NPG Biotechnology Volume 23 Issue 5 May

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ED I T OR I A L

Memo to FDA head: IT upgrade required May 1, 2005 Dear Lester (or if there’s a nomination hitch, To Whomever It May Concern), As Spring is the time of new beginnings and rebirths, we would like to congratulate you on becoming commissioner of the one of the world’s largest and most important government bureaucracies. You are now responsible for regulation and oversight of products that account for over a trillion dollars annually in the United States as well as the health and well-being of over 295 million citizens. The agency that you head is currently recuperating from a series of health scares in which concerns from internal advisors about toxicities associated with certain antidepressants and widely prescribed analgesics approved by the fast-track process appeared to fall on deaf ears. Criticism of your handling of aseptic standards at a UK facility of a major flu vaccine manufacturer hasn’t helped. Neither has US Republican congressmen, who cite a “crisis of confidence” in an agency that has developed an overly cozy relationship with industry. Many appear to suffer from collective amnesia, conveniently forgetting previous carping about risk aversion at the FDA and unacceptable delays in the approval of lifesaving medicines. You and we appreciate, however, that the FDA is not in crisis. Rather, it has suffered some blunt trauma and you must now orchestrate the healing. That is why it is critical that in your role as commissioner, you now take steps to ensure that the agency further streamlines the drug approval process and addresses problems in monitoring serious adverse events. In the next few years, it will not be pharmaceutical companies, but increasingly biotech that will be producing new experimental medicines and technologies. This means that unlike medicines of the past, which have taken decades of development, an increasing number of new drug applications at your agency will have their origins wholly or in large part in biological discoveries reported just a few years before. As a consequence, more and more of the products under review will be unfamiliar experimental treatments, requiring new knowledge and new reference points. The increasing use of pharmacogenomic information in defining indications, as laid out in your recent Guideline (see p. 510), will only add to this knowledge-based information torrent streaming in from applicants and trial sponsors. And yet it seems that under your leadership, the FDA is destined to move in the opposite direction. Your recent budget proposal was heavy on protecting Americans “from risky products and potential terrorist threats” but palpably light on mobilizing the increasing knowledge base of biology in the cause of better treatments. The one area you highlighted for savings was IT. Specifically, as acting FDA commissioner you commended “savings of $5,116,000 through continued consolidation and/or postponement of information technology expenditures” in order to “fully embrace the President’s Management Agenda and the Secretarial priorities.” But the FDA’s IT systems are woefully outdated, even by the standards of the average office computer. In December, according to Scott Gottlieb,

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a former senior adviser for medical technology to the FDA commissioner, your agency requested Neurocrine Biosciences to refile its new drug application for a sleep aid called Indiplon (a pyrazolopyrimidine). This was not because the company had omitted data or because the agency had identified safety concerns. It was because the FDA could not ‘navigate’ the company’s electronic application (rather like grandma struggling with the video remote). And then there are reports that the FDA cannot accept portable document formats (PDFs) submitted via e-mail: perhaps some of the $20 million worth of increased user fees to be collected under PDUFA (the Prescription Drug User Fee Act) could tackle the PDF deficit? Overhaul of your antiquated computer system might also provide a solution to the process of adverse event reporting. The current system is paper based and consequently severely hobbled because it is passive and depends on the diligence of overworked physicians, who fill out and send lengthy reports, via drug companies, to the FDA. The result is your agency becomes aware of life-threatening toxicities often only months or even years after a critical mass of fatalities appear on the radar. If you were to seriously upgrade IT, your agency could take advantage of the increasing amounts of medical data that are finding their way onto electronic medical records and into electronic prescriptions. That’s why your ‘to-do’ list should prioritize the development of computerized systems for proactively gathering toxicity information and the refinement of algorithms for spotting potential toxic events and establishing causal links between drugs and side effects. These tools would provide a powerful complement to existing passive reporting. Indeed, you already have pilot programs underway with healthcare networks, such as the Veteran Administration hospitals and New York-Presbyterian Hospital. It may still be unfashionable to commend European initiatives, especially among Republican congressmen, but it is becoming obvious that, at least on the question of electronic submission, the European Medicines Agency (EMEA) now leads the FDA. The FDA needs to keep pace with technological change and to foster a framework for evaluating drugs that are the fruits of secured knowledge and not mere hopeful endeavor. It is your job to fix the FDA’s inefficient and antiquated computer system. In doing so, you’ll go a long way to fixing drug safety monitoring and overwork within the agency. Lester (or Whomever), it’s your legacy we are concerned about. Oh yes, and other people’s lives.

C o n t en t r es hu ffle Sharp-eyed readers will notice that the Patents and Bioentrepreneur sections have been moved toward the front of the magazine. The Computational Biology section has also been relocated before the Research section. These changes have been implemented to bring news, opinion and feature content together, and to group technical content toward the back. Any feedback concerning these changes is welcome ([email protected]).

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gen o mics gu idan ce p es bio p ir acy debat e p e E S T p at en t abilit y p r y an d W hit e H o u s e p L E : J u lian T hu r s t o n p

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E ar lier s t age bio t echs at t r act p ar t n er s Even as the environment for going price and the probability of sucpublic remains hostile to most biocess,” contends venture capitalist tech companies and their investors, Jürgen Drews, former president of some are discovering that favorable R&D at Hoffmann-LaRoche. Big financing alternatives still exist. A pharma also now has better tools spate of recent acquisitions and a in-house to make fast go/no-go continuation of early-stage deal decisions on whether to take prodmaking signify that there are still ucts forward, in turn enabling them appealing ways for many compato take more ‘shots on goal’ before nies to sustain their drug developinvesting in costly last-stage drug ment programs, especially given developm ent—an argum ent in the needs of potential acquirers favor of early-stage alliances as well and licensees. as acquisitions of companies with For biotechs whose lead programs early-stage programs. are at or near the phase 2 clinical Thus at Pfizer, according to trial inflection point that validates Pfizer, who recently acquired Idun Pharmaceutical, has, like many Martin Mackay, senior vice presiproof of concept, potential partners other big pharma, been focusing on earlier stage biotech companies. dent worldwide research & technolincreasingly are considering acquiogy, “we tend to go earlier because sition. “People are far more willing we have good predictive tools, to to consider acquisition than they used to be,” Syrrx Pharmaceutical (by Pfizer and Takeda, be able to get to a decision point as quickly notes Roger Longman, managing partner at respectively), TransForm Pharmaceutical and as possible.” This was a large part of the healthcare business information specialist NeoGenesis, both located outside of Boston rationale behind its acquisition of Idun, Windhover Information. Plus, he says, “bio- (Johnson & Johnson and Schering-Plough), a developer of caspase modulators, which techs today may be willing to cut their upside and in 2004, metropolitan New York area- are proteins involved in apoptosis but that for a present-day payout.” based Aton Pharma (Merck). remain unproven as a drug target. “Idun Indeed, most companies don’t have that Acquisition is also an alternative to the has built up a know-how that we’ll seek kind of product visibility with which to old biotech standby—the big corporate deal. to understand,” he adds. With scale, Pfizer appeal to public investors. Five firms have “The question of whether to buy or ally is believes it can quickly ascertain the potengone public so far in 2005, to a lukewarm always a trade-off,” notes Steve Sands, an tial for caspase-targeting compounds, which response in terms of the initial public offer- investment banker with Lazard Freres in New Idun is developing for a broad range of dising (IPO) price and after-market reaction. York City. In the past, the trend in pharma orders including liver diseases, cancer and Moreover, an IPO, unlike an acquisition, and biotech was always to form an alliance. inflammation. is not necessarily an exit. “It’s a financing But a broad alliance is basically like givGiven the uncertainties around availabilopportunity,” Longman points out. The ing away the company anyway, he suggests, ity of future financing and the overall costs level of financing an IPO often provides is and many alliances that went south—in of drug development, “conversations for especially important for companies that need some cases because long-term social issues potentially important products often get to capital to take a product to the end stages arise when working together for extended the obvious point of considering an acquiof commercialization, leading to an increase periods—should have been acquisitions. sition,” summarizes Anthony Evnin of venin valuation without further fund-raising. Moreover, at the board level, potential licen- ture capital firm Venrock Associates. “With Among the recent class of IPOs, Eyetech sor/acquirees are now thinking about acqui- IPO valuations and timing uncertain now, it Pharmaceuticals, Pharmion, Nitromed, and sition earlier and in a more organized way, requires you to think more profoundly about Corgentech (had its product worked—it he says. acquisition. I do think there are elements that recently failed in phase 3) are all companies With most late-stage product candidates— begin to feel like a trend.” that went public for that reason, more so those that have been validated in phase 2 Mark Ratner, Cambridge, Massachusetts than to give private investors an exit. proof-of-concept clinical trials—either The vagaries of going public are one part already spoken for or very pricey, pharmaof the reason more and more biotechs are ceutical companies have also become more For more news and analysis go to considering a big pharma buyout—an option interested in earlier stage alliances. “Lateseveral have already chosen in 2005, including stage deals, while expensive, sometimes www.nature.com/news San Diego-based Idun Pharmaceutical and do not provide the optimal ratio between Spencer Platt/Getty Images

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The US Food and Drug To build the knowledge base Administration (FDA) on March necessary to scientifically validate 22 issued the long-awaited new biomarkers and create the final version of its Guidance needed regulatory infrastructure, for Industry on submission of industry will have to take up the pharmacogenomics data, giving FDA’s request of submitting volindustry a strong political signal untary genomics data. But how that it is open to applications quickly this trend will catch on concerning therapeutic products remains to be seen, experts say. ‘personalized’ to patients’ genetic In the past year, the agency has blueprints. The main challenge received only about five to ten volfor the regulator is to encouruntary data submissions—surprisage industry to submit voluntary ingly few, says Spear. data to help refine the pharmaTo some extent, notes Mendrick, cogenomics approval process and the low number of submissions speed up the arrival of the next may reflect a time lag—provisions wave of such products to the for submitting voluntary data were market. only announced a year and a half Breast cancer was the first condition for which a so-called personalized Industry experts have enthusi- medicine called Herceptin was approved. With the new Pharmacogenomics ago in the draft version of the astically embraced the new docu- guidelines, the FDA has given a clear political signal that it is ready to open guidance document, published in ment, calling it an “important first the door to many more personalized drugs. November 2003, and completing step” for bringing pharmacogethe studies necessary to submit an nomics-based drugs to market. Investigational New Drug applica“I applaud the FDA for taking the step,” says drug metabolism risk for a specific disease, tion often takes longer than that. Donald Halbert, executive vice president of drug metabolism or potential to respond to But companies may simply not yet be willR&D of Iconix Pharmaceuticals in Mountain a specific therapy. The guidance document ing to embrace voluntary submissions, experts View, California. “They have maybe intention- defines what the agency considers a valid bio- say. “There’s still a belief [among companies] ally put pressure on themselves to develop the marker, but what remains to be fleshed out that if this is voluntary, why jump if we don’t infrastructure, tools and training to work with is the process by which biomarkers come to have to?” says Spear. “The FDA has really tried these kinds of data sets.” be validated by the agency and the scientific to come up with reasons, but those reasons The guideline outlines the circumstances community. If a company is working with are more relevant for people generating the under which companies are required to submit an experimental marker for which it has not science” than those on the commercial end, pharmacogenomics data and the procedures been required to submit data, says Donna he says. “I think different companies will have for submitting them. Data must be submit- Mendrick, vice president of toxicogenomics at different levels of eagerness.” ted when they are based on valid biomarkers Gene Logic in Gaithersburg, Maryland, “the What could entice companies to submit that have been rigorously tested by the scien- concern is that next year the FDA will come their data, Spear suggests, is evidence that they tific community and explicitly affect how tri- to you and say it’s valid. Companies want to are benefiting from the process—for example, als for a product are designed. Using several know, is there a list of valid biomarkers? How by gaining credibility with the FDA, or by eduexamples, the document also defines a second do we get our hands on it?” cating the agency early on about techniques category of data as those obtained under the Although there are a few drugs to be and issues relevant to their product. According auspices of exploratory research. Data in this approved on the basis of pharmacogenom- to Carol Reed, vice president of medical affairs latter category do not have to be submitted; ics data—such as Herceptin (trastuzumab) at New Haven-based Genaissance, one of the instead, the agency encourages companies to first approved in 1998 in the US and since few companies who have submitted volunsubmit it voluntarily and in turn promises not followed by Gleevec (imatinib) and Erbitux tary genomics data, sitting down to a detailed to use it to make regulatory decisions. (cetuximab)—there hasn’t been that many informal discussion with the agency did in The document’s main impact, says Brian more since. Because the science is still so new, fact have very real benefits. “They had very Spear, director of pharmacogenomics at establishing the guidelines will be an evolving good questions, they’d done their homework, Abbott Laboratories in Abott Park, Illinois, process. The document incorporates many and they were very helpful to us scientifically,” is on reassuring companies that conducting suggestions from the Washington-based she said. early-stage pharmacogenomic experiments Biotechnology Industry Organization (BIO). All recognize that managing the expected will not bring negative regulatory conseAnd further refinement is expected. The volume of data will be a challenge as data quences. “This has been a real worry,” says FDA on March 28 released a draft concept acquisition and analysis could bog the FDA Spear. “In the past companies have avoided paper on codeveloping gene-based diagnostics officials down, perhaps even draining resources generating this data. But I’m guessing that the and therapeutics and are planning to publish from regulatory review. “Part of the problem risk has been so removed by this guidance that another one on the role of DNA in microar- for the FDA is, how much data do they want?” this is not going to be an issue.” rays. In October, an agency committee, of says Mendrick. “It’s going to add quite a bit of Pharmacogenomics involves identifying which Mendrick is a member, is scheduled to work to their plate.” biomarkers, genes that determine a patient’s discuss biomarker validation. Alla Katsnelson, New York David Eulitt/KRT

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Cautious welcome for FDA pharmacogenomics guidance

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Although the revocation on March 8 by the European Patent Office (EPO) of a patent based on the fungicidal properties of the neem tree was hailed as an historic victory over biopiracy, the decision may have mainly a symbolic value. Yet, it could send a signal to biotech companies operating in developing countries that they cannot equate the traditional knowledge of indigenous communities with freely available public domain information. The ruling by the EPO’s technical appeals board not to allow an appeal brought by the current patent holders, Certis, of Columbia, Maryland—part of Japan’s largest general trading company, Tokyo-based Mitsui—and the US Secretary of Agriculture, finally brings to a close a battle that has lasted for more than a decade. The EPO has yet to publish the technical appeals board’s full decision as the revocation was only announced orally at the Munich hearing in March. The EPO originally awarded the patent, number 436,257, to the agrochemical company WR Grace, of Columbia, Maryland, and the US Secretary of Agriculture on September 14, 1994. The patent disclosure described a method for controlling fungi on plants using a hydrophobic oil extracted from the seeds of the neem tree (Azadirachta indica). Neem is a subtropical member of the mahogany family, which is native to the Indian subcontinent, and now widely cultivated elsewhere. The disputed patent is one of several dozen based on the properties of the same plant, which are held by Indian and overseas companies (see Table 1). In 1995, a trio of activists launched their opposition. The group, which was represented throughout by Fritz Dolder, professor of intellectual property at the University of Basel, included the veteran Indian anti-biotechnology activist Vandana Shiva, Magda Aevolet, then president of the Green grouping in the European Parliament, and Linda Bullard, then vice president of the International Federation of Organic Agriculture Movements (IFOAM), a Bonn, Germany-based umbrella organization for organic farmers. An oral hearing of the EPO’s opposition division, held on May 9–10, 2000, found in their favor, ruling that the patent lacked novelty because its opponents had successfully demonstrated prior public use in India. Central to the opponents’ case was that the neem tree’s fungicidal properties were known about and used in India for centuries. Crucially, however, they backed this claim up with documentary and oral evidence of field trials of similar products conducted in India in the 1980s, as well as references in the scientific literature

that predated the original 1990 filing. “They have recognized that traditional knowledge is a potential prior public use but you have to provide evidence of it,” says Dolder, adding, “It all depends on the precision of the evidence.” “In a sense, the decision says to the EPO ‘widen your search in relation to novelty,’ and that’s a good thing,” says Julian Kinderlerer, assistant director of the Sheffield Institute of Biotechnological Law and Ethics in the UK. In practice, he says, patent examiners tend to check whether a claimed invention has been patented previously instead of establishing whether it is genuinely novel. Coincidentally, while the EPO was hearing the neem patent appeal in Munich, delegates from eight developing countries, including India, tabled a fresh proposal at a World Trade Organization (WTO) Council meeting on Trade-Related Intellectual Property Rights (TRIPS) during March 8–9 in Geneva. The proposal would impose additional rules regarding the sharing of benefits resulting from patents that involved the use of genetic resources. Biotech companies filing new patent applications would have to include evidence that such benefit-sharing agreements were in place. This is part of a wider, albeit unsuccessful, effort to mandate the inclusion of additional disclosures in patent applications, such as the country of origin of genetic materials and information on the extent to which an invention relies on traditional knowledge. However, Tony Taubman, director of the traditional knowledge division at the World Intellectual Property Organization (WIPO), the Geneva-based UN agency for intellectual property (IP) protection, says there has been a transformation in the level of recognition accorded to traditional knowledge in debates on IP over the past three years. Individual countries are implementing their own requirements on disclosure, while at the international level, practical measures, such as including traditional knowledge repositories in the patent examination process and amending the International Patent Classification system to include categories of traditional knowledge, are gaining ground. WIPO is also exploring deeper questions, such as “how to frame a true international norm” for preventing the misappropriation of traditional knowledge. The agency was, as Nature Biotechnology went to press, due to publish a new text on the issue. Several observers have pointed to the egregiousness of the original WR Grace patent. That it has been overturned now may well point to the weakness of the patent examination process

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Craig Fujii/AP Wide World

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

EPO neem patent revocation revives biopiracy debate

Caption: Neem oil, cultivated from the eponymous tree’s seeds, is used widely in India as home remedy thought to cure a wide variety of ailments

of the early 1990s and to the glacially slow nature of the EPO’s appeals process rather than to any significant implications for international multilateral agreements on IP or for biotechnology companies with legitimate bioprospecting activities. “It is possible to exaggerate the importance of this,” says Graham Dutfield, senior research fellow at the Queen Mary Intellectual Property Research Institute at the University of London. Yet, Calestous Juma, professor of the practice of international development at Harvard University, in Cambridge, Massachusetts, and former executive secretary of the United Nations’ Convention on Biodiversity (CBD) points out, “It energizes those who have been questioning for a long time the patenting of products based on traditional knowledge.” According to IFOAM’s Linda Bullard, the EPO decision may give some impetus to stalled discussions on the Doha Mandate. The mandate requires members of the WTO to consider the relationship between the CBD and the TRIPS agreement on internationally accepted IP protection standards, and to examine the protection of traditional knowledge, in their review of the TRIPS article on the patentability of life forms. “The next obvious step is to take this jurisprudence into other legal regimes and international patent agreements,” she says. Cormac Sheridan, Dublin

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T able 1 N eem p at en t s cu r r en t ly ex amin ed by E P O t hat co u ld be affect ed by t he r ev o cat io n Title

Applicant Proprietor

Relation to neem

Patent number

Extraction of substrates from components of the neem tree

Flavex Naturextrakte (Rehlingen, Germany)

Process for extracting substances from neem seeds and nuts

EP 874550 A1 19981104 WO 9725867

Azadirachtin compounds extracted from Azadirachta indica, compositions and use thereof as insecticidal

Fortune Biotech (Secunderabad, India)

Stable azadirachtin prepared from neem kernel extract

WO 0024731 A1 20000504 EP 1124818

Biological wood treatment agent based on extracts of herbs

Jobeck (Hausham, Germany)

Treatment agent including neem oil

EP 1149670 A1 20011031

Method for acaricidal and microbiocidal treatment of textile materials

Greenworker (Cyprus)

Textile comprising neem oil

WO 03002807 A2 20030109 EP 1432866

Topical cosmetic composition with skin rejuvenation benefits

Avon Products (New York)

Cosmetic composition comprising a blend of neem seed

WO 03041636 A2 20030522 EP 1441686

Extraction method

Neem Biotech (Cardiff, UK)

Extraction of azadirachtin

EP 1326870 A1 20030716 WO 02032907

Substrate, especially textile substrate, and method for producing the same

Terra Nostra Produkte mit Naturextrakten (Geisenfeld, Germany)

Substrate comprising extracts of neem tree

WO 03071871 A1 20030904 EP 1478230

Compositions and delivery methods for the treatment of wrinkles, fine lines and hyperhidrosis

Avon Products (New York)

Limonoids for treating skin; limonoids include the plant alkaloids toosendanin

WO 04060326 A1 20040722 EP 1471874

Pediculicidal compound

Natural Science.com (Powys, UK)

Composition comprising extract from EP 1465648 A1 Melia azadirachta for treating head 20041013 WO lice infestation in humans. 03057231

Source: Kein Patent auf Leben initiative (No patent on life), Munich Germany.

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On May 3, the first case appealed Although, Monsanto could not to the US Court of Appeals for the comment due to the pending litiFederal Circuit concerning the patgation, the court case could indeed entability of expressed sequence serve to codify the most recent tags (ESTs) is due to start, followguidelines issued by the USPTO ing a dispute between St. Louis, in 2001 the utility of an invention Missouri-based Monsanto and the intended to address patenting of US Patent and Trademark Office genetic and related information. (USPTO). The case is likely to Along with several other parclearly define how much knowlties including Eli Lilly and Dow edge a patentee needs to have of AgroSciences, both of Indianapolis, how newly discovered ESTs work Indiana, the National Academy of and whether the defined use meets Sciences and the AAMC are part of the USPTO requirement of specific an amicus brief in support of the and substantial utility. If favorable USPTO filed against Monsanto in to Monsanto, the judgment could the federal case. The basis of their annihilate or support longstandopposition is straightforward: by ing efforts by the biotech research many in the research community, community to work with a stricter ESTs are seen as research tools definition of ‘utility’ in patenting. that should be part of the public The issues related to DNA patdomain unless they are clearly enting have been debated for more defined in relation to specific biothan a decade. The May court case, Companies patenting express sequence tags “ are akin to Spanish logical processes. In re Fisher, is the latest develop- conquistadors” finding a new territory, who did not always know what Some are concerned that if EST they had discovered, but would claim it anyway. ment in a long series of discussions patent applications with overly since the early nineties between the broad claims—many are worded USPTO, the National Institutes of so as to potentially annex any adjaHealth, the National Academy of Sciences, to the DNA associated with it. This ‘linking’ cent nucleic acids which could theoretically and other government agencies and academic would qualify as “specific and substantial expand it out to the entire chromosome—or institutions, and the biotechnology and phar- utility,” if disclosed. The vexing intellectual nonspecific utility are approved, it could have maceutical industry attempting to define the property issue in Fisher is to determine when implications for other homologous, genetic appropriate scope of DNA-related patent researchers have enough knowledge of this processes. Although ESTs have largely faded claims and the invention’s utility. process to lay claim to particular ESTs. In from the current forefront of research, this In Fisher, the specific question is ‘how much this court case, a general, nonspecific utility would affect areas of biotech research still in does a patentee have to know about the func- is defined in the patent. Monsanto’s plant vogue such as kinases, which are also based on tion and role of ESTs to actually patent them’? ESTs described in the patent application homologous processes. The patent application under scrutiny was aren’t linked to any particular biological or If the court favored Monsanto, it would be initially submitted by Monsanto and then disease process. going against the utility requirements of the rejected by the USPTO on the grounds that This can be a problem, notes the USPTO. Such a scenario would potentially its claims in the written description of the Washington, DC-based Biotechnology Industry open the door for increasingly less specific invention were too broad and that it didn’t Organization’s director of intellectual property patents thus threatening a decade of work by sufficiently demonstrate “specific and sub- Lila Feisee. “It’s not very complicated to find the USPTO and the biotechnology research stantial” use of the invention. ESTs. It’s not like you’ve done invention of any community to define utility strictly. This decision was appealed by Monsanto sort, basically anyone can do it.” BIO does not “It’s akin to the old Spanish, English and within the USPTO and the rejection upheld, have an official position on EST patents. Portuguese explorers,” concludes Korn. “They although the issue of overly broad claims Monsanto, even though it is a voracious would take their boats until they found some was dismissed whereas the lack of utility was patent acquirer, may not be clinging fiercely edge of land, then they would go up and affirmed by the USPTO’s appeals board. The to this patent case out of the desire to further plant the flag of their king or queen. They federal circuit court is expected to rule on the expand its patent portfolio. Some industry didn’t know what they’d discovered; how big matter sometime this summer; then if either observers have speculated that Monsanto it is, where it goes to—but they would claim party appeals the decision, the case could floated this as a test case. “It’s a very impor- it anyway.” make its way to the US Supreme Court. tant test,” noted the senior vice president of Stacy Lawrence, San Francisco ESTs are a sequence or fragment of DNA, the division of biomedical and health sciwhich may or may not code for a particu- ences research of the Washington, DC-based lar protein. These proteins can enable a Association of American Medical Colleges For more news and analysis go to researcher to trace back and determine both (AAMC) David Korn. “I wouldn’t be amazed the proteins’ RNA and DNA, thereby poten- if somebody from Monsanto said they were www.nature.com/news tially linking a disease or biological process doing this deliberately to test the guidelines.” Brian Gable

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

US court case to define EST patentability

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because of the small chance that Late in March it was widely functional versions could transfer reported that the Swiss agribusifrom crops to microorganisms ness group Syngenta had inadverand spread problems of antibiotic tently mislabeled and sold Bt-10, resistance. “But for the purposes an unapproved genetically modiof the government’s investigation,” fied (GM) corn seed, as Bt-11, says Jim Rogers a spokesman for which is approved, to US farmers the USDA, “this is not a question between 2001 and 2004. Although about exactly how similar or difthe US government deemed the ferent they are, or about public matter to be a legal rather than safety. Both are nearly identical health or environmental matter, and both are safe. But, only one of this latest industry public relathem is approved.” tions mishap may strengthen calls Still, Syngenta did them to tighten legislation on genetiselves, newly installed US Trade cally modified (GM) products in the US. Meanwhile, the incident Syngenta recently admitted that unapproved genetically modified corn seed, Representative Rob Portman and the biotech industry no favors— provided new ammunition for an known as Bt10, found its way to US farmers between 2001 and 2004 not just by letting the Bt-10 seeds old gripe over trade between the slip off radar in the first place, US and the EU, which launched relative risk factors.” ( Nat. Biotechnol. 19, but also by taking nearly 4 months to pubits own investigation. licly release information that something After Nature broke the story on March 11, 2001). Although activists have charged that the was amiss. 24, Syngenta disclosed that its Bt-10 test line Friends of the Earth Europe was prompt somehow found its way into five produc- biotech industry is not to be trusted, Syngenta tion lines of Bt-11. Bt-10, which like Bt-11 did, in fact, report this incident immediately to issue a statement saying, “This is an contains a toxin gene from the soil bacte- upon discovering it in December to the US industry out of control … This [Syngenta] rium Bacillus thuringiensis ( Bt ), was kept Department of Agriculture (USDA), the US case makes a complete mockery of the US around purely for research purposes as it did Food and Drug Administration and the US regulatory system for GM crops.” Even not prosper quite as well in fields as Bt-11. Environmental Protection Agency (EPA) just the relatively m ild-m annered Council Syngenta says the amount of Bt-10 corn that as regulations require. US regulators quickly for Responsible Genetics in Cambridge, was sold as Bt-11 would cover an estimated confirmed that Bt-10 posed no human or Massachusetts, called it a “massive failure of the US regulatory system … This is cer37,000 acres. For a sense of scale, during that environmental threat. Because Bt-10 is not US government tainly going to be a big problem for the same time 113 million acres of GM corn were approved, planting, selling, distributing, United States.” planted in the US. Indeed, the White House has been dragged We may never know exactly how or when comingling or shipping it without a special the comingling occurred, to what extent the government permit is a violation of the Plant into this affair because of the potential of global food system was contaminated, or how Protection Act. Thus far, the USDA has deter- Bt-10 to further complicate trade negotiaSyngenta calculated its acreage proclamation. mined that Syngenta was guilty of breaking tions with Europe. The incident has, however, But, all agree that the fact that it did occur laws on GM plants and levied a $375,000 fine. not alarmed skittish agbiotech investors, who suggests that there was some sloppy handling The EU, on 15 April, announced its intention surely would have fled Syngenta shares by now of materials that should have been treated to require imports of corn-based feed to be if they believed, as they did with Monsanto with the utmost of care at all times for any certified as free of Bt - 10. Meanwhile, the EPA shares in 2000 in the wake of a laboratory number of reasons—some scientific, others and EU have launched their own investiga- study on the impact of Bt pollen on Monarch tions, which could result in more fines for the butterfly larvae ( Nat. Biotechnol. 17, 627, 1999), purely political. Quite predictably, the incident triggered a company—and, industry insiders fear, per- that political or regulatory trouble was in the chain reaction of high-voltage commentary haps new regulations for the whole of the GM offing. In fact, Syngenta’s share price, after an initial drop when news broke of the Bt-10 incifrom some European regulators and biotech crop industry. Syngenta made much of the fact that the dent, remains close to its 52-week high. critics who all but likened the event to the Syngenta is not in the clear yet, howrelease of a plume of radioactive particles Bt-10 corn is identical to Bt-11, which is into the atmosphere and chided the com- approved for human consumption in the US, ever. Regardless of what regulators decide pany and regulators for putting the public and the EU and Japan. In fact, they are similar but to do about Syngenta’s “unintended event,” the environment at risk. “Incidents like the not identical. Bt-10 differs from Bt-11 is that Margaret Mellon of the advocacy group Union one with Starlink and Syngenta,” says Steve it contains an inactive marker gene which of Concerned Scientists says the damage has Strauss, professor of Forest Science at Oregon originally conferred resistance to ampicil- been done to both the company and the State University in Corvalis, “unfortunately lin, a commonly used antibiotic. The inac- industry. “Environmentalists and the media strengthen the case for tightening regulations tive gene is a relic from the process used to might have overreacted to this incident,” she not loosening them at a time when regula- select transgenic corn cells during strain con- says. “But it was Syngenta that mishandled tions for [biotech versus nonbiotech crops] struction. The release of such genes into the things from beginning to end.” are already totally out of sync with actual and environment has been contested in the past Stephan Herrera, New York Teak Philips/St. Louis Post-Dispatch

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

Syngenta’s gaff embarrasses industry and White House

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© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

On March 31, the European Commission (EC) proposed to raise fees for the authorization of pharmaceuticals at the European Medicines Agency (EMEA). Although the agency received 51 new applications in 2004—a sharp rise from the 39 received in 2003—the increase was not introduced specifically to deal with higher numbers of applications as they are expected to slow. Instead, the fee hike would mainly cover additional tasks such as more intensive post-authorization monitoring. These will be undertaken starting in November 2005 as the new legislation comes into force. The annual fees will go up by 10% as of 2006 and a new and variable fee for yet unspecified ‘scientific services’ will be introduced. In addition, biotech companies falling in the small and medium-sized enterprises category would get deferments and discounts. The industry’s total contribution to EMEA’s budget would thus increase slightly from 68.1% this year to 68.4% in 2006. Another measure would introduce new fees that would range between those for new products and traditional generics for follow-on biologics, known as ‘biosimilars.’ The European bioindustry association EuropaBio in Brussels welcomes the new fee as an acknowledgment that biosimilars need more review than traditional generics. PV

I s r ael t ight en s I P law The Israeli Knesset in March approved a data exclusivity bill that will give foreign companies who register drugs in Israel five years’ protection from copying by the country’s strong generics industry. Unlike the US and Europe, Israeli patent law has, until now, included neither market exclusivity, which prevents generic companies from marketing generics for a number of years, nor data exclusivity, considered more stringent because it blocks generic companies from accessing the data from the drug’s regulatory file. Both the EU and the US have put pressure on Israel to apply stronger intellectual property protection. Last May, the issue became heated when the US threatened to put Israel back on its priority watch list of countries violating intellectual property rights, after having removed it in 2003. How the legislation will affect drug companies’ willingness to register

News in Brief written by Ichiko Fuyuno, K.S. Jayaraman, Alla Katsnelson, Stacy Lawrence, Sabine Louët, Cormac Sheridan & Peter Vermij.

I n dia’ s ap p r o v es fas t t r ack fo r G M

cr o p s

The approval process for genetically modified (GM) crops and recombinant medicines in India is to be put on the fast track under a new policy, which is part of a ‘National Biotechnology Development Strategy’ drafted by the Indian Department of Biotechnology (DBT), and is aimed at accelerating the pace of biotech product development. The policy, announced on April 2 , by science minister Kapil Sibal calls for strong support for indigenous discovery of new genes and promoters in agbiotech, but authorizes Indian institutions to license them from multinationals if it’s in the national interest (most of the 4 0 genes currently used in India are imports).The policy calls for the creation of an independent single regulatory authority with separate divisions to handle applications for commercializing genetically modified crops, recombinant drugs and food products and genetically engineered industrial products. Biosafety will be tested by DBT but exemptions will be made for biosafety tests or large-scale trials of a transgenic crop variety if the same transgene has already been released in another variety. The industry has also welcomed the curtailment of the role of the existing Genetic Engineering Approval Committee (GEAC)—the apex regulatory body under the ministry of environment. Its role has now been limited to ensuring that the released ‘event’ has no harmful impact on the environment. “ The policy has rightly defined GEAC’s role and this will help speed up introduction of GM products,” comments Varaprasada Reddy, CEO of Shantha Biotecnics Private in Hyderabad. KSJ EPA/STR

E M E A fee hike

drugs in Israel remains to be seen, say Tamar Morag-Sela and Ilan Cohn of the Tel Aviv law firm Reinhold Cohn and Partners. Some industry leaders have insisted that the lack of protection has kept foreign drug companies from building research and manufacturing facilities in Israel, resulting in a loss for the economy. But Chaim Hurvitz, vice president of generics pharmaceutical company Teva has said that the new bill will cost the national health system ILS300 ($68.5) million a year. AK

T y s abr i do w n an d o u t ? The retrospective reclassification of a patient who died during a phase 3 clinical trial of Tysabri (natalizum ab) as m onotherapy for Crohn’s disease as having developed progressive multifocal leukoencephalopathy (PML) appeared to eliminate one big imponderable that had clouded discussions of the drug’s future. The two previous PML cases—which prompted its voluntary withdrawal on February 28—had arisen during the Sentinel phase 3 multiple sclerosis trial comparing a combination of Tysabri plus Avonex (interferon β-1a) with Avonex alone. The possibility remained that those adverse

N ATU RE BI O TECH N O LO GY VOLUME 23 NUMBER 5 MAY 2005

events had arisen because of an additive effect of the two drugs, which enabled Elan, of Dublin, Ireland, and Biogen Idec, of Cambridge, Massachusetts, to hold out some hope that the monoclonal antibody could return to the market, albeit under restricted conditions. Such a return is now a “significant challenge,” says Richard Parks, analyst at ING Financial Markets in London. The association between Tysabri and PML, “is not just limited to combination therapy, and even shorter doses of Tysabri can result in a predisposition to this potentially fatal side effect,” he says. The patient in the Crohn’s disease trial had received the drug for just eight months, whereas those in the Sentinel trial had each been on Tysbari for more than two years. However, there were additional confounding factors attached to the third PML case, says Jack Gorman, analyst at Davy Stockbrokers in Dublin, as that patient had also received Remicade (infliximab) and the immunosuppressant azathioprine, which has also been linked to PML. CS

For more news and analysis go to www.nature.com/news

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© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

N ew p r o du ct t able Company

Details

Symlin (pramlintide acetate) (Amylin Pharmaceuticals, San Diego, California)

On March 16, the US Food and Drug Administration (FDA) announced the approval of the first-in-class antihyperglycemic drug Symlin, to be used in conjunction with insulin to treat type 2 and type 1 diabetes. Symlin is a synthetic analog of human amylin, a naturally occurring hormone that is made in the beta cells of the pancreas. Diabetes affects over 18 million Americans.

Mycamine (micafungin sodium (Fujisawa Healthcare, now Astellas Pharma US, Deerfield, Illinois)

The FDA approved, on March 17, the use of micafungin sodium, the antifungal product for prophylaxis of Candida albicans infections in patients undergoing hematopoietic stem cell transplantation and the treatment of esophageal candidiasis. Mycamine belongs to a new class of antifungal agents, the echinocandins, which inhibit the synthesis of fungus’ cell-wall. Invasive candidiasis kills 10–40% of infected immunocompromised patients. SL

R ep o r t flags U S p r icin g Future revenues of many biotech companies are at significant risk because of new US government powers to lower the costs of drugs administered by physicians and clinicians, the UK-based industry research firm Wood Mackenzie in Edinburgh warned in Medicare Insight, a report reviewing 520 drugs from 60 manufacturers, which was published on April 5. The study highlights the Medicare Modernization Act (MMA), affecting 40 million senior Americans and signed into law in 2003, which has given administrators at the US Centers for Medicare and Medicaid Services (CMS) authority to control prices in a market heavily targeted by biotech firms. This makes “20% per annum price increases in this class a thing of the past,” the report says. One new option for CMS is to link reimbursement levels to average market prices, and the practice is already “wreaking havoc in oncology,” says Keith Redpath, Wood Mackenzie’s vice president of life sciences. Many oncologists report they can no longer afford to supply patients because of lower reimbursements, forcing producers to market to wholesalers instead. Anticipating steep rises in overall Medicare costs, Redpath expects the US government to use all the new tools in its box. He adds, “I would not be surprised if CMS is going to press the [US Food and Drug Administration] to more quickly approve generic versions of existing biotech drugs.” PV

J ap an in v es t o r s s hu n bio t ech On March 29, Effector Cell Institute, a Tokyobased university spin-off that aims to develop cancer and anti-allergy drugs, was floated on the Nagoya Stock Exchange’s Centrex market. But it failed to trade because investors considered it overpriced. The next day, the stock first traded about 40% lower than the initial public offering price, and has subsequently fallen further. Many analysts say Effector’s dramatic downturn results from investors’ concern that the company’s outlook is too optimistic given that it has not even started clinical studies. That represents a shift among investors—most of which are individuals—towards a more prudent attitude. When a raft of Japanese biotech companies started to trade around 2002 and 2003, investors were bullish on them, leading the biotech sector to reach its peak in the spring of 2004 ( Nat. Biotechnol. 21, 1256–1257, 2003). “The market was new, so investors didn’t have clear judgment,” says Kenji Tsujimoto, manager at Nomura Research & Advisory, the research arm of brokerage giant Nomura Securities in Tokyo. But the overall sector plunged by roughy 50% late last year to regain value only this year. Now, Tsujimoto says, investors are looking more carefully to an individual company’s business conditions such as the status of clinical trials, current sales value and business partnership. IF

B as ic P C R p at en t s ex p ir e Eight patents held by Basel-based F. HoffmannLa Roche, covering PCR expired in the US on March 28. Otherwise known as the Mullis patents, they cover the fundamental processes behind PCR, the first technique to allow rapid amplification of DNA sequences. The patents were originally acquired from Cetus, now known as Chiron, of Emeryville, California, in 1991, for $300 million. The European and Japanese versions of these patents are set to expire in about a year. According to the company, Roche has more than 800 licensing deals for PCR. For companies needing the most current PCR technology, the PCR patent expiration may be of little consequence other than opening the door to performing basic PCR more freely, according to life sciences patent lawyer Simona Levi-Minzi, partner at McDermott Will & Emery in Chicago, Illinois. “But more recent methods are still under patent,” she adds. And Roche still holds hundreds of more current PCR patents. “Naturally, we will experience some reduction in royalty revenue over the short-term,” acknowledges Roche spokesperson Paula Evangelista. “However, we also expect to see increased growth in licensing revenues resulting from the adoption of the real-time PCR methods offsetting some of the losses.” StL

S elect ed r es ear ch co llabo r at io n s Partner 1

Partner 2

Genentech (S. San Francisco, California)

Curis (Cambridge, Massachusetts)

9

A two-year deal to discover small molecule modulators of an undisclosed pathway that regulates tissue formation and repair and its abnormal activation that is associated with certain cancers. Curis will use its technology based on use of proteins or small molecules to modulate the pathways of interest to Genentech, which will pay both licensing and research fees. Curis retains rights to resulting compounds for ex vivo cell therapy in areas outside of cancer and hematopoiesis.

GlaxoSmithKline (GSK; London)

Global Alliance for TB Drug Development (New York)

*

A partnership to discover compounds to treat tuberculosis (TB) that would cut two to four months off the current six- to nine-month treatment as well as to find agents that have fewer drug interactions with antiretrovirals, as patients are often infected with both TB and HIV. The deal covers four projects including the development of pleuromutilins, a new class of antibiotics, the study of two TB targets (isocitrate lyase and InhA) and the screening of GSK’s antimicrobial libraries for novel anti-TB agents.

* Financial details not disclosed.

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$ Details (million)

SL

VOLUME 23 NUMBER 5 MAY 2005 N ATU RE BI O TECH N O LO GY

P R OF I L E

Julian Thurston

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

A commercial lawyer in the UK who has been around the track more than most, Julian Thurston thinks shortsighted investment strategies in Europe are shackling early biotech venture development.

Julian Thurston started his career as general counsel for what was once the flagship of UK biotech—Celltech, now part of Belgian mid-sized pharma company UCB. For the past 26 years, he has been providing legal expertise on commercial exploitation, intellectual property (IP) protection and licensing, partnering and strategic planning to European biotech companies. Thurston wears many hats—he deals with technology transfer as a nonexecutive director for the UK Cancer Research Campaign and currently is a partner at London law firm Morrison and Foerster. He is now regarded as one of the top life sciences transactional attorneys in Europe. If nothing else, a quarter century of experience has taught Thurston that biotech is cyclical—its fortunes waxing and waning with investor whim. In Europe, the latest trend is the desertion of early-stage biotech by the venture capital (VC) community. “I think that the classic series A, B and C IPO [and initial public offering] model is not going to work in Europe in the future,” he remarks. To get around this, he believes the European biotech sector needs to rethink its business strategy. One option would be to promote consolidation. Thurston’s vision is that the European sector should “sell a package of several startups” with complementary technology or products to partners of choice. “There needs to be mechanisms across universities where a German university would link up with a UK university and with an Italian university,” he explains. By packaging several university spinouts together, companies would become more substantial and sustainable. Peter Heinrich, CEO of Medigene, of Martinsried, Germany, agrees, adding “those focused on niche markets would stand a better chance of being competitive.” Heinrich is, however, skeptical about whether European investors have the necessary vision to accomplish this. “VCs should drive this [type of] merger,” he says, but whether they will is another matter. Many mergers have failed and consolidation has not been as extensive as it could have been because of what he calls “the ego problem.” This affects both CEOs and venture capitalists: the former bickering over who should get top job at the new company, the latter obsessively focusing on their reputation and the relative valuation of the company they are backing. Simon Turton, an investor at private equity firm Warburg Pincus in London recognizes that ego has a lot to do with the process but points out that “mergers are so difficult to do!” and they may not be the only driver of growth of the sector. Once consolidation has taken place, Thurston believes that the aggregated companies could be sold to bigger players. And as far as suitable partners go, he thinks that the most likely acquiring candidates for this packaged technology would be mid-sized biotech companies, such as UK firms Cambridge Antibody Technology (CAT) in Cambridge or Vernalis in Winnersh, which are always on the lookout for partnering or acquisition opportunities. Where acquisition is not possible, Thurston thinks companies could enter partnership deals. This is exactly the kind of strategy that CAT has adopted. As CAT CEO Peter Chambré explains, “If we’re convinced of the quality of the opportunity, we will ideally want to take full rights to it. If they are not available, we will partner

N ATU RE BI O TECH N O LO GY VOLUME 23 NUMBER 5 MAY 2005

or take a license.” Leon Bushara, senior executive vice president of business development at Serono in Geneva concurs: “Larger, better capitalized companies [can] identify projects in [smaller] biotech companies in Europe [and] form partnerships with or without equity investment.” As an alternative to mature biotech, mid-sized pharmaceutical companies could also be partners, says Thurston. This might be particularly attractive because European family-owned pharma companies are not often quoted on the stock market and frequently “escape the radar screen of US-based analysts.” “It’s the Ipsens, the Pierre Fabres, the Esteves, the Menarinis and the Schwartz Pharmas—all that lot” that biotech should be focusing on, Thurston suggests. So why aren’t there more deals between mid-sized pharma and biotechs? Thurston cites several reasons: first, mid-sized pharmas are family run and are often unaware of which biotech company is doing what in Europe; second, they are generally risk averse, com-

…for early-stage European biotech to really succeed, he believes “ science has to be exceptional,” says Thurston

paratively smaller and have constraints on resources; and third, they “only look at a small number of projects each year.” Warburg Pincus’ Turton points out that the parochial nature of many typical family-run pharmas is the real deal killer. “The majority are regional companies who are strong in their territory,” he says. But they are often not the partner of choice for biotechs seeking to reach the pan-European market. With funding in Europe at such a premium, many firms are now turning to the United States for partnering. In March, one of the flagships of French biotech, Immuno-Designed Molecules of Paris (which failed to float in Europe last June), underwent a reverse merger with Californian Nasdaq-quoted company Epimmune of San Diego. According to Thurston, the sad truth is “more science in the US that’s average gets funded.” In contrast, for early-stage European biotech to really succeed, he believes “science has to be exceptional.” But the major stumbling block for Europe is parochial attitudes. European resources are “far too splintered and too nationalistic,” says Thurston. In Europe, old habits die hard and cultural differences make consensus difficult. Each country tries to grow its own biotech industry, regardless of the fact that European biotech can only thrive by being a borderless deal-making activity. “It is going to take time to be as good as San Francisco, San Diego or Boston,” he adds. “We should be focusing on just two or three clusters and cross-fertilize the science Europe-wide,” he adds. The UK and Switzerland would be good places to start. Sabine Louët, Dublin

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D ATA PA GE

M ix ed r es u lt s in Q 1 Anyone hoping for a resurgence of biotech stocks last quarter was disappointed. Even so, initial public offerings (IPOs) and follow-on offerings did make something of a recovery, up 55% and ~25%, respectively over the fourth quarter. IPOs on European and Japanese markets had three

out of the five largest IPOs of the quarter. Biotech venture capital held its ground, with most going to European companies. But total biotech funds raised from partnering and debt are down from the same quarter in 2004 by 41% and 74%, respectively.

B io t echn o lo gy s t o ck mar ket p er fo r man ce

G lo bal bio t ech in it ial p u blic o ffer in gs

Biotechnology indices have performed worse than the broader market, actually losing value since the beginning of 2004.

After decreasing through 2004, there was an upturn in IPO money raised last quarter.

120.0

$1,000.0

Nasdaq Biotechnology Swiss exchange Nasdaq Dow Jones Biocentury Biotechnology

115.0 110.0 105.0

Index

580.7 265.5 70.0

$900.0

North America

661.7 36.8 17.9

$800.0

Europe 396.9 161.0 0.0

$700.0 298.0 91.6 104.0

$600.0 $500.0

100.0

Asia-Pacific

264.3 78.8 16.4

$400.0

95.0

$300.0

90.0

$200.0

85.0

$100.0 $0.0 1Q04

3/2/2005

1/2/2005

11/2/2004

9/2/2004

7/2/2004

5/2/2004

3/2/2004

80.0

1/2/2004

2Q04

3Q04

4Q04

5Q04

Number of IPOs North america

8

14

6

6

7

Europe Asia-Pacific

2 1

1 2

3 1

5 1

3 0

Source: Multex, BioCentury

Source: BioCentury

G lo bal bio t ech v en t u r e cap it al in v es t men t

G lo bal bio t echn o lo gy in du s t r y fin an cin g

VC funding totals have been virtually unchanged for the last four quarters, but Europe has started claiming an increasingly large share.

After a big boost in the fourth quarter, mainly from closure of debt and partnership deals, the first quarter is down substantially.

1800 1600

1,343.2 220.2 61.6 968.7 302.0 0

1400

891.9 345.7 1.5

876.5 228.3 0

1200

2,010, 1,006, 1,625, 1,242, 916, 1,387

1Q04

North America 750.2 446.8 2.5

Partnering

Europe

Follow-on financing

2Q04

2,262, 553, 1,271, 1,874, 716, 409

Asia-Pacific

Venture Capital

1000

Debt and other financing

3,017, 792, 1,105, 949, 494, 430

3Q04

800

IPO financing

600

4Q04

3,644, 1,064, 1,239, 4,476, 360, 578

PIPEs

400 200

1Q05

2,151, 1,305, 1,200, 1,177, 558, 511

0

1Q 05

4Q 04

3Q 04

2Q 04

0 1Q 04

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

Stacy Lawrence

5000

10000

15000

Source: BioCentury, Burrill & Company

Source: BioCentury

N o t able fir s t q u ar t er bio t ech deals C o mp an y ( lead u n der w r it er ) IP O s

MediciNova (Daiwa Securities SMBC)

D at e lau n ched M er ger s an d acq u is it io n s

T ar get

A cq u ir er

V alu e ( $ millio n s )

D at e an n o u n ced

Hexal

Novartis

6,000

21-Feb

Warner Chilcott

Bain Capital Partners, DLJ Merchant Banking, J.P. Morgan Partners, Thomas H. Lee Partners

3,100

19-Jan

21-Mar

121

N/A

8-Feb

Aspreva Pharmaceuticals (Merrill Lynch, Pierce, Fenner & Smith)

79

11%

3-Mar

Intercell (Goldman Sachs, Lehman Brothers)

69

N/A

25-Feb

ViaCell (Credit Suisse First Boston)

60

-51%

21-Jan

Medicis Pharmaceutical

INAMED

2,800

Paion

52

N/A

11-Feb

Eon Labs

Novartis

1,700

21-Feb

CTI Molecular Imaging

Siemens

1,000

18-Mar

Kendro Laboratory Products

Thermo Electron

834

20-Jan

Angiosyn

Pfizer

527

20-Jan

ESP Pharma

Protein Design Labs

475

25-Jan

L icen s in g / co llabo r at io n

518

A mo u n t r ais ed P er cen t chan ge in s t o ck ( $ millio n s ) p r ice s in ce o p en in g1

R es ear cher

I n v es t o r

V alu e ( $ millio n s )

D eal t y p e

Coley Pharmaceutical Group

Pfizer

515

Collaboration, development, license

Basilia Pharmaceutica

Cilag AG

300

Co-promotion, development, license, manufacturing, marketing

ANDRx

First Horizon Pharmaceutical

85

Asset purchase

Savient Pharmaceuticals

Ferring

80

Asset purchase

Vertex

Avalon Pharmaceuticals

73

Co-promotion, license

Avecia

Merck

65

ICICI Ventures

Dr Reddy’s Laboratories

56

Durect

Endo Pharmaceuticals

45

Development, license

C o mp an y ( lead in v es t o r ) V en t u r e cap it al

FibroGen (Adage Capital Management)

A mo u n t in v es t ed ( $ millio n s ) 100

R o u n d n u mber

D at e clo s ed

N/A

15-Feb

Perlegen Sciences (CSK Venture Capital)

74

4

28-Feb

Alexza Molecular Delivery (NGN Capital)

52

4

6-Jan

Asset purchase

Five Prime Therapeutics (Domain Associates)

45

N/A

11-Feb

Collaboration, development, license

Predix Pharmaceuticals (Forward Ventures, Boston Millennia Partners, and CMEA Ventures)

43

3

25-Jan

Neuro3d (Gilde Investment Management)

43

3

3-Jan

1 Prices

as of March 30, 2005. Source: BioCentury, Hoover’s, Recombinant Capital

VOLUME 23 NUMBER 5 MAY 2005 N ATU RE BI O TECH N O LO GY

N E WS F E AT U R E

Can gene therapy ever live down its setbacks and live up to its initial promise? A chastened but determined group of pioneers believes it can, and they are pointing to a new generation of products to back up that claim. Malorye A. Branca investigates.

Karen Kasmauski/Corbis

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

G en e t her ap y : cu r s ed o r in chin g t o w ar ds cr edibilit y ?

Future gene therapy candidate? Nine-year-old child with Crigler-Najjar syndrome, a genetic disease that causes elevated bilirubin levels, sleeps under UV lights every night.

On February 9 the US Department of Justice announced a civil settlement in the government’s case related to Jesse Gelsinger’s death during a gene therapy trial at the University of Pennsylvania in 1999 (see News in Brief, p 515). Gelsinger, just 18 when he died, was not the first person to die while receiving experimental treatment, but both the circumstances of his death and the anxiety many people feel about genetic manipulation in general brought the case extraordinary attention. “There was a definite negative effect on the entire field [after Gelsinger’s death], as we all paused to reconsider what we were doing,” says Richard Gregory, head of research at Cambridge, Massachusetts’ Genzyme. This, plus the latest round of interruptions of some gene therapy trials, less than a year after a high profile French trial was resumed ( Box 1), has put this once promising field back on the hot seat, just when researchers and companies felt they were finally making some progress. Will this latest round of negative publicity put gene

therapy back in the doldrums or can an approval, believed by some to be coming in the next 2–3 years, finally bring some commercial success to the sector?

H eav y baggage Nowhere in biotechnology has the promise been more tantalizing and the failures more devastating than in gene therapy. The idea that scientists could treat the errors in our very DNA initially gave new hope to many, particularly the desperate parents of children with fatal inherited disorders such as cystic fibrosis or muscular dystrophy. Those dreams have been dashed, as repeated set backs have forced investigators to completely rethink the best ways to first move their brave new therapies into medical practice. Now that one of the bleakest episodes in gene therapy’s history has finally ended, at least officially, have we entered the dawn of better gene-based therapies? The answer seems to be yes, if the latest wave of products fulfill expectations.

N ATU RE BI O TECH N O LO GY VOLUME 23 NUMBER 5 MAY 2005

Most agree a pause for reflection was warranted, and that the result has been a much better understanding of the underlying science. But some investigators feel the new regulatory restrictions go overboard, unduly shackling those already using appropriate caution, by adding steps to an already difficult process. Ironically, although Paul Gelsinger, Jesse’s father, does believe the technology was hyped prematurely, he doesn’t blame gene therapy for his son’s death. “The problem wasn’t gene therapy,” he says. Rather he faults certain individuals and the system ( Box 2). Others saw it differently, however, and gene therapy was branded as an unusually risky field within the already volatile biotechnology sector. Some companies shifted their focus to new fields, or recast their work. “Because gene therapy has such a nasty reputation, people tried to rename it or call it ‘new and improved’ to free themselves of the stigma,” says Michael Zasloff, an analyst with Ferris, Baker Watts of Washington, DC. “That may fool the public, but the market sees through it,” he says. Wherever genetic manipulation is involved, no matter for how long or where in the body, investors typically treat it like gene therapy, despite what the product’s developers may say.

S afet y an d efficacy It is not just safety problems that have dogged gene therapy, efficacy has been much harder to achieve than expected. Katherine High, of Children’s Hospital of Philadelphia, has experienced the field’s ups and downs from the front row. Along with Mark Kay’s group at Stanford University and scientists at Alameda, California–based Avigen, she has spent years moving a gene therapy for hemophilia through to human studies. Mouse studies seemed promising and the dog studies were remarkable—with animals producing adequate levels of Factor IX for more than five years. Then, in the human trials, the therapy seemed to be working well in at least one patient. But that quickly proved to be an illusion. Five weeks later, the patient’s Factor IX levels started dropping and quickly reached baseline. “I have to admit, at first I was devastated. I couldn’t believe it didn’t last,” High says. Someone from Avigen reminded her to “think how the patient feels. For four weeks, he touched the rainbow.” The bottom line is that it has been extraordinarily difficult to get sustained delivery of any gene. Many diseases that originally seemed likely targets have also turned out to be devilishly tough. Cystic fibrosis, for example, was one of the first stops for gene therapy, and

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N E WS F E AT U R E many trials have been carried out by companies including Genzyme. “The CF lung is full of mucus and things like proteases that are hostile to vectors,” Gregory says. As a result, candidate gene therapies for genetic diseases are dropping like flies. Avigen recently announced it is refocusing entirely, moving into small molecule therapeutics and exploring options for keeping alive its gene therapy programs, which include the hemophilia treatment and one for Parkinson disease. “It has just been very tough on the business side,” says Avigen’s Glenn Pierce. “The timeline is long, and the hurdles are bigger than expected.” Strasbourg, France’s Transgene also completely abandoned ‘real’ gene therapy this year, and will now concentrate on its vaccine business. The company’s Duchenne/Becker’s muscular dystrophy program will continue through support from the French Association against Muscular Dystrophy. Finally, Targeted Genetics recently announced it is ceasing work in cystic fibrosis. Early results from the company’s latest phase 2 trial—the largest gene therapy trial ever conducted in this indication—did not confirm earlier encouraging results. The challenge still is getting enough gene expressed for a sufficient span of time. “The other sad or amusing thing was the gradual discovery of how much the immune system matters here,” says Doug Jolly, a gene therapy pioneer and now president and COO of Advantagene of Encinitas, California. An immune response is probably what derailed Avigen’s hemophilia treatment, and it is almost certainly what led to Jesse Gelsinger’s death. Miracle cures for genetic diseases are hard to deliver. Experts are still optimistic they will come, but the work on these conditions is now largely confined to academia and other research sponsored by nonprofits. Genzyme

B o x 1

O n t he S C I D S

Gene therapy’s big worry has always been that a genetic payload could integrate into the host genome in a trouble spot, where it would cause other diseases or even alter a patient’s germline. That concern now seems to have been validated by a series of events in France that have put gene therapy’s one successful treatment at risk. The work of Alain Fisher and Marina Cavazzanna-Calvo’s group at Necker Hospital was initially hailed as a stunning achievement, when in 2000 they reported successfully treating X-linked severe combined immunodeficiency (SCID) in infants using retrovirusbased gene therapy. It was called the first real validation of the field. But that victory was marred just a couple of years later, when one of the boys developed T-cell leukemia. Soon after the first case was discovered, another child developed cancer, and then another. One of the three boys died from leukemia last year. As these developments unfolded, SCID trials around the world were first stopped, then restarted, then stopped again. The FDA also held a special meeting in mid-March of this year to review the problem. Because X-SCID leads to early death if untreated, the consensus so far is that gene therapy should be considered only in children for whom there are no other treatment options. “ It’s still a wonderful success, but with very nasty possible side effects,” says Advantagene’s Douglas Jolly. In the first two cases, the cancer was triggered in the SCID kids after the retrovirus inserted near the LMO2 oncogene promoter. Something similar occurred in the third case, although a different oncogene was involved. Having reviewed the SCID cases, experts believe this side effect is caused by the very gene being treated in the French trial, because this effect has not been seen in other trials.

may be the one exception here, because it has a long history in the field and a good reason to stay in it. The company launched its first gene therapy trial (for cystic fibrosis) in 1992. It has yet to see a payoff from that work, but in the meantime Genzyme has built a major franchise around protein replacement treatment for the major form of Gaucher disease. That treatment, Cerezyme, is remarkably effectively, but patients with rarer forms of this condition are still incurable. “We are committed to serving all of these patients,” says Gregory. If a technology exists that will help more Gaucher patients, or improve upon what

Genzyme already has, the company wants to be the first to get it working. As a result, the company is investigating gene therapies for these alternative forms of Gaucher.

S o ber ly o p t imis t ic Those who’ve been in the field a while are philosophical about the problems. “We’ve finally been doing this long enough that bad things are cropping up,” says Gregory. Jolly, meanwhile, likes to point out that although trials began in 1990, it took longer for industry to get really involved. “We’ve only been doing serious drug development in this field for about

T able 1 S elect ed co mp an y - s p o n s o r ed gen e t her ap y t r ials Company/location

Indication/treatment site

Product/gene

Vector

Corautus Genetics Atlanta, Georgia

Severe angina/heart muscle

Vascular endothelial growth factor (VEGF) 2

Naked plasmid DNA 2b

Genzyme Cambridge, Massachusetts

Peripheral arterial disease/legs

HIF-1 α (an engineered form of the hypoxia-inducible factor 1 gene)

Adenovirus type 2

2b

BIOBYPASS/VEGF121 (proprietary form of VEGF)

Adenovirus

2b

Severe coronary artery disease, angina/ GenVec Gaithersburg, Maryland coronary arteries

Introgen Austin, Texas

Targeted Genetics Seattle, Washington

520

Clinical trial phase

Pancreatic cancer/tumor

TNFerade/tumor necrosis factor-α (TNFα)

Adenovirus

2

Age-related macular degeneration/eye

Pigment epithelium-derived factor (PEDF)

Adenovirus

1

Solid tumors/head and neck, lung, breast, esophagus, prostate, brain, pelvis

ADVEXIN/p53

Adenovirus

1–3

Solid tumors/various

INGN 241/mda7 (encodes IL24)

Adenovirus

1–2

Solid tumors/lung

INGN 401/FUS1 (a tumor suppressor gene)

Nanoparticle

1

Rheumatoid arthritis/joints

tgAAC94/TNFα

Adeno-associated virus (AAV)

1

VOLUME 23 NUMBER 5 MAY 2005 N ATU RE BI O TECH N O LO GY

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

N E WS F E AT U R E 12 years,” he says, pointing to the fabled 20 years it took monoclonal antibody developers to get it right. Important lessons have been learned, not just about side effects, but also about efficacy. “One rule is ‘Use vectors locally, before you try them systemically’” says High. The realization that different organs have different sideeffect profiles to the same vector has also been critical, reinforcing the importance of vector choice and route of administration. Finally, the explosion in knowledge about certain gene targets has been an unexpected boon. New research on angiogenesis and cancer, in particular, has revealed many attractive gene therapy targets. The result is a nice little wave of progress, albeit in more complicated diseases ( Table 1). Here the challenge will be finding strong end points to measure the therapies against. As Stephen Dunn of Boca Raton, Florida, securities firm Dawson James sums it up, the issue now is “Right vector? Right gene? Right target?” That’s a much different proposition than curing all genetic diseases with one magic vector, but it’s also a plan that is much more attractive to investors. “Do I like the new crop of gene therapies more than the old? Absolutely,” says Zasloff. Treating something locally and transiently with a gene therapy is much less risky than permanently altering gene expression. Even better, from the investor’s standpoint, these therapies are targeting more typical markets— conditions such as cancer, cardiovascular disease and rheumatoid arthritis. No one expects the floodgates to burst open soon, but there is widespread confidence that within the next 2–3 years, a gene therapy will be approved in the US. Others should follow, one by one. “Gene therapy’s first successes will be in something localized like solid tumors or eye diseases,” says Dunn. Work on new vectors will continue. Already, more research is going into the adeno-associated virus, lentivirus and nonviral vectors. High expect major progress on the vector front within the next ten years. Others agree. “In about 15 years, I like to think there will be several gene therapies hitting the market at once,” says Gregory. Although he cautions that if there is another major set back like the one at the University of Pennsylvania, “I can’t predict what the effect will be.” The first gene therapy approved in the US will most likely be Austin, Texas–based Introgen’s Advexin, which delivers normally functioning p53 to cells. Currently in late phase trials for head and neck, non-small-cell lung

B o x 2

A fin al r ecko n in g in G els in ger cas e

Just over five years ago Jesse Gelsinger went to the University of Pennsylvania’s Institute of Human Gene Therapy in Philadelphia to take part in a trial aimed at treating inborn ornithine transcarbamylase deficiency. But four days after he received the therapy, Gelsinger died of massive organ failure, apparently sparked by an immune reaction to the adenoviral vector used. In February of this year, the Department of Justice (DOJ) announced the final civil settlement in the case. The University of Pennsylvania and Children’s National Medical Center each agreed to pay more than $500,000 to the government. Both institutions have also been obliged to shore up patient safety procedures. Clinical research restrictions were placed on the three investigators involved—Penn’s James Wilson and Steven Raper, and Mark Batshaw, a former Penn doctor and now chief academic officer at Children’s National Medical Center. Neither the scientists nor the institutions named admit to any of the government’s allegations. Jesse’s father, Paul Gelsinger, is now vice president of Citizens for Responsible Care and Research. He says the settlement does not go far enough. Gelsinger maintains that Wilson’s industry ties played a role, that the Gelsingers were repeatedly misled throughout the ordeal and that the FDA should have stopped the trial earlier. “ This judgment lets everyone off the hook,” he says. David Hoffman, the US attorney general who prosecuted the case, says the DOJ, FDA, and NIH all approved the final settlement, and that it should serve as a lesson to investigators everywhere, “ To have the sense to always view subjects as people, not just ‘participants.’”

and breast cancer, Advexin is also in development as a mouthwash to treat precancerous lesions. The most intriguing thing about this treatment is how safe it appears to be. “These are highly specific, minimally toxic and very targeted products,” says Introgen’s president and CEO David G. Nance, who points out that the US Food and Drug Administration has approved trials of Advexin even in precancers. He adds that the company has tested its gene therapies in over 700 patients, and never had a trial stopped or put on hold. Introgen, which has several products in development, thus epitomizes the new gene therapy company. Its products are used transiently, tested in combination with other treatments, and locally delivered—Advexin can even be applied directly to a tumor during surgery. Once one or more gene therapies reach approval in the US, experts believe the big pharmaceutical companies will again start setting up partnerships in the sector. Major players such as Schering Plough of Kenilworth, New Jersey, and Novartis of Basel showed intense interest in gene therapy early on, but have all but abandoned the field over the last few years.

C hin es e checkmat e A wild card here is China’s bold move into this field. In 2003 the first gene therapy product was approved in that country,

N ATU RE BI O TECH N O LO GY VOLUME 23 NUMBER 5 MAY 2005

much to many people’s surprise. Probably most surprised were the management at Introgen, whose lead product is quite similar to the Chinese product—Shenzhen SiBiono GenTech’s recombinant Ad-p53 for head and neck squamous cell carcinoma (see Nat. Biotechnol. 22, 3, 2004). Dunn sees the Chinese connection as important. China is apparently positioning itself as a leader in the field, and hopes to attract medical tourists from afar with breakthrough therapies not available at home. Shenzhen SiBiono claims that about 400 Westerners have already visited China to receive the company’s treatment. “Find a Chinese partner,” is Dunn’s advice to gene therapy companies. Everyone in the field is already watching the developments in China closely. Introgen has chosen to file patents there judiciously, and try to work the political scene. It helps that some of the company’s Chinese patents have already been issued. “But we saw what happened to Pfizer,” says Nance. “They marched in with a strong patent for Viagra, and got nothing.” Advexin is so similar to Shenzhen SiBiono’s product that if the Chinese seek to commercialize their product in the US, “It would be an issue,” says an Introgen spokesperson. All the more reason for them to hope they get their US approvals soon and to keep an eye on the East. Malorye A. Branca, Boston, Massachusetts

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B I OE N EWS

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

A L S O O N W W W .N A T U R E .C O M /B IO E N T U K o n a q u es t fo r ear ly - s t age fin an cin g mo dels B r az il t o fo s t er p u blic- p r iv at e in n o v at iv e v en t u r es S T A R T - U P P R O F I L E : S er en ex

I n dia’ s s t r at egy t o br idge t he p u blic- p r iv at e div ide Public-private partnership is the cornerstone of India’s new draft National Biotechnology Development Strategy that aims at creating one million jobs and an annual turnover of $5 billion in the biotech industry by 2010. A draft of the policy for the National Biotechn ology Developm en t Strategy released on April 2 by science minister Kapil Sibal is due to be implemented later this year. The policy, which is geared towards encouraging public-private partnership, has already triggered a debate within the biotech industry because it would allow 100% foreign direct investment, an option that could threaten the local industry, according to opponents. The new strategy is designed to catapult India into the “global biotech league” says Maharaj Kishan Bhan, secretary to the New Dehli-based Department of Biotechnology (DBT) that drafted the policy. Bhan says the new strategy is built on the conviction that research for the “public good” and research “for profit” should become mutually reinforcing to help foster the development of innovation in biotech. The draft policy envisages that by 2010, biopharmaceuticals—mostly vaccines and bio-generics—will be contributing to $2 billion of the annual turnover of the sector in India. Clinical development services is forecasted to reach $1.5 billion and outsourced research services are estimated to reach $1 billion. The balance of $500 million is attributed to agricultural and industrial biotechnology. This is an ambitious target but given the growth of the industry in the previous year, the government has strong growth data to support its optimism. The Indian biotech industry grew by 39% between 2003 and 2004 to reach a value of $705 million. Total investment in the sector also increased by 26% during the same period to reach $137 million. Under the new policy, public funds can be spent on industrial projects while scientists who are employees of a public research institution can be seconded to private firms without losing any benefits. Furthermore, the policy states that “at least 30% of

Indranil Mukherjee/Agence France Presse

The Indian biotech industry has grown significantly over the past few years as it has successfully developed biotech drugs and vaccines

government-funded programs must have a commercial partner who will be responsible for directing research and development (R&D) towards commercialization.” “This kind of partnership is very welcome,” says Varaprasada Reddy, managing director of Shantha Biotechnics in Hyderabad, a company that pioneered recombinant vaccines in India in the 1990s. But one component of the strategy that allows “100% foreign direct investment” is causing resentment in the industry. “The policy should have also insisted on partnership between local and foreign companies in the ratio of at least 25:75 [rather] than allowing foreigners total ownership,” points out Reddy. Indeed, the new policy dispenses with the need for government approval for equity investment in the biotech sector unlike in other sectors like telecommunications or energy. “What this means is multinational companies can come with suitcases full of money, buy up plots, build plants, hire our scientists at low salaries and create wealth for themselves,” says Reddy, adding, “That is a prescription for killing the local industry. We all will be dead.” Others like Bhimsen Bajaj, president of the southern chapter of the All India Biotechnology Association in Hyderabad, disagree. In his experience, foreign companies are not so interested in forming

N ATU RE BI O TECH N O LO GY VOLUME 23 NUMBER 5 MAY 2005

joint ventures, but he recognizes that with this new policy, they will bring new technology and generate jobs, thus enabling the country to develop. “There is nothing wrong in this,” he says, adding, “we have to be open minded in these days of globalization.” The DBT says it will not finalize its strategy until it receives all opinions on the draft biotech strategy that is open for comments until May 15. After that, the strategy will be submitted to the cabinet for approval before implementation. Despite the opposition to this aspect of the policy, India’s priority in supporting publicprivate partnership could well benefit the industry. As proposed, public-private partnership can take several forms. In one form government institutes will partner with small and medium-sized companies such as biotech that have bright ideas but lack qualified staff. Once the project passes the proof-of-concept stage the company would become eligible for soft loans for product development and commercialization. The R&D expenditure of public sector institutes, while working on the projects of their private sector partners, will be met by grants from DBT. In another form, the strategy aims at creating several “Technology Transfer Cells” that will promote the transfer of knowledge generated within publicly funded research institutions to the private sector. Biotech parks promoted by private industry will also get 30% equity funding from government. And the DBT will promote and support at least ten biotech parks by 2010. “This will really give a boost to the biotech infrastructure in the country,” says Krishna Ella, managing director of vaccine and biologics company Bharat Biotech International in Hyderabad. K.S. Jayaraman, Hyderabad This story was reprinted with some modification from the BioE News section of the Bioentrepreneur web portal (http://www.nature.com/bioent), 7 April 2005, doi:10.1038/bioent857.

For more news and analysis go to www.nature.com/news

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B U I L D I N G A B U SI N ESS

T he r o le o f co mp et it iv e in t elligen ce in bio t ech s t ar t u p s Salvador Carlucci, Anthony Page & David Finegold C o mp et it iv e in t elligen ce ( C I ) gat her in g is es s en t ial t o dev elo p in g a bio t ech fir m’ s bu s in es s s t r at egy , bu t few s t ar t u p s hav e s u fficien t s u p p o r t s y s t ems in p lace t o do C I effect iv ely . T his ar t icle, t he fir s t o f t w o o n t he t o p ic, addr es s es w hy it is imp o r t an t an d ho w t o do it .

All biotech startups gather competitive intelligence (CI), although often they are not aware they are doing so. When a scientist attends a professional conference to learn about emerging technologies and who is working on them, when an employee stops at a rival’s booth at a trade show to pick up information about their products, when a business development expert reads a market report, or even when an executive chats with a friend about trends in their industry, they are gathering CI. The problem is that in most biotech startups no one manages this flow of information, nor is it organized in a systematic way1. As a consequence, the firm does not maximize the value of these critical resources. In this article, the first of a two-part series, we will describe the types of competitive intelligence and the benefits for a small startup, and provide some examples of CI both working well and not. In the next article, we will provide guidelines on structuring a framework for collecting and providing CI to the people in one’s company who need it, and we will examine the legal and ethical considerations of gathering CI.

C o mp et it iv e in t elligen ce defin ed Competitive intelligence is often mistakenly thought of in a narrow way, as a means of gathering ‘secret’ information that can be used to gain advantage over competitors2. We adopt a much broader view of CI, defining it as the analytical process that transforms Salvador Carlucci and Anthony Page are at HealthIQ, 770 The City Drive South, Suite 7400, Orange, California 92868, USA and David Finegold is at the Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, California 91711, USA. e-mail: [email protected]

disaggregated market and competitor data into relevant strategic knowledge that can be readily put to use by all relevant members of the company. From this broader perspective, CI is closely related to other core management concepts such as strategic planning, business intelligence, market analysis and knowledge management. Com petitive intelligence is an ongoing process that is useful at all levels of an organization (see Box 1). It allows forwardthinking business leaders to clearly define the marketplace, to ask disciplined questions, and to receive timely and reliable answers to them 2. It also can help scientists learn about new technologies in the market that could greatly benefit the company by improving discovery platforms or by reducing manufacturing costs. In addition, CI can be used to keep an organization functioning well when key employees leave, by ensuring that they do not take all of their knowledge with them. And it can be used to train new employees so that they more quickly understand the firm’s strategy and competitive

B o x 1 • • • • • • • • •

marketplace and therefore become fully productive faster 3. For all these reasons, CI done well can enhance a company’s probability of success in the highly risky biotech environment by reducing uncertainty and improving investment decisions, whereas a failure to obtain CI can threaten a firm’s survival (see Box 2).

W hat t y p es o f in t elligen ce ar e n eeded? To begin creating a CI system, a company must determine what knowledge it needs to set its strategy and operate its business. The specific types of knowledge needed and the priority placed on them will vary according to the company’s market, but companies generally should have knowledge in at least nine different areas: 1. Intellectual property. Depending on the company’s resources, one should do a comprehensive patent literature search at least once a year. When searching for patents, it is useful to start with the European Patent Office, which publishes

P o t en t ial ben efit s o f co mp et it iv e in t elligen ce2

,4

Identify opportunities and potential customers Gain competitive advantage by reducing reaction time Improve short- and long-term strategic planning Create company benchmarks and reveal how competitors do business Provide guidance on pricing, delivery, product development, outsourcing and clinical research decisions Anticipate changes in the regulatory and reimbursement environment that may profoundly affect the firm or its industry Identify emerging technologies and their potential impact on the competitive environment Assess merger and acquisitions candidates, joint-venture, academic and alliance partners Provide warning when key strategic assumptions are changing and prevent surprises

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T he ben efit s o f C I an d t he co s t s o f n o t do in g it

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

Example 1 : Bad. Company A, a small biotech firm, had its strategy

consulting firm do a limited CI study of the market for migraine medications to support projections that would be presented to potential investors. To conserve resources, the company focused on immediate competitors with similar compounds. As they were preparing for presentations, it was learned that a competitor product already on the market would be approved for this indication and would offer comparable efficacy at a fraction of the cost. The company’s market had disappeared. Retrospective analysis revealed that this competitive development was the result of a large clinical trial program (with over 6,000 patients) that had been going on for the past two years and that could have been easily detected by a more thorough CI program. The company subsequently abandoned the indication, laid off large numbers of employees, and saw its stock price plunge and its financial support evaporate. Example 2 : Better. Company B commissioned an in-depth CI study

while its drug was in phase 2 clinical trials. The study confirmed

that a major pharma company had a compound that was far ahead. This company already had a dominant position in the market, which would be very difficult for Company B to overcome without clearly superior efficacy. When interim phase 2 data showed that this was not the case, the company terminated development and avoiding sinking additional investment into a dead-end indication. The company estimates it saved at least $30 million. Example 3 : Best. Company C directed CI efforts at their

competitor’s product development program. Systematic discussions with dozens of researchers on the likely safety and efficacy benefits of products in development revealed that the competitor was including novel safety endpoints in their clinical trial. The company concluded that the competitor intended to use the resulting data to make an enhanced safety claim. The company refined its own protocol to one-up the competitor and was able to make even stronger safety claims that effectively differentiated its product and created a substantial competitive advantage.

all patent applications within 18 months of when they are filed, usually after one year, whereas the USPTO will not publish patents until 18 months after they are filed. This is valuable information, because most countries do not make a patent available to the public until it has been reviewed by the patent office.

and size will change. It is therefore vital to keep CI up to date by regularly consulting with key people, such as members of a carefully chosen Scientific Advisory Board and a broad sample of experts in the relevant fields. Networking at professional or industry conferences is a good way to do this.

2. Market need and size. Identifying target market segments will allow the company to know what markets competitors are planning to move into or are ignoring. During the long development periods required for most biotech products, the market needs

3. Partnerships. By monitoring new technologies entering the market and in development, the company can identify possible partnerships with other companies and academic institutions. Scientific journal and patent literature searches along with pro-

B o x 3

4. Competitive environment. It is important to continuously monitor the competition. Some players will drop out, while new, potentially disruptive technologies developed by small firms may enter the market that may not be readily apparent as competitors. The company has to be very expansive in thinking about the possible kinds of competitors. By attending conferences and examining relevant ads, the company can assess competitors’ product strategies.

C r eat iv ely u s in g in t er n al an d ex t er n al in fo r mat io n

Internal information. Company Y hired a CI provider to determine

why they were losing major accounts to a competitor. As a first step, the CI firm reviewed the company’s internal data, including e-mail correspondence from its own sales force reporting rumors they’d heard from customers. This correspondence included references to a sequence of activities that clearly indicated what had happened. Over a four-month period, different company sales reps reported: (i) competitor sales reps visited the customer, (ii) the customer requested information on Company Y’s discounting policy, (iii) the competitor was using “ a cost-effectiveness outcomes trial” as a marketing tool, and (iv) the competitor had initiated an “ outcomes trial” at the account’s facility. One month later the account switched to the competitor. These pieces were not assembled and analyzed because the company did not have any internal resources dedicated to systematically keeping an eye on competitor activity. External information. A large pharmaceutical company conducted

a CI assessment of a competitor’s product marketing strategy for a synthetically produced compound in development. An analysis of financial information revealed that the competitor had purchased a large volume of agricultural futures in a particular flower. A

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fessional conferences can all be potentially fruitful sources of new partners.

review of the company’s foreign marketing materials revealed that their product was being test-marketed in a European country using an “ all-natural” direct-to-consumer (DTC) message. Finally, a personality profile of the senior marketing executive showed a history of innovative DTC campaigns. Based on this information, it was determined that the competitor intended to produce the compound using the more expensive natural production method in order to use the “ all-natural” marketing position in the US market. The product’s key market positioning strategy was thus identified 12 months before its scheduled US launch. Creative thinking. While attending a symposium to collect

intelligence, a CI analyst heard one of his client’s trial investigators comment during his presentation that a particular treatment pathway (not the client’s) was likely to be an area of “ future development.” On returning home, the analyst researched the speaker and found that he had an association with the president of a startup company focused on this pathway. This startup company was funded by a major rival and was working on a next-generation product to leapfrog the company’s own product. Additional research revealed that this researcher also had an undisclosed financial relationship with the competing startup company.

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B U I L DI N G A B U SI N ESS 5. Marketing and distribution. By talking with distributors’ and competitors’ sales forces, the company can determine how competitors are getting their products to market. This information can help the company develop its own more efficient and targeted strategy for product marketing and distribution. The company can, for example, look at how much competitors spend on advertising or how big competitors’ sales forces are to create benchmarks for its own goals and performance. Although most people will decline to talk to a “competitor,” many will talk to their “peers” in other companies if the questions are asked in the right way. 6. Technology opportunities and risks. By reading the publications of competitors’ scientists and their academic partners and talking with them at conferences, the company can identify the bottlenecks that competitors have encountered when developing similar technologies. 7. Regulatory and reimbursement issues. Surveying the regulatory agencies is one way to determine the current regulatory requirements and identify new issues that might affect the approval of a product or the way it is labeled and marketed. With a CI process one can examine the various factors in the regulatory environment and anticipate changes that may profoundly affect the enterprise. 8. Financing options. One of the most vital tasks for the leader of any startup is ensuring

the resources that the firm needs to operate are available. CI can help determine which venture capitalists are investing in the firm’s technology area and what organizations might be interested in acquiring those technologies. This data can better establish the value of a company during financing and can potentially strengthen a firm’s negotiating position. In addition, CI can be used in examining merger and acquisition candidates, government grants and joint-venture partners that could provide alternative sources of funding, thereby increasing the firm’s negotiation leverage. 9. Human capital. Salary surveys and analyses of job ads can provide important insights into competitors’ staffing strategies. Likewise, recruiting agencies, while keeping their client information confidential, may be good sources for industry skill trends and the strategies of non-client firms. This kind of information can allow the company to determine the type of people it needs to succeed in a market niche and what it will take to attract and retain them.

C o n clu s io n Competitive intelligence is a vital part of creating a sound business strategy, and obtaining it effectively can bring multiple benefits to an organization. But small companies often do not have the expertise or systems in place to get the full value from CI. They must carefully identify their top CI priorities and the resources required to meet these specific intelligence needs, then track and control the allocation of

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resources to achieve these tasks. As the company grows, the CI function should also grow to maintain its competitive advantage and exploit the opportunities that CI provides. Although broad organizational involvement is important for gaining the full value of CI, there should always be one person accountable for clearly establishing and communicating the CI development objectives. This person should be responsible for ensuring the CI process is gathering the necessary information and then distributing it to the right people. Those employees directly involved in CI data-gathering and analysis need good industry and technical knowledge, especially at the tactical level, and a solid understanding of secondary research. In addition, CI staff should have strong interpersonal, problem-solving, written and oral communication skills, because they will be collating information from both internal and external sources (see Box 3). They should also have a thorough knowledge of ethical and legal implications of their activities, which will be discussed in the next article in this series. This story was reprinted with some modification from the Building a Business section of the Bioentrepreneur web portal (http://www.nature.com/bioent), 21 March 2005, doi:10.1038/bioent850. 1. Hodgson, J. The headache of knowledge management. Nat. Biotechnol. 19, BE44–BE46 (2001). 2. Nolan J. Confidential (HarperBusiness, New York, 1999). 3. Stewart, T.A. Intellectual Capital: The New Wealth Of Organizations (Doubleday/Currency, New York, 1997). 4. Ashton, W.B. & Klavans, R.A. Keeping Abreast of Science and Technology: Technical Intelligence for Businesses (Battelle Press, Columbus, Ohio, USA, 1997).

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COR R ESP ON D EN CE

T he o r igin s o f n ew dr u gs T o t he edit o r : There is some debate as to the relative contribution of publicly funded research (universities, government research institutes and academic medical centers), biotech companies and pharmaceutical companies to the discovery of new medicines. To gain a clearer understanding of the origin of newly marketed drugs, I have analyzed data from the US Food and Drug Administration (FDA, Rockville, MD, USA), US Securities and Exchange Commission (SEC, Washington, DC) and the US Patent and Trademark Office (PTO, Washington, DC) to determine the origin of most of the new molecular entities (NMEs) and new biological entities (NBEs) approved by the FDA from 1998 to 2003. To carry out this analysis, I obtained lists of NMEs and NBEs approved each year from 1998 to 2003 from the FDA website (http:// www.fda.gov/), which provided each drug’s sponsor (that is, the company seeking drug approval that usually owns the drug or holds an exclusive license to the patents covering the drug). In the case of NMEs, the sponsor must identify the patents (if any) describing

T able 1

the chemical compounds that constitute the NMEs (if such compounds are patentable), methods of NME manufacture or uses of the NME. I excluded from the analysis nine NMEs that are imaging agents and one chemical warfare protective paste developed by the US Army. I found patents covering all the other NMEs (some expired but still relevant as to origin) except Vioxx (rofecoxib; 1999), which Merck (Rahway, NJ) has recently withdrawn from the market, and nine other NMEs for which the FDA Orange Book states “no unexpired patents”. (SEC documents showed that one of these nine, Valstar (valrubicin; 1998, originated in Dana Farber.) In addition, a few NMEs only have recently filed use or method-of-delivery patents that do not provide clues as to origin. Nevertheless, the patent records combined with SEC documents and occasional internet searches give a fairly good picture of the main loci of early stage and preclinical development in the case of all but 14 of the total 145 NMEs. In the case of NBEs, I reviewed Recombinant Capital’s Signals Magazine (http://www.signalsmag. com), which periodically publishes analyses of

licensing data from its rDNA database (http:// www.recap.com/rdna.nsf). I also reviewed 10-K reports filed annually to the SEC by the companies that sought FDA approval for the NBEs. Small and mid-sized biotech companies often mention the existence of in-licenses covering their NBEs that have just received FDA approval, although pharmaceutical companies and large biotechs rarely mention such in-licenses. It is possible that I have not identified the principle origin of some of the NBEs submitted for approval by pharmaceutical companies and large biotechs. The results of the analysis are summarized in Table 1. The data reveal that at least 39% of all (171) drugs (both NMEs and NBEs) approved by the FDA from 1998 to 2003 originated from outside pharmaceutical companies: ~24% came from biotech companies and at least 15% came from public research. Of the drugs that originated from public research, 19% were licensed to pharmaceutical companies and 81% were licensed to biotech companies. In cases when a public research institution’s patents had expired, the drug

T he o r igin o f F D A - ap p r o v ed medicin es

C at ego r y

Y ear ( s ) ap p r o v ed by F D A 1 9 9 8

1 9 9 9

2 0 0 0

2 0 0 1

2 0 0 2

2 0 0 3

1 9 9 8 – 2 0 0 3

FDA drug approvals

Total

34

34

28

26

22

27

No. originating from biotech R&D

14

11

9

8

7

13

171 62

No. based on university invention

4

8

4

3

2

5

26

University inventions licensed directly to pharma company

1

2

2

0

0

0

5

145

New molecular entities (NMEs)

Total

29

33

26

21

15

21

No. originating from biotech R&D

10

10

7

4

2

7

40

No. based on university invention

4

7

4

1

1

3

20

University inventions licensed directly to pharma company

1

2

2

0

0

0

5

New biological entities (NBEs)

Total

5

1

2

5

7

6

26

No. originating from biotech R&D

4

1

2

4

5

6

22

No. based on university invention

0

1

0

2

1

2

6

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COR R E S P ON D E N CE was simply developed by a pharmaceutical or biotechnology company. Thus, biotech companies either discovered or played a major role in developing 36% of all new drugs (NMEs and NBEs). As expected, biotech companies have dominated the development of NBEs, discovering or playing a key role in the development of 22 (85%) of the 26 new NBEs. Of the 22 biotech NTBs, six (27%) were licensed to pharmaceutical companies, which then applied for FDA marketing approval. Biotech companies themselves applied for marketing approval for the remaining 16. In 4 of these 16 cases, the biotech that applied for marketing approval had in-licensed the NBE from another biotech. At least 6 (27%) of the 22 biotech-developed NBEs were based upon inventions made in public research. There appear to be no cases of a university directly licensing an invention covering an NBE to a pharmaceutical company. Biotech companies and public research also contributed to a significant but lesser degree to the discovery of new NMEs, at least 45 (31%) of which were discovered outside of pharmaceutical companies. Forty (27%) were discovered or developed in biotech companies, and in most of these cases, a biotech company pursued development all the way to obtaining marketing approval. In the case of 30 of the 40 biotech company-developed NMEs, the biotech company was also the applicant for FDA marketing approval. Nine of these 30 were licensed from one biotech company to another, which subsequently assumed responsibility for obtaining FDA approval. Twenty (14%) of the NMEs are covered by university patents. Five of the drugs of university origin were licensed directly to pharmaceutical companies rather than to biotechs. Fifteen (38%) of the 40 NMEs developed by biotech companies originated in public research institutions. Others have described the importance of linkages between universities, biotech companies and pharmaceutical companies for the discovery and development of new drugs1–6. The analysis described here provides an objective estimate of the contribution in drug discovery not only of biotech companies but also of public research (to the extent that university involvement is reflected in patents covering the new drugs). In the case of NBEs, the data indicating the contribution of public research or biotech companies to drug discovery are lower-bound estimates because the FDA does not publish information about the patents covering NBEs. Thus, it is difficult to know whether a pharmaceutical company that has received

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permission to market an NBE might have inlicensed the NBE from a biotech company or public research institution. It is also difficult to know whether a biotech company that has received marketing approval for an NBE might have in-licensed key discoveries from a public research institution, although the SEC filings often provide this information. In addition, patents reflect only a portion of the total contribution to drug discovery and development. Cockburn 1 has shown that even before university patenting of biomedical discoveries became commonplace, the vast majority of the most therapeutically important drugs approved in the 1960s and 1970s owed their discovery in large part to public research. On the other hand, even though patented discoveries in a university or biotech laboratory may have been important in the discovery or development of a new drug, subsequent R&D in the pharmaceutical or biotech company that ultimately applies for approval also reflects considerable scientific and innovative effort. Thus, these findings do not suggest a diminished contribution of pharmaceutical companies but rather confirm the integrated nature of drug discovery and development and the substantial contributions of biotechnology companies and universities. Compared with Cockburn’s earlier analysis, the data presented here also suggest that a larger proportion of university discoveries directly relevant to drug discovery are now being transferred as formal patent licenses to new small companies. These formal (and presumably exclusive) licenses undoubtedly

help biotech companies to obtain private investment and thereby continue drug development. These findings also indicate that biotech companies which are the original discoverers of drugs ultimately approved (whether NMEs or NBEs) more often than not pursue development of these drugs all the way through approval. One interpretation of this finding is that, when a biotech company discovers a drug that turns out to be a winner, it usually manages to obtain resources to pursue development all the way to marketing approval (that is, biotech companies and their investors do a pretty good job of picking and holding onto winners). However, size does matter. Small biotechs are more likely to out-license their winning drugs than large biotechs. Finally, although I show data for each year, clear time trends are not apparent. Robert Kneller University of Tokyo, RCAST, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan . e-mail: [email protected] 1. Cockburn, I. & Henderson, R. Public-Private Interaction and the Productivity of Pharmaceutical Research. National Bureau of Economic Research (NBER) Working Paper 6018 (NBER, Cambridge, MA, 1997). 2. Powell, W.W., Korput, K.W. & Smith-Doerr, L. Administr. Sci. Quart. 4 1 , 116–145 (1996). 3. Murray, F. Res. Policy 3 1 , 1389–1403 (2002). 4. Henderson, R., Orsenigo, L. & Pisano, G.P. in Sources of Industrial Leadership, Studies of Seven Industries (eds. Mowery, D. & Nelson, R.) 267–311 (Cambridge University Press, Cambridge, UK, 1999). 5. McKelvey, M. Evolutionary Innovations, the Business of Biotechnology (Oxford University Press, Oxford, 1996). 6. Zucker, L.G. & Darby, M.R. Proc. Natl. Acad. Sci. USA 9 3 , 12709–12716 (1996).

F r amin g t he is s u es o n t r an s gen ic fo r es t s T o t he edit o r : Your News Feature in the February issue ( Nat. Biotechnol. 23, 165–167, 2005) highlighted rapid advances being made in forest molecular domestication. Counter to Herrera’s assertion that “most of the global funding for forest biotech is being funneled to universities,” the pursuit of genetic engineering in forest research is principally corporate, shaped by the imperatives of private investment, market forces and government regulatory institutions. Novel forest tree phenotypes are thus created as a means to increase shareholder value of investor companies. And although potential benefits will accrue to shareholders, it is clear that ecological risks of certain transgenic traits engineered into trees are likely to be shared by all. Indeed, as the

forest-products companies driving adoption of transgenic technology hold less than 11% of US forest acreage, it is the remaining majority— public landowners and private small woodlot owners—that stands to lose the most. Herrera indicates in his article that for forest biotech, “investors are virtually nonexistent.” Even so, private investment in forest biotechnology is still sufficient to be fueling the creation of novel transgenic phenotypes in trees at a rate that is outstripping public policy deliberation and scientific assessment of environmental concerns specific to trees. For example, trees disperse their seed and pollen over unprecedented distances compared with crops. The sheer scale of gene flow dynamics

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COR R E S P ON D E N CE was simply developed by a pharmaceutical or biotechnology company. Thus, biotech companies either discovered or played a major role in developing 36% of all new drugs (NMEs and NBEs). As expected, biotech companies have dominated the development of NBEs, discovering or playing a key role in the development of 22 (85%) of the 26 new NBEs. Of the 22 biotech NTBs, six (27%) were licensed to pharmaceutical companies, which then applied for FDA marketing approval. Biotech companies themselves applied for marketing approval for the remaining 16. In 4 of these 16 cases, the biotech that applied for marketing approval had in-licensed the NBE from another biotech. At least 6 (27%) of the 22 biotech-developed NBEs were based upon inventions made in public research. There appear to be no cases of a university directly licensing an invention covering an NBE to a pharmaceutical company. Biotech companies and public research also contributed to a significant but lesser degree to the discovery of new NMEs, at least 45 (31%) of which were discovered outside of pharmaceutical companies. Forty (27%) were discovered or developed in biotech companies, and in most of these cases, a biotech company pursued development all the way to obtaining marketing approval. In the case of 30 of the 40 biotech company-developed NMEs, the biotech company was also the applicant for FDA marketing approval. Nine of these 30 were licensed from one biotech company to another, which subsequently assumed responsibility for obtaining FDA approval. Twenty (14%) of the NMEs are covered by university patents. Five of the drugs of university origin were licensed directly to pharmaceutical companies rather than to biotechs. Fifteen (38%) of the 40 NMEs developed by biotech companies originated in public research institutions. Others have described the importance of linkages between universities, biotech companies and pharmaceutical companies for the discovery and development of new drugs1–6. The analysis described here provides an objective estimate of the contribution in drug discovery not only of biotech companies but also of public research (to the extent that university involvement is reflected in patents covering the new drugs). In the case of NBEs, the data indicating the contribution of public research or biotech companies to drug discovery are lower-bound estimates because the FDA does not publish information about the patents covering NBEs. Thus, it is difficult to know whether a pharmaceutical company that has received

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permission to market an NBE might have inlicensed the NBE from a biotech company or public research institution. It is also difficult to know whether a biotech company that has received marketing approval for an NBE might have in-licensed key discoveries from a public research institution, although the SEC filings often provide this information. In addition, patents reflect only a portion of the total contribution to drug discovery and development. Cockburn 1 has shown that even before university patenting of biomedical discoveries became commonplace, the vast majority of the most therapeutically important drugs approved in the 1960s and 1970s owed their discovery in large part to public research. On the other hand, even though patented discoveries in a university or biotech laboratory may have been important in the discovery or development of a new drug, subsequent R&D in the pharmaceutical or biotech company that ultimately applies for approval also reflects considerable scientific and innovative effort. Thus, these findings do not suggest a diminished contribution of pharmaceutical companies but rather confirm the integrated nature of drug discovery and development and the substantial contributions of biotechnology companies and universities. Compared with Cockburn’s earlier analysis, the data presented here also suggest that a larger proportion of university discoveries directly relevant to drug discovery are now being transferred as formal patent licenses to new small companies. These formal (and presumably exclusive) licenses undoubtedly

help biotech companies to obtain private investment and thereby continue drug development. These findings also indicate that biotech companies which are the original discoverers of drugs ultimately approved (whether NMEs or NBEs) more often than not pursue development of these drugs all the way through approval. One interpretation of this finding is that, when a biotech company discovers a drug that turns out to be a winner, it usually manages to obtain resources to pursue development all the way to marketing approval (that is, biotech companies and their investors do a pretty good job of picking and holding onto winners). However, size does matter. Small biotechs are more likely to out-license their winning drugs than large biotechs. Finally, although I show data for each year, clear time trends are not apparent. Robert Kneller University of Tokyo, RCAST, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan . e-mail: [email protected] 1. Cockburn, I. & Henderson, R. Public-Private Interaction and the Productivity of Pharmaceutical Research. National Bureau of Economic Research (NBER) Working Paper 6018 (NBER, Cambridge, MA, 1997). 2. Powell, W.W., Korput, K.W. & Smith-Doerr, L. Administr. Sci. Quart. 4 1 , 116–145 (1996). 3. Murray, F. Res. Policy 3 1 , 1389–1403 (2002). 4. Henderson, R., Orsenigo, L. & Pisano, G.P. in Sources of Industrial Leadership, Studies of Seven Industries (eds. Mowery, D. & Nelson, R.) 267–311 (Cambridge University Press, Cambridge, UK, 1999). 5. McKelvey, M. Evolutionary Innovations, the Business of Biotechnology (Oxford University Press, Oxford, 1996). 6. Zucker, L.G. & Darby, M.R. Proc. Natl. Acad. Sci. USA 9 3 , 12709–12716 (1996).

F r amin g t he is s u es o n t r an s gen ic fo r es t s T o t he edit o r : Your News Feature in the February issue ( Nat. Biotechnol. 23, 165–167, 2005) highlighted rapid advances being made in forest molecular domestication. Counter to Herrera’s assertion that “most of the global funding for forest biotech is being funneled to universities,” the pursuit of genetic engineering in forest research is principally corporate, shaped by the imperatives of private investment, market forces and government regulatory institutions. Novel forest tree phenotypes are thus created as a means to increase shareholder value of investor companies. And although potential benefits will accrue to shareholders, it is clear that ecological risks of certain transgenic traits engineered into trees are likely to be shared by all. Indeed, as the

forest-products companies driving adoption of transgenic technology hold less than 11% of US forest acreage, it is the remaining majority— public landowners and private small woodlot owners—that stands to lose the most. Herrera indicates in his article that for forest biotech, “investors are virtually nonexistent.” Even so, private investment in forest biotechnology is still sufficient to be fueling the creation of novel transgenic phenotypes in trees at a rate that is outstripping public policy deliberation and scientific assessment of environmental concerns specific to trees. For example, trees disperse their seed and pollen over unprecedented distances compared with crops. The sheer scale of gene flow dynamics

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COR R E S P ON D E N CE for trees presents a daunting challenge in assessing the environmental impact of a transgenic trait ( Fig. 1). Second, trees produce an abundance of seed and pollen for many years before they are ready for timber harvesting. Thus, in contrast to seasonally harvested crops, pollen and seeds from trees disperse without hindrance into their surroundings for many years. As seed and pollen production increase with the age and height of a tree, each year more seed and pollen travel progressively farther by a process known as long-distance dispersal. And third, most commercially cultivated tree species have many wild relatives that grow in similar locations; thus there is a high potential for mating. In contrast, in the US at least, such crops as corn, cotton and soybeans have no wild or weedy relatives in the vicinity, making gene spread from transgenic varieties more unlikely. It is instructive to discuss these concerns in the context of loblolly pine ( Pinus taeda) , a tree indigenous to the southeastern United States and our major timber commodity. Pinus taeda grows as natural or plantation forests on nearly 58 million acres in the American South, providing 16% of the world’s annual timber supply. Annual planting demand is roughly one billion seedlings per year. Harvest age for P. taeda is between 25 and 35 years, so standing timber may be bought and sold several times before harvesting. Pinus taeda and other pines have been domesticated since the mid-20th century— relatively recent compared with food crops. Thus, it is likely that a conifer expressing a transgenic trait would thrive without human intervention after escape into an unmanaged ecosystem. Because traditional breeding is managed at a population level to conserve genetic diversity, neither inbred lines nor even breed structure exists for domesticated P. taeda trees. The cost and effort of traditional breeding has been borne by the private sector with the notable exception of a few state agencies. No gene conservation program has been formalized by the federal government for P. taeda. Certain biotechnology firms—Arborgen in Summerville, South Carolina, and CellFor in Vancouver, Canada—cited by Herrara in his article now offer clonal P. taeda trees to timber companies via somatic embryogenesis (the culture of undifferentiated cells from immature embryos to yield unlimited quantities of a single genotype). The availability of somatic embryogenesis for P. taeda makes genetic engineering of this species feasible on a commercial scale for the first time.

Flux of DNA Parcels Canopy air flow Canopy height Particle properties Amount of pollen and seed

≤ 0.1 km

Pollination Topography Pollen viability Opportunity for pollination

≤ 10 km

≤ 1000 km

• Experiments • Observations • Paleo-evidence of past invasions • Deterministic and stochastic modeling Figure 1 Gene flow from transgenic conifers is more complex than gene flow from annual row crops. Source: R. Oren, Duke University

As yet, no extensive analysis of the environmental impact of a P. taeda transgene has been undertaken. What is clear is that these trees would outcross and produce abundant windborne pollen and seeds each year. Consider that a fraction of seeds uplifted above the forest canopy will move by the long-distance dispersal process as far as 11.9 to 33.7 km. Out of 105 seeds produced per ha–1 yr –1 in a 16-year old plantation, roughly 70 seeds ha–1 will reach distances in excess of 1 km from the source, a distance too great to serve as a biocontainment zone. Pollen dispersal distances are even greater. The probability of long-distance dispersal of transgenic conifer seeds and pollen at distances exceeding 1 km approaches 100%. Although 99.9% of P. taeda seeds and pollen fall near the source tree, via a dispersal process known as local neighborhood diffusion, it is the remaining 0.01% that pose the greatest ecological concerns. Longdistance dispersal provides the biological mechanism for establishment of remote satellite colonies from transgenic P. taeda seeds and pollen, even though it is not the most common process of dispersal. To date, the benefits of specific transgenic traits in P. taeda have not been fully gauged because technology innovation is recent and transgenic wood products have not reached timber harvest age. But what will happen as these tests get older? At present, transgenic P. taeda test stands must be cut down at onset

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of reproduction, whereas the species reaches peak merchantable value only after the age of 25 years. This constitutes a regulatory impasse for collecting data on benefits, especially given prospects of transient expression or ‘genesilencing’ through harvest age. Risk analysis is similarly incomplete. Mathematical models suggest that movement of escaped transgenic seed and pollen on the scale of kilometers from the source is a certainty. Movement of transgenic pollen and seeds is problematic only if there is potential harm associated with a specific transgenic trait, but potential harm has not been tested. To be harmful, a tree must express a transgenic trait that exhibits enhanced invasiveness properties compared with a wild type. Increased invasiveness is harmful if it translates into displacement of local endemic species or even long-term forest maladaptation. No experimental evidence, pro or con, yet exists to show whether specific transgenic traits in the context of forests are harmful. The takehome message is that no experimental results for either benefits or risk associated with transgenic P. taeda are available. Commercial exploitation of transgenic trees, particularly indigenous conifer P. taeda, is technically imminent; putting this into practice will, however, be stymied by concerns over the environmental impact of gene flow and the unique pattern of ownership of forest lands in the United States.

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COR R E S P ON D E N CE The certainty of gene flow from transgenic forests is problematic because neighboring lands are often less intensively managed public and private forest lands. At present, the scale and staggering expense of regulatory oversight alone could drive the political outcome in the absence of risk-benefit analyses. Ecological consequences of investment decisions on private lands deserve closer scrutiny at a national level. Calls for public deliberation are coming late in the life of the forest product life cycle. I advocate that transgenic conifers be considered separately from agricultural biosafety policy due to the sheer scale and complexity of forest tree gene flow. Biocontainment zones suited to transgenic food crops cannot deter escape of seeds or pollen from transgenic P. taeda. Reproductive sterility research for conifers, a complex problem, remains in its infancy and has not received serious consideration as a national research priority. There is thus an urgent need for policy makers to move on two fronts. First, a gene conservation program should be formalized through the National Forest System. In Region 8 of the southern United States, for example, indigenous P. taeda forests need to be protected from the potential impact of transgenic varieties. Widespread use of clonal forests with or without genetic engineering will likely rapidly narrow the numbers of P. taeda genotypes, opening the question of

how to protect undomesticated germ plasm and close relatives, which remain largely undomesticated. Second, forestry-specific research programs that address key issues specific to the implementation of transgenic technology in forestry need to be promoted within the existing cadre of national competitive funding programs. We are in dire need of funding for research to gauge the environmental impact of gene flow from trees. At present, we remain ignorant on numerous aspects of tree biology and ecology that affect whether or not we should proceed. Can pine pollen move in the jet stream and, if so, will it remain viable? How does gene flow from transgenic P. taeda affect indigenous pine forests or small woodlot or public forest ownership patterns? A singular priority for forest research is determining the scale of regulatory oversight for transgenic forest trees. Responsible biotechnology governance is indeed questionable for transgenic conifer plantations located within less intensively managed forest ecosystems in the American South. The genetic composition of our nation’s indigenous forests is at issue. Claire G. Williams Claire G. Williams is at Duke University, Duke University, Department of Biology, Biological Sciences Building, Box 90338, Durham, North Carolina 27708, USA. e-mail: claire. [email protected]

L o s t in t he w o o ds T o t he edit o r : In “Struggling to see the forest through the trees” ( Nat. Biotechnol. 23 , 165–167, 2005), Herrera cites many of the important issues surrounding the state of forest biotechnology, yet at the same time fails to give an accurate impression of the extremely difficult state of the industry worldwide. First, there are serious technical problems that stand in the way of this industry maturing. Although it is abundantly clear that simple traits like herbicide resistance and insect resistance, when encoded by single genes as in transgenic agricultural crops, can provide major benefits in some species

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and geographies with responsible use1, it is not clear that these traits are valuable enough in forestry, given the costs of transformation, integration into breeding programs and associated field testing. For transformation, this is partly a result of the expected need to use new markers in place of antibiotic resistance genes to get broad international regulatory approvals2, even though the commercially authorized (USA) nptII gene for kanamycin resistance used in transgenic agricultural crops has never been shown to be a significant health or environmental risk. In addition, transformation methods

must be robust enough to work in the high diversity of germplasm used in most industrial forestry programs—which can include several species and dozens of genotypes. We know of no transformation systems up to this task. Were there to be a number of companies and/or public sector institutions seriously investing in technological solutions to these problems, we are certain they could be solved. But the reality, in contrast to the impression Herrera gave, is that there is a very low level of industrial activity worldwide. Of the companies listed in Table 1 of his article, only Arborgen in Summerville, South Carolina, is seriously pursuing transgenic breeding science. CellFor in Vancouver, Canada, has ended all transgenic and molecular biology research; SweTree of Umeå, Sweden, works primarily on basic genomics and has never had an applied breeding-related program, and the transgenic breeding research programs in Chile and New Zealand have all been dramatically cut back in recent years. Large, technologically advanced companies like Weyerhaeuser, Federal Way, Washington, have never had their own transgenic research, though they have supported some basic transgenic-related studies in universities, primarily for biosafety and wood quality. Most of the major forestry companies in Chile are effectively turning away from transgenic research because of concerns about activist boycotts and their European markets. Finally, with the high regulatory risks (discussed below), few forestry breeding programs would wish to encumber their efficient programs with transgenic-level regulatory costs and potential liabilities. Second, and most important, the thorny regulatory environment, designed without regard to the years of scientific consensus from national academies and ecological societies (e.g., see a position paper from the Ecological Society of America3), treats genetic engineering itself as dangerous by choosing to regulate every transgenic product in virtually the same way (the so-called ‘case-by-case’ approach). This extreme ‘precautionary’ system effectively precludes the use of trial-and-error, empirical methods that characterize all tree breeding programs. It is hard to imagine that changes to growth, wood chemistry or structure that are of significant economic benefit, but that do not also impair tree physiology and adaptation so important in all perennial crops, can be identified mainly in glasshouses and laboratories.

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COR R E S P ON D E N CE The certainty of gene flow from transgenic forests is problematic because neighboring lands are often less intensively managed public and private forest lands. At present, the scale and staggering expense of regulatory oversight alone could drive the political outcome in the absence of risk-benefit analyses. Ecological consequences of investment decisions on private lands deserve closer scrutiny at a national level. Calls for public deliberation are coming late in the life of the forest product life cycle. I advocate that transgenic conifers be considered separately from agricultural biosafety policy due to the sheer scale and complexity of forest tree gene flow. Biocontainment zones suited to transgenic food crops cannot deter escape of seeds or pollen from transgenic P. taeda. Reproductive sterility research for conifers, a complex problem, remains in its infancy and has not received serious consideration as a national research priority. There is thus an urgent need for policy makers to move on two fronts. First, a gene conservation program should be formalized through the National Forest System. In Region 8 of the southern United States, for example, indigenous P. taeda forests need to be protected from the potential impact of transgenic varieties. Widespread use of clonal forests with or without genetic engineering will likely rapidly narrow the numbers of P. taeda genotypes, opening the question of

how to protect undomesticated germ plasm and close relatives, which remain largely undomesticated. Second, forestry-specific research programs that address key issues specific to the implementation of transgenic technology in forestry need to be promoted within the existing cadre of national competitive funding programs. We are in dire need of funding for research to gauge the environmental impact of gene flow from trees. At present, we remain ignorant on numerous aspects of tree biology and ecology that affect whether or not we should proceed. Can pine pollen move in the jet stream and, if so, will it remain viable? How does gene flow from transgenic P. taeda affect indigenous pine forests or small woodlot or public forest ownership patterns? A singular priority for forest research is determining the scale of regulatory oversight for transgenic forest trees. Responsible biotechnology governance is indeed questionable for transgenic conifer plantations located within less intensively managed forest ecosystems in the American South. The genetic composition of our nation’s indigenous forests is at issue. Claire G. Williams Claire G. Williams is at Duke University, Duke University, Department of Biology, Biological Sciences Building, Box 90338, Durham, North Carolina 27708, USA. e-mail: claire. [email protected]

L o s t in t he w o o ds T o t he edit o r : In “Struggling to see the forest through the trees” ( Nat. Biotechnol. 23 , 165–167, 2005), Herrera cites many of the important issues surrounding the state of forest biotechnology, yet at the same time fails to give an accurate impression of the extremely difficult state of the industry worldwide. First, there are serious technical problems that stand in the way of this industry maturing. Although it is abundantly clear that simple traits like herbicide resistance and insect resistance, when encoded by single genes as in transgenic agricultural crops, can provide major benefits in some species

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and geographies with responsible use1, it is not clear that these traits are valuable enough in forestry, given the costs of transformation, integration into breeding programs and associated field testing. For transformation, this is partly a result of the expected need to use new markers in place of antibiotic resistance genes to get broad international regulatory approvals2, even though the commercially authorized (USA) nptII gene for kanamycin resistance used in transgenic agricultural crops has never been shown to be a significant health or environmental risk. In addition, transformation methods

must be robust enough to work in the high diversity of germplasm used in most industrial forestry programs—which can include several species and dozens of genotypes. We know of no transformation systems up to this task. Were there to be a number of companies and/or public sector institutions seriously investing in technological solutions to these problems, we are certain they could be solved. But the reality, in contrast to the impression Herrera gave, is that there is a very low level of industrial activity worldwide. Of the companies listed in Table 1 of his article, only Arborgen in Summerville, South Carolina, is seriously pursuing transgenic breeding science. CellFor in Vancouver, Canada, has ended all transgenic and molecular biology research; SweTree of Umeå, Sweden, works primarily on basic genomics and has never had an applied breeding-related program, and the transgenic breeding research programs in Chile and New Zealand have all been dramatically cut back in recent years. Large, technologically advanced companies like Weyerhaeuser, Federal Way, Washington, have never had their own transgenic research, though they have supported some basic transgenic-related studies in universities, primarily for biosafety and wood quality. Most of the major forestry companies in Chile are effectively turning away from transgenic research because of concerns about activist boycotts and their European markets. Finally, with the high regulatory risks (discussed below), few forestry breeding programs would wish to encumber their efficient programs with transgenic-level regulatory costs and potential liabilities. Second, and most important, the thorny regulatory environment, designed without regard to the years of scientific consensus from national academies and ecological societies (e.g., see a position paper from the Ecological Society of America3), treats genetic engineering itself as dangerous by choosing to regulate every transgenic product in virtually the same way (the so-called ‘case-by-case’ approach). This extreme ‘precautionary’ system effectively precludes the use of trial-and-error, empirical methods that characterize all tree breeding programs. It is hard to imagine that changes to growth, wood chemistry or structure that are of significant economic benefit, but that do not also impair tree physiology and adaptation so important in all perennial crops, can be identified mainly in glasshouses and laboratories.

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COR R E S P ON D E N CE Yet, costly requirements for containment of pollen and seed from trees of commercially relevant sizes, when grown in a representative diversity of environments, make such essential adaptive research virtually impossible to carry out. This is in spite of highly promising small-scale field results from Europe and elsewhere4, started in the optimistic 1990s. Finally, vandalism has led to local decisions in places such as British Columbia, Canada, to ban all transgenic field research with forest tree species, despite any scientific rationale to do so. Of course, as Herrera hints, such draconian regulations are in place owing largely to the scare tactics and pressure on government officials from antigenetically modified organism (GMO) activist organizations, which hope to see all transgenic trees regulated based on imagined worst case scenarios—not based on the increasing interest in modified expression of functionally native genes and pathways enabled by tree genomics. These regulations also ignore the reality that conventional breeding and silviculture, not just genetic engineering, also bring about substantial changes in wood structure, lignin, flowering, growth rate and many other attributes. Yet there is little call for their stringent regulation. It is time that the absurd, anti-scientific (that is, process not product) claims that all Agrobacterium tumefaciens or biolistics-delivered genes are somehow capable of causing ‘destruction and contamination’ of wild forests be identified as the scare-mongering that it is. Instead, lawyers and bureaucrats who have a limited understanding of breeding science or practice are working to insert language into local and national regulations, and into international treaties5, whose effect will be to completely or effectively (due to cost and liability risk) ban all genetic engineering from forestry and agriculture. Finally, these same groups, primarily by threat of boycott of retailers and corporations, rather than on advice from the leading scientific societies, continue to pressure companies for adoption of ‘green’ certification programs, such as the Forest Stewardship Council’s (FSC), that ban all field use of transgenic trees, even for contained research. For FSC, any use of transgenic trees is considered a major violation of their ‘principles,’ even where

it involves completely contained field research and is intended to solve a major environmental problem (e.g., to reduce chemical use during pulping, increase the rate of bioremediation or reduce the risk of invasiveness of forest trees when they are exotics6). As these programs slowly proliferate under the myth that avoidance of all genetic engineering is somehow an environmental good, companies’ willingness to engage in transgenic research understandably dissipates. These unwieldy social problems (for a review, see ref. 7), combined with the growing anti-commons caused by the fragmented patent estate of technologies important to forest biotechnology, make it a place where most companies understandably fear to tread. It will take strong political leaders and highly engaged scientists empowered by public funds for outreach, to stand-up and prevent green fundamentalist religion from trumping what could be a highly green new tool for breeding practice. Instead of genetic engineering helping to produce more efficient forms of plantation forestry that generate cost-efficient renewable energy and biobased products, we are instead being forced to continue planting more tree farms and harvesting more wild trees than necessary. How green is that? Sofia Valenzuela Forest Science Faculty, Universidad de Concepción, Concepción, Chile. e-mail: [email protected]

Steven H. Strauss Forest Science, Oregon State University, Corvallis, OR 97331-5752, USA. e-mail: [email protected] 1. Sedjo, R.A. in The BioEngineered Forest: Challenges to Science and Society, (eds. Strauss, S.H. & Bradshaw, H.D.) 23–35 (Resources for the Future, Washington, DC, 2004). 2. König, A.A Nat. Biotechnol. 2 1 , 1274–1279 (2003). 3. Snow, A.A. et al. Ecological Society of America Position Paper. Genetically Engineered Organisms and the Environment: Current Status and Recommendations (ESA, Washington, DC, 2004). http://www.esa.org/ pao/esaPositions/Papers/geo_position.htm 4. Pilate, G. et al. Nat. Biotechnol. 2 0 , 6 0 7 –6 1 2 (2002). 5. DeGreef, W. Nat. Biotechnol. 2 2 2 , 8 1 1 –8 1 2 (2004). 6. Strauss, S.H. et al . Int. Forestry Rev. 3 , 85–102 (2001). 7. Strauss, S.H. & Bradshaw, H.D. (eds.) The BioEngineered Forest: Challenges to Science and Society (Resources for the Future, Washington, DC, 2004).

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The Human Cancer Genome Project—one more misstep in the war on cancer George L Gabor Miklos Strap yourself in and get ready for some serious ‘more of the same.’ A recent proposal to sequence cancer genomes holds out the promise of personalized cures for each of 50 different cancers. The cost? A mere $12 billion at today’s prices1. This human cancer genome megaproject is the equivalent of 12,500 human genome projects and already has the backing of several prominent scientists. Harold Varmus believes that the project could “completely change how we view cancer”1; Eric Lander argues that “knowing the defects of the cancer cell points you to the Achilles’ heel of tumors”1; and Francis Collins predicts that he “can confidently tell you that something will happen here”1. More pragmatically, Craig Venter points out that “...it’s not clear what answer we’d get; there might be better ways to move cancer research forward”1. In a nutshell, the megaproject aims to catalog all somatic mutations from primary tumors as the basis for designer drugs to cure most cancers. Success is predicated on the assumption that drugs can be targeted to very specific mutated regions of gene products. However, most patients with a localized primary tumor are cured by surgery and local radiation. It is not the primary tumor, but the metastatic spread of a small population of deadly cells that ultimately compromises a normal tissue or organ, that kills in cancer 2 (for an excellent popular account, see ref. 3). Are primary tumors therefore the appropriate focus for such a massive project and are their bulk mutational spectra therapeutically useful?

George L. Gabor Miklos is at Secure Genetics Pty Limited, 19 Bungan Head Road, Newport Beach, Sydney, New South Wales, Australia 2106. He was an advisor to the Berkeley Drosophila Genome Project and to the human and mouse genome projects at Celera. e-mail: [email protected]

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T he clin ical t r ack r eco r d Cancer research has consumed hundreds of billions of dollars to date3 and yet the main killers—breast, prostate, lung and colorectal cancer—are essentially as deadly as ever 4 (see Fig. 1). Despite the glacial progress in treatment and the advent of ‘molecularly targeted’therapy, cancer research continues to focus myopically on individual oncogenes, tumor suppressors and repair genes5, with little effort devoted to alternative mechanisms and targets6–8. Although conventional chemotherapeutic agents remain the first-line treatment of choice, newer molecularly targeted therapies are now reaching the market. Thus far, however, these therapies have had very limited success against solid tumors, which after all make up 90% of all cancers. Success has been largely restricted to rare leukemias; for example, imatinib mesylate (Gleevec; Novartis, Basel) has initially proven effective in patients with chronic myelogenous leukemia (CML).

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Whereas the initial clinical success of imatinib in CML was spectacular, this has not been repeated in most of the succeeding cancer therapies against solid tumors. Gefitinib (Iressa; AstraZeneca, London)-based treatment shrinks tumors in only about 10% of advanced non-small cell lung cancer patients9. A recent study in one very small (35) patient group indicated that trastuzumab (Herceptin; Genentech, S. San Francisco) induces a partial response in only 23% of individuals with advanced HER2/neu -overexpressing breast cancers10; early indications are that bevacizumab (Avastin; Genentech, S. San Francisco) is not much better in colon cancer. All of these agents have serious associated toxicities11–13, most extend patient survival only by a matter of months and there is a variable period of remission before a resistant form of the cancer returns, even in the case of imatinib14. In the light of these findings, the concept of intervening in cancer networks at a single ‘oncoprotein’ or ‘tumor suppressor protein’

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has thus far had decidedly mixed results when translated to the clinic. This should not be surprising as the majority of solid tumors are characterized not by single genebased events, but by multiple genomic alterations, which are specific to each tumor in an individual.

C o r r elat io n s an d mu t at io n al dat a Analysis of somatic mutations in disseminated single tumor cells from the bone marrow of breast cancer patients reveals that contrary to dogma, mutation of TP53 (also known as p53) is not an early event in systemic breast cancer, but may occur in some individuals later during metastatic progression 2,15,16. Likewise, whereas mutations in the phosphatidylinositol 3-kinase gene have been documented in 74 out of 199 colorectal tumors and 4 out of 15 glioblastomas, they are found in only 1 out of 12 breast cancers, 1 out of 24 lung cancers, none out of 11 pancreatic cancers and none out of 12 medulloblastomas17. In the case of the epidermal growth factor receptor (EGFR), somatic mutations are found in 1 out of 61 non-small cell lung cancer tumors from US patients, but in 15 out of 58 Japanese patients with the same cancer 18. Furthermore, although somatic mutations in CDC4 are claimed to be a chief cause of chromosomal instability in cancers generally19, only 22 out of 190 colon tumor samples have these mutations. It is not known which of the above mutations existed in a homo-, hetero- or hemizygous condition within particular cells in these samples—a prerequisite for functional and causal interpretations. Thus, correlations remain weak and the above data are equally consistent with the majority of these mutations being innocent bystanders in the processes of tumor progression. There is enormous phenotypic variation in the extent of human cancer phenotypes, even among family members inheriting the same mutation in the adenomatous polyposis coli ( APC) gene believed to be causal for colon cancer. In the experimental mouse knockout of the catalytic gamma subunit of the phosphatidyl-3-OH kinase, there can be a high incidence of colorectal carcinomas or no cancers at all, depending on the mouse strain in which the knockout is created, or into which the knockout is crossed 20. Finally, the experimental centerpiece on which theories of human oncogenesis are based, namely that overexpression of only two oncogenes and a telomerase catalytic subunit is sufficient to create a malignant human tumor 21, simply does not hold up experimentally for a diploid human cell22.

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M as s iv e gen o mic imbalan ces an d met hy lat io n The Human Genome Project used diploid genomes in which the principles of mutation, reversion, suppression and methylation are reasonably understood, and in which the transcriptomes, proteomes and higher-order systems are characteristic of particular cell types at equilibrium. In contrast, all the genomes of the thousands of solid tumors that have been examined microscopically or molecularly depart from diploidy23–25, as do multiple myelomas26. Some regions of a cancer genome are differentially amplified, others are deleted and many are rearranged. Solid tumors consist of a heterogeneous population of aneuploid and/or segmentally aneuploid cells, where spontaneous mitotic nondisjunction at a single cell division can inexorably change the dosages of thousands of genes, microRNAs as well as other noncoding entities and the dosage-mediated interactions of thousands of noncoding single nucleotide polymorphisms, all without any mutational input whatsoever. Equally important, cancer genomes undergo massive changes in hyperand hypomethylation 27 leading to large alterations in gene activity. These clinically profound genome-wide methylation changes in genes and regulatory regions can occur completely independently of mutation. This huge departure from diploidy combined with a changing methylome introduces novel properties to cellular networks, such that conventional interpretations of phenotypic change through mutations have little traction. Transcriptomes emanating from variably aneuploid genomes are subjected to perturbations that far exceed anything that single gene effects can muster. The descriptions of homo-, hetero- and hemizygosity, dominance and recessiveness, and neomorphic, antimorphic, hypermorphic and hypomorphic alleles lose their conventional meaning because allelic and methylomic dosages are mere parameters in perturbed networks where the key is network flux. It is in such aneuploid contexts that multidrug resistance rapidly develops, even when major multidrug resistance genes are experimentally deleted from a genome28. As deficiencies involved in loss of heterozygosity cannot revert, and because back-mutation frequencies are low, conventional mutational interpretations of the rapidity of multidrug resistance and reversion do not hold 29. T he r eal killer — met as t as is Primary tumors are heterogeneous at all levels30,31 and many remain dormant 32. The heterogeneity is illustrated by microarray

data showing that the correlation among the expression profiles for three different parts of the same kidney tumor is poor 33. The heterogeneity among tumors from the same individual is also extensive. In prostate cancer, there are often many distinct foci in the same prostate, only one of which may be invasive and have a deleterious effect on the patient 34. Thus sequence data derived from a primary tumor are problematic because the important functional variation between cells is obliterated. Single-cell data are far more informative. Analysis of single disseminated tumor cells after curative resection of the primary breast cancer reveals that disseminated cells can exhibit changes completely different from those observed in the primary tumor 15. Similarly, when bone marrow micrometastases are compared with the primary colorectal tumors from the same patient, cells disseminated to the bone marrow do not always carry the same K- ras mutations as the primary tumor 35.

M o n ey w ell s p en t ? No one doubts that primary tumors accumulate somatic mutations over time. However, the Achilles’ heel of cancer is not the mutational baggage train of the primary tumor, but the genomic imbalances and methylation changes of the deadly cohort of cells that metastasize in different genetic backgrounds. As a megaproject in advancing cancer research and ultimate cures, the human cancer genome project thus is fundamentally flawed. First, as the mutational spectra of the primary tumor and its metastatic derivatives may only partially overlap, therapeutic strategies developed for specifically targeting mutations in primary tumors are unlikely to eradicate cells that have already left the primary tumor and are evolving along different genomic trajectories. The clinically significant entity is not the primary tumor per se but the rare cells within it that give rise to metastases and the particular genetic background within which they occur 36–40. Second, there is growing evidence for the profound clinical effects of genome-wide methylation changes in genes and regulatory regions41. These changes can take place completely independently of oncogenic or tumor suppressor or mismatch repair mutations and would not be detected by a human cancer genome sequencing effort. Thus, although a mutation-cataloging research megaproject may be a diverting occupation for sequencing centers and gene hunters, leading scientists should think carefully before they tout its therapeutic promise to patients and politicians. The simple truth is that the money would be much better spent

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COM M E N TA R Y if research priorities were reevaluated. A good place to start would be to dismiss the fallacious notion that single mutations in primary tumors are the optimal starting point for research that would lead to the discovery of new, more effective cancer drugs. The clinical reality is that it is not single genes, but rather the properties of aneuploid-based methylated networks that allow metastatic cancer cells to explore novel niches in different genetic backgrounds and to rapidly become resistant to drug-based therapies. Pollack, A. New York Times, March 28 , A1 (2005). Klein, C.A. Adv. Cancer Res. 8 9 , 35–67 (2003). Leaf, C. Fortune, 1 4 9 , 76–97 (2004). SEER Program (www.seer.cancer.gov) SEER* Stat Database; SEER 18 Regs, Nov 2004 Sub (1973-2002 varying), National Cancer Institute, DCCPS, released December 2004. 5. Vogelstein, B. & Kinzler, K.W. Nat. Med. 1 0 , 789–799 (2004). 6. SEER Program (http://www.seer.cancer.gov) SEER* Stat

1. 2. 3. 4.

Database; SEER 18 Regs, Nov 2004 Sub (1973-2002 varying), National Cancer Institute, DCCPS, released December 2004. 7. Sonnenschein, C. & Soto, A.M. Mol. Carcinogen. 2 9 , 205–211 (2000). 8. Harris, H. Nature 4 2 7 , 201 (2004). 9. Marx, J. Science 3 0 4 , 658–659 (2004). 10. Mohsin, S.K. et al. J. Clin Oncol. 2 3 , 2460–2468 (2005). 11. Hurwitz, H. N. Engl J. Med . 3 5 0 , 2 3 3 5 –2 3 4 2 (2004). 12. Ozcelik, C. et al. Proc. Natl. Acad. Sci. USA 9 9 , 8880– 8885 (2002). 13. Bendell, J.C. et al. Cancer 9 7 , 2972–2977 (2003). 14. Hofmann, W.-K. et al. Lancet 3 5 9 , 4 8 1 –4 8 6 (2002). 15. Schmidt-Kittler, O. et al. Proc. Natl. Acad. Sci. USA 1 0 0 , 7737–7742 (2003). 16. Klein, C.A. Cell Cycle 3 , 29–31 (2004). 17. Samuels, Y. et al. Science 3 0 4 , 554 (2004). 18. Paez, J.G. et al . Science 3 0 4 , 1497–1500 (2004). 19. Rajagopolan, H. et al. Nature 4 2 8 , 77–81 (2004). 20. Barbier, M. et al. Nature 4 1 3 , 796 (2001). 21. Hahn, W.C. et al. Nature 400, 464–468 (1999). 22. Akagi, T. et al. Proc. Natl. Acad. Sci. USA 1 0 0 , 13567–13572 (2003). 23. Pollack, J.R. et al. Proc. Natl. Acad. Sci. USA 9 9 , 12963–12968 (2002).

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24. Aneuploidy Conference Abstracts. Cell. Oncol. 2 6 , 171–269 (2004). 25. Duesberg, P. et al. Cell Cycle 3 , 823–828 (2004). 26. Fonseca, R. Blood 1 0 2 , 2562–2567 (2003). 27. Rush, L.J. Blood 97, 3226–3233 (2001). 28. Duesberg, P. et al. Proc. Natl. Acad. Sci. USA 9 8 , 11283–11288 (2001). 29. Duesberg, P. et al. Proc. Natl. Acad. Sci. USA 9 7 , 14295–14300 (2000). 30. Al-Hajj, M. et al. Proc. Natl. Acad. Sci. USA 1 0 0 , 3983–3988 (2003). 31. Al-Hajj, M. et al. Curr. Opin. Genet. Dev. 1 4 , 43–47 (2004). 32. Folkman, J. & Kalluri, R. Nature 4 2 7 , 787 (2004). 33. Vasselli, J.R. et al. Proc. Natl. Acad. Sci. USA 1 0 0 , 6958–6963 (2003). 34. Masters, J.R.W. & Lakhani, S.R. Nature 4 0 4 , 921 (2000). 35. Tortola, S. et al. J. Clin. Oncol . 1 9 , 2837–2843 (2001). 36. Fidler, I.J. & Kripke, M.L. Nat. Genet. 3 4 , 23 (2003). 37. Hunter, K. et al. Nat. Genet. 3 4 , 23–24 (2003). 38. Ramaswamy, S. et al. Nat. Genet. 3 4 , 25 (2003). 39. Dick, J.E. Proc. Natl. Acad. Sci. USA 1 0 0 , 3547–3549 (2003). 40. Kondo, T. et al. Proc. Natl. Acad. Sci. USA 1 0 1 , 781– 786 (2004). 41. Egger, G. Nature 4 2 9 , 457–463 (2004).

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I N VEST OR ’ S L A B

Beware the biotech barker Tom Jacobs Biotech company executives’ public statements can tell you a lot, but even the most experienced investors have trouble separating their spin from fact. This is especially difficult because most biotechs are development-stage companies lacking profits, dependent for years on selling hope and dreams. Yet knowing the difference between story and substance is the only way to determine whether you are investing or speculating—gambling, really—and making money and avoiding losses. Management is not necessarily to blame, but investors must recognize the game.

F illin g t he co o kie jar The game for development-stage biotechs has one invariable rule: like teenagers, they never have enough cash. Once public, companies desperate to fund a decade or more of lead product research and development must raise money through selling more of their shares and/or borrowing money (by selling their debt, often with the provision of converting debt to shares; Nat. Biotechnol. 21, 855, 2003). The greater the demand for shares, the higher the share price, the more money to be made by selling new shares. But with no profits to form a sound financial footing to a stock’s valuation, there is only hope and dreams—air—to boost the stock price, so the hotter the air, the better. Company executives perform a relentless round of public relations designed to garner attention. A wonderful story can vault a stock price to the heavens, but even if a company’s stock is stuck in one of the frequent cycles where investors desert biotech in droves, the story can still snare willing lenders. And so for hopeful biotechs, nothing is more important than the story and the storyteller.

Tom Jacobs is cofounder of Complete Growth Investor (http://www.completegrowth. com). He welcomes your comments at [email protected]. Tom owns no shares of companies mentioned in this article.

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S t ep r ight u p ! All development-stage biotech bigwigs have to develop their barker skills, tirelessly and convincingly telling a better story to divert investor attention from other rides and attract them to their own. But you and I must hear their message warily. Company management covets not us but institutional investors, the deep-pocketed buyers that can lay out the millions or even billions to buy enough shares or debt to make a real difference. Compared with them, you and I, so-called retail investors with 10, 100 or 1,000 shares, are mere insects, yet we hear the same pitch. Executive statements may come with legal disclaimers, but not “This is for Wall Street, not John/Jane Q. Investor.” The responsibility for caution is ours. B ar ker p ar ex cellen ce Consider perhaps the greatest biotech barker in biotech history, former Celera Genomics (Rockville, MD, and S. San Francisco, CA, USA; NYSE:CRA) president, J. Craig Venter. Thanks to management at Applera (Norwalk, CT, USA), the parent of both Applied Biosystems (Foster City, CA, USA; NYSE:ABI) and Celera, Venter in 1999 took the helm of Celera and also the tide in the affairs of men—the incredible public attention focused on sequencing the human genome—at its flood. It was a perfect storm, a once-in-a-millennium combination of public attention and investor interest in biotech. Venter was the consummate biotech barker, the rebellious, larger-than-life wild genomist who blew apart the scientific establishment with his shotgun method of gene sequencing. The hungry media went nuts. The government’s program was wrong, slow and perhaps even harmful in its delay! Celera’s celerity would bring us all personal DNA cards, individualized medicine and investor riches! The stock rose from $7.34 in June 1999 to a high close of $247 on March 6, 2000, about 35 times in less than nine months—an unparalleled gestation period. Whatever the combination of company spin and media attention, it did

the job for Celera. At the height of the frenzy, Celera sold almost $1 billion in stock to large institutional investors.

W hen t he car n iv al leav es t o w n Today, the stock is around $10, down 96% from its March 2000 peak, Venter has departed, and Celera’s genomic information business now belongs to sister company Applied Biosystems. Celera has become a drug maker with a pipeline but no products, along with a 50% share of Celera Diagnostics (Alameda, CA, USA) and its products. The cash raised at the carnival’s peak provided quite a lifeline. Even with this year’s expected $135 million to $150 million cash burn, Celera’s stash should last another five years. Many retail investors sport more serious burns, because they did not take the profits the carnival brought them. A r u le o f t hu mb fo r s p ecu lat io n Thus, it’s key to know the difference between investing and speculation, between the cold hard nature of company numbers in a quarterly or annual filing, and a pied piper CEO. We should invest all or most of our money by estimating what a profitable company is worth based on current product sales and profits and estimates of a reasonable future. We should only speculate, if at all, with small amounts we can afford to lose, buying but sparingly shares of unprofitable companies with promising stories. And when we do speculate, we should be happy, and take any profits flighted to us, with the hot air. I make it a general practice to consider selling half my shares of a speculation if and when the price doubles from my purchase price. Then no matter what happens, I’ve not lost anything, and—to use a gambling phrase—I’m playing with the house’s money. Let’s resolve to know the difference between investing and speculating and to know that while executives of development-stage biotechs may sing siren songs of biotech love, they aren’t for thee and me.

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Navigating an ethical patchwork—human gene banks Karen J Maschke

P o p u lat io n gen et ics r es ear ch co llabo r at io n s ar e r eachin g in cr eas in gly acr o s s n at io n al bo u n dar ies t o acces s hu man t is s u e r ep o s it o r ies . W ill dis cr ep an cies in n at io n al p o licies o n in fo r med co n s en t an d I P r ight s hin der p r o gr es s ? Several countries are establishing banks of human blood samples, or biobanks, associated with electronic health records in the hope that population genetics can speed the identification of disease susceptibility genes or diagnostic biomarkers. Concurrently, biotech companies are independently amassing private collections of DNA and tissues or seeking to collaborate with public population databases and biobanks. The collection and storage of biological samples raises several ethical and policy issues about access to, and use of, these samples, particularly around issues related to informed consent. However, no binding international regulatory framework addresses these issues. Instead, a patchwork of national laws, regulations and ethics advisory body guidelines govern the collection, storage and research use of biological samples. A review of policies at the major biobanks in North America, Europe and Asia reveals that although wide agreement exists on issues such as informed consent and patient confidentiality, there is no consensus on consent procedures for new research on previously stored samples, on informing individuals about (or even providing them access to) the results of research carried out on their samples, or on intellectual property (IP) restrictions on biospecimens and data. Andrew Brookes/Corbis

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T he r is e o f t he gen e ban k According to a 1999 Rand report 1, over 300 million samples obtained primarily during Karen J. Maschke is the associate for ethics and science policy at the Hastings Center, 21 Malcolm Gordon Road, Garrison, New York 10524-5555, USA. e-mail: [email protected]

routine clinical and surgical procedures are stored in the United States in a wide variety of public and private institutions. Human biological samples have also been collected in Europe and elsewhere, though little is known about where and how many samples are stored. In Scandinavian countries, for example, some long-standing public healthcare systems have been stockpiling collections of human tissues and blood for decades. However, not all of these samples are readily accessible to researchers and some may be unusable for purposes of genetics research because they were not stored properly, they were not properly annotated, or consent had not been obtained for their

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use in research. As a consequence, a new wave of national and local initiatives across the globe aims to establish genetics research biobanks (collections of blood or tissue samples) that can be linked to medical, genealogical or lifestyle information about a specific population gathered using a specific consent process. These biobank initiatives take the form of national efforts to collect health and genetic data on large populations (e.g., the Estonian Genome Project, Quebec CARTaGENE, UK Biobank, Singapore Tissue Network or Biobank Japan; see Table 1), collections of tissues from focused population groups created by provincial clinics or private ventures (see Box 1 and Table 2), or public-private partnerships (e.g., the collaboration between the Icelandic parliament (Althingi) and US-owned deCODE Genetics (Reykjavik, Iceland) or the partnership between the Swedish Medical Biobank and UmanGenomics in Umeå, Sweden). Apart from initiatives aimed at large populations at the national level (see Table 1), a diverse range of biobanks are also being created provincially. In the United States, for example Northwestern University (Chicago, IL, USA) created the NUgene Project to collect and store DNA samples and healthcare information from associated hospitals and clinics. In Wisconsin, the Marshfield Clinic has initiated the Marshfield Personalized Medicine Project to collect DNA samples from 40,000 local citizens. Biobanks also exist at hospitals and clinics affiliated with Duke University (Durham, NC), the University of Alabama (Birmingham) and the Mayo Clinic, Rochester, Minnesota. Other projects are underway to focus on specific diseases. For example, an Alzheimer’s

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F E AT U R E Genebank has been set up and is jointly sponsored by the US National Institute on Aging and the Alzheimer’s Association. To facilitate cancer research, The US National Cancer Institute (NCI; Bethesda, MD, USA) is currently setting up a National Biospecimen Network, which aims to centralize provincial repositories, such as those at Duke or the Mayo Clinic, into one place2. In Europe, there

T able 1

are existing projects such as the UK’s National Cancer Tissue Resource. Added to these initiatives are biobanks set up to study race (e.g., Howard University’s Genomic Research in African Diaspora Biobank), twins (e.g., the European GenomEUtwin project, a population-based project involving Danish, Finnish, Italian, Dutch and Swedish twins) or to provide anthropologic information, such as

the recently announced Genographic Project, which aims to collect DNA samples from over 100,000 people worldwide to trace human migrations throughout the globe.

I n fo r med co n s en t Five countries—Estonia, Iceland, Norway, Sweden and the United Kingdom—have national legislation governing the collection,

P o p u lat io n - bas ed bio ban k p r o ject s

Project (location)

Funding

Description

Consent

IP rights

URL

CARTaGENE (Quebec, Canada)

Supported by public money from Genome Canada.

Commencement imminent. DNA to be extracted and stored from 50,000+ adults in Quebec between 25 and 74 years.

Multi-layered options, samples double-coded. Results not shared with donors.

Researchers or biobank permitted to obtain IP on inventions.

http://www.rmga.qc.ca/en/ cartagene.htm

Estonian Genome Project (Tartu, Estonia)

Supported initially by private money from EGeen (Mountain View, CA, USA). Since January 2004, the project has received additional funding from the Estonian government.

Initiated in October 2002. DNA to be extracted from blood samples of 1 million Estonian adults and children together with health and genealogical data.

Blanket consent. Samples coded. Donor option for results.

Owned by EGP, IP policy unknown (since for profit company participation terminated in Dec. 2004).

http://www.genomics.ee/

Latvian Genome Supported by the Latvian Project Genome Foundation. (Riga, Latvia)

Pilot project initiated in 2002 with 60,000 pilot samples.

Blanket consent. Samples coded.

IP rights owned by Latvian Genome Foundation, which plans to market access to database.

http://bmc.biomed.lu.lv/ gene/

Icelandic Biobank (Reykjavík, Iceland)

A public-private collaboration with deCODE (Reykjavík).

Blood samples to be collected from 270,000 Icelandic citizens and linked to Iceland Health Sector Database and genealogical records.

Informed consent, donor informed of objectives. Presumed consent of previously collected samples. Results not shared with donors.

deCODE has a 12-year exclusive license to rights over samples, but does not own them. Icelandic health system takes a share of any profits (capped at $1 million per year).

http://www.decode.com/

UK Biobank (Manchester, UK)

Publicly funded by the Wellcome Trust, the Medical Research Council, the UK Department of Health, and Scottish Executive.

DNA, medical records and Blanket consent. Donors lifestyle questionnaires to not provided with research be acquired from 500,000 results. UK adult volunteers between 45 and 69 years old to begin in 2006. Subjects to be followed for 30 years.

WellcomeTrust owns samples and database. IP policies not in place.

http://www.ukbiobank. ac.uk/

Medical Biobank (Umeå, Västerbotten, Sweden)

Publicly funded by Swedish National Healthcare System.

Contains over 85,000 DNA samples from individuals of 40, 50 and 60 years of age in Västerbotten county together with medical records.

Informed consent acquired from previous donors for each new project; UmanGenomics has access only to coded samples. Donors not provided with research results.

UmanGenomics has exclusive rights to genetic samples from existing Medical Biobank and exclusive right to commercialize information derived from Biobank.

http://www.biobanks.se/ medicalbiobank.htm

Singapore Tissue Network (Biopolis, Singapore)

Publicly funded since March 2002 by Singapore Biomedical Research Council, Agency for Science, Technology and Research, Ministry of Health and the Genome Institute of Singapore.

Genomic information from Singapore population groups via a network of collaborating organizations and hospitals.

Informed consent acquired from donors for each new project using previously collected samples.

The Genome Institute of Singapore will avoid any commercialization of the project.

http://www.stn.org.sg/

Biobank Japan (Kanagawa, Japan)

Initiated in 2003 with public funding from the Japanese Ministry of Education, Culture, Sports, Science and Technology.

DNA samples to be acquired from 300,000 Japanese individuals of 20+ years of age suffering from 30 common illnesses.

Full informed consent acquired from donors. Donors provided with research results. If comprehensive consent obtained, use of existing samples allowed for new research.

IP policies not yet in place.

http://www.src.riken.go.jp/ eng/src/project/person. html

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storage and use of biological samples ( Table 3). Elsewhere, regulations governing research with humans and/or specific guidelines issued by national ethics advisory bodies or regulatory agencies apply (e.g., see Table 4).

B o x 1

Voluntary informed consent from individuals who participate in research is a core ethical principle of human research ethics. This must be balanced with the need to facilitate research using biological samples without

undue logistical and ethical hurdles (see Box 2). Extending this principle to research with biological samples means giving individuals the opportunity to decide whether they are willing to let researchers collect, store and study their

E t hics an d t he p r iv at e s ect o r

Following a firestorm of controversy in 1998 when the Icelandic government agreed to provide deCODE Genetics with exclusive rights to the nation’s medical and genealogical records, the biotech sector has been increasingly involved in collaborations with public population databases and biobanks. In Sweden, the Medical Biobank has also granted UmanGenomics exclusive rights to national medical and genetic data and, elsewhere, the Latvian Genome Project (and initially the Estonian Genome Project) was initiated with the aim of providing economic benefit from associated biotech ventures. Several business models are emerging in the sector. Certain companies are setting up their own tissue repositories and selling this information to interested parties; others are negotiating access to samples and patient information in public population databases and tissue repositories, and using these to identify new targets or biomarkers for internal research and development programs; others are providing expertise and technology to support public institutions or drug companies undertaking clinical work; and yet others are pursuing combinations of the above (see T able 2 ). The handling of human samples and medical records by corporations presents some unique ethical T able 2 S elect ed bio challenges. In the United States, Company although companies provide assurances AGT Biosciences that they safeguard donor privacy and (formerly Autogen, Victoria, Australia) interests, there is no legal requirement for companies to protect human Ardais (Lexington, MA, USA) subjects. And it is not clear what happens to patient medical records deCODE genetics and confidential information when a (Reykjavík, Iceland) private biobank goes bankrupt. Often guarantees made by a company during sample acquisition are not legally EGeen (Tartu, Estonia) binding on the trustee in a bankruptcy. When DNA Sciences of Fremont, California, went bankrupt in 2003, First Genetic Trust samples and data from 18,000 donors (Chicago, IL, USA) were part of its assets; in this case, DNA Sciences and its assets were bought by Genaissance Pharmaceuticals (New Genizon Biosciences (Quebec, Canada) Haven) for $1.3 million. In 2001, a court in Japan auctioned off a human Genomics Collaborative cell collection that a scientific society (Cambridge, MA, USA) had used as collateral on a loan. Reconciling the commercial Newfound Genomics imperatives of private companies with (St. John’s, Canada) the goals of public tissue banks to UmanGenomics improve the health of local populations (Umeå, Sweden) also presents problems. After intense criticism, deCODE Genetics agreed to give the Icelandic health system

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a portion of any profits accrued (with a cap of about $1 million a year). IDgene of Jerusalem, a now-defunct Israeli company collecting samples from Ashkenazi Jews, also had a similar arrangement to donate a percentage of future profits to the healthcare of Israeli society. In the case of the Icelandic project, perhaps the most contentious issue was its reliance on ‘presumed consent,’ whereby the entire population’s health records are automatically included in the database unless a citizen specifically requests otherwise. To date, up to 7,000 individuals have opted out. But deCODE is by no means unique in prompting intense public opposition. In 2000, Australia’s Autugen (now AGT Bioscience, Victoria) made an aborted attempt to form a collaboration with the Tongan Ministry of Health. This project fell through after the Ministry failed to give the company access to Tongan medical records; tribal leaders and opposition groups claimed there was insufficient public consultation and the informed consent procedures failed to take account of Tongan traditions in which the extended family is involved in decision making.

t ech co mp an ies in v o lv ed in bio ban ks an d gen et ics r es ear ch Activity Internal discovery program accesses a unique DNA collection from worldwide populations (>44,000 samples). External research collaborations with pharma. Provides informatics support and advisory services to facilitate biospecimen collection, management and distribution by biobanks and academics and drug companies involved in clinical work. Contracted by the Icelandic government to put the health records of all 270,000 citizens into a single database (~7,000 citizens have thus far elected to opt out). Using database and Icelandic Biobank, plans to carry out gene association and founder studies for internal drug target discovery and development program and external research collaborations. Analysis of disease and drug response data using DNA and biomarker profiles from donors in the Estonian Genome Project. Initial focus on hypertension. Provides informatics support and advisory services to biobank developers seeking to protect human subjects and ensure data privacy. Partnered with Howard University to launch the Genomic Research in African Diaspora Biobank. A bank of 50,000 Québecois patients and relatives with 28 different diseases. Partnership with Myriad Genetics (Salt Lake City, UT, USA) and Perlegen Sciences (Mountain View, CA, USA). Offers access to a clinically annotated tissue bank of >120,000 donors from around the globe. Provides tissue banking and consulting services to drug industry. Technical/consulting services for the Singapore Tissue Network. Banking samples from individuals in Newfoundland and Labrador provinces to study the causes of diabetes and obesity. Exclusive commercial access to Medical Biobank. DNA and plasma samples from more than 70,000 Swedish donors with detailed medical and lifestyle histories.

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F E AT U R E samples. In addition, individual consent may vary depending on what personal confidential information is connected with a sample: samples can be linked to an individual’s medical records, codified so that an individual’s information can be retrieved only using a specific key, anonymized so that connected personal information cannot be retrieved at a later date or anonymous where no personal information is connected to the sample (see Box 3). A survey of the main national biobanks reveals consensus across the policies that informed consent is required when collecting samples. The Act on Biobanks in Iceland (see Table 3) is, however, unique in that it permits presumed consent (that is, consent is assumed unless otherwise indicated) to govern the storage of samples in a biobank if they were obtained for the purpose of clinical tests or treatment. Consent options. Although informed consent is universally accepted, there is considerable divergence on policies regarding the use of consent options. With consent options, individuals have the option to decide whether researchers can recontact them to obtain consent for future studies with their sample and might have the option of opting out of future research. In the United States, the Health Insurance Portability and Accountability Act (HIPPA) Privacy Rule implemented in April 2003 states that patients have to authorize the use of protected health information for each use of their data. When that is impossible, three alternatives are possible: first, an anonymized data set can be constructed (see Box 3); second, a ‘limited’ data set can be provided with identifiers linked to a data use agreement making the institution liable for violations by the recipient; or third, an Institutional Review Board (IRB) can issue a waiver of authorization indicating that the study poses minimum risk to privacy and the research could not practicably be carried out without access to, and use of, the information.

B o x 2

B alan cin g in div idu al r ight s w it h r es ear ch p r o gr es s

From an individual rights perspective, informed consent should be required for research with all types biological samples, whether they are linked with healthcare information or coded, anonymized or anonymous (see B o x 3 , for explanation). On the other hand, the medical and public health perspectives favor relaxing the informed consent requirement so that important research can go forward 1 1 . When potential research harms are minimal or nonexistent, advocates for these perspectives place greater weight on the needs of science than on the right of self-determination, particularly for use of coded, anonymized and anonymous samples. Research harms are said to be minimal or nonexistent when using anonymized and anonymous samples, and when using coded samples if researchers do not have access to the code. Under these circumstances, the need to affirm and protect the right of selfdetermination is considered to be less compelling than when samples are identifiable. However, for human dignity and privacy to have meaning, individuals ought to have self-determination over their body and its materials, especially if research carries potential harms. Many commentators assert that research with coded, anonymous and anonymized samples may be harmful to individuals, families and communities. Information derived from population-based studies might be used to discriminate against or stigmatize individuals from these populations. Moreover, because there is cultural variation in the way the body and its materials are viewed, special handling practices may be required when samples are obtained from certain populations. Even where there is consensus for obtaining informed consent, disagreement exists over whether individuals should have the opportunity to opt in or out of certain types of research and to specify their preference regarding notification of research results. From the individual rights perspective, the more options individuals have the better, whereas a blanket consent approach gives researchers greater latitude in how they will use samples for which specific consent was not obtained, and affirms medical and public health goals of advancing science.

An approach taken for the collection of biological samples by the Icelandic BioBank involves the use of two consent forms. Individuals who sign consent form ‘a’ authorize researchers to use a blood sample for specific research with the understanding that when the research ends, the sample will be destroyed. Consent form ‘b’ authorizes researchers to use a blood sample for specific research and for additional similar research if

T able 3 S ev er al n at io n al law s p er t ain in g t o co n fiden t ialit y o f medical dat a lin ked w it h t is s u e r ep o s it o r ies Country

Law/rule (year enacted)

US

Health Insurance Portability and Accountability Act (HIPAA; 1996); associated Privacy Rule (2003)

UK

Human Tissue Act (2004)

Estonia

Human Genes Research Act (2000)

Latvia

Human Genome Research Law (2002)

Iceland

Act on Biobanks No. 110/2000 (2000); Act on a Health Sector Database No. 139/1998 (1998); Act on the Rights of Patients No. 74/1997 (1997)

Sweden

Act on Biobanks (2002)

Norway

The Norwegian Act on Biobanks (2003)

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approved by the Data Protection Commission and the National Bioethics Committee. By signing consent form ‘b,’ individuals also give consent for DNA to be extracted, coded, stored and used for any research approved by the Data Protection Commission and the National Bioethics Committee3. The Quebec CARTaGENE project (see Table 1) also uses consent options. Participants in this population-based semi-longitudinal project will give general consent for use of their anonymized samples. An additional consent mechanism will offer choices for opting into three specific research activities. Unlike the projects in Iceland and Quebec, the Estonian Genome Project and the UK Biobank use a blanket consent approach, that is, consent for unspecified uses of samples. The proposed Ethics Governance Framework (EGF) for the UK Biobank recommends that individuals not be given the opportunity to choose which data about themselves will be used or what kind of research can be conducted with their samples. Instead, the EGF recommends that individuals be told they can either opt in or out of the project, with the understanding that if they opt in their samples may be used in the future for unspecified research.

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F E AT U R E In Germany, the National Ethics Council (NEC) recommends that the purpose of the research should determine whether consent options are offered. The NEC contends that the absence of consent options does not violate the right of self determination because individuals can decide whether or not they want to participate in genetics research under specified conditions. Norway also favors a conditional approach to consent options: risk of research harms, sensitivity of the biological material and the vulnerability of the research subject are factors that determine what information is provided to individuals and the specificity of consent. None of the policies examined appear to explicitly endorse a consent option that gives individuals the opportunity to decide whether their identifiable sample can later be anonymized for research use. New uses of stored samples. There is no uniform approach regarding consent for new uses of stored samples. Policies in Canada, Germany, Norway, the Netherlands and the United States permit use of stored samples without consent if the samples are not identifiable. Iceland’s policy confers decisionmaking authority about the need for consent for new use of stored samples to its National Bioethics Committee. In Estonia and the United Kingdom, consent is not required for new use of stored samples because individuals gave blanket consent for research with their samples at the time of collection. Under the newly enacted UK Human Tissue Act of 2004, exceptions to the requirement for informed consent include the provision that consent is not required if samples are anonymized and if an ethics review committee approves the research. Minors and incapacitated adults. Policies in Denmark, Estonia, Sweden, the Netherlands and the United Kingdom contain special rules for minors and incapacitated adults. In the Netherlands, consent must be obtained from both parents or legal representatives for chil-

T able 4

B o x 3

T ier ed lin kin g o f healt hcar e dat a t o D N A s amp les

Complicating the consent issue are the various methods that are used for coding and identifying DNA samples in biobanks. Some stored samples either contain personal identifiers or can contain a code that links them to the individual source (identifiable samples). Others have identifiers, but will be anonymized at some later time (anonymized samples). A last category is a sample collected without any link to personal identifiers or for which all identifying information has been destroyed (anonymous samples). Although anonymous and anonymized samples retain some value for population-based genetics research, it is difficult to pool genetic data for pedigree/family members and look for disease markers. Thus, genetic information linked to individual medical records is of greatest potential for marker discovery and for developing personalized treatment and prevention therapies.

dren less than 12 years old; children between the ages of 12 and 18 must give consent, along with both of their parents or legal representatives. Parents in Estonia must give consent for children over the age of seven. Regulations in the United States governing research with children require parental consent and child or adolescent assent when certain conditions are met. However, it is unclear whether IRBs in the United States require researchers to obtain assent from children/ adolescents whose samples are collected or whether adolescents who reach the age of legal maturity are contacted to obtain their consent for ongoing use of their samples. The UK Biobank and the Quebec CARTaGENE projects will collect samples from adults, presumably only from adults with decisional capacity. The proposed EGF for the UK Biobanks states the “UK Biobank will have to have clear policies on how to respect participants’ wishes if they become incapacitated or die” but does not say whether samples will be collected from incapacitated adults with surrogate permission.

A cces s t o r es ear ch r es u lt s Whether individuals should have access to information obtained from research with their identifiable samples is still open to debate.

I n t er n at io n al gu idelin es co v er in g et hics fo r gen e ban ks

Organization

Guideline

World Medical Association (WMA)

Declaration of Helsinki (2000) Declaration of Ethical Considerations regarding Health Databases (2002)

Council of Europe

Recommendation on Human Tissue Banks (1994) Convention on Human Rights and Biomedicine (1997)

UNESCO

Draft Report on the Collection, Treatment, Storage and Use of Genetic Data (2001) Declaration on the Human Genome and Human Rights (1997)

Human Genome Organization

Statement on DNA Sampling: Control and Access (1998) Statement on Benefit Sharing (2000)

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Focus groups and surveys indicate that some people want access to research results and that the opportunity to obtain personal information is what motivates some of them to consent to use of their samples4. However, only the Estonian Genome Project requires researchers to give individuals access to personalized information if they want it. The proposed EGF for the UK Biobank says individual research results should not be given to participants because the Biobank is a research project, not a healthcare project. The German NEC mentions the issue of research results in its report “Biobanks for Research,” but does not make a recommendation for or against providing results to individuals.

C o n s u lt at iv e ap p r o aches Although there has been extensive discussion about community consultation for genetics research, none of the national policies examined requires this approach when researchers collect samples from socially identifiable groups. The German NEC mentions the notion of group consent, but notes that problems associated with research on indigenous populations are not present in Germany. Although several of the European countries examined here do not have indigenous populations and traditionally have had homogenous populations, nearly all of these countries have experienced an influx of immigrants from the Middle East, Africa and Asia. Consequently, researchers and biobank programs may need to address cultural issues surrounding the collection and use of genetic samples from these and other populations. If community consultation is defined as interaction with the broader public, rather than only with socially identifiable communities, then only two large-scale biobank projects, the UK Biobank and CARTaGENE, have incorporated some form of community consultation into the planning and development of the projects. Various communication

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F E AT U R E strategies were used in Iceland and Estonia to inform the public about the projects, though no formal consultative approach was used in establishing the projects’ collection, storage and access polices4. In contrast, the UK Biobank and CARTaGENE projects used a “partnership or collaborative approach” in addressing consent and other issues. Both projects used focus groups and surveys to measure public attitudes toward the projects and the ethical issues involved 4.

O w n er s hip an d I P r ight s Biological samples, personal information about sample donors, genealogies and the discoveries and inventions resulting from research with samples and associated data are likely to have commercial value. Yet, the general principle in international and national law is that there are no property rights on the human body. For example, in the case of Moore v. Regents of the University of California5, a California court ruled that individuals do not have an ownership interest in their cells after they are removed. More recently, a Florida court sided with the Moore ruling in holding that individuals do not retain an ownership interest in their donated tissue samples6. However, institutions and other entities that store samples and genetic databases have asserted control over samples and databases and over the management of IP rights.

B o x 4

The Estonian Genome Project was established as a public-private partnership between the Estonian Genome Project Foundation (EGPF) and EGeen, a biotech company in the United States. Under the original arrangement, the EGPF would own the samples and genetic data, and EGeen would be the exclusive commercial licensee of the database. In December 2004, EGPF and EGeen terminated this agreement, which means that EGeen is no longer the primary funder of the project. Media accounts indicate that because of economic realities, EGeen wanted to redirect the project to focus on particular priority diseases rather than follow the original agreement to conduct population-based research. EGPF has not provided information about continued financing for the project or about commercial rights to the data and inventions resulting from research under new financing agreements. In January 2005, the UK Biobank issued a draft IP and access policy that recommends how IP rights should be managed. IP accruing from the creation and development of the biobank is vested in the UK Biobank. When IP arises out of research using the biobank, it is vested in the researcher, his or her institution, or their assignee. In Quebec, the CARTaGENE project does not confer ownership rights to researchers or to the biobank, but the project permits these parties to obtain intellectual

D ebat e mo v es in t o t he co u r t s

Although the case law involving research with human biological samples is sparse, some ethical and policy controversies are finding their way into the courts. I celan d. In November 2003, the Icelandic Supreme Court ruled that the Icelandic Health Sector Database Act (see T able 3 ) does not adequately protect personal privacy in accordance with the Icelandic Constitution’s requirement that “ Everyone shall enjoy the privacy of his or her life, home, and family.” The Supreme Court agreed with the plaintiff’s claim that she has a personal interest in preventing her deceased father’s medical records being entered into the database because it might be possible to infer information about her from his records. The court ruled that under the Act’s presumed consent provision, deceased individuals and their living relatives are at a disadvantage in their ability to opt out of the Health Sector Database, which will contain the medical and genealogical records of all Icelanders and will link this information to genetic information obtained from the tissue samples of the Icelandic population deposited in a biobank. U n it ed S t at es . Two lawsuits seeking $75 million were filed in 2004 against the University of Arizona, its Institutional Review Board, several genetic researchers and other named defendants. Several members of the Havasupai Tribe filed one of the lawsuits, and the tribe itself filed the other one. The plaintiffs allege that blood samples obtained from tribal members for diabetes research were used without their consent for secondary research to identify an association between certain gene variants and schizophrenia and for ancestral migration studies. Additional charges against the defendants include allegations of breach of fiduciary duty, fraud and misrepresentation, infliction of emotional distress, conversion, violation of civil rights and various claims of negligence. The federal lawsuits are pending in the US District Court for Arizona.

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property rights over inventions obtained from genetic information. The Icelandic Act on Biobanks states that the biobank operator does not own the samples, but has rights over them. Pursuant to the Icelandic Health Sector Database Act (see Table 3), the Icelandic government issued an exclusive license to deCODE Genetics to establish and operate the Icelandic Health Sector Database, which includes the right to sell access to the database7 (deCODE is registered and headquartered in Reykjavik, Iceland, and is a wholly owned subsidiary of deCODE Genetics, registered in Delaware, USA). Legislation in Iceland does not place special legal restrictions on the licensee’s freedom to negotiate IP rights for itself8. Although deCODE Genetics is conducting research with samples obtained from over 100,000 individuals, it is unclear whether the Health Sector Database is fully operational. In November 2003, the Icelandic Supreme Court ruled that the Health Sector Database Act did not adequately protect personal privacy in accordance with the Iceland Constitution’s privacy guarantee. The Health Sector Database Act permits every citizen’s health data to be entered into the database without informed consent, but gives individuals the opportunity to opt out of the database. In the lawsuit heard by the court, the relative of a deceased individual argued that her right to privacy was violated because information about her could be inferred from data related to hereditary characteristics of her deceased relative, whose health data had been entered into the database ( Box 4).

C o n clu s io n s All of the policies discussed above require some form of ethical oversight for the collection, storage and research use of biological samples. Moreover, there is an emerging consensus that individuals should be told about policies for sample ownership, managing IP rights and protecting privacy and confidentiality of genetic information. The ethical situation is far less clear for biobanks containing stored samples that were collected without consent for use in genetics research. There is a notable lack of consensus on whether informed consent should be required for research with stored samples that were collected without consent for research or that were collected for research that differs from the proposed study. Even so, research with stored samples is going forward, and new samples are being collected for storage in research biobanks. It remains to be seen whether variation in consent policies will hinder these activities. Organized opposition to the large-scale projects in Iceland and the United Kingdom and

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F E AT U R E public opinion surveys and lawsuits in the United States and Iceland reveal public concern about consent issues, confidentiality of genetic information and the type of research conducted with their samples. Some commentators have called for harmonizing policies for research with human tissue across national boundaries9. Several transnational biobanking projects are attempting to develop a minimal threshold of policy harmonization within their respective spheres. These projects include GenomEUtwin; Population Project in Genomics (P3G), an open-access database that will integrate data from the UK Biobank, the Estonian Genome Project, CARTaGENE and GenomeEUtwin; and the International HapMap Consortium, which involves research groups from Canada, China, Japan, Nigeria, the United Kingdom and the

United States. Whether these transnational attempts at harmonization will succeed, or whether an international regulatory framework is possible, remains to be seen. There is a growing need for more explicit, enforceable and coordinated international policy guidelines. Consent issues remain a potential barrier to harmonization, as do national policies regarding data protection, genetic privacy and IP rights. Moreover, as the failed attempts to develop an international regulatory framework regarding human cloning reveals, global policymaking is fraught with deeply entrenched value conflicts and geopolitical realities10. 1. Eiseman, E. & Haga, S.B. Handbook of Human Tissue Sources: A National Resource of Human Tissue Samples (RAND, Santa Monica, CA, 1999). 2. Bouchie, A. Coming soon—a global grid for cancer research. Nat. Biotechnol . 2 2 , 1071–1073 (2004). 3. Árnason, V. Coding and consent: moral challenges of

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the database project in Iceland. Bioethics 1 8 , 27–49 (2004). 4. Godard, B. et al . Strategies for consulting with the community: the cases of four large-scale genetic databases. Science and Engineering Ethics 1 0 , 457–477 (2004). 5. Moore v. Regents of the University of California, 793 P.2d 479 (Cal. 1990). 6. Greenberg v. Miami Children’s Hosp. Research Inst., Inc., 64 F. Supp. 2d 1064 (S.D.Fla. 2003). 7. Árnason, E. Personal identifiability in the Icelandic Health Sector Database. J. Inform. Law Technol . 2 , (2 0 0 2 ). http://www2 .warwick.ac.uk/fac/soc/law/elj/ jilt/2002_2/arnason/ 8. Kaye, J. et al . Population genetic databases: a comparative analysis of the law in Iceland, Sweden, Estonia, and the UK. Trames 8 , 15–13 (2004). 9. Bauer, K. et al . Ethical issues in tissue banking for research: a brief review of existing organizational policies. Theor. Med. Bioethics 2 5 , 113–142 (2004). 10. Maschke, K.J. & Murray, T.H. Ethical issues in tissue banking for research: the prospects and pitfalls of setting international standards. Theoretical Medicine and Bioethics 2 5 , 142–155 (2004). 11. Andrews, L. Future Perfect: Confronting Decisions about Genetics (Columbia University Press, NY, 2002).

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T he challen ge t o p at en t law o f p u r e chemical p r o t ein s y n t hes is Aaron Xavier Fellmeth T he emer gen ce o f p u r e chemical p r o t ein s y n t hes is as a co mmer cially v iable met ho d o f dr u g des ign an d p r o du ct io n w ill cr eat e s er io u s p r o blems in t he p at en t s y s t em.

n the last five years, advances in the chemical synthesis of proteins has reached the point where it is conceivable that, in the near future, the vanguard of pharmaceutical development will be not recombinant genetics but chemistry. Because nearly all biological processes are controlled by the molecular recognition of peptides and proteins, the understanding and manipulation of these substances is central to neurobiology, enzymology, immunology, pharmacology and molecular biology and biochemistry more generally. Although experiments in chemically synthesizing peptides from their constituent amino acids without transcription date back some 100 years, only in the last 15 have chemists advanced from synthesizing amino acids, simple sugars and purines to short peptide chains, such as acyl carrier protein 1, and now to medium-sized proteins of 200 amino acids or more, such as erythropoietin 2–4. Smaller synthetic peptides have become widely available as important commercial products, from sweet aspartame to clinical hormones such as oxytocin, calcitonin and gonadotropinreleasing hormone (GnRH) super-agonists. Now that it is becoming commercially feasible to synthesize larger proteins chemically, a serious intellectual property challenge looms on the horizon. There is an urgent need to consider the role that chemical synthesis will assume in pharmacology, where recombinant genetics is still the standard, and where patents over “isolated and purified” natural peptides and proteins have long been granted with wild abandon.

I

Aaron Xavier Fellmeth is at Arizona State University College of Law, P.O. Box 877906, Tempe, Arizona 85287-7906, USA. e-mail: [email protected]

Corbis

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

PAT EN T S

It is important to realize that chemically synthesized proteins need not have precisely the same molecular makeup as their recombinant counterparts. The advantages of chemical synthesis of proteins over recombinant production are several. First and most important in terms of drug design, chemical synthesis offers predictability and customizability not available to geneticists. Through protein research, chemists can rationally design peptide ligands whose biochemical properties can be predicted a priori. Customization now takes chemists in directions so far foreign to geneticists. Chemists can attach unnatural amino acids, pseudopeptides or polymers to proteins at binding sites to suppress undesired characteristics. For example, the coupling of polyethylene glycol (PEG) polymers has yielded fully bioactive derivatives with reduced immunogenicity5. Polymers may also be added as macromolecular carriers for target-specific drug delivery or sustained release of drugs, with increased solubility and other beneficial pharmacologi-

N ATU RE BI O TECH N O LO GY VOLUME 23 NUMBER 5 MAY 2005

cal effects6. Polymeric and unnatural amino acid tweaking may change the functionality of the protein, resulting in entirely new characteristics or modifications to current bioactive characteristics, such as increased or decreased longevity or reactivity. Another advantage of chemical synthesis relates to the tricky matter of producing glycoproteins. Chemical synthesis offers an opportunity for greater homogeneity of glycoforms than is currently possible in recombinant proteins7. Finally, chemical synthesis tends to yield proteins having a high degree of purity. Although automated techniques can typically purify recombinant proteins with great success, there is less risk of DNA impurities or endotoxins contaminating a batch of chemically synthesized proteins.

P r o t ein p at en t s Despite the differences between pure chemical synthesis of proteins and recombinant production, there are and will continue to be cases in

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PAT E N T S which chemically synthesized proteins are identical to recombinant proteins. Unfortunately, the United States Patent and Trademark Office (USPTO), with the sanction of the US Court of Appeals for the Federal Circuit (but, tellingly, not the US Supreme Court), has permitted patents on naturally occurring proteins when “isolated and purified” from their natural solution or matrix. Already, hundreds of naturally occurring proteins have been patented 8–10. Such proteins as erythropoetin 11, mammary transforming protein 12 and human chemotactic protein 13 have been patented by the first researchers who showed that they purified and recombinantly reproduced the natural substance and found a substantial use for it. Such patents are highly problematic as a matter of both policy and law. They tend to impose tollbooth costs on the development of products and processes relating to biological functions, disorders and diseases involving the protein at issue. This raises the cost of biological and medical research unnecessarily by distributing funds from the developers of new and useful technologies to researchers who have merely identified and purified a protein without inventing anything new, contrary to the requirements of the Patent Act. The unnecessary costs imposed by patents on naturally occurring proteins raise an especially thorny problem for chemists seeking new ways to synthesize such proteins. A patent on a recombinant protein typically claims the isolated and purified protein regardless of how it is produced. In reality, of course, the patentee merely mimicked the natural process of protein production by expressing the DNA artificially. The more difficult task of chemical synthesis produces the same protein in an entirely different way. As a result, a patent on a recombinant protein may block the commercialization of an identical protein produced by pure chemical synthesis. To the extent that the protein has been substantially modified in the process of chemical synthesis (e.g., by docking polymers to the protein or including unnatural amino acids), it is at least possible that the protein will fall outside the scope of the patent claims. But where chemically synthesized proteins substantially preserve their natural form, a patent battle will inevitably ensue, thereby preempting (or raising the costs of) the development of chemically synthesized drugs.

S u bs t an t ial t r an s fo r mat io n o f p r o t ein s In an earlier article, Linda Demaine and I explained the origins of the aberrational patent policy that permits patents on “isolated and purified” naturally occurring biochemicals14. There, we explained at length why such patents are invalid as claiming products of nature.

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We also proposed a test for patentability—the “substantial transformation test”—based on whether the patent applicant had transformed the molecule at issue so as to give it a new and different biological function. The purpose of this test was to preserve the traditional prohibition on patenting either products of nature or merely purified, preexisting substances. These doctrines serve several important public policies, including inter alia prohibiting private persons from claiming monopolies over products not invented by them, preventing arbitrary upstream monopolies that impose gatekeeper costs and inefficiencies on vast fields of downstream research not invented by the patent owner and preempting costly confusion and litigation over blocking or overlapping patents covering different aspects of preexisting natural molecules. The chemical synthesis of proteins presents a paradigmatic case of why patents on naturally occurring biochemicals were traditionally—and should continue to be—disallowed. When a biologist first reproduces a newly discovered natural protein through recombinant

By granting patents on [“ isolated and purified” naturally occurring] proteins, the patent system necessarily preempts or discourages the development of new methods of reproducing these natural proteins. expression, he has done several things, none of which necessarily results in an ‘invention.’ By identifying the structure and chemistry of the protein, he merely reveals a fact about nature. This discovery is unpatentable as lacking statutory subject matter. By reproducing it recombinantly, he may merely use well-known methods for identifying and reproducing it. Assuming the reproduction process is not patentable (because, for example, the process is one commonly used), a purified version of a naturally occurring protein is equally not properly an invention in the sense of the patent law. Besides generally lacking the required quality of nonobviousness15, it, too, lacks patentable subject matter. The recombinant protein has the same structure and performs the same biological function as its naturally occurring analog.

P at en t av en u es t o p o licy o u t co mes By granting patents on such proteins, the patent system necessarily preempts or discourages the development of new methods of reproducing these natural proteins. Chemical synthesis

is a prototypical example of such a method. When a chemist reproduces the same protein through pure synthesis, there is again no product invention (although there may be a process invention), because the product is the same as the purified recombinant protein. If the patent system has allowed the biologist to patent the protein, the chemist not only can obtain no patent on the synthesized version of the same protein, but he cannot commercialize the synthesized protein at all without the permission of the biologist. Yet, the process used by the chemist is entirely different from that used by the biologist. The biologist cannot reasonably claim that the chemist has infringed a process patent, because pure chemical synthesis in no way resembles recombinant protein production. And the protein itself already existed in nature. The ineluctable deduction is that the notion that the chemist producing a pure protein synthetically infringes the biologist’s ‘invention’ (a purified, naturally occurring protein reproduced recombinantly) is a legal and scientific absurdity. Yet, it logically follows from the patent theories now commonly accepted by the USPTO and the Federal Circuit. If any product is patentable, it is a protein that has been substantially transformed by way of physical alteration so as to perform a new biological function. Biologists and especially proteomic chemists have already made significant advances in understanding structural motifs and post-translational modification. This knowledge has resulted in, and foreshadows more, great advances in designing new methods of altering proteins, resulting in longer-lasting biochemical effects, fewer unintended side effects, greater bioactivity and other benefits. The patent law should encourage such inventions, but it should also require sufficient proof that the subject matter claimed in the patent application is indeed substantially different from the natural protein. In this way, the benefits of encouraging invention can be preserved without imposing unnecessary costs on researchers. Beyond the substantial transformation of proteins and peptides, researchers retain other avenues for patenting proteomic inventions. The most common is the process patent. As a kind of tollbooth for research on any product using the patented process, such patents can be extremely lucrative, and the field remains relatively open for patents on novel, efficient methods of designing and synthesizing proteins, both existing and new. Among such patents are Genentech’s patent on a method for purifying proteins through Protein A chromatography16 and Stephen Kent’s novel method for preparing modified proteins through ligation of segmented peptides17. Because process

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© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

PAT E N T S patents do not encompass natural products, the tollbooth effect does not inhibit new developments in the field of proteomics insofar as an incentive remains to invent around the process patent. There are, in short, many ways to synthesize or produce a protein, but inventing around a protein itself is exceedingly difficult. The physical and chemical properties of proteins are highly determined by their structure, and nearly all natural proteins produced by the body are necessary to it in the form produced. Whatever incentive patents on natural proteins provide for researchers to investigate the structure and function of these proteins (which is a necessary precondition to modern rational drug design in any case) is more than offset by the costs imposed on wide fields of future research involving that protein. Chemists who cannot pay royalties and wish to avoid litigation are limited to one significant avenue of research—the invention and synthesis of unnatural proteins.

C o n clu s io n s Improved methods of pure chemical protein synthesis foreshadow a clash between existing patentees of “isolated and purified” naturally occurring proteins that have been produced recombinantly and researchers chemically synthesizing the same protein through new methods. The US patent system has created the possibility of this conflict through the myopic decision to allow patents to claim purified, naturally occurring biochemicals regardless of how they were produced. Probably it never occurred to the judges who created this exception to the general rule that such products are unpatentable or that proteins could be produced in any manner other than recombinant genetics. The impending litigation should serve as a wake-up call to Congress, the USPTO and the Federal Circuit. The very last science policy a sound patent system should pursue is the discouragement of new methods for creating natural biochemicals that are essential to advances in the biomedical sciences.

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1. Hancock, W.S. et al. J. Bio. Chem. 247, 6224 (1972). 2. Warren, J.D. et al. J. Am. Chem. Soc. 126, 6576 (2004). 3. Kent, S.B.H. et al. Science 299, 884 (2003). 4. Kent, S.B.H. et al. Science 266, 776 (1994). 5. Burnham, N.L. Amer. J. Hosp. Pharm. 51, 210 (1994). 6. Hudecz, F. in Self-Assembling Peptide Systems in Biology, Medicine and Engineering (ed. Aggeli, A. et al.) 139–140 (Kluwer Academic Publishers, Dordrecht, Netherlands, 2001). 7. Kochendoerfer. G.G. et al. Science 299, 884 (2003). 8. US Patent No. 6,806,065 (Oct. 19, 2004) (claiming inter alia “ an isolated nucleic acid molecule encoding a Rickettsia felis outer membrane protein” ). 9. US Patent No. 6,800,473 (Oct. 5, 2004) (claiming inter alia human cathepsin L2 protein and its coding gene). 10. US Patent No. 6,794,500 (Sept. 21, 2004) (claiming RNA-binding protein). 11. Amgen, Inc. v. Chugai Pharmaceutical Co., 927 F.2d 1200 (Fed. Cir.), cert. denied, 502 US 856 (1991). 12. US Patent No. 6,057,434 (May 2, 2000). 13. US Patent No. 5,880,263 (Mar. 9, 1999). 14. Demaine, L.J. & Fellmeth, A.X. Stanford Law Rev. 55, 303, 357 (2002). 15. 35 USC § 103 (2004). 16. US Patent No. 6,797,814 (Sept. 28, 2004). 17. US Patent No. 6,476,190 (Nov. 5, 2002).

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P AT E N T S

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

R ecen t p at en t ap p licat io n s in micr o ar r ay s Patent #

Subject

Assignee

Inventor(s)

WO 200518796

A reaction-conducting system comprising a porous reaction substrate with its top surface bounded to a rigid support having multiple through-going holes, which form reaction zones; useful, e.g., for polymer and peptide synthesis reactions and as a microarray support.

PamGene (Hertogenbosch, The Netherlands)

Kievits T, Ruijtenbeek R, van Beuningen MG

US 20050048580

Priority application date

Publication date

8/21/2003

3/3/2005

An array comprising a substrate having multiple addresses, Harvard College LaBaer J, Lau AY each address comprising a nucleic acid encoding an (Cambridge, MA, USA) affinity-tagged test amino acid sequence, a translation effector and a binding agent that recognizes the affinity tag. The binding agent is preferably attached to the substrate; useful for high-throughput analyses of protein interactions.

8/3/2004

3/3/2005

US 20050048554

A microarray with a platform comprising a solid substrate and one consecutive hybrid film coating the surface of the solid substrate, where the film comprises alternating polycationic and polyanionic polymer layers; useful for identifying protein-protein interactions, for drug screening, for characterizing antibodies and in enzyme assays.

Zhou J; Zhou X

Zhou J, Zhou X

8/18/2004

3/3/2005

US 20050046758

A method for transcribing biomolecular patterns, involving two-dimensionally arranging biomolecules on a board, forming a thin-film layer made of an inorganic substance on the biomolecules, forming a supporting layer on the thin film layer, and peeling the thin film layer and the supporting layer off of the biomolecules together; useful for manufacturing biochips or devices using quantum dots or photonic crystals.

Omron KK (Tokyo); Aoyama S; Matsushita T; Nisjikawa T; Norioka S; Tsuda Y; Wazawa T

Aoyama S, Matsushita T, Nisjikawa T, Norioka S, Tsuda Y, Wazawa T

7/29/2003

3/3/2005

US 20050048648

A medium for reformulating biological membranes that enhances assay performance and is useful for fabricating and prolonging the shelf-life of biological membrane arrays.

Fang Y; Ferrie AM

Fang Y, Ferrie AM

8/29/2003

3/3/2005

US 20050048531

An array of nucleic acid probes, where each probe consists essentially of any of 127811 fully defined nucleotide sequences; useful for genetic analysis.

Affymetrix Lockhart DJ, (Santa Clara, CA, USA) Mack DH, Mittman M

9/17/1998

3/3/2005

WO 200516869

New dendrimer compounds useful in biochips for detecting a target compound, and for methods of diagnosis and biochemical analysis.

Pohang Iron and Steel Co.; Pohang University of Science and Technology Foundation (Pohang, Korea)

Choi KY, Choi YS, Hong BJ, Kwon SH, Oh SJ, Park JW, Youn TO

8/19/2003

2/24/2005

US 20050042363

A microarray with a macroporous polymer substrate, manufactured by obtaining a macroporous polymer substrate and coating a surface with the substrate. The substrates have high immobilization capacity for large biomolecules and better accessibility of analytes to the immobilized biomolecules.

Chernov BK; Gemmell MA; Golovaj B; Kukhtin AV; Yershov GM

Chernov BK, Gemmell MA, Golovaj B, Kukhtin AV, Yershov GM

8/18/2003

2/24/2005

WO 200514852

A microarray of immobilized biomolecules comprising a surface carrying a pattern of separated regions, each containing several spots of biomolecules; for use in analysis and diagnosis.

SusTech GmbH & Co. (Darmstadt, Germany)

Groll J, Levi S, Moeller M, Rong H

7/18/2003

2/17/2005

KR 2004094982

A method for highly concentrating a target material in a Sogang University sample using a scanning probe microscope to manufacture (Seoul, Korea) a highly integrated nano-bioarray.

Choi JU, Chun BS, Nam YS, Oh BG

5/6/2003

11/12/2004

Source: Derwent Information, Alexandria, VA. The status of each application is slightly different from country to country. For further details, contact Derwent Information, 1725 Duke Street, Suite 250, Alexandria, VA 22314. Tel: 1 (800) DERWENT (info.derwent.com).

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T her ap eu t ic an t ibo dy gen e t r an s fer Wayne A Marasco A A V v ect o r s co n t ain in g t he co mbin ed fu r in - s it e an d 2 A ‘ s elf- cleav in g’ p ep t ide facilit at e high- lev el ex p r es s io n o f mo n o clo n al an t ibo dies in vivo. The field of human antibody engineering has seen important recent advances, including de novo methods for isolating high-affinity human antibodies and the creation of transgenic mice expressing human antibodies. Yet serious bottlenecks in the development process remain, related primarily to the costs and time involved in producing and manufacturing human monoclonal antibodies (mAbs) both at the preclinical and clinical scales. In this issue, Fang et al.1 provide convincing evidence that in vivo therapeutic antibody gene transfer is indeed possible—at least at the preclinical level, potentially accelerating the translation of therapeutic mAbs from bench to bedside. There are now around 20 US Food and Drug Administration (FDA)–approved therapeutic mAbs on the market today for the treatment of cancer and of autoimmune, inflammatory and infectious diseases, and many more mAbs are in preclinical and clinical development. It has become increasingly evident over the last several years that human mAb therapies are here to stay. And why shouldn’t they be? They have a long track record of safety in human trials, the FDA is quite familiar with them and specific manufacturing guidelines are established. In addition, as the humanization 2 of immunoglobulins (IgG) increases—for example, by conversion of rodent IgGs to chimeric IgGs to CDR-grafted IgGs to ‘fully human’ IgGs—they show reduced immunogenicity and improved therapeutic efficacy. The trick in mAb production has been to find ways of generating stoichiometric amounts of both the heavy and light chains in the mAbproducing cells, especially because an imbal-

Wayne A. Marasco is in the Department of Cancer Immunology & AIDS, Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA. e-mail: [email protected]

F igu r e 1

a

Antigen binding sites Variable Constant

Light chain

Fc

Heavy chain

b Furin cleavage site

AAV vector HF2AL Promotor

Ab heavy chain

2A

Ab light chain

Stop codon

Start codon

c

Poly A

Cotransfection of 293 cells

+ AAV2 rep/AAV8 cap + ‘Helper’ virus functions Adv E2a, E4, VA RNAs Vector purification

Encapsidated rAAV

d Tail vein injection of rAAV

Mouse tumor model Bob Crimi

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

N E WS A N D VI E WS

ance in chain production can be toxic to the cells or can result in nonfunctional IgG chains in the supernatant that can complicate the purification process ( Fig. 1). Fang et al. hit upon their simple yet elegant technological advance while trying to find a solution to the limited cloning space that exists in recombinant adenoassociated virus (rAAV) vectors, an attractive vector system for achieving long-term gene transfer in vivo3. Many investigators have relied on the use of internal ribosomal entry sites (IRES) to allow cap-independent expression of the second gene of interest in a bicis-

N ATU RE BI O TECH N O LO GY VOLUME 23 NUMBER 5 MAY 2005

Pathway to therapeutic antibody gene transfer. (a) The complexity of monoclonal antibody production lies in the fact that immunoglobulins of the gamma family, the most commonly used in the clinical setting, are composed of four chains: two identical heavy and two identical light chains, which heterodimerize and homodimerize in the endoplasmic reticulum to form the mature four-chain molecule of IgG. Each heavy and light chain contains an N-terminal variable region and a C-terminal constant region. (b) The rAAV8HF2AL mAb expression system can accommodate new variable region genes for different mAbs through PCR cloning with specific PCR primers. (c) Cotransfection of 293 cells with AAV-HF2AL vector and plasmids encoding AAV structural genes and adenovirus helper functions is followed by purification steps to produce rAAV particles. (d) rAAV is injected intravenously into mice bearing subcutaneous human or mouse tumors, and therapeutic levels of mAbs are produced.

tronic cassette. However, as is well known, this method is somewhat unpredictable, is both cell and gene dependent and can often result in lower expression of the gene encoded by the second cistron 4. In the current study, Fang et al. employed a modification of the 2A self-processing sequence derived from the foot-and-mouth disease virus to express a full-length mAb from a single open reading frame driven by a single promoter. The 2A sequence mediates enzyme-independent ‘cleavage’ to separate polypeptides during the post-translation process5. The particular

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© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

N E WS A N D VI E WS sequence used leads to cleavage between the last two amino acids at its C terminus. One of the tested configurations (heavy chain–2A sequence–light chain (H2AL)) yielded equivalent amounts of both heavy and light chains, with a C-terminal 21-amino-acid ‘tail’ on the heavy chain, as expected, and the secreted antibodies were functional. Extending the work of others who have used furin cleavage sites to process heterologous polypeptides6, the investigators showed that addition of a four-amino-acid furin cleavage site immediately proximal to the 2A sequence (HF2AL) resulted in a second cleavage event, so that the final mature heavy chain contained only two extra amino acids at its C terminus both in vitro and in vivo. Impressively, this configuration allowed higher levels of secretion of functional antibodies. These experimental findings alone provide a useful advance in the field of antibody engineering. However, Fang et al. went one important step further by incorporating their single mAb expression cassette into a newly reported rAAV serotype 8 vector. Recently isolated from rhesus macaques, this vector has the capacity to transduce hepatocytes and skeletal cells with very high efficiency when delivered directly by portal vein injection or intravenous infusion 7,8. By combining these experimental systems, the investigators achieved remarkably high levels (>1,000 ug/ml) and long-term expression (>140 days) of an anti-VEGFR2 mAb in mice and demonstrated therapeutic efficacy against two tumor cell lines in two mouse tumor models. The significance of this study for the field of therapeutic mAb development is manifold. First, the rAAV8-HF2AL mAb expression system should provide investigators with a rapid and relatively straightforward way to evaluate potential therapeutic mAbs with less cost and labor than are required to produce sufficient mAbs in vitro for eventual in vivo testing in animal models of disease, at least at the early stages of the discovery process. This applies most readily to mAbs that have attained leadcandidate status through in vitro biological studies. However, it is easy to imagine that the system could be adapted to accept new variableregion genes from human single-chain antibody (scFv) and Fab libraries that have been selected against a target protein of interest. Second, this system could be adapted to evaluate combination mAb therapies—an area of active investigation, particularly for cancer and infectious diseases. In this experimental setting, one could test important and underexplored questions concerning the initial timing and relative dosing of two or more mAbs. Third, as the authors imply, the system may facilitate the manufacture of therapeutic mAbs

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by providing a way of generating cell lines that produce high-titer, stable antibodies. Before this elegant work can advance the cause of in vivo therapeutic antibody gene transfer, however, several important obstacles must be overcome. First, chronic diseases such as cancer and HIV-1/AIDS, for which mAb therapies hold great promise, are characterized by genetic instability, and it is well established that immune pressure can lead to phenotypically altered tumors or viral escape mutants, respectively. Whether high levels of circulating mAbs in vivo would prevent or accelerate this process remains to be determined. A second concern is the immune response to the vector. Heterologous antisera raised against AAV serotypes 1–6 are not neutralizing against AAV8, and therefore prior immunity in humans to AAV serotypes 1–6 is unlikely to interfere with in vivo gene transfer9. Nevertheless, the high promiscuity of AAV8 gene transfer in vivo suggests that the consequences of potentially transducing unintended populations of cells must be completely evaluated from the safety standpoint.

Furthermore, the use of tissue-specific promoters to restrict gene expression should be explored. Finally, the 23-amino-acid 2A self-processing peptide, although cleaved, is a foreign sequence. Processing and presentation of this sequence by major histocompatibility complexes class I and II could still occur and prove detrimental to the host. Nevertheless, although many questions remain, this study offers new tools and avenues of investigation that should accelerate the process of therapeutic mAb discovery and shorten the time needed to reach the clinic. 1. Fang, J. et al. Nat. Biotechnol. 2 3 , 5 8 4 –5 9 0 (2005). 2. Lobo, E.D., Hansen R.J. & Balthasar J.P. J. Pharm. Sci. 9 3 , 2645–2668 (2004). 3. Flotte, T.R. Gene Ther. 1 1 , 805–810 (2004). 4. de Felipe, P. Genet. Vaccines & Ther. 2 , 13 (2004). 5. de Felipe, P. & Ryan, M.D. Traffic 5 , 6 1 6 –6 2 6 (2004). 6. Gaken, J. et al. Gene Ther. 7 , 1979–1985 (2000). 7. Gao, G.-P. et al. Proc. Natl. Acad. Sci. USA 9 9 , 11854–11859 (2002). 8. Nakai, H. et al . J. Virol . 7 9 , 214–224 (2005). 9. Jooss, K. & Chirmule, N. Gene Ther. 1 0 , 955–963 (2003).

B r in gin g amy lo id in t o fo cu s Todd E Golde & Brian J Bacskai A my lo id dep o s it s can be r ap idly det ect ed in t he br ain s o f liv in g mice u s in g a n o v el ligan d an d n ear - in fr ar ed flu o r es cen ce imagin g. Until recently using clinical imaging technologies such as positron emission tomography (PET) and magnetic resonance imaging (MRI), the amyloid plaques that accumulate in the brains of patients with Alzheimer disease have been difficult, if not impossible, to detect in vivo. In this issue Hintersteiner et al.1 describe a different approach to imaging amyloid. Using a near infrared (NIR) fluorescence probe that crosses the blood-brain barrier and binds amyloid plaques in the brains of mice, the amount of amyloid can be cost-effectively estimated using near infrared fluorescence imaging. Eventually such an approach may be adapted to visualize amyloid in humans. Todd E. Golde is in the Department of Neuroscience, Mayo Clinic, Mayo Clinic College of Medicine, 4500 San Pablo Road, Jacksonville, Florida 32224, USA, and Brian J. Bacskai is in the Alzheimer’s disease Research Unit, Mass. General Hospital, 114 16th St. Charlestown, Massachusetts 02129, USA. e-mail: [email protected] and [email protected]

The ability to image disease processes in living humans is one of the major technologic advances of modern medicine. In the context of disease management, in vivo imaging is one of the many, and often most informative, modalities that can be used to diagnose diseases and evaluate treatment outcomes. Largely because of costs, imaging is less commonly used to predict risk for the development of disease in asymptomatic individuals. Although not always thought of as such, tests that rely on imaging are fundamentally biomarker studies. As with any biomarker assay, the utility of such tests depends on the sensitivity and specificity of the biomarker and on the sensitivity and specificity of the test used to measure that biomarker. The former issue is extremely important to recognize. No matter how good the assay, its predictive ability is only as good as the predictive ability of the biomarker being studied. Deposition of the amyloid β-peptide into a fibrillar β-sheet structure referred to as amyloid is a diagnostic hallmark of the post-mortem Alzheimer-disease brain. In Alzheimer disease, amyloid β deposits as amyloid in senile plaques

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© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

N E WS A N D VI E WS sequence used leads to cleavage between the last two amino acids at its C terminus. One of the tested configurations (heavy chain–2A sequence–light chain (H2AL)) yielded equivalent amounts of both heavy and light chains, with a C-terminal 21-amino-acid ‘tail’ on the heavy chain, as expected, and the secreted antibodies were functional. Extending the work of others who have used furin cleavage sites to process heterologous polypeptides6, the investigators showed that addition of a four-amino-acid furin cleavage site immediately proximal to the 2A sequence (HF2AL) resulted in a second cleavage event, so that the final mature heavy chain contained only two extra amino acids at its C terminus both in vitro and in vivo. Impressively, this configuration allowed higher levels of secretion of functional antibodies. These experimental findings alone provide a useful advance in the field of antibody engineering. However, Fang et al. went one important step further by incorporating their single mAb expression cassette into a newly reported rAAV serotype 8 vector. Recently isolated from rhesus macaques, this vector has the capacity to transduce hepatocytes and skeletal cells with very high efficiency when delivered directly by portal vein injection or intravenous infusion 7,8. By combining these experimental systems, the investigators achieved remarkably high levels (>1,000 ug/ml) and long-term expression (>140 days) of an anti-VEGFR2 mAb in mice and demonstrated therapeutic efficacy against two tumor cell lines in two mouse tumor models. The significance of this study for the field of therapeutic mAb development is manifold. First, the rAAV8-HF2AL mAb expression system should provide investigators with a rapid and relatively straightforward way to evaluate potential therapeutic mAbs with less cost and labor than are required to produce sufficient mAbs in vitro for eventual in vivo testing in animal models of disease, at least at the early stages of the discovery process. This applies most readily to mAbs that have attained leadcandidate status through in vitro biological studies. However, it is easy to imagine that the system could be adapted to accept new variableregion genes from human single-chain antibody (scFv) and Fab libraries that have been selected against a target protein of interest. Second, this system could be adapted to evaluate combination mAb therapies—an area of active investigation, particularly for cancer and infectious diseases. In this experimental setting, one could test important and underexplored questions concerning the initial timing and relative dosing of two or more mAbs. Third, as the authors imply, the system may facilitate the manufacture of therapeutic mAbs

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by providing a way of generating cell lines that produce high-titer, stable antibodies. Before this elegant work can advance the cause of in vivo therapeutic antibody gene transfer, however, several important obstacles must be overcome. First, chronic diseases such as cancer and HIV-1/AIDS, for which mAb therapies hold great promise, are characterized by genetic instability, and it is well established that immune pressure can lead to phenotypically altered tumors or viral escape mutants, respectively. Whether high levels of circulating mAbs in vivo would prevent or accelerate this process remains to be determined. A second concern is the immune response to the vector. Heterologous antisera raised against AAV serotypes 1–6 are not neutralizing against AAV8, and therefore prior immunity in humans to AAV serotypes 1–6 is unlikely to interfere with in vivo gene transfer9. Nevertheless, the high promiscuity of AAV8 gene transfer in vivo suggests that the consequences of potentially transducing unintended populations of cells must be completely evaluated from the safety standpoint.

Furthermore, the use of tissue-specific promoters to restrict gene expression should be explored. Finally, the 23-amino-acid 2A self-processing peptide, although cleaved, is a foreign sequence. Processing and presentation of this sequence by major histocompatibility complexes class I and II could still occur and prove detrimental to the host. Nevertheless, although many questions remain, this study offers new tools and avenues of investigation that should accelerate the process of therapeutic mAb discovery and shorten the time needed to reach the clinic. 1. Fang, J. et al. Nat. Biotechnol. 2 3 , 5 8 4 –5 9 0 (2005). 2. Lobo, E.D., Hansen R.J. & Balthasar J.P. J. Pharm. Sci. 9 3 , 2645–2668 (2004). 3. Flotte, T.R. Gene Ther. 1 1 , 805–810 (2004). 4. de Felipe, P. Genet. Vaccines & Ther. 2 , 13 (2004). 5. de Felipe, P. & Ryan, M.D. Traffic 5 , 6 1 6 –6 2 6 (2004). 6. Gaken, J. et al. Gene Ther. 7 , 1979–1985 (2000). 7. Gao, G.-P. et al. Proc. Natl. Acad. Sci. USA 9 9 , 11854–11859 (2002). 8. Nakai, H. et al . J. Virol . 7 9 , 214–224 (2005). 9. Jooss, K. & Chirmule, N. Gene Ther. 1 0 , 955–963 (2003).

B r in gin g amy lo id in t o fo cu s Todd E Golde & Brian J Bacskai A my lo id dep o s it s can be r ap idly det ect ed in t he br ain s o f liv in g mice u s in g a n o v el ligan d an d n ear - in fr ar ed flu o r es cen ce imagin g. Until recently using clinical imaging technologies such as positron emission tomography (PET) and magnetic resonance imaging (MRI), the amyloid plaques that accumulate in the brains of patients with Alzheimer disease have been difficult, if not impossible, to detect in vivo. In this issue Hintersteiner et al.1 describe a different approach to imaging amyloid. Using a near infrared (NIR) fluorescence probe that crosses the blood-brain barrier and binds amyloid plaques in the brains of mice, the amount of amyloid can be cost-effectively estimated using near infrared fluorescence imaging. Eventually such an approach may be adapted to visualize amyloid in humans. Todd E. Golde is in the Department of Neuroscience, Mayo Clinic, Mayo Clinic College of Medicine, 4500 San Pablo Road, Jacksonville, Florida 32224, USA, and Brian J. Bacskai is in the Alzheimer’s disease Research Unit, Mass. General Hospital, 114 16th St. Charlestown, Massachusetts 02129, USA. e-mail: [email protected] and [email protected]

The ability to image disease processes in living humans is one of the major technologic advances of modern medicine. In the context of disease management, in vivo imaging is one of the many, and often most informative, modalities that can be used to diagnose diseases and evaluate treatment outcomes. Largely because of costs, imaging is less commonly used to predict risk for the development of disease in asymptomatic individuals. Although not always thought of as such, tests that rely on imaging are fundamentally biomarker studies. As with any biomarker assay, the utility of such tests depends on the sensitivity and specificity of the biomarker and on the sensitivity and specificity of the test used to measure that biomarker. The former issue is extremely important to recognize. No matter how good the assay, its predictive ability is only as good as the predictive ability of the biomarker being studied. Deposition of the amyloid β-peptide into a fibrillar β-sheet structure referred to as amyloid is a diagnostic hallmark of the post-mortem Alzheimer-disease brain. In Alzheimer disease, amyloid β deposits as amyloid in senile plaques

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and, more variably, in cerebral vessels. The amyloid in senile plaques forms a spherical core that ranges from ~2 to ~200 µm, but is typically 20– 60 µm in diameter. Accumulation of amyloid β in the brain is also hypothesized to be the cause of Alzheimer disease, although there is a great deal of debate as to whether the toxic form is amyloid β that accumulates as visible amyloid deposits or as smaller soluble extracellular or intracellular aggregates. In any case, there is a great deal of evidence from genetic, biochemical, pathologic and animal modeling studies to support the hypothesis that accumulation of amyloid β in the brain is the initiating event in Alzheimer-disease pathogenesis2. Thus, in theory, amyloid β deposition as amyloid appears to be a good diagnostic biomarker for Alzheimer disease and may also be a good predictive biomarker of the disease. Given the potential of amyloid as a biomarker, a number of groups have been developing approaches to visualize plaques. Conceptually, the simplest approach has been to use MRI to try to directly visualize amyloid plaques. Despite recent impressive technological advances in MRI resolution, this approach remains challenging because of the plaques’ small size. Several recent reports suggest that this may now be feasible, at least in transgenic mouse models and in mouse and human tissue slices3,4. However, only large plaques (>50 µM) can currently be visualized, and the scans require hours to complete. Thus, additional advances in MRI technology would be needed to translate this approach to humans or even to make it more widely accessible for preclinical studies of amyloid β deposition in mice. Compounds such as Congo red ( Fig. 1a) and thioflavin S have been used for many years to recognize amyloid deposits in postmortem tissue. Thus, another approach to visualizing amyloid β deposited in vivo is to modify known amyloid-binding compounds or identify new ones. The most advanced of these ligand-based approaches uses Pittsburgh compound B (PIB) to visualize plaque burden in living humans with PET5. PIB is a thioflavin derivative labeled with 11C so that it can be used as a PET probe ( Fig. 1a). PIB is the only amyloid probe currently used to image amyloid in Alzheimer disease patients, but because of its only recent approval for humans and somewhat limited availability, the clinical utility of PET imaging with PIB has not been definitively established. Although PIB has been used to image amyloid plaque in humans in vivo and in mice tissue ex vivo, microPET with PIB has not been able to detect amyloid in the brains of living Alzheimerdisease mouse models. Moreover, the inherent resolution limits of PET impose certain restric-

tions on the information that a this approach can provide. OH O NH S O For example, it is unlikely that HO S N N N PET scans will ever be able to N N O N H S H N O Amyloiddetect individual plaques or Congo red HO PIB binding even to provide precise localF ligands ization of amyloid deposition OH HO within structural subregions O HO FSB of the brain. AOI987 HO O Other studies suggest that combining MRI with an amyb loid ligand labeled with 1H or Intravenous 19F may enable visualization injection of AOI987 of amyloid plaques6–8. The most recent report shows that MRI can detect an 19F-labeled amyloid dye known as FSB and that FSB labeling is superior to other MRI methods for detecting amyloid plaques in Near-infrared Imaging living mice9. Although such light approaches provide more spatial resolution than PET studies and avoid the use of CCD radioactive isotopes with camera short half-lives, they are still subject to some of the techni- F igu r e 1 Amyloid ligands and near infrared fluorescence imaging of cal limitations of traditional plaques. (a) Examples of amyloid-binding ligands. (b) As described MRI studies and, at least with by Hintersteiner et al ., the near-infrared probe AOI987, injected current ligands, to suboptimal systemically in mice, crosses the blood-brain barrier and specifically labels amyloid plaque. Only a light source, appropriate filters and a signal-to-noise levels. sensitive charge-coupled-device camera are required to measure the Hintersteiner et al. describe near-infrared fluorescence emitted by the amyloid-binding probe. an alternative, all-optical approach to imaging an amyloid ligand in vivo. Using a novel fluores- and, in the case of PET, short-lived radioligands, cent amyloid-binding dye, AOI987 ( Fig. 1a), in vivo NIR imaging as described in this study is which absorbs and emits in the NIR spectrum, quite inexpensive and could be set up in virtuthey visualize amyloid load in the brain of ally any laboratory. living mice using NIR fluorescence imaging It should also be possible to improve the ( Fig. 1b). Fluorescent imaging in live animals spatial resolution. NIR imaging using the more with shorter wavelengths of light is severely elaborate techniques and equipment of optilimited because of tissue autofluorescence and cal tomography (which was not done in this scattering. However, NIR imaging solves this study) has attained a spatial resolution of 1 mm problem by reducing the background and scat- or better, approximately the resolution of PET tering through biological tissue10. Although the imaging11. It may also be possible to improve spatial resolution in the work of Hintersteiner upon the fluorescent signal by improving the et al. is limited, the authors do demonstrate that amyloid probe. For example, an amyloid dye the fluorescence signal intensity increases with that fluoresced more intensely, changed spectra, increasing plaque load in the mice and that or both, upon binding to amyloid could reduce amyloid deposition can be detected in mice as background and improve contrast. young as 9 months. Moreover images could be Currently, the major constraint on NIR fluoacquired in short time periods ranging from rescence imaging is the paucity of fluorescent 0.5 to 3.0 seconds, allowing measurement at probes. Probes described in the literature are multiple time points after dosing to calculate ligands tagged with NIR fluorophores. Thus, on specific binding. a more general level, the finding of Hintersteiner Despite the lack of spatial resolution, the et al. is rather exciting simply because they ability to noninvasively quantify amyloid β identify an NIR fluorescent ligand that binds deposited as amyloid in living mice using NIR the biomarker of interest. This proof-ofimaging is quite exciting. Unlike PET and MRI concept study will likely engage others in the imaging technologies, which require significant search for better NIR imaging agents for amyhardware investments, highly skilled operators, loid and other targets.

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N E WS A N D VI E WS NIR imaging has been used for many years to look at functional parameters in the human brain (for example, saturations of hemoglobin) 12,13. Although it is unlikely that NIR imaging will permit imaging of the entire brain, it is possible to image several centimeters below the human skull, potentially enabling, for example, detection of amyloid in the cortex. If amyloid imaging were used to diagnose Alzheimer disease, it is likely that simply detecting plaques in areas of the brain known to be affected by Alzheimer disease would be good enough. The development of amyloid probes that can be imaged in vivo is almost certain to expedite the preclinical and clinical evaluation of novel Alzheimer-disease therapeutics that target amyloid β. Such ligands may also be useful in the diagnosis of atypical Alzheimer-disease cases. But current clinical diagnosis of Alzheimer disease is reasonably accurate, so it is unlikely that amyloid imaging will become a routine diagnostic modality unless it were relatively quick, safe and inexpensive. With further advances in the technology and ligands, NIR imaging of amyloid may fulfill these criteria14. There is evidence to suggest that amyloid deposition predates the clinical signs of Alzheimer disease by years or even decades; however, the exact temporal relationship between amyloid deposition and cognitive dysfunction remains to be established. The utility of

existing amyloid probes for detecting very early stages of amyloid deposition in the brain of humans has not yet been determined, although most believe that significant improvements in sensitivity will be needed. As it is almost certain that Alzheimer disease will be easier to prevent than treat, a refined version of current amyloid imaging methods may ultimately be the diagnostic tool used to determine both who needs prophylactic treatment and when that treatment should be initiated. 1. Hintersteiner, M. et al . Nat. Biotechnol . 2 3 , 577–583 (2005). 2. Golde, T.E. J. Clin. Invest. 1 1 1 , 11–18 (2003). 3. Jack, C.R. Jr. et al. Magn. Reson. Med. 5 2 , 1263–1271 (2004). 4. Lee, S.P., Falangola, M.F., Nixon, R.A., Duff, K. & Helpern, J.A. Magn. Reson. Med. 5 2 , 538–544 (2004). 5. Klunk, W.E. et al. Ann. Neurol. 5 5 , 306–319 (2004). 6. Poduslo, J.F. et al. Biochemistry 4 3 , 6064–6075 (2004). 7. Skovronsky, D.M. et al. Proc. Natl. Acad. Sci. USA 9 7 , 7609–7614 (2000). 8. Wadghiri, Y.Z. et al. Magn. Reson. Med. 5 0 , 293–302 (2003). 9. Higuchi, M. et al. Nat. Neurosci. 8 , 527–533 (2005). 10. Frangioni, J.V. Curr. Opin. Chem. Biol. 7 , 626–634 (2003). 11. Graves, E.E., Ripoll, J., Weissleder, R. & Ntziachristos, V. Med. Phys. 3 0 , 901–911 (2003). 12. Strangman, G., Boas, D.A. & Sutton, J.P. Biol. Psychiatry 5 2 , 679–693 (2002). 13. Pouratian, N. et al. Magn. Reson. Med. 4 7 , 766–776 (2002). 14. Skoch, J., Dunn, A., Hyman, B.T. & Bacskai, B.J. J. Biomed. Opt. 1 0 , 011007 (2005).

R ev er s e en gin eer in g gen e r egu lat o r y n et w o r ks Alexander J Hartemink A n in fo r mat io n t heo r et ic algo r it hm t hat p r u n es aw ay p o t en t ially in dir ect in t er act io n s allo w s fo r imp r o v ed r eco n s t r u ct io n o f bio lo gical n et w o r ks . Biological systems are wondrously and notoriously complex. Over the last fifty years, molecular biology has helped to reveal the vast and stunning array of components in biological systems. Now, we face the even more daunting challenge of systems biology: determining how all these puzzle pieces come together to create living systems. A recent paper by Basso et al.1 published in Nature Genetics describes a statistical algorithm for more compactly and

Alexander J. Hartemink is in the Department of Computer Science, Duke University, Box 90129, Durham, North Carolina 27708-0129, USA. e-mail: [email protected]

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more accurately reverse engineering networks describing pair-wise interactions among genes and thin protein products. The network they recover from gene expression profiles of a variety of human B-cell populations suggests that the Bcell regulatory network has both a scale-free and hierarchical architecture, implying the presence of a few ‘hubs’ that are highly connected and preferentially connected to one another. Reverse engineering is the process of elucidating the structure of a system by reasoning backwards from observations of its behavior. In reverse engineering biological networks, one of the first hurdles to overcome is semantic. The term ‘network’ has come to mean different things throughout biology, and the semantic

overload is magnified when computational and statistical interpretations are added. Even in networks whose nodes are ostensibly the same objects (for examples, genes or their protein products), the network edges can mean vastly different things and should be interpreted with care. As just one example, edges can either be undirected (without an orientation) to capture relations that are symmetric or directed (with an orientation) to capture relations that are asymmetric. An undirected edge between two genes may indicate that the genes are coexpressed or coregulated, participate in a common pathway or regulatory ‘module’ or share a common biological function, location or process; or that their protein products coprecipitate, directly bind one another, or assemble into the same complex (a problematic term in its own right). On the other hand, a directed edge between two genes may be used to represent a step in a metabolic pathway, signal transduction cascade, or stage of development; or it may indicate a causal control or a regulatory relationship. This semantic caveat is important in trying to understand the myriad methods that have been proposed in the last decade for reverse engineering biological networks from system-wide data, especially gene expression data. Within this broader context, the ARACNe algorithm of Basso et al. is most closely related to an earlier method for producing ‘relevance networks’2,3. Both sets of authors use a pair-wise mutual information criterion across gene expression profiles to recover edges that are undirected, but ARACNe improves on this somewhat by using the data processing inequality to prune out interactions suspected to be indirect. After using synthetic data to assess the accuracy of their ARACNe algorithm, Basso et al. apply it to a rather sizable set of gene expression array data, collected from human B-cell populations with a variety of phenotypes, including both normal and malignantly transformed cells at different stages in the germinal center reaction process, from naive cells in the mantle zone to differentiated memory or plasma cells. This results in a network with about 129,000 undirected interactions between pairs of genes. Owing to the obvious complexity of such a network, the authors choose to focus on two simpler aspects: a statistical summary of the (global) connectivity distribution among all the nodes in the network, an approach that is quite in vogue; and a more detailed look at a specific (local) portion of the network centered around the proto-oncogene MYC, chosen both because of its clinical importance and because of the wealth of information available for corroboratory purposes.

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© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

N E WS A N D VI E WS NIR imaging has been used for many years to look at functional parameters in the human brain (for example, saturations of hemoglobin) 12,13. Although it is unlikely that NIR imaging will permit imaging of the entire brain, it is possible to image several centimeters below the human skull, potentially enabling, for example, detection of amyloid in the cortex. If amyloid imaging were used to diagnose Alzheimer disease, it is likely that simply detecting plaques in areas of the brain known to be affected by Alzheimer disease would be good enough. The development of amyloid probes that can be imaged in vivo is almost certain to expedite the preclinical and clinical evaluation of novel Alzheimer-disease therapeutics that target amyloid β. Such ligands may also be useful in the diagnosis of atypical Alzheimer-disease cases. But current clinical diagnosis of Alzheimer disease is reasonably accurate, so it is unlikely that amyloid imaging will become a routine diagnostic modality unless it were relatively quick, safe and inexpensive. With further advances in the technology and ligands, NIR imaging of amyloid may fulfill these criteria14. There is evidence to suggest that amyloid deposition predates the clinical signs of Alzheimer disease by years or even decades; however, the exact temporal relationship between amyloid deposition and cognitive dysfunction remains to be established. The utility of

existing amyloid probes for detecting very early stages of amyloid deposition in the brain of humans has not yet been determined, although most believe that significant improvements in sensitivity will be needed. As it is almost certain that Alzheimer disease will be easier to prevent than treat, a refined version of current amyloid imaging methods may ultimately be the diagnostic tool used to determine both who needs prophylactic treatment and when that treatment should be initiated. 1. Hintersteiner, M. et al . Nat. Biotechnol . 2 3 , 577–583 (2005). 2. Golde, T.E. J. Clin. Invest. 1 1 1 , 11–18 (2003). 3. Jack, C.R. Jr. et al. Magn. Reson. Med. 5 2 , 1263–1271 (2004). 4. Lee, S.P., Falangola, M.F., Nixon, R.A., Duff, K. & Helpern, J.A. Magn. Reson. Med. 5 2 , 538–544 (2004). 5. Klunk, W.E. et al. Ann. Neurol. 5 5 , 306–319 (2004). 6. Poduslo, J.F. et al. Biochemistry 4 3 , 6064–6075 (2004). 7. Skovronsky, D.M. et al. Proc. Natl. Acad. Sci. USA 9 7 , 7609–7614 (2000). 8. Wadghiri, Y.Z. et al. Magn. Reson. Med. 5 0 , 293–302 (2003). 9. Higuchi, M. et al. Nat. Neurosci. 8 , 527–533 (2005). 10. Frangioni, J.V. Curr. Opin. Chem. Biol. 7 , 626–634 (2003). 11. Graves, E.E., Ripoll, J., Weissleder, R. & Ntziachristos, V. Med. Phys. 3 0 , 901–911 (2003). 12. Strangman, G., Boas, D.A. & Sutton, J.P. Biol. Psychiatry 5 2 , 679–693 (2002). 13. Pouratian, N. et al. Magn. Reson. Med. 4 7 , 766–776 (2002). 14. Skoch, J., Dunn, A., Hyman, B.T. & Bacskai, B.J. J. Biomed. Opt. 1 0 , 011007 (2005).

R ev er s e en gin eer in g gen e r egu lat o r y n et w o r ks Alexander J Hartemink A n in fo r mat io n t heo r et ic algo r it hm t hat p r u n es aw ay p o t en t ially in dir ect in t er act io n s allo w s fo r imp r o v ed r eco n s t r u ct io n o f bio lo gical n et w o r ks . Biological systems are wondrously and notoriously complex. Over the last fifty years, molecular biology has helped to reveal the vast and stunning array of components in biological systems. Now, we face the even more daunting challenge of systems biology: determining how all these puzzle pieces come together to create living systems. A recent paper by Basso et al.1 published in Nature Genetics describes a statistical algorithm for more compactly and

Alexander J. Hartemink is in the Department of Computer Science, Duke University, Box 90129, Durham, North Carolina 27708-0129, USA. e-mail: [email protected]

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more accurately reverse engineering networks describing pair-wise interactions among genes and thin protein products. The network they recover from gene expression profiles of a variety of human B-cell populations suggests that the Bcell regulatory network has both a scale-free and hierarchical architecture, implying the presence of a few ‘hubs’ that are highly connected and preferentially connected to one another. Reverse engineering is the process of elucidating the structure of a system by reasoning backwards from observations of its behavior. In reverse engineering biological networks, one of the first hurdles to overcome is semantic. The term ‘network’ has come to mean different things throughout biology, and the semantic

overload is magnified when computational and statistical interpretations are added. Even in networks whose nodes are ostensibly the same objects (for examples, genes or their protein products), the network edges can mean vastly different things and should be interpreted with care. As just one example, edges can either be undirected (without an orientation) to capture relations that are symmetric or directed (with an orientation) to capture relations that are asymmetric. An undirected edge between two genes may indicate that the genes are coexpressed or coregulated, participate in a common pathway or regulatory ‘module’ or share a common biological function, location or process; or that their protein products coprecipitate, directly bind one another, or assemble into the same complex (a problematic term in its own right). On the other hand, a directed edge between two genes may be used to represent a step in a metabolic pathway, signal transduction cascade, or stage of development; or it may indicate a causal control or a regulatory relationship. This semantic caveat is important in trying to understand the myriad methods that have been proposed in the last decade for reverse engineering biological networks from system-wide data, especially gene expression data. Within this broader context, the ARACNe algorithm of Basso et al. is most closely related to an earlier method for producing ‘relevance networks’2,3. Both sets of authors use a pair-wise mutual information criterion across gene expression profiles to recover edges that are undirected, but ARACNe improves on this somewhat by using the data processing inequality to prune out interactions suspected to be indirect. After using synthetic data to assess the accuracy of their ARACNe algorithm, Basso et al. apply it to a rather sizable set of gene expression array data, collected from human B-cell populations with a variety of phenotypes, including both normal and malignantly transformed cells at different stages in the germinal center reaction process, from naive cells in the mantle zone to differentiated memory or plasma cells. This results in a network with about 129,000 undirected interactions between pairs of genes. Owing to the obvious complexity of such a network, the authors choose to focus on two simpler aspects: a statistical summary of the (global) connectivity distribution among all the nodes in the network, an approach that is quite in vogue; and a more detailed look at a specific (local) portion of the network centered around the proto-oncogene MYC, chosen both because of its clinical importance and because of the wealth of information available for corroboratory purposes.

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F igu r e 1 Comparison of the performance of ARACNe and a Bayesian network inference algorithm in reverse engineering a synthetic gene regulatory network. (a) The synthetic gene regulatory network used for assessing the reconstruction accuracy of network inference algorithms7 . The network has 8 disconnected nodes serving as a negative control (not shown) and 12 interconnected nodes, including a cyclic loop formed by nodes 0, 2, 3 and 7 (regulatory interactions between two genes (nodes) are shown as arrows (edges); black and green arrows represent up- and downregulation, respectively). Reverse engineering using a Bayesian network inference algorithm to recover a dynamic Bayesian network (DBN) on the full data set results in a reconstructed network with 100% accuracy, as reported earlier7 (blue arrows indicate correct edges with correct orientation; no incorrect edges were recovered). Reverse engineering using ARACNe on the full data set results in a reconstructed network with the same 13 correct edges as the DBN reconstruction, but without orientations; it includes two incorrect edges, between nodes 3 and 4 and nodes 7 and 8 (correct and incorrect edges are represented by blue and red, respectively; the ARACNe network is reproduced from Basso et al. ). (b) Performance of ARACNe and a Bayesian network inference algorithm on subsets of the full data set. Sensitivity and precision are plotted as a function of the number of samples used for the analysis. At roughly the same sensitivity, the Bayesian network inference algorithm appears to exhibit better precision over a wide range of sample sizes (the ARACNe plots are reproduced from Basso et al. ).

Analyzing the connectivity distribution of a network is currently popular for two reasons. First, it is a sensible first step in reasoning about networks so large that they are difficult to understand otherwise; for all intents and purposes, the interactions recovered by a tool like ARACNe are impossible to visualize directly in a way that facilitates insight. Second, articles and books4 suggesting that many kinds of networks—biological, social, and engineered—are scale-free have recently been published in a flurry. Indeed, the network recovered by ARACNe from B-cell expression profiles has a connectivity distribution that suggests that it, too, is scale-free. Basso et al. appropriately caution that the reported connectivity distribution is not conclusive because an explainable saturation occurs in the ‘low interaction count’ portion of the curve, resulting in a distribution that is scale-free over only one order of magnitude. Nevertheless, the results are consistent with a hypothesis that this network is scale-free. As for the subnetwork centered around MYC, it contains 56 genes adjacent to MYC, termed ‘first neighbors,’along with 2,007 genes adjacent to these first neighbors, termed ‘second neighbors.’Even a comparatively small subnetwork of this size is still a challenge to visualize insightfully, so the authors assess its quality in two ways. First, they determine whether the genes of the subnetwork are enriched for specific cellular process categories in the Gene Ontology database5, which they are. Second—and this is a wonderful strength of the paper—the authors experimentally validate some of the first neighbors of MYC.

The list of MYC first neighbors was pruned to exclude those with lowest mutual information scores, those that do not contain MYC binding sites near the transcription initiation site, and those already known to be bound directly by MYC. The remaining 12 genes were tested for direct MYC binding using a standard chromatin immunoprecipitation assay, and 11 predictions were positive. Although the authors’ resultant claim of over 90% specificity for ARACNe is perhaps optimistic as they excluded predictions with lowest mutual information scores and, more important, predictions not known to contain a MYC binding site, the results are still extremely encouraging. The success of this kind of experimental validation lends credence both to ARACNe and also to computational approaches more generally. In closing, two further points should be made. First, this paper provides evidence confirming a simple intuition that many in this field have had, namely that gene expression data need not necessarily be collected from perturbation experiments for reverse engineering to be successful. Although perturbation experiments are certainly useful for network inference, they are also costly, and in some cases infeasible for either technical or ethical reasons. Basso et al. demonstrate that as long as the available data explore a wide range in the ‘expression space’ of the system, biologically meaningful interactions can be recovered by computational algorithms. Second, the authors of this paper should be commended for evaluating the performance of ARACNe on synthetic data6, and indeed, it performs nobly. However, they seem to misrep-

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resent the performance of Bayesian networks on the same synthetic data. They report that ARACNe offers “substantially higher precision” in comparison with Bayesian networks, whereas we have observed exactly the opposite ( Fig. 1). The discrepancy is most likely due to the fact that Basso et al. used a static Bayesian network in place of a more appropriate dynamic Bayesian network. This is only a minor quibble because the B-cell expression profiles examined in the remainder of the study are of quite a different character from the synthetic expression data in many regards, and it is not clear which method would be best suited to network inference in the B-cell context. Indeed, given the earlier caveat that the networks recovered by these and other methods typically have different semantics, it is likely that multiple methods will be needed to completely understand the regulation and dysregulation of B-cell differentiation, as well as other similar problems in systems biology. 1 Basso, K. et al. Nat. Gen. 3 7 , 382 –390 (2005). 2. Butte, A.J. & Kohane, I.S. Pac. Symp. Biocomput. 2000, 418 –429 (2000). 3. Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R. & Kohane, I.S. Proc. Natl. Acad. Sci. USA 9 7 , 12182 – 12186 (2000). 4. Barabasi, A.L. Linked: How Everything Is Connected to Everything Else and What It Means (Plume, New York, NY, 2003). 5. The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nat. Gen. 2 5 , 25–29 (2000). 6. Smith, V.A., Jarvis, E.D. & Hartemink, A.J. Bioinformatics 1 8 , S216 –S224 (2002). 7. Yu, J., Smith, V.A., Wang, P.P., Hartemink A.J. & Jarvis, E.D. in 3 rd International Conference on Systems Biology (Karolinska Institute, Stockholm, Sweden, 2002).

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T u n able an t ibo dies Louis M Weiner & Paul Carter

Monoclonal antibodies (mAbs) have become the protein therapeutics of choice for targeting cancer, and increasingly for other indications. Recent setbacks involving the withdrawal of Biogen Idec’s (Cambridge, MA, USA) IgG4 mAb (Tysabri, natalizumab) for use in multiple sclerosis, however, serve to emphasize the incompleteness of our knowledge of the functional consequences of mAb-mediated target binding. In addition, for those mAbs ( e.g., IgG1 isotype) that trigger antibodydependent cellular cytotoxicity (ADCC)—the process by which immune cells ( e.g., natural killer (NK) cells, macrophages and neutrophils) are recruited to kill target cells such as tumors—the biological and therapeutic implications of enhancing cytotoxicity remain unclear. Several of many excellent presentations at a recent Keystone meeting in Santa Fe on antibody-based therapeutics for cancer 1 emphasized progress in our ability to modify different domains of mAbs and mAb fragments to both influence target affinity and modify ADCC. Alteration of the mAb antigen-combining site by site-directed mutagenesis2 or random mutagenesis with yeast surface display3,4 is now widely used to increase antibody affinity for target antigens. There is also the possibility that these approaches will be supplemented by other novel approaches. One such approach may be to exploit small-molecule specificity and capacity by modifying the IgG structure to contain defined chemical groups with their own binding and biologic properties. This reprograms the mAb’s exquisite targeting power while retaining the advantages of IgG with respect to pharmacokinetics, mechanisms of clearance and a larger surface of interaction with the target of the chemical entity than is possessed by the unmodified small molecule (Carlos Barbas III; Scripps Research Institute, La Jolla, CA, USA).

Louis M. Weiner is at the Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA, and Paul Carter is at Seattle Genetics Corporation, 21823 30th Drive SE, Bothell, Washington 98021, USA. email: [email protected] and [email protected]

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Although the range of protein-engineering approaches available for increasing affinity of mAbs to their targets continues to grow, much work remains in relating affinity to biological and therapeutic potency. To date, most work has proceeded with the assumption that higher affinity antibodies, by virtue of prolonged tumor retention, will have superior tumor targeting and efficacy properties. This dogma was challenged many years ago by Weinstein et al.5, who proposed and demonstrated the existence of a binding site barrier that impedes the penetration of antibodies into tumor masses because durable, high-affinity interactions between the antibody and its target block the diffusion of such antibodies throughout the tumor mass. This hypothesis was supported and extended several years ago by Adams et al.6, who showed that single-chain (sc)Fv molecules targeting HER2/ neu , a member of the epidermal growth factor receptor (EGFR) family, with high affinities exhibited unimproved quantitative tumor targeting, less tumor targeting

specificity and diminished penetration into solid tumor masses than did their loweraffinity counterparts. As revealed at the meeting, it is now becoming clear that IgG molecules derived by James Marks (University of California, San Francisco) from the same scFv molecules used in the earlier report 6 show similar results with respect to tumor retention and tumor targeting specificity (Gregory Adams; Fox Chase Cancer Center, Philadelphia). This indicates that the impact of binding site affinity on tumor targeting is not related to the antigen-binding format ( e.g., scFv versus IgG), an observation with broad potential significance as all antibodies approved for use in oncology and most in clinical development are in IgG format. Similarly, high-affinity variants of an anticarcinoembryonic antigen (CEA) scFv have been generated by yeast surface display7. As with the HER2/ neu targeting system described by Adams et al.6, high-affinity scFvs do not target CEA-expressing tumors any more efficiently than their low-affinity counterparts

F igu r e 1

Antibody Antigen Antigen structure can be binding site binding site manipulated to fit an intended therapeutic Variable application. When Increase affinity of antibodies are used antigen binding site to inhibit signaling by • Impede tumor Constant penetration Fab blocking ligand-receptor • Indirectly improve interactions, desirable ADCC properties will include • Affect cellular internalization high affinity for the tumor antigen, prolonged blood residence (which Increase affinity of can be accomplished Light Fc receptor binding chain by increased binding Fc • Modify pharmacokinetics to FcRn); the capacity (FcRn) Heavy to mediate ADCC may • Improve ADCC (Fcγ RIII) chain or may not be critical. When the intended therapeutic mechanism is ADCC, high affinity, multivalent binding to the tumor antigen, high-affinity binding to activating Fc receptors, diminished binding to inhibitory Fc receptors, and prolonged blood residence are desirable; the capacity to mediate signaling by the target cell may be important as well. When antibodies are used as carrier vehicles for immunoconjugates, a moderate affinity for the tumor target is desirable as this is associated with improved tumor penetration. The choice of target antigen and epitope on that antigen are important determinants of internalization, which is frequently required for optimal therapeutic efficacy of immunoconjugates. In contrast to unconjugated antibodies, a shorter blood residence time is frequently desirable, and can be achieved either through the use of antibody fragments or by engineering IgG antibodies with low affinity binding to FcRn. Bob Crimi

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A r ecen t K ey s t o n e meet in g highlight ed t he p r o gr es s in gen er at in g effect iv e an t i- can cer an t ibo dies by man ip u lat in g an t ibo dy bin din g affin it y an d t heir abilit y t o s u p p o r t A D C C .

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N E WS A N D VI E WS (K. Dane Wittrup; Massachusetts Institute for Technology, Cambridge, MA, USA; Kerry Chester and Richard Begent; Royal Free Hospital, London). Wittrup postulates that antibody consumption by tumor cells, in addition to high antigen-binding affinity, can pose a major ‘barrier’ to tumor penetration. Antibody consumption depends upon the rates of internalization and catabolism versus recycling of the antibody/antigen complex to the cell surface. These possibilities are under active investigation by experimentation and mathematical modeling. These observations have potentially important implications for the design of therapeutic antibodies. Thus, when rapid and complete tumor penetration is essential (e.g., for immunotoxins), a high-affinity binding interaction may be undesirable. In other situations, however, high-affinity binding to tumor antigens can indeed be useful. Work in one of our laboratories (Louis Weiner; Fox Chase Cancer Center, Philadelphia) has shown that both high-affinity binding and multivalency contribute to the ability of antibodies to mediate ADCC. Bispecific antibodies targeting HER2/ neu and FcγRIII are more efficient mediators of ADCC against HER2/ neu -expressing tumor cell lines when the affinity for HER2/ neu is higher and when such binding is multivalent. Focus is extending from the antigen-binding variable domains of mAb molecules, to their Fc region, which directly participates in activating complement via the classical pathway and in recruiting immune cells in ADCC. For example, CD20 is a tetra-spanning membrane protein expressed on malignant B cells (but not on plasma cells) that is the target for Genentech (S. San Francisco, CA, USA)/ Biogen Idec’s (San Diego, CA, USA) rituximab (Rituxan) for B-cell non-Hodgkin’s lymphoma. Fully human antibodies have been identified that are even more potent than rituximab in complement-mediated cytotoxicity assays (Martin Glennie; Tenovus Research Laboratories, Southampton, UK). Preliminary clinical results in lymphoma patients seem quite promising, with objective clinical responses described by Jan van de Winkel (Genmab, Utrecht, Netherlands). Manipulations of mAb Fc regions can also be exploited to influence their pharmacokinetics properties. By engineering Fc regions to alter the binding affinity for the major histocompatibility complex (MHC) class I-related receptor FcRn , it is now possible to produce significant changes in the blood clearance dynamics of IgG molecules (E. Sally Ward; University of Texas Southwestern, Dallas, TX, USA). This brings closer to reality the

engineering of IgG molecules so that their clearance from the circulation is tuned to match the desired clearance profile for the intended biological function and therapeutic application of the molecule. As infrequent dosing is generally more convenient, it is likely that therapy requiring the sustained presence of circulating antibody ( e.g., blocking ligand– receptor interactions) will benefit from the presence of such long-lived antibodies. Work is also progressing in developing mAbs that more efficiently bind to FcγRIII and thereby mediate ADCC. FcγRIII is expressed by NK cells and other leukocyte populations, and activates target cell lysis or phagocytosis. Elegant in silico algorithms are now being applied to introduce up to four amino acid mutations into the Fc domain to selectively tune the affinity for FcγRIII, and other Fcγ receptors. (Bassil Dahiyat; Xencor, Monrovia, CA, USA). The process consists of a combined computational and experimental method using a proprietary technology as a computational screen to search the entire sequence space. By eliminating sequences incompatible with the protein fold, Protein Design Automation rapidly reduces the number of sequences to a size amenable to experimental screening, resulting in a library that can be constructed and experimentally screened to select for variants with modified properties such as improved binding to FcγRIII. Antitumor antibodies containing such high-affinity Fc domains are very efficient mediators of ADCC. An alternative strategy to enhance ADCC by mAb is to engineer production cell lines to tune the Fc glycosylation— increase bisecting N-acetylglucosamine and decrease fucosylation (Pablo Umaña; Glycart, Schlieren-Zürich, Switzerland). The ultimate relevance of ADCC to therapeutic benefit, however, remains inferential, although progress is also being made here. For example, individuals with FcγRIII polymorphisms that improve the capacity to mediate in vitro ADCC by host natural killer cells using standard cytotoxicity assays have been observed to exhibit higher objective response rates than otherwise similar patients lacking those polymorphisms (Ronald Levy; Stanford University, CA, USA; refs. 8,9). Ongoing clinical trials with antibodies designed to have more efficient interactions with FcγRIII should address this consideration more directly. In related work, antibody Fc region interactions with cellular Fc receptors have been modified to enhance antigen presentation by dendritic cells based upon preferential association of the antibodies with activating or inhibitory Fc receptors (Raphael Clynes; Columbia University, New York); such

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presentation can be biased to promote or inhibit the generation of cytotoxic T-cell responses against the targeted antigen; for example, an antibody that preferentially associates with the activating isoform, FcγRIIA, is more likely to induce TH 1-dependent T-cell responses, whereas preferential association with FcγRIIB is more likely to induce TH 2 –dependent T-cell responses. Indeed, results from a phase 2 clinical trial in metastatic breast cancer run by one of our groups (Louis Weiner), which tested a bispecific mAb targeting HER2/ neu and FcγRIII (which promote ADCC), indicate that some of the people treated with the bispecific mAb developed host antibodies directed against HER2/ neu -expressing tumor cells as well as CD4+ and CD8+ Tcell immune responses against the HER2/ neu extracellular and intracellular domains. The available evidence thus supports the contention that the induction of ADCC can lead to adaptive immune responses. The capacity to measure antibody and T-cell responses in the peripheral blood of treated patients makes it possible to use these ‘footprints’ of immune response to determine the relevance of ADCC to therapeutic benefits of selected mAbs. For example, consider a clinical trial of an unconjugated monoclonal antibody with the capacity to mediate in vitro ADCC, and imagine that treatment with the antibody provides significant clinical benefit to some, but not all treated patients. Several such antibodies (e.g., rituximab, trastuzumab, cetuximab) are routinely used in cancer therapy today. It should prove possible to directly test the proposition that ADCC underlies at least some of the observed therapeutic benefit of such antibodies by tallying the induction of anti-tumor immune responses and determining if such responses occur more frequently in patients who clinically benefit from antibody therapy. More importantly, if such relationships are indeed demonstrated, it may prove possible to further shape, expand and prolong antibody-induced immune responses to the added clinical benefit of treated patients. 1 . Keystone Symposium: Antibody-based Therapeutics for Cancer, Hilton Hotel, Santa Fe, New Mexico, February 1 7 –2 2 , 2 0 0 5 . 2 . Schier, R. et al. J. Mol. Biol. 2 6 3 , 5 5 1 –5 6 7 (1 9 9 6 ). 3 . Colby, D.W. et al. Methods Enzymol. 3 8 8 , 3 4 8 –3 5 8 (2 0 0 4 ). 4 . Weaver-Feldhaus, J.M. FEBS Lett. 5 6 4 , 2 4 –3 4 (2 0 0 4 ). 5 . Weinstein, J.N. et al. Ann. NY Acad. Sci. 5 0 7 , 1 9 9 – 2 1 0 (1 9 8 7 ). 6 . Adams G.P. et al . Cancer Res. 6 1 , 4 7 5 0 –4 7 5 5 (2 0 0 1 ). 7. Graff, C.P. et al . Protein Eng. Des. Sel. 1 7 , 293–304 (2 0 0 4 ). 8 . Cartron, G. et al . Blood 9 9 , 7 5 4 –7 5 8 (2 0 0 2 ). 9 . Weng, W.K. & Levy, R. J. Clin. Oncol. 2 1 , 3940–3947 (2 0 0 1 ).

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C ell t her ap y fo r A lz heimer A phase I clinical trial in eight individuals with mild Alzheimer disease appears to have slowed the decline of cognitive function. The treatment involved transplantation into the brain of autologous fibroblasts genetically modified to express nerve growth factor (NGF). Although the benefits of NGF therapy have been extensively validated in animal models of cholinergic neuronal degeneration, clinical applications have been hindered by a lack of precise delivery methods, as indiscriminate delivery can cause serious side effects. Tuszynski and colleagues found that injection of NGF-expressing fibroblasts into the cholinergic basal forebrain had no adverse consequences during the monitoring period of 18–24 months. Moreover, the yearly rate of decline of cognitive function over an average of 22 months, as measured by the Mini-Mental Status Examination, decreased to 49% of the pretreatment rate. A second test, the Alzheimer Disease Assessment Scale–Cognitive subcomponent, indicated a modest benefit in the first 12 months after treatment. The authors caution that NGF therapy is not a cure for Alzheimer disease because it targets only one component of the pathological process, the cholinergic neuron. Nevertheless, if the results of this small noncontrolled trial are reproducible, the approach represents an important therapeutic advance. (Nat. Med. 11, 545–549, 2005) KA

SNPs on beads Effective genotyping studies will require hundreds of thousands of SNP assays. The latest estimate from the haplotyping project puts the number of genetic variants in humans at 10 million, of which 200,000 to 300,000 might be sufficient to genotype an individual. Although arrays that can perform massive multiplexing of SNPs assays exist, technologies are still needed to simplify the procedure. In their whole genome genotyping assay (WGA), researchers from Illumina, Ambion and Prognosys Bioscience describe a platform that combines several simple technologies to assay an unlimited number of sites at single base resolution. WGA uses whole genome amplification, followed by capture of fragments on high-density bead arrays containing sets of probes for each allele. Primer extension with biotin-labeled nucleotides labels the captured strand, followed by signal amplification with streptavidinphycoerythrin conjugates. In a pilot study, the authors demonstrate that their approach can genotype over 800 loci with 99% accuracy, which compares favorably with existing techniques. By increasing the number and density of hybridization sites, they estimate that they could assay 500,000 sites in a single experiment. The assay may also be applicable to other kinds of studies, such as expression profiling and loss of heterogeneity measurements. ( Nat. Genet. 37, 549–554, 2005) LD

Signals and noise Two papers in Science describe the use of synthetic gene regulatory cascades in Escherichia coli for determining what intrinsic and extrinsic Research Highlights written by Kathy Aschheim, Nadia Cervoni, Laura DeFrancesco, Teresa Moogan and Mark Zipkin.

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factors contribute to gene expression fluctuations and how these fluctuations or ‘noise’ are transmitted through gene cascades within single cells. Rosenfeld et al. investigate the quantitative relationship between transcription factor concentration and the production rate of downstream targets in a synthetic ‘λ-cascade’ system; Pedraza et al. determine how noise propagates in a gene network incorporating the lac repressor (LacI) and the tet repressor (TetR). To allow real-time ‘quantitative’ measurement of gene expression, the two teams tracked fluorescence of E. coli strains engineered with fusions of different fluorescent reporter genes, each linked to a specific protein in one of the pathways. Rosenfeld et al. then examined how factors, such as biochemical parameters, noise and varying cellular states, affect gene regulation; Pedraza et al. identified and measured what determines noise propagation in a single gene, including noise from other cellular factors affecting global gene expression (extrinsic noise), noise from fluctuations due to factors inherent to the expression of an individual gene (intrinsic noise) and noise transmitted from upstream genes. Both studies provide important insights for understanding noise in cellular gene expression and its implications for the design of synthetic networks. ( Science 307, 1962–1965, 2005; Science 307, 1965–1969, 2005) NC

Smaller carriers deliver Synthetic, positively charged and high-molecular-mass polyethylenimines (PEIs) have been a mainstay for liposomal delivery of drugs and nucleic acids. Thus far, however, these have proven to be much less efficient and organ-specific than viral vectors. Now, Thomas et. al. have significantly increased the delivery efficiency and specificity of PEIs by removing N -acyl groups from commercial PEIs. By deacylating a commercial, 25-kDa linear PEI (PEI25), they found the aliphatic polyamine to be 21 times more efficient in vitro. Three other de novo synthesized PEIs devoid of N -acyl groups proved even more efficient. When applied to mice, deacylated PEI25 delivered DNA with a 1,500-fold increase in specificity to lungs and an overall 10,000-fold jump in efficiency compared to the acylated molecule. Finally, to prove the therapeutic potential of deacylated PEIs, the authors applied one of the de novo synthesized PEIs, PEI87, together with a short interfering RNA (siRNA) to combat influenza in mouse and observed a 94% drop of virus titers in lung. ( Proc. Natl. Acad. Sci. USA 102 5679–5684, 2005) MZ

mAbs against West Nile virus The spread of West Nile virus, a zoonotic agent that can cause meningitis and encephalitis in the elderly and immunocompromised, is adding urgency to the search for new treatments to supplement human polyclonal antibodies. To develop a monoclonal antibody (mAb) with potent virus neutralizing capacity, Diamond and coworkers raised 46 mAbs by immunizing mice with the West Nile virus envelope (E) protein. Error-prone PCR mutagenesis was then used to rapidly map mutations in domain III (DIII) of E protein displayed on the surface of yeast that affect mAb binding. For in vitro protection assays, only two of the 46 mAbs rapidly neutralized the virus in human adrenal carcinoma cells. The most potent of these mAbs, E16, boosted survival of a West Nile Virus Disease mouse model from 10% to 90%. E16 further mapped to the same neutralizing epitope as human antibodies derived from people convalescing from West Nile viral infection, suggesting its potential as a human therapeutic. Indeed, chimeric mouse-human antibodies based on E16 increased the survival of wild-type mice from 46% to 67%. ( Nat. Med. 11, 522–530, 2005) TM

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A N A LYSI S

Systematic interpretation of genetic interactions using protein networks Ryan Kelley1,2 & Trey Ideker1,2

G en et ic in t er act io n an aly s is , in w hich t w o mu t at io n s hav e a co mbin ed effect n o t ex hibit ed by eit her mu t at io n alo n e, is a p o w er fu l an d w ides p r ead t o o l fo r es t ablis hin g fu n ct io n al lin kages bet w een gen es . I n t he y eas t Saccharomyces cerevisiae , o n go in g s cr een s hav e gen er at ed > 4 , 8 0 0 s u ch gen et ic in t er act io n dat a. W e demo n s t r at e t hat by co mbin in g t hes e dat a w it h in fo r mat io n o n p r o t ein - p r o t ein , p r o t e in - D N A o r met abo lic n et w o r ks , it is p o s s ible t o u n co v er p hy s ical mechan is ms behin d man y o f t he o bs er v ed gen et ic effect s . U s in g a p r o babilis t ic mo del, w e fo u n d t hat 1 , 9 2 2 gen et ic in t er act io n s ar e s ign ifican t ly as s o ciat ed w it h eit her bet w een - o r w it hin - p at hw ay ex p lan at io n s en co ded in t he p hy s ical n et w o r ks , co v er in g ~ 4 0 % o f kn o w n gen et ic in t er act io n s . T hes e mo dels p r edict n ew fu n ct io n s fo r 3 4 3 p r o t ein s an d s u gges t t hat bet w een - p at hw ay ex p lan at io n s ar e bet t er t han w it hin - p at hw ay ex p lan at io n s at in t er p r et in g gen et ic in t er act io n s iden t ified in s y s t emat ic s cr een s . T his s t u dy p r o v ides a r o ad map fo r ho w gen et ic an d p hy s ical in t er act io n s can be in t egr at ed t o r ev eal p at hw ay o r gan iz at io n an d fu n ct io n . A major biological challenge is to interpret observed genetic interactions in a physical cellular context 1–3. There are several major types of genetic interactions: synthetic-lethal interactions, in which mutations in two nonessential genes are lethal when combined; suppressor interactions, in which one mutation is lethal but when combined with a second, cell viability is restored; and an array of other effects such as enhancement and epistasis. Genetic interactions have been used extensively to shed light on pathway organization in model organisms1–4. In humans, genetic interactions are critical in linkage analysis of complex diseases5 and in discovery of new pharmaceuticals6. Although genetic interactions are classically identified by mutant screens7, recent studies have applied systematic ‘reverse’ methods such as synthetic genetic arrays (SGA) 8 or synthetic lethal analysis by microarrays (SLAM) 9 to catalog ~ 4,000 synthetic-lethal and synthetic-sick interactions in Saccharomyces cerevisiae. Because of the high-throughput nature of SGA, discovery of new genetic interactions is largely automated. However, interpreting the 1 Program

in Bioinformatics, 2 Department of Bioengineering, University of California, San Diego, 9500 Gilman Dr., San Diego, California 92093-0412, USA. Correspondence should be addressed to T.I. ([email protected]). Published online 5 May 2005; doi:10.1038/nbt1096

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functional significance of each result remains a relatively slow process. The problem is compounded by the large number of genetic interactions measured when screening one gene versus all others (~ 34 on average10) as well as possible false positives if the interactions are not confirmed by tetrad or random spore analysis. Thus, without further methods to aid in characterizing synthetic lethals, large-scale interpretation is a daunting prospect. A promising solution may be to integrate synthetic lethals with other types of high-throughput interactions. For instance, direct physical interactions among proteins are being mapped by systematic two-hybrid 11–15 or immunoprecipitation studies16,17, whereas physical interactions between transcription factors and promoter sites are determined using chromatin-immunoprecipitation in conjunction with DNA microarrays18,19. These interactions comprise a physical network, which correlates with the network of genetic interactions and provides potential clues as to the mechanisms behind particular synthetic-lethal effects. Previous studies have demonstrated this correlated structure in yeast, by showing that two proteins in the same region of the genetic network are likely to also physically interact 8,10, that genes with similar patterns of genetic interactions often occur within the same protein complex10 and that a protein with many interactions in the physical network typically has many interactions in the genetic network also 20. These studies suggest that it may be possible to interpret observed synthetic-lethal relationships explicitly using physical interactions. In this regard, previous authors1,21 have noted that synthetic-lethal interactions are typically associated with one of three types of physical interpretations: between-pathway models, within-pathway models and indirect effects ( Box 1). Here, we demonstrate a computational framework for assembling genetic and physical interactions into models corresponding to between- versus within-pathway interpretations. Regions of the physical network that correspond to each type of model are identified using a probabilistic scoring scheme. These models predict new protein functions and suggest that genetic interactions are more likely to bridge redundant or complementary processes than to combine additively within the same process.

C o n s t r u ct io n o f gen et ic an d p hy s ical n et w o r ks We assembled a genetic interaction network from two primary data sources ( Fig. 1). The first was generated by SGA, a large-scale screen 10 crossing 132 yeast gene deletion strains versus each of the ~4,700 available deletion strains22 and resulting in 2,012 observed synthetic-lethal

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A N A LYS I S B o x 1

I n t er p r et at io n s o f gen et ic in t er act io n s

B et w een - p at hw ay in t er p r et at io n s . The genetic interaction bridges genes operating in two pathways with redundant or complementary functions. Deletion of either gene is expected to abrogate the function of one but not both pathways.

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

W it hin - p at hw ay in t er p r et at io n s . The genetic interaction occurs between protein subunits within a single pathway. A single gene is dispensable for the function of the overall pathway, but the additive effects of several gene deletions are lethal.

I n dir ect effect s . The synthetic lethal phenotype is not mediated by a localized mechanism in the physical network. Indirect effects can occur because a deletion phenotype represents not just the absence of one particular gene, but also the response of the cell to its absence, involving many diverse pathways2 1 .

interactions and 2,113 synthetic-sick interactions. The second data source consisted of an additional 687 synthetic-lethal interactions culled from the literature and catalogued at the Munich Information Center for Protein Sequences (MIPS) 23. The combined genetic network synthesizing these data consisted of 1,434 proteins (genes) linked by 4,812 synthetic-lethal interactions. We also assembled a physical network of 5,993 yeast proteins connected by physical interactions of three types: 15,429 protein-protein interactions (the two proteins a and b display physical binding); 5,869 protein-DNA regulatory interactions ( a binds upstream of the gene encoding b) and 6,306 shared-reaction metabolic relationships ( a and b are enzymes that operate on at least one metabolite in common). The protein-protein interactions were downloaded from the DIP database24 as of July 2004 and predominantly included data from large-scale experiments13,15–17. The protein-DNA interactions were obtained from a large-scale chromatin-immunoprecipitation study of 106 transcription factors18 (interactions with P = 0.001). Enzymatic reactions linked by common metabolites were obtained from KEGG25, excluding metabolite cofactors such as ATP or H 2O (listed in Supplementary Table 1 online). The combined physical network covered 94.4% of all proteins in the genetic network. Both networks are provided at http://www.cellcircuits. org/Kelley2005/ in Cytoscape26 (SIF) format.

B et w een - p at hw ay in t er p r et at io n s fo r gen et ic in t er act io n s Preliminary statistical analyses confirm a limited relationship between genetic and physical interactions (see Supplementary Fig. 1 online and

Genetic network Type Synthetic lethal Synthetic sick Synthetic lethal

Source SGA SGA MIPS

Network model identification Between-pathway

No. 2,012 2,113 724

Tong et al.8,10), but demonstrate a need for structured models to efficiently separate signal from noise. Towards this goal, we implemented a probabilistic modeling procedure to capture the between-pathway interpretation of genetic interactions. This procedure involved a search for pairs of physical pathways that were densely connected by genetic interactions, in which a ‘pathway’ was loosely defined as any densely connected set of proteins in the physical network (this definition generically covers many network structures, including protein complexes). Pairs of pathways (constituting a single network model; see Fig. 1) were assigned a score proportional to the density of physical interactions falling within each pathway and the density of genetic interactions bridging between pathways ( Box 2). This search generated 360 significant models covering 401 pathways and incorporating a total of 1,573 genetic interactions (196 MIPS, 687 SGA synthetic lethal, 690 SGA synthetic sick) and 1,931 physical interactions (1,248 protein binding, 77 regulatory, 606 shared reaction). Significance of these models was assessed by comparison to random genetic and physical networks. Detailed information for all models is provided in Supplementary Tables 2 and 3 online and at http://www.cellcircuits.org/Kelley2005/. Pooling diverse genetic and physical interaction data sets widens the search but also has the potential to decrease the coverage of network models, because not all data sets may be equally predictive and highscoring network models are more likely to arise at random in large networks. To investigate the effect of data pooling, we repeated the search on a smaller network comprising large-scale synthetic-lethal (SGA) and protein-binding (DIP) interactions only. This reduced search identified 20 models containing a total of 137 synthetic-lethal and 120 proteinbinding interactions ( Fig. 2). In comparison to the complete search, fewer protein-binding and SGA synthetic-lethal interactions were incorporated into models, demonstrating the synergy obtained by data pooling (although models generated by the restricted search performed somewhat better in validation). Supplementary Table 4 online analyzes the impact of removing each physical and genetic data set from the modeling procedure.

W it hin - p at hw ay in t er p r et at io n s We next searched the physical and genetic networks for within-pathway explanations. This procedure assigned a high score to single sets of proteins that were densely connected by both physical and genetic interactions (see Fig. 1, Box 2 and Supplementary Fig. 2 online). This search yielded 91 significant models. In all, these contained 272 MIPS, 225 SGA synthetic lethal and 169 SGA synthetic-sick interactions associated with 318 protein-binding, 37 regulatory and 36 shared-reaction interactions. Four representative within-pathway models are shown in Figure 3.

Significant models Number of models 360 between 91 within Number of interactions 1,922 genetic 2,082 physical

Within-pathway

Physical network Type/direction Protein-protein Protein-DNA Reaction-reaction

Source DIP Lee et al. KEGG

No. 15,429 5,869 6,306

Validation Enriched functions Predicted interactions Genetic Physical

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F igu r e 1

Method overview. A combined physical and genetic network is searched to identify between- or within-pathway models of genetic interactions. The between-pathway model implies two groups of proteins (pathways) with many physical connections within each pathway (solid blue links) and genetic interactions spanning between pathways (dotted red links). The within-pathway model implies many physical and genetic interactions within the same group of proteins. In the search, 3 6 0 and 9 1 network models were identified that correspond to between- or within-pathway searches, respectively.

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A N A LYS I S F u n ct io n al en r ichmen t o f mo dels As initial validation of the between- and within-pathway models, we found that both types were significantly enriched for particular functional annotations recorded in the Gene Ontology database27. Two-hundred and fifty-one out of 401 pathways in between-pathway models were enriched for proteins with a common Molecular Function, Biological Process or Cellular Component annotation using the hypergeometric test ( P = 0.05; Bonferroni-corrected for multiple testing) 28. Similarly, 52 of the 91 within-pathway models were enriched for Gene Ontology annotations. Moreover, these functional enrichments were higher than expected based on the physical interaction network alone (see Supplementary Table 5 online). P r edict io n o f n ew p r o t ein fu n ct io n s Having established that proteins in many of the between- and withinpathway models were enriched for specific annotations, we used this concept to predict new protein functions. Specifically, for physical pathways in which a majority of proteins were already assigned a common significant annotation, we predicted this term for the remaining proteins in the pathway. To eliminate overly general predictions, significance was assigned only to those terms that were enriched at a level of P = 0.05 and were associated with fewer than 100 yeast proteins overall. For between-pathway models, this approach predicted 745 molecular function, biological process or cellular component annotations among 282 proteins. In comparison, the within-pathway models predicted 285

B o x 2

annotations involving 127 proteins, bringing the total to 973 annotations for 343 proteins accounting for repeated predictions. A list of novel functional predictions is provided in Supplementary Table 6 online. Less than a quarter of these predictions were attainable using a similar approach based on the physical network only ( Supplementary Table 7 online). Accuracy of these predictions was estimated using cross validation 29. Using a standard five-way procedure, the set of yeast proteins was partitioned such that annotations were hidden for one-fifth of the proteins and annotations for the remaining four-fifths of proteins were used to predict the hidden information. Each prediction for a protein in the ‘hidden set’ was scored as a success or failure depending on whether it recovered a hidden annotation. Using this approach, the success rate was estimated to be 63% for between-pathway models, 69% for within-pathway models.

P r edict io n o f n ew gen et ic in t er act io n s Finally, we investigated whether the network models could predict the existence of new genetic interactions ( Fig. 4). According to the betweenpathway model, proteins in one pathway genetically interact with many of the same partners in a second pathway. This leads to the occurrence of ‘complete bipartite motifs’ in the genetic interaction network, defined as four-protein subnetworks in which the first two proteins are connected to the second two proteins by all four possible genetic interactions ( Fig. 4a; see Milo et al.30 for an introduction to network motifs). When an incomplete motif (IM) is observed, for which only three of the four genetic interactions are present, the motif implies

S co r in g t he mo dels

S co r in g w it hin - p at hw ay ex p lan at io n s . The within-pathway model implies dense interactions within a single group of proteins in both the physical and genetic networks. We adopt a previously described log-odds score37 to assess the likelihood that a group of proteins is more densely connected than would be expected at random:

Swithin(V,E) = log

P (V, E ⎜ Model dense) P (V, E ⎜ Model random)

ΠβI

(a, b)∈V × V

= log

Πr

a, b (a, b)∈V × V

E

(a, b) + (1 –β ) (1 – IE(a, b))

yielding an overall score for the within-pathway model: S = Swithin(V,Ephysical ) + Swithin(V,Egenetic).

S co r in g bet w een - p at hw ay ex p lan at io n s . The between-pathway model implies dense genetic interactions connecting two separate, nonoverlapping groups of proteins, where each group is densely connected by physical interactions. The density of physical interactions is scored independently within two sets of proteins V1 and V2 using the above function S. A related log-odds score is used to evaluate the probability that the genetic interactions Egenetic bridging between these sets are denser than random:

Π

I E (a, b) + (1 – ra,b ) (1 – IE(a, b))

where V is a set of proteins and E a set of interactions among those proteins (genetic or physical). I E( a, b ) is an indicator function which equals 1 if and only if the interaction ( a, b ) occurs in E and otherwise 0 . For Model dense, interactions are expected to occur with high probability ( β) for every pair of proteins in V. In this work, β is set to 0 .9 ( S u p p lemen t ar y F ig. 2 shows how the results depend on choice of β). For Model random , the probability of observing each interaction ( ra,b ) is determined by estimating the fraction of all networks with identical degree distribution which also contain that interaction. Comparable random networks are generated by ‘crossing’ pairs of edges in a process similar to that described by Milo et al. 3 0 In this randomization, only edges of the same type are allowed to be crossed. In addition, for undirected types, either interacting node is allowed to serve as the ‘source’ in crossing the edges. Such randomization generates a family of random networks which resemble the original network and corrects for the presence of highly connected proteins, which score highly under both models. The interaction density is evaluated independently for the physical and genetic networks,

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Sbetween(V1 ,V2 ,Egenetic) = log

(a, b)∈V1 × V2

Π

(a, b)∈V1 × V2

β IE

ra,b IE

genetic

genetic

(a, b) + (1 –β ) (1 – IE

(a, b)) genetic

(a, b) + (1 – ra,b ) (1 – IE

(a, b)) genetic

The final scoring function for the between-pathway model is then: S(V1 , V2 , Eall ) =

ΣS

i =1,2

within

(Vi , Ephysical ) + Sbetween(V1 , V2 , Egenetic)

S ear ch an d S ign ifican ce. Sets of proteins that are well explained by either the within-pathway or between-pathway models are identified using a greedy network search procedure. The search is as previously described by Sharan et al . 3 7 except that it is seeded from each pair of genetically interacting proteins. Pathways that share more than 5 0 % of genetic interactions with a higher-scoring result are discarded. To determine the significance threshold, identical searches are performed over 1 0 0 random trials in which both the genetic and physical networks are randomized as described above. Models that score higher than the maximal-scoring models in 9 5 % of random trials are reported as significant.

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A N A LYS I S that the remaining interaction is true. Physical network information is incorporated by requiring that valid incomplete motifs fall within (i.e., are subgraphs of) a between-pathway model. We applied the technique of five-way cross-validation to estimate the accuracy of genetic interaction prediction versus the minimum number of required incomplete motifs ( Fig. 5). In each of five crossvalidation trials, approximately one-fifth of the genetic interaction data were withheld, including both positive and negative interactions measured for each genetic ‘bait’ in SGA. These positive and negative interactions were subsequently used to test prediction accuracy. For instance, at a prediction threshold of eight or more incomplete motifs, the between-pathway models predicted 43 new genetic interactions with 87% estimated accuracy ( Fig. 5). To assess the contribution of the physical models in the prediction process, we also predicted ‘naive’ genetic interactions by relaxing the requirement that incomplete motifs

a Interactions

fall in a between-pathway model. The estimated accuracy fell to 5% for these naive predictions, evaluated at the same threshold of eight incomplete motifs. For the within-pathway models, genetic interactions were implied between proteins that had genetic interactions with one or more common neighbors ( Fig. 4b). The physical network was incorporated by restricting the proteins and neighbors to fall into a single within-pathway model. The number of common neighbors was used as a measure of confidence in the implied genetic interaction, and cross validation was used to estimate the prediction accuracy as a function of this number. The maximal prediction accuracy was 38%, achieved at a prediction threshold of three or more common neighbors ( Supplementary Fig. 3 online). The corresponding success rate for naive predictions, made without constraining the proteins to occur in within-pathway models, was 15%. Thus, both types of models enhance the accuracy of prediction of genetic interactions, but between-pathway models appear to be better predictors than within-pathway models.

ex

pl

O

m

Genetic (SGA) Genetic bundle Physical binding Shared proteins

e

m

tro

o rc

Vps17

Lge1

bi

qu

Pep8

e )R

Y

Vps74

)U

iti

na

Bre1

Vps35

tio

n

Vps29

Yel043w Vps5

ex pl

T)

m

tin

co

c Jnm1

a yn

Ctf3

Yll049w

Nip100

Ctf19

Arp1

Pac11

Ric1

ne

Iml3

)D

N

Ki

to

ch

or

e

U) Retrograde transport

Chl4

Gim3

Vrp1

Pac10 Yke2

Ypt6 Rgp1

Chs3

Gim5 Gim4

M) Prefoldin complex

Bni4

Myo5

Bbc1 Rvs167 Bck1

Skt5

Slt2

V) Cell cortex

Q) Cell cortex

b Amino-terminal blocking Budding Cell cortex Chromosome DNA catabolism Dynactin Glycoprotein metabolism Motor activity Prefoldin complex Regulation Retromer complex F igu r e 2

Between-pathway explanations for genetic interactions. (a) Several high-scoring models are shown (M,N,T; Q,V; O,U,Y). Blue solid and red dotted links indicate physical and genetic interactions, respectively. (b) Bird’s-eye view of all between-pathway models obtained from a search on a reduced network composed of SGA and DIP interactions. Each node [A]–[Z] represents a physical pathway; groups of genetic interactions between pathways are condensed into a single link called a ‘bundle.’ Node colors indicate significant Gene Ontology annotations. Solid gray lines connect pathways that share one or more proteins; such pathways may represent different components of a larger mechanism.

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P r ep o n der an ce o f bet w een - p at hw ay in t er act io n s Given a systematic approach for associating genetic interactions with physical interpretations, it is of interest to ask which type of interpretation is most common. Focusing on large-scale SGA measurements, roughly threeand-a-half times as many genetic interactions are associated with between- as opposed to within-pathway models (1,377 versus 394 SGA interactions). These figures can be viewed as an a priori expectation that a newly determined SGA interaction will fall between versus within pathways, suggesting that SGA interactions typically span between multiple physical network regions instead of occurring within a single complex or pathway. One reason for the preference towards between-pathway models may be that SGA interactions are mainly targeted to nonessential genes (due to their use of complete gene deletions as opposed to, e.g., point mutations made by classical techniques). Using physical models, it is possible to characterize approxim ately 40% of the genetic interactions as occurring between or within pathways. Whether the remaining interactions belong to between-pathway models, within-pathway models or are best characterized as ‘indirect’ ( Box 1) cannot be reliably determined at this stage. For example, consider the case of two related pathways, each with only one protein required for pathway function. In this case, only the required proteins would be connected by a (single) genetic interaction across the pathways, making it difficult for the between-pathway model to achieve statistical significance. Further examination of the between-pathway models reveals that many of the genetically linked pathways have clear interdependent functional relationships. For example, pathway

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A N A LYS I S Cla4

Genetic interactions Rsr1

a

Cdc12

Bem1

Swi6

Gic2

Bni1

a

a'

Cdc24

d

b' f Common neighbors

Cdc28 Gin4

Ckb2

d'

Swe1

Ste20

Ckb1

Within pathway

e b

Cka1

b

Between pathway Motif 1

Cellular morphogenesis

c

c'

Observed

CK2 complex Gle1 Prp11

Predicted

Motif 2

Nup100

Prp21 Gle2

Nup116

F igu r e 4 Nup145 Nup84

Prp9

Spliceosome

Nup42

Nucleic acid and related transport

F igu r e 3

Within-pathway explanations for genetic interactions. A total of 91 pathways were identified, of which four examples are displayed. Color is used to indicate the data set from which each interaction was drawn.

M contains members of the prefoldin complex, which have syntheticlethal interactions with members of pathways N and T forming parts of the dynactin complex and kinetochore, respectively ( Fig. 2a). The prefoldin complex promotes folding of α- and β-tubulin into functional microtubules31. These are important for the function of dynactin, an adaptor complex involved in translocating the spindle and other molecular cargos along microtubules32, as well as the kinetochore, which anchors chromosomes to spindle microtubules during metaphase33. Apparently, deletion of proteins in the prefoldin complex reduces microtubule stability, leading to synthetic-lethal interactions with pathways that are directly dependent on microtubule function. These pathways also predict a new function for the uncharacterized protein Yll049w (pathway N). This protein binds Jnm1, a dynactin protein which is required for spindle partitioning in anaphase32. In addition, it has synthetic-lethal interactions with members of the prefoldin complex in a manner similar to dynactin genes. Together, these relationships suggest that Yll049w is associated with dynactin during spindle partitioning. However, because Jnm1 has 12 physical F igu r e 5

Success rate of genetic interaction prediction versus the stringency of prediction. Success rate is measured through cross-validation as (predicted positives)/(predicted positives and negatives). Stringency is defined by the minimum number of incomplete bipartite motifs required for prediction. Blue diamonds mark the success rate for predictions in which incomplete motifs must occur in a between-pathway model. The success rate is dramatically higher than for naive predictions (magenta) which predict interactions in the same manner, but are not constrained by the physical network. Even for much more stringent prediction criteria, the success rate of naive predictions fails to exceed that of the betweenpathway predictions (inset).

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Genetic interaction prediction schemes. Two different schemes are proposed for predicting genetic interactions, depending on the underlying network model. Observed genetic interactions are shown in red, while the corresponding predicted genetic interactions are shown in gray. (a) Under the between-pathway model, two incomplete bipartite motifs are shown which predict a genetic interaction between genes b and b′. (b) Under the withinpathway model, common genetic neighbors are used to predict a genetic interaction between genes d and d′. Note that these diagrams contain additional incomplete motifs which have been omitted for clarity: the motifs in a can be rearranged to predict genetic interactions (a to c′) and (c to a′); the motifs in b can be rearranged to predict (e to f ).

interactions overall, and Yll049w has a total of 14 interactions in the genetic network, this prediction would have been difficult to make without an integrated approach. Pathways O, U and Y provide another example of synergistic pathways linked by genetic interactions ( Fig. 2a). Pathways U and Y mediate retrograde transport of proteins to the Golgi apparatus34,35. Pathway O (Bre1, Lge1) is involved in histone ubiquitination and cell size control, where cell size is influenced by the histone ubiquitination activity by an unknown process36. The abundant genetic interactions between pathways O and U indicate a possible role for retrograde transport in histone ubiquitination, or reciprocally, for histone ubiquitination in retrograde transport. Moreover, the uncharacterized protein Yel043w is physically

100

Between pathway Naive

80

Naive predictions Accuracy (%)

Msl5

Mud2

Accuracy (%)

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

SGA (SL) SGA (SS) MIPS Physical interactions Binding Regulatory Reaction

Cka2

Swi4

Cdc42

60

100 80 60 40 20 0

40

0

50 100 150 200 250 Number of incomplete motifs

300

20

0

1

8

15

22

29

36

Number of incomplete motifs

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© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

A N A LYS I S associated with Bre1 and Lge1 and also has the same pattern of genetic interactions, suggesting that the three proteins may function together. In summary, we have presented a methodology for integrating largescale genetic and physical networks to capture the physical context behind observed genetic interactions. Approximately 40% of yeast syntheticlethal genetic interactions can be incorporated into high-level physical pathway models and are approximately three and a half times as likely to span pairs of pathways than to occur within pathways. Further studies will be needed to address other types of genetic effects to extend this approach from yeast to the growing number of other organisms for which protein networks are now available. As systematic approaches generate ever larger databases of interactions across a variety of species, integrative modeling approaches such as the one proposed here will be indispensable for selecting and organizing the information into predictive models. Note: Supplementary information is available on the Nature Biotechnology website. ACKNOWLEDGMENTS

We thank Jonathan Wang, Owen Ozier and Gopal Ramachandran for preliminary investigations and Vineet Bafna, Ben Raphael and Vikas Bansal for insightful commentary. Craig Mak, Silpa Suthram and Taylor Sittler provided helpful reviews of the text. Funding was provided by the National Institute of General Medical Sciences (GM070743-01) and the National Science Foundation (NSF 0425926). COMPETING INTERESTS STATEMENT

The authors declare that they have no competing financial interests. Published online at http://www.nature.com/naturebiotechnology/ 1. Guarente, L. Synthetic enhancement in gene interaction: a genetic tool come of age. Trends Genet. 9 , 362–366 (1993). 2. Avery, L. & Wasserman, S. Ordering gene function: the interpretation of epistasis in regulatory hierarchies. Trends Genet. 8 , 312–316 (1992). 3. Thomas, J.H. Thinking about genetic redundancy. Trends Genet. 9 , 395–399 (1993). 4. Hartman, J.L., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 2 9 1 , 1001–1004 (2001). 5. Sham, P. Shifting paradigms in gene-mapping methodology for complex traits. Pharmacogenomics 2 , 195–202 (2001). 6. Dolma, S., Lessnick, S.L., Hahn, W.C. & Stockwell, B.R. Identification of genotypeselective antitumor agents using synthetic lethal chemical screening in engineered human tumor cells. Cancer Cell 3 , 285–296 (2003). 7. Forsburg, S.L. The art and design of genetic screens: yeast. Nat. Rev. Genet. 2 , 659– 668 (2001). 8. Tong, A.H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 2 9 4 , 2364–2368 (2001). 9. Ooi, S.L., Shoemaker, D.D. & Boeke, J.D. DNA helicase gene interaction network defined using synthetic lethality analyzed by microarray. Nat. Genet. 3 5 , 277–286 (2003). 10. Tong, A.H. et al. Global mapping of the yeast genetic interaction network. Science 3 0 3 , 808–813 (2004).

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11. Li, S. et al. A map of the interactome network of the metazoan C. elegans. Science 3 0 3 , 540–543 (2004). 12. Rain, J.C. et al. The protein-protein interaction map of Helicobacter pylori. Nature 4 0 9 , 211–215 (2001). 13. Uetz, P. et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 4 0 3 , 623–627 (2000). 14. Giot, L. et al. A protein interaction map of Drosophila melanogaster. Science 3 0 2 , 1727–1736 (2003). 15. Ito, T. et al. Toward a protein-protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc. Natl. Acad. Sci. USA 9 7 , 1143–1147 (2000). 16. Ho, Y. et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 4 1 5 , 180–183 (2002). 17. Gavin, A-C., Bösche, M., Krause, R., Grandi, P. & Marzioch, M. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 4 1 5 , 141–147 (2002). 18. Lee, T.I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2 9 8 , 799–804 (2002). 19. Harbison, C.T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 4 3 1 , 99–104 (2004). 20. Ozier, O., Amin, N. & Ideker, T. Global architecture of genetic interactions on the protein network. Nat. Biotechnol. 2 1 , 490–491 (2003). 21. Tucker, C.L. & Fields, S. Lethal combinations. Nat. Genet. 3 5 , 204–205 (2003). 22. Winzeler, E.A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 2 8 5 , 901–906 (1999). 23. Mewes, H.W. et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 3 0 , 31–34 (2002). 24. Xenarios, I. et al. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 3 0 , 303–305 (2002). 25. Kanehisa, M., Goto, S., Kawashima, S. & Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Res. 3 0 , 42–46 (2002). 26. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 1 3 , 2498–2504 (2003). 27. Gene Ontology Consortium. Creating the gene ontology resource: design and implementation. Genome Res. 1 1 , 1425–1433 (2001). 28. Zar, J.H. Biostatistical Analysis, edn. 3 (Prentice Hall, New Jersey, 1996). 29. Kendall, S.M., Stuart, A. & Ord, J.K. Kendall’s Advanced Theory of Statistics, edn. 5 (Oxford University Press, NY, 1987). 30. Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 2 9 8 , 824–827 (2002). 31. Geissler, S., Siegers, K. & Schiebel, E. A novel protein complex promoting formation of functional alpha- and gamma-tubulin. EMBO J. 1 7 , 952–966 (1998). 32. Kahana, J.A. et al. The yeast dynactin complex is involved in partitioning the mitotic spindle between mother and daughter cells during anaphase B. Mol. Biol. Cell 9 , 1741–1756 (1998). 33. Pidoux, A.L. & Allshire, R.C. Centromeres: getting a grip of chromosomes. Curr. Opin. Cell Biol. 1 2 , 308–319 (2000). 34. Pfeffer, S.R. Membrane transport: retromer to the rescue. Curr. Biol. 1 1 , R109–R111 (2001). 35. Siniossoglou, S., Peak-Chew, S.Y. & Pelham, H.R. Ric1p and Rgp1p form a complex that catalyses nucleotide exchange on Ypt6p. EMBO J. 1 9 , 4885–4894 (2000). 36. Hwang, W.W. et al. A conserved RING finger protein required for histone H2B monoubiquitination and cell size control. Mol. Cell 1 1 , 261–266 (2003). 37. Sharan, R., Ideker, T., Kelley, B.P., Shamir, R. & Karp, R.M. Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. Proceedings of the Eighth Annual International Conference on Research in Computational Molecular Biology–RECOMB, 282–289 (ACM Press, New York, 2004)

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R EVI EW

Baculovirus as versatile vectors for protein expression in insect and mammalian cells Thomas A Kost1, J Patrick Condreay1 & Donald L Jarvis2 T o day , man y t ho u s an ds o f r eco mbin an t p r o t ein s , r an gin g fr o m cy t o s o lic en z y mes t o membr an e- bo u n d p r o t ein s , hav e been s u cces s fu lly p r o du ced in bacu lo v ir u s - in fect ed in s ect cells . Y et , in addit io n t o it s v alu e in p r o du cin g r eco mbin an t p r o t ein s in in s ect cells an d lar v ae, t his v ir al v ect o r s y s t em co n t in u es t o ev o lv e in n ew an d u n ex p ect ed w ay s . T his is ex emp lified by t he dev elo p men t o f en gin eer ed in s ect cell lin es t o mimic mammalian cell gly co s y lat io n o f ex p r es s ed p r o t ein s , bacu lo v ir u s dis p lay s t r at egies an d t he ap p licat io n o f t he v ir u s as a mammalian - cell gen e deliv er y v ect o r . N o v el v ect o r des ign an d cell en gin eer in g ap p r o aches w ill s er v e t o fu r t her en han ce t he v alu e o f bacu lo v ir u s t echn o lo gy .

Over the past 20 years the baculovirus–insect cell expression system has become one of the most widely used systems for routine production of recombinant proteins1–6. A number of technological improvements have eliminated the original tedious procedures required to identify and isolate recombinant viruses, increasing the popularity of the system. These include development of a wide variety of transfer vectors, simplified recombinant virus isolation and quantification methods, advances in cell culture technology and the commercial availability of reagents. These enhancements have resulted in a virus-based expression system that is safe, easy to use and readily amenable to scale-up. In addition, biotechnology now uses baculoviruses in applications beyond the production of proteins in insect cells and larvae. These include the development of strategies for displaying foreign peptides and proteins on virus particles and the insertion of mammalian cell– active expression cassettes in baculoviruses to express genes efficiently into many different mammalian cell types. Baculoviruses engineered to display foreign peptides and proteins on the viral surface have proven particularly useful as immunogens and both surface display and capsid fusions may provide further opportunities for enhancing and targeting baculovirus-mediated transduction of mammalian cells. Here, we review recent advances in baculovirus–insect cell protein production, baculovirus display and the development and application of baculoviruses as mammalian-cell gene-delivery vectors ( Fig. 1).

I s o lat io n an d q u an t ificat io n o f r eco mbin an t bacu lo v ir u s es Recombinant baculovirus expression vectors were initially isolated using a highly inefficient homologous recombination process. Insect cells cotransfected with baculovirus and transfer plasmid DNA produced a mixture of parental and recombinant viruses, with a recombination frequency of only about 0.1%. Progeny were usually resolved by 1 Gene Expression Protein Biochemistry, GlaxoSmithKline R&D, 5 Moore Drive, Research Triangle Park, North Carolina 27709, USA. 2 Department of Molecular Biology, University of Wyoming, Laramie, Wyoming 82071, USA. Correspondence should be addressed to T.A.K. ([email protected]).

Published online 5 May 2005; doi:10.1038/nbt1095

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plaque assay and recombinant clones identified microscopically by their distinctive occlusion-negative plaque phenotypes. This was tedious as recombinant plaques, surrounded by a sea of occlusion-positive parental virus plaques, were difficult to identify. A huge improvement came with the development of baculovirus DNA that could be linearized at a unique Bsu36I site in the polyhedrin locus7. When used together with a transfer plasmid to cotransfect insect cells, the linearized viral DNA gave rise to recombinants at a higher frequency, typically around 25%. Later, baculovirus DNAs were engineered to have multiple Bsu36I sites, one within an essential viral gene8. Bsu36I digestion created a large deletion that functionally inactivated the essential gene, thus precluding replication of parental virus and increasing the frequency of recombinant virus production to over 90%. This approach was commercialized and the use of predigested viral DNAs became status quo for recombinant baculovirus production. Still, baculovirus plaque assays remained an essential part of the technology, as recombinant baculoviruses were most frequently cloned using this approach. Efforts to eliminate the requirement for a plaque assay in virus isolation led to development of an in vivo bacterial transposition method, first described in 1993 and later commercialized as the Bac-to-Bac system 9. This method involves site-specific transposition of a foreign gene from a donor plasmid to a cloned baculoviral DNA, or ‘bacmid’ such that the foreign gene is controlled by the polyhedrin promoter. Since Escherichia coli clones containing recombinant bacmid DNA acquire an antibiotic resistance marker and lose a lacZ marker, they can be easily selected and identified. One simply isolates viral DNA from positive bacterial clones and uses this bacmid DNA to transfect insect cells and produce recombinant virus. Theoretically this method does not require a plaque assay to resolve parental from recombinant virus progeny; however, the virus stock may be, nevertheless, polyclonal. A recent improvement in the frequency of recombinant viruses produced using Tn7-mediated transposition has been described, which may allow for the efficient generation of baculoviral libraries10,11. It is important to note that recent reports have shown that BAC vector sequences can be spontaneously excised from bacmid-derived vectors upon passage in insect cells12,13. In our experience, this has not posed a major problem

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Insect larvae

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

Insect cells

Baculovirus

Mammalian cells

Baculodisplay

Figure 1 Versatility of baculovirus expression vectors. Baculovirus vectors

can be used for a variety of applications. These include producing proteins in insect larvae, insect cells and mammalian cells. The insect and mammalian cells in the photomigrographs were treated with baculoviruses expressing GFP. Viruses can also be produced that display peptides or proteins on the surface of viral particles. The red circles on the schematic virus particle represent displayed gp64 fusion proteins.

for routine protein production using bacmid-derived viruses, but could hinder large-scale production systems. Recently two additional approaches have been described for generating recombinant baculoviruses. An in vitro transposition system, adapted from Bac-to-Bac, commercially known as BaculoDirect, uses a purified transposase to move a foreign gene from a donor plasmid to a viral DNA acceptor, placing the foreign gene under the control of the polyhedrin promoter. The parental viral DNA has a herpes virus thymidine kinase gene, which serves as a negative selection marker that is eliminated upon transposition. Thus, insect cells are transfected with the mixture of parental and recombinant viral DNA created in the test tube and parental viruses are eliminated by gancyclovir treatment. As with Bac-to-Bac, plaque isolation is not required, but the virus stock may be polyclonal and an experienced virologist would probably undertake plaque purification. An alternative method uses a baculovirus with a lethal mutation in orf1629, which encodes an essential gene product required for virus replication. Viral DNA is maintained as a bacmid in E. coli. In this system cotransfection of insect cells with a transfer vector containing the gene of interest and the engineered viral DNA yields 100% recombinant viruses14. Studies have also been undertaken to develop rapid and facile methods for virus quantification. These efforts have been driven by the use of automated platforms for baculovirus production and expression 15–17 and the need to rapidly titrate baculovirus stocks destined for the production of multi-subunit protein complexes and mammalian cell transduction studies. A commercially available immunocytochemical assay using a monoclonal antibody against the baculovirus gp64 protein provides virus titers within 48 h 18. Two additional antibody-based assays have been developed that provide rapid virus titrations19,20. Quantification of baculovirus particles by

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flow cytometry21 and real-time PCR22 has been described. Several assays that use reporter proteins such as green fluorescent protein (GFP) or β-galactosidase have also been described; however, an inherent drawback of these methods is the need for expression of an additional protein.

P r o t ein gly co s y lat io n in t he bacu lo v ir u s – in s ect cell s y s t em It is often stated that the baculovirus–insect cell system has eukaryotic protein processing capabilities. It is generally true that insect cells can fold, modify, traffic and assemble newly synthesized polypeptides to produce highly authentic, soluble end products1,2,23. However, it is equally true that insect protein processing pathways are not necessarily equivalent to those of higher eukaryotes. One of the best examples of a similar, but distinct processing pathway is the protein N -glycosylation pathway. Early research on dipteran insect cells, which has been reviewed in detail elsewhere24,25, established a model of the insect protein N -glycosylation pathway which, with a few caveats, is still valid today. These studies indicated that insect cells could assemble N -glycans, transfer them to nascent polypeptides and trim the N -glycan precursors to produce high mannose or paucimannose end products ( Fig. 2). However, the cells failed to elongate the trimmed N -glycans to produce complex products containing terminal galactose and/or sialic acid residues. As recombinant glycoproteins began to be produced, it was recognized that the lepidopteran insect cell lines used as hosts for baculovirus expression vectors followed this general paradigm. In addition, enzyme assays showed that these cell lines had little or none of the galactosyltransferase and sialyltransferase activities involved in N -glycan elongation ( Fig. 2). Moreover, it was found that many of these cell lines have an unusual processing activity that converts an intermediate common to both the insect and mammalian pathways to the insect-specific paucimannose end product 26 ( Fig. 2). Today, it is generally recognized that most baculovirus-expressed recombinant glycoproteins will acquire authentic N -glycans only at sites occupied by high mannose structures on the native mammalian products. In contrast, they are most likely to acquire paucimannose N glycans at sites occupied by complex, terminally galactosylated and/or sialylated N -glycans on the native product. This latter fact is a clear limitation of the baculovirus–insect cell expression system because N -glycans, and particularly terminal sialic acids, contribute to glycoprotein functions in many different ways. For some clinical applications, such as in vivo administration of a therapeutic recombinant glycoprotein, the absence of terminal sialic acids would be unacceptable. Recent trends in the development of the baculovirus–insect cell system include extensive efforts to address this problem, the details of which have been reviewed elsewhere27–31. However, an overview of selected developments will be of interest to investigators using the baculovirus–insect cell system for recombinant glycoprotein production. An early step was the development of expression plasmids and methods for transforming lepidopteran insect cell lines containing stably integrated, constitutively expressed foreign genes32,33. These studies set the stage for the creation of transgenic lepidopteran insect cell lines containing mammalian genes encoding N -glycan processing activities that were absent in the parental cell lines. The first transgenic insect cell line of this type was produced by transformation with a bovine β1,4-galactosyltransferase gene. Baculovirus infection of this cell line, but not the parental Sf 9 cell line, led to the production of a foreign protein with terminally β-galactosylated N -glycans34. Subsequently, a transgenic Sf 9 line encoding both bovine β1,4-galactosyltransferase and rat α2,6-sialyltransferase was isolated that supported the production of terminally α2,6-sialylated N -glycans35. This was a surprising result because the donor substrate required by the rat sialyltransferase, CMP-sialic acid, is not found at detectable levels in Sf 9

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R E VI E W cells36,37. A possible explanation was obtained with the finding that Sf9 cells have a sialic acid Key to symbols High mannose scavenging pathway that can support de novo GlcNAc glycoprotein sialylation in transgenic cells Man Fucose expressing the sialyltransferase38. However, Gal detailed structural analyses revealed that only GalNAc the lower ( α1,3) branch of the N -glycans proSialic acid duced by these transgenic insect cells had been Mammalian Insect Asn elongated. Because monoantennary N -glycans N-acetylglucosaminidase N-acetylglucosaminyl are rarely found on mammalian glycoproteins, transferase II another transgenic insect cell line was designed Asn to express five mammalian glycosyltransferases, including one that initiates elongation of the Galactosyltransferases N-acetylgalactosaminyltransferase upper ( α1,6) N -glycan branch 39. These cells, Sialytransferases Sialytransferases Asn designated SfSWT-1 cells produced biantennary, terminally monosialylated N -glycans. Finally, Paucimannose because a sialic acid scavenging pathway might Asn Asn be an inefficient way to produce CMP-sialic acid for de novo glycoprotein sialylation, a transgenic Sialylated complex line designated SfSWT-3 was produced. These cells encode the same five glycosyltransferases Figure 2 Overview of processing pathways and major N-glycans produced by insect and mammalian cell of SfSWT-1, plus two murine enzymes that systems. The processing pathways in both systems yield a common intermediate. The major insect-cell convert the sialic acid precursor, N -acetylman- end product (paucimannose) is produced by further trimming of this intermediate (left-hand branch), nosamine, to CMP-sialic acid40. When cultured whereas the major mammalian-cell end products (including sialylated complex) are produced by in the presence of N -acetylmannosamine, this elongation of this intermediate (right-hand branch). new cell line produced high levels of intracellular CMP-sialic acid and a recombinant N -glycoprotein with a highly homogeneous, biantennary, monosialylated of a 21-base-pair (bp) element derived from a 5′ untranslated leader side chain. sequence of a lobster tropomyosin cDNA that contains the Kozak The transgenic insect cell line approach has begun to address the sequence and A-rich sequence found in the polyhedrin leader sequence inability of the baculovirus–insect cell system to produce authentic into a recombinant virus enhanced the expression of tropomyosin and recombinant N -glycoproteins; however, the humanization of protein luciferase 20- and sevenfold, respectively49. As with the addition of hr processing pathways in insect cells is a work in progress, with many elements, the effect of this 21-bp element will require further evaluation additional developments needed and yet to come. with additional proteins to determine the general applicability of this approach for enhancing protein yield. E n han cin g p r o t ein ex p r es s io n in in s ect cells Baculovirus infection of insect cells results in microscopically observIn many instances sufficient quantities of functional protein for able cell lysis within 3–5 d after infection. Cell disruption may lead to experimental needs can be readily obtained from baculovirus-infected increased proteolytic activity and other environmental factors that can insect cells. However, this is not always true and for numerous rea- result in degradation of recombinant protein. In an attempt to oversons increased yields of functional protein are often desirable. Various come this difficulty, a baculovirus with reduced capability for initiating approaches to increasing production of properly processed proteins cell lysis was isolated by random mutagenesis and the application of a were covered in an earlier review on this topic41. A number of studies novel fluorescence resonance energy transfer (FRET)-based assay for have documented enhanced protein production following cotrans- selecting the desired mutant 50. At 5 d after infection the mutant virus fection with baculoviruses expressing chaperone proteins, which are showed only 7% lysis of infected Sf21 cells, whereas the parent virus known to aid in the folding and modification of newly synthesized showed 60% lysis. Using this virus the authors demonstrated that a proteins. The expression of correctly assembled Shaker potassium chan- higher level of compactly folded, engineered luciferase protein could nels in Sf9 cells was enhanced by coexpression of the calcium-binding, be produced with less degradation as compared to the parental virus. lectin-chaperone calnexin together with substitution of the polyhedrin Another approach to reducing protein degradation is the developpromoter with the weaker basic protein promoter to drive expression ment of a chitinase and v-cathepsin negative bacmid. Generation of of the ion channel42. Coexpression of calreticulin promoted the pro- a recombinant virus designed to express the cattle parasite Theileria duction of properly folded human lipoprotein lipase43 and HLA-DR4 parva sporozoite surface protein p67 with this bacmid protected the tetramers44. Another approach has been to coexpress the chaperone secreted recombinant protein from degradation 51. Hsp70 and its cofactors Hsdj and Hsp40. Such coinfections have The baculovirus–insect cell system has been used successfully for resulted in increased yields of soluble Epstein-Barr virus replication the expression of thousands of diverse types of proteins. It has proven protein, BZLF145 and functionally active tumor suppression protein particularly valuable for the expression of G protein–coupled receptors LKB146. These studies demonstrate the potential value of coexpressing (GPCRs) 52,53 and coexpression with G proteins has proved valuable chaperones to enhance functional protein production. for studying receptor–G protein interactions54. The system has also Significant increases in expression levels have also been reported by the proven very useful for expression of cytochrome p450 enzymes55–57. addition of various DNA elements to the virus. The addition of baculovi- Irrespective of the protein being produced, a major advantage of the rus homology region 1 (hr1) 47 and hr3 (ref. 48) sequence regions to the baculovirus–insect cell expression system is the ease of scale-up from virus genome resulted in increased luciferase production. Incorporation the laboratory to a large-scale production system 58.

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R E VI E W P r o du ct io n o f v ir u s - like p ar t icles ( V L P s ) an d p r o t ein co mp lex es The baculovirus–insect cell system has been used extensively for the production of VLPs to study viral assembly processes in the absence of infectious virus, produce antigens for immunization and proteins for diagnostic assays and for gene transfer 59–62. This approach is particularly valuable in those cases where cell culture–based viral replication systems are not available, such as human papilloma virus (HPV) and hepatitis C virus (HCV). The baculovirus–insect cell system allows one to deliver individual viral structural proteins via coinfection with multiple baculoviruses, each expressing a single protein, or via a single virus designed to express multiple proteins63. By varying the multiplicity of infection and by using various promoters, one can attempt to control the amount of expressed proteins to optimize VLP production. A striking example of the application of this technology is the development of HPV VLP–based vaccines64,65. VLPs composed of the HPV types 16/18 L1 structural proteins produced in baculovirus-infected insect cells have been shown in clinical trials to be efficacious in preventing cervical infections with HPV-16 and HPV-18, together with the associated cytological abnormalities and lesions66. HCV VLPs have also been successfully produced using the baculovirus–insect cell system 67–71. VLPs containing the core, E1 and E2 proteins of HCV resemble putative HCV virions and have been shown to effectively induce HCV-specific humoral and cellular immune responses in baboons71. Recently the assembly of human severe acute respiratory syndrome (SARS) coronavirus–like particles in baculovirus-infected insect cells expressing the S, E and M structural proteins has been described 72,73. Budded VLPs could only be detected in the culture medium when the genes encoding the three proteins were carried by a single recombinant baculovirus73. These results provide impetus for further studies into the assembly and development of candidate VLPbased vaccines against this important disease. Insect cells infected with recombinant baculoviruses have also been used to produce infectious adeno-associated virus (AAV) type 2 vectors74. Insect cells were coinfected with three recombinant baculoviruses, one expressing the AAV replication proteins, a second expressing the AAV structural proteins and a third expressing GFP under the control of a cytomegalovirus (CMV) promoter bounded by the AAV inverted terminal repeat sequences. The yield of functional genome-containing AAV particles per Sf9 cell produced in this system approached 5 × 104 demonstrating the system can produce large quantities of AAV vectors. B acu lo v ir u s dis p lay A variety of strategies have been developed for displaying heterologous peptides or proteins on the surface of baculovirus particles by fusing the peptide or protein to the baculovirus surface glycoprotein, gp6475,76. In most instances the vector is designed so that baculovirus particles contain both wild-type gp64 and gp64 molecules containing the heterologous protein sequence. Baculoviruses displaying gp64fusion proteins have proven to be very effective immunogens. Since this approach was first used to raise monoclonal antibodies against the nuclear receptors LXRβ and FXR77, it has been used successfully to elicit antibody responses to a variety of displayed proteins. These include human peroxisome proliferator-activated receptor 78, Plasmodium berghei circumsporozoite protein 79, hemagglutinin protein of Rinderpest virus80, Theila parva sporozoite surface antigen p6781 and foot-andmouth disease virus proteins82. Baculovirus display strategies have also been used for modification of the viral surface to influence baculovirus-mediated transduction of mammalian cells. These studies will be discussed further below. In addition to gp64 fusions, GFP was recently fused to the baculovirus vp39 capsid protein 83. Capsid modifications may allow novel approaches for enhancing baculovirus mediated gene delivery into mammalian cells.

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It has also been shown that membrane proteins produced in infected insect cells can be incorporated into baculovirus particles in a functional form. This was first observed for the β-2-adrenergic receptor, which was recovered in a functional form complexed with heterotrimeric G proteins84 and more recently for the human leukotriene B4 receptor 85. This approach has been used successfully to produce a functional γ-secretase complex on the surface of baculovirus particles86. Coinfection of Sf 9 cells with viruses expressing the four putative γ-secretase components resulted in the production of virus particles with γ-secretase activity that was concentrated ~2.5-fold higher in the budded virus particles as compared to Sf 9 cell membranes. These studies show that baculovirus particles can provide a unique scaffold for the assembly and enrichment of functional membrane bound protein complexes.

R eco mbin an t p r o t ein p r o du ct io n in in s ect lar v ae The use of baculovirus-infected insect cell larvae as hosts for protein production was first described for the production of α-interferon in 1985 (ref. 87). Since that time larvae have been used successfully to produce a variety of recombinant proteins88–92. This approach has been more widely adopted in Asian countries, including China, Japan and India, where silkworms are abundantly available and more laboratories have experience in growing and maintaining larvae. In most studies the expression vectors were based on Bombyx mori nucleopolyhedrosis virus (BmNPV), which infects the silkworm Bombyx mori. However, there is at least some industrial interest in the larvae of the cabbage looper moth, Trichoplusia ni, as host for recombinant protein production by Autographa californica nuclear polyhedrosis virus (AcMNPV)based recombinant baculovirus vectors in the United States, as well. Protein expression levels in baculovirus-infected larvae can be very high, reducing costs for large-scale production. Nevertheless, due to a general unfamiliarity with larval systems and ready access to cell culture facilities, this approach has not gained widespread popularity in most molecular biology laboratories in North America and Europe. B acu lo v ir u s - mediat ed gen e deliv er y in mammalian cells The successful use of recombinant baculoviruses to direct gene expression in mammalian cells was first reported ten years ago 93,94. Since we reviewed this subject 95 there has been a remarkable increase in published reports of the use of this system. A number of publications have focused on improvements and demonstration of new cell types susceptible to baculovirus transduction, but most have described applications of this technology in areas such as genomics, pharmaceutical screening assays and in vivo applications such as gene therapy. In this section, we discuss advances that have been reported in the past few years. Host cells and transduction parameters. The use of recombinant baculoviruses containing mammalian cell–active expression cassettes, commonly referred to as BacMam viruses, for gene delivery to mammalian cells was first demonstrated in cells of liver origin 93,94. Subsequently, a number of labs reported gene delivery to a broad range of nonhepatic cell lines and primary cells96–98. Our 2002 review95 contains a table of reported susceptible cells; however, there have been many recent additions. Primary rat chondrocytes are efficiently transduced by baculovirus99 and the transduced cells retain their differentiated state. Mouse primary kidney cells can express genes delivered by baculovirus for up to 20 d 100. Hepatic stellate cells from rat and human are transduced at greater than 90% efficiency when the cells are activated by culturing on plastic surfaces, although transduction of fresh cultures is quite low (1,000 lg/ml of DC101 in mice, demonstrating significant anti-tumor efficacy. This report describes the first feasible gene therapy approach for stable delivery of mAbs at therapeutic levels, which may serve as an attractive alternative to direct injection of mAbs.

mAbs have become important therapeutic agents for the treatment of cancer, inflammation and infectious disease. With technical advancements in antibody engineering such as human antibody phage display1, mice transgenic for human IgG2 and antibody humanization techniques1, highly specific human monoclonal antibodies can be readily generated for various disease targets. Chronic diseases such as cancer require long-term therapies, and mAbs are often infused into people with cancer, frequently at high doses over a long period of time, which can induce adverse effects3. An alternative approach to long-term delivery of therapeutic antibodies is to express the antibodies in vivo after gene transfer. For most antibodies, however, therapeutic serum concentrations range from one to several hundred mg/ml, levels that have been impossible to achieve using gene transfer. Unfortunately, the recombinant adenoassociated virus (rAAV) vector, which is an attractive vector system for achieving long-term gene transfer in vivo4, cannot accommodate conventional antibody expression cassettes that drive the mAb heavy and light chains from two individual promoters, because the vector cannot package more than B5 kb efficiently. A potentially advantageous approach for in vivo delivery of antibodies is to express mAb heavy and light chains in a bicistronic vector that uses a single promoter. The conventional method for bicistronic expression cassettes, however, uses internal ribosomal entry sites (IRES) that leads to substantially lower expression of the second gene than the catabolite activator protein (CAP)-dependent first gene5. In this study, we describe an antibody expression system that uses the foot-and-mouth-disease virus (FMDV)-derived 2A self-processing sequence to express full-length antibodies from a single open reading frame (ORF). 2A sequences are oligopeptides located

between the P1 and P2 proteins in some members of the picornavirus family and can undergo self-cleavage to generate the mature viral proteins P1 and P2. Among various 2A or 2A-like sequences, FMDV 2A is particularly short (minimum of 13 amino acids) and is able to ‘cleave’ at its own C terminus between the last two amino acids through an enzyme-independent but undefined mechanism, probably by ribosomal skip, during protein translation6–11. Using a FMDV 2A sequence adjacent to a furin cleavage site to link the antibody heavy and light chain sequences, we were able to engineer a mAb expression cassette that, in the context of AAV-mediated gene transfer, results in high levels of full-length, functional monoclonal antibodies in vitro and in vivo. Sustained mAb serum levels of 41,000 mg/ml were achieved in mice with a single administration of an rAAV8 vector expressing DC101, an anti-angiogenic mAb targeting vascular endothelial cell growth factor receptor-2 (VEGFR2 or Flk-1)12. The rAAV8 mediated gene transfer of DC101 resulted in significant (P o 0.001) anti-tumor efficacy in two tumor models, demonstrating the generation of functional antibodies in vivo using this expression system. RESULTS 2A-mediated mAb expression from a single ORF DC101, a rat anti-mouse VEGFR2 (Flk-1) IgG1 mAb, which has been well characterized for its anti-angiogenic effects in mouse tumor models12, was chosen as a model antibody to evaluate expression of full-length antibodies from a single ORF using the FMDV 2A selfprocessing peptide. An expression cassette termed H2AL, in which the heavy and light chain sequences of the DC101 mAb were linked together by the FMDV 2A self-cleavage sequence, was generated and

Department of Preclinical Oncology and Immunology, Cell Genesys, Inc., 500 Forbes Blvd., S. San Francisco, California 94080, USA. Correspondence should be addressed to J.F. ([email protected]). Published online 17 April 2005; doi:10.1038/nbt1087

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Figure 1 Full-length mAb expression cassette using the FMDV 2A sequence. (a) Schematic illustration of the biosynthesis of mAb heavy and light chains by the 2A, peptide-containing expression cassette. (b) Antibody expression cassettes in which mAb heavy and light chain sequences are linked by the 2A sequence (H2AL), an IRES sequence (HIRESL) or a combination of furin cleavage site and 2A sequence (HF2AL).

cloned into an expression plasmid driven by the CAG promoter (Fig. 1). The CAG promoter is comprised of the cytomegalovirus (CMV) immediate early enhancer region, chicken b-actin promoter/ splice donor and rabbit b-globin enhancer13 and is constitutively active. A control plasmid, which linked the DC101 heavy and light chains by an IRES derived from encephalomyocarditis virus (EMCV) (Fig. 1b, HIRESL), was also generated. Both plasmids were transiently transfected into human embryonic kidney (HEK) 293 cells, and the antibody concentrations in the supernatants were determined by enzyme-linked immunosorbent assay (ELISA) after 48 h. The H2AL construct resulted in about 1.6 mg/ml of DC101, whereas the HIRESL construct resulted in 0.1 mg/ml of the mAbs (Fig. 2a). Thus, the 2A sequence efficiently facilitates antibody heavy and light chain expression from a single ORF. To further characterize the DC101 heavy and light chains expressed from the 2A-containing construct, the proteins in the supernatants of transiently transfected HEK 293 cells were separated on SDS-PAGE gels under both reducing and nonreducing conditions and subjected to western blot analysis using a polyclonal goat anti-rat IgG antibody that recognizes antibody heavy and light chains. Under reducing conditions, two protein bands at molecular weights of B55 and 25 kDa were detected, corresponding to the IgG heavy and light chain proteins of DC101, respectively (Fig. 2b). Bands of similar size were detected in samples from the parental DC101 hybridoma

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cells. The heavy chain protein from the H2AL plasmid migrates slightly more slowly than the native DC101 antibody heavy chain (Fig. 2b) due to 23–amino acid residues that are derived from the 2A sequence and remain after cleavage. Under nonreducing conditions, a single band of approximately 160 kDa was detected in the cell culture supernatant of H2AL-transfected HEK 293 cells (Fig. 2c), which is the expected size of a dimerized full-length antibody containing two heavy and two light chains. The molecular weight of the mAb expressed from the H2AL plasmid was slightly higher than that of the native antibody, because of the additional amino acid residues at the C terminus of the heavy chain. No additional protein bands, which are expected when the ratio between heavy and light chains is imbalanced, were detected under nonreducing conditions (Fig. 2c), suggesting that the antibodies in the supernatant of H2AL transfected cells are properly dimerized and neither heavy nor light chains are in excess. Biological activity of mAbs expressed from 2A plasmids The biological activity of the antibodies expressed from the 2A-containing plasmid was evaluated using a binding assay that measures mAb-binding activity to immobilized mVEGFR2 protein (see Methods). DC101 antibodies expressed from the H2AL construct in HEK 293 cells were able to recognize the mouse VEGFR2 with similar binding activity as the parental antibodies (Fig. 3a) and were capable of blocking the VEGF and mVEGFR2 interaction in a dosedependent manner in a VEGF-mVEGFR2 (ligand-receptor) binding assay with the same potency as the native antibodies (Fig. 3b). Thus, antibodies expressed from the 2A-containing construct retain full biological activity. Removal of 2A-derived amino acid residues by furin cleavage Since the 2A self-processing cleavage occurs between the last two amino acids at the C terminus of the 2A peptide, the first protein in the cassette (that is, the heavy chain in the H2AL construct) has 23 additional amino acid residues at its C terminus (Fig. 1a). To eliminate possible adverse effects caused by the remaining 2A residues, a DC101 expression cassette was engineered that included a furin cleavage site sequence (RAKR), located between the 2A sequence and the mAb heavy chain (HF2AL) (Fig. 1b, lower construct). mAb was expressed from the HF2AL construct in HEK 293 cells after transient plasmid transfection (Fig. 2a). The heavy chain proteins in the supernatants were separated by SDS-PAGE gel and analyzed by western blot using a goat anti-rat IgG antibody. The antibody heavy chains expressed from the HF2AL construct have a similar molecular weight as the native antibody heavy chains, suggesting successful cleavage at the furin cleavage site (Fig. 2b). Furthermore, the antibodies expressed from the HF2AL construct appeared as a single band at a molecular weight corresponding to the antibody dimer (Fig. 2c) and demonstrated full biological activity in antibody binding (Fig. 3a) and neutralization assays (Fig. 3b). To confirm successful removal of the residual 2A amino acids, we generated a construct that contained six histidine residues (his-tag) at the C terminus of the antibody light chain in the HF2AL cassette. The his-tagged antibody was expressed in vivo by hydrodynamic injection of the HF2AL expression plasmid into mice, and mAb was purified from mouse serum using a nickel column purification system. The purified antibody appears as two protein bands in a reducing SDSPAGE gel at B52 and 25 kDa (data not shown), corresponding to the antibody heavy and light chains, respectively. The heavy chain band was excised from the gel, digested with trypsin and analyzed by mass

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spectrometry. We detected no peaks that represent peptide fragments derived from the 2A sequence (APVK, QTLNFDLLK and LAGDVESNPG) in the mass spectrum, demonstrating that the 2A residues had been removed from the antibody heavy chain of the HF2AL construct. Moreover, the mass spectrum analysis revealed a 1,039.53-Da fragment corresponding to the C-terminal fragment sequence, SLSHSPGKRA, that contains the native C-terminal sequence of the antibody heavy chain plus two additional amino acids (arginine and alanine) derived from the furin cleavage site. The actual amino acid sequence of the 1039.53 Da fragment was further confirmed by the post-source decay (PSD) analysis. In summary, these data demonstrate that the addition of a furin cleavage site to the 2A self-processing cleavage site results in the removal of the 23–amino acids that remain after 2A cleavage. 2A-mediated, high-level expression of mAbs in vivo Using the 2A sequence, the expression cassette for DC101 could be fit into a rAAV vector. We generated rAAV vectors pseudotyped with the capsid proteins from AAV serotype-8 (ref. 14) that express the DC101 antibody from an H2AL or HF2AL cassette driven by the CAG promoter, termed rAAV8-DC101(H2AL) and rAAV8-DC101(HF2AL). Mice were injected through the hepatic portal vein with one of three

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1011 vector genomes (vg)/mouse), and DC101 serum levels were evaluated over time. We detected, 28 d after administration of the rAAV8-DC101(H2AL) vector, peak serum levels of 3,286 and 1,877 mg/ml DC101 in mice that had received 4
1011 or 2
1011 vg/animal, respectively (Fig. 4a). Antibody serum levels declined somewhat thereafter but remained at about 600 mg/ml in the animals treated with the highest dose of vector throughout the 4-month study (Fig. 4a). Interestingly, two- to tenfold higher DC101 serum levels were achieved when the antibody was expressed from the HF2AL expression cassette (Fig. 4b). Peak expression levels in mice receiving 4
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1011 vg/mouse of the rAAV-DC101(HF2AL) vector were 48,000 mg/ml and remained above 1,000 mg/ml up to 4 months after rAAV vector administration. Furthermore, DC101 antibody expressed from either cassette exhibited full biological activity in the antibody binding (Fig. 3a) and neutralization assays (Fig. 3b). We monitored serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in these animals to evaluate liver function after gene transfer and were not able to detect elevated ALT or AST serum levels in all vector-treated mice throughout the entire experiment (data not shown).

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Figure 2 In vitro expression of DC101 mAb using the 2A self-processing sequence–containing expression plasmids. HEK 293 cells were MW MW (kDa) (kDa) transiently transfected with DC101-expressing 3 188 plasmids and supernatants were harvested 2.5 98 for protein analyses. (a) ELISA analysis of 188 2 supernatants 48 h after transfection of HEK 293 62 98 1.5 cells with the H2AL, the HIRESL or the HF2AL 49 62 plasmids (mean 7 s.d.). (b) Western blot 1 38 analysis of DC101-containing supernatants under 49 0.5 28 reducing conditions. Proteins in the supernatants 38 0 of the parental hybridoma cells (hybridoma), 17 H2AL HIRESL HF2AL 28 the HEK 293 cells transfected with the H2AL 14 17 plasmid (H2AL) or the HF2AL plasmid (HF2AL), or untransfected HEK 293 cells (mock) were separated by SDS-PAGE under reducing conditions and probed with a goat anti-rat IgG (H+L) polyclonal antibody. (c) Western blot analysis of supernatants from the parental DC101 hybridoma cells (hybridoma), from HEK 293 cells transfected with the H2AL plasmid (H2AL) or HF2AL plasmid (HF2AL), or from untransfected HEK 293 cells (mock). Proteins in supernatants were separated in SDS-PAGE under nonreducing conditions and probed with a goat anti-rat IgG (H+L) polyclonal antibody.

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DISCUSSION This study demonstrates that a 2A self-processing peptide derived from FMDV facilitates efficient and apparent equimolar expression of full-length antibody heavy and light chains in vitro and in vivo from a single ORF and that the antibody chains self-assemble to form a functional antibody. To our knowledge, no one else has shown that full-length mAb gene transfer can provide potent anti-tumor activity in vivo, which may be useful as an alternative therapy to direct injection of monoclonal antibodies. Given the potential clinical benefits of antibody gene therapy, great effort has been devoted to the expression of full-length antibodies in vivo after gene transfer15. To achieve therapeutic effects, however, most antibodies require high and sustained serum levels, typically ranging from one to several hundred mg/ml16–18. Such high mAb serum levels can be achieved only by repeated administration of high doses of recombinant protein, typically ranging from 5 mg/kg to 20 mg/kg of body weight. The high mAb serum concentrations required for clinical efficacy and the typically low expression levels after gene transfer have made the development of antibody gene transfer technologies challenging. Electroporation of mAb plasmids in muscle achieved mAb serum levels of 1.5 mg/ml in mice19, whereas implantation of ex vivo transduced cells with retroviral vectors, such as myoblasts20 and fibroblasts21, resulted in mAb serum levels of 1–3 mg/ml. rAAV vectors encoding the heavy and light chains of a human anti-HIV mAb driven by a minimal CMV or EF1 alpha promoter yielded antibody serum levels of

Anti-tumor efficacy of 2A-mediated mAbs in vivo After demonstrating high-level antibody expression in mice after rAAV8-mediated gene transfer, we investigated whether DC101 generated from the rAAV8 vector results in anti-tumor efficacy in vivo as demonstrated for recombinant DC101 antibodies12. Given its higher levels of serum DC101 expression in vivo, only the furincontaining rAAV8-DC101(HF2AL) vector was tested. rAAV8-DC101(HF2AL) or rAAV8-null control vector (2
1011 vg/ mouse) was injected into nude mice through the tail vein, and DC101 antibody serum levels were evaluated by ELISA. At day 24 after vector administration (Fig. 5), the mice were injected subcutaneously with either 1
105 cells/mouse of murine B16F10 melanoma cells or 5
106 cells/mouse of human U87 malignant glioblastoma cells. rAAV8DC101 injection resulted in milligram levels of mAb in the serum of mice (Fig. 5a,d). Significant anti-tumor activity was observed in both models in mice treated with the rAAV8-DC101(HF2AL) vector (Fig. 5b,e; P o 0.05), which resulted in a significantly prolonged survival time (Fig. 5c,f; P o 0.001). In the B16F10 model, median survival time (MST) increased from 30 d in the control group to 41.5 d in the treated group. In the U87 MG model, DC101 antibody gene transfer resulted in tumor dormancy in 6 out of 11 mice for more than 3 months and three mice tumors that had reached a volume of 400–700 mm3 regressed completely (data not shown). In summary, a single administration of a rAAV8 vector expressing DC101 mAb results in stable and high mAb serum levels that are able to control tumor burden.

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Figure 5 Anti-tumor efficacy after rAAV8-mediated gene transfer of DC101. (a–f) rAAV8-CAG-HF2AL or rAAV8-null control vectors (2
1011 vg/mouse) were administered through the tail vein into NCr nude mice on day 0 and serum DC101 concentrations were determined by rat IgG1 ELISA (mean 7 s.e.m.) (a,d). On day 24, B16F10 (1
105 cells/mouse) (a–c) or U87 (5
106 cells/mouse) (d–f) tumor cells were injected subcutaneously into these mice and tumor volume (mean 7 s.e.m.) (b,e) and survival (c,f) were evaluated. For tumor growth (b,e) and survival (c,f), the day of tumor challenge was regarded as day 0. *, P o 0.05; ***, P o 0.001.

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ARTICLES 4–5 mg/ml22. The mAb serum levels described in these studies are below the therapeutic levels. So far, only high doses of recombinant adenoviral vectors have occasionally achieved mAb serum levels of B200 mg/ml23. However, mAb expression by adenoviral vectors is transient23, due to the instrinsic immunogenicity of the vector backbone24. rAAV is a preferred vector system when long-term gene expression is desired. rAAV vectors can stably transduce host cells and are capable of expressing therapeutic proteins at constant levels in vivo following a single vector administration. rAAV vectors have been shown to efficiently transduce quiescent cells of the muscle, liver, brain and eye24–27 and are currently under clinical evaluation28–31. One of the problems of using rAAV vectors as mAb expression vectors, however, is their limited packaging capacity, which makes it difficult to express mAb heavy and light chains from individual promoters in one vector. We used the short FMDV 2A sequence to mediate antibody heavy and light chain expression from a single ORF, which enabled us to construct a single rAAV vector for the production of a full-length mAb. In combining the 2A technology with the recently identified AAV8 serotype that efficiently transduces the liver, mAb serum levels of 41,000 mg/ml were achieved in mice after a single administration of the rAAV vector. Antibody expression remained at high levels for over 4 months. In summary, this expression system presents a feasible gene therapy approach for long-term delivery of antibodies at high levels in vivo. Furthermore, the high concentration of mAb achieved in vivo after rAAV8-mediated gene transfer will make vector manufacturing possible for human use. In our study, we were able to show that the 23 additional amino acids derived from the 2A sequence at the C terminus of the heavy chain can be efficiently removed by adding a furin cleavage site next to the 2A sequence. This modification results in a significant increase of mAb serum levels and generates an antibody that more closely resembles the native protein, thereby eliminating possible adverse effects. There are currently contrasting results regarding whether the leading signal peptide is necessary for the second protein to enter into the endoplasmic reticulum (ER) for protein secretion. In yeast, the lack of the signal peptide at the N terminus of the second protein resulted in cytosolic localization of the protein10. In contrast, in mammalian cells, the second protein can enter the ER lumen without a signal sequence11. It is currently not clear, how the signal sequence of the second protein affects overall antibody expression. Nevertheless, the mAb light chain in our H2AL and HF2AL constructs contains the native signal peptide at its N terminus. Since the signal peptide is cleaved during protein secretion, inclusion of the leading sequence removes the amino acid proline from the N terminus of the antibody light chain, that is derived from the 2A sequence. Therefore, the secreted antibody light chain expressed from these cassettes is expected to retain its native sequence. In addition to providing an option for an alternative long-term antibody therapy, we believe that this technology may be of great value for the generation of mAb producer cell lines. Current technologies used for the generation of stable mAb producer cell lines are labor intensive and time consuming. We are currently evaluating this technology for the generation of stable mAb producer cell lines and have preliminary data demonstrating that the 2A technology combined with viral vectors enables the rapid identification of high mAb producer cell clones. Furthermore, the 2A-furin/rAAV8 technology described within this paper may be a useful tool to validate mAb targets in vivo for drug development, study protein function in vivo by blocking their biological activity or carry out cell-depletion research to study the function of a particular cell type in an in vivo system. The 2A-furin/rAAV8 technology for mAb gene expression may also have a great potential

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for delivery of neutralizing mAbs to induce passive immunity in some infectious diseases. In summary, the 2A-furin technology may have broad application as an in vivo mAb therapy for the treatment of cancer or other chronic diseases, and as a research tool for studies in which high and sustained mAb serum levels are required or for the rapid generation of stable mAb producer cell lines. METHODS Plasmid construction. Total RNA from DC101 hybridoma cells was purified using RNeasy kit (Qiagen). First stream cDNA was synthesized from total RNA using specific primers to heavy or light chain constant region sequences. Variable regions of the antibody heavy and light chains, including their signal peptide sequences, were amplified with the rapid amplification of cDNA ends cloning kit (BD Biosciences Clontech). The VH and VL were cloned into the pCR 2.1 plasmid using the TA cloning kit (Invitrogen) and sequenced. The consensus VH and VL sequences were determined based on sequence data from the clones derived from multiple independent PCRs. Constant regions of both heavy and light chains were also cloned from cDNA. Variable and constant regions of heavy and light chains were joined together by PCR reaction to generate full-length heavy and light chains. To generate the constructs containing the 2A self-processing sequence, the cDNA oligo for a 24–amino acid FMDV 2A peptide was synthesized (Biosource) based on the sequence APVKQTLNFDLLKLAGDVESNPGP32. The cDNA encoding antibody heavy chain, 2A and light chain was assembled by PCR and was cloned into a plasmid downstream of a CAG promoter. The final plasmid, pH2AL, contains a single ORF consisting of a full-length heavy chain, the 2A sequence, and full-length light chain (Fig. 1). Both heavy and light chains include their native signal peptide sequences at their N termini. The plasmid also includes a bovine growth hormone poly A sequence at the 3¢ end. For the construct that also contains a furin cleavage site, the sequence that encodes the furin cleavage site, RAKR, was inserted by PCR between the antibody heavy chain and the 2A sequence. The cDNA that encodes an antibody heavy chain, a furin cleavage site (RAKR), the 2A sequence and an antibody light chain was cloned into the plasmid downstream of the CAG promoter (pHF2AL). To express full-length antibody heavy and light chains with IRES sequences, the DC101 heavy chain, an IRES sequence derived from the EMCV33 and the DC101 light chain were inserted into the plasmid downstream of the CAG promoter. Both heavy and light chain cDNAs end with a stop codon (Fig. 1). rAAV vector preparation. Subconfluent HEK 293 cells were cotransfected using the calcium phosphate method with the pAAV-CAG-DC101 vector plasmid in combination with the AAV8 serotype helper plasmid p5e18-VD2/8 (ref. 14) and pXX-6 (ref. 34). Forty-eight hours after transfection, cells were harvested using PBS/EDTA (10 mM) and lysed by three freeze/thaw cycles in cell lysis buffer (150 mM NaCl, 50 mM HEPES, pH 7.6). Lysates were treated with 250 U/ml benzonase for 15 min at 37 1C and cellular debris was removed by centrifugation. The cleared cell lysate was fractionated by ammonium sulfate precipitation and the rAAV virions were isolated on two sequential CsCl gradients. The gradient fractions containing rAAV were dialyzed against sterile PBS containing CaCl2 and MgCl2, and stored at –80 1C. Viral titers were determined by dot-blot analysis. Briefly, rAAV preparations were treated with DNaseI followed by proteinase K in the presence of 0.5% SDS and 10 mM EDTA to liberate the rAAV genomes, followed by phenol chloroform extraction and ethanol precipitation. Viral DNA was denatured in alkali and applied to a nylon membrane. Dilutions of the corresponding vector plasmid were used as standards to determine the rAAV virion copy number. A radioactive probe specific for the rAAV transgene was hybridized to DNA on the filter and the filter was exposed to film followed by quantification of radioactivity by a b-counter (Perkin Elmer). Biological activities of the purified rAAV were determined by DC101 antibody expression in HEK 293 or HuH7 cells following rAAV transduction in vitro. Cell culture and transfection. HEK 293, B16F10, U87MG and DC101 hybridoma cell lines were obtained from ATCC. HuH 7 cells were from JingHsiung Ou, University of Southern California. HEK 293 cells were cultured in Iscove’s Modified Dulbecco’s Medium (Invitrogen), supplemented with 3 mM

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L-glutamine

and 10% fetal bovine serum. HuH 7, B16F10, U87 and DC101 hybridoma cells were cultured in DMEM medium (Invitrogen), supplemented with 3 mM L-glutamine and 10% fetal bovine serum. To produce DC101 mAb from the hybridoma, exhausted cell culture supernatants were harvested. For DC101 expression in vitro, plasmid DNA was purified using a plasmid DNA mega purification kit (Qiagen) and cells were transfected in 6-well tissue culture plates or 10-cm dishes with FuGene 6 transfection reagent (Roche). Twenty-four hours after transfection, the cell culture medium was removed and the cells were fed fresh medium with or without 10% FBS. The supernatants were collected after 48 or 72 h. ELISA and western blots. The DC101 antibody concentrations in mouse serum or cell culture supernatants were determined using a commercial ELISA assay kit for rat IgG1 (Bethyl Lab). For protein analysis in polyacrylamide gels, protein samples were separated in precast Tris-glycine gels (Invitrogen) under reducing or nonreducing conditions. For western blot analysis, proteins in polyacrylamide gels were transferred to nitrocellulose membranes. The membranes were blocked with 5% nonfat dry milk and incubated with a goat antirat IgG antibody (Calbiochem) conjugated with horseradish peroxidase (HRP). Protein bands were visualized by exposure on X-ray films (Kodak) after the membranes were treated with enhanced chemiluminescence solution (Pierce). Antibody binding and blocking assay. DC101 mAb antibodies were expressed in the supernatants of hybridoma cells (hybridoma), from HEK 293 cells transfected with the H2AL (H2AL in vitro) or the HF2AL plasmids (HF2AL in vitro), or in the sera of mice injected with AAV8 H2AL (H2AL in vivo) or HF2AL (HF2AL in vivo) vectors. To evaluate binding activity of the DC101 antibody to mVEGFR2 (Flk-1), 96-well ELISA plates were coated with 200 ng/ ml of recombinant Flk-1-Fc protein (R&D Systems). The plates were blocked with 5% nonfat dry milk and incubated with various concentrations of DC101 antibody. The plates were incubated with goat anti-rat IgG antibody conjugated to HRP and staining revealed by peroxidase substrate. Medium from naive HEK 293 cells served as a negative control (control). The plates were read in a microplate reader at absorbance of 405 nm. To evaluate the neutralizing effect of DC101 mAb on VEGF-Flk-1 binding, 96-well plates were coated with 500 ng/ml of recombinant human VEGF165 (R&D Systems). Recombinant Flk-1-Fc protein was preincubated with various concentrations of DC101 mAb expressed from hybridoma cells (Hybridoma), HEK 293 cells transfected with the H2AL (H2AL in vitro) or the HF2AL (HF2AL in vitro) plasmids, or sera of mice injected with AAV8 H2AL (H2AL in vivo) or AAV8 HF2AL (HF2AL in vivo) vectors. After blocking with 5% nonfat dry milk, 50 ng/ml of recombinant Flk-1-Fc (R&D Systems), which had been preincubated with various concentrations of DC101 antibody, was added to each well and incubated at 37 1C for 1 h. The plates were washed with Tris-buffered saline, incubated with biotin-conjugated goat anti-Flk-1 antibody (R&D Systems), washed again and the staining revealed with streptavidin-HRP (DB Pharmingen) and peroxidase substrate. Medium from naive HEK 293 cells was used as a negative control (control). VEGF-Flk-1 binding was detected at an absorbance of 405 nm with an anti-Flk-1 antibody conjugated to HRP. His-tagged antibody expression, purification and mass spectrum analysis. A hydrodynamic gene transfer method35 was used to express his-tagged DC101 antibody from plasmid in mice. Briefly, a pAAV-CAG-DC101 antibody HF2AL construct with 6
histidine residues (his-tag) at the C terminus of the light chain was constructed. Plasmid DNA (50 mg in 2 ml of PBS) was rapidly injected into a NCr nu/nu mouse via the tail vein. His-tagged DC101 antibody in mouse serum was purified using a nickel column (Qiagen). Purified proteins were separated in a SDS-PAGE gel under reducing conditions and the antibody heavy chain band was isolated, trypsin digested and analyzed in a mass spectrometer. To determine the amino acid sequence, we isolated the peptide fragment peaks from the mass spectrum and analyzed them by post source decay (PSD) analysis. Antibody expression in vivo by rAAV vector–mediated gene transfer. Female NCr nu/nu mice (6–8 weeks old) were obtained from Taconic. All mice were housed under specific-pathogen-free conditions and treated according to the Institute for Laboratory Animal Research Guide for the Care and Use of

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Laboratory Animals. rAAV vector at 1
1011, 2
1011 and 4
1011 vg/ mouse was injected into mice (n ¼ 5 in each group) via a surgically implanted portal vein catheter. Mice were bled by alternate retro-orbital puncture at each scheduled time point for up to 6 months for analysis of DC101 expression. Blood samples may be collected from the orbital sinus of anesthetized mice at scheduled intervals. Mouse tumor models. Female NCr nu/nu mice (n ¼ 10–12 in each group) were injected with rAAV8-CAG-HF2AL vector through intravenous administration via tail veins at a dose of 2
1011 vg/mouse in 200 ml of PBS. Mice in the control group were injected with the same dose of rAAV8-null vector. To monitor serum DC101 levels, mice were bled weekly by alternate retro-orbital puncture. At day 24 after rAAV administration, B16F10 melanoma (1
105 cells/mouse in 200 ml PBS) or human U87 MG glioma (5
106 cells/mouse in 200 ml PBS/Matrigel at 1:1 ratio) cells were implanted subcutaneously into the flanks of mice. Tumor volume was measured twice a week with a caliper and calculated by the formula of [(width
length
height)/2]. For survival studies, end points were based on the pre-established criteria that include tumor volume, body weight loss, degree of tumor necrosis and the general health of animals. ACKNOWLEDGMENTS The authors would like to thank Mingxia Shi, Sandra Sanchez, Lei Xu, Gail Colbern and the animal service group of Cell Genesys for technical assistance, John Leszyk at the University of Massachusetts Medical School for carrying out mass spectrometry analysis and Peter Working for critical reading of the manuscript. COMPETING INTERESTS STATEMENT The authors declare competing financial interests (see the Nature Biotechnology website for details). Received 20 January; accepted 10 March 2005 Published online at http://www.nature.com/naturebiotechnology/ 1. Hudson, P.J. & Souriau, C. Engineered antibodies. Nat. Med. 9, 129–134 (2003). 2. Green, L.L. Antibody engineering via genetic engineering of the mouse: XenoMouse strains are a vehicle for the facile generation of therapeutic human monoclonal antibodies. J. Immunol. Methods 231, 11–23 (1999). 3. Maloney, D.G. et al. IDEC-C2B8 (Rituximab) anti-CD20 monoclonal antibody therapy in patients with relapsed low-grade non-Hodgkin’s lymphoma. Blood 90, 2188–2195 (1997). 4. Grimm, D. & Kay, M.A. From virus evolution to vector revolution: use of naturally occurring serotypes of adeno-associated virus (AAV) as novel vectors for human gene therapy. Curr. Gene Ther. 3, 281–304 (2003). 5. Mizuguchi, H., Xu, Z., Ishii-Watabe, A., Uchida, E. & Hayakawa, T. IRES-dependent second gene expression is significantly lower than cap-dependent first gene expression in a bicistronic vector. Mol. Ther. 1, 376–382 (2000). 6. Ryan, M.D. & Drew, J. Foot-and-mouth disease virus 2A oligopeptide mediated cleavage of an artificial polyprotein. EMBO J. 13, 928–933 (1994). 7. Donnelly, M.L., Gani, D., Flint, M., Monaghan, S. & Ryan, M.D. The cleavage activities of aphthovirus and cardiovirus 2A proteins. J. Gen. Virol. 78, 13–21 (1997). 8. Donnelly, M.L. et al. Analysis of the aphthovirus 2A/2B polyprotein ‘cleavage’ mechanism indicates not a proteolytic reaction, but a novel translational effect: a putative ribosomal ‘skip’. J. Gen. Virol. 82, 1013–1025 (2001). 9. Szymczak, A.L. et al. Correction of multi-gene deficiency in vivo using a single ‘selfcleaving’ 2A peptide-based retroviral vector. Nat. Biotechnol. 22, 589–594 (2004). 10. de Felipe, P., Hughes, L.E., Ryan, M.D. & Brown, J.D. Co-translational, intraribosomal cleavage of polypeptides by the foot-and-mouth disease virus 2A peptide. J. Biol. Chem. 278, 11441–11448 (2003). 11. de Felipe, P. & Ryan, M.D. Targeting of proteins derived from self-processing polyproteins containing multiple signal sequences. Traffic 5, 616–626 (2004). 12. Prewett, M. et al. Antivascular endothelial growth factor receptor (fetal liver kinase 1) monoclonal antibody inhibits tumor angiogenesis and growth of several mouse and human tumors. Cancer Res. 59, 5209–5218 (1999). 13. Niwa, H., Yamamura, K. & Miyazaki, J. Efficient selection for high-expression transfectants with a novel eukaryotic vector. Gene 108, 193–199 (1991). 14. Gao, G.P. et al. Novel adeno-associated viruses from rhesus monkeys as vectors for human gene therapy. Proc. Natl. Acad. Sci. USA 99, 11854–11859 (2002). 15. Bakker, J.M., Bleeker, W.K. & Parren, W.H.I. Therapeutic antibody gene transfer: an active approach to passive immunity. Mol. Ther. 10, 411–416 (2004). 16. Davis, T.A. et al. Rituximab anti-CD20 monoclonal antibody therapy in non-Hodgkin’s lymphoma: safety and efficacy of re-treatment. J. Clin. Oncol. 18, 3135–3143 (2000). 17. Lin, Y.S. et al. Preclinical pharmacokinetics, interspecies scaling, and tissue distribution of a humanized monoclonal antibody against vascular endothelial growth factor. J. Pharmacol. Exp. Ther. 288, 371–378 (1999). 18. Armbruster, C. et al. A phase I trial with two human monoclonal antibodies (hMAb 2F5, 2G12) against HIV-1. AIDS 16, 227–233 (2002).

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ARTICLES 19. Perez, N. et al. Regulatable systemic production of monoclonal antibodies by in vivo muscle electroporation. Genet. Vaccines Ther. 2, 2 (2004). 20. Noel, D. et al. In vitro and in vivo secretion of cloned antibodies by genetically modified myogenic cells. Hum. Gene Ther. 8, 1219–1229 (1997). 21. Noel, D., Pelegrin, M., Brockly, F., Lund, A.H. & Piechaczyk, M. Sustained systemic delivery of monoclonal antibodies by genetically modified skin fibroblasts. J. Invest. Dermatol. 115, 740–745 (2000). 22. Lewis, A.D., Chen, R., Montefiori, D.C., Johnson, P.R. & Clark, K.R. Generation of neutralizing activity against human immunodeficiency virus type 1 in serum by antibody gene transfer. J. Virol. 76, 8769–8775 (2002). 23. Noel, D. et al. High in vivo production of a model monoclonal antibody on adenoviral gene transfer. Hum. Gene Ther. 13, 1483–1493 (2002). 24. Jooss, K. & Chirmule, N. Immunity to adenovirus and adeno-associated viral vectors: implications for gene therapy. Gene Ther. 10, 955–963 (2003). 25. Monahan, P.E., Jooss, K. & Sands, M.S. Safety of adeno-associated virus gene therapy vectors: a current evaluation. Expert Opin. Drug Saf. 1, 79–91 (2002). 26. Lu, Y. Recombinant adeno-associated virus as delivery vector for gene therapy–a review. Stem. Cells. Dev. 13, 133–145 (2004). 27. Flotte, T.R. et al. Phase I trial of intramuscular injection of a recombinant adenoassociated virus alpha 1-antitrypsin (rAAV2-CB-hAAT) gene vector to AAT-deficient adults. Hum. Gene Ther. 15, 93–128 (2004).

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28. Moss, R.B. et al. Repeated adeno-associated virus serotype 2 aerosol-mediated cystic fibrosis transmembrane regulator gene transfer to the lungs of patients with cystic fibrosis: a multicenter, double-blind, placebo-controlled trial. Chest 125, 509–521 (2004). 29. Manno, C.S. et al. AAV-mediated factor IX gene transfer to skeletal muscle in patients with severe hemophilia B. Blood 101, 2963–2972 (2003). 30. Janson, C. et al. Clinical protocol. Gene therapy of Canavan disease: AAV-2 vector for neurosurgical delivery of aspartoacylase gene (ASPA) to the human brain. Hum. Gene Ther. 13, 1391–1412 (2002). 31. Luo, J. et al. J. Subthalamic GAD gene therapy in a Parkinson’s disease rat model. Science 298, 425–429 (2002). 32. Ryan, M.D., King, A.M. & Thomas, G.P. Cleavage of foot-and-mouth disease virus polyprotein is mediated by residues located within a 19 amino acid sequence. J. Gen. Virol. 72, 2727–2732 (1991). 33. Jang, S.K., Pestova, T.V., Hellen, C.U., Witherell, G.W. & Wimmer, E. Cap-independent translation of picornavirus RNAs: structure and function of the internal ribosomal entry site. Enzyme 44, 292–309 (1990). 34. Xiao, X., Li, J. & Samulski, R.J. Production of high-titer recombinant adeno-associated virus vectors in the absence of helper adenovirus. J. Virol. 72, 2224–2232 (1998). 35. Liu, F., Song, Y. & Liu, D. Hydrodynamics-based transfection in animals by systemic administration of plasmid DNA. Gene Ther. 6, 1258–1266 (1999).

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Gene knockdown by large circular antisense for high-throughput functional genomics Yun-Han Lee1,4,6, Ik-Jae Moon1,6, Bin Hur1,6, Jeong-Hoh Park1, Kil-Hwan Han1, Seok-Yong Uhm1, Yong-Joo Kim1, Koo-Jeong Kang2, Jong-Wook Park3, Young-Bae Seu4, Young-Ho Kim4 & Jong-Gu Park1,5 Single-stranded genomic DNA of recombinant M13 phages was tested as an antisense molecule and examined for its usefulness in high-throughput functional genomics. cDNA fragments of various genes (TNF-a, c-myc, c-myb, cdk2 and cdk4) were independently cloned into phagemid vectors. Using the life cycle of M13 bacteriophages, large circular (LC)-molecules, antisense to their respective genes, were prepared from the culture supernatant of bacterial transformants. LC-antisense molecules exhibited enhanced stability, target specificity and no need for target-site searches. High-throughput functional genomics was then attempted with an LC-antisense library, which was generated by using a phagemid vector that incorporated a unidirectional subtracted cDNA library derived from liver cancer tissue. We identified 56 genes involved in the growth of these cells. These results indicate that an antisense sequence as a part of single-stranded LC-genomic DNA of recombinant M13 phages exhibits effective antisense activity, and may have potential for high-throughput functional genomics.

Gene expression can be specifically reduced or ablated in cells after the uptake of antisense molecules complementary to a specific mRNA sequence. Antisense inhibition of gene expression is believed to be achieved through RNaseH activity after the formation of an antisense DNA-mRNA duplex or through steric hindrance of movement/ binding of the ribosomal complex1. Gene silencing by antisense treatment has been considered ideal for functional analysis of genes and further for drug target discovery2. Intense efforts have been made to develop antisense anticancer agents that eliminate aberrant expression of genes involved in tumor initiation and progression3–8. The efficacy of antisense oligonucleotides (AS-oligos) has been validated in animal models9–14. We have previously described a series of distinct antisense molecules, with closed structures lacking exonuclease active sites, resulting in much enhanced stability in biologic fluids15,16. These results prompted us to investigate the potential of the single-stranded circular genome of M13 bacteriophages (phages) as antisense molecules. A recombinant M13 phagemid vector was engineered to produce a single-stranded circular genome containing an antisense sequence, which was then tested for enhanced stability and specific antisense activity. Various methods have been devised to study gene expression17–23, however, the information generated has been limited to differential or sequential expression profiles of genes in different tissues or cells. Rapid accumulation of genomic sequence information and expression profiling has created a bottleneck in subsequent definitive gene functionalization and/or target validation. Most definitive functionalization of genes has been performed with various conventional

gain-of-function or loss-of-function studies. Loss-of-function studies have been done either with gene knockdown using conventional antisense24,25 or its related technologies26–28, or with gene knockout using homologous recombination29,30. These approaches are limited in that they must be done individually. Construction of an extensive antisense library may provide an answer to this information bottleneck for massive gene functionalization. AS-oligo libraries have been partially established and used to obtain functional data of a large number of genes. Constructing such a library, however, can be costly and time consuming because a target site search must be done31–33. Thus, another approach is needed to facilitate construction of an antisense library. LC-antisense constructs may provide a salient advantage in library construction because they do not require target site searches. In the present study, we tested the efficacy of single-stranded circular genomic DNA of M13 phagemids as antisense molecules with regard to enhanced stability and target-specific reduction of gene expression. An LC-antisense library was constructed by using cDNA prepared from hepatoblastoma tissue by subtractive hybridization, which was then used to screen genes involved in the growth of a liver cancer cell line using a high-throughput approach. RESULTS Construction and purification of LC-antisense molecule Covalently closed circular antisense molecules are unusually stable and effective in reducing target gene expression, suggesting the potential of the single-stranded circular genome of bacteriophage M13 as an antisense molecule. The F1 replication origin of the M13 phagemid

1WelGENE Inc., 71B 4L, Development Sector 2-3, Sungseo Industrial Park, Dalseogu, Daegu, 704-230, South Korea. 2Department of General Surgery, Dongsan Medical Center, Keimyung University, 3Department of Immunology, Keimyung University School of Medicine, 4Department of Microbiology, College of Natural Sciences, Kyungpook National University, 1370 Sangyeokdong, Bookgu, Daegu, 702-701, South Korea. 5Department of Medical Genetic Engineering, Keimyung University School of Medicine, Dongsan Medical Center, 194 Dongsandong, Joonggu, Daegu, 700-712, South Korea. 6These authors contributed equally to this work. Correspondence should be addressed to J.-G.P. ([email protected]).

Published online 1 May 2005; doi:10.1038/nbt1089

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Target specificity and antisense activity of LC-antisense If LC-antisense is to be effective, it must be target specific, especially in regard to its large length. Thus, sequence specificity of LC-antisense molecules was examined by an RNase protection assay (RPA) after treatment of HeLa cells with cdk2-LCAS. Whereas cdk2-LCAS reduced cdk2 expression in the cells at time points of 24 and 48 h, the antisense did not substantially affect expression of other genes, cdk1, p16, L32 and GAPDH (Fig. 2a). CDK2 levels in HeLa cells transfected with cdk2-LCAS were also examined by western blotting analysis. Whereas cdk2-LCAS at a concentration of 0.8 or 1.6 nM reduced the intracellular level of CDK2 by more than 80%, an equal amount of cdk2-LCSE

Control

Effective inhibition of target gene expression by LC-antisense Encouraged by the enhanced tolerance of LC-antisense molecules to nucleases, we tested TNFa-LCAS for antisense activity. TNFa-LCAS (1.4 nM) was complexed with cationic lipids and added to the rat monocytic WRT7/P2 cell line in which TNF-a expression was induced by lipopolysaccharide (LPS) treatment. When treated with TNFaLCAS, the cells were shown to have a substantially reduced level of TNF-a mRNA (Fig. 1c). In contrast, cells treated with either

TNFa-LCSE (the sense strand of TNF-a DNA) or LCSS (singlestranded vector genomic DNA) did not show much reduction of TNF-a mRNA. The RT-PCR band of TNF-a was confirmed by Southern hybridization with a probe that bound to the internal region of the amplified DNA fragments (Fig. 1c). To confirm that the treatment of LC-antisense leads to the eventual blockade of protein synthesis from target mRNA, we transfected WRT7/P2 cells with TNFa-LCAS and measured the level of TNF-a protein secreted from the transfectants. Commensurate with the reduction of TNF-a mRNA level, the level of TNF-a in the cell culture supernatant was also reduced by more than 90% after treatment with TNFa-LCAS (Fig. 1d). In contrast, none of the two control molecules, TNFa-LCSE and LCSS, significantly reduced the level of TNF-a protein in WRT7/P2 transfectants. After observing the effective antisense activity of TNFa-LCAS, we performed experiments to determine if LC-antisense molecules to other genes, such as c-myc and c-myb, would also block expression of their respective target genes. When 1.12 nM of c-myc-LCAS was added to K562 cells, c-myc mRNA was reduced by about 70% compared to that obtained after c-myc-LCSE transfection (Fig. 1e). Similarly, treatment of 1.12 nM of c-myb-LCAS to K562 cells reduced c-myb mRNA level by about 80% (Fig. 1f). The treatment of c-myc-LCAS did not affect the expression of the c-myb gene and vice versa (Fig. 1e,f). These results show that LC-antisense can efficiently reduce gene expression in smaller amounts than most conventional antisense molecules.

Size marker

was used to generate a single-stranded circular phage genome harboring either the antisense or sense sequence for a target gene. Rat TNF-a cDNA was cloned into a pBS KS ( ) vector to produce the antisense sequence as a part of the phage genome (see Supplementary Fig. 1 online). The phage genomic LC-antisense molecule, designated as TNFa-LCAS in this study, was isolated from the culture supernatant of bacterial cells that were transformed with recombinant phagemid harboring rat TNF-a cDNA34. Large-scale purification of the LCantisense molecule was done by gel filtration column chromatography. The antisense sequence in the single-stranded phage genomic DNA was confirmed by DNA sequencing using the T3 primer (data not shown). LC-antisense molecules to the c-myc, c-myb, cdk2 and cdk4 genes were also constructed by the same approach and designated as c-myc-LCAS, c-myb-LCAS, cdk2-LCAS and cdk4-LCAS, respectively. Similarly, both LCSE (single-stranded phage genome containing the sense sequence of each target gene) and LCSS (single-stranded phage genome devoid of an insert sequence) were also prepared as control molecules. LC-antisense molecules were expected to be resistant to exonucleases because of their circular structure15,16. When single-stranded (ss) TNFa-LCAS was incubated with either XhoI or exonuclease III, the antisense molecules were found to be largely intact after 3 h (Fig. 1a). In contrast, when XhoI was added to the double-stranded (ds) recombinant M13 phagemid DNA harboring the TNF-a cDNA, the dsDNA was restriction digested, generating two linear bands of 3.4 and 0.5 kb on an agarose gel. Furthermore, the dsDNA was digested to completion by the combination of XhoI and exonuclease III, leaving no detectable DNA band. The fact that TNFa-LCAS is ssDNA was reconfirmed by the efficient digestion of the circular molecules with S1 nuclease, which specifically cuts ssDNA regardless of sequence composition. When TNFa-LCAS was combined with cationic lipids, a large fraction of the antisense molecules remained intact after an extended period of incubation in fetal bovine serum (FBS), even after 24 h incubation in 30% FBS (Fig. 1b).

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Figure 1 Stability and antisense activity of LC-antisense. (a) Characterization of TNFa-LCAS molecules. Either double-stranded (ds) recombinant TNFa-phagemid or single-stranded (ss) TNFa-LCAS was incubated with the restriction endonuclease XhoI, S1 nuclease or XhoI/exonuclease III and run on a 1% agarose gel with sham-treated controls. (b) Stability test of LCantisense molecules. TNFa-LCAS plus cationic lipid complexes were treated with 30% FBS for different periods of time as indicated and run on a 1% agarose gel with a sham-treated control. (c) Antisense activity of TNFa-LCAS on TNF-a mRNA levels in WRT7/P2 cells. RT-PCR analysis was carried out with two sets of primers, either TNF-a primers or b-actin primers. Southern blotting, shown in the bottom panel, was carried out to detect TNF-a expression. (d) Reduced expression of TNF-a by TNFa-LCAS treatment. ELISA of TNF-a in medium: WRT7/P2 cells transfected with TNFa-LCAS, TNFa-LCSE or LCSS. Each bar value represents the mean 7 s.d. of triplicate experiments. Statistical significance was calculated with student’s t-test (analysis of variance, * P o 0.05). (e,f) Indicated amounts of LC-antisense molecules to c-myc (e) and c-myb (f) were transfected into K562 cells. Amplified PCR fragments of each target gene were run on a 1% agarose gel and visualized with ethidium bromide staining.

Size marker

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Absorbance (570 nm)

Normalized expression

Absorbance (570 nm)

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Sh

am Li (24 pi h) d L C s (2 SE 4 h ) LC (24 SS h ) (2 4 h)

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Control treatments (i) Sham control

Treated with LC-antisense molecules

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(vii)

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(ix)

0.8, 1.6 and 2.4 nM of either cdk2- or cdk4-LCAS was added to HeLa cells, target mRNA was reduced by about 40–75%, in a dose-dependent manner at 6, 12 and 24 h (see Supplementary Fig. 3a,b). In contrast, cells that were treated with either LCSE or LCSS did not show substantial reduction of either cdk2 or cdk4 gene expression. The influence on the expression of noncognate cdk genes was then examined after cdk2- and cdk4-LCAS treatment, respectively. When 1.6 nM of cdk2-LCAS was added to HeLa cells, the level of cdk4 mRNA was not changed substantially at 6 h and 12 h, but was decreased by about 48% at 24 h (Fig. 2b). Similarly, 50 nM cdk2 siRNA reduced target cdk2 RNA by about 48% compared to the sham treatment, but cdk4 RNA was not reduced at earlier time points. However, after the 24 h incubation with cdk2 siRNA, cdk4 mRNA was reduced by approximately 47% (see Supplementary Fig. 3c online). Further, when 1.6 nM of cdk4-LCAS was added to HeLa cells, the expression of cdk2 and cdk6 mRNA was not affected at 6 h and 12 h, but was decreased by approximately 64% and 47% at 24h, respectively (Fig. 2c). The lack of off-target expression interference among the functionally associated genes, cdk2, cdk4 and cdk6, at earlier time 120

HepG2 cell growth

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had no substantial effect (see Supplementary Fig. 2 online). Interestingly, the level of cdk4 mRNA was also reduced by the treatment of cdk2-LCAS. The result may be explained by the coordinate regulation between the two G1 phase-specific cell cycle regulators in a sequential fashion35,36. However, it was necessary to rule out the off-target effect of cdk2-LCAS, therefore, we monitored the changes in cdk2, cdk4 and cdk6 gene expression after either cdk2- or cdk4-LCAS treatment at earlier time points using real-time quantitative RT-PCR. Cdk4 shares conserved regions in some of its sequence to those of cdk2 and cdk6, the other two cell cycle regulators. Nucleotides 749–848 of the cdk4 cDNA (GenBank accession number NM_000075) exhibits 75% sequence similarity to a region of the cdk2 gene (NM_001798) and 74% to a region of cdk6 gene (NM_001259), respectively. There is, however, no sequence similarity between cdk2 and cdk6 genes. A timecourse experiment was initially carried out by transfection of 2  104 HeLa cells by various amounts of cdk2- or cdk4-LCAS to monitor the optimal concentration of the antisense and time points for effective knockdown of their respective gene expression. In addition, the activity of cdk2-LCAS was compared to that obtained with cdk2 siRNA. When

Percent of absorbance (570 nm)

© 2005 Nature Publishing Group http://www.nature.com/naturebiotechnology

Sh

am Li (24 pi ds h) LC (24 SE h) LC (24 SS h) (2 4 h)

b

Normalized expression

Cdk2-LCAS (48 h)

Cdk2-LCAS (24 h)

LCSS (48 h)

Sham (48 h)

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a

Yeast tRNA

Figure 2 Target specificity and antisense activity 120 of LC-antisense. Detection of gene expression 0.9 Cdk2 100 Cdk4 was done after the transfection of LC-antisense 80 into HeLa. (a) RPA assay for expression of various 60 0.5 40 genes in HeLa cells. Total RNA was extracted at Cdk1 20 24 h or 48 h after treatment with cdk2-LCAS or Cdk2 0 at 48 h after sham treatment or LCSS treatment Cdk3 6h 12 h 24 h 0.1 Cdk4 Cdk2-LCAS (1.6 nM) in 6-well plates. After hybridization of the (0.56 nM) AS1 AS2 LCSE LCSS p27 extracted RNA with biotin-labeled probes or yeast c-myb-LCAS p21 tRNA (negative control), the samples were run 120 Cdk2 together with unhybridized probes as markers PISSLRE 0.3 Cdk6 100 on a denaturing polyacrylamide gel. Reference p16 Cdk4 80 mRNAs: L32 and GAPDH. Irregular splotches 60 0.2 are shown on L32 and GAPDH bands of the two 40 L32 right-hand lanes. PISSLRE, a human CDC-2 20 0.1 0 GAPDH related protein kinase, (b,c) Real-time RT-PCR 6h 12 h 24 h analysis of cdk gene expression in HeLa cells Cdk4-LCAS (1.6 nM) 0 AS SE AS SE LCSS treated with cdk2-LCAS (b) or cdk4-LCAS (c). 0.28 0.56 0.56 (nM) Total RNA was extracted at 6 h, 12 h and 24 h after treatment with 1.6 nM of LC-antisense or at 24 h after sham, lipids alone, LCSE and LCSS treatments in 24-well plates. In all real-time RT-PCR experiments, expression is calculated relative to b-actin and is normalized to sham treatment. Each bar value represents the mean 7 s.d. of triplicate experiments. (d,e) Effect of LC-antisense on proliferation of human cancer cell lines was measured by MTT assays after transfection of LC-antisense molecules. (d) Effects of two types of c-myb-LCAS on K562 cell proliferation. C-myb-LCAS1 and c-myb-LCAS2 contain 0.5 kb or 1.5 kb of the c-myb cDNA sequence, respectively. (e) Cdk4-LCAS on MCF-7 cell proliferation. AS, cdk4-LCAS; SE, cdk4-LCSE. Each bar value represents the mean 7 s.d. of triplicate experiments.

Figure 3 High-throughput functional analysis to identify genes involved in the growth of liver cancer cells. (a) Growth inhibition of HepG2 cells after transfection with LC-antisense library was examined by light microscopy 4 d after transfection (200 magnification). (i)–(iii), control treatments as indicated; (iv)–(ix), HepG2 cells treated with different LC-antisense molecules. A representative example of the data acquired from treatments with 6 out of 1,200 kinds of LC-antisense is shown. (b) LC-antisense species of 56 random genes were transfected to a HepG2 cell line in a macroarray configuration. The transfectants were examined for growth inhibition by MTT assays in triplicate. Cells that were sham-treated, treated with lipids alone and treated with control DNA plus lipid complexes were assayed simultaneously. Each bar value represents the mean 7 s.d. of triplicate experiments.

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ARTICLES Table 1 List of genes involved in liver cancer cell growth

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Gene description and putative functional category Protein synthesis Homo sapiens ribosomal protein S25 (RPS25) Siboglinum ekmani 18S ribosomal RNA, partial sequence H. sapiens ribosomal protein S8 (RPS8) H. sapiens ribosomal protein, large P1 H. sapiens ribosomal protein S24 (RPS24) H. sapiens ribosomal protein S17 (RPS17) H. sapiens clone IMAGE:3543815 H. sapiens ribosomal protein L27 (RPL27) H. sapiens ribosomal protein S5 (RPS5) H. sapiens ribosomal protein L35 (RPL35) Translation factors H. sapiens eukaryotic translation initiation factor 3, subunit 6 interacting protein (EIF3S6IP) H. sapiens eukaryotic translation initiation factor 4A, isoform 2 (EIF4A2) H. sapiens eukaryotic translation elongation factor 1 gamma Structural proteins and their regulators H. sapiens tissue inhibitor of metalloproteinase 1 (TIMP1) H. sapiens clone MGC:5318 IMAGE:2900273 H. sapiens beta-2-microglobulin (B2M) H. sapiens syntaxin 7 (STX7) Metabolism H. sapiens glutamate dehydrogenase 1 (GLUD1) Human liver glutamate dehydrogenase H. sapiens similar to serine (or cysteine) proteinase inhibitor Human mRNA for glutamate dehydrogenase Other H. sapiens alpha-fetoprotein (AFP) H. sapiens ferritin, light polypeptide (FTL) H. sapiens cutaneous T-cell lymphoma-associated tumor antigen se20-4 (SE20-4) H. sapiens apolipoprotein A-II H. sapiens heat shock 70 kDa protein 8 (HSPA8)

No. of clones

Accession no.

WGSL5 WGSL16 WGSL19 WGSL23 WGSL33 WGSL38 WGSL39 WGSL45 WGSL46 WGSL55

BC004986 AF315062 NM_001012 NM_001003 NM_033022 M13932 BC020169 NM_000988 BC018151 NM_007209

WGSL3 WGSL28 WGSL41

NM_016091 NM_001967 BC028179

WGSL6 WGSL14 WGSL32 WGSL53

XM_033878 BC006781 NM_004048 XM_004526

WGSL18 WGSL25 WGSL34 WGSL42

NM_005271 J03248 BC011991 X07769

WGSL8 WGSL9 WGSL11 WGSL12 WGSL13

BC027881 NM_000146 BC024270 BC005282 NM_006597

Table 1 continued on following page

points and between the c-myb and c-myc genes by respective LCantisense molecules demonstrate the target specificity. We next tested LC-antisense molecules for growth inhibition of cancer cell lines by targeting c-myb and cdk4. To accomplish this, two different LC-antisense molecules to c-myb at 0.56 nM (c-myb-LCAS1 containing 0.5 kb c-myb antisense and c-myb-LCAS2 containing 1.5 kb c-myb antisense) were added to K562. When the cancer cell lines transfected with the antisense molecules were examined for growth with the MTT assay, both c-myb-LCAS1 and c-myb-LCAS2 were able to inhibit cancer cell growth by more than 60% (Fig. 2d). In contrast, c-myb-LCSE and LCSS did not substantially affect K562 cell growth. Similarly, when cdk4-LCAS was added to MCF-7 cells, the cell growth of antisense transfectants was inhibited by more than 70% at antisense concentrations of either 0.28 or 0.56 nM. In contrast, cdk4-LCSE showed only marginal inhibition of cell growth, less than 9% and 15% inhibition at the same concentrations (Fig. 2e). The changes of CDK4 levels in MCF-7 cells treated with various amounts of cdk4-LCAS were also examined with western blot analysis. Whereas cdk4-LCAS at a concentration of 0.8 or 1.6 nM reduced the CDK4 level by more than 60%, an equal amount of cdk4-LCSE had no substantial effect (see Supplementary Fig. 4 online). These results demonstrate that LC-antisense molecules may provide target specificity and effective antisense activity. En masse identification of liver cancer-related genes The fact that a phagemid vector can be easily used to construct a cDNA library, prompted us to investigate the feasibility of

594

high-throughput functional genomics using LC-antisense technology. Using an LC-antisense library can be appealing because target site searches are not required. Thus, we constructed an LC-antisense library to identify genes that are functionally involved in the growth of liver cancer cells (see Supplementary Fig. 5 online). To improve our chances of finding genes of interest, we prepared mRNA from both hepatoblastoma and noncancerous adjacent liver tissues, which was differentially amplified for liver cancer–specific mRNA by a suppression subtractive hybridization method37. Differentially amplified cDNAs were unidirectionally cloned into a phagemid vector, and the cDNA library constructed was transformed into Escherichia coli competent cells. From the cDNA library of 9,600 transformants, 1,200 clones with cDNA inserts of more than 500 base pairs were selected by a simplified method for plasmid isolation38. LC-antisense molecules were then purified from the culture supernatant of bacterial competent cells superinfected with helper bacteriophages. The random gene LC-antisense library of 1,200 member species was arrayed for transfection in 13 96-well plates that had been seeded with HepG2 cells for functional analysis. Each LC-antisense molecule (0.1 mg) was complexed with cationic lipids at a ratio of 1:3 (wt/wt) and transfected into 7  103 HepG2 cells in each well of 96-well plates. Cells were inspected for morphological changes with light microscopy (Fig. 3a) and measured quantitatively for growth inhibition with an MTT assay 4 d after transfection. Of the 1,200 antisense species selected by insert sizes, 153 (B13%) were found to be inhibitory to cancer cell growth in varying degrees. In contrast, cells treated with single-stranded control DNA (devoid of antisense insert sequences) exhibited a mild

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Gene description and putative functional category H. sapiens haplotype M*2 mitochondrion H. sapiens haptoglobin (HP) Human cytochrome P450IIE1 (ethanol-inducible) gene H. sapiens cytochrome b5 outer mitochondrial membrane precursor Human DNA sequence from clone RP4-792D7 on chromosome 1q42.2-43. Contains the 5¢ end of the TARBP1 gene for TAR (HIV) RNA-binding protein 1 H. sapiens interferon, gamma-inducible protein 30 (IFI30) H. sapiens fibrinogen, gamma polypeptide (FGG), transcript variant gamma-A H. sapiens hypothetical protein My014 (MY014) Human liver fatty acid binding protein (FABP) H. sapiens clone MGC:12445 IMAGE:3935036 Human gene for heterogeneous nuclear ribonucleoprotein (hnRNP) core protein A1 H. sapiens FK506 binding protein 3 (25kD) (FKBP3) Undefined functions Human chromosome 14 DNA sequence BAC R-123M6 of library RPCI-11 from chromosome 14 Human DNA sequence from clone CTA-175E3 on chromosome 22q12.1 Human DNA sequence from clone RP11-38P6 on chromosome 9 H. sapiens BAC clone RP11-620E11 from chromosome 4 H. sapiens hypothetical protein FLJ14075 (FLJ14075) H. sapiens PRO2675 mRNA H. sapiens mRNA; cDNA DKFZp762B195 H. sapiens clone RP11-56O18 from chromosome 2 H. sapiens cDNA FLJ35730 fis, highly similar to alpha-1-antichymotrypsin precursor H. sapiens BAC clone RP11-449G13 from chromosome 16 H. sapiens chromosome 5 clone RP11-412P18 H. sapiens clone IMAGE:3923943 H. sapiens chromosome 4 clone B366O24 map 4q25 H. sapiens BAC clone RP11-360H4 from chromosome 2 Human DNA sequence from clone RP11-334A14 on chromosome 1 H. sapiens genomic MHC class III complement gene cluster (MCGC@) on chromosome 6 H. sapiens BAC clone CTD-2324K8 from 7p14-p13 H. sapiens genomic DNA, chromosome 11q clone:RP11-680L20

level of growth inhibition that could also be seen in cells treated with a dsDNA-lipid complex. To eliminate redundancy, we compared the sequences of cDNA clones complementary to the 153 growthinhibiting LC-antisense molecules and searched the GenBank database for matching sequences. For instance, LCAS 3, 5, 8, 9, 10 and 23 contained the antisense sequences reversely complementary to the cDNA sequences of WGSL3 (nucleotides 1367–1885 of GenBank accession number NM_016091), WGSL5 (nt 21–489 of BC004986), WGSL8 (nt 1448–2017 of BC027881), WGSL9 (nt 421–863 of NM_000146), WGSL10 (nt 2119–2446 of NM_024894) and WGSL23 (nt 321–512 of NM_001003) genes, respectively. There were 56 unique sequences out of 153 cDNAs, and these were sorted and designated as clones WGSL 1–56. Putative functional categorization of each gene was then performed by motif-based searches on the basis of the revealed sequence information (Table 1). The LC-antisense molecules derived from the 56 clones were then designated as LCAS 1–56. Functional categorization indicated that 18 out of the 56 genes encode proteins of undefined functions. The remaining 38 genes have previously defined functions. The growth inhibiting activities of the 56 LC-antisense molecules were further confirmed by repetitive MTT assays (Fig. 3b). These 56 genes appear to have functions directly or indirectly related to cell growth of hepatoblastomas. Effects of LC-antisense on cell cycle progression and apoptosis The 56 LC-antisense species inhibitory to cancer cell growth were studied further to reconfirm their roles and to understand the underlying molecular mechanisms of their inhibitory effects. We

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No. of clones

Accession no.

WGSL21 WGSL26 WGSL30 WGSL31 WGSL35

AF382013 NM_005143 J02843 BC014431 AL136124

WGSL36 WGSL40 WGSL44 WGSL49 WGSL50 WGSL51 WGSL52

XM_038146 NM_000509 NM_030918 M10050 BC005348 X12671 NM_002013

WGSL1 WGSL2 WGSL4 WGSL7 WGSL10 WGSL15 WGSL17 WGSL20 WGSL22 WGSL24 WGSL27 WGSL29 WGSL37 WGSL43 WGSL47 WGSL48 WGSL54 WGSL56

AL117190 Z95113 AL354874 AC079926 NM_024894 AF119890 AL359585 AC019159 AK093049 AC020716 AC091952 BC024924 AC004067 AC019086 AL445183 NG_000013 AC011230 AP001102

used flow cytometry analysis to detect changes in cell cycle patterns in HepG2 cells that were treated with the LC-antisense species for 48 h. When compared to control treatments, using lipids alone or LCSS plus lipids, 53 (B95%) out of the 56 LC-antisense molecules exhibited an increased percentage of cells with sub-G0-G1 DNA content (Fig. 4a). We then determined whether cell death caused by antisense treatment reflected the induction of apoptosis. HepG2 cells treated with LC-antisense molecules were subjected to a DNA fragmentation assay. Twenty seven (LCAS 1, 3, 5, 8, 11, 12, 14, 15, 16, 20, 21, 22, 24, 29, 31, 33, 34, 35, 40, 41, 42, 43, 47, 48, 49, 51, and 53) out of the 53 LC-antisense species were found to cause characteristic DNA ladder formation 48 h after the transfection, indicating apoptotic progression caused by the antisense molecules (Fig. 4b). These results suggest that the LC-antisense library system is an effective means for en masse identification of genes involved in cancer cell growth. Functional validation of the identified genes using other antisense A large number of genes were rapidly identified to be involved in liver cancer cell growth with the LC-antisense library. Functional validations of the genes identified from the liver cancer cells were further carried out by using other antisense technologies including siRNA and PS end-capped AS-oligos. We chose an LC-antisense of clone WGSL 11 (accession number BC024270; gene description, H. sapiens cutaneous T-cell lymphoma-associated tumor antigen), as an example, out of the seven LC-antisense molecules that showed differential inhibition of HepG2 cell growth (data not shown).

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ds pi Li

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11

-M

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-M

-M

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11

11

-4

-3

11

-1

-2

11

11

11

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AS LC

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AS

AS

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AS

LC

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co am

on C

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M

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nt

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l

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Li

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800 1,000

56.9%

FL2-A

LC

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LC

0

600

LCAS 15

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021115.037

40 80 120 160 200

021122.022

400

021122.021

40 80 120 160 200

200

200

0

0

0

29

Counts 40 80 120 160 200

021115.033

200

40 80 120 160 200

600

0

400

is

200

11.0%

C

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Control DNA + Li.

0

3.5%

030121.004

40 80 120 160 200

030121.003

40 80 120 160 200

Sham

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40 80 120 160 200

030121.001

Counts 40 80 120 160 200

We studied the effect of two siRNAs, 11-1siRNA and 11-2siRNA, to WGSL 11, on target mRNA level. 11-1siRNA, was found to be effective in target RNA reduction, and was compared with LCAS 11 for target RNA reduction and cell proliferation blockade. Quantitative downregulation of the target gene expression by LCAS-11 and 11-1siRNA was done with real-time RT-PCR to detect target mRNA levels in HepG2 cells. 24-h treatment with LC-antisense (1.6 nM) and siRNA (50 nM) resulted in about a 70% and 60% reduction of target mRNA, respectively, when compared to that obtained with sham treatment (Fig. 5a). HepG2 cell growth was inhibited by about 70% and 67% by 0.8 nM LCAS-11 and 25 nM 11-1siRNA, respectively (Fig. 5b). Next, we set out to reconfirm the validity of the functional data by using other types of antisense molecules. To find an effective target site for antisense inhibition, a series of PS end-capped AS-oligos derived from the clone WGSL11 were designed and evaluated for their ability to inhibit target gene expression and HepG2 cell growth. Of five antisense sequences tested, the most active one, PS 11-1, was selected. When treated with PS 11-1, target mRNA levels were much reduced and cell growth was inhibited by about 70% at a concentration of 1.1 mM (Fig. 5c,d). In contrast, cells treated with either mismatch or sense

a

0

Figure 4 Effects of LC-antisense on cell cycle progression and apoptotic induction. A representative example of the data acquired from treatments with six kinds of LC-antisense is shown. (a) Cell cycle analysis after transfection of cells with LC-antisense molecules. HepG2 cells and controls were treated with the LC-antisense molecules, LCAS 11, LCAS 14, LCAS 15, LCAS 16, LCAS 29 and LCAS 31: sham treatment, lipids alone and control DNA + Li. (lipid) complexes. Cells were harvested at 48 h after transfection. Functional analysis was performed on an equal number of cells (104 events) by flow cytometry after staining of DNA with propidium iodide. (b) Induction of apoptotic DNA ladder formation by LC-antisense molecules. The LC-antisense molecules, LCAS 11, LCAS 14, LCAS 15, LCAS 16, LCAS 21 and LCAS 29, were treated to HepG2 cells along with controls: sham treatment, control DNA + Li. (lipid) complexes and cisplatin (positive control). Genomic DNA was extracted 48 h after transfection and run on a 1.6% agarose gel. M, 100 bp DNA ladder size marker.

Percent of absorbance (570 nm)

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M

control end-capped AS-oligos did not show much reduction of target mRNA and resulted in only marginal cell growth inhibition. The result was reconfirmed by Southern hybridization of the RT-PCR band of target mRNA (Fig. 5c). These results validate the utility of LC-antisense library in screening genes of interest in a high-throughput mode.

Figure 5 Validation of WGSL 11 as a potential target for the inhibition of cancer cell growth. LCAS 11, siRNA and a series of PS end-capped ASoligos directed against WGSL 11 were designed and transfected into HepG2 cells. (a) HepG2 cells were treated with 1.6 nM LCAS 11 or 50 nM 111siRNA or controls in a 24-well plate. Total RNA was isolated and subjected to real-time RT-PCR 24 h after the antisense treatments. Values represent the average of three independent experiments. Open bars, treatment by LCAS 11 or controls; solid bars, treatment by 11-1siRNA or controls. (b) HepG2 cells were treated with 0.8 nM of LCAS 11, 25 nM of 111siRNA or controls in a 96-well plate. The transfectants were examined for growth inhibition using MTT assays 72 h after the antisense treatments. Cells treated with sham, lipids alone and control molecules complexed with lipids were also assayed for comparisons. Open bars, treatment by LCAS 11 or controls; solid bars, treatment by 11-1siRNA or controls. (c) HepG2 cells were transfected with 0.8 nM LCAS 11 and 1.1 mM the PS end-capped AS-oligos, complexed with Lipofectin in a 48-well plate. Total RNA was subjected to RT-PCR 48 h after the AS-oligo treatments. DNA bands in b were then transferred onto a nylon membrane and subjected to Southern hybridization. (d) HepG2 cells were transfected with each PS end-capped AS-oligos (0.16 mM) complexed with Lipofectin at a ratio of 1:2.5 (wt/wt) in a 96-well plate. MTT assays were carried out to determine the inhibition of cell growth 72 h after AS-oligos treatment. Each bar value represents the mean 7 s.d. of triplicate experiments. Sham, sham treated; 11-1–5, treated with PS end-capped AS-oligos of five different sequences; 11-M1–M3, treated with mismatch control oligos of three different sequences to PS 11-1; and 11-S, treated with a sense control oligo of PS 11-1.

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ARTICLES DISCUSSION LC antisense molecules were generated as single-stranded genomic DNA of recombinant bacteriophages, and was tested for stability, antisense activity and, further, for its usefulness in high-throughput functional genomics. When LC-antisense molecules to TNF-a mRNA were used, the antisense molecules were found to be stable in the presence of nucleases and effective in reduction of target mRNA, which was reflected in the antisense activity that required less than 1/10 the amount of most other types of AS-oligos. LC-antisense to TNF-a was also found to substantially reduce production of rat TNFa in cells, confirming commensurate antisense activity at the protein level. Further, the broad utility of LC-antisense was confirmed with antisense-mediated expression blockade of several other genes (c-myc, c-myb, cdk2 and cdk4) of biological significance. The enhanced antisense activity of LC-antisense molecules may be explained in two ways. One reason may be that the long antisense sequence (1,000 bases on average) in the molecules allows the formation of a more stable duplex between the antisense sequence and the complementary sequence of target mRNA. The lengthy duplex may serve as a substrate for RNaseH activity for an extended period of time. Another reason is that mRNA tends to form extensive secondary and tertiary structures among its own sequences or interact with RNAbinding proteins in the cell cytoplasm, which can make some target sequences inaccessible. It is more likely, with its long length, that certain regions within LC-antisense molecules have a higher chance of binding to complementary sequences in target mRNA. As the antisense sequences are much longer in LC-antisense molecules, target-specificity of LC-antisense is of critical concern. To prove sequence specificity of LC-antisense in a rigorous manner, we carried out both multi-probe RPA and real time RT-PCR. The specific antisense activity was shown by the lack of off-target effects between LC-antisense molecules to c-myb and c-myc and was reconfirmed by LC-antisense molecules and siRNA targeting the same gene, cdk2. Because cdk4 expression was downregulated only by cdk4 LCAS but not by cdk2 LCAS, and vice versa, also indicates sequence specificity because the two genes have a region of localized homology. The delayed downregulation by functionally associated genes may be explained by the tightly coordinated regulation between cell cycle regulatory proteins. Perturbed expression of a growth regulatory gene has been reported to alter expression of other genes involved in the G1/S transition phase of cell cycle progression36. Primary reduction of cdk2 expression may have subsequently lowered the activity of CDK4 (ref. 35). Even with its long length, LC-antisense provides sequence specificity comparable or better to existing antisense technologies. As with other antisense technologies including siRNA, there may still be some off-target effects when a large amount of LC-antisense molecules is used. Knockdown conferred by antisense provides much faster means for gene functionalization than do the conventional knockout methods. To take advantage of antisense technologies, antisense libraries have been constructed using AS-oligos and used for selection of drug targets39,40. A vector system for expression of ssDNA in mammalian cells was also reported41. More recently, an approach using a genomewide synthetic siRNA or siRNA expression library was developed for unveiling gene functions42. Although the technology of siRNA has been reported to be effective, the efficiency in target reduction and specificity appears to be comparable to those of LC-antisense. It should be noted that both AS-oligos and siRNA, unlike LC-antisense, require target site searches that are time consuming and often inconclusive. LC-antisense, as with other antisense, appears to bring about somewhat varying degrees of target reduction even with its lower variability

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and better efficiency. Thus, antisense activities obtained from distinct LC-antisense molecules need to be analyzed with some prudence. By using the random gene LC-antisense library, we identified 56 genes functionally involved in liver cancer cell growth. Motif-based searches suggested that these include genes with novel functions and genes with defined functions, some of which were, as expected, involved in critical cellular metabolisms in DNA replication, transcription and translation. Yet, some others contribute to cancer cell growth in addition to their functions that appear irrelevant to cancer cell growth. Several genes that we found to be involved in liver cancer cell growth in the present study were shown to be overexpressed in hepatocellular carcinoma43,44. In fact, ribosomal proteins P1, S17, L35, fibrinogen gamma polypeptide and elongation factor-1 gamma (WGSL 23, 38, 55, 40 and 41 respectively) were overexpressed in liver cancer and involved in protein synthesis. These results suggest that these genes, although essential in their housekeeping roles, may have differential expression levels in liver cancer tissues and support cell growth in liver cancer. Similar findings of a large number of housekeeping genes in the identification of growth-related genes were also reported in other functional genomics using expressed antisense in Candida albicans45. If overexpressed above a normal level, these genes may play an important role in cancer or pathogenic cell growth. Recently, for example, a-fetoprotein (WGSL 8) was reported to stimulate expression of some oncogenes (c-fos, c-jun and N-ras) in liver cancer cells46. It would then be worth targeting these genes to curb cancer cell growth. Further studies are clearly warranted to investigate biochemical processes of protein products of the genes. An LC-antisense library can be constructed with the unidirectional cloning of cDNA fragments of known sequences (unigenes) into phagemid vectors. Contrary to the random gene antisense library, each antisense species in the unigene antisense library has a unique sequence. An advantage of the unigene antisense library is that a large panel of human genes can be individually targeted without redundancy, and genes of constant transcription levels with posttranslational modifications may be screened. The LC-antisense library system may provide a faster, more cost effective and analytically accurate tool for the study of functional genomics. It would be interesting to see if the LC-antisense molecules can also be used in animals as this would potentially provide a useful approach for in vivo functional genomics. METHODS Construction of recombinant phagemids. Various recombinant phagemids were constructed according to standard cloning procedure47. WRT7/P2 cells (1  105) were seeded in each well of a 48-well plate. Rat TNF-a expression was induced in the cells by the treatment of LPS (Sigma-Aldrich) at 30 mg/ml for 4–24 h. Cells were harvested at desired time points to examine the level of mRNA. The LPS incubation time that induced the highest expression level of TNF-a was chosen for further experiments. The RT-PCR fragment (708 bp) of TNF-a that comprises the entire coding sequence was amplified with a pair of PCR primers (5¢-GATCGTCGACGATGAGCACAGAAAGCATGATCC-3¢ and 5¢-GATCGAATTCGTCACAGAGCAATGACTCCAAAG-3¢) and sequence verified. To construct TNFa-LCAS, the rat TNF-a cDNA fragment was cloned into the multiple cloning site of the pBluescript (pBS) KS( ) vector (Stratagene) using SalI and EcoRI restriction sites in the same direction as the lacZ gene (see Supplementary Fig. 1 online). Control sense molecules were constructed similarly. Likewise, cDNA fragments of the c-myc, c-myb, cdk2 and cdk4 genes were amplified with a pair of PCR primers (see Supplementary Table 1 online) and cloned into the EcoRV site of pBS-KS (+) or ( ) vector. The recombinant phagemids were transformed into Epicurian Coli XL-1 Blue competent cells (Stratagene) by the calcium-chloride method. Cloning direction of amplified cDNA fragments were confirmed with both restriction digestion and DNA sequencing.

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Production and purification of either LC-antisense or control molecules. LCantisense or control molecules to target genes were produced by overnight culture of transformed bacterial cells that had previously been infected with helper bacteriophages, and purified by gel filtration column chromatography. These methods are described in detail in Supplementary Methods online. Structural analysis and stability test of LC-antisense molecules. For structural analysis, 1 mg of TNFa-LCAS was treated with XhoI (10 U/mg DNA), exonuclease III (160 U/mg DNA), or S1 nuclease (10 U/mg DNA) at 37 1C for 3 h, and subjected to phenol extraction, ethanol precipitation and gel electrophoresis on a 1% agarose gel. For stability test, 1 mg of the antisense molecules was tested alone or after complex formation with lipids at a ratio of 1:3 (wt/wt) of DNA/lipids. We added 30% FBS that was not heat inactivated to the antisense-lipids complex and incubated it at 37 1C for varying periods of time for up to 48 h. After incubation with FBS and exonucleases, LC-antisense was extracted with phenol, precipitated with ethanol and run on a 1% agarose gel. Transfection of LC-antisense and siRNA. Transfection of LC-antisense or siRNA was carried out to study the activity of the LC-antisense molecules. For LC-antisense transfection, cells were seeded on a 6-well (for RPA assay), 24-well (for real-time PCR) or 48-well plate (for RT-PCR) in an appropriate volume of culture medium. Cationic lipids, Lipofectamine, Lipofectamine 2000 or Lipofectamine plus reagents (Invitrogen) were mixed with the purified molecules in various ratios (wt/wt) for transfection into target cells. These lipid-DNA complexes were mixed with Opti-MEM (Invitrogen) and added to cells according to the manufacturer’s protocol. After 6 h transfection at 37 1C, the cells were added with fresh medium and incubated further for up to 48 h at 37 1C before assays. Expression of the Rat TNF-a gene was induced with LPS treatment (30 mg/ml) to WRTP7/P2 cell transfectants. To compare the effects of the LC-antisense molecules, identical quantities of lipids alone and control DNA plus lipid complexes were also added to the same number of cells in a different well plate and assayed simultaneously. For siRNA transfection, cells were seeded on a 24-well (for real-time PCR), or 96-well plate (for MTT reduction assay) in an appropriate volume of culture medium. Cdk2siRNA (sense sequence, 5¢-GGUACCGAGCUCC UGAAAUCTT-3¢; antisense, 5¢-GAU UUCAGGAGCUCGGUACCTT-3¢), 11-1siRNA (sense sequence, 5¢-GCAGG CACUGGAGGAUAUUCTT-3¢; antisense, 5¢-GAAUAUCCUCCAGUGCCUG CTT-3¢), and 11-2siRNA (sense sequence, 5¢-GAAGCAAGAAAUGAAGAAAC TT-3¢; antisense, 5¢-GUUUCUUCAUUUCUUGCUUCTT-3¢) duplexes were synthesized (Bioneer) and transfected into HeLa or HepG2 cells using siPORT Lipid (Ambion) as recommended by the manufacturer. To compare the effects of the siRNA molecules, identical quantities of lipids alone and negative control no. 1 siRNA (Ambion) plus lipid complexes were also added to the same number of cells in a different well plate and assayed simultaneously. Detection of target gene transcript. After the transfection of LC-antisense or siRNA, the change of target gene expression in mRNA level was detected with RT-PCR, RPA, and real-time quantitative RT-PCR methods. RNA preparation was carried out with Tri reagent (Molecular Research Center) according to the protocol recommended by the manufacturer. Purified RNA was subjected to RT-PCR in a 50-ml reaction volume by using the Access RT-PCR kit (Promega) and a thermal cycler (MJ Research) as recommended by the manufacturer. A pair of primers was used to amplify TNF-a, c-myc, c-myb and WGSL 11 genes (see Supplementary Table 2 online). PCR product was confirmed on a 1% agarose gel, and quantitative analysis of the amplified DNA was performed with AlphaImager 1220, a gel documentation apparatus (Alpha Innotech). To investigate the effect of LC-antisense molecule on the steady-state level of cdk2 mRNA, RPA was carried out according to the instruction of the RiboQuant Multi-Probe RPA System (BD Pharmingen). Total RNA was obtained from the transfectants at 24 h and 48 h after antisense or control treatment. The antisense RNA probes were synthesized from an hCC-1 template set (BD Pharmingen) in the presence of biotin-16-UTP (Roche). The biotin-labeled probes were hybridized in excess to 20 mg total RNA in solution. Unprotected probes and RNA were digested by RNases. The RNA/ probe hybrids were run on a denaturing polyacrylamide gel and then transferred onto a nylon membrane by a semi-dry blotting unit (Fisher Scientific). Immobilized hybrids were cross-linked to the membrane by

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exposing to UV light. The membrane was incubated with a streptavidinhorseradish peroxidase reagent before exposure to an X-ray film. Target gene expression was also measured by real-time quantitative RT-PCR. Total RNA (1 mg) was reverse transcribed by using random primers supplied in the Reverse Transcription System (Promega). To quantify gene expression, cDNA of cdk2, cdk4, cdk6 and WGSL 11 genes were amplified by using respective pair of primers (see Supplementary Table 3 online), the DyNAmo HS SYBR Green qPCR Kit (MJ Research), and the DNA Engine Opticon 2 System (MJ Research) according to the manufacturer’s instruction. To normalize the amount of total RNA present in each reaction, b-actin gene was amplified simultaneously. Triplicate assays were done with RNA samples isolated from at least two independent experiments. Detection of polypeptides with ELISA or western blotting. Quantification of each target protein after TNFa-LCAS, cdk2-LCAS, and cdk4-LCAS treatment was performed with the enzyme-linked immunosorbent assay (ELISA) or western blotting analysis (see Supplementary Methods online). MTT assay to determine inhibition of cell growth. Both LC-antisense molecules (c-myb and cdk4 LCAS) and siRNA to WGSL11 were studied for their growth inhibitory effects using the MTT assay. The MTT reagent (3-[4,5dimethylthiazol-2-yl]-2,5-diphenyl-tetrazolium bromide) (Sigma-Aldrich) was diluted with PBS to a concentration of 5 mg/ml, and 100 mg of the diluent was added to each well containing 100 ml culture medium. Cells were maintained in a CO2 incubator at 37 1C for 4 h and treated with an equal amount of isopropanol (containing 0.1N HCl) at 25 1C for 1 h. The cells were then measured for absorbance at 570 nm with an ELISA reader, SpectraMAX 190 (Molecular Devices). Construction of a unidirectional subtracted liver cDNA library. To clone differentially expressed genes in hepatoblastomas, a cDNA library was constructed by using a subtractive hybridization procedure37. The construction of the library is described in detail in Supplementary Methods online. Preparation of a liver cancer LC-antisense library. Bacterial competent cells containing recombinant pBS SK( ) phagemids were plated on Luria-Bertani agar plates containing 50 mg/ml of ampicillin and 50 mg/ml of tetracycline and incubated at 37 1C for 16 h. Isolated colonies were seeded in a well of 96-deepwell plates containing 1.4 ml of 2YT liquid medium (tryptone 16 g, yeast extract 10 g, NaCl 10 g per 1,000 ml) added with 50 mg/ml ampicillin. Cells were cultured for 7 h at 37 1C with vigorous shaking. To produce LC-antisense molecules from each phagemid, 20 ml of the bacterial culture was transferred to each well prefilled with 1.4 ml of fresh 2YT liquid medium containing 9 ml of helper bacteriophages, M13K07 (New England Biolabs). After 1 h incubation, 4.2 ml of 70 mg/ml kanamycin was added and cultured at 37 1C for 12 h. The superinfection was carried out in triplicate for each clone to maximize the yield of antisense molecules in a single purification step. Single-stranded LCantisense molecules were purified from the culture supernatant of bacterial cells using QIAprep 96 M13 Kits and QIAVAC vacuum manifolds (Qiagen) according to manufacturer’s instructions. To test both quantity and purity, we ran purified LC-antisense molecules on a 1% agarose gel along with control LCmolecules derived from pBS SK( ) phagemid without a cDNA insert. Transfection of an LC-antisense library into a liver cancer cell line. To identify genes involved in the growth of liver cancer cells, Lipofectamine 2000 was mixed with antisense molecules of the liver cancer–specific LC-antisense library for transfection into HepG2. The cells (7  103) were washed twice with Opti-MEM, seeded in each well of 96-well plates in 100 ml of Opti-MEM supplemented with 10% FBS and incubated for 12–18 h at 37 1C in a 5% CO2 incubator. LC-antisense molecules (0.1 mg) were complexed with 0.3 mg of the cationic lipids, and the antisense molecule plus lipid complexes were added to the cultured cells. The cultures were exchanged with fresh medium 24 h after transfection and incubated for 4 d further. To compare the effects of the LCantisense molecules on cell proliferation, we also added equal quantities of lipids alone and control DNA plus lipid complexes to the same number of cells in a different 96-well plate and assayed them simultaneously. Control DNA was single-stranded phage genomic DNA lacking a cDNA insert. After the transfection, microscopic observation or MTT reduction assay was performed to study

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the effect of antisense molecules on proliferation of cancer cells as described above. The percentage of growth inhibition of cells in each well treated with antisense plus lipids complex was calculated by comparing the optical density with those of sham treatments, using the following formula: 1 (absorbance of an experimental well/absorbance of a sham control well)  100. Gene identification and sequence motif search. To identify genes complementary to LC-antisense molecules that inhibited proliferation of liver cancer cells, we sequenced recombinant phagemids obtained by alkaline lysis from the 5¢ upstream of the (+) strand of cDNA inserts using the T3 primer. Sequences of cDNA inserts were compared with those of the GenBank database. Polypeptides deduced from cDNA sequences were then searched for amino acid motifs using the ProfileScan Server (http://hits.isb-sib.ch/cgi-bin/PFSCAN). Treatment of PS end-capped AS-oligos. To reconfirm the functional role of WGSL11 gene in the cell proliferation of liver cancer, we designed a series of PS end-capped AS-oligos (see Supplementary Table 4 online) and transfected HepG2 cells. The procedures are described in detail in Supplementary Methods online. Note: Supplementary information is available on the Nature Biotechnology website.

ACKNOWLEDGMENTS This study was supported by generous grants of the CDRC of Korean Science & Engineering Foundation (research grant no. R01-2000-00138, R13-2002-02801004-0), South Korea, and WelGENE Inc., a biotechnology company founded by Jong-Gu Park. COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Received 15 December 2004; accepted 14 March 2005 Published online at http://www.nature.com/naturebiotechnology/

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READS: a method for display of 3¢-end fragments of restriction enzyme-digested cDNAs for analysis of differential gene expression. Methods Enzymol. 303, 258–272 (1999). 24. Gottschalk, A.R. et al. p27Kip1 is required for PTEN-induced G1 growth arrest. Cancer Res. 61, 2105–2111 (2001). 25. Zeng, J.Z., Wang, H.Y., Chen, Z.J., Ullrich, A. & Wu, M.C. Molecular cloning and characterization of a novel gene which is highly expressed in hepatocellular carcinoma. Oncogene 21, 4932–4943 (2002). 26. Ahn, J.D. et al. Inhibitory effects of novel AP-1 decoy oligodeoxynucleotides on vascular smooth muscle cell proliferation in vitro and neointimal formation in vivo. Circ. Res. 90, 1325–1332 (2002). 27. Suyama, E., Kawasaki, H., Nakajima, M. & Taira, K. Identification of genes involved in cell invasion by using a library of randomized hybrid ribozymes. Proc. Natl. Acad. Sci. USA 100, 5616–5621 (2003). 28. Caplen, N.J. RNAi as a gene therapy approach. Expert Opin. Biol. Ther. 3, 575–586 (2003). 29. 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LETTERS

Migration and differentiation of neural precursors derived from human embryonic stem cells in the rat brain Viviane Tabar1,2, Georgia Panagiotakos1,2, Edward D Greenberg1,2, Bill K Chan1,2, Michel Sadelain3, Philip H Gutin2 & Lorenz Studer1,2 Human embryonic stem (hES) cells provide a potentially unlimited cell source for regenerative medicine. Recently, differentiation strategies were developed to direct hES cells towards neural fates in vitro. However, the interaction of hES cell progeny with the adult brain environment remains unexplored. Here we report that hES cell–derived neural precursors differentiate into neurons, astrocytes and oligodendrocytes in the normal and lesioned brain of young adult rats and migrate extensively along white matter tracts. The differentiation and migration behavior of hES cell progeny was region specific. The hES cell–derived neural precursors integrated into the endogenous precursor pool in the subventricular zone, a site of persistent neurogenesis. Like adult neural stem cells, hES cell–derived precursors traveled along the rostral migratory stream to the olfactory bulb, where they contributed to neurogenesis. We found no evidence of cell fusion, suggesting that hES cell progeny are capable of responding appropriately to host cues in the subventricular zone. Directing neural differentiation of hES cells in vitro has been of particular interest in view of the success of mouse ES cells in preclinical models of disease1–5. However, the capacity of hES cell– derived precursors to integrate into the adult brain and respond to environmental cues in this typically nonpermissive milieu remains unknown, whereas the behavior of human fetal neural stem cells in vivo is well described6–9. We derived stable EGFP-expressing hES cells from lines H1 (WA01)10 and HES3 (ES03)11 after transduction with the CPG phosphoglycerate kinase–enhanced green fluorescent protein selfinactivating (mPGK-EGFP SIN)-lentiviral vector. The CPG lentiviral vector expressing enhanced green fluorescent protein (EGFP) under control of the PGK promoter was derived from a multiply attenuated HIV vector system12 and included a U3 deletion and introduction of a cPPT element. Vectors were produced by triple transfection of human embryonic kidney 293 cells followed by ultracentrifugation and titration as previously described13. Undifferentiated hES cells growing on

mouse embryonic fibroblasts (MEF) were exposed to the virus at a titer of 0.5
108 transforming units/ml for 8 h followed by a 16-h recovery period for 3 consecutive days. EGFP was detected by native fluorescence at day 3 after transduction. Single EGFP-expressing hES cell colonies were transferred and replated repeatedly until uniform EGFP expression was observed among all cells within colonies. No loss in EGFP expression was observed during propagation or differentiation for up to 15 months after transduction (Fig. 1a–c). The absence of single cell derivation or selection suggests a polyclonal origin of the EGFP+ cell population. The phenotypic characteristics and differentiation profile of wild-type and EGFP-transduced hES cells were indistinguishable. Neural differentiation was induced under serum-free conditions by coculture on a stromal cell line (MS5)3,14. At 1 month neural precursors were isolated, replated feeder-free and maintained for 4 weeks in N2/basic fibroblast growth factor/epidermal growth factor (Fig. 1d). At this stage the majority of cells (490%) were immunoreactive for neural precursor markers (Nestin, Musashi, A2B5) and negative for undifferentiated ES cell markers such as Oct-4, SSEA-3 and SSEA-4. In addition to immature neural precursors (Fig. 1e–f), 8% of the cells expressed markers of immature neurons (Fig. 1g) and 2% were immunoreactive for glial fibrillary acidic protein (GFAP) suggesting astrocytic identity. The in vivo behavior of hES cell–derived (WA01-GFP) neural precursors was first monitored in the normal brain of 3-month-old rats. We transplanted 105 EGFP+ neural precursors or equivalent dead cell controls into the striatum (n ¼ 8). The animals received three daily injections of (+)-5-bromo-2¢-deoxyuridine (BrdU) (300 mg/kg/day for 3 consecutive days) immediately before being killed at 11 weeks after grafting. To assess whether modulation of host-derived cues is required for cell migration, differentiation and integration, we created lysolecithin lesions in the cingulum of a second group of animals (n ¼ 8). These lesions result in a focal demyelination that elicits a consistent repair response leading to complete spontaneous remyelination in 6–8 weeks15. Five days after the lesions were made, the animals received the same transplants as the unlesioned group, including dead cell controls, and were subjected to an identical BrdU regimen.

1Developmental

Biology, 2Neurosurgery and 3Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, New York 10021, USA. Correspondence should be addressed to V.T. ([email protected]).

Published online 24 April 2005; doi:10.1038/nbt1088

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LETTERS Figure 1 In vitro derivation of EGFP+ neural precursors from human ES cells. (a–c) Stable EGFP expressing hES cells (WA01) 14 months after transduction with the CPG PGK-EGFP SIN lentivirus. Over 95% of the Oct4+ undifferentiated hES cells (b) expressed EGFP (c). (d) Schematic representation of the neural differentiation protocol. HES cells are plated on MS5 stromal cells from days 0–28, neural rosettes are mechanically isolated and replated feeder-free on precoated dishes. Neural precursor cells derived from neural rosettes are propagated in the presence of FGF2 and EGF as attached monolayer cultures and passaged weekly. Cells were used at 4 weeks of precursor cell proliferation. (e,f) Typical markers for undifferentiated hES cells, neural rosettes and neural precursor stage are listed in red, absent markers are in blue. Characteristic markers expressed at the neural precursor stage were nestin (e) and A2B5 (f). (g) Pax 6 expression was observed in the majority of neural rosettes cells and greatly reduced at the neural precursor stage. Up to 8% Tuj1+ immature neurons (g) were observed among the neural precursor cells. EGFP and Pax6 are depicted in green, DAPI nuclear stain in blue and all other markers in red. Scale bar in (c): 25 mm and applies to Fig. a–c; scale bar in f, 10 mm, and applies to e also; scale bar in g, 25 mm.

EGFP+ human nuclear antigen (hNA)+ human cells were detected in all animals grafted with hES cell–derived neural precursors, whereas all dead cell control grafts were devoid of any EGFP+ or hNA+ labeling. Colabeling of human cells with hNA and EGFP was confirmed by double immunohistochemistry (Supplementary Fig. 1 online). Grafts consisted of a core and large numbers of migrating cells (Fig. 2). Most cells in the core were neuronal precursors (TuJ1+, 58.4% 7 4.7) and postmitotic neurons (NeuN+, 28.3% 7 2.3, Fig. 2a). The volumes of the graft core ranged from 0.31 to 2.09 mm3 (Cavalieri estimator). Stereological counts (fractionator16,17) yielded an average of 325,410 human cells within the graft core (range 95,664–703,296). Migrating human cells followed white matter tracts and were distributed both ipsi- and contralateral to the injection site within corpus callosum, fornix, fimbria, striatum and cortex. Stereological counts of the total number of migrating cells amounted to an average of 100,172 cells per animal (range 57,375–182,250). Within the striatum, hES cell–derived cells were located largely (79.6%) in DARPP32-/MBP+ zones. Most human cells in the corpus callosum expressed nestin (92.6% 7 1.4, Fig. 2b). These migrating cells exhibited a fivefold increase in BrdU uptake compared with cells within the graft core (13.4% 7 1.1 versus 2.6 7 0.5%, P o 0.001). Tunel labeling demonstrated a very low level of cell death 11 weeks after transplantation (o0.1% of all human cells per brain). GFAP+ human cells were rare and mostly visualized in the periphery of the core graft. Grafted cells were also colabeled for oligodendroglial markers such as NG2 (21.7% 7 1.4), cyclic nucleotide phosphodiesterase (CNP), myelin/oligodendrocyte–specific protein (MOSP), O1, O4 and myelin binding protein (o5%) (Supplementary Figs. 2 and 3 online). All animals with lysolecithin lesions (n ¼ 8) exhibited full repair by the time they were killed regardless of the graft status (dead

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versus alive). This was assessed by myelin histochemical stains (Luxol blue) and immunohistochemistry for MBP, MAG and NG2 (data not shown). Although human cells participated in the lesion repair, the lesion did not result in increased recruitment of hES cell–derived precursors into the area of demyelination or any detectable change in the overall differentiation profile of the grafted cells. Panels of human cells labeled for various oligodendrocyte markers in confocal and epifluorescence microscopy are provided as Supplementary Figures 2 and 3 online.

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e Figure 2 Transplantation of hES-derived neural precursors into the striatum of the young adult rodent brain. (a) Camera lucida drawing of a representative coronal section through the brain of a lesioned animal illustrating the distribution of hNA+ cells 11 weeks post grafting: (b) EGFP+/ NeuN+ neurons in the graft core (green outline in a), (c) Migrating hNA+/ Nestin+ cells in the contralateral corpus callosum, (d) z-stack confocal image of a EGFP+/MBP+ cell located in the remyelinated cingulum after lysolecithin lesion. (e) Diagram of a sagittal brain section illustrating the location of hNA+/Dlx2+ cells (f) and hNA+/GFAP+ (f inset) in the SVZ. (g) hNA+/doublecortin+ neuronal precursors in the RMS. (h) z-stack confocal image of hNA+/Calretinin+ neuron in the olfactory bulb. DAPI nuclear counterstains are in blue. Scale bars, 10 mm.

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Interestingly, human cells were detected in the ipsi- and contralateral SVZ, where they expressed markers typical of ‘transit amplifying’ type C cells (nestin+, BrdU+, dlx-2+, Fig. 2d), type A migrating neuronal precursors (doublecortin+) and GFAP+ type B astrocytes, the putative stem cells in the adult brain19 (Fig. 2d inset). Whereas human cells were detected in the fimbria (the white matter output of the hippocampus), none were found within the dentate gyrus, another site with a highly regulated neurogenic niche20. Human cells expressing doublecortin (Dcx) were found within the rostral migratory stream (RMS; Fig. 2e) and in the olfactory granular and periglomerular layers, where they gave rise to differentiated interneurons including calretinin+ olfactory neurons (Fig. 2f). The region-specific immunohistochemical profile of the human cells is identical to that exhibited by endogenous SVZ stem cell progeny. All colocalization was confirmed by serial 3D-reconstruction of 0.5- to 0.8-mm confocal sections (Supplementary Figs. 1,2 and 4). No human cells were found in other regions of the olfactory or orbital lobe, suggesting that they reached the olfactory bulb selectively by the RMS route rather than by random migration. Also noted was an absence of neuronal differentiation outside the core graft and the RMS/olfactory bulb area.

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Whereas the in vitro differentiation of hES cell–derived precursors into mature oligodendrocytes has proven challenging18, our data suggest that hES cell–derived precursors are capable of differentiation into myelin-expressing oligodendrocytes in the young adult brain without the need for demyelination-induced environmental cues. However, the lysolecithin model is suboptimal for evaluating the functional capacity of hES cell–derived myelin-producing cells. Future studies may include transplantation of hES cell progeny into models such as the shiverer mouse or the myelin-deficient rat, which constitute a more significant challenge for remyelination. None of the grafted animal brains harbored a teratoma (absence of alpha-fetoprotein, cytokeratin, myosin, SSEA-3 and SSEA-4) or other types of tumor as assessed by histological analysis on hematoxylin & eosin (H&E) sections. EGFP expression was maintained 11 weeks after grafting (Fig. 1a and Supplementary Figs. 1–3), indicating that lentiviral-mediated gene transfer might be an efficient way to achieve long-term expression of therapeutic transgenes in hES cell–derived neural progeny in vivo. The adult rodent brain harbors sites of persistent neurogenesis including the subventricular zone (SVZ) and the dentate gyrus.

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Figure 3 Kinetic analysis of human cell migration from the SVZ to the olfactory bulb post transplantation into the SVZ. (a–c) Examples of BrdU+/hNA+ cells (arrows) in the SVZ (a), RMS (b) and olfactory bulb (c). Inset in (c) shows a higher power magnification of the BrdU+ endogenous and human cells depicted in the marked area. HNA+/BrdU+ cells are indicated by arrows, olfactory glomeruli by arrowheads. (a–c) Images were taken 4 d after BrdU labeling, corresponding to 18 d after transplantation; hNA (green), BrdU (red). (d) Multi-image reconstruction of human NCAM+ neural precursors traveling along the RMS towards the olfactory bulb at day 18 after transplantation. (e) At 42 d after transplantation human cells persisted in the SVZ and were aligned in human NCAM+ chains. (f–g) Triple immunohistochemistry at the same timepoint revealed BrdU+ human GFAP cells adjacent to the SVZ (f, BrdU in red, hNA in green, GFAP in blue); and doublecortin+ cells in the RMS (g, BrdU in blue, hNA in green, doublecortin in red). (h–m) Kinetics of endogenous and hESderived precursor cell migration to the olfactory bulb: The number of total BrdU+ cells and the number of human BrdU+/hNA+ cells are shown at 1 d (h,i), 4 d (j,k), and 28 d (l,m) after BrdU exposure. Cells were quantified as average number of cells per uniform randomly selected section within a defined area of the proximal RMS (immediately adjacent to the anterior SVZ), the distal RMS (immediately proximal to the olfactory bulb) and the olfactory bulb (see also Fig. 4j). BrdU+ human cells within the graft core at the injection site are not included in this analysis, since their peri-SVZ location is in the immediate vicinity of the injection site. Data are presented as mean 7 s.e.m. Scale bars (a–c) 25 mm except for inset (10 mm); (d) 100 mm; (e–f) 25 mm; (g) 10 mm.

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The capacity of hES cell–derived neural precursors to reach the SVZ and to participate in olfactory bulb neurogenesis was further analyzed using kinetic studies of cell proliferation and migration along the RMS. Unlike the first experiment, in which hES cell–derived neural precursors were injected into the striatum, the second experiment involved injection into the rostral SVZ. We selected this site to increase the number of cells migrating along the RMS for time-based quantitative analyses. Normal young adult rats (n ¼ 8) were transplanted in the rostral SVZ with 0.5
105 hES cell–derived (WA01; non-GFP expressing) neural precursors21. Two weeks after grafting, animals received BrdU injections (three doses of 100 mg/kg/8 h) over a 24-h period. Animals were killed 1 d, 4 d and 28 d after BrdU administration. Histological analysis confirmed the progressive migration of BrdU+ human precursors from the SVZ to the RMS and olfactory bulb in parallel with endogenous precursor cell migration (Fig. 3a–c). At day 1, the distribution of all BrdU+ human cells was restricted to the graft core at the injection site. Because of the proximity of the injection site to the SVZ, it was difficult to distinguish actual migration from coincidental localization to the SVZ. Therefore our analysis did not include any BrdU-labeled cells in the SVZ but only those that migrated to the RMS or olfactory bulb. Endogenous BrdU+ cells were present in the proximal RMS at day 1 and to a lesser extent in the distal RMS and olfactory bulb. This difference between endogenous and hES cell–derived cells may be explained by a slower migration rate of the human cells compared with rat cells or by the presence of a mitotic population of endogenous rat cells in the RMS. At day 4, the majority of BrdU-labeled human cells were detected in the RMS, concomitant with an overall increase of endogenous BrdU+ cells observed in that region. Low power images revealed large numbers of human cells traveling in the RMS, as identified by immunohistochemistry for human-specific, neural cell–adhesion molecule (NCAM; Fig. 3d). By week 4, the overall BrdU label was Prox. RMS

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significantly diminished and mostly confined to the olfactory bulb, consistent with previous work demonstrating a 50% decrease in the number of thymidine-labeled cells between days 15 and 45 after their birth in the SVZ22. Similarly, the number of human cells that reach and persist in the olfactory bulb by day 28 is low. However, the majority of these human cells in the olfactory bulb were BrdU-labeled (88% on day 28 versus 49% on day 4), demonstrating precursor origin. The progressive increase in the proportion of BrdU+ human cells reaching the olfactory bulb is highly suggestive of a process of targeted migration from the SVZ that complies with the kinetics of the SVZ population (Fig. 3h–m). The increasing percentage of BrdU+ human cells in the olfactory bulb may be attributed to the slow migration of the labeled human cells and a high degree of BrdU labeling achieved by the regimen used. Representative phenotypic fates of human BrdU+ cells were confirmed by triple immunohistochemistry (Fig. 3f,g). At the end of week 4, human cells were detected in the SVZ lining the lateral wall of the posterior aspect of the lateral ventricle (Fig. 3e). These cells were discontinuous with the graft core, which was seen at the injection site more rostrally and may be suggestive of human cell contribution to the SVZ similar to the cells observed in the SVZ in the first experiment, 11 weeks after intrastriatal transplantation (Fig. 2d). To achieve labeling rates sufficient for detection of BrdU+ human cells in the olfactory bulb, our BrdU scheme (three discrete doses over 24 h) was aimed at labeling rapidly dividing transit amplifying cells23 rather than marking the slow cycling stem cells. hES cell–derived cells in the SVZ were largely negative for BrdU at day 28 after labeling, as were the majority of the host SVZ cells. Occasionally, a few BrdU+ human cells were detected in small clusters of human cells lining the SVZ (data not shown). The majority of endogenous and human BrdU+ cells were located in the distal RMS and olfactory bulb 4 weeks after labeling. Representative camera lucida drawings of the RMS and olfactory bulb at each time point examined demonstrate the

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LETTERS

Human BrdU + cell Endogenous BrdU + cell Human BrdU − cell

Figure 4 Camera lucida drawings of representative coronal sections at the level of proximal rostral migratory stream (RMS), distal RMS and olfactory bulb on days 1, 4 and 28 after BrdU injection. (a–i) Insets from are shown at higher magnification in (a¢–i¢). Blue circles represent endogenous BrdU+ cells, green triangles represent BrdU- human cells and red squares BrdU+ human cells. These markers indicate the location of individual cells analyzed at an initial magnification of 400
(one marker per cell). All sections were stained concomitantly for BrdU and human nuclear antigen. Brown outlines in the olfactory bulb sections represent the olfactory glomeruli. (j) Represents a camera lucida drawing of a parasagittal section taken 28 d after BrdU injection. Representation in Figures a and a¢ do not include the large number of human BrdU labeled cells that are found in the graft core and immediate vicinity of the injection site. Note the near exclusive distribution of human cells to the immediate vicinity of the graft, the RMS and the olfactory bulb. Black lines through the section indicate the approximate level of the coronal sections seen in this figure. Arrows indicate the approximate cell injection site and the location of the lateral ventricle.

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LETTERS spatial distribution of human cells, BrdU+ cells and BrdU+ human cells (Fig. 4). Unlike the striatal grafts, human cells injected into the rostral SVZ exhibited a more limited migration pattern and almost exclusively traveled along the RMS towards the olfactory bulb, whereas the majority remained within the graft core (Fig. 4j). Although marker expression and the kinetics of human cell migration to the olfactory bulb presented here are compatible with endogenous SVZ precursor behavior, future long-term BrdU labeling studies will be required to fully demonstrate the stem cell nature of the human cells integrated in the SVZ. Whereas mouse and human ES cells have demonstrated a potential for multilineage and neuron subtype specification in vitro, the in vivo neuronal differentiation potential of hES cell–derived neural precursors in the current study was restricted and similar to that exhibited by primary neural progenitors. However, the ability of hES cell–derived neural precursors to appropriately respond to environmental cues in vivo suggests that environmental factors will be crucial in determining the utility of hES cells in regenerative therapies. Recent studies have demonstrated that stem cell plasticity can be mimicked by a fusion of grafted and host cells24–26. We used two independent assays to test for cell fusion: DNA in situ hybridization with a rat-specific X chromosome probe combined with immunohistochemistry for hNA (Supplementary Fig. 5a online) and genomic in situ hybridization (GISH) of biotin-labeled rat and digoxigeninlabeled human total DNA (Supplementary Fig. 5b). Neither method showed evidence of nuclear fusion. Histochemical analysis (cresyl violet and H&E; ten slides/animal) and double-label immunohistochemistry (45,000 human cells examined) for hNA, DAPI and various cytoplasmic markers (nestin, beta-tubulin, S100-beta) did not detect any multinuclear human cells. The capacity of hES cell progeny to migrate extensively in the young adult brain and differentiate into neurons, astrocytes and oligodendrocytes underlines the potential utility of hES cells for cell therapies. That the cells integrate into the SVZ progenitor pool in young adult animals and adopt an adult neural stem cell–like fate demonstrates appropriate interactions with the stem cell niche in the SVZ. These findings also emphasize the potent role of environmental context in determining the migration behavior and cell fate of hES cells in vivo. METHODS Cell culture and viral transduction. Undifferentiated hES cells (lines H1 (WA01, XY, passages (P) 40–65), and HES327 (ES03, XX, P 50–65) were cultured on mitotically inactivated MEF (Specialty Media) and maintained under growth conditions and passaging techniques described previously28. The CPG PGK-EGFP SIN lentiviral vector was prepared as previously described13. Native EGFP fluorescence was detected 3 d after transduction. Green colonies were manually removed and replated repeatedly until uniform EGFP expression was confirmed. Neural induction was obtained by growing EGFP cells on MS5 cells in knock-out serum replacement (KSR) medium as described previously14. After 16 d, cells were switched from KSR medium to N2 medium. At 1 month, neural precursors were mechanically isolated, replated feeder-free onto polyornithine/laminin-coated culture dishes (50–100
103 cells/cm2) and maintained for 4 weeks in N2 supplemented with FGF-2 and EGF (20 ng/ml each, R&D). Cells were passaged weekly after exposure to Ca2/Mg2-free HBSS for 1 h at 25 1C, spun at 200g for 5 min and replated at 50–100
103 cells/cm2. Slightly enhanced neural induction was observed in the presence of Noggin 500 ng/ml Noggin Fc Chimera (R&D), applied from days 0–28. Animal surgery. All animal experiments were done in accordance with protocols approved by our Institutional Animal Care and Use Committee (IACUC) and following National Institutes of Health (NIH) guidelines for animal welfare. Young adult Sprague Dawley female rats (86- to 92-d old at time of grafting) were acquired from Taconic and used throughout the study.

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Stereotactic implantation of graft cells was performed under full anesthesia using a mixture of ketamine (Ketaset, Fort Dodge Animal Health) and xylazine (AnaSed, Lloyd Laboratories). For the lysolecithin lesion, we injected 2 ml of 2% lysolecithin in PBS (L-a-lyso-lecithin, Calbiochem) into the right cingulum (AP, 0.3; ML, 2.0; V, 2.6 mm; and TB, 3.0). For the striatal implants, the animals received a unilateral 1 ml (100,000 cells in sterile HBSS) injection at the following coordinates: striatum: anteroposterior, 0.3; mediolateral, 2.0; ventral, 2.8; tooth bar, 3.0. For the BrdU time-based analysis, the 50,000 cells were implanted in the SVZ: AP, +1.6; ML, 1.5; DV, 4.2; TB, 2.3. All coordinates relative to bregma and ventral coordinates relative to cortex. Immunosuppression. All rats received cyclosporine (Neoral 100 mg/ml; Novartis) at 20 mg/kg/day intraperitoneally (i.p.). This regimen was initiated 2 d before grafting and maintained until the day they were killed. BrdU administration. Two regimens of BrdU (97%; Aldrich) were used in the different experiments. The first regimen was given to the animals with the striatal implants. It consisted of 300 mg/kg/day given i.p. for the 3 consecutive days preceding death (11 weeks after grafting). The second regimen consisted of three doses of BrdU at 100 mg/kg administered every 8 h for a total of 3 doses over 24 h. This regimen was initiated 2 weeks after transplantation in the SVZ. Two animals were selected randomly and killed at the following time points after BrdU administration: 1 d, 4 d and 4 weeks. BrdU was dissolved in sterile normal saline and .007 M NaOH. Tissue processing. Rats were deeply anesthetized with a 25-mg intraperitoneal injection of pentobarbital solution (Nembutal Sodium Solution, Abbott Laboratories). They were then transcardially perfused with 0.1% heparinized normal saline at 4 1C (Sigma) followed by 4% paraformaldehyde (PFA) in PBS also at 4 1C (pH 7.4). The brains were carefully extracted, post-fixed overnight in 4% PFA at 4 1C, and subsequently transferred to 30% sucrose at 4 1C until embedding. Optimal cutting temperature compound (O.C.T. Compound, Tissue-Tek) was used to embed the brains and sections were cut on a freezing cryostat and stored at 80 1C. Immunohistochemistry. Sections were washed briefly with PBS 0.1% BSA (Sigma). For fluorescence double immunohistochemistry, sections were first blocked with 10% normal goat serum (Gibco) in PBS and 0.3% Triton X-100 (with the exception of surface antigens, where Triton X-100 was omitted). Some antibodies required a pretreatment step as follows: 30 min in 2N HCl at 25 1C for BrdU; 3 min in 100% acetone at 20 1C for human nuclear antigen. Primary antibodies were incubated overnight at 4 1C. Appropriate secondary antibodies and fluorochromes (AMCA and Alexa conjugates (Molecular Probes) or Cy-conjugates (Jackson Immunoresearch Labs)) were applied for 1 h at 25 1C followed by PBS washes, DAPI (Molecular Probes) counterstain and mounted in glycerol. Triple labeling was carried out in the following sequence: pretreatment for BrdU followed by a sodium borate wash, postfixation in 2% PFA then acetone at 20 1C for 3 min and incubation with all three primary antibodies combined overnight. Secondary antibodies were used as above. The primary antibodies included: rat anti-BrdU (1:40, Abcam, Cambridge, UK); Calretinin (1:2000, Swant); Nestin (gift from R.D. McKay, 1:1,000); Dlx2 (gift from S. Anderson and J. Rubenstein, 1:100); TuJ1 (Covance/BabCo, monoclonal 1:200, polyclonal 1:1,000); human NCAM (Eric-1, 1:100, Santa Cruz Biotechnology); EGFP (1:500, Molecular Probes); A2B5 (1:50, Roche Diagnostics); Oct-3/4 (1:100, Santa Cruz Biotechnology); Pax-6 (1:50, Covance/BabCo); DARPP-32 (1:200); SSEA-3 (MC631, 1:50, DSHB); SSEA-4 (MC 813-70, 1:75, DSHB); Pancytokeratin (1:300, Sigma); a-fetoprotein (1:300, Sigma); myosin (1:400, Sigma); CNP (Sternberger, 1:100); and human nuclear antigen (1:50), GFAP (1:1,000), rat MBP (1:200), MAG (1:200), NG2 (1:100), Neu-N (1:50), Musashi (1:100), DCX (1:3,000), O1 (1:50), O4 (1:50), MOSP (MAB 328, 1:1,000), Galc (1:50), all from Chemicon. Confocal sections were imaged on a Leica TCS SO2 AOBS set-up and reconstructed using a Leica Confocal Software Lite package. Tunel. Sections were blocked with 3% H2O2 in methanol for 15 min at 25 1C, rinsed and permeabilized in 0.1% Triton-X in 0.1% sodium citrate for 2 min on ice. They were then incubated in the Tunel mixture prepared according to the manufacturer’s instructions (in-situ cell death detection kit POD, Roche

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Diagnostics). In some cases, the FITC label was converted to DAB chromogen using the manufacturer’s POD converter kit. Quantification. Graft volumes were estimated using the Cavalieri estimator probe (Stereo Investigator version 6, Microbrightfield). Total cell number was assessed separately in the graft core and in the migrating population. Systematic random sampling was applied to the regions of interest (graft core and areas of distribution of human cells) as defined on serial sections. The stereological software was used to design and implement the fractionator probes at a coefficient of error (Gundersen) of r0.05. For the cell counts within the RMS and olfactory bulb (Fig. 3), cells were quantified as average number of cells per uniform randomly selected section within the regions of interest. The latter were defined as follows: proximal RMS (immediately adjacent to the anterior SVZ), distal RMS (immediately proximal to the olfactory bulb) and olfactory bulb (within the olfactory bulb). Data are presented as mean 7 s.e.m. In situ hybridization (GISH). Total genomic human and rat DNA was fragmented using nick translation and labeled with digoxigenin and biotin, respectively. Absence of cross-reactivity was confirmed by hybridization with normal human and rat cells. Brain sections were pretreated with pepsin (0.05% in 0.1 M HCl) for 5 min at 37 1C, washed in PBS and dehydrated in graded alcohol solutions. They were then postfixed in 4% PFA and washed. DNA probes (180 ng DNA per slide in 20 ml) were diluted in hybridization buffer (2
SSC, 50% formamide, 01% SDS, 1
Denhardt’s, 40 mM sodium phosphate, pH.7) and applied to the slides. The slides were sealed in rubber cement, denatured at 80 1C for 7 min and hybridized overnight at 37 1C. The sections were washed in 2
SSC, permeabilized in 0.1% Tween 4
SSC for 10 min at 37 1C and incubated in mouse anti-digoxigenin (1/500, Chemicon) for 1 h at 25 1C. Appropriate secondary antibodies conjugated to Alexa fluorochromes (Molecular Probes) were applied for 1 h at 25 1C followed by DAPI counterstain. For the combined immunofluorescence for hNA and rat-CMS X probe hybridization, slides were postfixed in 4% PFA for 15 min at 25 1C, pretreated in cold acetone for 3 min at 20 1C. Incubation with human nuclear antibody (1/50) was carried out at 4 1C overnight. This was followed by incubation in an Alexa 555-conjugated secondary antibody and PBS rinses. The sections then underwent pre-treatment with pepsin as described above, followed by dehydration in serial graded alcohols. In situ hybridization for the rat X chromosome was carried out using an FITC-labeled rat X Chromosome probe (Cambio and ID Labs) and according to manufacturer’s instructions. The slides were mounted in glycerol. Note: Supplementary information is available on the Nature Biotechnology website.

ACKNOWLEDGMENTS We thank R. McKay for the nestin antibody, S. Anderson and J. Rubenstein for the Dlx2 antibody and M. Leversha for the DNA probes and assistance with GISH. Supported by the National Institute of Neurological Disorders and Stroke, NIH, R21NS046045, the Michael W. McCarthy Foundation, the M.J. Fox Foundation, and the Kinetics Foundation. M.S. is supported by NIH grants HL57612 and CA08748. COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Received 12 November 2004; accepted 8 March 2005 Published online at http://www.nature.com/naturebiotechnology/

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1. Kim, J.H. et al. Dopamine neurons derived from embryonic stem cells function in an animal model of Parkinson’s disease. Nature 418, 50–56 (2002). 2. Bjorklund, L.M. et al. Embryonic stem cells develop into functional dopaminergic neurons after transplantation in a Parkinson rat model. Proc. Natl. Acad. Sci. USA 99, 2344–2349 (2002). 3. Barberi, T. et al Neural subtype specification of fertilization and nuclear transfer embryonic stem cells and application in parkinsonian mice. Nat. Biotechnol. 21, 1200–1207 (2003). 4. Brustle, O. et al. Embryonic stem cell-derived glial precursors: a source of myelinating transplants. Science 285, 754–756 (1999). 5. McDonald, J.W. et al. Transplanted embryonic stem cells survive, differentiate and promote recovery in injured rat spinal cord. Nat. Med. 5, 1410–1412 (1999). 6. Englund, U., Fricker-Gates, R.A., Lundberg, C., Bjorklund, A. & Wictorin, Y. Transplantation of human neural progenitor cells into the neonatal rat brain: extensive migration and differentiation with long-distance axonal projections. Exp. Neurol. 173, 1–21 (2002). 7. Englund, U., Bjorklund, A. & Wictorin, K. Migration patterns and phenotypic differentiation of long-term expanded human neural progenitor cells after transplantation into the adult rat brain. Dev. Brain Res. 134, 123–141 (2002). 8. Caldwell, M.A. et al. Growth factors regulate the survival and fate of cells derived from human neurospheres. Nat. Biotechnol. 19, 475–479 (2001). 9. Svendsen, C.N. et al. Long-term survival of human central nervous system progenitor cells transplanted into a rat model of Parkinson’s disease. Exp. Neurol. 148, 135–146 (1997). 10. Thomson, J.A. et al. Embryonic stem cell lines derived from human blastocysts. Science 282, 1145–1147 (1998). 11. Reubinoff, B.E. et al. Neural progenitors from human embryonic stem cells. Nat. Biotechnol. 19, 1134–1140 (2001). 12. Zufferey, R., Nagy, D., Mandel, R.J., Naldini, L. & Trono, D. Multiply attenuated lentiviral vector achieves efficient gene delivery in vivo. Nat. Biotechnol. 15, 871–875 (1997). 13. May, C. et al. Therapeutic haemoglobin synthesis in beta-thalassaemic mice expressing lentivirus-encoded human beta-globin. Nature 406, 82–86 (2000). 14. Perrier, A.L. et al. Derivation of midbrain dopamine neurons from human embryonic stem cells. Proc. Natl. Acad. Sci. USA 101, 12543–12548 (2004). 15. Gensert, J.M. & Goldman, J.E. Endogenous progenitors remyelinate demyelinated axons in the adult CNS. Neuron 19, 197–203 (1997). 16. West, M.J. Design-based stereological methods for counting neurons. Prog. Brain Res. 135, 43–51 (2002). 17. West, M.J. Design based stereological methods for estimating the total number of objects in histological material. Folia Morphol. (Warsz. ) 60, 11–19 (2001). 18. Studer, L. Stem cells with brainpower. Nat. Biotechnol. 19, 1117–1118 (2001). 19. Doetsch, F., Caille, I., Lim, D.A., Garcia-Verdugo, J.M. & Alvarez-Buylla, A. Subventricular zone astrocytes are neural stem cells in the adult mammalian brain. Cell 97, 703–716 (1999). 20. Monje, M.L., Toda, H. & Palmer, T.D. Inflammatory blockade restores adult hippocampal neurogenesis. Science 302, 1760–1765 (2003). 21. Suhonen, J.O., Peterson, D.A., Ray, J. & Gage, F.H. Differentiation of adult hippocampus-derived progenitors into olfactory neurons in vivo. Nature 383, 624–627 (1996). 22. Petreanu, L. & Alvarez-Buylla, A. Maturation and death of adult-born olfactory bulb granule neurons: role of olfaction. J. Neurosci. 22, 6106–6113 (2002). 23. Doetsch, F., GarciaVerdugo, J.M. & AlvarezBuylla, A. Cellular composition and threedimensional organization of the subventricular germinal zone in the adult mammalian brain. J. Neurosci. 17, 5046–5061 (1997). 24. Medvinsky, A. & Smith, A. Stem cells: fusion brings down barriers. Nature 422, 823– 825 (2003). 25. Alvarez-Dolado, M. et al. Fusion of bone-marrow-derived cells with Purkinje neurons, cardiomyocytes and hepatocytes. Nature 425, 968–973 (2003). 26. Weimann, J.M., Johansson, C.B., Trejo, A. & Blau, H.M. Stable reprogrammed heterokaryons form spontaneously in Purkinje neurons after bone marrow transplant. Nat. Cell Biol. 5, 959–966 (2003). 27. Reubinoff, B.E., Pera, M.F., Fong, C.Y., Trounson, A. & Bongso, A. Embryonic stem cell lines from human blastocysts: somatic differentiation in vitro. Nat. Biotechnol. 18, 399–404 (2000). 28. Zhang, S.C., Wernig, M., Duncan, I.D., Brustle, O. & Thomson, J.A. In vitro differentiation of transplantable neural precursors from human embryonic stem cells. Nat. Biotechnol. 19, 1129–1133 (2001).

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Transient inhibition of BMP signaling by Noggin induces cardiomyocyte differentiation of mouse embryonic stem cells Shinsuke Yuasa1,2, Yuji Itabashi1, Uichi Koshimizu4, Tomofumi Tanaka4, Keijiro Sugimura4, Masayoshi Kinoshita1, Fumiyuki Hattori2,4, Shin-ichi Fukami3, Takuya Shimazaki3, Hideyuki Okano3,5, Satoshi Ogawa1 & Keiichi Fukuda2 Embryonic stem (ES) cells are a promising source of cardiomyocytes, but clinical application of ES cells has been hindered by the lack of reliable selective differentiation methods. Differentiation into any lineage is partly dependent on the regulatory mechanisms of normal early development. Although several signals, including bone morphogenetic protein (BMP)1,2, Wnt3 and FGF4, are involved in heart development, scarce evidence is available about the exact signals that mediate cardiomyocyte differentiation. While investigating the involvement of BMP signaling in early heart formation in the mouse, we found that the BMP antagonist Noggin is transiently but strongly expressed in the heartforming region during gastrulation and acts at the level of induction of mesendoderm to establish conditions conducive to cardiogenesis. We applied this finding to develop an effective protocol for obtaining cardiomyocytes from mouse ES cells by inhibition of BMP signaling. BMP signaling is crucial in mesodermal induction and cardiac formation1,2. However, simple stimulation with BMP2/BMP4 did not augment or suppress cardiomyocyte induction from ES cells (data not shown). In the vertebrate nervous system, Noggin and other BMP inhibitors (chordin and follistatin) are involved in neural differentiation in a context-dependent fashion5,6. We hypothesized that BMP antagonists may also be involved in cardiomyocyte induction. Here, we performed whole-mount in situ hybridization for various BMP antagonists on mouse embryos at different gastrulation stages. The BMP antagonist Noggin was transiently but strongly expressed in the heart-forming area (Fig. 1a,b). It was clearly expressed at the cardiac crescent at mouse embryo day E7.5 and the late crescent stage at E8.0, but was barely detectable in the linear heart tube after E8.5. In contrast, the expression of Noggin at the notochord continued after E8.5, as reported previously7,8. Sectioning of wholemount samples from E7.5 and E8.0 showed expression of Noggin in

both the endodermal and mesodermal layers and made clear that Noggin was derived from the primary heart field (Fig. 1c,d). This marked difference in the time course of Noggin expression between the heart-forming region and notochord suggested that transient expression of Noggin functions in cardiomyocyte differentiation. We stimulated mouse ES cells in suspension cultures with Noggin in various ways (Fig. 2a,b). We administered Noggin before or after embryoid body formation to mimic the transient and strong expression of Noggin at the early gastrulation stage. Discontinuation of leukemia inhibitory factor (LIF) and addition of Noggin before or after embryoid body formation did not increase the incidence of formation of spontaneously beating embryoid bodies (Fig. 2b, rows 2,3). Interestingly, addition of Noggin on day 0 and discontinuation of LIF on day 3 slightly but substantially increased the beating embryoid body incidence (Fig. 2b, row 4), suggesting that the optimal timing for Noggin might be both before and after embryoid body formation. Next, we added Noggin at either 3, 0, +1, +2 or +3 d (Fig. 2b, rows 5–9), and LIF before embryoid body formation. Although Noggin at day 0 (Fig. 2b, row 6) slightly increased the beating embryoid body incidence, this incidence gradually decreased at the later time points. Based on these results, we administered Noggin at day –3 and day 0 from embryoid body formation. This led to a marked increase in beating embryoid body incidence to 95.3% at 10 d (Fig. 2b, rows 10–16), and continued growth of embryoid bodies to day 14. These results suggest that the cardiomyocyte inductive activity of Noggin was restricted to the period from 3 d before one day after embryoid body formation and that the ES cells must initially be undifferentiated. This protocol was effective in two independent ES cell lines, EB3 and R1, and the optimal concentration of Noggin was 150 ng/ml (Fig. 2c and Supplementary Fig. 1 online). To demonstrate that this effect was specific to inhibition of the BMP pathway, we administered various concentrations of BMP2 at day 0 (Fig. 2d). Even low doses of BMP2 strongly inhibited Noggin-dependent cardiomyocyte induction. To confirm that the inhibition of BMP signaling in the early

1Division of Cardiology, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan. 2Department of Regenerative Medicine and Advanced Cardiac Therapeutics, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan. 3Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan. 4Daiichi Suntory Biomedical Research Co. Ltd., 1-1-1 Wakayamadai, Shimamoto-cho, Mishima-gun, Osaka 618–8513, Japan. 5Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Kawaguchi, Saitama, 332-0012, Japan. Correspondence should be addressed to K.F. ([email protected]).

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phase of differentiation could accelerate cardiomyocyte induction, soluble BMP receptor-1A (BMPR-1A) or another BMP antagonist, chordin, was also administered, and cardiomyocyte induction was observed. Both interventions augmented the incidence of beating in individual embryoid bodies (Fig. 2e). In contrast, administration of various growth factors, including insulin-like growth factor-1 (IGF-1), fibroblast growth factor (FGF2) and BMP2, using the same protocol did not boost cardiomyocyte induction (Fig. 2f). These results suggest that inhibition of BMP signaling in the undifferentiated or immediate early phase of ES cell differentiation is crucial for cardiomyocyte differentiation. Next, we examined which step of cardiomyocyte development Noggin acted upon. Noggin-treated ES cells expressed markedly higher levels of brachyury T than untreated cells, and then showed strong induction of cardiomyocyte marker gene expression (Nkx2.5 and Tbx5). Despite this increase in brachyury-T expression, the expression of other early mesodermal markers transiently increased but then subsequently decreased (Fig. 2g). We also performed whole mount in situ hybridization of the embryoid bodies, and quantified these mesodermal marker-positive cells (Fig. 2h,i). Taken together, these data suggest that Noggin acts principally between the undifferentiated and brachyury-T-positive states. Brachyury T is a marker of mesendodermal progenitors that can differentiate into mesoderm or endoderm depending on culture conditions9. In our experiments, Noggin increased both the proportion of the cells expressing brachyury T by 1.8-fold and the level of brachyury-T mRNA per cell by sixfold (Fig. 2j). This suggests that an increase in mRNA per cell is essential for cardiomyocyte induction from undifferentiated ES cells, and that there may be subpopulations within the brachyury-T-positive cells that can be distinguished by their levels of expression. The increase in cells expressing high levels of brachyury T that formed mesendoderm resulted in the large increase in Nkx2.5positive cells. To quantify the incidence of cardiomyocyte induction with Noggin treatment, we immunostained for cardiac-specific proteins and

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Figure 1 Transient expression of noggin at the heart forming area. (a) Wholemount in situ hybridization of noggin and Nkx2.5 was performed at mouse embryo stages E7.5, E8.0, E8.25, E8.5 and E9.0. Note that noggin was strongly expressed at the cardiac crescent (E7.5) and late crescent stage (E8.0), but was undetected after E8.5. In contrast, Nkx2.5 was expressed thereafter. Arrows indicate the heart. (b) The schema of noggin and Nkx2.5 expression at E7.5. CC: cardiac crescent, NC: notochord, LHT: linear heart tube. (c) Section of samples at E7.5 and E8.0 with the whole mount in situ hybridization. i–vi represented the site of the section as shown in a. (d) The schema of noggin and Nkx2.5 expression at E7.5.

observed the results by confocal laser microscopy. Most cells in the Noggin-treated embryoid bodies stained positive for myosin heavy chain (MHC), myosin light chain (MLC), atrial natriuretic peptide (ANP), cardiac troponin I and sarcomeric actinin (Fig. 3a–c). In contrast, the cardiomyocyte content was markedly lower in the control or in embryoid bodies treated with other Noggin protocols. The optimal Noggin protocol led to synchronous beating of the entire embryoid body (see Supplementary Video online). The isolated cells expressed many cardiac markers and had a typical cardiac myocyte morphology. At day 10, the embryoid bodies were attached to the gelatin-coated dishes and stained with anti-MHC antibodies. There was an B100-fold increase in the number of cardiomyocytes compared with control. The Noggin protocol efficiently induced expression of cardiac transcription factors, including Nkx2.5, GATA4, TEF1, Tbx5 and MEF2C, whereas expression of the stem cell marker Oct3/4 rapidly decreased (Fig. 3d). Cardiac-specific proteins were also strongly induced, including ANP, brain natriuretic peptide, MLC-2v, MLC2a, a-MHC, b-MHC and a-cardiac actin. Western blot analysis revealed that the Noggin-treated embryoid bodies expressed GATA4, troponin I, MLC and ANP at levels that were 10- to 450-fold higher than those seen with the other protocol (Fig. 3e,f). To investigate whether this inductive phenomenon was cell autonomous or nonautonomous, we treated ES cells stably transfected with the gene encoding green fluorescent protein (GFP) with Noggin, and then combined them with untreated GFP– ES cells just before embryoid body formation. The majority of GFP+, Noggin-treated ES cells differentiated into cardiomyocytes in the embryoid bodies, whereas very few GFP– untreated ES cells became cardiomyocytes (Fig. 3g,h). These findings suggest that Noggin-mediated induction of cardiomyocyte formation is a cell-autonomous phenomenon. A number of growth factors and chemical compounds induce cardiomyocyte differentiation of mouse ES cells, including reactive oxygen species (2.7-fold increase in beating embryoid body incidence)10, TGFb plus BMP2 (threefold increase)11, targeting of RBP-Jk (downstream of notch signaling)-gene (20-fold increase)12, ascorbic acid (fivefold increase)13, as well as IGF-1, FGF, oxytocin, erythropoietin, retinoic acid and dimethyl sulfoxide14–16. To our knowledge, however, no previous protocol for increasing cardiomyocyte differentiation is as efficient as our present protocol (approximately 100-fold increase in the number of cardiomyocytes compared with control). The efficiency of our protocol may reflect the fact that it makes use of endogenous factors and is modeled on in vivo cardiomyoctye induction. Accumulating evidence implicates BMP signaling as a potent heartinductive signal. Administration of BMP-2/BMP-4 to explant cultures from chicken embryo induces full cardiac differentiation in stages 5–7 anterior medial mesoderm, a tissue that is normally not cardiogenic17,18. In contrast, before stage 3 or during early stages of gastrulation, both BMP2 and BMP4 inhibit cardiomyogenesis19.

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are also expressed in ventral somites or in the notochord, suggesting that some are involved in a counter gradient of BMP activity along the dorso-ventral axis. Based on the analogy to the central nervous system, we suspected that the context-dependent differential action of BMPs in cardiomyocyte induction might be explained by local action of Noggin and other BMP inhibitors. We found that Noggin is transiently but strongly expressed at the anterolateral plate in mouse embryos at E7.0–E8.0 and is critical in cardiomyocyte induction. The restricted and highly effective window of Noggin’s inductive action for cardiomyocyte differentiation from ES cells exactly matched the normal developmental conditions in the heart-forming area in E7.0-E8.0 embryos. From the present results, we propose that BMP signaling is essential for at least two steps in the cardiomyocyte induction

Although BMPs are expressed in lateral plate mesoderm including the anterior lateral plate20, stimulation of ES cells by BMP2 or BMP4 does not augment cardiomyocyte differentiation. Together, these findings suggest that BMPs play multiple roles in mesodermal induction and specific organ differentiation and that their temporal and spatial expression is critical in cardiomyocyte induction19. In the vertebrate nervous system, the local action of Noggin and other BMP inhibitors on BMP signaling is very important in neural induction, in patterning during embryonic development and in adult neurogenesis21. In Xenopus laevis gastrula-stage embryos, Noggin and other BMP inhibitors are secreted by the Spemann organizer and induce neural tissue from dorsal ectoderm7,22,23 by inhibiting ectodermal BMPs24. In the developing neural tubes, BMP has been shown to specify the dorsal fates of neural progenitor cells25. BMP inhibitors

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Figure 2 Protocol and efficiency of the cardiomyocyte induction from ES cells using noggin, chordin and soluble BMP receptor-1A. (a) Representative schema of the protocol of cardiomyocyte induction from ES cells. (b) The efficiency of various protocols for noggin (150 ng/ml) exposure were compared. (c) Dose-efficiency relationship of noggin administration was demonstrated using two different ES cell lines, EB3 and R1 (Supplementary Fig. 1 online). Both lines showed the same dose-efficiency relationship. (d) Administration of low dose of BMP2 abolished the effect of noggin for cardiomyocyte induction, indicating the BMP2 concentration was critical in this phenomenon. (e) Effect of other BMP antagonists, chordin and soluble BMPR-1A (BMP neutralizing receptor), on cardiomyocyte induction. Both chordin and BMPR-1A were administered using the same protocol as noggin (150 ng/ml). Both of these BMP antagonists induced cardiomyocyte induction from ES cells at the same level as noggin, indicating that transient relief from the intrinsic BMP signal is critical for cardiomyocyte induction. (f) Other factors including IGF-1, FGF-2 and BMP2 did not affect cardiomyocyte induction with this protocol. (g) Quantitative RT-PCR of early mesodermal markers and cardiac transcription factors. Black column, noggin-treated ES cells; white column, nontreated ES cells. Each column was normalized by GAPDH. (h) Section of embryoid bodies at 3, 5 and 7 d after embryoid body formation with the whole mount in situ hybridization. (i) To identify individual cells in embryoid body sections, nuclei were stained with propidium iodide. Cells positive for brachyury T, Nkx2.5 and Tbx5 were counted. Cell numbers are presented as a percentage of the total. (j) The increased number of brachyury-T-positive cells and the increased levels of brachyury-T mRNA at day 3 after embryoid body formation are shown. *P o 0.05, **P o 0.01 versus control; NS, not significant.

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Figure 3 Expression of stem cell marker, cardiac transcription factors and cardiac specific proteins in noggin-treated ES cells. (a) Immunostaining for anti-MHC, anti-troponin I, anti-ANP, anti-actinin and anti-MLC are shown. Most of the cells in the whole embryoid bodies were stained with cardiomyocyte-specific antibodies. (b) Isolated cells were stained with the same antibodies. Red represents nuclear staining with PI. In the last immunofluorescent photograph, ANP, MHC and nucleolus are stained with rhodamine, FITC and DAPI, respectively. (c) Embryoid bodies were attached to the gelatin-coated tissue culture plate, and stained with anti-MHC and examined for the number of cardiomyocytes. The number of cardiomyocytes with noggin-treated cells was 100-fold more than the control cells. **P o 0.01 versus control. (d) RT-PCR of Oct3/4, and cardiac transcription factors including Nkx2.5, GATA4, TEF1, Tbx5 and MEF2C is shown. Noggin treatment facilitated the extinction of Oct3/4 and accelerated the time and degree of cardiac transcription factor expression. (e) RT-PCR of cardiac-specific proteins. Noggin treatment augmented their expression. (f) Western blot analysis of cardiacspecific proteins. **P o 0.01 versus control. (g) Cell autonomy of the noggin-treated ES cells. GFP+ ES cells were treated with noggin, mixed with untreated GFP– ES cells and embryoid body formation was performed. GFP+ cells expressed actinin, whereas GFP– cells did not. (h) Quantitative analysis of g. ANP, atrial natriuretic peptide; MLC, myosin light chain; BMP, bone morphogenetic protein; MHC, myosin heavy chain; GFP, green fluorescent protein; BNP (brain natriuretic peptide).

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process: mesodermal induction26 and cardiomyocyte differentiation1,2. However, between these steps, a transient block of intrinsic BMP signaling may be the most important step for determining cardiomyogenic differentiation.

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EB3 cells (a kind gift from H. Niwa, Riken, Japan), which carry the blasticidin S-resistant selection marker gene driven by the Oct3/4 promoter (active in the undifferentiated status) were maintained in medium containing 20 mg/ml blasticidin S to eliminate differentiated cells. EB3 is a subline derived from E14tg2a ES cells30, and was generated by targeted integration of the Oct3/4IRES-BSD-pA vector28 into the Oct3/4 allele.

METHODS Whole-mount in situ hybridization. Pregnant ICR wild-type mice were purchased from Japan CLEA. All experiments were approved by the Keio University Ethics Committee for Animal Experiments. Mice from embryonic day (E) 7.5, 8.0, 8.25, 8.5 and 9.0 were removed, and whole-mount in situ hybridization was performed using digoxigenin-labeled RNA probes as described27. The full-length cDNAs for mouse Noggin and nkx2.5 (accession number NM_008711 and NM_008700, respectively) were obtained by RT-PCR and subcloned into pBluescript plasmid. The cDNAs for mouse Tbx5 and brachyury T were kindly provided by H. Yamagishi and H. Bernhard, respectively. The probes were transcribed with T3 or T7 RNA polymerase. Cell culture. Mouse embryonic fibroblast-free ES cells were used. Undifferentiated ES cells (EB328, R129) were maintained on gelatin-coated dishes in GMEM supplemented with 10% FBS (Equitechbio), 2 mM L-glutamine, 0.1 mM nonessential amino acids, 1 mM sodium pyruvate, 0.1 mM 2-mercaptoethanol and 2,000 U/ml murine LIF (Chemicon International).

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Differentiation of ES cells. ES cells were cultured on gelatin-coated dishes in a-MEM supplemented with 10% FBS (Equitechbio), 2 mM L-glutamine, 0.1 mM nonessential amino acids, 1 mM sodium pyruvate, 0.1 mM 2-mercaptoethanol, 2,000 U/ml LIF and 0.15 mg/ml Noggin (Noggin-Fc, R&D) for 3 d. Then, the cells were trypsinized, and cultured to form spheroids (embryoid bodies) from a single cell using a three-dimensional culture system in the same medium as described above minus the LIF on uncoated Petri dishes to induce embryoid bodies. FGF2, IGF-1, BMP2, chordin and BMP receptor-1A/Fc (BMPR-1A) were purchased from R&D. Histological and immunohistochemical analysis. Embryoid bodies (12–14 d) were fixed in 4% paraformaldehyde for 45 min and embedded using Tissue-Tek OCT (Sakura Finetek). In some experiments, the isolated cells were plated on gelatin-coated glass coverslips at low density and fixed in 4% paraformaldehyde for 5 min. The samples were exposed to primary antibodies including anti-MHC (MF20), anti-troponin I (C-19, Santa Cruz

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Biotechnology; 1:500), anti-actinin (EA-53, Sigma; 1:800), anti-ANP (CHEMICON; 1:100), and anti-MLC (P-18, Santa Cruz; 1:500). Bound antibodies were visualized using a secondary antibody conjugated with Alexa488. Nuclei were stained with 4¢,6-diamidine-2-phenylidole dihydrochloride (DAPI; Sigma Aldrich) or propidium iodide (PI, Sigma), TOTO3 (Molecular Probes). The percentage of MHC–expressing cells was quantified using the day-12 embryoid bodies. RT-PCR and real-time quantitative PCR. Total RNA was extracted using Trizol reagent (GIBCO) and RT-PCR was performed as described previously28. At least five replicates were done for each time point. The PCR primers are listed in the Supplementary Table 1 online. Before quantitative analysis, the linear range of the PCR cycles was measured for each gene, and the appropriate number of PCR cycles was determined. GAPDH was used as an internal control. For quantitative analysis of brachyury T, Nkx2.5, Tbx5, Flk1 and GATA1 expression, cDNA was used as template in a TaqMan real-time PCR assay using the ABI Prism 7700 sequence detection system (Applied Biosystems) according to the manufacturer’s instructions. All samples were run in triplicate. Data were normalized to GAPDH. The primers and TaqMan probe for brachyury T, Nkx2.5, Tbx5, Flk1 and GATA1 were Mm00436877_m1, Mm00657783_m1, Mm00803521_m1, Mm00440099_m1, and Mm00484678_m1 (Applied Biosystems), respectively. Western blotting. Embryoid bodies were lysed in a buffer containing 20 mmol/l Tris-HCl (pH 7.4), 100 mmol/l NaCl, 5 mmol/l EDTA, 1.0% Triton X-100, 10% glycerol, 0.1% SDS, 1.0% deoxycholic acid, 50 mmol/l NaF, 10 mmol/l Na3P2O7, 1 mmol/l Na3VO4, 1 mmol/l phenylmethylsulfonyl fluoride, 10 mg/ ml aprotinin, and 10 mg/ml leupeptin. Proteins were separated on 5% to 10% SDS-PAGE. Western blot analysis was performed as described previously29. Rabbit polyclonal antibodies against GATA4 (Santa Cruz Biotechnology), troponin I, MLC and ANP were used as primary antibodies, and peroxidaseconjugated goat anti-rabbit IgG was used as a secondary antibody. Signals were visualized with an ECL kit (Amersham). Statistical analysis. The data were processed using StatView J-4.5 software. Values are reported as means 7 s.d. Comparisons among values for all groups were performed by one-way ANOVA. The Scheffe’s F test was used to determine the level of significance. The probability level accepted for significance was P o 0.05. Note: Supplementary information is available on the Nature Biotechnology website.

ACKNOWLEDGMENTS This work was (partially) supported by a grant-in-aid from the 21st century Center of Excellence Program of the Ministry of Education, Culture, Sports, Science and Technology, Japan to Keio University. We are grateful to H. Niwa for kindly providing ES cell line EB3 and T. Yoshizaki and Y. Okada for their thoughtful advice and discussion. COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Received 21 September 2004; accepted 30 March 2005 Published online at http://www.nature.com/naturebiotechnology/

1. Winnier, G., Blessing, M., Labosky, P.A. & Hogan, B.L. Bone morphogenetic protein-4 is required for mesoderm formation and patterning in the mouse. Genes Dev. 9, 2105– 2116 (1995). 2. Zhang, H. & Bradley, A. Mice deficient for BMP2 are nonviable and have defects in amnion/chorion and cardiac development. Development 122, 2977–2986 (1996).

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3. Marvin, M.J., Di Rocco, G., Gardiner, A., Bush, S.M. & Lassar, A.B. Inhibition of Wnt activity induces heart formation from posterior mesoderm. Genes Dev. 15, 316–327 (2001). 4. Mima, T., Ueno, H., Fischman, D.A., Williams, L.T. & Mikawa, T. Fibroblast growth factor receptor is required for in vivo cardiac myocyte proliferation at early embryonic stages of heart development. Proc. Natl. Acad. Sci. USA 92, 467–471 (1995). 5. Sasai, Y., Lu, B., Steinbeisser, H. & De Robertis, E.M. Regulation of neural induction by the Chd and Bmp-4 antagonistic patterning signals in Xenopus. Nature 376, 333–336 (1995). 6. Lim, D.A. et al. A. Noggin antagonizes BMP signaling to create a niche for adult neurogenesis. Neuron 28, 713–726 (2000). 7. Smith, W.C. & Harland, R.M. Expression cloning of noggin, a new dorsalizing factor localized to the Spemann organizer in Xenopus embryos. Cell 70, 829–840 (1992). 8. McMahon, J.A. et al. Noggin-mediated antagonism of BMP signaling is required for growth and patterning of the neural tube and somite. Genes Dev. 12, 1438–1452 (1998). 9. Kubo, A. et al. Development of definitive endoderm from embryonic stem cells in culture. Development 131, 1651–1662 (2004). 10. Sauer, H., Rahimi, G., Hescheler, J. & Wartenberg, M. Role of reactive oxygen species and phosphatidylinositol 3-kinase in cardiomyocyte differentiation of embryonic stem cells. FEBS Lett. 476, 218–223 (2000). 11. Behfar, A. et al. Stem cell differentiation requires a paracrine pathway in the heart. FASEB J. 16, 1558–1566 (2002). 12. Schroeder, T. et al. Recombination signal sequence-binding protein Jkappa alters mesodermal cell fate decisions by suppressing cardiomyogenesis. Proc. Natl. Acad. Sci. USA 100, 4018–4023 (2003). 13. Takahashi, T. et al. Ascorbic acid enhances differentiation of embryonic stem cells into cardiac myocytes. Circulation 107, 1912–1916 (2003). 14. Boheler, K.R. et al. Differentiation of pluripotent embryonic stem cells into cardiomyocytes. Circ. Res. 91, 189–201 (2002). 15. Heng, B.C., Haider, H.K., Sim, E.K., Cao, T. & Ng, S.C. Strategies for directing the differentiation of stem cells into the cardiomyogenic lineage in vitro. Cardiovasc. Res. 62, 34–42 (2004). 16. Sachinidis, A. et al. Cardiac specific differentiation of mouse embryonic stem cells. Cardiovasc. Res. 58, 278–291 (2003). 17. Schultheiss, T.M., Burch, J.B. & Lassar, A.B. A role for bone morphogenetic proteins in the induction of cardiac myogenesis. Genes Dev. 11, 451–462 (1997). 18. Andree, B., Duprez, D., Vorbusch, B., Arnold, H.H. & Brand, T. BMP-2 induces ectopic expression of cardiac lineage markers and interferes with somite formation in chicken embryos. Mech. Dev. 70, 119–131 (1998). 19. Ladd, A.N., Yatskievych, T.A. & Antin, P.B. Regulation of avian cardiac myogenesis by activin/TGFbeta and bone morphogenetic proteins. Dev. Biol. 204, 407–419 (1998). 20. Lyons, K.M., Hogan, B.L. & Robertson, E.J. Colocalization of BMP 7 and BMP 2 RNAs suggests that these factors cooperatively mediate tissue interactions during murine development. Mech. Dev. 50, 71–83 (1995). 21. Lim, D.A. et al. Noggin antagonizes BMP signaling to create a niche for adult neurogenesis. Neuron 28, 713–726 (2000). 22. Smith, W.C., Knecht, A.K., Wu, M. & Harland, R.M. Secreted noggin protein mimics the Spemann organizer in dorsalizing Xenopus mesoderm. Nature 361, 547–549 (1993). 23. Lamb, T.M. et al. Neural induction by the secreted polypeptide noggin. Science 262, 713–718 (1993). 24. Zimmerman, L.B., De Jesus-Escobar, J.M. & Harland, R.M. The Spemann organizer signal noggin binds and inactivates bone morphogenetic protein 4. Cell 86, 599–606 (1996). 25. Liem, K.F. Jr., Jessell, T.M. & Briscoe, J. Regulation of the neural patterning activity of sonic hedgehog by secreted BMP inhibitors expressed by notochord and somites. Development 127, 4855–4866 (2000). 26. Winnier, G., Blessing, M., Labosky, P.A. & Hogan, B.L. Bone morphogenetic protein-4 is required for mesoderm formation and patterning in the mouse. Genes Dev. 9, 2105– 2116 (1995). 27. Sasaki, H. & Hogan, B.L. Differential expression of multiple fork head related genes during gastrulation and axial pattern formation in the mouse embryo. Development 118, 47–59 (1993). 28. Niwa, H., Miyazaki, J. & Smith, A.G. Quantitative expression of Oct-3/4 defines differentiation, dedifferentiation or self-renewal of ES cells. Nat. Genet. 24, 372– 376 (2000). 29. Nagy, A., Rossant, J., Nagy, R., Abramow-Newerly, W. & Roder, J.C. Derivation of completely cell culture-derived mice from early-passage embryonic stem cells. Proc. Natl. Acad. Sci. USA 90, 8424–8428 (1993). 30. Hooper, M., Hardy, K., Handyside, A., Hunter, S. & Monk, M. HPRT-deficient (LeschNyhan) mouse embryos derived from germline colonization by cultured cells. Nature 326, 292–295 (1987).

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Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets Hal Alper1, Kohei Miyaoku1,2 & Gregory Stephanopoulos1 Identification of genes that affect the product accumulation phenotype of recombinant strains is an important problem in industrial strain construction and a central tenet of metabolic engineering. We have used systematic (model-based) and combinatorial (transposon-based) methods to identify gene knockout targets that increase lycopene biosynthesis in strains of Escherichia coli. We show that these two search strategies yield two distinct gene sets, which affect product synthesis either through an increase in precursor availability or through (largely unknown) kinetic or regulatory mechanisms, respectively. Exhaustive exploration of all possible combinations of the above gene sets yielded a unique set of 64 knockout strains spanning the metabolic landscape of systematic and combinatorial gene knockout targets. This included a global maximum strain exhibiting an 8.5-fold product increase over recombinant K12 wild type and a twofold increase over the engineered parental strain. These results were further validated in controlled culture conditions. Optimization of metabolic phenotype often requires the simultaneous rerouting of metabolic intermediates and rewiring of regulatory networks. In prior work, this optimization has been accomplished by the modification of genes with well-defined structural or regulatory roles in the context of the particular metabolic pathway being considered1–3. Distant genes affecting a metabolic phenotype either through redistribution of metabolite precursors or indirect kinetic and global regulatory effects have been particularly challenging to identify. Models are relatively ineffective in the search for such genes because of their inability to capture the genes’ complex, nonlinear kinetic and regulatory interactions. In general, methods for identifying genetic targets are not as powerful as the molecular biological tools that are effectively used to modify such targets. These issues become more involved when one considers the possibility of multiple gene modulations4. In general, the complex nature of the metabolic landscape raises significant challenges in the development of an optimal search strategy because varying genetic backgrounds and culturing conditions have a profound impact on the type of gene targets identified by various strategies.

Recently, we reported on a method for the rational design of strains that identifies single and multiple gene knockout targets based on a global stoichiometric analysis. The method was applied successfully to increase lycopene production in recombinant strains of Escherichia coli5. Lycopene production was investigated in the context of the nonmevalonate6 pathway in which cells are recombinant, expressing the crtEBI operon to encode for the polymerization into the 40-carbon molecule product. The pre-engineered strain used for the study contained chromosomal overexpressions of dxs, idi and ispFD5 (Fig. 1a). There has been a significant effort to specifically engineer the isoprenoid pathway and downstream genes7–13; however, in the previous study5 and this current one, we investigate genome-wide gene knockout targets. A total of seven single and multiple stoichiometric gene deletions, (DgdhA, DaceE, DytjC (gpmB), DfdhF, DgdhA DaceE, DgdhA DytjC, DgdhA DaceE DfdhF), were predicted and experimentally validated to increase lycopene production through increasing the supply of precursors and cofactors that are important in the lycopene pathway5. These seven mutations along with the parental strain comprise the set of eight systematically designed genotypes. The left panel of Figure 1b depicts the methodology for identifying these systematic gene knockout targets. Lycopene production in these systematically identified knockout strains was still below the stoichiometric maximum, presumably limited by unknown kinetic or regulatory factors that are unaccounted for in stoichiometric models. To identify additional knockout targets that affect the lycopene phenotype via regulatory, kinetic or other unknown mechanisms, we undertook a global transposon library search in the background of the pre-engineered parental strain. Screening this transposon library on glucose plates identified three gene targets that correlated with lycopene overproduction. Upon sequencing, these combinatorial targets were identified as rssB (also known as hnr), yjfP and yjiD. In the case of yjiD, the transposon was found to be inserted between the identified promoter region and the gene for yjiD and will henceforth be referred to as DpyjiD. The right panel of Figure 1b shows the identity and annotated function of these selected gene targets along a representative location of the transposon insertion event. We note that none of the previously identified single stoichiometric genes surfaced in the combinatorial transposon search

1Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge, Massachusetts 02139, USA. 2On leave from Mitsubishi Chemical Corporation. Correspondence should be addressed to G.S. ([email protected]).

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production levels around 11,000 p.p.m. (mg/g dry cell weight). The first strain contains the DgdhA DaceE DfdhF genotype, which is a purely stoichiometrically designed strain. The other maximum is DgdhA DaceE DPyjiD, which is created through the combination of stoichiometric and combinatorial targets. Second, several local maximum points are present with production levels ranging from 8,400 to 9,400 p.p.m., each formed from the combination of systematic and combinatorial targets. Third, the left quadrant of the graph indicates that the combination or stacking of more than one combinatorial knockout target greatly reduces lycopene levels to below 2,000 p.p.m., and as low as only 500 p.p.m. for some constructs, which is below the production level of a recombinant wild-type E. coli K12 strain. Finally, visual inspection of this landscape suggests a highly nonlinear function with many local optima. Clustering methods have been routinely applied to the analysis of microarray (and other) data to determine sets of genes that exhibit similar expression profiles14. Likewise, the technique of hierarchical clustering may be applied to the metabolic landscape of Figure 2 to cluster gene knockout constructs exhibiting similar production profiles over the four time points. Presumably, strains clustering most

because of the relatively high threshold of the lycopene accumulation level imposed in the selection of candidate strains. Using these three identified targets, it is possible to create a total of seven gene combinations of single, double and triple combinatorial target mutations (DrssB, DyjfP, DPyjiD, DrssB DyjfP, DrssB DPyjiD, DyjfP DPyjiD and DrssB DyjfP DPyjiD). These seven combinations along with the parental strain constitute the combinatorial strain set comprising a total of eight strains. The previous results point to two distinct sets of stoichiometric and combinatorial gene targets. It is not clear how these targets interact when combined. To answer this question, we conducted an exhaustive study of the 64 strains comprising all combinations of the eight stoichiometric and eight combinatorial genotypes. These target genes were modified in the background of the pre-engineered recombinant E. coli strain. The resulting production profiles over the course of a 48-hour shake-flask fermentation process provided the information needed for the complete mapping of the lycopene metabolic landscape (Fig. 2). Several interesting observations arise from the topology of this metabolic landscape. First, two global maxima exist, each with

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Figure 1 Systematic and combinatorial gene knockout target identification. (a) Lycopene synthesis begins with the condensation of the key glycolytic intermediates, glyceraldehyde 3-phosphate and pyruvate and continues in a nearly linear pathway. In the engineered strain used in this study, the idi, ispFD, and dxs genes are overexpressed by chromosomal promoter replacement. To produce lycopene, a cluster of genes, crtEBI are expressed on a plasmid. (b) Systematic targets (illustrated on the left) were identified through the use of global, stoichiometric modeling (as more comprehensive models are unavailable) to identify gene knockouts which were predicted in silico to increase lycopene by increasing either cofactor or precursor supply5. Combinatorial targets (illustrated on the right) were identified through the use of transposon mutagenesis. The gene rssB is a response regulator responsible for recruiting the proteolysis of the stationary phase sigma factor, sS (encoded by rpoS)21,22, which has previously been implicated in the overproduction of carotenoids23. The gene yjfP is a 249–amino acid protein which is currently not annotated, but has been putatively categorized as either a nonpeptidase homolog24 or as a putative hydrolase (1st module)25. Finally, yjiD is a 130–amino acid protein with an unknown function25. For only the yjiD mutants, the transposon site was only found between the promoter region and the gene. These targets were combined to create the unique set of 64 mutant strains used in this study.

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LETTERS Figure 2 Visualization of the metabolic landscape. Eight systematically derived knockout genotypes were combined with eight systematically derived genotypes to create a unique collection of 64 strains. The maximum lycopene production (in p.p.m.) during the course of a 48-h shake-flask fermentation is plotted. Among the interesting features of this landscape is the presence of two global maxima (at around 11,000 p.p.m.) and several local maxima. Furthermore, certain combinations of combinatorial targets in most systematically derived genetic backgrounds resulted in a substantial decrease in lycopene production.

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concentric bubble-plot suggesting an additive effect of accumulating gene deletions. This is in concert with the presumed mode of action in these strains, namely the increasing availability of precursors and cofactors that are needed for lycopene biosynthesis. In contrast to Figure 3a, all combinatorial targets, as exemplified by rssB, force a split-tree shape in the dendrogram when performed in the background of each of the seven stoichiometric targets (Fig. 3b). Different time courses in lycopene accumulation suggest different modes of action for the effect of the combinatorial genes on this phenotype. Specifically, whereas each construct formed from the deletion of a single combinatorial target gene tends to exhibit similar behavior (increased production), the deletion of combinations of these genes yields phenotypes that are neither linear nor synergistic. In fact, double and triple knockout constructs arising from these combinatorial targets exhibit vastly different production profiles from the individual targets (Fig. 2). This nonlinearity suggests that the combinatorial targets are disrupting regulatory processes that are relatively incompatible, and in certain cases deleterious, when combined. Biological differences are observed when combinatorial genes are deleted together with stoichiometric ones. Strains in cluster Y (Fig. 3b) all exhibit an extended lag phase, which extends to 16–18 h before reaching a typical cell density OD 3.5–4.0. In contrast, strains in cluster Z do not posses such a lag phase and exhibit a steady increase of lycopene production with time. The average, scaled

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closely accumulate product by following similar modes of action in the mechanism of lycopene production. Upon clustering the entire set of 64 strains, two distinct organizations emerge for the two sets of gene targets previously identified. Clustering lycopene profiles (across the four time points) for the eight stoichiometric knockout strains revealed a fairly close, stacked dendrogram (see abscissa of Fig. 3a). When these strains are plotted against the lycopene accumulation level, they reveal an expanding

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Figure 3 Clustering analysis depicting the interaction of systematic and combinatorial targets. Lycopene production profiles across the 48-h shake-flask fermentation are clustered, resulting in the dendrograms illustrated. (a) The purely systematic strains have a stacked dendrogram which is visually illustrated with a concentric bubble plot. Strains that are more tightly clustered have similar modes of action, thus all systematic strains seem to be additive in nature. This is further evidenced by the close clustering of DfdhF and the parental strain, as the fdhF single knockout was determined from the stoichiometric analysis to bring about no enhancement of lycopene production. (b) Conversely, the addition of any combinatorial genotype, rssB in this case, decouples the systematic design and causes a disjoint pattern in the dendrogram and bubble plot. This has the implication that local, metabolic gene targets are more accessible through a sequential search than global, regulatory targets, which require a simultaneous search that is sensitive to the genetic background of the strain. c compares the average, relative production profiles for the three clusters shown in a and b. The biological differences in the production profiles for each of these clusters are evident.

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Glucose feeding profile 5 Glucose (g/l)

production profiles for the purely systematic cluster and the two clusters forced by an rssB deletion are compared in Figure 3c. It is noted that this branched pattern is exhibited by all strains constructed from the deletion of any combinatorial gene in the background of the stoichiometric targets, with different production profiles characterizing each of the clusters. Drawing from this analysis, it appears that stacking (that is, deleting) combinatorial target genes upon stoichiometric ones leads to a decoupling of the stoichiometric logic. This decoupling is evident in analyzing the impact of the deletion of rssB or any other combinatorial gene, on the shape of the dendrogram obtained from hierarchical clustering of the lycopene accumulation profiles for the eight stoichiometric strains; it is also quantified by covariance analysis (Supplementary Fig. 1 and Supplementary Discussion). The exhaustive exploration of the combinations of stoichiometric and combinatorial targets allowed the identification of several interesting strains on the basis of their performance in batch shake-flask cultivations. To better assess the production capacity of these knockout strains, fed-batch cultivations were carried out in shake-flasks and controlled bioreactors with staged glucose feed (Fig. 4). Several strains were thus evaluated. Optimized shake-flask fermentations highlight the capability of the global maximum strains to produce upwards of 18,000 p.p.m. in 24–40 h (Fig. 4). These global maximum strains were also grown in 500-ml bioreactors with a similar glucose feeding profile and pH control and showed enhanced lycopene production producing upwards of 23,000 p.p.m. in only 60 h (data not shown). Further improvements are possible through iterative bioreactor optimization. The good correspondence between fermentor and shaker-flasks results suggests that the performance of the strains selected by the described method is transferable to larger systems. Identification of multiple gene targets affecting a particular phenotype is an open problem. Among the complications are strong nonlinear effects, lack of accurate models capable of capturing genetic interactions and ineffective search strategies. To address these issues in the context of lycopene production, we undertook an exhaustive experimental search to investigate combinations of rationally selected genes with those identified through combinatorial methods. A number of promising strains were obtained, some of which were capable of producing upwards of 18,000 p.p.m. (or 18 mg/g dry cell weight) of lycopene in defined glucose medium using simple fed-batch conditions. This value represents a nearly fourfold increase over the parental strain when cultured in simple cultivation, a twofold increase over the preengineered parental strain in similar conditions and an 8.5-fold increase over recombinant wild-type K12 E. coli under similar conditions. The metabolic landscape defined through this unique set of 64 knockout strains allows for several observations of importance to metabolic engineering. First, rationally selected stoichiometric gene knockout targets have the potential of generating serious contenders in the quest for maximally producing strains. We note that one of the two maximum overproducing strains resulted from the knockout of three stoichiometric genes (gdhA, aceE, fdhF). Additionally, the knockout of specific combinatorial genes yielded substantially enhanced phenotypes in the background of particular stoichiometric knockout genes. Second, whereas combinatorial gene targets hold greater potential than stoichiometric ones as single knockout mutants, multiple knockouts of the combinatorial gene set led to a distinct deterioration of the lycopene phenotype. Yet, it proved invaluable in the creation of some important strains in the landscape. Third, the presence of many local maxima complicates the nature of the landscape and raises questions about general sequential search strategies. Previously, sequential search strategies were found to be quite effective

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Figure 4 Behavior of selected strains in optimized culturing conditions. Selected strains from the metabolic landscape were cultured in fed-batch shake-flasks with increased M9 salts and a staged glucose feed as represented in the glucose feeding profile. Strains presented from left to right are K12 (recombinant wild-type); engineered parental strain with dxs, idi, and ispFD overexpressions; DgdhA DaceE DfdhF; DgdhA DaceE DPyjiD; DgdhA DaceE DfdhF DrssB DyjfP; DgdhA DaceE DfdhF DyjfP; DgdhA DaceE DrssB DyjfP DPyjiD. The two global maxima were capable of producing upwards of 18,000 p.p.m. in 24 to 40 h. Strain behavior was transferable to these optimized conditions. These results highlight that strains isolated on solid-media plates retained the lycopene overproduction phenotype in the course of the scale-up process.

when applied to the space of stoichiometric genes5, which is due to their overall additive effect on phenotype. Figure 2 suggests that this result does not hold when combinatorial genes are also included in the search space, necessitating exhaustive combinatorial searches of the type undertaken in this study. Although identification of optimal gene targets will continue to be a demanding undertaking, searches for gene targets will be significantly aided by advanced models of cell function accounting for kinetic and regulatory mechanisms. It should be noted that the search of this study was limited to the effect of gene knockout only. Gene knockdown or overexpression adds an extra layer of complexity in the metabolic engineering of overproducing strains and could provide further drastic improvements of product overproduction phenotypes. This study underscores some important issues optimizing phenotype. First, high-throughput screening methods combined with detailed cellular models will aid in efficient strain optimization. Second, combinatorial targets influencing global cellular function should be invoked at later stages in the strain improvement process to avoid selecting those with limited utility or incompatible modes of action. Finally, metabolic genes seem to have a linear impact in the overall cellular phenotype whereas the effect of regulatory targets is definitely nonlinear and more complex. This work serves as a case study aiming to understand the complex interaction of the genotypephenotype space in the context of product overproduction phenotype. The lessons gained from the exhaustive exploration of systematic and combinatorial gene knockout sets can help shape future strain improvement programs as they are tested in diverse systems for divergent products. METHODS Strains and media. E. coli K12 PT5-dxs, PT5-idi, PT5-ispFD, provided by DuPont, was used as the lycopene expression strain when harboring the pACLYC plasmid containing the crtEBI operon15. Overexpression of dxs, idi, and

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LETTERS ispFD was chromosomally incorporated without an antibiotic marker through promoter delivery. Strains were grown at 37 1C with 225 r.p.m. orbital shaking in M9-minimal medium16 containing 5 g/l D-glucose and 68 mg/ml chloramphenicol. All simple cultures were 50 ml, grown in a 250-ml flask with an 1% (vol/vol) inoculation from an overnight 5-ml culture and assayed at 15, 24, 39 and 48 h. Optimized shaker-flasks were 50-ml cultures grown in 250-ml flasks with a 1% (vol/vol) inoculation from an overnight 5-ml culture with glucose feeds of 5 g/l at 0 and 15 h and 3 g/l at 24 h. The medium for these experiments was M9-minimal medium16 with double concentrations of all salts except CaCl2 and MgSO4. All experiments were performed in biological knockout replicates to validate data and calculate statistical parameters. Glucose monitoring was conducted periodically using an r-Biopharm kit to verify complete usage of glucose. Cell density was monitored spectrophotometrically at 600 nm. All PCR products were purchased from Invitrogen and used Taq polymerase. M9 Minimal salts were purchased from US Biological and all remaining chemicals were from Sigma-Aldrich. Transposon library screening and sequencing. Transposon libraries were generated using the pJA1 vector17. Cells were transformed with between 800 and 1,600 ng of the plasmid, then diluted and plated on M9-glucose-agar plates (containing 1 mM isopropyl-b-D-thiogalactoside) with a target density of 200 colonies per 150
15 mm Petri dishes. Plates were incubated at 37 1C for 36 h, then allowed to sit at 22 1C. Cells identified as exhibiting increased lycopene content (more red) were isolated and cultured throughout the culturing process. The identity of promising targets were sequenced using an altered version of Thermal Asymmetric Interlaced PCR (TAIL-PCR)18. For the TAIL1 reaction, 1.5 ml of genomic DNA isolated using a DNA purification kit (Promega) was used as the initial template. The TAIL3 reaction was increased to 30 cycles. Kanamycin-specific primers: TAIL1, 5¢-TATCAGGACATAG CGTTGGCTACCCG-3¢; TAIL2, 5¢-CGGCGAATGGGCTGACCGCT-3¢; TAIL3, 5¢-TCGTGCTTTACGGTATCGCCGCTC-3¢. The degenerate primer AD1 was used as described in the reference. The product of the TAIL3 reaction was purified by a PCR cleanup kit (Qiagen) after gel visualization. This product was sequenced using the primer TAIL-seq, 5¢-CATCGCCTTCTATCGCCTTCTT-3¢. Gene target identity was determined through BLAST nucleotide sequence comparison. Strains identified through transposon mutagenesis were subsequently constructed by using PCR product recombination and tested for maintenance of the lycopene overproduction phenotype. Knockout construction and verification. Gene deletions were conducted using PCR product recombination19 using the pKD46 plasmid expressing the lambda red recombination system and pKD13 as the template for PCR (see Supplementary Table 1 online for primer designs). Gene knockouts were verified through colony PCR. Phage transduction was used for creating multiple gene knockout strains. P1vir phage transduction was used to transfer knockout mutants between strains20. PCR primers used for knockout and verification may be found in Supplementary Table 1 online. Lycopene assay. Intracellular lycopene content was extracted from 1 ml of bacterial culture at the point of total glucose exhaustion. The cell pellet was washed, and then extracted in 1 ml of acetone at 55 1C for 15 min with intermittent vortexing. The lycopene content in the supernatant was quantified through absorbance at 475 nm12 and concentrations were calculated through a standard curve. The entire extraction process was performed in reduced light conditions to prevent photobleaching and degradation. Cell mass was calculated by correlating dry cell with OD600 for use in p.p.m. (mg lycopene/g dry cell weight) calculations. Hierarchical clustering routines. A complete linkage hierarchical clustering of the lycopene time profiles for the entire 8
8 strain matrix (containing values of the maximum lycopene production) using the Euclidean distance as the similarity metric was performed using Cluster Version 3.0. Dendrograms were visualized using Java TreeView Version 1.0.8. Note: Supplementary information is available on the Nature Biotechnology website.

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ACKNOWLEDGMENTS We acknowledge financial support of this work by the DuPont-MIT Alliance. In particular, we would like to thank Wonchul Suh for providing the parental E. coli strain. We also thank Joel Moxley for providing thoughtful suggestions and Veronica Godoy for providing the initial phage stock. COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Received 10 February; accepted 2 March 2005 Published online at http://www.nature.com/naturebiotechnology/ 1. Stephanopoulos, G., Aristidou, A. & Nielsen, J. Metabolic Engineering: Principles and Methodologies (Academic Press, San Diego, CA, 1998). 2. Ostergaard, S., Olsson, L., Johnston, M. & Nielsen, J. Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network. Nat. Biotechnol. 18, 1283–1286 (2000). 3. Stafford, D.E. et al. Optimizing bioconversion pathways through systems analysis and metabolic engineering. Proc. Natl. Acad. Sci. USA 99, 1801–1806 (2002). 4. Koffas, M.A., Jung, G.Y. & Stephanopoulos, G. Engineering metabolism and product formation in Corynebacterium glutamicum by coordinated gene overexpression. Metab. Eng. 5, 32–41 (2003). 5. Alper, H., Jin, Y.-S., Moxley, J. & Stephanopoulos, G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. (in the press) doi:10.1016/j.ymben.2004.12.003 (2005). 6. Adam, P. et al. Biosynthesis of terpenes: studies on 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase. Proc. Natl. Acad. Sci. USA 99, 12108–12113 (2002). 7. Matthews, P.D. & Wurtzel, E.T. Metabolic engineering of carotenoid accumulation in Escherichia coli by modulation of the isoprenoid precursor pool with expression of deoxyxylulose phosphate synthase. Appl. Microbiol. Biotechnol. 53, 396–400 (2000). 8. Misawa, N. & Shimada, H. Metabolic engineering for the production of carotenoids in non-carotenogenic bacteria and yeasts. J. Biotechnol. 59, 169–181 (1997). 9. Farmer, W.R. & Liao, J.C. Improving lycopene production in Escherichia coli by engineering metabolic control. Nat. Biotechnol. 18, 533–537 (2000). 10. Smolke, C.D., Martin, V.J.J. & Keasling, J.D. Controlling the metabolic flux through the carotenoid pathway using directed mRNA processing and stabilization. Metab. Eng. 3, 313–321 (2001). 11. Lee, P.C. & Schmidt-Dannert, C. Metabolic engineering towards biotechnological production of carotenoids in microorganisms. Appl. Microbiol. Biotechnol. 60, 1–11 (2002). 12. Kim, S.-W. & Keasling, J.D. Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production. Biotechnol. Bioeng. 72, 408–415 (2001). 13. Jones, K.L., Kim, S.-W. & Keasling, J.D. Low-copy plasmids can perform as well as or better than high-copy plasmids for metabolic engineering of bacteria. Metab. Eng. 2, 328–338 (2000). 14. Eisen, M., Spellman, P., Brown, P. & Botstein, D. Cluster analysis and display of genomewide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998). 15. Cunningham, F.X., Jr., Sun, Z., Chamovitz, D., Hirschberg, J. & Gantt, E. Molecular structure and enzymatic function of lycopene cyclase from the cyanobacterium Synechococcus sp strain PCC7942. Plant Cell 6, 1107–1121 (1994). 16. Maniatis, T., Fritsch, E.F. & Sambrook, J. Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 1982). 17. Badarinarayana, V. et al. Selection analyses of insertional mutants using subgenicresolution arrays. Nat. Biotechnol. 19, 1060–1065 (2001). 18. Liu, Y.-G. & Whittier, R.F. Thermal asymmetric interlaced pcr: automatable amplification and sequencing of insert end fragments from pi and yac clones for chromosome walking. Genomics 25, 674–681 (1995). 19. Datsenko, K.A. & Wanner, B.L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. USA 97, 6640– 6645 (2000). 20. Miller, J.H. A Short Course in Bacterial Genetics (Cold Springs Harbor Laboratory Press, Cold Springs Harbor, NY, 1992). 21. Muffler, A., Fischer, D., Altuvia, S., Storz, G. & Hengge-Aronis, R. The response regulator RssB controls stability of the sigma(S) subunit of RNA polymerase in Escherichia coli. EMBO J. 15, 1333–1339 (1996). 22. Sandmann, G., Woods, W. & Tuveson, R.W. Identification of carotenoids in Erwinia herbicola and in a transformed Escherichia coli strain. FEMS Microbiol. Lett. 59, 77–82 (1990). 23. Becker-Hapak, M., Troxtel, E., Hoerter, J. & Eisenstark, A. RpoS dependent overexpression of carotenoids from Erwinia herbicola in OXYR-deficient Escherichia coli. Biochem. Biophys. Res. Commun. 239, 305–309 (1997). 24. Rawlings, N., Tolle, D. & Barrett, A. MEROPS: the peptidase database. Nucleic Acids Res. 32, D160–D164 (2004). 25. Serres, M.H. et al. A functional update of the Escherichia coli K-12 genome. Genome Biol. 2, published online 20 August 2001 (doi:10.1186/gb-2001-2-9research0035).

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Quantitative mouse brain proteomics using culture-derived isotope tags as internal standards Yasushi Ishihama1,2, Toshitaka Sato1,2, Tsuyoshi Tabata1, Norimasa Miyamoto1, Koji Sagane1, Takeshi Nagasu1 & Yoshiya Oda1 An important challenge for proteomics is to be able to compare absolute protein levels across biological samples1,2. Here we introduce an approach based on the use of culturederived isotope tags (CDITs) for quantitative tissue proteome analysis. We cultured Neuro2A cells in a stable isotopeenriched medium and mixed them with mouse brain samples to serve as internal standards. Using CDITs, we identified and quantified a total of 1,000 proteins, 97–98% of which were expressed in both mouse whole brain and Neuro2A cells. CDITs also allow comprehensive and absolute protein quantification. Synthetic unlabeled peptides were used to quantify the corresponding proteins labeled with stable isotopes in Neuro2A cells, and the results were used to obtain the absolute amounts of 103 proteins in mouse whole brain. The expression levels correlated well with those in Neuro2A cells. Thus, the use of CDITs allows both relative and absolute quantitative proteome studies.

designating it as the reference, and subsequently labeling all other samples with a heavy isotope. Quantitative proteomic strategies have proven particularly advantageous for the discrimination of target proteins from contaminants copurified nonspecifically3–5. In vivo labeling strategies for MS-based proteomics entail growing cells in a medium in which an essential nutrient is labeled with a stable isotope. Quantitative data are obtained after analysis of amounts of digested peptide6 or proteins7. The advantages of this approach are simplified sample manipulation and comprehensive labeling. Recently, an in vivo labeling method for mammals by long-term administration of a diet enriched in a stable isotope was reported8. Although this is an

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Figure 1 Strategy of quantitative mouse brain proteomics using CDITs. (a) Quantitative tissue proteome analysis using stable isotope-labeled cultured cells as global internal standards. Tissue samples 1 and 2 are mixed with cultured cells early in the process to obviate the variations during sample preparation. After protein extraction and separation, digested proteins are analyzed by mass spectrometry to identify and quantify proteins. The ratio between the two isotopic distributions (one from a tissue sample and one from cultured cells labeled with isotopes) can then be determined from the mass spectra. Changes of protein level in two tissue samples are estimated by calculating the ratio of the two ratios, ratio 1/ratio 2, a procedure which cancels out the internal standards (cultured cells). (b) Method for semiquantifying a protein found in a tissue sample, but not in the cells cultured with stable isotopes. The ratio of a target peptide, which does not have a corresponding labeled peak in cultured cells, is obtained by using the peak ratio against an isotope-labeled, cultured-cell-derived peptide of different sequence, but with the closest (ideally the same) retention time in LC/MS.

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1Laboratory of Seeds Finding Technology, Eisai Co., Ltd., 5-1-3 Tokodai, Tsukuba, Ibaraki 300-2635, Japan. 2These authors contributed equally to this work. Correspondence should be addressed to Y.O. ([email protected]).

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Figure 2 Validation data of CDIT strategy. (a) Number of identified and quantified proteins from wild-type whole mouse brain and Neuro2A cells. After combining brain extracts with Neuro2A cell lysates, proteins were digested with trypsin, and then peptides were analyzed by LC/MS. Identification was performed based on leucine-containing peptides, because leucine residues were labeled with stable isotopes and the peak ratios of those peptides were measured for quantification. When there were no corresponding peaks from labeled Neuro2A cells, we judged that those proteins were expressed in brains, but not in Neuro2A cells. (b) The measured average ratio of 44 different proteins in unlabeled mouse brain to those in labeled Neuro2A cells versus the expected ratio showing the linearity and precision of the method. Neuro2A cell lysates were combined with three different amounts of whole brain extracts in the ratios were 1:5, 1:1 and 5:1. The detailed data are in part 2 of Supplementary Table 3 online. (c) Summary of the number of proteins identified and quantified by the CDIT method and cleavable ICAT method. Proteins were extracted from wild-type whole mouse brains. Starting protein amounts were 300 mg for CDIT, 300 mg for light ICAT labeling and 300 mg for heavy ICAT labeling. For the CDIT approach, the same amount of total proteins extracted from labeled Neuro2A cells were added as internal standards. The detailed data are in Supplementary Table 4 online.

interesting approach, it takes a long time (44 d) to obtain the labeled rat, and some tissues, such as brain, are not completely labeled with stable isotopes. We have developed an alternative quantitative approach for studying the proteome of mammalian tissues based on the application of the stable isotope labeling by amino acids in cell culture (SILAC)9–11 approach to the generation of an internal standard. We applied CDITs to quantify the mouse brain proteome by using mouse neuroblastoma Neuro2A cells cultured in 13C-labeled leucine-rich medium as an internal standard (Fig. 1). To validate our methodology, we used an affinity matrix-immobilized E7070-like compound, which is known to enrich cytosolic malate dehydrogenase (cMDH)3, to determine levels of cMDH. We diluted 0.1 ml of the soluble fraction of a mixture of wild-type brain with an appropriate amount of labeled Neuro2A cells in 0.9 ml of PBS, and the solution was loaded onto an E7070-immobilized column. The column was washed with 1 M sodium chloride in PBS, and cMDH was eluted with 10 mM NADH in PBS. The eluted fraction was concentrated by ultrafiltration, and then cMDH was separated on SDS-PAGE. The cMDH band was excised and in-gel-digested with trypsin. We also prepared ADAM22 (ref. 12) knockout mouse brain samples mixed with Neuro2A cells, and purified cMDH from them. We repeated the affinity purification procedures five times (total of ten samples, five wild-type and five ADAM22 knockout samples). Tryptic peaks of cMDH were measured with matrix-assisted laser desorption ionization (MALDI)-MS, and peaks due to leucine-containing peptides from brain cMDH were compared with those from Neuro2A cells. Although affinity purification steps are usually variable, precision was extremely good (the coefficient of variation was 4.11%; see Supplementary Table 1 online). Next, the amount of brain tissue was changed to confirm the linearity of the method. Peak intensity ratios of cMDH were found to be linearly correlated to relative amount (R ¼ 0.994) in the range from 1:20 to 5:1 (brain cMDH versus Neuro2A cMDH, see Supplementary Table 2 online). To confirm the suitability of Neuro2A cells as internal standard cells for the mouse brain proteome, we identified many proteins extracted from Neuro2A cells and mouse whole brain. A total of 957 different proteins identified by leucine-containing peptides were expressed in mouse whole brain. Among them, 14 proteins in the brain were not found in Neuro2A cells. There was 98% or more overlap among the 1,000 proteins in mouse whole brain and Neuro2A cells (Fig. 2a).

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For a larger scale validation experiment, 44 different proteins in wild-type mouse brains were carefully quantified by manually measuring peak ratios with the corresponding proteins in Neuro2A cells. The observed quantitative value was very close to the expected value (see Fig. 2b and part 1 of Supplementary Table 3 online). Since the CDIT approach allows the calculation of a ratio of ratios to obtain relative quantitative values, variation in the assessment of MS spectra is minimized by cancellation of systematic errors of internal standard intensities13. Since the dynamic range of MS detector is generally quite narrow, two or three different amounts of Neuro2A should be added into brain samples to increase the number of quantified proteins (see part 2 of Supplementary Table 3 online). When the extracts from Neuro2A were equivalently added to brain extracts, two-thirds of identified proteins in wild-type mouse whole brains had an appropriate expression level (0.1 o peak ratio o 10) in Neuro2A cells. Although in vivo labeling has advantages, chemical tagging strategies like isotope-coded affinity tags (ICATs)14 allow for quantitative tissue proteomics. Therefore we compared the performance of cleavable ICATs with the CDIT approach by using the same amount of mouse brain extracts. The major innovation of the ICAT approach was to purify cysteine-containing peptides by using an affinity tag for reducing the complexity of a peptide mixture by about a factor of 10. But our results indicate that the CDIT approach allows the efficient identification and quantification of more proteins (602 proteins, the average ratio was 0.99 7 0.41) than the ICAT method (339 proteins, the average ratio was 1.26 7 0.33) (Fig. 2c and Supplementary Table 4 online), suggesting the applicability of the method in the context of real world complex samples. Considering the inefficiency of ICAT, there might be two reasons for the difference. First, time-consuming and variable steps are required to attach chemical tags and remove excess reagents, which can lead to sample losses2,15; second, the presence of chemical tags like ICAT makes tandem MS (MS/MS) spectra complicated and thus peak identification becomes difficult3,16. Although an isotope-labeled peptide with the same sequence as the target was found for almost every sequence in this analysis, it is likely that some proteins found in brain, especially less abundant proteins, are not expressed in Neuro2A cells. In this situation, the peak ratio based on the corresponding isotope-labeled peak from Neuro2A cells cannot be calculated, so we selected an isotope-labeled peptide with a different sequence from Neuro2A cells as the internal standard for calculation of the peak ratio (Fig. 1b). We calculated peak ratios for

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Figure 3 Total scheme for absolute quantification using CDITs. (a) Absolute quantification using amplified isotope double dilution. An unlabeled synthetic peptide is used as an internal standard for a target protein expressed in CDIT cells. (b) Expression of 103 proteins in mouse cultured cell (Neuro2A) and brain (wild type). The plot is based on data listed in Supplementary Table 8 online.

155 different-sequence pairs and same-sequence pairs; the difference between them was 1.29 7 19.3% (see Supplementary Table 5-1 online). This result showed that quantitative precision became worse when different-sequence peptides were selected as internal standards instead of same-sequence, isotope-labeled peptides. Then, the target peptide in a brain was normalized to the average ratio calculated by four different sequences selected from Neuro2A at a similar elution time (see part 2 of Supplementary Table 5 online). The difference was only 1.02 7 28.9% (n ¼ 35); therefore normalization for ‘semiquantification’ was possible by using different-sequence peptides. Indeed, in other areas such as pharmacokinetic studies there are many examples of the successful use of internal standards with a different structure from that of the target molecules17–21. We next explored whether the CDIT method is an efficient tool for examining protein expression levels in animal tissues. The systematic treatment of animals with kainate induces generalized tonic-clonic seizures, which are due to necrosis and apoptosis of brain cells22,23. Isolated hippocampus treated with kainate was compared with nontreated counterparts. After adding Neuro2A cells, the hippocampus was fractionated into five parts. In total, 598 proteins were identified and quantified (see Supplementary Table 6 online). As in the case of whole brain, 497% of the proteins found in mouse hippocampus were expressed in Neuro2A cells. Although the mechanisms underlying kainate neurotoxicity are still not well understood, the expression levels of 21 different proteins were changed more than twofold (see Supplementary Table 7 online). But these relatively abundant proteins and metabolic enzymes could well be linked indirectly through general toxicity with kainate treatment. Absolute concentrations of proteins in samples are also important in addition to the relative concentrations between two samples. Conventionally, antibodies, enzymatic assays and staining dyes have been used to measure absolute protein amounts. Another technique for absolute quantification is to use MS after spiking known amounts of isotopically labeled analytes, so-called isotope dilution24 or AQUA25. One of the proteolytic peptides of a particular target protein is synthesized using isotope-labeled reagents and then the absolute

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amount is measured. However, this method is difficult to apply to quantitative analysis of large numbers of protein, because peptide synthesis usually requires a tenfold excess of reagents (expensive isotopically labeled reagents in this case), and also because the scale of conventional peptide synthesis is microgram to milligram, whereas MS requires only femtogram to picogram amounts of peptides. With CDITs, absolute amounts of target proteins in brain can be calculated, if absolute quantification of target proteins in Neuro2A cells is conducted in advance (Fig. 3a). Since all the proteins in the Neuro2A cells are already labeled with stable isotopes, conventional unlabeled synthetic peptides can be used to identify the absolute amounts of target proteins. It has been found that the AQUA approach does not work for in-gel-digested proteins because of low recovery in the digestion or extraction step26. In our strategy, quantified synthetic unlabeled peptides and the labeled cultured cells are used in the first step, in which cells are lysed and proteins are extracted by ultrasonication without any purification step. Then, tryptic digestion is done in the solution to maximize recovery. Because the contents of tryptic peptides from the same protein should be equivalent, the content of the tryptic peptide, which is quantified by matching to the unlabeled peptide, is the same as that of other tryptic peptides from the protein. Therefore, we used all labeled peptides from the cultured cells for quantification of the unlabeled tissue sample spiked with isotopelabeled cultured cells at the second step (Fig. 3a). In addition, we can use purification steps in addition to the in-gel digestion without affecting the quantification because the internal standards are not peptides, but proteins, unlike in the AQUA method. This approach was applied to mouse whole brain in conjunction with CDIT-labeled cells. We successfully quantified 103 proteins in mouse brain, and their expression levels correlated well with those of Neuro2A cells (Fig. 3b and Supplementary Table 8 online). The absolute amounts of proteins in a particular organelle are not calculated from the amounts of proteins in the whole lysate of the cultured cells, but are obtained by measuring the expressed amounts of proteins in the target organelle in advance. Because the cellular fractionation is not highly reproducible, the resultant amounts of proteins in the sample organelle are

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LETTERS influenced by the variations of two experiments, that is, fractionation for the cultured cells and for the samples spiked with cultured cells. Nevertheless, this approach is very attractive compared with conventional isotope dilution methods as a comprehensive approach due to its much lower cost. Moreover, because the amounts of target proteins in the cultured cells are calculated, the absolute amounts of corresponding proteins in the samples can be compared at the protein level, instead of at the peptide level, which should give better reliability. In conclusion, the CDIT-based method is a simple, convenient and cost-effective approach for relative and absolute quantification of a tissue proteome. The American Type Culture Collection (http:// www.atcc.org/) provides more than 1,000 mammalian cell lines by tissue source, and some of them, which can grow in stable isotope rich medium, are probably useful as internal standards for their tissues of origin, and proteins that do not have a ‘shared peptide’ can be semiquantified by using a different-sequence peptide from labeled cell lines. Finally, it is easy to obtain large numbers of cells (41010 cells) from animal tissues, but there is a limit to the scale of cell culture in general laboratories. Although the large-scale preparation of CDITlabeled cells could be a hurdle, many laboratories carrying out proteomic studies routinely handle 107–109 cells, which is the scale required for this method. The CDIT approach thus represents a robust alternative for tissue proteomics with the potential to provide relative and absolute quantitative data on proteins. METHODS Materials and reagents. Neuro2A cells were grown to a density of 7  107 cells/ 15-cm diameter dish in RPMI-1640 medium (Sigma) deficient in L-leucine, and U-13C 6 labeled L-leucine (Cambridge Isotope Laboratories) was added to the culture. TPCK-treated, sequencing-grade modified trypsin was obtained from Promega. 5-cyclohexyl-1-pentyl-beta-D-maltoside (CYMAL-5) was obtained from Anatrace. Negative gel stain MS kit was obtained from Wako. Gels with a thickness of 1.0 mm (Tris-HCl, 5–20% acrylamide gradient gel) were obtained from DRC. All other reagents were of analytical grade. Mouse brain sample preparation. All mice were treated ethically according to the rules of the Eisai Co., Ltd. Animal Use and Care Committee. Seventeen male C57BL/6 mice were used at 8–16 weeks old. Kainate in saline buffer was administered intraperitoneally (i.p.) at a dose of 30 mg/kg. The same volume of the solution in PBS was administered i.p. to control mice. These mice were killed 2–3 h after the injection by rapid decapitation. The brain was removed, the hippocampus was isolated on ice, and the protein amounts were measured by means of micro bicinchoninic acid (BCA) assay (Pierce). Each experimental hippocampus was combined with 4  109 labeled Neuro2A cells and the mixture was suspended in 0.32 M sucrose solution containing 1 mM sodium hydrogen carbonate and protease inhibitor cocktail (Roche Diagnostics). The suspension was homogenized in a Teflon Potter-type homogenizer and centrifuged at 710g for 10 min to remove the nuclear fraction. The supernatant was centrifuged at 13,800g for 10 min to separate the soluble fraction and insoluble materials. The pellets were resuspended in 0.32 M sucrose solution, and the suspension was layered on 1.2 M sucrose solution then centrifuged at 82,500g for 2 h to separate cytosol, trafficking and secretion-related organelles, and mitochondrial fractions. For whole brain analysis, frozen mouse brain with added Neuro2A cells was homogenized with a protease inhibitor cocktail, sonicated and then centrifuged at 100,000g for 1 h at 4 1C. The supernatant and the pellet were collected as the soluble fraction and membrane-nuclear fraction, respectively. Proteins from the pellet fraction were extracted with 8 M urea containing 1% CHAPS and 1 M sodium chloride. Protein solutions were concentrated with an Ultrafree-MC centrifugal filter (10,000 Da nominal molecular weight limit (NMWL)) (Millipore) to 100 ml. After SDS-PAGE, each lane was cut into twelve equal pieces, and in-gel digestion was carried out27. For cleavable ICAT analysis, ICAT kits were used according to the protocol recommended by Applied Biosystems.

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Mass spectrometric analysis. The dried samples were desalted with C18 Stagetips28 and then redissolved in 20 ml of acetonitrile/water/TFA, 5:95:0.1, for liquid chromatography (LC)/MS analysis using a ‘stone-arch’ column29. The eluent was directed to an ESI ion trap mass spectrometer (ThermoFinnigan Model LCQ) with a lab-made nano-spray ion source at a flow rate of 1 ml/min after flow splitting, or to an ESI QqTOF mass spectrometer (Applied Biosystems Model QSTAR pulsar i) with a lab-made nano-spray ion source at a flow rate of 200 nl/min after flow splitting. A linear gradient of B from 5–30% was run, using mobile phase A of 0.5% acetic acid and mobile phase B of 0.5% acetic acid/acetonitrile, 20:80. Data processing. MS/MS data were analyzed by MASCOT (Matrix Sciences) and Sonar MS/MS (ProteoMetrics). After protein identification by MS, leucinecontaining peptides were extracted from the search results. Their m/z and scan number information were used to extract mass chromatograms and in-house software determined the peak areas of peptides and manual confirmation was done to correct peak areas. Each peak was quantified relative to its corresponding isotope-labeled peak from Neuro2A cells, which were used as comprehensive internal standards to normalize the variations of sample preparation and analysis. Finally the amount of each peak was compared in different tissue samples relative to Neuro2A cells. Procedures for absolute quantification. Proteins from Neuro2A cells labeled with 13C6-Leu were dissolved in Tris buffer (pH 9) and 8 M urea, then reduced, alkylated and digested with Lys-C (Wako), followed by dilution with 50 mM ammonium bicarbonate buffer (pH 9.0) and digestion with trypsin. Candidates for peptide synthesis containing at least one leucine and one tyrosine, but not methionine or cysteine, were selected considering the sequences of tryptic peptides from proteins expressed in Neuro2A cells. One hundred and twentytwo peptides were synthesized using a Shimadzu PSSM-8 with F-moc chemistry and were purified by preparative high-performance liquid chromatography (HPLC). Amino acid analysis, peptide mass measurement and HPLCUV were carried out for purity and structural confirmation. Known amounts of these peptides were spiked into the peptide mixtures from Neuro2A cells and LC/MS analyses were carried out to obtain the ratio of labeled peptides to the unlabeled peptides. The spiked amounts were adjusted to obtain a ratio in the range of 0.1–10. The absolute amounts of mouse brain proteins were calculated from the peak ratio between Neuro2A labeled peptides and unlabeled brain peptides in the mass spectra. Note: Supplementary information is available on the Nature Biotechnology website.

ACKNOWLEDGMENTS This work was supported by funds from New Energy and Industrial Technology Development Organization, Japan (NEDO). COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Received 1 December 2004; accepted 3 March 2005 Published online at http://www.nature.com/naturebiotechnology/

1. Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003). 2. Sechi, S. & Oda, Y. Quantitative proteomics using mass spectrometry. Curr. Opin. Chem. Biol. 7, 70–77 (2003). 3. Oda, Y. et al. Quantitative chemical proteomics for identifying candidate drug targets. Anal. Chem. 75, 2159–2165 (2003). 4. Ranish, J.A. et al. The study of macromolecular complexes by quantitative proteomics. Nat. Genet. 33, 349–355 (2003). 5. Blagoev, B. et al. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nat. Biotechnol. 21, 315–318 (2003). 6. Oda, Y., Huang, K., Cross, F.R., Cowburn, D. & Chait, B.T. Accurate quantitation of protein expression and site-specific phosphorylation. Proc. Natl. Acad. Sci. USA 96, 6591–6596 (1999). 7. Pasa-Tolic, L. et al. High-thoughput proteome-wide precision measurements of protein expression using mass spectrometry. J. Am. Chem. Soc. 121, 7949–7950 (1999). 8. Wu, C.C., MacCoss, M.J., Howell, K.E., Matthews, D.E. & Yates, J.R. 3rd. Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal. Chem. 76, 4951–4959 (2004). 9. Jiang, H. & English, A.M. Quantitative analysis of the yeast proteome by incorporation of isotopically labeled leucine. J. Proteome Res. 1, 345–350 (2002).

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LETTERS 10. Ong, S.E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002). 11. Zhu, H., Pan, S., Gu, S., Bradbury, E.M. & Chen, X. Amino acid residue specific stable isotope labeling for quantitative proteomics. Rapid Mass Commun. Mass Spectrom. 16, 2115–2123 (2002). 12. Sagane, K., Yamazaki, K., Mizui, Y. & Tanaka, I. Cloning and chromosomal mapping of mouse ADAM11, ADAM22 and ADAM23. Gene 236, 79–86 (1999). 13. MacCoss, M.J., Wu, C.C., Liu, H., Sadygov, R. & Yates, J.R. 3rd. A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal. Chem. 75, 6912–6921 (2003). 14. Gygi, S.P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999 (1999). 15. Sakai, J., Kojima, S., Yanagi, K. & Kanaoka, M. (18)O-labeling quantitative proteomics using an ion trap mass spectrometer. Proteomics 5, 16–23 (2005). 16. Parker, K.C. et al. Depth of proteome issues: a yeast isotope-coded affinity tag reagent study. Mol. Cell. Proteomics 3, 625–659 (2004). 17. Mano, N., Oda, Y., Yamada, K., Asakawa, N. & Katayama, K. Simultaneous quantitative determination method for sphingolipid metabolites by liquid chromatography/ionspray ionization tandem mass spectrometry. Anal. Biochem. 244, 291–300 (1997). 18. Lensmeyer, G.L. & Poquette, M.A. Therapeutic monitoring of tacrolimus concentrations in blood: semi-automated extraction and liquid chromatography-electrospray ionization mass spectrometry. Ther. Drug. Monit. 23, 239–249 (2001). 19. Gunawan, S., Griswold, M.P. & Kahn, D.G. Liquid chromatographic-tandem mass spectrometric determination of amprenavir (agenerase) in serum/plasma of human immunodeficiency virus type-1 infected patients receiving combination antiretroviral therapy. J. Chromatogr. A. 914, 1–4 (2001).

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20. Cass, R.T., Villa, J.S., Karr, D.E. & Schmidt, D.E. Jr. Rapid bioanalysis of vancomycin in serum and urine by high-performance liquid chromatography tandem mass spectrometry using on-line sample extraction and parallel analytical columns. Rapid Commun. Mass Spectrom. 15, 406–412 (2001). 21. Wilkinson, A.P., Wahala, K. & Williamson, G. Identification and quantification of polyphenol phytoestrogens in foods and human biological fluids. J. Chromatogr. B 777, 93–109 (2002). 22. Collingridge, G.L. & Isaac, J.T. Functional roles of protein interactions with AMPA and kainate receptors. Neurosci. Res. 47, 3–15 (2003). 23. Lerma, J. Roles and rules of kainate receptors in synaptic transmission. Nat. Rev. Neurosci. 4, 481–495 (2003). 24. Barr, J.R. et al. Isotope dilution—mass spectrometric quantification of specific proteins: model application with apolipoprotein A-I. Clin. Chem. 42, 1676–1682 (1996). 25. Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W. & Gygi, S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 6940–6945 (2003). 26. Havlis, J. & Shevchenko, A. Absolute quantification of proteins in solutions and in polyacrylamide gels by mass spectrometry. Anal. Chem. 76, 3029–3036 (2004). 27. Katayama, H. et al. Efficient in-gel digestion procedure using 5-cyclohexyl-1-pentylbeta-D-maltoside as an additive for gel-based membrane proteomics. Rapid Commun. Mass Spectrom. 18, 2388–2394 (2004). 28. Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003). 29. Ishihama, Y., Rappsilber, J., Andersen, J.S. & Mann, M. Microcolumns with selfassembled particle frits for proteomics. J. Chromatogr. A 979, 233–239 (2002).

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LETTERS

Enzyme family–specific and activity-based screening of chemical libraries using enzyme microarrays Daniel P Funeriu1, Jo¨rg Eppinger2, Lucile Denizot1, Masato Miyake1 & Jun Miyake1 The potential of protein microarrays1 in high-throughput screening (HTS) still remains largely unfulfilled, essentially because of the difficulty of extracting meaningful, quantitative data from such experiments2,3. In the particular case of enzyme microarrays3, low-molecular-weight fluorescent affinity labels4–10 (FALs) can function as ideally suited activity probes of the microarrayed enzymes. FALs form covalent bonds with enzymes in an activity-dependent manner and therefore can be used to characterize enzyme activity at each enzyme’s address, as predetermined by the microarraying process11. Relying on this principle3, we introduce herein thematic enzyme microarrays (TEMA). In a kinetic setup we used TEMAs to determine the full set of kinetic constants and the reaction mechanism between the microarrayed enzymes (the theme of the microarray) and a family-wide FAL. Based on this kinetic understanding, in an HTS setup we established the practical and theoretical methodology for quantitative, multiplexed determination of the inhibition profile of compounds from a chemical library against each microarrayed enzyme. Finally, in a validation setup, Kiapp values and inhibitor profiles were confirmed and refined. Protein microarray technologies typically focus on information content. The higher the multiplexing power, the more relevant and valuable the information extracted but also the more challenging the task, since each protein’s behavior on the surface is difficult to probe, control and predict, which results in a need for multiple rounds of validation. Designed for use in the HTS activity-based discovery and selectivity profiling of enzyme inhibitors in one-inhibitor-versus-nenzymes format, TEMAs (Fig. 1) provide an enzyme microarray platform in which the multiplex power is purposely reduced to the components of a single enzyme family. Indeed, due to the enzymes’ different environmental requirements for activity, microarrays embedding large numbers of different and diverse enzymes may well be neither relevant nor realistic for highly parallel activitybased investigations. We set out to fully validate and demonstrate the capabilities of TEMA technology on the cathepsin cysteine-protease family. The differential involvement in disease (such as osteoporosis, arthritis, tumor invasiveness and parasital infections) of members of the

cathepsin family12–14 continues to generate important efforts for the identification of strong, specific inhibitors15–17. Therefore, the availability of a cathepsin microarray-based system for inhibitor discovery and profiling is of direct practical interest. Moreover, existing FALbased studies8 of this family provide a suitable starting point for technology validation. Several characteristics of the cathepsin family (such as fragility and pH instability) are also important for technology validation, allowing the challenge of TEMAs in an applicationrelevant, nontrivial context. Cathepsins C, H, L, S, K and B (from three different sources) were microarrayed in duplicate within 48 identical subarrays of a hydrogel-aldehyde functionalized glass slide, according to the pattern described in Figure 2. The prepared microarrays were used in the three experimental setups below, together with an epoxide containing FAL8 (Fig. 2d; Supplementary Methods online for synthesis). In the first experimental setup, the kinetic setup, after blocking and buffer preincubation of each subarray, FAL solutions at four different concentrations were added concomitantly to four subarrays, at distinct time intervals (Fig. 1a), and the entire microarray was vigorously washed 30 s after FAL was last dispensed, such as to prevent any further reaction of noncovalently bound FAL. The fluorescence of each address was measured (Fig. 2a,b) and the data for each enzyme analyzed according to a previously described algorithm3. The progress curves obtained (Fig. 2e for cathepsin L, Supplementary Figs. 1–8 online for all enzymes) were fitted to a series of possible theoretical models. The best fit revealed that for all enzymes, the reaction between the microarrayed enzymes and the FAL is a combination of a Michaelis-Menten–derived mechanism and a nonenzymatic background reaction3,18 (Fig. 2c). The dependence of initial velocities vini on the concentration of the FAL, (Fig. 2f) in all cases adheres to what is expected for an enzyme-catalyzed reaction. The characteristic kinetic constants obtained are reported in Supplementary Table 1 online (see Supplementary Methods online for calculation procedures). The extracted KMapp values allow the cathepsins to be sorted by their efficiency in converting the FAL in the following order (KMapp (mM)): H (16), K (15), B (6.5), S (5.4), L (5.3), C (1.2). To test the method’s robustness, the experiment was repeated three times with the same batch of enzymes at more than 1-week intervals. The derived kinetic constants of the individual experiments were found to differ by less than the experimental uncertainty.

1Research

Institute for Cell Engineering, National Institute of Advanced Industrial Science and Technology, 3-11-46 Nakouji, Amagasaki, Hyogo, 661-0974, Japan. Molekulare Katalyse, Lehrstuhl fu¨r Anorganische Chemie, Technische Universita¨t Mu¨nchen, Lichtenbergstr. 4, 85748 Garching, Germany. Correspondence should be addressed to D.P.F. ([email protected]) or J.E. ([email protected]).

2ForschungsDozentur

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LETTERS Several conclusions can be drawn from the kinetic analysis of the progress curves. Most importantly, they confirm the enzymatic nature of the reaction responsible for the fluorescent labeling of all the microarrayed proteases by the FAL. The contribution of the nonenzymatic background reaction to the overall signal is below 10% for up to 80% conversion of the enzymes. An exception to this trend is represented by cathepsin C (Supplementary Fig. 4). For cathepsin S and recombinant cathepsin B, the relative contribution of the background reaction increases if the microarray is dried for prolonged

times after blocking. Because the background reaction presumably results from the attack of an SH group of a deactivated fold of the enzyme on the epoxide of the FAL, we infer that on the microarray’s surface there is a loss of enzymatic activity attributed to folding collapse upon drying. Correspondingly, only in these two cases is the concentration of active enzyme, c(E), reduced by 60–70% and the conversion of the remaining active enzyme, as expressed by the pseudo second-order constant k2nd, slowed by 40–50%. These observations underline two important aspects. First, whereas the simple surface

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Figure 1 Schematic explanation of TEMA technology. Different members of an enzyme family are microarrayed within identical subarrays on a functionalized glass slide. (a) In the kinetic setup, after blocking and preincubation with reaction buffer, four different concentrations of FAL (one row of subarrays for each concentration) are reacted for 12 different reaction times (one column of subarrays for each reaction time) at one concentration and one time per subarray. Analysis of the data from this setup confirms the enzymatic nature of the reaction between the microarray enzymes and the FAL and the kinetic constants that characterize the activity of each enzyme are extracted. This kinetic characterization is used in defining a set of experimental conditions under which one can run a meaningful HTS experiment. (b) In the HTS setup, each of the subarrays is preincubated with a potential inhibitor (colored) or a blank (light blue) and subsequently treated with FAL at a concentration and for a reaction time defined by the kinetic constants derived in a. Analysis of the data provides each inhibitor’s activity profile against all enzymes of the subarray, importantly, under exactly the same experimental conditions, reducing reproducibility problems associated with HTS. (c) In the validation setup, the subarrays within one row of subarrays are preincubated with different concentrations of an inhibitor (including a blank) and subsequently treated with FAL in a manner similar to b. Analysis of the data provides an inhibitor titration curve, from which refined Kiapp values can be calculated.

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LETTERS obtained above allow estimation of whether, and under which conditions, the chosen FAL H, bov B, hum is appropriate for the multiplexed determinaL, hum 4.5 B, bov tion of inhibitor activity21 according to this S, rec B, rec 1.5 methodology: indeed, if two enzymes react at K, rec C, bov very different velocities, the fast-reacting 0.5 enzyme would impose a short reaction time 0 0.5 1 2 3 4 5 7 10 13 17 22 for Pi measurements (before saturation), Time (min) which in turn may not be enough for the c d slow-reacting enzyme to reach a sufficient E+S ES k+1; k–1 t signal/noise ratio. The FAL used in this ES P kcat study, at a final concentration of 0.4 mM Et + S P and reaction time of 120 s satisfies these kside FAL conditions. This method is suitable to generate inhibitor profiles independent of the e 1.4 c(FAL) = 13.5 µM f 25 mechanism of inhibition. c(FAL) = 4.5 µM 1.2 Microarrays were prepared similar to those c(FAL) = 1.5 µM 20 used for kinetic studies. Eight compounds c(FAL) = 0.5 µM 1.0 known to be cysteine-protease inhibitors (see 15 0.8 Supplementary Methods online for inhibitor 0.6 description) were randomly distributed 10 among 186 compounds with unknown inhi0.4 bitor activity. Seven of the known inhibitors 5 0.2 were screened twice on different microarrays. 0 0 After preincubating each subarray of the 0 2 4 6 8 10 12 14 0 2 4 6 8 10 TEMA with one of the library members, the c(FAL) (µM) Time (min) FAL was reacted for 2 min and the microarray Figure 2 Results of the kinetic setup experiments. (a) Fluorescent scanner image of a microarray was washed and scanned. The chosen inhiprocessed as described in the kinetic setup. (b) Typical image of a subarray, and the identity of the bitor concentrations (3 mM), in combination microarrayed cathepsins (each enzyme in duplicate). (c) Michaelis-Menten–derived, best-fit reaction with average experimental errors of 10%, model obtained for all the microarrayed enzymes. For numerical values of k+1, k 1, kcat and kside see allowed a range between 80 nM and 2 mM Supplementary Table 1 online. (d) Structure of the FAL used in this study. (e) Time progress curves ini in which Kiapp can be quantified (Fig. 3). derived from the microarray in a for cathepsin L. (f) Plot of initial velocities n versus [FAL] for all the From such experiments it is intrinsically posmicroarrayed enzymes. sible to derive only one of the two kinetic constants kon and koff that characterize the chemistry that we use reliably maintains the activity of the micro- inhibition process. Therefore kon was assumed to have the calculated arrayed enzymes under optimized conditions, further advances in value of the corresponding constant for the FAL-binding process (k+1), surface chemistry19 will increase the TEMAs’ robustness upon storage. and koff was obtained from the inhibition studies. The systematic error Second, they reveal that mere fluorescence observation at an enzyme’s on the calculated Kiapp values obtained under this assumption and for address is not necessarily related to enzymatic ‘activity’, making one our experimental conditions is at most 25% if kon is within two orders time point measurements unsuitable for such claims. of magnitude of k+1 (Supplementary Methods). All the known The second experimental setup, the HTS setup (Fig. 1b) is inhibitors were correctly found in this experimental setup (Fig. 3). established with the goal of quantifying the inhibition profile of a Comparison with literature values16,17,22–24 (Supplementary Table 2 compound against the entire panel of microarrayed enzymes from one online) reveals that known inhibition constants and profiles are well time point measurement. In general, Kiapp values are most straight- reflected in this assay. They are reproducible, as demonstrated by the forwardly extracted from initial velocities (vini) determined after data obtained on the seven inhibitors that were screened twice. Some equilibration of the enzyme/inhibitor system. However, because of very weak inhibition by so far unknown compounds could be observed the impossibility of directly measuring vini, the intrinsically different (Kiapp 4 800 nM). No false positives occurred for strong inhibitors. reactivity of the microarrayed enzymes towards the FAL and the In the third experimental setup, the validation setup, Kiapp values necessity of taking into consideration the background reaction, multi- are refined and inhibitor profiles confirmed (Fig. 1c). After the plexing the direct determination of Kiapp as recently described3,20 can demonstration that Kiapp can be obtained in a HTS format using result in significantly distorted inhibition profiles. If this different the accurately established conditions above, we set to demonstrate that kinetic-related behavior of the enzymes is ignored, the error of the this method is also suited for the refinement of the calculated Kiapp derived from initial velocity ratios was calculated to possibly inhibition constants for several enzymes in parallel. This setup is amount to up to 800% for the case under study. Therefore, to reliably particularly useful and necessary for compounds whose range of Kiapp quantify the inhibition profiles for the one-inhibitor/several-enzymes values for the different enzymes is too broad to be measured from the case from one-time-point experiments, we measure the exact con- data acquired from a single concentration experiment, as discussed in centration of the reaction product Pi (instead of vini) at a given time; the HTS setup. In this setup, each subarray is preincubated with we then apply the above complete kinetic description of the reaction different inhibitor concentrations, typically ranging from 120 mM to between the microarrayed enzymes and the FAL by including the 0.11 nM, followed by addition of the FAL at a final concentration of binding equilibrium for a reversible inhibitor. The kinetic data 0.42 mM for 2 min (Fig. 4). For high inhibitor concentrations, the

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LETTERS Kiapp can be determined as described recently3,20. Importantly, for inhibitor concentrations close to or below the total enzyme concentration, one has to take into account the fact that the ‘effective’ concentration of inhibitor to which a given enzyme is exposed is significantly depleted by other enzymes’ binding of this inhibitor. This affects the determined inhibition constants in the interesting case of strong inhibitors (when nearly all inhibitor is enzyme bound) if c(I) o 5*c(Et) (with Et the total concentration of all active enzymes). To correct for this effect, we set up an algorithm (Supplementary Methods) that alternately calculates the Kiapp values for each enzyme as described for the HTS setup and then uses those values to determine the concentration of free and bound inhibitor. Those concentrations are used to recalculate the Kiapp for each enzyme; this iterative process convergences to accurate Kiapp values. Neglecting this effect can shift the determined Kiapp values dramatically; in the case of leupeptin for example, the determined constants are more than

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Figure 3 Results of the HTS setup experiment. (a) Enzyme inhibitor profiles resulting from on-chip parallel screening of an inhibitor library (one compound/subarray) against eight cysteine proteases of the cathepsin family. The setting of the method (inhibitor concentration used) allows a quantification of the inhibition constant Kiapp in the range between 80 and 2,000 nM (shades of blue, see scale bar on the right). Higher values are shown as white, lower values as orange bars. Roughly, for the microarrays presented herein, an inhibitor concentration c(I), allows Kiapp values between c(I) and c(I)/500 to be estimated. Known cysteine protease inhibitors in the library are identified at the left of the diagram. All known inhibitors were found in this experimental setup. Their determined profiles match well the available literature data. (b) The resulting fluorescence intensities after preincubation with a selective (CNI: NC-D-Pro-Leu-(OBn)) and an unselective (E-64) inhibitor compared to the noninhibited reactivity (FALincubation time: 2 min). To test data reproducibility and to ensure the absence of systematic errors, in this setup the positions of cathepsin B human and cathepsin B bovine were reversed as compared to Figure 2b.

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one order of magnitude too low if the effect of parallel competition is neglected. The inhibition constants obtained in the microarray-based validation experiment (Supplementary Table 3 online) very closely correspond with the literature values of solution-state studies. This indirectly confirms that the studied, active enzymes are immobilized in a native-like state, with little interference of the solid support on their activity. To acquire information about the method’s robustness under a broad range of conditions, we evaluated the microarrayed enzyme’s behavior towards environmental factors, in particular surfactant concentration and pH (Supplementary Fig. 9 online). The subarrays were preincubated at different SDS concentrations or different pH and subsequently reacted with FAL solutions for 2 min. Cathepsin S and H were found to rapidly loose activity with increasing SDS concentration, whereas cathepsin K’s activity reaches a sharp maximum around the critical micelle concentration of SDS. This effect is similar for cathepsin B, C and L yet much less pronounced. The fact that environmental conditions can significantly enhance the solid-support-bound active fold of the enzyme indicates some degree of conformational dynamism for the covalently bound enzyme. The cathepsins’ higher activity at low pH was confirmed; cathepsin L was particularly sensitive to the pH increase, loosing all its activity at pH 4 6.5. In contrast, cathepsin S and C maintained substantial activity at slightly basic pH, in agreement with other studies25. Because of higher reactivity of the epoxide group at low pH, the contribution of the background reaction to the total signal increased at low pH. Therefore, the fluorescent signal at lower pH was higher than what would be expected from the classic bell-shaped activity versus pH curves obtained by alternative methods25. In conclusion, we have shown that TEMA technology can be used to generate an inhibitor’s profile against a family of enzymes in one experiment. The method is suited to HTS experiments where it provides not only binary yes/no answers, but gives access to at least semi-quantitative Kiapp values. By adapting the experimental setup, the exact quantification of on-chip inhibition constants is possible. Miniaturization, multiplexing power, straightforward automatization, throughput, low sample consumption, complemented by high robustness demonstrate the advantages of TEMAs over classical, well-plate assays. The quantitative, multiplexed data that it provides, as well as the inhibition mode3 are crucial for distinguishing between the more promising, enzyme-specific inhibitors and molecular motives that target the studied enzymes indiscriminatingly. This is of particular importance since it provides an integrated system for the early identification of cross-reactive inhibitors, saving considerable efforts for their further evaluation. Whereas in this study we use a relatively small library of molecules for technology validation, we estimate that under appropriate automation on the order of 105 compounds can be profiled daily against an entire family of enzyme targets. Although requiring purified enzymes, this technology adds high-throughput capabilities to existing FAL-based gel profiling methods8,21. In principle, TEMA is general for families of enzymes that form a covalent intermediate with their substrate. However, successful extension of TEMAs towards a comprehensive coverage of the targetable enzyme families depends on two essential factors: firstly, as our kinetic study has clearly shown, a family-wide FAL with finely tuned properties and low background reactivity must be employed. Although significant recent advances in FAL-related research5–10 few kinetic data relative to the described FALs are available21. Secondly, although the sensitivity of the method is such that only a tiny amount of properly folded enzyme must be present on the surface (0.05 fmoles/spot), the method will substantially benefit from advances in the active field of surface

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LETTERS 1.5 mM, 0.5 mM) to a column of four subarrays. Final concentrations of FAL: 4.5, 1.5, 0.5, 0.166 mM. Because of significant adsorption of the FAL to the plastic-ware used (pipette tips, well-plates, centrifuge tubes) all the plasticware was preconditioned CNI with a 20 mM solution of FAL. The FAL concentra8.4 7.4 6.4 5.4 4.4 3.4 2.4 1.4 Inh(–) 0.4 –0.6 –1.6 tion was estimated by UV measurements of the log4(c(Inh) (nM)) used solutions both before and after the experiment. At the end of the kinetic experiment the 1.2 1.2 Cath B Cath B b microarray is first flow washed with a 80 1C Cath C Cath C Cath H Cath H 1.0 1.0 solution of SDS buffer (35 mM SDS, 10 g/l glycine) Cath K Cath K at pH 5.5 (wash buffer), then submersed in a 80 1C Cath L Cath L 0.8 0.8 Cath S Cath S solution of wash buffer and vigorously shaken for 3 min, sonicated for 2 min, abundantly washed 0.6 0.6 with ddH2O and dried by centrifugation at 15g for 0.4 0.4 2 min. Repeated washings under these conditions resulted in no significant differences in fluores0.2 0.2 cence. The obtained microarray was scanned using 0 0 an ArrayWorks microarray scanner (Applied Pre–1 0 1 2 3 4 5 6 –1 0 1 2 3 4 5 6 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 cision) and the fluorescence quantified using c(CNI) (nM) c(E-64) (nM) Imagene software (Biodiscovery). Figure 4 Results of the validation setup experiment for two inhibitors (CNI, reversible and E-64, HTS setup. After the microarray preparatirreversible). (a) Image of the relevant subarrays for E-64 and CNI. To avoid possible systematic errors, ion described above, each subarray was preincuwe placed the 0 inhibitor concentration (blank) subarrays fourth from right. Enzyme identity is the bated for 90 min with 8 ml reaction buffer same as in Figure 3. (b) Evolution of the ratio between the initial product formation (Pi/Pmax) as a containing the appropriate concentration of inhifunction of inhibitor concentration, derived for the two microarray experiments presented in a. It can bitor. In the experiments described herein be estimated that for the microarrays described herein, a minimum inhibitor concentration of about the inhibitor concentration was chosen to be 0.1 nM (1fmole inhibitor) is necessary to observe an effect for Kiapp values in the nM range. For 3 mM. After the preincubation, we added 4 ml of simplification, only one curve for cathepsin B is shown (source: human). a solution of FAL in reaction buffer at 1.25 mM using a multichannel pipetting device. Final concentrations were 2 mM for c(I) and 0.41 mM for chemistry19. Moreover, in the case of enzymes that do not form c(FAL). The microarray was washed 2 min after FAL addition, as described covalently-bound intermediates with their substrates, we are currently in the kinetic setup. Validation setup. Each subarray was preincubated with 8 ml reaction buffer investigating the use of alternatives to FALs, such as tagged photocontaining decreasing inhibitor concentrations (120 mM, 30 mM, 7.5 mM, 26–28 reactive, mechanism-based inhibitors . However, as our kinetic 1.87 mM, 0.47 mM, 117 nM, 30.3 nM, 7.32 nM, 0 nM, 1.83 nM, 0.45 nM, studies have shown, a detailed analysis and understanding of the on0.11 nM) for 90 min. Then 4 ml of a solution of FAL in reaction buffer at chip processes is needed before robust use of TEMAs is possible. 1.25 mM was concomitantly added to the subarrays using a multichannel pipette. The microarray was washed 2 min after FAL addition, as described in METHODS the kinetic setup. In the check of the technology’s robustness, each subarray was General. The compounds used for HTS screening were purchased from preincubated for 90 min with 8 ml reaction buffer containing decreasing Nanosyn Inc. Eight known cathepsin inhibitors and a parent compound of concentrations of SDS and glycine (Supplementary Fig. 9) or 8 ml reaction one of the inhibitors, known for its inability to inhibit cathepsins, were buffer at different pH (from 3.5 to 9 by 0.5 pH units increment). Then 4 ml of a randomly distributed among the screened compounds. These eight compounds solution of FAL in reaction buffer at appropriate pH at 1.25 mM is concomiwere either commercially available (see below) or synthesized according to tantly added to the subarrays using a multi-channel pipette. 2 min after FAL literature procedures. addition, the microarray was washed as described in the kinetic setup. Further details of the experimental setup and data analysis procedure are provided in Microarray preparation. The enzymes were microarrayed using a 200 mm pin the Supplementary Methods online. and a commercial microarrayer (LabNext) on a hydrogel-aldehyde (NoAb Pi /Pmax

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a

Diagnostics) functionalized glass slides (Matsunami Glass) comprised of 48 subarrays (12 rows and 4 columns) at about 1 mg/ml in the respective buffer at 73% humidity and incubated for about 1 h in a wet atmosphere (humidity from 60–80%), at 25 1C. The subarrays are spatially separated by a hydrophobic coating, which allows them to be treated both individually and collectively. The surface was blocked with 2% BSA (wt/vol) in 50 mM TRIS, pH 5.5 (TRISA) for 10 min. Then the microarray was dip-washed with TRISA and dried by centrifugation at 15g for 2 min. Other surfaces, such as aldehyde, hydrogelNHS, NHS, hydrogel-BSA-NHS provided significantly inferior results, resulting either from poorer immobilization or enzyme denaturation onto the surface. Microarray processing. Kinetic setup. After the microarray preparation described above, we preincubated each sub-array with 8 ml reaction buffer (TRISA containing 5 mM CaCl2, 5 mM MgCl2, 2 mM DTT, 2% DMSO (vol/vol)) for 30 min. Preliminary experiments have established that the enzymes’ activity is largely independent of the preincubation time in the range of 5 to 90 min. After the preincubation, 4 ml of a solution of FAL in reaction buffer was concomitantly added at the times described into the text, using a multichannel pipette, at four different stock concentrations (13.5 mM, 4.5 mM,

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Note: Supplementary information is available on the Nature Biotechnology website.

ACKNOWLEDGMENTS We thank Robert Menard for providing recombinant cathepsin B and recombinant cathepsin L. This work was supported by the National Institute of Advanced Industrial Science and Technology of Japan and by the Stifterverband fu¨r die Deutsche Wissenschaft (Projekt-Nr. 11047: ForschungsDozentur Molekulare Katalyse). COMPETING INTERESTS STATEMENT The authors declare competing financial interests (see the Nature Biotechnology website for details). Received 15 December 2004; accepted 1 March 2005 Published online at http://www.nature.com/naturebiotechnology/

1. Kambhampati, D. (ed.). Protein microarray technology (Wiley-VCH, Heidelberg, 2004). 2. Zhu, H. et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).

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LETTERS 3. Eppinger, J., Funeriu, D.P., Miyake, M., Denizot, L. & Miyake, J. Enzyme microarrays: on-chip determination of inhibition constants based on affinity-label detection of enzymatic activity. Angew. Chem. Int. Ed. 43, 3806–3810 (2004). 4. Chen, G.Y., Uttamchandani, M., Zhu, Q., Wang, G. & Yao, S.Q. Developing a strategy for activity-based detection of enzymes in a protein microarray. ChemBioChem 4, 336–339 (2003). 5. Liu, Y., Patricelli, M.P. & Cravatt, B.F. Activity-based protein profiling: the serine hydrolases. Proc. Natl. Acad. Sci. USA 96, 14694–14699 (1999). 6. Campbell, D.A. & Szardenings, A.K. Functional profiling of the proteome with affinity labels. Curr. Opin. Chem. Biol. 7, 296–303 (2003). 7. Speers, A.E. & Cravatt, B.F. Profiling enzyme activities in vivo using click chemistry methods. Chem. Biol. 5, 535–546 (2004). 8. Greenbaum, D.C. et al. Small molecule affinity fingerprinting. A tool for enzyme family subclassification, target identification, and inhibitor design. Chem. Biol. 9, 1085– 1094 (2002). 9. Jessani, N. & Cravatt, B.F. The development and application of methods for activitybased protein profiling. Curr. Opin. Chem. Biol. 8, 54–59 (2004). 10. Goulet, B. et al. A cathepsin L isoform that is devoid of a signal peptide localizes to the nucleus in S phase and processes the CDP/Cux transcription factor. Mol. Cell 14, 207– 219 (2004). 11. Gosalia, D.N. & Diamond, S.L. Printing chemical libraries on microarrays for fluid phase nanoliter reactions. Proc. Natl. Acad. Sci. USA 100, 8721–8726 (2003). 12. Berdowska, I. Cysteine proteases as disease markers. Clin. Chim. Acta 342, 41–69 (2004). 13. Greenbaum, D.C. et al. A role for the protease falcipain 1 in host cell invasion by the human malaria parasite. Science 298, 2002–2006 (2002). 14. Lecaille, F., Kaleta, J. & Bro¨mme, D. Human and parasitic papain-like cysteine proteases: their role in physiology and pathology and recent developments in inhibitor design. Chem. Rev. 102, 4459–4488 (2002). 15. Kang, K. & Kim, W. Recent developments of cathepsin inhibitors and their selectivity. Exp. Opin. Therap. Pat. 12, 419–432 (2002).

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16. Powers, J.C., Asgian, J.L., Ekici, O¨.D. & James, K.E. Irreversible inhibitors of serine, cysteine, and threonine proteases. Chem. Rev. 102, 4639–4750 (2002). 17. Otto, H.-H. & Schirmeister, T. Cysteine proteases and their inhibitors. Chem. Rev. 97, 133–171 (1997). 18. Kuzmic, P. Program DYNAFIT for the analysis of enzyme kinetic data: application to HIV proteinase. Anal. Biochem. 237, 260–273 (1996). 19. Carrillo, A., Gujraty, K.V. & Kane, R.S. Surfaces and substrates. in Microarray Technology and Its Applications (eds. Mueller, U.R. & Nicolau, D.V.) 45–61 (Springer GmbH, Berlin, 2005). 20. Kuzmic, P. et al. High-throughput screening of enzyme inhibitors: automatic determination of tight-binding inhibition constants. Anal. Biochem. 281, 62–67 (2000). 21. Leung, D., Hardouin, C., Boger, D.L. & Cravatt, B.F. Discovering potent and selective reversible inhibitors of enzymes in complex proteomes. Nat. Biotechnol. 21, 687–691 (2003). 22. Sasaki, T. et al. Inhibitory effect of di- and tripeptidyl aldehydes on calpains and cathepsins. J. Enzyme Inhib. 3, 195–201 (1990). 23. Yamashita, D.S. et al. Structure and design of potent and selective cathepsin K inhibitors. J. Am. Chem. Soc. 119, 11351–11352 (1997). 24. Rydzewski, R.M. et al. Peptidic 1-cyanopyrrolidines: synthesis and SAR of a series of potent, selective cathepsin inhibitors. Bioorg. Med. Chem. 10, 3277–3284 (2002). 25. Kirschke, H., Barrett, A.J. & Rawlings, N.D. Proteinases 1: lysosomal cysteine proteases. Protein Profile 2, 1581–1643 (1995). 26. Gilbert, B.A. & Rando, R.R. Modular design of biotinylated photoaffinity probes: synthesis and utilization of a biotinylated pepstatin photoprobe. J. Am. Chem. Soc. 117, 8061–8066 (1995). 27. Hagenstein, M.C. et al. Affinity-based tagging of protein families with reversible inhibitors: a concept for functional proteomics. Angew. Chem. Int. Ed. 42, 5635– 5638 (2003). 28. Saghatelian, A., Jessani, N., Joseph, A., Humphrey, M. & Cravatt, B.F. Activity-based probes for the proteomic profiling of metalloproteases. Proc. Natl. Acad. Sci. USA 101, 10000–10005 (2004).

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A n t ibo dies

Rabbit monoclonal antibodies

Broad specificity antibodies

Six new rabbit monoclonal antibodies have been added to Vector Laboratories’ line of primary antibodies for immunohistochemical staining: CD3, COX-2, Cyclin D1, Ki67, Estrogen Receptor and Progesterone Receptor. These antibodies provide excellent results on formalinfixed, paraffin embedded tissue sections using standard immunohistochemistry methods. They can be applied to sections just like mouse monoclonals, and then detected using anti-rabbit IgG secondary detection reagents. ht t p : / / w w w . v ect o r labs . co m/

Three antibodies from Upstate are validated to recognize over 80 serine/threonine and tyrosine kinases: 4G10 antibodies detect twice as many phospho-proteins as PY-20 and PT-66 using the most frequently referenced phospho-tyrosine antibody available; MPM2 antibodies are able to recognize more than 40 mitotic proteins; and highly specific phospho-MBP monoclonal antibodies and conjugates are available as detection reagents for numerous kinase assays. ht t p : / / w w w . u p s t at e. co m/

Mouse anti-human antibodies Allophycocyanin-, fluorescein- and phycoerythrin-conjugated mouse anti-human CCL3/MIP-1α antibodies are available from R&D Systems. CCL3, a member of the CC or β-chemokine subfamily, was originally purified from the conditioned media of LPS-stimulated macrophages. It acts as a chemoattractant to a variety of cell types including monocytes, T cells, B cells and eosinophils. ht t p : / / w w w . R an dD s y s t ems . co m/

G en e clo n in g karyotic, mammalian, viral or insect expression systems. ht t p : / / w w w . o p en bio s y s t ems . co m/

Antibodies for cell-signaling research Human ORF collection Open Biosystems offers a collection of clones containing full open reading frames (ORFs) for over 8,000 human genes, created by the Center for Cancer Systems Biology of the Dana-Farber Institute. Derived from fully sequenced Mammalian Gene Collection fulllength cDNAs, they are cloned into recombinational entry vectors. ORF clones save time by allowing users to skip PCR, cloning into an expression vector, and verifying the ends of the ORF DNA sequence. The GatewayT entry vectors ensure easy transfer into pro-

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Comprehensive human genes GenScript’s GenPool ORF collection includes ever y single hum an gene with known sequence. All the ORFs are cloned into the pDream 2.1 vector, which allows expression in bacteria, Sf9 cells and mammalian cells. Each gene has a FLAG tag to facilitate purification and detection, which is also removable if needed. pDream2.1 vector is also compatible with Gateway and LIC systems. ht t p : / / w w w . gen s cr ip t . co m/

Epitomics’ rabbit monoclonal antibodies offer more diverse epitope recognition, improved response to less immunogenic antigens, and greatly improved response to rodent proteins. All Epitomics RabMAbs offer high affinity and are extensively characterized and tested for use in WB, IHC, ICC, IF, IP and flow cytometry. Antibodies are available for key proteins involved in various cell-signaling pathways including apoptosis, cell cycle control, cytokine signaling and many phosphospecific proteins (Above: Phospho-EGFR (pY1173) RabMAb). ht t p : / / w w w . ep it o mics . co m/

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CAREERS AN D RECRU I TM EN T

US biotechnology companies and foreign nationals: the changing dynamics of access to H-1B visas A Stephen Dahms & Stephen C Trow R ecen t chan ges in t he H - 1 B v is a p r o gr am hav e left bio t ech emp lo y er s s ho r t han ded an d co n fu s ed.

egional biotechnology industry clusters cite access to a skilled labor pool as one of the top two or three most significant hurdles to commercialization. In the United States, a shortfall in the labor pool has required access to foreign workers in a variety of areas and expertise. Starting with efforts in California, the industry was surveyed in 1998–2001, which led to findings that, depending upon the region, between 6% and 10% of the US biotechnology workforce had H-1B visas, with an estimate of 18,000 in the biotech industry nationally, and projected needs of 25% annual increases in some of the clusters. Relatedly, the US Department of Commerce reported in 2003 that the biotech industry workforce grew annually by 12% over the period 1997–2003. Biotechnology industry surveys of H-1B visa usage1 also found that 80% of biotechnology H-1Bs were from US universities, 75% were graduate-degreed (40% PhD, 35% MS, 20% BS and 5% MD), 85% eventually acquired permanent residency in the US and companies were spending an average of $10,200 on each H-1B worker for processing fees and legal expenses through to a green card, leading to the conclusion that US companies had spent in excess of $150 million over the previous five years to acquire and keep their H-1B workers. These and other survey data also clearly showed that the H-1B worker skill sets sought by biotech companies

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A. Stephen Dahms is at the California State University System Biotechnology Program, San Diego State University, San Diego, California, USA and Stephen C. Trow is at Trow & Rahal, PC, an immigration law firm in Washington, DC, USA. e-mail: [email protected] or [email protected]

identically match the most pressing employment needs of the industry overall and that their compensation was equal to, or in most cases, higher than US nationals.

R ecen t his t o r y Access to H-1B visas is limited by a ‘cap’ or annual limit on the number of new foreign workers who can be granted H-1B visa status. Increasing demand for H-1B visa status first exceeded the cap in September 1997, and then caused major disruptions in May 1998 when processing of petitions for new H-1B workers was suspended for the remaining five months of fiscal year (FY) 1998. In response to pressure exerted by the information technology (IT) industry and the Biotechnology Industry Organization (BIO; Washington, DC, USA), the H-1B cap was raised from 65,000 to 115,000 in FY 1999 and then again to 195,000 in FY 2001, with a provision that the cap would revert to 65,000 on October 1, 2004. Starting in mid-2001, discussions began within BIO about legislative strategies to raise the cap from 65,000 back to a level that would assure access to these talented foreign nationals. This initiative was derailed by the events of September 11, after which any discussion of increasing the entry of foreign workers was counter to public and congressional opinion. Although there has not been a biotechnology industry H-1B needs survey since early 2001, it is thought that demands are increasing in the range of 3,500 to 5,000 per year. Fortunately, the law raising the cap for FY 2001 also exempted from the cap all H-1B workers employed at an institution of higher education or a related or affiliated nonprofit entity, or at a nonprofit research organization or a governmental research organization, thus removing approximately 10,000 workers from the H-1B cap.

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T he cu r r en t s it u at io n On October 1, 2004, when FY 2005 began and the H-1B cap reverted to 65,000, the US Citizenship and Immigration Services (USCIS) announced that it had received enough H-1B petitions during the preceding six months to use up the entire supply for FY 2005. The USCIS indicated that it would continue to process petitions that it received before October 1, but it would not accept any new H-1B petitions that are subject to the cap for FY 2005. It also announced that it would start to accept petitions for FY 2006 on April 1, 2005, six months before the start of FY 2006. Many employers expected that the USCIS would receive enough petitions to exhaust the FY 2006 supply well before October 1, 2005, and made plans to file their petitions in April 2005. In December 2004, the FY 2005 Omnibus Appropriations Act exempted from the H-1B cap up to 20,000 foreign nationals per year who have earned a master’s degree or higher from a US university. This change was prompted by renewed pressure from the IT, semiconductor and engineering industrial sectors, especially the National Association of Manufacturers (which BIO has partnered with on the H-1B issue since the mid-1990s). The new exemption is vitally important for foreign students who graduated from US universities during 2004 and are now working in F-1 or J-1 visa status with practical training authorization that expires before October 1, 2005. The exemption was scheduled to take effect on March 8, 2005, and it was expected that the 20,000 new slots would be taken quickly, so employers made plans to file petitions for qualifying workers on March 8. There were no changes in the other exemptions from the H-1B cap, so employers continued with their plans to file petitions for nonexempt workers on April 1.

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CA R E E R S A N D R E CR U I T M E N T However, on March 4, 2005, the USCIS announced that it was not ready to accept petitions for these 20,000 new H-1B slots as scheduled on March 8, and then it announced to great surprise that these 20,000 slots would not be limited to foreign nationals with a master’s/PhD degree from a US university. The USCIS did not publish a rationale for this dramatic change, but it appears to be based on a determination that at least 20,000 petitions for master’s/PhD graduates of US universities had already been approved during FY 2005, making the 20,000 new slots available to any qualified applicants. In addition, in late March the USCIS announced that it had mistakenly approved at least 10,000 more H-1B petitions during FY 2005 than authorized by the 65,000 cap. This series of announcements left employers wondering whether the 10,000 excess approvals would be deducted from the 20,000 new slots, leaving only 10,000 new slots to be given to any and all qualified applicants. As of April 20 the USCIS had still not announced the filing date and procedure for the 20,000 new H-1B slots that were expected to be available on March 8, nor had it indicated whether the 10,000 excess approvals would be deducted from the 20,000 new slots. The prospects for recent master’s/PhD graduates of US universities to avoid gaps in their work authorization before October 1, 2005, are looking much worse, whereas graduates of foreign universities and bachelor’s degree graduates of US universities can now hope for a windfall H-1B approval before October 1. Regardless of how these issues are resolved,

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the delay and uncertainty have made life more difficult for employers and workers seeking H-1B status.

F ee hikes an d u s age Employers struggling with the limited supply of H-1B visas and confusion over eligibility for the 20,000 new slots are also facing sharply higher filing fees for H-1B petitions. The FY 2005 Omnibus Appropriations Act reinstated a ‘training’ fee that had lapsed in October 2003 and increased that fee from $1,000 to $1,500 for most H-1B petitions. The training fee is reduced to $750 for employers that have no more than 25 full-time equivalent employees, including employees of affiliates and subsidiaries. This fee will fund job training and scholarships for US workers, and government processing of H-1B cases. The Appropriations Act also imposed an additional $500 ‘fraud prevention’ fee for each petition seeking an initial grant of H-1B status or authorization to change employers in H-1B status. This will provide additional funding for visa fraud prevention programs at USCIS and other government agencies. These new fees are separate from the mandatory $185 base fee for an H-1B visa petition, and the optional $1,000 fee for premium processing (faster service) from the USCIS. The total filing fee for a large employer seeking premium processing of an H-1B visa petition is now $3,185. Over $500 million of H-1B training fees have been collected and routed by the US Department of Labor since 1999 to fund new training programs to help reduce dependency

upon foreign nationals, but unfortunately none of these funds has been directed at the graduate-degree education that the biotechnology industry needs. Biotech employers are paying H-1B training fees to create programs to relieve their dependency on foreign workers, yet most H-1B workers in the biotech and high-tech industries are coming from US educational institutions. The problem arises on the supply side of the labor equation, not the demand side. Currently, over 25% of all PhDs in the US are foreign nationals, and over 50% of all graduate students are foreign students. Funding graduate-level training programs will not reduce the demand for H1B workers if US students decline to enroll in those programs.

C o n clu s io n s Clearly, the H-1B visa program provides a temporary solution to shortages in the national and domestic biotech labor pools— shortages that mirror the inadequate production of appropriately trained US nationals by US institutions of higher learning. The reality is that universities have inadequate resources for expanding their training pipelines, especially in specialized areas that follow the basic-research phase of company product development. Efforts should be directed toward influencing greater congressional and federal agency attention to these important topics, especially an increase in the H-1B cap and effective use of the very sizable H-1B fee–derived training funds. 1. Sevier, E.D. & Dahms, A.S. Nat. Biotechnol. 20, 955– 956 (2002).

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P EOP L E

Stressgen Biotechnologies (Victoria, BC, Canada) has appointed Gregory M . M cKee as the company’s president and CEO, succeeding Dan Korpolinski who is leaving to pursue other interests. Mr. McKee, who has served as Stressgen’s chief financial officer and vice president of corporate development for the past two years, will focus on the commercialization of HspE7 , the company’s lead product candidate targeting a broad spectrum of human papillomavirus (HPV)-related diseases. Mr. McKee previously served as senior director, corporate development for Valentis, as well as director of Genzyme’s operations in Asia.

Michael J. Astrue has been appointed to the board of directors of ArQule (Woburn, MA, USA). Mr. Astrue is currently president and CEO of Transkaryotic Therapies, and previously served as vice president, secretary and general counsel for Biogen as well as chairman of the Massachusetts Biotechnology Council.

Benitec (Mountain View, CA, USA) has named Michael Catelan i ch ief financial officer. Mr. Catelani had served as a consultant to the company since Januar y 2005. Mr. Catelani previously held senior financial management positions including vice president and CFO at Axon Instruments. David Chiswell has been named nonexecutive chairman of DanioLabs (Cambridge, UK), replacing Roger Brimblecombe who has retired from the DanioLabs board. Dr. Chiswell was a founder and CEO of Cambridge Antibody Technologies. In addition to this new chairmanship, he also serves as chairman of Arrow Therapeutics, Sosei Co. and the UK BioIndustry Association, and as a nonexecutive director of Arakis Ltd.

Innovive Pharmaceuticals (New York) has announced the appointment of Adam R. Craig as vice president and chief medical officer. He joins the company from ArQule where he served as medical director and vice president, clinical development. Dr. Craig was also senior director, clinical development for Ilex Oncology and medical advisor, oncology for Antisoma.

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Illumina (San Diego, CA, USA) has appointed Scott D. Kahn as vice president and chief information officer, a new position. Dr. Kahn joins the company from Accelrys, where he served as chief scientific officer. Illumina also named Paul Grint to its board of directors. Dr. Grint is currently senior vice president and chief medical officer of Zephyr Sciences, and previously served as vice president and head of clinical R&D for Pfizer in La Jolla, California. Bryan Koontz has been promoted to senior vice president and general manager of discovery informatics at Tripos (St. Louis, MO, USA). He succeeds Trevor Heritage, who is leaving to pursue other interests. The cofounder, and formerly CEO, of Optive Research, Mr. Koontz joined Tripos as vice president of marketing and corporate development when Tripos acquired Optive in January 2005. John T. Henderson has been elected chairman of the board of Myriad Genetics (Salt Lake City, UT, USA). He succeeds Dale Stringfellow who recently died of complications associated with pancreatic cancer. Dr. Henderson, a board member since March 2004, was previously with Pfizer for over 25 years, most recently as a vice president in the pharmaceuticals group. In addition, Myriad’s board of directors has selected Linda S. Wilson to fill the vacancy on the audit committee left by Dr. Stringfellow’s passing. Dr. Wilson has been a director since 1999. She was the seventh president of Radcliffe College, and served as vice president for research at the University of Michigan, and in similar roles at the University of Illinois and Washington University.

Topigen Pharm aceuticals (Montreal, Quebec, Canada) has announced the appointment of G. John Mohr as chief bu sin ess officer. Mr. Mohr was most recently a corporate officer and vice president of business development and licensing at AtheroGenics, and previously president, US operations at Fournier Pharma. Protagen (Dor tm und, Germ any) has appointed Stefan Müllner as chief scientific officer, succeeding Helmut E. Meyer. Dr. Müllner previously served in positions in R&D management at Henkel and Hoechst. Dr. Meyer will join Protagen’s scientific advisory board and the board of directors, replacing Achim Riemann , former CEO of Arthur D. Little Germany. Tercica (S. San Francisco, CA, USA) has appointed Chris E. Rivera to the newly created position of senior vice president, commercial operations. He joins the company from Corixa, where he was vice president of sales. Mr. Rivera also served as senior vice president of Genzyme Therapeutics. August M. Watanabe has been named to the board of directors of Ambrx (San Diego, CA, USA). Dr. Watanabe was formerly a senior executive at Eli Lilly, where he occupied the positions of executive vice president of science and technology and president of Lilly Research Laboratories, and was a member of the board of directors. Keith Yamamoto has been appointed as chairperson to the scientific advisory board of Sirna Therapeutics (Boulder, CO, USA). Dr. Yamamoto is currently executive vice dean at the University of California, San Francisco School of Medicine. He has been a member of the UCSF faculty for more than 25 years, serving as director of the biochemistry and molecular biology graduate program from 1988 to 2001 and as chairperson of the department of cellular and molecular pharmacology from 1994 to 2003.

VOLUME 23 NUMBER 5 MAY 2005 N ATU RE BI O TECH N O LO GY