Pathogenic Neisseria : Genomics, Molecular Biology and Disease Intervention [1 ed.] 9781908230614, 9781908230478

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Pathogenic Neisseria Genomics, Molecular Biology and Disease Intervention Edited by John K. Davies and Charlene M. Kahler

Caister Academic Press

Pathogenic Neisseria

Genomics, Molecular Biology and Disease Intervention

Edited by John K. Davies Department of Microbiology Monash University Clayton, VIC Australia

and Charlene M. Kahler School of Pathology and Laboratory Medicine University of Western Australia Nedlands, WA Australia

Caister Academic Press

Copyright © 2014 Caister Academic Press Norfolk, UK www.caister.com British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-908230-47-8 (hardback) ISBN: 978-1-908230-61-4 (ebook) Description or mention of instrumentation, software, or other products in this book does not imply endorsement by the author or publisher. The author and publisher do not assume responsibility for the validity of any products or procedures mentioned or described in this book or for the consequences of their use. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the publisher. No claim to original U.S. Government works. Cover design adapted from Figure 6.3

Contents

Contributorsv Prefaceix 1

Genomics and Reference Libraries

Keith A. Jolley and Martin C.J. Maiden

1

2

Transcriptional Regulatory Proteins in the Pathogenic Neisseria17

3

The Regulatory Small RNAs of Neisseria41

4

Inter- and Intraspecies Transformation in the Neisseria: Mechanism, Evolution and DNA Uptake Sequence Specificity

59

Pathogenic Neisseria: Neither Aerobes nor True Anaerobes, but Dedicated Microaerophiles

77

6

Peptidoglycan Metabolism and Fragment Production

97

7

The Glycome of Neisseria spp.: How does this Relate to Pathogenesis?

115

8

Neisseria Biofilms: Biology, Formation and Role in Pathogenesis

147

9

Molecular Mechanisms of Antibiotic Resistance Expressed by the Pathogenic Neisseria161

Nadine Daou, Ryan McClure and Caroline A. Genco

Yvonne Pannekoek, Dave Speijer and Arie van der Ende

Ole H. Ambur

5

Jeffrey A. Cole

Joseph P. Dillard

Stephanie N. Bartley and Charlene M. Kahler Michael A. Apicella

Magnus Unemo, Robert A. Nicholas, Ann E. Jerse, Christopher Davies and William M. Shafer

10

The Roadblocks in Developing a Gonococcal Vaccine and Reasons for Optimism Lee M. Wetzler

193

iv  | Contents

11

Interactions of Pathogenic Neisseria with Neutrophils in the Context of Host Immunity Alison K. Criss

12

205

Modelling Infection by the Pathogenic Neisseria233 Epshita A. Islam and Scott D. Gray-Owen

Index

255

Contributors

Ole H. Ambur Department of Microbiology and Infection Control Akershus University Hospital Lørenskog Norway [email protected] Michael A. Apicella The University of Iowa Carver College of Medicine Iowa City, IA USA [email protected] Stephanie N. Bartley School of Pathology and Laboratory Medicine The University of Western Australia Perth Australia [email protected] Jeffrey A. Cole School of Biosciences University of Birmingham Birmingham UK [email protected] Alison K. Criss Department of Microbiology, Immunology and Cancer Biology University of Virginia School of Medicine Charlottesville, VI USA [email protected]

Nadine Daou Department of Medicine Section of Infectious Diseases Boston University School of Medicine Boston, MA USA [email protected] Christopher Davies Department of Biochemistry and Molecular Biology Medical University of South Carolina Charleston, SC USA [email protected] Joseph P. Dillard Department of Medical Microbiology and Immunology University of Wisconsin–Madison Madison, WI USA [email protected] Arie van der Ende Department of Medical Microbiology Center for Infection and Immunity Amsterdam (CINIMA) Academic Medical Center Amsterdam The Netherlands [email protected]

vi  | Contributors

Caroline A. Genco Department of Medicine Section of Infectious Diseases; and Department of Microbiology Boston University School of Medicine Boston, MA USA

Ryan McClure Department of Medicine Section of Infectious Diseases; and Department of Microbiology Boston University School of Medicine Boston, MA USA

[email protected]

[email protected]

Scott D. Gray-Owen Department of Molecular Genetics University of Toronto Toronto, ON Canada

Martin C.J. Maiden Department of Zoology University of Oxford Oxford UK

[email protected]

[email protected]

Epshita A. Islam Department of Molecular Genetics University of Toronto Toronto, ON Canada

Robert A. Nicholas Department of Pharmacology; and Department of Microbiology and Immunology Chapel Hill School of Medicine University of North Carolina Chapel Hill, NC USA

[email protected] Ann E. Jerse Department of Microbiology and Immunology Uniformed Services University of the Health Sciences Bethesda, MD USA [email protected] Keith A. Jolley Department of Zoology University of Oxford Oxford UK [email protected] Charlene M. Kahler School of Pathology and Laboratory Medicine; The Marshall Center for Infectious Disease Research and Training; and Telethon Institute of Child Health Research University of Western Australia Perth Australia [email protected]

[email protected] Yvonne Pannekoek Department of Medical Microbiology Center for Infection and Immunity Amsterdam (CINIMA) Academic Medical Center Amsterdam The Netherlands [email protected] William M. Shafer Department of Microbiology and Immunology Emory University School of Medicine Atlanta, GA USA; and Laboratories of Bacterial Pathogenesis Atlanta VA Medical Center Decatur, GA USA [email protected]

Contributors |  vii

Dave Speijer Department of Medical Biochemistry Academic Medical Center Amsterdam The Netherlands [email protected] Magnus Unemo WHO Collaborating Centre for Gonorrhoea and other Sexually Transmitted Infections Örebro University Hospital Örebro Sweden [email protected]

Lee M. Wetzler Department of Medicine Section of Infectious Diseases; and Department of Microbiology Boston University School of Medicine Boston, MA USA [email protected]

Current Books of Interest

Antibiotics: Current Innovations and Future Trends 2015 Leishmania: Current Biology and Control 2015 Acanthamoeba: Biology and Pathogenesis (2nd edition) 2015 Microarrays: Current Technology, Innovations and Applications2014 Metagenomics of the Microbial Nitrogen Cycle: Theory, Methods and Applications2014 Proteomics: Targeted Technology, Innovations and Applications2014 Biofuels: From Microbes to Molecules2014 Human Pathogenic Fungi: Molecular Biology and Pathogenic Mechanisms2014 Applied RNAi: From Fundamental Research to Therapeutic Applications2014 Halophiles: Genetics and Genomes2014 Molecular Diagnostics: Current Research and Applications2014 Phage Therapy: Current Research and Applications2014 Bioinformatics and Data Analysis in Microbiology2014 The Cell Biology of Cyanobacteria2014 Pathogenic Escherichia coli: Molecular and Cellular Microbiology2014 Campylobacter Ecology and Evolution2014 Burkholderia: From Genomes to Function2014 Myxobacteria: Genomics, Cellular and Molecular Biology2014 Next-generation Sequencing: Current Technologies and Applications2014 Omics in Soil Science2014 Applications of Molecular Microbiological Methods2014 Mollicutes: Molecular Biology and Pathogenesis2014 Genome Analysis: Current Procedures and Applications2014 Bacterial Toxins: Genetics, Cellular Biology and Practical Applications2013 Bacterial Membranes: Structural and Molecular Biology2014 Cold-Adapted Microorganisms2013 Fusarium: Genomics, Molecular and Cellular Biology2013 Prions: Current Progress in Advanced Research2013 RNA Editing: Current Research and Future Trends2013 Real-Time PCR: Advanced Technologies and Applications2013 Microbial Efflux Pumps: Current Research2013 Cytomegaloviruses: From Molecular Pathogenesis to Intervention2013 Oral Microbial Ecology: Current Research and New Perspectives2013 Bionanotechnology: Biological Self-assembly and its Applications2013 Full details at www.caister.com

Preface

Neisseria meningitidis and Neisseria gonorrhoeae are exquisitely adapted to life within the human host, their only natural niche. N. meningitidis infection can result in meningitis and septic shock, while N. gonorrhoeae is the causative agent of sexually transmitted gonorrhoea. In this book leading Neisseria authorities review recent research on pathogenic Neisseria to provide a timely overview of the field. Important recent developments include the emergence of gonococcal strains with resistance to all previously effective antibiotics, pointing to the need for new and more effective control strategies. Significant advances have been made in recent years in the development of vaccines to protect against meningococcal disease. In contrast there are no current prospects for a vaccine to protect against gonococcal infections, although there is some reason for optimism in

this regard. Another recent development has been the accumulation of enormous amounts of genomic sequence data for Neisseria species. The platforms and databases hosting Neisseria data are discussed, including how they can be used to extract relevant information and perform comparative genomics. Other topics covered include genetic regulation by both regulatory proteins and small RNA molecules, the natural transformation systems, peptidoglycan metabolism, aspects of microaerobic metabolism, and the glycome of these species. Finally the interaction of both species with the immune system is explored with separate chapters exploring biofilm formation, interactions with neutrophils, and advances in humanized mouse models. John K. Davies and Charlene M. Kahler

Genomics and Reference Libraries Keith A. Jolley and Martin C.J. Maiden

Abstract Whole-genome sequencing is now available at a cost that means it will very soon become a routine tool in epidemiology and public health. There is a challenge, however, in making sense of the vast amount of information encoded in a bacterial genome and in being able to relate this to the large collection of legacy data that exists for many bacterial species. The Neisseria community has led the way in embracing nucleotide sequencing for typing and epidemiology and is at the forefront of the push to apply whole-genome sequencing to research and public health. In this chapter we review the platform and databases hosting Neisseria allelic diversity data and discuss how they can be used to extract relevant information and perform comparative genomics. Introduction The past few years have seen a rapid reduction in the costs of generating whole genome sequence data, with a concomitant increase in the quantity publicly available. While in the past this has been the preserve of large genome centres with highthroughput pipelines and expensive sequencing machines, whole-genome sequencing of bacterial isolates has become feasible for smaller laboratories with the introduction of lower-throughput bench top machines (Loman et al., 2012). In many cases it is already cheaper to produce a whole bacterial genome than it is to perform Sanger sequencing when more than a handful of genetic loci are targeted. With this opportunity, however, come new challenges related to the handling of large numbers of data and the extraction

1

of relevant information that can be used to address research or clinical questions. For many analyses it is necessary to be able to identify the sequences at specific loci. In order to place a strain in context with the large archival collections of isolates gathered over the past few decades, sequence variants of loci that together provide a strain type that can relate to existing typing methods need to be determined. These may relate to fragments of genes used in multilocus sequence typing (MLST) (Maiden et al., 1998), or the genes that encode outer membrane proteins used originally for serological grouping, such as porA (Russell et al., 2004). While it has been suggested that these legacy typing methods will eventually be replaced by whole-genome sequencing (Inouye et al., 2012), this is confusing data with nomenclature. It is more likely that their definitions will continue to be used since they provide the nomenclature that relates to characteristics, including transmission and virulence, and genome sequencing will be used as a means of easily obtaining them. While typing is important for epidemiology, having whole-genome data available can clearly provide much more. It could be invaluable for clinical use and for informing public health measures since the sequences of specific genes together encode phenotypic properties, for instance how susceptible the strain is to antimicrobial treatment or whether its antigenic repertoire is covered by existing vaccines. Here we describe the databases hosting allelic diversity and isolate provenance available to the Neisseria research and public health communities. The way these data are linked and the means of

2  | Jolley and Maiden

extracting clinically and epidemiologically relevant information are discussed, together with an overview of the online tools that can be used to leverage and make inferences from these data. Bacterial typing using nucleotide sequencing The ease of use and widespread availability of standard techniques such as the polymerase chain reaction (PCR) and fluorescent nucleotide sequencing lead to the development of a range of typing techniques for Neisseria species in the last decades of the twentieth century. The relative ease of creating new methods based on a particular laboratory’s favoured genetic target, however, had the potential to create more confusion than clarity since none were widely adopted resulting in competing nomenclatures without a ready means of cross-referencing (Achtman, 1996). The introduction of MLST in 1998 (Maiden et al., 1998) changed this for two reasons: (1) the typing scheme was developed by a consortium of laboratories at the forefront of investigating meningococcal population biology, resulting in sufficient community buy-in to promote large scale adoption; and (2) the development of online databases that provided dissemination of sequences and typing nomenclature (Chan et al., 2001). Any typing method that relies upon a single genetic target can provide inaccurate results due to the confounding effects of recombination, where different parts of the genome may have different evolutionary histories. This is the rationale behind MLST which uses the nucleotide sequences of fragments of multiple (usually seven) housekeeping loci to define alleles and combinations of alleles. Because these loci are located in different parts of the genome, where the distances between loci are considerably longer than the size of fragment exchanged in recombination events the method is robust to its effects (Holmes et al., 1999). The number and length of fragments was a pragmatic choice based on the level of observed discrimination and the ease of data generation. Fragment lengths of approximately 500bp are used for most schemes because this was the length of sequence that

could be readily obtained from a single sequencing reaction using the gel-based fluorescent sequencing technology at the time. Every unique sequence at each locus is given an arbitrary allele number, starting at one and incremented in the order of discovery. Every unique combination of alleles is then defined by an arbitrary sequence type (ST) number. Consequently, a ST number can be considered shorthand for a specific, unique, sample of the genome represented by approximately 3.5 kbp. Because the numbering of both alleles and STs is arbitrary it is important to realize that no phylogenetic inference can be made based on these numbers and the sequences of closely numbered alleles and STs are not necessarily any more similar than any other. This was a deliberate choice and ensures that the typing nomenclature is completely value-free, with no preconceived idea of the population biology of an organism influencing the naming. This does not prevent higher-level organization of the STs (see ‘Clonal complexes’, below), but ensures that the most fundamental level of typing nomenclature, the allele and ST numbers, never need to change when understanding of the organism’s biology changes. The use of allele numbers to indicate genetic changes has an advantage over solely sequence-based, phylogenetic, methods for recombining organisms such as Neisseria. This is because a single recombination event may introduce 50 or so nucleotide changes, but by treating all allelic changes as equal these changes, introduced by a single genetic event, are weighted the same as a single mutation (Maiden, 2006). As the MLST loci are housekeeping genes chosen because they were under stabilizing selection and, therefore, less likely to change rapidly, the method provides the underlying genetic type of a strain. This is invaluable for global epidemiology but for questions of local outbreak management the resolution is often insufficient to unambiguously resolve strains. This is an issue of choosing the appropriate resolution for a given problem. For this reason, the results of MLST can be complemented with sequence data from genes that are under selection for change, such as genes encoding surface antigen proteins. The outermembrane porins, PorA and PorB, are highly variable, and it is the variation in their protein

Genomics and Reference Libraries |  3

sequences that have formed the basis of serosubtyping and serotyping respectively. The use of these serological methods has been largely superseded by sequencing of the genes encoding the proteins (Russell et al., 2004; Urwin et al., 2004). The use of sequence data from the two variable regions of porA and the similarly diverse variable region of fetA/frpB (Thompson et al., 2003) are now recommended, along with MLST and serogroup designation, as the basis for meningococcal strain designation ( Jolley et al., 2007). Clonal complexes Although pathogenic Neisseria are highly diverse, it was first recognized using multilocus enzyme electrophoresis (MLEE) (Selander et al., 1986) that the bacterial population is actually highly structured with only small subsets of strains frequently isolated from cases of disease (Caugant et al., 1986, 1987; Moore et al., 1989). The total diversity of the genus has been sampled by MLST and currently over 10,000 sequence types have been defined. Some of these STs are observed frequently, both over time and geographical distance, whereas others appear transiently and may never be seen again. Inspection of the allelic profiles defined by these STs demonstrates that many share alleles at multiple loci. Clustering methods used on these profiles identify that the most frequently observed STs occupy central positions with many more related STs that vary at only one or two loci compared to others. Further, strains belonging to these groups of related STs exhibit epidemiologically and pathologically distinct phenotypes, with some associated frequently with severe invasive disease and others mainly with asymptomatic carriage (Yazdankhah et al., 2004). These groups are generally referred to as clonal complexes. One popular method for defining these groups in bacterial species has been e-BURST (Feil et al., 2004). This is a method that identifies clusters of STs by counting the number of single, double and triple-locus variants for each ST and graphically displaying these in concentric rings linked by spokes to represent the number of variant loci. The default group definition for e-BURST with a seven locus scheme is that group members share

five loci with any other member of the group. While this is useful in identifying the centrally positioned STs, there is a fundamental disadvantage of its use in defining clonal complexes used in a typing nomenclature. Because the e-BURST groupings depend on the number and diversity of variants in the dataset they can change following the addition of new data. Clearly for typing purposes this is not ideal. Furthermore, groups can chain together due to the presence of rare, intermediate, allelic profiles such that with an extensively sampled population you start to see super-clusters with some peripheral members not sharing any loci with the ST at the centre (Francisco et al., 2009, 2012). For these reasons, a more pragmatic solution is utilized for clonal complex definition in Neisseria and some other species. This defines clonal complexes as those STs that match a pre-determined central ST at four or more loci. This ensures that the complex to which a ST has been assigned remains constant and also constrains the size of the complex. This algorithm is also very straightforward to automate and can therefore be implemented within a MLST database to ensure that all newly identified STs are assigned to defined clonal complexes if appropriate. The central STs are identified by a variety of heuristic methods, including e-BURST, as well as split decomposition and identification of prominent circulating genotypes in epidemiological reports. It is important to note, however, that an exhaustive search for new complexes has not been conducted; rather they are defined as the need arises, and so many STs do not currently belong to a known complex. Sequence typing databases The MLST databases for Neisseria are hosted on PubMLST.org ( Jolley et al., 2004; Jolley and Maiden, 2010). Data are stored in two linked, but independent, databases. The first of these is for sequence and profile definitions and can be considered to be the authoritative nomenclature server, or sequence dictionary, for MLST. It simply contains information linking specific sequences to allele identifiers for each of the MLST loci and defines each unique combination of these alleles as a ST, along with standard

4  | Jolley and Maiden

Sequence bin >abcZ1 TTTGATACTGTT… >abcZ2 TTTGATACCGTT… >abcZ3 TTTGATACCGTT…

BLAST

Sequence definition database

CCATCCCGTTGTCGAACAGCAGGTACGCCA AACCTTCAGACGGCATTGCCGCAGCTTCAAA CTTCACCGCCAACCACACCGACCTTGACCAC AAGCCGCCAAAGCCGCCGCCGCGCTGGAC GTGTTGTCCACATTTTCAGCCTTGGCAAAAG AAACACCGCCTCATGCTGCTCACCGGCCCC AGGAACCCTCAAAGCCGTTTTCCCGGAAAAC AGCGGAACTTCGTCCGCCCCGAGTTTGCCG AATATGGGCGGCAAATCCACCTACATGCGCA CTATCCACAGCCGAACAGCTCCGCCAAGCC ACTATCCGGTTATCCACATCGAAAACGGCCG GGAACCCTCAAAGCCGTTTTCCCGGAAAACC ATTTTGCCCGAACCTTCCGTCTGGCTGAAAG CCATCCCGTTGTCGAACAGCAGGTACGCCA TATCCACAGCCGAACAGCTCCGCCAAGCCA ACGGCAATGTCATCAACCACGGTTTTCATCC CTTCACCGCCAACCACACCGACCTTGACCAC TTTTGCCCGAACCTTCCGTCTGGCTGAAAGA CGAACTGGACGAATTGCGCCGCATTCAAAAC AGGAACCCTCAAAGCCGTTTTCCCGGAAAAC CGGCAATGTCATCAACCACGGTTTTCATCCC CATGGCGACGAATTTTTGCTGGATTTGGAAG CTATCCACAGCCGAACAGCTCCGCCAAGCC GAACTGGACGAATTGCGCCGCATTCAAAACC CCAAGGAACGCGAACGTACCGGTTTGTCCA ATTTTGCCCGAACCTTCCGTCTGGCTGAAAG ATGGCGACGAATTTTTGCTGGATTTGGAAGC CACTTAAAGTCGAGTTCAACCGCGTTCACGG ACGGCAATGTCATCAACCACGGTTTTCATCC CAAGGAACGCGAACGTACCGGTTTGTCCAC CTTTTACATTGAATTGTCCAAAACCCAAGCC CGAACTGGACGAATTGCGCCGCATTCAAAAC ACTTAAAGTCGAGTTCAACCGCGTTCACGGC GAACAAGCACCTGCCGACTACCAACGCCGG CATGGCGACGAATTTTTGCTGGATTTGGAAG TTTTACATTGAATTGTCCAAAACCCAAGCCG CAAACCCTTAAAAACGCCGAACGCTTCATCA CCAAGGAACGCGAACGTACCGGTTTGTCCA AACAAGCACCTGCCGACTACCAACGCCGGC CGCCGGAACTGAAAGCCTTTGAAGACAAAGT CACTTAAAGTCGAGTTCAACCGCGTTCACGG AAACCCTTAAAAACGCCGAACGCTTCATCAC GCTGACTGCTCAAGAGCAAGCCCTCGCCTT CTTTTACATTGAATTGTCCAAAACCCAAGCC GCCGGAACTGAAAGCCTTTGAAGACAAAGT AGAAAAACAACTCTTTGACGGCGTATTGAAA GCCCCGAGTTTGCCGACTATCCGGTTATCCA GCTGACTGCTCAAGAGCAAGCCCTCGCCTT AACCTTCAGACGGCATTGCCGCAGCTTCAAA ACAAGTCGCGCTGATTGTTT CATCGAAAACGGCCGCCATCCCGTTGTCGA AGAAAAACAACTCTTTGACGGCGTATTGAAA AAGCCGCCAAAGCCGCCGCCGCGCTGGAC ACAGCAGGTACGCCACTTCACCGCCAACCA AACCTTCAGACGGCATTGCCGCAGCTTCAAA GTGTTGTCCACATTTTCAGCCTTGGCAAAAG CACCGACCTTGACCACAAACACCGCCTCATG AAGCCGCCAAAGCCGCCGCCGCGCTGGAC AGCGGAACTTCGTCCGCCCCGAGTTTGCCG CTGCTCACCGGCCCCAATATGGGCGGCAAA GTGTTGTCCACATTTTCAGCCTTGGCAAAAG ACTATCCGGTTATCCACATCGAAAACGGCCG TTT TCCACCTACATGCGCCAAGTCGCGCTGATTG AGCGGAACTTCGTCCGCCCCGAGTTTGCCG CCATCCCGTTGTCGAACAGCAGGTACGCCA CTTCACCGCCAACCACACCGACCTTGACCAC AAACACCGCCTCATGCTGCTCACCGGCCCC AATATGGGCGGCAAATCCACCTACATGCGC CAAGTCGCGCTGATTGTTT

Isolate record Provenance metadata Allelic designations

Figure 1.1  The Neisseria PubMLST site uses two linked databases powered by the BIGSdb platform. The Isolate database sequence definition database contains allele sequence and MLST profile definitions whereas the isolate database contains provenance and epidemiological information along with the genome sequence of the isolate if available. To identify alleles for a specific locus within a genome, the entire genome is queried, using BLAST, against the database containing all known allelic variants. Identified allele designations and the locus positions within contigs from the sequence bin are stored within the isolate database so this only needs to be performed once.

housekeeping information such as who submitted the sequence and when. The second database holds isolate provenance and other metadata with allele numbers identified for specific loci (Fig. 1.1). Separation of the definition and isolate databases was an early design decision that facilitates a network of interlinked databases where single nomenclature servers can interface with multiple isolate databases, some of which may be public and others private, with different requirements for metadata inclusion. Likewise, a single isolate database can connect to different definition databases, allowing cross-referencing of multiple typing methods hosted separately. Because the databases are linked, sequence types can be automatically set for any isolate once allele definitions have been added. This link is live such that any changes in the nomenclature stored in the definitions database are immediately reflected in the isolate record. The definitions database currently has over 10,000 STs defined, with each MLST locus represented by between 425 and 679 unique alleles, with more identified every week.

Antigen typing As well as MLST definitions, the Neisseria PubMLST databases host antigen sequence data. In particular, peptide sequences for the PorA (Russell et al., 2004) and FetA (Thompson et al., 2003) variable regions used in the standard strain designation ( Jolley et al., 2007) are defined. The PorA porin has two variable regions thought to be surface-exposed loops originally used in sero-subtyping (Barlow et al., 1989; Saukkonen et al., 1989). The extremely high levels of variation observed within these, however, resulted in many isolates where the sero-subtype could not be determined due to incomplete coverage of available monoclonal antibodies and also situations where variants with similar binding affinities were impossible to differentiate. For these reasons it was proposed in 2003 at the European Meningococcal Disease Society (EMGM) meeting that all meningococcal subtyping should be performed using nucleotide sequencing which has now been almost universally accepted and implemented. Because of the requirement to maintain a level of backwards compatibility with the old

Genomics and Reference Libraries |  5

nomenclature, naming of variants uses a binomial system of family and variant (Russell et al., 2004). Because most of these families were defined by serology their numbering is mixed between the two variable regions where, for example, a strain may be defined as PorA: 5-2, 10-1, which means that variable region 1 is family 5 variant 2 and variable region 2 is family 10 variant 1. Lists of these families and variants, linked to legacy nomenclature where possible, are maintained at http:// pubmlst.org/neisseria/PorA/ and sequences can be queried against the database. As of the end of 2012, there were a total of 242 VR1 sequences represented by 10 families, and 672 VR2 sequences represented by 20 families. The single variable region of FetA is, along with the two variable regions of PorA, also used in the recommended strain designation. Owing to their similarity in diversity, length and usage the nomenclature of these sequences follow the naming of PorA, with a family and variant name (Thompson et al., 2003). Similarly, lists of these families and variants can be found at http:// pubmlst.org/neisseria/FetA/. As of the end of 2012, there were a total of 413 variant sequences representing five families. There has been considerable interest in recent years in Factor H-binding protein as a component of vaccines against serogroup B meningococci (Beernink et al., 2007; Brehony et al., 2009; Murphy et al., 2009; Pizza et al., 2008). Extensive investigation of its sequence diversity has been undertaken by groups working with two major pharmaceutical companies, Novartis and Pfizer, where sequence variants have been placed in to either two or three distinct family groups. This had the consequence of competing nomenclatures that incorporated the family name within the allele identifier. Further complexity was then added by the identification of modular regions of the protein, each of which could be defined separately resulting in an additional naming scheme (Beernink and Granoff, 2009; Pajon et al., 2010). Additionally, a selected smaller region of the fHbp gene has been sequenced in a specific study with unique alleles defined facilitating determination of the family group using a single sequencing reaction (Hong et al., 2012). All these nomenclatures

have been incorporated in to the PubMLST reference database (http://pubmlst.org/neisseria/ f Hbp/), with alleles and peptide sequences now identified by a value-free allele identifier using integers, as is the case with the MLST loci. The library allows the alternative names for each sequence to be cross-referenced and searched as required, and it is hoped that the allele numbering defined here will be used going forward to provide consistency within the field. Antibiotic resistance genes Other genetic targets have been added over the years with a multinational team investigating the sequences of the penA (Taha et al., 2007), rpoB (Taha et al., 2010) and gyrA genes (Hong et al., 2013) linked to the antibiotic resistance to penicillin, rifampicin and ciprofloxacin respectively. These studies have linked specific mutations incorporated within allele sequences now defined in the PubMLST database to specific MIC ranges for the respective antibiotics. Corresponding isolates can be retrieved from sequence searches so that other characteristics of the strains, such as clonal complex, can be readily cross-referenced. Data management Owing to the importance of authoritative nomenclature servers for typing schemes that are widely used internationally, the entire PubMLST.org website and all the databases hosted on it are mirrored in multiple geographical locations providing resilience to the service. Apart from the primary host in the UK, there are mirrors located in the USA, Denmark, Norway and the Netherlands. The Neisseria PubMLST database is overseen by an international management committee drawn from delegates of the International Pathogenic Neisseria Conference (http://pubmlst.org/neisseria/info/). The primary role of the committee has been in defining clonal complexes but it is envisaged that this will expand with the era of whole-genome sequencing to include issues concerning annotation and nomenclature. Since 2010, the Neisseria PubMLST databases have been hosted using the Bacterial Isolate Genome Sequence Database (BIGSdb) platform

6  | Jolley and Maiden

and have started to accept whole genome data associated with isolate records. The Bacterial Isolate Genome Sequence Database (BIGSdb) platform The BIGSdb platform ( Jolley and Maiden, 2010) was developed to flexibly store and analyse nucleotide sequence data ranging from single Sanger sequencing assemblies, through contiguous sequences (contigs) generated by parallel sequencing technologies such as Illumina and 454, to complete finished genomes. The design rationale was based on the development of the earlier generation of MLST databases ( Jolley et al., 2004) where unique sequences of loci of interest are assigned allele numbers. As with the MLST database idea, BIGSdb works on the principle of two (or more) linked, but independent, databases: one for sequence definitions and one for isolate data. Conceptually, each record in the isolate database has a sequence bin associated with it containing all sequence data generated for that isolate. Isolates can be grouped into projects or linked by PubMed id to a publication such that specific sets of isolates can be analysed as a coherent group. Any number of loci can then be defined, based either on nucleotide or peptide sequence, with the locus configuration containing information telling the system where it can find the allele definitions. This way, an isolate database can pull in allele sequence data from any number of definition databases. Once sequence data have been uploaded for an isolate, the entire sequence bin can then be queried using the BLAST algorithm (Altschul et al., 1997) against the allele definitions for the loci of interest, a process known as scanning. This process is very rapid even though the query sequence may be a complete genome, generally taking of the order of one second per locus on current hardware. Where known alleles are identified these are marked in the database and the location of the locus tagged within the sequence bin. Once this has been performed for a locus and an allele identified it does not have to be done again as any comparative analysis of isolates can be performed against the designated allele numbers. When new alleles are identified

based on similarity to previously defined alleles, these can be added to the definition database and assigned new allele numbers, and the isolate rescanned. This results in an iterative improvement in the scanning capability with more and more of the total diversity of a locus being identified as more data are added to the database. To facilitate hierarchical analysis of genomes, the loci themselves can be grouped in to schemes. A scheme is a collection of loci, the unique combinations of alleles of which can be defined by, or associated with, field values. One such scheme is that for MLST, where the unique combination of alleles for the MLST loci together define the ST number. Non-unique fields can also be associated with an allelic combination, or profile. This enables associated data to be linked, such as clonal complex in the case of MLST. The concept of a scheme is totally generic within BIGSdb though, so that schemes can be constructed for any set of related loci, such as genes encoding antibiotic resistance or those encoding enzymes involved in specific biochemical pathways. Schemes themselves can also be grouped, as can these groups, allowing an ordering of loci by whatever criteria necessary, but with the flexibility that loci can belong to multiple schemes and schemes can belong to multiple groups. The gene-by-gene concept embodied in BIGSdb facilitates the analysis of genomes using a MLST-like approach offering scalability and speed (Bratcher et al., 2012; Jolley and Maiden, 2013). The use of allele numbers act as shorthand for a sequence and remove the need to constantly re-analyse data as new comparator genomes are added to a dataset. It is computational straightforward and efficient to identify and count the alleles that are the same between isolates when they have been tagged with an allele number, rather than having to compare the sequences directly. This is the idea behind the Genome Comparator plugin. Genome Comparator and whole genome MLST Genome Comparator is an analysis tool available as a plugin within BIGSdb (Fig. 1.2). It was written initially to facilitate the identification of closely matching genomes within a set (Omer

Genomics and Reference Libraries |  7

A

B

C

Figure 1.2  Use of the Genome Comparator plugin to perform comparative genomics on PubMLST.org. (A) isolates are selected for analysis from the list and an annotated reference chosen to use for comparisons. Alternatively an annotated genome file may be uploaded. (B) Each coding sequence from the reference is BLASTed against each of the genomes in turn. Sequences that are identical among isolates are given the same allele number. This builds up a whole genome MLST profile. (C) A network is constructed by counting the allelic differences between each pair of genomes.

8  | Jolley and Maiden

et al., 2011) but has been further developed to enable analysis of the inter-relationships of selected isolates and provide information about which coding sequences are found throughout and can consequently be considered core genes. As with BIGSdb itself, the concept of Genome Comparator is very straightforward. Isolates are analysed using a set of comparator loci defined within an annotated genome record (Fig. 1.2A). A GenBank accession number can be entered and the software will automatically retrieve the genome record and extract all the coding sequences from the genome and use these for analysis. Each of the genomes in the analysis set is queried against the sequence from the comparator genome and the best matching sequence extracted. Where these are identical to the reference they are designated as allele 1. The first allele that is different from the reference genome is called allele 2 and so on, effectively building up a whole genome MLST profile based on the coding sequences of a finished reference. For Neisseria this is of the order of approximately 2000 genes. The resultant table is colour-coded with respect to allele number making it very easy to identify the most highly variable or conserved loci within the genome (Fig. 1.2B). A distance matrix is then constructed by counting all the differences in allele numbers between every pair of isolates. A NeighborNet (Bryant and Moulton, 2004) representation of these distances is then constructed automatically using Splitstree (Huson, 1998) (Fig. 1.2C). Because Genome Comparator compares each genome in turn against a reference and does not rely on pairwise analysis of complete genome sequences the method is highly scalable with the amount of time taken to analyse genomes increasing only linearly with additional genomes. In practice, comparison of ten genomes compared at every coding sequence of an annotated reference can be completed in 15–20 minutes ( Jolley et al., 2012b). Optionally, the analysis will also output alignments and concatenated sequences of any variable loci allowing additional phylogenetic analyses to be performed offline. Further analysis of these sequences allows the exact differences between genome coding sequences to be forensically analysed and often the location of recombination events can be determined where

runs of allelic differences are found in adjacent coding sequences. An alternative mode for Genome Comparator is to use the loci already defined in the database, or any subset of them, for comparisons. One advantage of this is that if the alleles for these loci have already been tagged within the database then the comparison is very quick since no BLAST searching has to be performed. Selecting loci based on scheme membership allows comparisons to be performed based on specific aspects of the biology of the organism. Neisseria genomes on PubMLST.org The Neisseria PubMLST databases began hosting whole genome data with the introduction of the BIGSdb platform. At the end of 2012 there were over 740 genomes in the isolate database, including the fully annotated reference strains FAM18 (Bentley et al., 2007), Z2491 (Parkhill et al., 2000), and MC58 (Tettelin et al., 2000). Other genomes in the collection include those from the 107 isolates first used to validate MLST and representing global diversity in the latter part of the last century (Maiden et al., 1998) and over 500 from the Meningitis Research Foundation (MRF) meningococcus genome library. This latter set is a resource that includes data for every single invasive meningococcal isolate from England, Wales and Northern Ireland in the 2010/11 epidemiological year, assembled as a joint project between the University of Oxford, the UK Health Protection Agency and the Sanger Institute, and funded by the MRF. The majority of the genomes have been sequenced using the Illumina platform with a de novo method of assembly, usually Velvet (Zerbino, 2010; Zerbino and Birney, 2008). Because this is a short-read sequencing technology, this results in a relatively large number of contigs (often 200–300) for each genome. Incomplete assembly occurs because of repeat regions larger than the sequencing read length such that fragments cannot be placed together unambiguously. This tends to occur in regions outside of the coding sequences so while a complete, closed, genome would be preferable, this number of contigs does not usually cause a problem for genome analysis

Genomics and Reference Libraries |  9

using the BIGSdb gene-by-gene approach with the majority of coding sequences located completely within a contig. As soon as a genome is uploaded to the database a first pass annotation is performed automatically by an autotagger script that scans the genome against all known alleles for each defined locus. Further annotation is performed periodically in a semi-automated manner using an iterative approach of scanning, identifying new alleles based on similarity to existing alleles, defining these in the definitions database and then rescanning (identifying alleles) and tagging (updating designations in the database). As new alleles are added to the database all the time, a periodic run of the autotagger against all the records in the database further annotates genomes containing newly identified alleles not previously tagged. Community-based annotation At the time of writing there were over 1300 loci defined in the PubMLST databases, the majority of which were complete coding sequences of genes thought to be core over the Neisseria genus (Bennett et al., 2010), along with some legacy gene fragment loci used in previous studies and typing schemes. Locus nomenclature is a potentially confusing issue since every annotated reference genome has used its own standard. The commonly used names for genes can mislead since in many cases these have been derived based on perceived function due to their sequence similarity to genes from other organisms in archival databases such as GenBank. These functions can sometimes not be accurate or they may not be the primary function of the protein encoded by the gene. Finally, because of the standard practice of giving genes three- or four-letter common names, there are cases where two completely separate genes, coding for different functions, have been assigned the same name in the literature. One example of this that has caused confusion is nadA – the extensively studied vaccine component NadA (Beernink et al., 2007; Comanducci et al., 2002; Lucidarme et al., 2009, 2010) is encoded by the gene designated NMC1969 (adhesin/invasin) in the FAM18 genome (Bentley et al., 2007), not by NMC1772 (nadA), which codes for quinolinate

synthetase, part of the NAD biosynthesis pathway. A standardized, value-free, nomenclature has now been introduced for loci defined in PubMLST. This uses the four letter string ‘NEIS’ (designating the Neisseria genus) followed by an integer. To maintain some link to existing annotations, the integer used is the same as in the FAM18 genome where these loci exist in FAM18. Where they do not exist in FAM18, the numbers are incremented from the last designated locus in the order of definition. The BIGSdb platform allows a locus to also have a common name (which need not be unique) and any number of alternative aliases. All of these can be searched equally. It also has functionality to provide descriptions for loci that can also link to primary literature in PubMed or include URLs to any other information page located on the worldwide web. Using this, it is possible for a curator to provide a definitive overview of the locus in question, and link alternative nomenclatures, which can be accessible from any page of returned results within the website that refer to the locus. Genome annotation has, in the past and by necessity, been performed by a single researcher or small team in a genome centre. While this was appropriate in the days of the first genomes, when the exemplar sequences were determined, it fails to leverage the accumulated knowledge of the research community. There is considerable expertise in narrow areas of the biology of specific organisms that can be tapped to improve the annotations for the benefit of the whole community. BIGSdb has a fine-grained permission system that allows specific users to annotate specific loci. With this, experts of particular loci or biochemical schemes can be recruited to provide detailed information about the locus and to assess what differences in the sequence may mean in respect to phenotype. At the International Pathogenic Neisseria Conference in Würzburg in 2012, a call to delegates was made to become specialist curators and this has started to be implemented with initial areas focusing on lipooligosaccharide biosynthesis, pilins, and small regulatory RNAs. These join existing curator teams that have overseen the assignment of new MLST alleles and profiles, antigen variant types and antibiotic resistance genes (Table 1.1). Curation, in this context, does not necessarily involve the day-to-day

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Table 1.1 Locus schemes available on the Neisseria PubMLST sequence definition database Category

Schemes

Loci

Mean number of alleles/locus

Capsule

Region A

48

32

Region C

4

68

DNA replication

16

131

Nucleotide excision repair

8

140

RNA polymerase

4

107

Aminoacyl-tRNA biosynthesis

24

142

Glycolysis

11

157

Pyruvate dehydrogenase complex

3

148

TCA cycle

16

155

LOS Biosynthesis

41

102

Purine metabolism

39

122

Pyrimidine metabolism

32

120

MLST

7

623

Finetyping antigens

3

443

Antigen genes

5

368

eMLST (20 complete genes)

20

171

Antibiotic resistance

3

159

Factor H-binding protein

9

210

Iron acquisition

4

392

Pilus genes

3

225

Protein glycosylation

11

123

Small regulatory RNAs

3

1

Genetic information processing

Metabolism

Typing

Other schemes

scanning of genomes to identify new variants, but rather to provide a comprehensive description of the gene and its product, how this fits in to biochemical pathways and how its products interact with others. Automated extraction of clinically relevant information Typing information can be extracted after generating whole genome sequence data. A number of services and software are available to do this specifically for MLST. If short read data have been assembled in to contigs then directly querying the PubMLST databases is the easiest method. Multiple contigs up to whole genome size can be pasted in to the sequence query forms on PubMLST and loci or scheme data extracted, so by selecting ‘MLST’ as the scheme, the genome sequence is queried against all seven MLST loci

and the ST and clonal complex returned if a match is found. This can be performed for any loci defined in the database, so it can readily be used to determine any antigen or other sequence of interest. The Neisseria sequence definition database also has a new rule-based query that can provide much more clinically relevant data in one go without the need to specify individual loci. Selecting the tool to ‘extract finetype’ from the sequence definition database contents page presents the user with a web form in which multiple FASTA files for the genome can be pasted or alternatively these can be uploaded directly from a local file. After starting the analysis, the genome is queried against pre-selected loci in turn – specifically the MLST loci, the finetyping loci (PorA VR1, PorA VR2 and FetA VR), and genes involved in antibiotic resistance for which there is linked MIC data (currently penA and rpoB). After about 40 seconds, the complete strain designation (ST,

Genomics and Reference Libraries |  11

clonal complex, PorA and FetA designations) is provided, along with the penA and rpoB alleles with an indication of the expected resistance of the strain to penicillin and rifampicin based on matching data in the isolate database. While queries on PubMLST require assembled sequence data, other methods are available that can be used directly with short-read data without the need for prior assembly. The first of these is a web service hosted at the Center for Genomic Epidemiology at the Danish Technical University (Larsen et al., 2012). With this service, short-read data from various sequencing platforms can be uploaded and the MLST profile and ST extracted for Neisseria and many other species. This process can take quite a while to perform and necessarily requires the uploading of a large amount of data per genome so may be appropriate only for a small number of samples or as a one-off analysis. An alternative method involves the local installation of software called SRST (Inouye et al., 2012). This has an advantage over the previous service in that there is no need to upload large amounts of data and it can be used against any loci for which an allele definition file is available. This would enable it to be incorporated in to a local sequence assembly pipeline. Apart from the SRST scripts, the software also requires installation of BWA (Li and Durbin, 2009), SAMtools (Li et al., 2009) and BLAST (Altschul et al., 1997). Neisseria speciation using standard and ribosomal MLST Although meningococcal isolates comprise the majority of the Neisseria MLST database records, the standard MLST scheme can be used with other species of the Neisseria genus. It has been specifically validated for N. lactamica (Bennett et al., 2005) and N. gonorrhoeae (Bennett et al., 2007), although optimization of primer sequences can be an issue when looking at more variable species. Such optimization is, of course, not required when whole genome sequence methods are employed and the Neisseria PubMLST isolate database now has full MLST profiles for 106 isolates not belonging to these three named species. A new technique has recently been developed that allows high resolution strain differentiation

across the bacterial domain. Ribosomal MLST, or rMLST ( Jolley et al., 2012a), uses the 53 genes that encode the ribosomal proteins in a MLST scheme. Owing to variation in bacterial biology, one of the issues with conventional MLST is that the genes utilized are not necessarily found everywhere, sometimes not even within the same genus, which results in the scheme only working for a single species or, as in the case with Neisseria, a single genus. Since ribosomes are absolutely essential for protein translation and their structure is largely conserved, these genes can be used for speciation. As there are 53 of these genes, the method also provides high-level typing within a species, in many cases better than a conventional MLST scheme designed for that species. This has been successfully employed to investigate the Neisseria genus (Bennett et al., 2012). While amplifying and sequencing this many genes is impractical using standard molecular techniques, the introduction of whole-genome sequencing has made this a feasible proposition, allowing a researcher to not only identify an unknown isolate at the species level but to obtain a specific strain type, all using a single typing scheme. Discussion The gene-by-gene approach to genome analysis, as exemplified by BIGSdb and the PubMLST Neisseria databases provides a straightforward and scalable means of identifying and comparing variation. Because comparisons are at the level of individual genes, isolates that are very different from each other can be compared as easily as those that are closely related and there is no requirement to align large parts of the genome. Genes are also relatively small sequences of DNA that can be readily compared using highly optimized tools such as BLAST. Further, because the sequence of each allele can be condensed to a single integer, analysis can be simplified using tools developed for allelic based typing methods such as MLST. The approach is additive to existing data based on nucleotide sequence, so these never become obsolete and can always be used for historical comparisons with new data extracted from genomes. Analysis of genomic diversity using the reference libraries on PubMLST require that the

12  | Jolley and Maiden

sequences to be compared are assembled. This means taking the short reads produced by the sequencer and placing them together to form contigs that are longer than the genes to be compared. Assembly can be performed using either a reference or a reference-free approach. With reference-based assembly, a complete reference genome is used as a template against which short reads are matched. In the past, this was often necessary since the read lengths produced by the first generation of parallel sequencing machine were very short. The disadvantage of using a reference is that short reads will only be matched against sequence that is present in the reference, so accessory genes that are not found in all isolates can be excluded. Using a reference-free assembly, where the short-reads are matched against overlaps of other short reads removes this disadvantage and is computationally feasible with the longer read lengths that can now be routinely obtained. Even with longer read lengths, however, it is important to realize that some parts of the genome remain difficult to assemble. These are typically regions containing repetitive sequence that are found in multiple parts of the genome where the repeat sequence is longer that the sequence read length. These parts of the genome will be excluded from the final assembly since reads from these regions cannot be unambiguously matched with other reads to create longer contigs. Depending on the nature of the analysis, however, this may not be a problem since these regions usually fall outside the coding sequences of genes. Use of a gene-by-gene approach facilitates a hierarchical manner of analysis where the number and choice of loci are selected depending on the type of question being asked. For situations, such as disease outbreaks, where isolates need to be ruled in or out of a transmission chain, often the standard typing loci are sufficient. The tools on PubMLST make it a relatively trivial process to extract the sequences for the full MLST and finetyping loci to identify strain type – indeed whole-genome sequencing is now the easiest and most cost effective method of determining these. If isolates are different at these loci then it can be determined that they are not closely related – at least not in the timeframe of an outbreak. If they cannot be differentiated at these loci, then it will

be necessary to use progressively more loci to look for differences. The rMLST loci provide an intermediate level of discrimination when used together, but ultimately when looking for transmission partners it may be necessary to use all the coding sequences present in a reference genome to identify small differences. The Genome Comparator tool on PubMLST allows this hierarchical analysis to be performed on genomes submitted to the database. The BIGSdb software that runs PubMLST is also freely available and can be run locally ( Jolley and Maiden, 2010). In other organisms whole genome data used for epidemiological purposes has largely concentrated on the identification of single nucleotide polymorphisms (SNPs) against a reference genome (Croucher et al., 2011; Eyre et al., 2012; Grad et al., 2012; Harris et al., 2010; Köser et al., 2012; Young et al., 2012). This methodology has been borrowed from the field of human genetics where variation is considerably lower than seen in most bacteria and assumes novel mutations are clonally inherited. While this method is effective when investigating local transmission chains where all isolates involved are closely related to an available reference strain, and does not require de novo assembly of short read data, it has fundamental disadvantages for integrating whole genome data for global epidemiology and for phenotypic studies that may include disparate isolates. Only sites present in the reference sequence can be identified, so large parts of the accessory genome are necessarily excluded from analysis. SNP-based analyses also do not scale well when hundreds or thousands of isolates are to be compared and introduction of a new reference necessarily requires repeating the SNP discovery. On a biological level it is somewhat unsatisfactory to divorce analysis from genes. Selective forces act at the level of genes and they cannot all be treating equivalently as is often done when counting SNP differences. While SNPs can be traced back to genes it is an unnecessary complication that is as an adjunct to the primary analysis. Future trends The genomics and reference libraries for Neisseria are an exemplar for gene-by-gene analysis of

Genomics and Reference Libraries |  13

whole genome data. Currently, loci representing the core genome of the Neisseria genus are represented but there is a large amount of information from the accessory genome that is not yet available. It will require an effort from large parts of the research community for this to change, to take ownership of specific loci and pathways so they can be annotated with expert input. A significant start has been made on this and members of the community have engaged with the process Identification of novel sequences will increasingly become an automated process, with new definitions made as genome sequences from new isolates are uploaded to the database. Future challenges will centre on making further sense of sequence variation. How sequence variation at specific loci affects the phenotype of the organism is the ultimate goal of many pathogen researchers. The ability to predict likely transmission rates and disease severity based on particular alleles is possible at a crude level already where these are markers for clonal complexes, but the genome offers much finer levels of insight if we can unravel its complexities. Apart from knowing which variants of specific genes are present there is also information available about the likely expression of those genes. Phase variation of contingency loci can help strains adapt to new host environments (Bayliss et al., 2000; Martin et al., 2003) and the likely expression of these can often be interpreted from the genome. Whole genome sequences of large numbers of bacterial isolates have only been available for a few years and we are still at the beginning of a journey that will result in a fuller understanding of pathogen biology. Web resources

PubMLST (http://pubmlst.org/neisseria/) – hosts the reference libraries and hundreds of genome sequences for Neisseria isolates. http://cge.cbs.dtu.dk/services/MLST/- web resource that facilitates extraction of MLST data direct from short-read sequence data.

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Lucidarme, J., Comanducci, M., Findlow, J., Gray, S.J., Kaczmarski, E.B., Guiver, M., Kugelberg, E., Vallely, P.J., Oster, P., Pizza, M., et al. (2009). Characterisation of fHbp, nhba (gna2132), nadA, porA, Sequence Type and the genomic presence of IS1301 in group. meningococcal ST269 clonal complex case-isolates from England and Wales. J. Clin. Microbiol. 47, 3577–3585. Lucidarme, J., Comanducci, M., Findlow, J., Gray, S.J., Kaczmarski, E.B., Guiver, M., Vallely, P.J., Oster, P., Pizza, M., Bambini, S., et al. (2010). Characterisation of fHbp, nhba (gna2132), nadA, porA and sequence type in group B meningococcal case isolates collected in England and Wales during January 2008, and potential coverage OF an investigational group B meningococcal vaccine. Clin Vaccine Immunol. 17, 919–929. Maiden, M.C. (2006). Multilocus Sequence typing of bacteria. Annu Rev Microbiol 60, 561–588. Maiden, M.C.J., Bygraves, J.A., Feil, E., Morelli, G., Russell, J.E., Urwin, R., Zhang, Q., Zhou, J., Zurth, K., Caugant, D.A., et al. (1998). Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. Proc. Natl. Acad. Sci. U.S.A. 95, 3140–3145. Martin, P., van de Ven, T., Mouchel, N., Jeffries, A.C., Hood, D.W., and Moxon, E.R. (2003). Experimentally revised repertoire of putative contingency loci in Neisseria meningitidis strain MC58: evidence for a novel mechanism of phase variation. Mol. Microbiol. 50, 245–257. Moore, P.S., Reeves, M.W., Schwartz, B., Gellin, B.G., and Broome, C.V. (1989). Intercontinental spread of an epidemic group A Neisseria meningitidis strain. Lancet ii, 260–262. Murphy, E., Andrew, L., Lee, K., Dilts, D.A., Nunez, L., Fink, P.S., Ambrose, K., Borrow, R., Findlow, J., Taha, M.K., et al. (2009). Sequence diversity of the factor H-binding protein vaccine candidate in epidemiologically relevant strains of serogroup B Neisseria meningitidis. J. Infect. Dis. 200, 379–389. Omer, H., Rose, G., Jolley, K.A., Frapy, E., Zahar, J.R., Maiden, M.C.J., Bentley, S.D., Tinsley, C.R., Nassif, X., and Bille, E. (2011). Genotypic and phenotypic modifications of Neisseria meningitidis after an accidental human passage. Plos One 6, e17145. Pajon, R., Beernink, P.T., Harrison, L.H., and Granoff, D.M. (2010). Frequency of factor H-binding protein modular groups and susceptibility to cross-reactive bactericidal activity in invasive meningococcal isolates. Vaccine 28, 2122–2129. Parkhill, J., Achtman, M., James, K.D., Bentley, S.D., Churcher, C., Klee, S.R., Morelli, G., Basham, D., Brown, D., Chillingworth, T., et al. (2000). Complete DNA sequence of a serogroup A strain of Neisseria meningitidis Z2491. Nature 404, 502–506. Pizza, M., Donnelly, J., and Rappuoli, R. (2008). Factor H-binding protein, a unique meningococcal vaccine antigen. Vaccine 26 Suppl. 8, I46-I48.

Russell, J.E., Jolley, K.A., Feavers, I.M., Maiden, M.C., and Suker, J. (2004). PorA variable regions of Neisseria meningitidis. Emerg. Infect. Dis. 10, 674–678. Saukkonen, K., Leinonen, M., Abdillahi, H., and Poolman, J.T. (1989). Comparative evaluation of potential components for group B meningococcal vaccine by passive protection in the infant rat and in vitro bactericidal assay. Vaccine 7, 325–328. Selander, R.K., Caugant, D.A., Ochman, H., Musser, J.M., Gilmour, M.N., and Whittam, T.S. (1986). Methods of multilocus enzyme electrophoresis for bacterial population genetics and systematics. Appl. Environ. Microbiol. 51, 837–884. Taha, M.K., Vazquez, J.A., Hong, E., Bennett, D.E., Bertrand, S., Bukovski, S., Cafferkey, M.T., Carion, F., Christensen, J.J., Diggle, M., et al. (2007). Target gene sequencing to characterize the penicillin G susceptibility of Neisseria meningitidis. Antimicrob. Agents Chemother. 51, 2784–2792. Taha, M.K., Hedberg, S.T., Szatanik, M., Hong, E., Ruckly, C., Abad, R., Bertrand, S., Carion, F., Claus, H., Corso, A., et al. (2010). Multicenter study for defining the breakpoint for rifampin resistance in Neisseria meningitidis by rpoB sequencing. Antimicrob. Agents Chemother. 54, 3651–3658. Tettelin, H., Saunders, N.J., Heidelberg, J., Jeffries, A.C., Nelson, K.E., Eisen, J.A., Ketchum, K.A., Hood, D.W., Peden, J.F., Dodson, R.J., et al. (2000). Complete genome sequence of Neisseria meningitidis serogroup B strain MC58. Science 287, 1809–1815. Thompson, E.A.L., Feavers, I.M., and Maiden, M.C.J. (2003). Antigenic diversity of meningococcal enterobactin receptor FetA, a vaccine component. Microbiology 149, 1849–1858. Urwin, R., Russell, J.E., Thompson, E.A., Holmes, E.C., Feavers, I.M., and Maiden, M.C. (2004). Distribution of Surface Protein Variants among Hyperinvasive Meningococci: Implications for Vaccine Design. Infect. Immun. 72, 5955–5962.Yazdankhah, S.P., Kriz, P., Tzanakaki, G., Kremastinou, J., Kalmusova, J., Musilek, M., Alvestad, T., Jolley, K.A., Wilson, D.J., McCarthy, N.D., et al. (2004). Distribution of serogroups and genotypes among disease-associated and carried isolates of Neisseria meningitidis from the Czech Republic, Greece, and Norway. J. Clin. Microbiol. 42, 5146–5153. Young, B.C., Golubchik, T., Batty, E.M., Fung, R., Larner-Svensson, H., Votintseva, A.A., Miller, R.R., Godwin, H., Knox, K., Everitt, R.G., et al. (2012). Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease. Proc. Natl. Acad. Sci. U.S.A. 109, 4550–4555. Zerbino, D. (2010). Using the Velvet de novo Assembler for Short-Read Sequencing Technologies. Curr. Protoc. Bioinformatics 11, 1–12. Zerbino, D.R., and Birney, E. (2008). Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829.

Transcriptional Regulatory Proteins in the Pathogenic Neisseria Nadine Daou, Ryan McClure and Caroline A. Genco

Abstract The exclusive human pathogens Neisseria gonorrhoeae and Neisseria meningitidis are the only two pathogenic members of the Neisseria genus. N. gonorrhoeae infects the human genital tract while N. meningitidis typically infects the bloodstream or cerebrospinal fluid. Because of rapidly changing environments encountered during infection, including changing oxygen levels, reactive oxygen species, pH, and iron, both pathogens must be able to quickly and accurately control gene expression. In other microbial pathogens, response to these stimuli often involves a repertoire of regulatory proteins, which collectively function to ensure tight regulation of gene expression. However, unlike other bacteria, there are relatively few classical DNA binding proteins expressed by Neisseria species. The following is a discussion of Neisseria gene regulation involving transcriptional regulatory proteins with a specific emphasis on Fur, a global regulatory protein in Neisseria species. While Fur has classically been known to respond to levels of free iron, new studies show that it engages in complex cross-talk with other DNA binding proteins in response to stimuli encountered during infection. Introduction N. meningitidis and N. gonorrhoeae are the only pathogenic members of the Neisseria genus and both are exclusively human pathogens. The two species are closely related, sharing a high level of DNA sequence similarity (80–100%) between the genomes (Dillard and Seifert, 2001; Guibourdenche et al., 1986; Klee et al., 2000). In

2

spite of this similarity, N. meningitidis and N. gonorrhoeae inhabit different ecological niches and can cause distinct clinical symptoms and diseases. N. meningitidis normally inhabits the nasopharynx in an asymptomatic manner without causing disease (DeVoe, 1982), but can traverse the mucosal barrier and cause life-threatening meningococcal disease infecting either the bloodstream or cerebrospinal fluid (CSF) (van Deuren et al., 2000). N. gonorrhoeae inhabits the urogenital tract causing the sexually transmitted infection gonorrhoea, typically characterized by a symptomatic localized inflammatory response (Naumann et al., 1999). Although the nasopharynx and the urogenital tract are both mucosal membranes, they differ with respect to oxygen tension, iron availability, reactive oxygen species, pH, commensal flora, and immune responses. The different lifestyles of both N. meningitidis and N. gonorrhoeae and their ability to adapt to different environments in the host is likely related to differences in genetic content (Perrin et al., 2002) and in transcriptional regulation that result in differential gene expression (Schielke et al., 2009). Although extensive transcriptional regulation accompanies the infection process, only a few transcriptional regulators are found in these pathogenic Neisseria (Dietrich et al., 2003; Pareja et al., 2006). In N. meningitidis 35 putative regulators have been found while 34 have been found in N. gonorrhoeae. This is in comparison to E. coli, which harbours more than 200 transcriptional regulators. It is possible this is related to the restricted human ecological niche of the Neisseria species. To date, only few of these regulators have been characterized in N. gonorrhoeae and N. meningitidis. The role

18  | Daou et al.

of major transcriptional regulators in controlling the expression of potential virulence factors will be the focus of this chapter including the difference in regulatory targets between N. gonorrhoeae and N. meningitidis with a specific emphasis on Fur, a global regulatory protein in Neisseria species. The role of these regulators in the adaptation to the different environmental conditions in distinct niches during colonization will also be highlighted. Clinical and biological relevance of Neisseria transcriptional regulators in general and Fur in particular During infection N. gonorrhoeae and N. meningitidis encounter multiple environmental conditions within the host making a rapid adaptation crucial for bacterial survival. The role of transcriptional regulatory proteins in the adaptation of Neisseria to these environmental conditions and their contribution to Neisseria survival in the host will be discussed here. Antimicrobial resistance adaptation Gonococci are specialized to survive in the presence of abundant hydrophobic compounds present in the urogenital tract (Willcox, 1981). This survival is mediated by dual transcriptional regulation through MtrR and MtrA to control the expression of the MtrCDE efflux pump system conferring resistance to both antibiotics and host derived immune compounds such as the antimicrobial peptides (Folster et al., 2009; Hagman et al., 1995; Hagman and Shafer, 1995). This efflux pump has been shown to be essential for resistance to β-lactamase (penicillin G and nafcillin), macrolides (erythromycin), and host produced compounds (peptide LL-37 and progesterone) (Hagman et al., 1995; Shafer et al., 1998) and is essential for growth of gonococci in the lower genital tract of infected female mice ( Jerse et al., 2003). The mtrCDE efflux pump operon is jointly regulated by the repressor MtrR, a member of the TetR family, (Hagman and Shafer, 1995; Lucas et al., 1997) and the activator MtrA, a member of the AraC family (Rouquette et al., 1999). Inactivation of MtrA reduces the fitness of gonococci by

approximately 500-fold compared to the parent strain in a competitive female mouse model of lower genital tract infection (Warner et al., 2008). Correspondingly, inactivation of the repressor MtrR increases the fitness of gonococci compared to the parent strain (Warner et al., 2008). This strongly suggests that the regulation of mtrCDE levels is correlated with the in vivo fitness of gonococci. The dual regulatory properties of these two transcriptional factors in gonococci are important in determining bacterial fitness in vivo by enhancing their resistance to antimicrobials. In N. meningitidis, however, the MtrR protein is not functional and expression of the mtrCDE locus encoding the efflux pump is independent of MtrA (Rouquette-Loughlin et al., 2004). Down-regulation of mtrC expression is fulfilled through IHF insertions of Correia Elements preventing a constitutively active MtrCDE efflux pump (Abadi et al., 1996; Correia et al., 1986; Pan and Spratt, 1994; Rouquette-Loughlin et al., 2004). This suggests that expression of this efflux pump in the nasopharynx is not essential. A separate transcriptional regulatory protein, FarR, is responsible for controlling the expression of the efflux pump system FarAB, which provides gonococcal resistance to long-chain fatty acids (Lee et al., 2003; Lee and Shafer, 1999). Such fatty acids are well known for their antimicrobial activity, as they block electron transport, inhibit oxygen and amino acid uptake, uncouple oxidative phosphorylation and inhibit bacterial fatty acid synthesis (Miller et al., 1977; Sheu and Freese, 1973; Zheng et al., 2005). Free fatty acids are also used by the human host acting non-specifically against a broad range of microorganisms (Drake et al., 2008). FarR in N. gonorrhoeae interacts directly with the promoter region of the farAB operon, repressing transcription of the FarAB efflux pump (Lee et al., 2003; Lee and Shafer, 1999). An atypical sensitivity to hydrophobic agents has been described in gonococci (Guymon and Sparling, 1975; Lysko and Morse, 1981; Sparling et al., 1975), and deletion of the FarR regulator considerably increased the fatty acid resistance due to an increased expression of FarAB efflux pump for fatty acids (Lee et al., 2003; Lee and Shafer, 1999). Although the meningococcal FarR protein shares high sequence similarity with its

Transcriptional Regulatory Proteins in the Pathogenic Neisseria |  19

homologue in gonococci (98%), it assumes a different role as deletion of farR did not reveal any effect on the resistance against hydrophobic compounds of long-chain fatty acids. Instead, FarR has been shown to strongly repress the adhesin NadA (Neisseria adhesion A) (Schielke et al., 2009). NadA is a highly antigenic, surface-exposed trimeric protein that is present in most hyper virulent strains and therefore part of a putative vaccine against serogroup B meningococci, which is currently entering phase III clinical trials (Bowe et al., 2004; Comanducci et al., 2002; Giuliani et al., 2006; Pizza et al., 2000). A crucial role of FarR in the pathogenesis of meningococci was shown through infection experiments with Chang epithelial cells, indicating that a FarR-deficient strain adhered to cells significantly more than the wildtype strain (Schielke et al., 2009). This result is not surprising since FarR repressed the expression of NadA, which contributes to the adhesion and penetration of human epithelial cells (Capecchi et al., 2005). Furthermore, expression of farR is regulated during meningococcal exponential growth phase and repressed upon contact with active complement components, indicating an important role for this regulator during meningococcal interactions with host cells. The nadA gene is distributed irregularly among meningococcal strains, is not present in commensal strains and absent in gonococci (Bowe et al., 2004; Comanducci et al., 2002, 2004; Giuliani et al., 2006). Therefore, FarR can be viewed as an outcome of divergent host niche adaptation of the two human pathogenic Neisseria species: while FarR provides N. gonorrhoeae with fatty acid resistance for survival in the urogenital tract, this transcriptional regulator mediates immune evasion by repression of the highly antigenic NadA in N. meningitidis and at the same time regulates attachment to epithelial cells by this adhesin, clearing the way for a persistent colonization (Schielke et al., 2009). Anaerobiosis adaptation Another physiologically relevant state encountered during infection of N. gonorrhoeae and N. meningitidis is anaerobiosis. In fact, N. gonorrhoeae is often recovered from infected patients with other anaerobic bacteria such as Peptococcus and Bacteroides species (Newkirk, 1996). N.

meningitidis is also exposed to limited concentrations of oxygen during the invasive stage of the infection as it occupies different anatomical compartments within the host containing fluctuating oxygen levels. To be able to grow and survive anaerobically and/or microaerobically, N. gonorrhoeae and N. meningitidis use a partial denitrification pathway in which nitrate is reduced to nitric oxide (NO) by AniA (nitrite reductase), NO is then reduced to nitrous oxide by NorB (nitric oxide reductase). The regulation of adaptation from aerobic to anaerobic growth in Neisseria is very complex and involves several transcriptional regulators, FNR (oxygen-sensing), NsrR (NO sensing repressor), ArsR, Fur (Ferric uptake regulator) and the two component system NarPQ (Heurlier et al., 2008; Isabella et al., 2008; Lissenden et al., 2000; Overton et al., 2006; Rock et al., 2007). FNR is the main regulatory protein responsible for the switch from aerobic to anaerobic metabolism in many bacteria (Green et al., 1996). It has been shown that subtle differences in FNR regulation contribute to differential expression of target genes which contribute to the adaptation of N. gonorrhoeae and N. meningitidis to different niches within the host. Apart from aniA and nosR, none of the genes activated by FNR in N. meningitidis were found to be regulated by FNR in N. gonorrhoeae (Table 2.1) (Bartolini et al., 2006; Whitehead et al., 2007). Although the FNR proteins of N. meningitidis serogroup B and N. gonorrhoeae are highly conserved showing 97% of amino acid sequence identity, they have subtly different roles in the two pathogenic Neisseria species. Studies focusing on aniA, of which FNR is the master regulator, have shown that there are differences in aniA regulation between N. meningitidis and N. gonorrhoeae. In addition to these differences several independent studies have found that 32% of sequenced N. meningitidis strains contain a frame shift mutation resulting in premature termination of translation or absence of aniA (Ku et al., 2009; Stefanelli et al., 2008). This suggests that AniA does not play a major role in meningococcal survival. However, all sequenced gonococcal strains contain a fully intact aniA gene suggesting that this nitrate reductase may be more important for the survival of gonococcus within the

–

–

–

–

–

–

–

–

Delany et al. (2006), Grifantini et al. (2003)

(Delany et al., 2006)

Grifantini et al. (2003)

fetA (frpB), ferric enterobactin receptor

tonB, energy transducer

NMB1730

Delany et al. (2006), Grifantini et al. (2003)

zupT, zinc transporter

lbpB, lactoferrin-binding protein B

NMB1541

Grifantini et al. (2003)

NMB1988

lbpA, lactoferrin-binding protein A

NMB1540

Grifantini et al. (2003) Grifantini et al. (2003)

NMB0175

tbpA, lactoferrin-binding protein A

(Delany et al., 2006; Grifantini et al., 2003)

tbpB, lactoferrin-binding protein B

NMB0460

NMB0461

NGO2109

NGO0553

NGO0024

NGO2092

NGO2093

–

–

NGO2176

NGO1495

NGO1496

NGO1318

hpuB, haemoglobin–haptoglobin utilization protein B

tdfG, putative TonB–dependent receptor

putative FetB2 protein

fetB, ferric enterobactin periplasmicbinding protein

fetA, ferric enterobactin receptor

–

–

tonB, energy transducer

lbpB, lactoferrin-binding protein B

lbpA, lactoferrin-binding protein A

tbpA, transferrin-binding protein A

tbpB, transferrin-binding protein B

hemO/hemR, haem utilization protein

NGO0217– fbpABC, ferric iron binding protein 0215

Delany et al. (2006), Grifantini et al. (2003) Delany et al. (2006)

NGO1779 fur, ferric uptake regulator protein

Function

Grifantini et al. (2003)

NMB1728–1729 exbD, and exbB biopolymer transport protein

hmbR, haemoglobin receptor

NMB1668

NMB0205 Iron fur, ferric uptake regulator protein acquisition and transport NMB0633–0634 fbpAB, iron (III) ABC transporter permease and periplasmic protein

Reference

Gene

Function

Gene

Fur-repressed regulon

Category

N. gonorrhoeae FA1090

N. meningitidis MC58

Table 2.1 Transcriptional regulatory proteins and target genes in N. meningitidis and N. gonorrhoeae

Jackson et al. (2010)

Jackson et al. (2010)

Jackson et al. (2010)

Jackson et al. (2010)

Jackson et al. (2010)

Sebastian et al. (2002)

(Biswas et al., 1999)

Biswas and Sparling (1995), Genco and Desai (1996)

Jackson et al. (2010)

Jackson et al. (2010)

Jackson et al. (2010), Sebastian et al. (2002)

Desai et al. (1996), Forng et al. (1997), Jackson et al. (2010)

Jackson et al. (2010), Sebastian et al. (2002)

Reference

Energy metabolism

RTX toxin/ virulence

Adaption/ stress response

fumC, fumarase C in TCA cycle

–

–

NMB1458

–

–

Delany et al. (2006), Grifantini et al. (2003)

Delany et al. (2006), Grifantini et al. (2003)

lldD, l-lactate dehydrogenase

NMB1377

–

– Delany et al. (2006), Grifantini et al. (2003)

–

–

NMB1395–1396 Alcohol dehydrogenase/mutY, A/G-specific adenine glycosylase

–

Grifantini et al. (2003)

–

Grifantini et al. (2003)

FrpC operon protein

–

–

Grifantini et al. (2003)

NMB1412–1415 FrpC operon protein

–

–

NMB1405

Putative transposase

NMB1798

Grifantini et al. (2003)

Delany et al. (2006), Grifantini et al. (2003)

Putative transposase

NMB0101

Delany et al. (2006) Grifantini et al. (2003)

NMB0584–0585 FrpC operon protein

recN, DNA repair protein

Delany et al. (2006), Grifantini et al. (2003)

clpB, chaperone

NMB1472

NMB0740

Delany et al. (2006)

NMB0364–0365 FrpC operon protein

dnaK, heat shock protein, chaperone

NMB0544

fumC, fumarase C in TCA cycle Putative oxidoreductase Putative glutaredoxin

NGO1029 NGO0108 NGO0114

–

–

–

secY, translocase

–

Opa A–K (opacity-associated protein)

IgA1, IgA-specific metalloendopeptidase

–

–

–

NGO1822

NGO0275

–

–

–

–

Thioredoxin I

NGO0652 –

sodB, superoxide dismutase B

–

–

recN, DNA repair protein

–

–

NGO0449

–

–

NGO0318

–

–

Jackson et al. (2010)

Jackson et al. (2010)

Sebastian et al. (2002)

Sebastian et al. (2002)

Sebastian et al. (2002)

Jackson et al. (2010)

Jackson et al. (2010)

Sebastian et al. (2002)

Sebastian et al. (2002)

YciI-like protein

nadA, quinolinate synthetase

nadC, nicotinate–nucleotide pyrophosphorylase

HesB/YadR/YfhF family protein

nifU nitrogen fixation

NMB0343

NMB0394

NMB0396

NMB1381

NMB1380

Hypothetical

Delany et al. (2006) Delany et al. (2006)

Hypothetical protein

NMB1796

NMB1879–1880 Hypothetical protein

NMB0175

Hypothetical protein

Delany et al. (2006)

Hypothetical protein

NMB1491

–

–

–

–

–

Grifantini et al. (2003)

Hypothetical protein

NMB1340

Grifantini et al. (2003)

– –

Grifantini et al. (2003)

–

–

–

–

–

–

–

–

Hypothetical protein

NGO0554 –

Hypothetical protein

mpeR, AraC–like regulator

nrrF, small RNA transcriptional regulator

–

–

–

–

NGO0322

NGO0025

–

–

–

–

Hypothetical protein

NMB0861

Grifantini et al. (2003)

–

– –

–

–

–

–

Function

–

Gene

N. gonorrhoeae FA1090

NMB0865–0864 Hypothetical protein

Hypothetical protein

Hypothetical protein

NMB0744

NMB0821

Grifantini et al. (2003)

Delany et al. (2006) Delany et al. (2006), Grifantini et al. (2003)

mpeR, AraC-like transcriptional regulator

Mellin et al., (2007), Mercante et al. (2012)

Grifantini et al. (2003)

Grifantini et al. (2003)

Grifantini et al. (2003)

Grifantini et al. (2003)

Grifantini et al. (2003)

Grifantini et al. (2003)

Grifantini et al. (2003)

Grifantini et al. (2003)

Reference

NMB0034–0036 Hypothetical protein

NMB1879

dsbA-2, disulfide interchange protein

NMB0294

nrrF, small RNA transcriptional regulator

7-Cyano-7-deazaguanine reductase

NMB0317

Regulators

Mlp, lipoprotein

NMB1898

Biosynthesis

Function

Gene

Category

N. meningitidis MC58

Table 2.1 (Continued)

Jackson et al. (2010)

Jackson et al. (2010)

Jackson et al. (2010)

Ducey et al. (2009)

Reference

– –

kat, catalase

NMB0216

–

Unpublished data arsR, transcriptional regulator

NGO1562

Hypothetical protein

Hypothetical

–

NGO1851

NMB0298

Yu and Genco (2012) Yu and Genco (2012)

Transcription termination factor Rho DNA direct RNA polymerase subunit β

NGO0199

Jackson et al. (2010), Yu and Genco (2012)

Jackson et al. (2010), Yu and Genco (2012)

Jackson et al. (2010), Yu and Genco (2012)

Yu and Genco (2012)

Jackson et al. (2010), Yu and Genco (2012)

– Grifantini et al. (2003)

–

–

–

aniA, nitrate reductase

norB, nitric oxide reductase

nspA, outer membrane protein

ATP-binding protein

NGO0904– Hypothetic Fe–S protein; hypothetical 0906 protein, Fe–S oxidoreductase

–

Grifantini et al. (2003) Delany et al. (2006), Grifantini et al. (2003)

–

–

NGO1276

NGO1275

Grifantini et al. (2003)

Delany et al. (2006)

Delany et al. (2006), Grifantini et al. (2003)

Delany et al. (2006)

Grifantini et al. (2003), NGO0233 Shaik et al. (2007)

NGO2116

Jackson et al. (2010), Yu and Genco (2012)

Yu and Genco (2012)

Jackson et al. (2010), Yu and Genco (2012)

–

NMB1436–1438 oxidative stress resistance

nsgA, outer membrane protein

aniA, nitrite reductase

NMB1623

sodB, superoxide dismutase

norB, nitric oxide reductase

NMB1622

NMB0884

nspA, neisserial surface protein A

NMB0663

NMB0663

–

–

– –

–

Delany et al. (2006) Delany et al. (2006)

Cytochrome c4

NMB1805

NMB2051–2053 petABC, ubiquinol–cytochrome c reductase

–

–

–

Delany et al. (2006)

pyrC, pyrimidine ribonucleotide biosynthesis

Putative TonB-dependent receptor

–

NGO1751– nuoB–nuoD, NADH–quinone 1748 oxidoreductase subunit ABCDE

NGO1205

–

bfrA, bacterioferritin A

NMB0682

–

Delany et al. (2006) Delany et al. (2006)

NGO0794

Delany et al. (2006), Grifantini et al. (2003)

–

NMB1613

Delany et al. (2006)

NMB0242–0244 nuoA–nuoD, NADH dehydrogenase subunit ABCDE

bfrB, bacterioferritin B

fumB, fumarate hydratase

NMB1206

bfrA, bacterioferritin A

NMB1207

Transcription/ – regulation –

Adaption/ stress response

Energy metabolism

Iron acquisition/ storage

Fur-activated regulon

Gene

–

–

–

–

–

–

–

–

–

–

–

Phage proteins

Regulators

Adaptation/ stress response

Iron acquisition

Function

–

–

–

–

–

–

–

–

–

–

–

nosR, regulatory protein

Cytochrome C5

NMB0577

NMB1677

mapA–pgmβ

Bartolini et al. (2006)

Bartolini et al. (2006)

Bartolini et al. (2006)

galM

NMB0388

Cytochrome C4

Bartolini et al. (2006)

aniA, nitrate reductase

NMB1623

NMB1805

Bartolini et al. (2006)

–

–

– Bartolini et al. (2006)

Reference

–

FNR-activated regulon

Adaption/ stress response

Biosynthesis

FNR-repressed regulon

Category

N. meningitidis MC58

Table 2.1 (Continued)

NGO1688

ompU, Putative iron uptake OMP

NGO1615

NGO0472

NGO1622

NGO1621

NGO0602

–

Putative phage-associated protein

Putative phage-associated protein

Putative phage-associated protein

Putative phage-associated protein

Putative MerR family transcriptional regulator

nosR, Regulatory protein

–

–

– –

–

–

aniA, nitrite reductase

Putative NRAMP family manganese/ iron transporter

–

NGO1276

NGO1455

Short Neisseria-specific protein

norB, nitric oxide reductase

NGO1275 NGO1428

dnrN, Putative NO– response protein

glnQ, amino acid ABC transporter

cysK, cysteine synthetase

Putative phosphotransferase

Function

NGO0653

NGO0374

NGO0340

NGO1716

Gene

N. gonorrhoeae FA1090

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Reference

–

NMB1870

–

aniA, nitrite reductase

nirV, putative nitrite reductase assembly protein

dnrN, putative reactive species response protein

mobA, gene associated with molybdenum metabolism

NMB1623

NMB1624

NMB1365

NMB1248

norB, nitric oxide reductase

NMB1622

Hypothetical proteins

Translational proteins

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

Receptors – and transport

PerR-repressed regulon

Adaptation/ stress response

–

Hypothetical protein

NMB1806

–

Hypothetical protein

Hypothetical protein

NMB0363

NsrR-repressed regulon

Biosynthesis

Hypothetical proteins Bartolini et al. (2006)

Heurlier et al. (2008)

Heurlier et al. (2008)

Heurlier et al. (2008)

Heurlier et al. (2008)

Heurlier et al. (2008)

Bartolini et al. (2006)

Bartolini et al. (2006)

–

–

Hypothetical protein

NGO0087

NGO0474

NGO1049

NGO0930

NGO0931

NGO0952

NGO1205

NGO0166

NG0168

NGO0169

NGO0170

–

NGO0653

Hypothetical protein NT01NG0302

Hypothetical protein

Conserved hypothetical protein

RpmE, 50s ribosomal protein L31

RpmJ, ribosomal protein L36

TdfH, TonB-dependent receptor

TonB-dependent receptor

HmcD, putative periplasmic protein

MntC, periplasmic binding protein

MntB, membrane protein

MntA, ATP-binding protein (ABC transporter)

–

dnrN, putative reactive species response protein

aniA, nitrite reductase –

NGO1276

norB, nitric oxide reductase

res, Type III restriction–modification system EcoPI enzyme

–

NGO1275

NGO0546

Neisseria-specific protein

Conserved hypothetical protein

NGO0473

–

NGO1215

–

–

–

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Wu et al. (2006)

Overton et al. (2006)

Overton et al. (2006)

Overton et al. (2006)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

Whitehead et al. (2007)

–

Kat, catalase

–

–

NMB0216

–

–

Kat, catalase

–

Ieva et al. (2008)

Ieva et al. (2008)

NGO0926

NGO0925

–

NGO1767

NGO1442

prx, Peroxiredoxin

gor, Glutathione reductase

–

Kat, catalase

AdhA, alcohol dehydrogenase

Function

Seib et al. (2007)

Seib et al. (2007)

Seib et al. (2007)

Wu et al. (2006)

Reference

Genes in bold represent Fur cross-talk genes with other regulatory circuits. Underlined genes represent Fur-regulated genes under both iron and anaerobic conditions.

Oxidative stress

OxyR-activated regulon

NMB0216

OxyR-repressed regulon

Oxidative stress

Reference

Gene

Function

Gene

PerR-activated regulon

Category

N. gonorrhoeae FA1090

N. meningitidis MC58

Table 2.1 (Continued)

Transcriptional Regulatory Proteins in the Pathogenic Neisseria |  27

urogenital tract (Barth et al., 2009). In addition, AniA was found to be expressed during infection in women suggesting that it plays a critical role during infection (Clark et al., 1988). Moreover, antibodies against AniA were detected in patients infected by N. gonorrhoeae suggesting a possible role in protection of the bacterium from attack by the host immune system (Clark et al., 1988). The differences in responses of N. gonorrhoeae and N. meningitidis to oxygen availability may be related to their underlying physiological niches. The urogenital tract occupied by N. gonorrhoeae is frequently anoxic (Paavonen, 1983), whereas N. meningitidis occupies a more aerobic environment and appears to be in the process of evolving to lose the ability to respire nitrite (Moir, 2011). While anaerobic respiration is lacking in N. meningitidis it is likely that FNR contributes to the virulence of meningococci through the regulation of the expression of the galM and mapA transcripts involved in maltose catabolism (Table 2.1). Inactivation of the fnr gene from N. meningitidis MC58 attenuated proliferation of the organism in both an infant rat and adult mouse model by more than one order of magnitude. However, inactivation of galM and mapA reduced the ability of N. meningitidis to survive in the blood by two and three orders of magnitude respectively (Bartolini et al., 2006). These observations suggest that these FNR-dependent genes are important for the survival and proliferation of N. meningitidis in the bloodstream of the animals. It also strongly suggests that when oxygen is limiting, N. meningitidis requires a sugar fermentation pathway for both a carbon and energy source. In support of this hypothesis, six FNR-regulated genes (NMB0388, NMB1805, NMB0577, NMB1677, NMB1623, NMB1870) showed increased expression in the human whole blood as compared to N. meningitidis grown in the GC medium (Echenique-Rivera et al., 2011). However, FNR itself was not found to be up-regulated in the blood, suggesting that even though there is no difference in expression levels, the proportion of active FNR is altered during growth in the blood (Echenique-Rivera et al., 2011). Another regulatory protein, NsrR, is also involved in adaptation to anaerobiosis. NsrR is the nitric oxide (NO) sensitive repressor acting

principally as a NO detoxifying agent (Rock et al., 2007). The regulation of the NsrR detoxifying system through norB, offers potential for N. gonorrhoeae and N. meningitidis to resist the normal microbicidal activity of nitric oxide which can be generated via the inducible NO synthase expressed within macrophages, neutrophils and epithelial cells as part of a successful response to infection (Aktan, 2004). In addition, NO is present in high concentration in the nasopharynx and is detected in exhaled nasal breath (Kimberly et al., 1996). Studies have shown that upon infection of primary human macrophages or within nasopharyngeal mucosa model, NorB was required to enhance survival of N. meningitidis (Stevanin et al., 2005). In addition, it was shown that the reduction of NO by N. meningitidis modifies the release of NO-regulated cytokines and chemokines by human macrophages (Stevanin et al., 2007). These studies demonstrate the requirement of an active NO detoxification system for optimal survival of N. meningitidis during nasopharyngeal colonization and interactions with human pathogens. In N. gonorrhoeae, NsrR plays a critical role in enabling the gonococci to evade NO generated by the host as a defence mechanism (Overton et al., 2006). In addition, NsrR, through its regulation of norB, can reduce NO levels generated by the host to provide a potential source of energy during oxygen-limited growth and thus enhance survival and growth of N. gonorrhoeae even during oxygen starvation. Regulation mediated by NsrR in gonococci to reduce NO have an immune modulatory effect during the course of infection. Several pieces of evidence suggest that the gonococcal reduction of the NO produced by the host may be responsible or at least contributes to, the asymptomatic nature of disease in women. To support this hypothesis, in vitro studies have shown that N. gonorrhoeae is capable of reducing the NO steady-state level from a proinflammatory concentration to a new steady-state level in the anti-inflammatory range (Cardinale and Clark, 2005). This phenomenon prevents NFκB activation and subsequent cytokine production (Katsuyama et al., 1998; Reynaert et al., 2004). Recent studies have shown that gonococcal activation of iNOS promoted bacterial survival in an in vitro cervical cell model. In this study, it was also

28  | Daou et al.

suggested that host derived nitric oxide is not protective against gonococci, rather nitric oxide may actually be required to sustain cervical bacterial disease (Edwards, 2010). Oxidative stress adaptation Several studies revealed significant differences between N. gonorrhoeae and N. meningitidis, with respect to their mechanisms of oxidative defence (Seib et al., 2004). Gonococci are equipped with two peroxide-responsive transcriptional regulators for oxidative stress response, PerR and OxyR that have a relatively small regulon (Table 2.1) (Seib et al., 2007; Tseng et al., 2003; Wu et al., 2006). While PerR was not observed in N. meningitidis (Seib et al., 2004), a recent study revealed the presence of OxyR in N. meningitidis and reported its ability to activate and to repress the kat gene at the same time (Table 2.1) (Ieva et al., 2008). A similar mechanism of regulation of the kat gene was not observed in N. gonorrhoeae. The differences in the regulation of oxidative stress defences in the pathogenic Neisseria are most likely a result of their localization in different ecological niches. The presence of highly efficient mechanisms to remove the toxicity of H2O2 was associated with the ability of N. gonorrhoeae to adapt to the female urogenital tract where it is exposed to a high level of H2O2 produced by resident lactic acid bacteria (McNeeley, 1989) and activated polymorphonuclear leucocytes (PMNs) (Archibald and Duong, 1986). Despite the presence of this toxic environment, N. gonorrhoeae can be isolated from PMN-laden purulent exudates (Archibald and Duong, 1986). A N. gonorrhoeae perR mutant strain was impaired in its ability to invade and survive within the human ectocervical epithelial cells (Wu et al., 2006), which have oxidative defence capacity and are able to kill bacteria by oxidative mechanisms (Battistoni et al., 2000; Rochelle et al., 1998; Schmidt and Walter, 1994). Similar results were observed with the N. gonorrhoeae oxyR mutant in epithelial cells (Seib et al., 2007). Both results were surprising as both mutants were hyperresistant to in vitro oxidative stress (Seib et al., 2007). This suggests that complex interactions of factors are involved in the regulation of oxidative

stress defence that require the presence of both PerR and OxyR (Wu et al., 2006). In addition, the N. gonorrhoeae oxyR mutant strain has a decreased ability to form a biofilm (Seib et al., 2007). It has been suggested that the formation of biofilms by N. gonorrhoeae may contribute to its ability to persist in an asymptomatic state in the female genital tract. Thus, OxyR-mediated regulation may contribute to asymptomatic disease that is often encountered in infected women. Iron availability adaptation The regulons of the previously described transcriptional regulators is relatively small compared to that of the Ferric uptake regulator (Fur) whose regulon comprises more than 200 genes (Grifantini et al., 2003; Jackson et al., 2010). Fur is a global regulatory protein involved in the adaptation to iron stress conditions. It is best known for its regulatory role in the expression of genes implicated in iron homeostasis ensuring a crucial balance between iron growth requirements and avoidance of iron toxicity. Fur plays a critical role in both meningococcal and gonococcal disease as fur itself and genes belonging to the Fur regulon, and specifically those involved in iron acquisition, were up-regulated during N. meningitidis infection in human blood as well as in cervical swab specimens from women suffering from uncomplicated gonorrhoea or urethral swab specimens from men with urethral infections (Agarwal et al., 2005; Agarwal et al., 2008; Echenique-Rivera et al., 2011). In addition, Fur has been shown to be important for the survival of N. meningitidis in human whole blood (Echenique-Rivera et al., 2011). Additional studies have shown that iron starvation of meningococci (Brener et al., 1981) and gonococci (Keevil et al., 1989) increased the virulence phenotype in vivo suggesting an important role of Fur repressed genes in virulence. Iron-uptake was shown to be a crucial factor in meningococcal infection in a mouse model as injection of either iron dextran or human transferrin followed by intraperitoneal infection with live N. meningitidis resulted in a lethal infection. In contrast mice injected with only N. meningitidis suffered a transient bacteraemia and recovered

Transcriptional Regulatory Proteins in the Pathogenic Neisseria |  29

quickly (Holbein, 1980, 1981; Holbein et al., 1979). Generally, the host niches colonized by N. gonorrhoeae and N. meningitidis are iron-deplete environments due to the presence of iron-binding proteins such as haemoglobin, transferrin and lactoferrin, rendering the environment deficient in iron. However, these niches differ with respect to their availability of iron binding proteins. Haemoglobin is predominantly sequestered within red blood cells and is a tetrameric protein with each subunit capable of binding one molecule of haem. Transferrin is predominantly found in serum and on inflamed mucosal surfaces, while lactoferrin is found on mucosal surfaces, in secretion and in polymorphonuclear leucocytes (Noinaj et al., 2012). In addition, the concentration of lactoferrin can change with the menstrual cycle in human vaginal mucus (Cohen et al., 1987). During infection, both pathogenic Neisseria species express high affinity outer membrane receptors that mediate direct extraction and import of iron from the human host iron binding proteins haemoglobin, lactoferrin and transferrin (Cornelissen, 2011). All these iron acquisition systems are tightly regulated by Fur and play a critical role in initiating infection, survival and persistence of N. gonorrhoeae and N. meningitidis within the host. All strains of N. meningitidis are able to use haemoglobin, lactoferrin and transferrin (Marri et al., 2010; Mickelsen et al., 1982; Mickelsen and Sparling, 1981) through their corresponding Fur regulated receptors and thus support the survival of N. meningitidis in the different host niches colonized during the invasive stage of infection. The gonococcal transferrin outermembrane receptor encoded by tbpBA has been reported to be expressed during natural mucosal infection in male volunteers and to contribute to gonococcal colonization in the male urethral challenge model (Agarwal et al., 2005; Cornelissen et al., 1998), supporting the importance of these iron transport system in initiating infection and survival in humans. It is interesting to note that the transferrin outer membrane receptor was not required for efficient colonization in the female mucosal mouse model of infection suggesting that this receptor may be one of the factors responsible

for the difference in gonococcal disease between men and women. It is likely that the transferrin receptor plays an important role during the invasive phase of meningococcal infection because of its high concentration in the serum. Meningococcal transferrin binding proteins are immunogenic in animals (Rokbi et al., 1997) and humans (Gorringe et al., 1995), however the gonococcal transferrin binding protein did not generate high-titre anti-transferrin antibodies which may mediate protective immunity (Rokbi et al., 1997). This is consistent with the hypothesis that gonococci are capable of immune suppression during infection (Liu et al., 2011). The lactoferrin receptor is believed to be an important virulence factor of meningococci as lactoferrin is the predominant iron source in the nasopharynx, the entry site of N. meningitidis into the human body. Additionally, lactoferrin has been shown to cross the blood–brain barrier during acute inflammation which may serve as a source of iron during the invasive phase of infection (Huettinger et al., 1998). Supporting this role, the lactoferrin receptor (Lbpa/Lbpb) has been found in all meningococcal strains (Pettersson et al., 1998). In contrast, approximately half of gonococcal isolates have a large deletion in the lbpA/B genes encoding the lactoferrin receptor, rendering this system inactive (Biswas et al., 1999). In fact, the lactoferrin receptor is not essential in gonococcal colonization or invasive disease (Cornelissen et al., 1998). However, other studies of human gonococcal infection indicate that in the absence of the transferrin receptor, expression of the lactoferrin receptor is sufficient for initiating infection (Anderson et al., 2003). In addition, the expression of both transferrin and lactoferrin receptors results in a competitive advantage over a strain expressing only the transferrin receptor (Anderson et al., 2003). Although every environment within the host where N. meningitidis survive and cause disease has low amount of haemoglobin (Genco and Dixon, 2001; Stojiljkovic and Perkins-Balding, 2002), the haemoglobin receptor shows a profound effect on the survival of meningococci in an infant rat model of infection indicating that

30  | Daou et al.

haemoglobin utilization is important for N. meningitidis virulence (Stojiljkovic et al., 1995). In addition to regulating genes involved in iron metabolism, Fur also regulates genes implicated in the colonization and pathogenesis of Neisseria species. In N. meningitidis, the aniA (nitrite reductase), kat (catalase) and nspA (Neisseria surface protein A) genes are under direct Fur activation and have been demonstrated to be up-regulated during N. meningitidis colonization in human blood (Echenique-Rivera et al., 2011). The kat and aniA genes have been shown to play a role in N. meningitidis survival in the presence of reactive oxygen and nitrogen species secreted from neutrophils and macrophages (Anjum et al., 2002; Seib et al., 2004). The nspA gene encodes for a human factor H binding protein that facilitates the resistance to human complement, which results in enhancing the survival of N. meningitidis in the blood (Echenique-Rivera et al., 2011; Lewis et al., 2010). Interestingly, the kat gene is not under iron or Fur-mediated control in the gonococcus ( Jackson et al., 2010). Furthermore, nspA and aniA, were not detected in specimens isolated from women with gonococcal infection (Agarwal et al., 2008). Another significant difference between N. meningitidis and N. gonorrhoeae Fur regulons during infection may relate to the expression of Opa proteins, which are involved in bacterial adherence and invasion of human epithelial cells and neutrophils (Criss and Seifert, 2012). N. meningitidis contains three or four Opa proteins, and at least one of them (NMB1636) is up-regulated in human blood (Echenique-Rivera et al., 2011). None of these meningococcal opa genes is regulated via Fur (Delany et al., 2006; Grifantini et al., 2003). In contrast, Fur has been demonstrated to bind to the putative promoter regions of all 11 gonococcal opa genes (A–K) suggesting Fur mediated direct regulation (Sebastian et al., 2002). In addition, these gonococcal Opa proteins are expressed during natural infections as well as in experimentally infected volunteers ( James and Swanson, 1978; Jerse et al., 1994; Swanson et al., 1988). Furthermore, Opa–CEACAM interactions are shown to promote gonococcal colonization in mouse models (Muenzner et al., 2010; Muenzner et al., 2005; Sadarangani

et al., 2011). Overall, these results suggest that differential gene regulation of Fur is associated with the specific colonization niches leading to distinct associated pathogenic processes of the two pathogenic Neisseria species. Mechanism of regulation of the ferric uptake regulator (Fur) In the majority of pathogenic bacteria that have been examined, the Fur protein regulates the expression of iron homeostasis genes in response to iron intracellular levels. This ensures a crucial balance between the requirement of iron as an essential element for growth and the avoidance of iron toxicity via the production of hydroxyl or peroxide radicals (Escolar et al., 1999; Hantke, 2001). Fur is a 15- to 17-kDa protein that forms a dimer together with the ferrous iron (Fe2+) or other divalent cations (Bagg and Neilands, 1987; Hamed, 1993). The iron-bound Fur dimer has been classically characterized as a repressor that binds the DNA at a consensus sequence (Fur box) that overlaps the –10 and –35 promoter regions excluding the binding of RNA polymerase and resulting in the inhibition of transcriptional initiation (Fig. 2.1A) (Desai et al., 1996; Escolar et al., 1997, 1998). Fur-mediated gene repression is a highly characterized model that has been well known for decades (Fig. 2.1A). Under iron-rich conditions Fur binds ferrous iron, acquires a configuration that allows it to bind to DNA and inhibits transcription of a subset of iron regulated genes. In contrast, when iron is scarce, Fur monomers do not bind to DNA allowing access of the RNA polymerase complex to gene promoter regions and initiation of transcription. (Griggs et al., 1987; Klebba et al., 1982). Crystal structure analysis of several Fur proteins of different microorganisms reveals that the amino terminus is responsible for its binding to DNA whereas the carboxyl terminus is required for dimer formation. In addition, structural analysis revealed multiple metal-binding sites (Dian et al., 2011; Pohl et al., 2003; Sheikh and Taylor, 2009). The standard interpretation considers the Fur box as a palindromic sequence composed of two 9-bp inverted repeat 9-1-9 (GATAATGAT A ATCATTATC) (Fig. 2.2). Indeed, the prototype

Transcriptional Regulatory Proteins in the Pathogenic Neisseria |  31

A

B

C

D

Figure 2.1  Fur-mediated regulation in the pathogenic Neisseria. (A) Classical mechanism of Fur-mediated regulation via Fur binding directly to DNA sequences to inhibit transcription. Fur (light grey rectangles) exists as inactive monomers in the absence of ferrous iron (small dark grey circles). Fur binding to ferrous iron leads to dimerization and binding to DNA promoter regions (dotted lines). This acts to block subsequent binding by RNA polymerase (large light grey circles) and leads to decreased transcription of target genes (black arrows). (B) Fur has also been shown to act as a regulator in the absence of iron in what is termed apo-Fur mediated regulation. (C) Under certain conditions, Fur can act as a direct activator through binding to promoter regions and facilitating the binding of RNA polymerase leading to increased transcription of target genes. (D) Fur can regulate genes indirectly through repressing a repressor (such as another DNA binding protein or sRNA). The targets of such repressor are then transcribed and translated at higher rates.

Fur box consensus sequence is within the bacillibactin siderophore operon (dhb) in B. subtilis and is bound with high affinity by Fur (Bsat and Helmann, 1999). Closely related Fur box sequences are found in a variety of Gram-positive and Gram-negative bacteria including N. gonorrhoeae and N. meningitidis (Delany et al., 2004; Ducey et al., 2005; Grifantini et al., 2003; Jackson

Figure 2.2  Interpretation of the Fur box consensus sequence. The classical Fur box defined as a 19-bp inverted repeat sequence (hatched black rectangles). Alternative Fur box composed of three or more copies of the hexamer GATAAT (solid grey rectangles). Alternative Fur box composed of the 19-bp Fur box resulting from two overlapping heptamer inverted repeats [(7–1–7)2] that together define a 21-bp sequence (solid black arrows).

et al., 2010; Yu and Genco, 2012). An alternative interpretation of the consensus Fur box was proposed by Escolar et al, as they noted that the 19 bp of the Fur box sequence can be also viewed as an hexameric repeat of two directed and one inverted (6-6-1-6) of the invariable sequence GATAAT (GATAAT GATAAT C ATTATC) (Fig. 2.2) (Escolar et al., 1998, 1999). Reinterpretation by Baichoo and Helmann (2002) proposed that the 19 bp of the Fur box result from two overlapping heptamers (7-1-7) that together define a 21-bp sequence (Fig. 2.2). Based on this reinterpretation, together with the analysis of the affinity and stoichiometry of DNA binding by Fur in B. subtilis, it was predicted that two Fur dimers bind to opposite faces of the DNA helix (Baichoo and Helmann, 2002; Ochsner et al., 1999; Ochsner and Vasil, 1996; Pohl et al., 2003). However, regardless of the multiple views of the Fur Box, the use of the classic consensus sequence has been very successful in predicting novel Fur-regulated genes in silico in different microorganisms as well

32  | Daou et al.

as in Neisseria species (Baichoo and Helmann, 2002; Ducey et al., 2005; Grifantini et al., 2003; Jackson et al., 2010; Panina et al., 2001). Recent studies in Helicobacter pylori have described the ability of Fur to repress transcription in the absence of the ferrous cofactor in a process termed apo-Fur mediated regulation (Fig. 2.1B) (Ernst et al., 2005; Miles et al., 2010). Such a mechanism may also be present in Neisseria, since several genes of N. gonorrhoeae have shown to be repressed by Fur in the absence of iron (Yu and Genco, 2012). Additional studies show that Fur can also function as a transcriptional activator in a direct (Fig. 2.1C) and indirect manner (Fig. 2.1D) (Butcher et al., 2011; Danielli et al., 2006; Ernst et al., 2005; Gao et al., 2008; Yu and Genco, 2012). Fur acts as a global regulator in the pathogenic Neisseria In N. meningitidis the Fur regulon has been defined by transcriptome analysis using a fur mutant strain. This analysis revealed that Fur can both repress and activate iron responsive genes by direct and indirect mechanisms (Delany et al., 2003; Delany et al., 2004). Interestingly, 44 of these genes were repressed and 38 were activated, defining a new role for Neisseria Fur in activation of gene expression. In N. gonorrhoeae, the Fur regulon is only beginning to be analysed. Microarray analysis of N. gonorrhoeae FA1090 in response to iron availability reported that 300 genes were iron repressed and 107 iron-activated (Ducey et al., 2005; Jackson et al., 2010). Based on the consensus Fur binding sequences from N. gonorrhoeae, P. aeruginosa and E. coli, at least 92 genes revealed a predicted Fur Box by in silico analysis, among which 28 were able to bind Fur in a Fur titration assay (FURTA) (Ducey et al., 2005; Jackson et al., 2010) suggesting that Fur regulates gene transcription either directly or indirectly. However, the Fur-dependent repression or activation of these genes was not examined in a gonococcal fur mutant. Recently we have determined that ironbound Fur-mediated activation in N. gonorrhoeae can occur via both direct and indirect mechanisms (Yu and Genco, 2012).

Direct Fur regulation In N. meningitidis Fur repressed genes or operons under direct control can be classified into four major categories based on their function in iron uptake and transport, energy metabolism and biosynthesis, virulence and bacterial adaptation and regulation (Table 2.1) (Delany et al., 2006; Delany et al., 2004; Grifantini et al., 2003; Mellin et al., 2007; Shaik et al., 2007). The first category involves genes encoding iron uptake and transport proteins such as tbpA, tbpB, lbpA, lbpB, hmbR and fetA that are repressed by Fur under ironreplete conditions which is in agreement with the primary role of Fur as a maintainer of iron homeostasis (Table 2.1). The second category includes genes involved in energy metabolism and biosynthesis (Table 2.1). The protein products of these genes enable bacterial growth, whereas their roles in pathogenesis have not yet been investigated in detail. Interestingly, these genes have few homologues in N. gonorrhoeae (Table 2.1). The third category includes genes involved in virulence and bacterial adaptation such as FrpC-like proteins of N. meningitidis, which may play a role in pathogenesis (Osicka et al., 2001) (Table 2.1). Several chaperone proteins and putative transposases, in addition to RecN, involved in DNA recombination and repair processes are proposed to be involved in bacterial adaptation to the host environment (Table 2.1) (Grifantini et al., 2003). The last category of Fur repressed genes includes regulatory proteins. In fact, a large number of Fur-dependent genes are not directly regulated by Fur as demonstrated by the inability of Fur to bind to the promoter regions, which suggests the involvement of secondary regulatory proteins (Delany et al., 2006; Grifantini et al., 2003). Conversely, nine genes/operons directly activated by Fur fall into three major categories including iron storage such as bfrA and bfrB, oxidative stress resistance such as sodB, kat, norB, aniA and NMB1438-1436; and energy metabolisms such as nuo complex (NMB0242–0244) (Table 2.1) (Delany et al., 2006; Grifantini et al., 2003). In addition, a large number of hypothetical proteins under Fur regulation await further investigation. Similar to N. meningitidis, a large category of Fur repressed genes in N. gonorrhoeae includes genes whose proteins products are involved in

Transcriptional Regulatory Proteins in the Pathogenic Neisseria |  33

iron acquisition (Table 2.1). However, protein products involved in energy metabolism and biosynthesis include only three Fur repressed genes (fumC, NGO0108 and NGO0114) in N. gonorrhoeae (Table 2.1) ( Jackson et al., 2010; Sebastian et al., 2002). The genes encoding proteins involved in virulence and bacterial adaptation are different from those in N. meningitidis (Table 2.1). The igA, secY and opa (A-K) genes encoding for IgA1 protease, a putative pre-protein translocase, and the opacity-associated protein Opa (A-K) respectively are directly repressed by Fur only in N. gonorrhoeae (Table 2.1). In addition to sodB, only two genes, recN and NGO0652 have been identified as stress response genes in N. gonorrhoeae (Table 2.1). Interestingly, the sodB gene, one of the Fur activated genes in N. meningitidis, is repressed by Fur in N. gonorrhoeae (Table 2.1). A large number of Fur activated genes/operons have been also identified in the N. gonorrhoeae (Table 2.1). In addition to bfrA, nspA, norB, aniA and nuo operon that have been already reported in N. meningitidis, Fur-activated genes in N. gonorrhoeae include genes involved in iron storage and transport (NGO1205), transcription and regulation (NGO0199, NGO1851, arsR) and energy metabolism (NGO2116) (Table 2.1) ( Jackson et al., 2010; Yu and Genco, 2012). The role of these genes in gonococcal pathogenesis has yet to be investigated. Indirect Fur regulation In addition to binding to DNA sequences and controlling transcription of genes directly, Fur can also indirectly control gene expression through the regulation of additional regulatory proteins (Fig. 2.1D). These include other DNA binding regulatory proteins, as well as regulatory small RNA (sRNA) transcripts. Regulation of the MpeR regulator by Fur is described below. We also have recently observed that the expression of the ArsR regulator is activated at the transcriptional level by Fur in N. gonorrhoeae (Table 2.1) (unpublished data). Bacterial sRNAs consist of short (50–300 nucleotides) single stranded RNA transcripts that act post-transcriptionally to regulate target mRNAs or bind to proteins directly (Repoila and

Darfeuille, 2009; Waters and Storz, 2009). Many sRNAs are controlled themselves at the transcriptional level by DNA binding proteins including Fur. The first sRNA regulated by Fur to be found in Neisseria was the neisserial RNA responsive to iron (NrrF) (Mellin et al., 2007). This sRNA binds to the transcripts coding for subunits of the succinate dehydrogenase (Sdh) complex and leads to their degradation. Fur enters into this regulatory circuit through repression of the transcription of NrrF. In this way, Fur leads to the increased stability and translation of the sdh transcripts and higher expression of the Sdh protein complex (Mellin et al., 2007). While Fur-mediated regulation of nrrF were elucidated in N. meningitidis, a homologue has been reported in N. gonorrhoeae (Ducey et al., 2009). Though only one Fur-regulated sRNA has been identified to date in Neisseria, several others are likely to exist. A knockout of the common sRNA protein cofactor Hfq in N. meningitidis showed the dysregulation of several mRNAs as well as un-annotated transcripts (possibly representing sRNAs) (Mellin et al., 2010). Thus, there are likely a large number of unidentified sRNAs in this organism. Several of these transcripts and mRNAs were also regulated via the influence of iron suggesting a further role for Fur in controlling their expression and stability (Mellin et al., 2010). Fur cross-talk with other regulatory circuits The role of Fur has been extended beyond the regulation of iron metabolism to involve the regulation of genes implicated in the adaptation to different signals found in the host environment such as oxygen limited conditions (anaerobiosis), oxidative stress as well as acid and antimicrobial resistance. The most complex and best-studied regulatory network involving Fur is the adaptation to anaerobiosis. Fur interacts with several regulatory proteins including FNR, NsrR, ArsR, and the two component systems NarP/NarQ to influence the regulation of their targets principally the aniA and norB genes (Table 2.1). In addition, several genes belonging to the Fur regulon are also regulated under anaerobiosis conditions in N. gonorrhoeae

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(Table 2.1) (Isabella and Clark, 2011). When oxygen is limited, FNR is first utilized to up-regulate nitrite respiration leading to the accumulation of toxic NO, and Fur is then utilized to up-regulate the expression of the nitric oxide reductase (norB) gene leading to elimination of the toxic effects of NO (Delany et al., 2004). Therefore, the bacteria utilize the same regulator Fur to simultaneously activate nitrite respiration, iron scavenging and detoxification from NO, and iron overload. Fur also regulates genes belonging to the OxyR regulon responsible for the resistance to oxidative stress. The transcription of the kat gene, which is activated by Fur, is also repressed by OxyR in the absence of H2O2 and activated by OxyR in the presence of H2O2 (Table 2.1) (Ieva et al., 2008). A very recent example of a Fur regulatory cascade involves regulation of MpeR, which controls gonococcal resistance to antimicrobials (Mercante et al., 2012). Fur regulates MpeR, which in turn regulates the expression of the gene encoding the major transcriptional repressor MtrR of the mtrCDE efflux pump operon. During infection the gonococci are confronted with an iron-limited environment that increases MpeR expression through Fur mediated regulation. Increased MpeR expression leads to a decrease in MtrR expression level that increases the expression levels of the MtrCDE efflux pump. As a consequence, the potential of gonococcal infection is enhanced as a result of a decrease of bacterial susceptibility to the antimicrobials recognized by the efflux pump. The most likely explanation for why several Neisseria regulatory proteins interface and cooperate to regulate the adaptation of a certain condition in vivo is that N. gonorrhoeae and N. meningitidis encounter a broad range of different signals during infection. Thus, for an effective infection, the bacteria must sense those specific signals and respond appropriately involving multiple transcriptional regulators to coordinate differential expression of potential virulence genes or disease-associated microbial signatures. Conclusions N. meningitidis and N gonorrhoeae, the humanpathogenic bacteria in the Neisseria genus, are

closely related. However, despite their high genetic similarity, N. meningitidis and N. gonorrhoeae colonize and survive in distinct environments within the human host and are able to cause distinct diseases, of the nasopharynx in the case of N. meningitidis and of the urogenital tract in the case of N. gonorrhoeae. As a consequence, these pathogens have developed diverse strategies to control gene expression in response to the specific host niche in which they are found. Although our understanding of complicated genetic regulatory circuits is still largely undefined in the pathogenic Neisseria, the present chapter serves to highlight recent advances in our understanding of the regulatory cascades of the Fur protein outlining similar and differential functions of this protein which may aid in understanding the molecular basis of in sitespecific colonization of these organisms. Moreover, we highlight how the Fur protein responds together with different regulatory proteins which sense and respond to an array of environmental conditions including anaerobiosis, oxidative stress and acid and antimicrobial resistance. We propose that Fur-mediated global regulation results in a multitude of consequences on gene expression and associated pathogenic mechanisms. Moreover, the complexity of Fur regulation combined with the presence of relatively few transcriptional regulators in the Neisseria species genome suggests that Fur protein might have a compensatory role in gene regulation facilitating bacterial adaptation and survival within the host environment. The role of Fur in global transcriptional regulation has been linked to the ability of both these organisms to respond to stimuli encountered within the human host. As such further characterization of Fur regulatory networks should provide valuable insights into the pathogen-specific global genetic regulation during natural infection and may lead to the discovery of new therapeutic strategies to target infections caused by these two pathogens. Future trends The classical role of Fur as a transcriptional regulatory protein which responds to iron is well established. Recent work has highlighted additional mechanisms by which this protein controls

Transcriptional Regulatory Proteins in the Pathogenic Neisseria |  35

gene expression in the pathogenic Neisseria. Furthermore, it appears that Fur functions together with other transcriptional regulatory proteins to respond to additional environmental cues encountered during human infection. The relatively small number of DNA-binding proteins in Neisseria suggests that in these organisms, cross-talk between such transcriptional regulatory proteins may allow for a wide range of regulatory pathways. New studies utilizing global RNA sequencing of these organisms during growth under various environmental conditions should provide critical information on the indirect and direct regulons of various DNA binding proteins. Such experiments combined with ChiP-Seq will reveal which target genes are directly regulated by proteins such as Fur (i.e. Fur binds directly the DNA promoter regions of such genes to alter subsequent transcription) and which are indirectly regulated (no such binding is observed but dysregulation of the target gene is observed in a knock-out strain of the DNA-binding protein). The large number of data obtained from RNA-sequencing can be analysed with metatranscriptomic programs (Faith et al., 2007) to reveal previously unknown interactions between different regulatory proteins to complete the map of global regulation in pathogenic Neisseria species. Acknowledgements This work is supported by NIH/NIAID grants R01AI048611 and 1U19AI084048. References

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The Regulatory Small RNAs of Neisseria Yvonne Pannekoek, Dave Speijer and Arie van der Ende

Abstract The discovery and characterization of regulatory small RNAs (sRNAs) in bacteria has exploded in recent years. These sRNAs act by base-pairing with target mRNAs with which they share either limited or extended complementarity. Many of them base-pair at or near the Shine–Dalgarno (SD) sequence of their targets and block translation by preventing entry of ribosomes, while others base-pair in regions influencing the stability of their cognate mRNA. Those binding at or near SD regions are the most well studied among bacteria and carry promoters that are often responsive to environmental signals. In the last years, using a variety of approaches, among which are biocomputional prediction, highdensity micro array analysis and high-throughput transcriptome analysis, novel sRNAs of Neisseria species were identified. Some of them (NrrF, AniS and a sRNA involved in pilin antigenic variation) were also functionally analysed. In this chapter we will focus on strategies used to identify sRNAs in Neisseria and will highlight studies on those sRNAs for which base-pairing mRNAs were identified and functionality has been demonstrated by experimental approaches. Introduction The genus Neisseria contains two strictly human pathogens, causing infections resulting in serious public health problems worldwide. Neisseria meningitidis or meningococcus frequently colonizes the upper respiratory mucosal surfaces but sporadically can cross the epithelial layer, causing septicaemia and meningitis, a life-threatening

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disease, especially in childhood (de Souza and Seguro, 2008). Its closest relative, Neisseria gonorrhoeae or the gonococcus primarily infects the urogenital and/or anorectal mucosa following intimate contact. It is the causative agent of gonorrhoea, the second most reported notifiable disease in the USA (Edwards and Apicella, 2004).These genetically closely related pathogens have to adapt to the many different environments encountered in the host. Changing gene expression patterns is a major adaptive response to the different challenges posed by each of these surroundings. Meningococci and gonococci possess a variety of genes involved in environmental responses. They are regulated and controlled by specific factors and environmental signals, including temperature, pH, oxygen and nutrient availability. They also have to react to exposure to toxic compounds such as oxygen radicals produced by the host (Basler et al., 2006; Cole, 2012; Criss et al., 2009; Del-Tordello et al., 2012; Delany et al., 2003, 2004; Ducey et al., 2005; Edwards et al., 2010; Edwards et al., 2012; Hedman et al., 2012; Isabella and Clark, 2011; Moir, 2011; Pannekoek et al., 1992; Schielke et al., 2010; Sebastian et al., 2002; Yu and Genco, 2012b; Stohl et al., 2005). Of interest, as predicted from their genome sequences, these two pathogens have many orthologues of regulators, although their infectious processes and niches are quite distinct. Gene expression can of course be influenced on many levels: transcription, mRNA stability, translation and protein stability. In bacteria, it is mostly controlled by a variety of mechanisms at the level of transcription and mRNA stability, translation, or both. Gene expression switches are usually

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mediated by specific regulatory proteins known as transcription factors that sense an appropriate signal and trigger the specific transcriptional response by binding to a specific DNA sequence (operator) in the promoter region. Another class of gene expression regulators, more recently identified, comprises regulatory small RNAs (sRNAs) (Rodionov, 2007).

sRNAs based on detection by microarray analysis or deep sequencing (Del-Tordello et al., 2012; Georg et al., 2009; Guell et al., 2011; Sharma et al., 2010; Toledo-Arana and Solano, 2010; Sittka et al., 2009). However, only a few of these signals have been verified in independent experiments. What is more, only a very limited number of antisense sRNAs have been characterized functionally.

RNA sequences influencing gene expression There are basically two types of sRNAs found in bacteria. The first class of sRNAs are so-called antisense sRNAs, transcribed opposite the open reading frames of annotated genes. They thus share extensive complementarity with these corresponding transcripts with which they can interact (Georg et al., 2009; Guell et al., 2009; Sharma et al., 2010; Thomason and Storz, 2010; Toledo-Arana et al., 2009). They are also referred to as cis-encoded sRNA. The second class of sRNAs also act by base-pairing but have more limited complementarity with their target mRNAs. They are generally encoded somewhere else, far away from genes they regulate. This type is often found in intergenic (IG) regions of the genome. Pairing generally involves a seed region of 6–8 contiguous base pairs. These sRNAs are usually between 50 and 300 nucleotides (nt) long and have been referred to as trans-encoded sRNAs. They are the most well studied among bacteria, carry promoters that are often (but not always!) responsive to environmental signals and have ρ-independent terminators followed by a T-stretch (Gottesman and Storz, 2010). Stretches of regulatory RNA are also found as elements present in the 5′ untranslated regions (5′-UTR) of the mRNAs they regulate (for example, riboswitches, thermosensors and pH sensors) (Breaker, 2012; Kortmann and Narberhaus, 2012). During the last decade, numerous sRNAs have been identified in various bacterial species using a wide variety of both biocomputational and experimental approaches. For any bacterium the exact number of sRNAs is still not known because in many cases short transcripts have only been detected by one approach and have not been functionally analysed. There are reports of hundreds of

Mechanisms of regulation of gene expression by sRNAs The mechanisms by which antisense sRNAs act have only been established in a small number of instances. In most cases studied, the antisense sRNA represses the amount of corresponding protein by interfering with translation. The mechanism by which this occurs may vary with different mRNA antisense sRNA pairs, because the antisense sRNA can overlap the 5′ end of the mRNA, the open reading frame of the mRNA, or the 3′ end of the mRNA. Another activity invoked by antisense sRNAs is the directed cleavage of the target transcript (the ‘opposite’ strand). This later process is dependent on the (combined) activity of RNases (enzymes that cleave RNA which are involved in RNA processing, degradation and quality control) (Gerdes and Wagner, 2007; Sesto et al., 2013; Weaver, 2007). In addition, recently a new concept in bacterial antisense RNA-mediated gene regulation has been described and designated as the ‘excludon’. The excludon defines a genomic locus encoding an unusually long antisense RNA that spans divergent genes or operons with related or opposing functions. These long ‘antisense’ RNAs can inhibit expression of one operon while functioning as an mRNA for an adjacent operon, thereby acting as fine-tuning regulatory switches in bacteria (Sesto et al., 2013). Base-pairing sRNAs with limited complementarity also modulate transcription, translation and mRNA stability and are often tightly associated with the activity of RNases. Many of these sRNAs base-pair at or near the Shine–Dalgarno (SD) sequence of the target mRNA. This may lead to translational inhibition and concomitant rapid degradation of sRNA-mRNA duplexes (see Fig. 3.1A). However, translational blocking is not clearly restricted to the ribosome binding site. It

sRNA in Neisseria |  43

A

sRNA

Translation repression 5’

5’

mRNA

SD

5’ mRNA

B

C

mRNA

5’

Translation activation

5’

SD

sRNA

C

C sRNA

mRNA degradation

5’

5’

mRNA

ribonuclease

5’ mRNA

D mRNA

5’

mRNA stabilization ribonuclease

mRNA

5’

sRNA

5’

Figure 3.1  Some modes of action of sRNAs basepairing with mRNAs resulting in changes in translation or stability. (A) Translational repression. sRNA, either with or without help from Hfq, blocks translational repression by directly pairing with the Shine–Dalgarno (SD) sequence in the 5′-UTR of the target RNA, preventing ribosomal entry. Translational repression is not restricted to binding in the direct neighbourhood of the SD sequence but can also occur downstream within ‘a five codon window’ and over 50 nt upstream (Darfeuille et al., 2007; Desnoyers and Masse, 2012; Sharma et al., 2007). Furthermore, examples of sRNAs targeting the coding region to repress translation or destabilize mRNA have also been reported (Bouvier et al., 2008; Pfeiffer et al., 2009). (B) Translational activation. The sRNA can activate translation by preventing or overcoming the formation of a secondary structure. Binding uncovers the SD sequence (Majdalani et al., 2005; Morfeldt et al., 1995; Prevost et al., 2007). (C) mRNA degradation. Binding of the sRNA possibly initiates mRNA degradation by uncovering (or creating) recognition sites or structures for ribonucleases such as RNase E (Caron et al., 2010; Desnoyers et al., 2013). (D) mRNA stabilization. On the other hand, binding of sRNA to mRNA may promote mRNA stability in case of masking a potential ribonuclease recognition site or structure (Ramirez-Pena et al., 2010).

has been shown that base-pairing as far as 50 nt upstream of the ribosome binding site and as far as the fifth codon into the open reading frame (ORF) can still block translation (Bouvier et al., 2008). Besides repressing translation, base-pairing of sRNAs can also remodel mRNA structure, thereby for example uncovering the SD sequence, increasing the stability of the transcript and activating translation (Majdalani et al., 2005; Morfeldt et al., 1995; Prevost et al., 2007) (Fig. 3.1B). It should be noted that some sRNAs use both modes of action, positively regulating some targets, while negatively acting on others (Fig. 3.1), and that some mRNAs are subjected to regulation by multiple sRNAs (Corcoran et al., 2012a; Desnoyers and Masse, 2012; Huntzinger et al., 2005; Lease et al., 1998; Morfeldt et al., 1995; Caron et al., 2010).

The majority of the base-pairing sRNAs that act via limited complementarity in enteric bacteria require the RNA chaperone Hfq to facilitate pairing. Hfq is a member of the Sm\Lm family of proteins involved in splicing and binds both sRNAs and target mRNAs. There are several mechanisms of Hfq mediated regulation at the levels of translation and RNA stability, and these have been recently reviewed by Vogel and Luisi (2011). The hallmark of Hfq activity is to stimulate base-pairing in vitro. Hfq is widely conserved among a variety of bacterial species, among which are Neisseria (Brennan and Link, 2007; Dietrich et al., 2009; Fantappie et al., 2009; Mellin et al., 2010; Pannekoek et al., 2009). Unlike enteric bacteria, several base-pairing sRNAs of Gram-positive bacteria such as Staphylococcus aureus and Bacillus subtilis do not require the function of Hfq,

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although Hfq is present (Boisset et al., 2007; Heidrich et al., 2006; Silvaggi et al., 2005). It is not clear why Hfq is required in some cases but not in others. One explanation might be that a higher proportion of G:C base pairs obviates the need for Hfq for a subset of sRNAs to establish more extended pairing. Another explanation might be that (an)other RNA chaperone(s) may be playing a role, as has been proposed for B. subtilis (Gaballa et al., 2008). This might well be the case in other bacterial species like Chlamydia, encoding functional sRNAs, but in which the gene encoding Hfq is absent (Grieshaber et al., 2006; Tattersall et al., 2012). Almost all of the sRNAs acting via limited complementarity are expressed under specific conditions. A wide range of environmental stimuli affect the expression of this class of sRNAs, among which are anaerobic growth, oxidative stress, availability of glucose, iron and magnesium, and osmotic imbalance. In this way, these sRNAs provide a fast regulatory network to (pre)adapt to rapidly varying growth conditions and stress signals (Gottesman and Storz, 2010). In the last six years, using a variety of experimental approaches, among which biocomputational prediction, high-density micro array analysis and high-throughput transcriptome analysis, novel sRNAs of Neisseria species were identified. Some of them were also functionally analysed. In addition, proteomic and high-density array analysis studies have been used to identify genes and IG regions differently expressed in wild type gonococci and meningococci versus Hfq knockout (Δhfq) strains. Approximately 150 genes were found differently expressed in Δhfq cells in meningococci and approximately 370 genes in gonococci (Dietrich et al., 2009; Fantappie et al., 2009; Mellin et al., 2010; Pannekoek et al., 2009). For almost all coding genes it is still unknown whether observed differential expression patterns result from Hfq function directly or indirectly. In this chapter we will instead focus on strategies used to identify sRNAs in Neisseria and will highlight studies on those sRNAs for which base-pairing mRNAs were identified and functionality has been demonstrated by experimental approaches.

Identification of sRNAs of N. meningitidis and N. gonorrhoeae Biocomputational approaches Several biocomputational approaches have been developed to identify sRNA-coding genes in IG regions by searching for the co-localization of genetic features such as predicted ρ-independent transcription terminators, promoters and transcription factor binding sites, intergenic conservation among closely related species and/ or conserved secondary structure (Livny, 2007; Livny et al., 2008; Mellin et al., 2007; Pannekoek and van der Ende, 2012). It should be stressed, however, that searches using these programs to predict regions encoding candidate sRNAs in a single genome do not guarantee that these candidates are in indeed expressed. They also provide only limited functional annotation and thus little insight in the potential biological roles of the candidate loci. The ferric uptake transcriptional regulator (Fur) is essential for iron homeostasis in many prokaryotes (Hantke, 2001). Fur inhibits expression of genes crucial for iron-acquisition by means of binding to a specific consensus transcription binding site (Fur-box) in their promoters, but also acts as a positive regulator affecting the production of factors that store or contain iron. Fur responds to iron-replete environments by complexing with the available Fe2+, causing a change in the conformation of Fur, leading to binding to the Fur-box. This inhibits binding of RNA polymerase, leading to repression of transcription (Hantke, 2001). Fur has been well studied in meningococci and gonococci. Fur has been demonstrated to repress the transcription of many iron-regulated genes (Agarwal et al., 2008; Delany et al., 2003, 2004, 2006; Grifantini et al., 2003; Grifantini et al., 2004; Sebastian et al., 2002; Shaik et al., 2007; Yu and Genco, 2012b; Yu and Genco, 2012a). Microarray studies examining an N. meningitidis fur knockout strain (Δfur) demonstrated that also a large number of genes are activated by Fur in the presence of iron. The underlying mechanism of Fur-dependent gene activation in meningococci and gonococci is largely unknown (Delany et al., 2004; Delany et al., 2006; Grifantini et al., 2003).

sRNA in Neisseria |  45

In a variety of microorganisms, the expression of a particular class of sRNAs has been shown to be Fur-regulated, demonstrating cross-talk between the riboregulated and the Fur-regulated networks (Massé et al., 2007). One of the sRNAs first identified, the Fur-regulated sRNA of Escherichia coli was designated RyhB. RyhB was shown to contain a Fur-box in its promoter region. It is repressed in a Fur-dependent manner when iron is limiting (Massé and Gottesman, 2002; Massé et al., 2003, 2005). RyhB orthologues (while noting that there is little sequence homology between functional sRNAs orthologues from different bacteria), have been identified in other bacterial species among which are Vibrio cholerae and Pseudomonas aeruginosa (Davis et al., 2005; Massé and Gottesman, 2002; Massé et al., 2003, 2005; Wilderman et al., 2004). Using a bioinformatics approach to screen for Fur-regulated sRNAs in meningococci, combined with experimental analyses, Mellin et al. (2007) identified the first neisserial sRNAs. The biocomputational strategy used was straightforward. It was reasoned that a Fur-regulated sRNA would likely have at least two characteristic features: a Fur-box in its promoter region and a ρ-independent terminator tail at the end of its coding sequence. IG regions of N. meningitidis strain MC58 were extracted using tools present on the Comprehensive Microbial Resource website and were parsed for a 24-bp consensus neisserial Fur-binding site (Grifantini et al., 2003; Shaik et al., 2007) using the pattern search algorithm Fuzznuc, available as part of the EMBOSS software suite. Nine mismatches were allowed. Next, a separate analysis, using the program TranstermHP, parsed IG regions for predicted ρ-independent terminator sequences ( Jacobs et al., 2009). The two databases were compared to identify regions where a Fur-binding site occurred within 0 to 250 bp of the beginning of a predicted ρ-independent terminator sequence. After the exclusion of tRNAs, nineteen regions meeting these criteria were identified. Next, the transcription levels were analysed using S1 protection analysis, RT-PCR and Northern blotting. Only 5 out of 19 regions predicted to encode sRNAs were indeed found to be transcribed. The transcription of only one of them was shown to be truly iron

regulated in a Fur-dependent manner. This particular sRNA was designated NrrF (for neisserial regulatory RNA involved with iron [Fe]) (Mellin et al., 2007). Biocomputational approaches: comparative genomics Livny et al. (2008) developed SIPHT (sRNA identification protocol using high-through put technologies). Candidate sRNA-encoding loci of N. meningitidis and N. gonorrhoeae were identified based on the presence of putative ρ-independent terminators downstream of conserved IG sequences. Each locus found was annotated for several features, including conservation in other species, association with one of several transcription factor binding sites and homology to any previously identified sRNAs and cis-regulatory RNA in other bacterial species. Using SIPHT,16 novel candidate sRNAs in addition to NrrF were identified. Two of the novel candidates (Candidate_87 and Candidate_225) were only found in the genomes of meningococci and were apparently absent in gonococci. The remaining 14 predicted sRNAs were found in both gonococci and meningococci but not in the genomes of other commensal Neisseria species (with one exception, Candidate_191, which was also present in the genome of N. lactamica) (Livny et al., 2008). In as far as we know, the expression of these sRNAs still needs to be validated and none of these sRNAs have been functionally analysed. Transcriptomics Using a oligonucleotide tiling microarray, consisting of 60-mer tiling probes of both strands of all IG regions of strain MC58, the transcriptional profile of N. meningitidis wild type, Δhfq and complemented Δhfq cells cultured under high and low iron growth conditions was examined. In addition to NrrF seventeen Hfq and/or iron-regulated IG regions were identified that showed at least 2.5-fold regulation. In four of these transcripts a ρ-independent terminator was predicted but no further functional analyses were conducted (Mellin et al., 2010). The transcriptome of N. meningitidis cultured in blood was determined by using a customized 60-mer oligonucleotide tiling microarray. The

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array contained probes for the coding sequence of the genes and probes covering head to tail the antisense strands of each gene and all the IG regions in both strands. N. meningitidis was incubated in human blood in a time course experiment. Samples were collected at different time points: immediately after mixing bacteria with blood (time zero, used as a reference point) and after 30, 60 and 90 min of incubation in blood at 37°C and 5% CO2. Total RNA was extracted and enriched for bacterial RNA. Transcriptional changes throughout the course of incubation in human blood were defined by comparison of the expression levels at various time points to that at time zero. This screening resulted in the identification of 91 transcripts with a median length of 126 nt and range from 60 nt (corresponding to a single probe) to 457 nt. In only one case, a larger transcript (801 nt) was detected. Thirteen of these transcripts were found to be present and annotated as genes encoding hypothetical proteins in the genomes of other neisserial species and excluded from further analyses. Eight highly up-regulated transcripts were selected for experimental confirmation using 5′ rapid amplification of cDNA ends (5′-RACE) and the expression of seven of them was confirmed (called Bns, for blood induced neisserial sRNA) (Del-Tordello et al., 2012). Among these, one corresponded to NrrF and one (Bns2) corresponded to the sRNAs mc05 and M07, as identified by Mellin et al. (2010). To date, to our best knowledge, no further experimental analyses of these sRNAs were conducted. Specific response genes, which are regulated by a subset of alternative σ70-like transcription factors, have evolved to rapidly respond to changing environments. In reaction to specific external stimuli, these σ factors recruit RNA polymerases to the appropriate response genes. Extracytoplasmic function (ECF) σ factors or σE factors, are characterized by the fact that most of the genes under their control encode proteins residing in the outer membrane or periplasmic space (Brooks and Buchanan, 2008; Cases and deLorenzo, 2005). Previous investigations in Salmonella enterica and E. coli have shown several sRNAs to be regulated by their respective σE orthologues (Corcoran et al., 2012a; Papenfort et al., 2006; Udekwu and

Wagner, 2007). Recently, we have described the existence of a σE operon in Neisseria meningitidis and identified the anti-σE factor MseR (Hopman et al., 2010). In order to unravel the complete σE regulon in N. meningitidis the total transcriptional content of wild type meningococci was analysed by SOLiD deep sequencing and compared with that of mseR knockout cells (H44/76ΔmseR) in which σE is highly expressed (Huis in ‘t Veld et al., 2011). Analysis of differential expression in IG regions identified a σE dependent small noncoding RNA. This sRNA is encoded in the IG region between NMH_1566 and NMH_1568 (analogous to NMB1826 and dnaE in N. meningitidis strain MC58). It is 74 nt long and ends with the typical T-stretch of a ρ-independent (intrinsic) terminator. The gene encoding this sRNA was found to be conserved in all currently sequenced complete and whole genome shotgun genomes of N. meningitidis, N. gonorrhoeae and N. lactamica strains and in several, but not all, commensal Neisseria species. Further study is currently under way to assess the function of this interesting non-coding sRNA (Pannekoek et al., unpublished). The major transcriptional regulator controlling the physiological switch between aerobic and anaerobic growth conditions in E. coli is the DNA binding transcription factor protein FNR (fumarate and nitrate reduction). This transcription factor regulates gene expression in response to oxygen deprivation in many bacteria by binding its target sequences, the so-called FNR-boxes, located in several different promoters. Binding usually results in activation but repression of transcription also occurs (Kiley and Beinert, 1998) In E. coli and several other enterobacterial species, recent studies have described the anaerobic induction and function of a small regulatory RNA referred to as fnrS. Regulation of this sRNA was shown to be relatively complex. Maximal fnrS expression occurred anaerobically, but the available carbon source and alternative terminal electron acceptor present also influenced levels to a lesser extent. Induction of fnrS under anaerobic conditions was shown to be FNR-dependent and mediated by FNR binding to a class-II activation site centred –41.5 upstream of the transcription start site (Durand and Storz, 2010). FnrS

sRNA in Neisseria |  47

orthologues have so far not been identified in bacteria outside the Enterobacteriaceae family. Of course the Neisseria genomes contain genes encoding other components involved in denitrification and/or adaptation to anaerobic growth as well. They are subject to a variety of transcriptional regulators, such as the nitric oxidesensitive repressor NsrR, the nitrite-insensitive two-component system NarQP as well as the oxygen-sensitive regulator FNR (Edwards et al., 2010; Moir, 2011). Previous microarray analysis of the meningococcal FNR regulon identified a small FNR-activated transcript of unknown function (Bartolini et al., 2006). Coincidently, this transcript was also determined to be the most highly induced by FNR in gonococci (Whitehead et al., 2007). Deep sequencing whole transcriptome exploration of the anaerobic stimulon in Neisseria gonorrhoeae confirmed both expression and regulation of this sRNA: likely the functional equivalent of fnrS in the Enterobacteriaceae family (Isabella and Clark, 2011). Almost simultaneously a novel Hfq-dependent sRNA under the control of FNR, induced anaerobically, named AniS (anaerobically induced sRNA) was identified in meningococci (Fantappie et al., 2011). The sRNA was originally annotated as a 147 bp hypothetical gene (NMB1205 in the N. meningitidis MC58 genome) and predicted to encode a protein of 48 amino acids of unknown function (Tettelin et al., 2000). The detected transcript is approximately 100 nt long and comparative analysis of this sequence in the available neisserial genomes from diverse species revealed the presence of an sRNA orthologue in all the genomes which was highly conserved over 56 nt at the 3′ end. Although the 5′ part of the sequences is variable in length, the presence of conserved regulatory elements of the promoter (FNR-box upstream of a putative –10 promoter element) and the transcriptional terminator suggests that this FNR-regulated sRNA is synthesized in each species. Since this particular sRNA does not exhibit sequence similarity to the FNR-regulated sRNA FnrS of E. coli, and is found at different genomic locations, it was suggested to call this neisserial sRNA AniS (Fantappie et al., 2011). Though this seems to be the functional equivalent of FnrS, in this chapter we will follow this nomenclature.

Functional analysis of sRNAs in Neisseria The Fur-regulated sRNA NrrF The gene encoding the sRNA NrrF (169 bp long) is located in the IG region between two predicted converging genes annotated as hypothetical proteins (NMB2073 and NMB2074) in the genome of N. meningitidis strain MC58 (Mellin et al., 2007; Metruccio et al., 2009; Fantappie et al., 2011; Tettelin et al., 2000). The predicted Fur-binding site of the nrrF coding region identified in the initial biocomputational screen carries high homology with the Fur-box consensus of 24 bp (Fig. 3.2). Nrrf is conserved with more than 93% identity in the genome of N. gonorrhoeae strain FA 1090, where it is located in the IG region between NGO2002 and NGO2003 (orthologues of NMB2073 and NMB2073 respectively) and it is also synthesized under iron-limiting conditions (Ducey et al., 2009). The binding of Fur to the predicted Fur-box sequence in N. meningitidis and the specificity of binding was investigated by electrophoretic gel mobility shift assays (EMSA) using different concentrations of recombinant meningococcal Fur protein and mutagenesis of the predicted Fur-binding region. In this way, it was found that the 24-bp region of the nrrF promoter was indeed essential for Fur-binding, suggesting that transcription of nrrF is controlled by Fur (Mellin et al., 2007). Putative targets regulated by NrrF were identified using the computational tool ‘TargetRNA’ (Tjaden et al., 2006). TargetRNA scans the regions up- and downstream of putative SD sites of each 5′-UTR of the mRNA transcript for complementarity to a given sRNA. This way, 11 transcripts carrying stretches of complementarity to NrrF in their 5′-UTRs were identified (Mellin et al., 2007). Possible regulation by NrrF was assessed by RT-PCR for two predicted targets, namely sdhA and sdhC, two of the four genes encoding subunits of the succinate dehydrogenase complex (SdhCDAB). These genes are expressed from four collinear genes, which appear to be co-regulated in meningococci, as assessed by microarray analysis (Metruccio et al., 2009). The choice was made to

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Fur-box

NMB2073

(-67)

taaaataawaataattatcattat CAAAAAATCTGCATTTATTTTTAAatttttattgataattattattattagcgtataatcaa +1

nrrF

-35

-10

(-4)

(-5)

aaccaCTCGGAAGCCGTCCGTTCCGAACCATTAAACACCATATTTCCCCATCATCACTTTCA

(57)

(58)

CACTTGGAGTCGGCATATACGAGACATACATTCCCTTTTTATATATCAGATACTCAAAACCG

(121)

(122)

AAACGCCAAACCCACCTTCGCGGTGGGTTTGGCGTTTatcgtccggctttcgcgcCTATTTG

(181)

NMB2074

Figure 3.2  The nrrf locus in N. meningitidis strain MC58. Sequence of the nrrF locus, flanked by NMB2073 and NMB2074. The Fur-box is indicated and lower bold typed nt in the Fur-box are identical to the Fur-box consensus sequence shown above it (Grifantini et al., 2003). The promoter elements –35 and –10 are underlined and the transcriptional start (+1) is indicated by an arrow. A putative Hfq binding site is indicated in uppercase bold typed nt. A, further downstream, predicted ρ-independent terminator is underlined, with bold typed nt predicted to form a stem–loop (adapted from Mellin et al., 2007; Metruccio et al., 2009). W = either A or T.

investigate NrrF-mediated regulation of sdhA and sdhC because in E. coli the putative orthologues of these genes are post-transcriptionally regulated by the Fur-regulated small RNA RyhB (Masse and Gottesman, 2002). In wild-type meningococci, both sdhA and sdhC showed repression upon iron depletion, corresponding with an increase in nrrF transcription. In nrrF knockout cells (∆nrrF) however, sdhA and sdhC were not repressed under such conditions (Mellin et al., 2010). Metruccio et al. (2009) performed an additional detailed analysis of NrrF and its role in Fur-mediated regulation of the sdhCDAB genes. They showed that Hfq binds NrrF in vitro and could mediate Fur and Hfq-dependent NrrF-regulation of succinate dehydrogenase in vivo. NrrF forms a duplex with a region of complementarity within the sdhDA region of the succinate dehydrogenase transcript. Hfq enhances the binding of NrrF to the identified target in the sdhCDAB mRNA. However, Mellin et al. (2010) showed that the stability of NrrF, as well as the regulation of sdhC and sdhA in vivo was unaltered ∆hfq cells. In this context, it should be noted that binding of Hfq to NrrF in vitro can not be taken as ultimate proof that Hfq is the required cofactor of NrrF in vivo, as Hfq has been shown to bind to a wide variety of RNA and DNA species (Takada et al., 1997;

Windbichler et al., 2008; Zhang et al., 1998). Thus, the specificity of binding of Hfq to NrrF as assessed in in vitro systems remains questionable in this particular case. However, although in the genetic background of ∆fur cells SdhA protein levels are drastically down-regulated, in ∆fur∆hfq cells, levels of SdhA protein, though still lower than wild-type levels, are not as low as in ∆fur cells, indicating that down-regulation is indeed more efficient in the presence of Hfq (Metruccio et al., 2009). So far, succinate dehydrogenase subunits remain the only identified true target of NrrF for meningococci. In order to identify additional targets of NrrF, genes that were previously found to be positively regulated in ∆fur cells, suggesting regulation through indirect mechanisms (Delany et al., 2004, 2006), were assessed for direct regulation by NrrF using a ∆fur cells and/ or ∆fur∆nrrF cells. The levels of none of the putative targets (NMB0952-NMB0954, nuoA, norB, pan1, NMB1436, sodB, fumB, aniA and bfrA) were significantly affected by NrrF status (Mellin et al., 2010; Metruccio et al., 2009). The FNR-regulated sRNA AniS AniS, originally annotated as NMB1205 in the genome of N. meningitidis strain MC58 is flanked upstream by the converging bacterioferritin B and

sRNA in Neisseria |  49

A genes (NMB1207 and NMB1206 respectively) and downstream by converging genes encoding a transcriptional regulator and a protein-PII uridyltransferase (NMB1204 and NMB1203 respectively) (Fig. 3.3) (Fantappie et al., 2011; Tettelin et al., 2000). By the combination of primer extension analysis and in vitro transcription, a run off transcript of approximately ~100 nt long was found from NMB1205 (Fantappie et al., 2011). Transcript levels of AniS were compared in wild type and fnr knockout cells (∆fnr) cultured with or without oxygen. Expression of AniS was strongly induced in wild-type cells upon anaerobic culture conditions but no transcript could be detected in the ∆fnr cells, irrespective of growth conditions. This indicated that the synthesis of AniS is positively regulated by FNR and that FNR activates its transcription in response to anaerobiosis (or oxygen limitation). In vitro binding assays in combination with DNAse I footprinting analysis using purified FNR protein demonstrated binding of FNR to the predicted FNR-box, further substantiating the role of FNR as a transcriptional activator of AniS (Fantappie et al., 2011). Putative targets of AniS were identified by comparison of global gene expression profiles of cells overexpressing FNR and wild type cells, and cells overexpressing FNR in which aniS was deleted. Three genes whose expression levels varied at least twofold between experiments and of which the regulation was confirmed by RT-PCR analysis were identified (NMB0214 and the predicted operon NMB1468/1469). The

NMB1206

down-regulation of these putative target genes in the strain overexpressing FNR and up-regulation in response to deletion of aniS suggests that AniS down-regulates these target mRNAs when its synthesis is induced by FNR. Indeed, overexpression of AniS resulted in a significant down-regulation of the NMB1468 protein and mRNA levels of both NMB0214 and NMB1468. Of interest, deletion of Hfq in the overexpression FNR genetic background still resulted in high levels of AniS but the target genes were no longer down-regulated. This strongly suggests a role for Hfq in mediating the AniS-dependent regulation. Furthermore, it was shown that overproduction of AniS resulted in time dependent decay of the transcripts of both NMB0214 and NMB1468 (Fantappie et al., 2011). Direct targeting of NMB1468 by AniS was assessed using a reporter system in E. coli (Urban and Vogel, 2007). For this, the 5′-UTR of NMB1468 was fused in frame with the gene encoding the green fluorescent protein (GFP) on a low copy vector and cotransformed with either a plasmid expressing aniS or with the proper control plasmid expressing a non-sense sRNA. Expression of aniS in trans resulted in translational down-regulation of the GFP-fusion, suggesting that AniS directly interacts with the 5′-UTR of NMB1468 as was also predicted and suggested by biocomputational tools. To validate the interaction and to validate its biocomputational predicted location of interaction, EMSA assays were carried out using radio labelled AniS and in vitro generated mRNA corresponding to the first 127 nt of

FNR-box

ttgatnnnnatcaat CTAA//aataaaatctaaattgatttgaatcaatacctgataagggtttttgtttgataataccgat +1

(1)

aniS

-10

ATGAAATTCAGGGTGCTTGTTTGCTGTTTCCCAATCTGTCTTGATTTTATCTCTTTCCTCTTGATGT (67) NMB1204

(68) GTGTGTGTTTGGGTGTGGCTGCCGCCACCCCTTTTTTTTggcttttatgtgaagtaaaa//ATGATG

Figure 3.3  The AniS locus in N. meningitidis strain MC58. Sequence of the aniS locus flanked by NMB1206 and NMB1204. The FNR-box is indicated with lower bold typed nt in the FNR box identical to its consensus sequence (shown above the aniS sequence) (Bartolini et al., 2006). The promoter elements –35 and –10 are underlined and the transcriptional start (+1) is indicated by an arrow. The predicted ρ-independent terminator is underlined, with bold typed nt predicted to form a stem–loop (adapted from Fantappie et al., 2011).

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the NM1468 transcript, containing the predicted region of interaction. In this fashion a direct interaction between AniS and the mRNA transcript was confirmed, at least in vitro. Mutations in aniS disrupted the interaction, which however could be restored by compensatory substitutions in the target. Taken together, these experiments provided ultimate proof that AniS interacts with NMB1468, most likely through a Hfq-stabilized base-pairing of the proposed region. AniS regulates NMB1468 expression through transcriptional inhibition or post-transcriptional down-regulation mechanisms, or both (Fantappie et al., 2011). EMSA experiments showed direct binding of Hfq to AniS, but of more significance, AniS transcripts are rapidly degraded in vivo in the cells overexpressing hfq (half-life 10 min). This was somewhat unexpected, since Hfq is known to play a role in stabilizing many sRNAs (Aiba, 2007; Valentin-Hansen et al., 2004). On the other hand, these observations might point to another model of how Hfq functions. Hfq facilitates base-pairing of sRNAs with mRNAs targets whenever there are adequate mRNA targets present in the cell. This could promote degradation of both target and sRNA itself by the subsequent recruitment of RNases (Bandyra et al., 2012; DeLay and Gottesman, 2012; Massé et al., 2003). Whether this explains the phenomenon of enhanced turnover of AniS in cells overexpressing hfq awaits further studies. NMB0214 and NMB1468 encode a PrlC oligopeptidase and a lipoprotein respectively. The precise functions of these proteins are largely unknown, although PrlC has been implicated in cell cycle regulation, protein export and degradation ( Jain and Chan, 2007; Jiang et al., 1998; Trun and Silhavy, 1989). Further functional analysis of both these components hopefully will provide a rationale for down-regulation under conditions of oxygen limitation. A sRNA of Neisseria gonorrhoeae required for pilin antigenic variation Pathogenic Neisseria species express only one, type IV, pilus (Swanson et al., 1971). Type IV pili are expressed by many Gram-negative species of bacteria. Almost all of the mechanistic

studies on pili were conducted in N. gonorrhoeae but it is generally assumed that the mechanisms used by N. meningitidis are similar, if not identical. The N. gonorrhoeae type IV pilus is essential for establishing infection (Cohen and Cannon, 1999; Swanson et al., 1987). Pili are of importance for epithelial cell adherence and cell aggregation. They are also involved in twitching motility (Park et al., 2001; Rudel et al., 1992; Wolfgang et al., 1998). Although pili are antigenic structures, total protective immunity never develops, partially because gonococci can evade the host immune system by varying the composition and expression of surface antigens such as pili, lipooligosaccharides, and the opacity family of outer membrane proteins (Seifert, 1992). Pilin antigenic variation (Av) is the result of a high frequency diversification system that operates via a specialized recombination pathway utilizing enzymes participating in general recombination and repair pathways, as well as enzymes that do not participate in either (Cahoon and Seifert, 2011). The recombination factors RecA, RecG and RuvB are essential for pilin Av (Cahoon and Seifert, 2011; Koomey et al., 1987; Sechman et al., 2005, 2006). RecA is a DNA recombinase and RecG is a 3′ to 5′ helicase involved in branch migration of Holliday junctions. RuvB, in concerted action with RuvA, is a 5′ to 3′ helicase also involved in branch migration of Holliday junctions. Gonococci carry one pilin expression locus (pilE) and up to 19 silent pilin loci (pilS) residing in up to 6 discrete locations in the genome (Hamrick et al., 2001). Pilin Av occurs as a result of non-reciprocal DNA recombination between any pilS copy and pilE, giving rise to the expression of a new variant protein (Hagblom et al., 1985). By using a transposon-based genetic screen, a DNA region upstream of pilE was identified as being essential for further recombination between any pilS and pilE although the expression of pilin itself was not blocked as a consequence of the transposon insertion. A targeted genetic screen in this region identified 11 GC base pairs that when individually mutated completely blocked pilin Av. Mutation of a 12th base pair resulted in intermediate Av and mutation of an adjacent 13th GC base pair had no effect on Av but mutation of this GC

sRNA in Neisseria |  51

in addition to the 12th resulted in complete loss of pilin Av, suggesting that the 13th GC base pair could partially substitute for the 12th (Cahoon and Seifert, 2009; Cahoon and Seifert, 2011). Mutation of the AT base pairs within the region containing the 12 GC base pairs had no effect on pilin Av, nor did mutation of bases directly outside this region. The identification of 12 GC base pairs arranged in four sets of three was consistent with a guanine quadruplex (G4)-forming sequence and this was further confirmed by biophysical studies. Additional mutation studies demonstrated that single base pair alterations blocking pilin Av change the structure and growth of N. gonorrhoeae. N-methyl mesoporphyrin IX, a compound that specifically binds G4 structures but not double or single stranded DNA (Ren and Chaires, 1999), indeed decreased the frequency of pilin Av (Cahoon and Seifert, 2009). Point mutations that block pilin Av and G4 structure formation prevented single stranded nicks from being detected in both the G4 forming sequence and complement strand (Cahoon and Seifert, 2009). Taken together, these data suggest that the formation of the pilE G4 structure is required for pilin Av and it was suggested that the structure forms only when the DNA duplex is melted, possibly during DNA replication as the G-rich sequence is on the lagging strand, resulting in a temporary single stranded

A

condition. Whatever the mechanism, in order for a G4 structure to form, duplex DNA must first somehow be converted into single stranded DNA. In a recent study, Cahoon and Seifert have identified an sRNA of N. gonorrhoeae encoded within the pilE G4 sequence, the transcription of which is essential for pilin Av (Cahoon and Seifert, 2013). The gene encoding this sRNA is adjacent to the pilE G4 sequence and consists of a promoter containing the most conserved bases of the –10 element (TAGAAT) and all the bases in the –35 element (TTGAGA) (Shultzaberger et al., 2007) (see Fig. 3.4). The relative location of this putative promoter to the pilE G4 sequence, as well as the –10 and –35 elements are conserved in all sequenced genomes of N. gonorrhoeae and N. meningitidis strains except in N. meningitidis strain FAM18. FAM18 expresses a different pilin class devoid of pilin Av and does not have a pilE G4 sequence (Aho et al., 1997; Helm and Seifert, 2010). 5′-RACE analysis in combination with in vitro transcription with E. coli σ70 RNA polymerase revealed a run-off transcript of 74 nt, initiated within the G4 forming sequence and three adjacent transcriptional start sites could be identified (Fig. 3.4). The existence of this transcript, although at low abundances, was confirmed by

sRNA promoter pilE promoter

pilE

G4

+1

B (2038315)

tgattgggtcggaatttgagatttttgaatttacgcgttagaataGGGtGGGTTGGGTGGGG

(2038377)

-35

-10

AATTTTCTATTTTTTAAAAAGCTCCGTTTTCTTGGAAAGCATTTGAAATCGGCGCGTGGTGT

Figure 3.4 The sRNA locus of N. gonorrhoeae involved in antigenic variation of pilin. (A) Schematic representation of the pilE G4-associated locus in N. gonorrhoeae. The sRNA promoter is adjacent to the pilE G4 (arrow pointing left) and located upstream from the pilE promoter (arrow pointing right) in the opposite orientation of transcription. (B) The pilE G4-associated sRNA sequence. Promoter elements at –35 and -10 are underlined. The three transcriptional start sites are indicated under the arrow and the G4 sequence is indicated in bold nts. The numbering corresponds to the genomic localization in the genome of N. gonorrhoeae strain FA1090 (GenBank AE004969) (adapted from Cahoon and Seifert, 2013).

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RNA deep sequence analysis of N. meningitidis strain alpha 14 (Cahoon and Seifert, 2013). To determine whether the pilE-G4 associated sRNA is required for pilin Av, promoter loss-of function mutants were constructed and analysed using the kinetic pilus-dependent colony morphology phase variation assay (Sechman et al., 2005). Mutation of the –10 element or both the –10 and –35 elements caused a complete block of pilin Av. Mutation of the –35 elements only resulted in intermediate pilin Av. Point mutations in these elements resulted in decreased pilin Av, suggesting that transcript levels of the sRNA correlate with the amount of pilin Av. Replacement of the guanine residue in the –10 element with thymidine to create a stronger promoter (i.e. more in line with the consensus sequence) (Shultzaberger et al., 2007) did not result in enhancement of pilin Av. Further mutagenesis of regions upstream of the promoter element, or insertions or deletions downstream of the G4 forming sequence but within the predicted sRNA transcript, also had no effect on pilin Av. Since the insertions and deletions downstream of the pilE-G4 sequence have no effect on pilin Av and since a ribosome binding site or open reading frame is apparently absent, it was concluded that this sRNA is indeed non-coding. Taken together, these results and the mutagenesis outcomes discussed above, are consistent with a model where pilin Av requires transcription of a sRNA, originating in the pilE-G4 sequence (Cahoon and Seifert, 2013). Pilin Av also requires the recombination factors RecA, RecG and RuvB (Koomey et al., 1987; Sechman et al., 2005, 2006). The Holliday junction double mutant recG/ruvB is synthetically lethal upon induction of recA expression in gonococci. This lethal phenotype is rescued by a pilE G4 mutation that prevents the formation of the G4 structure, demonstrating that the formation of the G4 structure is functionally upstream of the involvement of RecG, RuvB and RecA during pilin Av (Cahoon and Seifert, 2009). By using a G4-associated promoter mutant, introduced in the recG/ruvB double mutant, recA inducible strain, it was shown that upon recA induction –10 and –35 promoter mutants also rescue the lethal phenotype of the recG/ruvB double mutant under these conditions. This indicates that transcription

of the pilE G4-associated sRNA, as well as the formation of the G4 structure act before RecG, RuvB and RecA in the pilin Av mechanism (Cahoon and Seifert, 2013). Expression of the pilE G4-associated noncoding sRNA was shown to be essential for pilin Av. However, the specific identity of the RNA polymerase providing transcription is not crucial. The replacement of the –10 promoter element with a minimal T7 promoter sequence resulted in a pilin Av-deficient phenotype, but expression of T7 RNA polymerase in trans restored pilin Av to wild type levels. In addition, expression of the G4 sRNA region in trans could not complement a Av pilin-deficient promoter mutant at the endogenous locus indicating that the pilE G4-associated sRNA acts in cis (Cahoon and Seifert, 2013). Taken together, a direct link between transcription and G4 structure formation in the process of pilin Av seems to be established. As transcription proceeds through the pilE G4 sequence, an RNA:DNA hybrid could form between the sRNA and the C-rich strand leaving the G-rich strand unpaired to aid G4 structure formation, possibly in concert with the help of an as yet unknown protein. How the G4 structure then initiates gene conversion is presently unknown but a model is proposed where the G4 structure acts to recruit recombination factors to the pilE region of the chromosome. This is supported by the affinity for the pilE G4 structure of RecA and the ability of the pilE G4 to stimulate RecA-mediated strand exchange. Though possible other functions of the pilE G4-associated sRNA remain to be explored, the function described could be shared by other sRNAs. This can be deduced from the exciting observation that two other gonococcal genome sequences, predicted to be G4 forming, have been found to have putative promoters in similar locations (Cahoon and Seifert, 2013). Conclusions Over the last years, numerous sRNAs have been identified in Neisseria by using biocomputational methods, comparative genomics and experimental approaches, such as microarray analysis and deep sequencing. However, only a few of these

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potential sRNAs have been verified in independent experiments and, in addition, only a very limited number of sRNAs have been characterized functionally. Meningococci and gonococci possess a variety of genes involved in environmental responses, which are regulated and controlled by specific regulators and environmental factors, including temperature, pH, oxygen as well as nutrient availability. Exposure to toxic compounds, among which oxygen radicals produced by the host are important, also leads to changes in neisserial gene expression (Basler et al., 2006; Cole, 2012; Criss et al., 2009; Del-Tordello et al., 2012; Delany et al., 2003, 2004; Ducey et al., 2005; Edwards et al., 2010, 2012; Hedman et al., 2012; Isabella and Clark, 2011; Moir, 2011; Pannekoek et al., 1992; Schielke et al., 2010; Sebastian et al., 2002; Yu and Genco, 2012b; Stohl et al., 2005). sRNAs, functionally involved in the adaptive response to important environmental factors, such as iron availability and in the physiological switch between aerobic and anaerobic growth conditions, have now been convincingly identified. This strongly indicates that sRNAs of these pathogens are also key regulators of metabolic, physiological and possibly of pathogenic processes. The recent identification and functional characterization of a sRNA involved in the establishment of pilin Av suggests that sRNAs of Neisseria might also be involved in more antigenic variation processes. As such, sRNAs could make even more contributions to virulence properties of both meningococci and gonococci. Theoretically, this would make them prime targets for clinical interventions. Future trends To identify regulatory sRNAs to explore the complete transcriptional riboregulated network of Neisseria it will be important to pay attention to culture conditions used. Even ‘minor’ changes in culture conditions could be important, and many environmental alterations having impact, are probably not yet known. In light of this, is not unlikely to suggest that many sRNAs with true regulatory functions could still have been missed. It can also not be ruled out that some of these have been overlooked because

they have structures preventing detection by the methods used. Another possibility is that they are processed from mRNAs and are difficult to distinguish from 5′ or 3′-UTRs. Lastly, they can be missed because they are only expressed under highly specific conditions. Escherichia coli, expressing around 4000 proteins, is thought to contain 80–100 sRNAs (Thomason and Storz, 2010). It is difficult to predict a number, but it seems likely that the genomes of Neisseria will encode a lower number of sRNAs considering its smaller genome size. However, it should be noted that a thorough exploration and comparison of the genome architectures of Neisseria species with other bacterial species. reveals relatively large intergenic regions. These DNA stretches could potentially contain yet to be discovered genes coding for novel sRNAs. In contrast to the increasing number of reports describing identification of sRNAs in Neisseria our understanding of sRNA functionality in Neisseria is still very limited. A key task in the functional characterization is the identification of all interacting partners. Experimental transcriptomics and proteomics approaches, leading to mRNA target identification, have to be complemented by in silico methods for the prediction of sRNA targets. Prediction of mRNA targets is one thing. Experimental validation of all sRNA–target interactions is another. So far, the method of choice to unravel the function of these versatile regulators has been the use of in vivo gene fusion reporter systems. Recently improved gene fusion reporter systems, such as the superfolder GFP constructs, and additional experimental tools to identify RNA–protein interactions will facilitate experimental approaches in the future (Corcoran et al., 2012a,b; Rieder et al., 2012). All this could lead to a much better understanding of the functionality of sRNAs in Neisseria and the further identification of riboregulated networks in these important human pathogens. Web resources Comprehensive Microbial Resource: http://cmr.jcvi.org/ cgi-bin/CMR/CmrHomePage.cgi EMBOSS software suite: http://emboss.bioinformatics. nl/cgi-bin/emboss/fuzznuc SIPHT: http://bio.cs.wisc.edu/sRNA. TargetRNA: http://cs.wellesley.edu/~btjaden/TargetRNA2/ TransTermHP: http://transterm.cbcb.umd.edu/index.php

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Inter- and Intraspecies Transformation in the Neisseria: Mechanism, Evolution and DNA Uptake Sequence Specificity

4

Ole H. Ambur

Life without the means of change is without the means of its conservation. (Quote inspired by Edmund Burke) Abstract The evolution of the pathogenic neisseriae, Neisseria meningitidis and Neisseria gonorrhoeae, is closely linked to their ability to be transformed with extracellular DNA. A strong tradition of studying these organisms has documented that they exchange alleles with the commensal Neisseria, and that they also repeatedly utilize DNA from their own separate clonal lineages for this purpose. This chapter present studies that describe inter- and intraspecies transformation and discuss potential effects of a flexible gene pool. The molecular mechanism for transformation is highlighted, and the most important components and recent developments in this field are presented. Neisseria is one of very few bacterial genera to exhibit sequence specific transformation, and the mechanism and evolutionary implications of this is discussed. Also, the negative influence that restriction has on transformation and sexual isolation are described. Finally, a model is proposed that emphasizes the regenerative aspects of transformation. Introduction Three different means of horizontal gene transfer (HGT) between bacteria are known. These are plasmid-mediated conjugation, phage-mediated transduction and transformation which encompass the uptake and recombination of naked

DNA. The mechanisms, genetic consequences and evolutionary rationales are fundamentally distinct among these processes. Transformation, like sexual reproduction, stands alone in that it seems to have evolved for the transfer of homologous DNA whereas conjugation and transduction primarily transfers novel genetic sequence. Transformation in the pathogenic Neisseria has been studied for more than half a century and has led to important contributions to our understanding of this highly complex and evolved process found in many bacteria. Transformation has been particularly well characterized in meningococci and gonococci since this process provides means for the spread of antibiotic resistance genes and generates genetic variation relevant for pathogenicity. Recent developments in the field have, however, highlighted the importance of transformation in genome maintenance and repair. Terminology • Transformation or genetic transformation in bacteria is defined as the uptake and recombination of naked DNA. • Organisms able to undergo transformation are defined as competent. • Transformation is a three stage process: DNA binding, DNA uptake and finally recombination. • Transformation is studied in the laboratory by monitoring DNA binding/uptake and/or marker rescue and previous recombination events can be investigated in silico by comparing sequences in sampled material.

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• Interspecies transformation/recombination: transformation of one species with DNA from another. • Intraspecies transformation/recombination: transformation of one species with DNA from the same species. • Intrachromosomal recombination: recombination between loci located in different locations in the same chromosome. • Homologous recombination: the exchange of similar or identical nucleotide sequences. Inter- and intraspecies transformation in the neisseriae The history of microbiology is a tale of continuous identification of new species. But what is in essence a bacterial species? The species concept is readily defined in higher sexually reproducing (meiotic) organisms as individuals in a group that are able to produce fertile offspring. In the realm of bacteria however, this approach to define individual species may not be applied due to their reproduction by cell division (mitotic) and the high frequency of interspecies (from one species to another) transfer of DNA. Bacteria may be sexual since they exchange homologous DNA but this activity is not directly linked to reproduction. Microbiologists therefore remain interested in the challenges of defining bacterial species, not the least within the transformation-competent genus Neisseria which encompasses closely related pathogenic and commensal species. Only a decade after Avery and co-workers demonstrated that DNA was the substance of inheritance by studying transformation in pneumococci (Avery et al., 1944), transformation of type specificity by DNA was described in Neisseria meningitidis (Alexander and Redman, 1953). The simple experimental transformation protocol involving the short incubation of a bacterial suspension with DNA encoding a novel trait, termination of DNA uptake by the addition of DNaseI followed by seeding and selection for the novel trait has remained virtually unchanged since, and highlights the fact that meningococci are readily transformed. In the years to follow the initial discovery, interest in genetic transformation of

the neisseriae was sparked in several laboratories. B.W. Catlin introduced antibiotic resistance transformations in meningococci in the late 1950s and thereby allowed accurate quantitative measures to be made (Catlin, 1960). Interestingly, Catlin (1960) investigated transformation with DNA obtained both from cells and what she denoted as culture slime. Today culture slime is recognized as biofilms and the high content of DNA in these matrices was ‘rediscovered’ in 2002 (Whitchurch et al., 2002) and has also been found important for the production and structure of meningococcal biofilms (Lappann et al., 2010) (see Chapter 8). It is not expected that homologous DNA, either secreted (Dillard and Seifert, 2001) or from lysed bacteria (Morse and Bartenstein, 1974), is in short supply for transformation. The nasopharyngeal mucosa of man are polymicrobial and inhabited by several distinguishable species of Neisseria and Catlin and Cunningham initiated the study of interspecies genetic transfer among this group (Catlin and Cunningham, 1961) that later become important methods for the characterization of phylogeny and molecular processes responsible for transformation and its barriers. DNA preparations from streptomycin-resistant mutants of N. meningitidis, Neisseria perflava, Neisseria flava, Neisseria subflava, Neisseria sicca and Neisseria flavescens were found to confer resistance upon streptomycin-susceptible parent strains of the corresponding species (intraspecies transformation) and of each other species (interspecies transformation) (Catlin and Cunningham, 1961). Despite substantial variation between the transformability of individual strains and species, intraspecies transformation frequencies were repeatedly reported to be higher than interspecies transformation frequencies. This work alluded to the presence of a barrier or barriers of unknown mechanism(s) that opposes the free mobility of DNA between different species. Also, Neisseria catarrhalis, Neisseria ovis and Neisseria caviae were disqualified as a true Neisseria sp. mainly on the basis of inter- vs. intraspecies transformation and the base composition of their respective DNA, and are today recognized as Moraxella sp. (Baumann et al., 1968; Bøvre, 1964; Catlin and Cunningham, 1961; Henriksen and Bøvre, 1968). Transformation quickly became an important

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tool for taxonomic investigations of the Neisseria genus ( Jyssum and Lie, 1965; Sparling, 1966). These early studies of transformation signify the defining and complete influence that DNA characteristics and later DNA sequence would have on bacterial taxonomy. Siddiqui and Goldberg (1975) found, based on transformation experiments, that N. gonorrhoeae and N. perflava were more closely related than previously expected. They documented that preventative measures to transformation differed between species of Neisseria by studying a range of inter- and intraspecies transformations. They showed that the number of transformants obtained in interspecies N. perflava (donor) → N. gonorrhoeae (recipient) transformations were 100-fold lower than the number obtained in intraspecies N. gonorrhoeae → N. gonorrhoeae transformations (Siddiqui and Goldberg, 1975). In the reciprocal experiment the number of transformants in interspecies N. gonorrhoeae → N. perflava transformations and the number obtained in intraspecies N. perflava → N. perflava transformations ranged between 600- and 1000-fold, highlighting that N. perflava was more selective with respect to heterologous DNA than was N. gonorrhoeae (Siddiqui and Goldberg, 1975). Despite the evident variation in transformability and the presence of barriers to the free flux of DNA within the Neisseria genus, several reports document the prevalence of such interspecies transfers that have challenged the taxonomic species definitions. In a traditional clinical laboratory setting a definitive distinction between gonococci and meningococci may be given by the ability of the latter to oxidize maltose as well as glucose. Ison et al. (1982) demonstrated that this distinctive marker, the ability to oxidize maltose, was easily transferred with meningococcal DNA (and Neisseria lactamica) to live gonococci. Worryingly, antibiotic resistance proved to be easily transferred between species in the Neisseria genus. In the pathogenic Neisseria penicillin resistance may be achieved either by the acquisition of a gene-encoding β-lactamase that inactivates the antibiotic or by alterations of the chromosomal genes that encodes the penicillin-binding proteins (PBPs) combined with reductions in the permeability of the outer membrane (Spratt, 1988).

The β-lactamase gene is carried on plasmids in N. gonorrhoeae (Elwell et al., 1977; Roberts et al., 1977) which are horizontally transferred via conjugation whereas the evolution and spread of PBP-mediated penicillin resistance has been shown to be associated with the transformation of penA alleles between pathogenic and commensal Neisseria (Lujan et al., 1991; Spratt et al., 1992). The source of a PBP with lower affinity to penicillin compared to the PBPs in N. meningitidis and N. gonorrhoeae was initially shown to be N. flavescens isolates that predated the introduction of antibiotics (Lujan et al., 1991). Using an analysis developed by Maynard Smith the mosaic gene structures of penA alleles was traced to their multispecies origins including that of N. cinera (Spratt et al., 1992). Individual alleles may successfully be re-assorted between members of the Neisseria genus by means of transformation and selectiondependent beneficial alleles, such as penA alleles encoding low-affinity PBPs, may spread and thereby increase in frequency in multispeciespopulations exposed to antibiotic selection. Interspecies recombination of PBP-mediated penicillin resistance was later demonstrated in laboratory experiments using donor and recipient strains (Bowler et al., 1994). Genotyping of the antigenic class 1 outer membrane protein (PorA) in a large collection of serogroup A meningococci allowed subgrouping based on the limited number of alleles. Interestingly, each PorA gene type was found to assort independently of other variable genes (pil and iga) suggesting that the gene types predated the subgroups in which they occurred and had spread by transformation (Suker et al., 1994). Frequent interspecies recombination between commensal neisseriae and meningococci was observed in the glutamine synthetase (glnA) and shikimate dehydrogenase (aroE) genes demonstrating that a polyphyletic population structure in meningococci could be studied also in housekeeping genes that generally experience weaker selection pressures for generating novel genotypes compared to genes encoding antigenic epitopes (Zhou et al., 1997). Except in laboratory settings it is generally not possible to reconstruct the order or date of individual recombination events as pointed out by Maiden et al. (1996). They proposed that interpretations of past

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recombination events in sampled strain collections need to consider a global gene pool concept which corresponds to the total genetic material that is shared and available to all strains within the Neisseria (Maiden et al., 1996; Suker et al., 1994). Finding the same allele in two different strains does not indicate that either is the donor. In this perspective, that emphasizes the potential influence of the relatively low barriers for interspecies recombination within the Neisseria, any allele of a given gene in any member of the genus is available to a global recombination network (Maiden et al., 1996). Frequent interspecies genetic exchange between commensal Neisseria and N. meningitidis suggests that effective vaccination against serogroup B meningococcal disease could be difficult to achieve (Linz et al., 2000). In addition to transformation, meningococci have evolved different means for genome plasticity and hence the ability to generate antigenic variation such as intrachromosomal recombination and phase variation by slipped-strand mispairing (Davidsen and Tonjum, 2006). Despite these challenges to vaccine design, new vaccines against N. meningitidis serogroup B that utilize several immunogenic targets have been developed (Sanders et al., 2013; Serruto et al., 2012). Prospects for a gonococcal vaccine is discussed in Chapter 10. Multilocus sequence typing (MLST) proved a powerful tool to investigate clonality, mutation and recombination and in assigning isolates to specific clones (Maiden et al., 1998). The influence of transformation (recombination) on the evolution of meningococci was estimated to be 80-fold higher than by single nucleotide mutation using MLST (Feil et al., 1999). MLST, which was originally developed for meningococci, was readily adapted to typing of N. gonorrhoeae and genetic variation consistent with frequent recombination was evident also in this pathogen (Bennett et al., 2007). Interestingly, recombination in the context of N. gonorrhoeae seemed to primarily mean intraspecies recombination which is in line with the physical separation of niche (urogenital tract) from the other neisseriae studied (oro-pharynx). MLST was found to sufficiently resolve N. meningitidis, N. lactamica and N. gonorrhoeae into species specific clusters suggesting minimal interspecies recombination between the housekeeping genes

of these three Neisseria sp. (Bennett et al., 2007). Infrequent recombination between N. meningitidis and N. lactamica, which both reside in the throat, may be influenced by the different prevalence of these species in adults and children. High prevalence of N. lactamica in children is likely to have a dietary explanation in that children consume larger quantities of milk that creates a suitable habitat for an organism able to ferment lactose (Bennett et al., 2005). Several whole genome initiatives have brought further understanding to the extent of interspecies gene transfer in neisseriae and have improved resolution in phylogenetic analysis of the genus. Van Passel showed in genome comparisons that previously annotated pathogenicity-associated genes are not exclusively present in the pathogenic Neisseria and drew attention to the ambiguity in regard to the concept of virulence (van Passel et al., 2006). Marri and co-workers reported widespread exchange of virulence genes among human Neisseria species including eight newly sequenced commensal Neisseria and 11 published N. meningitidis, N. gonorrhoeae and N. lactamica genomes (Marri et al., 2010). The method applied in their analysis relies on phylogenetic congruence of individual gene trees in a manner that defines that if a gene tree does not match a robust species tree it is likely that the gene has been subject to horizontal gene transfer. The robust species tree was in this study generated using a concatenate of 636 core genes shared with the outgroup Chromobacterium violaceum. Forty-five per cent of the 69 virulence genes and 35% of the core genes rejected the concatenated tree suggesting that virulence genes are exchanged at a slightly higher frequency than core genes (Marri et al., 2010). Whether these past recombination events were due to intra- or interspecies recombination was investigated using a recombination prediction algorithm (RDPv3.18). This analysis identified the involvement of at least one commensal genome in more than 50% of the predicted recombination events in the 69 virulence genes, suggesting that commensals may act as reservoirs of new virulence genes (Marri et al., 2010). The bidirectional transfer of rifampicin resistance encoded by a variant form of rpoB was demonstrated in co-culture experiments of

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Neisseria elongata and N. gonorrhoeae, illustrating the interspecies mobility of homologous alleles (Higashi et al., 2011). A comprehensive analysis of 34 meningococcal and four other Neisseria genomes documented extensive intraspecies homologous recombination in several gene categories (Hao et al., 2011). The relative impact of recombination, expressed as the median ratio of recombination/mutation in virulence genes, was observed as 5.37 in this study and confirms previous estimates (Feil et al., 2001; Vos and Didelot, 2009). It was further shown that up to 28% of the virulence-associated genes could differ between strains of identical sequence types (STs) drawing the attention to a limitation of MLST, particularly in associating to phenotypic characteristics such as antigenic variability (Hao et al., 2011). The MLST concept, however, is so powerful due to its simplicity and applicability in taxonomy and the study of the evolution of lineages that it has been further developed to improve resolution of genus Neisseria (Bennett et al., 2012; Jolley et al., 2012). The impact of genomics on taxonomy and reference libraries is discussed in Chapter 1. More than 60 years of research on transformation in Neisseria has demonstrated the effects of allelic exchange in regard to evolution of the genus and we are starting to see the outlines of the importance of bacterial sex on fitness. Restriction modification and transformation Restriction modification systems (RMSs) are important barriers against the free movement of DNA between strains and species and such systems are common in Neisseria (Budroni et al., 2011; Korch et al., 1983; Stein et al., 1988; Stein et al., 1995). Generally a RMS is constituted by a sequence specific restriction endonuclease and a corresponding methylase. Methylated DNA is protected from endonucleolytic degradation by the nuclease whereas unmethylated DNA, e.g. from other organisms, may quickly undergo degradation. As such, RMSs protect their hosts against harmful genetic interference from alien DNA and contribute in an evolutionary context to sexual isolation of lineages (Budroni et al., 2011). That DNA was restricted by endonucleases

during transformation became evident when Sox and co-workers in Fred Sparling’s group identified deletions in plasmids that had undergone transformation in gonococci (Sox et al., 1979). Also the identification of linearized plasmids following transformation supported the notion that transforming DNA was susceptible to restriction by endonucleases although sequence specificity was not initially identified (Biswas et al., 1986). Interestingly, restriction of plasmid DNA was observed during transformation but not conjugation suggesting that RMSs in gonococci are a barrier against transforming DNA and not against DNA transferred by conjugation (Stein et al., 1988). However, Butler and Gotschlich (1991) showed later that methylation of donor DNA allowed high-frequency mobilization of conjugative plasmids into N. gonorrhoeae suggesting that conjugating DNA is also restricted by RMS. In any case, the essential difference between these two models of gene exchange is that conjugation involves the transfer of ssDNA via a conjugal bridge whereas the substrate for transformation is free dsDNA. The presence of both dsDNA and ssDNA in cell lysates following transformation has been described (Biswas and Sparling, 1981; Chaussee and Hill, 1998). The model for transformation (next section and Fig. 4.1) is not completely understood since ssDNA is predicted to enter the cytoplasm where transforming DNA is restricted by RMSs. RMSs have evolved high specificity to dsDNA although activity against ssDNA has been documented (Nishigaki et al., 1985). A gonococcal strain with specific deletion of five different RMSs was found competent for transformation using an unmethylated donor DNA whereas the parent strain with intact RMSs remained incompetent using the same DNA (Gunn and Stein, 1996). An inverse relationship between the transformation frequency and number of restriction sites (NlaIV) in transforming DNA has been demonstrated in experiments using plasmids harbouring heterologous regions (Ambur et al., 2012). For one DNA template the transformation frequency was shown to increase more than 100-fold in the mutant background with a single RMS (NlaIV) inactivated and demonstrates the potential strength of a single RMS-barrier against

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Figure 4.1  A model of the transformation machine of the pathogenic Neisseria spp. Double stranded (ds) DNA binds to the surface of the bacterium and makes DUS–specific interactions with ComP (P) in the protruding pilus, which is constituted primarily of PilE (E) monomers. DNA is pulled into the periplasm by the retracting pilus driven by the cytoplasmic PilT ATPase (T). DNA interacts with several components in the periplasmic space, including ComE, and is channelled through ComA in a single stranded (ss) form. Once in the cytoplasm ssDNA binds SSB, DprA and RecA in a sequential manner and homologous recombination and allelic exchange takes place with the chromosome. Drawing by S. Frye, with permission.

transformation with heterologous DNA. No such effect was observed when homologous DNA was used in transformation, suggesting that the restriction phenotype in Neisseria is sensitive to differences other than just the methylation status of transforming DNA (Ambur et al., 2012). However, Claus and co-workers identified novel meningococcal RMS that were differentially distributed among the most important lineages and were able to demonstrate partial restriction of DNA transfer from meningococci of the ET-37 complex to meningococci of the ET-5 complex using co-cultivation experiments (Claus et al., 2000). Whole-genome sequencing and analysis of 20 meningococcal genomes enabled the structuring of individual strains into distinct clades (Budroni et al., 2011). A remarkable correlation between the distribution of RMSs and phylogeny was identified, suggesting that the evolutionary

impact of RMSs is substantial in driving sexual isolation among the highly recombinogenic meningococci (Budroni et al., 2011). Transformation mechanism Transformation is a highly complex process that involves a range of specialized and dedicated proteins. A naked piece of DNA, which for example is integrated in the matrix of an extracellular biofilm, faces several challenges in entering a transformation pathway. At all levels DNA must avoid degradation by extra- and intracellular nucleases, it must be transported across two membranes (in Gram-negative species) and must exhibit a high degree of homology with the recipient chromosome to allow RecA-mediated recombination and the fulfilment of allelic replacement. Frequent and efficient transformation is achieved via dynamic

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molecular processes involving pilus and recombination components. An association between pili and competence in meningococci was first established in 1973 (Frøholm et al., 1973) although a connection between competence and morphological distinct clonal types of gonococci had been made previously (Sparling, 1966). Pili are fimbrial structures that emanate from the surface of many bacteria and are important for the attachment to surfaces and colonization, twitching motility, aggregation and transformation (Pelicic, 2008). N. gonorrhoeae may spontaneously lose competence and virulence upon cultivation (Sparling, 1966) and this is related to the unstable expression of pili (Tønjum and Koomey, 1997; Aas et al., 2002b). The pathogenic Neisseria are unusual in that they are competent throughout their growth cycle and do not require induction by soluble competence factors, which is common in other naturally competent bacteria (Biswas et al., 1977; Johnsborg et al., 2007). Another characteristic feature of transformation in Neisseria is the requirement for a specific DNA uptake sequence in the transforming DNA, which is thoroughly described in the next section of this chapter. A simplified illustration of the current model for the transformation machinery in the neisseriae, including pilus biogenesis components, an inner-membrane translocase, recombination proteins and transforming DNA is given in Fig. 4.1. In the context of Neisseria, it would be an understatement to say that pili are well-characterized as no other organelle has received more scientific examination than these antigenic and multipurpose fimbrial structures. Here, a brief overview of the mechanisms undertaking transformation is presented and the reader is well advised to seek more detailed information in some of several excellent reviews describing form, function and variability of pili ( Jain et al., 2011; Koomey, 2009; Pelicic, 2008; Vink et al., 2012) and complete competence machineries in Neisseria and other species (Claverys et al., 2009; Hamilton and Dillard, 2006). In the current model for transformation extracellular double-stranded (ds) DNA binds sequence specifically to ComP at the bacterial surface and is pulled into the periplasm by the retracting pilus (Cehovin et al., 2013). The energy

required for pilus retraction is provided by the intracellular ATPase PilT (Maier et al., 2002; Merz et al., 2000; Wolfgang et al., 1998; Carbonelle et al., 2006). The retraction force of pili has been monitored in a laser tweezer and found to exceed 100 pN, which renders the pilus machinery the strongest molecular motor characterized to date (Maier et al., 2002; Clausen et al., 2009a). Also two different speeds of retraction, ≈ 1 µm/s and ≈ 2 µm/s, have been characterized (Clausen et al., 2009a,b). The pilus itself is a right-handed helix of pilin subunits, mostly PilE, with hypervariable domains exposed to allow antigenic variation (Craig et al., 2006; Parge, 1995). Antigenic variation of PilE has been very well-characterized in the pathogenic Neisseria and is caused by gene conversion between silent- and expression loci directed by a guanine-rich region in the chromosome (Cahoon and Seifert, 2009). The fibre is highly dynamic allowing the rapid assembly and dis-assembly of pilins to accommodate extrusion and retraction of the pilus, respectively. Long and co-workers demonstrated that even very low expression of pilin allowed for substantial transformation indicating that long pilus fibres may not be required for competence (Long et al., 2003). The pilins are synthesized as longer prepilins from which the hydrophobic N-terminus is cleaved by the PilD leader peptidase prior to assembly and extrusion, a process aided by the hexameric ATPase PilF and the polytopic inner membrane protein PilG (Collins et al., 2007; Freitag et al., 1995; Strom and Lory, 1993; Tønjum et al., 1995). Some of the minor pilins are not required for pilus biogenesis, such as ComP, PilV and PilX (Carbonnelle et al., 2006) but play modulatory roles in pilus biology and sequence specific transformation (Cehovin et al., 2013; Helaine et al., 2007; Takahashi et al., 2012; Aas et al., 2002a,b). Also, a set of five proteins sharing structural similarities to pilins, PilH-L, influence the function and dynamics of pili, including transformation in N. gonorrhoeae (Winther-Larsen et al., 2005). The specific functions of PilV and ComP in competence is discussed in the next section. The PilM/N/O/P proteins are associated with the inner membrane and function in the assembly of the pilus and may co-ordinate the extraction/retraction ATPases (Carbonnelle et al., 2006; Takhar et al., 2013; Tammam et al.,

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2011). PilW is essential in securing fibre stability and in the function of PilQ (Carbonnelle et al., 2005; Szeto et al., 2011; Trindade et al., 2008). PilC seems to affect the adhesive properties of pili to human cells and may also be involved in competence when located at the tip of the pilus ( Jonsson et al., 1991; Morand et al., 2004, 2009; Nassif et al., 1997; Rudel, 1995; Rudel et al., 1995; Winther-Larsen et al., 2005). The pilus extrudes through a large outer membrane pore, PilQ, which is a complex of 12 PilQ monomers forming a doughnut shaped trans-periplasmic channel for pilus and DNA (Assalkhou et al., 2007; Berry et al., 2012; Collins et al., 2001, 2005). In addition to the pilus proteins there are components involved in transformation which are located in the inner membrane and in the cytoplasm. ComA forms a pore in the inner membrane allowing the passage of ssDNA (Facius et al., 1996). Also required in transformation is the non-specific DNA-binding protein ComE, which is proposed to be involved in the transfer of DNA onto ComA in the current model (Chen and Gotschlich, 2001). The comE gene is present in four identical copies in the genomes of meningococci and gonococci and their expression is derepressed under iron-starved conditions when transformability is also increased (Chen and Gotschlich, 2001; Ducey et al., 2005; Serkin and Seifert, 2000). The deletion mutants of the peptidoglycan-associated protein ComL and Tpc are shown to be incompetent and exert their functions at a step following DNA uptake and upstream of recombination (Benam et al., 2011; Fussenegger et al., 1996). A putative periplasmic nuclease to process one of the strands in dsDNA awaits identification. Once in the cytoplasm, ssDNA is protected from degradation and processed in a sequential manner by SSBs, DprA and RecA (Mortier-Barriere et al., 2007). SSB is essential in the pathogenic Neisseria while both DprA and RecA mutants are incompetent (Koomey and Falkow, 1987; Sun et al., 2005). DprA is involved in securing the loading of RecA onto ssDNA, at least in Bacillus subtilis, (Mortier-Barriere et al., 2007) and the ssDNA-RecA filament searches the recipient chromosome for homologous sequence. Where DprA seems dedicated to transformation in Neisseria (Hovland et al., 2010), RecA is in addition an important factor in DNA repair. Yet

other recombination proteins (RecBCD), are to a variable extent involved in homologous recombination to conclude transformation (Mehr and Seifert, 1998). Competence is constitutive in the pathogenic neisseriae, which also lack SOS-like systems regulating the expression of DNA repair proteins, that functionally may overlap with transformation (Ambur et al., 2009; Biswas et al., 1977; Black et al., 1998; Davidsen et al., 2007). DNA uptake sequence specificity of the Neisseria Some bacteria have the ability to discriminate against alien and non-homologous DNA at an early stage in the transformation process by recognizing and engaging specific uptake sequences present in the extracellular DNA. This phenomenon is a well-studied characteristic and seems a unique property of two very phylogenetically distinct bacterial families, the Pasteurellaceae and the Neisseriaceae. The uptake sequences are genomic imprints of previous recombination events and the study of their distributions has revealed important aspects regarding the evolution of these organisms and the role of transformation thereto. Identity and function of DUS The presence and identity of the uptake signal sequence (USS), 5′-AAGTGCGGTCA-3′, responsible for selective DNA uptake in Pasteurellaceae family members was first described in Haemophilus influenzae and Haemophilus parainfluenzae (Danner et al., 1980; Sisco and Smith, 1979). That N. gonorrhoeae also displayed sequence-specific DNA uptake in transformation was shown soon thereafter (Graves et al., 1982). It was further determined experimentally that the gonococcal DNA uptake sequence (DUS) had to be different from that of Haemophilus spp. (Graves et al., 1982). Identifying the true sequence identity of DUS from consensus sequences of transformable restriction fragments proved a true puzzle (Burnstein et al., 1988). The correct DUS was first identified as a 10-mer, 5′-GCCGTCTGAA-3′, present in DNA fragments able to competitively inhibit transformation in N. gonorrhoeae (Goodman and Scocca, 1988). The functional identity of DUS was later expanded to

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encompass the 12-mer 5′-atGCCGTCTGAA-3′ where the lowercase letters denote two semiconserved nucleotides of DUS (Ambur et al., 2007). The 12-mer DUS has been shown in different reports to modestly outperform the 10-mer DUS in transformation (Ambur et al., 2007; Duffin and Seifert, 2010; Frye et al., 2013). The number of DUS in DNA has been shown in competitive inhibition experiments to linearly relate to the affinity of DNA, independent of absolute fragment size (Goodman and Scocca, 1991). In transformation assays however (not competitive binding), this linearity between DUS numbers and assay-performance was not observed (Ambur et al., 2012). There are nearly 2000 DUS in the 2.2 Mb genomes of the two pathogenic Neisseria spp. which amounts to more than 1% of the chromosome (Ambur et al., 2007; Smith et al., 1999) and the significance of this exceptional overrepresentation in evolution and fitness is discussed in the sections below. In order to detail the molecular interactions responsible for transformation in the Neisseria investigations of the exact location of DUS-specificity was undertaken by Aas and co-workers. They found that DNA binding and uptake were two resolvable events and that sequence specificity was imposed at the level of binding under the influence of both PilE and ComP (Aas et al., 2002b). Overexpression of ComP was further found to enhance sequence– specific DNA binding in the absence of uptake, emphasizing a role of ComP at the first step of the transformation pathway (Aas et al., 2002b). PilV and ComP were found dispensable for Type IV pilus biogenesis and rather displayed antagonistic behaviours in DUS-dependent DNA uptake (Aas et al., 2002a). This double-act revealed a novel mechanism by which transformation may be modulated that still warrants further investigations since the molecular interactions at play remain largely unresolved. Recently however, a conclusive study documented binding between ComP and DUS using ELISA, EMSA, NMR and transformation assays (Cehovin et al., 2013). EMSAs showed further that three other pilins, PilV, PilX and PilE, did not exhibit DUS-binding (Cehovin et al., 2013). ComP is remarkably conserved for a surface exposed protein in this group of bacteria and DNA binding was found

to correlate with an electropositive stripe on the surface of ComP (Cehovin et al., 2013). Future studies will seek to explore individual residues involved in the interaction with DUS with the aim of manipulating specificity to better understand the molecular dynamics and evolution of sequence-specific transformation. Although our understanding of DUS-mediated transformation has taken a major step with the confirmation of the DUS-specificity of ComP, many questions still remain and new arise. For example, (how) does double stranded (ds) DNA become single stranded prior to entry into the cytoplasm? The recent ComP–DUS associations were studied using dsDNA (Cehovin et al., 2013) although it has been shown previously that ssDNA with DUS also transforms gonococci (Duffin and Seifert, 2012; Stein, 1991). Does ComP interact strongly with the electronegative phospho-backbone of dsDNA and does it separate the two strands at DUS? Following a laborious purification procedure for ssDNA encoding a selectable trait, it has been shown that the Crick strand of DUS (5′-TTCAGACGGCAT-3′) has greater effect on transformation than the Watson strand of DUS (5′-ATGCCGTCTGAA-3′) (Duffin and Seifert, 2012). In light of the DUS–ComP association this observation may facilitate the modelling of their exact interactions. If ComP binds DUS in the extracellular environment and pulls DNA into the periplasm with the retracting and disassembling pilus, is there DNA processing taking place involving a periplasmic nuclease to degrade a single strand? It has been demonstrated that the localization of DUS relative to recombinogenic regions of transforming DNA affects transformability; demonstrating a link between DNA processing or channelling and DUS (Ambur et al., 2012). Duffin and Seifert have shown that DNA binding and uptake do not fully correlate with transformation in different gonococcal strains, suggesting that DUS may influence more than one level in the transformation process (Duffin and Seifert, 2010). What is the association between ComP and the periplasmic ComE? It has been known for a long time that DUS-independent transformation exists in gonococci (Boyle-Vavra and Seifert, 1996; Duffin and Seifert, 2010) but the molecular reason for this phenomenon remains unresolved.

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Is DUS-independent transformation related to the expression-level of proteins involved in the DUS-dependent pathway? Distribution and genomics of DUS DUS are extremely abundant in the genomes of Neisseria, by far the most frequent small repeats (Davidsen et al., 2004; Smith et al., 1999) (Fig. 4.2). The 2.2 Mb genomes of N. gonorrhoeae and N. meningitidis harbour nearly 2000 DUS, which is striking when considering the complexity of the DUS-repeat. What were the selective pressures that allowed or drove the establishment of so many DUS and why sequence-specific transformation in the first place? The evolutionary cause for this overrepresentation of DUS in Neisseriaceae and Pasteurellaceae genomes is debated and hypotheses range from a blind molecular drive to classical Darwinian natural selection. The genomic distributions of DUS shed light on these questions. DUS are an integrated part of their host genomes and are located both inside coding regions and in between genes. The abundance of DUS across the chromosome allows for transformation of most parts of the genome. Goodman and Scocca noted in their pre-genomic analysis that DUS in three different loci was arranged as an inverted repeats able to form stem–loop structures on RNA that potentially could attenuate or terminate transcription (Goodman and Scocca, 1988). It therefore seems that DUS was involved in two important processes: sequence-specific transformation and regulation of transcription. This dual function of DUS was later supported by genomic DUS-distribution analysis and correlated with experimental observations (Ambur et al., 2007; Smith et al., 1999). In fact, half of the 2000 DUS in the gonococcal and meningococcal genomes are arranged as inverted repeats predicted to be involved in rho-independent transcriptional termination (Ambur et al., 2007). Generally DUS and the reverse complement DUS constitute the stem of the transcriptional terminator and are separated by five random nucleotides on average that constitute the loop (Smith et al., 1999). Of the two possible configurations of the inverted DUS-repeat, one is most common (5′-atGCCGTCTGAA (N) TTCAGACGGCat-3′) (Kingsford et al., 2007).

DUS distribution in the genome of N. meningitidis Z2491

Figure 4.2 The genomic distribution of DUS in a representative meningococcal genome. Nearly 2000 DUS are very evenly spread throughout the chromosome rendering nearly all parts subject to DUS-specific transformation. Small regions devoid of DUS are typically alien DNA of phage origin. The sheer number and careful incorporation of DUS in permissive regions of the genome suggests that DUS-dependent transformation is an integrated part of the neisserial lifestyle.

Also the USSs of the Pasteurellaceae exhibit a biased arrangement to transcriptional terminators although to a lesser extent than DUS in the Neisseria (Ambur et al., 2007; Smith et al., 1999). A single copy of DUS has been demonstrated to be functional in transformation and DNA binding and the inverted arrangement was not found to increase levels of either of these measures (Ambur et al., 2007; Elkins et al., 1991; Goodman and Scocca, 1988). The abundance of inverted repeat DUS in intergenic locations may reflect the evolutionary costs of harbouring DUS inside coding regions. Notably, there is a stop codon in one of the reading frames of DUS which imposes an obvious limitation to the positioning of DUS in intragenic positions. The distributions of intragenic DUS in Neisseria and USS in Pasteurellaceae have been shown biased towards genome maintenance genes, suggestive of facilitated recovery of alleles encoding genome preserving functions (Davidsen et al., 2004). To a considerable extent, it seems that homologous genes in Neisseria and Pasteurellaceae

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have accumulated DUS/USS inside their coding regions during evolution. DUS and USS are nonhomologous sequences and their bias towards genome maintenance genes is suggestive of convergent evolution and that sequence specific transformation has exerted similar influences on the evolution of these organisms. The absence of a ComP homologue in the Pasteurellaceae (Cehovin et al., 2013) supports the notion of convergent evolution. Since the genes with high DUS-content are generally very well conserved in the two phylogenetically distant bacterial families and encode proteins involved in basic and essential processes such as recombination, replication and repair, DUS-dependent transformation seems to have been important for survival and fitness. Genes that have been repeatedly replaced and reassorted during the course of evolution are likely to be associated with DUS. The increasing number of sequenced whole genomes from meningococci and close relatives has enabled closer investigations of the evolutionary past of DUS and their genetic contexts. Studying DUS in an alignment of six genomes from N. meningitidis, N. gonorrhoeae and N. lactamica revealed the close link between DUS and recombination (Treangen et al., 2008). DUS was indeed found to be a marker of recombination and was found to be highly overrepresented in the core genome (the part of the genome common to all six genomes). The spacing of DUS in the genomes was found to correlate with the sizes of in silico predicted recombination fragments and genomes with shorter conversion fragments were shown to harbour more conserved DUS. Singly occurring DUS exhibited too high divergence from the homologous regions in the alignment to have arisen by point mutation, linking their appearance to recombination. The very strong conservation of DUS is suggestive of stringent selection of DUS during the course of evolution. DUS are much more conserved (97% sequence identity) than the average conserved regions (ca. 85% sequence identity) in which they reside. 71% of all DUS in the three neisserial genomes were found to be exactly conserved in all six genomes, and above 90% of DUS were conserved in the meningococcal group of four genomes. Since the evolutionary role of transformation is generally considered to

be to generate genetic variation, it was surprising that laterally transferred regions (present in only one of the six genomes) did not contain DUS. Conversely, recently lost sequences (present in all but a single genome) were also found to be completely devoid of DUS. These regions of alien DNA were likely to have originated by transduction and not by way of transformation. Furthermore genes predicted to encode surface exposed epitopes, and hence under selection for diversification, were found to contain significantly fewer DUS than expected from the core genome average. Inside the core genome, DUS were identified in permissive regions which are regions under weaker selection than highly conserved regions. This is in line with the frequent presence of DUS in intergenic locations described above. The inverted repeat arrangement may be an economic adaptation to accommodate the need for transformation and transcriptional regulation. Interestingly, DUS inside of coding regions are located in permissive regions expected to experience less stringent selection. DUS inside coding regions are translated and a permissive region of a protein is typically a structural region away from the active site. The gentle establishment of DUS in the genomes of Neisseria demonstrates that sequence-specific transformation has been established and maintained in these bacteria over a long evolutionary time span and has not been caused by the rapid spread of selfish genetic elements. A recent analysis of 20 meningococcal genomes revealed a significant correlation between the recombination rate and the density of DUS confirming DUS as a marker of recombination (Budroni et al., 2011). Recently, analysis of the genomes of Neisseriaceae family members has revealed that many, but not all, contain DUS or completely new dialects of DUS, outlining an evolving ‘sexual language’ (Frye et al., 2013). In total, eight DUS dialects have been detected that display considerable variation between species but that are remarkably well conserved and overrepresented within each genome. Each dialect was found to be distributed in correlation with the robust phylogeny based on core genomes of 23 representative Neisseriaceae species. Importantly, an experimentally defined 5′-CTG-3′ DUS core (5′-atGCCGTCTGAA-3′) was found

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to be conserved in all eight dialects of DUS. Transformation of four phylogenetically separate Neisseriaceae species – Kingella denitrificans, Eikenella corrodens, Neisseria elongata and N. meningitidis – using DNA with different DUS-dialects attached, show that DUS dialects can function as efficient barriers to inter-species transformation/ sex although their corresponding DUS share the same core (Frye et al., 2013). Finding that DUS have evolved into different dialects, each carefully maintained and integrated in respective genomes in the family Neisseriaceae (Frye et al., 2013), supports the notion of the adaptive value of DUS. DUS/USS-specificity has been around for a very long time and consequently goes deep into the phylogenetic trees of the Neisseriaceae (Frye et al., 2013) and the Pasteurellaceae (Bakkali et al., 2004). During the course of evolution DUS has manifested itself in the genomes and reveal aspects of the role of transformation in these organisms. The main evolutionary rationale behind sequence-specific transformation is not to acquire novel genes or to generate antigenic variation (Treangen et al., 2008). Transformation in bacteria rather seems to have evolved for the similar reasons as the evolution of sex in higher organisms and this model is discussed below. Discussion and conclusions Sex is one of the most fundamental processes in biology. Sex in bacteria by means of transformation and meiotic processes in humans utilize homologous proteins (e.g. RecA/Rad51) and the study of the Neisseria as model organisms may therefore reveal basal characteristics of nature. Sex is defined and recognized by the relocation of homologous DNA from one individual to another by means of a highly coordinated process in (at least) the recipient, followed by homologous recombination. Sex may be symmetric, as in eukaryotes where an entire (haploid) genome is passed on to the next generation, or asymmetric, as in bacterial transformation, where only parts of the chromosome, or even individual alleles, are passed on in a single event. Why is sex so common in nature? The origin and evolution of sex is seemingly a paradox since, compared to asexual reproduction, sex is burdened with several costs.

In eukaryotes, sex evolved alongside multicellularity and the oxygenation of the atmosphere (Butterfield, 2011) and was probably predated by the evolution of similar processes in prokaryotes. The most substantial cost is often considered to be the two-fold cost of sex, which is the cost of allowing only one of the two sexes to produce offspring (Smith, 1971). Finding a mate or suitable DNA also requires time and energy. Yet, sex in various forms is found in all domains of life, in plants, algae, animals, fungi and in microbes such as the Neisseria. Sex is poorly understood and several hypotheses aiming to explain the evolution of sex have been put forward. Did sex evolve for the ability to acquire beneficial mutations, to purge deleterious mutations from a population, for DNA repair or for all of these reasons? The most favoured hypotheses addressing the evolution of sex consider the advantages of breaking down genetic associations in a population and could therefore potentially be due to all of the above (Kouyos et al., 2009; Otto and Lenormand, 2002). The study of bacterial transformation, however, has traditionally had a primary focus on the potential for spreading novel traits such as antibiotic resistance or antigenicity. The emphasis that competent bacteria put into selecting homologous DNA for transformation, contrasts with the focus on new genetic information. It is therefore important to distinguish between alleles encoding novel functions, such as antibiotic resistant forms of e.g. penA or rpoB that may float around in genus Neisseria (Higashi et al., 2011; Spratt et al., 1992), and novel genes from other species such as superoxide dismutase sodC acquired from H. influenzae (Kroll et al., 1998). Acquisition of the latter is very rare and it is difficult to see how transformation could have evolved to accommodate these few chance events. Transformation most likely evolved for the ability to shuffle homologous alleles and have adaptations such as DUS and RMSs to secure this process. The biased distribution of DUS in conserved genes involved in genome maintenance suggests that transformation fulfils a regenerative function causing conservation of the core genome. As such transformation in Neisseria allows populations to purge deleterious mutations, drive the rapid combination of beneficial alleles in individual genomes and is in effect DNA repair at

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well-adapted genotype

well-adapted genotype lost

new genotypes made

Figure 4.3  A model explaining the conservative output of transformation and potentially the establishment of DUS. Successful pathogenic bacteria thrive in their human hosts and have well-adapted genotypes that secure clonal expansion. Individual alleles in a population are continuously and randomly being made by mutations caused by mutagens such as reactive oxygen species. Most mutations are not beneficial and some are dramatic causing death and cell lysis. DNA is released and serves as substrate for allelic reassortment in surviving bacteria. Novel genotypes are made that in the larger perspective may not be completely novel but rather represent the original well-adapted genotype. Through repeated cycles DUS may appear by mutation, may be maintained by recombination and finally become fixed in a population. DUS may be carefully established in the genome by hitch-hiking with genes under positive selection for repeated allelic replacement, i.e. genes that generally are less tolerant to mutations.

the population level. The regenerative potential of transformation is illustrated in a model in Fig. 4.3. Michod and co-workers have alluded to the possibility that sex is a response to stress and DNA damage based on observations of stress-induced expression of sex genes in the alga Volvox carteri (Nedelcu et al., 2004) and DNA damage has been shown to trigger genetic exchange in another competent bacterium, Helicobacter pylori (Dorer et al., 2010). A dynamic genome where nearly all genes are exchangeable may provide a prominent selective advantage responsible for the successes of the Neisseria that live in environments of high oxidative stress. Future trends More genetic data are becoming available through initiatives such as the Human Microbiome Project and we will learn more about the drivers and limitations of inter- and intraspecies transformation in the Neisseria. Whole-genome sequencing may be used as a tool to monitor and discover small genetic changes that has taken place in controlled

laboratory experiments, e.g. interchromosomal transformation and transformation. Functional studies in the laboratory are becoming increasingly more important in order to test predictions made from the vast amounts of digital information, be it genome sequences, microarray data or other high-throughput technologies. Here, almost 30 different proteins were briefly listed that are involved with the competence machinery of the Neisseria. This number may increase yet further in the future with new technological developments to measure e.g. the actions of single molecules. Also genetic association studies in this and other groups of bacteria will likely shed more light on the molecular mechanisms that takes place during transformation. Particularly interesting for future study in Neisseria are the observations in Bacillus subtilis describing the close spatiotemporal association between DNA uptake and the recombination machinery (Kidane et al., 2012; Tadesse and Graumann, 2007). Also the recently elaborated association between ComP and the DNA uptake specificity in Neisseria (Cehovin et al., 2013) and the new dialects of DUS (Frye et

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al., 2013) warrants further investigations. The prospects of conducting experimental evolution experiments on strains with manipulated DUS specificity are also promising for the study of the selective forces that has shaped the evolution of transformation. The use of animal models (Weyand et al., 2013) in combination with whole-genome sequencing may also prove valuable in future studies aiming to trace the fate(s) of individual alleles in a dynamic population of Neisseria spp. In conclusion, therefore, the pathogenic Neisseria are fascinating microbes that also in the future may serve as excellent models for the student interested in learning more about topics ranging from evolution, sex and pathogenesis. References

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Smith, H.O., Gwinn, M.L., and Salzberg, S.L. (1999). DNA uptake signal sequences in naturally transformable bacteria. Res. Microbiol. 150, 603–616. Smith, J.M. (1971). What use is sex? Journal of theoretical biology 30, 319–335. Sox, T.E., Mohammed, W., and Sparling, P.F. (1979). Transformation-derived Neisseria gonorrhoeae plasmids with altered structure and function. J. Bacteriol. 138, 510–518. Sparling, P.F. (1966). Genetic transformation of Neisseria gonorrhoeae to streptomycin resistance. J. Bacteriol. 92, 1364–1371. Spratt, B.G. (1988). Hybrid penicillin-binding proteins in penicillin-resistant strains of Neisseria gonorrhoeae. Nature 332, 173–176. Spratt, B.G., Bowler, L.D., Zhang, Q.Y., Zhou, J., and Smith, J.M. (1992). Role of interspecies transfer of chromosomal genes in the evolution of penicillin resistance in pathogenic and commensal Neisseria species. J. Mol. Evol.n 34, 115–125. Stein, D.C. (1991). Transformation of Neisseria gonorrhoeae: physical requirements of the transforming DNA. Can. J. Microbiol. 37, 345–349. Stein, D.C., Gregoire, S., and Piekarowicz, A. (1988). Restriction of plasmid DNA during transformation but not conjugation in Neisseria gonorrhoeae. Infect. Immun. 56, 112–116. Stein, D.C., Gunn, J.S., Radlinska, M., and Piekarowicz, A. (1995). Restriction and modification systems of Neisseria gonorrhoeae. Gene 157, 19–22. Strom, M.S., and Lory, S. (1993). Structure-function and biogenesis of the type IV pili. Annu. Rev. Microbiol. 47, 565–596. Suker, J., Feavers, I.M., Achtman, M., Morelli, G., Wang, J.F., and Maiden, M.C. (1994). The porA gene in serogroup A meningococci: evolutionary stability and mechanism of genetic variation. Mol. Microbiol. 12, 253–265. Sun, Y.H., Exley, R., Li, Y., Goulding, D., and Tang, C. (2005). Identification and characterization of genes required for competence in Neisseria meningitidis. J. Bacteriol. 187, 3273–3276. Szeto, T.H., Dessen, A., and Pelicic, V. (2011). Structure/ function analysis of Neisseria meningitidis PilW, a conserved protein that plays multiple roles in type IV pilus biology. Infect. Immun. 79, 3028–3035. Tadesse, S., and Graumann, P.L. (2007). DprA/Smf protein localizes at the DNA uptake machinery in competent Bacillus subtilis cells. BMC Microbiol.7, 105. Takahashi, H., Yanagisawa, T., Kim, K.S., Yokoyama, S., and Ohnishi, M. (2012). Meningococcal PilV potentiates Neisseria meningitidis type IV pilus-mediated internalization into human endothelial and epithelial cells. Infect. Immun. 80, 4154–4166. Takhar, H.K., Kemp, K., Kim, M., Howell, P.L., and Burrows, L.L. (2013). The platform protein is essential

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Pathogenic Neisseria: Neither Aerobes nor True Anaerobes, but Dedicated Microaerophiles

5

Jeffrey A. Cole

Abstract Neisseria meningitidis and N. gonorrhoeae are found in contrasting sites in the human body. Meningococci are rarely oxygen deficient, but in women gonococci become trapped in biofilms surrounded by anaerobic, fermentative bacteria. When starved of oxygen, both species exploit low levels of nitrite for energy generation and substrate oxidation. However, too little is known about whether they are able to exploit oxidants other than nitrite and nitric oxide to survive in anaerobic environments. Although five proteins have been implicated in resistance to nitrosative stress, only for two of them has the mechanism of protection been defined. These are the nitric oxide-binding cytochrome c′, and the di-iron protein, DnrN. Both pathogens maintain very high respiration rates: multiple mechanisms contributing to defence against oxidative stress have been identified. Only subtle differences between them have so far been identified. They include the meningococcal transcription factor, FNR, which regulates adaptation to anaerobic growth, that is more tolerant to oxygen than the gonococcal FNR. The truncated denitrification pathway is totally conserved in gonococci but not in all meningococcal strains. Only in the gonococcus is there an FNR-activated cytochrome c peroxidase, and a third haem group on the cytochrome oxidase CcoP subunit that contributes significantly to electron transfer from the cytoplasmic membrane to the nitrite reductase in the outer membrane. Finally adhC, which encodes a functional S-nitrosoglutathione reductase in the meningococcus, is a pseudogene in the gonococcus. It is proposed that both species have evolved

a microaerobic rather than a fully aerobic or anaerobic metabolism: both can adapt to periods of oxygen starvation, but this ability appears to be more important for the gonococcus than for the meningococcus. Introduction The two Neisseria species that are pathogenic to humans, N. meningitidis and N. gonorrhoeae, are found in contrasting niches within the human body. Meningococci invade the nasopharynx where they might be expected to be oxygen sufficient. In contrast, at least in the female genital tract, gonococci survive in biofilms of anaerobic bacteria where they are starved of oxygen. However, obligately anaerobic bacteria can be isolated even from the nasopharynx, and gonococci are probably oxygen sufficient in males. This perhaps explains why both species have evolved multiple overlapping mechanisms to survive oxygen stress, and their physiology is typical of microaerophiles that are adapted to life with a limited supply of oxygen. Nevertheless, there are several reasons for believing that neither species is able to grow under rigorously anaerobic conditions: indeed, genomic analysis would predict that they are both obligate aerobes (Rock et al., 2005). This superficial overview of the physiology of gonococci and meningococci raises many fascinating questions. In laboratory experiments, both species can survive prolonged periods of oxygen starvation – but can they grow anaerobically? Given that they are both obligate human pathogens and their host is certainly an obligate aerobe, not an anaerobe, why should they ever need to

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do so? In a range of bacteria, the master regulator of anaerobic growth is FNR (the regulator of fumarate and nitrate reduction). In vivo studies of other pathogens are revealing that they have evolved mechanisms to cope with anaerobiosis, but in readiness for re-exposure to oxygen as a key determinant of their pathogenicity. For example, Shigella flexneri must produce a type 3 secretion system to invade host cells. Components of this system are regulated by FNR, which is active only when oxygen is absent. This provides a mechanism by which the bacteria can respond to contact with oxygen-evolving host cells that inactivate FNR, triggering an invasive response (Martyn et al. 2010). Have similar mechanisms evolved in the pathogenic Neisseria? There is little evidence that gonococci or meningococci can ferment glucose and they are certainly unable to ferment their preferred source of carbon and energy, lactate. This would require the cobalamindependent enzyme to convert succinyl coenzyme A to propionyl Co-A, which is absent in the Neisseriaceae. However, lactate supports a higher anaerobic respiratory capacity than glucose. This statement is based upon studies of both oxygen and nitrite reduction, but is it also true for nitric oxide reduction? Is useful energy conserved during nitric oxide reduction? Gonococci synthesize a cytochrome c peroxidase that is absent from the meningococcus: does this provide an energy conservation mechanism during gonococcal oxygen starvation that is not required by meningococci? This seems highly unlikely, given that in their natural environments, both species are exposed to an abundant supply of hydrogen peroxide from neighbouring bacteria or host macrophages. Their extremely active catalases ensure that any hydrogen peroxide is rapidly dismutated to oxygen and water, hence providing a supply of oxygen for respiration and critical key biosynthetic pathways. Is the gonococcal cytochrome c peroxidase therefore completely redundant? Finally, these pathogens are able to exploit a truncated denitrification pathway to reduce nitrite via nitric oxide to nitrous oxide. Given that nitric oxide is extremely toxic, why is the synthesis of the nitric oxide reductase that is required to remove the toxic metabolite not coordinated with synthesis of the nitrite reductase that forms it?

Other questions will arise as we seek answers to these questions from the literature and genomic databases. The article mentions many genes, some of which are poorly annotated or have different functions in different bacteria. Table 5.1 lists the most relevant genes mentioned in this article, together with gene numbers for N. meningitidis strain MC58 and N. gonorrhoeae strain F62. Aerobic growth of neisserial species In the absence of whole-genome sequencing data, the early literature on neisserial respiratory chains was full of errors. In part this was due to the fact that most redox proteins are membrane attached: the preparation of biochemically useful quantities of membranes from pathogens for protein purification and spectroscopic studies is both difficult and hazardous. It was only recently that whole-genome sequencing data revealed that the neisserial electron transfer chain to oxygen terminates in a single cytochrome oxidase, cytochrome cbb3. This enzyme was first discovered in symbiotic, nitrogen fixing bacteria where it is essential to reduce traces of oxygen that otherwise would prevent nitrogen fixation by inactivating nitrogenase (Preisig et al., 1993, 1996; Rey and Maier, 1997; Pitcher and Watmough, 2004). It has a very high affinity for oxygen and is therefore effective in scavenging low concentrations of oxygen. Consequently, cytochrome oxidase cbb3 is now assumed to be associated with a micro-aerobic lifestyle where a high affinity for oxygen is more important than a high capacity. In the female genital tract, gonococci become trapped in biofilms where they are surrounded by anaerobic fermentative bacteria such as lactobacilli. High population densities of anaerobic fermentative bacteria convert glucose and other sugars predominantly to lactate. Lactobacilli are sufficiently aerotolerant to reduce a limited supply of oxygen to hydrogen peroxide, so gonococci in the human body are always exposed to reactive oxygen species ( Johnson et al., 1996; Seib et al., 2004). Gonococci are therefore found where oxygen and glucose are in limited supply, but lactate is abundant. Neither gonococci nor meningococci are especially adaptable species. In

Anaerobic Survival of Gonococci and Meningococci |  79

Table 5.1 Gene designations for Neisseria gonorrhoeae strain F62 and N. meningitidis strain MC58 N. gonorrhoeae NG number

N. meningitidis NMB number

adhC

Premature stop codon: inactive

1241

Alcohol dehydrogenase; 5(hydroxymethyl) glutathione dehydrogenase

aniA

1246

1623

Cu-containing nitrite reductase

cccA

0292

0717

ccp

1769

absent

Cytochrome c2

cycA

0101

1805

cycB

1328

1677

dnrN

0128

1365

estD

0600

1305

Esterase D

fnr

0734

1785

Transcription factor: regulates anaerobic growth

fnrS

Small RNA

Small RNA

Regulates translation of Fnr-dependent transcripts

ftsEX

1921–1922

2145–2146

Cell division

galM

1571

1778

Aldose 1-epimerase-like protein

hemA

1403

0518

Haem synthesis

mapA

1865

1777

Maltose phosphorylase

minCDE

1814–1816

0160–0162

Cell division

nmlR

0602

NMA1517

MerR-type regulator (note that the meningococcal entry is for serotype A strain.

norB

1275

1622

Nitric oxide reductase

nosZ

a1400

aAbsent

Nitrous-oxide reduction

nosRD

a

a

Nitrous-oxide reduction

nsrR

1519

0437

Repressor protein that is inactivated by nitric oxide.

siaA/lst

1081

0899

CMP-NANA sialyltransferase

trxB

0580

1261

Thioredoxin reductase

Gene

1401–1402

0577–0578

Function

Cytochrome c peroxidase Cytochrome c4 Cytochrome c5

Iron–sulfur cluster repair protein DnrN

aDue

to the presence of multiple premature translation stop codons in the nos genes, no strain of N. gonorrhoeae has been shown to reduce nitrous oxide to dinitrogen. The NosZ protein from N. meningitidis strain MC58 is severely truncated and therefore non-functional, and there are also stop codons in other nos genes. However, some N. meningitidis strains are fully functional for nitrous oxide reduction.

the laboratory they require rich media for growth and oxidize at least some of the amino acids available as secondary sources of energy. Nevertheless, Steve Morse and his colleagues in the 1970s established that gonococci are able to grow rapidly in oxygen-sufficient media. Glucose, pyruvate and lactate are used as primary carbon sources for energy conservation and biosynthesis (Morse and Bartenstein, 1974). However, consistent with the abundance of lactate in their natural environment, it was reported more than 80 years ago that lactate is oxidized more rapidly than pyruvate or glucose (Barton and Miller, 1932). This lactate can also be produced by neutrophil metabolism, resulting in a synergistic oxygen consumption by mixtures of

gonococci and mammalian cells (Britigan et al., 1988). Several amino acids, for example proline, are also oxidized quite rapidly and support limited growth. Despite characteristics typical of a microaerobic lifestyle, four points are especially striking. 1

2

Neisseria are able to reduce oxygen at rates comparable to aerobic bacteria – up to 250 nmol of oxygen reduced per minute per mg of bacterial dry mass, which is essentially the same rate as an aerobic culture of Escherichia coli (Li et al., 2010). They are a prolific source of six c-type cytochromes, and also express genes for two

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3

4

more c-type cytochromes under specific growth conditions (Strange and Judd, 1994). There are two independent pathways for electron transfer from the cytochrome bc1 complex to the terminal cytochrome oxidase. Deletion of the gene for either of them, cycA encoding cytochrome c4 or cycB encoding cytochrome c5, has only a marginal effect on the capacity for electron transfer to oxygen (Li et al., 2010). Although potential rates of oxygen reduction are little affected when the cytochrome c4 or cytochrome c5 pathway has been lost, the single mutants are much more sensitive than the parent to oxygen toxicity in air-saturated cultures. This provided strong supporting evidence for the proposal from whole genome microarray analysis of RNA from N. meningitidis strain MC58 that expression of cycA and cycB is regulated by FNR in order to provide protection against reactive oxygen species (Bartolini et al. 2006).

These observations led us to conclude that their high respiratory capacity has evolved primarily to provide protection against reactive oxygen species rather than to achieve rapid growth in air saturated environments (Li et al., 2010). If so, the high respiratory capacity will also provide protection against reactive oxygen species generated by the defence mechanisms of the host. Consistent with this interpretation, Neisseria have extremely high catalase activity that protects their cytoplasm from hydrogen peroxide from whatever source. In summary, the pathogenic Neisseria are essentially microaerophiles. Why might Neisseria not be able to grow anaerobically? Two key biosynthetic pathways in obligate aerobes require oxygen, but the critical steps are bypassed by alternative enzymes in anaerobic bacteria or facultative anaerobes. These are enzymes involved in nucleotide and haem biosynthesis. The first oxygen-dependent enzyme is ribonucleotide reductase, which catalyses a critical step in ribonucleotide biosynthesis. According to Härtig et al. (2002), ribonucleotide reductases fall

into essentially three types, I, II and III. All of them function by a mechanism in which a free radical activates the substrate. Type I enzymes are typical of aerobic organisms, from bacteria to mammals. They use a di-iron protein to form a tyrosyl radical in the presence of molecular oxygen. They can be subdivided into two subgroups, the nrdAB group and the nrdEF group. Type II enzymes are less widely distributed amongst both aerobic and anaerobic prokaryotes and a few eukaryotes. In contrast to type I ribonucleotide reductases, type II enzymes can function aerobically or anaerobically by a cobalamin-dependent mechanism. Type III ribosyl reductases are restricted to obligate anaerobes. In these bacteria, a glycyl radical is generated from S-adenosylmethionine by an extremely oxygen-sensitive iron–sulphur protein. Type III enzymes are therefore restricted to obligately anaerobic microorganisms. Pathogenic Neisseria synthesize only one ribonucleotide reductase, which is a type I enzyme. Oxygen is essential for the formation of the tyrosyl radical at the active site of the neisserial type I reductase. The second oxygen-dependent pathway in many bacteria is haem synthesis. In Escherichia coli and other facultatively anaerobic bacteria, the products of the hemA and hemN genes are enzymes that catalyse the synthesis of coproporphyrin in the haem biosynthesis pathway during aerobic or anaerobic growth, respectively (Troup et al., 1994; 1995; Tyson et al., 1997). The HemA protein requires oxygen to function. As hemN can also be identified in Neisseria species, they should be competent for haem synthesis even in the absence of oxygen. So can we dogmatically exclude an ability of meningococci or gonococci to grow anaerobically? There are reasons why we should be reluctant to do so. First, as the roles of genes of unknown function are gradually being revealed, new and totally unprecedented enzyme activities are being discovered. Neisseria are known to be exposed to and to generate nitric oxide. One of the most remarkable recent discoveries is a group of enzymes that generate molecular oxygen from nitric oxide under otherwise strictly anaerobic conditions. This is the molecular basis for anaerobic methane oxidation using nitrite as the oxidizing substrate (Ettwig et al., 2010). There

Anaerobic Survival of Gonococci and Meningococci |  81

is no current evidence that pathogenic Neisseria can do likewise – but who has looked for such an activity? A second reason for being cautious is that NrdE and NrdF are essential for anaerobic growth of Bacillus subtilis (Härtig et al., 2002), despite the previous assumption that this enzyme complex is totally dependent upon oxygen for function. Effect of oxygen limitation on serum resistance and pathogenicity The origins of ideas that anaerobiosis increases the pathogenicity of Neisseria are difficult to trace. The author first encountered them in the 1990s, in posters presented at the biennial Pathogenic Neisseria meetings. However, characterization of the E. coli FNR protein and its global role in activating expression of genes required for anaerobic electron transfer pathways prompted speculation that an FNR orthologue was also likely to be involved in neisserial gene regulation. The background to these speculations was as follows. A

Based upon the seminal paper of Ward et al. (1970), Harry Smith, the Guru of microbial pathogenicity, recognized that there was something in human serum that the gonococcus could exploit to protect itself against complement mediated killing. Identification of this chemical would provide a mechanistic explanation of how gonococci survive in the human body. The simple observation was that if gonococci from patients are challenged with any human serum, even from a healthy individual, the bacteria survive (Fig. 5.1A). However, if the bacteria are grown outside the human body on laboratory media and then challenged with the same serum, they are now killed (Fig. 5.1B). He and many others tried to identify the mechanism of induced serum resistance. It was not until the late 1980s that we were able to establish that the active component in serum is the nucleotide, CMP-NANA, the natural donor of sialic acid for glycoproteins in the blood stream (Nairn et al., 1988). We showed that LPS on the gonococcal surface includes as a major component the tetrasaccharide, lactoneotetraose (Parsons et al., 1988). Exactly the same four sugars are a normal

Gonococci from patients

Survive

Human serum

Gonococci from a lab culture B

Killed

Gonococci from an agar plate Incubate with CMP-NANA

Sialylated gonococci

Killed

Human serum

Survive

Figure 5.1  Dependence of gonococcal serum resistance upon the nucleotide, cytidine-5′-monophosphoN-acetyl neuraminic acid derived from the host.

82  | Cole

component of the human body – for example, it is the carbohydrate component of transferrin and many blood group antigens. The pathogenicity mechanism exploits a constitutive enzyme on the bacterial surface, a sialyltransferase that confers the ability to exploit a normal component of the human body to avoid complement mediated killing. So the answer to the question how the gonococcus survives in the human body is very simple – it is a lovely example of molecular mimicry. As soon as the gonococcus gets into the blood stream, it is sialylated by CMP-NANA and now looks exactly like many other blood components: it has acquired its passport for survival. However, the gonococcus lacks genes for CMP-NANA biosynthesis, so when gonococci are grown in the laboratory, their LOS in not sialylated, and they are immediately killed by complement-mediated killing. FNR in the gonococcus These discoveries prompted other research groups to probe the regulation of induced serum resistance. First it was demonstrated that sialylation by CMP-NANA is greater during oxygen-limited than oxygen-sufficient growth (Frangipane and Rest, 1993), leading to the then-unsupported hypothesis that a gonococcal FNR regulates sialyltransferase synthesis. This predated the availability of whole-genome sequence data, but prompted us to establish first that there is indeed a gonococcal FNR, and that it regulates expression of aniA and a few other genes, but not siaA

AniA NO2-

encoding the critical sialyltransferase (Lissenden et al., 1999; Whitehead et al., 2007). Subsequently McGee and Rest (1996) showed that it was not the sialyltransferase that is up-regulated by anaerobiosis, but the 4.5 kDa lipooligosaccharide that is the target for sialylation. Nevertheless, reports that anaerobically grown gonococci are less serum sensitive than aerobically grown bacteria persist. Gonococcal survival during oxygen starvation It was Knapp and Clark (1984) who first showed that when oxygen is scarce, gonococci exploit traces of nitrite available in human body fluids as a secondary source of energy and oxidizing power. First nitrite is reduced to nitric oxide by a coppercontaining nitrite reductase of the NirK family. The product of nitrite reduction, nitric oxide, is then reduced to nitrous oxide by a single-subunit nitric oxide reductase, NorB (Fig. 5.2). These two enzymes thus provide a truncated denitrification pathway in both gonococci and meningococci. The copper-containing nitrite reductase has become known as AniA (Clark et al., 1987, 1988; Mellies et al., 1997). This designation, which is the abbreviation for anaerobically induced protein A, arose because it was the major protein that accumulated specifically during anaerobic growth. Antisera from gonorrhoea patients were shown to react with AniA, providing clear evidence that the aniA gene is expressed in vivo. Subsequently it was established that anaerobic induction requires a functional FNR protein, a transcription factor that

NorB NO

N2 O NO

CycP

CycP

Figure 5.2  The truncated denitrification pathway found in pathogenic Neisseria. Nitrite and nitric oxide are sequentially reduced by AniA and NorB. Cytochrome c′ limits nitrosative damage by binding nitric oxide as it crosses the outer membrane into the periplasm.

Anaerobic Survival of Gonococci and Meningococci |  83

activates the expression of many genes for anaerobic energy conservation in a range of enteric and other bacteria (Householder et al., 1999; Lissenden et al., 2000; Overton et al., 2006). As FNR is active only in the absence of oxygen, the detection of antibodies to AniA in human sera provided clear proof that gonococci can survive in vivo in an oxygen limited environment. In both species, a two-component regulatory system also increases expression of aniA. This involves the environmental sensor protein, NarQ, and the response regulator, NarP (Overton et al., 2006). These names are based upon the E. coli NarQ-NarP system that responds to very low concentrations of nitrate and to much higher concentrations of nitrite. Except for the environmental sensing protein, NarQ, for which the activating signal remains to be identified, regulation of the truncated denitrification pathway is also highly conserved in the two pathogens. Reduction of nitric oxide, the toxic product of nitrite reduction Nitric oxide produced by nitrite reduction by NirK (AniA) is toxic. Nitrate is far more abundant in body fluids than nitrite, but pathogenic Neisseria lack both the structural genes for nitrate reductase synthesis, and genes required for the synthesis of the active site of nitrate reductase, which is molybdopterin guanine dinucleotide. However, dietary nitrate is reduced to nitrite by Enterobacteriaceae and many other types of bacteria. Nitrite is readily protonated, so it diffuses across membranes to provide an external source of nitrite at µM concentrations for other bacteria, including the Neisseria. To avoid nitric oxide toxicity, γ-proteobacteria reduce nitrite not to nitric oxide, but to ammonia. Many of the Enterobacteriaceae express dual pathways for nitrate reduction via nitrite to ammonium, one located in the cytoplasm, the other in the periplasm. Expression of the genes for both of these parallel pathways is coordinately regulated by FNR activation during anaerobic growth, and both of the nitrite reductases protect bacteria like Escherichia coli against nitrosative stress (Vine and Cole, 2011; Vine et al., 2011). One might therefore expect FNR in pathogenic

Neisseria also to coordinate expression of their nitrite and nitric oxide reductases, but this is not what has evolved. The reason is that pathogens must be able to protect themselves against three different sources of NO: that generated by AniA during anaerobic nitrite reduction; NO generated by other bacteria in their local environment; and NO synthesized from arginine by macrophages as part of the host defences. As host cells are aerobic, if NorB synthesis is activated by FNR, bacteria would be unable to protect themselves from nitrosative stress generated by the host. This problem is avoided in the Neisseria and also in many other denitrifying bacteria by regulating nitric oxide reduction independently of FNR. When nitric oxide is absent, expression of the neisserial norB gene is repressed by the transcription factor, NsrR (Lissenden et al., 2000; Householder et al., 2000; Overton et al., 2006; Rock et al., 2007; Whitehead et al., 2007; Heurlier et al., 2008; Isabella et al., 2009). NsrR binds to conserved 29-base pair DNA sites where it represses norB, aniA and nsrR transcription. NsrR regulation of aniA transcription thus provides a mechanism to coordinate nitrite and nitric oxide reduction that does not depend upon a functional FNR. Gonococcal NsrR contains a [2Fe–2S] iron– sulfur centre that is nitrosylated by concentrations of NO that are too low to inactivate transcription activation of aniA by FNR (Isabella et al., 2009). In the presence of NO, NsrR repression is relieved, and NorB is synthesized (Fig. 5.3). A similar principle is used by other denitrifying bacteria in soil or wastewater treatment plants, but various transcription factors have evolved to achieve the same outcome (Rodionov et al., 2005). For example, expression of the two Pseudomonas stutzeri genes required for NorBC synthesis is regulated by NNR, which is a member of the CRP-FNR protein family (Zumft, 1997). There are many reports that several of the global transcription factors respond to nitric oxide. They include FNR, Fur (the repressor of functions required for scavenging iron), and SoxRS, all of which are inactivated by the binding of nitric oxide. The problem with these studies is that, despite statements to the contrary, the concentrations of nitric oxide or nitric oxide donors used were far greater than those that are likely to

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+NO

NO

NsrR Active

NO NsrR

Repair by DnrN

Inactive

NsrR pnorB

pnorB

Figure 5.3  Regulation of NorB synthesis by NsrR. In the absence of nitric oxide, NsrR binds to and represses the norB and aniA promoters. Binding of nitric oxide to the iron–sulfur centre inactivates NsrR, relieving norB repression.

occur in vivo. Consequently, the ‘regulation’ of gene expression observed was more likely due to chemical inactivation of the transcription factors rather than to physiologically relevant responses of signalling pathways. A fuller account of this controversy was reviewed by Spiro (2007). Neisserial denitrification and pathogenicity Genome database analyses coupled with laboratory experiments have revealed variations in competence and capacity to reduce nitrite or nitric oxide. Almost a third of sequenced meningococci lack a functional aniA gene. In contrast, aniA is fully conserved in gonococci (Barth et al., 2009a). Both species have fully conserved norB genes, with more than 95% sequence identity across all Neisseria species, both commensal and pathogenic, suggesting that a functional NorB is essential for survival in various habitats within the human host. All strains tested were able to reduce exogenously supplied NO to anti-inflammatory levels (Barth et al., 2009a). Both species have retained residual nitrous oxide reductase operons in which frame shift or deletion mutations disrupt function. Consequently neither species reduces nitrous oxide to dinitrogen. The maximum rate of nitrite reduction was higher than nitric oxide reduction by gonococci, but the reverse was true for meningococci.

The conventional view is that host-derived nitric oxide is produced to protect the host against bacterial attack. However, there is evidence that this is not an effective defence strategy. Edwards (2010) reported that far from being toxic, nitric oxide might actually be required to sustain cervical bacterial disease. Cardinale and Clark (2005) reported that gonococci are able to reduce nitric oxide to concentrations that are anti-inflammatory, irrespective of whether the NO is generated internally by nitrite reduction, or externally by host iNOS. They proposed that it is significant that nosZ and parts of nosR and nosD have been deleted in meningococci, but the nos genes of gonococci are disrupted by premature stop codons. They argued that because loss of nos gene functions had been selected by independent mechanisms, there must be a selective advantage in maintaining finite levels of NO that is greater than potential benefits of conserving energy during nitrous oxide reduction to dinitrogen. This advantage was proposed to be the ability of 32 µg/ml) in meningococci (Stefanelli et al., 2001; Taha et al., 2006; Taha et al., 2010). The H552N mutation has also been shown to result in high-level resistance to rifampin (>32 µg/ml) in N. gonorrhoeae (Unemo et al., 2009). Interplay of resistance mechanisms Certain mutations by themselves can increase antibiotic resistance to a level that results in treatment failure for the antibiotic in question. In other instances, however, the increase in resistance conferred by a single determinant or mutation is small and of less clinical significance. However, whereas acquisition of a single determinant often confers only an incremental increase in resistance, the cumulative effects of several determinants and their complex interactions can ultimately result in clinical levels of resistance. This is precisely the scenario that resulted in the demise of penicillin as an effective treatment for gonorrhoea, and which threatens the continued use of ceftriaxone. The genetics of the step-wise transfer of chromosomally mediated resistance determinants has been studied in detail for both penicillin (Ropp et al., 2002) and expanded-spectrum cephalosporins (Zhao et al., 2009). DNA isolated from a penicillin-resistant strain (FA6140) or a CephI strain (35/02) was used to sequentially transform a penicillin- and cephalosporin-susceptible laboratory strain (FA19) to higher levels of resistance (Fig. 9.2). When FA19 is transformed with DNA from either a penicillin-resistant or CephI strain and transformants are selected on antibiotic concentrations slightly above the MIC, the first resistance determinant transferred is penA (Fig. 9.2). This order is consistent with the observation that in wild-type strains, PBP2 has an ~10- to 100-fold higher rate of acylation than PBP1 and thus is the lethal target of most β-lactam antibiotics at concentrations near their MIC (there are four PBPs in Neisseria species, but only PBPs 1 and 2 are essential) (Barbour, 1981). The next determinant transferred, mtrR, results in

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Figure 9.2  Stepwise transfer of resistance determinants from a penicillin-resistant (FA6140) or CephI (35/02) donor strain to a wild-type (FA19) strain. PCR products of the indicated resistance determinants were used in a step-wise manner to transform FA19 to the indicated genotypes. The MICs of penicillin for the transformants from donor FA6140 (left panel) and ceftriaxone for the transformants from donor 35/02 (right panel) are shown, with the fold increase in the MIC at each step depicted by the arrows. Note that ponA1 does not increase the MIC of either penicillin or ceftriaxone when introduced into strains containing the first three resistance determinants, but replacement of ponA1 in FA6140 (but not 35/02) with the ponAWT allele decreases the MIC nearly 2-fold. The difference in MICs between FA19 transformed with the four determinants relative to the donor strain represents the contribution of the unknown determinant(s) that cannot be transformed in the laboratory. See text for more details on each of the resistance determinants. Adapted from Zhao et al. (2009).

over-expression of the MtrC-MtrD-MtrE efflux pump, which confers resistance to a wide range of hydrophobic compounds and antibiotics. The level of resistance conferred by mtrR is at most 2-fold, but the mtrR determinant is required for high-level resistance (see below). The third step in resistance is mediated by penB, which encodes mutations in porB1B (Fig. 9.2). penB mutations also increase resistance to a broad range of antibiotics that depend on porins to diffuse into the periplasmic space. As described above, resistance conferred by penB mutations is observed only in strains also carrying the mtrR determinant. While the first three determinants are transferred with high efficiency, transformation of a third-level transformant up to the level of resistance of clinical isolates has not been achieved in the laboratory, and as a consequence the genes involved have not been identified (Faruki and Sparling, 1986; Dougherty, 1986; Zhao et al., 2009). One possibility for a candidate gene involved in resistance is ponA, which encodes PBP1. Indeed, Ropp et al. (2002) identified a single missense mutation in ponA in a set of

high-level penicillin-resistant strains that lowered the rate of acylation for penicillin, but introduction of this determinant into an FA19 penA mtrR penB third-level transformant had no effect on resistance. However, when the mutant ponA allele in the donor penicillin-resistant strain (FA6140) was replaced by the wild type ponA1 gene, the MIC decreased 2-fold (Fig. 9.2). Thus, mutation of PBP1 appears to have a role in penicillin resistance, but it has no phenotype unless the unknown determinant(s) is(are) present. In contrast to penicillin resistance, the ponA gene appears to play no role in resistance to expanded-spectrum cephalosporins (Zhao et al., 2009), as indicated by the lack of an effect on the MIC following replacement of the ponA1 allele in the CephI strain 35/02 with wild-type ponA (Fig. 9.2). The difficulty in transforming wild-type strains to higher levels of resistance with DNA isolated from penicillin-resistant and CephI strains perhaps suggests that multiple genes are required; if so, this would mean that the individual determinants have no effect on resistance by themselves, and that these genes, much like mtrR and penB,

Mechanisms of Antibiotic Resistance |  177

have complex interactions that require one other to exert their function effects. Other antibiotic resistance properties also can be influenced by the interplay of multiple mutations. For instance, chromosomally mediated resistance to tetracycline results from a combination of decreased entry due to penB, increased efflux due to mtrR mutations and decreased target recognition due to missense mutations in rpsJ (Sparling et al., 1975; Gill et al., 1998; Olesky et al., 2002; Hu et al., 2005). Mutations that impact efflux pump gene expression (see below) in conjunction with other resistance mechanisms can also influence neisserial susceptibility to quinolones and macrolides.

Regulation of antibiotic resistance genes Bacteria often regulate expression of their antibiotic resistance genes. Control of resistance gene expression may help to counteract a possible fitness cost in the absence of antibiotics, which has been observed with certain resistance mechanisms (see below). Regulation can be constitutive or inducible, and both trans- and cis-acting factors have been identified in gonococci and meningococci as being important in gene regulation. Perhaps the best understood example of how regulatory systems can impact antibiotic resistance levels expressed by gonococci is with trans- and cis-acting elements that modulate expression of mtrCDE (Fig. 9.3).

mtrA (I)

Fur+Fe

mpeR

mtrF

mtrR

mtrCDE

IHF MC CE

Figure 9.3  Trans- and cis-acting regulatory elements that control expression of the mtrCDE efflux pump operon in N. gonorrhoeae and N. meningitidis. Trans-acting elements behaving as repressors or genes that encode them are shown as barred lines (⊥) while those encoding activators are shown by arrows. IHF regulation of mtrCDE occurs in meningococci (and in rare strains of gonococci) due to the presence of a Correia element (CE), shown as the inverted triangle in the DNA sequence, and this regulation requires IHF-binding to target DNA (Rouquette-Loughlin et al., 2004; Lee et al., 2006) as shown by the barred line. The promoters responsible for transcription of mtrR and mtrCDE are shown with their respective –10 and –35 hexamer sequences (see bars over hexamers). Note that mtrR and mtrCDE are transcriptionally divergent and only the mtrCDE-coding strand is shown. The position of the 13 bp inverted sequence (I.R.) between the –10 and –35 hexamers of the mtrR promoter is shown within the 14 nucleotide sequence within the dotted box and a T nucleotide that is frequently deleted in strains that over-express mtrCDE and exhibit a high level of resistance to pump substrates is bolded. The position of the new –10 hexamer sequence generated by the point mutation (C to T) that acts as a new promoter (mtr120) for mtrCDE transcription (Ohneck et al., 2011) is shown. MtrR repression of mtrCDE is due to binding of two homodimers to the mtrCDE promoter as shown by the barred line that extends to the region shown in the nucleotide sequence. MtrA binds upstream of the mtrCDE promoter (Zalucki et al., 2012).

178  | Unemo et al.

The mtr system was originally proposed to modulate the influx of structurally diverse hydrophobic antimicrobial compounds into gonococci by altering the permeability property of the OM (Guymon et al., 1978). It was identified as such because a single step mutation event increased gonococcal resistance to structurally diverse hydrophobic drugs, dyes and detergents (Maness and Sparling, 1973). Mutations that reversed this resistance, and rendered gonococci hypersusceptible to hydrophobic agents (HA) and antibiotics, were subsequently identified and such mutants were termed env (Sarubbi et al., 1975; Guymon and Sparling, 1975). Pan and Spratt (1994) cloned and sequenced a region of the chromosome from an mtr strain that conferred HA-resistance when introduced into a wild-type strain and found that resistance was due to a missense mutation in a gene that encoded a DNA-binding protein (MtrR) structurally and functionally similar to the TetR family of repressors. MtrR contains a helix– turn–helix (HTH) domain (residues 32–53) in the N-terminal half of the protein and mutations in this domain were subsequently found to inhibit its DNA-binding activity (Lucas et al., 1997). MtrR acts as two homodimers (Hoffmann et al., 2005) and binds to a DNA sequence that lies just upstream of the mtrCDE operon (Fig. 9.3) (Lucas et al., 1997). Mutations in the MtrR HTH region, as well as missense mutations in the C-terminal region, which probably affect dimer formation, can enhance transcription of mtrCDE and elevate gonococcal resistance to substrates (host-derived antimicrobials and antibiotics such as β-lactams and macrolides) for the efflux pump system. MtrR binds to the mtrCDE promoter in a specific manner and a 15-bp binding site containing two pseudo-direct repeats has been identified (Hoffmann et al., 2005). In addition to regulating mtrCDE, MtrR can positively or negatively regulate more than 70 other genes, including glnA, glnE, ponA, pilMNOPQ, and rpoH (Folster et al., 2007, 2009; Johnson et al., 2011) and its regulatory activity modulates gonococcal fitness in a murine genital tract infection model (see below). The mtrRCDE locus is also found in N. meningitidis and the efflux pump is functional. However, many meningococci examined to date encode an MtrR protein that is predicted to be non-functional due

to nonsense mutations that prematurely truncate the protein, or missense mutations that abrogate DNA-binding; as described below, meningococci use an insertion sequence between mtrR and mtrCDE to control expression of the efflux pump genes (Rouquette-Loughlin et al., 2004). Another transcription factor (MpeR) possessed by both gonococci and meningococci was shown to be important in regulating expression of the gonococcal MtrC-MtrD-MtrE efflux pump system through its ability to negatively regulate mtrF expression (Fig. 9.3) (Folster and Shafer, 2005). mtrF encodes a putative cytoplasmic membrane protein that is required by the MtrCMtrD-MtrE pump to direct high-level efflux of substrates (host antimicrobials and antibiotics) for the pump system (Veal and Shafer, 2003). MpeR contains an HTH at its C-terminal end and is similar to other DNA-binding proteins in the AraC family of regulators. MtrR and MpeR both regulate mtrF expression by independent processes (Fig. 9.3). Expression of mpeR is subject to repression by Fur plus iron and, under ironlimiting conditions, expression is increased. Thus, it is likely that that mpeR expression is favoured at sites of infection that are low in iron. Microarray analysis (Mercante et al., 2012) revealed that MpeR controls different genes in a growth phase-dependent manner. In addition to mtrF, MpeR regulates mtrR (Mercante et al., 2012) and fetA (Hollander et al., 2011), the latter of which encodes a siderophore receptor. Due to the capacity of MpeR to repress mtrR expression and the known ability of MtrR to repress expression of the mtrCDE efflux pump operon, it is likely that during infection, when free iron levels are low, the levels of the MtrC-MtrD-MtrE pump will be higher than under laboratory growth conditions. Hence, resistance to antibiotics may be greater during infection than would be predicted from laboratory MIC testing. Integration host factor (IHF), which is a welldescribed DNA-binding protein that bends DNA, also has been found to regulate mtrCDE in meningococci (Rouquette-Loughlin et al., 2004). IHF negatively regulates mtrCDE efflux pump gene expression in meningococci (Rouquette-Loughlin et al., 2004) by binding to a sequence within a Correia element (CE) (Correia et al., 1988)

Mechanisms of Antibiotic Resistance |  179

positioned upstream of mtrCDE (Fig. 9.3); a CE upstream of mtrCDE is absent in most gonococci (see exception below). The mtrC-mtrD-mtrE efflux pump locus can be inducibly expressed when gonococci are grown in the presence of a sub-lethal level of an antimicrobial agent recognized by the pump (Rouquette et al., 1999). This induction is independent of MtrR but requires the presence of an AraC/XylS-like protein, termed MtrA (Rouquette et al., 1999; Zalucki et al., 2012). A high percentage of gonococci contain an 11 bp deletion at the 5′-end of the mtrA-coding sequence that prematurely truncates the protein, and such strains are non-inducible. Meningococci possess an intact mtrA gene, but for unknown reasons fail to activate mtrCDE when grown in the presence of antimicrobials. MtrA, like MtrR, can directly or indirectly regulate (positively or negatively) several genes in gonococci ( Johnson and Shafer, unpublished) and an intact mtrA gene in gonococci enhances fitness in a murine infection model (Warner et al., 2007). While mutations in the mtrR coding sequence can enhance transcription of mtrCDE and elevate the efflux of substrates in gonococci, high-level efflux requires a cis-acting mutation within the overlapping mtrR and mtrCDE promoters (Hagman and Shafer, 1995). The mtrR promoter contains a 13 bp inverted repeat sequence between the –10 and –35 hexamers (Fig. 9.3). A single bp deletion (Hagman and Shafer, 1995) or a dinucleotide insertion (Zarantonelli et al., 2001) within this inverted repeat is sufficient to dramatically reduce transcription of mtrR; these cis-acting promoter mutations are observed in clinical isolates expressing high levels of HA-resistance. It has been proposed (Hagman and Shafer, 1995; Lucas et al., 1997) that because the mtrR and mtrCDE promoters overlap on opposite strands at their –35 hexamer region (Fig. 9.3), the single bp deletion or the dinucleotide insertion enhances mtrCDE expression by both reducing mtrR expression and making the mtrCDE promoter more available for interaction with RNA polymerase. A single point mutation (C to T) located 120 bp upstream of mtrCDE in the gonococcal strain MS11 was shown by Warner et al. (2008) to result in high levels of mtrCDE expression and

efflux of substrates for the efflux pump system that were comparable to those observed with the single bp deletion in the mtrR promoter described above. Subsequent work by Ohneck et al. (2011) revealed that this mutation generated a consensus –10 hexamer (TATAAT) and exhibited promoter activity that was exempt from control by the transcriptional factors MtrR and MtrA. Examination of 121 clinical isolates exhibiting the Mtr phenotype revealed only one isolate with the MS11-like mutation compared to 86 isolates with the single bp deletion, suggesting that it is rare in gonococcal populations. Interestingly, this point mutation has a small but significant impact on expression of genes other than mtrCDE (Ohneck et al., manuscript in preparation). This mutation also results in enhanced expression of ccpR, which encodes cytochrome C peroxidase. The significance of increased expression of ccpR is not yet known but may influence the susceptibility of gonococci to hydrogen peroxide that escapes the action of other detoxifying systems. Nevertheless, this observation emphasizes the notion that transcriptional control systems can have global effects that impact the overall biology of gonococci and its resistance to antimicrobials. Transcription of mtrCDE in meningococci utilizes the same promoter as in gonococci (Rouquette-Loughlin et al., 2004). Since many meningococci are natural mtrR mutants, the transcription of the efflux pump operon was expected to be similar to mtrR mutants of gonococci. Instead, a novel regulatory process was uncovered. Central to regulation of mtrCDE in meningococci is a 155–159 bp CE (Fig. 9.3). Multiple copies of this element are found scattered throughout the gonococcal and meningococcal chromosomes (Correia et al., 1988). In meningococci, but not most gonococci, it is positioned between the mtrR and mtrCDE genes just downstream of the mtrCDE promoter (Rouquette-Loughlin et al., 2004), where it appears to regulate the mtrCDE operon. This regulation requires an intact IHF-binding site, as its deletion from the CE enhances mtrCDE transcription. The CE also modulates expression post-transcriptionally through RNase III cleavage at sites located within inverted repeats. A small percentage of meningococci do not contain this CE (or have deletions),

180  | Unemo et al.

but the significance of its absence is not yet known (Enriquez et al., 2010). In 1999, a group of gonococcal isolates from Kansas City, MO, USA, were identified that exhibited resistance to azithromycin (MIC of 2–4 µg/ ml) and erythromycin (4–8 µg/ml) by a mechanism that required an active MtrC-MtrD-MtrE efflux pump ( Johnson et al., 2003). These strains are of particular interest because, unlike most gonococci, they contain a CE between mtrR and mtrCDE, much like what is observed in meningococci (Rouquette-Loughlin et al., 2004). Some of these macrolide-resistant gonococci also had nonsense or missense mutations in the mtrR-coding region that would result in truncation of MtrR, but transformation experiments revealed that the CE alone conferred resistance. The emergence of these isolates suggests that either a gonococcal CE element located elsewhere on the chromosome was repositioned to the mtr locus or that meningococcal CE DNA sequences containing flanking mtr DNA were imported and recombined at this site. Mutations positioned upstream of the macABand norM-encoded efflux pump genes have been shown to modulate levels of neisserial susceptibility to antibiotics by altering gene expression (Rouquette-Loughlin et al., 2003, 2005). The MacA-MacB efflux system in gonococci and meningococci, which exports macrolides, is similar to the ABC-type MacA-MacB efflux system previously recognized in E. coli (Kobayashi et al., 2001). The macA and macB genes in gonococci and meningococci are organized as an operon and the start point of macAB transcription is located 37 nucleotides upstream of the translational start codon. Inspection of the putative –10 and –35 hexamer sequences, which are separated by an optimal 17 nucleotides, revealed that the –10 sequence (5′-TAGAAT-3′) contained a point mutation (the underlined G instead of the normal T) in nine gonococcal strains including FA19 and FA1090. This T→G mutation was found to dampen transcription of macAB, because transcription was enhanced when it was replaced by the more optimal T. Importantly, this mutation also increased gonococcal resistance to macrolides by 10- to 30-fold when placed into a strain lacking the MtrC-MtrD-MtrE efflux pump (RouquetteLoughlin et al., 2003, 2005).

The NorM efflux pump is a Na+-drug antiporter in the MATE family. It was observed that a point mutation in the –35 hexamer (5′-CTGACG-3′ to 5′-TTGACG-3′) in the gonococcal and meningococcal norM promoter could enhance transcription of norM, resulting in decreased neisserial susceptibility to norfloxacin and ciprofloxacin (Rouquette-Loughlin et al., 2003). Resistance levels could be further increased by a second mutation that mapped to the ribosomebinding site (5′-TGAA-3′ to 5′-TGGA-3′). While the levels of resistance were insufficient by themselves to provide clinical resistance, they could be significant in strains expressing a level of ciprofloxacin resistance near the MIC breakpoint. Antibiotic resistance and neisserial fitness Although strains expressing resistance to an antibiotic have an advantage both in vitro and in vivo over antibiotic-susceptible strains in the presence of the antibiotic in question, they are frequently less fit in the absence of the antibiotic. However, compensatory mutations that restore fitness but do not change antibiotic resistance occur frequently in the laboratory and probably in the microbe’s natural environment as well. It is noteworthy that many years after penicillin, tetracycline and fluoroquinolones were removed from the treatment guidelines for gonorrhoea, resistant strains continue to represent a significant percentage of current isolates globally (Tapsall, 2001; Unemo and Shafer, 2011; Unemo and Nicholas, 2012). The continued prevalence of these strains suggests that the resistance mechanisms are unlikely to have a negative impact on bacterial fitness in human hosts. The literature regarding bacterial antibiotic resistance is replete with examples of how resistance can decrease fitness in vivo in the absence of antibiotic pressure (Nagaev et al., 2001; Trzcinski et al., 2006; Rozen et al., 2007). However, reports where resistance can actually enhance fitness in vivo are rare; one of these is through derepression of the gonococcal mtrCDE efflux pump operon. Using a female mouse model of lower genital tract infections, Jerse and co-workers (2003) determined that null mutations in mtrD or mtrE

Mechanisms of Antibiotic Resistance |  181

decrease the capacity of strain FA19 to cause a sustained (12-day) infection. This observation was likely the result of the inability of the infecting strain to export host-derived HA (such as antimicrobial peptides and/or progesterone) recognized by the pump (Hagman et al., 1997; Shafer et al., 1998). Unexpectedly, in competition experiments that employed wild-type strain FA19 and an isogenic derivative with an mtrR null mutation, loss of MtrR, which results in moderate overexpression of mtrCDE and enhanced antimicrobial resistance, greatly increased gonococcal fitness in vivo. In contrast, loss of mtrA, which would abrogate the inducible over-expression of the efflux pump, decreased fitness in vivo; however, this decrease in fitness could be reversed by second site mutations that mapped to mtrR (Warner et al., 2007). Importantly, clinically isolated mutations in the mtr locus differ in the degree to which they derepress the mtrCDE operon, and the resultant gradient in erythromycin MICs parallels the degree of resistance to host-derived substrates and in vivo fitness conferred by these mutations (Warner et al. 2008) (Fig. 9.4). While additional experiments documented the importance of the pump in these fitness changes, other MtrR- and/ or MtrA-regulated genes also may contribute. In collaboration with D. Dyer and L. Jackson (Oklahoma Health Sciences Center), we have determined that these DNA-binding proteins can control numerous gonococcal genes and some of these may be important in fitness. For example, MtrR has been shown to bind to DNA sequences upstream of ponA and pilMNOPQ and control their transcription (Folster et al., 2007). There is also suggestive clinical evidence that efflux pumps likely enhance the ability of gonococci to survive during human infection. First, mtrR mutants are frequently isolated from the rectum of infected patients (Morse et al., 1982; Shafer et al, 1995; Xia et al., 2000), presumably because this environment is rich in HAs such as long chain fatty acids and bile salts. The MtrC-MtrD-MtrE efflux pump is also likely to help gonococci evade innate immune responses involving antimicrobial peptides, since the pump can recognize the human cathelicidin LL-37 (Shafer et al., 1998). Bloodstream isolates from patients with disseminated gonococcal infections

(DGI) are often hypersusceptible to HAs and penicillin (Eisenstein and Sparling, 1978) due to small deletions in mtrC or mtrD (Veal et al., 1998) that render the pump inactive. However, these strains also frequently contain phenotypically suppressed mtrR mutations and can donate HA-resistance by transformation to other strains (Eisenstein and Sparling, 1978; Shafer et al., 1995; Veal et al., 1998). Kunz et al. (2012) recently studied whether gonococci expressing intermediate or high-level resistance to ciprofloxacin experience a fitness cost or benefit relative to an isogenic susceptible strain. As described above, intermediate-level fluoroquinolone resistance results from mutations in gyrA, whereas high-level resistance most frequently requires mutations in both gyrA and parC. Interestingly, S91F and D95N mutations in gyrA were found to enhance gonococcal fitness in vivo as assessed by competition with the parental (wild type or mtrR-deletion) strain in the lower genital tract of female mice. The in vivo fitness benefit was not observed when the strains were cultured competitively in vitro, and in fact, strains with both gyrA and mtrR mutations were attenuated for growth relative to the parent strain. Addition of the D86N parC allele reversed the in vivo fitness benefit and resulted in a substantial fitness cost in vitro. However, compensatory mutations that increased fitness while maintaining ciprofloxacin resistance were selected in some mice. Subsequent studies have shown that gyrA together with parC mutations in other strain backgrounds have a fitness benefit that is similar to that conferred by gyrA mutations alone. This finding suggests that some ciprofloxacin-resistant strains may spread even without acquiring compensatory fitness mutations. The mechanism(s) for the in vivo fitness benefit afforded by gyrA is under investigation, but the results suggest that gonococci containing gyrA mutations may have a competitive advantage in the community and develop parC mutations when resistance to ciprofloxacin is required for the bacterium to avoid treatment regimens that include a fluoroquinolone. There is still no clear comprehension as to why antibiotic resistance is so infrequent in meningococci as compared with gonococci. However, recent in vitro growth and mouse infection studies

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