• Loading metrics

What Makes a Bacterial Species Pathogenic?:Comparative Genomic Analysis of the Genus Leptospira

  • Derrick E. Fouts , (DEF); (JMV)

    Affiliation J. Craig Venter Institute, Rockville, Maryland, United States of America

  • Michael A. Matthias,

    Affiliation Division of Infectious Diseases, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California, United States of America

  • Haritha Adhikarla,

    Affiliation Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America

  • Ben Adler,

    Affiliation Australian Research Council Centre of Excellence in Structural and Functional Microbial Genomics, Department of Microbiology, Monash University, Clayton, Australia

  • Luciane Amorim-Santos,

    Affiliations Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America, Centro de Pesquisas Gonçalo Moniz, Fundação Oswaldo Cruz/MS, Salvador, Bahia, Brazil

  • Douglas E. Berg,

    Affiliation Division of Infectious Diseases, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California, United States of America

  • Dieter Bulach,

    Affiliation Victorian Bioinformatics Consortium, Monash University, Clayton, Victoria, Australia

  • Alejandro Buschiazzo,

    Affiliations Institut Pasteur de Montevideo, Laboratory of Molecular and Structural Microbiology, Montevideo, Uruguay, Institut Pasteur, Department of Structural Biology and Chemistry, Paris, France

  • Yung-Fu Chang,

    Affiliation Department of Population Medicine & Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America

  • Renee L. Galloway,

    Affiliation Centers for Disease Control and Prevention (DHHS, CDC, OID, NCEZID, DHCPP, BSPB), Atlanta, Georgia, United States of America

  • David A. Haake,

    Affiliations VA Greater Los Angeles Healthcare System, Los Angeles, California, United States of America, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America

  • Daniel H. Haft,

    Current address: National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America

    Affiliation J. Craig Venter Institute, Rockville, Maryland, United States of America

  • Rudy Hartskeerl,

    Affiliation WHO/FAO/OIE and National Collaborating Centre for Reference and Research on Leptospirosis, KIT Biomedical Research, Royal Tropical Institute (KIT), Amsterdam, The Netherlands

  • Albert I. Ko,

    Affiliations Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America, Centro de Pesquisas Gonçalo Moniz, Fundação Oswaldo Cruz/MS, Salvador, Bahia, Brazil

  • Paul N. Levett,

    Affiliation Government of Saskatchewan, Disease Control Laboratory Regina, Canada

  • James Matsunaga,

    Affiliations VA Greater Los Angeles Healthcare System, Los Angeles, California, United States of America, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America

  • Ariel E. Mechaly,

    Affiliation Institut Pasteur de Montevideo, Laboratory of Molecular and Structural Microbiology, Montevideo, Uruguay

  • Jonathan M. Monk,

    Affiliation Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America

  • Ana L. T. Nascimento,

    Affiliations Centro de Biotecnologia, Instituto Butantan, São Paulo, SP, Brazil, Programa Interunidades em Biotecnologia, Instituto de Ciências Biomédicas, USP, São Paulo, SP, Brazil

  • Karen E. Nelson,

    Affiliation J. Craig Venter Institute, Rockville, Maryland, United States of America

  • Bernhard Palsson,

    Affiliation Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America

  • Sharon J. Peacock,

    Affiliation Department of Medicine, University of Cambridge, Cambridge, United Kingdom

  • Mathieu Picardeau,

    Affiliation Institut Pasteur, Biology of Spirochetes Unit, National Reference Centre and WHO Collaborating Center for Leptospirosis, Paris, France

  • Jessica N. Ricaldi,

    Affiliation Instituto de Medicina Tropical Alexander von Humboldt; Facultad de Medicina Alberto Hurtado, Universidd Peruana Cayetano Heredia, Lima, Peru

  • Janjira Thaipandungpanit,

    Affiliation Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

  • Elsio A. Wunder Jr.,

    Affiliations Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America, Centro de Pesquisas Gonçalo Moniz, Fundação Oswaldo Cruz/MS, Salvador, Bahia, Brazil

  • X. Frank Yang,

    Affiliation Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

  • Jun-Jie Zhang,

    Affiliation Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

  •  [ ... ],
  • Joseph M. Vinetz (DEF); (JMV)

    Affiliations Division of Infectious Diseases, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California, United States of America, Instituto de Medicina Tropical Alexander von Humboldt; Facultad de Medicina Alberto Hurtado, Universidd Peruana Cayetano Heredia, Lima, Peru, Instituto de Medicina “Alexander von Humboldt,” Universidad Peruana Cayetano Heredia, Lima, Peru

  • [ view all ]
  • [ view less ]

What Makes a Bacterial Species Pathogenic?:Comparative Genomic Analysis of the Genus Leptospira

  • Derrick E. Fouts, 
  • Michael A. Matthias, 
  • Haritha Adhikarla, 
  • Ben Adler, 
  • Luciane Amorim-Santos, 
  • Douglas E. Berg, 
  • Dieter Bulach, 
  • Alejandro Buschiazzo, 
  • Yung-Fu Chang, 
  • Renee L. Galloway


Leptospirosis, caused by spirochetes of the genus Leptospira, is a globally widespread, neglected and emerging zoonotic disease. While whole genome analysis of individual pathogenic, intermediately pathogenic and saprophytic Leptospira species has been reported, comprehensive cross-species genomic comparison of all known species of infectious and non-infectious Leptospira, with the goal of identifying genes related to pathogenesis and mammalian host adaptation, remains a key gap in the field. Infectious Leptospira, comprised of pathogenic and intermediately pathogenic Leptospira, evolutionarily diverged from non-infectious, saprophytic Leptospira, as demonstrated by the following computational biology analyses: 1) the definitive taxonomy and evolutionary relatedness among all known Leptospira species; 2) genomically-predicted metabolic reconstructions that indicate novel adaptation of infectious Leptospira to mammals, including sialic acid biosynthesis, pathogen-specific porphyrin metabolism and the first-time demonstration of cobalamin (B12) autotrophy as a bacterial virulence factor; 3) CRISPR/Cas systems demonstrated only to be present in pathogenic Leptospira, suggesting a potential mechanism for this clade’s refractoriness to gene targeting; 4) finding Leptospira pathogen-specific specialized protein secretion systems; 5) novel virulence-related genes/gene families such as the Virulence Modifying (VM) (PF07598 paralogs) proteins and pathogen-specific adhesins; 6) discovery of novel, pathogen-specific protein modification and secretion mechanisms including unique lipoprotein signal peptide motifs, Sec-independent twin arginine protein secretion motifs, and the absence of certain canonical signal recognition particle proteins from all Leptospira; and 7) and demonstration of infectious Leptospira-specific signal-responsive gene expression, motility and chemotaxis systems. By identifying large scale changes in infectious (pathogenic and intermediately pathogenic) vs. non-infectious Leptospira, this work provides new insights into the evolution of a genus of bacterial pathogens. This work will be a comprehensive roadmap for understanding leptospirosis pathogenesis. More generally, it provides new insights into mechanisms by which bacterial pathogens adapt to mammalian hosts.

Author Summary

Leptospirosis is an emerging and re-emerging globally important zoonotic infectious disease caused by spirochetes of the genus Leptospira. This genus is complex, with members that cause lethal human disease, yet mechanisms that underlie pathogenesis remain obscure. Leptospira species are divided into those that are infectious for mammals, and those that are non-infectious environmental saprophytes. Based on biological characteristics and molecular phylogeny, infectious Leptospira are further divided into pathogenic and intermediately pathogenic members. The pan-genus genomic analysis of 20 Leptospira species reported here shows the evolutionary relationship of the different Leptospira clades, and various genetic factors related to virulence and pathogenesis. Infectious Leptospira show key adaptations to mammals, for example sialic acid biosynthesis, pathogen-specific porphyrin metabolism, and the observation that pathogenic Leptospira are vitamin B12 autotrophs, able to synthesize it from a simple amino acid precursor, L-glutamine. A large novel protein family of unknown function—the Virulence Modifying proteins—is found uniquely in pathogenic Leptospira. Similarly, the CRISPR/Cas system was only found in pathogenic Leptospira. A comparative genomic analysis of a complex bacterial genus allowed us to identify large-scale changes that provides new insights into general processes by which bacteria evolve to become pathogenic.


Leptospirosis is a globally widespread zoonotic disease with important health consequences for humans and domesticated animals [1, 2]. Infectious Leptospira have significant affinity for specific mammals but vary in how strictly they adapt to specific hosts [1]. Rodent reservoirs (e.g., reservoir hosts (rats, mice) do not exhibit disease but have long-term renal colonization and excrete organisms in the urine, which is key to leptospiral ecology and its life cycle. Infected livestock (e.g.,. cattle, pigs) and companion animals (e.g.,. dogs) may suffer fetal loss and acute kidney, liver and lung injury in response to infection. Infected humans variably exhibit clinical manifestations including asymptomatic infection [3] with or without long-term renal carriage [4], undifferentiated fever, renal failure, jaundice, hemorrhage (especially the severe pulmonary hemorrhage syndrome), meningitis, shock and death.

Past taxonomy divided the Leptospira genus into a single pathogenic and a single saprophytic species denoted as L. interrogans and L. biflexa, respectively, which, in turn, were divided into more than 250 serovars based on the cross-agglutinin absorption (CAAT) assay [1, 5]. In the 1990s, DNA hybridization (DDH) identified 17 ‘genomospecies’ [6], which also distinguished DDH from serovar. DDH complemented by molecular methods and experimental studies have since confirmed the existence of at least 22 species [712], and grouping of species as infectious (sometimes referred to as group I and group II pathogens, corresponding to “pathogenic” and “intermediately pathogenic”, respectively) and non-infectious (“saprophytic”) [13]. Technical challenges in performing DDH has led to the development of many different molecular approaches to species identification [14, 15]. The International Committee on Systematics of Prokaryotes, Subcommittee on the Taxonomy of Leptospiraceae recently agreed that genome sequence comparison should replace DDH for species definition [16]. Such methods include sequence-based phylogeny and calculation of in silico genomic similarities between isolates by using draft genomes [1720].

Leptospiral typing is important for carrying out outbreak investigations and in identifying likely mammalian host reservoir sources of infection. Two commonly used molecular methods performed are pulsed-field gel electrophoresis and multilocus sequencing typing (MLST). MLST has the advantage that it reflects the underlying population genetic structure, is reproducible, is robustly supported by experimental data, and even can be used directly to identify infecting Leptospira in clinical samples [2127]. Genome sequencing, which has become widely available, together with automated tools that assign MLST sequence types directly from sequence data, has demonstrated an important potential for typing, with the expectation that automated analysis tools will become sufficiently user-friendly for rapid and efficient whole genome analysis and comparison, including phylogenetic analysis based on the identification of single nucleotide polymorphisms (SNPs) in the core genome.

The goal of the Leptospira Genome Project, initiated in 2011, has been to obtain and compare whole genome information for all known Leptospira species. Among the goals of this analysis are the following: i) identifying Leptospira pathogenesis mechanisms that might explain heterogeneity in clinical manifestations of leptospirosis; ii) understanding the relationship of genomic content and context to pathogenesis; iii) determining the definitive evolutionary relationship of Leptospira towards understanding how infectious Leptospira diverged from saprophytes; and iv) identifying common antigens for improving diagnosis and vaccine development. Prior to this project, there were 9 known pathogenic Leptospira species, 5 intermediate Leptospira species, and 6 saprophytic Leptospira species [28], for which whole genome sequence analysis was available for two pathogenic species (two serovars of L. interrogans, Lai [29] and Copenhageni [30], two serovars of L. borgpetersenii [31]), one intermediate pathogen L. licerasiae [32], and one saprophyte species, L. biflexa [33]. Since the advent of the present large-scale project the whole genome sequence of another pathogen, L. santarosai serovar Sherman has been reported [34] but without comparative analysis.

The present study reports a systematic comparative genome analysis of the 20 Leptospira species known when this project began. These species comprise the pathogenic, intermediately pathogenic and saprophytic clades, defined by 16S rRNA gene sequence [2, 35, 36] and complemented by DNA-DNA hybridization [7, 11, 32]. This analysis focuses on the main genomic features and content distinguishing infectious from non-infectious Leptospira, and on how specific genes and gene families have ramified in the pathogenic and intermediately pathogenic Leptospira.


Leptospira isolates

A globally representative collection of the 20 Leptospira species known at the advent of this project was analyzed here, and provided by members of the leptospirosis research community (Table 1).

DNA preparation from isolates

A standard operating procedure was established for all contributing laboratories to follow in preparing DNA for whole genome sequencing. Leptospira were considered to be like Gram-negative bacteria for the purpose of DNA extraction because of the presence of lipopolysaccharide and a thin peptidoglycan cell wall. Either ~1012 bacterial cells or 30 mL of the densest possible culture of Leptospira (in EMJH medium) were centrifuged and the pellet resuspended in 180 μl Buffer ATL (all buffer abbreviations are according to the manufacturer and related specifically to components of the kit) (Qiagen Tissue Kit, Valencia, CA, USA), and then purified according to the protocol “Purification of Total DNA from Animal Tissues (Spin-Column Protocol),” including the use of proteinase K, thorough vortexing throughout the procedure, and incubated at 56°C until the cells were completely lysed, according to the manufacturer’s instructions. Lysis was usually complete in 1–3 hr. Before adding Buffer AL, 10 μl of RNAse cocktail was added (mixture of two highly purified ribonucleases, RNase A (500 U/ml) and RNase T1 (20,000 U/ml); Ambion, Life Technologies, Carlsbad, CA), and incubated for 30–60 min at 37°C. After vortexing for 15 s, Buffer AL was added to the sample, which was again mixed thoroughly by vortexing, followed by adding ethanol (96–100%), and mixed again by vortexing. It was considered essential that the sample, Buffer AL, and ethanol were mixed immediately and thoroughly by vortexing or pipetting to yield a homogeneous solution. After this point samples were handled with large bore, genomic DNA-compatible tips. The mixture from step 3 (including any precipitate) was pipetted into the DNeasy Mini spin column placed in a 2 ml collection tube (provided), centrifuged at 6000 x g for 1 min and the flow-through discarded. The DNeasy Mini spin column was placed in a new 2 ml collection tube, and washed with 500 μl Buffer AW1. The DNeasy Mini spin column was placed in a new 2 ml collection tube, washed with 500 μl Buffer AW2, and centrifuge for 3 min at 20,000 x g (14,000 rpm) to dry the DNeasy membrane; remaining ethanol was considered to interfere with sequencing reactions and processing. The DNeasy Mini spin column was eluted twice by adding 2 x 100 μl Buffer AE directly onto the DNeasy membrane, incubating at room temperature for 1 min, and then centrifuged for 1 min at 6000 x g (8000 rpm). DNA was shipped on dry ice to JCVI for sequencing. Quality control included certification of intact, high molecular weight DNA and required 15–30 μg for fragment libraries, complementarily documented by agarose gel image containing a DNA Mass Ladder, OD260/280 determination, and an estimated DNA concentration from a fluorometric assay (SYBR Green, Quant-IT PicoGreen dsDNA Assay Kits).

Genome sequencing, draft assembly and annotation

The genomes of 17 Leptospira species (the whole genome sequences of the remaining 3 species studied in the present analysis, L. interrogans serovar Copenhageni strain L1-130 and L. biflexa serovar Patoc Strain Patoc I, and L. licerasiae serovar Varillal strain VAR010, already were published [33, 37, 38]) were sequenced at JCVI by a combination of Illumina HiSeq (2x100 bp) and 454 FLX Titanium. Briefly, paired-end libraries were constructed for each sequencing technology from random nebulized genomic DNA in the 300–800 bp (Illumina) and 2–3 kb (454) size ranges. Sequence reads were generated with a target average read depth of ~ 20–30 fold (454) and ~60-fold (Illumina) coverage. Sequences for all 18 strains were assembled using the Celera Assembler version 6.1 [38], and ordered using NUCmer [39] to align the contigs to the best-matching closed Leptospira reference genome. All 18 new genome sequences underwent manual gap closure to elevate the genome status to improved high-quality draft (Table 1). Contigs were annotated for protein- and RNA-encoding features using the JCVI automated annotation pipeline essentially as described [4043] except HMMs were run using HMMER3 [44].

Phylogenetic analysis

16S rRNA trees were generated by first creating a multiple sequence alignment to the bacterial 16S rRNA reference alignment using Ribosomal Database Project release 10 (RDP-X) [45]. The aligned FASTA sequences were downloaded and trimmed to remove gapped columns using Belvu (v2.31) [46]. Based on the alignment, a bootstrapped Maximum-likelihood tree was subsequently inferred using phylipFasta, an in-house wrapper script [47] for the Phylip program [48, 49].

SecY trees were created by first aligning secY nucleotide sequences using Clustal Omega [50] with 100 combined guide-tree/HMM iterations. The multiple sequence alignment was trimmed to remove gapped columns and a bootstrapped Maximum-likelihood tree was inferred as was done for the 16S rRNA trees.

The nucleotide sequences of 7 MLST housekeeping genes were extracted from the 20 genomes, and sequence types (STs) assigned using the MLST website ( [51]. A multiple sequence alignment of the concatenated sequences of 7 MLST loci was performed using ClustalW implemented in MEGA version 5 [52]. A maximum likelihood tree was re-constructed using an algorithm implemented in PhyML version 3.0.1 [53]. The model of sequence evolution used was the generalized time-reversible (GTR) model with gamma-distributed rate variation. The CLC Main Work Bench version 7.0 was used to edit and display the tree (Qiagen, USA).

Universal protein marker trees were constructed using a set of 39 proteins that are universally conserved among bacteria and produce monophyletic phylogenies, suggesting that they undergo minimal horizontal transfer (S1 Table) [5456]. Protein sequences were aligned using ClustalW (v1.83) [57] using default settings. The alignment was trimmed to remove gapped columns using trimAl (v1.2r59) using–nogaps option and–fasta output option [58]. Aligned and trimmed predicted amino acid sequences of each species were concatenated as described previously [40], in the following order: AspS, FusA, GyrB, InfB, LepA, LeuS, PyrG, RplA, RplB, RplC, RplE, RplF, RplK, RplM, RplN, RplO, RplP, RplR, RplV RpoA, RpoB, RpsB, RpsC, RpsD, RpsE, RpsG, RpsH, RpsI, RpsK, RpsL, RpsM, RpsO, RpsQ, SecY, SerS, TopA, TsaD, Tuf, and YchF. The resulting alignment of 11241 amino acids was used to generate a Maximum-likelihood tree from 100 bootstrapped replicates using raxmlFasta, an in-house wrapper script for the raxmlHPC (v7.0.4) [59].

A pan-genome tree was constructed using the mean of the BLASTP Score Ratio (BSR) as described previously [60]. The PanOCT output file 100_pairwise_BSR_distance_matrix_phylip.txt was used as input for Neighbor [48, 49] to build an unrooted UPGMA Neighbor-Joining tree. This PanOCT output file is a Phylip-style distance matrix derived from the pairwise mean BSR of core proteins present in 100% of genomes where a single value is presented for each pair of genomes in the pan-genome.

In silico DNA-DNA hybridization

Genome relatedness among Leptospira strains was determined pairwise from fully or partially sequenced genomes using the genome-to-genome distances (GGD) calculator (S2 Table) [61]. This analysis was complemented by in silico DNA-DNA hybridization as previously reported [11].

Pan-genome analysis

Clusters of orthologous proteins were generated using version ver3_13 of PanOCT [62]. Since PanOCT does not place paralogs into its ortholog clusters, but does produce a paralogs.txt file that specifies which clusters are paralogs, an in-house PERL script,, was created to merge paralogous clusters. This approach was necessary because analysis of core and novel genes has historically been defined for clusters containing all paralogs [6368]. The R script, compute_pangenome.R, from Park et al. [67] and were used to construct the pan-genome, core and novel genes plot. We initially chose not to compute permutations in genome order for the reasons described in [69]. As a consequence of a lack of permutations, compute_pangenome.R was modified to load in a defined genome order of addition.

Orthologous protein content was compared and illustrated in a Venn diagram that was constructed using output from an in-house PERL script, that uses output from PanOCT and a file that describes how genomes are to be grouped. Genomes were grouped by whether they are infectious (group I or group II), non-infectious (saprophytic), or an outgroup (Leptonema illini). Since there were multiple genomes per group (except for Leptonema), clusters were counted if there was a majority (50%), all-but-one, or all protein members from a particular group or groups. Clusters not matching these criteria were not counted.

Metabolic reconstructions

Four draft metabolic network reconstructions were created for representative species chosen from pathogenic, intermediate and saprophytic clades including the following: L. interrogans (serovar Copenhageni strain L1-130), L. licerasiae (serovar Varillal strain VAR010), L. biflexa (serovar Patoc strain Patoc 1 (Ames)) and L. kmetyi (serovar Malaysia strain Bejo-Iso9). L. kmetyi was included in these comparisons because in addition to having recently been reported to infect humans in the Caribbean islands [70, 71], initial genome examination suggested that L. kmetyi could belong to a transitional group between the group I and group II pathogens and distinct from the other group I pathogens, here represented by L. interrogans. The reconstructions were built using the ModelSeed framework [72].

The COBRApy toolbox [73] was used to perform Flux Balance Analysis (FBA) [74] simulations and constraint-based analyses using the gurobi linear programming solver [75]. The constraint-based model consists of an S matrix composed of distinct metabolites and reactions including exchange and biomass reactions (S3 Table). Each of the reactions has an upper and lower bound on the flux it can carry. Reversible reactions have an upper bound of 1,000 mmol gDW−1h−1 and a lower bound of −1,000 mmol gDW−1 h−1, making them practically unconstrained, while irreversible reactions have a lower bound of zero. By default, the biomass reaction was set as the objective to be maximized. The exchange reactions that allow for extracellular metabolites to pass in and out of the system were defined such that a positive flux indicates flow out. The GapFind MILP algorithm [76] encoded in the COBRApy Toolbox was performed for the models unable to grow in minimal medium (biomass objective function equal to zero), to find exchange reactions allowing for in vitro growth, indicating strain-specific auxotrophies.

An in silico minimal medium was constructed that supported growth for all of the Leptospira models, consisting of trace elements (magnesium, manganese zinc, sulfate, calcium, copper, phosphate, cobalt, chlorine, potassium, ferrous and ferric iron and ammonia), water, oxygen, heme, CO2, the vitamins thiamine (B1), folate and menaquinone, glycerol and the fatty acids lauric acid, stearic acid, and decanoate and aminoethanol, meso-2,6, diaminopimelate, as well a variety of amino acids (glutamate, aspartate, tyrosine, phenylalanine, asparagine (S3 Table).

Genomic location and genetic organization of vitamin B12 biosynthesis -related gene clusters in Leptospira

PanOCT data were used to identify the cob I/III and cob II gene clusters in infectious and non-infectious Leptospira (when present), which encode proteins predicted to participate in B12 transport or synthesis. To determine the genomic locations of the btuB and cob II and I/III clusters, a custom Postgresql database was created using the annotated genomes of 20 species. Orthologs were identified with blastp (-F “m S” -s T) and conserved genomic neighborhood using the Prokaryotic Sequence homology Analysis Tool (PSAT) [77].


General genomic descriptions

The genomes of 17 Leptospira spp. isolates were newly determined for this study, representing 8 pathogenic, 4 intermediate and 5 saprophytic clades and used in pan-genomic comparative analyses along with the previously reported genomes of L. interrogans serovar Copenhageni strain Fiocruz L1-130 [30], L. biflexa serovar Patoc strain Patoc I (Paris) [33] and L. licerasiae serovar Varillal strain VAR010T [32]. Thirteen of these isolates were sequenced to a genome finishing status [78, 79] of "Improved High-Quality Draft" (IHQD) and 5 to a status a “High-Quality Draft" (HQD) (Table 1). To achieve a genome finishing status of IHQD, manual finishing was conducted consisting of contig sequence extension, sequence gap closure, and PCR to link physical ends. On average, the genomes assembled into 36 contigs [range 4 (L. kmetyi) to 89 (L. vanthielii)], with an average genome size of 4.26 Mbp in length [range 3.89 Mbp (L. borgpetersenii to 4.71 Mbp (L. noguchii)] at an average of 59.7-fold sequence coverage. The average G+C% was 40.7% [range 35.5% (L. noguchii) to 45.6% (L. wolfii)]. These genomes were predicted to encode an average of 4,197 protein-coding sequences per genome [range 3,932 (L. terpstrae) to 4,582 (L. alexanderi)].

Phylogenetic analysis of the Leptospira Genus to determine evolutionary relatedness

Twenty genome sequences (17 new, 3 previously published) of isolates representing 20 of the 22 known Leptospira spp. (Table 1) were used to determine phylogenetic distances between species; two recently reported species (L. idonii [80] and L. mayottensis [81]) are not included here. Phylogenetic relationships among all Leptospira species were analyzed in five independent ways (Fig 1): A, a core set of 39 concatenated genes coding for housekeeping proteins (universal markers); B, a pan-genus set of 1350 proteins; C, 3) multilocus sequence typing (MLST)[51]; D, 16S rRNA (highly conserved); and E, secY (highly variable)[70]. Leptonema illini was used as the outgroup for all analyses. Each approach yielded different nodes and branches of the species, except for 16S rRNA sequences, for which deduced phylogeny did not discriminate between L. meyeri and L. yanagawae. Additionally, these five approaches revealed three clades correctly clustering members of the nine pathogenic (group I) and five intermediately pathogenic (group II) and six non-pathogenic (saprophytic) species. Only the trees based on secY, the universal markers and the pan-genome clearly separated the closely related pathogenic species L. interrogans, L. kirschneri and L. noguchii from the other 6 pathogenic species. As expected, phylogenetic positions shifted to some extent between the single locus-based analyses and became more consistent using the multi-locus approaches. The following pairs of species showed close relationships: the pathogenic species (except in the secY tree) L. interrogans and L. kirschneri, L alexanderi and L. weilii; the group II species, L. inadai and L. broomii; and the non-pathogenic (except 16S and secY trees) L. wolbachii and L. vanthielii.

Fig 1. Phylogenetic analysis of Leptospira species.

Consensus maximum-likelihood trees are depicted using multiple alignments of 16S rRNA (A), secY (B), MLST (C) and 39 concatenated protein data sets (D). The numbers along the branches denote percent occurrence of nodes among 100 bootstrap replicates. A pan-genome tree was generated based on the mean of the BLASTP score ratio of core 1135 proteins (E). The scale bar represents the number of nucleotide (A-C), amino acid (D & E) substitutions.

Furthermore, the genome relatedness between pairs of representative strains of each of the 20 species from fully or partially sequenced genomes confirmed the genetic relatedness among Leptospira species as established by DDH (Fig 1/ S2 Table).

Pan-genome analysis

The pan-genome is defined as a core set of genes shared by all isolates plus a variable set of genes shared by a subset of isolates, and strain-specific or novel genes. Based on these 20 representative genomes and raw PanOCT output, the size of the core- and pan-genome was determined to contain 1,764 and 17,477 genes, respectively; however, with paralogs collapsed, the size of the core- and pan-genome was determined to be 1,592 and 13,822 genes, respectively (S1A Fig). The number of species-specific or novel genes ranged from 233 to 892 (S1A Fig) for each new genome added. After the addition of the third genome (L. noguchii), the size of the core gene set plateaued, while the pan-genome continued to rise (S1A Fig).

To determine whether the Leptospira pan-genome is open or closed (as defined below, the number of new genes identified (i.e., unique or strain-specific genes) for each genome added was determined and fit to a power law function (n = κN) as described previously [63]. Conceptually, a pan-genome is closed when sequencing the genomes of additional isolates fails to increase gene number (i.e., the entire gene repertoire has been discovered) [82]. The exponent (α) indicates whether the pan-genome is open (α ≤ 1) or closed (α > 1) [83]. Using this equation, the pan-genome of Leptospira was inferred to be open (α = 0.49 ± 0.02) (S1B Fig). From an exponential decay function, the number of new genes predicted for each genome (species) added was extrapolated and calculated to be 409 ± 12 on average (S1B Fig).

The distribution of protein clusters representing gene families among the three groups (pathogenic, intermediate, saprophytic) is depicted in a Venn diagram (Fig 2A). Because there were multiple genomes per group (except for the Leptonema outgroup), clusters were counted if there was a majority (50%), all-but-one, or all protein cluster members from a particular group or groups. Focusing on the majority criteria, pathogens and intermediates had nearly equal numbers of group-specific genes (416 and 424, respectively), and the highest number of shared genes between two groups (369). Binary comparisons of pathogens and intermediates with saprophytes revealed just 52, and 78 genes shared, respectively. When comparing only Leptospira-specific genes, the core genome was comprised of 737 genes, with the majority of genes being shared with Leptonema illini. Closer examination of species-specific genes showed that pathogenic Leptospira have more species-specific genes on average (637±129) than do intermediates (418±126) or saprophytes (321±90). L. noguchii sv. Panama str. CZ 214T had the greatest number of species-specific genes among species compared in this study. To understand the function of genes shared among infectious Leptospira, the distribution of protein functions was examined for clusters shared among infectious and non-infectious Leptospira (Fig 2B). The only functional category dominated by pathogenic Leptospira was “mobile and extrachromosomal elements.” The functional categories that stood out most among genes shared between pathogens and intermediates was "biosynthesis of cofactors, prosthetic groups, and carriers" and "fatty acid and phospholipid metabolism." Saprophyte-specific genes dominated 10 of the 16 functional role categories, many of which were involved in central intermediary and energy metabolism, gene regulation, signal transduction, protein fate, cell envelope, and transport functions.

Fig 2. Pan-genomic comparisons of 20 Leptospira species.

Panel A: Orthologous protein clusters were binned, counted and placed into a Venn diagram by whether clusters contained proteins from genomes in each of the three Leptospira groups: pathogenic (A), intermediate (B), saprophytic (C) and the Leptonema outgroup (D). Clusters were counted if there was a majority (50%), all-but-one, or all protein members from a particular group or groups (separated by colons). Singleton clusters, representing species-specific or strain-specific genes are noted in circles surrounding the Venn diagram. Clusters not matching any of these criteria or containing at least one protein from another group were considered as ambiguous groupings. The Venn diagram is not to scale. Panel B: Protein clusters unique to pathogenic, intermediate, and saprophytic groups or shared only between pathogenic and intermediate groups were counted by main functional role categories. See key for group colors.

Protein secretion systems

All Leptospira clades are predicted to have type II protein secretion systems but do not appear to contain a type III secretion system. Lipoproteins in Leptospira have particular pathogenetic significance because of their potential as vaccine targets and virulence factors involved in host-pathogen interactions [8493].

An unusual sec system in Leptospira

Leptospira contains the Sec system for signal-peptide-containing proteins and signal peptidase to remove the signal peptide at the time of secretion. Genes encoding the signal recognition particle (SRP) protein Ffh and receptor protein FtsY were not found in any Leptospira genome, nor was the SRP structural RNA. Generally, the lack of SRP and its receptor is unusual in bacteria, although the system is missing in the genus Dehalococcoides and also, apparently, in the uncultivated marine lineage SAR86 [94]. The narrow, elongated spiral shape limits the distance a ribosome can be from the Leptospira plasma membrane and may obviate the need for translation arrest by SRP. However, in looking for novel features near Sec system genes in Leptospira showed a novel gene inserted between the normally consecutive genes for Sec system proteins SecY and YajC, encoding a non-globular protein with an N-terminal signal peptide and a transmembrane segment towards the C-terminus, with the majority of residues in between consisting of low complexity, poorly conserved sequence especially rich in Lys, Glu, and Asn. No homologs to this low-complexity protein occur outside the Leptospira genus. We postulate that this novel gene could be involved in protein secretion.

Unusual sec-independent (twin-arginine) translocation system

Twin-arginine translocation (TAT) in prokaryotes allows completion of complete protein folding prior to Sec-independent secretion through the plasma membrane. Except in the halophiles, where high salt outside the cell explains the need for folding prior to export, TAT substrates tend to be redox cofactor-binding proteins [95]. These proteins fold and bind their cofactors before crossing the membrane. The tatA and tatC gene-encoded components of the translocase are evident in Leptospira genomes, but the twin-arginine signal itself proved elusive. The TIGRFAMs collection [96] hidden Markov model (HMM) TIGR01409 finds no sequence scoring near the trusted cutoff in any species of Leptospira, nor in Leptonema illini. However, alignments of full-length homologs in Leptospira to recognizable TAT translocation substrates from other lineages could be extended into the N-terminal signal region. Such alignments often showed a Lys-Arg dipeptide in the Leptospira sequence aligned to the Arg-Arg motif of recognizable TAT signal sequences. This observation triggered a review of all candidate families of TAT translocation substrates in Leptospira, and produced iterative refinement of the lineage-specific TAT signal, and a catalog of TAT substrates.

Eleven protein families were confirmed as TAT substrates by multiple criteria, including strong conservation of the putative TAT signal within the protein family, alignment to non-spirochete homologs that extended N-terminally into the TAT signal region, and strong sequence similarity of the putative TAT signal motif, usually RKxFL, across the different Leptospira putative TAT translocation substrate families. A continuous 18-residue stretch from each protein in each of these was used to construct a seed alignment of Leptospira TAT signal sequences, including the modified Twin-Arg motif and the adjacent hydrophobic region. Comparative analysis predicted a conserved TAT signal sequence in all Leptospira [97] (Fig 3). Eleven protein families comprising the defining alignment and for two additional families are strong candidates for TAT-dependent, Sec-independent translocation (S4 Table). Only one of the 13 families, the PhoX alkaline phosphatase family, was observed to be largely restricted to pathogenic species of Leptospira [98].

Fig 3. TAT signal sequence in Leptospira sp.

The X-axis shows position in an ungapped alignment. The Y-axis shows information content, measured in bits.

Inspecting the family of LIC_10874 (a 4Fe-4S dicluster domain protein family of LIC_10874) within and outside the genus Leptospira demonstrated conservation of the putative TAT signal in both, and the substitution in Leptospira of the second Arg by Lys, as in other families. This family is notable, however, because in multiple species from phylogenetically distant clades, translation start sites can be assigned with high confidence, and the TAT signal begins rather far (some 50 residues) from that start. Member sequences in this family all share a well-conserved prefix domain, ~50 amino acids in length, between the start of translation, and star of the recognizable TAT signal.

An unexpected feature in the Leptospira TAT system cassette is a probable serine phosphatase encoded next to tatC (family TIGR04400), which either overlaps it or is present within five base pairs in all 20 leptospiral species examined. It is not known whether this putative phosphatase is involved in Sec-independent translocation per se, rather than in its regulation, or in some unrelated process.

Lipoprotein secretion

Cleavage of pro-lipoproteins by the type II signal peptidase occurs within a short lipobox sequence, which includes the invariant cysteine that is targeted for covalent modification with lipids. In E. coli lipoproteins, the -1 position immediately preceding the peptidase cleavage site is highly conserved, being occupied by the small nonpolar amino acids Ala or Gly in the vast majority of cases [99]. A previous analysis noted somewhat larger residues, such as Asn, Ser, and Cys, at the -1 position of experimentally-verified spirochete lipoproteins [100]. Examination of L. interrogans lipoprotein orthologs in saprophytic and intermediate species revealed a number of unexpected amino acids at the -1 position (S5 Table). For example, the bulky amino acid Tyr was found in the -1 position in LipL21 in all intermediate Leptospira spp., whereas those from pathogenic and saprophytic species possess typical -1 residues Ala and Ser, respectively. Conversely, some lipoproteins of saprophytic or intermediate species with expected amino acids at the -1 position were found to have orthologs in L. interrogans with variant amino acids at the -1 position (S5 Table). For example, the outer membrane lipoprotein Loa22 of saprophytic species has the allowed residue Asn at -1 while its orthologs in pathogenic and intermediate species has Leu or Phe at the -1 position. Similarly, LIC11088 orthologs in most intermediate and saprophytic species possess permitted residues at -1, whereas the pathogens have Gln or charged residues. Thus, the availability of genome sequences from across the genus Leptospira has confirmed the much higher flexibility in the leptospiral lipobox and is anticipated to lead to redefinition of the pan-leptospiral lipobox to accommodate increased amino acid flexibility at the -1 position.

The substrate specificities of the first two enzymes in lipoprotein biogenesis, prolipoprotein diacylglyceryl transferase (Lgt) and signal peptidase II (Lsp), are likely to be influenced by amino acids at the -1 position relative to the lipoprotein cleavage site [101]. For this reason, these enzymes would be expected to possess novel structural properties that allow recognition of an expanded set of residues at the -1 position of the lipobox. Consistent with this notion, Lgt orthologs of all 20 Leptospira strains lack the signature sequence that defines most Lgt proteins (Prosite accession PS01311) [102]. Interestingly, Leptonema illini, the bacterium most closely related to Leptospira among sequenced organisms, harbors two Lgt paralogs: one quite close in sequence to leptospiral Lgt orthologs and another with signature sequence similar to Lgt of all other organisms. This arrangement suggests duplication of lgt in an ancestor common to Leptospira and Leptonema, with subsequent loss of the latter and functional divergence of the former to accommodate bulkier -1 lipobox residues. Similarly, Lsp of Leptospira species possesses an extra 22 or 24 residues at a position corresponding to a location within the second periplasmic loop of E. coli Lsp [103], which is missing from the Lsp sequence of other bacteria with well-characterized lipoproteins, including the spirochetes B. burgdorferi and T. pallidum. The sequence features of leptospiral Lgt and Lsp suggest the presence of novel structural features at the active sites of these enzymes consistent with variability at the -1 position of the leptospiral lipobox.

Metabolic reconstructions

In silico, genomically-based metabolic network reconstructions were created for four representative Leptospira species occupying different clades: L. interrogans and L. kmetyi (pathogen; group I), L. licerasiae (intermediate pathogen; group II) and L. biflexa (non-pathogenic). L. kmetyi was chosen for analysis because preliminary genomic inspection suggested unusual features of this species with regard to pathogenesis-related genes (vide infra). The base in silico media and default computational bounds (S3 Table) represent every compound allowed to enter the system for cellular uptake to allow all models to produce biomass. Removal of some of these compounds leads to species-specific growth (a unique model-predicted auxotrophy). The use of a steady-state assumption does not allow the flux balance analysis to take into account specific concentrations of a given metabolite but, rather, the predicted rate of uptake, secretion or transformation. The default uptake bounds for each metabolite are provided (S3 Table). Negative bounds represent entry of the metabolite into the extra-cellular compartment where they can then be consumed by the model. The bounds are only constraints on the maximum rate of consumption of a given compound. The actual rate of consumption is predicted by the model. Units are in mmol gDW-1 hr-1. Analogously to core and pan genomes, the reaction content of each model can be used to construct core and pan metabolic networks. The core network consists of those reactions occurring in all representatives of Leptospira, while the pan network consists of all reactions that can potentially occur in any individual Leptospira species (Fig 4A).

Fig 4. Core and pan-metabolic capabilities of the Leptospira genus.

The core and pan metabolic content was determined for genome-scale metabolic models of 4 different Leptospira species. A) Core content, illustrated by the intersection of the Venn diagram, shared with all species. The pan content consists of all content in any model and includes the core content. The Venn diagram is not to scale. B) Classification of reactions in the core and pan reactomes by metabolic subsystem.

Major differences in the metabolic networks between the four species arose in amino acid metabolism, biosynthesis of cofactors and vitamins and carbohydrate metabolism (Fig 4B). These large groups were further divided into specific pathways, specifically those of porphyrin metabolism, folate metabolism, starch and sucrose metabolism, as well as phenylalanine and tyrosine metabolism. A large difference was observed in porphyrin metabolism, specifically for the biosynthesis of cobalamin (vitamin B12) in L. interrogans (see below).

The conversion of static metabolic network reconstructions into computable mathematical models allows computation of phenotypes based on the content of each reconstruction [104]. Thus, the four strain-specific reconstructed networks were converted into genome-scale metabolic models that allow for the computational/simulation prediction of phenotype. This set of genomic scale models (GEMs) allows for a meaningful interpretation of the content of each reconstruction and allows for the prediction of each strain’s different metabolic capabilities [105]. Because reactions belonging to the amino acid metabolism subsystem made up the majority of reactions in the pan-reactome, it was hypothesized that these capabilities may reflect functional differences between different Leptospira species. To test this hypothesis, different minimal media formulations were created in silico and used to test each model’s growth capabilities. The models predicted all of the Leptospira tested to be auxotrophic for the amino acids aspartate, histidine and asparagine as well as vitamins B1 (thiamin) and vitamin K2 (menaquinone).

Beyond the auxotrophies predicted to be shared by all 4 Leptospira models, potential species-specific auxotrophies for other vitamins and amino acids were also identified. All of the strain-specific models were predicted to be auxotrophic for phenylalanine except for L. interrogans, which was predicted to have the enzyme prephenate dehydrogenase encoded for by novF that converts chorismate to prephanate, a precursor to tyrosine and phenylalanine. The model for L. interrogans lacks prephanate oxidoreductase, which would predict inability to convert prephanate to tyrosine. Only L. kmetyi was found to have the enzymatic machinery capable of synthesizing tyrosine from phenylalanine. Among these representative species of the three clades, only L. interrogans was predicted to be an L-glutamate auxotroph due to the lack of L-glutamate oxidoreductase.

Additional major differences were observed between the pathogenic and the non-pathogen Leptospira. A major difference in the lysine biosynthesis pathway was observed for the models of the pathogens, L. kmetyi and L. interrogans. Only these models possessed the dapABCDE genes required to convert L-aspartate 4-semialdehyde to LL-2,6, diaminopimelate required for peptidoglycan and lysine biosynthesis. Therefore both L. licerasiae and L. biflexa were predicted to be LL-2,6, diaminopimelate auxotrophs. Furthermore, only the pathogens L. interrogans and L. kmetyi possessed a full folate (vitamin B9) biosynthesis pathway using as precursor guanosine 5’-triphosphate. L. biflexa and L. licerasiae could produce vitamin B2 (riboflavin), but lack the reactions to convert it to folate including dihydroneopterin aldolase encoded for by folB; therefore, the models for L. biflexa and L. licerasiae were folate auxotrophs while the models for L. kmetyi and L. interrogans were not.

Vitamin B12 biosynthesis

The vitamin B12 biosynthesis genes in infectious Leptospira are grouped into two clusters: cob I/III and cob II (Table 2). Though the exact number of reactions for each pathway in Leptospira remains unknown, in Salmonella enterica Typhimurium cob I comprises genes for the biosynthesis of adenosylcobinamide, cob II genes for the synthesis of the lower axial ligand 5,6-dimethylbenzimidazole (DMB) and a third cluster cob III, the nucleotide loop that joins DMB to the corrin ring to complete B12 biosynthesis. In infectious Leptospira, cob II is a five-gene cluster that includes three genes cobTSC that participate in the synthesis of DMB (and two genes that may or may not participate in B12 biosynthesis), suggesting that the first cluster encodes enzymes for the synthesis of adenosylcobinamide guanine diphosphate. The first 12 genes encode enzymes that participate in the synthesis of the corrin ring (cob I) whereas the last five, enzymes for the addition of the nucleotide loop (cob III). Intriguingly, cob III of the infectious species includes a gene cbiZ encoding an enzyme that participates in an alternative cobinamide salvage pathway first described in the archeon, strain Göl [106].

Cob I/III gene clusters in the sequenced pathogenic Leptospira vary in length, from 16 in L. santarosai Shermani 1342KT to 19 in L. alexanderi Manhoa 3L60T and L. borgpetersenii Javanica UI 0993. Presumably owing to repeated in vitro sub-culture in medium containing B12, several genes have been inactivated or deleted in the strains tested. For example, the Javanica UI 0993 cobI/III cluster contains a gene fragment resulting from a premature stop codon in a gene encoding a histidine phosphatase superfamily branch 1 (hps_1) protein present in all other pathogenic Leptospira including other L. borgpetersenii strains (Hardjo L550 and JB197); and, cob I/III in L. alexanderi 3L 60T, contains a disrupted cobyrinic acid a,c-diamide synthase, inactivated by a frame shift mutation. A gene encoding a flavodoxin reductase present in cobI/III of other infectious has been deleted in L. santarosai 1342KT and L. alstoni 80–412. The cob I/III clusters of L. kmetyi (group I) and all group II pathogens contain three genes, two encoding a putative cobalt transporter (cbtBA) and a gene encoding an additional hps_1 protein (Table 2).

Cob II also varies in length among all infectious (pathogenic and intermediately pathogenic) Leptospira, from three genes in L. broomii Hurstbridge 5399 and L. inadai Lyme 10T to seven in L. noguchii Panama CZ214T. The genes comprising the cob II cluster in non-pathogenic Leptospira are found in two discrete clusters in non-pathogenic species (e.g., LEPBI_I2857 and LEPBI_I2858, LEPBI_I2938 –LEPBI_I2940) in L. biflexa (Table 2), suggesting that homologs in pathogenic Leptospira were acquired en bloc after the divergence of pathogenic and non-pathogenic Leptospira.

Leptospiral glycobiology: structure and diversity of rfb/O-antigen loci, lipid A, and sialic acid biosynthesis-encoding regions

General features of Leptospira rfb loci.

Lipopolysaccharide (LPS) has long been a major focus of leptospiral microbiology not because of its (low potency) endotoxigenic activities (see below in the lipid A section) and because, notably, leptospiral LPS is the basis for serovar identification and vaccine development [36, 107109]. Because of the importance of LPS in leptospiral biology, we carried out a comprehensive analysis of the genomic locations, structures and neighborhood of leptospiral rfb loci, also known as the O-antigen loci, in 20 species of Leptospira.

Using previously described leptospiral O-antigen gene clusters as a guide [110112], we identified and schematized all clades of Leptospira rfb loci (Fig 5; S2 Fig depicts the rfb locus in genomic context). Of the genomes representing 20 Leptospira species, 17 known serovars were compared. The O-antigen biosynthesis gene clusters were located in three different genomic locations and ranged in size from 3,768 bp (L. wolffii sv. Khorat) to 121,402 bp (L. alexanderi sv. Manhao3). This region in L. wolffii sv. Khorat is now the smallest predicted leptospiral rfb biosynthesis cluster, consisting of just 4 genes, replacing the locus of L. licerasiae sv. Varillal [32]. All pathogenic leptospiral species, and the intermediates L. inadai, L. broomii and L. fainei have their rfb loci located in the same genomic position, sandwiched between a copper-binding protein on the left and the ribosomal protein S6 on the right (S2 Fig). The same protein-encoding genes (viz., MarR and DASS) define the start and end of the O-antigen cluster, respectively. Of the rfb loci of pathogenic Leptospira, serovars Manhao 3, Javanica, and Pingchang were most similar in size and gene content (Fig 5). Notably, L. broomii [113] (and L. fainei [114116], both of which are serovar Hurstbridge, had nearly identical rfb gene clusters, predicting that L. broomii would also be serovar Hurstbridge, and confirmed by serology [113]. The presence of a specific serovar in different species has been previously observed in isolates of both L. interrogans and L. borgpetersenii serovar Hardjo, which have highly similar gene content in their O-antigen biosynthetic loci [117]. The rfb gene cluster of saprophytic Leptospira is downstream of the gene encoding ribosomal protein S6, lacks DASS, and is smaller (median 60,710 bp vs. ~99,520 bp) than pathogenic Leptospira rfb gene clusters (S2 Fig). O-antigen gene loci in serovars Varillal and Khorat are located in a third location, between murC and purK, consistent with a novel branching in the phylogenetic tree.

Fig 5. Structure of Leptospira rfb locus gene clusters.

The rfb region and beginning and ending CDSs (blue) 9 of pathogenic (A), 5 intermediate (B), and 6 saprophytic (C) representative Leptospira species were compared. rfb region CDSs are labeled by locus identifier and colored by functional role categories as noted in the boxed key. Gene symbols, when present, are noted above their respective genes. BLASTP matches between CDSs are colored by protein percent identity (see key).

Consistently, the downstream flanking genes in the rfb loci are far more conserved than the upstream genes (Fig 5; S2 Fig). This finding is especially true for the pathogenic serovars and between three of the five intermediate serovars represented. For pathogenic serovars and serovar Lyme, a conserved block of genes is involved in O polysaccharide processing via the Wzy-dependent pathway. This export system was also identified in two saprophytic serovars (e.g., Holland and Codice). Overall, 12 of the representative 20 species genomes encoded the Wzy-dependent system and one genome (L. licerasiae sv. Varillal only encoded a putative flippase (Wzx) with no identifiable Wzy ortholog. There were no orthologs of Wzz, the O-antigen chain length determinant, in the 20 genomes studied. Also conserved in the 3-prime region of only those serovars with the Wzy-dependent pathway is a gene encoding a protein with homology to E. coli WcaJ/S. enterica WbaP, which are members of the PHPT family of polyisoprenyl-phosphate hexose-1-phosphate transferases that function to transfer glycosyl-1-phosphate to a lipid undecaprenol carrier, initiating formation of the O-unit in O-antigen assembly. In L. borgpeterseni serovar Hardjobovis, this protein, encoded by orfH13, is an UND-pp-galactosyltransferase [118].

The other major pathway of O-antigen polysaccharide biosynthesis is the Wzm/Wzt–encoded or ABC-transporter dependent pathway [119, 120]. Six of the 20 representative genomes encoded orthologs of Wzm and Wzt. Two of the genomes were from intermediate and four were from saprophytic groups, representing 5 known serovars (e.g. Hurstbridge, Saopaulo, Hualin, Hardjo type Went, and Patoc). The genome analysis did not provide a clear indication of the export system used by serovar Khorat. One other known O-antigen biosynthesis pathway the synthase-dependent pathway [119]. BLASTP searches of WbbE and WbbF from the only known example of this pathway, from the plasmid-encoded O:54 antigen of S. enterica serovar Borreze [121], failed to identify any homologs in the representative 20 Leptospira genomes. It is possible that serovar Khorat uses a novel mechanism for O -antigen biosynthesis.

A dTDP-rhamnose biosynthesis gene cluster, encoding rfbABCD was found in the conserved 3-prime end of the predicted O-antigen biosynthetic gene clusters of only pathogenic Leptospira spp. serovar Saopaulo, found in the saprophytic species L. yanagawae, encoded homologs of all four of these genes, but in the order rfbCBAD, where the genes rfbABC appear to have been inverted (Fig 5C). The genes rfbAB and rfbC, were found in a different location with rfbC separated by several genes in serovars Hualin, Hardjo, and Patoc. These same isolates lacked an rfbD homolog. Only rfbAB homologs were identified in serovar Hurstbridge.

L. licerasiae-type surface polysaccharide cassettes.

We previously reported that L. licerasiae, which is antigenically unique, lacks the type of extremely large O-antigen biosynthesis region found in L. interrogans and nearly all other Leptospira [122]. Instead, the one serovar of L. licerasiae, Varillal, has a six-gene cluster with three glycosyltransferase genes between two normally adjacent, convergently transcribed genes: the murC gene involved in cell wall biosynthesis and purK gene of purine biosynthesis. Leptospira wolffii had a similar genomic rfb locus, again with a six-gene cluster positioned between murC and purK (Fig 5B); antigenic relatedness to L. licerasiae serovar Varillal remains to be confirmed experimentally. The first glycosyltransferase in this cassette, LEP1GSC185_2122 (GenBank EIE02925) in L. licerasiae and LEP1GSC061_3728 (GenBank EPG64090) in L. wolffii, are highly conserved and would be a useful marker for this extremely small O-antigen gene cluster. No other protein in the replacement six-gene cassette is conserved across the different variants. Genes in these regions have no close homologs in any other Leptospira, in the O-antigen region or anywhere else, supporting the notion that these cassettes provide unique carbohydrate chemistry and serology, and is not simply an unusual gene neighborhood for otherwise common leptospiral enzymes.

Lipid A biosynthesis.

The lipid A of leptospiral LPS is not as potent an endotoxin as lipid A moieties of other bacteria such as the Enterobacteriaceae or Neisseria spp.; the mechanistic explanation for this observation is that L. interrogans lipid A has different acyl chains and novel phosphorylation on the position of the lipid A that abrogate endotoxinogenicity [123]. The lipid A biosynthetic pathway of L. interrogans serovar Lai involves 13 enzymes, encoded by genes lpxA, lpxC, lpxD1, lpxD2, lpxB1, lpxB2, lpxK, kdtA, kdsB1, kdsB2, lnt, kdsA (also found as waaA) and htrB. The presence and homology of amino acid (aa) sequences of these enzymes was compared between 21 different species and/or serovars of Leptospira spp classified in three different groups: pathogenic (PT, 10 species), intermediate (IM, 5 species) and non-pathogenic or commensal (NP, 6 species). Most proteins were found in all Leptospira species (S6 Table). However lpxB2, was found only in 4 pathogenic species/serovar and 1 non-pathogenic, lpxD2 was not found in intermediate species/serovar and htrB was only present in 1 pathogenic and 1 non-pathogenic species/serovar. The kdsB1 and kdsB2 were only found in two species/serovar (L. interrogans sv. Lai and L. inadai sv. Lyme), all other species/serovar had only one kdsB that showed a higher level of similarity with kdsB2 from L. interrogans sv. Lai than with kdsB1. Although we found that some genomes lack one or two lipid A biosynthetic genes (e.g. lpxD2 and kdsB2), the computation analysis is still consistent with functional biosynthetic pathways still being present in all species, because, for the genomes lacking one of the duplicate genes, the remaining ones (e. g. lpxD1 and kdsB1) may be able to complement the function of the lipid A biosynthetic pathway. Another possibility is that the genes are present in the genomes, but we missed the genes because of gene diverge or gaps in genome sequence obtained. Finally, the variable presence of lipid A biosynthesis genes may relate to some as yet undiscovered structural differences in lipid A moieties among Leptospira.

The predicted protein sequences of individual lipid A biosynthesis pathway were nearly identical among Leptospira as predicted using an identity matrix (S7A Table). The homology between two sequences is expressed within the range of 0 to 1 (identical or completely homologous). The results presented hereby are expressed as the mean of homology values within each group, compared to the pathogenic species group. The lpxA amino acid sequence was found in all species, although the average similarity within pathogens was 0.928, while the homology of intermediates and saprophytes was 0.694 and 0.581, respectively, when compared to pathogen sequences. This analysis was carried out for each amino acid sequence (S7B Table).

Sialic acids as post-translational modifications restricted to pathogenic Leptospira.

Previous studies have demonstrated that pathogenic Leptospira endogenously synthesize Neu5Ac, the most common sialic acid, and that an observed gene fusion event suggested that L. interrogans uses a Neu5Ac biosynthetic pathway that is more similar to that of animals than to other bacteria. Lectin-based affinity purification of NulO-modified molecules, followed by mass spectrometric identification suggested post-translational modification of surface lipoproteins, including the putative virulence factor Loa22 [124, 125]. In the genomes analyzed for this study, 3 of the 9 pathogens had the complete cluster of genes involved in the production of sialic acids; 3 more lacked 1 gene in the cluster (Table 3). L. weilii contains only 2 genes from the cluster (spsE and rfbB3) and L. kirschneri and L. noguchii have only the spsE gene

All genomes, except L. licerasiae and L. wolffii have a N-acetylneuraminic (sialic) acid synthetase (spsE) gene (NP_711790.1). Phylogenetic analysis of this protein shows 2 distinct groups (S3 Fig). The first group contains the proteins from pathogens that contain the whole cluster. These proteins are related to the synthetases involved in the production of legionaminic acids. The second group contains the proteins from the intermediate species, the saprophytes and the pathogens L. kirschneri and L. noguchii. This group of synthetases is related to those producing pseudaminic acids.

The lack of a second sialic acid synthetase (NP_711794.1) in L. kirschneri and L. noguchii differentiates these pathogens from L. interrogans, which does contain this gene. These synthetases contain a phosphatase domain in addition to the NeuB domain, which suggests an animal-like Neu5Ac biosynthetic pathway. The pathogen L. weilli lacks NP_711794.1 but saprophyte L. vanthielli contains a similar synthetase but one that is missing the N-terminal transferase domain present in leptospiral pathogens. Finally, a UDP-N-acetylglucosamine diphosphorylase (NP_714003.1) was found in all leptospiral genomes studied. This gene is not located within the sialic acid gene cluster, and is also annotated as a MobA-like NTP transferase domain, therefore its role in sialic acid biosynthesis is unclear.

The sialic acid biosynthetic genes in leptospiral pathogens have some notable characteristics. L. alexanderi lacks O-acyltransferase (neuD) and this species and L. borgpetersenii have a truncated version of a nucleoside-diphosphate-sugar epimerase (NP_711787.1) (S3 Fig; Table 3). Only L. santarosai has a N-acetylneuraminic (sialic) acid synthetase with a phosphoglycerate dehydrogenase domain. Notably, none of the intermediate or saprophyte species contain the metabolic machinery to synthesize sialic acids, confirming previous suggestions [124].

Leptospiral mobile elements: phage and CRISPR-Cas systems


Bacteriophages are abundant biological entities that have significant effects on bacterial evolution. Some estimates suggest that there are approximately ten-fold more phages than bacteria [126, 127]. However, our current knowledge of phages infecting Leptospira spp. is limited. Three distinct L. biflexa phages were isolated from sewage water in Paris. Morphological analysis by electron microscopy revealed that these three phages belong to the Myoviridae family and seem to be morphologically similar with polyhedral heads and contractile tails [128]. One of these phages, the 74-kb LE1 prophage, was shown to replicate as a double-stranded circular replicon in L. biflexa [129]. The genome of LE1 has a GC content of 36%, similar to that of Leptospira spp., and most of the 79 predicted ORFs display no similarity to known ORFs, but 21 ORFs appeared to be organized in clusters that might encode head and tail structural proteins and immunity repressor proteins [130].

Next generation sequencing and refinement of computational methods have allowed comparative genome analysis to discover new prophage and genomic islands [131]. A few phage related genomic islands have thus been characterized in L. interrogans and L. licerasiae [122, 132, 133] and it was previously shown that one of these genomic islands can excise from the L. interrogans chromosome [133].

To determine the distribution of prophages within the Leptospira genus, Phage_Finder [60] was run under both strict (-S) and non-strict modes to identify predicted prophage regions. Phage_Finder predicted a total of 14 major prophage regions across the 20 genomes, most of which were found to be shared between the Leptospira species (Table 4). Among the prophage sequences, the LE1-like prophage is found in many genomes, suggesting that double-stranded DNA tailed phages, which are the most frequently observed phages in bacteria [134], are common phages infecting Leptospira. The presence of numerous phage-associated sequences in the genome of pathogens and intermediates, in comparison to the saprophytes, suggests that phages have played an important part in the evolution of these lineages, as has been experimentally shown in L. biflexa [135].

Further experimental studies of Leptospira phages, which would include both electron microscopic visualization and production of phage in vitro, will be important to determine whether recombinant Leptospira phage might be useful for genetic manipulation studies of different Leptospira species, particularly pathogens and intermediates.

CRISPR/Cas systems.

Three described types of CRISPR/Cas systems are common in Leptospira genomes and only found in infectious members of the genus (Table 5): the E. coli (type I-E), DVULG (type I-C), and MYXAN systems [96, 136, 137]. A single sequenced genome, L. inadai serovar Lyme str. 10, has the recently described PreFran type, which has been found in Prevotella and Francisella [96]. Four of the 20 representative strains have components of two CRISPR/Cas types, suggesting that for some isolates there is redundancy in CRISPR/Cas machinery. Surprisingly, half of the 20 representative Leptospira strains contained predicted CRISPR repeats, which were more common in pathogens and intermediates than in saprophytes, which lacked CRISPR systems. In none of the six saprophytes examined—L. biflexa, L. meyeri, L. wolbachii, L. vanthielii, L. yanagawae, and L. terpstrae—were CRISPR/Cas systems detected, suggesting that these species rely on some other mechanism for escaping phage/plasmid attack. In these saprophytes, we were also unable to detect sequences encoding prophage, while CRISPR systems and prophage occurred together in several, but not all, representative pathogenic and intermediate strains.

Table 5. CRISPR/Cas Systems Identified in Leptospira Species Representatives.

When present, between one and six CRISPR repeat arrays were detected, containing between three and 25 spacer sequences (Table 5). Since CRISPR spacer sequences in other organisms are known to target phage sequences for destruction, we wondered if any of the 239 predicted spacer sequences targeted any of the known Leptospira spp. phage or predicted prophages. A database containing the nucleotide sequences of the 19 predicted prophages from this study plus the LE1 phage and the prophage from Qin et al. [32, 132] was constructed and used to search all 239 predicted spacer sequences using BLAST+ 2.2.30 [138]. Upon filtering the data for matches spanning the entire spacer sequence, with 3 or fewer mismatches and with a bitscore of 30+ revealed three spacer sequences matching two predicted prophage sequences (Fig 6). Two different L. noguchii spacers matched the same predicted L. santarosai prophage (Table 5). One of the same L. noguchii spacers also matched a predicted L. weilii prophage and was also recognized by an L. weilii spacer (Fig 6).

Fig 6. CRISPR Spacer Sequences that Recognize Leptospira Predicted Prophages.

The CRISPR sequences are shown, which correspond to specific prophage accession numbers as listed in Table 4.

Virulence and Survival Mechanisms

Adhesion to Extracellular Matrix (ECM).

The presence of genes encoding putative adhesive proteins through the 20 sequenced species was analyzed by BLAST and comparative genome analysis (S8 Table). The widespread distribution of these genes within the Leptospira genus suggests that their functions arose independent of mammalian adaptation, but any potential role in adaptation to an environmental lifestyle remains speculative. These predicted adhesion-related proteins were generally distributed among the 20 species except for the predicted adhesin-encoding gene LenB, identified only in pathogenic L. interrogans serovars Lai and Copenhageni strains; in the saprophyte L. meyeri; and Lsa27, Lsa21, LipL53 present in two, three and five pathogen species, respectively. Three predicted adhesin-encoding genes were restricted to pathogenic species: Lsa30, Lsa44 and Mfn6. Lsa30 was present in all species, Lsa44 was absent in L. interrogans serovar Lai and L. weilli, and Mfn6 was absent in L. weilli and L. alexanderi. The genes encoding Lsa23, Lsa26, Lsa33, Lsa45, Lsa66, LipL32 and Mfn1 were found in all infectious species (pathogens and intermediates) but absent in saprophytes. The Len protein family members were variably distributed. Lsa24/LenA was identified in all sequenced strains, while LenB, LenC, LenD, LenE and LenF were found in both pathogens and saprophytes; LenD was also found in the intermediate L. wolffii. Lsa23, Lsa26, Lsa33, Lsa45, Lsa66, LipL32 and Mfn1 were found in all pathogenic and intermediate but are absent in saprophyte strains. The Len protein family showed a random distribution among the genome species: Lsa24/LenA was identified in all sequenced strains, while LenB, LenC, LenD, LenE and LenF were found in both pathogenic and saprophytic Leptospira, and LenD was also found in L. wolffii, an intermediate. The adhesins Lsa20, Lsa25, Lsa36, Lsa63, TlyC, OmpL1, OmpL37, OmpL47, Mfn7 and rLIC12976 were identified in all pathogenic, intermediate and saprophyte species; Mfn9 was found in all except in L. santarosai. Also listed are plasminogen- and complement regulator-binding proteins (S8 Table) that have functions related to the predicted proteins listed above.

Complement evasion and ECM degradation via metalloproteases.

Two leptospiral proteases have been suggested as virulence factors: thermolysin and collagenase. Thermolysins are members of the M4 metalloprotease family that can be identified bioinformatically by the presence of two N-terminal propeptide (FTP, and PepSY) and two C-terminal protease domains (Peptidase_M4 and Peptidase_M4_C) [139]. Using Pfam HMMs targeting these four domains (e.g., PF07504, PF03413, PF012868 and PF01447), we identified LIC13322 and four additional predicted thermolysin orthologs only in pathogenic Leptospira spp.: LIC10715, LIC13320, LIC13321, and LEP1GSC059_0182 (S9 Table), primarily among L. interrogans, L. kirschneri and L. noguchii. LEP1GSC059_0182 was found only in one species, L. noguchii. No thermolysin ortholog was found in intermediate or saprophytic species.

Collagenase has been suggested to be a virulence factor in Leptospira based on observed in vivo expression, detection of specific anti-collagenase antibodies induced by infection, and the effects of ColA mutagenesis and complementation on traversal of cell monolayers and outcome of experimental animal infection [140, 141]. Comparative genomic analysis identified two collagenase genes, restricted to pathogenic Leptospira spp.: orthologs of one (LIC_12760) were found in all pathogens except L. kmetyi; an additional paralog, EMN46521, was restricted to L. weilii and L. alexanderi, and based on nearly identical size and closely related amino acid sequences, likely arose by gene duplication (S9 Table). The implications of this latter finding for pathogenesis are unclear.

Resistance to oxidative stress.

Three enzyme systems have conventionally been associated with the ability of pathogenic bacteria to defend against host-derived oxidative stress-related mediators such as hydrogen peroxide and superoxide radicals: catalases, peroxidases and superoxide dismutase. While catalases generate water and oxygen from H202, peroxidases generate water and an oxygen radical.

KatA and another but uncharacterized predicted catalase ortholog (LEP1GSC062_4039) were only found in pathogenic Leptospira, suggesting that these enzymes play an important role for Leptospira living within the mammalian host (Table 6). Conversely, superoxide dismutase was not found in pathogenic Leptospira but only in saprophytic Leptospira, suggesting either that pathogenic Leptospira are not exposed to oxygen radicals in the environment, or, more likely, that this clade of Leptospira has developed alternative ways to detoxify oxygen radicals.

Immunodominant proteins of Leptospira.

Previously published protein microarray analysis demonstrated the presence of immunodominant proteins of L. interrogans serovar Copenhageni using sera from confirmed leptospirosis cases in Bahia state, Brazil. The top 24 immunogenic hits from this analysis were analyzed throughout the genus Leptospira, focusing on the presence of orthologs and their amino acid similarities (S10A Table). Only 1 of these genes (a methyltransferase, NC_005823.1) was restricted to L. interrogans, and only 1 (Lig A) was found in only L. interrogans and kirschneri, with the caveat that a 56% homologous LigA domain was found in L. alstoni, a leptospire with unclear disease potential. Orthologs of 20 of these 24 hits were detected in all Leptospira species but with variable amino acid similarities, suggesting that species- (and perhaps serovar-) specific protein microarrays might be necessary for accurate assessment of immune responses induced by different Leptospira in humans. Further, to validate such arrays with well-defined sera from leptospirosis cases, identification of the infecting leptospire will be necessary.

The extensively-studied leptospiral immunoglobulin-like (Lig) protein family is comprised of three proteins, LigA, LigB and LigC [142, 143], which have bacterial immunoglobulin-like (Big) repeat domains, a motif found in virulence factors of other bacterial pathogens [144, 145]. The three genes encoding Lig proteins were believed to be pathogen-specific [142, 146148]. Comparative analysis of 20 genomes confirmed that ligA and ligB were present exclusively in pathogenic Leptospira: ligB was identified in all pathogenic species, ligA was found in three of the nine pathogenic species, L. alstoni, L. kirschneri and L. interrogans (S10B Table). While ligC was found in the five intermediate species and five of nine pathogenic species (S10B Table), none of the lig genes were identified in genomes of saprophytes. ligC was previously identified as a pseudogene from sequence analysis of a limited number of strains [142, 147, 148].

The unique structure of LigA, LigB and LigC proteins, which includes a large number of tandem Big domains, is conserved across species for which lig genes were found. ligB and ligC encode molecules which are comprised of a lipobox sequence, 12 tandem Big2 type domains and a C-terminal non-Big domain (S10B Table), whereas ligA encodes a protein with 13 tandem Big domains which lacks a C-terminal non-Big domain. Of note, further prospection of the genome sequence and ortholog families identified four additional genes that encode Big2 and Big3_4 domain-type containing proteins (S10B Table). These genes are different from the conventional lig gene family in that they encode proteins with a small (1–2) number of Big domains. Interestingly, a gene (LIC13050) encoding a protein with two Big3_4 domains was found in all Leptospira species, including the saprophytes.

PF07598 paralogous gene family.

Previous work identified a group I-specific family of proteins corresponding to Pfam model PF07598 [149] that was expressed in vivo in a hamster model of acute leptospirosis [149], and expanded in strains, e.g., Copenhageni and Lai, that commonly cause severe disease, suggesting that these proteins contribute to Leptospira virulence. These prior studies focused on finding PF07598 orthologs to the L. interogans Lai attenuated strain in the 20 representative Leptospira spp., but did not look for strain-specific homologs that match PF07598 HMM. To identify novel PF07598 family members, we identified clusters of protein orthologs from our pan-genome run that matched PF07598 above trusted cut-offs; this analysis also included matches within the genomes of previously sequenced strains L. borgpetersenii Hardjo L550 and JB197 (and previously annotated as conserved hypothetical proteins). At least 26 distinct orthologs ranging in length from 47 (LEP1GSC049_1303 unique to L. kirschneri) amino acids to 651 (LEP1GSC193_2756; L. alstoni 80–412) were identified (Table 7; only homologs longer than 200 amino acids shown). As previously reported, L. santarosai 1342KT contains two distinct homologs, while L. kirschneri 3522 CT at least 15 and L. noguchii CZ214 at least 14 including 5 (LEP1GSC059_0232, LEP1GSC059_3018, LEP1GSC059_3019, LEP1GSC059_3599 and LEP1GSC059_3600) without an apparent ortholog in any of the other strains tested (Table 7). L. borgpetersenii Javanica (4 total) and the previously sequenced genomes of two Hardjo strains (3) contain two orthologs in common, while Javanica has two distinct copies (LEP1GSC103_4030 and LEP1GSC103_0672) not present in Hardjo and both Hardjo strains share an ortholog (LBJ_1339 and LBL_1564) not present in Javanica. In addition, L. interrogans L1-130 contains an ortholog (LIC_10639) shared with L. noguchii CZ214T and L. kirschneri 3522 CT not present in L. interrogans 56601 consistent with the hypothesis that serovar Lai has lost this ortholog.

Table 7. Distribution of the PF07598 paralogous gene family in Leptospira.

To better understand the evolution of this paralogous gene family, a phylogenetic tree of all PF07598 members detected in infectious Leptospira was constructed using homologs longer than 200 amino acids shown (Fig 7A). A complex web of lineage specific gene duplications and loss was revealed. For example, as highlighted in (Fig 7A), successive gene duplications and subsequent gene loss have led to four distinct clusters containing proteins in L. interrogans, L. kirschneri and L. noguchii PF07598 family members. The initial duplication event led to divergence of the first orthologous group comprising LEP1GSC059_0224 (L. noguchii) and LIC_12985 and LA0591 (L. interrogans) and apparent loss of the corresponding ortholog in L. kirschneri (*). The second, an L. interrogans-specific event (**), led to the divergence of two orthologous groups comprised of LA_0589, LIC_12986 and LEP1GSC049_3370, and LA_3388, LIC_10778 and LEP1GSC049_0186, respectively. L. interrogans Lai 56601 has seemingly lost an ortholog belonging to a group containing LIC10639 and LEP1GSC049_1381 comprised of proteins belonging to L. interrogans Copenhageni and L. kirschneri, respectively; and L. kirschneri 3522CT, L. alexanderi and both L. borgpetersenii Hardjo strains seem to have lost an ortholog present in the other pathogenic strains (***). This pattern of species and serovar specific gene duplication and gene deletion occurs throughout the tree. K-means clustering with Kendall rank correlation grouped the L. interrogans Lai orthologs into three clusters comprising family members with >90% inclusion probability: LA_1400 and LA_1402 (cluster A); LA_0589, LA_0591, LA_0835 and LA_3388 (cluster B); and LA_0620, LA_0769, LA_0934, LA_2628 LA_3271 and LA_3490 (cluster C) (Fig 7B).

Fig 7. Phylogenetic Relationship of PF07598 Paralogous Family in Leptospira.

(A) Unrooted bootstrapped phylogenetic tree; (*) Gene duplication event; (**) gene duplication event; (***) gene deletion. (B) Principal components analysis was used to arrange PF07598 family members. Color legend indicates the PF07598 family members from specific serovars depicted as diamonds. Arrowheads indicate L. noguchii-specific orthologs. Only PF07598 family members longer than 200 amino acids are included in the analysis. Clusters (A, B and C) were defined by K-means clustering with Kendall rank correlation.

Motility and chemotaxis.

Since motility is required for pathogenesis [150, 151], it is plausible that there are differences in motility and chemotaxis gene content that distinguish infectious from non-infectious species. We identified a total of 76 CDSs encoding proteins involved in leptospiral motility and chemotaxis, using the annotated genome of the Leptospira interrogans serovar Copenhageni strain Fiocruz L1-130 as a reference. We established the amino acid sequence identity of CDSs in 20 Leptospira genomes based on their respective orthologs in the strain Fiocruz L1-130 genome (S4 Fig; S11 Table). Among these, 37 CDSs were predicted to encode proteins in the basal body assembly and export apparatus; 7 CDSs were predicted to encode proteins in the flagellar hook assembly; 7 CDSs were predicted to encode proteins involved in the filament assembly; and 25 CDSs were predicted to encode proteins in chemotaxis (S4 Fig; S11 Table).

Proteins involved in motility were highly conserved among all the 20 Leptospira species according to BLAST analysis and PanOCT ortholog clusters (S4 Fig). The filament is the portion of the flagella which demonstrated the highest amino acid sequence identity, with a mean of 97.9%, 86.4 and 72.4% amino acid sequence identity among pathogenic, intermediate and saprophytic species, respectively (S11 Table). The ORFs that encode the flagellar hook proteins also demonstrate high amino acid sequence conservation, with an average sequence identity of 86.0% and 61.7% in pathogenic and saprophytic species, respectively (S11 Table). FliK, a bi-functional protein involved in determining hook length and modulating export-pathway specificity at the hook–filament checkpoint [152, 153], was the only protein that showed a low level of identity among the three species groups, including within the pathogenic species (69.7%, S4 Fig and S11 Table).

Although CDSs encoding basal body proteins showed the lower amino-acid identity among motility genes, the average identity was high, ranging from 93, 73, and 60% within pathogenic, intermediate and saprophytic species (S4 Fig and S11 Table). In this category, three proteins showed 50% identity or lower when comparing pathogenic species with intermediates and saprophytes. The protein FlgA is involved in the P-ring formation, whereas the FliO and FliJ are involved in the export apparatus. In addition, five CDSs showed an amino acid sequence identity below 50% between pathogenic and saprophytic species, which were CDSs encoding proteins FlgH and FlgL involved in the L- and P-ring formation, respectively, proteins FlgN and FlhX involved in the export apparatus, and the FliG1 protein, which is involved in the motor switch. The P- and L-ring form the outer cylinder and acts as a bushing for the central rod [153, 154] and is believed to participate only passively in the motor mechanism, while the FliG1 protein is believed to be partly responsible for the asymmetrical rotation of the flagella [155].

CDSs encoding chemotaxis proteins are highly conserved among pathogenic species (87% amino acid sequence identity). In contrast, ORFs encoding such proteins are less conserved when comparing pathogenic species with intermediates and saprophytes species groups and have lower amino acid sequence identity (48 and 43%, respectively, S4 Fig and S11 Table). More than 70% of the orthologs of chemotaxis proteins within intermediate and saprophyte species had less than 50% amino acid sequence identity when compared to pathogenic species. Among these proteins, the majority were methyl-accepting protein (MCP) homologs, but include also chemotaxis regulators like cheA, cheR, cheB and cheY (S4 Fig and S11 Table). Whereas orthologs associated with construction of the flagellar filament are conserved across pathogenic, intermediate and saprophytic species, ORFs encoding two chemotaxis proteins, one MCP homolog and cheR1, in pathogenic species had no orthologs in intermediate and saprophytic species. Furthermore, one MCP homolog protein in pathogenic and intermediate species had no orthologs in saprophyte species, suggesting a degree of divergence with respect to chemotaxis between pathogenic, intermediate and saprophyte Leptospira species.

Gene regulation and sensory transduction

Alternative sigma (σ) factors.

σ factors are a class of proteins constituting essential dissociable subunits of prokaryotic RNA polymerase. σ factors provide promoter recognition specificity to the polymerase and contribute to DNA strand separation. All bacterial species have a housekeeping σ-factor (σ70) responsible for transcription from the majority of promoters. Most bacteria encode additional alternative σ-factors that redirect RNAP to distinct sets of promoters, which can contribute both directly and indirectly to environmental adaptation and bacterial virulence. In addition to a housekeeping sigma factor σ70 (LIC11701, RpoD), all Leptospira species have an alternative sigma factors σ54 (LIC11545, RpoN) involved in nitrogen and many cellular and environmental regulations, σF involved in flagella gene expression (LIC11380 (FliA,), 5–11 extracytoplasmic function (ECF) σ factors (σE) involved in regulation of membrane and periplasmic stress, and more than 30 anti-σ regulators (S12 Table).

Leptospiral species differ in σ-factors. First, pathogenic Leptospira have two activators (enhancer-binding protein, EBP) for σ54, whereas saprophytic Leptospira species has only one. σ54 is a unique sigma factor that is phylogenetically different from other σ actors. It recognizes a unique −24/−12 promoter sequence (instead of -35/-10 sequence for σ70) and its activation always requires an activator, EBP. Signals feed into EBP and activate σ54–dependent genes. Each EBP-σ54 pairs responds to different signals and activates a set of genes. Our analyses show that while all pathogenic Leptospira appear to have two activators (herein named as Leptospira enhancer-binding protein A and B that can be denominated EBP-A and EBP-B, saprophytic Leptospira have only one EBP (EBP-A) (S12 Table). Although the upstream signals and downstream targets remain to be elucidated, we speculate that LepA-σ54 modulates a group of genes involved in environmental survival for both pathogenic and saprophytic Leptospira, whereas LepB-σ54 is important for pathogenic Leptospira species to adapt to host environment. Second, pathogenic and saprophytic leptospiral species differ in ECF σ factors. Pathogenic and intermediately pathogenic Leptospira have 9 to 10 ECFs, and saprophytic species often have 5 ECFs. One ECF (LIC10599) is only found in highly pathogenic Leptospira, while 2 ECFs are only associated with saprophytic Leptospira (S5 Fig; S12 Table). Lastly, Leptospira have more than 30 regulators predicted as anti-σfactors, anti-anti-σfactors, and regulators of anti-anti-σfactors. Although their functions remain unclear, some of these regulators may modulate ECF functions as observed in B. subtilis. Nevertheless, there are some obvious differences in their distributions among Leptospira species (S12 Table). It is conceivable that the ECFs and regulators of σ factors present only in saprophytic Leptospira are involved in responding to environmental stress, whereas the ECFs and regulators of σ factors present only in pathogenic Leptospira are likely important for Leptospira’s life cycle in mammalian hosts.

Two Component Systems (TCS).

TCSs are the predominant molecular switches controlling signaling events in bacteria. Typically, TCSs consist of a sensor histidine kinase (HK) and an effector response regulator (RR). A single polypeptide merging both components results in hybrid histidine kinases (HHKs). HKs and RRs are usually found adjacent to each other in the genome. Orphan TCS proteins are unpaired HKs/RRs, which work with their cognate partners that are far apart in the genome. In addition, multistep phosphorelays may include intermediate histidine phosphotransferase proteins (Hpt), adding further complexity to TCS networks. Pathogenic, intermediate and saprophytic Leptospira species encode an unusually large and diverse set of TCSs, including orphan HKs and RRs, HHKs and Hpts in addition to classical paired HK/RR systems (Table 8 and S13 Table). Of note, more than 60% of the TCS genes found in Leptospira genomes encode non-classical orphan HK, orphan RR, HHK and Hpt proteins (Table 8). Overall, pathogenic species had the lowest average number of TCS genes (76), while saprophytic ones had the largest (102) (Table 8). Genome size-normalized TCS data revealed that pathogenic Leptospira species have roughly 35% less TCS genes in comparison to intermediate and saprophytic species (S6 Fig and Table 8). Additionally, pathogenic species had a proportionally lower number of strain-specific TCS genes compared to intermediate and saprophytic species (S7 Fig and S14 Table). We also identified a core set of 16 TCS genes shared among all the Leptospira genomes being analyzed (S7 Fig). Half of these TCS genes, conserved among all Leptospira species, were orphan HK/RRs (S15 Table). Taking into account their high conservation throughout the species, irrespective of saprophytic or pathogenic mode of lives, this core set of TCSs probably regulates pivotal cellular pathways in Leptospira.

Table 8. Summary of two component systems identified in 20 Leptospira genomes.

Although there were 15 TCS genes conserved among pathogen and intermediate species, we did not identify genes that were shared between saprophyte:pathogen or saprophyte: intermediate species (S7 Fig). This finding is in agreement with the previous observation that intermediate Leptospira spp. are more closely related to pathogens rather than to saprophytes, and that the gene order is more conserved in pathogenic and intermediate species [32]. Among the 15 TCS genes found in both pathogenic and intermediate species, 5 were orphan RRs, 4 were orphan HKs, 2 were HHKs, and only 1 was a classical HK:RR pair (S13 Table).

It is worth noting that L. kmetyi and L. alstoni were outliers with respect to being pathogenic species that harbored the largest number of species-specific TCS genes (27 and 17, respectively) and the largest overall number of TCSs within this species group (S7 Fig and S14 Table). In contrast, L. alexanderi did not contain any species-specific TCS genes (S7 Fig and S14 Table). The seven TCS genes present in all pathogenic species (S16 Table) may represent moieties involved in common signaling pathways and may play a role in host colonization and pathogenetic mechanisms.


Here we used comparative whole genome analysis to answer the overall question, “what makes a bacterial genus pathogenic?” This analysis delineated the definitive phylogenetic relationship among 20 species of Leptospira, and demonstrated that infectious species and clades of Leptospira contain unique genes that are not found in non-infectious Leptospira (summarized in Table 9).

Table 9. Summary of Some Key Genomic Differences in Leptospira Species that Suggest Role in Evolution from Saprophyte to Infectious Pathogen.

In a general sense, the comprehensiveness of this analysis is fundamentally important for understanding large-scale evolutionary mechanisms by which saprophytic bacteria acquire genes to enable infectiousness and pathogenicity. More specifically, considering how complex the genus Leptospira is—among the most complex genera of pathogenic bacteria—our analyses indicate that many genetic events over evolutionary time have given rise to pathogenic Leptospira of diverse biological properties. The genus Leptospira contains non-infectious environmental saprophytes and those members infectious to mammals. Infectious Leptospira are subdivided further into phylogenetically separated groups: pathogens (group I) and intermediate pathogens (group II). Previous systems (pathogenomic) approaches began to yield insights into the large scale set of genes that enable infectious Leptospira to adhere, invade, colonize, persist, evade the immune system and cause disease in mammalian reservoirs hosts as well as accidental hosts [32, 149, 156]. By identifying novel gene families, differences in pathogen clade-related gene content, and key potential metabolic differences among infectious Leptospira species as well as Leptospira species that have contrasting potential for causing severe disease, for example, the present work contributes towards the next generation of leptospirosis pathogenesis experimental studies. The data and analyses resulting from this Leptospira Genome Project will contribute to new research directions in diagnostics, vaccine and therapeutics development to prevent and ameliorate leptospirosis, with One Health relevance for the health of humans and animals of veterinary importance alike.

This comparative analysis of the genus Leptospira assessed phylogenetic relationships among species in several independent ways, including single locus, multilocus [51] and whole genome approaches. All approaches robustly confirmed the separation of the 20 Leptospira species into three clades: pathogens, intermediate pathogens and saprophytes; infectious Leptospira include members of the pathogen and intermediate pathogen clades. Whole genome analysis produced consistent dendrograms, similar but not identical to those obtained by multi-locus sequence typing (MLST) [51]; MLST has been most useful for characterizing Leptospira isolates [22, 51, 70, 157, 158] but also has been used to identify Leptospira strains directly in clinical samples. Speciation based on Bayesian analysis of 16s rDNA (rrs) gene sequences has become generally accepted [35] for Leptospira, especially differentiation of pathogen from non-pathogen (potential contaminant). However, the Leptospira 16S rRNA gene is highly conserved so that species cannot be further subdivided. For example, 16S rDNA sequence-deduced phylogeny could not distinguish L. meyeri from L. yanagawae. However, the data provided here and elsewhere demonstrate that the gold standard for future definitive taxonomical definition of any Leptospira isolate will be based on whole genome sequence-based in silico DNA-DNA hybridization [11]. Genome-to-genome distances (GGD) analysis confirmed in silico DNA-DNA hybridization results. As previously determined by the classical in vitro-performed DNA-DNA hybridization studies, each of the representative strains represents a distinct species (estimated hybridization between pair of strains <70%) (S2 Table). For example, L. interrogans serovar Icterohaemorrhagiae strain M20 is phylogenetically related to L. kirschneri strain 3522C (estimated hybridization 42.30% ± 2.53) and L. noguchii strain CZ214 (estimated hybridization 37.80% ± 2.49), while other pathogenic, intermediate and saprophytic species are distantly related to L. interrogans (S2 Table). One serovar may belong to more than one species (Fig 5) so that serovar does not have precise taxonomic implications, although the present analysis is limited by not exploring within-species O-antigen loci in the genomic data from the 320 isolates for which genomic sequence information was generated. Finally, serovars belonging to the same species had GGD values higher than 70% DDH similarity. In silico DDH values therefore accurately reflect whole genome relatedness and may be used for the purpose of species delineation [159, 160], thus replacing the classical DNA-DNA hybridization technique which, we argue, is now obsolete. The use of genome sequences also provides reusable data and reproducible results. The GC content and a set of core genes (including ribosomal genes) can also be extracted from genome sequences to verify that the data are phylogenetically consistent.

Lipopolysaccharide rfb biosynthetic loci in 20 Leptospira species

A remarkable feature of Leptospira—one that has often dominated the study of Leptospira over the past century—is the serologically-determined variety of Leptospira serovars. Serovar identification based on serological agglutination tests previously was the basis of Leptospira taxonomy, and continues to be important for understanding leptospirosis epidemiology related to human and animal infection and mammalian reservoir hosts. The present comparative genome analysis of 20 Leptospira species, as well as hundreds of additional Leptospira whole genome sequences not yet completely analyzed, demonstrates genetic determinants of the antigenic carbohydrates (putatively O-antigens). Therefore molecular analysis of genes in the rfb O-antigen locus rather than serological tests will be the future basis of serovar identification.

Leptospiral LPS has long been known to be non-endotoxigenic, the basis for which is thought to be due to unusual modifications of the core Lipid A component of LPS resulting in altered Toll-like receptor-mediated innate immune responses [161164]. Leptospiral serovar, determined by LPS antigenicity, seems to have some association with mammalian host predilection—for example, serovar Copenhageni with Rattus spp., serovar Canicola with dogs [1, 2, 165]—but a causal, mechanistic role here remains to be experimentally demonstrated. Indirect evidence from other microbial systems suggests the speculative hypothesis that the diversity of leptospiral LPS may be driven by environmental ecology-mediated selection pressures [166, 167], such as has been reported in Salmonella spp. which has many (>2000) serotypes). The genus Leptospira has been reported to contain more than 300 serovars [1, 2, 165], the basis for which remains essentially unexplored.

Metabolic reconstructions

Here we report the first genomically-predicted metabolic network analysis [168, 169] of Leptospira, comparing members of the pathogen, intermediate pathogen and saprophyte clades. These large-scale reconstructions allow classification of the conserved metabolic capabilities (core metabolic network) and the unique metabolic capabilities (pan metabolic network). These reconstructions can be further converted to metabolic models of metabolism to probe metabolic capabilities computationally.

The most striking differences between infectious/pathogenic Leptospira and non-pathogens arose in porphyrin and vitamin biosynthetic capabilities. L. interrogans was shown to have a nearly complete vitamin B12 biosynthetic pathway that enables de novo B12 synthesis from an L-glutamate precursor, while L. biflexa completely lacked this pathway. Only pathogenic LeptospiraL. interrogans and L. kmetyi—were predicted to have a full folate biosynthetic pathway. These differences in biosynthetic capabilities may allow such pathogens to survive in nutrient-limited niches within the mammalian host. These observations are consistent with previous observations that found that L. interrogans serovar Canicola can grow in vitro in the absence of B12 but not B1 [170], but in contrast with observations of others who concluded that pathogenic Leptospira could grow in the absence of B1 but not B12 [171].

An open question in Leptospira biology is why L. interrogans grows more slowly than do intermediate pathogens and saprophytes, such as L. licerasiae and L. biflexa, which grow rapidly in defined EMJH medium [38]. The metabolic network model of L. interrogans was shown to lack L-glutamate oxidoreductase, an enzyme involved in recruiting ammonia as a nitrogen source [172, 173]), predicting a lower growth yield compared to the other Leptospira models in our in-silico minimal media analysis. The model of L. biflexa predicted the greatest yield with this reaction because this Leptospira contains the L-aspartate ammonia-lyase reaction allowing it to convert L-aspartate into fumarate and ammonia, in addition to using this component solely for biomass generation. These observations hint at one possible solution to the question of different growth rates, but model-guided experimentation is required to validate this prediction. Predictions made using these metabolic networks depend on an accurate reactome [174], and must be validated experimentally. If the models reported here are further curated and experimentally validated they would be the first such mode for a pathogenic spirochete. Such an approach will yield fundamental insights into diverse metabolic capabilities of this diverse phylum, including Borrelia spp., which cause Lyme borreliosis and relapsing fever, and Treponema spp., which cause syphilis, yaws, periodontitis and other diseases.

Vitamin autotrophy

Vitamin B12 (cobalamin) is the largest and most complex of natural organometallic cofactors and coenzymes, its de novo synthesis requiring ~30 energetically costly enzymatic steps [175]. Mammals have evolved highly complex, regulated mechanisms to absorb, transport and store cobalamin [176], reminiscent of the baroque processes involved in iron uptake, transport and storage in humans [177] [178]. We found that the genetic machinery for B12 autotrophy is found in infectious but not saprophytic Leptospira, leading us to speculate that such autotrophy allows Leptospira to infect mammals in the face of B12 sequestration by the host.

All Leptospira survive in the external environment, but our analysis predicts that only pathogenic strains make cobalamin de novo from L-glutamate, suggesting that this process is critical in vivo. Important in this context, cobalamin absorption and utilization in mammals is mediated by an elaborate set of carrier proteins, receptors and transporters [176] that generally are presumed necessary to process and protect this very large molecule. Considering the mechanistic details of cobalamin handling in mammals and the B12 autotrophy of infectious Leptospira, we hypothesize that mammalian B12 systems deprive invasive microbes of cobalamin, akin to the role of iron absorption, transport and sequestration known to sequester iron from pathogens, which have evolved siderophore mechanisms to acquire iron in vivo in mammalian hosts. Comparative analysis of de novo cobalamin biosynthesis in Leptospira predicts that infectious Leptospira are autotrophic for synthesizing this compound while saprophytes are auxotrophic, and suggests lines of experimentation to explore further the details cobalamin biosynthesis in Leptospira. The significance of the absence of complete cobalamin biosynthetic pathways in some group I Leptospira remains unclear.

Detoxification of reactive oxygen species suggests resistance to host defense and differences in ecological niche

Previous comparative biochemical studies of spirochetes demonstrated catalase activity only in pathogenic Leptospira (all of which were previously classified inclusively as L. interrogans), and superoxide dismutase activity only in the saprophytic L. biflexa; peroxidase activity was present in both clades [179]. An important finding in our comparative genome analysis was that the Leptospira catalases, KatA and the putative catalase ortholog (LEP1GSC062_4039), were only found in pathogenic Leptospira, while the single leptospiral superoxide dismutase gene sod was found only in saprophytic Leptospira. Catalase has classically been associated with resistance to phagocyte-produced oxidative burst-mediated killing of pathogens (viz. hydrogen peroxide), typically intracellularly after phagocytosis. The presence of catalases only in pathogenic Leptospira suggests the testable hypothesis that this enzyme class may be involved in intracellular resistance to intracellular host cell killing, following on the published observations of pathogenic Leptospira within phagolysosomes [180, 181]; whether pathogenic Leptospira survive and proliferate in this subcellular compartment has not been conclusively demonstrated. Conversely, the absence of sod in pathogenic Leptospira suggests that this clade occupies an environmental niche not exposed to oxidative radicals. Presumably the observation that sod is restricted to saprophytes suggests that this clade is exposed to a different context in which oxidative radicals are found in the environment.


Extracellular matrix (ECM)-binding proteins potentially mediate Leptospira adhesion to mammalian host cells, movement of Leptospira in and through tissue interstitium, and generally promoting invasion/colonization processes [37, 182195]. A diverse array of ECM-binding proteins has been identified suggesting a redundancy of adhesion molecules that are probably part of invasion strategies of Leptospira. Indeed, many putative adhesins are multifunctional as they bind plasminogen and generate plasmin [185, 196, 197], increasing proteolytic processes associated with infection, or they could participate in immune evasion strategies by interacting with complement regulators (see below). Moreover, several of these proteins are recognized by human leptospirosis serum samples indicating their expression during infection. A caveat is that almost all these putative adhesins have been identified based on binding studies with recombinant proteins. Although site-directed mutagenesis of pathogenic Leptospira spp remains difficult, relatively straightforward methods have been developed for functional analysis of putative adhesins genes through gain-of-function studies in L. biflexa [92] [198][199]. Many putative adhesins are present in saprophytic Leptospira but the presence of DNA sequences does not mean that the proteins are expressed. Although non-pathogenic Leptospira species may encode the genes for putative adhesins, transcripts of ompL1 and the protein itself, for example, have not been detected in L. biflexa serovar Patoc (190). In any event, adhesion to environmental biotic or abiotic structures may be part of the biology of saprophytic Leptospira but such a concept remains speculative at this time [200].

Many adhesins are multipurpose proteins as they bind PLG and produce PLA. The generation of fully active PLA, aside from its other functions, also contributes to the enhanced degradation of complement components. Indeed, it has been shown that in Leptospira, PLA decreases C3b and human IgG deposition, most probably through their degradation, thereby hampering opsonization, restricting complement antibacterial functions [201]. Another mechanism of complement evasion is through the acquisition of host regulators of complement activation. Surface microbial proteins that bind to complement inhibitors and activate them permit pathogens to inhibit the complement response on the bacteria [202]. Binding of Leptospira to factor H (FH), factor H-like protein (FHL-1) and C4 binding protein (C4BP) has been reported [203, 204] and several complement regulators-binding proteins have been identified [205211]. Lsa23 is an example of multifunctional protein capable of binding ECM, PLG/PLA and complement regulators and should play a role in leptospiral virulence [211].

Immune evasion via proteolysis of complement

The protein family includes several metalloprotease members that are considered virulence factors in several pathogens [212]. Aureolysin, a zinc-dependent metalloprotease of S. aureus, acts in synergy with host regulators to inactivate C3 thus potentially inactivating host immune response [213]. Indeed, in the case of Leptospira, thermolysins were only found in pathogenic strains. Recently, experimental evidence of the sequence LIC13322 encoding a putative thermolysin on the direct degradation of complement factors has been reported, suggesting its role in immune evasion by pathogenic leptospiral strains [214]. Thus, it seems that pathogenic Leptospira, like other successful pathogens, utilize at least two strategies to circumvent the complement system: acquisition of host complement inhibitors and degradation of complement components, either thorough PLG/PLA generation or by the presence of bacterial proteases.

Sialic acids

We show here a distinctive presence of the sialic acid cluster in most pathogenic Leptospira species and notably absent from intermediately pathogenic and saprophytic Leptospira species. This fact suggests a role for sialic acids as virulence determinants. The data shown are consistent with previously published data in which L. interrogans and L. alexanderi produced di-acetylated nonulosonic acids and L. santarosai did not produce identifiable nonulosonic acid species, and this observation could be associated with some species missing particular enzymes of the pathway [215].

The pathogens that contain the whole sialic acid biosynthesis cluster have N-acetylneuraminic acid synthetases predicted to produce legionaminic acids, while other species have a cluster predicted to produce pseudaminic acid. Pseudaminic acid has been shown to be required for flagella biogenesis in Campylobacter spp. and Helicobacter spp. and function as virulence factors [216]. Leptospiral flagella are located between the inner membrane (IM) and outer membrane (OM) and drive motility [217]. The presence of a pseudaminic acid pathway in all species suggests glycosylation could have a similar role in flagella biogenesis.

Legionaminic acid has been associated with virulence in Legionella pneumophila and Campylobacter coli where its cell surface location seems to be involved in adhesion, cell-cell interaction and immune evasion [218]. We hypothesize that similar mechanisms occur in pathogenic leptospires.

PF07598 paralogous gene family

A novel gene family—first identified in a pathogenomic screen of L. interrogans serovar Lai [219]—was found to have orthologs present in pathogenic Leptospira but not in intermediate or saprophytic Leptospira. In L. interrogars serovar Lai, these genes were reported to be upregulated in vivo [219] and here we report that the numbers of paralogs varies among the pathogens, with the L. interrogans, L. kirschneri and L. noguchi having the most. These observations suggest that the PF07598 genes contribute to leptospiral virulence but the mechanism(s) by which they do so remain to be elucidated; no functional annotations for the PF07598 gene family is yet possible. Experimental studies of this gene family will likely provide insight into leptospirosis pathogenesis.

Motility and chemotaxis

Although motility is essential for pathogenesis of Leptospira [150, 151], all Leptospira spirochetes, including those belonging to intermediate and saprophytic species, are motile. Consistent with this observation, we found that pathogenic, intermediate and saprophytic species of Leptospira have all the genes necessary to assemble a functional flagellar apparatus. Furthermore, the flagella-encoding genes are highly conserved within the genus, indicating that pathogenic and non-pathogenic Leptospira do not differ significantly with respect to their flagella apparatus and structure.

In contrast to what we found with regard to motility, there was high diversity with respect to predicted amino acid sequence identity among genes encoding chemotaxis proteins from different species of Leptospira. Furthermore, not all chemotaxis proteins are present in all the species, which corresponds to different chemotactic behaviors observed in pathogenic and saprophytic Leptospira [220]. The majority of the diversity among the chemotaxis proteins was observed in MCPs, trans-membrane sensor proteins that trigger the intracellular signal transduction in bacterial chemotaxis [221], and located at the cell poles near the basal body and flagellar motor as other chemotaxis proteins [217, 222]. This finding, together with the observation that there is a higher diversity among genes associated with the basal body and that asymmetrical periplasmic flagellar rotation occurs with the interaction of basal body and chemotaxis proteins, suggests that the sensing and chemotactic response regulated by this proteins may impact their survival in specific environments, including their ability to infect a mammalian host.

Gene regulation

Transcriptional regulation is the most common form of regulation in bacteria, often via a specific transcriptional regulator (activator or repressor) or an alternative sigma factor. In this study, we found that Leptospira have only three types of alternative sigma (σ) factors (σ54, σF, σE), which is fewer than E. coli which have genome sizes similar to those of Leptospira. Our analysis revealed that there is a distinct difference in the σ54 regulatory networks between pathogenic and saprophytic Leptospira species. All pathogenic species have two σ54 regulatory networks, LepA-σ54 and LepB-σ54, while saprophytic Leptospira only have LepA-σ54. Historically, σ54 is known to be involved in nitrogen assimilation, and now is well recognized to regulate diverse functions in response to various stimuli [223]. In addition, σ54 has also been shown to be essential for infection in some pathogenic bacteria. For example, in another spirochetal pathogen, B. burgdorferi, the EBP activator, Rrp2, and σ4, controls production of RpoS which in turn, governs expression of many virulence factors important for mammalian infection such as OspC [224, 225]. Thus, it is logical to postulate that LepB-σ54, is involved in survival in natural environments for both pathogenic and saprophytic Leptospira, while LepA-σ54 plays a role in survival in the host for pathogenic species. This hypothesis merits experimental testing.

Extracytoplasmic function σ factors (ECF σ factors) are the most diverse alternative σ factors found in many bacteria [226, 227]. Many bacteria contain multiple ECF σ factors. For example, Pseudomonas aeruginosa has more than 19 ECF σ factors [226]. Based on sequence analysis, ECF σ factors have been grouped into over 40 classes [226]. Our analyses showed that Leptospira have 5–10 ECF σ factors, and pathogenic Leptospira have 5 more ECFσ factors than saprophytic species (S11 Table), which is consistent with the more complex life cycle of pathogenic species than of saprophytic species. All Leptospira spp. have one copy of ECF31 and ECF43 with unknown functions. Pathogenic Leptospira have additional 5 unclassified ECFσ factors. Saprophytic Leptospira have one copy of ECF41 and ECF42 that are not found in pathogenic species. Although functions of ECF41 and ECF42-type remain unknown, one report showed that one of the ECF41 σ factors, SigJ in Mycobacterium tuberculosis, is involved in resistance to hydrogen peroxide [228]. It is unclear whether ECF41 in saprophytic Leptospira (LEPBI_I1070) has a similar function as SigJ, and if so, how it contributes to the survival of saprophytic Leptospira in the environment.

The activity of ECF σ factors is often regulated by an anti-σ factor, a transmembrane protein that binds and inhibits the activity of ECF σ [229]. Cleavage of anti-σ factor by proteases leads to release and activate σE. Extracellular signals regulate this intramembrane proteolysis often via an anti-anti-σ factor (or called anti-σ antagonist). Our analyses revealed that both pathogenic and saprophytic Leptospira have more than 30 σE regulators. Among them, 17 are only found in pathogenic/intermediate Leptospira, while 19 are found solely in saprophytic Leptospira (S11 Table). These differences likely reflect the variety of signals sensed by pathogenic and saprophytic Leptospira.

In addition to alternative σ factors, both pathogenic and saprophytic Leptospira species have many putative transcriptional regulators, far more than is found in other pathogenic spirochetes such as Borrelia burgdorferi and Treponema pallidum [230, 231]. Our initial analyses of transcriptional regulators among Leptospira species did not yield a distinct pattern of correlation with pathogenicity. Further in silico and experimental analyses to confirm the prediction and more importantly, to determine their regulatory role in Leptospira, is needed.

Sensory transduction

Leptospira species have a high number of two-component sensory systems (TCSs) (70–100) compared to Borrelia, Treponema and Bradyspira (6–20). The number of TCS genes found in a particular Leptospira species strongly correlated with the likely diversity of ecological niches that the species encounters, a phenomenon observed in other bacteria [203]. The lower number of TCSs found in pathogenic species may be linked to the process of host adaptation, whereas the larger numbers of unique TCSs in intermediates and, even more in saprophytes, may be instrumental for sensing and adapting to a more diverse range of environmental conditions. Regardless, almost all of the pathogenic species encode more than 70 TCS genes, indicating that the Leptospira pathogen requires a highly complex network of signaling processes for its life cycle. Interestingly, the proportion of TCS genes encoding orphan HK/RR and HHK proteins are higher in Leptospira (> 60%) than in other bacteria where orphan TCS proteins are unusual. These findings suggest that branched signaling pathways may be relevant in this genus and could confer added physiological advantages to Leptospira under specific circumstances.

One limitation in the present analytical approach is the difficulty in defining a robust and confident automatic method to segregate orthologous clusters among all Leptospira strains, and especially those that relate to TCS function. As in most in silico analyses, further biochemical experiments are needed to confirm the role of the various TCS categories identified in this study. Another limitation of this cross-species comparative analysis is that differences in serovars/strains with Leptospira species were not studied; such analyses will be a future priority given the strength of the approach and the depth of existing data. This in silico approach will also not identify novel virulence factors nor mechanisms of pathogenesis based on sequence analysis alone.

In summary, the large-scale comparative genomic analysis of 20 Leptospira species has provided broad insights into how infectious members of this genus acquired the genes necessary to acquire pathogenicity and virulence, placing these species within a definitive phylogeny. Novel, Leptospira species-specific genes and gene families were identified. Genomically-based metabolic reconstruction predictions predict novel adaptation of infectious Leptospira to mammals (summarized in Table 9), including sialic acid biosynthesis, pathogen-specific porphyrin metabolism and the first-time demonstration of riboswitch-regulated cobalamin (B12) autotrophy as a bacterial virulence factor. Only pathogenic Leptospira contain CRISPR/Cas systems, suggesting not only a potential mechanism for this clade’s refractoriness to gene targeting but also possible novel means to be able to genetically modify pathogenic Leptospira. Whether restriction modification systems might contribute to gene targeting has yet to be analyzed in detail, but the publicly available whole genome data sets provided in support of the present work will be contribute to carrying out such analyses. A novel virulence-related genes/gene family epitomized by the PF07598 group of paralogs suggests adaptation and diversification of this protein family within the pathogenic clade. Identifying large scale changes in infectious (pathogenic and intermediate pathogenic) as compared to non-infectious Leptospira has yield large-scale, novel insights into the evolution of a bacterial pathogen, provides the basis for new directions in leptospirosis pathogenesis research. It also makes novel genomic and pathogenomic contributions to the field of bacterial pathogenesis, which is of general interest.

Supporting Information

S1 Fig. Pan-genome, core and novel genes of the 20 sequenced Leptospira species.

The blue and red lines denote the pan-genome and core genes as genomes are added in the order noted on along the x-axis (A). The bars indicate the number of novel gene families discovered for each genome added. The color of the bars illustrate the three main groupings of Leptospira: pathogenic (red), intermediate (blue), and saprophytic (green). The number of novel genes discovered with the addition of each new genome (B) was estimated using a pan-genome model based on the original model presented by Tetellin et al. [63]. Purple circles are the median of each distribution (grey circles). Power law (red lines) and exponential (blue lines) regressions were plotted to determine (α), and tg(θ), respectively. The exponent (α) indicates whether the pan-genome is open (α ≤ 1) or closed (α > 1) [83] and tg(θ) denotes the average extrapolated number of strain-specific/novel genes.



S2 Fig. Flanking Genes Surrounding the Leptospira rfb locus gene clusters.

The rfb region and flanking CDSs (blue) 9 of pathogenic (A), 5 intermediate (B), and 6 saprophytic (C) representative Leptospira species were compared. rfb region CDSs are labeled by locus identifier and colored by functional role categories as noted in the boxed key. Gene symbols, when present, are noted above their respective genes. BLASTP matches between CDSs are colored by protein percent identity (see key).



S3 Fig. Phylogenetic analysis of leptospiral N-acetylneuraminic (Sialic) Acid Synthetase (NeuB) protein sequences.

Maximum-likelihood tree shows pathogens (red lines), intermediates (green lines) and saprophytes (blue lines). Numbers denote node support. A red box highlights those proteins that are part of a complete sialic acid cluster.



S4 Fig. Heat map of ORFs encoding 51 motility and 25 chemotaxis proteins identified in analysis of the 20 Leptospira genomes.

ORFs are identified according to their L. interrogans serovar Copenhageni strain Fiocruz L1-130 number. The heat map shows the degree of amino acid sequence identity of ORFs with their respective orthologs in the L. interrogans strain Fiocruz L1-130 genome.



S5 Fig. Comparison of ECF Sigma (σ) Factors Among Leptospira.

Venn diagram showing distribution of ECF σfactors unique or shared among the pathogenic (L. interrogans L1-130), intermediately pathogenic (L. kmetyi) and saprophytic (L. biflexa) species. The number and locus ID of ECF σ factors that are unique or shared among these Leptospira species are labeled in each sector of the diagram.



S6 Fig. Normalized number of Leptospiral two component systems by genome size.

The number of TCS genes was normalized per Mbp genome (y-axis) of representative Leptospiral species (x-axis). See key for shading of pathogenic, intermediate and saprophyte genomes.



S7 Fig. Venn diagram showing the distribution of TCS genes among Leptospira species.

The ratios depicted inside each one of the major groupings, correspond to the number of TCS ortholog genes present in the [majority:all-but-one:all] species of that particular group. True cut-off values for these Figs correspond to the presence of the gene in 50% (majority), 90% (all but one) or 100% (all) of the particular group of species. Sequence clusters that do not match the indicated cut-off value or those from unexpected groupings are included in the “ambiguous grouping” set. Singleton clusters, representing species-specific genes are noted in circles surrounding the Venn diagram.



S1 Table. Universal protein markers.



S2 Table. Estimates of genome relatedness of Leptospira species.



S3 Table. Metabolites and reactions including exchange and biomass reactions used for metabolic reconstructions.



S4 Table. Candidate substrates for the leptospiral TAT protein secretion system.



S5 Table. Examination of the -1 position of leptospiral lipobox sequences.



S6 Table. Identification of Lipid A biosynthesis proteins in Leptospira.



S7 Table.

A. Identity matrix comparison of lipid A biosynthesis pathway genes across the genus Leptospira. B. Homology comparison of amino acid sequences of enzymes involved in the synthesis of cell wall lipid A from Leptospira species.



S8 Table. Leptospiral proteins involved in adhesion to extracellular matrix, plasminogen binding and complement evasion.



S9 Table. Proteases with a potential role in host-pathogen interactions.



S10 Table.

A. Amino acid sequence identity comparison of proteins orthologous to Leptospira interrogans serovar Copenhageni immunodominant proteins. B. Amino acid identity of leptospiral proteins to Lig proteins and domains.



S11 Table. Comparison of leptospiral proteins involved in motility and chemotaxis.



S12 Table. Sigma factors and accessory proteins involved in gene regulation in Leptospira species.



S13 Table. Inventory of two component systems in Leptospira species.



S14 Table. Identification of strain-specific two component systems in Leptospira species.



S15 Table. Core three component system proteins conserved among Leptospira species.



S16 Table. Two component systems specific to pathogenic Leptospira species.




We are grateful to the J. Craig Venter Institute sequencing, bioinformatics and IT departments for supporting the infrastructure required to determine the genome sequences, annotation and pan-genome and other analyses carried out in this project.

Author Contributions



  1. 1. Levett PN. Leptospirosis. Clin Microbiol Rev. 2001;14(2):296–326. pmid:11292640
  2. 2. Bharti AR, Nally JE, Ricaldi JN, Matthias MA, Diaz MM, Lovett MA, et al. Leptospirosis: A zoonotic disease of global importance. Lancet Infect Dis. 2003;3:757–71. pmid:14652202
  3. 3. Ashford DA, Kaiser RM, Spiegel RA, Perkins BA, Weyant RS, Bragg SL, et al. Asymptomatic infection and risk factors for leptospirosis in Nicaragua. Am J Trop Med Hyg. 2000;63(5–6):249–54. pmid:11421372.
  4. 4. Ganoza CA, Matthias MA, Saito M, Cespedes M, Gotuzzo E, Vinetz JM. Asymptomatic renal colonization of humans in the peruvian Amazon by Leptospira. PLoS Negl Trop Dis. 2010;4(2):e612. Epub 2010/02/27. doi: 10.1371/journal.pntd.0000612 pmid:20186328; PubMed Central PMCID: PMC2826405.
  5. 5. Dikken H, Kmety E. Serological typing methods of leptospires. In: B T., NJ R., editors. Methods in Microbiology. 11. London: Academic Press; 1978. p. 259–307.
  6. 6. Brenner DJ, Kaufmann AF, Sulzer KR, Steigerwalt AG, Rogers FC, Weyant RS. Further determination of DNA relatedness between serogroups and serovars in the family Leptospiraceae with a proposal for Leptospira alexanderi sp. nov. and four new Leptospira genomospecies. International journal of systematic bacteriology. 1999;49 Pt 2:839–58. Epub 1999/05/13. pmid:10319510.
  7. 7. Brenner DJ, Kaufmann AF, Sulzer KR, Steigerwalt AG, Rogers FC, Weyant RS. Further determination of DNA relatedness between serogroups and serovars in the family Leptospiraceae witha proposal for Leptospira alexanderi sp. nov. and four new Leptospira genomospecies. Int J Syst Bacteriol. 1999;49:839–58. pmid:10319510
  8. 8. Ramadass P, Jarvis BDW, Corner RJ, Penny D, Marshall RB. Genetic characterization of pathogenic Leptospira species by DNA hybridization. Int J Syst Bacteriol. 1992;42:215–9. pmid:1581182
  9. 9. Ramadass P, Jarvis BDW, Corner RJ, Cinco M, Marshall RB. DNA relatedness among strains of Leptospira biflexa. Int J Syst Bacteriol 1990;40:231–5. pmid:2397191
  10. 10. Yasuda PH, Steigerwalt AG, Sulzer KR, Kaufmann AF, Rogers FC, Brenner DJ. Deoxyribonucleic acid relatedness between serogroups and serovars in the family Leptospiraceae with proposals for seven new Leptospira species. Int J Syst Bacteriol. 1987;37:407–15.
  11. 11. Bourhy P, Collet L, Brisse S, Picardeau M. Leptospira mayottensis sp. nov., a pathogenic species of the genus Leptospira isolated from humans. Int J Syst Evol Microbiol. 2014;64(Pt 12):4061–7. doi: 10.1099/ijs.0.066597-0 pmid:25249563.
  12. 12. Saito M, Villanueva SY, Kawamura Y, Iida K, Tomida J, Kanemaru T, et al. Leptospira idonii sp. nov., isolated from environmental water. International journal of systematic and evolutionary microbiology. 2013;63(Pt 7):2457–62. doi: 10.1099/ijs.0.047233-0 pmid:23203626.
  13. 13. Ko AI, Goarant C, Picardeau M. Leptospira: the dawn of the molecular genetics era for an emerging zoonotic pathogen. Nat Rev Microbiol. 2009;7(10):736–47. Epub 2009/09/17. nrmicro2208 [pii] doi: 10.1038/nrmicro2208 pmid:19756012.
  14. 14. Ahmed A, Grobusch MP, Klatser PR, Hartskeerl RA. Molecular approaches in the detection and characterization of Leptospira. J Bacteriol Parasitol. 2011:S5–002.
  15. 15. Cerqueira GM, Picardeau M. A century of Leptospira strain typing. Infect Genet Evol. 2009;9:760–8. doi: 10.1016/j.meegid.2009.06.009. pmid:19540362
  16. 16. Levett PN, Smythe L. International Committee on Systematics of Prokaryotes Subcommittee on the taxonomy of Leptospiraceae. Minutes of the closed meeting, 9 October 2013, Fukuoka, Japan. Int J Syst Evol Micr. 2014;64:in press.
  17. 17. Auch AF, vonJan M, Klenk HP G M. Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison. Stand Genomic Sci. 2010;2:117–34. doi: 10.4056/sigs.531120. pmid:21304684
  18. 18. Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Micr. 2007;57:81–91.
  19. 19. Wolf YI, Rogozin I, Grishin N, Tatusov R, Koonin E. Genome trees constructed using five different approaches suggest new major bacterial clades. BMC Evol Biol. 2001;1:8. pmid:11734060
  20. 20. Auch AF, Klenk HP, Göker MS. Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs. Stand Genomic Sci. 2010;2:142–8. doi: 10.4056/sigs.541628. pmid:21304686
  21. 21. Boonsilp S, Thaipadungpanit J, Amornchai P, Wuthiekanun V, Bailey MS, Holden MT, et al. A single multilocus sequence typing (MLST) scheme for seven pathogenic Leptospira species. PLoS neglected tropical diseases. 2013;7(1):e1954. Epub 2013/01/30. doi: 10.1371/journal.pntd.0001954 pmid:23359622; PubMed Central PMCID: PMC3554523.
  22. 22. Ahmed A, Ferreira AS, Hartskeerl RA. Multilocus sequence typing (MLST): markers for the traceability of pathogenic Leptospira strains. Methods in molecular biology. 2015;1247:349–59. doi: 10.1007/978-1-4939-2004-4_25 pmid:25399108.
  23. 23. Nalam K, Ahmed A, Devi SM, Francalacci P, Baig M, Sechi LA, et al. Genetic affinities within a large global collection of pathogenic Leptospira: implications for strain identification and molecular epidemiology. PLoS One. 2010;5(8):e12637. doi: 10.1371/journal.pone.0012637 pmid:20805987; PubMed Central PMCID: PMC2929200.
  24. 24. Romero EC, Blanco RM, Galloway RL. Analysis of multilocus sequence typing for identification of Leptospira isolates in Brazil. J Clin Microbiol. 2011;49(11):3940–2. doi: 10.1128/JCM.01119-11 pmid:21880969; PubMed Central PMCID: PMC3209071.
  25. 25. Ahmed A, Thaipadungpanit J, Boonsilp S, Wuthiekanun V, Nalam K, Spratt BG, et al. Comparison of two multilocus sequence based genotyping schemes for Leptospira species. PLoS Negl Trop Dis. 2011;5(11):e1374. doi: 10.1371/journal.pntd.0001374 pmid:22087342; PubMed Central PMCID: PMC3210738.
  26. 26. Agampodi SB, Moreno AC, Vinetz JM, Matthias MA. Utility and limitations of direct multi-locus sequence typing on qPCR-positive blood to determine infecting Leptospira strain. The American journal of tropical medicine and hygiene. 2013;88(1):184–5. doi: 10.4269/ajtmh.2012.12-0526 pmid:23208890; PubMed Central PMCID: PMCPMC3541733.
  27. 27. Chiani Y, Jacob P, Varni V, Landolt N, Schmeling MF, Pujato N, et al. Isolation and clinical sample typing of human leptospirosis cases in Argentina. Infect Genet Evol. 2016;37:245–51. doi: 10.1016/j.meegid.2015.11.033 pmid:26658064.
  28. 28. Lehmann JS, Matthias MA, Vinetz JM, Fouts DE. Leptospiral pathogenomics. Pathogens. 2014;3(2):280–308. doi: 10.3390/pathogens3020280 pmid:25437801; PubMed Central PMCID: PMC4243447.
  29. 29. Ren SX, Fu G, Jiang XG, Zeng R, Miao YG, Xu H, et al. Unique physiological and pathogenic features of Leptospira interrogans revealed by whole-genome sequencing. Nature. 2003;422(6934):888–93. doi: 10.1038/nature01597 pmid:12712204.
  30. 30. Nascimento AL, Ko AI, Martins EA, Monteiro-Vitorello CB, Ho PL, Haake DA, et al. Comparative genomics of two Leptospira interrogans serovars reveals novel insights into physiology and pathogenesis. J Bacteriol. 2004;186(7):2164–72. Epub 2004/03/19. pmid:15028702; PubMed Central PMCID: PMC374407.
  31. 31. Bulach DM, Zuerner RL, Wilson P, Seemann T, McGrath A, Cullen PA, et al. Genome reduction in Leptospira borgpetersenii reflects limited transmission potential. Proc Natl Acad Sci U S A. 2006;103(39):14560–5. Epub 2006/09/16. doi: 10.1073/pnas.0603979103 pmid:16973745; PubMed Central PMCID: PMC1599999.
  32. 32. Ricaldi JN, Fouts DE, Selengut JD, Harkins DM, Patra KP, Moreno A, et al. Whole Genome Analysis of Leptospira licerasiae Provides Insight into Leptospiral Evolution and Pathogenicity. PLoS Negl Trop Dis. 2012;6(10):e1853. doi: 10.1371/journal.pntd.0001853 pmid:23145189; PubMed Central PMCID: PMC3493377.
  33. 33. Picardeau M, Bulach DM, Bouchier C, Zuerner RL, Zidane N, Wilson PJ, et al. Genome sequence of the saprophyte Leptospira biflexa provides insights into the evolution of Leptospira and the pathogenesis of leptospirosis. PLoS One. 2008;3(2):e1607. Epub 2008/02/14. doi: 10.1371/journal.pone.0001607 pmid:18270594; PubMed Central PMCID: PMC2229662.
  34. 34. Chou LF, Chen YT, Lu CW, Ko YC, Tang CY, Pan MJ, et al. Sequence of Leptospira santarosai serovar Shermani genome and prediction of virulence-associated genes. Gene. 2012;511(2):364–70. doi: 10.1016/j.gene.2012.09.074 pmid:23041083.
  35. 35. Matthias MA, Diaz MM, Campos KJ, Calderon M, Willig MR, Pacheco V, et al. Diversity of bat-associated Leptospira in the Peruvian Amazon inferred by bayesian phylogenetic analysis of 16S ribosomal DNA sequences. Am J Trop Med Hyg. 2005;73(5):964–74. pmid:16282313; PubMed Central PMCID: PMC2270400.
  36. 36. Levett PN. Systematics of leptospiraceae. Current topics in microbiology and immunology. 2015;387:11–20. doi: 10.1007/978-3-662-45059-8_2 pmid:25388130.
  37. 37. Nascimento AL, Ko AI, Martins EA, Monteiro-Vitorello CB, Ho PL, Haake DA, et al. Comparative genomics of two Leptospira interrogans serovars reveals novel insights into physiology and pathogenesis. J Bacteriol. 2004;186(7):2164–72. Epub 2004/03/19. pmid:15028702; PubMed Central PMCID: PMC374407.
  38. 38. Matthias MA, Ricaldi JN, Cespedes M, Diaz MM, Galloway RL, Saito M, et al. Human leptospirosis caused by a new, antigenically unique Leptospira associated with a Rattus species reservoir in the Peruvian Amazon. PLoS Negl Trop Dis. 2008;2(4):e213. Epub 2008/04/03. doi: 10.1371/journal.pntd.0000213 pmid:18382606; PubMed Central PMCID: PMC2271056.
  39. 39. Delcher AL, Phillippy A, Carlton J, Salzberg SL. Fast algorithms for large-scale genome alignment and comparison. Nucleic acids research. 2002;30(11):2478–83. Epub 2002/05/30. pmid:12034836; PubMed Central PMCID: PMC117189.
  40. 40. Fouts DE, Mongodin EF, Mandrell RE, Miller WG, Rasko DA, Ravel J, et al. Major Structural Differences and Novel Potential Virulence Mechanisms from the Genomes of Multiple Campylobacter Species. PLoS Biol. 2005;3(1):e15. pmid:15660156; PubMed Central PMCID: PMC539331.
  41. 41. Fouts DE, Tyler HL, DeBoy RT, Daugherty S, Ren Q, Badger JH, et al. Complete Genome Sequence of the N2-Fixing Broad Host Range Endophyte Klebsiella pneumoniae 342 and Virulence Predictions Verified in Mice. PLoS Genet. 2008;4(7):e1000141. PMCID: PMC2453333. pmid:18654632; PubMed Central PMCID: PMC2453333. doi: 10.1371/journal.pgen.1000141.
  42. 42. Davidsen T, Beck E, Ganapathy A, Montgomery R, Zafar N, Yang Q, et al. The comprehensive microbial resource. Nucleic acids research. 2010;38(Database issue):D340–5. Epub 2009/11/07. doi: 10.1093/nar/gkp912 pmid:19892825; PubMed Central PMCID: PMC2808947.
  43. 43. Chen Y, Stine OC, Badger JH, Gil AI, Nair GB, Nishibuchi M, et al. Comparative Genomic Analysis of Vibrio parahaemolyticus: Serotype Conversion and Virulence. BMC Genomics. 2011;12:294. Epub 2011/06/08. doi: 10.1186/1471-2164-12-294 pmid:21645368; PubMed Central PMCID: PMC3130711.
  44. 44. Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic acids research. 2011;39(Web Server issue):W29–37. Epub 2011/05/20. doi: 10.1093/nar/gkr367 pmid:21593126; PubMed Central PMCID: PMC3125773.
  45. 45. Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic acids research. 2009;37(Database issue):D141–5. Epub 2008/11/14. doi: 10.1093/nar/gkn879 pmid:19004872; PubMed Central PMCID: PMC2686447.
  46. 46. Sonnhammer EL, Hollich V. Scoredist: a simple and robust protein sequence distance estimator. BMC Bioinformatics. 2005;6:108. Epub 2005/04/29. doi: 10.1186/1471-2105-6-108 pmid:15857510; PubMed Central PMCID: PMC1131889.
  47. 47. phylipFasta—Wrapper for the Phylip Package Written in Ruby. Available from:
  48. 48. Felsenstein J. PHYLIP—Phylogeny Inference Package (Version 3.2). Cladistics. 1989;5:164–6.
  49. 49. Felsenstein J. PHYLIP (Phylogeny Inference Package) version 3.69. Distributed by the author, Department of Genome Sciences, University of Washington, Seattle. 2009.
  50. 50. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539. Epub 2011/10/13. doi: 10.1038/msb.2011.75 pmid:21988835; PubMed Central PMCID: PMC3261699.
  51. 51. Boonsilp S, Thaipadungpanit J, Amornchai P, Wuthiekanun V, Bailey MS, Holden MT, et al. A single multilocus sequence typing (MLST) scheme for seven pathogenic Leptospira species. PLoS Negl Trop Dis. 2013;7(1):e1954. Epub 2013/01/30. doi: 10.1371/journal.pntd.0001954 pmid:23359622; PubMed Central PMCID: PMC3554523.
  52. 52. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Molecular biology and evolution. 2011;28(10):2731–9. Epub 2011/05/07. doi: 10.1093/molbev/msr121 pmid:21546353; PubMed Central PMCID: PMC3203626.
  53. 53. Guindon S, Gascuel O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Systematic biology. 2003;52(5):696–704. Epub 2003/10/08. pmid:14530136.
  54. 54. Brown JR, Douady CJ, Italia MJ, Marshall WE, Stanhope MJ. Universal trees based on large combined protein sequence data sets. Nat Genet. 2001;28(3):281–5. Epub 2001/06/30. doi: 10.1038/90129 pmid:11431701.
  55. 55. Santos SR, Ochman H. Identification and phylogenetic sorting of bacterial lineages with universally conserved genes and proteins. Environ Microbiol. 2004;6(7):754–9. Epub 2004/06/10. doi: 10.1111/j.1462-2920.2004.00617.x pmid:15186354.
  56. 56. Ciccarelli FD, Doerks T, von Mering C, Creevey CJ, Snel B, Bork P. Toward automatic reconstruction of a highly resolved tree of life. Science. 2006;311(5765):1283–7. Epub 2006/03/04. doi: 10.1126/science.1123061 pmid:16513982.
  57. 57. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic acids research. 1994;22(22):4673–80. Epub 1994/11/11. pmid:7984417; PubMed Central PMCID: PMC308517.
  58. 58. Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25(15):1972–3. Epub 2009/06/10. doi: 10.1093/bioinformatics/btp348 pmid:19505945; PubMed Central PMCID: PMC2712344.
  59. 59. Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22(21):2688–90. Epub 2006/08/25. doi: 10.1093/bioinformatics/btl446 pmid:16928733.
  60. 60. Fouts DE. Phage_Finder: Automated Identification and Classification of Prophage Regions in Complete Bacterial Genome Sequences. Nucleic Acids Res. 2006;34(20):5839–51. pmid:17062630; PubMed Central PMCID: PMC1635311.
  61. 61. Meier-Kolthoff JP, Auch AF, Klenk HP, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics. 2012;(14):60.
  62. 62. Fouts DE, Brinkac L, Beck E, Inman J, Sutton G. PanOCT: Automated Clustering of Orthologs Using Conserved Gene Neighborhood for Pan-Genomic Analysis of Bacterial Strains and Closely Related Species. Nucleic Acids Res. 2012;40(22):e172. Epub 2012/08/21. doi: 10.1093/nar/gks757 pmid:22904089; PubMed Central PMCID: PMC3526259.
  63. 63. Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome". Proc Natl Acad Sci U S A. 2005;102(39):13950–5. Epub 2005/09/21. doi: 10.1073/pnas.0506758102 pmid:16172379; PubMed Central PMCID: PMC1216834.
  64. 64. Hogg JS, Hu FZ, Janto B, Boissy R, Hayes J, Keefe R, et al. Characterization and modeling of the Haemophilus influenzae core and supragenomes based on the complete genomic sequences of Rd and 12 clinical nontypeable strains. Genome Biol. 2007;8(6):R103. Epub 2007/06/07. doi: 10.1186/gb-2007-8-6-r103 pmid:17550610; PubMed Central PMCID: PMC2394751.
  65. 65. Rasko DA, Rosovitz MJ, Myers GS, Mongodin EF, Fricke WF, Gajer P, et al. The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates. J Bacteriol. 2008;190(20):6881–93. Epub 2008/08/05. doi: 10.1128/JB.00619-08 pmid:18676672; PubMed Central PMCID: PMC2566221.
  66. 66. Davie JJ, Earl J, de Vries SP, Ahmed A, Hu FZ, Bootsma HJ, et al. Comparative analysis and supragenome modeling of twelve Moraxella catarrhalis clinical isolates. BMC Genomics. 2011;12:70. Epub 2011/01/29. doi: 10.1186/1471-2164-12-70 pmid:21269504; PubMed Central PMCID: PMC3045334.
  67. 67. Park J, Zhang Y, Buboltz AM, Zhang X, Schuster SC, Ahuja U, et al. Comparative genomics of the classical Bordetella subspecies: the evolution and exchange of virulence-associated diversity amongst closely related pathogens. BMC Genomics. 2012;13:545. Epub 2012/10/12. doi: 10.1186/1471-2164-13-545 pmid:23051057; PubMed Central PMCID: PMC3533505.
  68. 68. Gordienko EN, Kazanov MD, Gelfand MS. Evolution of pan-genomes of Escherichia coli, Shigella spp., and Salmonella enterica. J Bacteriol. 2013;195(12):2786–92. Epub 2013/04/16. doi: 10.1128/JB.02285-12 pmid:23585535; PubMed Central PMCID: PMC3697250.
  69. 69. Jacobsen A, Hendriksen RS, Aaresturp FM, Ussery DW, Friis C. The Salmonella enterica pan-genome. Microb Ecol. 2011;62(3):487–504. Epub 2011/06/07. doi: 10.1007/s00248-011-9880-1 pmid:21643699; PubMed Central PMCID: PMC3175032.
  70. 70. Bourhy P, Herrmann Storck C, Theodose R, Olive C, Nicolas M, Hochedez P, et al. Serovar diversity of pathogenic Leptospira circulating in the French West Indies. PLoS Negl Trop Dis. 2013;7(3):e2114. doi: 10.1371/journal.pntd.0002114 pmid:23516654; PubMed Central PMCID: PMC3597474.
  71. 71. Hochedez P, Escher M, Decoussy H, Pasgrimaud L, Martinez R, Rosine J, et al. Outbreak of leptospirosis among canyoning participants, Martinique, 2011. Euro Surveill. 2013;18(18):20472. pmid:23725775.
  72. 72. Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28(9):977–82. Epub 2010/08/31. doi: 10.1038/nbt.1672 pmid:20802497.
  73. 73. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. 2013;7:74. Epub 2013/08/10. doi: 10.1186/1752-0509-7-74 pmid:23927696; PubMed Central PMCID: PMC3751080.
  74. 74. Orth JD, Thiele I, Palsson BO. What is flux balance analysis? Nat Biotechnol. 2010;28(3):245–8. Epub 2010/03/10. doi: 10.1038/nbt.1614 pmid:20212490; PubMed Central PMCID: PMC3108565.
  75. 75. Gurobi Optimization I. Gurobi Optimizer Reference Manual 2014 [2014 Nov 17]. Available from:
  76. 76. Kumar VS, Maranas CD. GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput Biol. 2009;5(3):e1000308. Epub 2009/03/14. doi: 10.1371/journal.pcbi.1000308 pmid:19282964; PubMed Central PMCID: PMC2645679.
  77. 77. Fong C, Rohmer L, Radey M, Wasnick M, Brittnacher MJ. PSAT: a web tool to compare genomic neighborhoods of multiple prokaryotic genomes. BMC Bioinformatics. 2008;9:170. Epub 2008/03/28. doi: 10.1186/1471-2105-9-170 pmid:18366802; PubMed Central PMCID: PMC2358893.
  78. 78. Chain PS, Grafham DV, Fulton RS, Fitzgerald MG, Hostetler J, Muzny D, et al. Genomics. Genome project standards in a new era of sequencing. Science. 2009;326(5950):236–7. Epub 2009/10/10. doi: 10.1126/science.1180614 pmid:19815760; PubMed Central PMCID: PMC3854948.
  79. 79. Nelson KE, Weinstock GM, Highlander SK, Worley KC, Creasy HH, Wortman JR, et al. A catalog of reference genomes from the human microbiome. Science. 2010;328(5981):994–9. Epub 2010/05/22. doi: 10.1126/science.1183605 pmid:20489017; PubMed Central PMCID: PMC2940224.
  80. 80. Saito M, Villanueva SY, Kawamura Y, Iida K, Tomida J, Kanemaru T, et al. Leptospira idonii sp. nov., isolated from environmental water. Int J Syst Evol Microbiol. 2013;63(Pt 7):2457–62. Epub 2012/12/04. doi: 10.1099/ijs.0.047233-0 pmid:23203626.
  81. 81. Bourhy P, Collet L, Brisse S, Picardeau M. Leptospira mayottensis sp. nov., a pathogenic species of the genus Leptospira isolated from humans. Int J Syst Evol Microbiol. 2014;64(Pt 12):4061–7. Epub 2014/09/25. doi: 10.1099/ijs.0.066597-0 pmid:25249563.
  82. 82. Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan-genome analyses. Current opinion in microbiology. 2014;23C:148–54. Epub 2014/12/09. doi: 10.1016/j.mib.2014.11.016 pmid:25483351.
  83. 83. Tettelin H, Riley D, Cattuto C, Medini D. Comparative genomics: the bacterial pan-genome. Current opinion in microbiology. 2008;11(5):472–7. Epub 2008/12/18. pmid:19086349.
  84. 84. Nally JE, Whitelegge JP, Aguilera R, Pereira MM, Blanco DR, Lovett MA. Purification and proteomic analysis of outer membrane vesicles from a clinical isolate of Leptospira interrogans serovar Copenhageni. Proteomics. 2005;5(1):144–52. pmid:15672460.
  85. 85. Haake DA, Martinich C, Summers TA, Shang ES, Pruetz JD, McCoy AM, et al. Characterization of leptospiral outer membrane lipoprotein LipL36: downregulation associated with late-log-phase growth and mammalian infection. Infection and immunity. 1998;66(4):1579–87. pmid:9529084
  86. 86. Barnett JK, Barnett D, Bolin CA, Summers TA, Wagar EA, Cheville NF, et al. Expression and distribution of leptospiral outer membrane components during renal infection of hamsters. Infection and immunity. 1999;67(2):853–61. pmid:9916100
  87. 87. Haake DA, Mazel MK, McCoy AM, Milward F, Chao G, Matsunaga J, et al. Leptospiral outer membrane proteins OmpL1 and LipL41 exhibit synergistic immunoprotection. Infection and immunity. 1999;67(12):6572–82. pmid:10569777
  88. 88. Haake DA, Chao G, Zuerner RL, Barnett JK, Barnett D, Mazel M, et al. The leptospiral major outer membrane protein LipL32 is a lipoprotein expressed during mammalian infection. Infection and immunity. 2000;68(4):2276–85. pmid:10722630.
  89. 89. Guerreiro H, Croda J, Flannery B, Mazel M, Matsunaga J, Galvao Reis M, et al. Leptospiral proteins recognized during the humoral immune response to leptospirosis in humans. Infection and immunity. 2001;69(8):4958–68. pmid:11447174
  90. 90. Matsunaga J, Sanchez Y, Xu X, Haake DA. Osmolarity, a key environmental signal controlling expression of leptospiral proteins LigA and LigB and the extracellular release of LigA. Infection and immunity. 2005;73(1):70–8. pmid:15618142.
  91. 91. Matsunaga J, Lo M, Bulach DM, Zuerner RL, Adler B, Haake DA. Response of Leptospira interrogans to physiologic osmolarity: relevance in signaling the environment-to-host transition. Infection and immunity. 2007;75(6):2864–74. Epub 2007/03/21. IAI.01619-06 [pii] doi: 10.1128/IAI.01619-06 pmid:17371863; PubMed Central PMCID: PMC1932867.
  92. 92. Pinne M, Matsunaga J, Haake DA. Leptospiral outer membrane protein microarray, a novel approach to identification of host ligand-binding proteins. J Bacteriol. 2012;194(22):6074–87. doi: 10.1128/JB.01119-12 pmid:22961849; PubMed Central PMCID: PMC3486348.
  93. 93. Ristow P, Bourhy P, da Cruz McBride FW, Figueira CP, Huerre M, Ave P, et al. The OmpA-like protein Loa22 is essential for leptospiral virulence. PLoS Pathog. 2007;3(7):e97. pmid:17630832.
  94. 94. Dupont CL, Rusch DB, Yooseph S, Lombardo MJ, Richter RA, Valas R, et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. Isme J. 2012;6(6):1186–99. Epub 2011/12/16. doi: 10.1038/ismej.2011.189 pmid:22170421; PubMed Central PMCID: PMC3358033.
  95. 95. Rose RW, Bruser T, Kissinger JC, Pohlschroder M. Adaptation of protein secretion to extremely high-salt conditions by extensive use of the twin-arginine translocation pathway. Molecular microbiology. 2002;45(4):943–50. Epub 2002/08/16. pmid:12180915.
  96. 96. Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. TIGRFAMs and Genome Properties in 2013. Nucleic acids research. 2013;41(Database issue):D387–95. Epub 2012/12/01. doi: 10.1093/nar/gks1234 pmid:23197656; PubMed Central PMCID: PMC3531188.
  97. 97. Crooks GE, Hon G, Chandonia JM, Brenner SE. WebLogo: a sequence logo generator. Genome Res. 2004;14(6):1188–90. Epub 2004/06/03. doi: 10.1101/gr.849004 pmid:15173120; PubMed Central PMCID: PMC419797.
  98. 98. Drozd M, Gangaiah D, Liu Z, Rajashekara G. Contribution of TAT system translocated PhoX to Campylobacter jejuni phosphate metabolism and resilience to environmental stresses. PLoS One. 2011;6(10):e26336. Epub 2011/10/27. doi: 10.1371/journal.pone.0026336 pmid:22028859; PubMed Central PMCID: PMC3197622.
  99. 99. Gonnet P, Rudd KE, Lisacek F. Fine-tuning the prediction of sequences cleaved by signal peptidase II: a curated set of proven and predicted lipoproteins of Escherichia coli K-12. Proteomics. 2004;4(6):1597–613. Epub 2004/06/03. doi: 10.1002/pmic.200300749 pmid:15174130.
  100. 100. Setubal JC, Reis M, Matsunaga J, Haake DA. Lipoprotein computational prediction in spirochaetal genomes. Microbiology. 2006;152(Pt 1):113–21. Epub 2005/12/31. doi: 10.1099/mic.0.28317-0 pmid:16385121; PubMed Central PMCID: PMC2667199.
  101. 101. Paetzel M, Dalbey RE, Strynadka NC. Crystal structure of a bacterial signal peptidase apoenzyme: implications for signal peptide binding and the Ser-Lys dyad mechanism. J Biol Chem. 2002;277(11):9512–9. Epub 2001/12/14. doi: 10.1074/jbc.M110983200 pmid:11741964.
  102. 102. Sigrist CJ, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A, et al. New and continuing developments at PROSITE. Nucleic acids research. 2013;41(Database issue):D344–7. Epub 2012/11/20. doi: 10.1093/nar/gks1067 pmid:23161676; PubMed Central PMCID: PMC3531220.
  103. 103. Munoa FJ, Miller KW, Beers R, Graham M, Wu HC. Membrane topology of Escherichia coli prolipoprotein signal peptidase (signal peptidase II). J Biol Chem. 1991;266(26):17667–72. Epub 1991/09/15. pmid:1894646.
  104. 104. Bordbar A, Monk JM, King ZA, Palsson BO. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet. 2014;15(2):107–20. Epub 2014/01/17. doi: 10.1038/nrg3643 pmid:24430943.
  105. 105. Monk JM, Charusanti P, Aziz RK, Lerman JA, Premyodhin N, Orth JD, et al. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc Natl Acad Sci U S A. 2013;110(50):20338–43. Epub 2013/11/28. doi: 10.1073/pnas.1307797110 pmid:24277855; PubMed Central PMCID: PMC3864276.
  106. 106. Woodson JD, Escalante-Semerena JC. CbiZ, an amidohydrolase enzyme required for salvaging the coenzyme B12 precursor cobinamide in archaea. Proc Natl Acad Sci U S A. 2004;101(10):3591–6. doi: 10.1073/pnas.0305939101 pmid:14990804; PubMed Central PMCID: PMC373507.
  107. 107. Adler B. History of leptospirosis and leptospira. Current topics in microbiology and immunology. 2015;387:1–9. doi: 10.1007/978-3-662-45059-8_1 pmid:25388129.
  108. 108. Adler B. Vaccines against leptospirosis. Current topics in microbiology and immunology. 2015;387:251–72. doi: 10.1007/978-3-662-45059-8_10 pmid:25388138.
  109. 109. Faine M, Adler B, Bolin C, Perolat P. Leptospira and Leptospirosis. Melbourne: MedScience; 1999.
  110. 110. Bulach DM, Kalambaheti T, de la Pena-Moctezuma A, Adler B. Lipopolysaccharide biosynthesis in Leptospira. J Mol Microbiol Biotechnol. 2000;2(4):375–80. pmid:11075908
  111. 111. de la Pena-Moctezuma A, Bulach DM, Adler B. Genetic differences among the LPS biosynthetic loci of serovars of Leptospira interrogans and Leptospira borgpetersenii. FEMS Immunol Med Microbiol. 2001;31(1):73–81. Epub 2001/07/31. S0928-8244(01)00245-0 [pii]. pmid:11476985.
  112. 112. de la Pena-Moctezuma A, Bulach DM, Kalambaheti T, Adler B. Comparative analysis of the LPS biosynthetic loci of the genetic subtypes of serovar Hardjo: Leptospira interrogans subtype Hardjoprajitno and Leptospira borgpetersenii subtype Hardjobovis. FEMS microbiology letters. 1999;177(2):319–26. pmid:10474199
  113. 113. Levett PN, Morey RE, Galloway RL, Steigerwalt AG. Leptospira broomii sp. nov., isolated from humans with leptospirosis. Int J Syst Evol Microbiol. 2006;56(Pt 3):671–3. Epub 2006/03/04. 56/3/671 [pii] doi: 10.1099/ijs.0.63783-0 pmid:16514048.
  114. 114. Perolat P, Chappel RJ, Adler B, Baranton G, Bulach DM, Billinghurst ML, et al. Leptospira fainei sp. nov., isolated from pigs in Australia. Int J Syst Bacteriol. 1998;48 Pt 3:851–8. pmid:9734039.
  115. 115. Petersen AM, Boye K, Blom J, Schlichting P, Krogfelt KA. First isolation of Leptospira fainei serovar Hurstbridge from two human patients with Weil's syndrome. J Med Microbiol. 2001;50(1):96–100. pmid:11192512
  116. 116. Chappel RJ, Khalik DA, Adler B, Bulach DM, Faine S, Perolat P, et al. Serological titres to Leptospira fainei serovar hurstbridge in human sera in Australia. Epidemiol Infect. 1998;121(2):473–5. pmid:9825801.
  117. 117. de la Pena-Moctezuma A, Bulach DM, Kalambaheti T, Adler B. Comparative analysis of the LPS biosynthetic loci of the genetic subtypes of serovar Hardjo: Leptospira interrogans subtype Hardjoprajitno and Leptospira borgpetersenii subtype Hardjobovis. FEMS microbiology letters. 1999;177(2):319–26. Epub 1999/09/04. pmid:10474199.
  118. 118. Bulach DM, Kalambaheti T, de la Pena-Moctezuma A, Adler B. Functional analysis of genes in the rfb locus of Leptospira borgpetersenii serovar Hardjo subtype Hardjobovis. Infection and immunity. 2000;68(7):3793–8. Epub 2000/06/17. pmid:10858186; PubMed Central PMCID: PMC101650.
  119. 119. Raetz CR, Whitfield C. Lipopolysaccharide endotoxins. Annual review of biochemistry. 2002;71:635–700. Epub 2002/06/05. doi: 10.1146/annurev.biochem.71.110601.135414 pmid:12045108; PubMed Central PMCID: PMC2569852.
  120. 120. Cuthbertson L, Mainprize IL, Naismith JH, Whitfield C. Pivotal roles of the outer membrane polysaccharide export and polysaccharide copolymerase protein families in export of extracellular polysaccharides in gram-negative bacteria. Microbiology and molecular biology reviews: MMBR. 2009;73(1):155–77. Epub 2009/03/05. doi: 10.1128/MMBR.00024-08 pmid:19258536; PubMed Central PMCID: PMC2650888.
  121. 121. Keenleyside WJ, Perry M, Maclean L, Poppe C, Whitfield C. A plasmid-encoded rfbO:54 gene cluster is required for biosynthesis of the O:54 antigen in Salmonella enterica serovar Borreze. Molecular microbiology. 1994;11(3):437–48. Epub 1994/02/01. pmid:7512186.
  122. 122. Ricaldi JN, Fouts DE, Selengut JD, Harkins DM, Moreno A, Lehmann JS, et al. Whole genome analysis of Leptospira licerasiae provides insight into Leptospiral evolution and pathogenicity. PLoS Negl Trop Dis. 2012;6:e1853. doi: 10.1371/journal.pntd.0001853. pmid:23145189
  123. 123. Que-Gewirth NL, Ribeiro AA, Kalb SR, Cotter RJ, Bulach DM, Adler B, et al. A methylated phosphate group and four amide-linked acyl chains in Leptospira interrogans lipid A. The membrane anchor of an unusual lipopolysaccharide that activates TLR2. J Biol Chem. 2004;279(24):25420–9. Epub 2004/03/27. doi: 10.1074/jbc.M400598200 pmid:15044492; PubMed Central PMCID: PMC2556802.
  124. 124. Ricaldi JN, Matthias MA, Vinetz JM, Lewis AL. Expression of sialic acids and other nonulosonic acids in Leptospira. BMC Microbiol. 2012;12:161. Epub 2012/08/03. doi: 10.1186/1471-2180-12-161 pmid:22853805; PubMed Central PMCID: PMC3438082.
  125. 125. Ristow P, Bourhy P, da Cruz McBride FW, Figueira CP, Huerre M, Ave P, et al. The OmpA-like protein Loa22 is essential for leptospiral virulence. PLoS pathogens. 2007;3(7):e97. Epub 2007/07/17. doi: 10.1371/journal.ppat.0030097 pmid:17630832; PubMed Central PMCID: PMC1914066.
  126. 126. Brussow H, Hendrix RW. Phage genomics: small is beautiful. Cell. 2002;108:13–6. pmid:11792317
  127. 127. Rohwer F. Global phage diversity. Cell. 2003;113:141. pmid:12705861
  128. 128. Saint Girons I, Margarita D, Amouriaux P, Baranton G. First isolation of bacteriophages for a spirochaete: potential genetic tools for Leptospira. Res Microbiol. 1990;141:1131–8. pmid:2092364
  129. 129. Saint Girons I, Bourhy P, Ottone C, Picardeau M, Yelton D, Hendrix RW, et al. The LE1 bacteriophage replicates as a plasmid within Leptospira biflexa: construction of an L. biflexa-Escherichia coli shuttle vector. J Bacteriol. 2000;182:5700–5. pmid:11004167
  130. 130. Bourhy P, Frangeul L, Couve E, Glaser P, Saint Girons I, Picardeau M. Complete nucleotide sequence of the LE1 prophage from the spirochete Leptospira biflexa and characterization of its replication and partition functions. J Bacteriol. 2005;187:3931–40. pmid:15937155
  131. 131. Dobrindt U, Hochhut B, Hentschel U, Hacker J. Genomic islands in pathogenic and environmental microorganisms. Nat Rev Microbiol. 2004;2:414–24. pmid:15100694
  132. 132. Qin JH, Zhang Q, Zhang ZM, Zhong Y, Yang Y, Hu BY, et al. Identification of a novel prophage-like gene cluster actively expressed in both virulent and avirulent strains of Leptospira interrogans serovar Lai. Infect Immun. 2008;76:2411–9. doi: 10.1128/IAI.01730-07. pmid:18362131
  133. 133. Bourhy P, Salaün L, Lajus A, Médigue C, Boursaux-Eude C, Picardeau M. A genomic island of the pathogen Leptospira interrogans serovar Lai can excise from its chromosome. Infect Immun. 2007;75:677–83. pmid:17118975
  134. 134. Ackermann H-W. 5500 Phages examined in the electron microscope. Arch Virol. 2006;152:227–43. pmid:17051420
  135. 135. Saint Girons I, Bourhy P, Ottone C, Picardeau M, Yelton D, Hendrix RW, et al. The LE1 bacteriophage replicates as a plasmid within leptospira biflexa: construction of an L. biflexa-escherichia coli shuttle vecto. J Bacteriol. 2000;182(20):5700–5. pmid:11004167
  136. 136. Haft DH, Selengut J, Mongodin EF, Nelson KE. A guild of 45 CRISPR-associated (Cas) protein families and multiple CRISPR/Cas subtypes exist in prokaryotic genomes. PLoS Comput Biol. 2005;1(6):e60. Epub 2005/11/18. doi: 10.1371/journal.pcbi.0010060 pmid:16292354; PubMed Central PMCID: PMC1282333.
  137. 137. Makarova KS, Haft DH, Barrangou R, Brouns SJ, Charpentier E, Horvath P, et al. Evolution and classification of the CRISPR-Cas systems. Nature reviews Microbiology. 2011;9(6):467–77. Epub 2011/05/10. doi: 10.1038/nrmicro2577 pmid:21552286; PubMed Central PMCID: PMC3380444.
  138. 138. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. Epub 2009/12/17. doi: 10.1186/1471-2105-10-421 pmid:20003500; PubMed Central PMCID: PMC2803857.
  139. 139. Nickerson NN, Joag V, McGavin MJ. Rapid autocatalytic activation of the M4 metalloprotease aureolysin is controlled by a conserved N-terminal fungalysin-thermolysin-propeptide domain. Mol Microbiol. 2008;69(6):1530–43. doi: 10.1111/j.1365-2958.2008.06384.x pmid:18673454.
  140. 140. Kassegne K, Hu W, Ojcius DM, Sun D, Ge Y, Zhao J, et al. Identification of collagenase as a critical virulence factor for invasiveness and transmission of pathogenic Leptospira species. J Infect Dis. 2014;209(7):1105–15. doi: 10.1093/infdis/jit659 pmid:24277745.
  141. 141. Janwitthayanan W, Keelawat S, Payungporn S, Lowanitchapat A, Suwancharoen D, Poovorawan Y, et al. In vivo gene expression and immunoreactivity of Leptospira collagenase. Microbiol Res. 2013;168(5):268–72. doi: 10.1016/j.micres.2012.12.005 pmid:23305770.
  142. 142. Matsunaga J, Barocchi MA, Croda J, Young TA, Sanchez Y, Siqueira I, et al. Pathogenic Leptospira species express surface-exposed proteins belonging to the bacterial immunoglobulin superfamily. Molecular microbiology. 2003;49(4):929–45. Epub 2003/08/02. pmid:12890019; PubMed Central PMCID: PMC1237129.
  143. 143. Chang YF, Chen CS, Palaniappan RU, He H, McDonough SP, Barr SC, et al. Immunogenicity of the recombinant leptospiral putative outer membrane proteins as vaccine candidates. Vaccine. 2007;25(48):8190–7. Epub 2007/10/16. doi: 10.1016/j.vaccine.2007.09.020 pmid:17936448.
  144. 144. Hamburger ZA, Brown MS, Isberg RR, Bjorkman PJ. Crystal structure of invasin: a bacterial integrin-binding protein. Science. 1999;286(5438):291–5. Epub 1999/10/09. pmid:10514372.
  145. 145. Luo Y, Frey EA, Pfuetzner RA, Creagh AL, Knoechel DG, Haynes CA, et al. Crystal structure of enteropathogenic Escherichia coli intimin-receptor complex. Nature. 2000;405(6790):1073–7. Epub 2000/07/13. doi: 10.1038/35016618 pmid:10890451.
  146. 146. Palaniappan RU, Chang YF, Jusuf SS, Artiushin S, Timoney JF, McDonough SP, et al. Cloning and molecular characterization of an immunogenic LigA protein of Leptospira interrogans. Infection and immunity. 2002;70(11):5924–30. Epub 2002/10/16. pmid:12379666; PubMed Central PMCID: PMC130282.
  147. 147. McBride AJ, Cerqueira GM, Suchard MA, Moreira AN, Zuerner RL, Reis MG, et al. Genetic diversity of the Leptospiral immunoglobulin-like (Lig) genes in pathogenic Leptospira spp. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2009;9(2):196–205. Epub 2008/11/26. doi: 10.1016/j.meegid.2008.10.012 pmid:19028604; PubMed Central PMCID: PMC2812920.
  148. 148. Cerqueira GM, McBride AJ, Picardeau M, Ribeiro SG, Moreira AN, Morel V, et al. Distribution of the leptospiral immunoglobulin-like (lig) genes in pathogenic Leptospira species and application of ligB to typing leptospiral isolates. J Med Microbiol. 2009;58(Pt 9):1173–81. Epub 2009/06/17. doi: 10.1099/jmm.0.009175-0 pmid:19528180; PubMed Central PMCID: PMC2887549.
  149. 149. Lehmann JS, Fouts DE, Haft DH, Cannella AP, Ricaldi JN, Brinkac L, et al. Pathogenomic Inference of Virulence-Associated Genes in Leptospira interrogans. PLoS neglected tropical diseases. 2013;7(10): e2468. doi: 10.1371/journal.pntd.0002468. pmid:24098822
  150. 150. Lambert A, Picardeau M, Haake DA, Sermswan RW, Srikram A, Adler B, et al. FlaA proteins in Leptospira interrogans are essential for motility and virulence but are not required for formation of the flagellum sheath. Infection and immunity. 2012;80(6):2019–25. Epub 2012/03/28. doi: 10.1128/IAI.00131-12 pmid:22451522; PubMed Central PMCID: PMC3370569.
  151. 151. Liao S, Sun A, Ojcius DM, Wu S, Zhao J, Yan J. Inactivation of the fliY gene encoding a flagellar motor switch protein attenuates mobility and virulence of Leptospira interrogans strain Lai. BMC Microbiol. 2009;9:253. Epub 2009/12/17. doi: 10.1186/1471-2180-9-253 pmid:20003186; PubMed Central PMCID: PMC3224694.
  152. 152. Keener JP. A molecular ruler mechanism for length control of extended protein structures in bacteria. Journal of theoretical biology. 2010;263(4):481–9. Epub 2009/12/23. doi: 10.1016/j.jtbi.2009.12.015 pmid:20026337.
  153. 153. Pallen MJ, Penn CW, Chaudhuri RR. Bacterial flagellar diversity in the post-genomic era. Trends Microbiol. 2005;13(4):143–9. Epub 2005/04/09. doi: 10.1016/j.tim.2005.02.008 pmid:15817382.
  154. 154. Jones CJ, Homma M, Macnab RM. L-, P-, and M-ring proteins of the flagellar basal body of Salmonella typhimurium: gene sequences and deduced protein sequences. J Bacteriol. 1989;171(7):3890–900. Epub 1989/07/01. pmid:2544561; PubMed Central PMCID: PMC210140.
  155. 155. Charon NW, Cockburn A, Li C, Liu J, Miller KA, Miller MR, et al. The unique paradigm of spirochete motility and chemotaxis. Annual review of microbiology. 2012;66:349–70. Epub 2012/09/22. doi: 10.1146/annurev-micro-092611-150145 pmid:22994496; PubMed Central PMCID: PMC3771095.
  156. 156. Lehmann JS, Matthias MA, Vinetz JM, Fouts DE. Leptospiral Pathogenomics. Pathogens. 2014;3(2):280–308; doi: 10.3390/pathogens3020280. pmid:25437801
  157. 157. Ahmed N, Devi SM, Valverde Mde L, Vijayachari P, Machang'u RS, Ellis WA, et al. Multilocus sequence typing method for identification and genotypic classification of pathogenic Leptospira species. Ann Clin Microbiol Antimicrob. 2006;5:28. pmid:17121682.
  158. 158. Thaipadungpanit J, Wuthiekanun V, Chierakul W, Smythe LD, Petkanchanapong W, Limpaiboon R, et al. A dominant clone of Leptospira interrogans associated with an outbreak of human leptospirosis in Thailand. PLoS Negl Trop Dis. 2007;1(1):e56. Epub 2007/11/09. doi: 10.1371/journal.pntd.0000056 pmid:17989782.
  159. 159. Richter M, Rosselló-Móra R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci USA. 2009;106:19126–31. doi: 10.1073/pnas.0906412106. pmid:19855009
  160. 160. Tindall BJ, Rosselló-Móra R, Busse HJ, Ludwig W, Kämpfer P. Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol Microbiol. 2010;60:249–66. doi: 10.1099/ijs.0.016949-0. pmid:19700448
  161. 161. Werts C, Tapping RI, Mathison JC, Chuang TH, Kravchenko V, Saint Girons I, et al. Leptospiral lipopolysaccharide activates cells through a TLR2-dependent mechanism. Nat Immunol. 2001;2(4):346–52. pmid:11276206
  162. 162. Nahori MA, Fournie-Amazouz E, Que-Gewirth NS, Balloy V, Chignard M, Raetz CR, et al. Differential TLR recognition of leptospiral lipid A and lipopolysaccharide in murine and human cells. J Immunol. 2005;175(9):6022–31. Epub 2005/10/21. 175/9/6022 [pii]. pmid:16237097.
  163. 163. Que-Gewirth NLS, Ribeiro AA, Kalb SR, Cotter RJ, Bulach DM, Adler B, et al. A methylated phosphate group and four amide-linked acyl chains in Leptospira interrogans lipid A. J Biol Chem. 2004;279:25420–429. pmid:15044492
  164. 164. Viriyakosol S, Fierer J, Brown GD, Kirkland TN. Innate immunity to the pathogenic fungus Coccidioides posadasii is dependent on TLR2 and Dectin-1. Infection and immunity. 2005;In press.
  165. 165. Faine S. Leptospira and Leptospirosis. Boca Raton, Florida: CRC Press; 1994.
  166. 166. Feasey NA, Dougan G, Kingsley RA, Heyderman RS, Gordon MA. Invasive non-typhoidal salmonella disease: an emerging and neglected tropical disease in Africa. Lancet. 2012;379(9835):2489–99. doi: 10.1016/S0140-6736(11)61752-2 pmid:22587967; PubMed Central PMCID: PMC3402672.
  167. 167. Wildschutte H, Wolfe DM, Tamewitz A, Lawrence JG. Protozoan predation, diversifying selection, and the evolution of antigenic diversity in Salmonella. Proc Natl Acad Sci U S A. 2004;101(29):10644–9. doi: 10.1073/pnas.0404028101 pmid:15247413; PubMed Central PMCID: PMC489988.
  168. 168. Fondi M, Lio P. Genome-scale metabolic network reconstruction. Methods in molecular biology. 2015;1231:233–56. doi: 10.1007/978-1-4939-1720-4_15 pmid:25343869.
  169. 169. Fondi M, Lio P. Multi -omics and metabolic modelling pipelines: Challenges and tools for systems microbiology. Microbiol Res. 2015;171C:52–64. doi: 10.1016/j.micres.2015.01.003 pmid:25644953.
  170. 170. Stalheim OH, Wilson JB. Cultivation of Leptospirae. I. Nutrition of Leptospira Canicola. J Bacteriol. 1964;88:48–54. Epub 1964/07/01. pmid:14197904; PubMed Central PMCID: PMC277255.
  171. 171. Shenberg E. Growth of pathogenic Leptospira in chemically defined media. J Bacteriol. 1967;93(5):1598–606. pmid:6025446; PubMed Central PMCID: PMC276655.
  172. 172. Murachi T, Tabata M. Use of a bioreactor consisting of sequentially aligned L-glutamate dehydrogenase and L-glutamate oxidase for the determination of ammonia by chemiluminescence. Biotechnol Appl Biochem. 1987;9(4):303–9. pmid:3663333.
  173. 173. Bohmer A, Muller A, Passarge M, Liebs P, Honeck H, Muller HG. A novel L-glutamate oxidase from Streptomyces endus. Purification and properties. Eur J Biochem. 1989;182(2):327–32. pmid:2737205.
  174. 174. Monk J, Nogales J, Palsson BO. Optimizing genome-scale network reconstructions. Nat Biotechnol. 2014;32(5):447–52. Epub 2014/05/09. doi: 10.1038/nbt.2870 pmid:24811519.
  175. 175. Roth JR, Lawrence JG, Bobik TA. Cobalamin (coenzyme B12): synthesis and biological significance. Annual review of microbiology. 1996;50:137–81. doi: 10.1146/annurev.micro.50.1.137 pmid:8905078.
  176. 176. Nielsen MJ, Rasmussen MR, Andersen CB, Nexo E, Moestrup SK. Vitamin B12 transport from food to the body's cells—a sophisticated, multistep pathway. Nature reviews Gastroenterology & hepatology. 2012;9(6):345–54. doi: 10.1038/nrgastro.2012.76 pmid:22547309.
  177. 177. Zhao N, Zhang AS, Enns CA. Iron regulation by hepcidin. J Clin Invest. 2013;123(6):2337–43. doi: 10.1172/JCI67225 pmid:23722909; PubMed Central PMCID: PMC3668831.
  178. 178. Andrews NC. Disorders of iron metabolism. N Engl J Med. 1999;341(26):1986–95. doi: 10.1056/NEJM199912233412607 pmid:10607817.
  179. 179. Austin FE, Barbieri JT, Corin RE, Grigas KE, Cox CD. Distribution of superoxide dismutase, catalase, and peroxidase activities among Treponema pallidum and other spirochetes. Infection and immunity. 1981;33(2):372–9. pmid:7024127; PubMed Central PMCID: PMC350708.
  180. 180. Li S, Ojcius DM, Liao S, Li L, Xue F, Dong H, et al. Replication or death: distinct fates of pathogenic Leptospira strain Lai within macrophages of human or mouse origin. Innate Immun. 2010;16(2):80–92. Epub 2009/07/10. doi: 10.1177/1753425909105580 pmid:19587003.
  181. 181. Toma C, Okura N, Takayama C, Suzuki T. Characteristic features of intracellular pathogenic Leptospira in infected murine macrophages. Cell Microbiol. 2011;13(11):1783–92. Epub 2011/08/09. doi: 10.1111/j.1462-5822.2011.01660.x pmid:21819516.
  182. 182. Evangelista KV, Coburn J. Leptospira as an emerging pathogen: a review of its biology, pathogenesis and host immune responses. Future Microbiology. 2010;5(9):1413–25. Epub 2010/09/24. doi: 10.2217/fmb.10.102 pmid:20860485; PubMed Central PMCID: PMC3037011.
  183. 183. Evangelista KV, Hahn B, Wunder EA Jr., Ko AI, Haake DA, Coburn J. Identification of Cell-Binding Adhesins of Leptospira interrogans. PLoS Negl Trop Dis. 2014;8(10):e3215. doi: 10.1371/journal.pntd.0003215 pmid:25275630; PubMed Central PMCID: PMC4183468.
  184. 184. Evangelista K, Franco R, Schwab A, Coburn J. Leptospira interrogans binds to cadherins. PLoS Negl Trop Dis. 2014;8(1):e2672. doi: 10.1371/journal.pntd.0002672 pmid:24498454; PubMed Central PMCID: PMC3907533.
  185. 185. Fernandes LG, Vieira ML, Alves IJ, de Morais ZM, Vasconcellos SA, Romero EC, et al. Functional and immunological evaluation of two novel proteins of Leptospira spp. Microbiology. 2014;160(Pt 1):149–64. Epub 2013/10/29. doi: 10.1099/mic.0.072074-0 pmid:24162609.
  186. 186. Siqueira GH, Atzingen MV, Alves IJ, de Morais ZM, Vasconcellos SA, Nascimento AL. Characterization of three novel adhesins of Leptospira interrogans. The American journal of tropical medicine and hygiene. 2013;89(6):1103–16. doi: 10.4269/ajtmh.13-0205 pmid:23958908; PubMed Central PMCID: PMC3854887.
  187. 187. Oliveira R, Domingos RF, Siqueira GH, Fernandes LG, Souza NM, Vieira ML, et al. Adhesins of Leptospira interrogans mediate the interaction to fibrinogen and inhibit fibrin clot formation in vitro. PLoS neglected tropical diseases. 2013;7(8):e2396. doi: 10.1371/journal.pntd.0002396 pmid:24009788; PubMed Central PMCID: PMC3757074.
  188. 188. Souza NM, Vieira ML, Alves IJ, de Morais ZM, Vasconcellos SA, Nascimento AL. Lsa30, a novel adhesin of Leptospira interrogans binds human plasminogen and the complement regulator C4bp. Microbial pathogenesis. 2012;53(3–4):125–34. doi: 10.1016/j.micpath.2012.06.001 pmid:22732096.
  189. 189. Domingos RF, Vieira ML, Romero EC, Goncales AP, de Morais ZM, Vasconcellos SA, et al. Features of two proteins of Leptospira interrogans with potential role in host-pathogen interactions. BMC microbiology. 2012;12:50. doi: 10.1186/1471-2180-12-50 pmid:22463075; PubMed Central PMCID: PMC3444417.
  190. 190. Oliveira R, de Morais ZM, Goncales AP, Romero EC, Vasconcellos SA, Nascimento AL. Characterization of novel OmpA-like protein of Leptospira interrogans that binds extracellular matrix molecules and plasminogen. PLoS One. 2011;6(7):e21962. Epub 2011/07/15. doi: 10.1371/journal.pone.0021962 pmid:21755014; PubMed Central PMCID: PMC3130794.
  191. 191. Mendes RS, Von Atzingen M, de Morais ZM, Goncales AP, Serrano SM, Asega AF, et al. The novel leptospiral surface adhesin Lsa20 binds laminin and human plasminogen and is probably expressed during infection. Infection and immunity. 2011;79(11):4657–67. Epub 2011/08/17. doi: 10.1128/IAI.05583-11 pmid:21844229; PubMed Central PMCID: PMC3257903.
  192. 192. Vieira ML, de Morais ZM, Goncales AP, Romero EC, Vasconcellos SA, Nascimento AL. Lsa63, a newly identified surface protein of Leptospira interrogans binds laminin and collagen IV. The Journal of infection. 2010;60(1):52–64. doi: 10.1016/j.jinf.2009.10.047 pmid:19879894.
  193. 193. Oliveira TR, Longhi MT, Goncales AP, de Morais ZM, Vasconcellos SA, Nascimento AL. LipL53, a temperature regulated protein from Leptospira interrogans that binds to extracellular matrix molecules. Microbes and infection / Institut Pasteur. 2010;12(3):207–17. doi: 10.1016/j.micinf.2009.12.004 pmid:20026283.
  194. 194. Longhi MT, Oliveira TR, Romero EC, Goncales AP, de Morais ZM, Vasconcellos SA, et al. A newly identified protein of Leptospira interrogans mediates binding to laminin. Journal of medical microbiology. 2009;58(Pt 10):1275–82. doi: 10.1099/jmm.0.011916-0 pmid:19541787.
  195. 195. Maciel EA, de Carvalho AL, Nascimento SF, de Matos RB, Gouveia EL, Reis MG, et al. Household transmission of Leptospira infection in urban slum communities. PLoS neglected tropical diseases. 2008;2(1):e154. Epub 2008/03/22. doi: 10.1371/journal.pntd.0000154 pmid:18357340; PubMed Central PMCID: PMC2270796.
  196. 196. Domingos R, Fernandes L, Romero E, de Morais Z, Vasconcellos S, Nascimento AL. The novel Leptospira interrogans protein Lsa32 is expressed during infection and binds laminin and plasminogen. Microbiology. 2015.
  197. 197. Vieira ML, Fernandes LG, Domingos RF, Oliveira R, Siqueira GH, Souza NM, et al. Leptospiral extracellular matrix adhesins as mediators of pathogen-host interactions. FEMS Microbiol Lett. 2014;352(2):129–39. doi: 10.1111/1574-6968.12349 pmid:24289724.
  198. 198. Choy HA, Kelley MM, Croda J, Matsunaga J, Babbitt JT, Ko AI, et al. The multifunctional LigB adhesin binds homeostatic proteins with potential roles in cutaneous infection by pathogenic Leptospira interrogans. PLoS One. 2011;6(2):e16879. Epub 2011/02/25. doi: 10.1371/journal.pone.0016879 pmid:21347378; PubMed Central PMCID: PMC3036719.
  199. 199. Figueira CP, Croda J, Choy HA, Haake DA, Reis MG, Ko AI, et al. Heterologous expression of pathogen-specific genes ligA and ligB in the saprophyte Leptospira biflexa confers enhanced adhesion to cultured cells and fibronectin. BMC microbiology. 2011;11:129. doi: 10.1186/1471-2180-11-129 pmid:21658265; PubMed Central PMCID: PMC3133549.
  200. 200. Fernandes LG, Vieira ML, Kirchgatter K, Alves IJ, de Morais ZM, Vasconcellos SA, et al. OmpL1 is an extracellular matrix- and plasminogen-interacting protein of Leptospira spp. Infection and immunity. 2012;80(10):3679–92. Epub 2012/07/18. doi: 10.1128/IAI.00474-12 pmid:22802342; PubMed Central PMCID: PMC3457549.
  201. 201. Vieira ML, Atzingen MV, Oliveira R, Mendes RS, Domingos RF, Vasconcellos SA, et al. Plasminogen binding proteins and plasmin generation on the surface of Leptospira spp.: the contribution to the bacteria-host interactions. J Biomed Biotechnol. 2012;2012:758513. Epub 2012/11/03. doi: 10.1155/2012/758513 pmid:23118516; PubMed Central PMCID: PMC3481863.
  202. 202. Potempa M, Potempa J. Protease-dependent mechanisms of complement evasion by bacterial pathogens. Biol Chem. 2012;393(9):873–88. Epub 2012/09/05. doi: 10.1515/hsz-2012-0174 pmid:22944688; PubMed Central PMCID: PMC3488274.
  203. 203. Meri T, Murgia R, Stefanel P, Meri S, Cinco M. Regulation of complement activation at the C3-level by serum resistant leptospires. Microb Pathog. 2005;39(4):139–47. Epub 2005/09/20. doi: 10.1016/j.micpath.2005.07.003 pmid:16169184.
  204. 204. Barbosa AS, Abreu PA, Vasconcellos SA, Morais ZM, Goncales AP, Silva AS, et al. Immune evasion of Leptospira species by acquisition of human complement regulator C4BP. Infection and immunity. 2009;77(3):1137–43. Epub 2008/12/31. doi: 10.1128/IAI.01310-08 pmid:19114549; PubMed Central PMCID: PMC2643629.
  205. 205. Verma A, Hellwage J, Artiushin S, Zipfel PF, Kraiczy P, Timoney JF, et al. LfhA, a novel factor H-binding protein of Leptospira interrogans. Infection and immunity. 2006;74(5):2659–66. Epub 2006/04/20. doi: 10.1128/IAI.74.5.2659-2666.2006 pmid:16622202; PubMed Central PMCID: PMC1459737.
  206. 206. Barbosa AS, Monaris D, Silva LB, Morais ZM, Vasconcellos SA, Cianciarullo AM, et al. Functional characterization of LcpA, a surface-exposed protein of Leptospira spp. that binds the human complement regulator C4BP. Infection and immunity. 2010;78(7):3207–16. Epub 2010/04/21. doi: 10.1128/IAI.00279-10 pmid:20404075; PubMed Central PMCID: PMC2897400.
  207. 207. Choy HA. Multiple activities of LigB potentiate virulence of Leptospira interrogans: inhibition of alternative and classical pathways of complement. PLoS One. 2012;7(7):e41566. Epub 2012/08/23. doi: 10.1371/journal.pone.0041566 pmid:22911815; PubMed Central PMCID: PMC3402383.
  208. 208. Domingos RF, Vieira ML, Romero EC, Goncales AP, de Morais ZM, Vasconcellos SA, et al. Features of two proteins of Leptospira interrogans with potential role in host-pathogen interactions. BMC Microbiol. 2012;12:50. Epub 2012/04/03. doi: 10.1186/1471-2180-12-50 pmid:22463075; PubMed Central PMCID: PMC3444417.
  209. 209. Souza NM, Vieira ML, Alves IJ, de Morais ZM, Vasconcellos SA, Nascimento AL. Lsa30, a novel adhesin of Leptospira interrogans binds human plasminogen and the complement regulator C4bp. Microb Pathog. 2012;53(3–4):125–34. Epub 2012/06/27. doi: 10.1016/j.micpath.2012.06.001 pmid:22732096.
  210. 210. Castiblanco-Valencia MM, Fraga TR, Silva LB, Monaris D, Abreu PA, Strobel S, et al. Leptospiral immunoglobulin-like proteins interact with human complement regulators factor H, FHL-1, FHR-1, and C4BP. J Infect Dis. 2012;205(6):995–1004. Epub 2012/02/01. doi: 10.1093/infdis/jir875 pmid:22291192.
  211. 211. Siqueira GH, Atzingen MV, Alves IJ, de Morais ZM, Vasconcellos SA, Nascimento AL. Characterization of three novel adhesins of Leptospira interrogans. Am J Trop Med Hyg. 2013;89(6):1103–16. Epub 2013/08/21. doi: 10.4269/ajtmh.13-0205 pmid:23958908; PubMed Central PMCID: PMC3854887.
  212. 212. Adekoya OA, Sylte I. The thermolysin family (M4) of enzymes: therapeutic and biotechnological potential. Chem Biol Drug Des. 2009;73(1):7–16. doi: 10.1111/j.1747-0285.2008.00757.x pmid:19152630.
  213. 213. Laarman AJ, Ruyken M, Malone CL, van Strijp JA, Horswill AR, Rooijakkers SH. Staphylococcus aureus metalloprotease aureolysin cleaves complement C3 to mediate immune evasion. J Immunol. 2011;186(11):6445–53. Epub 2011/04/20. doi: 10.4049/jimmunol.1002948 pmid:21502375.
  214. 214. Fraga TR, Courrol Ddos S, Castiblanco-Valencia MM, Hirata IY, Vasconcellos SA, Juliano L, et al. Immune evasion by pathogenic Leptospira strains: the secretion of proteases that directly cleave complement proteins. J Infect Dis. 2014;209(6):876–86. Epub 2013/10/29. doi: 10.1093/infdis/jit569 pmid:24163418.
  215. 215. Ricaldi JN, Matthias MA, Vinetz JM, Lewis AL. Expression of sialic acids and other nonulosonic acids in Leptospira. BMC microbiology. 2012;12:161. doi: 10.1186/1471-2180-12-161 pmid:22853805; PubMed Central PMCID: PMC3438082.
  216. 216. McNally DJ, Schoenhofen IC, Houliston RS, Khieu NH, Whitfield DM, Logan SM, et al. CMP-pseudaminic acid is a natural potent inhibitor of PseB, the first enzyme of the pseudaminic acid pathway in Campylobacter jejuni and Helicobacter pylori. ChemMedChem. 2008;3(1):55–9. Epub 2007/09/26. doi: 10.1002/cmdc.200700170 pmid:17893902.
  217. 217. Raddi G, Morado DR, Yan J, Haake DA, Yang XF, Liu J. Three-dimensional structures of pathogenic and saprophytic Leptospira species revealed by cryo-electron tomography. J Bacteriol. 2012;194(6):1299–306. Epub 2012/01/10. doi: 10.1128/JB.06474-11 pmid:22228733; PubMed Central PMCID: PMC3294836.
  218. 218. Schoenhofen IC, Vinogradov E, Whitfield DM, Brisson JR, Logan SM. The CMP-legionaminic acid pathway in Campylobacter: biosynthesis involving novel GDP-linked precursors. Glycobiology. 2009;19(7):715–25. Epub 2009/03/14. doi: 10.1093/glycob/cwp039 pmid:19282391.
  219. 219. Lehmann JS, Fouts DE, Haft DH, Cannella AP, Ricaldi JN, Brinkac L, et al. Pathogenomic inference of virulence-associated genes in Leptospira interrogans. PLoS Negl Trop Dis. 2013;7(10):e2468. doi: 10.1371/journal.pntd.0002468 pmid:24098822; PubMed Central PMCID: PMC3789758.
  220. 220. Lambert A, Takahashi N, Charon NW, Picardeau M. Chemotactic behavior of pathogenic and nonpathogenic Leptospira species. Applied and environmental microbiology. 2012;78(23):8467–9. Epub 2012/09/25. doi: 10.1128/AEM.02288-12 pmid:23001652; PubMed Central PMCID: PMC3497369.
  221. 221. Islam MS, Takabe K, Kudo S, Nakamura S. Analysis of the chemotactic behaviour of Leptospira using microscopic agar-drop assay. FEMS microbiology letters. 2014;356(1):39–44. Epub 2014/06/05. doi: 10.1111/1574-6968.12495 pmid:24894019.
  222. 222. Malmstrom J, Beck M, Schmidt A, Lange V, Deutsch EW, Aebersold R. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature. 2009;460(7256):762–5. Epub 2009/07/17. doi: 10.1038/nature08184 pmid:19606093; PubMed Central PMCID: PMC2723184.
  223. 223. Buck M, Gallegos MT, Studholme DJ, Guo Y, Gralla JD. The bacterial enhancer-dependent sigma 54 (sigma N) transcription factor. J Bacteriol. 2000;182(15):4129–36. pmid:10894718
  224. 224. Hubner A, Yang X, Nolen DM, Popova TG, Cabello FC, Norgard MV. Expression of Borrelia burgdorferi OspC and DbpA is controlled by a RpoN-RpoS regulatory pathway. Proc Natl Acad Sci U S A. 2001;98(22):12724–9. Epub 2001/10/25. doi: 10.1073/pnas.231442498 98/22/12724 [pii]. pmid:11675503; PubMed Central PMCID: PMC60121.
  225. 225. Yang XF, Alani SM, Norgard MV. The response regulator Rrp2 is essential for the expression of major membrane lipoproteins in Borrelia burgdorferi. Proc Natl Acad Sci U S A. 2003;100(19):11001–6. pmid:12949258
  226. 226. Staroń A, Sofia HJ, Dietrich S, Ulrich LE, Liesegang H, Mascher T. The third pillar of bacterial signal transduction: classification of the extracytoplasmic function (ECF) σ factor protein family. Molecular microbiology. 2009;74(3):557–81. doi: 10.1111/j.1365-2958.2009.06870.x. pmid:19737356
  227. 227. Helmann JD. The extracytoplasmic function (ECF) sigma factors. Adv Microb Physiol. 2002;46:47–110. pmid:12073657
  228. 228. Hu Y, Kendall S, Stoker NG, Coates ARM. The Mycobacterium tuberculosis sigJ gene controls sensitivity of the bacterium to hydrogen peroxide2004 2004-08-01 00:00:00. 415–23 p.
  229. 229. Ho TD, Ellermeier CD. Extra cytoplasmic function σ factor activation. Current Opinion in Microbiology. 2012;15(2):182–8. doi: 10.1016/j.mib.2012.01.001. pmid:22381678
  230. 230. Fraser CM, Casjens S, Huang WM, Sutton GG, Clayton R, Lathigra R, et al. Genomic sequence of a Lyme disease spirochaete, Borrelia burgdorferi. Nature. 1997;390:580–6. pmid:9403685
  231. 231. Fraser CM, Norris SJ, Weinstock GM, White O, Sutton GG, Dodson R, et al. Complete genome sequence of Treponema pallidum, the syphilis spirochete. Science. 1998;281:375–88. pmid:9665876