Skip to content

Advertisement

  • Research article
  • Open Access

The variome of pneumococcal virulence factors and regulators

  • Gustavo Gámez1, 2, 3, 4Email author,
  • Andrés Castro1, 2,
  • Alejandro Gómez-Mejia1, 3,
  • Mauricio Gallego1, 2,
  • Alejandro Bedoya1, 2,
  • Mauricio Camargo1 and
  • Sven Hammerschmidt3
Contributed equally
BMC Genomics201819:10

https://doi.org/10.1186/s12864-017-4376-0

Received: 3 August 2017

Accepted: 11 December 2017

Published: 3 January 2018

Abstract

Background

In recent years, the idea of a highly immunogenic protein-based vaccine to combat Streptococcus pneumoniae and its severe invasive infectious diseases has gained considerable interest. However, the target proteins to be included in a vaccine formulation have to accomplish several genetic and immunological characteristics, (such as conservation, distribution, immunogenicity and protective effect), in order to ensure its suitability and effectiveness. This study aimed to get comprehensive insights into the genomic organization, population distribution and genetic conservation of all pneumococcal surface-exposed proteins, genetic regulators and other virulence factors, whose important function and role in pathogenesis has been demonstrated or hypothesized.

Results

After retrieving the complete set of DNA and protein sequences reported in the databases GenBank, KEGG, VFDB, P2CS and Uniprot for pneumococcal strains whose genomes have been fully sequenced and annotated, a comprehensive bioinformatic analysis and systematic comparison has been performed for each virulence factor, stand-alone regulator and two-component regulatory system (TCS) encoded in the pan-genome of S. pneumoniae. A total of 25 S. pneumoniae strains, representing different pneumococcal phylogenetic lineages and serotypes, were considered. A set of 92 different genes and proteins were identified, classified and studied to construct a pan-genomic variability map (variome) for S. pneumoniae. Both, pneumococcal virulence factors and regulatory genes, were well-distributed in the pneumococcal genome and exhibited a conserved feature of genome organization, where replication and transcription are co-oriented. The analysis of the population distribution for each gene and protein showed that 49 of them are part of the core genome in pneumococci, while 43 belong to the accessory-genome. Estimating the genetic variability revealed that pneumolysin, enolase and Usp45 (SP_2216 in S. p. TIGR4) are the pneumococcal virulence factors with the highest conservation, while TCS08, TCS05, and TCS02 represent the most conserved pneumococcal genetic regulators.

Conclusions

The results identified well-distributed and highly conserved pneumococcal virulence factors as well as regulators, representing promising candidates for a new generation of serotype-independent protein-based vaccine(s) to combat pneumococcal infections.

Keywords

VariomeVirulence factorsTwo component systems Streptococcus Pneumoniae

Background

Streptococcus pneumoniae, also known as the pneumococcus, is a Gram-positive, α-hemolytic and facultative aerobic bacterium. This microorganism is normally found as a harmless commensal in the upper respiratory tract of humans. Pneumococi have a great epidemiological importance due to their high impact on public health, causing more than one and a half million of deaths per year around the world [1]. S. pneumoniae is the main etiologic agent of community-acquired pneumonia. However, this is not its only clinical manifestation, because other kind of diseases such as otitis media, sinusitis, septicemia and meningitis are also caused by this pathogen and associated with high mortality rates [2].

Given the particular biochemical and molecular features of Streptococcus pneumoniae (Gram-positive, catalase-negative, optochin-sensitive and bile-soluble bacteria), its identification process in the laboratory is relatively simple. Nevertheless, the great molecular, biochemical and immunological diversity of its capsule and other antigens such as choline-binding proteins make them one of the hardest bacterial pathogens to face because of its variability [3, 4]. The “Quellung Reaction”, developed over 100 years ago by Neufeld, allows the specifical and reliable identification of each one of the >94 serotypes that have been discovered up to date. The capsular polysaccharide is the sine qua non virulence factor, however the pathogenic potential of serotypes may vary and similarly, the frequencies or prevalence varies from one geographic region to the other [5]. Despite this, the capsule is not the only factor required to induce disease by S. pneumoniae. In fact, the surface of the pneumococcus is decorated by various proteins, which have been already associated with its high pathogenic potential. In addition, their interaction level with the host cellular receptors has been proved, exhibiting crucial pathogenic functions such as adhesion, colonization, breaching tissue barriers and immune evasion [6].

An important group of regulatory proteins of great interest are the histidine kinases (HK), located in the bacterial surface and functioning as the sensors of two-component regulatory systems (TCS). The sensing of environmental signals via TCS, regulates the genetic expression of cellular processes that are of great importance such as natural competence, antibiotic resistance, adaptation to different environmental situations, surface proteins expression, and others [7, 8]. In general, TCS are composed of a histidine kinase, a membrane protein sensing the extracellular signals and transmitting these signals to a cytoplasmatic regulator/effector protein refered to as response regulator (RR). This happens via the HK autophosphorylation and a subsequent trans-phosphorylation process. In Streptococcus pneumoniae, 13 TCS and one orphan RR have been identified [7].

The relevance of the cellular, physiological and pathogenic functions that these pneumococcal proteins fulfill, have aroused a great scientific and biotechnological interest, given their potential pharmaceutical applications as vaccine candidates [9]. Nowadays, the antibiotic treatment of the infections caused by the pneumococcus is often complicated due to the increase of antibiotic resistance [10]. Furthermore, prevention by the use of the pneumococcal polysaccharide vaccines and/or pneumococcal conjugated vaccines only helps to control the disease caused by some of the serotypes and has an indirect impact on colonization [9]. Thus, there is an urge to define more global and effective strategies for the treatment and/or prevention, and to fight the pneumococcus and its local and invasive diseases. Consequently, the idea of a protein-based vaccine has taken great importance in the last years. However, in order to be considered or included in a recombinant vaccine formulation, a bacterial protein has to fulfill specific criteria such as: (1) playing an important role in the bacterial fitness and/or pathogenesis of S. pneumoniae, (2) possessing a wide distribution among the circulating strains and clinical isolates, (3) exhibiting a major conservation at its genetic and protein sequence, (4) being inmunogenic, (5) demonstrating protectivity in experimental assays, and (6) having favorable physico-chemical properties for expression and purification of its recombinant products.

Streptococcus pneumoniae is a pathogen exhibiting a fratricide behavior and an enormous capacity for natural competence, acquiring foreign genetic material and integrating it into its genome [11]. These processes, in addition to the mutation rates [12, 13], greatly stimulate the horizontal gene transfer with other microorganisms, and explains pneumococcal genetic variability and genome plasticity [14, 15]. This model of pneumococcal population evolution, where recombination highly outpasses mutation, is also caused by the relatively high numbers of repetitive sequences in the genome thereby facilitating the incorporation of foreign DNA in the chromosome [1518]. In consequence, these events contribute to structural reorganizations, and influence the presence or absence of protein-encoding genes in differente subsets of the global pneumococcal population, making them highly heterogeneous from the core- and pan-genomic point of view [15]. Likewise, the generation and fixation of particular changes in the genome affect the mutation rates, which in turn influence the evolution and conservation of genes and contribute to adaptative changes that potentially lead to an increased virulence and a more complex interaction with the host [19].

Due to these molecular events and their importance, there is a need to fully and globally understand the genetic heterogeneity and variability among the different pneumococcal strains/serotypes (variome), and to get a deeper and detailed molecular undestanding of the different physiological and pathogenic mechanisms that this microorganim uses to cause severe and life-threatening diseases. Definitely, obtaining this knowledge will allow to identify potential pharmaceutical targets for new antimicriobial therapies. By the recognizition of their conservation and distribution degree among pneumococcal strains, this will confirm protein candidates for vaccines. However, despite the availability of a high number of completely sequenced genomes and the importance to analyse the genetic differences among pneumococci, only a few studies have focused on studying its variability from a global perspective, similarly as the Human variome databases do [20]. To date only the “Microbial Variome Database” [21], which possesses and organizes the available information of the variome of the two Gram-negative bacterial species Escherichia coli and Salmonella enterica, is providing such information for microorganisms. Remarkably, there are no open-source data of this nature for any Gram-positive bacterial genome. Hence, this study focused on the construction of the first S. pneumoniae Variome model, starting with the identification of all allellic and protein variants, a mutation and distribution analysis (presence and absence) of the virulence factors and regulators, among a set of pneumococcal strains that possess a fully sequenced and annotated genome.

Methods

Definition of the study population set and determination of the optimal representation of the entire population of pneumococci

The search and selection of the Streptococcus pneumoniae strains for the analysis in this study was done using the microbial database of the “National Center for Biotechnology Information” NCBI (http://www.ncbi.nlm.nih.gov/genome) [22]. Likewise, in order to ensure an optimal representation of the global pneumococcal population, a genomic BLAST of 8290 available S. pneumoniae genomes was carried out. In brief, DNA alignments, employing the tool “Microbial Nucleotide BLAST” [23], that can be found in the website http://blast.ncbi.nlm.nih.gov/Blast.cgi, were performed for all the currently reported draft or complete sequenced genomes. The comparative data was then employed to construct a DNA-based Phylogenetic Tree (dendrogram), by using the Genome Tree Report Tool of the NCBI (ncbi.nlm.nih.gov/genome/tree/176). Afterwards, the file containing the dendrogram, constructed for the 8290 strains, was downloaded from the NCBI database. Finally, the dendrogram file was viewed, analyzed and adapted in order to generate circular, slanted and/or rectangular cladograms, by using the online NCBI Tool “Tree Viewer 1.17.0”, which is available online at the website: ncbi.nlm.nih.gov/projects/treeview (Fig. 1).
Figure 1
Fig. 1

Phylogenetic tree (slanted cladogram) of the pneumococcal genome / strains. By using the online NCBI Tools Genome Tree Report (ncbi.nlm.nih.gov/genome/tree/176) and the Tree Viewer 1.17.0 (ncbi.nlm.nih.gov/projects/treeview), a phylogenetic tree was constructed from the analysis by genomic BLAST of 8290 sequencing projects of pneumococci reported in the NCBI database. The topology of this slanted cladogram showed different pneumococcal lineages, where the selected set of 25 pneumococcal strains can be identified in red as external nodes (the “well-distributed” key features also highlighted in red), evidencing an optimal representation of the pneumococcal population. The overall number of sequenced pneumococcal genomes is provided for each external node. The blue lines depicted those external nodes where fully sequenced and annotated genomes are located

Definition of the virulence factors and two-component regulatory systems to be studied in S. pneumoniae

The search and selection of genes and proteins widely known as virulence factors or gene encoding factors possessing a proven interaction with the human host was done by an exhaustive bioinformatic screening in the database “Virulence Factors DataBase - VFDB” [24], available at the website http://www.mgc.ac.cn/VFs. Aditionally, the virulence factors and proteins involved in interactions with the host were confirmed and completed by a systematic review of the literature [14, 25]. The common names of each one of the selected virulence factors were then introduced in the database UNIPROT [26], available at http://www.uniprot.org/, with the aim of obtaining the locus tag for S. pneumoniae TIGR4 genome/strain. In addition, the genes encoding the HK or RR of the pneumococcal TCS were identified by using the database Prokaryotic Two-Component Systems - P2CS [27], available at the website http://www.p2cs.org/index.php. Likewise, the corresponding locus tag for S. pneumoniae TIGR4 genome / strain, of each one of the histidine kinases genes (hk) and response regulator genes (rr), were also recovered from the same database.

Chromosomal localization of the virulence factor and two-component regulatory systems genes in S. pneumoniae

The chromosomal location of all the genes in the genome of S. pneumoniae TIGR4 and the construction of the genomic maps, in linear or circular representation, was done by using the software SnapGene® (GSL Biotech), available at http://www.snapgene.com. In brief, the studied genomes of S. pneumoniae were imported through its corresponding access code in GenBank (ie: NC_003028.3 for TIGR4). Then, the chromosomal location of each virulence factor gene, and the factors involved in the interaction with the host and the genes encodying for proteins of simple or two-component regulatory systems were identified. Finally, the lineal maps for the scale genomic localization for the virulence factors and the circular maps for the genomic periphery of the genes that form the two-component regulatory systems were constructed.

Distribution of the virulence factors and two-component regulatory systems in the different strains of S. pneumoniae

The identification of the genetic and protein sequences of interest to perform the comparative analysis was done, having as reference the codes (Locus Tag) in the genomes of S. pneumoniae TIGR4 and/or R6 in the database Kyoto Encyclopedia of Genes and Genomes – KEGG [28], available at http://www.kegg.jp/kegg/. Once every gene of interest was established in the database, a series of comparisons (BLASTs) were performed using the GenomeNet [29], available at http://www.genome.jp/, using only the fully sequenced and annotated genomes of S. pneumoniae. For the nucleotide sequences the search was performed using the program BLASTN 2.2.29+, which uses nucleotide vs nucleotide alignments based on a punctuation matrix BLOSUM62 [23, 30]. In the same way, the search was done for the amino acid sequences using the program BLASTP 2.2.29+ [31, 32], that performs amino acids vs amino acids alignments based on a similar matrix. Once the BLAST was finalized for each virulence factor, the list was purged using as selection criteria genes with an expectancy value: e-Value = 0. The inclusion of genes with an e-value >0 was done by direct visual inspection of the alignments to check that it was indeed the same sequence. By having defined the list with the genes and proteins that fulfilled the selection criteria, it was defined to which strains of S. pneumoniae they belong. All the DNA and protein sequences were downloaded and stored in an organized way using the fasta format.

Genetic variability (variome) of the virulence factors and two-component regulatory systems among the different pneumocococal strains

The multiple comparative alignments of pneumococcal sequences were done using the web tool MultAlin [33], available at http://multalin.toulouse.inra.fr/multalin/, for which an identity matrix 1–0 was used to assign a penalty even for the slightest change in the nucleotides or amino acids sequences, covering substitution, deletions, insertions and variations in the length. From these analyses, the number of allelic and protein variants were determined for each gene according to the registry value assigned by the program to each sequence, where equal sequences have the same registry value, while different sequences possess different values. The results of the alignments were manually curated and stored for further analysis. Finally, the precise determination of the total mutations, synonymous and nonsynonymous was done using the software DnaSP V.5.1 [34, 35], available at http://www.ub.edu/dnasp/. There, all the sequences found for a determined gene were introduced and the calculations were perfomed for the corresponding type of mutation as mentioned before.

Results and discussion

“Hundreds to thousands” of S. pneumoniae strains and clinical isolates recovered from the nasopharynx, blood or cerebrospinal fluid (CSF) have been included up to date in genomic sequencing projects worldwide. However, pneumococcal strains, whose genomes are fully sequenced, annotated and publicly available, are the focus of this study. Therefore, a set of 25 pneumococcal strains were selected from the NCBI database, as population study, to perform the bioinformatic analysis needed to accomplish the construction of the variome of the virulence factors and two-component regulatory systems of Streptococcus pneumoniae (Table 1).
Table 1

The study population set of 25 S. pneumoniae strains included in this study and their serotypes

S. pneumoniae Strain

Serotype

# of Genes

NCBI Annotation

D39

2

2069

NC_008533.1

R6

No Capsule

1967

NC_003098.1

TIGR4

4

2228

NC_003028.3

INV104

1

2003

NC_017591.1

AP200

11A

2284

NC_014494.1

JJA

14

2235

NC_012466.1

ATCC 700669

23F

2224

NC_011900.1

INV200

14

2113

NC_017593.1

CGSP14

14

2276

NC_010582.1

G54

19F

2186

NC_011072.1

gamPNI0373

1

2226

NC_018630.1

P1031

1

2254

NC_012467.1

SPN034156

3

1956

NC_021006.1

SPN994039

3

1974

NC_021005.1

SPN994038

3

1974

NC_021026.1

SPN034183

3

1985

NC_021028.1

OXC141

3

2037

NC_017592.1

670-6B

6B

2430

NC_014498.1

A026

19F

2153

NC_022655.1

Taiwan19F-14

19F

2205

NC_012469.1

ST556

19F

2219

NC_017769.1

TCH8431/19A

19A

2355

NC_014251.1

SPNA45

3

1921

NC_018594.1

70,585

5

2323

NC_012468.1

Hungary19A 6

19A

2402

NC_010380.1

A Variome model of the Pneumococcal Virulence Factors and Regulators is an intraspecific study, aiming to highlight variable genetic loci on the genome of Streptococcus pneumonie. A perfect and ultimate Variome model would be that constructed with the 100% of the genomic information correctly assessed from the entire pneumococcal population. However, the current state of the art is far away from this scenario and an optimal representation of the pneumococcal sets assessed up to date would be appropriate in order to validate these genomic analyzes. Currently, 8290 pneumococcal sequencing projects are reported as draft or complete genomes in the Genome Assembly and Annotation Report of the NCBI database. Therefore, a global genomic BLAST (DNA alignment) of those 8290 available S. pneumoniae genomes/strains was performed and a DNA-based Phylogenetic Tree was constructed by using the Genome Tree Report Tool of the NCBI. The topology of this phylogenetic tree (slanted cladogram) showed different pneumococcal lineages, where the selected set of 25 pneumococcal genomes/strains can be identified as external nodes (“well-distributed” key features highlighted in red), evidencing an optimal representation of the pneumococcal population (Fig. 1). In addition, it is important to highlight that the serotypes (1, 2, 3, 4, 5, 6B, 11A, 14, 19A, 19F and 23F), represented in this study population set, have been described as the pneumococcal types with the highest pathogenic potencial, due to the high burden of invasive pneumococcal diseases (IPDs) they cause worldwide. This is the reason why the majority of them (except serotypes 2 and 11A) have been included in the pneumococcal conjugate vaccines (PCVs) currently used for immunization [1].

An initial considerable number of pneumococcal virulence factor genes were identified, by employing the database VFDB [24]. This database provided further detailed information to establish their function, pathogenic role and type of interaction with a receptor in its human host. Aditionally, a systematic screening of the literature [14] did not only allow the confirmation of identified factors, but also ensured the posibility to complement the list with additional factors that have not been included in the databases. Likewise, the number of the tcs genes (27) was determined using the database Prokaryotic 2-Component Systems - P2CS [27]. In total, 92 different genes encoding 61 surface proteins, 4 stand alone transcriptional regulators, 13 HKs and 14 RRs have been selected and included in this work for the construction of the variome, after being classified by their function and grouped according to their molecular mechanisms of surface-exposure (Table 2).
Table 2

Function or pathogenic role of the virulence factors and two-component regulatory systems of S. pneumoniae

Virulence Factors

Protein Name

Function and/or Pathogenic Role

LPxTG - Proteins

BgaA

β-Galactosidase

β-Galactosidase Enzyme

EndoD

Endo-β-N-Acetylglucosaminidase D

Virulence

PclA

Pneumococcal Collagen-Like Protein

Adherence and Invasion

SpGH101

Endo-α-N-Acetylgalactosaminidase

Virulence

StrH

β-N-Acetylhexosaminidase

β-N-Acetylhexosaminidase Enzyme

NanA

Neuraminidase A

Hydrolytic Enzyme, Adherence and Colonization

PfbA

Plasmin- and Fibronectin-Binding Protein A

Adherence, Immune Evasion and Antiphagocytosis

PrtA

Subtilysin-Like Serine Protease

Virulence

PavB

Pneumococcal Adherence and Virulence Protein B

Adherence and Colonization

KsgA

Dimethyladenosine Transferase

Virulence

SpuA

Alkaline Amyllopullullanase

Pullullanase Enzyme and Immune Evasion

HysA

Hyaluronate Lyase

Hyaluronidase Enzyme and Colonization

SP_1492

Cell Wall Surface Anchor Protein Family, Mucin-Binding Protein

Virulence

ZmpA

Zinc Metalloprotease A, IgA1

IgA1 Protease Enzyme and Colonization

ZmpB

Zinc Metalloprotease B

Immune Evasion and Colonization

ZmpC

Zinc Metalloprotease C

Immune Evasion and Colonization

ZmpD

Zinc Metalloprotease D, IgA1 Paralog Protease

Immune Evasion and Colonization

PsrP

Pneumococcal Serine-Rich Repeats Protein

Adherence

RrgA

Pilus-1 Tip Protein (Adhesin)

Adherence

RrgB

Pilus-1 Backbone Protein

Adherence

RrgC

Pilus-1 Anchore Protein

Adherence

PitA

Pilus-2 Subunit, Ancillary Protein

Adherence

PitB

Pilus-2 Subunit, Backbone Protein

Adherence

Choline-Binding Proteins (CBPs)

LytA

Autolysin (N-Acetyl-Muramoyl-L-Alanine Amidase)

Autolytic Enzyme, Cell Wall Digestion and Autolysis

LytB

Endo-β-N-Acetylglucosamidase

Immune Evasion and Colonization

LytC

Lysozyme (1,4-β-N-Acetylmuramidase)

Adherence, Immune Evasion and Colonization

Pce

Choline-Binding Protein E, Phosphorylcholine Estearase

Phosphorylcholine Estearase Enzyme, Adherence, Colonization and Cellular Metabolism

PcpA

Pneumococcal Choline-Binding Protein A

Protection Against Lung Infection and Sepsis

PspA

Pneumococcal Surface Protein A

Cellular Metabolism and Immune Evasion

PspC

Pneumococcal Surface Protein C, Choline-Binding Protein A

Adherence, Immune Evasion, Colonization and Invasion

CbpC

Choline-Binding Protein C

Virulence

CbpD

Choline-Binding Protein D

Colonization

CbpF

Choline-Binding Protein F

Virulence

CbpG

Choline-Binding Protein G

Adherence and Colonization

CbpI

Choline-Binding Protein I

Virulence

CbpJ

Choline-Binding Protein J

Virulence

SP_0667

Pneumococcal Surface Protein (Putative Lysozyme)

Virulence

Lipoproteins

GlnQ

Glutamine Transporter

Cellular Metabolism

PiaA

Iron-Compound ABC Transporter

Peptidil-Prolil Isomerase (PPIase) Enzyme

PiuA

Iron-Compound ABC Transporter

Peptidil-Prolil Isomerase (PPIase) Enzyme

PpiA

Streptococcal Lipoprotein Rotamase A, Peptidil-Prolil Isomerase (PPIases) Enzyme

Peptidil-Prolil Isomerase (PPIase) Enzyme

PsaA

Peptide Permease Enzyme, Manganese ABC Transporter, Manganese-Binding Lipoprotein

Immune Evasion

PpmA

Foldase Protein PrsA, Proteinase Maturation A

Adherence, Immune Evasion, Strain-Specific Colonization and Evasion of Phagocytosis

AliA

Oligopeptide ABC Trasporter

Adherence

PhtA

Pneumococcal Histidine Triad A

Adherence and Immune Evasion

PhtB

Pneumococcal Histidine Triad B

Adherence and Immune Evasion

PhtD

Pneumococcal Histidine Triad D

Adherence and Immune Evasion

PhtE

Pneumococcal Histidine Triad E

Adherence and Immune Evasion

Non-Classical Surface-Exposed Proteins

Eno

Enolase (2-Phosphoglycerate Dehydratase)

Glycolytic Enzyme, Adherence and Colonization

GAPDH

Glyceraldehyde-3-Phosphate Dehydrogenase

Glycolytic Enzyme, Adherence and Colonization

HtrA

High-Temperature Requirement A, Serine Protease (Heat Shock Protein)

Serine Protease Enzyme

PavA

Pneumococcal Adherence and Virulence Protein A

Adherence, Immune Evasion, Colonization and Translocation

Pbp1B

Penicillin-Binding Protein 1B

Antibiotic Resistance

StkP

Serine/Threonine Protein Kinase

Cellular Metabolism and Fitness

Usp45

PcsB, Secreted 45-KDa Protein

Virulence

NanB

Neuraminidase B

Hydrolytic Enzyme, Adherence and Colonization

PppA

Pneumococcal Protective Protein A, Non-Heme Iron-Containing Ferritine

Colonization

Fic-Like

Fic-Like Cell Fillamentation Protein

Putative Cytotoxicity

6PGD

6-Phosphogluconate Dehydrogenase

Virulence

Ply

Pneumolysin

Cytolytic Toxin, Adherence, Immune Evasion, Invasion, Dissemination and Complement Activation

NanC

Neuraminidase C

Virulence

Regulators

RlrA

Pathogenicity Island rlrA Transcriptional Regulator

Virulence

MgrA

MgrA Family Transcriptional Regulator

Virulence

MerR

MerR Family Transcriptional Regulator

Virulence

PsaR

Iron-Dependent Transcriptional Regulator

Virulence

Histidine Kinases (HKs)

HK01

Sensor Histidine Kinase

Virulence

HK02

Sensor Histidine Kinase, VicK

Antibiotic Resistance, Virulence and Fitness

HK03

Sensor Histidine Kinase, LiaS

Antibiotic Resistance and Stress Protection

HK04

Sensor Histidine Kinase, PnpS

Genetic Competence, Fitness, Immune Evasion

HK05

Sensor Histidine Kinase, CiaH

Antibiotic Resistance, Genetic Competence and Pathogenesis

HK06

Sensor Histidine Kinase

Colonization and Invasion

HK07

Sensor Histidine Kinase, YesM

Fitness

HK08

Sensor Histidine Kinase, SaeS

Pathogenesis and Fitness

HK09

Sensor Histidine Kinase

Virulence

HK10

Sensor Histidine Kinase, VncS

Antibiotic Resistance

HK11

Sensor Histidine Kinase

Biofilm Formation

HK12

Sensor Histidine Kinase, ComD

Genetic Competence

HK13

Sensor Histidine Kinase, BlpH

Virulence

Response Regulators (RRs)

RR01

Response Regulator

Virulence

RR02

Response Regulator, VicR

Antibiotic Resistance, Virulence and Fitness

RR03

Response Regulator, LiaR

Antibiotic Resistance and Stress Protection

RR04

Response Regulator, PnpR

Genetic Competence, Fitness, Immune Evasion

RR05

Response Regulator, CiaR

Antibiotic Resistance, Genetic Competence, Pathogenesis

RR06

Response Regulator

Colonization and Invasion

RR07

Response Regulator, YesN

Fitness

RR08

Response Regulator, SaeR

Pathogenesis and Fitness

RR09

Response Regulator

Virulence

RR10

Response Regulator, VncR

Antibiotic Resistance

RR11

Response Regulator

Biofilm Formation

RR12

Response Regulator, ComE

Genetic Competence

RR13

Response Regulator, BlpR

Virulence

RR14

Response Regulator

Virulence

The proteins are grouped by classes, depending on their surface-exposure mechanism. The names, abreviations and function of the proteins were obtained from literature references

The genomes of 25 analyzed pneumococcal strains comprise genome sizes ranging from 2,024,476 bp in SPN034156 up to 2,245,615 bp in Hungary 19A-6. Likewise, the G + C content varies between 39.50% in CGSP14 and 39.90% in SPN034156. 670-6B is the strain with the highest number of genes (2430) and proteins (2352) and SPN034156 is the strain with the lowest number of genes (1956) and proteins (1799). Hence, the difference among genomes, regarding the number of genes and proteins can be up to 474 genes and 553 proteins, respectively. The overall number of genes for each pneumococcal genome evaluated here overmatches the overall number of proteins because the reported number of genes includes all the tRNA-, rRNA- and protein-encoding genes.

Considering the chromosomal localization of pneumococcal virulence factors genes, they are all distributed along the pneumococcal genome (Fig. 2). Interestingly, these genes are located in a co-oriented manner in relation with the origin of replication (oriC: 2.160.822–196). During the bidirectional replication of the genome, gene transcription must be simultaneous [36]. Hence, for the genes oriented in opposite direction to the corresponding replication fork, both molecular machineries will run into a frontal collision that might affect at least one of the processes. For replication, this phenomenon implies a genomic instability, while the gene transcription is probably inefficient. Previous studies have proven that the essential and highly constitutively expressed genes are co-oriented [36]. For the pneumococcus, 30 of the 36 genes encoding virulence factors are localized in the first half of the genome, on the forward strand, and co-oriented with the replication fork clockwise. Similarly, 21 of the 27 virulence factor genes localized on the second half of the genome, are located on the reverse strand and co-oriented with the replication fork moving anti-clockwise (Fig. 2). A similar genome organization is observed for the 27 genes that encode the TCSs in S. pneumoniae, where only one operon, the tcs04 genes (TCS04), is not co-oriented with the replication fork (Fig. 3). These data reinforce the idea that the virulence factor genes and the genes of the tcs are highly important for the pneumococcal interaction with the human host, and its pathogenic potential in processes such as adherence, colonization, invasion, immune evasion, fitness, antibiotic resistance and natural competence (Table 2).
Figure 2
Fig. 2

Chromosomal localization and direction of the virulence factor genes of S. pneumoniae TIGR4. Lineal representation of the pneumococcus genome. The arrows, drawn at scale, localize 62 of the 65 virulence factors and simple regulation genes considered in this study (pitA, pitB and zmpD are not present in the genome of TIGR4). Each color represents a different class of codified protein: blue = sortase-anchored proteins with an LPxTG cleavage motif; violet = choline-binding proteins (CBPs); green = lipoproteins, yellow = non-classical surface proteins (NCSP), and red = stand-alone regulators. This map was constructed using the Software SnapGene® (GSL Biotech; Available at snapgene.com)

Figure 3
Fig. 3

Localization and direction of the two component systems genes in S. pneumoniae TIGR4. Circular representation of the pneumococcal genome. The arrows, not drawn at scale, localize the 27 genes which codifies for the proteins of the 13 two component systems +1 incomplete. Each color indicates a different class of codified protein: red = histidine kinase sensors and blue = response regulators Proteins. This map was constructed using the Software SnapGene® (GSL Biotech; Available at snapgene.com)

The analysis of the distribution of genes associated with virulence and host-pathogen interactions among the studied pneumococcal strains revealed that only 26 of the 65 genes considered here are present in the all 25 strains. These genes encode for products involved in different functions such as cell wall hydrolysis, ABC transporters and structural proteins implied in the adherence to host tissue, the so-called adhesins. Interestingly, after preliminar inspection (by locus tag, identifier names and/or product sizes) of the datasets and supplementary material reported by van Tonder and colleagues in 2017, only a few of the pneumococcal virulence factors (PspC, KsgA, and 4 hypothetical lipoproteins) and regulators (RR04, HK08, RR08, RR09, RR10) were found in the pneumococcal “supercore” genomic list of 303 genes, based on the analysis of 3121 pneumococci recovered from healthy individuals from four different subsets of the global pneumococcal population [15]. These findings, if confirmed after deeper analysis of the datasets based on sequence comparison, may indicate that pneumococcal pathogenesis is a much more complex process than thought before. While most of the genes have a single copy in the genome, the lytA gene, encoding the major pneumococcal autolysin, is found also in two and even three copies in 13, and 2 strains, respectively. This is most likely due to the multiple integration of prophages in the chromosomal DNA [37] (Table 3). In strain SPNA45, the gene gnd, encoding the enzyme 6-phophogluconate dehydrogenase, is duplicated and fused with a second copy of its downstream neighbor gene, which encodes the orphan response regulator (rr14). The remaining 39 of the 65 virulence factor genes were found to belong to the accesory genome, presenting different degrees of absence in the 25 strains. Thus, all these genes are not essential but are beneficial for fitness and pathogenesis. Striking examples are the genes encoding the Pilus-1 and Pilus-2 structures that have been identified to mediate adherence, contribute to virulence and promote invasion [3842]. These genes are located on pathogenicity islands (PAI) and these islands contain also the genes required for cell surface anchoring and regulation [3841]. Remarkably, strains like ST556, Taiwan19F-14 and TCH8431/19A, were detected here as positive for both types of pili (1 and 2). Among the other genes with restricted presence in some strains it is important to mention that they encode for sortase-anchored proteins or choline-binding proteins (CBPs), as well as histidine triad proteins (pht genes). These gene products are associated with different processes of bacterial fitness and pathogenesis (Tables 3 and 2) [6, 43, 44]. Regarding the distribution and data of the analyzed strains for the TCS most of them were found in the 25 pneumococcal strains. Exceptions are presented by the TCS07 and TCS12, which contribute to fitness and competence, respectively [7, 45]. These TCS are absent in a couple of strains (Table 4). In some other strains genes like hk01, hk12 and rr04, presented incomplete sequences, an artefact leading to truncated and hence non-functional proteins/regulators (Tables 3 and 2). Interestingly, only the genes encoding the hk08, rr08, rr09, rr06 and rr04 were found to belong to the “supercore” genomic set of genes reported by van Tonder et al., in 2017 [15], indicating the important role these highly conserved and well-distributed regulatory proteins play in the pneumococcus and in its interplay with the environment.
Table 3

Distribution of the virulence factor and regulation genes of S. pneumoniae

Virulence Factors

TIGR4

D39

R6

70,585

Hungary19A 6

SPN994038

SPN994039

SPN034183

OXC141

SPN034156

ST556

Taiwan19F-14

TCH8431/19A

A026

INV200

CGSP14

INV104

G54

AP200

P1031

gamPNI0373

SPNA45

ATCC 700669

JJA

670-6B

LPxTG - Proteins

bgaA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

endoD

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

pclA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

spGH101

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

strH

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

nanA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

pfbA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

prtA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

pavB

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

0

1

1

ksgA

1

1

1

1

1

1

1

1

0

1

1

1

1

1

1

1

1

1

1

1

0

1

0

1

1

spuA

1

1

1

1

1

1

1

1

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hysA

1

1

1

1

1

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

SP_1492

1

1

1

1

1

0

0

0

0

0

1

1

1

1

1

0

1

1

1

1

1

1

0

1

1

zmpA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

1

0

1

0

0

0

0

0

1

zmpB

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

0

1

zmpC

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

0

0

0

0

0

0

zmpD

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

0

1

0

1

1

0

1

1

0

psrP

1

0

0

1

1

0

0

0

0

0

0

0

0

0

0

1

1

0

0

0

0

0

0

0

0

rrgA

1

0

0

0

1

0

0

0

0

0

1

1

1

1

0

0

0

0

0

0

0

0

0

0

1

rrgB

1

0

0

0

1

0

0

0

0

0

1

1

1

1

0

0

0

0

0

0

0

0

0

0

1

rrgC

1

0

0

0

1

0

0

0

0

0

1

1

1

1

0

0

0

0

0

0

0

0

0

0

1

pitA

0

0

0

0

0

1

0

0

0

0

1

1

1

0

0

0

1

0

1

0

0

0

0

0

0

pitB

0

0

0

0

0

1

0

0

0

0

1

1

1

0

0

0

1

0

1

0

0

0

0

0

0

Choline-Binding Proteins (CBPs)

lytA

1

1

1

2

2

2

2

2

2

2

2

1

1

1

1

1

1

1

2

2

2

2

2

3

3

lytB

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

lytC

1

1

1

1

1

1

0

0

0

0

1

1

1

0

1

1

1

1

1

1

1

1

1

1

1

pce

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

pcpA

1

1

1

1

1

0

0

0

0

0

1

1

1

1

1

1

1

1

1

0

0

1

1

1

1

pspA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

0

1

pspC

1

1

1

1

1

0

0

0

0

1

1

1

1

1

0

1

1

0

1

1

1

1

1

1

1

cbpC

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

1

0

0

1

1

cbpD

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

cbpF

1

1

1

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

1

cbpG

1

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

cbpI

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

0

0

0

0

0

0

cbpJ

1

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

1

1

1

SP_0667

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

Lipoproteins

glnQ

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

piaA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

piuA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

ppiA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

psaA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

ppmA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

0

0

1

1

1

1

1

1

aliA

1

1

1

1

1

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

phtA

1

1

1

1

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

1

1

0

0

0

1

phtB

1

0

0

0

0

0

0

0

0

0

1

1

0

0

1

0

1

1

1

0

1

0

0

0

0

phtD

1

1

1

1

1

1

1

1

1

0

1

1

1

0

1

1

1

1

1

1

1

1

1

1

1

phtE

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Non-Classical Surface-Exposed Proteins

eno

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

gap

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

htrA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

pavA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

pbp1B

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

stkP

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

usp45

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

nanB

1

1

1

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

pppA

1

1

1

1

1

1

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

fic-Like

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

1

1

gnd

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

1

1

1

ply

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

nanC

1

0

0

0

1

0

0

0

0

0

0

0

0

0

1

1

1

1

1

0

0

0

1

1

1

Regulators

mgrA

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

psaR

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

merR

1

0

1

0

0

1

1

1

1

1

1

0

1

1

1

1

0

1

1

1

1

0

1

1

1

rlrA

1

0

0

0

1

0

0

0

0

0

1

1

1

1

0

0

0

0

0

0

0

0

0

0

1

The present table shows the absence (0) or presence (1, 2 or 3) of each considered genes in the 25 strains selected for this study. The number (1, 2 or 3) indicates the amount of copies of each gene in the genome. lytA is the only factor with more than one copy per genome. In the strain SPNA45, the gene gnd was found duplicated (2) and fused with a duplication of its neighbor gene (rr14) downstream. In the gene nanA of TIGR4 (1) a shift in its ORF was found. However, it has also been reported that NanA is expressed in this pneumoccoccal strain. Gene defective copies (genes with any alteration in their primary DNA sequences) are depicted in bold and italics: In the SPNA45 strain ply is fused with a copy of lytA, and pspA is defective in the ATCC700669 pneumococcal strain

Table 4

Distribution of the genes that conform the two-component systems in S. pneumoniae

Two-Component Systems (TCSs)

TIGR4

D39

R6

70,585

Hungary19A 6

SPN994038

SPN994039

SPN034183

OXC141

SPN034156

ST556

Taiwan19F-14

TCH8431/19A

A026

INV200

CGSP14

INV104

G54

AP200

P1031

gamPNI0373

SPNA45

ATCC 700669

JJA

670-6B

Histidine Kinases (HKs)

hk01

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk02

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk03

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk04

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk05

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk06

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk07

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

1

1

1

1

1

hk08

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk09

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk10

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk11

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hk12

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

0

1

1

1

hk13

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Response Regulators (RRs)

rr01

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr02

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr03

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr04

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr05

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr06

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr07

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

1

1

1

1

1

rr08

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr09

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr10

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr11

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr12

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

1

1

1

rr13

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

rr14

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

1

1

1

The table shows, the absence (0) or presence (1 or 2) of each gene considered in the 25 strains selected for this study. The number (1 or 2) indicates the amount of copies per gene in each genome. rr14 is the only gene with more than one copy per genome (2), which is actually fused with its neighbor gene (gnd) upstream. In bold and italics, three genes are observed (hk01, hk12 and rr04) in two different strains which might have some alteration (insertion or deletion) in their primary DNA and protein Sequence. The two-component system NisK-NisR of the pneumococcus is rare, of the 25 strains analyzed in this study only in the strain 70,585 was found

The estimation of the variability for each individual virulence factor and pneumococcal regulator (at the DNA and protein level) allowed the construction of a partial variome for the analysed 25 pneumococcal strains. Briefly, the variome takes into consideration the estimation of (1) the presence, absence or the number of copies of genes in the different strains, (2) the number of total synonymous and nonsynonymous mutations, and (3) the number of allelic and protein variants explaining the variability for each factor. The results summarized in Tables 5 and 6, contain the data for the genes and proteins associated to virulence and host-pathogen interaction, and also the data for the stand-alone and TCS regulators. Specifically there are some identified factors with the best distribution and highest evolutionary conservation, These were (1) the ply gene encoding the sole pneumococcal cytolysin and cytotoxin pneumolysin [46], (2) the enolase, which encodes the enzyme enolase (2-phosphoglycerate dehydratase) and has an essential function in the metabolism [47], but also interacts specifically with plasmin(ogen) and is therefore involved in fibrinolytic processes, adherence and virulence, and (3) the pcsB (Usp45) gene, which encodes for a 45-kDa secreted and immunogenic protein that is involved in cell division and stress response [48]. As for the mutations, these three proteins presented a minor number of changes, in comparison with others proteins that were also analyzed. The variome of the TCS (Table 6) allowed to conclude that the most conserved genes from the evolutionary point of view, are the genes hk05 and rr05 of ciaR/H (tcs05). The TCS CiaRH is involved in the resistance to cefotaxime, regulation of genetic competence and increase in pathogenicity in the respiratory tract in murine models [7, 49, 50]. Meanwhile, hk02 and rr02 (WalR/K, MicA/B or VicR/K), have been associated with resistance to erythromycin and are essential for the bacterial growth. Nevertheless, the latter was proven to be due to its regulon (pcsB), and was no longer essential upon ectopic expression of PcsB [7, 48]. Pneumococcal TCS08 is involved in the genetic regulation of pilus-1 [41]. The mutation analysis showed that the response regulators exhibited a lower rate of variations in comparison to the histidine kinases, being the response regulators rr05, rr02, rr06, and rr08 the most conserved. All the results obtained in this study support the global idea of a new generation of protein-based and serotype-independent vaccines for Streptococcus pneumoniae. The basis is the high degree of distribution and conservation of the virulence proteins in combination with the importance of their functions and immunogenic capacities. This probably makes them ideal pharmacological targets to treat the pneumococcus and its diseases. This might be an alternative to the immunization with the conjugated serotypes, or represent a strategy to combine immunogenic and highly conserved proteins with capsular polysaccharides to generate a serotype-independent immune response.
Table 5

Analysis of the Variome of the virulence factor genes of S. pneumoniae

Virulence Factor Genes

Length

Mutations

Variants

Analyzed Sequences

Name

Locus in TIGR4 / R6

Gene (bp)

Protein (a.a.)

Overall

Synonimous

Non-Synonimous

Allelles

Protein

lytA

sp_1937 / spr1754

957

318

208

154

54

25

20

42

gnd

sp_0375 / spr0335

1425

474

82

75

7

19

11

26

strH

sp_0057 / spr0057

3939

1312

87

32

55

21

21

25

lytB

sp_0965 / spr0867

1977

658

132

84

48

22

20

25

endoD

sp_0498 / spr0040

4980

1659

139

70

69

20

20

25

nanA

sp_1693 / spr1536

3108

1035

460

282

178

20

19

25

bgaA

sp_0648 / spr0565

6702

2233

415

247

168

19

18

25

spGH101

sp_0368 / spr0328

5304

1767

348

238

110

18

18

25

glnQ

sp_0609 / spr0534

765

254

40

22

18

17

17

25

phtE

sp_1004 / spr0908

3120

1039

56

28

28

20

16

25

pbp1B

sp_2099 / spr1909

2466

821

81

16

65

17

16

25

pce

sp_0930 / spr0831

1884

627

148

80

68

16

16

25

pavA

sp_0966 / spr0868

1656

551

40

18

22

17

15

25

cbpD

sp_2201 / spr2006

1347

448

49

28

21

18

14

25

piuA

sp_1872 / spr1687

966

321

22

7

15

14

12

25

htrA

sp_2239 / spr2045

1182

393

12

8

4

14

10

25

pclA

sp_1546 / spr1402

630

209

19

9

10

13

10

25

mgrA

sp_1800 / spr1622

1482

493

21

15

6

13

9

25

ppiA

sp_0771 / spr0679

804

267

16

8

8

12

9

25

stkP

sp_1732 / spr1577

1980

659

52

46

6

18

7

25

gap

sp_2012 / spr1825

1008

335

14

12

2

13

7

25

psaA

sp_1650 / spr1494

930

309

53

41

12

13

6

25

psaR

sp_1638 / spr1480

651

216

10

5

5

9

6

25

piaA

sp_1032 / spr0934

1026

341

9

2

7

7

6

25

usp45

sp_2216 / spr2021

1179

392

9

6

3

8

4

25

eno

sp_1128 / spr1036

1305

434

17

16

1

12

2

25

spuA

sp_0268 / spr0247

3843

1280

121

77

44

18

18

24

prtA

sp_0641 / spr0561

6423

2140

436

272

164

18

16

24

nanB

sp_1687 / spr1531

2094

697

45

20

25

18

16

24

pfbA

sp_1833 / spr1652

2127

708

209

80

129

15

15

24

pppA

sp_1572 / spr1430

537

178

69

43

23

16

12

24

ply

sp_1923 / spr1739

1416

471

20

19

1

14

2

24

pavB

sp_0082 / spr0075

2574

857

(−)

(−)

(−)

20

17

23

phtD

sp_1003 / spr0907

2520

839

604

331

273

17

17

23

zmpB

sp_0664 / spr0581

5646

1881

(−)

(−)

(−)

15

15

23

pspA

sp_0117 / spr0121

2235

744

(−)

(−)

(−)

18

17

22

cbpC

sp_0377 / spr0337

1023

340

176

87

89

15

14

22

ksgA

sp_1992 / spr1806

666

221

36

8

28

14

14

22

ppmA

sp_0981 / spr0884

942

313

9

6

3

9

6

22

hysA

sp_0314 / spr0286

3201

1066

91

41

50

18

17

20

lytC

sp_1573 / spr1431

1473

490

80

20

60

17

16

20

pspC

sp_2190 / spr1995

2082

693

(−)

(−)

(−)

19

19

19

aliA

sp_0366 / spr0327

1986

661

67

50

17

17

17

19

SP_0667

sp_0667 / spr0583

999

332

87

49

38

13

13

19

merR

sp_0739 / spr0649

741

246

17

9

8

12

9

19

pcpA

sp_2136 / spr1945

1866

621

43

25

18

17

13

18

SP_1492

sp_1492 / spr1345

609

202

18

6

12

11

11

18

cbpG

sp_0390 / spr0349

858

285

106

47

59

15

15

17

zmpA

sp_1154 / spr1042

6015

2004

(−)

(−)

(−)

10

10

17

cbpJ

sp_0378 / Absent

999

332

87

52

35

10

9

15

nanC

sp_1326 / Absent

2223

740

64

39

25

7

7

10

phtA

sp_1175 / spr1061

2409

802

48

34

14

8

7

9

phtB

sp_1174 / Absent

2460

819

331

202

129

7

7

8

cbpF

sp_0391 / spr0351

1023

340

57

29

28

7

7

8

zmpD

Absent / Absent

5238

1745

(−)

(−)

(−)

6

5

7

rrgA

sp_0462 / Absent

2682

893

420

225

195

5

5

7

rrgC

sp_0464 / Absent

1182

393

19

8

11

5

5

7

rrgB

sp_0463 / Absent

1998

665

738

290

448

4

4

7

rlrA

sp_0461 / Absent

1530

509

0

0

0

2

2

7

pitA

Absent / Absent

1770

589

1

0

1

5

5

6

pitB

Absent / Absent

1233

410

0

0

0

3

3

6

psrP

sp_1772 / Absent

14,331

4776

(−)

(−)

(−)

5

5

5

zmpC

sp_0071 / Absent

5571

1856

2

1

1

2

2

3

cbpI

sp_0069 / Absent

636

211

0

0

0

1

1

3

Data of the punctual genetic variability (total mutations, synonymous and nonsynonymous + allelic and protein variants) estimated for each one of the virulence factors and simple regulators genes. The analyzed sequences depend on the presence, absence or number of copies of the genes in the different strains. The size of the sequences and loci are also shown in TIGR4 and R6, pneumococcal representative strains. Factors in bold were identified as the most conserved. (−) = mutations could not be estimated for different reasons, like repetitive sequences

Table 6

Analysis of the genetic variation (Variome) of the genes that conform the two-component systems in S. pneumoniae

Virulence Factor Genes

Length

Mutations

Variants

Analyzed Sequences

Name

Locus in TIGR4 / R6

Gene (bp)

Protein (a.a.)

Overall

Synonimous

Non-Synonimous

Allelles

Protein

rr14

sp_0376 / spr0336

690

229

17

15

2

14

5

26

hk11

sp_2001 / spr1815

1098

365

247

142

105

18

18

25

hk10

sp_0604 / spr0529

1329

442

33

16

17

17

16

25

hk06

sp_2192 / spr1997

1332

443

33

23

10

17

12

25

hk13

sp_0527 / spr0464

1341

446

272

153

119

12

11

25

rr13

sp_0526 / spr0463

738

245

78

65

13

11

11

25

rr11

sp_2000 / spr1814

600

199

77

56

21

15

10

25

hk03

sp_0386 / spr0343

996

331

36

26

10

13

10

25

hk01

sp_1632 / spr1473

975

324

20

11

9

13

10

25

hk09

sp_0662 / spr0579

1692

563

23

17

6

15

8

25

rr01

sp_1633 / spr1474

678

225

13

10

3

13

8

25

rr09

sp_0661 / spr0578

738

245

14

10

4

12

7

25

rr04

sp_2082 / spr1893

708

235

49

17

32

9

6

25

rr03

sp_0387 / spr0344

633

210

14

10

4

12

5

25

rr10

sp_0603 / spr0528

657

218

15

10

5

11

5

25

rr06

sp_2193 / spr1998

654

217

9

5

4

7

5

25

rr08

sp_0083 / spr0076

699

232

10

6

4

10

4

25

hk04

sp_2083 / spr1894

1332

443

12

9

3

10

4

25

hk08

sp_0084 / spr0077

1053

350

14

12

2

11

3

25

rr05

sp_0798 / spr0707

675

224

6

5

1

11

3

25

hk02

sp_1226 / spr1106

1350

449

12

10

2

10

3

25

rr02

sp_1227 / spr1107

705

234

7

7

0

11

2

25

hk05

sp_0799 / spr0708

1335

444

11

10

1

9

2

25

hk07

sp_0155 / spr0153

1647

548

68

46

22

19

17

24

rr07

sp_0156 / spr0154

1287

428

50

33

17

17

14

24

rr12

sp_2235 / spr 2041

753

250

11

8

3

10

3

24

hk12

sp_2236 / spr2042

1326

441

42

16

26

11

9

23

Data of the punctual genetic variability (total mutations, synonymous and non-synonymous + allelic and protein variants) estimated for each one of the two-component system genes. The analyzed sequences depend on the presence, absence or number of copies of the genes in the different strains. The size of the sequences and loci are also shown in TIGR4 and R6, pneumococcal representative strains. Factors in bold are the most conserved

Conclusions

The construction of this “low-scale” Variome model for the virulence factors and regulators of Streptococcus pneumoniae was achieved from 25 pneumococcal strains with fully sequenced and annotated genomes. According to the Molecular Phylogenetic Analysis performed on the NCBI website, this selected set of pneumococcal genomes ensured an optimal representation of the pneumococcal population (8290 strains) reported in the NCBI database up to date. Similarly, this study population set also represented an important group of highgly pathogenic pneumococcal serotypes (1, 2, 3, 4, 5, 6B, 11A, 14, 19A, 19F and 23F), which have been also included in the current pneumococcal conjugate vaccine formulations (except serotypes 2 and 11A), used to prevent penumococal infections. A total of 92 different genes and proteins were identified, classified, and studied for the construction of the variome. The genes of the pneumococcal virulence factors and TCS, are distributed along the genome, and are located in such a manner that transcription is co-oriented with replication. The analysis of the gene distribution in this study population set showed that 26 of them were found in the 100% of the 25 pneumococcal genomes/strains (core genome), while 39 are part of the flexible genome. The estimation of the variability for each individual virulence factors, stand-alone regulator or TCS, indicated that the virulence factors with the lowest variability in the pneumococcus are pneumolysin, enolase and PcsB, while the regulators with the highest conservation are TCS05 (CiaR/H), TCS02 (VicR/K) and TCS08. Finally, all the results obtained here with the bioinformatic analysis performed, constitute the first model to compare, visualize and understand the future flood of new genomic data about the genetic variation (in terms of gene presence/absence or mutation) of pneumococcal virulence factors and regulators [5153]. The applicability offered by this variome model, together with further population genomic analysis of pneumococci, will provide relevant information on potential targets for vaccines, supporting the idea of a new generation of protein-based formulations to combat Streptococcus pneumoniae and its disease burden.

Abbreviations

BLAST: 

Basic local alignment search tool

BLOSUM: 

Blocks substitution matrix

CBPs: 

Choline binding proteins

CSF: 

Cerebrospinal fluid

DnaSP: 

DNA sequence polymorphism

HK: 

Histidine kinase

IPDs: 

Invasive pneumococcal diseases

KEGG: 

Kyoto encyclopedia of genes and genomes

MultAlin: 

Multiple sequence alignment

NCBI: 

National center for biotechnology information

NCSP: 

Non-classical surface proteins

P2CS: 

Prokaryotic two-component systems

PAI: 

Pathogenicity Islands

RR: 

Response regulator

S. p.

Streptococcus pneumoniae

TCS: 

Two-Component regulatory Systems

UNIPROT: 

The Universal Protein Resource

Variome: 

Pan-genomic variability map

VFDB: 

Virulence factors data base

Declarations

Acknowledgements

The authors thank to Prof. Vanessa Cienfuegos, School of Microbiology, University of Antioquia for her critical evaluation of this research work and manuscript. We express our acknowledgements to peer reviewers for critical review of the manuscript. Their suggestions and comments significantly improved the quality of this piece of work.

Funding

The fundings for this research work have been provided by the Committee for Development of Research at the University of Antioquia (CODI, CIEMB-097-13) in Colombia, and the DFG GRK 1870/1 (Bacterial Respiratory Infections) in Germany. Both funding sources had no involvement in the design of this study, in the collection, analysis and interpretation of data, in the writing of this manuscript, and in the decision to submit this article for publication.

Availability of data and materials

Sequence data that support the findings of this study were already-published information, retrieved from GenBank (accession numbers are provided in Table 1). All the bioinformatic-analyzed data generated here are included in this published study. However, supplementary raw information files (mainly DNA and protein sequence comparisons) are available from the corresponding author on reasonable request.

Authors’ contributions

All the authors have contributed to this research work, participating in the conception and design (GG, AC, MG, AB, SH), collection and analysis of information (GG, AC, MG, AB), discussion of results (GG, AC, MG, AB, AGM, MC, SH), manuscript draft preparation (GG, AC), and critical revision and edition (GG, AC, AGM, MC, SH) of the manuscript. GG and AC have contributed equally to this research work and manuscript. All the authors have read and approved the final version of this manuscript.

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests in relation with this research work and manuscript.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Genetics, Regeneration and Cancer Research Group (GRC), University Research Centre (SIU), Universidad de Antioquia (UdeA), Medellín, Colombia
(2)
Basic and Applied Microbiology Research Group (MICROBA), School of Microbiology, Universidad de Antioquia (UdeA), Medellín, Colombia
(3)
Department of Molecular Genetics and Infection Biology, Interfaculty Institute of Genetics and Functional Genomics, Center for Functional Genomics of Microbes, Ernst-Moritz-Arndt University of Greifswald, Greifswald, Germany
(4)
School of Microbiology, University of Antioquia, Medellín, Colombia

References

  1. World Health Organization. The global burden of disease: 2004 update. Geneva: WHO; 2008.Google Scholar
  2. Bridy-Pappas AE, Margolis MB, Center KJ, Isaacman DJ. Streptococcus Pneumoniae: description of the pathogen, disease epidemiology, treatment, and prevention. Pharmacotherapy. 2005;25(9):1193–212.PubMedView ArticleGoogle Scholar
  3. Brueggemann AB, Griffiths DT, Meats E, Peto T, Crook DW, Spratt BG. Clonal relationships between invasive and carriage Streptococcus Pneumoniae and serotype- and clone-specific differences in invasive disease potential. J Infect Dis. 2003;187(9):1424–32.PubMedView ArticleGoogle Scholar
  4. Johnson HL, Deloria-Knoll M, Levine OS, Stoszek SK, Freimanis Hance L, Reithinger R, Muenz LR, O'Brien KL. Systematic evaluation of serotypes causing invasive pneumococcal disease among children under five: the pneumococcal global serotype project. PLoS Med. 2010;7(10):1–13.Google Scholar
  5. Jedrzejas MJ. Pneumococcal virulence factors: structure and function. Microbiol Mol Biol Rev. 2001;65(2):187–207. first page, table of contentsPubMedPubMed CentralView ArticleGoogle Scholar
  6. Voss S, Gamez G, Hammerschmidt S. Impact of pneumococcal microbial surface components recognizing adhesive matrix molecules on colonization. Mol Oral Microbiol. 2012;27(4):246–56.PubMedView ArticleGoogle Scholar
  7. Throup JP, Koretke KK, Bryant AP, Ingraham KA, Chalker AF, Ge Y, Marra A, Wallis NG, Brown JR, Holmes DJ, et al. A genomic analysis of two-component signal transduction in Streptococcus Pneumoniae. Mol Microbiol. 2000;35(3):566–76.PubMedView ArticleGoogle Scholar
  8. McCluskey J, Hinds J, Husain S, Witney A, Mitchell TJ. A two-component system that controls the expression of pneumococcal surface antigen a (PsaA) and regulates virulence and resistance to oxidative stress in Streptococcus Pneumoniae. Mol Microbiol. 2004;51(6):1661–75.PubMedView ArticleGoogle Scholar
  9. Gamez G, Hammerschmidt S. Combat pneumococcal infections: adhesins as candidates for protein-based vaccine development. Curr Drug Targets. 2012;13(3):323–37.PubMedView ArticleGoogle Scholar
  10. Centers for Disease Control and Prevention. Active Bacterial Core Surveillance Report, Emerging Infections Program Network, Streptococcus pneumoniae. Atlanta: CDC; 2015.Google Scholar
  11. Eldholm V, Johnsborg O, Straume D, Ohnstad HS, Berg KH, Hermoso JA, Havarstein LS. Pneumococcal CbpD is a murein hydrolase that requires a dual cell envelope binding specificity to kill target cells during fratricide. Mol Microbiol. 2010;76(4):905–17.PubMedView ArticleGoogle Scholar
  12. Donkor ES. Understanding the pneumococcus: transmission and evolution. Front Cell Infect Microbiol. 2013;3:7.PubMedPubMed CentralGoogle Scholar
  13. Feil EJ, Smith JM, Enright MC, Spratt BG. Estimating recombinational parameters in Streptococcus Pneumoniae from multilocus sequence typing data. Genetics. 2000;154(4):1439–50.PubMedPubMed CentralGoogle Scholar
  14. Donati C, Hiller NL, Tettelin H, Muzzi A, Croucher NJ, Angiuoli SV, Oggioni M, Dunning Hotopp JC, Hu FZ, Riley DR, et al. Structure and dynamics of the pan-genome of Streptococcus Pneumoniae and closely related species. Genome Biol. 2010;11(10):R107.PubMedPubMed CentralView ArticleGoogle Scholar
  15. van Tonder AJ, Bray JE, Jolley KA, Quirk SJ, Haraldsson G, Maiden MCJ, Bentley SD, Haraldsson A, Erlendsdottir H, Kristinsson KG et al. Heterogeneity Among Estimates Of The Core Genome And Pan-Genome In Different Pneumococcal Populations. bioRxiv 2017, doi:https://doi.org/10.1101/133991.
  16. Aras RA, Kang J, Tschumi AI, Harasaki Y, Blaser MJ. Extensive repetitive DNA facilitates prokaryotic genome plasticity. Proc Natl Acad Sci U S A. 2003;100(23):13579–84.PubMedPubMed CentralView ArticleGoogle Scholar
  17. Chewapreecha C, Harris SR, Croucher NJ, Turner C, Marttinen P, Cheng L, Pessia A, Aanensen DM, Mather AE, Page AJ, et al. Dense genomic sampling identifies highways of pneumococcal recombination. Nat Genet. 2014;46(3):305–9.PubMedPubMed CentralView ArticleGoogle Scholar
  18. Croucher NJ, Harris SR, Fraser C, Quail MA, Burton J, van der Linden M, McGee L, von Gottberg A, Song JH, Ko KS, et al. Rapid pneumococcal evolution in response to clinical interventions. Science. 2011;331(6016):430–4.PubMedPubMed CentralView ArticleGoogle Scholar
  19. Sokurenko EV, Gomulkiewicz R, Dykhuizen DE. Source-sink dynamics of virulence evolution. Nat Rev Microbiol. 2006;4(7):548–55.PubMedView ArticleGoogle Scholar
  20. Ring HZ, Kwok PY, Cotton RG. Human Variome project: an international collaboration to catalogue human genetic variation. Pharmacogenomics. 2006;7(7):969–72.PubMedView ArticleGoogle Scholar
  21. Chattopadhyay S, Taub F, Paul S, Weissman SJ, Sokurenko EV. Microbial variome database: point mutations, adaptive or not, in bacterial core genomes. Mol Biol Evol. 2013;30(6):1465–70.PubMedPubMed CentralView ArticleGoogle Scholar
  22. Tatusova TA, Karsch-Mizrachi I, Ostell JA. Complete genomes in WWW Entrez: data representation and analysis. Bioinformatics. 1999;15(7–8):536–43.PubMedView ArticleGoogle Scholar
  23. Zhang Z, Schwartz S, Wagner L, Miller W. A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000;7(1–2):203–14.PubMedView ArticleGoogle Scholar
  24. Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, Jin Q. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 2005;33(Database issue):D325–8.PubMedView ArticleGoogle Scholar
  25. Engel P, Goepfert A, Stanger FV, Harms A, Schmidt A, Schirmer T, Dehio C. Adenylylation control by intra- or intermolecular active-site obstruction in Fic proteins. Nature. 2012;482(7383):107–10.PubMedView ArticleGoogle Scholar
  26. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, et al. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2004;32(Database issue):D115–9.PubMedPubMed CentralView ArticleGoogle Scholar
  27. Barakat M, Ortet P, Jourlin-Castelli C, Ansaldi M, Mejean V, Whitworth DE. P2CS: a two-component system resource for prokaryotic signal transduction research. BMC Genomics. 2009;10:315.PubMedPubMed CentralView ArticleGoogle Scholar
  28. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Kanehisa M. Linking databases and organisms: GenomeNet resources in Japan. Trends Biochem Sci. 1997;22(11):442–4.PubMedView ArticleGoogle Scholar
  30. Morgulis A, Coulouris G, Raytselis Y, Madden TL, Agarwala R, Schaffer AA. Database indexing for production MegaBLAST searches. Bioinformatics. 2008;24(16):1757–64.PubMedPubMed CentralView ArticleGoogle Scholar
  31. Schaffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF. Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res. 2001;29(14):2994–3005.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402.PubMedPubMed CentralView ArticleGoogle Scholar
  33. Corpet F. Multiple sequence alignment with hierarchical clustering. Nucleic Acids Res. 1988;16(22):10881–90.PubMedPubMed CentralView ArticleGoogle Scholar
  34. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25(11):1451–2.PubMedView ArticleGoogle Scholar
  35. Sokurenko EV, Feldgarden M, Trintchina E, Weissman SJ, Avagyan S, Chattopadhyay S, Johnson JR, Dykhuizen DE. Selection footprint in the FimH adhesin shows pathoadaptive niche differentiation in Escherichia Coli. Mol Biol Evol. 2004;21(7):1373–83.PubMedView ArticleGoogle Scholar
  36. Srivatsan A, Tehranchi A, MacAlpine DM, Wang JD. Co-orientation of replication and transcription preserves genome integrity. PLoS Genet. 2010;6(1):e1000810.PubMedPubMed CentralView ArticleGoogle Scholar
  37. Morales M, Garcia P, de la Campa AG, Linares J, Ardanuy C, Garcia E. Evidence of localized prophage-host recombination in the lytA gene, encoding the major pneumococcal autolysin. J Bacteriol. 2010;192(10):2624–32.PubMedPubMed CentralView ArticleGoogle Scholar
  38. van Kooyk Y, Geijtenbeek TB. DC-SIGN: escape mechanism for pathogens. Nat Rev Immunol. 2003;3(9):697–709.PubMedView ArticleGoogle Scholar
  39. Figueira M, Moschioni M, De Angelis G, Barocchi M, Sabharwal V, Masignani V, Pelton SI. Variation of pneumococcal Pilus-1 expression results in vaccine escape during experimental Otitis media [EOM]. PLoS One. 2014;9(1):e83798.PubMedPubMed CentralView ArticleGoogle Scholar
  40. Soriani M, Telford JL. Relevance of pili in pathogenic streptococci pathogenesis and vaccine development. Future Microbiol. 2010;5(5):735–47.PubMedView ArticleGoogle Scholar
  41. Song XM, Connor W, Hokamp K, Babiuk LA, Potter AA. The growth phase-dependent regulation of the pilus locus genes by two-component system TCS08 in Streptococcus Pneumoniae. Microb Pathog. 2009;46(1):28–35.PubMedView ArticleGoogle Scholar
  42. Iovino F, Hammarlöf DL, Garriss G, Brovall S, Nannapaneni P, Henriques-Normark B. Pneumococcal meningitis is promoted by single cocci expressing pilus adhesin RrgA. J Clin Invest. 2016;126(8):2821–6.PubMedPubMed CentralView ArticleGoogle Scholar
  43. AlonsoDeVelasco E, Verheul AF, Verhoef J, Snippe H. Streptococcus Pneumoniae: virulence factors, pathogenesis, and vaccines. Microbiol Rev. 1995;59(4):591–603.PubMedPubMed CentralGoogle Scholar
  44. Blue CE, Paterson GK, Kerr AR, Berge M, Claverys JP, Mitchell TJ. ZmpB, a novel virulence factor of Streptococcus Pneumoniae that induces tumor necrosis factor alpha production in the respiratory tract. Infect Immun. 2003;71(9):4925–35.PubMedPubMed CentralView ArticleGoogle Scholar
  45. Martin B, Granadel C, Campo N, Henard V, Prudhomme M, Claverys JP. Expression and maintenance of ComD-ComE, the two-component signal-transduction system that controls competence of Streptococcus Pneumoniae. Mol Microbiol. 2010;75(6):1513–28.PubMedView ArticleGoogle Scholar
  46. Shak JR, Ludewick HP, Howery KE, Sakai F, Yi H, Harvey RM, Paton JC, Klugman KP, Vidal JE. Novel role for the Streptococcus Pneumoniae toxin pneumolysin in the assembly of biofilms. MBio. 2013;4(5):e00655–13.PubMedPubMed CentralView ArticleGoogle Scholar
  47. Bergmann S, Schoenen H, Hammerschmidt S. The interaction between bacterial enolase and plasminogen promotes adherence of Streptococcus Pneumoniae to epithelial and endothelial cells. Int J Med Microbiol. 2013;303(8):452–62.PubMedView ArticleGoogle Scholar
  48. Ng WL, Robertson GT, Kazmierczak KM, Zhao J, Gilmour R, Winkler ME. Constitutive expression of PcsB suppresses the requirement for the essential VicR (YycF) response regulator in Streptococcus Pneumoniae R6. Mol Microbiol. 2003;50(5):1647–63.PubMedView ArticleGoogle Scholar
  49. Sebert ME, Patel KP, Plotnick M, Weiser JN. Pneumococcal HtrA protease mediates inhibition of competence by the CiaRH two-component signaling system. J Bacteriol. 2005;187(12):3969–79.PubMedPubMed CentralView ArticleGoogle Scholar
  50. Muller M, Marx P, Hakenbeck R, Bruckner R. Effect of new alleles of the histidine kinase gene ciaH on the activity of the response regulator CiaR in Streptococcus Pneumoniae R6. Microbiology. 2011;157(Pt 11):3104–12.PubMedView ArticleGoogle Scholar
  51. Gámez G, Castro F, Gómez-Mejia A, Gallego M, Bedoya A, Hammerschmidt S. Bioinformatic analysis and construction of the variome of the virulence factors and genetic regulators in Streptococcus Pneumoniae. In: Annual Conference of the Association for General and Applied Microbiology (VAAM). Marburg. Germany: Biospektrum; 2015.Google Scholar
  52. Castro AF, Gómez-Mejia A, Gallego M, Bedoya A, Hammerschmidt S, Gámez GA. Variome of the Pneumococcal Surface-Exposed Proteins and other Virulence Factors: A Bioinformatics Analysis. [Abstract EuroPneumo-P1.27]. Pneumonia. 2015;7:17.Google Scholar
  53. Gámez GA, Castro AF, Gómez-Mejia A, Gallego M, Bedoya A, Hammerschmidt S. Análisis Bioinformático y Construcción del Varioma de los Factores de Virulencia y Sistemas de Regulación por Dos-Componentes de Streptococcus pneumoniae. [Abstract 3rd Colombian Congress on Computational Biology and Bioinformatics-CCBCOL3]. Medellín - Colombia; 2015, Oral Presentation 129.Google Scholar

Copyright

© The Author(s). 2018

Advertisement