The human small-intestinal microbiota is characterised by relatively large and dynamic Streptococcus populations. In this study, genome sequences of small-intestinal streptococci from S. mitis, S. bovis, and S. salivarius species-groups were determined and compared with those from 58 Streptococcus strains in public databases. The Streptococcus pangenome consists of 12,403 orthologous groups of which 574 are shared among all sequenced streptococci and are defined as the Streptococcus core genome. Genome mining of the small-intestinal streptococci focused on functions playing an important role in the interaction of these streptococci in the small-intestinal ecosystem, including natural competence and nutrient-transport and metabolism. Analysis of the small-intestinal Streptococcus genomes predicts a high capacity to synthesize amino acids and various vitamins as well as substantial divergence in their carbohydrate transport and metabolic capacities, which is in agreement with observed physiological differences between these Streptococcus strains. Gene-specific PCR-strategies enabled evaluation of conservation of Streptococcus populations in intestinal samples from different human individuals, revealing that the S. salivarius strains were frequently detected in the small-intestine microbiota, supporting the representative value of the genomes provided in this study. Finally, the Streptococcus genomes allow prediction of the effect of dietary substances on Streptococcus population dynamics in the human small-intestine.
Citation: Van den Bogert B, Boekhorst J, Herrmann R, Smid EJ, Zoetendal EG, Kleerebezem M (2013) Comparative Genomics Analysis of Streptococcus Isolates from the Human Small Intestine Reveals their Adaptation to a Highly Dynamic Ecosystem. PLoS ONE 8(12): e83418. https://doi.org/10.1371/journal.pone.0083418
Editor: Willem van Schaik, University Medical Center Utrecht, The Netherlands
Received: July 8, 2013; Accepted: November 4, 2013; Published: December 30, 2013
Copyright: © 2013 Van den Bogert et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by a project from the Top Institute Food and Nutrition, Wageningen, The Netherlands and by the Netherlands Bioinformatics Centre (NBIC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The affiliation to NIZO Food Research B.V. by two co-authors does not alter the authors′ adherence to all the PLOS ONE policies on sharing data and materials.
Streptococcus is a genus of Gram-positive, low GC-rich species belonging to the lactic acid bacteria (LAB) in the family Streptococcaceae . While several Streptococcus species, such as S. pyogenes  and S. pneumonia  are recognized as human pathogens, others like S. salivarius, S. mitis, S. parasanguinis are commonly detected as relatively dominant inhabitants in the upper respiratory tract , oral cavity , , throat , esophagus , stomach , and small-intestine , ,  of healthy individuals.
Studies carried out by our laboratory focused on elucidating the composition and function of the microbial community in the small-intestine, using ileostoma effluent samples as a representation of the luminal content of the small-intestine , , , . In ileostomy subjects the terminal ileum is connected to an abdominal stoma making this region of the intestinal tract accessible for non-invasive and repetitive sampling of the luminal fraction of the small-intestinal microbiota , , . The microbial composition in ileostoma effluent resembled the microbiota that resides in the proximal part of the small-intestine from individuals with an intact intestinal tract , . Although Streptococcus spp. were detected in each of collected ileostoma effluent samples, their relative abundance fluctuated greatly between individuals and even between samples obtained from the same individual , . Furthermore, metatranscriptomic analysis of ileostoma effluent identified carbohydrate transport systems, including several phosphotransferase systems (PTS) among the highly represented expressed functions in the small-intestinal streptococci, suggesting that the activity of these bacteria is focused on efficient uptake and fermentation of the available (diet-derived) carbohydrates in the human small-intestine . This is interesting considering that the human small-intestine represents the first region where food components and the intestinal microbiota meet , .
Based on the above, the Streptococcus populations in the small-intestine are predicted to play a prominent role in the metabolic conversion of primary carbohydrates that are present in this ecosystem, and may thereby effectively compete for dietary carbohydrate nutrients with the host mucosa. Therefore, a cultivation approach was employed to obtain representative Streptococcus isolates from the small-intestinal ecosystem . Classification of the isolates on the basis of molecular typing methodologies showed that from one ileostoma sample alone 3 different Streptococcus species were recovered belonging to the S. mitis group, S. bovis group, and S. salivarius group. Isolates from the S. salivarius group could be further divided in 4 genetic lineages (strain level). Although considerable temporal fluctuations of distinguishable genetic lineages were observed when a second sample was collected and investigated one year later, isolates belonging to a single lineage were recovered from both ileostoma effluent samples. Moreover, the Streptococcus lineages displayed different carbohydrate conversion and growth patterns . However, the mechanisms underlying the dynamics at the genetic lineage level is unclear. Therefore, specific aspects of the environmental interaction-potential and the metabolic capacity of 6 small-intestinal Streptococcus strains were investigated through analysis of their genome sequences in this study. Furthermore, the genomes enabled the comparison with other streptococci from other niches, allowing the identification of genetic targets for strain-specific PCR-based detection in intestinal samples from different individuals.
Materials and Methods
Small intestinal samples used in this study were collected as part of previous projects, results of which are reported elsewhere ,  (Leimena and Van den Bogert, et al., Unpublished data). These studies were approved by the University Hospital Maastricht Ethical Committee, and were conducted in full accordance with the principles of the ‘Declaration of Helsinki’ (52nd WMA General Assembly, Edinburgh, Scotland, October 2000). Subjects were informed about the study orally and in writing and signed a written informed consent before participation.
Fecal samples used in this study were collected as part of a previous project , which was approved by the METC of Wageningen University. Subjects were able to understand the written study information and signed an informed consent.
Streptococcus isolates and chromosomal DNA extraction
The isolation of the small-intestinal Streptococcus strains and their molecular typing was described previously . In short, isolates were obtained from ileostoma effluent plated on Mitis Salivarius (MS) agar (Becton Dickinson, Breda, the Netherlands) supplemented with Tellurite solution 1% (Becton Dickinson). The streptococcal isolates were classified by DNA fingerprinting into 6 genetic lineages that belonged to the S. mitis (1 lineage), S. bovis (1 lineage) and S. salivarius (4 lineages) species-groups. A randomly picked representative isolate of each lineage was selected for whole genome sequencing.
Genomic DNA of the isolates was extracted from overnight cultures in 10 ml MS medium at 37°C. Cells were pelleted by centrifugation at 7250 g at 4°C for 15 minutes and subsequently frozen at −20°C. Thawed cell-pellets were resuspended in 2 ml THMS (30 mM TRIS-HCl [Sigma, St. Louis, MO, USA; pH = 8.0], 25% (w/v) sucrose [Sigma], and 3 mM MgCl2 [Riedel-de Haën, Seelze, Germany]) supplemented with 10 mg/ml lysozyme (Sigma) and 40 µl mutanolysin (Sigma; 5000 U/ml), aliquoted in equal amount into 2 eppendorf tubes, and incubated at 37°C for 30 minutes. After centrifugation at 14.000 g for 5 minutes and discarding the supernatant, cells were resuspended in 100 µl THMS and mixed with 400 µl TES (50 mM TRIS-HCl [Sigma; pH = 8.0], 20 mM EDTA [Sigma; pH = 8.0], 50 mM NaCl [Merck], containing 0.5% (v/v) SDS [Ambion, Austin, TX, USA]) and 20 µl Proteinase K (20 mg/ml) followed by incubation at 56°C for 15 minutes. Nucleic acids were subsequently purified by sequential extraction with acid-phenol (Phenol:Water (3.75:1 v/v); pH = 4.45−5.68; Invitrogen, Carlsbad, CA, USA), acid-phenol:chloroform (1:1), and chloroform (Sigma-Aldrich, Zwijndrecht, Netherlands) using standard procedures as described by Sambrook, et al. . DNA was precipitated from the water-phase by standard ethanol precipitation . After drying, the DNA pellets were dissolved in 50 µl nuclease free water (Promega, Leiden, Netherlands). One µl RNAse A (10 mg/ml; Qiagen GmbH, Hilden, Germany) was added to the solution followed by incubation at 37°C for 30 minutes. Samples were stored at 4°C. DNA quality and concentrations were determined by nanodrop and on a 1.0% (w/v) agarose gel containing 0.4 µg/ml ethidium bromide (Bio-rad).
Genome sequencing and annotation
DNA from bacterial isolates was sequenced using 454 GS FLX (Roche) technology in combination with titanium chemistry, producing 350−450 bp reads (234,320 ± 86,626 reads per genome), and by using Illumina HiSeq 2000 technology, producing 11,884,010 ± 1,026,060 paired reads of 50 bp per genome from 3 kb mate pair libraries (Table S1; GATC-Biotech, Konstanz, Germany). Pyrosequence reads were assembled using the Celera Assembler v6.1 (http://sourceforge.net/apps/mediawiki/wgs-assembler/index.php?title=Main_Page), and the resulting contigs were subsequently combined with paired-read Illumina sequencing data to generate scaffolds using the SSPACE software v1.1 . Genome pseudo-assemblies were constructed by placing scaffolds in their likely order based on comparisons with the genomes from closely related bacteria: Streptococcus parasanguinis ATCC 15912 ([Genbank: NC_015678]; S. mitis species-group), Streptococcus gallolyticus UCN34 ([Genbank: NC_013798], S. bovis species-group), and Streptococcus salivarius CCHSS3 ([Genbank: NC_015760]; S. salivarius species-group). These comparisons were manually screened for inconsistencies using the Artemis comparison tool . Genomes were annotated using the RAST server . The genes predicted in the genomes of the six small-intestinal isolates were assigned to Cluster of Orthologous groups (COG; ) categories, using blastp comparison with the COG database (NCBI, ftp://ftp.ncbi.nih.gov/pub/COG/COG) using an alignment E-value cut-off of 10−3.
Strain identifiers and accession numbers
The Whole Genome Shotgun projects of the human small intestinal Streptococcus strains have been deposited at DDBJ/EMBL/GenBank under the following strain identifiers (and accession): the S. parasanguinis strain from the S. mitis species-group: HSISM1 (ASKI00000000), the S. equinus from the S. bovis species-group: HSISB1 (ASKA00000000), S. salivarius strain 1: HSISS1 (ASKB00000000), S. salivarius strain 2: HSISS2 (ASKC00000000), S. salivarius strain 3: HSISS3 (ASKH00000000), and S. salivarius strain 4: HSISS4 (ASKD00000000). The version described in this paper is version XXXX01000000.
Orthology relationships were identified by comparing all predicted gene products from all 6 small-intestinal Streptococcus genomes with the genes predicted to be encoded by the 58 other Streptococcus genomes (See Table S2 for accession numbers) that were available within the NCBI database on February 22nd, 2012 using OrthoMCL v2.0.2 with default parameters. Genome metadata (e.g. isolation site) from the Streptococcus genomes was retrieved from the Genome OnLine database (GOLD; http://genomesonline.org) on February 27th, 2012 (Table S2).
Streptococcus phylogenetic tree reconstruction
Multiple protein sequence alignments of the 450 orthologous groups with exactly one member in each Streptococcus genome were generated using MUSCLE . The variable positions were concatenated into a single alignment (length 5605 residues) and a maximum-likelihood phylogenetic tree was generated using PhyML . The phylogenetic tree was visualized using the TREEVIEW program .
Genome mining and metabolic mapping
Bacterial genomes were mined for systems involved in responses to external stimuli, focusing on bacteriocins, identified using BAGEL2 employing no re-annotation , and two-component systems (TCS) consisting of sensor histidine kinase (HK) and response regulator (RR) pairs .
Moreover, genomes were screened for gene clusters involved in regulation of natural competence: comCDE, present in the S. mitis group species, or comRS in S. bovis and S. salivarius streptococci .
Protein sequences for genes annotated as transposases were assigned to Insertion Sequences (IS; See  for a review) families using blastp comparison with the ISfinder database . IS families were assigned based on the best hit. Multiple alignment of the protein sequences of transposases were performed using ClustalX2 . Small interspersed repeats (e.g. BOX elements , Repeat Unit of Pneumococcus (RUP) , , and Streptococcus pneumoniae Rho-Independent Terminator-like Element (SPRITE) ) were identified with HMMER2  using the Hidden Markov Models (HMMs) generated for S. pneumoniae and S. suis by Croucher, et al. .
Genomes were further screened for sugar transport systems including constituents of the bacterial phosphotransferase system and ABC transporters. Metabolic and amino acid biosynthesis pathways were constructed for the newly sequenced genomes by mapping EC numbers from the genome annotations onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways . Pathways from individual KEGG maps that were represented in at least one of the Streptococcus genomes were included in combined metabolic visualizations for sugar metabolism and amino acid biosynthesis that were manually constructed. In cases where genes of key enzymes in specific pathways of interest were apparently absent from the genome-based predictions, a further effort was made to identify homologous gene candidates by dedicated BLAST searches .
Unique gene identification and PCR detection
Each of the newly sequenced genomes was screened for ‘unique’ genes that were not present in other small-intestinal Streptococcus genomes or other genomes in the NCBI database. Single copy unique genes with a sequence length of at least 750 nt were used for primer design employing the Primer-BLAST tool (http://www.ncbi.nlm.nih.gov/tools/primer-blast/), which uses the Primer3 program . Default parameters were used, except for the following changes: PCR product size: 150 to 300 bp; maximum primer size: 23 nt; minimum GC content: 40%; maximum poly-X (mononucleotide repeats): 3; maximum self-complementarity: 3.
Primer specificity was checked by submitting each primer to Primer-BLAST using genomes, “Genomes (chromosomes from all organisms)” from all Bacteria, as a reference database. An in-house perl script was used to determine if the primers designed had exact matches in small-intestinal Streptococcus genomes other than the intended Streptococcus strain target. This revealed that primers developed for S. salivarius lineage 4 were not exclusively specific for the intended target strain, but were predicted to be cross reactive with S. salivarius lineage 1. By decreasing the minimal gene sequence length to 500 nt, primers were developed that were specific for S. salivarius lineage 4.
After the recent addition of novel Streptococcus genomes to the DNA databases a reassessment of primer specificity showed that primers targeted to the S. salivarius strains of lineage 1 and 4 (HSISS1 and HSISS4) have predicted matches in the genome of S. salivarius JIM8777. However, the predicted PCR-products are exceptionally large (e.g. >4000 bp) and/or the primers show between 1 and 5 mismatches with the S. salivarius JIM8777 target. These features make it highly unlikely that a PCR-amplicon is formed using employed PCR conditions. The primers targeted to HSISS4 are predicted to form a PCR-product for which the primers only show a single mismatch with S. salivarius JIM8777. Therefore, the primers targeted to the S. salivarius strains of lineage 1 and 4 (HSISS1 and HSISS4) may not be exclusively specific to their intended targets, but can be used to detect streptococci belonging to the S. salivarius species group that carry the target functional gene.
Primers that passed each screening step, were specific for their target strain, and had a minimal tendency to form secondary structures, including hairpin loops, heterodimers, and homodimers (analysed by the IDTDNA Oligoanalyzer 3.1; Integrated DNA Technologies) were ordered (Biolegio BV, Nijmegen, Netherlands) and tested for their application in strain specific PCR detection assays (see below; Table S3).
All PCRs were performed on a C1000™ Thermal Cycler (Bio-rad) with a CFX96 optic module (Bio-rad) employing CFX Manager 2.1 (Bio-rad) software for analysis. Reactions were carried out in Hard-Shell semi skirted clear 96 well plates (Bio Rad) sealed with Microseal B film (Bio Rad) in 25 µl volumes using IQ SYBR green supermix (Bio-Rad) according to the manufacturer’s instructions with 200 nM of forward and reverse primer and either 5 µl gDNA (10−20 ng/µl) or glycerol stock of the strain as a template source.
The optimal annealing temperature (60°C) for each primer pair was determined by an 8-degree temperature (53°C to 64°C) gradient PCR using gDNA from target strains as template (data not shown).
The PCR program started with a denaturation step at 95°C for 5 minutes, followed by 40 cycles consisting of denaturation at 95°C for 15 s, annealing for 60°C for 30 s and elongation at 72°C for 20 s with data collection, and a final elongation step at 72°C for 10 minutes. Ct values above 35 were considered negative. Melting curve analysis was carried out by incrementally increasing the temperature from 55°C to 95°C at 30 s per 0.5°C with continuous fluorescence collection. Control PCRs were performed alongside each separate amplification without addition of template and consistently yielded no product.
Small-intestinal and fecal sample collection
In total, 30 ileostoma effluent samples were collected in the morning or afternoon (at least 3 h apart) on separate days (at least two days apart) from 6 ileostomy subjects (4 male and 2 female; aged 55 to 79; A-F), as part of previous projects, results of which are reported elsewhere ,  (Leimena and Van den Bogert, et al., Unpublished data). Small-intestinal fluid samples were obtained from 3 healthy individuals (3 males; 24 ± 4.5 years; G-I) and included a jejunal sample and an ileum sample from subject H and a single ileum sample from subjects G and I . Fecal samples were collected from 10 individuals (4 male and 6 female; aged 19 to 33; J-S) as part of a previous project .
DNA was extracted using the Repeated Bead Beating method described in  or using a method adapted from Zoetendal, et al. , , , depending on the study they originated from, and was used to screen for the unique targets of the Streptococcus genetic lineages.
General features of small-intestinal streptococcal genome sequences
The entire genome set analysed in this study consisted of 64 genomes, encompassing 20 Streptococcus species. Six draft genome sequences were obtained from strains originating from the small-intestine, which were determined in this study and ranged in genome size from 1.9 Mbp (Streptococcus strain from the bovis species-group) to 2.4 Mbp (S. salivarius lineage 3; See table S1 for genome statistics). The full complement of genes (pangenome) of the Streptococcus genome set consisted of 12,403 orthologous groups (OG), of which 4,232 OG were represented in the genomes of at least one of the six small-intestinal Streptococcus strains. The size of the Streptococcus pangenome estimated here is somewhat larger as has been suggested in previous studies , . However, these studies based their pangenome estimates on a smaller genome set comprising fewer species. Furthermore, the Streptococcus pangenome defined here does not seem to be exceptionally high compared to, for example, the Lactobacillus pangenome estimated to consist of over 13,000 protein-encoding genes  or gene families . Further analysis revealed that all 64 Streptococcus genomes shared 574 orthologous groups (OG), defining the core Streptococcus genome. All OG belonging to the core Streptococcus genome could be classified to a COG, although 26% of these OGs was assigned to poorly characterized COG categories (Figure 1). Most OG in the core Streptococcus genome were predicted to be involved in information storage and processing (29.2%), with most genes belonging to typically conserved functions such as ‘Translation, ribosomal structure and biogenesis’ and ‘Replication, recombination and repair’. Metabolic functions accounted for 28.4% of the core Streptococcus OG, followed by 15.7% of OG that were involved in cellular processes and signalling. Most OG belonging to ‘metabolism’ were assigned to functions in transport and metabolism of nucleotides and carbohydrates.
Most streptococcal genome sequences from the public databases that were included in the analysis here, were derived from clinical bacterial isolates cultivated from different human body sites (Table S2). Due to frequent obscurity concerning the source of isolation it is far from trivial to identify niche-specific OG. Nonetheless, 197 OG, mostly belonging to ‘metabolism’ (Table S4), were found to be present in at least two of the small-intestinal Streptococcus genomes, but not in any of the genomes from the public databases. An assessment of the orthology relationships between the Streptococcus strains analysed here as well as the recently made available genome of Streptococcus sp. HPH0090 (accession: NZ_ATCD00000000), a strain isolated from a biopsy of ileal-anal pouch mucosa as part of the Human Microbiome Project (http://www.hmpdacc.org) , revealed that only a fraction of the OG were shared by three or more small-intestinal Streptococcus genomes (e.g. 10 genes of HPH0090 belonged to the 197 OG). This suggests that there is considerable variation in the number of streptococcal genes with functions that may contribute to the lifestyle in the small-intestine ecosystem. Notably, a considerable fraction of the 197 OG (65; 33.0%) could not be assigned to a COG, and were predominantly annotated as hypothetical proteins (44; Figure 2). This suggests that for a substantial amount of OG that in this analysis were exclusively encountered in the streptococcal genomes from the small-intestine, the function needs to be further elucidated (Figure 2).
Phylogenetic analysis of Streptococcus genomes
The subset of genes of the core Streptococcus genome (450) that were present in single copy in each genome was used to construct a phylogenetic tree (Figure 3). This core-genome-based phylogeny revealed a division of 5 distinct clusters of Streptococcus strains that belong to the Streptococcus species-groups: Pyogenic (e.g. S. pyogenes), S. bovis, S. mutans, S. salivarius, and S. mitis groups. S. suis genomes could not be assigned to one of these Streptococcus species-groups , but represented an additional and separate phylogenetic branch (Figure 3). Notably, 5−140 orthologous groups were exclusively present in all genomes belonging to one of the clusters (cluster-specific genes) and might be used as cluster-markers for molecular detection and quantification (Table S5). The small-intestinal Streptococcus genomes clustered within the S. mitis, S. bovis, and S. salivarius groups, corroborating previous classifications based on MALDI-TOF MS analysis and 16S rRNA gene analysis , which showed that the strain belonging to the S. mitis species-group showed highest similarity to S. parasanguinis (> 99%), the strain from S. bovis species-group showed highest similarity to S. equinus (> 98.5%) and S. lutetiensis (> 99.7%;), and the strains belonging to the S. salivarius species-group showed highest similarity to S. salivarius subsp. salivarius (> 98.7%) and S. vestibularis (> 99.3%). To specify the classification of the isolate belonging to the S. bovis species-group a phylogenetic analysis of the SodA encoding gene  was performed, revealing that the small-intestinal strain clustered together with strains from the species S. equinus (data not shown). To improve species classifications of the salivarius group streptococci, we focused on the genetic organization of the region encoding the transketolase gene, that is known to differ between S. salivarius and S. vestibularis . In the genomes of all small-intestinal strains belonging to the S. salivarius species-group the transketolase gene is situated in a region flanked by genes encoding triose phosphate isomerase and thymidylate kinase, which is typical for S. salivarius species (; data not shown).
Unrooted maximum-likelihood phylogenetic tree based on multiple protein sequence alignments (length 5605 residues) of the 450 orthologous groups with exactly one member in 64 Streptococcus genomes. Genomes belonging to the Pyogenic group are highlighted in red, S. bovis species-group in green, S. mutans species-group in blue, S. salivarius species-group in purple, and S. mitis species-group in orange. Small-intestinal Streptococcus strains are highlighted and bulleted. Visualisation of the nodes (i.e. multiple leaves) of the S. pyogenes, S. equi, S. agalactiae, S. mutans, S. pneumoniae, and S. suis genomes were collapsed into triangles. Values in the tree are approximate likelihood ratio test (aLRT scores (SH-like) as calculated by PhyML).
The distance between the strains from S. salivarius lineage 1 and 4 was relatively small (Figure 3), which is in agreement with the similarity of fermentation and growth  as well as the immunostimulatory (Van den Bogert and Meijerink, et al., Unpublished data) profiles that were previously determined for these strains. To further assess the similarity between S. salivarius lineage 1 and 4, the number of shared genes was determined. This revealed that both lineages shared 1730 OG, which is high compared to the number of shared genes between the two other S. salivarius lineages (Table S6). Nonetheless, S. salivarius lineage 1 and 4 were still predicted to have 128 and 237 strain-specific OG, respectively. The set of 128 lineage 1 specific OGs were manually inspected for potential sequencing and/or gene-calling artifacts (See table S7). These genome sequence analyses confirmed that the representative isolates of S. salivarius lineages 1 and 4 are closely related, and confirmed and extended our previous observations based on AFLP and Rep-PCR fingerprinting .
Genomic mining to decipher environmental interaction potential
To obtain an impression how the analysed Streptococcus strains may react to external stimuli, we mined their genomes for the canonical TCS regulatory modules, consisting of HK and RR, that are known to play a prominent role in bacterial interaction with their environment . The strains appeared to encode 12−18 HK/RR) pairs, which are predicted to respond to a wide variety of environmental responses (Table S8). TCS annotated as CiaRH, ComDE, VraSR, and CsrSR were identified in all strains.
The CiaRH system responds to environmental Ca2+  and has been shown to be important for intracellular survival of group B Streptococcus . It has been shown that this system is involved in regulation of numerous functions in S. pneumoniae, including those associated with natural competence, which is a driver of evolution , . Analogously, the comCDE encoded TCS (ComDE), present in the S. mitis group species , has been shown to be the central regulatory module in the control of natural competence, involving a comC encoded extracellular competence stimulatory peptide (CSP) as its autoregulatory environmental cue . The small-intestinal S. parasanguinis strain appeared to encode two candidate comDE TCS, but a putative CSP encoding comC gene upstream of comDE could not be identified. The S. equinus and S. salivarius strains from the small-intestine were found to encode a distinct competence regulatory module consisting of a transcriptional regulator and a putative oligopeptide pheromone, that share similarity with ComR and ComS , and are genetically linked to conserved comX promoter structures . The oligopeptide predicted for the S. equinus strain (MKVFSILLTGWWLG) contains the conserved double-tryptophan (WW) motif, which is a conserved feature of ComS from bovis streptococci . The oligopeptides predicted for the strains from S. salivarius lineage 1 and 4 are identical (MKKLKLFTLFSLLITILPYFAGCL) and resemble that of S. salivarius SK126 , , but have a single amino acid substitution (“T” in S. salivarius SK126 to “A” in S. salivarius lineage 1 and 4) in the predicted 7 amino acid long C-terminal peptide that is likely to prevent inter-strain crosstalk. Moreover, the lineage 1 representing strain appears to contain a frameshift in the region encoding the N-terminal end of the oligopeptide. The oligopeptides predicted for the salivarius strains from lineage 2 and 3 are also identical, but are distinct (MKNLRKFLVLLIAAAPFFIIYY) from the sequence presented above. It is likely that competence could be induced in these strains via extracellular addition of the unmodified small peptides, especially since all genomes presented here appear to encode a complete competence regulon including genes encoding a competence specific sigma factor ComX, and late competence complexes (e.g. comEA/C, and comGA/B/C/D/E/F/G), which are involved in DNA uptake and DNA processing (see  for review).
To investigate if there is any evidence of horizontal gene transfer among the small-intestinal streptococci their genomes were mined for mobile elements, including IS, RUP, BOX and SPRITE. This revealed that the strains appeared to encode 1 (S. equinus HSISB1) to 22 (S. salivarius HSISS3) transposases that belonged to 7 IS families (IS3, IS30, IS1182, IS200/IS605, IS110, ISL3, and IS256). While S. equinus HSISB1 encoded a single transposase belonging to the IS 30 family, S. parasanguinis HSISM1 encoded 10 transposases that belonged the IS3 family. The latter could be further divided into 3 groups based on alignment of the protein sequences. The transposases encoded by the S. salivarius strains belonged to different families (IS3, ISL3, IS26, IS30, IS110, IS200/IS605, and IS1182). Interestingly, transposases from the IS3, ISL3, and IS256 families encoded in the genomes of the S. salivarius strains aligned perfectly suggesting that there is genetic exchange between the streptococci in the small-intestine (Figure S1). Though the HMMs produced by Croucher, et al.  are based on alignment of repeat sequences from S. pneumoniae and S. suis, their application to the small-intestinal Streptococcus genomes found 14 BOX elements, present in all genomes except in that of S. equinus, and 3 SPRITE elements, exclusively present in the S. parasanguinis genome, indicating that horizontal gene transfer could occur between small-intestinal streptococci (Table S9).
The genomes of all the small-intestinal Streptococcus strains described here, appeared to encode a TCS that resembles the NisK-NisR and/or SpaK-SpaR TCS modules involved in quorum-sensing controlled autoregulation of nisin and subtilin biosynthesis in Lactococcus lactis and Bacillus subtilis, respectively (for a review see ). Both nisin and subtilin are antimicrobial peptides (bacteriocins) that contain extensive post-translational modification and belong to the class of the lantibiotics (for a review see ) and their biosynthesis depends on multi-gene clusters encoding modification, export, immunity, and the mentioned TCS functions . To investigate whether the identified streptococcal homologues of these lantibiotic TCS may be involved in regulation of lantibiotic production by these strains, the genetic context of the TCS encoding genes was investigated. However, this analysis failed to identify additional genes that were predicted to be involved in lantibiotic biosynthesis in these organisms. To perform a genome wide analysis of the capacity to produce antimicrobial peptides, we employed the BAGEL2 software module  that identified at least one putative bacteriocin encoding gene in the genomes of the S. equinus and S. salivarius strains. All candidate genes belonged to the non-lanthionine-containing bacteriocins of the pediocin-like (class IIA) and/or miscellaneous (class IID) class according to the scheme proposed by Cotter, et al.  (Table S10). This analysis indicates that despite their resemblance to NisRK-like TCS modules, these TCS systems are not involved in regulation of genetically linked or distantly located lantibiotic encoding gene clusters, and are thus most likely involved in regulation of other functions.
All Streptococcus strains analysed here appeared to encode the CsrSR system, although S. parasanguinis appeared to lack a HK paired to the RR similar to CsrR. The CsrSR TCS module is known to play a major role in regulating the virulence of group A and B streptococci , . Group A streptococcal CsrSR regulates the expression of virulence factors (e.g. pyrogenic exotoxin A, DNase, streptolysin O, streptokinase, and hyaluronic acid capsule synthesis) depending on environmental Mg2+, as well as human antimicrobial peptide LL-37 concentrations . The CsrSR TCS in group B streptococci is known to repress the expression of certain genes (e.g. coding for β-haemolysin and secreted adhesins) while it stimulates expression of other genes (e.g. the cps operon coding for capsular polysaccharide ). All newly sequenced Streptococcus genomes were predicted to encode genes with similarity to hemolysin III. However, only the S. parasanguinis and S. equinus strains displayed partial (α) hemolysis and none displayed complete (β) hemolysis of blood cells when grown on blood agar (data not shown). The streptococcal genomes were predicted to encode capsular polysaccharide biosynthesis and appeared to encode a gene similar to exfoliative exotoxin B. The strains representing S. salivarius lineage 1 and 4 also contained a gene with homology to the C5a peptidase precursor. The latter enzyme inactivates C5a, a chemotactic attractant of phagocytes to infection sites, and promotes streptococcal invasion , . Although the strains described here are not known to be virulent, they appear to encode at least remnants of the virulence genes known in related streptococci, which may be regulated by the conserved CsrSR TCS module, analogous to what is observed for group A and B streptococci. Remnants of virulence related genes were also encountered in the genomes of strains of S. themophilus , suggesting that these benign streptococci share specific functions with their known pathogenic relatives.
The VraSR TCS that appeared to be encoded by all streptococcal genomes reported here, has been extensively studied in Staphylococcus aureus where it belongs to the cell-wall-stress stimulon that is involved in maintenance of cell wall integrity under stress conditions . In S. aureus VraSR plays an important role in regulation of resistance to antibiotics that target the bacterial cell wall biosynthesis pathway. Whether the VraSR homologues in the small-intestinal streptococci play a similar role in cell-wall stress and possible antibiotic resistance control remains to be established.
Amino acid and vitamin requirements
The predicted enzyme functions of the newly sequenced Streptococcus genomes were mapped onto KEGG pathways to assess their predicted potential for amino acid biosynthesis. Each of the genomes was predicted to encode the necessary enzymes for the biosynthesis of at least 18 amino acids (Table 1; Figure S2). However, none of the strains found to encode the enzymes required to synthesize lysine. Moreover, the biosynthesis of histidine from the pentose phosphate pathway intermediate phosphoribosyl pyrophosphate (PRPP) appears to be incomplete in the genomes of the S. salivarius strains representing lineage 2 and 3, as well as the S. parasanguinis strain. Alanine biosynthesis appeared to depend on distinct enzymatic conversion of pyruvate to alanine, involving alanine transaminase dehydrogenase (EC 126.96.36.199) in the S. equinus strain and the S. salivarius strain representing lineage 3, while involving an alanine dehydrogenase (EC 188.8.131.52) in all other strains (Figure S2).
Although the small-intestinal streptococci encode the capacity for synthesis of the majority of the amino acids, they also were predicted to encode the oligopeptide import system, oppABCDF , but lacked a gene resembling an extracellular protease function (e.g. PrtP; ). These findings may reflect the adaptation to the peptide and exogenous protease-rich environment that is probably encountered in the human small-intestine.
Next we investigated the predicted capacity to produce B-vitamins, which is known to be variable among streptococci . Genome analyses indicate that all small-intestine derived streptococci presented here encode the capacity to produce folate from phenylalanine. All strains, except S. parasanguinis, also appeared to encode a complete pyridoxal-5-phosphate (B6) biosynthetic pathway. In addition, S. equinus was predicted to also encode the capacity to synthesize riboflavin (B2), nicotinate (B3), and pantothenate (B5), which appear to be lacking in S. parasanguinis and S. salivarius. None of the strains is predicted to encode thiamine (B1), biotin (B8) and cobalamin (B12) biosynthesis pathways.
Primary carbon metabolism and pyruvate dissipation
As streptococci belong to the facultative heterofermentative LAB and generate energy through homolactic and mixed acid fermentation , we screened the genomes of the small-intestinal streptococci for genes involved in glycolysis and the pentose phosphate pathway. All strains encoded the required enzymes for glycolytic conversion of glucose to pyruvate (Table S11). Notably, only the S. parasanguinis strain appeared to encode a complete and intact pentose phosphate pathway. However, the S. equinus and S. salivarius strains, appeared to code for a transketolase (EC 184.108.40.206) that interconnects the glycolysis and the pentose phosphate pathway, enabling the synthesis of the precursor required in de novo purine and pyrimidine synthesis, phosphoribosyl pyrophosphate (PRPP; Figure S3). In addition, the S. equinus strain, codes for a putative xylulose-5-phosphate phosphoketolase (EC 220.127.116.11; Table S11 and figure S3), suggesting that this strain can ferment pentoses (e.g. arabinose ; see below) that enter the pentose phosphate pathway as xylulose-5-phosphate. As expected, genomic analyses showed that none of the small-intestinal streptococci code for a complete tricarboxylic acid (TCA) cycle, albeit that several enzymes (e.g. EC 18.104.22.168, Fumarate reductase) from this pathway are predicted in the genome annotations. The presence of fumarate reductase in the genomes may indicate that these streptococci possess a rudimentary electron transport chain, similar to what is observed for other LAB, including L. plantarum WCFS1 .
As expected, all the small-intestinal Streptococcus genomes have the necessary enzymes to convert pyruvate to L-lactate (lactate dehydrogenase [EC 22.214.171.124]). Although the genomes appeared to lack the genes to produce a complete pyruvate dehydrogenase complex, they do encode the necessary enzymes for mixed acid fermentation via the formate lyase (EC 126.96.36.199), phosphate acetyltransferase (EC 188.8.131.52), and acetate kinase (EC 184.108.40.206) pathway. In addition, the genomes also encompass acetaldehyde dehydrogenase (EC 220.127.116.11) and alcohol dehydrogenase (EC 18.104.22.168) encoding genes, implying their capacity to produce ethanol (Figure S3). Finally, all streptococci appeared to encode both acetolactate synthase (EC 22.214.171.124) and acetolactate decarboxylase (EC 126.96.36.199) that could catalyze the conversion of pyruvate to acetoin.
Streptococcus spp. have been proposed to contribute to microbial uptake and fermentation of the simple dietary carbohydrates in the small-intestine . Therefore, we especially focused our genome annotation efforts on carbohydrate transport functions and metabolism that can be used as fuel for the downstream energy-generating pathways (e.g. glycolysis and pentose phosphate pathway). All small-intestinal strains encode the general cytoplasmic enzyme I (EI) and phosphor-carrier protein (HPr; Figure 4) involved in phospho-donation to several PTS transport systems. In total, 11 distinct PTS transporter functions were found to be encoded by the small-intestinal Streptococcus genomes. Those with predicted specificities for glucose/maltose, mannose, fructose, sucrose, β-glucosides, and trehalose were redundantly present in some of the genomes (Figure 4).
Dots indicate for which genome the corresponding PTS (component) was encoded. Numbers in dots represent the number of times a complete PTS complex was encoded in the genome. Squared dots indicate which isolates were able to ferment the corresponding substrate, double squared dots indicate which isolates were able to ferment and grow on the corresponding substrate . Components of PTS that are encoded by the same orthologous group are indicated with faded purple lines a: S. parasanguinis and S. salivarius lineage 1 carried an additional phosphocarrier protein (Hpr), each belonging to different orthologous compared to the Hpr protein that belonged to the same OG that was shared between all Streptococcus b: complete PTS were encoded by genes belonging to different OG c: Component EIIa and EIIb from 2 complete cellobiose specific PTS were encoded by genes belonging to the same OG while component EIIc of the two systems belonged to different OG.
All sequenced genomes were predicted to encode complete enzyme II (EII) PTS complexes, consisting of IIa, IIb, and IIc (as well as IId in some cases) components, involved in import of glucose/maltose, mannose, fructose, and sucrose (Figure 4).
Complete PTS transport systems with predicted specificity for cellobiose, β-glucosides, and trehalose appeared to be encoded in the S. equinus genome, while the PTS-mediated import capacity for the latter two substrates was also predicted for the lineage 1 and 4 representing S. salivarius isolates. Next to these complete PTS EII complexes, all genomes also appeared to encode orphan PTS EIIc transport component(s), which lack the accompanying EIIb and EIIa encoding genes, and in all cases were predicted to have a cellobiose substrate-specificity. This is a feature that has been recognized in many other bacterial genomes, and has been proposed to play a role in environmental signalling , , , . The S. parasanguinis and S. equinus genomes are predicted to encode β–glucosidases (EC 188.8.131.52) that are required for the conversion of cellobiose to β–D–glucose (figure S3). However, only S. equinus is able to ferment cellobiose , which appears to be in agreement with the presence of genes encoding a complete cellobiose PTS, which was exclusively encountered in the S. equinus genome (Figure 4).
These genome-based predictions are in good agreement with the differential carbohydrate-fermentation and growth patterns that were previously determined . However, all S. salivarius genomes lack the PTS for lactose, while all strains were able to ferment this substrate . It is known that S. salivarius, and its close relative S. thermophilus, can effectively ferment lactose and import this substrate by a dedicated lactose permease (LacS) that belongs to the galactoside-pentose-hexuronide translocator family , . Indeed, all S. salivarius, and S. equinus genomes presented here appeared to encode a lacS homologue. The S. parasanguinis strain also encode a complete PTS EII complex predicted to be involved in import of galactose and galactosamine. To metabolize lactose and its galactose moiety, S. parasanguinis and S. equinus encode the tagatose phosphate and/or Leloir pathways . The S. salivarius lineages, lacking the PTS for lactose, relied on the latter pathway for metabolism of these substrates (Figure S3).
All sequenced genomes encode maltose/maltodextrin ABC transporters, while S. parasanguinis and S. equinus also appear to encode ABC transporters for multiple sugars (the so-called MSM system). These transporters have been previously described for S. mutans and can import multiple sugars, including raffinose and melibiose . Notably, the S. parasanguinis and S. equinus strains, also encode the downstream enzymes required for raffinose and melibiose metabolism. Finally, the S. equinus genome also contained genes encoding an “α-arabinosides ABC transport permease (araP)” as well as the necessary enzymes to metabolize this sugar. These findings are in excellent agreement with the observation that only S. equinus was able to grow on arabinose, while only S. parasanguinis and S. equinus could ferment melibiose and grow on raffinose .
Though all Streptococcus strains encoded at least one α-amylase, S. equinus could grow on media with starch as the sole carbohydrate source . However, this could be explained by the predicted subcellular location of the α-amylase enzymes. The enzymes encoded by S. parasanguinis and S. salivarius genomes were all predicted to be cytoplasmic, while only the S. equinus strain appeared to encode an excreted α-amylase (as well as a cytoplasmic one). Thereby, it seems likely that only S. equinus can access extracellular starch as a substrate for growth, whereas S. parasanguinis and the S. salivarius strains may use intracellular polysaccharides (IPS) for energy-storage. The latter is supported by the fact the S. parasanguinis and S. salivarius strains encoded three key enzymes required for IPS synthesis, namely glycogen synthase (EC 184.108.40.206), glucose-1-phosphate adenylyltransferase (EC 220.127.116.11), and branching enzyme (EC 18.104.22.168) . However, to the best of our knowledge there is no experimental evidence to support the capacity for IPS synthesis in any of the streptococcal species studied here, which may suggest that this only occurs under specific circumstances that were not studied to date.
Since the observed physiological characteristics of the Streptococcus strains are in excellent agreement with genome predictions , the small-intestinal Streptococcus genomes are helpful to predict the effect of dietary changes on the Streptococcus populations in the small-intestine.
Detection of small-intestinal streptococcal strains and lineages
To monitor the dynamics of the small-intestinal Streptococcus populations during dietary intervention studies, fast and high throughput, PCR-based detection assays were developed that targeted genes that were solely encountered in a single small-intestinal Streptococcus strain or lineage.
To evaluate the specificity and conservation of the genes selected for detection among the representative isolates that belong to the different streptococcal lineages, they were amplified from 92 Streptococcus isolates and 28 Enterococcus isolates as negative control . The PCR amplicons robustly discriminated the 92 Streptococcus isolates into 3 S. parasanguinis isolates, 13 S. equinus isolates, 1 S. salivarius lineage 2 isolate, and 8 S. salivarius lineage 3 isolates, thereby perfectly matching with their grouping according to AFLP and Rep-PCR (Table 2 and S12; ). These results showed that among representative strains of the identified lineages, the selected gene is conserved and specific for the isolates of that lineage. In contrast, the primers designed to selectively amplify S. salivarius strain 1 and 4, failed to consistently discriminate between isolates of these two lineages, albeit that not every bacterial isolate belonging to S. salivarius lineage 4 revealed a PCR product with primers for S. salivarius lineage 1. Nonetheless, these results confirm the close relatedness of these lineages, which was already apparent from the identical Rep-PCR profiles they generated (see above) . Furthermore, the Streptococcus PCR assays developed here provide a simple and effective means to detect the small-intestinal S. parasanguinis, S. equinus, and S. salivarius lineages 2, 3 and the group of S. salivarius lineage 1 and 4.
Considering that all six Streptococcus strains from the small-intestine were cultivated from a single ileostoma effluent sample, the PCR assays were further evaluated with total DNA from 30 ileostoma effluent samples obtained from 6 ileostomists, 4 ileal fluid samples from 3 healthy individuals, and fecal samples from 10 healthy individuals to investigate the distribution of these genetic targets beyond the ileostomist they were derived from. As anticipated, the selected genes from all sequenced Streptococcus genomes were amplified in other ileostoma effluent samples collected from the ileostomist from which the strains were isolated (Subject A; Table 3). However, the S. equinus targeted amplicon could only be detected in a single ileostoma effluent sample (Subject A) and in several fecal samples (Table 3). The S. parasanguinis targeted amplicon was exclusively detected in ileostoma samples, which were obtained from subject A and subject B. The latter samples were collected on two consecutive days, while additional samples that were collected from the same individual 5 years later did not allow the detection of this genetic marker. Considering that the S. parasanguinis and, to a lesser extent, S. equinus belong to the predominant streptococci in the small-intestine in these samples , these findings imply that the small-intestinal microbiota in other individuals is encompassing other S. parasanguinis and S. equinus lineages as compared to the strain targeted here. In contrast, the unique genes from at least 2 S. salivarius lineages (mostly lineage 2 and 4) were detected in all but one ileostoma effluent sample, all ileal fluid samples as well as several fecal samples obtained from other individuals (Table 3). These findings suggest that the S. salivarius lineages are highly conserved in the small-intestine and, to a lesser extent in the terminal part of the gastrointestinal tract, among the different subjects.
Streptococci are common colonizers of the human small-intestine and are important in metabolic conversion of diet-derived carbohydrates that are present in this ecosystem , . To further our knowledge of the environmental interaction-potential and the metabolic capacity, the genomes of six small-intestinal Streptococcus isolates were determined by next generation sequencing technologies and were compared with Streptococcus genomes from the public databases.
Phylogenetic analysis of the small-intestinal Streptococcus genomes placed one strain into the S. mitis species-group, one strain into the S. bovis species-group, and 4 into the S. salivarius species-group, matching species identifications that were previously based on the 16S rRNA gene sequence alone . The genomes of two of the S. salivarius strains (lineage 1 and 4) were highly similar, which was expected based on their highly similar genetic typing profiles and their conserved physiological characteristics .
The Streptococcus pangenome consisted of 12,403 orthologous DNA sequences, which is double the size predicted by Lefébure and Stanhope based on 26 Streptococcus finished and whole genome shotgun genomes . However, this genome set was represented by 6 species while the current study included as many as 20 different species. The core Streptococcus genome was defined here as a set of 574 OG shared by all Streptococcus genomes, which is in line with earlier predictions . Analysis of the core Streptococcus OG revealed that the function of most genes was well defined and belonged to typically conserved cellular processes like transcription, translation and replication. Nonetheless, a significant portion of core orthologous groups were involved in metabolism, especially transport and metabolism of nucleotides and carbohydrates. Likewise, a group of 197 OG that in the current analysis are only shared among the streptococci analysed here was predominated by OG involved in metabolism and may represent functions that contribute to the lifestyle of these bacteria in the (human small) intestine.
Mining of the genomes revealed that the small-intestinal streptococci coded for two-component regulatory modules, such as those involved in natural competence. Since natural competence is a mediator for evolution and genomic plasticity , we focused on functions that play an important role in this system. We found gene repertoires that imply that the competence regulon in the streptococcal genomes analysed here is complete. Moreover, the regulatory circuits involved in controlling expression of the competence genes contain conserved components in the S. salivarius genomes, including the communication peptide pheromone and their cognate receptors. These appeared to be identical for strain HSISS1 and 4, but were distinct from the matching peptides found for HSISS2 and 3). This finding suggests that within the small-intestinal habitat these strains could activate competence via inter-strain crosstalk, thereby stimulating genetic exchange between streptococcal members of this ecosystem. Furthermore, mobile elements were identified in the small-intestinal Streptococcus genomes, testifying that there is potential for the streptococci to engage in horizontal transfer of genes.
The genomes of the small-intestinal Streptococcus strains encoded the capacity to synthesize a large number of amino acids, as well as a number of B-vitamins. As expected, all streptococcal genomes analysed here encoded a complete glycolytic pathway and a (partially) intact pentose phosphate pathway for energy generation, yielding lactate, acetate, formate, and possibly acetoin as fermentation products. However, the strains differed considerably in their predicted capacity to transport and metabolize specific sugars. The Streptococcus genomes encoded a complement of 11 different complete PTS, which in some cases were present in multiple copies in a single strain. This could indicate that some substrates may be more important for certain strains. The S. equinus genome encoded for 9 different PTS, which was higher compared to the number of PTS encoded by S. parasanguinis (8) and the S. salivarius strains (4−6). In addition, this strain was also the only sequenced small-intestinal strain that appeared to encode transporters for arabinose and extracellular amylases for the degradation of starch. Nevertheless, the number of PTS was relatively low compared to a closely related strain S. gallolyticus UCN34, which encodes 25 PTS .
While the S. equinus strain encoded for extra- and intracellular α-amylases, the S. parasanguinis and S. salivarius strains were found to only code for those that remain intracellular, which have been postulated to play a role in breakdown of IPS . However, investigations into α-amylase of S. mutans revealed that intracellular α-amylase was not essential for breakdown of IPS and dextrins from starch digested by exogenous α-amylase . Therefore, the role of intracellular α-amylases remains to be elucidated.
The encoded carbohydrate transporters, and the reconstructions of the metabolic pathways based on genome analysis were in excellent agreement with physiological characteristics that were determined previously . The variation between their metabolic capacities may explain their dynamic abundance in a harsh and fluctuating environment such as the small-intestine  (Leimena and Van den Bogert, et al., Unpublished data). As streptococci are fast-growing and efficient fermenters of simple carbohydrates  the combined metabolic capacity of the small-intestinal Streptococcus population may make a considerable contribution to the primary digestion of food components in this ecosystem that competes with that of the host . The streptococcal fermentation products (e.g. lactate and acetate) may serve as an energy source for the intestinal mucosa. In addition, the short chain fatty acids support growth of secondary fermenters in the small intestinal ecosystem, including Veillonella that together with streptococci have a potential to form a food-chain relationship  as well as members of the genus Clostridium that produce butyrate from acetate (Leimena and Van den Bogert, et al., Unpublished data).
The concordance between genome-based metabolic pathways and physiological characteristtics suggests that the small-intestinal genomes are useful in the prediction of the carbohydrate utilization capacities of these bacterial strains. This predictive value of the Streptococcus genomes presented here may be of use in studies that aim to determine the effect of food components on the small-intestinal microbiota in situ with a special focus on these Streptococcus populations. One prerequisite to this concept is the capacity to effectively detect the Streptococcus lineages, using for example unique genes as genetic markers, in intestinal samples. To this end, PCR-based screening assays were designed for each of the small-intestinal streptococci and tested with 92 Streptococcus isolates. These assays correctly amplified isolates belonging to the same lineage as their target Streptococcus strain, based on strain-level groupings as was done with AFLP and Rep-PCR analysis . Although primer assays for S. salivarius lineage 1 and 4 isolates were developed using genes that were not encountered in other genomes and strict primer design parameters to ensure primer specificity, both primer sets showed cross-reaction with isolates belonging to the non-target S. salivarius lineage. Determining the exact causes for this is not trivial and are likely related to the reasons underlying the inaccurate estimation of strain-specific genes (Table S7). Nonetheless, the Streptococcus PCR assays developed here provide a simple and rapid method for the screening of large numbers of samples from, for example, dietary intervention studies, for the genes that were exclusively encountered in the genomes of the small-intestinal S. parasanguinis, S. equinus, and S. salivarius strains or lineages.
Application of the assays on 34 intestinal and 10 fecal samples collected from 19 human individuals revealed that at least two S. salivarius lineages were present in almost all small-intestinal samples and several fecal samples, indicating that these strains are common colonizers and represent an important population of, in particular, the small-intestinal microbiota. Only one ileostoma effluent sample showed no amplification within any of the assays. However, the Streptococcus population in this sample is most likely represented by one or more Streptococcus strains that do not carry the unique genes targeted by PCR-based detection assays.
In conclusion, the work presented here describes a comparative genomics study of Streptococcus spp. that focused on strains from the human small-intestine. Comparative genomic analysis revealed that the small-intestinal strains differed in their predicted transport and metabolism of sugars, which was in agreement with physiological data. Therefore, the small-intestinal Streptococcus genomes are useful to construct metabolic models to predict the effect of different dietary substances on Streptococcus population dynamics in the human small-intestine. Furthermore, assays designed for detection of two S. salivarius strains were positive for most of the small-intestinal samples from different individuals, suggesting that strains, carrying the target functional gene, represent an important population of the small-intestinal ecosystem.
Multiple alignment of protein sequences from transposases identified in small-intestinal Streptococcus genomes.
Pathways for amino acid metabolism identified in small-intestinal Streptococcus genomes.
Metabolic pathways for sugar metabolism identified in small-intestinal Streptococcus genomes.
Genome statistics for small-intestinal Streptococcus after scaffolding.
Characteristics of finished Streptococcus genomes*.
Locus tags from 197 orthologous groups represented in 2-6 small-intestinal genomes.
Cluster specific orthologous groups.
Number of shared and unshared orthologous genes between S. salivarius genomic lineages 1-4.
Potential causes for inaccurate estimation of specific orthologous genes.
Number and description of two component systems predicted for small-intestinal Streptococcus strains.
BOX, RUP, and SPRITE repeats found small-intestinal genomes*.
Candidate bacteriocins identified by BAGEL2.
Locus tags of enzymes involved in glycolysis and pentose phosphate pathway.
We thank Sebastian Tims for providing fecal sample DNA, Maria Stolaki for her help with performing the PCR-based screening assays, and Roland Siezen for critical reading of the manuscript. We appreciate the help of Christopher Bauser and Julia Löcherbach of GATC-Biotech (Konstanz, Germany) for assistance in the set-up of genome sequencing.
Conceived and designed the experiments: BB JB ES EZ MK. Performed the experiments: BB JB RH. Analyzed the data: BB JB RH. Contributed reagents/materials/analysis tools: BB JB. Wrote the paper: BB JB RH ES EZ MK.
- 1. De Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, et al.. (2009) Bergey's Manual of Systematic Bacteriology. New York: Springer.
- 2. Luca-Harari B, Darenberg J, Neal S, Siljander T, Strakova L, et al. (2009) Clinical and microbiological characteristics of severe Streptococcus pyogenes disease in Europe. J Clin Microbiol 47: 1155–1165.
- 3. Kadioglu A, Weiser JN, Paton JC, Andrew PW (2008) The role of Streptococcus pneumoniae virulence factors in host respiratory colonization and disease. Nat Rev Microbiol 6: 288–301.
- 4. Keijser BJ, Zaura E, Huse SM, van der Vossen JM, Schuren FH, et al. (2008) Pyrosequencing analysis of the oral microflora of healthy adults. J Dent Res 87: 1016–1020.
- 5. Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE (2005) Defining the normal bacterial flora of the oral cavity. J Clin Microbiol 43: 5721–5732.
- 6. Andersson AF, Lindberg M, Jakobsson H, Backhed F, Nyren P, et al. (2008) Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS One 3: e2836.
- 7. Pei Z, Bini EJ, Yang L, Zhou M, Francois F, et al. (2004) Bacterial biota in the human distal esophagus. Proc Natl Acad Sci U S A 101: 4250–4255.
- 8. Bik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, et al. (2006) Molecular analysis of the bacterial microbiota in the human stomach. Proc Natl Acad Sci U S A 103: 732–737.
- 9. Booijink CC, El-Aidy S, Rajilić-Stojanović M, Heilig HG, Troost FJ, et al. (2010) High temporal and inter-individual variation detected in the human ileal microbiota. Environ Microbiol 12: 3213–3227.
- 10. van den Bogert B, de Vos WM, Zoetendal EG, Kleerebezem M (2011) Microarray analysis and barcoded pyrosequencing provide consistent microbial profiles depending on the source of human intestinal samples. Appl Environ Microbiol 77: 2071–2080.
- 11. Zoetendal EG, Raes J, van den Bogert B, Arumugam M, Booijink CC, et al. (2012) The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates. Isme J 6: 1415–1426.
- 12. van den Bogert B, Erkus O, Boekhorst J, de Goffau M, Smid EJ, et al. (2013) Diversity of human small intestinal Streptococcus and Veillonella populations. FEMS Microbiol Ecol 85: 376–388.
- 13. Van den Bogert B, Leimena MM, De Vos WM, Zoetendal EG, Kleerebezem M (2011) Functional Intestinal Metagenomics. In: De Bruin FJ, editor. Handbook of Molecular Microbial Ecology. Hoboken, NJ: Wiley-Blackwell. pp. 170−190.
- 14. Leser TD, Molbak L (2009) Better living through microbial action: the benefits of the mammalian gastrointestinal microbiota on the host. Environ Microbiol 11: 2194–2206.
- 15. Booijink CC, Zoetendal EG, Kleerebezem M, de Vos WM (2007) Microbial communities in the human small intestine: coupling diversity to metagenomics. Future Microbiol 2: 285–295.
- 16. Tims S, Derom C, Jonkers DM, Vlietinck R, Saris WH, et al. (2013) Microbiota conservation and BMI signatures in adult monozygotic twins. Isme J 7: 707–717.
- 17. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: A laboratory manual. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press.
- 18. Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W (2011) Scaffolding pre-assembled contigs using SSPACE. Bioinformatics 27: 578–579.
- 19. Carver TJ, Rutherford KM, Berriman M, Rajandream MA, Barrell BG, et al. (2005) ACT: the Artemis Comparison Tool. Bioinformatics 21: 3422–3423.
- 20. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, et al. (2008) The RAST Server: rapid annotations using subsystems technology. BMC genomics 9: 75.
- 21. Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT, et al. (2001) The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res 29: 22–28.
- 22. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32: 1792–1797.
- 23. Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52: 696–704.
- 24. Page RD (1996) TreeView: an application to display phylogenetic trees on personal computers. Comput Appl Biosci 12: 357–358.
- 25. de Jong A, van Heel AJ, Kok J, Kuipers OP (2010) BAGEL2: mining for bacteriocins in genomic data. Nucleic Acids Res 38: W647–651.
- 26. Stock AM, Robinson VL, Goudreau PN (2000) Two-component signal transduction. Annu Rev Biochem 69: 183–215.
- 27. Havarstein LS (2010) Increasing competence in the genus Streptococcus. Mol Microbiol 78: 541–544.
- 28. Siguier P, Filee J, Chandler M (2006) Insertion sequences in prokaryotic genomes. Curr Opin Microbiol 9: 526–531.
- 29. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M (2006) ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res 34: D32–36.
- 30. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, et al. (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23: 2947–2948.
- 31. Martin B, Humbert O, Camara M, Guenzi E, Walker J, et al. (1992) A highly conserved repeated DNA element located in the chromosome of Streptococcus pneumoniae. Nucleic Acids Res 20: 3479–3483.
- 32. Claverys JP, Martin B (1998) Competence regulons, genomics and streptococci. Mol Microbiol 29: 1126–1127.
- 33. Oggioni MR, Claverys JP (1999) Repeated extragenic sequences in prokaryotic genomes: a proposal for the origin and dynamics of the RUP element in Streptococcus pneumoniae. Microbiology 145 ( Pt 10): 2647–2653.
- 34. Croucher NJ, Vernikos GS, Parkhill J, Bentley SD (2011) Identification, variation and transcription of pneumococcal repeat sequences. BMC Genomics 12: 120.
- 35. Durbin R, S.R E, A K, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge, United Kingdom: Cambridge University Press.
- 36. Kanehisa M (2002) The KEGG database. Novartis Found Symp 247: 91−101; discussion 101−103, 119−128, 244−152.
- 37. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403–410.
- 38. Rozen S, Skaletsky H (2000) Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol 132: 365–386.
- 39. Salonen A, Nikkila J, Jalanka-Tuovinen J, Immonen O, Rajilić-Stojanović M, et al. (2010) Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J Microbiol Methods 81: 127–134.
- 40. Zoetendal EG, Booijink CC, Klaassens ES, Heilig HG, Kleerebezem M, et al. (2006) Isolation of RNA from bacterial samples of the human gastrointestinal tract. Nat Protoc 1: 954–959.
- 41. Zoetendal EG, Heilig HG, Klaassens ES, Booijink CC, Kleerebezem M, et al. (2006) Isolation of DNA from bacterial samples of the human gastrointestinal tract. Nat Protoc 1: 870–873.
- 42. Lefébure T, Stanhope MJ (2007) Evolution of the core and pan-genome of Streptococcus: positive selection, recombination, and genome composition. Genome Biol 8: R71.
- 43. Lukjancenko O, Ussery DW, Wassenaar TM (2012) Comparative genomics of Bifidobacterium, Lactobacillus and related probiotic genera. Microb Ecol 63: 651–673.
- 44. Kant R, Blom J, Palva A, Siezen RJ, de Vos WM (2011) Comparative genomics of Lactobacillus. Microb Biotechnol 4: 323–332.
- 45. Human Microbiome Jumpstart Reference Strains C, Nelson KE, Weinstock GM, Highlander SK, Worley KC, et al. (2010) A catalog of reference genomes from the human microbiome. Science 328: 994–999.
- 46. Poyart C, Quesne G, Trieu-Cuot P (2002) Taxonomic dissection of the Streptococcus bovis group by analysis of manganese-dependent superoxide dismutase gene (sodA) sequences: reclassification of 'Streptococcus infantarius subsp. coli' as Streptococcus lutetiensis sp. nov. and of Streptococcus bovis biotype 11.2 as Streptococcus pasteurianus sp. nov. International Journal of Systematic and Evolutionary Microbiology 52: 1247–1255.
- 47. Delorme C, Poyart C, Ehrlich SD, Renault P (2007) Extent of horizontal gene transfer in evolution of Streptococci of the salivarius group. J Bacteriol 189: 1330–1341.
- 48. Giammarinaro P, Sicard M, Gasc AM (1999) Genetic and physiological studies of the CiaH-CiaR two-component signal-transducing system involved in cefotaxime resistance and competence of Streptococcus pneumoniae. Microbiology 145 ( Pt 8): 1859–1869.
- 49. Quach D, van Sorge NM, Kristian SA, Bryan JD, Shelver DW, et al. (2009) The CiaR response regulator in group B Streptococcus promotes intracellular survival and resistance to innate immune defenses. J Bacteriol 191: 2023–2032.
- 50. Mashburn-Warren L, Morrison DA, Federle MJ (2010) A novel double-tryptophan peptide pheromone controls competence in Streptococcus spp. via an Rgg regulator. Mol Microbiol 78: 589–606.
- 51. Mascher T, Zahner D, Merai M, Balmelle N, de Saizieu AB, et al. (2003) The Streptococcus pneumoniae cia regulon: CiaR target sites and transcription profile analysis. J Bacteriol 185: 60–70.
- 52. Fontaine L, Boutry C, de Frahan MH, Delplace B, Fremaux C, et al. (2010) A novel pheromone quorum-sensing system controls the development of natural competence in Streptococcus thermophilus and Streptococcus salivarius. J Bacteriol 192: 1444–1454.
- 53. Kleerebezem M (2004) Quorum sensing control of lantibiotic production; nisin and subtilin autoregulate their own biosynthesis. Peptides 25: 1405–1414.
- 54. van Kraaij C, de Vos WM, Siezen RJ, Kuipers OP (1999) Lantibiotics: biosynthesis, mode of action and applications. Nat Prod Rep 16: 575–587.
- 55. Cotter PD, Hill C, Ross RP (2005) Bacteriocins: developing innate immunity for food. Nat Rev Microbiol 3: 777–788.
- 56. Lamy MC, Zouine M, Fert J, Vergassola M, Couve E, et al. (2004) CovS/CovR of group B streptococcus: a two-component global regulatory system involved in virulence. Mol Microbiol 54: 1250–1268.
- 57. Tran-Winkler HJ, Love JF, Gryllos I, Wessels MR (2011) Signal transduction through CsrRS confers an invasive phenotype in group A Streptococcus. PLoS Pathog 7: e1002361.
- 58. Cheng Q, Stafslien D, Purushothaman SS, Cleary P (2002) The group B streptococcal C5a peptidase is both a specific protease and an invasin. Infect Immun 70: 2408–2413.
- 59. Wexler DE, Chenoweth DE, Cleary PP (1985) Mechanism of action of the group A streptococcal C5a inactivator. Proc Natl Acad Sci U S A 82: 8144–8148.
- 60. Bolotin A, Quinquis B, Renault P, Sorokin A, Ehrlich SD, et al. (2004) Complete sequence and comparative genome analysis of the dairy bacterium Streptococcus thermophilus. Nat Biotechnol 22: 1554–1558.
- 61. Utaida S, Dunman PM, Macapagal D, Murphy E, Projan SJ, et al. (2003) Genome-wide transcriptional profiling of the response of Staphylococcus aureus to cell-wall-active antibiotics reveals a cell-wall-stress stimulon. Microbiology 149: 2719–2732.
- 62. Detmers FJ, Kunji ER, Lanfermeijer FC, Poolman B, Konings WN (1998) Kinetics and specificity of peptide uptake by the oligopeptide transport system of Lactococcus lactis. Biochemistry 37: 16671–16679.
- 63. Kunji ER, Fang G, Jeronimus-Stratingh CM, Bruins AP, Poolman B, et al. (1998) Reconstruction of the proteolytic pathway for use of beta-casein by Lactococcus lactis. Mol Microbiol 27: 1107–1118.
- 64. Rogers AH (1973) The vitamin requirements of some oral streptococci. Arch Oral Biol 18: 227–232.
- 65. Kandler O (1983) Carbohydrate metabolism in lactic acid bacteria. Antonie Van Leeuwenhoek 49: 209–224.
- 66. Kleerebezem M, Boekhorst J, van Kranenburg R, Molenaar D, Kuipers OP, et al. (2003) Complete genome sequence of Lactobacillus plantarum WCFS1. Proc Natl Acad Sci U S A 100: 1990–1995.
- 67. Glaser P, Frangeul L, Buchrieser C, Rusniok C, Amend A, et al. (2001) Comparative genomics of Listeria species. Science 294: 849–852.
- 68. Siezen RJ, Bayjanov JR, Felis GE, van der Sijde MR, Starrenburg M, et al. (2011) Genome-scale diversity and niche adaptation analysis of Lactococcus lactis by comparative genome hybridization using multi-strain arrays. Microb Biotechnol 4: 383–402.
- 69. Kreft J, Vazquez-Boland JA (2001) Regulation of virulence genes in Listeria. Int J Med Microbiol 291: 145–157.
- 70. Hutkins RW, Ponne C (1991) Lactose Uptake Driven by Galactose Efflux in Streptococcus thermophilus: Evidence for a Galactose-Lactose Antiporter. Appl Environ Microbiol 57: 941–944.
- 71. Lessard C, Cochu A, Lemay JD, Roy D, Vaillancourt K, et al. (2003) Phosphorylation of Streptococcus salivarius lactose permease (LacS) by HPr(His ∼ P) and HPr(Ser-P)(His ∼ P) and effects on growth. J Bacteriol 185: 6764–6772.
- 72. Von Wright A, Axelsson L (2012) Lactic Acid Bacteria: An Introduction. In: Lahtinen S, Ouwehand AC, Salminen S, Von Wright A, editors. Lactic Acid Bacteria, Microbiological and functional aspects: CRC Press, Taylor & Francis Group.
- 73. Russell RR, Aduse-Opoku J, Sutcliffe IC, Tao L, Ferretti JJ (1992) A binding protein-dependent transport system in Streptococcus mutans responsible for multiple sugar metabolism. J Biol Chem 267: 4631–4637.
- 74. Busuioc M, Mackiewicz K, Buttaro BA, Piggot PJ (2009) Role of intracellular polysaccharide in persistence of Streptococcus mutans. J Bacteriol 191: 7315–7322.
- 75. Rusniok C, Couve E, Da Cunha V, El Gana R, Zidane N, et al. (2010) Genome sequence of Streptococcus gallolyticus: insights into its adaptation to the bovine rumen and its ability to cause endocarditis. J Bacteriol 192: 2266–2276.
- 76. Whitehead TR, Cotta MA (1995) Identification of intracellular amylase activity in Streptococcus bovis and Streptococcus salivarius. Curr Microbiol 30: 143–148.
- 77. Simpson CL, Russell RR (1998) Intracellular alpha-amylase of Streptococcus mutans. J Bacteriol 180: 4711–4717.