Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals
© The Author(s). 2016
Received: 31 March 2016
Accepted: 23 June 2016
Published: 7 July 2016
Our view of host-associated microbiota remains incomplete due to the presence of as yet uncultured constituents. The Bacteroidales family S24-7 is a prominent example of one of these groups. Marker gene surveys indicate that members of this family are highly localized to the gastrointestinal tracts of homeothermic animals and are increasingly being recognized as a numerically predominant member of the gut microbiota; however, little is known about the nature of their interactions with the host.
Here, we provide the first whole genome exploration of this family, for which we propose the name “Candidatus Homeothermaceae,” using 30 population genomes extracted from fecal samples of four different animal hosts: human, mouse, koala, and guinea pig. We infer the core metabolism of “Ca. Homeothermaceae” to be that of fermentative or nanaerobic bacteria, resembling that of related Bacteroidales families. In addition, we describe three trophic guilds within the family, plant glycan (hemicellulose and pectin), host glycan, and α-glucan, each broadly defined by increased abundance of enzymes involved in the degradation of particular carbohydrates.
“Ca. Homeothermaceae” representatives constitute a substantial component of the murine gut microbiota, as well as being present within the human gut, and this study provides important first insights into the nature of their residency. The presence of trophic guilds within the family indicates the potential for niche partitioning and specific roles for each guild in gut health and dysbiosis.
The host microbiome has been firmly established as critical to host physiology. Evidence now supports the microbiome as influential in diverse processes ranging from infection susceptibility  to behavior . Unique anatomical sites are occupied by microbiota of distinct composition , supporting alternative functions being carried out at each site. Of clear significance is the gut microbiome, as metabolic capacity is a product of the capabilities encoded within both the host and the microbiome. The typical vertebrate gut microbiome is dominated by the Firmicutes and Bacteroidetes, and the divergent nature of gut-associated genera in comparison to other phylum members not associated with this environment indicates host selection and evolution occurring over a long period . The relationship is also dynamic, evidenced by shifts in the composition of the gut microbiota encountered with perturbations to a person’s diet, as well as with many acute and chronic, non-communicable diseases, such as inflammatory bowel diseases, or with their treatment (reviewed in [5, 6]). Despite our advances in describing these fluctuations, many members of the communities have yet to be cultured and characterized. As such, it remains difficult to ascribe their contributions to gut and systemic function, and thereby, host health and well-being.
One such uncharacterized inhabitant of the gastrointestinal tract is a novel branch of the “Bacteroides group” first recognized in 2002 . This branch was subsequently classified by Greengenes  and Silva  as an uncultured family of the order Bacteroidales, named after one of the earliest environmental clones belonging to the lineage, S24-7 (acc. AJ400263, ). Multiple studies have since reported the altered abundance of S24-7 family members in association with different environmental conditions, e.g., S24-7 is more abundant in diabetes-sensitive mice fed a high-fat diet, in particular when chow is supplemented with gluco-oligosaccharides  and following treatment-induced remission of colitis in mice . Members of the S24-7 family are also differentiated by their degree of IgA-labeling [12, 13] suggesting at least some members of the group are targeted by the innate immune system. While these observations are currently limited to murine-based studies, they do suggest that S24-7 is involved in host-microbe interactions that impact on gut function and health.
To further our understanding of the S24-7 family, we obtained 30 population genomes from four different hosts (human, mouse, koala, and guinea pig) and performed a comparative genomics analysis. The recovered genomes define a family that is most closely related to, but distinct from, the genera Barnesiella and Coprobacter. Analysis of 16S rRNA gene databases suggests a strong habitat preference for the homeothermic gut. Metabolically, we infer S24-7 are fermentative or nanaerobic  species, consistent with their environmental niche. We describe three trophic guilds within the family focusing on α-glucan, host glycan, or plant glycan-based carbohydrates suggesting the capacity for niche partitioning and/or divergent spatial organization of its members.
“Candidatus Homeothermaceae” (S24-7) members are found almost exclusively in the guts of homeothermic animals
“Ca. Homeothermaceae” population genomes
We obtained 30 near complete “Ca. Homeothermaceae” draft genomes from fecal metagenomic datasets: 10 from human (genomes H1 to H10), 14 from mouse (Mus musculus, order: Rodentia, family: Muridae; genomes M1 to M14), four from guinea pig (Cavia porcellus, order: Rodentia, family: Caviidae; genomes GP1 to GP4), and two from koala (Phascolarctos cereus, order: Diprotodontia, family: Phascolarctidae; K1 and K10). Average genome size was 2.69 Mb, with a notable outlier from the koala gut of 4.46 Mb (Additional file 1: Table S1). Abundance of each population bin within their respective metagenomic dataset varied from 0.4 to 14.8 % indicating that members of this family represent large fractions of the gut community in some animal hosts (Additional file 1: Table S1). We selected the most complete genome with the lowest inferred contamination, M4 (99.4 % complete; 0.4 % contamination), as a representative of the “Ca. Homeothermaceae” family, for which we propose the name “Candidatus Homeothermus arabinoxylanisolvens.” Identification of protein orthologs between each of the genomes revealed a core of 503 proteins present in at least 28 of the 30 assembled “Ca. Homeothermaceae” genomes with an average of 14 % unique genes (minimum 6 % (H9), maximum 28 % (K1), Additional file 2: Table S2). Unique genes were distributed throughout each genome, including the large genome obtained from koala, with only a small number clustered in apparent genomic islands (Additional file 3: Figure S1).
Genome-based phylogenetic classification of the “Ca. Homeothermaceae”
The assembled “Ca. Homeothermaceae” population genomes were phylogenetically placed within the Bacteriodales order by generating a genome tree based on 120 single-copy marker genes within 300 Bacteroidales reference genomes obtained from NCBI (Fig. 1b). The closest characterized relatives of “Ca. Homeothermaceae” are members of the bacterial genera Barnesiella and Coprobacter. These genera are currently classified as members of the family Porphyromonadaceae but according to our genome-based inference, we propose that they be reclassified as a separate family, Barnesiellaceae fam. nov. (Fig. 1b and Additional file 4: Figure S2). Calculation of average nucleotide sequence identity (ANI) between “Ca. Homeothermaceae” genomes supports the dataset representing 27 different species, with only three examples of members of the same species having been sampled on two independent occasions (ANI >95 %, ). Two of these genome pairs were recovered from different hosts: M5 and H6, originating from mouse and human, and H1 and K10, originating from human and koala (Additional file 5: Figure S3). The third pair, H8 and H9, shares a human origin. Thus, “Ca. Homeothermaceae” species are not restricted to specific hosts. Above the species level, there are also five clades within the dataset sharing increased average amino acid identity that may represent distinct genera (Fig. 1b and Additional file 5: Figure S3). Nine population genomes fall outside of both inferred species and genera indicating further diversity exists within the family.
Shared features of “Ca. Homeothermaceae” genomes
All genomes encode the capacity for the production of vitamins B1 (thiamine), B2 (riboflavin), B3 (niacin), B5 (pantothenate), B7 (biotin), and B9 (folate), a range which is consistent with other Bacteroidetes . No homolog of PdxH, the final enzyme required for active vitamin B6 production, was identified in any of the genomes, despite the remainder of the pathway being present. The lack of PdxH has also been noted in some Bacteroides species . The complete vitamin B12 (cobalamin) production pathway is absent from all “Ca. Homeothermaceae,” however, a subset encode a partial pathway originating from adenosyl cobyrinic acid. Vitamin B12 transporters were identified within 28 of the 30 genomes (Fig. 2). Therefore, members of this family are predicted to rely on neighboring populations for the production of B12, an important cofactor, as is seen in other Bacteroidetes .
In addition to fermentation capacity, “Ca. Homeothermaceae” also encode elements of an electron transport chain indicating possible alternative modes of energy production. Complex I is found in the majority of “Ca. Homeothermaceae” genomes (25 of 30, Additional file 7: Figure S4) and comprises 11 of the 14 canonical subunits , lacking NuoEFG, the NADH dehydrogenase module. Such complexes are found in multiple phyla; however, their redox function is often unclear . Of the remaining five genomes, four (GP3, GP4, M10, and M14) have loci adjacent to contig boundaries suggesting incomplete assembly as the reason for the missing elements. The remaining genome, H5, entirely lacks all components of complex I. While this genome has a relatively low level of completeness (85 %), other genomes with similar completion levels contain complex I, suggesting true absence in H5. The closest relative of this population, M8, contains a complete (11/14 subunit) complex, indicating this would be a recent loss from H5 (Fig. 1b). An F-type ATP synthase was identified in 26 “Ca. Homeothermaceae” genomes and is integrated within the genomic locus of complex I. Consequently, several genomes with incomplete complex I also harbor incomplete ATP synthases, including H5 in which all subunits are absent.
In addition to complex I, complex II (fumarate reductase/succinate dehydrogenase) is present in 28 genomes and is composed of three subunits: a flavoprotein, an iron-sulfur protein, and a single transmembrane protein, indicating a type B structure . Both genomes with incomplete complex II gene sets, GP3 (missing two genes) and GP4 (missing all genes), were also missing elements of complex I, consistent with their lower completeness and increased fragmentation (Additional file 7: Figure S4 and Additional file 1: Table S1). The flavoprotein catalytic subunit of the complex resembles that of other related anaerobic Bacteroidales species suggesting fumarate as the terminal electron acceptor in an anaerobic respiratory chain, as is described in Bacteroides (Additional file 8: Figure S5, ). However, also like Bacteroides and most other members of the Bacteroidales order, the majority of “Ca. Homeothermaceae” genomes contain a vertically inherited aerobic reductase operon, cydAB (Additional file 9: Figure S6), a high-affinity bd-type oxidase induced under low oxygen conditions permitting growth in nanomolar concentrations of oxygen [29, 30]. Thus, “Ca. Homeothermaceae” are likely nanaerobes, able to inhabit both anoxic and marginally oxic environments . Four genomes lack the cydAB operon (H4, H9, M9, and GP4), one of which is 97 % complete, suggesting that not all “Ca. Homeothermaceae” have this capacity. We were unable to confirm the operons’ absence through genomic context due to a lack of synteny in the surrounding regions despite close relatives in some instances (H8-H9, M9-GP2). If truly absent, as with complex I in H5, this would indicate relatively recent independent loss of this operon from multiple “Ca. Homeothermaceae” lineages. The variable presence of respiratory complexes in “Ca. Homeothermaceae” suggests a level of energetic flexibility within the family and potentially relatively recent purging of non-essential respiratory elements in the gastrointestinal environment.
In addition to the typical electron transport chain elements, the Na+ translocating NADH:ubiquinone oxidoreductase Nqr complex was identified in 27 “Ca. Homeothermaceae” genomes (Additional file 7: Figure S4). Nqr permits the use of a Na+ gradient for energy production and suggests NADH may be oxidized by this complex in “Ca. Homeothermaceae” rather than by complex I, which is missing the NADH dehydrogenase module. All six Nqr subunits are present within the 27 genomes, which include H5. Three genomes, H2, H9, and M11, are missing all six components. At least one H+/Na+ antiporter is found within all “Ca. Homeothermaceae” genomes supporting the use of this system.
To support a prediction of “Ca. Homeothermaceae” as nanaerobes, we looked for proteins within the genomes that would provide oxidative stress protection. Superoxide dismutase (O2 − to O2 or H2O2) was identified in 24 of the genomes, and eight of these also encode a catalase (H2O2 to H2O + O2) protein (Additional file 7: Figure S4). All eight catalase-positive genomes were obtained from mice. Peroxide reduction may also be achieved via several peroxiredoxins that are present within the “Ca. Homeothermaceae” genomes. Firstly, the alkyl hydroperoxide reductase, AhpC, and associated disulfide reductase, AhpF, were identified in nine genomes. A further six contained AhpC only, representing a separate cluster within generated gene trees (Additional file 7: Figure S4 and Additional file 10: Figure S7). There are also between one and four copies of rubrerythrin in all “Ca. Homeothermaceae” genomes. Finally, 25 genomes encode the thiol peroxidase bacterioferritin comigratory protein. Protein stability and reduced state regeneration during oxidative stress is supported by the presence of between two and six TRX family (group I) thioredoxins within each genome and a single copy of the thioredoxin reductase TrxB in all but three genomes: GP1 and M13 lack TrxB, while M12 contains two copies. In addition, 26 genomes contain a non-heme ferritin protein permitting the storage of excess iron. Overall, “Ca. Homeothermaceae” members appear well equipped to deal with oxidative stress, supporting potential microaerobic growth.
The “Ca. Homeothermaceae” secretome
Proteins secreted by gut-inhabiting bacteria can influence interactions with both the host and other microbes. Approximately 15 % of “Ca. Homeothermaceae” proteins carry the general secretory pathway signal peptide, as predicted by SignalP , which is at the lower end of the range predicted for Gram-negative species (13 to 42 %) . Within this secretome, ~15 % of the proteins were annotated with carbohydrate-based activity and thus potentially provide nutrients for “Ca. Homeothermaceae” or neighboring populations. Several immune-related peptidases are also secreted: 20 of the 30 population genomes contain a metalloprotease belonging to peptidase family M6 (immune inhibitor A family) (Additional file 7: Figure S4). Members of this family have been demonstrated to degrade antimicrobial peptides  and components of the extracellular matrix  and may therefore play a role in invasiveness or persistence within the host (reviewed in ). In addition, 11 genomes contain an IgA degrading peptidase (peptidase family M64) that may assist with immune evasion by these populations .
Working in concert with the general secretory pathway, a type IX secretion system (T9SS) was also identified in the majority of the “Ca. Homeothermaceae” genomes. All 10 components of the system (PorK, PorL, PorM, PorN, PorP, PorT, PorU, PorV, PorW, and Sov) were present in 22 genomes (other genomes contained incomplete gene sets), in addition to the regulatory two-component sensor system, PorX (response regulator) and PorY (histidine kinase), responsible for the co-regulation of a subset of T9SS genes (Additional file 7: Figure S4, ). No other secretion system was identified within “Ca. Homeothermaceae”. Within the T9SS, PorU acts as a peptidase for proteins containing a conserved C-terminal domain (TIGR04183) that dictates the use of the system and is cleaved during translocation [38, 39]. We identified 161 proteins containing this domain within the “Ca. Homeothermaceae” genomes, ~75 % of which also carried a general secretory pathway signal peptide, supporting their movement to the periplasm and subsequent secretion by the T9SS. The majority of proteins within this group are annotated as hypothetical (60 %); however, there is a homolog of a characterized immune-related peptidase, streptopain (SpeB). SpeB, encoded by Streptococcus pyogenes, contains the peptidase C10 domain and is capable of degrading multiple components of the immune system (reviewed in ). Streptopain homologs are present in 26 “Ca. Homeothermaceae” genomes.
Potential metabolic guilds within “Ca. Homeothermaceae”
The α-glucan and plant glycan guilds constitute the majority of the “Ca. Homeothermaceae” genomes, comprising 13 and 12 genomes respectively, with the host glycan group composed of five members. We used a combination of indicator species identification and pairwise differential abundance analysis to confirm the enriched enzymes within each group, retaining those enzymes identified by both methods as defining the guild (Additional files 13 and 14: Tables S6 and S7). The α-glucan guild is the most highly selective group, with only two significantly enriched enzyme categories, both starch related (Additional file 13: Table S6). The plant guild is equipped for the degradation of arabinan, xylan, and pectin, all plant cell wall constituents. Finally, the host glycan guild is enriched in β-hexosaminidases, capable of cleaving glucosamine and galactosamine residues, α-fucosidases, capable of cleaving fucose residues and comprises the only genomes to contain the sialic acid cleaving sialidase, supporting a capacity for host glycan degradation (other enzymes carrying the GH33 domain do not display homology to known sialidase enzymes, Additional file 15: Figure S8). Integration of trophic guild membership with phylogeny reveals the presence of some clades with a shared substrate focus, while others are mixed (Fig. 1b). Each guild is also mixed in terms of host distribution, with no foci found in only one host. There are, however, dominant guilds within both guinea pig and human samples: 70 % of human origin genomes are α-glucan focused and 75 % of guinea pig origin genomes are plant focused. This may reflect diet preference of these hosts; however, more genomes are required to provide support for this theory. We also predict that these guilds may occupy distinct spatial niches within the gut; the host glycan group primarily associating with the mucus layer, as has been demonstrated for the known mucin degrader Akkermansia muciniphila , and the plant and starch groups associating primarily with the digesta.
Polysaccharide utilization loci
Carbohydrate degrading enzymes may be clustered together in polysaccharide utilization loci (PUL), first described in Bacteroides thetaiotaomicron  and now known to be typical of Bacteroidetes . Such loci are defined by the presence of orthologs of the starch utilization system (Sus) components responsible for starch and maltooligosaccharide binding (SusD) and transfer to the periplasmic space (SusC) [45, 46]. Multiple susCD-like gene pairs are found within all “Ca. Homeothermaceae” genomes, and most also contain the gene pair in association with carbohydrate-active enzymes (Additional file 16: Table S8). In addition, ~10 % of “Ca. Homeothermaceae” PULs are located in close proximity to a hybrid two-component system protein, which have been demonstrated to regulate PUL expression (Additional file 16: Table S8) [47, 48]. While homologs of extracytoplasmic function σ-factors, also linked to PUL expression , were identified in many “Ca. Homeothermaceae” genomes, they were not located near PULs. The average number of both susCD pairs and pairs with associated carbohydrate enzymes is highest in the plant guild, suggesting these as requiring the most varied carbohydrate degradative machinery (Additional file 16: Table S8). Both susC and susD are present in large numbers throughout the Bacteroidetes with variable sequence identity existing both within and between species [20, 50, 51]. While initially described as a starch binding system, the association of susCD pairs with enzymes targeting a variety of substrates supports the broader use of this system, and the differential regulation of distinct susCD pairs in response to dietary changes has been demonstrated in B. thetaiotaomicron . We constructed gene trees based on both genes (data not shown) to determine whether there was greater variability within a particular “Ca. Homeothermaceae” guild, which could reflect an ability to bind and transport a wider variety of substrates. However, average phylogenetic diversity scores  for all three guilds were similar for both susC (α-glucan:1.6, host glycan: 1.7, plant:1.5) and susD (α-glucan:1.8, host glycan:1.8, plant:1.7) suggesting that equivalent sequence diversity exists within each group and therefore potentially a similar, although not necessarily overlapping, diversity of substrates available to this system.
Broader functional comparative analysis of “Ca. Homeothermaceae”
To determine whether the metabolic guilds extend to broader genome content, we annotated each genome using both KEGG and COG orthology detection. Ordination plots generated from both annotation systems clustered the metabolic guilds discretely, although with low levels of separation (Fig. 4a, b). Only a single KEGG orthology group was found to be significantly enriched; K12373 (hexosaminidase), which was increased within the host glycan guild, consistent with the previous CAZy-based observations. COG annotation yielded several enriched protein families within each guild (Fig. 4c and Additional file 17: Table S9), all of which are associated with carbohydrate-active enzymes, indicating these as the key differentiating characteristic of each group. In addition to functions noted previously, the host glycan guild is also enriched for arylsulfatase-related enzymes (COG3119), which plays a role in the degradation of host glycans and is therefore consistent with the guild focus.
Using COG-based annotations, we then extended the analysis to compare “Ca. Homeothermaceae” to other Bacteroidales families. “Ca. Homeothermaceae,” Bacteroidaceae, and Prevotellaceae separated clearly based on COG annotations, while Porphyromonadaceae members were intermingled with other families, potentially reflecting the phenotypic diversity of this family (Additional file 18: Figure S9, ). The abundance of over 450 individual COGs was found to be significantly different within “Ca. Homeothermaceae” compared to the other families; ~75 % of which were decreased (Additional file 19: Table S10). Out of those with increased abundance, several are of interest. Multiple urease-associated COGs were significantly enriched, and the urease gene cluster was subsequently identified in twelve genomes, suggesting a role in nitrogen recycling and a source of ammonia (Additional file 7: Figure S4). Gene trees produced for each of the urease subunits confirm that the presence of urease is unusual within the Bacteroidales and suggest lateral transfer of the gene cluster to the common ancestor of “Ca. Homeothermaceae” from an Alistipes- or Odoribacter-like ancestor and subsequent loss from a subset of the “Ca. Homeothermaceae” genomes (Additional file 20: Figure S10, data not shown). Putative formyl-CoA transferases (COG1804) were also enriched in “Ca. Homeothermaceae,” revealing the presence of the oxalate degrading gene pair formyl-CoA transferase and oxalyl-CoA decarboxylase in 19 of the 30 genomes (Additional file 7: Figure S4). Oxalate is likely transported into the cell via a permease located adjacent to oxalyl-CoA decarboxylase in all 19 oxalate degraders (and which is not found in “Ca. Homeothermaceae” lacking the oxalate degrading gene pair) as suggested by genomic analysis of other oxalate degrading species . Oxalate degradation appears to be linked to metabolic guilds, that is, 10 of 12 plant, and four of five host glycan-focused “Ca. Homeothermaceae” have oxalate degrading genes, whereas only five of the 13 α-glucan-focused guild have them. As with urease, gene trees confirm that this function is rare within the Bacteroidales (Additional file 21: Figure S11, data not shown).
Relative abundance and prevalence within the sampled mammalian hosts
A large portion of “Ca. Homeothermaceae” sequences within 16S rRNA databases originate from mice suggesting high prevalence of the family in the murine host. To determine whether this reflects true prevalence or database bias, we searched for evidence of “Ca. Homeothermaceae” in available gut metagenome datasets from both mice and humans. We found “Ca. Homeothermaceae,” as represented by the analyzed population genomes, present in ~50 % of mice samples, with an average relative abundance of 6 % of the gut community as estimated by read mapping (from ~100 metagenomes, Additional file 22: Table S11). The prevalence in humans was lower, ~20 %, with an average abundance of ~2 % of the community (from ~300 metagenomes). “Ca. Homeothermaceae” is therefore more common in mice than humans but is nonetheless likely to be present in a sizable fraction of the human population.
The most prevalent “Ca. Homeothermaceae” species was H8/H9 in 20 % of human datasets, and M6 in 35 % of murine datasets (Additional file 22: Table S11), both members of the α-glucan guild. We were interested to see if this prevalence was consistent across different dietary backgrounds as a potential source of support for our proposed guild structure. To do this, we subdivided the analyzed datasets according to available diet-related metadata. We found mice fed a high-fat diet carried a narrower range of “Ca. Homeothermaceae” populations, and these were present at a lower abundance than those fed a standard chow diet (Additional file 22: Table S11). However, no particular guild showed dominance, and M6 remained the most prevalent population overall. Within the public human datasets sampled, very few non-α-glucan guild representatives were detected (Additional file 22: Table S11) in agreement with the dominance of this guild within the “Ca. Homeothermaceae” genomes recovered from humans and suggestive of the human diet as most supportive of members of the α-glucan guild. We found a higher prevalence of “Ca. Homeothermaceae” in obese individuals (23 %) than in lean (10 %), suggesting that a higher energy diet may better support the family, although there was no dietary composition information available for this dataset. We also identified a substantial increase in the prevalence of “Ca. Homeothermaceae” within the Hadza hunter-gatherer population; 70 % of individuals were found to carry at least one population. Only two species were identified within this group of individuals: the typically prevalent H8/H9 and species H4, also a member of the α-glucan guild. The Hadza diet is heavily plant based, composed of tubers, meat, honey, foliage, and berries, with tubers being particularly important due to their constant availability . Tubers consumed by the Hadza contain a large portion of indigestible fibers that are expectorated after chewing. As such, tubers provide a source of moisture, simple sugars, starch, and soluble fiber . The increased prevalence of “Ca. Homeothermaceae” within the Hadza suggests this diet is particularly amenable to the maintenance of the identified species, although does not result in any increase in their overall abundance.
The Bacteroidales family S24-7 is encountered frequently in culture-independent studies and is gaining recognition due to both its prevalence, particularly in murine-based datasets, and its fluctuating abundance in cross-sectional and intervention type studies. Increased abundance has been described in mice fed a low-fat diet and, in association with increased exercise , in diabetes-sensitive mice fed a high-fat diet  and following remission of colitis in a mouse model . S24-7 is also the dominant family during hibernation of arctic ground squirrels . The consequence of these fluctuations in the abundance of S24-7 is currently unknown, as they remain uncultured and no genomic studies have been undertaken. Here, we recover a set of S24-7 population genomes from metagenomic samples, enabling inference of their core metabolism, and propose the name “Ca. Homeothermaceae” reflecting an ecological distribution limited to the guts of homeothermic animals (Fig. 1a).
Carbohydrate composition and availability is known to be a primary driver of microbial community structure in gut ecosystems [48, 59–61]. We identified three trophic guilds within the “Ca. Homeothermaceae” based on their encoded carbohydrate-active enzymes (Fig. 3) that suggest the family has the capacity to occupy multiple niches within the gut, which may include spatial partitioning. Similar metabolic differentiation is observed in other gut-inhabiting genera such as Bacteroides [48, 50, 62] and Bifidobacterium  where different species encode alternative carbohydrate utilization machinery. Some gut inhabitants may be able to occupy multiple carbohydrate-based trophic niches: B. thetaiotaomicron displays a preference for diet-derived polysaccharides, such as xylan-, pectin-, and arabinose-based compounds, over host glycans; however, when such polysaccharides are scarce, host glycan degradation activity is upregulated . “Ca. Homeothermaceae” guild representatives may also utilize this strategy; all analyzed population genomes encode starch-degrading ɑ-amylases and as such, while they may have a preference for their specialist carbohydrate source, starch can serve as a foundation carbohydrate for all family members.
We identified two characteristics within a subset of “Ca. Homeothermaceae” populations that were relatively unusual for members of the Bacteriodales: the capacity for oxalate degradation and the production of urease (Additional files 20 and 21: Figure S10 and S11). Plants are the primary source of dietary oxalate, which in excess contributes to the formation of renal stones by complexing calcium (reviewed in ). In agreement with this dietary source, the majority of “Ca. Homeothermaceae” populations within the plant-focused guild encode the necessary components for oxalate degradation. Oxalate degrading potential is also encoded within four of the five host glycan guild population genomes while only 40 % of α-glucan guild members contain the necessary genes (Additional file 7: Figure S4). Oxalobacter formigenes is the best characterized oxalate degrading gut bacterium, and colonization is associated with the decreased incidence of calcium oxalate stone formation . While O. formigenes is dependent on the presence of oxalate and uses it as a sole carbon source , other oxalate degraders, such as lactic acid bacteria, require the presence of an additional carbon source [55, 66]. “Ca. Homeothermaceae” oxalate degraders are likely in the latter category due to sporadic distribution of the trait across the family. This distribution appears to be the result of lineage-specific loss rather than multiple independent lateral acquisitions (Additional file 20: Figure S10). Urease releases ammonia from urea, which can then be incorporated into microbial amino acids  and contributes to nitrogen level stability of the host particularly when protein consumption is low . Urease activity can therefore be advantageous for both host and microbe. However, urease-positive microbes can be detrimental in combination with elevated circulating ammonia levels associated with liver disease . Urease is also a recognized virulence factor in both bacterial and fungal infection (reviewed in ). The abundance of urease genes within the gut microbiota in humans differs with age and geography and is potentially reflective of diet . We identified urease positive “Ca. Homeothermaceae” populations in all four hosts (Additional file 7: Figure S4), and presence did not correlate with a particular trophic guild making it difficult to predict a role for urease within the group; however, a metabolic role for the enzyme is supported by the presence of glutamine synthetase in the majority of genomes (Fig. 2).
A key question relating to newly characterized members of the microbiota is whether they are friend or foe. “Ca. Homeothermaceae” representatives have been shown to be IgA coated . IgA production is induced by both commensal and pathogenic intestinal inhabitants, and both are believed to be able to induce the production of highly specific IgA leading to microbial cell coating . “Ca. Homeothermaceae” are found in both IgA+ and IgA− fractions of fecal microbiota [12, 13], however, on the whole, are not highly coated with IgA in contrast to families known to be inflammatory in murine models of colitis, such as Prevotellaceae . The presence within some “Ca. Homeothermaceae” genomes of an IgA protease (Additional file 7: Figure S4) may contribute to their identification within both IgA+ and IgA− community fractions, although the significance of IgA proteases in vivo remains unclear (reviewed in ). The majority of “Ca. Homeothermaceae” genomes also contain a homolog of SpeB (Additional file 7: Figure S4), a peptidase capable of degrading multiple immunologically relevant proteins (reviewed in ). SpeB homologs are found in other gut bacteria including Bacteroides fragilis and B. thetaiotaomicron [74, 75] and in the periodontal pathogen Prevotella intermedia where the homolog interpain A is involved in the inhibition of the immune response via complement degradation . The presence of multiple potential immune evasion peptidases within members of “Ca. Homeothermaceae” does not preclude a typically commensal relationship with the host; however, they may provide the capacity for opportunistic infection under the appropriate conditions .
Overall, this study provides the first genomic insights into the uncultured gut-inhabiting “Ca. Homeothermaceae” family through the comparative analysis of 30 draft genomes obtained from metagenomic datasets. We describe varied carbohydrate utilization mechanisms existing within the family in agreement with other genera occupying the same environmental niche. As a group that is particularly prevalent within a key experimental environment, the mouse gut (and also present in the human gut and potentially relevant to human health), further reports confirming the roles of “Ca. Homeothermaceae” in vivo are likely to appear in the future.
Description of “Candidatus Homeothermus”
Homeothermus (Ho.me.o.ther’mus Gr. adj. homoios, similar, Gr. n. thermē, heat. Homeothermus of homeothermic origin). Inferred to be Gram-negative, non-motile, nanaerobic, and able to ferment a wide range of carbohydrates including arabinose, cellobiose, fructan, fructose, glycerol, lactose, maltose, raffinose, sucrose, xylan, and xylose, with a focus on arabinan and xylan based on enzyme abundance.
Description of “Ca. Homeothermus arabinoxylanisolvens”
Homeothermus arabinoxylanisolvens (Ho.me.o.ther’mus Gr. adj. homoios, similar, Gr. n. thermē, heat. a.rab.in.o.xy.lan.i.sol’vens n. arabin, a carbohydrate derived from gum arabic, M.L. n. xylan, xylan, M. L. part. adj. solvere, to loosen, untie, free up). Description is the same as that for genus “Ca. Homeothermus.” Represented by population genome M4 (acc. no. LUJO00000000) obtained from metagenomic sequencing of Mus musculus fecal sample.
Description of “Ca. Homeothermaceae”
The description is the same as for the genus “Ca. Homeothermus” with the following additions; -aceae ending to denote a family. Additional fermentation substrates include pectin, rhamnose, and trehalose. Three trophic guilds are proposed within the family based on the relative abundance of carbohydrate-active enzymes with different substrates: α-glucans, complex plant cell wall components, and host glycans. Type genus: “Ca. Homeothermus.” Order: Bacteroidales.
Emended description of the family Porphyromonadaceae Krieg, Staley et al. 2011
The description is the same as that given by Krieg et al.  with the following amendment. The genera, Barnesiella, Butyricimonas, Coprobacter, Dysgonomonas, Odoribacter, Paludibacter, Parabacteroides, Proteiniphilum, “Ca. Sanguibacteroides,” and Tannerella have been removed as they do not form a monophyletic group with the type genus, Porphyromonas.
Description of Barnesiellaceae fam. nov.
Includes the genera Barnesiella (type genus) and Coprobacter. Description is drawn from that of Barnesiella given by Sakamoto et al.  and Coprobacter given by Shkoporov et al. : -aceae ending to denote a family. Cells are Gram-negative, obligately anaerobic, non-spore-forming, and non-motile. Saccharolytic. Type genus: Barnesiella. Order: Bacteroidales.
Description of Dysgonamonadaceae fam. nov.
Includes the genera Dysgonomonas (type genus) and Proteiniphilum. Description is drawn from that of Dysgonomonas given by Hofstad et al.  and Proteiniphilum given by Chen et al. : -aceae ending to denote a family. Cells are Gram-negative, fermentative, and facultatively (Dysgonomonas) or obligately (Proteiniphilum) anaerobic. Type genus: Dysgonomonas. Order: Bacteroidales.
Emended description of the family Marinifilaceae Iino, Mori et al. 2014
The description is drawn from that of Marinifilaceae given by Iino et al.  with the following amendment. The family Marinifilaceae contains the genera Butyricimonas, Marinifilum (type genus), Odoribacter, and “Ca. Sanguibacteroides.” Cells are Gram-negative, non-spore-forming, non-motile, and facultatively (Marinifilum) or obligately (Butyricimonas, Odoribacter) anaerobic.
Description of Paludibacteraceae fam. nov.
Includes the genus Paludibacter (type genus). Description is the same as for the genus Paludibacter given by Ueki et al. : -aceae ending to denote a family. Type genus: Paludibacter. Order: Bacteroidales.
Description of Tannerellaceae fam. nov.
Includes the genera Parabacteroides and Tannerella (type genus). Description is drawn from that of Tannerella given by Sakamoto et al.  and Parabacteroides given by Sakamoto et al. : -aceae ending to denote a family. Cells are Gram-negative, non-motile, and obligately anaerobic. Type genus: Tannerella. Order: Bacteroidales.
Sample collection and sequencing
Fecal samples were obtained from six 12-week-old female C57BL/6 mice housed in accordance with the University of Newcastle Animal Care and Ethics Committee; reference number A-2013-303. DNA was extracted from feces using the PowerSoil DNA Isolation Kit (MO BIO Laboratories, CA, USA) according to the manufacturer’s instructions. Library preparation was performed using the Nextera DNA Library Preparation Kit (Illumina, CA, USA). Libraries were sequenced at the Diamantina Institute, The University of Queensland, using the Illumina HiSeq 2500 platform generating ~9 Gbp of 100 bp paired-end reads per sample.
Koala fecal samples originated from a 12-year-old male as previously described . Public metagenomic datasets generated from fecal samples of both human and guinea pig were downloaded from the NCBI sequence read archive (SRA); human samples were obtained from multiple projects, specifically runs (ERR209459, ERR209707, ERR525737, ERR688517, ERR688528, ERR710427, ERR710429, SRR413598, SRR413599); guinea pig samples were obtained from a single project (BioProject: SRP012966).
Sequence assembly and population genome recovery
Reads from mouse fecal samples were adapter trimmed and merged using SeqPrep (https://github.com/jstjohn/SeqPrep) then remaining pairs were quality trimmed using Nesoni v0.128 (https://github.com/Victorian-Bioinformatics-Consortium/nesoni) with a minimum Phred quality threshold of 20. Assembly of pooled reads was performed using CLC Genomics Workbench v7.0.4 (QIAGEN, Aarhus, Denmark) using a word size of 30 and bubble size of 1500. Scaffolding was performed during assembly, and reads were mapped back to contigs with default settings. Minimum contig length was 300. Mapping of reads to final assemblies was performed using BWA v0.7.10  with default settings.
SRA data from guinea pig and human datasets was quality and adapter trimmed using Trimmomatic v0.3.2  with default settings plus a head crop of 10 and minimum read length of 30. Trimmed reads were merged using BBMerge (https://sourceforge.net/projects/bbmap/). Assembly was performed either per individual run (human) or pooled (guinea pig) using CLC Genomics Workbench v8.5.1 (QIAGEN, Aarhus, Denmark) either using default settings (human) or using a word size of 30 and a bubble size of 1000 (guinea pig). Minimum contig length was 500. Scaffolding was performed during assembly, and reads were mapped back to contigs with default settings. Gap filling was performed on assemblies from human datasets using Abyss-sealer  with default settings. Mapping of reads to final assemblies was performed using BamM v1.5.1 (http://ecogenomics.github.io/BamM/) with default settings.
Population genomes were obtained either from previous studies  or de novo from metagenomic datasets using GroopM v0.2 (, mouse) or MetaBAT v0.25.4 (, human, guinea pig). Phylogenetic analysis and estimation of contamination and completeness of recovered genomes was performed using CheckM v1.0.3 , which utilizes a set of single-copy marker genes. “Ca. Homeothermaceae” genomes were refined by removing contigs with incongruent coverage profiles as identified by RefineM v0.0.3 (https://github.com/dparks1134/RefineM). Gap filling was performed on these assemblies using FinishM v0.0.6 (https://github.com/wwood/finishm). Reads mapping to each genome were extracted from a complete assembly mapping (incorporating refined genomes and additional unrefined genomes as the reference) using BamM v1.5.1 (http://ecogenomics.github.io/BamM/) then remapped to each individual genome using CLC Genomics Workbench v8.5.1 (QIAGEN, Aarhus, Denmark) with default settings. These mappings were used for manual investigation of specific genomic features.
A maximum-likelihood tree of “Ca. Homeothermaceae” population genomes in the context of the order Bacteroidales was generated based on an alignment of 120 concatenated single-copy, bacterial-specific, marker genes implemented within an in-house pipeline. Marker genes from 300 Bacteroidales and 37 Sphingobacteriales (outgroup) genomes were obtained from publicly available genomes within the NCBI database and aligned using Mafft v7.221 . A maximum-likelihood tree was inferred using FastTree v2.1.7  under the WAG + GAMMA model based on alignment positions containing a residue within ≥90 % of sequences (36,713 positions). Bootstrap support was derived from 100 replicates.
Individual gene trees were generated using FastTree v2.1.7  implemented within Mingle v0.0.15 (https://github.com/Ecogenomics/mingle) utilizing the BLAST  workflow, identifying homologs within the IMG database . Default Mingle settings used for all genes except for oxalyl-CoA decarboxylase where percent identity was increased to 40 % due to an alignment length of <100 amino acids with default settings. Bootstrap support was derived from 100 replicates, also implemented within Mingle. Phylogenetic diversity scores for susC and susD gene trees were calculated using the picante R package . All trees were visually inspected using ARB v6.0.2 .
Genome annotation and core metabolic analysis
Genomes were annotated using Prokka v1.11 . All subsequent gene-based analysis was performed using the output of this annotation process. Orthologous proteins within each genome were identified using Proteinortho v5.11 . Average nucleotide identity was calculated using the Goris method  implemented in calculate_ani.py (available at: https://github.com/widdowquinn/scripts/blob/master/bioinformatics/calculate_ani.py). Average amino acid identity between each genome pair was calculated using CompareM v0.0.5 (https://github.com/dparks1134/CompareM). Protein families were identified using pfam_scan.pl against the Pfam database release 28  employing HMMER v3.1b2 . Archetypal cell envelope families as per Albertsen et al.  were used for predicting cell structure. TIGRFAMs were identified using hmmscan from HMMER v3.1b2  against database release 15.0 (downloaded from ftp://ftp.jcvi.org/pub/data/TIGRFAMs/). Antibiotic resistance genes were predicted using the Resfams database .
Kyoto Encyclopedia of Genes and Genomes (KEGG) term annotation was performed using KAAS  and KEGG maps in combination with RAST , KBase (http://kbase.us/), and Pathway Tools v19.0 , and curated gene lists  were used to elucidate the general metabolic profile of “Ca. Homeothermaceae.” Clusters of Orthologous Groups (COG) profiles were identified using BLASTP v2.2.30+  against 2014 update of the 2003 COG database downloaded from NCBI (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/COG/, last accessed July 2015) with e-value cutoff of 1e-6.
Carbohydrate-active enzymes (CAZy) were identified using hmmscan from HMMER v3.1b2  against the dbCAN database v4 . Percentage abundance of each CAZy category was assessed against the total number of genes with CAZy annotation within each genome. Signal peptide sequences within CAZy annotated genes were predicted using SignalP v4.1  and LipoP v1.0  with a margin cutoff of 4 for LipoP. Prediction of membrane positioning was based on the presence of a transmembrane domain (SignalP) or a type II signal peptide (LipoP).
Differential abundance comparisons
Differential abundance of annotated features (COG, KO, CAZy) was analyzed using DESeq2 R package  based on count data. Heatmaps were generated using pheatmap  following variance stabilizing normalization of data by DESeq2. Indicator annotations were identified using the labdsv R package . PCA plots were created using the vegan R package .
Prevalence and relative abundance
Prevalence of each population genome and overall abundance of “Ca. Homeothermaceae” were assessed by mapping public human and mouse gut metagenome datasets downloaded from the SRA against all genomes using BWA v0.7.12 . Genome coverage amounting to ≥0.5 % of total reads was used as the minimum cutoff for the presence of a given population in a particular sample. Cutoff was based on minimum coverage within read mappings of datasets used to produce “Ca. Homeothermaceae” population bins and thus represents a conservative estimate. Relative abundance of each population was based on the percentage of total reads attributed to each genome exceeding the minimum cutoff percentage, normalized for genome size. Diet-related datasets were PRJEB7759 (mouse), PRJNA278393 (Hadza), and PRJEB2054 (lean vs. obese). Additional human samples originated from projects PRJEB1220, PRJEB4410, PRJEB6456, and PRJEB7774
ANI, average nucleotide identity; AR, antibiotic resistance; CAZy, carbohydrate-active enzyme; COG, Clusters of Orthologous Groups; KEGG, Kyoto Encyclopedia of Genes and Genomes; PUL, polysaccharide utilization loci; Sus, starch utilization system.
We thank Nicola Angel and Serene Low for the library preparation and sequencing; Donovan Parks, Adam Skarshewski, and Pierre-Alain Chaumeil for providing early access to the Genome Tree Database; and Jennifer Hosmer, Emily Hoedt, Gene Tyson, and Rick Webb for their fruitful discussions.
KLO was supported by the NHMRC Program Grant APP1071822, SLG was supported by a Lung Foundation Australia Fellowship, and the mouse work was supported by the NHMRC Project Grant APP1059239.
Availability of data and materials
The population genomes supporting the conclusions of this article have been uploaded to the NCBI BioProject accession number PRJNA313232.
KLO, JND, and JP generated the population genomes. KLO and DLAW performed the bioinformatic analysis. NL, SLG, and PMH performed the sample collection and generation of murine data. KLO, CGOD, RWP, and LKN performed the metabolic analysis and interpretation of the results. LKN, MAC, MM, PMH, and PH assisted in study design and data interpretation. KLO and PH wrote the manuscript. All authors read, edited, and approved the final manuscript.
The authors declare that they have no competing interests.
Fecal samples were obtained in accordance with the University of Newcastle Animal Care and Ethics Committee; reference number A-2013-303.
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.
- Sekirov I, Tam NM, Jogova M, Robertson ML, Li YL, Lupp C, et al. Antibiotic-induced perturbations of the intestinal microbiota alter host susceptibility to enteric infection. Infect Immun. 2008;76(10):4726–36. doi:10.1128/iai.00319-08.PubMedPubMed CentralView ArticleGoogle Scholar
- Cryan JF, O’Mahony SM. The microbiome-gut-brain axis: from bowel to behavior. Neurogastroenterol Motil. 2011;23(3):187–92. doi:10.1111/j.1365-2982.2010.01664.x.PubMedView ArticleGoogle Scholar
- Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13(4):260–70. doi:10.1038/nrg3182.PubMedPubMed CentralGoogle Scholar
- Bäckhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Host-bacterial mutualism in the human intestine. Science. 2005;307(5717):1915–20. doi:10.2307/3841877.PubMedView ArticleGoogle Scholar
- Wlodarska M, Kostic Aleksandar D, Xavier Ramnik J. An integrative view of microbiome-host interactions in inflammatory bowel diseases. Cell Host Microbe. 2015;17(5):577–91. doi:10.1016/j.chom.2015.04.008.PubMedPubMed CentralView ArticleGoogle Scholar
- Spor A, Koren O, Ley R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Microbiol. 2011;9(4):279–90. doi:10.1038/nrmicro2540.PubMedView ArticleGoogle Scholar
- Salzman NH, de Jong H, Paterson Y, Harmsen HJ, Welling GW, Bos NA. Analysis of 16S libraries of mouse gastrointestinal microflora reveals a large new group of mouse intestinal bacteria. Microbiology. 2002;148(Pt 11):3651–60.PubMedView ArticleGoogle Scholar
- McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6(3):610–8. doi:10.1038/ismej.2011.139.PubMedView ArticleGoogle Scholar
- Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(D1):D590–6. doi:10.1093/nar/gks1219.PubMedView ArticleGoogle Scholar
- Serino M, Luche E, Gres S, Baylac A, Bergé M, Cenac C, et al. Metabolic adaptation to a high-fat diet is associated with a change in the gut microbiota. Gut. 2012;61(4):543–53. doi:10.1136/gutjnl-2011-301012.PubMedView ArticleGoogle Scholar
- Rooks MG, Veiga P, Wardwell-Scott LH, Tickle T, Segata N, Michaud M, et al. Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission. ISME J. 2014;8(7):1403–17. doi:10.1038/ismej.2014.3.PubMedPubMed CentralView ArticleGoogle Scholar
- Palm Noah W, de Zoete Marcel R, Cullen Thomas W, Barry Natasha A, Stefanowski J, Hao L, et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell. 2014;158(5):1000–10. doi:10.1016/j.cell.2014.08.006.PubMedPubMed CentralView ArticleGoogle Scholar
- Bunker Jeffrey J, Flynn Theodore M, Koval Jason C, Shaw Dustin G, Meisel M, McDonald Benjamin D, et al. Innate and adaptive humoral responses coat distinct commensal bacteria with immunoglobulin A. Immunity. 2015;43(3):541–53. doi:10.1016/j.immuni.2015.08.007.PubMedView ArticleGoogle Scholar
- Morris RL, Schmidt TM. Shallow breathing: bacterial life at low O2. Nat Rev Microbiol. 2013;11(3):205–12. doi:10.1038/nrmicro2970.PubMedPubMed CentralView ArticleGoogle Scholar
- Dick LK, Bernhard AE, Brodeur TJ, Domingo JWS, Simpson JM, Walters SP, et al. Host distributions of uncultivated fecal Bacteroidales bacteria reveal genetic markers for fecal source identification. Appl Environ Microbiol. 2005;71(6):3184–91. doi:10.1128/aem.71.6.3184-3191.2005.PubMedPubMed CentralView ArticleGoogle Scholar
- Layton A, McKay L, Williams D, Garrett V, Gentry R, Sayler G. Development of Bacteroides 16S rRNA gene TaqMan-based real-time PCR assays for estimation of total, human, and bovine fecal pollution in water. Appl Environ Microbiol. 2006;72(6):4214–24. doi:10.1128/aem.01036-05.PubMedPubMed CentralView ArticleGoogle Scholar
- Van Valkenburgh B. Iterative evolution of hypercarnivory in canids (Mammalia: Carnivora): evolutionary interactions among sympatric predators. Paleobiology. 1991;17(4):340–62.Google Scholar
- Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol. 2007;57(1):81–91. doi:10.1099/ijs.0.64483-0.PubMedView ArticleGoogle Scholar
- Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol. 2013;31(6):533–8. doi:10.1038/nbt.2579.PubMedView ArticleGoogle Scholar
- Xu J, Bjursell MK, Himrod J, Deng S, Carmichael LK, Chiang HC, et al. A genomic view of the human-Bacteroides thetaiotaomicron symbiosis. Science. 2003;299(5615):2074–6. doi:10.1126/science.1080029.PubMedView ArticleGoogle Scholar
- Yoshimura F, Murakami Y, Nishikawa K, Hasegawa Y, Kawaminami S. Surface components of Porphyromonas gingivalis. J Periodontal Res. 2009;44(1):1–12. doi:10.1111/j.1600-0765.2008.01135.x.PubMedView ArticleGoogle Scholar
- Wexler HM. Bacteroides: the good, the bad, and the nitty-gritty. Clin Microbiol Rev. 2007;20(4):593–621. doi:10.1128/cmr.00008-07.PubMedPubMed CentralView ArticleGoogle Scholar
- Magnúsdóttir S, Ravcheev DA, de Crécy-Lagard V, Thiele I. Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes. Front Genet. 2015;6(148). doi:10.3389/fgene.2015.00148.
- Goodman AL, McNulty NP, Zhao Y, Leip D, Mitra RD, Lozupone CA, et al. Identifying genetic determinants needed to establish a human gut symbiont in its habitat. Cell Host Microbe. 2009;6(3):279–89. doi:10.1016/j.chom.2009.08.003.PubMedPubMed CentralView ArticleGoogle Scholar
- Friedrich T, Scheide D. The respiratory complex I of bacteria, archaea and eukarya and its module common with membrane-bound multisubunit hydrogenases. FEBS Lett. 2000;479(1–2):1–5. doi:10.1016/S0014-5793(00)01867-6.PubMedView ArticleGoogle Scholar
- Moparthi VK, Hagerhall C. The evolution of respiratory chain complex I from a smaller last common ancestor consisting of 11 protein subunits. J Mol Evol. 2011;72(5-6):484–97. doi:10.1007/s00239-011-9447-2.PubMedPubMed CentralView ArticleGoogle Scholar
- Lemos RS, Fernandes AS, Pereira MM, Gomes CM, Teixeira M. Quinol:fumarate oxidoreductases and succinate:quinone oxidoreductases: phylogenetic relationships, metal centres and membrane attachment. Biochim Biophys Acta. 2002;1553(1–2):158–70. doi:10.1016/S0005-2728(01)00239-0.PubMedView ArticleGoogle Scholar
- Fischbach MA, Sonnenburg JL. Eating for two: how metabolism establishes interspecies interactions in the gut. Cell Host Microbe. 2011;10(4):336–47. doi:10.1016/j.chom.2011.10.002.PubMedPubMed CentralView ArticleGoogle Scholar
- Baughn AD, Malamy MH. The strict anaerobe Bacteroides fragilis grows in and benefits from nanomolar concentrations of oxygen. Nature. 2004;427(6973):441–4. doi:10.1038/nature02285.PubMedView ArticleGoogle Scholar
- Borisov VB, Gennis RB, Hemp J, Verkhovsky MI. The cytochrome bd respiratory oxygen reductases. Biochim Biophys Acta. 2011;1807(11):1398–413. doi:10.1016/j.bbabio.2011.06.016.PubMedPubMed CentralView ArticleGoogle Scholar
- Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8(10):785–6. doi:10.1038/nmeth.1701.PubMedView ArticleGoogle Scholar
- Song C, Kumar A, Saleh M. Bioinformatic comparison of bacterial secretomes. Genomics Proteomics Bioinformatics. 2009;7(1-2):37–46. doi:10.1016/s1672-0229(08)60031-5.PubMedView ArticleGoogle Scholar
- Dalhammar G, Steiner H. Characterization of inhibitor A, a protease from Bacillus thuringiensis which degrades attacins and cecropins, two classes of antibacterial proteins in insects. Eur J Biochem. 1984;139(2):247–52.PubMedView ArticleGoogle Scholar
- Vaitkevicius K, Rompikuntal PK, Lindmark B, Vaitkevicius R, Song T, Wai SN. The metalloprotease PrtV from Vibrio cholerae: purification and properties. FEBS J. 2008;275(12):3167–77. doi:10.1111/j.1742-4658.2008.06470.x.PubMedPubMed CentralView ArticleGoogle Scholar
- Singh B, Fleury C, Jalalvand F, Riesbeck K. Human pathogens utilize host extracellular matrix proteins laminin and collagen for adhesion and invasion of the host. FEMS Microbiol Rev. 2012;36(6):1122–80. doi:10.1111/j.1574-6976.2012.00340.x.PubMedView ArticleGoogle Scholar
- Kosowska K, Reinholdt J, Rasmussen LK, Sabat A, Potempa J, Kilian M, et al. The Clostridium ramosum IgA proteinase represents a novel type of metalloendopeptidase. J Biol Chem. 2002;277(14):11987–94. doi:10.1074/jbc.M110883200.PubMedView ArticleGoogle Scholar
- Nakayama K. Porphyromonas gingivalis and related bacteria: from colonial pigmentation to the type IX secretion system and gliding motility. J Periodontal Res. 2015;50(1):1–8. doi:10.1111/jre.12255.PubMedView ArticleGoogle Scholar
- Seers CA, Slakeski N, Veith PD, Nikolof T, Chen YY, Dashper SG, et al. The RgpB C-terminal domain has a role in attachment of RgpB to the outer membrane and belongs to a novel C-terminal-domain family found in Porphyromonas gingivalis. J Bacteriol. 2006;188(17):6376–86. doi:10.1128/jb.00731-06.PubMedPubMed CentralView ArticleGoogle Scholar
- Glew MD, Veith PD, Peng B, Chen YY, Gorasia DG, Yang Q, et al. PG0026 is the C-terminal signal peptidase of a novel secretion system of Porphyromonas gingivalis. J Biol Chem. 2012;287(29):24605–17. doi:10.1074/jbc.M112.369223.PubMedPubMed CentralView ArticleGoogle Scholar
- Nelson DC, Garbe J, Collin M. Cysteine proteinase SpeB from Streptococcus pyogenes - a potent modifier of immunologically important host and bacterial proteins. Biol Chem. 2011;392(12):1077–88. doi:10.1515/bc-2011-208.PubMedView ArticleGoogle Scholar
- Berry D, Stecher B, Schintlmeister A, Reichert J, Brugiroux S, Wild B, et al. Host-compound foraging by intestinal microbiota revealed by single-cell stable isotope probing. Proc Natl Acad Sci. 2013;110(12):4720–5. doi:10.1073/pnas.1219247110.PubMedPubMed CentralView ArticleGoogle Scholar
- Derrien M, Vaughan EE, Plugge CM, de Vos WM. Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int J Syst Evol Microbiol. 2004;54(5):1469–76. doi:10.1099/ijs.0.02873-0.PubMedView ArticleGoogle Scholar
- Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee Ying S, De Vadder F, Arora T, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015;22(6):971–82. doi:10.1016/j.cmet.2015.10.001.PubMedView ArticleGoogle Scholar
- Martens EC, Koropatkin NM, Smith TJ, Gordon JI. Complex glycan catabolism by the human gut microbiota: the Bacteroidetes Sus-like paradigm. J Biol Chem. 2009;284(37):24673–7. doi:10.1074/jbc.R109.022848.PubMedPubMed CentralView ArticleGoogle Scholar
- Reeves AR, D’Elia JN, Frias J, Salyers AA. A Bacteroides thetaiotaomicron outer membrane protein that is essential for utilization of maltooligosaccharides and starch. J Bacteriol. 1996;178(3):823–30.PubMedPubMed CentralGoogle Scholar
- Reeves AR, Wang GR, Salyers AA. Characterization of four outer membrane proteins that play a role in utilization of starch by Bacteroides thetaiotaomicron. J Bacteriol. 1997;179(3):643–9.PubMedPubMed CentralGoogle Scholar
- Sonnenburg ED, Sonnenburg JL, Manchester JK, Hansen EE, Chiang HC, Gordon JI. A hybrid two-component system protein of a prominent human gut symbiont couples glycan sensing in vivo to carbohydrate metabolism. Proc Natl Acad Sci. 2006;103(23):8834–9. doi:10.1073/pnas.0603249103.PubMedPubMed CentralView ArticleGoogle Scholar
- Sonnenburg ED, Zheng H, Joglekar P, Higginbottom SK, Firbank SJ, Bolam DN, et al. Specificity of polysaccharide use in intestinal Bacteroides species determines diet-induced microbiota alterations. Cell. 2010;141(7):1241–52. doi:10.1016/j.cell.2010.05.005.PubMedPubMed CentralView ArticleGoogle Scholar
- Martens EC, Chiang HC, Gordon JI. Mucosal glycan foraging enhances fitness and transmission of a saccharolytic human gut bacterial symbiont. Cell Host Microbe. 2008;4(5):447–57. doi:10.1016/j.chom.2008.09.007.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu J, Mahowald MA, Ley RE, Lozupone CA, Hamady M, Martens EC, et al. Evolution of symbiotic bacteria in the distal human intestine. PLoS Biol. 2007;5:e156. 2007/06/21.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhu A, Sunagawa S, Mende DR, Bork P. Inter-individual differences in the gene content of human gut bacterial species. Genome Biol. 2015;16(1):82. doi:10.1186/s13059-015-0646-9.PubMedPubMed CentralView ArticleGoogle Scholar
- Sonnenburg JL, Xu J, Leip DD, Chen C-H, Westover BP, Weatherford J, et al. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science. 2005;307(5717):1955–9. doi:10.1126/science.1109051.PubMedView ArticleGoogle Scholar
- Faith DP. Conservation evaluation and phylogenetic diversity. Biol Conserv. 1992;61(1):1–10. doi:10.1016/0006-3207(92)91201-3.View ArticleGoogle Scholar
- Krieg NR, Staley JT, Brown DR, Hedlund BP, Paster BJ, Ward NL, et al., editors. Bergey’s manual of systematic bacteriology, 2 edn. New York: Springer; 2011.Google Scholar
- Turroni S, Bendazzoli C, Dipalo SCF, Candela M, Vitali B, Gotti R, et al. Oxalate-degrading activity in Bifidobacterium animalis subsp. lactis: impact of acidic conditions on the transcriptional levels of the oxalyl coenzyme A (CoA) decarboxylase and formyl-CoA transferase genes. Appl Environ Microbiol. 2010;76(16):5609–20. doi:10.1128/aem.00844-10.PubMedPubMed CentralView ArticleGoogle Scholar
- Schnorr SL, Candela M, Rampelli S, Centanni M, Consolandi C, Basaglia G et al. Gut microbiome of the Hadza hunter-gatherers. Nat Commun. 2014;5. doi:10.1038/ncomms4654.Google Scholar
- Evans CC, LePard KJ, Kwak JW, Stancukas MC, Laskowski S, Dougherty J, et al. Exercise prevents weight gain and alters the gut microbiota in a mouse model of high fat diet-induced obesity. PLoS One. 2014;9:e92193. Public Library of Science.PubMedPubMed CentralView ArticleGoogle Scholar
- Stevenson TJ, Duddleston KN, Buck CL. Effects of season and host physiological state on the diversity, density, and activity of the arctic ground squirrel cecal microbiota. Appl Environ Microbiol. 2014;80(18):5611–22. doi:10.1128/aem.01537-14.PubMedPubMed CentralView ArticleGoogle Scholar
- Kolida S, Meyer D, Gibson GR. A double-blind placebo-controlled study to establish the bifidogenic dose of inulin in healthy humans. Eur J Clin Nutr. 2007;61(10):1189–95.PubMedView ArticleGoogle Scholar
- Cantarel BL, Lombard V, Henrissat B. Complex carbohydrate utilization by the healthy human microbiome. PLoS One. 2012;7:e28742. 2012/06/22.PubMedPubMed CentralView ArticleGoogle Scholar
- Milani C, Andrea Lugli G, Duranti S, Turroni F, Mancabelli L, Ferrario C, et al. Bifidobacteria exhibit social behavior through carbohydrate resource sharing in the gut. Sci Rep. 2015;5:15782. Macmillan Publishers Limited.PubMedPubMed CentralView ArticleGoogle Scholar
- Larsbrink J, Rogers TE, Hemsworth GR, McKee LS, Tauzin AS, Spadiut O, et al. A discrete genetic locus confers xyloglucan metabolism in select human gut Bacteroidetes. Nature. 2014;506(7489):498–502. doi:10.1038/nature12907.PubMedPubMed CentralView ArticleGoogle Scholar
- Whiteside SA, Razvi H, Dave S, Reid G, Burton JP. The microbiome of the urinary tract: a role beyond infection. Nat Rev Urol. 2015;12(2):81–90. doi:10.1038/nrurol.2014.361.PubMedView ArticleGoogle Scholar
- Troxel SA, Sidhu H, Kaul P, Low RK. Intestinal Oxalobacter formigenes colonization in calcium oxalate stone formers and its relation to urinary oxalate. J Endourol. 2003;17(3):173–6. doi:10.1089/089277903321618743.PubMedView ArticleGoogle Scholar
- Dawson KA, Allison MJ, Hartman PA. Isolation and some characteristics of anaerobic oxalate-degrading bacteria from the rumen. Appl Environ Microbiol. 1980;40(4):833–9.PubMedPubMed CentralGoogle Scholar
- Campieri C, Campieri M, Bertuzzi V, Swennen E, Matteuzzi D, Stefoni S, et al. Reduction of oxaluria after an oral course of lactic acid bacteria at high concentration. Kidney Int. 2001;60(3):1097–105.PubMedView ArticleGoogle Scholar
- Metges CC, Petzke KJ, El-Khoury AE, Henneman L, Grant I, Bedri S, et al. Incorporation of urea and ammonia nitrogen into ileal and fecal microbial proteins and plasma free amino acids in normal men and ileostomates. Am J Clin Nutr. 1999;70(6):1046–58.PubMedGoogle Scholar
- Meakins TS, Jackson AA. Salvage of exogenous urea nitrogen enhances nitrogen balance in normal men consuming marginally inadequate protein diets. Clin Sci (London). 1996;90(3):215–25.View ArticleGoogle Scholar
- Shen T-CD, Albenberg L, Bittinger K, Chehoud C, Chen Y-Y, Judge CA, et al. Engineering the gut microbiota to treat hyperammonemia. J Clin Invest. 2015;125(7):2841–50. doi:10.1172/JCI79214.PubMedPubMed CentralView ArticleGoogle Scholar
- Mora D, Arioli S. Microbial urease in health and disease. PLoS Pathog. 2014;10:e1004472. Public Library of Science.PubMedPubMed CentralView ArticleGoogle Scholar
- Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486(7402):222–7. doi:10.1038/nature11053.PubMedPubMed CentralGoogle Scholar
- Pabst O. New concepts in the generation and functions of IgA. Nat Rev Immunol. 2012;12(12):821–32. doi:10.1038/nri3322.PubMedView ArticleGoogle Scholar
- Mistry D, Stockley RA. IgA1 protease. Int J Biochem Cell Biol. 2006;38(8):1244–8. doi:10.1016/j.biocel.2005.10.005.PubMedView ArticleGoogle Scholar
- Thornton RF, Kagawa TF, O’Toole PW, Cooney JC. The dissemination of C10 cysteine protease genes in Bacteroides fragilis by mobile genetic elements. BMC Microbiol. 2010;10(1):1–15. doi:10.1186/1471-2180-10-122.View ArticleGoogle Scholar
- Thornton RF, Murphy EC, Kagawa TF, O’Toole PW, Cooney JC. The effect of environmental conditions on expression of Bacteroides fragilis and Bacteroides thetaiotaomicron C10 protease genes. BMC Microbiol. 2012;12(1):1–11. doi:10.1186/1471-2180-12-190.View ArticleGoogle Scholar
- Potempa M, Potempa J, Kantyka T, Nguyen K-A, Wawrzonek K, Manandhar SP, et al. Interpain A, a cysteine proteinase from Prevotella intermedia, inhibits complement by degrading complement factor C3. PLoS Pathog. 2009;5:e1000316.PubMedPubMed CentralView ArticleGoogle Scholar
- Sakamoto M, Lan PT, Benno Y. Barnesiella viscericola gen. nov., sp. nov., a novel member of the family Porphyromonadaceae isolated from chicken caecum. Int J Syst Evol Microbiol. 2007;57(2):342–6. doi:10.1099/ijs.0.64709-0.PubMedView ArticleGoogle Scholar
- Shkoporov AN, Khokhlova EV, Chaplin AV, Kafarskaia LI, Nikolin AA, Polyakov VY, et al. Coprobacter fastidiosus gen. nov., sp. nov., a novel member of the family Porphyromonadaceae isolated from infant faeces. Int J Syst Evol Microbiol. 2013;63(11):4181–8. doi:10.1099/ijs.0.052126-0.PubMedView ArticleGoogle Scholar
- Hofstad T, Olsen I, Eribe ER, Falsen E, Collins MD, Lawson PA. Dysgonomonas gen. nov. to accommodate Dysgonomonas gadei sp. nov., an organism isolated from a human gall bladder, and Dysgonomonas capnocytophagoides (formerly CDC group DF-3). Int J Syst Evol Microbiol. 2000;50(6):2189–95. doi:10.1099/00207713-50-6-2189.PubMedView ArticleGoogle Scholar
- Chen S, Dong X. Proteiniphilum acetatigenes gen. nov., sp. nov., from a UASB reactor treating brewery wastewater. Int J Syst Evol Microbiol. 2005;55(6):2257–61. doi:10.1099/ijs.0.63807-0.PubMedView ArticleGoogle Scholar
- Iino T, Mori K, Itoh T, Kudo T, Suzuki K-i, Ohkuma M. Description of Mariniphaga anaerophila gen. nov., sp. nov., a facultatively aerobic marine bacterium isolated from tidal flat sediment, reclassification of the Draconibacteriaceae as a later heterotypic synonym of the Prolixibacteraceae and description of the family Marinifilaceae fam. nov. Int J Syst Evol Microbiol. 2014;64(11):3660–7. doi:10.1099/ijs.0.066274-0.PubMedView ArticleGoogle Scholar
- Ueki A, Akasaka H, Suzuki D, Ueki K. Paludibacter propionicigenes gen. nov., sp. nov., a novel strictly anaerobic, Gram-negative, propionate-producing bacterium isolated from plant residue in irrigated rice-field soil in Japan. Int J Syst Evol Microbiol. 2006;56(1):39–44. doi:10.1099/ijs.0.63896-0.PubMedView ArticleGoogle Scholar
- Sakamoto M, Suzuki M, Umeda M, Ishikawa I, Benno Y. Reclassification of Bacteroides forsythus (Tanner et al. 1986) as Tannerella forsythensis corrig., gen. nov., comb. nov. Int J Syst Evol Microbiol. 2002;52(3):841–9. doi:10.1099/00207713-52-3-841.PubMedGoogle Scholar
- Sakamoto M, Benno Y. Reclassification of Bacteroides distasonis, Bacteroides goldsteinii and Bacteroides merdae as Parabacteroides distasonis gen. nov., comb. nov., Parabacteroides goldsteinii comb. nov. and Parabacteroides merdae comb. nov. Int J Syst Evol Microbiol. 2006;56(7):1599–605. doi:10.1099/ijs.0.64192-0.PubMedView ArticleGoogle Scholar
- Soo RM, Skennerton CT, Sekiguchi Y, Imelfort M, Paech SJ, Dennis PG, et al. An expanded genomic representation of the phylum cyanobacteria. Genome Biol Evol. 2014;6(5):1031–45. doi:10.1093/gbe/evu073.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. doi:10.1093/bioinformatics/btp324.PubMedPubMed CentralView ArticleGoogle Scholar
- Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. doi:10.1093/bioinformatics/btu170.PubMedPubMed CentralView ArticleGoogle Scholar
- Paulino D, Warren RL, Vandervalk BP, Raymond A, Jackman SD, Birol I. Sealer: a scalable gap-closing application for finishing draft genomes. BMC Bioinformatics. 2015;16(1):1–8. doi:10.1186/s12859-015-0663-4.View ArticleGoogle Scholar
- Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P, Tyson GW. GroopM: an automated tool for the recovery of population genomes from related metagenomes. In: Peer J, vol. 2. San Francisco: PeerJ Inc; 2014. p. e603.Google Scholar
- Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. In: PeerJ. Edited by Rahmann S, vol. 3; 2015: e1165.Google Scholar
- Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043–55. doi:10.1101/gr.186072.114.PubMedPubMed CentralView ArticleGoogle Scholar
- Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–80. doi:10.1093/molbev/mst010.PubMedPubMed CentralView ArticleGoogle Scholar
- Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490. 2010/03/13.PubMedPubMed CentralView ArticleGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.PubMedView ArticleGoogle Scholar
- Markowitz VM, Chen I-MA, Palaniappan K, Chu K, Szeto E, Pillay M, et al. IMG 4 version of the integrated microbial genomes comparative analysis system. Nucleic Acids Res. 2014;42(D1):D560–7. doi:10.1093/nar/gkt963.PubMedView ArticleGoogle Scholar
- Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26(11):1463–4. doi:10.1093/bioinformatics/btq166.PubMedView ArticleGoogle Scholar
- Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32(4):1363–71. doi:10.1093/nar/gkh293.PubMedPubMed CentralView ArticleGoogle Scholar
- Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9. doi:10.1093/bioinformatics/btu153.PubMedView ArticleGoogle Scholar
- Lechner M, Findeiß S, Steiner L, Marz M, Stadler PF, Prohaska SJ. Proteinortho: detection of (Co-)orthologs in large-scale analysis. BMC Bioinformatics. 2011;12(1):1–9. doi:10.1186/1471-2105-12-124.View ArticleGoogle Scholar
- Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42(Database issue):D222–30. doi:10.1093/nar/gkt1223.PubMedView ArticleGoogle Scholar
- Eddy SR. A new generation of homology search tools based on probabilistic inference. Genome Inform. 2009;23(1):205–11.PubMedGoogle Scholar
- Gibson MK, Forsberg KJ, Dantas G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 2015;9(1):207–16. doi:10.1038/ismej.2014.106.PubMedView ArticleGoogle Scholar
- Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35(Web Server issue):W182–5. doi:10.1093/nar/gkm321.PubMedPubMed CentralView ArticleGoogle Scholar
- Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. The RAST server: rapid annotations using subsystems technology. BMC Genomics. 2008;9:75. doi:10.1186/1471-2164-9-75.PubMedPubMed CentralView ArticleGoogle Scholar
- Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, et al. Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform. 2010;11(1):40–79. doi:10.1093/bib/bbp043.PubMedView ArticleGoogle Scholar
- Wu M, McNulty NP, Rodionov DA, Khoroshkin MS, Griffin NW, Cheng J et al. Genetic determinants of in vivo fitness and diet responsiveness in multiple human gut Bacteroides. Science. 2015;350(6256). doi:10.1126/science.aac5992.
- Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40:W445–51. Oxford University Press.PubMedPubMed CentralView ArticleGoogle Scholar
- Juncker AS, Willenbrock H, Von Heijne G, Brunak S, Nielsen H, Krogh A. Prediction of lipoprotein signal peptides in Gram-negative bacteria. Protein Sci. 2003;12(8):1652–62. doi:10.1110/ps.0303703.PubMedPubMed CentralView ArticleGoogle Scholar
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi:10.1186/s13059-014-0550-8.PubMedPubMed CentralView ArticleGoogle Scholar
- Kolde R. pheatmap: Pretty Heatmaps. 2015. R package version 107 from http://cran.r-project.org/package=pheatmap.
- Roberts D. Labdsv: ordination and multivariate analysis for ecology. 2007. R package version 18-0 from https://cran.r-project.org/web/packages/labdsv/index.html.
- Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB et al. Vegan: community ecology package. 2015. R package version 23-1 from http://cran.r-project.org/package=vegan.