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Inflammatory bowel disease (IBD) is associated with an altered gut microbiota composition, including expansion of Proteobacteria . Many species in this phylum are thought to contribute to disease under certain conditions, including adherent-invasive Escherichia coli (AIEC) strains, which are enriched in some patients.

Prebiotic fibers, polyphenols and other molecular components of food crops significantly affect the composition and function of the human gut microbiome and human health. The abundance of these, frequently uncharacterized, microbiome-active components vary within individual crop species. Here, we employ high throughput in vitro fermentations of pre-digested grain using a human microbiome to identify segregating genetic loci in a food crop, sorghum, that alter the composition and function of human gut microbes. Evaluating grain produced by 294 sorghum recombinant inbreds identifies 10 loci in the sorghum genome associated with variation in the abundance of microbial taxa and/or microbial metabolites. Two loci co-localize with sorghum genes regulating the biosynthesis of condensed tannins. We validate that condensed tannins stimulate the growth of microbes associated with these two loci. Our work illustrates the potential for genetic analysis to systematically discover and characterize molecular components of food crops that influence the human gut microbiome.

The effects of fiber, complex carbohydrates, lipids, and small molecules from food matrices on the human gut microbiome have been increasingly studied. Much less is known about how dietary protein can influence the composition and function of the gut microbial community. Here, we used near-isogenic maize lines of conventional popcorn and quality-protein popcorn (QPP) to study the effects of the opaque-2 mutation and associated quality-protein modifiers on the human gut microbiome. Opaque-2 blocks the synthesis of major maize seed proteins (α-zeins), resulting in a compensatory synthesis of new seed proteins that are nutritionally beneficial with substantially higher levels of the essential amino acids lysine and tryptophan. We show that QPP lines stimulate greater amounts of butyrate production by human gut microbiomes in in vitro fermentation of popped and digested corn from parental and QPP hybrids. In human gut microbiomes derived from diverse individuals, bacterial taxa belonging to the butyrate-producing family Lachnospiraceae, including the genera Coprococcus and Roseburia were consistently increased when fermenting QPP vs. parental popcorn lines. We conducted molecular complementation to further demonstrate that lysine-enriched seed protein can stimulate growth and butyrate production by microbes through distinct pathways. Our data show that organisms such as Coprococcus can utilize lysine and that other gut microbes, such as Roseburia spp., instead, utilize fructoselysine produced during thermal processing (popping) of popcorn. Thus, the combination of seed composition in QPP and interaction of protein adducts with carbohydrates during thermal processing can stimulate the growth of health-promoting, butyrate-producing organisms in the human gut microbiome through multiple pathways.

Background:Waxy starches contain >90% amylopectin and are derived from grain crops carrying naturally-occurring mutations that block amylose biosynthesis. The absence of amylose in waxy starches produces unique physiochemical properties that are desirable for food processing, but the effects of increased amylopectin/amylose ratios in waxy starches on the gut microbiome and physiological characteristics of the host are not well characterized. Here, we used a whole-grain model with isogenic pairs of wild type sorghum lines and their waxy derivatives to test the hypothesis that major differences in amylose/amylopectin ratio produce significant effects on the human gut microbiome. Results:Fermentation of grain from waxy versus wild type derivatives produced substantial differences in overall microbiome composition, abundances of multiple taxa, and production of microbial metabolites (butyrate). Several of the taxonomic and metabolic signatures of fermentations from parental versus waxy lines were shared across fermentations with microbiomes from different human donors, including reduced levels of butyrate production and lower abundances of Roseburia and other amylolytic, butyrate-producing members of Lachnospiraceae in fermentations of waxy lines. Using a human microbiome-associated mouse model, we also detected significant differences in microbiome composition in animals fed low-fiber diets supplemented with 20% grain from isogenic pairs of parental versus waxy derivatives of sorghum. Remarkably, these microbiome changes were accompanied by significant differences in weight gain, with animals consuming waxy sorghum gaining significantly more weight. Conclusions:We conclude that the benefits of waxy starches on food functionality can have trade-off effects on the gut microbiome and host physiology that could be particularly relevant in human populations consuming large amounts of waxy grains.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -BAPS1 -ST -cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turn-around, while aiming at generating two main outcomes: 1) Species level identification; and 2) Variant mapping at different levels of genotypic resolution for population-based tracking, in addition to predicting traits such as antimicrobial resistance (AMR). With the recent advances and continual dissemination of whole-genome sequencing technologies, large-scale population-based genotyping of bacterial pathogens has become possible. Since bacterial populations often present a high degree of clonality in the genomic backbone (i.e., low genetic diversity), the choice of genotyping scheme can even facilitate the understanding of ancestral relationships and can be used for prediction of co-inherited traits such as AMR. Multi-locus sequence typing (MLST) fits that purpose and can identify sequence types (ST) based on seven ubiquitous genome-scattered loci that aid in genotyping isolates beneath the species level. ST-based mapping also standardizes genotyping across laboratories and is used by laboratories worldwide. However, algorithms for inferring ST from Illumina paired-end sequencing data typically rely on genome assembly prior to classification. Genome assembly is computationally intensive and is a bottleneck for speed and scalability, which are important aspects of genomic epidemiology. The stringMLST program uses an assembly-free, kmer-based algorithm for inferring STs, which can overcome the speed and scalability bottlenecks. Here we have systematically studied the accuracy and scalability of stringMLST relative to the standard MLST program across a wide array of phylogenetically divergent Public Health-relevant bacterial pathogens. Our data shows that optimal kmer length for stringMLST is species-specific and that genome-intrinsic and -extrinsic features can affect performance and accuracy of the program. While suitable parameters could be identified for most organisms, there were a few instances where this program may not be directly deployable in its current format. More importantly, we integrated stringMLST into our freely available and scalable hierarchical-based population genomics platform, ProkEvo, and further demonstrated how the implementation facilitates automated, reproducible bacterial population analysis. The ProkEvo implementation provides a rapidly deployable genomic epidemiology tool for ST mapping along with other pan-genomic data mining strategies, while providing specific guidance on how to optimize stringMLST performance for a wide variety of bacterial pathogens.

The recent incorporation of bacterial whole-genome sequencing (WGS) into Public Health laboratories has enhanced foodborne outbreak detection and source attribution. As a result, large volumes of publicly available datasets can be used to study the biology of foodborne pathogen populations at an unprecedented scale. To demonstrate the application of a heuristic and agnostic hierarchical population structure guided pan-genome enrichment analysis (PANGEA), we used populations of S. enterica lineage I to achieve two main objectives: (i) show how hierarchical population inquiry at different scales of resolution can enhance ecological and epidemiological inquiries; and (ii) identify population-specific inferable traits that could provide selective advantages in food production environments. Publicly available WGS data were obtained from NCBI database for three serovars of Salmonella enterica subsp. enterica lineage I (S. Typhimurium, S. Newport, and S. Infantis). Using the hierarchical genotypic classifications (Serovar, BAPS1, ST, cgMLST), datasets from each of the three serovars showed varying degrees of clonal structuring. When the accessory genome (PANGEA) was mapped onto these hierarchical structures, accessory loci could be linked with specific genotypes. A large heavy-metal resistance mobile element was found in the Monophasic ST34 lineage of S. Typhimurium, and laboratory testing showed that Monophasic isolates have on average a higher degree of copper resistance than the Biphasic ones. In S. Newport, an extra sugE gene copy was found among most isolates of the ST45 lineage, and laboratory testing of multiple isolates confirmed that isolates of S. Newport ST45 were on average less sensitive to the disinfectant cetylpyridimium chloride than non-ST45 isolates. Lastly, data-mining of the accessory genomic content of S. Infantis revealed two cryptic Ecotypes with distinct accessory genomic content and distinct ecological patterns. Poultry appears to be the major reservoir for Ecotype 1, and temporal analysis further suggested a recent ecological succession, with Ecotype 2 apparently being displaced by Ecotype 1. Altogether, the use of a heuristic hierarchical-based population structure analysis that includes bacterial pan-genomes (core and accessory genomes) can (1) improve genomic resolution for mapping populations and accessing epidemiological patterns; and (2) define lineage-specific informative loci that may be associated with survival in the food chain.

The common marmoset (Callithrix jacchus) is an omnivorous New World primate whose diet in the wild includes large amounts of fruit, seeds, flowers, and a variety of lizards and invertebrates. Marmosets also feed heavily on tree gums and exudates, and they have evolved unique morphological and anatomical characteristics to facilitate gum feeding (gummivory). In this study, we characterized the fecal microbiomes of adult and infant animals from a captive population of common marmosets at the Callitrichid Research Center at the University of Nebraska at Omaha under their normal dietary and environmental conditions. The microbiomes of adult animals were dominated by species of Bifidobacterium, Bacteroides, Prevotella, Phascolarctobacterium, Megamonas, and Megasphaera. Culturing and genomic analysis of the Bifidobacterium populations from adult animals identified four known marmoset-associated species (B. reuteri, B. aesculapii, B. myosotis, and B. hapali) and three unclassified taxa of Bifidobacterium that are phylogenetically distinct. Species-specific quantitative PCR (qPCR) confirmed that these same species of Bifidobacterium are abundant members of the microbiome throughout the lives of the animals. Genomic loci in each Bifidobacterium species encode enzymes to support growth and major marmoset milk oligosaccharides during breastfeeding; however, metabolic islands that can support growth on complex polysaccharide substrates in the diets of captive adults (pectin, xyloglucan, and xylan), including loci in B. aesculapii that can support its unique ability to grow on arabinogalactan-rich tree gums, were species-specific. IMPORTANCE Bifidobacterium species are recognized as important, beneficial microbes in the human gut microbiome, and their ability colonize individuals at different stages of life is influenced by host, dietary, environmental, and ecological factors, which is poorly understood. The common marmoset is an emerging nonhuman primate model with a short maturation period, making this model amenable to study the microbiome throughout a life history. Features of the microbiome in captive marmosets are also shared with human gut microbiomes, including abundant populations of Bifidobacterium species. Our studies show that several species of Bifidobacterium are dominant members of the captive marmoset microbiome throughout their life history. Metabolic capacities in genomes of the marmoset Bifidobacterium species suggest species-specific adaptations to different components of the captive marmoset diet, including the unique capacity in B. aesculapii for degradation of gum arabic, suggesting that regular dietary exposure in captivity may be important for preserving gum-degrading species in the microbiome.

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research, diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of individual microbial species poses a tremendous opportunity for discovery and hypothesis-generating research into ecology and evolution of these microorganisms. Scalability and user-friendliness of existing pipelines for population-scale inquiry, however, limit applications of systematic, population-scale approaches. Here, we present ProkEvo, an automated, scalable, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: 1) Automation and scaling of complex combinations of computational analyses for many thousands of bacterial genomes from inputs of raw Illumina paired-end sequence reads; 2) Use of workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; 3) Use of high-performance and high-throughput computational platforms; 4) Generation of hierarchical population-based genotypes at different scales of resolution based on combinations of multi-locus and Bayesian statistical approaches for classification; 5) Detection of antimicrobial resistance (AMR) genes, putative virulence factors, and plasmids from curated databases and association with genotypic classifications; and 6) Production of pan-genome annotations and data compilation that can be utilized for downstream analysis. The scalability of ProkEvo was measured with two datasets comprising significantly different numbers of input genomes (one with ~2,400 genomes, and the second with ~23,000 genomes). Depending on the dataset and the computational platform used, the running time of ProkEvo varied from ~3-26 days. ProkEvo can be used with virtually any bacterial species and the Pegasus WMS facilitates addition or removal of programs from the workflow or modification of options within them. All the dependencies of ProkEvo can be distributed via conda environment or Docker image. To demonstrate versatility of the ProkEvo platform, we performed population-based analyses from available genomes of three distinct pathogenic bacterial species as individual case studies (three serovars ofSalmonella enterica, as well asCampylobacter jejuniandStaphylococcus aureus). The specific case studies used reproducible Python and R scripts documented in Jupyter Notebooks and collectively illustrate how hierarchical analyses of population structures, genotype frequencies, and distribution of specific gene functions can be used to generate novel hypotheses about the evolutionary history and ecological characteristics of specific populations of each pathogen. Collectively, our study shows that ProkEvo presents a viable option for scalable, automated analyses of bacterial populations with powerful applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance.

This study addressed a knowledge gap about longitudinal changes in the gut microbiome metabolites during animal pairing. This research in the laboratory common marmoset can control for the confounding factors such as diet and other environmental conditions. Phascolarctobacterium showed the highest contribution to the sex-biased transmission of the female to the male after pairing. Here, we observed the sex difference in the increase in short-chain fatty acid concentration in the feces of newly paired marmosets, which may be caused by the sex-biased gut microbiome transmission after pairing., The role by which the gut microbiome influences host health (e.g., energy equilibrium and immune system) may be partly mediated by short-chain fatty acids, which are bacterial fermentation products from the dietary fibers. However, little is known about longitudinal changes in gut microbiome metabolites during cohabitation alongside social contact. In common marmosets (Callithrix jacchus), the gut microbiome community is influenced by social contact, as newly paired males and females develop convergent microbial profiles. Here, we monitored the dynamics of short-chain fatty acid concentrations in common marmoset feces from the prepairing (PRE) to postpairing (POST) stages. In males, we observed that the concentrations of acetate, propionate, isobutyrate, and isovalerate significantly increased in the POST stage compared to the PRE stage. However, no significant changes were found in females. We further found that the propionate concentration was significantly positively correlated with the abundance of Phascolarctobacterium in the male feces. Thus, the sex difference in the changes in the concentrations of short-chain fatty acids might be related to sex-biased gut microbiome transmission after pairing. We suggest that the significant changes in the gut microbiomes and some short-chain fatty acids of the common marmoset during cohabitation may contribute to physiological homeostasis during pairing. IMPORTANCE This study addressed a knowledge gap about longitudinal changes in the gut microbiome metabolites during animal pairing. This research in the laboratory common marmoset can control for the confounding factors such as diet and other environmental conditions. Phascolarctobacterium showed the highest contribution to the sex-biased transmission of the female to the male after pairing. Here, we observed the sex difference in the increase in short-chain fatty acid concentration in the feces of newly paired marmosets, which may be caused by the sex-biased gut microbiome transmission after pairing.

In this controlled study, we collected longitudinal fecal samples from 16 male and female marmoset monkeys for 2 weeks prior to and for 8 weeks after pairing in male-female dyads. We report for the first time that marmoset monkeys undergo significant changes to the gut microbiome following pairing and that these changes are sex-biased; i.e., females transmit more microbes to their social partners than males do. Marmosets exhibit pair bonding behavior such as spatial proximity, physical contact, and grooming, and sex biases in these behavioral patterns may contribute to the observed sex bias in social transmission of gut microbiomes., Social behavior can alter the microbiome composition via transmission among social partners, but there have been few controlled experimental studies of gut microbiome transmission among social partners in primates. We collected longitudinal fecal samples from eight unrelated male-female pairs of marmoset monkeys prior to pairing and for 8 weeks following pairing. We then sequenced 16S rRNA to characterize the changes in the gut microbiome that resulted from the pairing. Marmoset pairs had a higher similarity in gut microbiome communities after pairing than before pairing. We discovered sex differences in the degrees of change in gut microbiome communities following pairing. Specifically, the gut microbiome communities in males exhibited greater dissimilarity from the prepairing stage (baseline) than the gut microbiome communities in females. Conversely, females showed a gradual stabilization in the rate of the gut microbiome community turnover. Importantly, we found that the male fecal samples harbored more female-source gut microbes after pairing, especially early in pairing (paired test, P

Shiga toxin-producing Escherichia coli (STEC) is a foodborne disease with worldwide outbreaks. STEC serotypes O157, O26, O45, O103, O111, O121, and O145 cause the most outbreaks. There is little published information regarding the other serotypes. We report the draft genome sequences for 11 uncommon STEC serotypes from Nebraska.

Scope Previous studies have suggested that diets rich in omega-3 and low in omega-6 long-chain polyunsaturated fatty acids (PUFAs) can limit the development of metabolic syndrome (MetS). Transgenic soybeans yielding oils enriched for omega-3 PUFAs represent a new and readily-available option for incorporating omega-3 PUFAs into diets to provide health benefits. Methods and results Transgenic soybean oils, enriched for either stearidonic acid (SDA) or eicosapentaenoic acid (EPA), were incorporated in diets to test their effects on limiting the development of MetS in a mouse model of diet-induced obesity. Supplementation with SDA- but not EPA-enriched oils improved features of MetS compared to feeding a control wild-type oil. Because previous studies have linked the gut microorganism Akkermansia muciniphila to the metabolic effects of feeding omega-3 PUFAs, we investigated the causal contribution of A. muciniphila to mediating the metabolic benefits provided by SDA-enriched diets. Although A. muciniphila was not required for SDA-induced metabolic improvements, this microorganism did modulate levels of saturated and mono-unsaturated fatty acids in host adipose tissues. Conclusion Together, these findings support the utilization of SDA-enriched diets to modulate weight gain, glucose metabolism, and fatty acid profiles of liver and adipose tissue. This article is protected by copyright. All rights reserved.

AIMS: The objective of this study was to determine if the faecal microbiome has an association with Escherichia coli O157:H7 prevalence and enumeration. METHODS AND RESULTS: Pyrosequencing analysis of faecal microbiome was performed from feedlot cattle fed one of three diets: (i) 94 heifers fed low concentrate (LC) diet, (ii) 142 steers fed moderate concentrate (MC) diet, and (iii) 132 steers fed high concentrate (HC) diet. A total of 322 585 OTUs were calculated from 2,411,122 high‐quality sequences obtained from 368 faecal samples. In the LC diet group, OTUs assigned to the orders Clostridiales and RF39 (placed within the class Mollicutes) were positively correlated with both E. coli O157:H7 prevalence and enumeration. In the MC diet group, OTUs assigned to Prevotella copri were positively correlated with both E. coli O157:H7 prevalence and enumeration, whereas OTUs assigned to Prevotella stercorea were negatively correlated with both E. coli O157:H7 prevalence and enumeration. In both the MC diet group and the HC diet group, OTUs assigned to taxa placed within Clostridiales were both positively and negatively correlated with both E. coli O157:H7 prevalence and enumeration. However, all correlations were weak. In both the MC diet group and the HC diet group, stepwise linear regression through backward elimination analyses indicated that these OTUs were significantly correlated (P

Changes in the gastrointestinal microbial community are frequently associated with chronic diseases such as Inflammatory Bowel Diseases. However, understanding the relationship of any individual taxon within the community to host physiology is made complex due to the diversity and individuality of the gut microbiota. Defined microbial communities such as the Altered Schaedler Flora (ASF) help alleviate the challenges of a diverse microbiota by allowing one to interrogate the relationship between individual bacterial species and host responses. An important aspect of studying these relationships with defined microbial communities is the ability to measure the population abundance and dynamics of each member. Herein, we describe the development of an improved ASF species-specific and sensitive real-time quantitative polymerase chain reaction (qPCR) for use with SYBR Green chemistry to accurately assess individual ASF member abundance. This approach targets hypervariable regions V1 through V3 of the 16S rRNA gene of each ASF taxon to enhance assay specificity. We demonstrate the reproducibility, sensitivity and application of this new method by quantifying each ASF bacterium in two inbred mouse lines. We also used it to assess changes in ASF member abundance before and after acute antibiotic perturbation of the community as well as in mice fed two different diets. Additionally, we describe a nested PCR assay for the detection of lowly abundant ASF members. Altogether, this improved qPCR method will facilitate gnotobiotic research involving the ASF community by allowing for reproducible quantification of its members under various physiological conditions.

Background & AimsAdherent-invasiveEscherichia coli(AIEC) are enriched in ileal Crohn’s disease patients and implicated in disease etiology. However, AIEC pathogenesis is poorly understood, and it is unclear if the expansion of these organisms contributes to inflammatory bowel disease (IBD). Questions also remain as to what extent the variousin vitrophenotypes used to classify AIEC are pathologically relevant.MethodsWe utilized a combination ofin vitrophenotyping and a murine model of intestinal inflammation to systematically relate AIEC phenotypes to pathogenicity for 30 mucosa-associated human-derivedE. colistrains.In vitroassays used included survival/replication in and TNF-α production by J774 macrophages as well as invasion/replication in Caco2 intestinal epithelial cells.ResultsAIEC do not form a phenotypic group that is clearly separated from non-AIEC. However,E. colistrains displayingin vitroAIEC phenotypes caused, on average, more severe intestinal inflammation. Survival/replication of strains in J774 and Caco2 cells were positively correlated with diseasein vivo, while adherence to Caco2 cells and TNF-α production by J774 cells were not. Importantly, co-colonization with adherent non-AIEC strains ameliorated AIEC-mediated disease.ConclusionOur findings do not support the existence of an AIEC pathovar that can be clearly separated from commensalE. coli. However, intracellular survival/replication phenotypes do contribute to murine intestinal inflammation, suggesting that the AIEC overgrowth observed in human IBD makes a causal contribution to disease. The ability to differentiate pathologically-relevant AIEC phenotypes from those that are not provides an important foundation for developing strategies to predict, diagnose and treat human IBD through characterizing and modulating patientE. colipopulations.

The Nebraska Sandhills region contains over 1,500 geochemically diverse interdunal lakes, some of which are potassium rich, alkaline, and hypersaline. Here, we report 16S rRNA amplicon pyrosequencing data on the water and sediment microbial communities of eight alkaline lakes in the Sandhills of western Nebraska.

Recent studies have demonstrated differences in the intestinal microbiota between patients with irritable bowel syndrome (IBS) and healthy controls (HC), suggesting a role for the intestinal microbiota in the pathogenesis of IBS. Alterations in the microbiota have also been implicated in the pathogenesis of abdominal bloating, a commonly reported symptom in IBS. We investigated the relationship between the intestinal microbiota, abdominal bloating, and altered bowel patterns in a cohort of patients with IBS and HC. The 16S rRNA gene from fresh fecal samples was amplified and pyrosequenced by using Roche-454 Titanium chemistry. A Core Measurable Microbiome (CMM) was generated for Operational Taxonomic Unit (OTU) detected in >75% of all samples and compositional features of CMM were compared between groups by Linear Discriminant Analysis (LDA). IBS differentiated from HC by LDA using continuous variation in the species/OTUs or the CMM genera. When subcategorized based on bloating symptoms and bowel characteristics, the same subjects were also well differentiated from one another and from HC. ANOVA analysis showed quantitative species/OTU differences between the subgroups including IBS with and without bloating, and subtypes based on bowel characteristics. The clear LDA differentiation and the significant microbial taxa differences between the groups imply a significant association of the microbiota with bloating symptoms and bowel characteristics in IBS. These changes in the microbiota may serve as a biomarker for IBS and its clinical subtypes and suggest a role for the intestinal microbiota in the pathogenesis of the main symptoms of the disorder.

Escherichia coli is a facultative anaerobic symbiont found widely among mammalian gastrointestinal tracts. Several human studies have reported increased commensal E. coli abundance in the intestine during inflammation; however, host immunological responses toward commensal E. coli during inflammation are not well-defined. Here, we show that colonization of gnotobiotic mice with different genotypes of commensal E. coli isolated from healthy conventional microbiota mice and representing distinct populations of E. coli elicited strain-specific disease phenotypes and immunopathological changes following treatment with the inflammatory stimulus, dextran sulfate sodium (DSS). Production of the inflammatory cytokines GM-CSF, IL-6, and IFN-γ was a hallmark of the severe inflammation induced by E. coli strains of Sequence Type 129 (ST129) and ST375 following DSS administration. In contrast, colonization with E. coli strains ST150 and ST468 caused mild intestinal inflammation and triggered only low levels of pro-inflammatory cytokines, a response indistinguishable from that of E. coli-free control mice treated with DSS. The disease development observed with ST129 and ST375 colonization was not directly associated with their abundance in the GI tract as their levels did not change throughout DSS treatment, and no major differences in bacterial burden in the gut were observed among the strains tested. Data mining and in vivo neutralization identified IL-6 as a key cytokine responsible for the observed differential disease severity. Collectively, our results show that the capacity to exacerbate acute intestinal inflammation is a strain-specific trait that can potentially be overcome by blocking the pro-inflammatory immune responses that mediate intestinal tissue damage.

The factors that govern assembly of the gut microbiota are insufficiently understood. Here, we test the hypothesis that inter-individual microbiota variation can arise solely from differences in the order and timing by which the gut is colonized early in life. Experiments in which mice were inoculated in sequence either with two complex seed communities or a cocktail of four bacterial strains and a seed community revealed that colonization order influenced both the outcome of community assembly and the ecological success of individual colonizers. Historical contingency and priority effects also occurred in Rag1-/- mice, suggesting that the adaptive immune system is not a major contributor to these processes. In conclusion, this study established a measurable effect of colonization history on gut microbiota assembly in a model in which host and environmental factors were strictly controlled, illuminating a potential cause for the high levels of unexplained individuality in host-associated microbial communities.

Strain-specificity of probiotic effects has been a cornerstone principle of probiotic science for decades. Certainly, some important mechanisms are present in only a few probiotic strains. But scientific advances now reveal commonalities among members of certain taxonomic groups of probiotic microbes. Some clinical benefits likely derive from these shared mechanisms, suggesting that sub-species-specific, species specific or genus-specific probiotic effects exist. Human trials are necessary to confirm specific health benefits. However, a strain that has not been tested in human efficacy trials may meet the minimum definition of the term `probiotic' if it is a member of a well-studied probiotic species expressing underlying core mechanisms and it is delivered at an effective dose.

Inflammatory bowel diseases (IBD) are likely driven by aberrant immune responses directed against the resident microbiota. Although IBD is commonly associated with a dysbiotic microbiota enriched in putative pathobionts, the etiological agents of IBD remain unknown. Using a pathobiont-induced intestinal inflammation model and a defined bacterial community, we provide new insights into the immune-microbiota interactions during disease. In this model system, the pathobiont Helicobacter bilis instigates disease following sub-pathological dextran sulfate sodium treatment. We show that H. bilis causes mild inflammation in mono-associated mice, but severe disease in the presence of a microbiota, demonstrating synergy between the pathobiont and microbiota in exacerbating pathology. Remarkably, inflammation depends on the presence of H. bilis, but is marked by a predominant Th17 response against specific members of the microbiota and not the pathobiont, even upon the removal of the most immune-dominant taxa. Neither increases in pathobiont burden nor unique changes in immune-targeted microbiota member abundances are observed during disease. Collectively, our findings demonstrate that a pathobiont instigates inflammation without being the primary target of a Th17 response or by altering the microbiota community structure. Moreover, our findings point toward monitoring pathobiont-induced changes in microbiota immune targeting as a new concept in IBD diagnotics.

Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data.

Comparative analyses of the control of mammalian microbiomes by host genetic architecture reveal striking conserved features that have implications for the evolution of host–microbiome interactions. See related Research article: http://www.genomebiology.com/2015/16/1/191

As the first series of genetic analyses of gut microbiome composition in humans is now emerging, the results should be met with enthusiasm, but also with caution. Findings from the initial offerings demonstrate how population-scale approaches can provide deeper insights into host-microbiome interactions while at the same time illustrating that our understanding of the architecture of highly complex microbiome 'traits' is still rudimentary.