The human gut microbiome harbors a diverse array of metabolic pathways contributing to its development and homeostasis via a complex web of diet-dependent metabolic interactions within the microbial community and host. Genomics-based reconstruction and predictive modeling of these interactions would provide a framework for diagnostics and treatment of dysbiosis-related syndromes via rational selection of therapeutic prebiotics and dietary nutrients. Of particular interest are micronutrients, such as B-group vitamins, precursors of indispensable metabolic cofactors, that are produced de novo by some gut bacteria (prototrophs) but must be provided exogenously in the diet for many other bacterial species (auxotrophs) as well as for the mammalian host. Cross-feeding of B vitamins between prototrophic and auxotrophic species is expected to strongly contribute to the homeostasis of microbial communities in the distal gut given the efficient absorption of dietary vitamins in the upper gastrointestinal tract. To confidently estimate the balance of microbiome micronutrient biosynthetic capabilities and requirements using available genomic data, we have performed a subsystems-based reconstruction of biogenesis, salvage and uptake for eight B vitamins (B1, B2, B3, B5, B6, B7, B9, and B12) and queuosine (essential factor in tRNA modification) over a reference set of 2,228 bacterial genomes representing 690 cultured species of the human gastrointestinal microbiota. This allowed us to classify the studied organisms with respect to their pathway variants and infer their prototrophic vs. auxotrophic phenotypes. In addition to canonical vitamin pathways, several conserved partial pathways were identified pointing to alternative routes of syntrophic metabolism and expanding a microbial vitamin “menu” by several pathway intermediates (vitamers) such as thiazole, quinolinate, dethiobiotin, pantoate. A cross-species comparison was applied to assess the extent of conservation of vitamin phenotypes at distinct taxonomic levels (from strains to families). The obtained reference collection combined with 16S rRNA gene-based phylogenetic profiles was used to deduce phenotype profiles of the human gut microbiota across in two large cohorts. This analysis provided the first estimate of B-vitamin requirements, production and sharing capabilities in the human gut microbiome establishing predictive phenotype profiling as a new approach to classification of microbiome samples. Future expansion of our reference genomic collection of metabolic phenotypes will allow further improvement in coverage and accuracy of predictive phenotype profiling of the human microbiome.

Cross-feeding on intermediary and end-point metabolites plays an important role in the dynamic interactions of host-associated microbial communities. While gut microbiota possess inherent resilience to perturbation, variations in the intake of certain nutrients may lead to changes in the community composition with potential consequences on host physiology. Syntrophic relationships and mutualism at the level of major carbon and energy sources have been documented, however, relatively little is known about metabolic interactions involving micronutrients, such as B-vitamins, biosynthetic precursors of essential cofactors in the mammalian host and numerous members of the gut microbiota alike. In silico genomic reconstruction and prediction of community-wide metabolic phenotypes for eight major B-vitamins (B1, B2, B3, B5, B6, B7, B9, and B12), suggests that a significant fraction of microbial gut communities (>20% by abundance) are represented by auxotrophic species whose viability is strictly dependent on acquiring one or more B-vitamins from diet and/or prototrophic microbes via committed salvage pathways. Here, we report the stability of gut microbiota using humanized gnotobiotic mice and in vitro anaerobic fecal culture in the context of extreme variations of dietary B-vitamin supply as revealed by phylotype-to-phenotype prediction from 16S rRNA profiling and metabolomic measurements. The observed nearly unaltered relative abundance of auxotrophic species in gut communities in the face of diet or media lacking B-vitamins or containing them in great excess (∼30-fold above normal) points to a strong contribution of metabolic cooperation (B-vitamin exchange and sharing) to the stability of gut bacterial populations.

The prebiotic potential of nervine herbal medicines has been scarcely studied. We therefore used anaerobic human fecal cultivation to investigate whether medicinal herbs commonly used as treatment in neurological health and disease in Ayurveda and other traditional systems of medicine modulate gut microbiota. Profiling of fecal cultures supplemented with either Kapikacchu, Gotu Kola, Bacopa/Brahmi, Shankhapushpi, Boswellia/Frankincense, Jatamansi, Bhringaraj, Guduchi, Ashwagandha or Shatavari by 16S rRNA sequencing revealed profound changes in diverse taxa. Principal coordinate analysis highlights that each herb drives the formation of unique microbial communities predicted to display unique metabolic potential. The relative abundance of approximately one-third of the 243 enumerated species was altered by all herbs. Additional species were impacted in an herb-specific manner. In this study, we combine genome reconstruction of sugar utilization and short chain fatty acid (SCFA) pathways encoded in the genomes of 216 profiled taxa with monosaccharide composition analysis of each medicinal herb by quantitative mass spectrometry to enhance the interpretation of resulting microbial communities and discern potential drivers of microbiota restructuring. Collectively, our results indicate that gut microbiota engage in both protein and glycan catabolism, providing amino acid and sugar substrates that are consumed by fermentative species. We identified taxa that are efficient amino acid fermenters and those capable of both amino acid and sugar fermentation. Herb-induced microbial communities are predicted to alter the relative abundance of taxa encoding SCFA (butyrate and propionate) pathways. Co-occurrence network analyses identified a large number of taxa pairs in medicinal herb cultures. Some of these pairs displayed related culture growth relationships in replicate cultures highlighting potential functional interactions among medicinal herb-induced taxa.

Although the impact of medicinal and culinary herbs on health and disease has been studied to varying extents, scarcely little is known about the impact of these herbs on gut microbiota and how such effects might contribute to their health benefits. We applied in vitro anaerobic cultivation of human fecal microbiota followed by 16S rRNA sequencing to study the modulatory effects of 4 culinary spices: Curcuma longa (turmeric), Zingiber officinale (ginger), Piper longum (pipli or long pepper), and Piper nigrum (black pepper). All herbs analyzed possessed substantial power to modulate fecal bacterial communities to include potential prebiotic and beneficial repressive effects. We additionally analyzed the sugar composition of each herb by mass spectrometry and conducted genome reconstruction of 11 relevant sugar utilization pathways, glycosyl hydrolase gene representation, and both butyrate and propionate biosynthesis potential to facilitate our ability to functionally interpret microbiota profiles. Results indicated that sugar composition is not predictive of the taxa responding to each herb; however, glycosyl hydrolase gene representation is strongly modulated by each herb, suggesting that polysaccharide substrates present in herbs provide selective potential on gut communities. Additionally, we conclude that catabolism of herbs by gut communities primarily involves sugar fermentation at the expense of amino acid metabolism. Among the herbs analyzed, only turmeric induced changes in community composition that are predicted to increase butyrate-producing taxa. Our data suggests that substrates present in culinary spices may drive beneficial alterations in gut communities thereby altering their collective metabolism to contribute to the salubrious effects on digestive efficiency and health. These results support the potential value of further investigations in human subjects to delineate whether the metabolism of these herbs contributes to documented and yet to be discovered health benefits.

Many studies have focused on the metabolic capacity of human gut microbiota to produce short-chain fatty acids and subsequent effects on host physiology. Given scarce data on how SCFAs produced by gut bacteria participate in cross-feeding to influence community structure and function, we evaluated the potential of SCFAs to modulate human gut microbiota in vitro. We employed anaerobic fecal cultivation in chemically defined medium supplemented with one of nine SCFAs to determine effects on both gut microbial community structure via 16S rRNA sequencing and function via genome reconstruction analysis. Each SCFA displayed significant and unique modulatory potential with respect to the relative abundance of bacterial taxa. Analysis of SCFA-supplemented communities revealed that alterations of individual closely related phylotypes displayed coherent changes, although exceptions were also observed which suggest strain-dependent differences in SCFA-induced changes. We used genome reconstruction to evaluate the functional implications of SCFA-mediated restructuring of fecal communities. We note that some SCFA-supplemented cultures displayed a reduction in the predicted abundance of SCFA producers, which suggests a possible undefined negative feedback mechanism. We conclude that SCFAs are not simply end-products of metabolism but also serve to modulate the gut microbiota through cross-feeding that alters the fitness of specified taxa. These results are important in the identification of prebiotics that elevate specific SCFAs for therapeutic benefit and highlight SCFA consumers as a salient part of the overall metabolic flux pertaining to bacterial fermentative processes.

Infant formula feeding, compared with human milk, has been associated with development of a distinct infant gut microbiome, but no previous study has examined effects of formula with added sugars. This work examined differences in gut microbiota among 91 Hispanic infants who consumed human milk [at breast (BB) vs. pumped in bottle (BP)] and 2 kinds of infant formula [(traditional lactose-based (TF) vs. lactose-reduced with added sugar (ASF)]. At 1 and 6 months, infant stool was collected to characterize gut microbiota. At 6 months, mothers completed 24-hour dietary recalls and questionnaires to determine infant consumption of human milk (BB vs. BP) or formula (TF vs. ASF). Linear regression models were used to determine associations of milk consumption type and microbial features at 6 months. Infants in the formula groups exhibited a significantly more 'mature' microbiome than infants in the human milk groups with the most pronounced differences observed between the ASF vs. BB groups. In the ASF group, we observed reduced log-normalized abundance of Bifidobacteriaceae (TF-BB Mean Difference = −0.71, ASF-BB Mean Difference = −1.10), and increased abundance of Lachnospiraceae (TF-BB Mean Difference = +0.89, ASF-BB Mean Difference = +1.20). We also observed a higher Community Phenotype Index of propionate, most likely produced by Lachnospiraceae, in the ASF group (TF-BB Mean Difference = +0.27, ASF-BB Mean Difference = +0.36). This study provides the first evidence that consumption of infant formula with added sugar may have a stronger association than birth delivery mode, infant caloric intake, and maternal BMI on the infant's microbiome at 6 months of age.

The human gut microbiota (HGM) have an impact on host health and disease. Amino acids are building blocks of proteins and peptides, also serving as precursors of many essential metabolites including nucleotides, cofactors, etc. Many HGM community members are unable to synthesize some amino acids (auxotrophs), while other members possess complete biosynthetic pathways for these nutrients (prototrophs). Metabolite exchange between auxotrophs and prototrophs affects microbial community structure. Previous studies of amino acid biosynthetic phenotypes were limited to model species or narrow taxonomic groups of bacteria. We analyzed over 2800 genomes representing 823 cultured HGM species with the aim to reconstruct biosynthetic pathways for proteinogenic amino acids. The genome context analysis of incomplete pathway variants allowed us to identify new potential enzyme variants in amino acid biosynthetic pathways. We further classified the studied organisms with respect to their pathway variants and inferred their prototrophic vs. auxotrophic phenotypes. A cross-species comparison was applied to assess the extent of conservation of the assigned phenotypes at distinct taxonomic levels. The obtained reference collection of binary metabolic phenotypes was used for predictive metabolic profiling of HGM samples from several large metagenomic datasets. The established approach for metabolic phenotype profiling will be useful for prediction of overall metabolic properties, interactions, and responses of HGM microbiomes as a function of dietary variations, dysbiosis and other perturbations.

The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by “shotgun” metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)—high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.