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Several studies have examined the impact of prebiotics on gut microbiota and associated changes in host physiology. Here, we used the in vitro cultivation of human fecal samples stimulated with a series of chemically related prebiotics and medicinal herbs commonly used in Ayurvedic medicine, followed by 16S rRNA sequencing. We applied a genome-wide metabolic reconstruction of enumerated communities to compare and contrast the structural and functional impact of prebiotics and medicinal herbs. In doings so, we examined the relationships between discrete variations in sugar composition and sugar linkages associated with each prebiotic to drive changes in microbiota composition. The restructuring of microbial communities with glycan substrates alters community metabolism and its potential impact on host physiology. We analyzed sugar fermentation pathways and products predicted to be formed and prebiotic-induced changes in vitamin and amino acid biosynthesis and degradation. These results highlight the utility of combining a genome-wide metabolic reconstruction methodology with 16S rRNA sequence-based community profiles to provide insights pertaining to community metabolism. This process also provides a rational means for prioritizing in vivo analysis of prebiotics and medicinal herbs in vivo to test hypotheses related to therapeutic potential in specific diseases of interest.

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.

Parkinson’s disease (PD) is a neurodegenerative disease characterized by the loss of dopaminergic neurons. Although the etiology of PD remains elusive, it has been hypothesized that initial dysregulation may occur in the gastrointestinal tract and may be accompanied by gut barrier defects. A strong clinical interest in developing therapeutics exists, including for the treatment of gut microbiota and physiology. We previously reported the impact of healthy fecal microbiota anaerobic cultures supplemented with nootropic herbs. Here, we evaluated the effect of nootropic Ayurvedic herbs on fecal microbiota derived from subjects with PD in vitro using 16S rRNA sequencing. The microbiota underwent substantial change in response to each treatment, comparable in magnitude to that observed from healthy subjects. However, the fecal samples derived from each participant displayed unique changes, consistent with a personalized response. We used genome-wide metabolic reconstruction to predict the community’s metabolic potential to produce products relevant to PD pathology, including SCFAs, vitamins and amino acid degradation products. These results suggest the potential value of conducting in vitro cultivation and analyses of PD stool samples as a means of prescreening patients to select the medicinal herbs for which that individual is most likely to respond and derive benefit.

Short-chain fatty acids (SCFAs) including acetate, formate, propionate, and butyrate are the end products of dietary fiber and host glycan fermentation by the human gut microbiota (HGM). SCFAs produced in the column are of utmost importance for host physiology and health. Butyrate and propionate improve gut health and play a key role in the neuroendocrine and immune systems. Prediction of HGM metabolic potential is important for understanding the influence of diet and HGM-produced metabolites on human health. We conducted a detailed metabolic reconstruction of pathways for the synthesis of SCFAs and L- and D-lactate, as additional fermentation products, in a reference set of 2,856 bacterial genomes representing strains of >800 known HGM species. The reconstructed butyrate and propionate pathways included four and three pathway variants, respectively, that start from different metabolic precursors. Altogether, we identified 48 metabolic enzymes, including five alternative enzymes in propionate pathways, and propagated their occurrences across all studied genomes. We established genomic signatures for reconstructed pathways and classified genomes according to their simplified binary phenotypes encoding the ability (“1”) or inability (“0”) of a given organism to produce SCFAs. The resulting binary phenotypes combined into a binary phenotype matrix were used to assess the SCFA synthesis potential of HGM samples from several public metagenomic studies. We report baseline and variance for Community Phenotype Indices calculated for SCFAs production capabilities in 16S metagenomic samples of intestinal microbiota from two large national cohorts (American Gut Project, UK twins), the Hadza hunter-gatherers, and the young children cohort of infants with high-risk for type 1 diabetes. We further linked the predicted SCFA metabolic capabilities with available SCFA concentrations both for in vivo fecal samples and in vitro fermentation samples from previous studies. Finally, we analyzed differential representation of individual SCFA pathway genes across several WGS metagenomic datasets. The obtained collection of SCFA pathway genes and phenotypes enables the predictive metabolic phenotype profiling of HGM datasets and enhances the in silico methodology to study cross-feeding interactions in the gut microbiomes.

Several studies have examined the impact of prebiotics on gut microbiota and associated changes in host physiology. Here, we used the in vitro cultivation of human fecal samples stimulated with a series of chemically related prebiotics and medicinal herbs commonly used in Ayurvedic medicine, followed by 16S rRNA sequencing. We applied a genome-wide metabolic reconstruction of enumerated communities to compare and contrast the structural and functional impact of prebiotics and medicinal herbs. In doings so, we examined the relationships between discrete variations in sugar composition and sugar linkages associated with each prebiotic to drive changes in microbiota composition. The restructuring of microbial communities with glycan substrates alters community metabolism and its potential impact on host physiology. We analyzed sugar fermentation pathways and products predicted to be formed and prebiotic-induced changes in vitamin and amino acid biosynthesis and degradation. These results highlight the utility of combining a genome-wide metabolic reconstruction methodology with 16S rRNA sequence-based community profiles to provide insights pertaining to community metabolism. This process also provides a rational means for prioritizing in vivo analysis of prebiotics and medicinal herbs in vivo to test hypotheses related to therapeutic potential in specific diseases of interest.

Introduction: Diabetic nephropathy (DN) and hypertensive nephrosclerosis (HN) represent the most common causes of chronic kidney disease (CKD) and many patients progress to ­end-stage renal disease. Patients are treated primarily through the management of cardiovas­cular risk factors and hypertension; however patients with HN have a more favorable outcome. A noninvasive clinical approach to separate these two entities, especially in hypertensive patients who also have diabetes, would allow for targeted treatment and more appropriate resource allocation to those patients at the highest risk of CKD progression. Meth­ods: In this preliminary study, high-spatial-resolution matrix-assisted laser desorption/ion­ization (MALDI) mass spectrometry imaging (MSI) was integrated with high-mass accuracy MALDI-FTICR-MS and nLC-ESI-MS/MS analysis in order to detect tissue proteins within kidney biopsies to discriminate cases of DN (n = 9) from cases of HN (n = 9). Results: Differences in the tryptic peptide profiles of the 2 groups could clearly be detected, with these becoming even more evident in the more severe histological classes, even if this was not evident with routine histology. In particular, 4 putative proteins were detected and had a higher signal intensity within regions of DN tissue with extensive sclerosis or fibrosis. Among these, 2 proteins (PGRMC1 and CO3) had a signal intensity that increased at the latter stages of the disease and may be associated with progression. Discussion/Conclusion: This preliminary study represents a valuable starting point for a future study employing a larger cohort of patients to develop sensitive and specific protein biomarkers that could reliably differentiate between diabetic and hypertensive causes of CKD to allow for improved diagnosis, fewer biopsy procedures, and refined treatment approaches for clinicians.

The profiling of 16S rRNA revolutionized the exploration of microbiomes, allowing to describe community composition by enumerating relevant taxa and their abundances. However, taxonomic profiles alone lack interpretability in terms of bacterial metabolism, and their translation into functional characteristics of microbiomes is a challenging task. This bottom-up approach minimally requires a reference collection of major metabolic traits deduced from the complete genomes of individual organisms, an accurate method of projecting these traits from a reference collection to the analyzed amplicon sequence variants (ASVs), and, ultimately, an approach to a microbiome-wide aggregation of predicted individual traits into physiologically relevant cumulative metrics to characterize and compare multiple microbiome samples. In this study, we extended a previously introduced computational approach for the functional profiling of complex microbial communities, which is based on the concept of binary metabolic phenotypes encoding the presence (“1”) or absence (“0”) of various measurable physiological properties in individual organisms that are termed phenotype carriers or non-carriers, respectively. Derived from complete genomes via metabolic reconstruction, binary phenotypes provide a foundation for the prediction of functional traits for each ASV identified in a microbiome sample. Here, we introduced three distinct mapping schemes for a microbiome-wide phenotype prediction and assessed their accuracy on the 16S datasets of mock bacterial communities representing human gut microbiome (HGM) as well as on two large HGM datasets, the American Gut Project and the UK twins study. The 16S sequence-based scheme yielded a more accurate phenotype predictions, while the taxonomy-based schemes demonstrated a reasonable performance to warrant their application for other types of input data (e.g., from shotgun metagenomics or qPCR). In addition to the abundance-weighted Community Phenotype Indices (CPIs) reflecting the fractional representation of various phenotype carriers in microbiome samples, we employ metrics capturing the diversity of phenotype carriers, Phenotype Alpha Diversity (PAD) and Phenotype Beta Diversity (PBD). In combination with CPI, PAD allows to classify the robustness of metabolic phenotypes by their anticipated stability in the face of potential environmental perturbations. PBD provides a promising approach for detecting the metabolic features potentially contributing to disease-associated metabolic traits as illustrated by a comparative analysis of HGM samples from healthy and Crohn’s disease cohorts.

Historically, the health benefits and immunomodulatory potential of medicinal herbs have been considered an intrinsic quality of the herb itself. We have hypothesized that the health benefits of medicinal herbs may be partially due to their prebiotic potential that alter gut microbiota leading to changes in short chain fatty acids and vitamin production or biotransformation of herb encoded molecules and secondary metabolites. Accumulating studies emphasize the relationship between the gut microbiota and host immune function. While largely unknown, these interactions are mediated by secreted microbial products that activate or repress a variety of immune cell types. Here we evaluated the effect of immunomodulatory, medicinal Ayurvedic herbs on gut microbiota in vitro using 16S rRNA sequencing to assess changes in community composition and functional potential. All immunomodulatory herbs displayed substantial prebiotic potential, targeting unique taxonomic groups. Application of genome reconstruction and analysis of biosynthetic capacity of herb selected communities suggests that many of the 11 herbs tested altered the community metabolism as the result of differential glycan harvest and sugar utilization and secreted products including multiple vitamins, butyrate, and propionate that may impact host physiology and immune function. Taken together, these results provide a useful framework for the further evaluation of these immunomodulatory herbs in vivo to maintain immune homeostasis or achieve desired regulation of immune components in the context of disease.

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.

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.

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 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.

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.