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Time-restricted feeding (TRF) during the active phase protects against high-fat diet (HFD)-induced obesity, and its impact on gut microbiota has been previously investigated using bacterial taxa as functional units. However, in the gut ecosystem, bacteria from different taxonomic backgrounds form coherent functional groups called guilds, whose members exhibit co-abundant behavior. Thirty-five co-abundance groups (CAGs), clustered from 297 prevalent amplicon sequence variants (ASVs), showed greater concordance with beta-diversity plots based on all 1131 ASVs than the 130 classifiable genera, leading to a significantly improved preservation of community-level information. TRF-enriched CAGs positively correlated with metabolic improvement, while TRF-reduced CAGs negatively correlated. TRF restored the diurnal rhythm of most of these key CAGs. Novel ASVs, unclassifiable at the genus level, were identified in these key CAGs. Overall, this suggests that the key bacterial guilds may mediate the beneficial metabolic effects of TRF through the restoration of diurnal oscillation.

The alteration of gut microbiota structure plays a pivotal role in the pathogenesis of abnormal glycometabolism. However, the microbiome features identified in patient groups stratified solely based on glucose levels remain controversial among different studies.. In this study, we stratified 258 participants (discovery cohort) into three clusters according to an unsupervised method based on 16 clinical parameters involving the levels of blood glucose, insulin, and lipid. We found 67 cluster-specific microbiome features (i.e., amplicon sequence variants, ASVs) based on 16S rRNA gene V3-V4 region sequencing. Specifically, ASVs belonging toBarnesvilleandAlistipeswere enriched in Cluster 1, in which participants had the lowest blood glucose levels, high insulin sensitivity, and high-fecal short-chain fatty acid concentration. ASVs belonging toPrevotella copriandRuminococcus gnavuswere enriched in Cluster 2, which was characterized by a moderate level of blood glucose, serious insulin resistance, and high levels of cholesterol and triglyceride. Cluster 3 was characterized by a high level of blood glucose and insulin deficiency, enriched with ASVs inP. copriandBacteroides vulgatus. In addition, machine learning classifiers using the 67 cluster-specific ASVs were used to distinguish individuals in one cluster from those in the other two clusters both in discovery and testing cohorts (N = 83). Therefore, microbiome features identified based on the unsupervised stratification of patients with more inclusive clinical parameters may better reflect microbiota alterations associated with the progression of abnormal glycometabolism.IMPORTANCEThe gut microbiota is altered in patients with type 2 diabetes (T2D) and prediabetes. The association of particular bacteria with T2D, however, varied among studies, which has made it challenging to develop precision medicine approaches for the prevention and alleviation of T2D. Blood glucose level is the only parameter in clustering patients when identifying the T2D-related bacteria in previous studies. This stratification ignores the fact that patients within the same blood glucose range differ in their insulin resistance and dyslipidemia, which also may be related to disordered gut microbiota. In addition to parameters of blood glucose levels, we also used additional parameters involving insulin and lipid levels to stratify participants into three clusters and further identified cluster-specific microbiome features. We further validated the association between these microbiome features and glycometabolism with an independent cohort. This work highlights the importance of stratification of patients with blood glucose, insulin, and lipid levels when identifying the microbiome features associated with the progression of abnormal glycometabolism.

BackgroundDysbiotic gut microbiome, genetically predisposed or chemically disrupted, has been linked with insulin-dependent diabetes (IDD) including autoimmune type 1 diabetes (T1D) in both humans and animal models. However, specific IDD-inducing gut bacteria remain to be identified and their casual role in disease development demonstrated via experiments that can fulfill Koch’s postulates.ResultsHere, we show that novel gut pathobionts in the Muribaculaceae family, enriched by a low-dose dextran sulfate sodium (DSS) treatment, translocated to the pancreas and caused local inflammation, beta cell destruction and IDD in C57BL/6 mice. Antibiotic removal and transplantation of gut microbiota showed that this low DSS disrupted gut microbiota was both necessary and sufficient to induce IDD. Reduced butyrate content in the gut and decreased gene expression levels of an antimicrobial peptide in the pancreas allowed for the enrichment of members in the Muribaculaceae family in the gut and their translocation to the pancreas. Pure isolate of one such members induced IDD in wildtype germ-free mice on normal diet either alone or in combination with normal gut microbiome after gavaged into stomach and translocated to pancreas.ConclusionThe pathobionts that are chemically enriched in dysbiotic gut microbiota are sufficient to induce insulin-dependent diabetes after translocation to the pancreas. This indicates that IDD can be mainly a microbiome-dependent disease, inspiring the need to search for novel pathobionts for IDD development in humans.

Summary ParagraphOver eons of co-evolution, the gut microbiota has become an essential organ for humans1,2. However, it is unclear what core members and their ecological organization ensures the stable provision of this organ’s essential health-relevant functions to the host. With high quality metagenome-assembled genomes as network nodes, here we identified two competing guilds3of the most stably and highly connected bacteria that together correlate with a wide range of host health conditions. Genomes in these two guilds kept their ecological relationship unchanged despite experiencing profound abundance changes during a 3-month high fiber intervention and 1-year follow-up in patients with type 2 diabetes (T2DM). The genomes of one guild harbored more genes for plant polysaccharide degradation and butyrate production, while the other guild had more genes for virulence or antibiotic resistance. A Random Forest regression model showed that the abundance distributions of these genomes were associated with 41 out of 43 bio-clinical parameters in the study cohort. With these genomes as reference, Random Forest modeling successfully classified case and control of T2DM, atherosclerotic cardiovascular disease, liver cirrhosis, inflammatory bowel diseases, colorectal cancer, ankylosing spondylitis, schizophrenia, and Parkinson’s disease in 12 independent metagenomic datasets from 1,816 participants across ethnicity and geography. This core microbiome signature may serve as a common target for health recovery.

SummaryGut-microbiota plays a pivotal role in development of type 2 diabetes (T2D), yet the molecular mechanism remains elusive. Here, we show that tryptamine, a microbial metabolite of tryptophan, impairs glucose tolerance and insulin sensitivity. Tryptamine presents a higher level in monkeys with spontaneous diabetes and human with T2D and positively correlated with the glucose tolerance. In parallel, tryptamine level was suppressed by dietary fibers intervention in T2D subjects and negatively correlated with improvement of glucose tolerance. The inhibitory effect of tryptamine on insulin signaling as shown was dependent on a trace amine-associated receptor 1 (TAAR1)-extracellular signal-regulated kinase (ERK) signaling axis. Monoassociation of T2D-associated tryptamine-producing bacteria Ruminococcus gnavus impairs insulin sensitivity in pseudo germ-free mice. Our findings indicate gut microbiota-derived tryptamine contributes to the development of insulin resistance in T2D and may serve as a new target for intervention.Graphical Abstract

Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual's response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of the gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin and resistant starch compared to the non-fermentable fiber cellulose. We found that inulin produced a generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the time-series data, which revealed a group of bacteria whose growth significantly increased in response to inulin and whose baseline abundance and interspecies competition explained the baseline dependence of microbiome density and community composition dynamics. Fecal levels of SCFAs, such as propionate, were associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impeded the prediction of SCFAs by machine learning models. We showed that our methods and major findings were generalizable to dietary resistant starch. Finally, we analyzed time-series data of synthetic and natural human gut microbiome in response to dietary fiber and validated the inferred interspecies interactions in vitro. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.