Disease-specific loss of microbial cross-feeding interactions in the human gut

Moving from genome-scale to community-scale metabolic models for the human gut microbiome

Metabolic models of individual microorganisms or small microbial consortia have become standard research tools in the bioengineering and systems biology fields. However, extending metabolic modelling to diverse microbial communities, such as those in the human gut, remains a practical challenge from both modelling and experimental validation perspectives. In complex communities, metabolic models accounting for community dynamics, or those that consider multiple objectives, may provide optimal predictions over simpler steady-state models, but require a much higher computational cost. Here we describe some of the strengths and limitations of microbial community-scale metabolic models and argue for a robust validation framework for developing personalized, mechanistic and accurate predictions of microbial community metabolic behaviours across environmental contexts. Ultimately, quantitatively accurate microbial community-scale metabolic models could aid in the design and testing of personalized prebiotic, probiotic and dietary interventions that optimize for translationally relevant outcomes.

Comprehensive molecular portrait reveals genetic diversity and distinct molecular subtypes of small intestinal neuroendocrine tumors

Small intestinal neuroendocrine tumors (siNETs) are rare bowel tumors arising from malignant enteroendocrine cells, which normally regulate digestion throughout the intestine. Though infrequent, their incidence is rising through better diagnosis, fostering research into their origin and treatment. To date, siNETs are considered to be a single entity and are clinically treated as such. Here, by performing a multi-omics analysis of siNETs, we unveil four distinct molecular groups with strong clinical relevance and provide a resource to study their origin and clinical features. Transcriptomic, genetic and DNA methylation profiles identify two groups linked to distinct enteroendocrine differentiation patterns, another with a strong immune phenotype, and the last with mesenchymal properties. This latter subtype displays the worst prognosis and resistance to treatments in line with infiltration of cancer-associated fibroblasts. These data provide insights into the origin and diversity of these rare diseases, in the hope of improving clinical research into their management.

Disrupted microbial cross-feeding and altered L-phenylalanine consumption in people living with HIV

This work aims to (1) identify microbial and metabolic alterations and (2) reveal a shift in phenylalanine production-consumption equilibrium in individuals with HIV. We conducted extensive searches in multiple databases [MEDLINE, Web of Science (including Cell Press, Oxford, HighWire, Science Direct, IOS Press, Springer Nature, PNAS, and Wiley), Google Scholar, and Embase] and selected two case-control 16S data sets (GenBank IDs: SRP039076 and EBI ID: ERP003611) for analysis. We assessed alpha and beta diversity, performed univariate tests on genus-level relative abundances, and identified significant microbiome features using random forest. We also utilized the MICOM model to simulate growth and metabolic exchanges within the microbiome, focusing on the Metabolite Exchange Score (MES) to determine key metabolic interactions. We found that L-phenylalanine had a higher MES in HIV-uninfected individuals compared with their infected counterparts. The flux of L-phenylalanine consumption was significantly lower in HIV-infected individuals compared with healthy controls, correlating with a decreased number of consuming species in the chronic HIV stage. Prevotella, Roseburia, and Catenibacterium were demonstrated as the most important microbial species involving an increase in L-phenylalanine production in HIV patients, whereas Bacteroides, Faecalibacterium, and Blautia contributed to a decrease in L-phenylalanine consumption. We also found significant alterations in both microbial diversity and metabolic exchanges in people living with HIV. Our findings shed light on why HIV-1 patients have elevated levels of phenylalanine. The impact on essential amino acids like L-phenylalanine underscores the effect of HIV on gut microbiome dynamics. Targeting the restoration of these interactions presents a potential therapeutic avenue for managing HIV-related dysbiosis.

Biombalance™, an Oligomeric Procyanidins-Enriched Grape Seed Extract, Prevents Inflammation and Microbiota Dysbiosis in a Mice Colitis Model

Inflammatory bowel disease (IBD) results from genetic factors, environmental factors, and intestinal microbiota interactions. This study investigated the effects of Biombalance™ (BB) in dextran sulphate sodium (DSS)-induced colitis in mice. BB extract exhibits high antioxidant activity, as determined by DPPH and ORAC tests. Mice were fed a standard diet, and BB was administered by gavage for ten days, before administration of 2.75% DSS in drinking water. BB significantly protected mice against DSS effects, as assessed by colon length, disease activity index (DAI) scores and colonic pathological damage. In addition, BB inhibited the expression of proinflammatory markers, such as IL-6, IL-17, CXCL1 and TNF-α, and the inflammatory mediators iNOS, TGF-β, FoxP3 and F4/80, while increasing IL-10 expression in the colon. BB modified microbiota composition, attenuating the microbial diversity lost due to DSS, increasing beneficial bacteria like Muribaculum, Lactobacillus, Muscispirillum, Roseburia and Bifidobacterium, and decreasing potentially harmful bacteria such as Proteobacteria and Enterococcus. Interestingly, microbiota-predicted functions using PICRUSt revealed that BB extract increases the antioxidant superpathway of ubiquinol biosynthesis, including ubiquinol-7, 8, 9 and 10 (CoenzymesQ). These findings suggest that Biombalance™ administration may help to reduce gut inflammation and oxidation, at least partly through modifications of the microbiota and its metabolites.

Pre-exposure of abundant species to disturbance improves resilience in microbial metacommunities

Understanding factors influencing community resilience to disturbance is critical for mitigating harm at various scales, including harm from medication to gut microbiota and harm from human activity to global biodiversity, yet there is a lack of data from large-scale controlled experiments. Factors expected to boost resilience include prior exposure to the same disturbance and dispersal from undisturbed patches. Here we set up an in vitro system to test the effect of disturbance pre-exposure and dispersal represented by community mixing. We performed a serial passage experiment on a 23-species bacterial model community, varying pre-exposure history and dispersal rate between three metacommunity patches subjected to different levels of disturbance by the antibiotic streptomycin. As expected, pre-exposure caused evolution of resistance, which prevented decrease in species abundance. The more abundant the pre-exposed species had been in the undisturbed community, the less the entire community changed. Pre-exposure of the most dominant species also decreased abundance change in off-target species. In the absence of pre-exposure, increasing dispersal rates caused increasing spread of the disturbance across the metacommunity. However, pre-exposure kept the metacommunity close to the undisturbed state regardless of dispersal rate. Our findings demonstrate that pre-exposure is an important modifier of ecological resilience in a metacommunity setting.

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

A meta-analysis of the gut microbiome in inflammatory bowel disease patients identifies disease-associated small molecules

Gut microbiome changes have been associated with several human diseases, but the molecular and functional details underlying these associations remain largely unknown. Here, we performed a meta-analysis of small molecule biosynthetic gene clusters (BGCs) in metagenomic samples of the gut microbiome from inflammatory bowel disease (IBD) patients and matched healthy subjects and identified two Clostridia-derived BGCs that are significantly associated with Crohn's disease (CD), a main IBD type. Using synthetic biology, we discovered and solved the structures of six fatty acid amides as the products of the CD-enriched BGCs, which we subsequently detected in fecal samples from IBD patients. Finally, we show that the discovered molecules disrupt gut permeability and exacerbate disease in chemically or genetically susceptible mouse models of colitis. These findings suggest that microbiome-derived small molecules may play a role in the etiology of IBD and represent a generalizable approach for discovering molecular mediators of disease-relevant microbiome-host interactions.

A mechanistic framework for complex microbe-host symbioses

Virtually all multicellular organisms on Earth live in symbiotic associations with complex microbial communities: the microbiome. This ancient relationship is of fundamental importance for both the host and the microbiome. Recently, the analyses of numerous microbiomes have revealed an incredible diversity and complexity of symbionts, with different mechanisms identified as potential drivers of this diversity. However, the interplay of ecological and evolutionary forces generating these complex associations is still poorly understood. Here we explore and summarise the suite of ecological and evolutionary mechanisms identified as relevant to different aspects of microbiome complexity and diversity. We argue that microbiome assembly is a dynamic product of ecology and evolution at various spatio-temporal scales. We propose a theoretical framework to classify mechanisms and build mechanistic host-microbiome models to link them to empirical patterns. We develop a cohesive foundation for the theoretical understanding of the combined effects of ecology and evolution on the assembly of complex symbioses.

Framework nucleic acid strategy enables closer microbial contact for programming short-range interaction

Programming precise and specific microbial intraspecies or interspecies interaction would be powerful for microbial metabolic regulation, signal pathway mechanism understanding, and therapeutic application. However, it is still of great challenge to develop a simple and universal method to artificially encode the microbial interactions without interfering with the intrinsic cell metabolism. Here, we proposed an extensible and flexible framework nucleic acid strategy for encoding the specific and precise microbial interactions upon self-assembly. With this spatial manipulation tool, we propose the microbial spatial heterogeneity and short-range interaction mechanism that the microbial assembly facilitates the gene expressions of the surface sensors including flagella and pili in Pseudomonas aeruginosa, leading to a more sensitive response to quorum sensing. The microbial interaction programming strategy proposed in this work is expected to provide a powerful and designable nanoplatform for better understanding of distance-dependent bacterial communication networks.

Gut microbial features and circulating metabolomic signatures of frailty in older adults

Frailty, a multidimensional indicator of suboptimal aging, reflects cumulative declines across multiple physiological systems. Although age-related changes have been reported in gut microbiota, their role in healthy aging remains unclear. In this study, we calculated frailty index (FI) from 33 health-related items to reflect the overall health status of 1,821 older adults (62-96 years, 55% female) and conducted multi-omics analysis using gut metagenomic sequencing data and plasma metabolomic data. We identified 18 microbial species and 17 metabolites shifted along with frailty severity, with stronger links observed in females. The associations of nine species, including various Clostridium species and Faecalibacterium prausnitzii, with FI were reproducible in two external populations. Plasma glycerol levels, white blood cell count and kidney function partially mediated these associations. A composite microbial score derived from FI significantly predicted 2-year mortality (adjusted hazard ratio across extreme quartiles, 2.86; 95% confidence interval, 1.38-5.93), highlighting the potential of microbiota-based strategies for risk stratification in older adults.

Microbial reaction rate estimation using proteins and proteomes

Microbes transform their environments using diverse enzymatic reactions. However, it remains challenging to measure microbial reaction rates in natural environments. Despite advances in global quantification of enzyme abundances, the individual relationships between enzyme abundances and their reaction rates have not been systematically examined. Using matched proteomic and reaction rate data from microbial cultures, we show that enzyme abundance is often insufficient to predict its corresponding reaction rate. However, we discovered that global proteomic measurements can be used to make accurate rate predictions of individual reaction rates (median R 2 = 0.78). Accurate rate predictions required only a small number of proteins and they did not need explicit prior mechanistic knowledge or environmental context. These results indicate that proteomes are encoders of cellular reaction rates, potentially enabling proteomic measurements in situ to estimate the rates of microbially mediated reactions in natural systems.

Hydrogen sulfide produced by the gut microbiota impairs host metabolism via reducing GLP-1 levels in male mice

Dysbiosis of the gut microbiota has been implicated in the pathogenesis of metabolic syndrome (MetS) and may impair host metabolism through harmful metabolites. Here, we show that Desulfovibrio, an intestinal symbiont enriched in patients with MetS, suppresses the production of the gut hormone glucagon-like peptide 1 (GLP-1) through the production of hydrogen sulfide (H2S) in male mice. Desulfovibrio-derived H2S is found to inhibit mitochondrial respiration and induce the unfolded protein response in intestinal L cells, thereby hindering GLP-1 secretion and gene expression. Remarkably, blocking Desulfovibrio and H2S with an over-the-counter drug, bismuth subsalicylate, improves GLP-1 production and ameliorates diet-induced metabolic disorder in male mice. Together, our study uncovers that Desulfovibrio-derived H2S compromises GLP-1 production, shedding light on the gut-relayed mechanisms by which harmful microbiota-derived metabolites impair host metabolism in MetS and suggesting new possibilities for treating MetS.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Excess fermentation and lactic acidosis as detrimental functions of the gut microbes in treatment-naive TB patients

Introduction

The link between gut microbiota and host immunity motivated numerous studies of the gut microbiome in tuberculosis (TB) patients. However, these studies did not explore the metabolic capacity of the gut community, which is a key axis of impact on the host's immunity.

Methods

We used deep sequencing of fecal samples from 23 treatment-naive TB patients and 48 healthy donors to reconstruct the gut microbiome's metabolic capacity and strain/species-level content.

Results

We show that the systematic depletion of the commensal flora of the large intestine, Bacteroidetes, and an increase in Actinobacteria, Firmicutes, and Proteobacteria such as Streptococcaceae, Erysipelotrichaceae, Lachnospiraceae, and Enterobacteriaceae explains the strong taxonomic divergence of the gut community in TB patients. The cumulative expansion of diverse disease-associated pathobionts in patients reached 1/4 of the total gut microbiota, suggesting a heavy toll on host immunity along with MTB infection. Reconstruction of metabolic pathways showed that the microbial community in patients shifted toward rapid growth using glycolysis and excess fermentation to produce acetate and lactate. Higher glucose availability in the intestine likely drives fermentation to lactate and growth, causing acidosis and endotoxemia.

Discussion

Excessive fermentation and lactic acidosis likely characterize TB patients' disturbed gut microbiomes. Since lactic acidosis strongly suppresses the normal gut flora, directly interferes with macrophage function, and is linked to mortality in TB patients, our findings highlight gut lactate acidosis as a novel research focus. If confirmed, gut acidosis may be a novel potential host-directed treatment target to augment traditional TB treatment.

A meta-analysis of the gut microbiome in inflammatory bowel disease patients identifies disease-associated small molecules

Changes in the gut microbiome have been associated with several human diseases, but the molecular and functional details underlying these associations remain largely unknown. Here, we performed a multi-cohort analysis of small molecule biosynthetic gene clusters (BGCs) in 5,306 metagenomic samples of the gut microbiome from 2,033 Inflammatory Bowel Disease (IBD) patients and 833 matched healthy subjects and identified a group of Clostridia-derived BGCs that are significantly associated with IBD. Using synthetic biology, we discovered and solved the structures of six fatty acid amides as the products of the IBD-enriched BGCs. Using two mouse models of colitis, we show that the discovered small molecules disrupt gut permeability and exacerbate inflammation in chemically and genetically susceptible mice. These findings suggest that microbiome-derived small molecules may play a role in the etiology of IBD and represent a generalizable approach for discovering molecular mediators of microbiome-host interactions in the context of microbiome-associated diseases.