Project description:The human microbiome has been associated with health status, and risk of disease development. While the etiology of microbiome-mediated disease remains to be fully elucidated, one mechanism may be through microbial metabolism. Metabolites produced by commensal organisms, including in response to host diet, may affect host metabolic processes, with potentially protective or pathogenic consequences. We conducted multi-omic phenotyping of healthy subjects (N = 136), in order to investigate the interaction between diet, the microbiome, and the metabolome in a cross-sectional sample. We analyzed the nutrient composition of self-reported diet (3-day food records and food frequency questionnaires). We profiled the gut and oral microbiome (16S rRNA) from stool and saliva, and applied metabolomic profiling to plasma and stool samples in a subset of individuals (N = 75). We analyzed these multi-omic data to investigate the relationship between diet, the microbiome, and the gut and circulating metabolome. On a global level, we observed significant relationships, particularly between long-term diet, the gut microbiome and the metabolome. Intake of plant-derived nutrients as well as consumption of artificial sweeteners were associated with significant differences in circulating metabolites, particularly bile acids, which were dependent on gut enterotype, indicating that microbiome composition mediates the effect of diet on host physiology. Our analysis identifies dietary compounds and phytochemicals that may modulate bacterial abundance within the gut and interact with microbiome composition to alter host metabolism.
Project description:Background/Objectives: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. Method: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. Results: Our results revealed biologically meaningful biomarkers, such as Bacteroides vulgatus and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. Conclusions: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research.
Project description:Alterations of the brain-gut-microbiome system (BGM) have been implicated in the pathophysiology of irritable bowel syndrome (IBS), yet bowel habit-specific alterations have not been elucidated. In this cross-sectional study, we apply a systems biology approach to characterize BGM patterns related to predominant bowel habit. Fecal samples and resting state fMRI were obtained from 102 premenopausal women (36 constipation-predominant IBS (IBS-C), 27 diarrhea-predominant IBS (IBS-D), 39 healthy controls (HCs)). Data integration analysis using latent components (DIABLO) was used to integrate data from the phenome, microbiome, metabolome, and resting-state connectome to predict HCs vs IBS-C vs IBS-D. Bloating and visceral sensitivity, distinguishing IBS from HC, were negatively associated with beneficial microbes and connectivity involving the orbitofrontal cortex. This suggests that gut interactions may generate aberrant central autonomic and descending pain pathways in IBS. The connection between IBS symptom duration, key microbes, and caudate connectivity may provide mechanistic insight to the chronicity of pain in IBS. Compared to IBS-C and HCs, IBS-D had higher levels of many key metabolites including tryptophan and phenylalanine, and increased connectivity between the sensorimotor and default mode networks; thus, suggestingan influence on diarrhea, self-related thoughts, and pain perception in IBS-D ('bottom-up' mechanism). IBS-C's microbiome and metabolome resembled HCs, but IBS-C had increased connectivity in the default mode and salience networks compared to IBS-D, which may indicate importance of visceral signals, suggesting a more 'top-down' BGM pathophysiology. These BGM characteristics highlight possible mechanistic differences for variations in the IBS bowel habit phenome. This article is part of the Special Issue on 'Microbiome & the Brain: Mechanisms & Maladies'.
Project description:Mastitis is commonly recognized as a localized inflammatory udder disease induced by the infiltration of exogenous pathogens. In the present study, our objective was to discern fecal and milk variations in both microbiota composition and metabolite profiles among three distinct groups of cows: healthy cows, cows with subclinical mastitis and cows with clinical mastitis. The fecal microbial community of cows with clinical mastitis was significantly less rich and diverse than the one harbored by healthy cows. In parallel, mastitis caused a strong disturbance in milk microbiota. Metabolomic profiles showed that eleven and twenty-eight molecules exhibited significant differences among the three groups in feces and milk, respectively. Similarly, to microbiota profile, milk metabolome was affected by mastitis more extensively than fecal metabolome, with particular reference to amino acids and sugars. Pathway analysis revealed that amino acids metabolism and energy metabolism could be considered as the main pathways altered by mastitis. These findings underscore the notable distinctions of fecal and milk samples among groups, from microbiome and metabolomic points of view. This observation stands to enhance our comprehension of mastitis in dairy cows.
Project description:IntroductionAlthough pre/pro/postbiotics have become more prevalent in dermatologic and cosmetic fields, the mode of action when topically applied is largely unknown. A multi-omic approach was applied to decipher the impact of the skincare products with pre/postbiotics on skin microbiome and metabolome.MethodsSubjects with dry skin applied a body wash and body lotion with or without pre/postbiotics for 6 weeks. Skin hydration was measured at baseline, 3 and 6 weeks. Skin swabs were collected for 16S rRNA gene sequencing, metagenomics and metabolomics analysis.ResultsSkin hydration significantly increased in both groups. The prebiotic group significantly reduced opportunistic pathogens, e.g., Pseudomonas stutzeri and Sphingomonas anadarae, and increased the commensals, e.g., Staphylococcus equorum, Streptococcus mitis, Halomonas desiderata. Bacterial sugar degradation pathways were enriched in the prebiotic group, while fatty acid biosynthesis pathways were reduced in control. The changes on skin metabolome profiles by the products were more prominent. The prebiotic group performed greater modulation on many clinically-relevant metabolites compared to control. Correlation analysis showed H. desiderata and S. mitis positively correlated with skin hydration, P. stutzeri and S. anadarae negatively correlated with the metabolites that are positively associated with skin hydration improvement.ConclusionThis holistic study supported a hypothesis that the pre/postbiotics increased skin hydration through the modulation of skin microbiome, metabolic pathways and metabolome.
Project description:Dietary factors modulate interactions between the microbiome, metabolome, and immune system. Sulforaphane (SFN) exerts effects on aging, cancer prevention and reducing insulin resistance. This study investigated effects of SFN on the gut microbiome and metabolome in old mouse model compared with young mice. Young (6-8 weeks) and old (21-22 months) male C57BL/6J mice were provided regular rodent chow ± SFN for 2 months. We collected fecal samples before and after SFN administration and profiled the microbiome and metabolome. Multi-omics datasets were analyzed individually and integrated to investigate the relationship between SFN diet, the gut microbiome, and metabolome. The SFN diet restored the gut microbiome in old mice to mimic that in young mice, enriching bacteria known to be associated with an improved intestinal barrier function and the production of anti-inflammatory compounds. The tricarboxylic acid cycle decreased and amino acid metabolism-related pathways increased. Integration of multi-omic datasets revealed SFN diet-induced metabolite biomarkers in old mice associated principally with the genera, Oscillospira, Ruminococcus, and Allobaculum. Collectively, our results support a hypothesis that SFN diet exerts anti-aging effects in part by influencing the gut microbiome and metabolome. Modulating the gut microbiome by SFN may have the potential to promote healthier aging.
Project description:BackgroundAs a typical medicinal food homology species, Chinese herbal medicine Astragali radix (AR) has been widely used to regulate the human immune system worldwide. However, the human immunomodulation of AR and its corresponding mechanisms remain unclear.MethodsFirst, following a fortnight successive AR administration, the changes in immune cytokines and immune cells from 20 healthy human subjects were used as immune indicators to characterize the immunomodulatory effects of AR. Subsequently, ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) based lipidomics and metabolomics analysis was performed on human serum, urine, and feces samples to investigate the changes in metabolic profiles. Then, 16S rRNA gene sequencing of feces samples was adopted for the changes of human gut microbiota. Finally, correlation analysis was conducted on the gut microbiome, metabolome/lipidome data, and immune indicators.ResultsAR displayed good safety in clinical use and posed a minor impact on gut microbiota major genera, global metabolic profiles, and immune cells. Meanwhile, AR could significantly up-regulate anti-inflammatory cytokines, down-regulate serum creatinine and pro-inflammatory cytokines, promote the anabolism of arginine, glycerolipid, sphingolipid, and purine, and the catabolism of phenylalanine and glycerophospholipid. Moreover, these AR-induced changes were closely correlated with significantly decreased Granulicatella, slightly higher Bifidobacterium, Ruminococcus, and Subdoligranulum, and slightly lower Blautia.ConclusionThe study clearly demonstrated that AR could modulate the human immune, by modifying the metabolism of amino acids, lipids, and purines in a microbiota-related way. Trial registration ChiCTR, ChiCTR2100054765. Registered 26 December 2021-Prospectively registered, https://www.chictr.org.cn/historyversionpub.html?regno=ChiCTR2100054765.
Project description:The Human Microbiome Project (HMP) explored microbial communities of the human body in both healthy and disease states. Two phases of the HMP (HMP and iHMP) together generated >48TB of data (public and controlled access) from multiple, varied omics studies of both the microbiome and associated hosts. The Human Microbiome Project Data Coordination Center (HMPDACC) was established to provide a portal to access data and resources produced by the HMP. The HMPDACC provides a unified data repository, multi-faceted search functionality, analysis pipelines and standardized protocols to facilitate community use of HMP data. Recent efforts have been put toward making HMP data more findable, accessible, interoperable and reusable. HMPDACC resources are freely available at www.hmpdacc.org.