Identification of metabolites produced by six gut commensal Bacteroidales strains using non-targeted LC-MS/MS metabolite profiling.

As the most abundant gram-negative bacterial order in the gastrointestinal tract, Bacteroidales bacteria have been extensively studied for their contribution to various aspects of gut health. These bacteria are renowned for their involvement in immunomodulation and their remarkable capacity to break down complex carbohydrates and fibers. However, the human gut microbiota is known to produce many metabolites that ultimately mediate important microbe-host and microbe-microbe interactions. To gain further insights into the metabolites produced by the gut commensal strains of this order, we examined the metabolite composition of their bacterial cell cultures in the stationary phase. Based on their abundance in the gastrointestinal tract and their relevance in health and disease, we selected a total of six bacterial strains from the relevant genera Bacteroides, Phocaeicola, Parabacteroides, and Segatella. We grew these strains in modified Gifu anaerobic medium (mGAM) supplemented with mucin, which resembles the gut microbiota's natural environment. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolite profiling revealed 179 annotated metabolites that had significantly differential abundances between the studied bacterial strains and the control growth medium. Most of them belonged to classes such as amino acids and derivatives, organic acids, and nucleot(s)ides. Of particular interest, Segatella copri DSM 18205 (previously referred to as Prevotella copri) produced substantial quantities of the bioactive metabolites phenylethylamine, tyramine, tryptamine, and ornithine. Parabacteroides merdae CL03T12C32 stood out due to its ability to produce cadaverine, histamine, acetylputrescine, and deoxycarnitine. In addition, we found that strains of the genera Bacteroides, Phocaeicola, and Parabacteroides accumulated considerable amounts of proline-hydroxyproline, a collagen-derived bioactive dipeptide. Collectively, these findings offer a more detailed comprehension of the metabolic potential of these Bacteroidales strains, contributing to a better understanding of their role within the human gut microbiome in health and disease.

Changes in liver metabolic pathways demonstrate efficacy of the combined dietary and microbial therapeutic intervention in MASLD mouse model.

OBJECTIVE: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most prevalent liver disease globally, yet no therapies are approved. The effects of Escherichia coli Nissle 1917 expressing aldafermin, an engineered analog of the intestinal hormone FGF19, in combination with dietary change were investigated as a potential treatment for MASLD. METHODS: MASLD was induced in C57BL/6J male mice by American lifestyle-induced obesity syndrome diet and then switched to a standard chow diet for seven weeks. In addition to the dietary change, the intervention group received genetically engineered E. coli Nissle expressing aldafermin, while control groups received either E. coli Nissle vehicle or no treatment. MASLD-related plasma biomarkers were measured using an automated clinical chemistry analyzer. The liver steatosis was assessed by histology and bioimaging analysis using Fiji (ImageJ) software. The effects of the intervention in the liver were also evaluated by RNA sequencing and liquid-chromatography-based non-targeted metabolomics analysis. Pathway enrichment studies were conducted by integrating the differentially expressed genes from the transcriptomics findings with the metabolites from the metabolomics results using Ingenuity pathway analysis. RESULTS: After the intervention, E. coli Nissle expressing aldafermin along with dietary changes reduced body weight, liver steatosis, plasma aspartate aminotransferase, and plasma cholesterol levels compared to the two control groups. The integration of transcriptomics with non-targeted metabolomics analysis revealed the downregulation of amino acid metabolism and related receptor signaling pathways potentially implicated in the reduction of hepatic steatosis and insulin resistance. Moreover, the downregulation of pathways linked to lipid metabolism and changes in amino acid-related pathways suggested an overall reduction of oxidative stress in the liver. CONCLUSIONS: These data support the potential for using engineered microbial therapeutics in combination with dietary changes for managing MASLD.

Associations of altered hepatic gene expression in American lifestyle-induced obesity syndrome diet-fed mice with metabolic changes during NAFLD development and progression.

Non-alcoholic fatty liver disease (NAFLD) pathogenesis remains poorly understood due to the complex metabolic and inflammatory changes in the liver. This study aimed to elucidate hepatic events related to inflammation and lipid metabolism and their linkage with metabolic alterations during NAFLD in American lifestyle-induced obesity syndrome (ALIOS) diet-fed mice. Forty-eight C57BL/6J male mice were fed with ALIOS diet (n=24) or control chow diet (n=24) for 8, 12, and 16 weeks. At the end of each timepoint, eight mice were sacrificed where plasma and liver were collected. Hepatic fat accumulation was followed using magnetic resonance imaging and confirmed with histology. Further, targeted gene expression and non-targeted metabolomics analysis were conducted. Our results showed higher hepatic steatosis, body weight, energy consumption, and liver mass in ALIOS diet-fed mice compared to control mice. ALIOS diet altered expression of genes related to inflammation (Tnfa and IL-6) and lipid metabolism (Cd36, Fasn, Scd1, Cpt1a, and Ppara). Metabolomics analysis indicated decrease of lipids containing polyunsaturated fatty acids such as LPE(20:5) and LPC(20:5) with increase of other lipid species such as LPI(16:0) and LPC(16:2) and peptides such as alanyl-phenylalanine and glutamyl-arginine. We further observed novel correlations between different metabolites including sphingolipid, lysophospholipids, peptides, and bile acid with inflammation, lipid uptake and synthesis. Together with the reduction of antioxidant metabolites and gut microbiota-derived metabolites contribute to NAFLD development and progression. The combination of non-targeted metabolomics with gene expression in future studies can further identify key metabolic routes during NAFLD which could be the targets of potential novel therapeutics.

Tissue-wide metabolomics reveals wide impact of gut microbiota on mice metabolite composition.

The essential role of gut microbiota in health and disease is well recognized, but the biochemical details that underlie the beneficial impact remain largely undefined. To maintain its stability, microbiota participates in an interactive host-microbiota metabolic signaling, impacting metabolic phenotypes of the host. Dysbiosis of microbiota results in alteration of certain microbial and host metabolites. Identifying these markers could enhance early detection of certain diseases. We report LC-MS based non-targeted metabolic profiling that demonstrates a large effect of gut microbiota on mammalian tissue metabolites. It was hypothesized that gut microbiota influences the overall biochemistry of host metabolome and this effect is tissue-specific. Thirteen different tissues from germ-free (GF) and conventionally-raised (MPF) C57BL/6NTac mice were selected and their metabolic differences were analyzed. Our study demonstrated a large effect of microbiota on mammalian biochemistry at different tissues and resulted in statistically-significant modulation of metabolites from multiple metabolic pathways (p ≤ 0.05). Hundreds of molecular features were detected exclusively in one mouse group, with the majority of these being unique to specific tissue. A vast metabolic response of host to metabolites generated by the microbiota was observed, suggesting gut microbiota has a direct impact on host metabolism.

Effects of exercise on NAFLD using non-targeted metabolomics in adipose tissue, plasma, urine, and stool.

The mechanisms by which exercise benefits patients with non-alcoholic fatty liver disease (NAFLD), the most common liver disease worldwide, remain poorly understood. A non-targeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics analysis was used to identify metabolic changes associated with NAFLD in humans upon exercise intervention (without diet change) across four different sample types-adipose tissue (AT), plasma, urine, and stool. Altogether, 46 subjects with NAFLD participated in this randomized controlled intervention study. The intervention group (n = 21) performed high-intensity interval training (HIIT) for 12 weeks while the control group (n = 25) kept their sedentary lifestyle. The participants' clinical parameters and metabolic profiles were compared between baseline and endpoint. HIIT significantly decreased fasting plasma glucose concentration (p = 0.027) and waist circumference (p = 0.028); and increased maximum oxygen consumption rate and maximum achieved workload (p < 0.001). HIIT resulted in sample-type-specific metabolite changes, including accumulation of amino acids and their derivatives in AT and plasma, while decreasing in urine and stool. Moreover, many of the metabolite level changes especially in the AT were correlated with the clinical parameters monitored during the intervention. In addition, certain lipids increased in plasma and decreased in the stool. Glyco-conjugated bile acids decreased in AT and urine. The 12-week HIIT exercise intervention has beneficial ameliorating effects in NAFLD subjects on a whole-body level, even without dietary changes and weight loss. The metabolomics analysis applied to the four different sample matrices provided an overall view on several metabolic pathways that had tissue-type specific changes after HIIT intervention in subjects with NAFLD. The results highlight especially the role of AT in responding to the HIIT challenge, and suggest that altered amino acid metabolism in AT might play a critical role in e.g. improving fasting plasma glucose concentration.Trial registration ClinicalTrials.gov (NCT03995056).

Identification and Distribution of Sterols, Bile Acids, and Acylcarnitines by LC-MS/MS in Humans, Mice, and Pigs-A Qualitative Analysis.

Sterols, bile acids, and acylcarnitines are key players in human metabolism. Precise annotations of these metabolites with mass spectrometry analytics are challenging because of the presence of several isomers and stereoisomers, variability in ionization, and their relatively low concentrations in biological samples. Herein, we present a sensitive and simple qualitative LC-MS/MS (liquid chromatography with tandem mass spectrometry) method by utilizing a set of pure chemical standards to facilitate the identification and distribution of sterols, bile acids, and acylcarnitines in biological samples including human stool and plasma; mouse ileum, cecum, jejunum content, duodenum content, and liver; and pig bile, proximal colon, cecum, heart, stool, and liver. With this method, we detected 24 sterol, 32 bile acid, and 27 acylcarnitine standards in one analysis that were separated within 13 min by reversed-phase chromatography. Further, we observed different sterol, bile acid, and acylcarnitine profiles for the different biological samples across the different species. The simultaneous detection and annotation of sterols, bile acids, and acylcarnitines from reference standards and biological samples with high precision represents a valuable tool for screening these metabolites in routine scientific research.

Positive Effects of Exercise Intervention without Weight Loss and Dietary Changes in NAFLD-Related Clinical Parameters: A Systematic Review and Meta-Analysis.

One of the focuses of non-alcoholic fatty liver disease (NAFLD) treatment is exercise. Randomized controlled trials investigating the effects of exercise without dietary changes on NAFLD-related clinical parameters (liver parameters, lipid metabolism, glucose metabolism, gut microbiota, and metabolites) were screened using the PubMed, Scopus, Web of Science, and Cochrane databases on 13 February 2020. Meta-analyses were performed on 10 studies with 316 individuals who had NAFLD across three exercise regimens: aerobic exercise, resistance training, and a combination of both. No studies investigating the role of gut microbiota and exercise in NAFLD were found. A quality assessment via the (RoB)2 tool was conducted and potential publication bias, statistical outliers, and influential cases were identified. Overall, exercise without significant weight loss significantly reduced the intrahepatic lipid (IHL) content (SMD: -0.76, 95% CI: -1.04, -0.48) and concentrations of alanine aminotransaminase (ALT) (SMD: -0.52, 95% CI: -0.90, -0.14), aspartate aminotransaminase (AST) (SMD: -0.68, 95% CI: -1.21, -0.15), low-density lipoprotein cholesterol (SMD: -0.34, 95% CI: -0.66, -0.02), and triglycerides (TG) (SMD: -0.59, 95% CI: -1.16, -0.02). The concentrations of high-density lipoprotein cholesterol, total cholesterol (TC), fasting glucose, fasting insulin, and glycated hemoglobin were non-significantly altered. Aerobic exercise alone significantly reduced IHL, ALT, and AST; resistance training alone significantly reduced TC and TG; a combination of both exercise types significantly reduced IHL. To conclude, exercise overall likely had a beneficial effect on alleviating NAFLD without significant weight loss. The study was registered at PROSPERO: CRD42020221168 and funded by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 813781.

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from 14C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.