The fecal metabolome is associated with gestational diabetes mellitus

Deep metagenomic characterization of the gut virome in pregnant women with preeclampsia

Preeclampsia (PE), a pregnancy-specific syndrome, has been associated with the gut bacteriome. Here, to investigate the impact of the gut virome on the development of PE, we identified over 8,000 nonredundant viruses from the fecal metagenomes of 40 early-onset PE and 37 healthy pregnant women and profiled their abundances. Comparison and correlation analysis showed that PE-enriched viruses frequently connected to Blautia species enriched in PE. By contrast, bacteria linked to PE-depleted viruses were often the Bacteroidaceae members such as Bacteroides spp., Phocaeicola spp., Parabacteroides spp., and Alistipes shahii. In terms of viral function, PE-depleted viruses had auxiliary metabolic genes that participated in the metabolism of simple and complex polysaccharides, sulfur metabolism, lipopolysaccharide biosynthesis, and peptidoglycan biosynthesis, while PE-enriched viruses had a gene encoding cyclic pyranopterin monophosphate synthase, which seemed to be special, that participates in the biosynthesis of the molybdenum cofactor. Furthermore, the classification model based on gut viral signatures was developed to discriminate PE patients from healthy controls and showed an area under the receiver operating characteristic curve of 0.922 that was better than that of the bacterium-based model. This study opens up new avenues for further research, providing valuable insights into the PE gut virome and offering potential directions for future mechanistic and therapeutic investigations, with the ultimate goal of improving the diagnosis and management of PE.IMPORTANCEThe importance of this study lies in its exploration of the previously overlooked but potentially critical role of the gut virome in preeclampsia (PE). While the association between PE and the gut bacteriome has been recognized, this research takes a pioneering step into understanding how the gut virome, represented by over 8,000 nonredundant viruses, contributes to this condition. The findings reveal intriguing connections between PE-enriched viruses and specific gut bacteria, such as the prevalence of Blautia species in individuals with PE, contrasting with bacteria linked to PE-depleted viruses, including members of the Bacteroidaceae family. These viral interactions and associations provide a deeper understanding of the complex dynamics at play in PE.

Gut metabolic changes during pregnancy reveal the importance of gastrointestinal region in sample collection

INTRODUCTION

Studies of gastrointestinal physiology and the gut microbiome often consider the influence of intestinal region on experimental endpoints. However, this same consideration is not often applied to the gut metabolome. Understanding the contribution of gut regionality may be critically important to the rapidly changing metabolic environments, such as during pregnancy.

OBJECTIVES

We sought to characterize the difference in the gut metabolome in pregnant mice stratified by region-comparing the small intestine, cecum, and feces. Pre-pregnancy feces were collected to understand the influence of pregnancy on the fecal metabolome.

METHODS

Feces were collected from CD-1 female mice before breeding. On gestation day (GD) 18, gut contents were collected from the small intestine, cecum, and descending colon. Metabolites were analyzed with LC-MS/MS using the Biocrates MetaboINDICATOR™ MxP® Quant 500 kit.

RESULTS

Of the 104 small molecule metabolites meeting analysis criteria, we found that 84 (81%) were differentially abundant based on gut region. The most significant regional comparison observed was between the cecum and small intestines, with 52 (50%) differentially abundant metabolites. Pregnancy itself altered 41 (39.4%) fecal small molecule metabolites.

CONCLUSIONS

The regional variation observed in the gut metabolome are likely due to the microbial and physiological differences between the different parts of the intestines. Additionally, pregnancy impacts the fecal metabolome, which may be due to evolving needs of both the dam and fetus.

Automated identification and quantification of metabolites in human fecal extracts by nuclear magnetic resonance spectroscopy

We report the development of a software program, called MagMet-F, that automates the processing and quantification of 1D 1 H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D 1 H NMR of six human fecal extracts using manual profiling and a literature review of known fecal metabolites. We acquired pure versions of those metabolites and then acquired their 1D 1 H NMR spectra at 700 MHz to generate a fecal metabolite spectral library for MagMet-F. The fitting of these metabolites by MagMet-F was iteratively optimized to replicate manual profiling. We validated MagMet-F's automated profiling using a test set of six fecal extracts. It correctly identified 80% of the compounds and quantified those within <20% of the values determined by manual profiling using Chenomx. We also compared MagMet-F's profiling performance to two other open-access NMR profiling tools, Bayesil and Batman. MagMet-F outperformed both. Bayesil repeatedly overestimated metabolite concentrations by 10% to 40% while Batman was unable to properly quantify any compounds and took 10-20× longer. We have implemented MagMet-F as a freely accessible web server to enable automated, fast and convenient 1D 1 H NMR spectral profiling of fecal samples. MagMet-F is available at https://www.magmet.ca.

An integrated fecal metabolomic based on 1 H-NMR and UPLC-QTOF-MS revealed the preventive mechanism of Gushudan on glucocorticoid-induced osteoporotic rats

Gushudan (GSD) has the effect of strengthening bones and nourishing kidneys. However, its specific intervention mechanism still remains unclear. In this study, to investigate the pathogenesis of glucocorticoid-induced osteoporosis (GIOP) and the preventive mechanism of GSD on GIOP, fecal metabolomics based on 1 H-NMR and ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry method was established. The changes in endogenous metabolites and the relevant metabolic pathways in the control group, model group, and GSD treatment group were investigated via multivariate statistical analysis. As a result, a total of 39 differential metabolites were identified. Of these, 22 metabolites, such as L-methionine, guanine, and sphingosine, were newly discovered as differential metabolites of GIOP. Amino acid metabolism, energy metabolism, intestinal flora metabolism, and lipid metabolism were significantly changed in the fecal profiles of GIOP rats, and GSD could play an anti-osteoporosis role by regulating these metabolic pathways. Finally, compared with our previous study of the GSD to prevent kidney yang deficiency syndrome, this study suggested that there were some identical differential metabolites and metabolic pathways. It showed that there was some correlation among the metabolic profiles of the intestine, kidney, and bone in GIOP rats. Therefore, this study offered new insights into the in-depth understanding of the pathogenesis of GIOP and the intervention mechanism of GSD.

Point-of-care applicable metabotyping using biofluid-specific electrospun MetaSAMPs directly amenable to ambient LA-REIMS

In recent years, ambient ionization mass spectrometry (AIMS) including laser ablation rapid evaporation IMS, has enabled direct biofluid metabolome analysis. AIMS procedures are, however, still hampered by both analytical, i.e., matrix effects, and practical, i.e., sample transport stability, drawbacks that impede metabolome coverage. In this study, we aimed at developing biofluid-specific metabolome sampling membranes (MetaSAMPs) that offer a directly applicable and stabilizing substrate for AIMS. Customized rectal, salivary, and urinary MetaSAMPs consisting of electrospun (nano)fibrous membranes of blended hydrophilic (polyvinylpyrrolidone and polyacrylonitrile) and lipophilic (polystyrene) polymers supported metabolite absorption, adsorption, and desorption. Moreover, MetaSAMP demonstrated superior metabolome coverage and transport stability compared to crude biofluid analysis and was successfully validated in two pediatric cohorts (MetaBEAse, n = 234 and OPERA, n = 101). By integrating anthropometric and (patho)physiological with MetaSAMP-AIMS metabolome data, we obtained substantial weight-driven predictions and clinical correlations. In conclusion, MetaSAMP holds great clinical application potential for on-the-spot metabolic health stratification.

Changes of gut microbiota and tricarboxylic acid metabolites may be helpful in early diagnosis of necrotizing enterocolitis: A pilot study

Purpose

We aimed to explore the value of gut microbiota and tricarboxylic acid (TCA) metabolites in early diagnosis of necrotizing enterocolitis (NEC) among infants with abdominal manifestations.

Methods

Thirty-two preterm infants with abdominal manifestations at gestational age ≤ 34 weeks were included in the study and were divided into non-NEC (n = 16) and NEC (n = 16) groups. Faecal samples were collected when the infants were enrolled. The gut microbiota was analysed with high-throughput sequencing, and TCA metabolites were measured with multiple reaction monitoring (MRM) targeted metabolomics. Receiver operating characteristic (ROC) curves were generated to explore the predictive value of the obtained data.

Results

There was no significant difference in alpha diversity or beta diversity between the two groups (p &gt; 0.05). At the phylum level, Proteobacteria increased, and Actinomycetota decreased in the NEC group (p &lt; 0.05). At the genus level, Bifidobacterium and Lactobacillaceae decreased significantly, and at the species level, unclassified Staphylococcus, Lactobacillaceae and Bifidobacterium animalis subsp. lactis decreased in the NEC group (p &lt; 0.05). Further Linear discriminant analysis effect sizes (LEfSe) analysis showed that the change in Proteobacteria at the phylum level and Lactobacillaceae and Bifidobacterium at the genus level scored higher than 4. The concentrations of succinate, L-malic acid and oxaloacetate in the NEC group significantly increased (p &lt; 0.05), and the areas under the ROC curve for these metabolites were 0.6641, 0.7617, and 0.7344, respectively.

Conclusion

Decreased unclassified Staphylococcus, Lactobacillaceae and Bifidobacterium animalis subsp. lactis at the species level as well as the increase in the contents of some TCA metabolites, including succinate, L-malic acid and oxaloacetate, have potential value for the early diagnosis of NEC.

Gut Microbiota and Their Associated Metabolites in Diabetes: A Cross Talk Between Host and Microbes-A Review

Dysbiosis of the gut microbiota's composition and function is important in developing insulin resistance and diabetes. Diabetes has also been linked to changes in the circulating and fecal metabolites. Evidence suggests the associations between the gut microbiota and the aberrant diabetes-related metabolome. Metabolites play a crucial role in the host-microbiota interactions. Researchers have used a combination of metagenomic and metabolomic approaches to investigate the relationships between gut microbial dysbiosis and metabolic abnormalities in diabetes. We summarized current discoveries on the associations between the gut microbiota and metabolites in type 1 diabetes, type 2 diabetes, and gestational diabetes mellitus in the scoping review. According to research, the gut microbiota changes might involve in the development of diabetes through modulating the host's metabolic pathways such as immunity, energy metabolism, lipid metabolism, and amino acid metabolism. These results add to our understanding of the interplay between the host and gut microbiota metabolism.

Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion

MOTIVATION

Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed.

RESULTS

In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.

Gut Microbiota Associated with Gestational Health Conditions in a Sample of Mexican Women

Gestational diabetes (GD), pre-gestational diabetes (PD), and pre-eclampsia (PE) are morbidities affecting gestational health which have been associated with dysbiosis of the mother's gut microbiota. This study aimed to assess the extent of change in the gut microbiota diversity, short-chain fatty acids (SCFA) production, and fecal metabolites profile in a sample of Mexican women affected by these disorders. Fecal samples were collected from women with GD, PD, or PE in the third trimester of pregnancy, along with clinical and biochemical data. Gut microbiota was characterized by high-throughput DNA sequencing of V3-16S rRNA gene libraries; SCFA and metabolites were measured by High-Pressure Liquid Chromatography (HPLC) and (Fourier Transform Ion Cyclotron Mass Spectrometry (FT-ICR MS), respectively, in extracts prepared from feces. Although the results for fecal microbiota did not show statistically significant differences in alfa diversity for GD, PD, and PE concerning controls, there was a difference in beta diversity for GD versus CO, and a high abundance of Proteobacteria, followed by Firmicutes and Bacteroidota among gestational health conditions. DESeq2 analysis revealed bacterial genera associated with each health condition; the Spearman's correlation analyses showed selected anthropometric, biochemical, dietary, and SCFA metadata associated with specific bacterial abundances, and although the HPLC did not show relevant differences in SCFA content among the studied groups, FT-ICR MS disclosed the presence of interesting metabolites of complex phenolic, valeric, arachidic, and caprylic acid nature. The major conclusion of our work is that GD, PD, and PE are associated with fecal bacterial microbiota profiles, with distinct predictive metagenomes.

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

Human Fecal Metabolome Reflects Differences in Body Mass Index, Physical Fitness, and Blood Lipoproteins in Healthy Older Adults

This study investigated how body mass index (BMI), physical fitness, and blood plasma lipoprotein levels are related to the fecal metabolome in older adults. The fecal metabolome data were acquired using proton nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry on 163 healthy older adults (65-80 years old, 80 females and 83 males). Overweight and obese subjects (BMI ≥ 27) showed higher levels of fecal amino acids (AAs) (valine, alanine, and phenylalanine) compared to normal-weight subjects (BMI ≤ 23.5). Adults classified in the high-fitness group displayed slightly lower concentrations of fecal short-chain fatty acids, propionic acid, and AAs (methionine, leucine, glutamic acid, and threonine) compared to the low-fitness group. Subjects with lower levels of cholesterol in low-density lipoprotein particles (LDLchol, ≤2.6 mmol/L) displayed higher fecal levels of valine, glutamic acid, phenylalanine, and lactic acid, while subjects with a higher level of cholesterol in high-density lipoprotein particles (HDLchol, ≥2.1 mmol/L) showed lower fecal concentration of isovaleric acid. The results from this study suggest that the human fecal metabolome, which primarily represents undigested food waste and metabolites produced by the gut microbiome, carries important information about human health and should be closely integrated to other omics data for a better understanding of the role of the gut microbiome and diet on human health and metabolism.

Influence of Age, Sex, and Diet on the Human Fecal Metabolome Investigated by 1H NMR Spectroscopy

The human fecal metabolome is increasingly studied to explore the impact of diet and lifestyle on health and the gut microbiome. However, systematic differences and confounding factors related to age, sex, and diet remain largely unknown. In this study, absolute concentrations of fecal metabolites from 205 healthy Danes (105 males and 100 females, 49 ± 31 years old) were quantified using ¹H NMR spectroscopy and the newly developed SigMa software. The largest systemic variation was found to be highly related to age. Fecal concentrations of short-chain fatty acids (SCFA) were higher in the 18 years old group, while amino acids (AA) were higher in the elderly. Sex-related metabolic differences were weak but significant and mainly related to changes in SCFA. The concentrations of butyric, valeric, propionic, and isovaleric acids were found to be higher in males compared to females. Sex differences were associated with a stronger, possibly masking, effect from differential intake of macronutrients. Dietary fat intake decreased levels of SCFA and AA of both sexes, while carbohydrate intake showed weak correlations with valeric and isovaleric acids in females. This study highlights some possible demographic confounders linked to diet, disease, lifestyle, and microbiota that have to be taken into account when analyzing fecal metabolome data.