Dataset Information

Gut Microbiota and Predicted Metabolic Pathways in a Sample of Mexican Women Affected by Obesity and Obesity Plus Metabolic Syndrome.

ABSTRACT: Obesity is an excessive fat accumulation that could lead to complications like metabolic syndrome. There are reports on gut microbiota and metabolic syndrome in relation to dietary, host genetics, and other environmental factors; however, it is necessary to explore the role of the gut microbiota metabolic pathways in populations like Mexicans, where the prevalence of obesity and metabolic syndrome is high. This study identify alterations of the gut microbiota in a sample of healthy Mexican women (CO), women with obesity (OB), and women with obesity plus metabolic syndrome (OMS). We studied 67 women, characterizing their anthropometric and biochemical parameters along with their gut bacterial diversity by high-throughput DNA sequencing. Our results indicate that in OB or OMS women, Firmicutes was the most abundant bacterial phylum. We observed significant changes in abundances of bacteria belonging to the Ruminococcaceae, Lachnospiraceae, and Erysipelotrichaceae families and significant enrichment of gut bacteria from 16 different taxa that might explain the observed metabolic alterations between the groups. Finally, the predicted functional metagenome of the gut microbiota found in each category shows differences in metabolic pathways related to lipid metabolism. We demonstrate that Mexican women have a particular bacterial gut microbiota characteristic of each phenotype. There are bacteria that potentially explain the observed metabolic differences between the groups, and gut bacteria in OMS and OB conditions carry more genes of metabolic pathways implicated in lipid metabolism.

SUBMITTER: Chavez-Carbajal A

PROVIDER: S-EPMC6358992 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gut Microbiota and Predicted Metabolic Pathways in a Sample of Mexican Women Affected by Obesity and Obesity Plus Metabolic Syndrome.

Chávez-Carbajal Alejandra A Nirmalkar Khemlal K Pérez-Lizaur Ana A Hernández-Quiroz Fernando F Ramírez-Del-Alto Silvia S García-Mena Jaime J Hernández-Guerrero César C

International journal of molecular sciences 20190121 2

Obesity is an excessive fat accumulation that could lead to complications like metabolic syndrome. There are reports on gut microbiota and metabolic syndrome in relation to dietary, host genetics, and other environmental factors; however, it is necessary to explore the role of the gut microbiota metabolic pathways in populations like Mexicans, where the prevalence of obesity and metabolic syndrome is high. This study identify alterations of the gut microbiota in a sample of healthy Mexican women ...[more]

PMID: 30669548

Similar Datasets

Project description:Like humans, outbred Sprague-Dawley CD rats exhibit a polygenic pattern of inheritance of the obese phenotype and not all individuals exposed to a high calorie intake develop obesity. We hypothesized that differences in gut microbiota composition account for phenotype differences between obese prone (OP) and obese resistant (OR) rats. We studied the gut microbiota composition of OPand OR rats after a high fat (HF) diet and how they respond to fermentation of resistant starch (RS). In phase 1 of the study 28 OP and 28 OR rats were fed a HF diet. In order to determine causal role of microbiota on phenotypes, In phase 2, a microbiota transplant between the two phenotypes was performed before switching all rats to a HF diet supplemented with 20% RS. We determined microbiota composition by 16S sequencing and predicted microbiota function by PICRUSt2. Despite a similar calorie intake, in phase 2 OP rats gained more weight and accumulated more abdominal fat in both phase 1 and 2 compared to OR rats (P < 0.001; n = 6). The OP rats fermented RS more robustly compared with OR rats with an increase in total bacteria, short chain fatty acids, and increased weight of the cecum, but microbiota of OP rats had much lower alpha diversity and evenness. The microbiota of OP rats, had higher amounts of bacteria from order Bacteroidales, specifically family Muribaculaceae (S24-7), which is known to possess several starch degrading enzymes and was reported in previous studies to increase with fermentation of RS. The OR rats fermented RS less but had higher bacterial diversity and evenness and had significantly higher bacterial counts from phylum Firmicutes particularly order Clostridiales, genus Clostridium and an uncultured bacterium of the genus Akkermansia. The microbiota of OR rats had enhanced bacterial chemotaxis, phosphotransferase system (PTS), and fatty acid biosynthesis compared to OP rats whose microbiota had higher glycan degradation and LPS biosynthesis pathways. The microbiota transplant did not change obesity phenotype or microbiota composition. In conclusion, a higher alpha-diversity and evenness of the microbiota and higher proportions of Clostridiales and Akkermansia in OR rats were associated with a better metabolic phenotype with lower body fat. However, robust RS fermentation caused a lower diversity and evenness and did not result in a leaner phenotype.

Project description:An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the Drosophila gut microbiome (from single taxa to the five-member community of Acetobacter and Lactobacillus species) under three nutrient regimes. We show that the metabolic function of Drosophila gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating Drosophila gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.IMPORTANCEDrosophila is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how Drosophila interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of Drosophila This revealed that metabolic interactions between Drosophila gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the Drosophila gut and the diversity of metabolites available to influence host traits.

Project description:Cesarean (C-section) delivery, recently shown to cause excess weight gain in mice, perturbs human neonatal gut microbiota development due to the lack of natural mother-to-newborn transfer of microbes. Neonates excrete first the in-utero intestinal content (referred to as meconium) hours after birth, followed by intestinal contents reflective of extra-uterine exposure (referred to as transition stool) 2 to 3 days after birth. It is not clear when the effect of C-section on the neonatal gut microbiota emerges. We examined bacterial DNA in carefully-collected meconium, and the subsequent transitional stool, from 59 neonates [13 born by scheduled C-section and 46 born by vaginal delivery] in a private hospital in Brazil. Bacterial DNA was extracted, and the V4 region of the 16S rRNA gene was sequenced using the Illumina MiSeq (San Diego, CA, USA) platform. We found evidence of bacterial DNA in the majority of meconium samples in our study. The bacterial DNA structure (i.e., beta diversity) of meconium differed significantly from that of the transitional stool microbiota. There was a significant reduction in bacterial alpha diversity (e.g., number of observed bacterial species) and change in bacterial composition (e.g., reduced Proteobacteria) in the transition from meconium to stool. However, changes in predicted microbiota metabolic function from meconium to transitional stool were only observed in vaginally-delivered neonates. Within sample comparisons showed that delivery mode was significantly associated with bacterial structure, composition and predicted microbiota metabolic function in transitional-stool samples, but not in meconium samples. Specifically, compared to vaginally delivered neonates, the transitional stool of C-section delivered neonates had lower proportions of the genera Bacteroides, Parabacteroides and Clostridium. These differences led to C-section neonates having lower predicted abundance of microbial genes related to metabolism of amino and nucleotide sugars, and higher abundance of genes related to fatty-acid metabolism, amino-acid degradation and xenobiotics biodegradation. In summary, microbiota diversity was reduced in the transition from meconium to stool, and the association of delivery mode with microbiota structure, composition and predicted metabolic function was not observed until the passing of the transitional stool after meconium.

Project description:BackgroundObservational studies have hinted at a correlation between the gut microbiota and spinal pain (SP). However, the impact of the gut microbiota on SP remains inconclusive.MethodsIn this study, we employed a two-sample Mendelian randomization (MR) analysis to explore the causal relationship between the gut microbiota and SP, encompassing neck pain (NP), thoracic spine pain (TSP), low back pain (LBP), and back pain (BP). The compiled gut microbiota data originated from a genome-wide association study (GWAS) conducted by the MiBioGen consortium (n = 18,340). Summary data for NP were sourced from the UK Biobank, TSP from the FinnGen Biobank, and LBP from both the UK Biobank and FinnGen Biobank. Summary data for BP were obtained from the UK Biobank. The primary analytical approach for assessing causal relationships was the Inverse Variance Weighted (IVW) method, supplemented by various sensitivity analyses to ensure result robustness.ResultsThe IVW analysis unveiled 37 bacterial genera with a potential causal relationship to SP. After Benjamini-Hochberg corrected test, four bacterial genera emerged with a strong causal relationship to SP. Specifically, Oxalobacter (OR: 1.143, 95% CI 1.061-1.232, P = 0.0004) and Tyzzerella 3 (OR: 1.145, 95% CI 1.059-1.238, P = 0.0007) were identified as risk factors for LBP, while Ruminococcaceae UCG011 (OR: 0.859, 95% CI 0.791-0.932, P = 0.0003) was marked as a protective factor for LBP, and Olsenella (OR: 0.893, 95% CI 0.839-0.951, P = 0.0004) was recognized as a protective factor for low back pain or/and sciatica. No significant heterogeneity or horizontal pleiotropy was observed through alternative testing methods.ConclusionThis study establishes a causal relationship between the gut microbiota and SP, shedding light on the "gut-spine" axis. These findings offer novel perspectives for understanding the etiology of SP and provide a theoretical foundation for potential interventions targeting the gut microbiota to prevent and treat SP.

Project description:The gut microbiota (GM) is related to obesity and other metabolic diseases. To detect GM markers for obesity in patients with different metabolic abnormalities and investigate their relationships with clinical indicators, 1,914 Chinese adults were enrolled for 16S rRNA gene sequencing in this retrospective study. Based on GM composition, Random forest classifiers were constructed to screen the obesity patients with (Group OA) or without metabolic diseases (Group O) from healthy individuals (Group H), and high accuracies were observed for the discrimination of Group O and Group OA (areas under the receiver operating curve (AUC) equal to 0.68 and 0.76, respectively). Furthermore, six GM markers were shared by obesity patients with various metabolic disorders (Bacteroides, Parabacteroides, Blautia, Alistipes, Romboutsia and Roseburia). As for the discrimination with Group O, Group OA exhibited low accuracy (AUC = 0.57). Nonetheless, GM classifications to distinguish between Group O and the obese patients with specific metabolic abnormalities were not accurate (AUC values from 0.59 to 0.66). Common biomarkers were identified for the obesity patients with high uric acid, high serum lipids and high blood pressure, such as Clostridium XIVa, Bacteroides and Roseburia. A total of 20 genera were associated with multiple significant clinical indicators. For example, Blautia, Romboutsia, Ruminococcus2, Clostridium sensu stricto and Dorea were positively correlated with indicators of bodyweight (including waistline and body mass index) and serum lipids (including low density lipoprotein, triglyceride and total cholesterol). In contrast, the aforementioned clinical indicators were negatively associated with Bacteroides, Roseburia, Butyricicoccus, Alistipes, Parasutterella, Parabacteroides and Clostridium IV. Generally, these biomarkers hold the potential to predict obesity-related metabolic abnormalities, and interventions based on these biomarkers might be beneficial to weight loss and metabolic risk improvement.

Dataset Information

Gut Microbiota and Predicted Metabolic Pathways in a Sample of Mexican Women Affected by Obesity and Obesity Plus Metabolic Syndrome.

Publications

Gut Microbiota and Predicted Metabolic Pathways in a Sample of Mexican Women Affected by Obesity and Obesity Plus Metabolic Syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets