Alternation of the gut microbiota in metabolically healthy obesity: An integrated multiomics analysis

The interaction between Lactobacillus delbrueckii ssp. bulgaricus M-58 and Streptococcus thermophilus S10 can enhance the texture and flavor profile of fermented milk: Insights from metabolomics analysis

Lactobacillus delbrueckii ssp. bulgaricus M-58 (M58) and Streptococcus thermophilus S10 (S10) are both dairy starter strains known for their favorable fermentation characteristics. Therefore, this research aimed to study the effects of 1-d low-temperature ripening on the physicochemical properties and metabolomics of fermented milk. Initially, the performance of single (M58 or S10) and dual (M58+S10) strain fermentation was assessed, revealing that the M58+S10 combination resulted in a shortened fermentation time, a stable gel structure, and desirable viscosity, suggesting positive strain interactions. Subsequently, nontargeted metabolomics analyses using liquid chromatography-MS and GC-MS were performed to comparatively analyze M58+S10 fermented milk samples collected at the end of fermentation and after 1 d of low-temperature ripening. The results showed a significant increase in almost all small peptides and dodecanedioic acid in the samples after 1 d of ripening, although there was a substantial decrease in indole and amino acid metabolites. Moreover, notable increases were observed in high-quality flavor compounds, such as geraniol, delta-nonalactone, 1-hexanol,2-ethyl-, methyl jasmonate, and undecanal. This study provides valuable insights into the fermentation characteristics of the dual bacterial starter consisting of M58 and S10 strains and highlights the specific contribution of the low-temperature ripening step to the overall quality of fermented milk.

Effects of Clostridium butyricum and inulin supplementation on intestinal microbial composition in high-fat diet fed mice

Obesity has become a serious epidemic problem in the world, and probiotics and prebiotics have been used to treat obesity. The effectiveness of diet therapy such as Clostridium butyricum (CB) and inulin supplementation in obesity and whether they can cooperate to produce better effects are still unclear. And during this process, intestinal flora play an important role, while the bacteria involved and the metabolic mechanism need to be explored. In this study, we successfully established a mouse obesity model with a high-fat diet (HFD) and divided it into three experimental groups: 7% CB (CB7), 7% CB + 1% inulin (C7G1), and 10% CB + 1% inulin (C10G). Dietary supplementation with CB and inulin could improve the glucose tolerance and intestinal microbial composition of obese mice, among which the simultaneous supplementation with 7% CB and 1% inulin (C7G1) has the most significant effect on obese mice fed with a HFD. It could significantly reduce the amount of total cholesterol, triglyceride, and low-density lipoprotein, improve abnormal glucose tolerance, and reduce abnormal blood glucose in obese mice. The intestinal flora of obese mice changed significantly, among which Lachnospiraceae_unclassified, Porphyromonaceae_unclassified, Olsenella, Bacteria_unclassified and Clostridiales_unclassified decreased due to the HFD, while Megamonas and Clostridium XIVa increased. After the supplementation with CB and inulin, the enrichment of three kinds of beneficial bacteria, Parabacteroides, Bacteroides, and Ruminococcaceae unclassified increased. The high-fat diet could upregulate the expression of FGF21, and the Clostridium butyricum and inulin supplemented diet could decrease the upregulation.

Effect of green banana and pineapple fibre powder consumption on host gut microbiome

Purpose

To determine whether green banana powder (GBP) and pineapple fibre powder (PFP) promote beneficial bacterial species, directly improve human gut health and modulate the gut microbiome and understand their utility as functional foods and dietary supplements.

Methods

Over 14 days, 60 adults followed protocol requirements, completed food diaries and study questionnaires, avoided consuming supplements with prebiotics, probiotics or postbiotics, and ingested food containing 5 g of total daily fibre [placebo (10.75 g), GBP (10.75 g) or PFP (7.41 g)]. Participants' medical and baseline wellness histories, as well as stool samples, were collected at baseline, day 7 and 14. Stool DNA was processed for sequencing.

Results

Dietary fibre and resistant starches (RS) in GBP and PFP promoted temporal increases in beneficial bacteria. GBP significantly elevated 7 species (F. prausnitzii, B. longum, B. bifidum, B. adolescentis, B. pseudocatenulatum, B. obeum, and R. inulinivorans), while PFP enriched 6 species (B. ovatus, B. cellulosilyticus, B. bifidum, B. intestinalis, R. inulinivorans, and E. siraeum). These bacteria, found to be deficient in younger adults, were promoted by both powders. PFP benefitted both genders aged 16-23, while GBP benefitted overweight/obese individuals, including females. GBP and PFP fiber and RS improved bowel regularity and health as well as metabolism by promoting histidine, branched-chain amino acids, short-chain fatty acids, and biotin production. The additional fiber caused "low" bloatedness and reduced "fairly bad" sleep disruptions, without affecting sleep durations.

Conclusion

GBP and PFP supplementation increased beneficial bacteria and metabolites, improved host gut health, and present a valuable nutritional strategy for enhancing human health.

Clinical trial registration

AMILI Institutional Review Board, Identifier 2023/0301.

A permutable MLP-like architecture for disease prediction from gut metagenomic data

Metagenomic data plays a crucial role in analyzing the relationship between microbes and diseases. However, the limited number of samples, high dimensionality, and sparsity of metagenomic data pose significant challenges for the application of deep learning in data classification and prediction. Previous studies have shown that utilizing the phylogenetic tree structure to transform metagenomic abundance data into a 2D matrix input for convolutional neural networks (CNNs) improves classification performance. Inspired by the success of a Permutable MLP-like architecture in visual recognition, we propose Metagenomic Permutator (MetaP), which applied the Permutable MLP-like network structure to capture the phylogenetic information of microbes within the 2D matrix formed by phylogenetic tree. Our experiments demonstrate that our model achieved competitive performance compared to other deep neural networks and traditional machine learning, and has good prospects for multi-classification and large sample sizes. Furthermore, we utilize the SHAP (SHapley Additive exPlanations) method to interpret our model predictions, identifying the microbial features that are associated with diseases.

A universal plasma metabolites-derived signature predicts cardiovascular disease risk in MAFLD

BACKGROUND

Metabolic associated fatty liver disease (MAFLD) is a novel concept proposed in 2020, which is more practical for identifying patients with fatty liver disease with high risk of disease progression. Fatty liver is a driver for extrahepatic complications, particularly cardiovascular diseases (CVD). Although the risk of CVD in MAFLD could be predicted by carotid ultrasound test, a very early stage prediction method before the formation of pathological damage is still lacking.

METHODS

Stool microbiomes and plasma metabolites were compared across 196 well-characterized participants encompassing normal controls, simple MAFLD patients, MAFLD patients with carotid artery pathological changes, and MAFLD patients with diagnosed coronary artery disease (CAD). 16S rDNA sequencing data and untargeted metabolomic profiles were interrogatively analyzed using differential abundance analysis and random forest (RF) machine learning algorithm to identify discriminatory gut microbiomes and metabolomic.

RESULTS

Characteristic microbial changes in MAFLD patients with CVD risk were represented by the increase of Clostridia and Firmicutes-to-Bacteroidetes ratios. Faecalibacterium was negatively correlated with mean-intima-media thickness (IMT), TC, and TG. Megamonas, Bacteroides, Parabacteroides, and Escherichia were positively correlated with the exacerbation of pathological indexes. MAFLD patients with CVD risk were characterized by the decrease of lithocholic acid taurine conjugate, and the increase of ethylvanillin propylene glycol acetal, both of which had close relationship with Ruminococcus and Gemmiger. Biotin l-sulfoxide had positive correlation with mean-IMT, TG, and weight. The general auxin pesticide beta-naphthoxyacetic acid and the food additive glucosyl steviol were both positively correlated with the increase of mean-IMT. The model combining the metabolite signatures with 9 clinical parameters accurately distinguished MAFLD with CVD risk in the proband and validation cohort. It was found that citral was the most important discriminative metabolite marker, which was validated by both in vitro and in vivo experiments.

CONCLUSIONS

Simple MAFLD patients and MAFLD patients with CVD risk had divergent gut microbes and plasma metabolites. The predictive model based on metabolites and 9 clinical parameters could effectively discriminate MAFLD patients with CVD risk at a very early stage.

Non-traumatic osteonecrosis of the femoral head induced by steroid and alcohol exposure is associated with intestinal flora alterations and metabolomic profiles

OBJECTIVE

Osteonecrosis of the femoral head (ONFH) is a severe disease that primarily affects the middle-aged population, imposing a significant economic and social burden. Recent research has linked the progression of non-traumatic osteonecrosis of the femoral head (NONFH) to the composition of the gut microbiota. Steroids and alcohol are considered major contributing factors. However, the relationship between NONFH caused by two etiologies and the microbiota remains unclear. In this study, we examined the gut microbiota and fecal metabolic phenotypes of two groups of patients, and analyzed potential differences in the pathogenic mechanisms from both the microbial and metabolic perspectives.

METHODS

Utilizing fecal samples from 68 NONFH patients (32 steroid-induced, 36 alcohol-induced), high-throughput 16 S rDNA sequencing and liquid chromatography with tandem mass spectrometry (LC-MS/MS) metabolomics analyses were conducted. Univariate and multivariate analyses were applied to the omics data, employing linear discriminant analysis effect size to identify potential biomarkers. Additionally, functional annotation of differential metabolites and associated pathways was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Subsequently, Spearman correlation analysis was employed to assess the potential correlations between differential gut microbiota and metabolites.

RESULTS

High-throughput 16 S rDNA sequencing revealed significant gut microbial differences. At the genus level, the alcohol group had higher Lactobacillus and Roseburia, while the steroid group had more Megasphaera and Akkermansia. LC-MS/MS metabolomic analysis indicates significant differences in fecal metabolites between steroid- and alcohol-induced ONFH patients. Alcohol-induced ONFH (AONFH) showed elevated levels of L-Lysine and Oxoglutaric acid, while steroid-induced ONFH(SONFH) had increased Gluconic acid and Phosphoric acid. KEGG annotation revealed 10 pathways with metabolite differences between AONFH and SONFH patients. Correlation analysis revealed the association between differential gut flora and differential metabolites.

CONCLUSIONS

Our results suggest that hormones and alcohol can induce changes in the gut microbiota, leading to alterations in fecal metabolites. These changes, driven by different pathways, contribute to the progression of the disease. The study opens new research directions for understanding the pathogenic mechanisms of hormone- or alcohol-induced NONFH, suggesting that differentiated preventive and therapeutic approaches may be needed for NONFH caused by different triggers.

The animal protein hydrolysate attenuates sarcopenia via the muscle-gut axis in aged mice

Age-related muscle loss and dysfunction, sarcopenia, is a common condition that results in poor quality of life in the elderly. Protein supplementation is a potential strategy for preventing sarcopenia and increasing muscle synthesis, but the effectiveness of protein type and level in improving sarcopenia is not well understood. In this study, we compared animal protein hydrolysate (APH), which has a high protein digestibility-corrected amino acid score (PDCAAS) and low molecular weight, with casein as a control group to investigate the effects and mechanisms of sarcopenia improvement, with a particular focus on the gut-muscle axis. APH supplementation improved age-related declines in muscle mass, grip strength, hind leg thickness, muscle protein level, muscle fiber size, and myokine levels, compared to the control group. In particular, levels of plasma cortisol, muscle lipids, and muscle collagen were markedly reduced by APH supplements in the aged mice. Furthermore, APH efficiently recovered the concentration of total SCFAs including acetic, propionic, and isovaleric acids decreased in aged mice. Finally, APH induced changes in gut microbiota and increased production of SCFAs, which were positively correlated with muscle protein level and negatively correlated with pro-inflammatory cytokines. In conclusion, APH can help to inhibit age-related sarcopenia by increasing muscle synthesis, inhibiting muscle breakdown, and potentially modulating the gut-muscle axis.

Contribution of the microbiome for better phenotyping of people living with obesity

Obesity has reached epidemic proportion worldwide and in all ages. Available evidence points to a multifactorial pathogenesis involving gene predisposition and environmental factors. Gut microbiota plays a critical role as a major interface between external factors, i.e., diet, lifestyle, toxic chemicals, and internal mechanisms regulating energy and metabolic homeostasis, fat production and storage. A shift in microbiota composition is linked with overweight and obesity, with pathogenic mechanisms involving bacterial products and metabolites (mainly endocannabinoid-related mediators, short-chain fatty acids, bile acids, catabolites of tryptophan, lipopolysaccharides) and subsequent alterations in gut barrier, altered metabolic homeostasis, insulin resistance and chronic, low-grade inflammation. Although animal studies point to the links between an "obesogenic" microbiota and the development of different obesity phenotypes, the translational value of these results in humans is still limited by the heterogeneity among studies, the high variation of gut microbiota over time and the lack of robust longitudinal studies adequately considering inter-individual confounders. Nevertheless, available evidence underscores the existence of several genera predisposing to obesity or, conversely, to lean and metabolically health phenotype (e.g., Akkermansia muciniphila, species from genera Faecalibacterium, Alistipes, Roseburia). Further longitudinal studies using metagenomics, transcriptomics, proteomics, and metabolomics with exact characterization of confounders are needed in this field. Results must confirm that distinct genera and specific microbial-derived metabolites represent effective and precision interventions against overweight and obesity in the long-term.