Gut Microbial SNPs Induced by High-Fiber Diet Dominate Nutrition Metabolism and Environmental Adaption of Faecalibacterium prausnitzii in Obese Children

Metabolic Regulation and Engineering Strategies of Carbon and Nitrogen Metabolism in Escherichia coli

The intricacies of carbon and nitrogen metabolism in Escherichia coli indeed present both challenges and opportunities for metabolic engineering aimed at optimizing microbial production processes. Carbon is the primary energy source and building block for biomolecules at the cellular level, while nitrogen is vital for synthesizing amino acids, nucleotides, and other nitrogen-containing compounds. This review provides a comprehensive summary of the metabolic regulation of central metabolism and outlines engineering strategies for carbon and nitrogen metabolism in E. coli. This perspective enhances our understanding of the molecular mechanisms involved and enables the development of rational metabolic engineering strategies. One key aspect of metabolic engineering consists of understanding the regulatory networks that govern these processes. Both carbon and nitrogen metabolisms are tightly regulated to ensure cellular homeostasis. By elucidating the interconnected nature of carbon and nitrogen metabolism, this review serves not just to better inform the academic community but also to stimulate advancements in biotechnological applications. Metabolic engineering in E. coli, targeting these complex networks, holds immense promise for the sustainable production of chemicals, biofuels, and pharmaceuticals.

Genetic architecture of hippocampus subfields volumes in Alzheimer's disease

BACKGROUND

The hippocampus is a heterogeneous structure, comprising histologically and functionally distinguishable hippocampal subfields. The volume reductions in hippocampal subfields have been demonstrated to be linked with Alzheimer's disease (AD). The aim of our study is to investigate the hippocampal subfields' genetic architecture based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set.

METHODS

After preprocessing the downloaded genetic variants and imaging data from the ADNI database, a co-sparse reduced rank regression model was applied to analyze the genetic architecture of hippocampal subfields volumes. Homology modeling, docking, molecular dynamics simulations, and Co-IP experiments for protein-protein interactions were used to verify the function of target protein on hippocampal subfields successively. After that, the association analysis between the candidated genes on the hippocampal subfields volume and clinical scales were performed.

RESULTS

The results of the association analysis revealed five unique genetic variants (e.g., ubiquitin-specific protease 10 [USP10]) changed in nine hippocampal subfields (e.g., the granule cell and molecular layer of the dentate gyrus [GC-ML-DG]). Among five genetic variants, USP10 had the strongest interaction effect with BACE1, which affected hippocampal subfields verified by MD and Co-IP experiments. The results of association analysis between the candidated genes on the hippocampal subfields volume and clinical scales showed that candidated genes influenced the volume and function of hippocampal subfields.

CONCLUSIONS

Current evidence suggests that hippocampal subfields have partly distinct genetic architecture and may improve the sensitivity of the detection of AD.

Dietary fiber for the prevention of childhood obesity: a focus on the involvement of the gut microbiota

Given the worldwide epidemic of overweight and obesity among children, evidence-based dietary recommendations are fundamentally important for obesity prevention. Although the significance of the human gut microbiome in shaping the physiological effects of diet and obesity has been widely recognized, nutritional therapeutics for the mitigation of pediatric obesity globally are only just starting to leverage advancements in the nutritional microbiology field. In this review, we extracted data from PubMed, EMBASE, Scopus, Web of Science, Google Scholar, CNKI, Cochrane Library and Wiley online library that focuses on the characterization of gut microbiota (including bacteria, fungi, viruses, and archaea) in children with obesity. We further review host-microbe interactions as mechanisms mediating the physiological effects of dietary fibers and how fibers alter the gut microbiota in children with obesity. Contemporary nutritional recommendations for the prevention of pediatric obesity are also discussed from a gut microbiological perspective. Finally, we propose an experimental framework for integrating gut microbiota into nutritional interventions for children with obesity and provide recommendations for the design of future studies on precision nutrition for pediatric obesity.

Prediction of BMI traits in the Chinese population based on the gut metagenome

BACKGROUND

Identifying individual characteristics based on trace evidence left at a crime scene is crucial in forensic identification. Microbial communities found in fecal traces have high individual specificity and could serve as potential markers for forensic characterization. Previous research has established that predicting body type based on the relative abundance of the gut microbiome is relatively accurate. However, the long-term stability and high individual specificity of the gut microbiome are closely linked to changes at the genome level of the microbiome. No studies have been conducted to deduce body shape from genetic traits. Therefore, in this study, the vital role of gut bacterial community characteristics and genetic traits in predicting body mass index (BMI) was investigated using gut metagenomic data from a healthy Chinese population.

RESULTS

Regarding the gut microbial community, the underweight group displayed increased α-diversity in comparison to the other BMI groups. There were significant differences in the relative abundances of 19 species among these three BMI groups. The BMI prediction model, based on the 31 most significant species, showed a goodness of fit (R2) of 0.56 and a mean absolute error (MAE) of 2.09 kg/m2. The overweight group exhibited significantly higher α-diversity than the other BMI groups at the level of gut microbial genes. Furthermore, there were significant variations observed in the single-nucleotide polymorphism (SNP) density of 732 contigs between these three BMI groups. The BMI prediction model, reliant on the 62 most contributing contigs, exhibited a model R2 of 0.72 and an MAE of 1.56 kg/m2. The model predicting body type from 44 contigs correctly identified the body type of 93.55% of the study participants.

CONCLUSION

Based on metagenomic data from a healthy Chinese population, we demonstrated the potential of genetic traits of gut bacteria to predict an individual's BMI. The findings of this study suggest the effectiveness of a novel method for determining the body type of suspects in forensic applications using the genetic traits of the gut microbiome and holds great promise for forensic individual identification.

Association between body weight and distal gut microbes in Hainan black goats at weaning age

Gut microbiota plays a critical role in the healthy growth and development of young animals. However, there are few studies on the gut microbiota of young Hainan black goats. In this study, 12 three-month-old weaned lambs with the same birth date were selected and divided into the high body weight group (HW) and low body weight group (LW). The microbial diversity, composition, and predicted function in the feces of HW and LW groups were analyzed by collecting fecal samples and sequencing the 16S rRNA V3-V4 region. The results indicated that the HW group exhibited higher community diversity compared with the LW group, based on the Shannon index. The core phyla of the HW and LW groups were both Firmicutes and Bacteroidetes. Parabacteroides, UCG-005, and Bacteroides are the core genera of the HW group, and Bacteroides, Escherichia-Shigella, and Akkermansia are the core genera of the LW group. In addition, genera such as Ruminococcus and Anaerotruncus, which were positively correlated with body weight, were enriched in the HW group; those genera, such as Akkermansia and Christensenellaceae, which were negatively correlated with body weight, were enriched in the LW group. Differential analysis of the KEGG pathway showed that Amino Acid Metabolism, Energy Metabolism, Carbohydrate Metabolism, and Nucleotide Metabolism were enriched in the HW group, while Cellular Processes and Signaling, Lipid Metabolism, and Glycan Biosynthesis and Metabolism were enriched in the LW group. The results of this study revealed the gut microbial characteristics of Hainan black goats with different body weights at weaning age and identified the dominant flora that contributed to their growth.

Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome

Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.

Chlorella pyrenoidosa Polysaccharides as a Prebiotic to Modulate Gut Microbiota: Physicochemical Properties and Fermentation Characteristics In Vitro

This study was conducted to investigate the prebiotic potential of Chlorella pyrenoidosa polysaccharides to provide useful information for developing C. pyrenoidosa as a green healthy food. C. pyrenoidosa polysaccharides were prepared and their physicochemical characteristics were determined. The digestibility and fermentation characteristics of C. pyrenoidosa polysaccharides were evaluated using in vitro models. The results revealed that C. pyrenoidosa polysaccharides were composed of five non-starch polysaccharide fractions with monosaccharide compositions of Man, Rib, Rha, GlcA, Glc, Gal, Xyl and Ara. C. pyrenoidosa polysaccharides could not be degraded under saliva and the gastrointestinal conditions. However, the molecular weight and contents of residual carbohydrates and reducing sugars of C. pyrenoidosa polysaccharides were significantly reduced after fecal fermentation at a moderate speed. Notably, C. pyrenoidosa polysaccharides could remarkably modulate gut microbiota, including the promotion of beneficial bacteria, inhibition of growth of harmful bacteria, and reduction of the ratio of Firmicutes to Bacteroidetes. Intriguingly, C. pyrenoidosa polysaccharides can promote growth of Parabacteroides distasonis and increase short-chain fatty acid contents, thereby probably contributing to the promotion of intestinal health and prevention of diseases. Thus, these results suggested that C. pyrenoidosa polysaccharides had prebiotic functions with different fermentation characteristics compared with conventional prebiotics such as fructooligosaccharide, and they may be a new prebiotic for improving human health.

Metagenome-Scale Metabolic Network Suggests Folate Produced by Bifidobacterium longum Might Contribute to High-Fiber-Diet-Induced Weight Loss in a Prader-Willi Syndrome Child

Gut-microbiota-targeted nutrition intervention has achieved success in the management of obesity, but its underlying mechanism still needs extended exploration. An obese Prader-Willi syndrome boy lost 25.8 kg after receiving a high-fiber dietary intervention for 105 days. The fecal microbiome sequencing data taken from the boy on intervention days 0, 15, 30, 45, 60, 75, and 105, along with clinical indexes, were used to construct a metagenome-scale metabolic network. Firstly, the abundances of the microbial strains were obtained by mapping the sequencing reads onto the assembly of gut organisms through use of reconstruction and analysis (AGORA) genomes. The nutritional components of the diet were obtained through the Virtual Metabolic Human database. Then, a community model was simulated using the Microbiome Modeling Toolbox. Finally, the significant Spearman correlations among the metabolites and the clinical indexes were screened and the strains that were producing these metabolites were identified. The high-fiber diet reduced the overall amount of metabolite secretions, but the secretions of folic acid derivatives by Bifidobacterium longum strains were increased and were significantly relevant to the observed weight loss. Reduced metabolites might also have directly contributed to the weight loss or indirectly contribute by enhancing leptin and decreasing adiponectin. Metagenome-scale metabolic network technology provides a cost-efficient solution for screening the functional microbial strains and metabolic pathways that are responding to nutrition therapy.

Targeting the Gut Microbiome in Prader-Willi Syndrome

Overwhelming evidence demonstrates an important role of the gut microbiome in the development of a wide range of diseases, including obesity, metabolic disorders, and mental health symptoms. Indeed, interventions targeting the gut microbiome are being actively investigated as a therapeutic strategy to tackle these diseases. Given that obesity and mental health symptoms are both hallmarks of Prader-Willi syndrome, targeting the gut microbiome may be a promising therapeutical strategy. Only a few studies have investigated the gut microbiome in the context of Prader-Willi syndrome and assessed the efficacy of probiotic supplementation as a therapeutic strategy for this disease. Here, we review the knowledge obtained to this date regarding the gut microbiome in individuals with Prader-Willi syndrome. The limited evidence available indicate that probiotic supplementation improves some metabolic and mental health aspects, however further studies are warranted to determine whether targeting the gut microbiome may constitute a safe and efficient strategy to treat individuals with Prader-Willi syndrome.