A genome and gene catalog of the aquatic microbiomes of the Tibetan Plateau.

The Tibetan Plateau supplies water to nearly 2 billion people in Asia, but climate change poses threats to its aquatic microbial resources. Here, we construct the Tibetan Plateau Microbial Catalog by sequencing 498 metagenomes from six water ecosystems (saline lakes, freshwater lakes, rivers, hot springs, wetlands and glaciers). Our catalog expands knowledge of regional genomic diversity by presenting 32,355 metagenome-assembled genomes that de-replicated into 10,723 representative genome-based species, of which 88% were unannotated. The catalog contains nearly 300 million non-redundant gene clusters, of which 15% novel, and 73,864 biosynthetic gene clusters, of which 50% novel, thus expanding known functional diversity. Using these data, we investigate the Tibetan Plateau aquatic microbiome's biogeography along a distance of 2,500 km and >5 km in altitude. Microbial compositional similarity and the shared gene count with the Tibetan Plateau microbiome decline along with distance and altitude difference, suggesting a dispersal pattern. The Tibetan Plateau Microbial Catalog stands as a substantial repository for high-altitude aquatic microbiome resources, providing potential for discovering novel lineages and functions, and bridging knowledge gaps in microbiome biogeography.

Microbial biogeochemical cycling reveals the sustainability of the rice-crayfish co-culture model.

Aquaculture has great potential in nourishing the global growing population, while such staggering yields are coupled with environmental pollution. Rice-crayfish co-culture models (RCFP) have been widely adopted in China due to their eco-friendliness. However, little is known about RCFP's microbiome pattern, which hinders our understanding of its sustainability. This study has conducted metagenomic analysis across aquaculture models and habitats, which revealed aquaculture model-specific biogeochemical cycling pattern (e.g., nitrogen (N), sulfur (S), and carbon (C)): RCFP is advantageous in N-assimilation, N-contamination, and S-pollutants removal, while non-RCFP features N denitrification process and higher S metabolism ability, producing several hazardous pollutants in non-RCFP (e.g., nitric oxide, nitrogen monoxide, and sulfide). Moreover, RCFP has greater capacity for carbohydrate enzyme metabolism compared with non-RCFP in environmental habitats, but not in crayfish gut. Collectively, RCFP plays an indispensable role in balancing aquaculture productivity and environmental protection, which might be applied to the blue transformation of aquaculture.

Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship.

Microbes live in diverse habitats (i.e. biomes), yet their species and genes were biome-specific, forming enrichment patterns. These enrichment patterns have mirrored the biome-species-function relationship, which is shaped by ecological and evolutionary principles. However, a grand picture of these enrichment patterns, as well as the roles of external and internal factors in driving these enrichment patterns, remain largely unexamined. In this work, we have examined the enrichment patterns based on 1705 microbiome samples from four representative biomes (Engineered, Gut, Freshwater, and Soil). Moreover, an "enrichment sphere" model was constructed to elucidate the regulatory principles behind these patterns. The driving factors for this model were revealed based on two case studies: (1) The copper-resistance genes were enriched in Soil biomes, owing to the copper contamination and horizontal gene transfer. (2) The flagellum-related genes were enriched in the Freshwater biome, due to high fluidity and vertical gene accumulation. Furthermore, this enrichment sphere model has valuable applications, such as in biome identification for metagenome samples, and in guiding 3D structure modeling of proteins. In summary, the enrichment sphere model aims towards creating a bluebook of the biome-species-function relationships and be applied in many fields.

MicroEXPERT: Microbiome profiling platform with cross-study metagenome-wide association analysis functionality.

The framework of the MicroEXPERT platform. Our Platform was composed of five modules. Data management module: Users upload raw data and metadata to the system using a guided workflow. Data processing module: Uploaded data is processed to generate taxonomical distribution and functional composition results. Metagenome-wide association studies module (MWAS): Various methods, including biomarker analysis, PCA, co-occurrence networks, and sample classification, are employed using metadata. Data search module: Users can query nucleotide sequences to retrieve information in the MicroEXPERT database. Data visualization module: Visualization tools are used to illustrate the metagenome analysis results.

Multi-omics approaches for in-depth understanding of therapeutic mechanism for Traditional Chinese Medicine.

Traditional Chinese Medicine (TCM) is extensively utilized in clinical practice due to its therapeutic and preventative treatments for various diseases. With the development of high-throughput sequencing and systems biology, TCM research was transformed from traditional experiment-based approaches to a combination of experiment-based and omics-based approaches. Numerous academics have explored the therapeutic mechanism of TCM formula by omics approaches, shifting TCM research from the "one-target, one-drug" to "multi-targets, multi-components" paradigm, which has greatly boosted the digitalization and internationalization of TCM. In this review, we concentrated on multi-omics approaches in principles and applications to gain a better understanding of TCM formulas against various diseases from several aspects. We first summarized frequently used TCM quality assessment methods, and suggested that incorporating both chemical and biological ingredients analytical methods could lead to a more comprehensive assessment of TCM. Secondly, we emphasized the significance of multi-omics approaches in deciphering the therapeutic mechanism of TCM formulas. Thirdly, we focused on TCM network analysis, which plays a vital role in TCM-diseases interaction, and serves for new drug discovery. Finally, as an essential source for storing multi-omics data, we evaluated and compared several TCM databases in terms of completeness and reliability. In summary, multi-omics approaches have infiltrated many aspects of TCM research. With the accumulation of omics data and data-mining resources, deeper understandings of the therapeutic mechanism of TCM have been acquired or will be gained in the future.

Intestinal microbiome-mediated resistance against vibriosis for Cynoglossus semilaevis.

BACKGROUND: Infectious diseases have caused huge economic loss and food security issues in fish aquaculture. Current management and breeding strategies heavily rely on the knowledge of regulative mechanisms underlying disease resistance. Though the intestinal microbial community was linked with disease infection, there is little knowledge about the roles of intestinal microbes in fish disease resistance. Cynoglossus semilaevis is an economically important and widely cultivated flatfish species in China. However, it suffers from outbreaks of vibriosis, which results in huge mortalities and economic loss. RESULTS: Here, we used C. semilaevis as a research model to investigate the host-microbiome interactions in regulating vibriosis resistance. The resistance to vibriosis was reflected in intestinal microbiome on both taxonomic and functional levels. Such differences also influenced the host gene expressions in the resistant family. Moreover, the intestinal microbiome might control the host immunological homeostasis and inflammation to enhance vibriosis resistance through the microbe-intestine-immunity axis. For example, Phaeobacter regulated its hdhA gene and host cyp27a1 gene up-expressed in bile acid biosynthesis pathways, but regulated its trxA gene and host akt gene down-expressed in proinflammatory cytokines biosynthesis pathways, to reduce inflammation and resist disease infection in the resistant family. Furthermore, the combination of intestinal microbes and host genes as biomarkers could accurately differentiate resistant family from susceptible family. CONCLUSION: Our study uncovered the regulatory patterns of the microbe-intestine-immunity axis that may contribute to vibriosis resistance in C. semilaevis. These findings could facilitate the disease control and selective breeding of superior germplasm with high disease resistance in fish aquaculture. Video Abstract.

Limosilactobacillus fermentum-fermented ginseng improved antibiotic-induced diarrhoea and the gut microbiota profiles of rats.

AIMS: This study investigated the efficacy of Limosilactobacillus fermentum-fermented ginseng for improving colitis and the gut microbiota profiles in rats and explored the benefits of the L. fermentum fermentation process to ginseng. METHODS AND RESULTS: Ginseng polysaccharide and ginsenoside from fermented ginseng were analysed by UV and HPLC. Antibiotic-fed rats were treated with fermented ginseng and a L. fermentum-ginseng mixture. Histopathology- and immune-related factors (TNF-α, IL-1ß, IL-6 and IL-10) of the colon were assayed by using pathological sections and ELISA. After treatment, fermented ginseng relieved the symptoms of antibiotic-induced diarrhoea and colon inflammation, and the expression of colon immune factors returned to normal. The gut microbial communities were identified by 16S rRNA gene sequencing. The results showed that the alterations in the gut microbiota returned to normal. In addition, the gut microbiota changes were correlated with immune factor expression after treatment. The fermented ginseng had better biological functions than a L. fermentum-ginseng mixture. CONCLUSIONS: Fermented ginseng can relieve diarrhoea and colon inflammation and restore the gut microbiota to its original state. The process of L. fermentum fermentation can expand the therapeutic use of ginseng. SIGNIFICANCE AND IMPACT OF THE STUDY: This research suggested the potential function of fermented ginseng to relieve diarrhoea and recover the gut microbiota to a normal level and explored the benefits of the Limosilactobacillus fermentum fermentation process to ginseng.

Paecilomyces cicadae-fermented Radix astragali ameliorate diabetic nephropathy in mice by modulating the gut microbiota.

The occurrence and development of diabetic nephropathy (DN) are closely related to gut microbiota. Paecilomyces cicadae is a medicinal and edible fungus. Radix astragali is a therapeutic material for unifying Chinese Qi. They can delay the occurrence and development of kidney disease. In recent years, solid-state fermentation of edible fungi and traditional Chinese medicine has become a hot issue.Hypothesis/Gap Statement. We assumed that solid-state fermentation products of R. astragali and Paecilomyces cicadidae (RPF) could ameliorate diabetic nephropathy and modulate gut microbiota composition. We aimed to study the function and mechanism of the RPF for ameliorating DN in mice. We investigated the effect of the potential roles of RPF in DN mice and interaction between DN and gut microbiota using animal experiments and gut microbiota measurements. We found that RPF dramatically reduced urine protein, serum creatinine and blood urea nitrogen in DN mice. Furthermore, RPF ameliorated the physiological condition of DN mice by regulating the abundance of intestinal microbiota such as Ruminococcaceae_UCG-014, Allobaculum, Unclassified_f__Lachnospiraceae Alloprevotella and Bacteroides. RPF can ameliorate diabetic nephropathy and modulate gut microbiota composition.

Microbial Dark Matter: from Discovery to Applications.

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.

Microbiome Resilience and Health Implications for People in Half-Year Travel.

Travel entail change in geography and diet, both of which are known as determinant factors in shaping the human gut microbiome. Additionally, altered gut microbiome modulates immunity, bringing about health implications in humans. To explore the effects of the mid-term travel on the gut microbiome, we generated 16S rRNA gene and metagenomic sequencing data from longitudinal samples collected over six months. We monitored dynamic trajectories of the gut microbiome variation of a Chinese volunteer team (VT) in their whole journey to Trinidad and Tobago (TAT). We found gut microbiome resilience that VT's gut microbial compositions gradually transformed to the local TAT's enterotypes during their six-month stay in TAT, and then reverted to their original enterotypes after VT's return to Beijing in one month. Moreover, we identified driven species in this bi-directional plasticity that could play a role in immunity modulation, as exemplified by Bacteroides dorei that attenuated atherosclerotic lesion formation and effectively suppressed proinflammatory immune response. Another driven species P. copri could play a crucial role in rheumatoid arthritis pathogenesis, a chronic autoimmune disease. Carbohydrate-active enzymes are often implicated in immune and host-pathogen interactions, of which glycoside hydrolases were found decreased but glycosyltransferases and carbohydrate esterases increased during the travel; these functions were then restored after VT' returning to Beijing. Furthermore, we discovered these microbial changes and restoration were mediated by VT people's dietary changes. These findings indicate that half-year travel leads to change in enterotype and functional patterns, exerting effects on human health. Microbial intervention by dietary guidance in half-year travel would be conducive to immunity modulation for maintaining health.

Analysis of antibiotic resistance genes reveals their important roles in influencing the community structure of ocean microbiome.

Antibiotic resistance gene (ARG) content is a well-established driver of microbial abundance and diversity in an environment. By reanalyzing 132 metagenomic datasets from the Tara Oceans project, we aim to unveil the associations between environmental factors, the ocean microbial community structure and ARG contents. We first investigated the structural patterns of microbial communities including both prokaryotes such as bacteria and eukaryotes such as protists. Additionally, several ARG-dominant horizontal gene transfer events between Protist and Prokaryote have been identified, indicating the potential roles of ARG in shaping the ocean microbial communities. For a deeper insight into the role of ARGs in ocean microbial communities on a global scale, we identified 1926 unique types of ARGs and discovered that the ARGs are more abundant and diverse in the mesopelagic zone than other water layers, potentially caused by limited resources. Finally, we found that ARG-enriched genera were often more abundant compared to their ARG-less neighbors in the same environment (e.g. coastal oceans). A deeper understanding of the ARG-microbiome relationships could help in the conservation of the oceanic ecosystem.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives.

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

TCM-Suite: A comprehensive and holistic platform for Traditional Chinese Medicine component identification and network pharmacology analysis.

DNA-based biological ingredient identification and downstream pharmacology network analysis are commonly used in research for Traditional Chinese Medicine preparations (TCM formulas). Advancements in bioinformatics tools and the accumulation of related data have become driving forces for progress in this field. However, a lack of a platform integrating biological ingredient identification and downstream pharmacology network analysis hinders the deep understanding of TCM. In this study, we developed the TCM-Suite platform composed of two sub-databases, Holmes-Suite and Watson-Suite, for TCM biological ingredient identification and network pharmacology investigation, respectively, both are among the most complete: In the Holmes-Suite, we collected and processed six types of marker gene sequences, accounting for 1,251,548 marker gene sequences. In the Watson-Suite, we curated and integrated a massive number of entries from more than 10 public databases. Importantly, we developed a comprehensive pipeline to integrate TCM biological ingredient identification and downstream network pharmacology research, allowing users to simultaneously identify components of a TCM formula and analyze its potential pharmacology mechanism. Furthermore, we designed search engines and a user-friendly platform to better search and visualize these rich resources. TCM-Suite is a comprehensive and holistic platform for TCM-based drug discovery and repurposing. TCM-Suite website: http://TCM-Suite.AImicrobiome.cn.

Understanding of the Site-Specific Microbial Patterns towards Accurate Identification for Patients with Diarrhea-Predominant Irritable Bowel Syndrome.

Fecal microbial community could not fully represent the intestinal microbial community. However, most studies analyzing diarrhea-dominant irritable bowel syndrome (IBS-D) were mainly based on fecal samples. We aimed to characterize the IBS-D microbial community patterns using samples at multiple intestinal sites. This study recruited 74 IBS-D patients and 20 healthy controls (HC). 22.34%, 8.51%, 14.89%, and 54.26% of them contributed to one, two, three, and four sites: duodenal mucosa (DM), duodenal lumen (DL), rectal mucosa (RM), and rectal lumen (RL) of intestinal samples, respectively. Then 16S rRNA gene analysis was performed on these 283 samples. The result showed that IBS-D microbial communities have specific patterns at each intestinal site differing from that of HC. Across hosts and sites, Bacillus, Burkholderia, and Faecalibacterium were the representative genera in duodenum of IBS-D, duodenum of HC, and rectum of HC, respectively. Samples from mucosa and lumen in rectum were highly distinguishable, regardless of IBS-D and HC. Additionally, IBS-D patients have lower microbial co-abundance network connectivity. Moreover, RM site-specific biomarker: Bacteroides used alone or together with Prevotella and Oscillospira in RM showed outstanding performance in IBS-D diagnosis. Furthermore, Bacteroides and Prevotella in RM were strongly related to the severity of abdominal pain, abdominal discomfort, and bloating in IBS-D patients. In summary, this study also confirmed fecal microbial community could not fully characterize intestinal microbial communities. Among these site-specific microbial communities, RM microbial community would be more applicable in the diagnosis of IBS-D. IMPORTANCE Microbial community varied from one site to another along the gastrointestinal tract, but current studies about intestinal microbial community in IBS-D were mainly based on fecal samples. Based on 283 intestinal samples collected from DM, DL, RM, and RL of HC and IBS-D, we found different intestinal sites had their site-specific microbial patterns in IBS-D. Notably, RM site-specific microbes Bacteroides, Prevotella, and Oscillospira could be used to discriminate IBS-D from HC accurately. Our findings could help clinicians realize the great potential of the intestinal microbial community in RM for better diagnosis of IBS-D patients.

Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction.

Information derived from metagenome sequences through deep-learning techniques has significantly improved the accuracy of template free protein structure modeling. However, most of the deep learning-based modeling studies are based on blind sequence database searches and suffer from low efficiency in computational resource utilization and model construction, especially when the sequence library becomes prohibitively large. We proposed a MetaSource model built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil, and Fermentor) to decode the inherent linkage of microbial niches with protein homologous families. Large-scale protein family folding experiments on 8,700 unknown Pfam families showed that a microbiome targeted approach with multiple sequence alignment constructed from individual MetaSource biomes requires more than threefold less computer memory and CPU (central processing unit) time but generates contact-map and three-dimensional structure models with a significantly higher accuracy, compared with that using combined metagenome datasets. These results demonstrate an avenue to bridge the gap between the rapidly increasing metagenome databases and the limited computing resources for efficient genome-wide database mining, which provides a useful bluebook to guide future microbiome sequence database and modeling development for high-accuracy protein structure and function prediction.

The ncRNA-Mediated Overexpression of Ferroptosis-Related Gene EMC2 Correlates With Poor Prognosis and Tumor Immune Infiltration in Breast Cancer.

Ferroptosis is an iron-dependent programmed cell death process. Although ferroptosis inducers hold promising potential in the treatment of breast cancer, the specific role and mechanism of the ferroptosis-related gene EMC2 in breast cancer have not been entirely determined. The potential roles of EMC2 in different tumors were explored based on The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Tumor Immune Estimation Resource (TIMER), Shiny Methylation Analysis Resource Tool (SMART), starBase, and cBioPortal for cancer genomics (cBioPortal) datasets. The expression difference, mutation, survival, pathological stage, DNA methylation, non-coding RNAs (ncRNAs), and immune cell infiltration related to EMC2 were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify the differences in biological processes and functions among different related genes. The expression levels of core prognostic genes were then verified in breast invasive carcinoma samples using immunohistochemistry and breast invasive carcinoma cell lines using real-time polymerase chain reaction. High expression levels of EMC2 were observed in most cancer types. EMC2 expression in breast cancer tissue samples correlated with poor overall survival. EMC2 was mutated and methylated in a variety of tumors and affected survival. The LINC00665-miR-410-3p axis was identified as the most potential upstream ncRNA-related pathway of EMC2 in breast cancer. EMC2 levels were significantly positively correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoint expression. Our study offers a comprehensive understanding of the oncogenic roles of EMC2 across different tumors. The upregulation of EMC2 expression mediated by ncRNAs is related to poor prognosis and tumor immune infiltration in breast cancer.

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany.

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.

Monascus ruber fermented Panax ginseng ameliorates lipid metabolism disorders and modulate gut microbiota in rats fed a high-fat diet.

ETHNOPHARMACOLOGICAL RELEVANCE: Ginseng (Panax ginseng Meyer) is rich in a variety of biologically active ingredients, which shows good effect in the treatment of metabolic diseases. Monascus has lipid-lowering activity and one of its metabolites, lovastatin, is widely used in clinical practice. AIM OF THE STUDY: The main purpose of this study was to clarify the effects of fermented Panax ginseng by Monascus ruber (PM) on lipid metabolism and gut microbiota in rats fed a high-fat diet. MATERIALS AND METHODS: SPF Sprague-Dawley rats were randomly divided into 5 groups, the therapeutic effect of PM on HFD-induced obesity, hyperlipidemia, hepatic steatosis, and disordered gut microbiota were determined in rats. RESULTS: PM could attenuate features of obesity in rats, decrease serum TC, LDL-C and IgA levels, increase excretion of bile acids in feces. Hepatic histopathologic analysis revealed that PM decrease lipid accumulation in hepatocytes. Consistently, mRNA expression levels of cholesterol metabolism-related genes were regulated in the livers of HFD-fed rats administered with PM. In addition, PM could enhance the diversity and relative abundance of gut microbiota, reduce the Firmicutes/Bacteroidetes (F/B) ratio, increase significantly the relative abundance of Prevotella_9, and decrease these of Muribaculaceae. CONCLUSIONS: PM could regulate lipid metabolism and the structure of the gut microbiota in the HFD rats. Our findings provide valuable experience for the development of ginseng. PM could be a potentially effective strategy to prevent and treat metabolic diseases and alleviate the gut microbiota disturbance caused by it.

Effects of fermented ginseng on the gut microbiota and immunity of rats with antibiotic-associated diarrhea.

ETHNOPHARMACOLOGICAL RELEVANCE: Ginseng (Panax ginseng Meyer) is a well-known herb in traditional Chinese medicine and has been used to treat many diseases for thousands of years. Recent studies have shown that ginseng is a promising agent for improving the gut microbiota and treating ulcerative colitis. Fermentation is a common process in traditional Chinese medicine making that can be used to enhance efficacy and reduce toxicity. AIM OF THE STUDY: The purpose of the present study was to research the efficacy of ginseng fermented with probiotics (Lactobacillus fermentum) on the gut microbiota and immunity of rats with antibiotic-associated diarrhea (AAD). MATERIALS AND METHODS: SPF Sprague-Dawley rats were randomly divided into eight groups: control group, antibiotic group, natural recovery group, and five groups treated with different doses of fermented ginseng (FG1 to FG5). A model of AAD was established by treating the rats with triple antibiotics, and obvious symptoms of AAD were observed. A histopathological analysis of the colon was performed. The total bacteria in the intestinal microbiota and five types of gut microbes in the feces were detected by quantitative PCR. The expression levels of related immune factors TLR4 and NF-κB in the colon were assayed. RESULTS: An appropriate dose of fermented ginseng (0.5 g/kg/d) relieved some of the symptoms of AAD and colon inflammation and reduced the expression of the immune factors TLR4 and NF-κB in the colon. The alteration of the gut microbiota observed in the rats treated with antibiotics also returned to normal after treatment with fermented ginseng. Moreover, different doses of fermented ginseng exerted different influences on the gut microbiota, and excessively high or low doses of fermented ginseng were disadvantageous for resolving the symptoms of AAD and promoting recovery. CONCLUSIONS: These results demonstrate that fermented ginseng can treat AAD symptoms and colon inflammation and restore the gut microbiota to its original state.

Stratification of athletes' gut microbiota: the multifaceted hubs associated with dietary factors, physical characteristics and performance.

Gut microbial communities of athletes differ from that of sedentary persons in both diversity and the presence of certain taxa. However, it is unclear to what degree elite athletes and non-elite athletes harbor different gut microbial community patterns and if we can effectively monitor the potential of athletes based on microbiota. A team of professional female rowing athletes in China was recruited and 306 fecal samples were collected from 19 individuals, which were separated into three cohorts: adult elite athlete's (AE), youth elite athlete's (YE), and youth non-elite athlete's (YN). The differences in gut microbiome among different cohorts were compared, and their associations with dietary factors, physical characteristics, and athletic performance were investigated. The microbial diversities of elite athletes were higher than those of youth non-elite athletes. The taxonomical, functional, and phenotypic compositions of AE, YE and YN were significantly different. Additionally, three enterotypes with clear separation were identified in athlete's fecal samples, with majority of elite athletes stratified into enterotype 3. And this enterotype-dependent gut microbiome is strongly associated with athlete performances. These differences in athlete gut microbiota lead to establishment of a random forest classifier based on taxonomical and functional biomarkers, capable of differentiating elite athletes and non-elite athletes with high accuracy. Finally, these versatilities of athlete microbial communities of athletes were found to be associated with dietary factors and physical characteristics, which can in concert explain 41% of the variability in gut microbiome.