shinyTempSignal: an R shiny application for exploring temporal and other phylogenetic signals.

The molecular clock model is fundamental for inferring species divergence times from molecular sequences. However, its direct application may introduce significant biases due to sequencing errors, recombination events, and inaccurately labeled sampling times. Improving accuracy necessitates rigorous quality control measures to identify and remove potentially erroneous sequences. Furthermore, while not all branches of a phylogenetic tree may exhibit a clear temporal signal, specific branches may still adhere to the assumptions, with varying evolutionary rates. Supporting a relaxed molecular clock model better aligns with the complexities of evolution. The root-to-tip regression method has been widely used to analyze the temporal signal in phylogenetic studies and can be generalized for detecting other phylogenetic signals. Despite its utility, there remains a lack of corresponding software implementations for broader applications. To address this gap, we present shinyTempSignal, an interactive web application implemented with the shiny framework, available as an R package and publicly accessible at https://github.com/YuLab-SMU/shinyTempSignal. This tool facilitates the analysis of temporal and other phylogenetic signals under both strict and relaxed models. By extending the root-to-tip regression method to diverse signals, shinyTempSignal helps in the detection of evolving features or traits, thereby laying the foundation for deeper insights and subsequent analyses.

Dynamic changes in butyrate levels regulate satellite cell homeostasis by preventing spontaneous activation during aging.

The gut microbiota plays a pivotal role in systemic metabolic processes and in particular functions, such as developing and preserving the skeletal muscle system. However, the interplay between gut microbiota/metabolites and the regulation of satellite cell (SC) homeostasis, particularly during aging, remains elusive. We propose that gut microbiota and its metabolites modulate SC physiology and homeostasis throughout skeletal muscle development, regeneration, and aging process. Our investigation reveals that microbial dysbiosis manipulated by either antibiotic treatment or fecal microbiota transplantation from aged to adult mice, leads to the activation of SCs or a significant reduction in the total number. Furthermore, employing multi-omics (e.g., RNA-seq, 16S rRNA gene sequencing, and metabolomics) and bioinformatic analysis, we demonstrate that the reduced butyrate levels, alongside the gut microbial dysbiosis, could be the primary factor contributing to the reduction in the number of SCs and subsequent impairments during skeletal muscle aging. Meanwhile, butyrate supplementation can mitigate the antibiotics-induced SC activation irrespective of gut microbiota, potentially by inhibiting the proliferation and differentiation of SCs/myoblasts. The butyrate effect is likely facilitated through the monocarboxylate transporter 1 (Mct1), a lactate transporter enriched on membranes of SCs and myoblasts. As a result, butyrate could serve as an alternative strategy to enhance SC homeostasis and function during skeletal muscle aging. Our findings shed light on the potential application of microbial metabolites in maintaining SC homeostasis and preventing skeletal muscle aging.

Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches.

BACKGROUND: Osteoporosis is a prevalent disease for the aged population. Chinese herbderived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throughput approach with the integration of cheminformatics and deep-learning methods to explore their mechanistic action, especially herb/drug-gene interaction networks. METHODS: Ten medicinal herbs for clinical osteoporosis treatment were selected. Chemical ingredients of the top 10 herbs were retrieved from the TCMIO database, and their predicted targets were obtained from the SEA server. Anti-osteoporosis clinical drugs and targets were collected from multidatabases. Chemical space, fingerprint similarity, and scaffold comparison of the compounds between herbs and clinical drugs were analyzed by RDKit and SKlearn. A network of herb-ingredient-target was constructed via Gephi, and GO and KEGG enrichment analyses were performed using clusterProfiler. Additionally, the bioactivity of compounds and targets was predicted by DeepScreening. Molecular docking of YYH flavonoids to HSD17B2 was accomplished by AutoDockTools. RESULTS: Cheminformatics result depicts a pharmacological network consisting of 89 active components and 30 potential genes. The chemical structures of plant steroids, flavonoids, and alkaloids are key components for anti-osteoporosis effects. Moreover, bioinformatics result demonstrates that the active components of herbs mainly participate in steroid hormone biosynthesis and the TNF signaling pathway. Finally, deep-learning-based regression models were constructed to evaluate 22 anti-osteoporosis-related protein targets and predict the activity of 1350 chemical ingredients of the 10 herbs. CONCLUSION: The combination of cheminformatics and deep-learning approaches sheds light on the exploration of medicinal herbs mechanisms, and the identification of novel and active compounds from medical herbs in complex molecular systems.

DenovoProfiling: A webserver for de novo generated molecule library profiling.

Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

Probiotic Consortia and Their Metabolites Ameliorate the Symptoms of Inflammatory Bowel Diseases in a Colitis Mouse Model.

Inflammatory bowel disease (IBD) has become a global public health problem. Although the pathogenesis of the disease is unknown, a potential association between the gut microbiota and inflammatory signatures has been established. Probiotics, especially Lactobacillus or Bifidobacterium, are orally taken as food supplements or microbial drugs by patients with IBD or gastrointestinal disorders due to their safety, efficacy, and power to restore the gut microenvironment. In the current study, we investigated the comprehensive effects of probiotic bacterial consortia consisting of Lactobacillus reuteri, Lactobacillus gasseri, Lactobacillus acidophilus (Lactobacillus spp.), and Bifidobacterium lactis (Bifidobacterium spp.) or their metabolites in a dextran sodium sulfate (DSS)-induced colitis mouse model. Our data demonstrate that probiotic consortia not only ameliorate the disease phenotype but also restore the composition and structure of the gut microbiota. Moreover, the effect of probiotic consortia is better than that of any single probiotic strain. The results also demonstrate that mixed fermentation metabolites are capable of ameliorating the symptoms of gut inflammation. However, the administration of metabolites is not as effective as probiotic consortia with respect to phenotypic characteristics, such as body weight, disease activity index (DAI), and histological score. In addition, mixed metabolites led only to changes in intestinal flora composition. In summary, probiotic consortia and metabolites could exert protective roles in the DSS-induced colitis mouse model by reducing inflammation and regulating microbial dysbiosis. These findings from the current study provide support for the development of probiotic-based microbial products as an alternative therapeutic strategy for IBD. IMPORTANCE IBD is a chronic nonspecific inflammatory disease. IBD is characterized by a wide range of lesions, often involving the entire colon, and is characterized mainly by ulcers and erosions of the colonic mucosa. In the present study, we investigated the efficacy of probiotics on the recovery of gut inflammation and the restoration of gut microecology. We demonstrate that probiotic consortia have a superior effect in inhibiting inflammation and accelerating recovery compared with the effects observed in the control group or groups administered with a single strain. These results support the utilization of probiotic consortia as an alternative therapeutic approach to treat IBD.

Gut Microbiota Exceeds Cervical Microbiota for Early Diagnosis of Endometriosis.

The diagnosis of endometriosis is typically delayed by years for the unexclusive symptom and the traumatic diagnostic method. Several studies have demonstrated that gut microbiota and cervical mucus potentially can be used as auxiliary diagnostic biomarkers. However, none of the previous studies has compared the robustness of endometriosis classifiers based on microbiota of different body sites or demonstrated the correlation among microbiota of gut, cervical mucus, and peritoneal fluid of endometriosis, searching for alternative diagnostic approaches. Herein, we enrolled 41 women (control, n = 20; endometriosis, n = 21) and collected 122 well-matched samples, derived from feces, cervical mucus, and peritoneal fluid, to explore the nature of microbiome of endometriosis patients. Our results indicated that microbial composition is remarkably distinguished between three body sites, with 19 overlapped taxa. Moreover, endometriosis patients harbor distinct microbial communities versus control group especially in feces and peritoneal fluid, with increased abundance of pathogens in peritoneal fluid and depletion of protective microbes in feces. Particularly, genera of Ruminococcus and Pseudomonas were identified as potential biomarkers in gut and peritoneal fluid, respectively. Furthermore, novel endometriosis classifiers were constructed based on taxa selected by a robust machine learning method. These results demonstrated that gut microbiota exceeds cervical microbiota in diagnosing endometriosis. Collectively, this study reveals important insights into the microbial profiling in different body sites of endometriosis, which warrant future exploration into the role of microbiota in endometriosis and highlighted values on gut microbiota in early diagnosis of endometriosis.

Gut Microbiota Serves a Predictable Outcome of Short-Term Low-Carbohydrate Diet (LCD) Intervention for Patients with Obesity.

To date, much progress has been made in dietary therapy for obese patients. A low-carbohydrate diet (LCD) has reached a revival in its clinical use during the past decade with undefined mechanisms and debatable efficacy. The gut microbiota has been suggested to promote energy harvesting. Here, we propose that the gut microbiota contributes to the inconsistent outcome under an LCD. To test this hypothesis, patients with obesity or patients who were overweight were randomly assigned to a normal diet (ND) or an LCD group with ad libitum energy intake for 12 weeks. Using matched sampling, the microbiome profile at baseline and end stage was examined. The relative abundance of butyrate-producing bacteria, including Porphyromonadaceae Parabacteroides and Ruminococcaceae Oscillospira, was markedly increased after LCD intervention for 12 weeks. Moreover, within the LCD group, participants with a higher relative abundance of Bacteroidaceae Bacteroides at baseline exhibited a better response to LCD intervention and achieved greater weight loss outcomes. Nevertheless, the adoption of an artificial neural network (ANN)-based prediction model greatly surpasses a general linear model in predicting weight loss outcomes after LCD intervention. Therefore, the gut microbiota served as a positive outcome predictor and has the potential to predict weight loss outcomes after short-term LCD intervention. Gut microbiota may help to guide the clinical application of short-term LCD intervention to develop effective weight loss strategies. (This study has been registered at the China Clinical Trial Registry under approval no. ChiCTR1800015156). IMPORTANCE Obesity and its related complications pose a serious threat to human health. Short-term low-carbohydrate diet (LCD) intervention without calorie restriction has a significant weight loss effect for overweight/obese people. Furthermore, the relative abundance of Bacteroidaceae Bacteroides is a positive outcome predictor of individual weight loss after short-term LCD intervention. Moreover, leveraging on these distinct gut microbial structures at baseline, we have established a prediction model based on the artificial neural network (ANN) algorithm that could be used to estimate weight loss potential before each clinical trial (with Chinese patent number 2021104655623). This will help to guide the clinical application of short-term LCD intervention to improve weight loss strategies.

Effect of dietary histamine on intestinal morphology, inflammatory status, and gut microbiota in yellow catfish (Pelteobagrus fulvidraco).

The toxic effect of dietary histamine on the intestine of aquatic animals has been demonstrated, but reports on the morphological observation of the intestine are limited. Thus, a feeding trial was conducted to determine the effect of dietary histamine on intestinal histology, inflammatory status and gut microbiota of yellow catfish (Pelteobagrus fulvidraco). Here, we showed that histamine-rich diets caused severe abnormality and damage to the intestine, including a decreased villi length and reduced villi number. In addition, the quantitative real-time PCR (qRT-PCR) demonstrates that histamine-rich diets increased the expression of pro-inflammatory genes (Tnfα, Il1ß, and Il8) and decreased the expression of an anti-inflammatory gene (Il10). Furthermore, the alpha-diversity (observed OTUs, Chao1, Shannon and Simpson) and beta-diversity (non-metric multidimensional scaling, with the stress value of 0.17) demonstrated that histamine-rich diets caused alterations in gut microbiota composition and diversity. Co-occurrence networks analysis of the gut microbiota community showed that the histamine influenced the number and the relationship between bacteria species in the phyla of Acidobacteria, Proteobacteria, and Bacteroidetes, which caused the instability of the intestinal microbiota community. Additionally, random forest selected six bacterial species as the biomarkers to separate the three groups, which are Lachnospiraceae Blautia (V520), Bacteroidales S24.7 (V235), Chloroplast Streptophyta (V368), Actinomycetales Streptomycetaceae (V152), Clostridia Clostridiales (V491) and Paraprevotellaceae Prevotella (V245). Finally, Pearson correlation analysis demonstrated that V520, V235, and V491 were negatively correlated with pro-inflammatory factors (Tnfα, Il1ß, and Il8) and positively correlated with an anti-inflammatory factor (Il10), which indicated that V520, V235, and V491 might be anti-inflammatory. These findings improved our understanding of the toxic effect of dietary histamine to intestinal histological damage, the induction of mucosa inflammatory status, and the alteration of gut microbiota.

Transferrin receptor 1 ablation in satellite cells impedes skeletal muscle regeneration through activation of ferroptosis.

BACKGROUND: Satellite cells (SCs) are critical to skeletal muscle regeneration. Inactivation of SCs is linked to skeletal muscle loss. Transferrin receptor 1 (Tfr1) is associated with muscular dysfunction as muscle-specific deletion of Tfr1 results in growth retardation, metabolic disorder, and lethality, shedding light on the importance of Tfr1 in muscle physiology. However, its physiological function regarding skeletal muscle ageing and regeneration remains unexplored. METHODS: RNA sequencing is applied to skeletal muscles of different ages to identify Tfr1 associated to skeletal muscle ageing. Mice with conditional SC ablation of Tfr1 were generated. Between Tfr1SC/WT and Tfr1SC/KO (n = 6-8 mice per group), cardiotoxin was intramuscularly injected, and transverse abdominal muscle was dissected, weighted, and cryosectioned, followed by immunostaining, haematoxylin and eosin staining, and Masson staining. These phenotypical analyses were followed with functional analysis such as flow cytometry, tread mill, Prussian blue staining, and transmission electron microscopy to identify pathological pathways that contribute to regeneration defects. RESULTS: By comparing gene expression between young (2 weeks old, n = 3) and aged (80 weeks old, n = 3) mice among four types of muscles, we identified that Tfr1 expression is declined in muscles of aged mice (~80% reduction, P < 0.005), so as to its protein level in SCs of aged mice. From in vivo and ex vivo experiments, Tfr1 deletion in SCs results in an irreversible depletion of SCs (~60% reduction, P < 0.005) and cell-autonomous defect in SC proliferation and differentiation, leading to skeletal muscle regeneration impairment, followed by labile iron accumulation, lipogenesis, and decreased Gpx4 and Nrf2 protein levels leading to reactive oxygen species scavenger defects. These abnormal phenomena including iron accumulation, activation of unsaturated fatty acid biosynthesis, and lipid peroxidation are orchestrated with the occurrence of ferroptosis in skeletal muscle. Ferroptosis further exacerbates SC proliferation and skeletal muscle regeneration. Ferrostatin-1, a ferroptosis inhibitor, could not rescue ferroptosis. However, intramuscular administration of lentivirus-expressing Tfr1 could partially reduce labile iron accumulation, decrease lipogenesis, and promote skeletal muscle regeneration. Most importantly, declined Tfr1 but increased Slc39a14 protein level on cellular membrane contributes to labile iron accumulation in skeletal muscle of aged rodents (~80 weeks old), leading to activation of ferroptosis in aged skeletal muscle. This is inhibited by ferrostatin-1 to improve running time (P = 0.0257) and distance (P = 0.0248). CONCLUSIONS: Satellite cell-specific deletion of Tfr1 impairs skeletal muscle regeneration with activation of ferroptosis. This phenomenon is recapitulated in skeletal muscle of aged rodents and human sarcopenia. Our study provides mechanistic information for developing novel therapeutic strategies against muscular ageing and diseases.

EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study.

Advances in next-generation sequencing (NGS) have revolutionized microbial studies in many fields, especially in clinical investigation. As the second human genome, microbiota has been recognized as a new approach and perspective to understand the biological and pathologic basis of various diseases. However, massive amounts of sequencing data remain a huge challenge to researchers, especially those who are unfamiliar with microbial data analysis. The mathematic algorithm and approaches introduced from another scientific field will bring a bewildering array of computational tools and acquire higher quality of script experience. Moreover, a large cohort research together with extensive meta-data including age, body mass index (BMI), gender, medical results, and others related to subjects also aggravate this situation. Thus, it is necessary to develop an efficient and convenient software for clinical microbiome data analysis. EasyMicroPlot (EMP) package aims to provide an easy-to-use microbial analysis tool based on R platform that accomplishes the core tasks of metagenomic downstream analysis, specially designed by incorporation of popular microbial analysis and visualization used in clinical microbial studies. To illustrate how EMP works, 694 bio-samples from Guangdong Gut Microbiome Project (GGMP) were selected and analyzed with EMP package. Our analysis demonstrated the influence of dietary style on gut microbiota and proved EMP package's powerful ability and excellent convenience to address problems for this field.

Deep learning enables discovery of highly potent anti-osteoporosis natural products.

A pre-trained self-attentive message passing neural network (P-SAMPNN) model was developed based on our anti-osteoclastogenesis dataset for virtual screening purpose. Validation processes proved that P-SAMPNN model was significantly superior to the other base line models. A commercially available natural product library was virtually screened by the P-SAMPNN model and resulted in confirmed 5 hits from 10 selected virtual hits. Among the confirmed hits, compounds AP-123/40765213 and AE-562/43462182 are the nanomolar inhibitors against osteoclastogenesis with a new scaffold. Further studies indicate that AP-123/40765213 and AE-562/43462182 significantly suppress the mRNA expression of RANK and downregulate the expressions of osteoclasts-related genes Ctsk, Nfatc1, and Tracp. Our work demonstrated that P-SAMPNN method can guide phenotype-based drug discovery.

The Gut Microbiome Is Associated With Therapeutic Responses and Toxicities of Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients-A Pilot Study.

Responses to neoadjuvant chemoradiotherapy (nCRT) and therapy-related toxicities in rectal cancer vary among patients. To provide the individualized therapeutic option for each patient, predictive markers of therapeutic responses and toxicities are in critical need. We aimed to identify the association of gut microbiome with and its potential predictive value for therapeutic responses and toxicities. In the present study, we collected fecal microbiome samples from patients with rectal cancer at treatment initiation and just after nCRT. Taxonomic profiling via 16S ribosomal RNA gene sequencing was performed on all samples. Patients were classified as responders versus non-responders. Patients were grouped into no or mild diarrhea and severe diarrhea. STAMP and high-dimensional class comparisons via linear discriminant analysis of effect size (LEfSe) were used to compare the compositional differences between groups. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was utilized to predict differences in metabolic function between groups. Ten patients were classified as responders and 12 patients were classified as non-responders. Fourteen patients experienced no or mild diarrhea and 8 patients experienced severe diarrhea. Several bacteria taxa with significantly different relative abundances before and after nCRT were identified. Similarly, several baseline bacteria taxa and predicted pathways with significantly different relative abundances between responders and non-responders or between patients no or mild diarrhea and severe diarrhea were identified. Specifically, Shuttleworthia was identified as enriched in responders and several bacteria taxa in the Clostridiales order etc. were identified as enriched in non-responders. Pathways including fatty acid metabolism were predicted to be enriched in responders. In addition, Bifidobacterium, Clostridia, and Bacteroides etc. were identified as enriched in patients with no or mild diarrhea. Pathways including primary bile acid biosynthesis were predicted to be enriched in patients with no or mild diarrhea. Together, the microbiota and pathway markers identified in this study may be utilized to predict the therapeutic responses and therapy-related toxicities of nCRT in patients with rectal cancer. More patient data is needed to verify the current findings and the results of metagenomic, metatranscriptomic, and metabolomic analyses will further mine key biomarkers at the compositional and functional level.

Kuijieling, a Chinese medicine alleviates DSS-induced colitis in C57BL/6Jmouse by improving the diversity and function of gut microbiota.

Ulcerative colitis (UC) is a gastrointestinal disease. The link between gut microbiota and the inflammatory response in the gut has been recently established. Restoration of gut microbiota suppresses inflammatory signaling. Kuijieling (KJL) decoction, an experimental Chinese medicine formula could ameliorate the symptom of colitis. However, the involvement of gut microbiota in its curative effect remains known. Here, we would like to assess the therapeutic effect of KJL in DSS-induced UC model. Mouse feces were collected, followed by 16S rRNA sequencing. Kuijieling decoction improved gut microbial homeostasis and suppressed inflammation in the UC model. A 5-fold cross-validation and random forest analysis identified seven signature bacterial taxa representing the DSS-mediated pathogenic condition and recovery stage upon KJL decoction treatment. Overall, the findings support the notion of KJL decoction-mediated restoration of gut microbiota as a critical step of inducing remission and alleviating UC symptoms. In the present investigation, we aimed to address the question of whether KJL decoction alleviates the UC symptoms by manipulating the gut microbial structure and function.

TCMIO: A Comprehensive Database of Traditional Chinese Medicine on Immuno-Oncology.

Advances in immuno-oncology (IO) are making immunotherapy a powerful tool for cancer treatment. With the discovery of an increasing number of IO targets, many herbs or ingredients from traditional Chinese medicine (TCM) have shown immunomodulatory function and antitumor effects via targeting the immune system. However, knowledge of underlying mechanisms is limited due to the complexity of TCM, which has multiple ingredients acting on multiple targets. To address this issue, we present TCMIO, a comprehensive database of Traditional Chinese Medicine on Immuno-Oncology, which can be used to explore the molecular mechanisms of TCM in modulating the cancer immune microenvironment. Over 120,000 small molecules against 400 IO targets were extracted from public databases and the literature. These ligands were further mapped to the chemical ingredients of TCM to identify herbs that interact with the IO targets. Furthermore, we applied a network inference-based approach to identify the potential IO targets of natural products in TCM. All of these data, along with cheminformatics and bioinformatics tools, were integrated into the publicly accessible database. Chemical structure mining tools are provided to explore the chemical ingredients and ligands against IO targets. Herb-ingredient-target networks can be generated online, and pathway enrichment analysis for TCM or prescription is available. This database is functional for chemical ingredient structure mining and network analysis for TCM. We believe that this database provides a comprehensive resource for further research on the exploration of the mechanisms of TCM in cancer immunity and TCM-inspired identification of novel drug leads for cancer immunotherapy. TCMIO can be publicly accessed at http://tcmio.xielab.net.

Corrigendum: Gut Microbiota as an Objective Measurement for Auxiliary Diagnosis of Insomnia Disorder.

[This corrects the article on p. 1770 in vol. 10, PMID: 31456757.].

Fecal microbiota as a noninvasive biomarker to predict the tissue iron accumulation in intestine epithelial cells and liver.

Iron is an essential trace mineral required for growth, metabolism, and immune response. Dysregulation of iron homeostasis is linked with the development and progression of various diseases. Iron accumulation is associated with inflammatory diseases and cancer, while iron deficiency leads to the growth retardation. Several studies have suggested that iron imbalance results in alteration of gut microbiota, leading to the disruption of microbial diversity, the increase of pathogen abundance, and the induction of intestinal inflammation. However, in screening studies done in the past decades, the association between the iron availability and gut microbiota has not been systemically explored. Furthermore, a noninvasive and convenient approach to determine the iron levels in tissues is lacking. In the present study, a murine model for iron dysregulation was established. 16S rRNA amplicon sequencing and bioinformatic algorithms were used to identify the key taxa. Using the key taxa identified and machine learning models, we established an easily accessible prediction model, which could accurately distinguish between iron-deprived or iron-fortified condition. This prediction model could precisely predict the iron level of the intestinal epithelial cells and the liver and could be used for early diagnosis of iron dysbiosis-related diseases, in a noninvasive manner, in the future.

Characteristics of Gut Microbiota in Patients with Hypertension and/or Hyperlipidemia: A Cross-Sectional Study on Rural Residents in Xinxiang County, Henan Province.

Human gut microbiota can be affected by a variety of factors, including geography. This study aimed to clarify the regional specific characteristics of gut microbiota in rural residents of Xinxiang county, Henan province, with hypertension and hyperlipidemia and evaluate the association of specific gut microbiota with hypertension and hyperlipidemia clinical indices. To identify the gut microbes, 16S rRNA gene sequencing was used and a random forest disease classifier was constructed to discriminate between the gut microbiota in hypertension, hyperlipidemia, and the healthy control. Patients with hypertension and hyperlipidemia presented with marked gut microbiota dysbiosis compared to the healthy control. The gut microbiota related to hypertension and hyperlipidemia may consist of a large number of taxa, influencing each other in a complex metabolic network. Examining the top 35 genera in each group showed that Lactococcus, Alistipes, or Subdoligranulum abundances were positively correlated with systolic blood pressure (SBP) or diastolic blood pressure (DBP) in hypertensive patients with treatment-naive hypertension (n = 63). In hypertensive patients undergoing anti-hypertensive treatment (n = 104), the abundance of Megasphaera or Megamonas was positively correlated to SBP. In the hyperlipidemia group, some of the top 35 genera were significantly correlated to triglyceride, total cholesterol, and fasting blood glucose levels. This study analyzed the characteristics of the gut microbiota in patients with hypertension and/or hyperlipidemia, providing a theoretical basis for the prevention and control of hypertension and hyperlipidemia in this region.

Gut Microbiota as an Objective Measurement for Auxiliary Diagnosis of Insomnia Disorder.

Insomnia is a type of sleep disorder which is associated with various diseases' development and progression, such as obesity, type II diabetes and cardiovascular diseases. Recent investigation of the gut-brain axis enhances our understanding of the role of the gut microbiota in brain-related diseases. However, whether the gut microbiota is associated with insomnia remains unknown. In the present investigation, leveraging the 16S rDNA amplicon sequencing of V3-V4 region and the novel bioinformatic analysis, it was demonstrated that between insomnia and healthy populations, the composition, diversity and metabolic function of the gut microbiota are significantly changed. Other than these, redundancy analysis, co-occurrence analysis and PICRUSt underpin the gut taxa composition, signaling pathways, and metabolic functions perturbed by insomnia disorder. Moreover, random forest together with cross-validation identified two signature bacteria, which could be used to distinguish the insomnia patients from the healthy population. Furthermore, based on the relative abundance and clinical sleep parameter, we constructed a prediction model utilizing artificial neural network (ANN) for auxiliary diagnosis of insomnia disorder. Overall, the aforementioned study provides a comprehensive understanding of the link between the gut microbiota and insomnia disorder.

Nr4a1 as a myogenic factor is upregulated in satellite cells/myoblast under proliferation and differentiation state.

Myogenic differentiation is precisely regulated with a cascade of genes and pathways. The previous study has demonstrated the muscle-specific deletion of Nr4a1 impairs muscle growth. However, it is still unclear whether muscular Nr4a1 deletion may directly impact myoblast physiology. Here, the present study delves into the molecular mechanism of Nr4a1 in C2C12. Through the analysis of RNAseq and microarray data, Nr4a1 was identified to highly correlate with the expression of myogenic factors. In C2C12, except confirming the induction of Nr4a1 mRNA and protein levels upon the initiation of differentiation, we observed a novel shuttling phenomenon of Nr4a1 from nucleus to cytoplasm in myoblast with a higher expression of MyoD or differentiated myotubes. Furthermore, Nr4a1 overexpression in C2C12 accelerates myoblasts' differentiation and increases myoblast fusion. In contrast, ablation of Nr4a1 expression in C2C12 inhibits the differentiation and fusion process. Meanwhile, in quiescent satellite cells, Nr4a1 expressed is not detected, while its protein level is highly induced in both BaCl2-induced muscle regeneration followed with satellite cells activation and satellite cells of cultured single myofiber. The mechanism may be through the Nr4a1-mediated expression of myogenic factors, e.g. MyoD and MyoG. In summary, the current investigation demonstrates that Nr4a1 is an essential myogenic factor involved in myoblast differentiation.

G Protein-Coupled Receptor 109A and Host Microbiota Modulate Intestinal Epithelial Integrity During Sepsis.

The intestinal epithelial barrier is important to mucosal immunity, although how it is maintained after damage is unclear. Here, we show that G protein-coupled receptor 109A (GPR109A) supports barrier integrity and decreases mortality in a mouse cecum ligation and puncture (CLP) sepsis model. Data from 16S RNA sequencing showed that the intestinal microbiota of WT and Gpr109a-/- mice clustered differently and their compositions were disrupted after CLP surgery. GPR109A-deficient mice showed increased mortality, intestinal permeability, altered inflammation, and lower tight junction gene expression. After eliminating the intestinal flora with antibiotics, all experimental mice died within 48 h of CLP surgery. This demonstrates the critical role of the gut microbiota in CLP-induced sepsis. Importantly, mortality and other pathologies in the model were decreased after Gpr109a-/- mice received WT gut microbiota. These findings indicate that GPR109A regulates the gut microbiota, contributing to intestinal epithelial barrier integrity and decreased mortality in CLP-induced sepsis.