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.

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.

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.

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.

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.

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.

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.

ABSTRACT 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.