Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.

microbioTA (http://bio-annotation.cn/microbiota) was constructed to provide a comprehensive, user-friendly resource for the application of microbiome data from diseased tissues, helping users improve their general knowledge and deep understanding of tissue-derived microbes. Various microbes have been found to colonize cancer tissues and play important roles in cancer diagnoses and outcomes, with many studies focusing on developing better cancer-related microbiome data. However, there are currently no independent, comprehensive open resources cataloguing cancer-related microbiome data, which limits the exploration of the relationship between these microbes and cancer progression. Given this, we propose a new strategy to re-align the existing next-generation sequencing data to facilitate the mining of hidden sequence data describing the microbiome to maximize available resources. To this end, we collected 417 publicly available datasets from 25 human and 14 mouse tissues from the Gene Expression Omnibus database and use these to develop a novel pipeline to re-align microbiome sequences facilitating in-depth analyses designed to reveal the microbial profile of various cancer tissues and their healthy controls. microbioTA is a user-friendly online platform which allows users to browse, search, visualize, and download microbial abundance data from various tissues along with corresponding analysis results, aimimg at providing a reference for cancer-related microbiome research.