SEECancer: a resource for somatic events in evolution of cancer genome.

Cancer cells progressively evolve from a premalignant to a malignant state, which is driven by accumulating somatic alterations that confer normal cells a fitness advantage. Improvements in high-throughput sequencing techniques have led to an increase in construction of tumor phylogenetics and identification of somatic driver events that specifically occurred in different tumor progression stages. Here, we developed the SEECancer database (http://biocc.hrbmu.edu.cn/SEECancer), which aims to present the comprehensive cancer evolutionary stage-specific somatic events (including early-specific, late-specific, relapse-specific, metastasis-specific, drug-resistant and drug-induced genomic events) and their temporal orders. By manually curating over 10 000 published articles, 1231 evolutionary stage-specific genomic events and 5772 temporal orders involving 82 human cancers and 23 tissue origins were collected and deposited in the SEECancer database. Each entry contains the somatic event, evolutionary stage, cancer type, detection approach and relevant evidence. SEECancer provides a user-friendly interface for browsing, searching and downloading evolutionary stage-specific somatic events and temporal relationships in various cancers. With increasing attention on cancer genome evolution, the necessary information in SEECancer will facilitate understanding of cancer etiology and development of evolutionary therapeutics, and help clinicians to discover biomarkers for monitoring tumor progression.

Systematic Microbiome Dysbiosis Is Associated with IgA Nephropathy.

IgA nephropathy (IgAN) is reportedly associated with microbial dysbiosis. However, the microbiome dysregulation of IgAN patients across multiple niches remains unclear. To gain a systematic understanding of microbial dysbiosis, we conducted large-scale 16S rRNA gene sequencing in IgAN patients and healthy volunteers across 1,732 oral, pharynx, gut, and urine samples. We observed a niche-specific increase of several opportunistic pathogens, including Bergeyella and Capnocytophaga in the oral and pharynx, whereas some beneficial commensals decreased in IgAN patients. Similar alterations were also observed in the early versus advanced stage of chronic kidney disease (CKD) progression. Moreover, Bergeyella, Capnocytophaga, and Comamonas in the oral and pharynx were positively associated with creatinine and urea, indicating renal lesions. Random forest classifiers were developed by using the microbial abundance to predict IgAN, achieving an optimal accuracy of 0.879 in the discovery phase and 0.780 in the validation phase. IMPORTANCE This study provides microbial profiles of IgAN across multiple niches and underlines the potential of these biomarkers as promising, noninvasive tools with which to differentiate IgAN patients for clinical applications.

dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms.

MicroRNAs (miRNAs) are important regulators in gene expression. The dysregulation of miRNA expression is widely reported in the transformation from physiological to pathological states of cells. A large number of differentially expressed miRNAs (DEMs) have been identified in various human cancers by using high-throughput technologies, such as microarray and miRNA-seq. Through mining of published studies with high-throughput experiment information, the database of DEMs in human cancers (dbDEMC) was constructed with the aim of providing a systematic resource for the storage and query of the DEMs. Here we report an update of the dbDEMC to version 3.0, which contains two-fold more data entries than the second version and now includes also data from mice and rats. The dbDEMC 3.0 contains 3268 unique DEMs in 40 different cancer types. The current datasets for differential expression analysis have expanded to 9 generalized categories. Moreover, the current release integrates functional annotations of DEMs obtained by using experimentally validated targets. The annotations can be of great benefit to the intensive analysis of the roles of DEMs in cancer. In summary, dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human cancers. The dbDEMC 3.0 is freely accessible at https://www.biosino.org/dbDEMC.

Mining of novel secondary metabolite biosynthetic gene clusters from acid mine drainage.

Acid mine drainage (AMD) is usually acidic (pH < 4) and contains high concentrations of dissolved metals and metalloids, making AMD a typical representative of extreme environments. Recent studies have shown that microbes play a key role in AMD bioremediation, and secondary metabolite biosynthetic gene clusters (smBGCs) from AMD microbes are important resources for the synthesis of antibacterial and anticancer drugs. Here, 179 samples from 13 mineral types were used to analyze the putative novel microorganisms and secondary metabolites in AMD environments. Among 7,007 qualified metagenome-assembled genomes (MAGs) mined from these datasets, 6,340 MAGs could not be assigned to any GTDB species representative. Overall, 11,856 smBGCs in eight categories were obtained from 7,007 qualified MAGs, and 10,899 smBGCs were identified as putative novel smBGCs. We anticipate that these datasets will accelerate research in the field of AMD bioremediation, aid in the discovery of novel secondary metabolites, and facilitate investigation into gene functions, metabolic pathways, and CNPS cycles in AMD.

Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts.

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.

A Novel Mutation of the KLK6 Gene in a Family With Knee Osteoarthritis.

To investigate the correlation between gene mutation and knee osteoarthritis (KOA), a whole-exome sequencing (WES) was applied to analyze blood samples of four KOA patients and two normal subjects in a family. Gene mutations were identified by gene-trapping and high-throughput sequencing analysis across the differences between the patients and normal subjects. The interactive gene network analysis on the retrieval of interacting genes (STRING) database and the KOA-related genes expression data sets was performed. A possibly detrimental and nonsynonymous mutation at the kallikrein-related peptidase 6 (KLK6) gene (rs201586262, c. C80A, P27H) was identified and attracted our attention. KLK6 belongs to the kallikrein family of serine proteases and its serum level is known as a prevalent biomarker in inflammatory and malignant diseases. KLK6 expresses in the extracellular compartment for matrix degradation, highlighting that KLK6 plays a role in the pathogenesis of KOA. By using the gene databases, the KOA-related genes were mined after de-duplication and IL6 was selected as the most relevant gene through interactive analysis of protein-protein interaction (PPI) network. The data suggested that KLK6 gene mutation and the related expression alteration of IL6 gene might determine the occurrence of hereditary KOA. The is the first study discovering the gene mutation of KLK6 as a factor of pathogenesis of KOA, especially the hereditary KOA.

GTDB: an integrated resource for glycosyltransferase sequences and annotations.

Glycosyltransferases (GTs), a large class of carbohydrate-active enzymes, adds glycosyl moieties to various substrates to generate multiple bioactive compounds, including natural products with pharmaceutical or agrochemical values. Here, we first collected comprehensive information on GTs, including amino acid sequences, coding region sequences, available tertiary structures, protein classification families, catalytic reactions and metabolic pathways. Then, we developed sequence search and molecular docking processes for GTs, resulting in a GTs database (GTDB). In the present study, 520 179 GTs from approximately 21 647 species that involved in 394 kinds of different reactions were deposited in GTDB. GTDB has the following useful features: (i) text search is provided for retrieving the complete details of a query by combining multiple identifiers and data sources; (ii) a convenient browser allows users to browse data by different classifications and download data in batches; (iii) BLAST is offered for searching against pre-defined sequences, which can facilitate the annotation of the biological functions of query GTs; and lastly, (iv) GTdock using AutoDock Vina performs docking simulations of several GTs with the same single acceptor and displays the results based on 3Dmol.js allowing easy view of models.