m6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species.

N 6-Methyladenosine (m6A) is one of the most abundant internal chemical modifications on eukaryote mRNA and is involved in numerous essential molecular functions and biological processes. To facilitate the study of this important post-transcriptional modification, we present here m6A-Atlas v2.0, an updated version of m6A-Atlas. It was expanded to include a total of 797 091 reliable m6A sites from 13 high-resolution technologies and two single-cell m6A profiles. Additionally, three methods (exomePeaks2, MACS2 and TRESS) were used to identify >16 million m6A enrichment peaks from 2712 MeRIP-seq experiments covering 651 conditions in 42 species. Quality control results of MeRIP-seq samples were also provided to help users to select reliable peaks. We also estimated the condition-specific quantitative m6A profiles (i.e. differential methylation) under 172 experimental conditions for 19 species. Further, to provide insights into potential functional circuitry, the m6A epitranscriptomics were annotated with various genomic features, interactions with RNA-binding proteins and microRNA, potentially linked splicing events and single nucleotide polymorphisms. The collected m6A sites and their functional annotations can be freely queried and downloaded via a user-friendly graphical interface at: http://rnamd.org/m6a.

m7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome.

With recent progress in mapping N7-methylguanosine (m7G) RNA methylation sites, tens of thousands of experimentally validated m7G sites have been discovered in various species, shedding light on the significant role of m7G modification in regulating numerous biological processes including disease pathogenesis. An integrated resource that enables the sharing, annotation and customized analysis of m7G data will greatly facilitate m7G studies under various physiological contexts. We previously developed the m7GHub database to host mRNA m7G sites identified in the human transcriptome. Here, we present m7GHub v.2.0, an updated resource for a comprehensive collection of m7G modifications in various types of RNA across multiple species: an m7GDB database containing 430 898 putative m7G sites identified in 23 species, collected from both widely applied next-generation sequencing (NGS) and the emerging Oxford Nanopore direct RNA sequencing (ONT) techniques; an m7GDiseaseDB hosting 156 206 m7G-associated variants (involving addition or removal of an m7G site), including 3238 disease-relevant m7G-SNPs that may function through epitranscriptome disturbance; and two enhanced analysis modules to perform interactive analyses on the collections of m7G sites (m7GFinder) and functional variants (m7GSNPer). We expect that m7Ghub v.2.0 should serve as a valuable centralized resource for studying m7G modification. It is freely accessible at: www.rnamd.org/m7GHub2.

RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.

Recent advances in epitranscriptomics have unveiled functional associations between RNA modifications (RMs) and multiple human diseases, but distinguishing the functional or disease-related single nucleotide variants (SNVs) from the majority of 'silent' variants remains a major challenge. We previously developed the RMDisease database for unveiling the association between genetic variants and RMs concerning human disease pathogenesis. In this work, we present RMDisease v2.0, an updated database with expanded coverage. Using deep learning models and from 873 819 experimentally validated RM sites, we identified a total of 1 366 252 RM-associated variants that may affect (add or remove an RM site) 16 different types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G, A-to-I, ac4C, Am, Cm, Um, Gm, hm5C, D and f5C) in 20 organisms (human, mouse, rat, zebrafish, maize, fruit fly, yeast, fission yeast, Arabidopsis, rice, chicken, goat, sheep, pig, cow, rhesus monkey, tomato, chimpanzee, green monkey and SARS-CoV-2). Among them, 14 749 disease- and 2441 trait-associated genetic variants may function via the perturbation of epitranscriptomic markers. RMDisease v2.0 should serve as a useful resource for studying the genetic drivers of phenotypes that lie within the epitranscriptome layer circuitry, and is freely accessible at: www.rnamd.org/rmdisease2.

m6A-Maize: Weakly supervised prediction of m6A-carrying transcripts and m6A-affecting mutations in maize (Zea mays).

With the rapid development of high-throughput sequencing techniques nowadays, extensive attention has been paid to epitranscriptomics, which covers more than 150 distinct chemical modifications to date. Among that, N6-methyladenosine (m6A) modification has the most abundant existence, and it is also significantly related to varieties of biological processes. Meanwhile, maize is the most important food crop and cultivated throughout the world. Therefore, the study of m6A modification in maize has both economic and academic value. In this research, we proposed a weakly supervised learning model to predict the situation of m6A modification in maize. The proposed model learns from low-resolution epitranscriptome datasets (e.g., MeRIP-seq), which predicts the m6A methylation status of given fragments or regions. By taking advantage of our prediction model, we further identified traits-associated SNPs that may affect (add or remove) m6A modifications in maize, which may provide potential regulatory mechanisms at epitranscriptome layer. Additionally, a centralized online-platform was developed for m6A study in maize, which contains 58,838 experimentally validated maize m6A-containing regions including training and testing datasets, and a database for 2,578 predicted traits-associated m6A-affecting maize mutations. Furthermore, the online web server based on proposed weakly supervised model is available for predicting putative m6A sites from user-uploaded maize sequences, as well as accessing the epitranscriptome impact of user-interested maize SNPs on m6A modification. In all, our work provided a useful resource for the study of m6A RNA methylation in maize species. It is freely accessible at www.xjtlu.edu.cn/biologicalsciences/maize.

Development of specific and selective bactericide by introducing exogenous metabolite of pathogenic bacteria.

The widespread and repeated use of broad-spectrum bactericides has led to an increase in resistance. Developing novel broad-spectrum bactericides cannot solve the resistance problem, and may even aggravate it. The design of specific and selective bactericides has become urgent. A specific bactericidal design strategy was proposed by introducing exogenous metabolites in this study. This strategy was used to optimize two known antibacterial agents, luteolin (M) and Isoprothiolane (D), against Xoo. Based on the prodrug principles, target compound MB and DB were synthesized by combing M or D with exogenous metabolites, respectively. Bactericidal activity test results demonstrated that while the antibacterial ability of target compounds was significantly improved, their selectivity was also well enhanced by the introducing of exogenous metabolites. Comparing with the original compound, the antibacterial activity of target compound was significantly increased 92.0% and 74.5%, respectively. The optimized target compounds were more easily absorbed, and the drug application concentrations were much lower than those of the original agents, which would greatly reduce environmental pollution and relieve resistance risk. Our proposed strategy is of great significance for exploring the specific and selective bactericides against other pathogens.

An analysis of the mechanism underlying photocatalytic disinfection based on integrated metabolic networks and transcriptional data.

Photocatalytic disinfection has long been used to combat pathogenic bacteria. However, the specific mechanism underlying photocatalytic disinfection and its corresponding targets remain unclear. In this study, an analysis of the potential mechanism underlying photocatalytic disinfection was performed based on integrated metabolic networks and transcriptional data. Two sets of RNA-seq data (wild type and a photocatalysis-resistant mutant mediated by titanium dioxide (TiO2)) were processed to constrain the genome scale metabolic models (GSMM) of E. coli. By analyzing the metabolic network, the differential metabolic flux of every reaction was computed in constrained GSMM, and several significantly differential metabolic fluxes in reactions were extracted and analyzed. Most of these reactions were involved in the transmembrane transport of substances and occurred on the inner membrane or were an important component of the cell membrane. These results, which are consistent with the reported information, validated our analysis process. In addition, our work also identified other new and valuable metabolic pathways, such as the reaction ALCD2x, which has a great effect on the energy production process under bacterial anaerobic conditions. The DHAK reaction is also related to the metabolic process of ATP. These reactions with large differential metabolic fluxes merit further research. Additionally, to provide a strategy to address photocatalysis-resistant mutant bacteria, a metabolic compensation analysis was also performed. The metabolic compensation analysis results provided suggestions for a combined method that can effectively combat resistant bacteria. This method could also be used to explore the mechanisms of drug resistance in other microorganisms.