MVIP: multi-omics portal of viral infection.

Virus infections are huge threats to living organisms and cause many diseases, such as COVID-19 caused by SARS-CoV-2, which has led to millions of deaths. To develop effective strategies to control viral infection, we need to understand its molecular events in host cells. Virus related functional genomic datasets are growing rapidly, however, an integrative platform for systematically investigating host responses to viruses is missing. Here, we developed a user-friendly multi-omics portal of viral infection named as MVIP (https://mvip.whu.edu.cn/). We manually collected available high-throughput sequencing data under viral infection, and unified their detailed metadata including virus, host species, infection time, assay, and target, etc. We processed multi-layered omics data of more than 4900 viral infected samples from 77 viruses and 33 host species with standard pipelines, including RNA-seq, ChIP-seq, and CLIP-seq, etc. In addition, we integrated these genome-wide signals into customized genome browsers, and developed multiple dynamic charts to exhibit the information, such as time-course dynamic and differential gene expression profiles, alternative splicing changes and enriched GO/KEGG terms. Furthermore, we implemented several tools for efficiently mining the virus-host interactions by virus, host and genes. MVIP would help users to retrieve large-scale functional information and promote the understanding of virus-host interactions.

Biochip Systems for Intelligence and Integration

Disease is one of the major threats to human life and health, and historically there have been many cases which threatened human life due to infectious diseases. In almost all cases, specific triggers for the emergence of disease can be identified, so there is an urgent need for effective detection and identification of most diseases, including infectious diseases. Therefore, this article proposes biochip systems as a tool for disease detection and risk assessment, and explains why they are effective in detecting disease, in terms of their working mechanisms, advantages and disadvantages, specific application scenarios and future trends.

Translation landscape of SARS-CoV-2 noncanonical subgenomic RNAs.

The ongoing COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a positive-stranded RNA genome. Current proteomic studies of SARS-CoV-2 mainly focus on the proteins encoded by its genomic RNA (gRNA) or canonical subgenomic RNAs (sgRNAs). Here, we systematically investigated the translation landscape of SARS-CoV-2, especially its noncanonical sgRNAs. We first constructed a strict pipeline, named vipep, for identifying reliable peptides derived from RNA viruses using RNA-seq and mass spectrometry data. We applied vipep to analyze 24 sets of mass spectrometry data related to SARS-CoV-2 infection. In addition to known canonical proteins, we identified many noncanonical sgRNA-derived peptides, which stably increase after viral infection. Furthermore, we explored the potential functions of those proteins encoded by noncanonical sgRNAs and found that they can bind to viral RNAs and may have immunogenic activity. The generalized vipep pipeline is applicable to any RNA viruses and these results have expanded the SARS-CoV-2 translation map, providing new insights for understanding the functions of SARS-CoV-2 sgRNAs.

SARS-CoV-2 causes a significant stress response mediated by small RNAs in the blood of COVID-19 patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a serious impact on the world. In this study, small RNAs from the blood of COVID-19 patients with moderate or severe symptoms were extracted for high-throughput sequencing and analysis. Interestingly, the levels of a special group of tRNA-derived small RNAs (tsRNAs) were found to be dramatically upregulated after SARS-CoV-2 infection, particularly in coronavirus disease 2019 (COVID-19) patients with severe symptoms. In particular, the 3'CCA tsRNAs from tRNA-Gly were highly consistent with the inflammation indicator C-reactive protein (CRP). In addition, we found that the majority of significantly changed microRNAs (miRNAs) were associated with endoplasmic reticulum (ER)/unfolded protein response (UPR) sensors, which may lead to the induction of proinflammatory cytokine and immune responses. This study found that SARS-CoV-2 infection caused significant changes in the levels of stress-associated small RNAs in patient blood and their potential functions. Our research revealed that the cells of COVID-19 patients undergo tremendous stress and respond, which can be reflected or regulated by small non-coding RNA (sncRNAs), thus providing potential thought for therapeutic intervention in COVID-19 by modulating small RNA levels or activities.

TMBIM6, a potential virus target protein identified by integrated multiomics data analysis in SARS-CoV-2-infected host cells.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we collected open access data to analyze the mechanisms associated with SARS-CoV-2 infection. Gene set enrichment analysis (GSEA) revealed that apoptosis-related pathways were enriched in the cells after SARS-CoV-2 infection, and the results of differential expression analysis showed that biological functions related to endoplasmic reticulum stress (ERS) and lipid metabolism were disordered. TMBIM6 was identified as a potential target for SARS-CoV-2 in host cells through weighted gene coexpression network analysis (WGCNA) of the time course of expression of host and viral proteins. The expression and related functions of TMBIM6 were subsequently analyzed to illuminate how viral proteins interfere with the physiological function of host cells. The potential function of viral proteins was further analyzed by GEne Network Inference with Ensemble of trees (GENIE3). This study identified TMBIM6 as a target protein associated with the pathogenesis of SARS-CoV-2, which might provide a novel therapeutic approach for COVID-19 in the future.