Shared molecular signatures between coronavirus infection and neurodegenerative diseases provide targets for broad-spectrum drug development.

Growing evidences have suggested the association between coronavirus infection and neurodegenerative diseases. However, the molecular mechanism behind the association is complex and remains to be clarified. This study integrated human genes involved in infections of three coronaviruses including SARS-CoV-2, SARS-CoV and MERS-CoV from multi-omics data, and investigated the shared genes and molecular functions between coronavirus infection and two neurodegenerative diseases, namely Alzheimer's Disease (AD) and Parkinson's Disease (PD). Seven genes including HSP90AA1, ALDH2, CAV1, COMT, MTOR, IGF2R and HSPA1A, and several inflammation and stress response-related molecular functions such as MAPK signaling pathway, NF-kappa B signaling pathway, responses to oxidative or chemical stress were common to both coronavirus infection and neurodegenerative diseases. These genes were further found to interact with more than 20 other viruses. Finally, drugs targeting these genes were identified. The study would not only help clarify the molecular mechanism behind the association between coronavirus infection and neurodegenerative diseases, but also provide novel targets for the development of broad-spectrum drugs against both coronaviruses and neurodegenerative diseases.

Virome analysis provides new insights into the association between viruses and Parkinson's disease.

Parkinson's disease (PD) is a kind of neurodegenerative disease that causes a huge burden to society. Previous studies have suggested the association between PD and multiple viruses. However, there is still a lack of a virome study about PD. This study systematically identified viruses from the public RNA-sequencing data of more than 700 samples from both PD patients and the control group (most were healthy people). Only nine viruses such as human betaherpesvirus 5 and Merkel cell polyomavirus have been detected in several human brain tissues of the central nervous system, the appendix, and blood of PD patients, and all of these viruses were also detected in the control group. Most viruses were observed to have low abundance in no more than three tissues. No statistically significant differences were observed between the virus abundance in the PD patients and the control group for all viruses. The positive rates of most viruses in PD patients were higher or similar to that in the control group, although those were less than 5% for most viruses. Overall, this is the first study to systematically investigate the virome in PD patients, and provides new insights into the association between viruses and PD.

Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms.

The coronavirus 3C-like (3CL) protease, a cysteine protease, plays an important role in viral infection and immune escape. However, there is still a lack of effective tools for determining the cleavage sites of the 3CL protease. This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein, and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus, Betacoronavirus and Gammacoronavirus. Strong residue preferences were observed at the neighboring positions of the cleavage sites. A random forest (RF) model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes, and the model achieved an AUC of 0.96 in cross-validations. The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts. It achieved an AUC of 0.95 and predicted correctly 80% of the cleavage sites. Then, 1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model. These proteins were enriched in several GO terms related to the cytoskeleton, such as the microtubule, actin and tubulin. Finally, a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model. Overall, the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.

Microbiome Profiling Using Shotgun Metagenomic Sequencing Identified Unique Microorganisms in COVID-19 Patients With Altered Gut Microbiota.

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut-lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman's correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

virusMED: an atlas of hotspots of viral proteins.

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2.

The life-threatening coronaviruses MERS-CoV, SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) have caused and will continue to cause enormous morbidity and mortality to humans. Virus-encoded noncoding RNAs are poorly understood in coronaviruses. Data mining of viral-infection-related RNA-sequencing data has resulted in the identification of 28 754, 720 and 3437 circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2, respectively. MERS-CoV exhibits much more prominent ability to encode circRNAs in all genomic regions than those of SARS-CoV-1/2. Viral circRNAs typically exhibit low expression levels. Moreover, majority of the viral circRNAs exhibit expressions only in the late stage of viral infection. Analysis of the competitive interactions of viral circRNAs, human miRNAs and mRNAs in MERS-CoV infections reveals that viral circRNAs up-regulated genes related to mRNA splicing and processing in the early stage of viral infection, and regulated genes involved in diverse functions including cancer, metabolism, autophagy, viral infection in the late stage of viral infection. Similar analysis in SARS-CoV-2 infections reveals that its viral circRNAs down-regulated genes associated with metabolic processes of cholesterol, alcohol, fatty acid and up-regulated genes associated with cellular responses to oxidative stress in the late stage of viral infection. A few genes regulated by viral circRNAs from both MERS-CoV and SARS-CoV-2 were enriched in several biological processes such as response to reactive oxygen and centrosome localization. This study provides the first glimpse into viral circRNAs in three deadly coronaviruses and would serve as a valuable resource for further studies of circRNAs in coronaviruses.

Compositional diversity and evolutionary pattern of coronavirus accessory proteins.

Accessory proteins play important roles in the interaction between coronaviruses and their hosts. Accordingly, a comprehensive study of the compositional diversity and evolutionary patterns of accessory proteins is critical to understanding the host adaptation and epidemic variation of coronaviruses. Here, we developed a standardized genome annotation tool for coronavirus (CoroAnnoter) by combining open reading frame prediction, transcription regulatory sequence recognition and homologous alignment. Using CoroAnnoter, we annotated 39 representative coronavirus strains to form a compositional profile for all of the accessary proteins. Large variations were observed in the number of accessory proteins of 1-10 for different coronaviruses, with SARS-CoV-2 and SARS-CoV having the most (9 and 10, respectively). The variation between SARS-CoV and SARS-CoV-2 accessory proteins could be traced back to related coronaviruses in other hosts. The genomic distribution of accessory proteins had significant intra-genus conservation and inter-genus diversity and could be grouped into 1, 4, 2 and 1 types for alpha-, beta-, gamma-, and delta-coronaviruses, respectively. Evolutionary analysis suggested that accessory proteins are more conservative locating before the N-terminal of proteins E and M (E-M), while they are more diverse after these proteins. Furthermore, comparison of virus-host interaction networks of SARS-CoV-2 and SARS-CoV accessory proteins showed that they share multiple antiviral signaling pathways, those involved in the apoptotic process, viral life cycle and response to oxidative stress. In summary, our study provides a tool for coronavirus genome annotation and builds a comprehensive profile for coronavirus accessory proteins covering their composition, classification, evolutionary pattern and host interaction.

Prediction of the Receptorome for the Human-Infecting Virome.

The virus receptors are key for the viral infection of host cells. Identification of the virus receptors is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level, high number of interaction partners and high expression level. Here, a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences. A total of 1424 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting virome. In addition, the combination of the random-forest model with protein-protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses, such as the enterovirus, norovirus and West Nile virus. Finally, the candidate alternative receptors of the SARS-CoV-2 were also predicted in this study. As far as we know, this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.

Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China.

An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV.