VIGA: a one-stop tool for eukaryotic virus identification and genome assembly from next-generation-sequencing data.

Identification of viruses and further assembly of viral genomes from the next-generation-sequencing data are essential steps in virome studies. This study presented a one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules, namely, identification, taxonomic annotation, assembly and novel virus discovery, which integrated several third-party tools such as BLAST, Trinity, MetaCompass and RagTag. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data and performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of prediabetes, Crohn's disease and ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes, especially the known viruses, in future studies.

Boost immunizations with NA-derived peptide conjugates achieve induction of NA inhibition antibodies and heterologous influenza protections.

Neuraminidase is suggested as an important component for developing a universal influenza vaccine. Targeted induction of neuraminidase-specific broadly protective antibodies by vaccinations is challenging. To overcome this, we rationally select the highly conserved peptides from the consensus amino acid sequence of the globular head domains of neuraminidase. Inspired by the B cell receptor evolution process, a reliable sequential immunization regimen is designed to result in immuno-focusing by steering bulk immune responses to a selected region where broadly protective B lymphocyte epitopes reside. After priming neuraminidase protein-specific antibody responses in C57BL/6 or BALB/c inbred mice strains by immunization or pre-infection, boost immunizations with certain neuraminidase-derived peptide-keyhole limpet hemocyanin conjugates significantly strengthened serum neuraminidase inhibition activities and cross-protections. Overall, this study provides proof of concept for a peptide-based sequential immunization strategy for achieving targeted induction of cross-protective antibody response, which provides references for designing universal vaccines against other highly variable pathogens.

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.

Computed Tomography-Based Machine Learning Differentiates Adrenal Pheochromocytoma From Lipid-Poor Adenoma.

Objectives: To assess the accuracy of computed tomography (CT)-based machine learning models for differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in patients with adrenal incidentalomas. Patients and Methods: The study included 188 tumors in the 183 patients with LPA and 92 tumors in 86 patients with sPHEO. Pre-enhanced CT imaging features of the tumors were evaluated. Machine learning prediction models and scoring systems for differentiating sPHEO from LPA were built using logistic regression (LR), support vector machine (SVM) and random forest (RF) approaches. Results: The LR model performed better than other models. The LR model (M1) including three CT features: CTpre value, shape, and necrosis/cystic changes had an area under the receiver operating characteristic curve (AUC) of 0.917 and an accuracy of 0.864. The LR model (M2) including three CT features: CTpre value, shape and homogeneity had an AUC of 0.888 and an accuracy of 0.832. The S2 scoring system (sensitivity: 0.859, specificity: 0.824) had comparable diagnostic value to S1 (sensitivity: 0.815; specificity: 0.910). Conclusions: Our results indicated the potential of using a non-invasive imaging method such as CT-based machine learning models and scoring systems for predicting histology of adrenal incidentalomas. This approach may assist the diagnosis and personalized care of patients with adrenal tumors.

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.

VirusCircBase: a database of virus circular RNAs.

Circular RNAs (circRNAs) are covalently closed long noncoding RNAs critical in diverse cellular activities and multiple human diseases. Several cancer-related viral circRNAs have been identified in double-stranded DNA viruses (dsDNA), yet no systematic study about the viral circRNAs has been reported. Herein, we have performed a systematic survey of 11 924 circRNAs from 23 viral species by computational prediction of viral circRNAs from viral-infection-related RNA sequencing data. Besides the dsDNA viruses, our study has also revealed lots of circRNAs in single-stranded RNA viruses and retro-transcribing viruses, such as the Zika virus, the Influenza A virus, the Zaire ebolavirus, and the Human immunodeficiency virus 1. Most viral circRNAs had reverse complementary sequences or repeated sequences at the flanking sequences of the back-splice sites. Most viral circRNAs only expressed in a specific cell line or tissue in a specific species. Functional enrichment analysis indicated that the viral circRNAs from dsDNA viruses were involved in KEGG pathways associated with cancer. All viral circRNAs presented in the current study were stored and organized in VirusCircBase, which is freely available at http://www.computationalbiology.cn/ViruscircBase/home.html and is the first virus circRNA database. VirusCircBase forms the fundamental atlas for the further exploration and investigation of viral circRNAs in the context of public health.

Nematode-Encoded RALF Peptide Mimics Facilitate Parasitism of Plants through the FERONIA Receptor Kinase.

The molecular mechanism by which plants defend against plant root-knot nematodes (RKNs) is largely unknown. The plant receptor kinase FERONIA and its peptide ligands, rapid alkalinization factors (RALFs), regulate plant immune responses and cell expansion, which are two important factors for successful RKN parasitism. In this study, we found that mutation of FERONIA in Arabidopsis thaliana resulted in plants showing low susceptibility to the RKN Meloidogyne incognita. To identify the underlying mechanisms associated with this phenomenon, we identified 18 novel RALF-likes from multiple species of RKNs and showed that two RALF-likes (i.e., MiRALF1 and MiRALF3) from M. incognita were expressed in the esophageal gland with high expression during the parasitic stages of nematode development. These nematode RALF-likes also possess the typical activities of plant RALFs and can directly bind to the extracellular domain of FERONIA to modulate specific steps of nematode parasitism-related immune responses and cell expansion. Genetically, both MiRALF1/3 and FERONIA are required for RKN parasitism in Arabidopsis and rice. Collectively, our study suggests that nematode-encoded RALFs facilitate parasitism via plant-encoded FERONIA and provides a novel paradigm for studying host-pathogen interactions.

Influenza A virus utilizes a suboptimal Kozak sequence to fine-tune virus replication and host response.

The segment-specific non-coding regions (NCRs) of influenza A virus RNA genome play important roles in controlling viral RNA transcription, replication and genome packaging. In this report, we present, for the first time to our knowledge, a full view of the segment-specific NCRs of all influenza A viruses by bioinformatics analysis. Our systematic functional analysis revealed that the eight segment-specific NCRs identified could differentially regulate viral RNA synthesis and protein expression at both transcription and translation levels. Interestingly, a highly conserved suboptimal nucleotide at -3 position of the Kozak sequence, which downregulated protein expression at the translation level, was only present in the segment-specific NCR of PB1. By reverse genetics, we demonstrate that recombinant viruses with an optimized Kozak sequence at the -3 position in PB1 resulted in a significant multiple-cycle replication reduction that was independent of PB1-F2 expression. Our detailed dynamic analysis of virus infection revealed that the mutant virus displays slightly altered dynamics from the wild-type virus on both viral RNA synthesis and protein production. Furthermore, we demonstrated that the level of PB1 expression is involved in regulating type I IFN production. Together, these data reveal a novel strategy exploited by influenza A virus to fine-tune virus replication dynamics and host antiviral response through regulating PB1 protein expression.