Ten computational challenges in human virome studies.

In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases. Despite this progress, the field of human virome research remains nascent, primarily hindered by the absence of effective methods, particularly in the domain of computational tools. This perspective systematically outlines ten computational challenges spanning various types of virome studies. These challenges arise due to the vast diversity of viromes, the absence of a universal marker gene in viral genomes, the low abundance of virus populations, the remote or minimal homology of viral proteins to known proteins, and the highly dynamic and heterogeneous nature of viromes. For each computational challenge, we discuss the underlying reasons, current research progress, and potential solutions. The resolution of these challenges necessitates ongoing collaboration among computational scientists, virologists, and multidisciplinary experts. In essence, this perspective serves as a comprehensive guide for directing computational efforts in human virome studies.

PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network.

Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.

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.

Transcription factor FOXM1 specifies chromatin DNA to extracellular vesicles.

Extracellular vesicle DNAs (evDNAs) hold significant diagnostic value for various diseases and facilitate transcellular transfer of genetic material. Our study identifies transcription factor FOXM1 as a mediator for directing chromatin genes or DNA fragments (termed FOXM1-chDNAs) to extracellular vesicles (EVs). FOXM1 binds to MAP1LC3/LC3 in the nucleus, and FOXM1-chDNAs, such as the DUX4 gene and telomere DNA, are designated by FOXM1 binding and translocated to the cytoplasm before being released to EVs through the secretory autophagy during lysosome inhibition (SALI) process involving LC3. Disrupting FOXM1 expression or the SALI process impairs FOXM1-chDNAs incorporation into EVs. FOXM1-chDNAs can be transmitted to recipient cells via EVs and expressed in recipient cells when they carry functional genes. This finding provides an example of how chromatin DNA fragments are specified to EVs by transcription factor FOXM1, revealing its contribution to the formation of evDNAs from nuclear chromatin. It provides a basis for further exploration of the roles of evDNAs in biological processes, such as horizontal gene transfer.

Multi-omics data integration reveals the complexity and diversity of host factors associated with influenza virus infection.

Influenza viruses pose a significant and ongoing threat to human health. Many host factors have been identified to be associated with influenza virus infection. However, there is currently a lack of an integrated resource for these host factors. This study integrated human genes and proteins associated with influenza virus infections for 14 subtypes of influenza A viruses, as well as influenza B and C viruses, and built a database named H2Flu to store and organize these genes or proteins. The database includes 28,639 differentially expressed genes (DEGs), 1,850 differentially expressed proteins, and 442 proteins with differential posttranslational modifications after influenza virus infection, as well as 3,040 human proteins that interact with influenza virus proteins and 57 human susceptibility genes. Further analysis showed that the dynamic response of human cells to virus infection, cell type and strain specificity contribute significantly to the diversity of DEGs. Additionally, large heterogeneity was also observed in protein-protein interactions between humans and different types or subtypes of influenza viruses. Overall, the study deepens our understanding of the diversity and complexity of interactions between influenza viruses and humans, and provides a valuable resource for further studies on such interactions.

Prediction of mammalian virus cross-species transmission based on host proteins.

Most emerging viruses are spilled over from mammals. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of newly emerging viruses. This study systematically investigated the host proteins associated with the cross-species transmission of mammalian viruses based on 1,271 pairs of virus-mammal interactions including 382 viruses from 33 viral families and 73 mammal species from 11 orders. Numerous host proteins were found to contribute to the cross-species transmission of mammalian viruses. Host proteins potentially contributing to virus cross-species transmission are specific to viral families, and few overlaps of such host proteins are observed in different viral families. Based on these host proteins, the random-forest (RF) models were built to predict the cross-species transmission potential of mammalian viruses. Moderate performance was obtained when using all viruses together. However, when modeling by viral family, the performance of the RF models varied much among viral families. In 13 viral families such as Flaviviridae, Retroviridae, and Poxviridae, the AUC of the RF model was greater than 0.8. Finally, the contribution of virus receptors to cross-species transmission was evaluated, and the virus receptor was found to have a minor effect in predicting the cross-species transmission of mammalian viruses. The study deepens our understanding of the mechanism of virus cross-species transmission and provides a framework for predicting the cross-species transmission of mammalian viruses. IMPORTANCE Emerging viruses pose serious threats to humans. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of emerging viruses. This study systematically identified host factors associated with cross-species transmission of mammalian viruses and further built machine-learning models for predicting cross-species transmission of the viruses based on host factors including virus receptors. The study not only deepens our understanding of the mechanism of virus cross-species transmission but also provides a framework for predicting the cross-species transmission of mammalian viruses based on host factors.

An updated database of virus circular RNAs provides new insights into the biogenesis mechanism of the molecule.

Virus circular RNAs (circRNA) have been reported to be extensively expressed and play important roles in viral infections. Previously we build the first database of virus circRNAs named VirusCircBase which has been widely used in the field. This study significantly improved the database on both the data quantity and database functionality: the number of virus circRNAs, virus species, host organisms was increased from 46440, 23, 9 to 60859, 43, 22, respectively, and 1902 full-length virus circRNAs were newly added; new functions were added such as visualization of the expression level of virus circRNAs and visualization of virus circRNAs in the Genome Browser. Analysis of the expression of virus circRNAs showed that they had low expression levels in most cells or tissues and showed strong expression heterogeneity. Analysis of the splicing of virus circRNAs showed that they used a much higher proportion of non-canonical back-splicing signals compared to those in animals and plants, and mainly used the A5SS (alternative 5' splice site) in alternative-splicing. Most virus circRNAs have no more than two isoforms. Finally, human genes associated with the virus circRNA production were investigated and more than 1000 human genes exhibited moderate correlations with the expression of virus circRNAs. Most of them showed negative correlations including 42 genes encoding RNA-binding proteins. They were significantly enriched in biological processes related to cell cycle and RNA processing. Overall, the study provides a valuable resource for further studies of virus circRNAs and also provides new insights into the biogenesis mechanisms of virus circRNAs.

Are the original SARS-CoV-2 novel mutants from in vitro culture able to escape the immune response?

Monitoring variations in the virus genome to understand the SARS-CoV-2 evolution and spread of the virus is extremely important. Seven early SARS-CoV-2 isolates in China were cultured in vitro and were analyzed for their viral infectivity through viral growth assay, tissue culture infectious dose (TCID50 ) assay, spike protein quantification, and next generation sequencing analysis, and the resultant mutations in spike protein were used to generate the corresponding pseudoviruses for analysis of immune escape from vaccination and postinfection immunity. The results revealed that in vitro cultured SARS-CoV-2 virus had much higher mutation frequency (up to ~20 times) than that in infected patients, suggesting that SARS-CoV-2 diversify under favorable conditions. Monitoring viral mutations is not only helpful for better understanding of virus evolution and virulence change, but also the key to prevent virus transmission and disease progression. Compared with the D614G reference strain, a pseudovirus strain of SARS-CoV-2 was constructed with a high mutation rate site on the spike protein. We found some novel spike mutations during in vitro culture, such as E868Q, conferred further immune escape ability.

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.

Regulation of PKR-dependent RNA translation inhibition by TRIM21 upon virus infection or other stress.

The host always employs various ways to defend against viral infection and spread. However, viruses have evolved their own effective strategies, such as inhibition of RNA translation of the antiviral effectors, to destroy the host's defense barriers. Protein synthesis, commonly controlled by the α-subunit of eukaryotic translation initiation factor 2 (eIF2α), is a basic cellular biological process among all species. In response to viral infection, in addition to inducing the transcription of antiviral cytokines by innate immunity, infected cells also inhibit the RNA translation of antiviral factors by activating the protein kinase R (PKR)-eIF2α signaling pathway. Regulation of innate immunity has been well studied; however, regulation of the PKR-eIF2α signaling pathway remains unclear. In this study, we found that the E3 ligase TRIM21 negatively regulates the PKR-eIF2α signaling pathway. Mechanistically, TRIM21 interacts with the PKR phosphatase PP1α and promotes K6-linked polyubiquitination of PP1α. Ubiquitinated PP1α augments its interaction with PKR, causing PKR dephosphorylation and subsequent translational inhibition release. Furthermore, TRIM21 can constitutively restrict viral infection by reversing PKR-dependent translational inhibition of various previously known and unknown antiviral factors. Our study highlights a previously undiscovered role of TRIM21 in regulating translation, which will provide new insights into the host antiviral response and novel targets for the treatment of translation-associated diseases in the clinic.

Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1) pdm09 viruses.

The Influenza A (H1N1) pdm09 virus caused a global pandemic in 2009 and has circulated seasonally ever since. As the continual genetic evolution of hemagglutinin in this virus leads to antigenic drift, rapid identification of antigenic variants and characterization of the antigenic evolution are needed. In this study, we developed PREDAC-H1pdm, a model to predict antigenic relationships between H1N1pdm viruses and identify antigenic clusters for post-2009 pandemic H1N1 strains. Our model performed well in predicting antigenic variants, which was helpful in influenza surveillance. By mapping the antigenic clusters for H1N1pdm, we found that substitutions on the Sa epitope were common for H1N1pdm, whereas for the former seasonal H1N1, substitutions on the Sb epitope were more common in antigenic evolution. Additionally, the localized epidemic pattern of H1N1pdm was more obvious than that of the former seasonal H1N1, which could make vaccine recommendation more sophisticated. Overall, the antigenic relationship prediction model we developed provides a rapid determination method for identifying antigenic variants, and the further analysis of evolutionary and epidemic characteristics can facilitate vaccine recommendations and influenza surveillance for H1N1pdm.

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.

Systematic identification and characterization of viral small RNAs from small RNA-Seq data.

Virus-encoded small RNAs (vsRNAs) have been reported to play an important role in viral infections. Unfortunately, there is still a lack of a systematic characterization and resource of vsRNAs. Herein, we identified a total of 19 734 high-confidence vsRNAs including 2746 microRNAs (miRNAs) in 64 viral species from more than 800 samples of public small RNA-Seq data. The number of vsRNAs identified in viruses varied from 1 to 2489 with a median of 170. The length distribution of vsRNAs peaked at 21 and 22 nt. Plant viruses were found to express larger number and higher levels of vsRNAs than those of animal viruses. Besides, the number of vsRNAs identified increased as the viral infection persisted. Interestingly, the vsRNA showed strong expression specificity as little overlap was observed among vsRNAs identified in different strains of a virus, or in different hosts, cells, or tissues infected by the same virus. Little conservation was observed among vsRNAs of different viruses. The viral miRNAs were found to interact with host genes involved in multiple biological processes related to organization, development, action potential, polarity establishment, methylation, immune response, gene regulation, localization, and so on. To facilitate the usage of vsRNAs, a database named vsRNAdb was built for organizing and storing vsRNAs which is available at http://www.computationalbiology.cn/vsRNAdb/#/vsRNAdb/#/. Overall, the study deepens our understanding about the diversity and complexity of vsRNAs and provides a rich resource for further studies of vsRNAs.

The bZIP transcription factor BATF3/ZIP-10 suppresses innate immunity by attenuating PMK-1/p38 signaling.

Innate immunity is the first line of host defense against pathogenic invasion in metazoans. The transcription factor basic leucine zipper transcriptional factor ATF-like 3 (BATF3) plays a crucial role in the development of conventional dendritic cells and the program of CD8 + T cell survival and memory, but the role of BATF3 in innate immune responses remains unclear. Here, we show an evolutionarily conserved basic-region leucine zipper (bZIP) transcription factor BATF3/ZIP-10 suppresses innate immune response through repressing the p38/PMK-1 mitogen-activated protein kinase (MAPK) pathway in vitro and in vivo. The worm mutant lacking the Caenorhabditis elegans homolog BATF3, ZIP-10, exhibited enhanced resistance to PA14 infection, which was completely rescued by transgenic expression of either endogenous zip-10 or mouse or human Batf3 cDNA driven by the worm zip-10 promoter. ZIP-10 expression was inhibited by a microRNA miR-60 that was downregulated upon PA14 infection. Moreover, the level of phosphorylated but not total PMK-1/p38 was attenuated by ZIP-10 and stimulated by miR-60. The human HEK293 cells with Batf3 overexpression or RNA-interference knockdown exhibited a reduction or increase of the cell viability upon Pseudomonas aeruginosa PA14 infection, respectively. The overexpression of either worm ZIP-10 or human BATF3 abolished the activation of p38 and inhibited the expression of antimicrobial peptides and cytokine genes in HEK293 cells. Our findings indicate that the genetic transcriptional program of the evolutionally conserved bZIP transcription factor BATF3/ZIP-10 suppresses innate immunity by attenuating the p38 MAPK signaling activity, which expands our understanding of the pathological mechanisms underlying relevant infectious 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.

Systematic identification and characterization of repeat sequences in African swine fever virus genomes.

African swine fever virus (ASFV) is a large DNA virus that infects domestic pigs with high morbidity and mortality rates. Repeat sequences, which are DNA sequence elements that are repeated more than twice in the genome, play an important role in the ASFV genome. The majority of repeat sequences, however, have not been identified and characterized in a systematic manner. In this study, three types of repeat sequences, including microsatellites, minisatellites and short interspersed nuclear elements (SINEs), were identified in the ASFV genome, and their distribution, structure, function, and evolutionary history were investigated. Most repeat sequences were observed in noncoding regions and at the 5' end of the genome. Noncoding repeat sequences tended to form enhancers, whereas coding repeat sequences had a lower ratio of alpha-helix and beta-sheet and a higher ratio of loop structure and surface amino acids than nonrepeat sequences. In addition, the repeat sequences tended to encode penetrating and antimicrobial peptides. Further analysis of the evolution of repeat sequences revealed that the pan-repeat sequences presented an open state, showing the diversity of repeat sequences. Finally, CpG islands were observed to be negatively correlated with repeat sequence occurrences, suggesting that they may affect the generation of repeat sequences. Overall, this study emphasizes the importance of repeat sequences in ASFVs, and these results can aid in understanding the virus's function and evolution.

Surveillance of avian influenza viruses in live bird markets of Shandong province from 2013 to 2019.

Avian influenza viruses (AIVs) seriously affect the poultry industry and pose a great threat to humans. Timely surveillance of AIVs is the basis for preparedness of the virus. This study reported the long-term surveillance of AIVs in the live bird market (LBM) of 16 cities in Shandong province from 2013 to 2019. A total of 29,895 samples were obtained and the overall positive rate of AIVs was 9.7%. The H9 was found to be the most predominant subtype in most of the time and contributed most to the monthly positve rate of AIVs as supported by the univariate and multivariate analysis, while H5 and H7 only circulated in some short periods. Then, the whole-genome sequences of 62 representative H9N2 viruses including one human isolate from a 7-year-old boy in were determined and they were genetically similar to each other with the median pairwise sequence identities ranging from 0.96 to 0.98 for all segments. The newly sequenced viruses were most similar to viruses isolated in chickens in mainland China, especially the provinces in Eastern China. Phylogenetic analysis showed that these newly sequenced H9N2 viruses belonged to the same clade for all segments except PB1. Nearly all of these viruses belonged to the G57 genotype which has dominated in China since 2010. Finally, several molecular markers associated with human adaptation, mammalian virulence, and drug resistance were identified in the newly sequenced H9N2 viruses. Overall, the study deepens our understanding of the epidemic and evolution of AIVs and provides a basis for effective control of AIVs in China.

vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data.

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.

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.

An atlas of human viruses provides new insights into diversity and tissue tropism of human viruses.

MOTIVATION: Viruses continue to threaten human health. Yet, the complete viral species carried by humans and their infection characteristics have not been fully revealed. RESULTS: This study curated an atlas of human viruses from public databases and literature, and built the Human Virus Database (HVD). The HVD contains 1131 virus species of 54 viral families which were more than twice the number of the human-infecting virus species reported in previous studies. These viruses were identified in human samples including 68 human tissues, the excreta and body fluid. The viral diversity in humans was age-dependent with a peak in the infant and a valley in the teenager. The tissue tropism of viruses was found to be associated with several factors including the viral group (DNA, RNA or reverse-transcribing viruses), enveloped or not, viral genome length and GC content, viral receptors and the virus-interacting proteins. Finally, the tissue tropism of DNA viruses was predicted using a random-forest algorithm with a middle performance. Overall, the study not only provides a valuable resource for further studies of human viruses but also deepens our understanding toward the diversity and tissue tropism of human viruses. AVAILABILITY AND IMPLEMENTATION: The HVD is available at http://computationalbiology.cn/humanVirusBase/#/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.