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      Transcriptome profiling in swine macrophages infected with African swine fever virus at single-cell resolution

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          Significance

          African swine fever virus (ASFV) causes a severe and highly contagious disease in pigs and wild boars, but no commercial vaccines or antivirals are available currently. Understanding the mutual antagonism between virus and host factors during ASFV infection may facilitate the development of new vaccines and antivirals. Our work profiled transcriptomes of swine macrophages infected with ASFV through single-cell RNA-sequencing technology. Identified dynamic transcriptome events of viral genes provide molecular characteristics of ASFV during infection. Moreover, virus–host interactions imply the regulation pathway of viral replication in host cells, which may guide research on antiviral strategies and dissection of ASFV pathogenesis.

          Abstract

          African swine fever virus (ASFV) is the causative agent of African swine fever, a highly contagious and usually fatal disease in pigs. The pathogenesis of ASFV infection has not been clearly elucidated. Here, we used single-cell RNA-sequencing technology to survey the transcriptomic landscape of ASFV-infected primary porcine alveolar macrophages. The temporal dynamic analysis of viral genes revealed increased expression of viral transmembrane genes. Molecular characteristics in the ASFV-exposed cells exhibited the activation of antiviral signaling pathways with increased expression levels of interferon-stimulated genes and inflammatory- and cytokine-related genes. By comparing infected cells with unexposed cells, we showed that the unfolded protein response (UPR) pathway was activated in low viral load cells, while the expression level of UPR-related genes in high viral load cells was less than that in unexposed cells. Cells infected with various viral loads showed signature transcriptomic changes at the median progression of infection. Within the infected cells, differential expression analysis and coregulated virus–host analysis both demonstrated that ASFV promoted metabolic pathways but inhibited interferon and UPR signaling, implying the regulation pathway of viral replication in host cells. Furthermore, our results revealed that the cell apoptosis pathway was activated upon ASFV infection. Mechanistically, the production of tumor necrosis factor alpha (TNF-α) induced by ASFV infection is necessary for cell apoptosis, highlighting the importance of TNF-α in ASFV pathogenesis. Collectively, the data provide insights into the comprehensive host responses and complex virus–host interactions during ASFV infection, which may instruct future research on antiviral strategies.

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          Most cited references46

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          SCENIC: Single-cell regulatory network inference and clustering

          Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
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            Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

            We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Copyright 2001 Academic Press.
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              SignalP 4.0: discriminating signal peptides from transmembrane regions.

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                Author and article information

                Contributors
                Journal
                Proceedings of the National Academy of Sciences
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                May 10 2022
                May 04 2022
                May 10 2022
                : 119
                : 19
                Affiliations
                [1 ]CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Sciences (CAS), Beijing 100101, China
                [2 ]State Key Laboratory of Veterinary Biotechnology, National High-Containment Laboratory for Animal Diseases Control and Prevention, and National African Swine Fever Para-Reference Laboratory, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150069, China
                [3 ]Savaid Medical School, University of the Chinese Academy of Sciences, Beijing 100049, China
                [4 ]Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing 100871, China
                [5 ]Biomedical Pioneering Innovation Center (BIOPIC), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University, Beijing 100871, China
                [6 ]Peking-Tsinghua Center for Life Sciences (CLS), Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
                Article
                10.1073/pnas.2201288119
                1c1d4d1a-36b9-4940-a824-6a9105d036b6
                © 2022

                https://creativecommons.org/licenses/by-nc-nd/4.0/

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