CMPK2 is a host restriction factor that inhibits infection of multiple coronaviruses in a cell-intrinsic manner.

Coronaviruses (CoVs) comprise a group of important human and animal pathogens. Despite extensive research in the past 3 years, the host innate immune defense mechanisms against CoVs remain incompletely understood, limiting the development of effective antivirals and non-antibody-based therapeutics. Here, we performed an integrated transcriptomic analysis of porcine jejunal epithelial cells infected with porcine epidemic diarrhea virus (PEDV) and identified cytidine/uridine monophosphate kinase 2 (CMPK2) as a potential host restriction factor. CMPK2 exhibited modest antiviral activity against PEDV infection in multiple cell types. CMPK2 transcription was regulated by interferon-dependent and interferon regulatory factor 1 (IRF1)-dependent pathways post-PEDV infection. We demonstrated that 3'-deoxy-3',4'-didehydro-cytidine triphosphate (ddhCTP) catalysis by Viperin, another interferon-stimulated protein, was essential for CMPK2's antiviral activity. Both the classical catalytic domain and the newly identified antiviral key domain of CMPK2 played crucial roles in this process. Together, CMPK2, viperin, and ddhCTP suppressed the replication of several other CoVs of different genera through inhibition of the RNA-dependent RNA polymerase activities. Our results revealed a previously unknown function of CMPK2 as a restriction factor for CoVs, implying that CMPK2 might be an alternative target of interfering with the viral polymerase activity.

Lysosomal enzyme trafficking factor LYSET enables nutritional usage of extracellular proteins.

Mammalian cells can generate amino acids through macropinocytosis and lysosomal breakdown of extracellular proteins, which is exploited by cancer cells to grow in nutrient-poor tumors. Through genetic screens in defined nutrient conditions, we characterized LYSET, a transmembrane protein (TMEM251) selectively required when cells consume extracellular proteins. LYSET was found to associate in the Golgi with GlcNAc-1-phosphotransferase, which targets catabolic enzymes to lysosomes through mannose-6-phosphate modification. Without LYSET, GlcNAc-1-phosphotransferase was unstable because of a hydrophilic transmembrane domain. Consequently, LYSET-deficient cells were depleted of lysosomal enzymes and impaired in turnover of macropinocytic and autophagic cargoes. Thus, LYSET represents a core component of the lysosomal enzyme trafficking pathway, underlies the pathomechanism for hereditary lysosomal storage disorders, and may represent a target to suppress metabolic adaptations in cancer.

Structural heterogeneity and precision of implications drawn from cryo-electron microscopy structures: SARS-CoV-2 spike-protein mutations as a test case.

Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged "ensemble" estimates for groups of residues when multiple structures are available.

The human disease gene LYSET is essential for lysosomal enzyme transport and viral infection.

Lysosomes are key degradative compartments of the cell. Transport to lysosomes relies on GlcNAc-1-phosphotransferase-mediated tagging of soluble enzymes with mannose 6-phosphate (M6P). GlcNAc-1-phosphotransferase deficiency leads to the severe lysosomal storage disorder mucolipidosis II (MLII). Several viruses require lysosomal cathepsins to cleave structural proteins and thus depend on functional GlcNAc-1-phosphotransferase. We used genome-scale CRISPR screens to identify lysosomal enzyme trafficking factor (LYSET, also named TMEM251) as essential for infection by cathepsin-dependent viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). LYSET deficiency resulted in global loss of M6P tagging and mislocalization of GlcNAc-1-phosphotransferase from the Golgi complex to lysosomes. Lyset knockout mice exhibited MLII-like phenotypes, and human pathogenic LYSET alleles failed to restore lysosomal sorting defects. Thus, LYSET is required for correct functioning of the M6P trafficking machinery and mutations in LYSET can explain the phenotype of the associated disorder.

Mapping the proteo-genomic convergence of human diseases.

Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.

The reactome pathway knowledgebase 2022.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis.

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.

CATH: increased structural coverage of functional space.

CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.

HVIDB: a comprehensive database for human-virus protein-protein interactions.

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Sequence similarity in 3D for comparison of protein families.

Homologous proteins are often compared by pairwise sequence alignment, and structure superposition if the atomic coordinates are available. Unification of sequence and structure data is an important task in structural biology. Here, we present the Sequence Similarity 3D (SS3D) method of integrating sequence and structure information. SS3D is a distance and substitution matrix-based method for straightforward visualization of regions of similarity and difference between homologous proteins. This work details the SS3D approach, and demonstrates its utility through case studies comparing members of several protein families. The examples show that SS3D can effectively highlight biologically important regions of similarity and dissimilarity. We anticipate that the method will be useful for numerous structural biology applications, including, but not limited to, studies of binding specificity, structure-function relationships, and evolutionary pathways. SS3D is available with a manual and tutorial at https://github.com/0x462e41/SS3D/.

Genes with 5' terminal oligopyrimidine tracts preferentially escape global suppression of translation by the SARS-CoV-2 Nsp1 protein.

Viruses rely on the host translation machinery to synthesize their own proteins. Consequently, they have evolved varied mechanisms to co-opt host translation for their survival. SARS-CoV-2 relies on a nonstructural protein, Nsp1, for shutting down host translation. However, it is currently unknown how viral proteins and host factors critical for viral replication can escape a global shutdown of host translation. Here, using a novel FACS-based assay called MeTAFlow, we report a dose-dependent reduction in both nascent protein synthesis and mRNA abundance in cells expressing Nsp1. We perform RNA-seq and matched ribosome profiling experiments to identify gene-specific changes both at the mRNA expression and translation levels. We discover that a functionally coherent subset of human genes is preferentially translated in the context of Nsp1 expression. These genes include the translation machinery components, RNA binding proteins, and others important for viral pathogenicity. Importantly, we uncovered a remarkable enrichment of 5' terminal oligo-pyrimidine (TOP) tracts among preferentially translated genes. Using reporter assays, we validated that 5' UTRs from TOP transcripts can drive preferential expression in the presence of Nsp1. Finally, we found that LARP1, a key effector protein in the mTOR pathway, may contribute to preferential translation of TOP transcripts in response to Nsp1 expression. Collectively, our study suggests fine-tuning of host gene expression and translation by Nsp1 despite its global repressive effect on host protein synthesis.

Evolution of biophysical tools for quantitative protein interactions and drug discovery.

With millions of signalling events occurring simultaneously, cells process a continuous flux of information. The genesis, processing, and regulation of information are dictated by a huge network of protein interactions. This is proven by the fact that alterations in the levels of proteins, single amino acid changes, post-translational modifications, protein products arising out of gene fusions alter the interaction landscape leading to diseases such as congenital disorders, deleterious syndromes like cancer, and crippling diseases like the neurodegenerative disorders which are often fatal. Needless to say, there is an immense effort to understand the biophysical basis of such direct interactions between any two proteins, the structure, domains, and sequence motifs involved in tethering them, their spatio-temporal regulation in cells, the structure of the network, and their eventual manipulation for intervention in diseases. In this chapter, we will deliberate on a few techniques that allow us to dissect the thermodynamic and kinetic aspects of protein interaction, how innovation has rendered some of the traditional techniques applicable for rapid analysis of multiple samples using small amounts of material. These advances coupled with automation are catching up with the genome-wide or proteome-wide studies aimed at identifying new therapeutic targets. The chapter will also summarize how some of these techniques are suited either in the standalone mode or in combination with other biophysical techniques for the drug discovery process.

Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis.

The development of therapeutic targets for COVID-19 relies on understanding the molecular mechanism of pathogenesis. Identifying genes or proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, and HSPA1A, which are central and also associated with multiple diseases.

Functional and tissue enrichment analyses suggest that SARS-CoV-2 infection affects host metabolism and catabolism mediated by interference on host proteins.

Infection by SARS-CoV-2, the causative agent of COVID-19, is critically connected with host metabolism. Through functional enrichment analysis, the present study aims to evaluate the biological processes involving host proteins interfered by SARS-CoV-2 to verify the potential metabolic impact of the infection. Furthermore, tissue enrichment analyses and differential gene expression of host proteins were applied to understand the interference by SARS-CoV-2 on tissue levels. Results based on functional and tissue-specific enrichment analyses, presented in this study, suggest that SARS-CoV-2, mediated interference on host proteins, can affect the metabolism and catabolism of molecular building blocks and control intracellular mechanisms, including gene expression in metabolism-related organs, to support viral demands. Thus, SARS-CoV-2 can broadly affect the host metabolism and catabolism at tissue and physiological levels contributing to a more severe disease.

Long non-coding RNAs: Promising new targets in pulmonary fibrosis.

Pulmonary fibrosis is characterized by progressive and irreversible scarring in the lungs with poor prognosis and treatment. It is caused by various factors, including environmental and occupational exposures, and some rheumatic immune diseases. Even the rapid global spread of the COVID-19 pandemic can also cause pulmonary fibrosis with a high probability. Functions attributed to long non-coding RNAs (lncRNAs) make them highly attractive diagnostic and therapeutic targets in fibroproliferative diseases. Therefore, an understanding of the specific mechanisms by which lncRNAs regulate pulmonary fibrotic pathogenesis is urgently needed to identify new possibilities for therapy. In this review, we focus on the molecular mechanisms and implications of lncRNAs targeted protein-coding and non-coding genes during pulmonary fibrogenesis, and systematically analyze the communication of lncRNAs with various types of RNAs, including microRNA, circular RNA and mRNA. Finally, we propose the potential approach of lncRNA-based diagnosis and therapy for pulmonary fibrosis. We hope that understanding these interactions between protein-coding and non-coding genes will contribute to the development of lncRNA-based clinical applications for pulmonary fibrosis.

Genetic architecture of host proteins involved in SARS-CoV-2 infection.

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

TRK-Fused Gene (TFG), a protein involved in protein secretion pathways, is an essential component of the antiviral innate immune response.

Antiviral innate immune response to RNA virus infection is supported by Pattern-Recognition Receptors (PRR) including RIG-I-Like Receptors (RLR), which lead to type I interferons (IFNs) and IFN-stimulated genes (ISG) production. Upon sensing of viral RNA, the E3 ubiquitin ligase TNF Receptor-Associated Factor-3 (TRAF3) is recruited along with its substrate TANK-Binding Kinase (TBK1), to MAVS-containing subcellular compartments, including mitochondria, peroxisomes, and the mitochondria-associated endoplasmic reticulum membrane (MAM). However, the regulation of such events remains largely unresolved. Here, we identify TRK-Fused Gene (TFG), a protein involved in the transport of newly synthesized proteins to the endomembrane system via the Coat Protein complex II (COPII) transport vesicles, as a new TRAF3-interacting protein allowing the efficient recruitment of TRAF3 to MAVS and TBK1 following Sendai virus (SeV) infection. Using siRNA and shRNA approaches, we show that TFG is required for virus-induced TBK1 activation resulting in C-terminal IRF3 phosphorylation and dimerization. We further show that the ability of the TRAF3-TFG complex to engage mTOR following SeV infection allows TBK1 to phosphorylate mTOR on serine 2159, a post-translational modification shown to promote mTORC1 signaling. We demonstrate that the activation of mTORC1 signaling during SeV infection plays a positive role in the expression of Viperin, IRF7 and IFN-induced proteins with tetratricopeptide repeats (IFITs) proteins, and that depleting TFG resulted in a compromised antiviral state. Our study, therefore, identifies TFG as an essential component of the RLR-dependent type I IFN antiviral response.

Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools.

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.