1
|
Rezatofighi SE. Exogenous interactome analysis of bovine viral diarrhea virus-host using network based-approach and identification of hub genes and important pathways involved in virus pathogenesis. Biochem Biophys Rep 2024; 40:101825. [PMID: 39318471 PMCID: PMC11421936 DOI: 10.1016/j.bbrep.2024.101825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 09/08/2024] [Accepted: 09/11/2024] [Indexed: 09/26/2024] Open
Abstract
Bovine viral diarrhea (BVD) is one of the most important diseases in livestock, caused by BVD virus (BVDV). During the pathogenesis of the virus, many interactions occur between host and viral proteins. Studying these interactions can help better understand the pathogenesis of the virus, identify putative functional proteins, and find new treatment and prevention strategies. To this aim, a BVDV-host protein-protein interaction (PPI) network map was constructed using Cytoscape and analyzed with cytoHubba, Kyoto Encyclopedia of Genes and Genomics (KEGG), Gene Ontology (GO), and Protein Analysis Through Evolutionary Relationships (PANTHER). Npro with 125 connections had the greatest number of interactions with host proteins. CD46, EEF-2, and TXN genes were detected as hub genes using different ranking algorithms in cytoHubba. BVDV interactions with its host mainly focus on targeting translation, protein synthesis, and cellular metabolism pathways. Different classes of proteins including translational proteins, nucleic acid metabolism proteins, metabolite interconversion enzymes, and protein-modifying enzymes are affected by BVDV. These findings improve our understanding of the effects of the virus on the cell. Hub genes and key pathways identified in the present study can serve as targets for novel BVDV prevention or treatment strategies.
Collapse
|
2
|
Pan J, Zhang G, Yang Y, Yang W, Mao N, You Z, Feng J, Wang S, Sun Y. MHIPM: Accurate Prediction of Microbe-Host Interactions Using Multiview Features from a Heterogeneous Microbial Network. J Chem Inf Model 2024; 64:7793-7805. [PMID: 39289839 DOI: 10.1021/acs.jcim.4c01296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
Current studies have demonstrated that microbe-host interactions (MHIs) play important roles in human public health. Therefore, identifying the interactions between microbes and hosts is beneficial to understanding the role of the microbiome and their underlying mechanisms. However, traditional wet-lab experimental approaches are insufficient for large-scale exploration of candidate microbes, as they are costly, laborious, and time-consuming. Thus, it is critical to prioritize microbe-interacting hosts by computational approaches for further biological experimental validation. In this work, we proposed a novel deep learning-based method called MHIPM, to predict MHIs by utilizing multisource biological information. Specifically, we first constructed a heterogeneous microbial network that consisted of human proteins, viruses, bacteriophages (phages), and pathogenic bacteria. Next, we used one of the largest protein language models, ESM-2, and a document embedding model, doc2vec, combined with a self-attention mechanism to extract the interview features from protein sequences. Then, an inductive learning-based model, GraphSAGE, was used to capture the intraview features from the heterogeneous network. Experimental results on three prediction tasks indicated that the MHIPM model consistently achieved better performance than seven baseline algorithms and its four variants. In addition, case studies and molecular docking experiments for two human proteins further confirmed the effectiveness of our model. In conclusion, MHIPM is an efficient and robust method in predicting MHIs and provides plausible candidate microbes for biological experiments. MHIPM is available at https://github.com/JIENWU/MHIPM.
Collapse
Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Guangming Zhang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Yong Yang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Wenli Yang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Ning Mao
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Jie Feng
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| |
Collapse
|
3
|
Zhang Y, Thomas JP, Korcsmaros T, Gul L. Integrating multi-omics to unravel host-microbiome interactions in inflammatory bowel disease. Cell Rep Med 2024; 5:101738. [PMID: 39293401 DOI: 10.1016/j.xcrm.2024.101738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/11/2024] [Accepted: 08/21/2024] [Indexed: 09/20/2024]
Abstract
The gut microbiome is crucial for nutrient metabolism, immune regulation, and intestinal homeostasis with changes in its composition linked to complex diseases like inflammatory bowel disease (IBD). Although the precise host-microbial mechanisms in disease pathogenesis remain unclear, high-throughput sequencing have opened new ways to unravel the role of interspecies interactions in IBD. Systems biology-a holistic computational framework for modeling complex biological systems-is critical for leveraging multi-omics datasets to identify disease mechanisms. This review highlights the significance of multi-omics data in IBD research and provides an overview of state-of-the-art systems biology resources and computational tools for data integration. We explore gaps, challenges, and future directions in the research field aiming to uncover novel biomarkers and therapeutic targets, ultimately advancing personalized treatment strategies. While focusing on IBD, the proposed approaches are applicable for other complex diseases, like cancer, and neurodegenerative diseases, where the microbiome has also been implicated.
Collapse
Affiliation(s)
- Yiran Zhang
- Department of Surgery & Cancer, Imperial College London, London W12 0NN, UK; Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK
| | - John P Thomas
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; UKRI MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, London W12 0HS, UK
| | - Tamas Korcsmaros
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; NIHR Imperial BRC Organoid Facility, Imperial College London, London W12 0NN, UK; Quadram Institute Bioscience, Norwich Research Park, Norwich NR4 7UQ, UK.
| | - Lejla Gul
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; Quadram Institute Bioscience, Norwich Research Park, Norwich NR4 7UQ, UK
| |
Collapse
|
4
|
Patra AT, Tan E, Kok YJ, Ng SK, Bi X. Temporal insights into molecular and cellular responses during rAAV production in HEK293T cells. Mol Ther Methods Clin Dev 2024; 32:101278. [PMID: 39022743 PMCID: PMC11253160 DOI: 10.1016/j.omtm.2024.101278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 06/04/2024] [Indexed: 07/20/2024]
Abstract
The gene therapy field seeks cost-effective, large-scale production of recombinant adeno-associated virus (rAAV) vectors for high-dosage therapeutic applications. Although strategies like suspension cell culture and transfection optimization have shown moderate success, challenges persist for large-scale applications. To unravel molecular and cellular mechanisms influencing rAAV production, we conducted an SWATH-MS proteomic analysis of HEK293T cells transfected using standard, sub-optimal, and optimal conditions. Gene Ontology and pathway analysis revealed significant protein expression variations, particularly in processes related to cellular homeostasis, metabolic regulation, vesicular transport, ribosomal biogenesis, and cellular proliferation under optimal transfection conditions. This resulted in a 50% increase in rAAV titer compared with the standard protocol. Additionally, we identified modifications in host cell proteins crucial for AAV mRNA stability and gene translation, particularly regarding AAV capsid transcripts under optimal transfection conditions. Our study identified 124 host proteins associated with AAV replication and assembly, each exhibiting distinct expression pattern throughout rAAV production stages in optimal transfection condition. This investigation sheds light on the cellular mechanisms involved in rAAV production in HEK293T cells and proposes promising avenues for further enhancing rAAV titer during production.
Collapse
Affiliation(s)
- Alok Tanala Patra
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A∗STAR), Singapore 138668, Singapore
| | - Evan Tan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A∗STAR), Singapore 138668, Singapore
| | - Yee Jiun Kok
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A∗STAR), Singapore 138668, Singapore
| | - Say Kong Ng
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A∗STAR), Singapore 138668, Singapore
| | - Xuezhi Bi
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A∗STAR), Singapore 138668, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore
- Food, Chemical and Biotechnology Cluster, Singapore Institute of Technology, Singapore 138683, Singapore
| |
Collapse
|
5
|
Onisiforou A, Zanos P. From Viral Infections to Alzheimer's Disease: Unveiling the Mechanistic Links Through Systems Bioinformatics. J Infect Dis 2024; 230:S128-S140. [PMID: 39255398 PMCID: PMC11385591 DOI: 10.1093/infdis/jiae242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND Emerging evidence suggests that viral infections may contribute to Alzheimer's disease (AD) onset and/or progression. However, the extent of their involvement and the mechanisms through which specific viruses increase AD susceptibility risk remain elusive. METHODS We used an integrative systems bioinformatics approach to identify viral-mediated pathogenic mechanisms, by which Herpes Simplex Virus 1 (HSV-1), Human Cytomegalovirus (HCMV), Epstein-Barr virus (EBV), Kaposi Sarcoma-associated Herpesvirus (KSHV), Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Influenza A Virus (IAV) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) could facilitate AD pathogenesis via virus-host protein-protein interactions (PPIs). We also explored potential synergistic pathogenic effects resulting from herpesvirus reactivation (HSV-1, HCMV, and EBV) during acute SARS-CoV-2 infection, potentially increasing AD susceptibility. RESULTS Herpesviridae members (HSV-1, EBV, KSHV, HCMV) impact AD-related processes like amyloid-β (Aβ) formation, neuronal death, and autophagy. Hepatitis viruses (HBV, HCV) influence processes crucial for cellular homeostasis and dysfunction, they also affect microglia activation via virus-host PPIs. Reactivation of HCMV during SARS-CoV-2 infection could potentially foster a lethal interplay of neurodegeneration, via synergistic pathogenic effects on AD-related processes like response to unfolded protein, regulation of autophagy, response to oxidative stress, and Aβ formation. CONCLUSIONS These findings underscore the complex link between viral infections and AD development. Viruses impact AD-related processes through shared and distinct mechanisms, potentially influencing variations in AD susceptibility.
Collapse
Affiliation(s)
- Anna Onisiforou
- Department of Psychology, Translational Neuropharmacology Laboratory, University of Cyprus, Nicosia 2109, Cyprus
| | - Panos Zanos
- Department of Psychology, Translational Neuropharmacology Laboratory, University of Cyprus, Nicosia 2109, Cyprus
| |
Collapse
|
6
|
Vello F, Filippini F, Righetto I. Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research. Viruses 2024; 16:1425. [PMID: 39339901 PMCID: PMC11437414 DOI: 10.3390/v16091425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 08/21/2024] [Accepted: 09/03/2024] [Indexed: 09/30/2024] Open
Abstract
Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.
Collapse
Affiliation(s)
| | - Francesco Filippini
- Synthetic Biology and Biotechnology Unit, Department of Biology, University of Padua, 35131 Padua, Italy; (F.V.); (I.R.)
| | | |
Collapse
|
7
|
Volzhenin K, Bittner L, Carbone A. SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale. iScience 2024; 27:110371. [PMID: 39055916 PMCID: PMC11269938 DOI: 10.1016/j.isci.2024.110371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/04/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
Ab initio computational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights into cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging the latest generation protein language models and recurrent neural networks, we present SENSE-PPI, a sequence-based deep learning model that efficiently reconstructs ab initio PPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins. SENSE-PPI demonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow. In this regard, it demonstrates the important role of parameters in protein language models. SENSE-PPI is very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.
Collapse
Affiliation(s)
- Konstantin Volzhenin
- Sorbonne Université, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France
| | - Lucie Bittner
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum national d’Histoire naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
- Institut Universitaire de France, Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France
- Institut Universitaire de France, Paris, France
| |
Collapse
|
8
|
Pan J, Zhang Z, Li Y, Yu J, You Z, Li C, Wang S, Zhu M, Ren F, Zhang X, Sun Y, Wang S. A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts. Brief Bioinform 2024; 25:bbae119. [PMID: 38555472 PMCID: PMC10981679 DOI: 10.1093/bib/bbae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/23/2024] [Accepted: 03/02/2024] [Indexed: 04/02/2024] Open
Abstract
Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.
Collapse
Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhen Zhang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Ying Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Jiaoyang Yu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Chenyu Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shixu Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Minghui Zhu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Fengzhi Ren
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Xuexia Zhang
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| |
Collapse
|
9
|
Chakraborty N. Metabolites: a converging node of host and microbe to explain meta-organism. Front Microbiol 2024; 15:1337368. [PMID: 38505556 PMCID: PMC10949987 DOI: 10.3389/fmicb.2024.1337368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/13/2024] [Indexed: 03/21/2024] Open
Abstract
Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.
Collapse
Affiliation(s)
- Nabarun Chakraborty
- Medical Readiness Systems Biology, CMPN, WRAIR, Silver Spring, MD, United States
| |
Collapse
|
10
|
Idrees S, Paudel KR, Hansbro PM. Prediction of motif-mediated viral mimicry through the integration of host-pathogen interactions. Arch Microbiol 2024; 206:94. [PMID: 38334822 PMCID: PMC10858152 DOI: 10.1007/s00203-024-03832-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/01/2024] [Accepted: 01/02/2024] [Indexed: 02/10/2024]
Abstract
One of the mechanisms viruses use in hijacking host cellular machinery is mimicking Short Linear Motifs (SLiMs) in host proteins to maintain their life cycle inside host cells. In the face of the escalating volume of virus-host protein-protein interactions (vhPPIs) documented in databases; the accurate prediction of molecular mimicry remains a formidable challenge due to the inherent degeneracy of SLiMs. Consequently, there is a pressing need for computational methodologies to predict new instances of viral mimicry. Our present study introduces a DMI-de-novo pipeline, revealing that vhPPIs catalogued in the VirHostNet3.0 database effectively capture domain-motif interactions (DMIs). Notably, both affinity purification coupled mass spectrometry and yeast two-hybrid assays emerged as good approaches for delineating DMIs. Furthermore, we have identified new vhPPIs mediated by SLiMs across different viruses. Importantly, the de-novo prediction strategy facilitated the recognition of several potential mimicry candidates implicated in the subversion of host cellular proteins. The insights gleaned from this research not only enhance our comprehension of the mechanisms by which viruses co-opt host cellular machinery but also pave the way for the development of novel therapeutic interventions.
Collapse
Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia.
- Centre for Inflammation, School of Life Sciences, Faculty of Science, Centenary Institute and the University of Technology Sydney, Sydney, NSW, Australia.
| | - Keshav Raj Paudel
- Centre for Inflammation, School of Life Sciences, Faculty of Science, Centenary Institute and the University of Technology Sydney, Sydney, NSW, Australia
| | - Philip M Hansbro
- Centre for Inflammation, School of Life Sciences, Faculty of Science, Centenary Institute and the University of Technology Sydney, Sydney, NSW, Australia
| |
Collapse
|
11
|
Valero-Rello A, Baeza-Delgado C, Andreu-Moreno I, Sanjuán R. Cellular receptors for mammalian viruses. PLoS Pathog 2024; 20:e1012021. [PMID: 38377111 PMCID: PMC10906839 DOI: 10.1371/journal.ppat.1012021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/01/2024] [Accepted: 02/02/2024] [Indexed: 02/22/2024] Open
Abstract
The interaction of viral surface components with cellular receptors and other entry factors determines key features of viral infection such as host range, tropism and virulence. Despite intensive research, our understanding of these interactions remains limited. Here, we report a systematic analysis of published work on mammalian virus receptors and attachment factors. We build a dataset twice the size of those available to date and specify the role of each factor in virus entry. We identify cellular proteins that are preferentially used as virus receptors, which tend to be plasma membrane proteins with a high propensity to interact with other proteins. Using machine learning, we assign cell surface proteins a score that predicts their ability to function as virus receptors. Our results also reveal common patterns of receptor usage among viruses and suggest that enveloped viruses tend to use a broader repertoire of alternative receptors than non-enveloped viruses, a feature that might confer them with higher interspecies transmissibility.
Collapse
Affiliation(s)
- Ana Valero-Rello
- Institute for Integrative Systems Biology (I2SysBio), Consejo Superior de Investigaciones Científicas-Universitat de València, Paterna, València, Spain
| | - Carlos Baeza-Delgado
- Institute for Integrative Systems Biology (I2SysBio), Consejo Superior de Investigaciones Científicas-Universitat de València, Paterna, València, Spain
| | - Iván Andreu-Moreno
- Institute for Integrative Systems Biology (I2SysBio), Consejo Superior de Investigaciones Científicas-Universitat de València, Paterna, València, Spain
| | - Rafael Sanjuán
- Institute for Integrative Systems Biology (I2SysBio), Consejo Superior de Investigaciones Científicas-Universitat de València, Paterna, València, Spain
| |
Collapse
|
12
|
Amahong K, Zhang W, Liu Y, Li T, Huang S, Han L, Tao L, Zhu F. RVvictor: Virus RNA-directed molecular interactions for RNA virus infection. Comput Biol Med 2024; 169:107886. [PMID: 38157777 DOI: 10.1016/j.compbiomed.2023.107886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.
Collapse
Affiliation(s)
- Kuerbannisha Amahong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Teng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Lianyi Han
- Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, 315211, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
| |
Collapse
|
13
|
Meyniel-Schicklin L, Amaudrut J, Mallinjoud P, Guillier F, Mangeot PE, Lines L, Aublin-Gex A, Scholtes C, Punginelli C, Joly S, Vasseur F, Manet E, Gruffat H, Henry T, Halitim F, Paparin JL, Machin P, Darteil R, Sampson D, Mikaelian I, Lane L, Navratil V, Golinelli-Cohen MP, Terzi F, André P, Lotteau V, Vonderscher J, Meldrum EC, de Chassey B. Viruses traverse the human proteome through peptide interfaces that can be biomimetically leveraged for drug discovery. Proc Natl Acad Sci U S A 2024; 121:e2308776121. [PMID: 38252831 PMCID: PMC10835127 DOI: 10.1073/pnas.2308776121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/06/2023] [Indexed: 01/24/2024] Open
Abstract
We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases.
Collapse
Affiliation(s)
| | | | | | | | - Philippe E. Mangeot
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | | | - Anne Aublin-Gex
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Caroline Scholtes
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Claire Punginelli
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | | | - Florence Vasseur
- Université de Paris, INSERM U1151, CNRS UMR 8253, Institut Necker Enfants Malades, Département “Croissance et Signalisation”, Paris75015, France
| | - Evelyne Manet
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Henri Gruffat
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Thomas Henry
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | | | | | | | | | | | - Ivan Mikaelian
- Université de Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de recherche en cancérologie de Lyon, Lyon69373, France
| | - Lydie Lane
- Computer and Laboratory Investigation of Proteins of Human Origin Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Vincent Navratil
- Pôle Rhône-Alpes de bioinformatique, Rhône-Alpes Bioinformatics Center, Université Lyon 1, Villeurbanne69622, France
- European Virus Bio-informatiques Center, Jena07743, Germany
- Institut Français de Bioinformatique, IFB-core, UMS 3601, Évry91057, France
| | - Marie-Pierre Golinelli-Cohen
- Université Paris-Saclay, CNRS, Institut de Chimie des Substances Naturelles, Unité Propre de Recherche 2301, Gif-sur-Yvette91198, France
| | - Fabiola Terzi
- Université de Paris, INSERM U1151, CNRS UMR 8253, Institut Necker Enfants Malades, Département “Croissance et Signalisation”, Paris75015, France
| | - Patrice André
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Vincent Lotteau
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | | | | | | |
Collapse
|
14
|
Yang X, Wuchty S, Liang Z, Ji L, Wang B, Zhu J, Zhang Z, Dong Y. Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM. Brief Bioinform 2024; 25:bbae005. [PMID: 38279649 PMCID: PMC10818167 DOI: 10.1093/bib/bbae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/25/2023] [Accepted: 01/01/2021] [Indexed: 01/28/2024] Open
Abstract
The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.
Collapse
Affiliation(s)
- Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami FL, 33146, USA
- Department of Biology, University of Miami, Miami FL, 33146, USA
- Institute of Data Science and Computation, University of Miami, Miami, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Zeyin Liang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Li Ji
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bingjie Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Jialin Zhu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yujun Dong
- Department of Hematology, Peking University First Hospital, Beijing, China
| |
Collapse
|
15
|
Wani AK, Chopra C, Dhanjal DS, Akhtar N, Singh H, Bhau P, Singh A, Sharma V, Pinheiro RSB, Américo-Pinheiro JHP, Singh R. Metagenomics in the fight against zoonotic viral infections: A focus on SARS-CoV-2 analogues. J Virol Methods 2024; 323:114837. [PMID: 37914040 DOI: 10.1016/j.jviromet.2023.114837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/03/2023]
Abstract
Zoonotic viral infections continue to pose significant threats to global public health, as highlighted by the COVID-19 pandemic caused by the SARS-CoV-2 virus. The emergence of SARS-CoV-2 served as a stark reminder of the potential for zoonotic transmission of viruses from animals to humans. Understanding the origins and dynamics of zoonotic viruses is critical for early detection, prevention, and effective management of future outbreaks. Metagenomics has emerged as a powerful tool for investigating the virome of diverse ecosystems, shedding light on the diversity of viral populations, their hosts, and potential zoonotic spillover events. We provide an in-depth examination of metagenomic approaches, including, NGS metagenomics, shotgun metagenomics, viral metagenomics, and single-virus metagenomics, highlighting their strengths and limitations in identifying and characterizing zoonotic viral pathogens. This review underscores the pivotal role of metagenomics in enhancing our ability to detect, monitor, and mitigate zoonotic viral infections, using SARS-CoV-2 analogues as a case study. We emphasize the need for continued interdisciplinary collaboration among virologists, ecologists, and bioinformaticians to harness the full potential of metagenomic approaches in safeguarding public health against emerging zoonotic threats.
Collapse
Affiliation(s)
- Atif Khurshid Wani
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Chirag Chopra
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Daljeet Singh Dhanjal
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Nahid Akhtar
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Himanshu Singh
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Poorvi Bhau
- School of Biotechnology, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Anjuvan Singh
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Varun Sharma
- NMC Genetics India Pvt. Ltd, Gurugram, Harayana, India
| | - Rafael Silvio Bonilha Pinheiro
- School of Veterinary Medicine and Animal Science, Department of Animal Production, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Juliana Heloisa Pinê Américo-Pinheiro
- Department of Forest Science, Soils and Environment, School of Agronomic Sciences, São Paulo State University (UNESP), Ave. Universitária, 3780, Botucatu, SP 18610-034, Brazil; Graduate Program in Environmental Sciences, Brazil University, Street Carolina Fonseca, 584, São Paulo, SP 08230-030, Brazil
| | - Reena Singh
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India.
| |
Collapse
|
16
|
Chen HM, Liu JX, Liu D, Hao GF, Yang GF. Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection. Rev Med Virol 2024; 34:e2517. [PMID: 38282401 DOI: 10.1002/rmv.2517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/12/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.
Collapse
Affiliation(s)
- Hui-Min Chen
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Di Liu
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| |
Collapse
|
17
|
Idrees S, Paudel KR. Bioinformatics prediction and screening of viral mimicry candidates through integrating known and predicted DMI data. Arch Microbiol 2023; 206:30. [PMID: 38117335 DOI: 10.1007/s00203-023-03764-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
Domain-motif interactions (DMIs) represent transient bonds formed when a Short Linear Motif (SLiM) engages a globular domain via a compact contact interface. Understanding the mechanics of DMIs is critical for maintaining diverse regulatory processes and deciphering how various viruses hijack host cellular machinery. However, identifying DMIs through traditional in vitro and in vivo experiments is challenging due to their degenerate nature and small contact areas. Predictions often carry a high rate of false positives, necessitating rigorous in-silico validation before embarking on experimental work. This study assessed the binding energy changes in predicted SLiM instances through in-silico peptide exchange experiment, elucidating how they interact with known 3D DMI complexes. We identified a subset of potential mimicry candidates that exhibited effective binding affinities with native DMI structures, suggesting their potential to be true mimicry candidates. The identified viral SLiMs can be potential targets in developing therapeutics, opening new opportunities for innovative treatments that can be finely tuned to address the complex molecular underpinnings of various diseases. To gain a comprehensive understanding of identified DMIs, it is imperative to conduct further validation through experimental approaches.
Collapse
Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia.
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, Faculty of Science, School of Life Sciences, Sydney, NSW, Australia.
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, Faculty of Science, School of Life Sciences, Sydney, NSW, Australia
| |
Collapse
|
18
|
Berenson A, Lane R, Soto-Ugaldi LF, Patel M, Ciausu C, Li Z, Chen Y, Shah S, Santoso C, Liu X, Spirohn K, Hao T, Hill DE, Vidal M, Fuxman Bass JI. Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors. Nat Commun 2023; 14:6570. [PMID: 37853017 PMCID: PMC10584920 DOI: 10.1038/s41467-023-42445-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023] Open
Abstract
Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.
Collapse
Affiliation(s)
- Anna Berenson
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Ryan Lane
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Luis F Soto-Ugaldi
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA
| | - Mahir Patel
- Department of Computer Science, Boston University, Boston, MA, 02215, USA
| | - Cosmin Ciausu
- Department of Computer Science, Boston University, Boston, MA, 02215, USA
| | - Zhaorong Li
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Yilin Chen
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Sakshi Shah
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Clarissa Santoso
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Xing Liu
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Juan I Fuxman Bass
- Department of Biology, Boston University, Boston, MA, 02215, USA.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
| |
Collapse
|
19
|
Zhu Z, You R, Li H, Feng S, Ma H, Tuo C, Meng X, Feng S, Peng Y. Multi-omics data integration reveals the complexity and diversity of host factors associated with influenza virus infection. PeerJ 2023; 11:e16194. [PMID: 37842064 PMCID: PMC10569165 DOI: 10.7717/peerj.16194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/06/2023] [Indexed: 10/17/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Zhaozhong Zhu
- College of Biology, Hunan University, Changsha, China
- School of Public Health, University of South China, Hengyang, China
| | - Ruina You
- College of Biology, Hunan University, Changsha, China
| | - Huiru Li
- College of Biology, Hunan University, Changsha, China
| | - Shuidong Feng
- School of Public Health, University of South China, Hengyang, China
| | - Huan Ma
- College of Biology, Hunan University, Changsha, China
| | - Chaohao Tuo
- College of Biology, Hunan University, Changsha, China
| | | | - Song Feng
- Xiangya Hospital, Central South University, Changsha, China
| | - Yousong Peng
- College of Biology, Hunan University, Changsha, China
| |
Collapse
|
20
|
Tan M, Xia J, Luo H, Meng G, Zhu Z. Applying the digital data and the bioinformatics tools in SARS-CoV-2 research. Comput Struct Biotechnol J 2023; 21:4697-4705. [PMID: 37841328 PMCID: PMC10568291 DOI: 10.1016/j.csbj.2023.09.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023] Open
Abstract
Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.
Collapse
Affiliation(s)
- Meng Tan
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Jiaxin Xia
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Haitao Luo
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Geng Meng
- College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Zhenglin Zhu
- School of Life Sciences, Chongqing University, Chongqing, China
| |
Collapse
|
21
|
Mihalič F, Benz C, Kassa E, Lindqvist R, Simonetti L, Inturi R, Aronsson H, Andersson E, Chi CN, Davey NE, Överby AK, Jemth P, Ivarsson Y. Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets. Nat Commun 2023; 14:5636. [PMID: 37704626 PMCID: PMC10499821 DOI: 10.1038/s41467-023-41312-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
The virus life cycle depends on host-virus protein-protein interactions, which often involve a disordered protein region binding to a folded protein domain. Here, we used proteomic peptide phage display (ProP-PD) to identify peptides from the intrinsically disordered regions of the human proteome that bind to folded protein domains encoded by the SARS-CoV-2 genome. Eleven folded domains of SARS-CoV-2 proteins were found to bind 281 peptides from human proteins, and affinities of 31 interactions involving eight SARS-CoV-2 protein domains were determined (KD ∼ 7-300 μM). Key specificity residues of the peptides were established for six of the interactions. Two of the peptides, binding Nsp9 and Nsp16, respectively, inhibited viral replication. Our findings demonstrate how high-throughput peptide binding screens simultaneously identify potential host-virus interactions and peptides with antiviral properties. Furthermore, the high number of low-affinity interactions suggest that overexpression of viral proteins during infection may perturb multiple cellular pathways.
Collapse
Affiliation(s)
- Filip Mihalič
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Caroline Benz
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eszter Kassa
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Richard Lindqvist
- Department of Clinical Microbiology, Umeå University, 90185, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90187, Umeå, Sweden
| | - Leandro Simonetti
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Raviteja Inturi
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Hanna Aronsson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eva Andersson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Celestine N Chi
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Norman E Davey
- Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
| | - Anna K Överby
- Department of Clinical Microbiology, Umeå University, 90185, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90187, Umeå, Sweden
| | - Per Jemth
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden.
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden.
| |
Collapse
|
22
|
Chaudhary Y, Jain J, Gaur SK, Tembhurne P, Chandrasekar S, Dhanavelu M, Sehrawat S, Kaul R. Nucleocapsid Protein (N) of Peste des petits ruminants Virus (PPRV) Interacts with Cellular Phosphatidylinositol-3-Kinase (PI3K) Complex-I and Induces Autophagy. Viruses 2023; 15:1805. [PMID: 37766213 PMCID: PMC10536322 DOI: 10.3390/v15091805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 09/29/2023] Open
Abstract
Autophagy is an essential and highly conserved catabolic process in cells, which is important in the battle against intracellular pathogens. Viruses have evolved several ways to alter the host defense mechanisms. PPRV infection is known to modulate the components of a host cell's defense system, resulting in enhanced autophagy. In this study, we demonstrate that the N protein of PPRV interacts with the core components of the class III phosphatidylinositol-3-kinase (PI3K) complex-I and results in the induction of autophagy in the host cell over, thereby expressing this viral protein. Our data shows the interaction between PPRV-N protein and different core components of the autophagy pathway, i.e., VPS34, VPS15, BECN1 and ATG14L. The PPRV-N protein can specifically interact with VPS34 of the PI3K complex-I and colocalize with the proteins of PI3K complex-I in the same sub-cellular compartment, that is, in the cytoplasm. These interactions do not affect the intracellular localization of the different host proteins. The autophagy-related genes were transcriptionally modulated in PPRV-N-expressing cells. The expression of LC3B and SQSTM1/p62 was also modulated in PPRV-N-expressing cells, indicating the induction of autophagic activity. The formation of typical autophagosomes with double membranes was visualized by transmission electron microscopy in PPRV-N-expressing cells. Taken together, our findings provide evidence for the critical role of the N protein of the PPR virus in the induction of autophagy, which is likely to be mediated by PI3K complex-I of the host.
Collapse
Affiliation(s)
- Yash Chaudhary
- Department of Microbiology, University of Delhi, South Campus, New Delhi 110021, India; (Y.C.); (J.J.); (S.K.G.)
| | - Juhi Jain
- Department of Microbiology, University of Delhi, South Campus, New Delhi 110021, India; (Y.C.); (J.J.); (S.K.G.)
| | - Sharad Kumar Gaur
- Department of Microbiology, University of Delhi, South Campus, New Delhi 110021, India; (Y.C.); (J.J.); (S.K.G.)
| | - Prabhakar Tembhurne
- Department of Microbiology, Nagpur Veterinary College, Nagpur 440006, India;
| | - Shanmugam Chandrasekar
- Division of Virology, Indian Veterinary Research Institute, Mukteshwar, Nainital 263138, India; (S.C.); (M.D.)
| | - Muthuchelvan Dhanavelu
- Division of Virology, Indian Veterinary Research Institute, Mukteshwar, Nainital 263138, India; (S.C.); (M.D.)
| | - Sharvan Sehrawat
- Department of Biological Sciences, Indian Institute of Science Education and Research Mohali, Mohali 140306, India;
| | - Rajeev Kaul
- Department of Microbiology, University of Delhi, South Campus, New Delhi 110021, India; (Y.C.); (J.J.); (S.K.G.)
| |
Collapse
|
23
|
Dolata KM, Pei G, Netherton CL, Karger A. Functional Landscape of African Swine Fever Virus-Host and Virus-Virus Protein Interactions. Viruses 2023; 15:1634. [PMID: 37631977 PMCID: PMC10459248 DOI: 10.3390/v15081634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Viral replication fully relies on the host cell machinery, and physical interactions between viral and host proteins mediate key steps of the viral life cycle. Therefore, identifying virus-host protein-protein interactions (PPIs) provides insights into the molecular mechanisms governing virus infection and is crucial for designing novel antiviral strategies. In the case of the African swine fever virus (ASFV), a large DNA virus that causes a deadly panzootic disease in pigs, the limited understanding of host and viral targets hinders the development of effective vaccines and treatments. This review summarizes the current knowledge of virus-host and virus-virus PPIs by collecting and analyzing studies of individual viral proteins. We have compiled a dataset of experimentally determined host and virus protein targets, the molecular mechanisms involved, and the biological functions of the identified virus-host and virus-virus protein interactions during infection. Ultimately, this work provides a comprehensive and systematic overview of ASFV interactome, identifies knowledge gaps, and proposes future research directions.
Collapse
Affiliation(s)
- Katarzyna Magdalena Dolata
- Institute of Molecular Virology and Cell Biology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493 Greifswald-Insel Riems, Germany
| | - Gang Pei
- Institute of Immunology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493 Greifswald-Insel Riems, Germany
| | | | - Axel Karger
- Institute of Molecular Virology and Cell Biology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493 Greifswald-Insel Riems, Germany
| |
Collapse
|
24
|
Xie P, Zhuang J, Tian G, Yang J. Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction. BIOSAFETY AND HEALTH 2023; 5:152-158. [PMID: 37362223 PMCID: PMC10166638 DOI: 10.1016/j.bsheal.2023.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/23/2023] [Accepted: 04/23/2023] [Indexed: 06/28/2023] Open
Abstract
Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human-SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.
Collapse
Affiliation(s)
- Pengfei Xie
- College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China
| | - Jujuan Zhuang
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing 100102, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
| | - Jialiang Yang
- Geneis Beijing Co., Ltd., Beijing 100102, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
| |
Collapse
|
25
|
Mihalič F, Simonetti L, Giudice G, Sander MR, Lindqvist R, Peters MBA, Benz C, Kassa E, Badgujar D, Inturi R, Ali M, Krystkowiak I, Sayadi A, Andersson E, Aronsson H, Söderberg O, Dobritzsch D, Petsalaki E, Överby AK, Jemth P, Davey NE, Ivarsson Y. Large-scale phage-based screening reveals extensive pan-viral mimicry of host short linear motifs. Nat Commun 2023; 14:2409. [PMID: 37100772 PMCID: PMC10132805 DOI: 10.1038/s41467-023-38015-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 04/12/2023] [Indexed: 04/28/2023] Open
Abstract
Viruses mimic host short linear motifs (SLiMs) to hijack and deregulate cellular functions. Studies of motif-mediated interactions therefore provide insight into virus-host dependencies, and reveal targets for therapeutic intervention. Here, we describe the pan-viral discovery of 1712 SLiM-based virus-host interactions using a phage peptidome tiling the intrinsically disordered protein regions of 229 RNA viruses. We find mimicry of host SLiMs to be a ubiquitous viral strategy, reveal novel host proteins hijacked by viruses, and identify cellular pathways frequently deregulated by viral motif mimicry. Using structural and biophysical analyses, we show that viral mimicry-based interactions have similar binding strength and bound conformations as endogenous interactions. Finally, we establish polyadenylate-binding protein 1 as a potential target for broad-spectrum antiviral agent development. Our platform enables rapid discovery of mechanisms of viral interference and the identification of potential therapeutic targets which can aid in combating future epidemics and pandemics.
Collapse
Affiliation(s)
- Filip Mihalič
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Leandro Simonetti
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Girolamo Giudice
- European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, CB10 1SD, UK
| | - Marie Rubin Sander
- Department of Pharmaceutical Biosciences, Uppsala University, Husargatan 3, Box 591, SE-751 24, Uppsala, Sweden
| | - Richard Lindqvist
- Department of Clinical Microbiology, Umeå University, 90187, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90186, Umeå, Sweden
| | - Marie Berit Akpiroro Peters
- Department of Clinical Microbiology, Umeå University, 90187, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90186, Umeå, Sweden
| | - Caroline Benz
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eszter Kassa
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Dilip Badgujar
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Raviteja Inturi
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Muhammad Ali
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Izabella Krystkowiak
- Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
| | - Ahmed Sayadi
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eva Andersson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Hanna Aronsson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Ola Söderberg
- Department of Pharmaceutical Biosciences, Uppsala University, Husargatan 3, Box 591, SE-751 24, Uppsala, Sweden
| | - Doreen Dobritzsch
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Evangelia Petsalaki
- European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, CB10 1SD, UK
| | - Anna K Överby
- Department of Clinical Microbiology, Umeå University, 90187, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90186, Umeå, Sweden
| | - Per Jemth
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden.
| | - Norman E Davey
- Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK.
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden.
| |
Collapse
|
26
|
Mann JT, Riley BA, Baker SF. All differential on the splicing front: Host alternative splicing alters the landscape of virus-host conflict. Semin Cell Dev Biol 2023; 146:40-56. [PMID: 36737258 DOI: 10.1016/j.semcdb.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
Alternative RNA splicing is a co-transcriptional process that richly increases proteome diversity, and is dynamically regulated based on cell species, lineage, and activation state. Virus infection in vertebrate hosts results in rapid host transcriptome-wide changes, and regulation of alternative splicing can direct a combinatorial effect on the host transcriptome. There has been a recent increase in genome-wide studies evaluating host alternative splicing during viral infection, which integrates well with prior knowledge on viral interactions with host splicing proteins. A critical challenge remains in linking how these individual events direct global changes, and whether alternative splicing is an overall favorable pathway for fending off or supporting viral infection. Here, we introduce the process of alternative splicing, discuss how to analyze splice regulation, and detail studies on genome-wide and splice factor changes during viral infection. We seek to highlight where the field can focus on moving forward, and how incorporation of a virus-host co-evolutionary perspective can benefit this burgeoning subject.
Collapse
Affiliation(s)
- Joshua T Mann
- Infectious Disease Program, Lovelace Biomedical Research Institute, Albuquerque, NM, USA
| | - Brent A Riley
- Infectious Disease Program, Lovelace Biomedical Research Institute, Albuquerque, NM, USA
| | - Steven F Baker
- Infectious Disease Program, Lovelace Biomedical Research Institute, Albuquerque, NM, USA.
| |
Collapse
|
27
|
Ozdemir ES, Nussinov R. Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions. Front Oncol 2023; 13:1061595. [PMID: 36910650 PMCID: PMC9997845 DOI: 10.3389/fonc.2023.1061595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
Collapse
Affiliation(s)
- Emine Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
28
|
Murakami Y, Mizuguchi K. Recent developments of sequence-based prediction of protein-protein interactions. Biophys Rev 2022; 14:1393-1411. [PMID: 36589735 PMCID: PMC9789376 DOI: 10.1007/s12551-022-01038-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2022] [Indexed: 12/25/2022] Open
Abstract
The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.
Collapse
Affiliation(s)
- Yoichi Murakami
- grid.440890.10000 0004 0640 9413Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-Ku, Chiba, 265-8501 Japan
| | - Kenji Mizuguchi
- grid.136593.b0000 0004 0373 3971Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita-Shi, Osaka, 565-0871 Japan ,grid.482562.fNational Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085 Japan
| |
Collapse
|
29
|
Liu X, Wang L, Liang CH, Lu YP, Yang T, Zhang X. An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms. J Biomol Struct Dyn 2022; 40:10592-10602. [PMID: 34251992 DOI: 10.1080/07391102.2021.1946429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo amino acid compositions, which were then carried out at two levels: categories and features. In brief, extra-tree was initially used for feature selection while SVM was then used to build the classification model. After that, the most suitable models for each category and each feature were selected by comparing with the three ensemble learning algorithms, that is, Random Forest, Adaboost, and Xgboost. According to our results, profile-based features were more suitable for building predictive models among the four categories. AUC value of the model constructed by Xgboost algorithm on independent data set could reach 92.66%. Moreover, Distance-based Residue, Physicochemical Distance Transformation and Profile-based Physicochemical Distance Transformation performed much better among the 17 features. AUC value of the Adaboost classifier constructed by Profile-based Physicochemical Distance Transformation on the independent dataset achieved 93.74%. Taken together, we proposed a better model with improved prediction capacity for protein-protein interactions between human and HCV in this study, which could provide practical reference for further experimental investigation into HCV-related diseases in future.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Xin Liu
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Liang Wang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.,Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Cheng-Hao Liang
- School of Life Science, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ya-Ping Lu
- College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China
| | - Ting Yang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiao Zhang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| |
Collapse
|
30
|
Jiang Y, Zhang JX, Liu R. Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing. PLoS Comput Biol 2022; 18:e1010744. [PMID: 36534703 PMCID: PMC9810203 DOI: 10.1371/journal.pcbi.1010744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 01/03/2023] [Accepted: 11/17/2022] [Indexed: 12/24/2022] Open
Abstract
The synergy between human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (MTB) could accelerate the deterioration of immunological functions. Previous studies have explored the pathogenic mechanisms of HIV mono-infection (HMI), MTB mono-infection (MMI) and MTB/HIV co-infection (MHCI), but their similarities and specificities remain to be profoundly investigated. We thus designed a computational framework named IDEN to identify gene pairs related to these states, which were then compared from different perspectives. MMI-related genes showed the highest enrichment level on a greater number of chromosomes. Genes shared by more states tended to be more evolutionarily conserved, posttranslationally modified and topologically important. At the expression level, HMI-specific gene pairs yielded higher correlations, while the overlapping pairs involved in MHCI had significantly lower correlations. The correlation changes of common gene pairs showed that MHCI shared more similarities with MMI. Moreover, MMI- and MHCI-related genes were enriched in more identical pathways and biological processes, further illustrating that MTB may play a dominant role in co-infection. Hub genes specific to each state could promote pathogen infections, while those shared by two states could enhance immune responses. Finally, we improved the network proximity measure for drug repurposing by considering the importance of gene pairs, and approximately ten drug candidates were identified for each disease state.
Collapse
Affiliation(s)
- Yao Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Jia-Xuan Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Rong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
- * E-mail:
| |
Collapse
|
31
|
Minadakis G, Tomazou M, Dietis N, Spyrou GM. Vir2Drug: a drug repurposing framework based on protein similarities between pathogens. Brief Bioinform 2022; 24:6895455. [PMID: 36513376 PMCID: PMC9851336 DOI: 10.1093/bib/bbac536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/25/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.
Collapse
Affiliation(s)
- George Minadakis
- Corresponding author: George Minadakis, Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus. Tel.: +357-22-392852; Fax: +357-22-358238; E-mail:
| | - Marios Tomazou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
- PO Box 23462, 1683 Nicosia, Cyprus,The Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus
| | - Nikolas Dietis
- Medical School, University of Cyprus, Nicosia 1678, Cyprus
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
- PO Box 23462, 1683 Nicosia, Cyprus,The Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus
| |
Collapse
|
32
|
Asim MN, Fazeel A, Ibrahim MA, Dengel A, Ahmed S. MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses. Front Med (Lausanne) 2022; 9:1025887. [PMID: 36465911 PMCID: PMC9709337 DOI: 10.3389/fmed.2022.1025887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/17/2022] [Indexed: 09/19/2023] Open
Abstract
Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.
Collapse
Affiliation(s)
- Muhammad Nabeel Asim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Ahtisham Fazeel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Andreas Dengel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| |
Collapse
|
33
|
Tang K, Tang J, Zeng J, Shen W, Zou M, Zhang C, Sun Q, Ye X, Li C, Sun C, Liu S, Jiang G, Du X. A network view of human immune system and virus-human interaction. Front Immunol 2022; 13:997851. [PMID: 36389817 PMCID: PMC9643829 DOI: 10.3389/fimmu.2022.997851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/11/2022] [Indexed: 11/30/2022] Open
Abstract
The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.
Collapse
Affiliation(s)
- Kang Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jing Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Wei Shen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
- Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Min Zou
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Chi Zhang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Qianru Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Ye
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chunwei Li
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Caijun Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Xiangjun Du,
| |
Collapse
|
34
|
Onisiforou A, Spyrou GM. Systems Bioinformatics Reveals Possible Relationship between COVID-19 and the Development of Neurological Diseases and Neuropsychiatric Disorders. Viruses 2022; 14:2270. [PMID: 36298824 PMCID: PMC9611753 DOI: 10.3390/v14102270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Coronavirus Disease 2019 (COVID-19) is associated with increased incidence of neurological diseases and neuropsychiatric disorders after infection, but how it contributes to their development remains under investigation. Here, we investigate the possible relationship between COVID-19 and the development of ten neurological disorders and three neuropsychiatric disorders by exploring two pathological mechanisms: (i) dysregulation of host biological processes via virus-host protein-protein interactions (PPIs), and (ii) autoreactivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epitopes with host "self" proteins via molecular mimicry. We also identify potential genetic risk factors which in combination with SARS-CoV-2 infection might lead to disease development. Our analysis indicated that neurodegenerative diseases (NDs) have a higher number of disease-associated biological processes that can be modulated by SARS-CoV-2 via virus-host PPIs than neuropsychiatric disorders. The sequence similarity analysis indicated the presence of several matching 5-mer and/or 6-mer linear motifs between SARS-CoV-2 epitopes with autoreactive epitopes found in Alzheimer's Disease (AD), Parkinson's Disease (PD), Myasthenia Gravis (MG) and Multiple Sclerosis (MS). The results include autoreactive epitopes that recognize amyloid-beta precursor protein (APP), microtubule-associated protein tau (MAPT), acetylcholine receptors, glial fibrillary acidic protein (GFAP), neurofilament light polypeptide (NfL) and major myelin proteins. Altogether, our results suggest that there might be an increased risk for the development of NDs after COVID-19 both via autoreactivity and virus-host PPIs.
Collapse
Affiliation(s)
| | - George M. Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia 2370, Cyprus
| |
Collapse
|
35
|
Adams C, Boonen K, Laukens K, Bittremieux W. Open Modification Searching of SARS-CoV-2-Human Protein Interaction Data Reveals Novel Viral Modification Sites. Mol Cell Proteomics 2022; 21:100425. [PMID: 36241021 PMCID: PMC9554009 DOI: 10.1016/j.mcpro.2022.100425] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 09/18/2022] [Accepted: 10/09/2022] [Indexed: 01/18/2023] Open
Abstract
The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification-mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus-host protein-protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.
Collapse
Affiliation(s)
- Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium,Centre for Proteomics (CFP), University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Centre for Proteomics (CFP), University of Antwerp, Antwerp, Belgium,Sustainable Health Department, Flemish Institute for Technological Research (VITO), Antwerp, Belgium
| | - Kris Laukens
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA,For correspondence: Wout Bittremieux
| |
Collapse
|
36
|
Amahong K, Zhang W, Zhou Y, Zhang S, Yin J, Li F, Xu H, Yan T, Yue Z, Liu Y, Hou T, Qiu Y, Tao L, Han L, Zhu F. CovInter: interaction data between coronavirus RNAs and host proteins. Nucleic Acids Res 2022; 51:D546-D556. [PMID: 36200814 PMCID: PMC9825556 DOI: 10.1093/nar/gkac834] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/07/2022] [Accepted: 09/16/2022] [Indexed: 01/29/2023] Open
Abstract
Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.
Collapse
Affiliation(s)
| | | | | | - Song Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Tianci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Zixuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Lin Tao
- Correspondence may also be addressed to Lin Tao.
| | - Lianyi Han
- Correspondence may also be addressed to Lianyi Han.
| | - Feng Zhu
- To whom correspondence should be addressed. Tel: +86 189 8946 6518; Fax: +86 571 8820 8444;
| |
Collapse
|
37
|
Chatr-aryamontri A, Hirschman L, Ross KE, Oughtred R, Krallinger M, Dolinski K, Tyers M, Korves T, Arighi CN. Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII. Database (Oxford) 2022; 2022:baac084. [PMID: 36197453 PMCID: PMC9534061 DOI: 10.1093/database/baac084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/18/2022] [Accepted: 09/08/2022] [Indexed: 11/06/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system's ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/.
Collapse
Affiliation(s)
- Andrew Chatr-aryamontri
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Marcelle-Coutu Pavilion, 2950 Chem. de Polytechnique Montreal, Quebec H3T 1J4, Canada
| | - Lynette Hirschman
- MITRE Labs, The MITRE Corporation, 202 Burlington Rd., Bedford, MA 01730, USA
| | - Karen E Ross
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 2115 Wisconsin Ave NW, DC 20007, USA
| | - Rose Oughtred
- Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, South Drive, Princeton, NJ 08544, USA
| | - Martin Krallinger
- Barcelona Supercomputing Center (BSC), Plaça d'Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Kara Dolinski
- Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, South Drive, Princeton, NJ 08544, USA
| | - Mike Tyers
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Marcelle-Coutu Pavilion, 2950 Chem. de Polytechnique Montreal, Quebec H3T 1J4, Canada
| | - Tonia Korves
- MITRE Labs, The MITRE Corporation, 202 Burlington Rd., Bedford, MA 01730, USA
| | - Cecilia N Arighi
- Computer and Information Sciences Department, University of Delaware, Ammon-Pinizzotto Biopharmaceutical Innovation Building, 590 Avenue 1743, Newark, DE 19713, USA
| |
Collapse
|
38
|
Wang L, Tan H, Medina-Puche L, Wu M, Garnelo Gomez B, Gao M, Shi C, Jimenez-Gongora T, Fan P, Ding X, Zhang D, Ding Y, Rosas-Díaz T, Liu Y, Aguilar E, Fu X, Lozano-Durán R. Combinatorial interactions between viral proteins expand the potential functional landscape of the tomato yellow leaf curl virus proteome. PLoS Pathog 2022; 18:e1010909. [PMID: 36256684 PMCID: PMC9633003 DOI: 10.1371/journal.ppat.1010909] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 11/03/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022] Open
Abstract
Viruses manipulate the cells they infect in order to replicate and spread. Due to strict size restrictions, viral genomes have reduced genetic space; how the action of the limited number of viral proteins results in the cell reprogramming observed during the infection is a long-standing question. Here, we explore the hypothesis that combinatorial interactions may expand the functional landscape of the viral proteome. We show that the proteins encoded by a plant-infecting DNA virus, the geminivirus tomato yellow leaf curl virus (TYLCV), physically associate with one another in an intricate network, as detected by a number of protein-protein interaction techniques. Importantly, our results indicate that intra-viral protein-protein interactions can modify the subcellular localization of the proteins involved. Using one particular pairwise interaction, that between the virus-encoded C2 and CP proteins, as proof-of-concept, we demonstrate that the combination of viral proteins leads to novel transcriptional effects on the host cell. Taken together, our results underscore the importance of studying viral protein function in the context of the infection. We propose a model in which viral proteins might have evolved to extensively interact with other elements within the viral proteome, enlarging the potential functional landscape available to the pathogen. Viruses are obligate intracellular parasites that depend on the molecular machinery of their host cell to complete their life cycle. For this purpose, viruses co-opt host processes, modulating or redirecting them. Most viruses have small genomes, and hence limited coding capacity. During the viral invasion, virus-encoded proteins will be produced in large amounts and coexist in the infected cell, which enables physical or functional interactions among viral proteins, potentially expanding the virus-host functional interface by increasing the number of potential targets in the host cell and/or synergistically modulating the cellular environment. Examples of interactions between viral proteins have been recently documented for both animal and plant viruses; however, the hypothesis that viral proteins might have a combinatorial effect, which would lead to the acquisition of novel functions, lacks systematic experimental validation. Here, we use the geminivirus tomato yellow leaf curl virus (TYLCV), a plant-infecting virus with reduced proteome and causing devastating diseases in crops, to test the idea that combinatorial interactions between viral proteins exist and might underlie an expansion of the functional landscape of the viral proteome. Our results indicate that viral proteins prevalently interact with one another in the context of the infection, which can result in the acquisition of novel functions.
Collapse
Affiliation(s)
- Liping Wang
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Huang Tan
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
- Department of Plant Biochemistry, Center for Plant Molecular Biology (ZMBP), Eberhard Karls University, Tübingen, Germany
| | - Laura Medina-Puche
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- Department of Plant Biochemistry, Center for Plant Molecular Biology (ZMBP), Eberhard Karls University, Tübingen, Germany
| | - Mengshi Wu
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Borja Garnelo Gomez
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Man Gao
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Chaonan Shi
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- Department of Plant Biochemistry, Center for Plant Molecular Biology (ZMBP), Eberhard Karls University, Tübingen, Germany
| | - Tamara Jimenez-Gongora
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Pengfei Fan
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Xue Ding
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Dan Zhang
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Yi Ding
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Tábata Rosas-Díaz
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yujing Liu
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Emmanuel Aguilar
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- Instituto de Hortofruticultura Subtropical y Mediterránea “La Mayora” (IHSM-UMA-CSIC), Area de Genética, Facultad de Ciencias, Universidad de Málaga, Campus de Teatinos s/n, Málaga, Spain
| | - Xing Fu
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Rosa Lozano-Durán
- Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
- Department of Plant Biochemistry, Center for Plant Molecular Biology (ZMBP), Eberhard Karls University, Tübingen, Germany
- * E-mail:
| |
Collapse
|
39
|
Gómez Borrego J, Torrent Burgas M. Analysis of Host–Bacteria Protein Interactions Reveals Conserved Domains and Motifs That Mediate Fundamental Infection Pathways. Int J Mol Sci 2022; 23:ijms231911489. [PMID: 36232803 PMCID: PMC9569774 DOI: 10.3390/ijms231911489] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 11/16/2022] Open
Abstract
Adhesion and colonization of host cells by pathogenic bacteria depend on protein–protein interactions (PPIs). These interactions are interesting from the pharmacological point of view since new molecules that inhibit host-pathogen PPIs would act as new antimicrobials. Most of these interactions are discovered using high-throughput methods that may display a high false positive rate. The absence of curation of these databases can make the available data unreliable. To address this issue, a comprehensive filtering process was developed to obtain a reliable list of domains and motifs that participate in PPIs between bacteria and human cells. From a structural point of view, our analysis revealed that human proteins involved in the interactions are rich in alpha helix and disordered regions and poorer in beta structure. Disordered regions in human proteins harbor short sequence motifs that are specifically recognized by certain domains in pathogenic proteins. The most relevant domain–domain interactions were validated by AlphaFold, showing that a proper analysis of host-pathogen PPI databases can reveal structural conserved patterns. Domain–motif interactions, on the contrary, were more difficult to validate, since unstructured regions were involved, where AlphaFold could not make a good prediction. Moreover, these interactions are also likely accommodated by post-translational modifications, especially phosphorylation, which can potentially occur in 25–50% of host proteins. Hence, while common structural patterns are involved in host–pathogen PPIs and can be retrieved from available databases, more information is required to properly infer the full interactome. By resolving these issues, and in combination with new prediction tools like Alphafold, new classes of antimicrobials could be discovered from a more detailed understanding of these interactions.
Collapse
|
40
|
Madan S, Demina V, Stapf M, Ernst O, Fröhlich H. Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings. PATTERNS (NEW YORK, N.Y.) 2022; 3:100551. [PMID: 36124304 PMCID: PMC9481957 DOI: 10.1016/j.patter.2022.100551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 03/28/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022]
Abstract
Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.
Collapse
Affiliation(s)
- Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Institute of Computer Science, University of Bonn, 53115 Bonn, Germany
| | | | - Marcus Stapf
- NEUWAY Pharma GmbH, In den Dauen 6A, 53117 Bonn, Germany
| | - Oliver Ernst
- NEUWAY Pharma GmbH, In den Dauen 6A, 53117 Bonn, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
| |
Collapse
|
41
|
Distinct evolutionary trajectories of SARS-CoV-2-interacting proteins in bats and primates identify important host determinants of COVID-19. Proc Natl Acad Sci U S A 2022; 119:e2206610119. [PMID: 35947637 PMCID: PMC9436378 DOI: 10.1073/pnas.2206610119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
The coronavirus disease 19 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a coronavirus that spilled over from the bat reservoir. However, the host genetic determinants that drive SARS-CoV-2 susceptibility and COVID-19 severity are largely unknown. Understanding how cellular proteins interacting with SARS-CoV-2 have evolved in primates and bats is of primary importance to decipher differences in the infection outcome between humans and the viral reservoir in bats. Here, we performed comparative functional genetic analyses of hundreds of SARS-CoV-2-interacting proteins to study virus–host interface adaptation over millions of years, pointing to genes similarly—or differentially—engaged in evolutionary arms races and that may be at the basis of in vivo pathogenic differences. The coronavirus disease 19 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a coronavirus that spilled over from the bat reservoir. Despite numerous clinical trials and vaccines, the burden remains immense, and the host determinants of SARS-CoV-2 susceptibility and COVID-19 severity remain largely unknown. Signatures of positive selection detected by comparative functional genetic analyses in primate and bat genomes can uncover important and specific adaptations that occurred at virus–host interfaces. We performed high-throughput evolutionary analyses of 334 SARS-CoV-2-interacting proteins to identify SARS-CoV adaptive loci and uncover functional differences between modern humans, primates, and bats. Using DGINN (Detection of Genetic INNovation), we identified 38 bat and 81 primate proteins with marks of positive selection. Seventeen genes, including the ACE2 receptor, present adaptive marks in both mammalian orders, suggesting common virus–host interfaces and past epidemics of coronaviruses shaping their genomes. Yet, 84 genes presented distinct adaptations in bats and primates. Notably, residues involved in ubiquitination and phosphorylation of the inflammatory RIPK1 have rapidly evolved in bats but not primates, suggesting different inflammation regulation versus humans. Furthermore, we discovered residues with typical virus–host arms race marks in primates, such as in the entry factor TMPRSS2 or the autophagy adaptor FYCO1, pointing to host-specific in vivo interfaces that may be drug targets. Finally, we found that FYCO1 sites under adaptation in primates are those associated with severe COVID-19, supporting their importance in pathogenesis and replication. Overall, we identified adaptations involved in SARS-CoV-2 infection in bats and primates, enlightening modern genetic determinants of virus susceptibility and severity.
Collapse
|
42
|
Shuler G, Hagai T. Rapidly evolving viral motifs mostly target biophysically constrained binding pockets of host proteins. Cell Rep 2022; 40:111212. [PMID: 35977510 DOI: 10.1016/j.celrep.2022.111212] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/11/2022] [Accepted: 07/22/2022] [Indexed: 11/28/2022] Open
Abstract
Evolutionary changes in host-virus interactions can alter the course of infection, but the biophysical and regulatory constraints that shape interface evolution remain largely unexplored. Here, we focus on viral mimicry of host-like motifs that allow binding to host domains and modulation of cellular pathways. We observe that motifs from unrelated viruses preferentially target conserved, widely expressed, and highly connected host proteins, enriched with regulatory and essential functions. The interface residues within these host domains are more conserved and bind a larger number of cellular proteins than similar motif-binding domains that are not known to interact with viruses. In contrast, rapidly evolving viral-binding human proteins form few interactions with other cellular proteins and display high tissue specificity, and their interfaces have few inter-residue contacts. Our results distinguish between conserved and rapidly evolving host-virus interfaces and show how various factors limit host capacity to evolve, allowing for efficient viral subversion of host machineries.
Collapse
Affiliation(s)
- Gal Shuler
- Shmunis School of Biomedicine and Cancer Research, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tzachi Hagai
- Shmunis School of Biomedicine and Cancer Research, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
| |
Collapse
|
43
|
Koca MB, Nourani E, Abbasoğlu F, Karadeniz İ, Sevilgen FE. Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses. Comput Biol Chem 2022; 101:107755. [PMID: 36037723 DOI: 10.1016/j.compbiolchem.2022.107755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/07/2022] [Accepted: 08/10/2022] [Indexed: 11/03/2022]
Abstract
Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of pathogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3-23% better area under curve (AUC) score than its competitors.
Collapse
Affiliation(s)
- Mehmet Burak Koca
- Department of Computer Engineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkey
| | - Esmaeil Nourani
- Department of Information Technology, Faculty of Computer Engineering and Information Technology, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Ferda Abbasoğlu
- Department of Computer Engineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkey
| | - İlknur Karadeniz
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Işık University, İstanbul, Turkey.
| | - Fatih Erdoğan Sevilgen
- Department of Computer Engineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkey; Institute for Data Science and Artificial Intelligence, Boğaziçi University, İstanbul, Turkey
| |
Collapse
|
44
|
Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
Collapse
Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
| |
Collapse
|
45
|
Fang Y, Yang Y, Liu C. New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions. Front Cell Infect Microbiol 2022; 12:931072. [PMID: 35982784 PMCID: PMC9378789 DOI: 10.3389/fcimb.2022.931072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
MotivationThe understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.ResultsThis work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.Availability and implementationThe KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.
Collapse
Affiliation(s)
- Yang Fang
- Key Laboratory of Bio-Resources and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- Department of Laboratory Medicine, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yi Yang
- Key Laboratory of Bio-Resources and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- *Correspondence: Chengcheng Liu, ; Yi Yang,
| | - Chengcheng Liu
- State Key Laboratory of Oral Diseases, Department of Periodontics, National Clinical Research Center for Oral Diseases, West China School & Hospital of Stomatology, Sichuan University, Chengdu, China
- *Correspondence: Chengcheng Liu, ; Yi Yang,
| |
Collapse
|
46
|
Chen H, Hu X, Hu Y, Zhou J, Chen M. CoVM2: Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method. Biomolecules 2022; 12:biom12081067. [PMID: 36008961 PMCID: PMC9405999 DOI: 10.3390/biom12081067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 02/01/2023] Open
Abstract
The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2–human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2–human protein–protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale.
Collapse
Affiliation(s)
- Hongjun Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
| | - Xiaotian Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
| | - Yanshi Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
| | - Jiawen Zhou
- Chu Kochen Honors College, Zhejiang University, Hangzhou 310058, China;
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
- Institute of Hematology, Zhejiang University School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-(0)571-8820-6612
| |
Collapse
|
47
|
Onisiforou A, Spyrou GM. Immunomodulatory effects of microbiota-derived metabolites at the crossroad of neurodegenerative diseases and viral infection: network-based bioinformatics insights. Front Immunol 2022; 13:843128. [PMID: 35928817 PMCID: PMC9344014 DOI: 10.3389/fimmu.2022.843128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Bidirectional cross-talk between commensal microbiota and the immune system is essential for the regulation of immune responses and the formation of immunological memory. Perturbations of microbiome-immune system interactions can lead to dysregulated immune responses against invading pathogens and/or to the loss of self-tolerance, leading to systemic inflammation and genesis of several immune-mediated pathologies, including neurodegeneration. In this paper, we first investigated the contribution of the immunomodulatory effects of microbiota (bacteria and fungi) in shaping immune responses and influencing the formation of immunological memory cells using a network-based bioinformatics approach. In addition, we investigated the possible role of microbiota-host-immune system interactions and of microbiota-virus interactions in a group of neurodegenerative diseases (NDs): Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Parkinson’s disease (PD) and Alzheimer’s disease (AD). Our analysis highlighted various aspects of the innate and adaptive immune response systems that can be modulated by microbiota, including the activation and maturation of microglia which are implicated in the development of NDs. It also led to the identification of specific microbiota components which might be able to influence immune system processes (ISPs) involved in the pathogenesis of NDs. In addition, it indicated that the impact of microbiota-derived metabolites in influencing disease-associated ISPs, is higher in MS disease, than in AD, PD and ALS suggesting a more important role of microbiota mediated-immune effects in MS.
Collapse
|
48
|
Valiente G. The Landscape of Virus-Host Protein–Protein Interaction Databases. Front Microbiol 2022; 13:827742. [PMID: 35910656 PMCID: PMC9335289 DOI: 10.3389/fmicb.2022.827742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/17/2022] [Indexed: 11/25/2022] Open
Abstract
Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.
Collapse
|
49
|
Saravanakumar K, Santosh SS, Ahamed MA, Sathiyaseelan A, Sultan G, Irfan N, Ali DM, Wang MH. Bioinformatics strategies for studying the molecular mechanisms of fungal extracellular vesicles with a focus on infection and immune responses. Brief Bioinform 2022; 23:6632620. [PMID: 35794708 DOI: 10.1093/bib/bbac250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/16/2022] [Accepted: 05/28/2022] [Indexed: 01/19/2023] Open
Abstract
Fungal extracellular vesicles (EVs) are released during pathogenesis and are found to be an opportunistic infection in most cases. EVs are immunocompetent with their host and have paved the way for new biomedical approaches to drug delivery and the treatment of complex diseases including cancer. With computing and processing advancements, the rise of bioinformatics tools for the evaluation of various parameters involved in fungal EVs has blossomed. In this review, we have complied and explored the bioinformatics tools to analyze the host-pathogen interaction, toxicity, omics and pathogenesis with an array of specific tools that have depicted the ability of EVs as vector/carrier for therapeutic agents and as a potential theme for immunotherapy. We have also discussed the generation and pathways involved in the production, transport, pathogenic action and immunological interactions of EVs in the host system. The incorporation of network pharmacology approaches has been discussed regarding fungal pathogens and their significance in drug discovery. To represent the overview, we have presented and demonstrated an in silico study model to portray the human Cryptococcal interactions.
Collapse
Affiliation(s)
- Kandasamy Saravanakumar
- Department of Bio-Health convergence, Kangwon National University, Chuncheon 200-701, Republic of Korea
| | | | - MohamedAli Afaan Ahamed
- School of Life Sciences, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu 600048, India
| | - Anbazhagan Sathiyaseelan
- Department of Bio-Health convergence, Kangwon National University, Chuncheon 200-701, Republic of Korea
| | - Ghazala Sultan
- Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India
| | - Navabshan Irfan
- Crescent School of Pharmacy, B.S Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600048, India
| | - Davoodbasha Mubarak Ali
- School of Life Sciences, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu 600048, India
| | - Myeong-Hyeon Wang
- Department of Bio-Health convergence, Kangwon National University, Chuncheon 200-701, Republic of Korea
| |
Collapse
|
50
|
Le TD, Nguyen PD, Korkin D, Thieu T. PHILM2Web: A high-throughput database of macromolecular host–pathogen interactions on the Web. Database (Oxford) 2022; 2022:6625823. [PMID: 35776535 PMCID: PMC9248916 DOI: 10.1093/database/baac042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 04/27/2022] [Accepted: 05/31/2022] [Indexed: 12/02/2022]
Abstract
During infection, the pathogen’s entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host–pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein–protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen–Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live
Collapse
Affiliation(s)
- Tuan-Dung Le
- Department of Computer Science, Oklahoma State University , Stillwater, OK, USA
| | - Phuong D Nguyen
- Department of Biochemistry and Molecular Biology, Oklahoma State University , Stillwater, OK, USA
| | - Dmitry Korkin
- Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute , Worcester, MA, USA
| | - Thanh Thieu
- Machine Learning Department, Moffitt Cancer Center and Research Institute , Tampa, FL, USA
| |
Collapse
|