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Guo X, Zhao Y, You F. MOI is a comprehensive database collecting processed multi-omics data associated with viral infection. Sci Rep 2024; 14:14725. [PMID: 38926513 PMCID: PMC11208532 DOI: 10.1038/s41598-024-65629-6] [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: 01/26/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
Viral infections pose significant public health challenges, exemplified by the global impact of COVID-19 caused by SARS-CoV-2. Understanding the intricate molecular mechanisms governing virus-host interactions is pivotal for effective intervention strategies. Despite the burgeoning multi-omics data on viral infections, a centralized database elucidating host responses to viruses remains lacking. In response, we have developed a comprehensive database named 'MOI' (available at http://www.fynn-guo.cn/ ), specifically designed to aggregate processed Multi-Omics data related to viral Infections. This meticulously curated database serves as a valuable resource for conducting detailed investigations into virus-host interactions. Leveraging high-throughput sequencing data and metadata from PubMed and Gene Expression Omnibus (GEO), MOI comprises over 3200 viral-infected samples, encompassing human and murine infections. Standardized processing pipelines ensure data integrity, including bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). MOI offers user-friendly interfaces presenting comprehensive cell marker tables, gene expression data, and epigenetic landscape charts. Analytical tools for DNA sequence conversion, FPKM calculation, differential gene expression, and Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment enhance data interpretation. Additionally, MOI provides 16 visualization plots for intuitive data exploration. In summary, MOI serves as a valuable repository for researchers investigating virus-host interactions. By centralizing and facilitating access to multi-omics data, MOI aims to advance our understanding of viral pathogenesis and expedite the development of therapeutic interventions.
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Affiliation(s)
- Xuefei Guo
- Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, NHC Key Laboratory of Medical Immunology, Peking University Health Science Center, Beijing, China.
| | - Yang Zhao
- Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, NHC Key Laboratory of Medical Immunology, Peking University Health Science Center, Beijing, China
| | - Fuping You
- Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, NHC Key Laboratory of Medical Immunology, Peking University Health Science Center, Beijing, China
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2
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Satish KS, Saraswathy GR, Ritesh G, Saravanan KS, Krishnan A, Bhargava J, Ushnaa K, Dsouza PL. Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:355-415. [PMID: 38942544 DOI: 10.1016/bs.pmbts.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.
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Affiliation(s)
- Kshreeraja S Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
| | - Giri Ritesh
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Aarti Krishnan
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Janhavi Bhargava
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kuri Ushnaa
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
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3
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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.
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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
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Chakraborty S, Askari M, Barai RS, Idicula‐Thomas S. PBIT V3 : A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates. Protein Sci 2024; 33:e4892. [PMID: 38168465 PMCID: PMC10804677 DOI: 10.1002/pro.4892] [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: 09/30/2023] [Revised: 12/15/2023] [Accepted: 12/28/2023] [Indexed: 01/05/2024]
Abstract
Rise of life-threatening superbugs, pandemics and epidemics warrants the need for cost-effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high-throughput discovery platforms to prioritize potential drug-targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug-targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad-spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno-informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug-resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/.
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Affiliation(s)
- Shuvechha Chakraborty
- Biomedical Informatics Centre, ICMR‐National Institute for Research in Reproductive and Child HealthMumbaiMaharashtraIndia
| | - Mehdi Askari
- Department of BioinformaticsGuru Nanak Khalsa College, Nathalal Parekh MargMumbaiMaharashtraIndia
| | - Ram Shankar Barai
- Biological Sciences DivisionICMR‐National Institute of Occupational HealthAhmedabadGujratIndia
| | - Susan Idicula‐Thomas
- Biomedical Informatics Centre, ICMR‐National Institute for Research in Reproductive and Child HealthMumbaiMaharashtraIndia
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5
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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/.
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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
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6
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Srivastava M, Dukeshire MR, Mir Q, Omoru OB, Manzourolajdad A, Janga SC. Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes. Brief Funct Genomics 2024; 23:46-54. [PMID: 36752040 PMCID: PMC10799312 DOI: 10.1093/bfgp/elac050] [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: 09/06/2022] [Revised: 10/24/2022] [Accepted: 11/11/2022] [Indexed: 02/09/2023] Open
Abstract
Long-range ribonucleic acid (RNA)-RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA-RNA hybrids have shown that these long-range structures exist in severe acute respiratory syndrome coronavirus (SARS-CoV)-2 on both genomic and sub-genomic levels and in dynamic topologies. Furthermore, co-evolution of coronaviruses with their hosts is navigated by genetic variations made possible by its large genome, high recombination frequency and a high mutation rate. SARS-CoV-2's mutations are known to occur spontaneously during replication, and thousands of aggregate mutations have been reported since the emergence of the virus. Although many long-range RRIs have been experimentally identified using high-throughput methods for the wild-type SARS-CoV-2 strain, evolutionary trajectory of these RRIs across variants, impact of mutations on RRIs and interaction of SARS-CoV-2 RNAs with the host have been largely open questions in the field. In this review, we summarize recent computational tools and experimental methods that have been enabling the mapping of RRIs in viral genomes, with a specific focus on SARS-CoV-2. We also present available informatics resources to navigate the RRI maps and shed light on the impact of mutations on the RRI space in viral genomes. Investigating the evolution of long-range RNA interactions and that of virus-host interactions can contribute to the understanding of new and emerging variants as well as aid in developing improved RNA therapeutics critical for combating future outbreaks.
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Affiliation(s)
- Mansi Srivastava
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Biology, Indiana University, 1001 East 3 St, Bloomington, Indiana 47405, USA
| | - Matthew R Dukeshire
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Quoseena Mir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Okiemute Beatrice Omoru
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Amirhossein Manzourolajdad
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Computer Science, Colgate University, Hamilton, NY, USA
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, Indiana 46202, USA
- Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, Indiana 46202, USA
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7
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Harrison PW, Amode MR, Austine-Orimoloye O, Azov A, Barba M, Barnes I, Becker A, Bennett R, Berry A, Bhai J, Bhurji SK, Boddu S, Branco Lins PR, Brooks L, Ramaraju S, Campbell L, Martinez MC, Charkhchi M, Chougule K, Cockburn A, Davidson C, De Silva N, Dodiya K, Donaldson S, El Houdaigui B, Naboulsi T, Fatima R, Giron CG, Genez T, Grigoriadis D, Ghattaoraya G, Martinez JG, Gurbich T, Hardy M, Hollis Z, Hourlier T, Hunt T, Kay M, Kaykala V, Le T, Lemos D, Lodha D, Marques-Coelho D, Maslen G, Merino G, Mirabueno L, Mushtaq A, Hossain S, Ogeh D, Sakthivel MP, Parker A, Perry M, Piližota I, Poppleton D, Prosovetskaia I, Raj S, Pérez-Silva J, Salam A, Saraf S, Saraiva-Agostinho N, Sheppard D, Sinha S, Sipos B, Sitnik V, Stark W, Steed E, Suner MM, Surapaneni L, Sutinen K, Tricomi FF, Urbina-Gómez D, Veidenberg A, Walsh TA, Ware D, Wass E, Willhoft N, Allen J, Alvarez-Jarreta J, Chakiachvili M, Flint B, Giorgetti S, Haggerty L, Ilsley G, Keatley J, Loveland J, Moore B, Mudge J, Naamati G, Tate J, Trevanion S, Winterbottom A, Frankish A, Hunt SE, Cunningham F, Dyer S, Finn R, Martin F, Yates A. Ensembl 2024. Nucleic Acids Res 2024; 52:D891-D899. [PMID: 37953337 PMCID: PMC10767893 DOI: 10.1093/nar/gkad1049] [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: 09/15/2023] [Revised: 10/20/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
Ensembl (https://www.ensembl.org) is a freely available genomic resource that has produced high-quality annotations, tools, and services for vertebrates and model organisms for more than two decades. In recent years, there has been a dramatic shift in the genomic landscape, with a large increase in the number and phylogenetic breadth of high-quality reference genomes, alongside major advances in the pan-genome representations of higher species. In order to support these efforts and accelerate downstream research, Ensembl continues to focus on scaling for the rapid annotation of new genome assemblies, developing new methods for comparative analysis, and expanding the depth and quality of our genome annotations. This year we have continued our expansion to support global biodiversity research, doubling the number of annotated genomes we support on our Rapid Release site to over 1700, driven by our close collaboration with biodiversity projects such as Darwin Tree of Life. We have also strengthened support for key agricultural species, including the first regulatory builds for farmed animals, and have updated key tools and resources that support the global scientific community, notably the Ensembl Variant Effect Predictor. Ensembl data, software, and tools are freely available.
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Affiliation(s)
- Peter W Harrison
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - M Ridwan Amode
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Olanrewaju Austine-Orimoloye
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Andrey G Azov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Matthieu Barba
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - If Barnes
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Arne Becker
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Ruth Bennett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Andrew Berry
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Jyothish Bhai
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Simarpreet Kaur Bhurji
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Sanjay Boddu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Paulo R Branco Lins
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Lucy Brooks
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Shashank Budhanuru Ramaraju
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Lahcen I Campbell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Manuel Carbajo Martinez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Mehrnaz Charkhchi
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, NY 11724, USA
| | - Alexander Cockburn
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Claire Davidson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Nishadi H De Silva
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Kamalkumar Dodiya
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Sarah Donaldson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Bilal El Houdaigui
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Tamara El Naboulsi
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Reham Fatima
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Carlos Garcia Giron
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Thiago Genez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Dionysios Grigoriadis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Gurpreet S Ghattaoraya
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Jose Gonzalez Martinez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Tatiana A Gurbich
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Matthew Hardy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Zoe Hollis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Mike Kay
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Vinay Kaykala
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Tuan Le
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Diana Lemos
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Disha Lodha
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Diego Marques-Coelho
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Gareth Maslen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Gabriela Alejandra Merino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Louisse Paola Mirabueno
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Aleena Mushtaq
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Syed Nakib Hossain
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Denye N Ogeh
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Manoj Pandian Sakthivel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Anne Parker
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Malcolm Perry
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Ivana Piližota
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Daniel Poppleton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Irina Prosovetskaia
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Shriya Raj
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - José G Pérez-Silva
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Ahamed Imran Abdul Salam
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Shradha Saraf
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Nuno Saraiva-Agostinho
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Dan Sheppard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Swati Sinha
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Botond Sipos
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Vasily Sitnik
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - William Stark
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Emily Steed
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Marie-Marthe Suner
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Likhitha Surapaneni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Kyösti Sutinen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Francesca Floriana Tricomi
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - David Urbina-Gómez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Andres Veidenberg
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Thomas A Walsh
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Doreen Ware
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, NY 11724, USA
- USDA ARS NAA Robert W. Holley Center for Agriculture and Health, Agricultural Research Service, Ithaca, NY 14853, USA
| | - Elizabeth Wass
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Natalie L Willhoft
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Jamie Allen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Jorge Alvarez-Jarreta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Marc Chakiachvili
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Bethany Flint
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Stefano Giorgetti
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Leanne Haggerty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Garth R Ilsley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Jon Keatley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Jane E Loveland
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Benjamin Moore
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Guy Naamati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - John Tate
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Stephen J Trevanion
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Andrea Winterbottom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Sarah E Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Sarah Dyer
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Fergal J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
| | - Andrew D Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, Cambridgeshire CB10 1SD, UK
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8
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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.
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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
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9
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Tusnády GE, Zeke A, Kálmán ZE, Fatoux M, Ricard-Blum S, Gibson TJ, Dobson L. LeishMANIAdb: a comparative resource for Leishmania proteins. Database (Oxford) 2023; 2023:baad074. [PMID: 37935582 PMCID: PMC10627299 DOI: 10.1093/database/baad074] [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: 05/10/2023] [Revised: 08/09/2023] [Accepted: 10/06/2023] [Indexed: 11/09/2023]
Abstract
Leishmaniasis is a detrimental disease causing serious changes in quality of life and some forms can lead to death. The disease is spread by the parasite Leishmania transmitted by sandfly vectors and their primary hosts are vertebrates including humans. The pathogen penetrates host cells and secretes proteins (the secretome) to repurpose cells for pathogen growth and to alter cell signaling via host-pathogen protein-protein interactions). Here, we present LeishMANIAdb, a database specifically designed to investigate how Leishmania virulence factors may interfere with host proteins. Since the secretomes of different Leishmania species are only partially characterized, we collated various experimental evidence and used computational predictions to identify Leishmania secreted proteins to generate a user-friendly unified web resource allowing users to access all information available on experimental and predicted secretomes. In addition, we manually annotated host-pathogen interactions of 211 proteins and the localization/function of 3764 transmembrane (TM) proteins of different Leishmania species. We also enriched all proteins with automatic structural and functional predictions that can provide new insights in the molecular mechanisms of infection. Our database may provide novel insights into Leishmania host-pathogen interactions and help to identify new therapeutic targets for this neglected disease. Database URL https://leishmaniadb.ttk.hu/.
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Affiliation(s)
- Gábor E Tusnády
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Magyar Tudósok körútja 2, Budapest 1117, Hungary
- Department of Bioinformatics, Semmelweis University, Tűzoltó u. 7, Budapest 1094, Hungary
| | - András Zeke
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Magyar Tudósok körútja 2, Budapest 1117, Hungary
| | - Zsófia E Kálmán
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Marie Fatoux
- ICBMS UMR CNRS 5246, University Lyon 1, Rue Victor Grignard, Villeurbanne 69622, France
- UMR CNRS 5086, University Lyon 1, 7 Passage du Vercors, Lyon 69367, France
| | - Sylvie Ricard-Blum
- ICBMS UMR CNRS 5246, University Lyon 1, Rue Victor Grignard, Villeurbanne 69622, France
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, Heidelberg 69117, Germany
| | - Laszlo Dobson
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Magyar Tudósok körútja 2, Budapest 1117, Hungary
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, Heidelberg 69117, Germany
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10
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Kabir AR, Chaudhary AA, Aladwani MO, Podder S. Decoding the host-pathogen interspecies molecular crosstalk during oral candidiasis in humans: an in silico analysis. Front Genet 2023; 14:1245445. [PMID: 37900175 PMCID: PMC10603195 DOI: 10.3389/fgene.2023.1245445] [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: 06/23/2023] [Accepted: 09/18/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction: The objective of this study is to investigate the interaction between Candida albicans and human proteins during oral candidiasis, with the aim of identifying pathways through which the pathogen subverts host cells. Methods: A comprehensive list of interactions between human proteins and C. albicans was obtained from the Human Protein Interaction Database using specific screening criteria. Then, the genes that exhibit differential expression during oral candidiasis in C. albicans were mapped with the list of human-Candida interactions to identify the corresponding host proteins. The identified host proteins were further compared with proteins specific to the tongue, resulting in a final list of 99 host proteins implicated in oral candidiasis. The interactions between host proteins and C. albicans proteins were analyzed using the STRING database, enabling the construction of protein-protein interaction networks. Similarly, the gene regulatory network of Candida proteins was reconstructed using data from the PathoYeastract and STRING databases. Core module proteins within the targeted host protein-protein interaction network were identified using ModuLand, a Cytoscape plugin. The expression levels of the core module proteins under diseased conditions were assessed using data from the GSE169278 dataset. To gain insights into the functional characteristics of both host and pathogen proteins, ontology analysis was conducted using Enrichr and YeastEnrichr, respectively. Result: The analysis revealed that three Candida proteins, HHT21, CYP5, and KAR2, interact with three core host proteins, namely, ING4 (in the DNMT1 module), SGTA, and TOR1A. These interactions potentially impair the immediate immune response of the host against the pathogen. Additionally, differential expression analysis of fungal proteins and their transcription factors in Candida-infected oral cell lines indicated that Rob1p, Tye7p, and Ume6p could be considered candidate transcription factors involved in instigating the pathogenesis of oral candidiasis during host infection. Conclusion: Our study provides a molecular map of the host-pathogen interaction during oral candidiasis, along with potential targets for designing regimens to overcome oral candidiasis, particularly in immunocompromised individuals.
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Affiliation(s)
- Ali Rejwan Kabir
- Computational and System Biology Lab, Department of Microbiology, Raiganj University, Raiganj, West Bengal, India
| | - Anis Ahmad Chaudhary
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Malak O Aladwani
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Soumita Podder
- Computational and System Biology Lab, Department of Microbiology, Raiganj University, Raiganj, West Bengal, India
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11
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Sarker P, Mitro A, Hoque H, Hasan MN, Nurnabi Azad Jewel GM. Identification of potential novel therapeutic drug target against Elizabethkingia anophelis by integrative pan and subtractive genomic analysis: An in silico approach. Comput Biol Med 2023; 165:107436. [PMID: 37690289 DOI: 10.1016/j.compbiomed.2023.107436] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 07/08/2023] [Accepted: 08/28/2023] [Indexed: 09/12/2023]
Abstract
Elizabethkingia anophelis is a human pathogen responsible for severe nosocomial infections in neonates and immunocompromised patients. The significantly higher mortality rate from E. anophelis infections and the lack of available regimens highlight the critical need to explore novel drug targets. The current study investigated effective novel drug targets by employing a comprehensive in silico subtractive genomic approach integrated with pangenomic analysis of E. anophelis strains. A total of 2809 core genomic proteins were found by pangenomic analysis of non-paralogous proteins. Subsequently, 156 pathogen-specific, 442 choke point, 202 virulence factor, 53 antibiotic resistant and 119 host-pathogen interacting proteins were identified in E. anophelis. By subtractive genomic approach, at first 791 proteins were found to be indispensable for the survival of E. anophelis. 558 and 315 proteins were detected as non-homologous to human and gut microflora respectively. Following that 245 cytoplasmic, 245 novel, and 23 broad-spectrum targets were selected and finally four proteins were considered as potential therapeutic targets of E. anophelis based on highest degree score in PPI network. Among those, three proteins were subjected to molecular docking and subsequent MD simulation as one protein did not contain a plausible binding pocket with sufficient surface area and volume. All the complexes were found to be stable and compact in 100 ns molecular dynamics simulation studies as measured by RMSD, RMSF, and Rg. These three short-listed targets identified in this study may lead to the development of novel antimicrobials capable of curing infections and pave the way to prevent and control the disease progression caused by the deadly agent E. anophelis.
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Affiliation(s)
- Parth Sarker
- Dept. of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, University Ave, Sylhet-3114, Bangladesh; Computational Biology and Bioinformatics Lab, Dept. of GEB, SUST, Sylhet-3114, Bangladesh
| | - Arnob Mitro
- Dept. of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, University Ave, Sylhet-3114, Bangladesh; Computational Biology and Bioinformatics Lab, Dept. of GEB, SUST, Sylhet-3114, Bangladesh
| | - Hammadul Hoque
- Dept. of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, University Ave, Sylhet-3114, Bangladesh
| | - Md Nazmul Hasan
- Dept. of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, University Ave, Sylhet-3114, Bangladesh
| | - G M Nurnabi Azad Jewel
- Dept. of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, University Ave, Sylhet-3114, Bangladesh; Computational Biology and Bioinformatics Lab, Dept. of GEB, SUST, Sylhet-3114, Bangladesh.
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12
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Shah M, Anwar A, Qasim A, Jaan S, Sarfraz A, Ullah R, Ali EA, Nishan U, Shehroz M, Zaman A, Ojha SC. Proteome level analysis of drug-resistant Prevotella melaninogenica for the identification of novel therapeutic candidates. Front Microbiol 2023; 14:1271798. [PMID: 37808310 PMCID: PMC10556700 DOI: 10.3389/fmicb.2023.1271798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/30/2023] [Indexed: 10/10/2023] Open
Abstract
The management of infectious diseases has become more critical due to the development of novel pathogenic strains with enhanced resistance. Prevotella melaninogenica, a gram-negative bacterium, was found to be involved in various infections of the respiratory tract, aerodigestive tract, and gastrointestinal tract. The need to explore novel drug and vaccine targets against this pathogen was triggered by the emergence of antimicrobial resistance against reported antibiotics to combat P. melaninogenica infections. The study involves core genes acquired from 14 complete P. melaninogenica strain genome sequences, where promiscuous drug and vaccine candidates were explored by state-of-the-art subtractive proteomics and reverse vaccinology approaches. A stringent bioinformatics analysis enlisted 18 targets as novel, essential, and non-homologous to humans and having druggability potential. Moreover, the extracellular and outer membrane proteins were subjected to antigenicity, allergenicity, and physicochemical analysis for the identification of the candidate proteins to design multi-epitope vaccines. Two candidate proteins (ADK95685.1 and ADK97014.1) were selected as the best target for the designing of a vaccine construct. Lead B- and T-cell overlapped epitopes were joined to generate potential chimeric vaccine constructs in combination with adjuvants and linkers. Finally, a prioritized vaccine construct was found to have stable interactions with the human immune cell receptors as confirmed by molecular docking and MD simulation studies. The vaccine construct was found to have cloning and expression ability in the bacterial cloning system. Immune simulation ensured the elicitation of significant immune responses against the designed vaccine. In conclusion, our study reported novel drug and vaccine targets and designed a multi-epitope vaccine against the P. melaninogenica infection. Further experimental validation will help open new avenues in the treatment of this multi-drug-resistant pathogen.
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Affiliation(s)
- Mohibullah Shah
- Department of Biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Amna Anwar
- Department of Biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Aqsa Qasim
- Department of Biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Samavia Jaan
- Department of Biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Asifa Sarfraz
- Department of Biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Riaz Ullah
- Medicinal Aromatic and Poisonous Plants Research Center, College of Pharmacy King Saud University, Riyadh, Saudi Arabia
| | - Essam A. Ali
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Umar Nishan
- Department of Chemistry, Kohat University of Science and Technology, Kohat, Pakistan
| | - Muhammad Shehroz
- Department of Bioinformatics, Kohsar University Murree, Murree, Pakistan
| | - Aqal Zaman
- Department of Microbiology and Molecular Genetics, Bahauddin Zakariya University, Multan, Pakistan
| | - Suvash Chandra Ojha
- Department of Infectious Diseases, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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13
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Liu T, Gao H, Ren X, Xu G, Liu B, Wu N, Luo H, Wang Y, Tu T, Yao B, Guan F, Teng Y, Huang H, Tian J. Protein-protein interaction and site prediction using transfer learning. Brief Bioinform 2023; 24:bbad376. [PMID: 37870286 DOI: 10.1093/bib/bbad376] [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: 07/26/2023] [Revised: 09/14/2023] [Accepted: 10/02/2023] [Indexed: 10/24/2023] Open
Abstract
The advanced language models have enabled us to recognize protein-protein interactions (PPIs) and interaction sites using protein sequences or structures. Here, we trained the MindSpore ProteinBERT (MP-BERT) model, a Bidirectional Encoder Representation from Transformers, using protein pairs as inputs, making it suitable for identifying PPIs and their respective interaction sites. The pretrained model (MP-BERT) was fine-tuned as MPB-PPI (MP-BERT on PPI) and demonstrated its superiority over the state-of-the-art models on diverse benchmark datasets for predicting PPIs. Moreover, the model's capability to recognize PPIs among various organisms was evaluated on multiple organisms. An amalgamated organism model was designed, exhibiting a high level of generalization across the majority of organisms and attaining an accuracy of 92.65%. The model was also customized to predict interaction site propensity by fine-tuning it with PPI site data as MPB-PPISP. Our method facilitates the prediction of both PPIs and their interaction sites, thereby illustrating the potency of transfer learning in dealing with the protein pair task.
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Affiliation(s)
- Tuoyu Liu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Han Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Xiaopu Ren
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guoshun Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Bo Liu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ningfeng Wu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huiying Luo
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yuan Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Tao Tu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Bin Yao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Feifei Guan
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yue Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China
| | - Huoqing Huang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jian Tian
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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14
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Jayaprakash A, Roy A, Thanmalagan RR, Arunachalam A, P T V L. Understanding the mechanism of pathogenicity through interactome studies between Arachis hypogaea L. and Aspergillus flavus. J Proteomics 2023; 287:104975. [PMID: 37482270 DOI: 10.1016/j.jprot.2023.104975] [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: 02/06/2023] [Revised: 06/28/2023] [Accepted: 07/15/2023] [Indexed: 07/25/2023]
Abstract
Aspergillus flavus (A. flavus) infects the peanut seeds during pre-and post-harvest stages, causing seed quality destruction for humans and livestock consumption. Even though many resistant varieties were developed, the molecular mechanism of defense interactions of peanut against A. flavus still needs further investigation. Hence, an interologous host-pathogen protein interaction (HPPI) network was constructed to understand the subcellular level interaction mechanism between peanut and A. flavus. Out of the top 10 hub proteins of both organisms, protein phosphatase 2C and cyclic nucleotide-binding/kinase domain-containing protein and different ribosomal proteins were identified as candidate proteins involved in defense. Functional annotation and subcellular localization based characterization of HPPI identified protein SGT1 homolog, calmodulin and Rac-like GTP-binding proteins to be involved in defense response against fungus. The relevance of HPPI in infectious conditions was assessed using two transcriptome data which identified the interplay of host kinase class R proteins, bHLH TFs and cell wall related proteins to impart resistance against pathogen infection. Further, the pathogenicity analysis identified glycogen phosphorylase and molecular chaperone and allergen Mod-E/Hsp90/Hsp1 as potential pathogen targets to enhance the host defense mechanism. Hence, the computationally predicted host-pathogen PPI network could provide valuable support for molecular biology experiments to understand the host-pathogen interaction. SIGNIFICANCE: Protein-protein interactions execute significant cellular interactions in an organism and are influenced majorly by stress conditions. Here we reported the host-pathogen protein-protein interaction between peanut and A. flavus, and a detailed network analysis based on function, subcellular localization, gene co-expression, and pathogenicity was performed. The network analysis identified key proteins such as host kinase class R proteins, calmodulin, SGT1 homolog, Rac-like GTP-binding proteins bHLH TFs and cell wall related to impart resistance against pathogen infection. We observed the interplay of defense related proteins and cell wall related proteins predominantly, which could be subjected to further studies. The network analysis described in this study could be applied to understand other host-pathogen systems generally.
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Affiliation(s)
- Aiswarya Jayaprakash
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Abhijeet Roy
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Raja Rajeswary Thanmalagan
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Annamalai Arunachalam
- Department of Food Science & Technology, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Lakshmi P T V
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India.
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15
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Thatai AKS, Ammankallu S, Devasahayam Arokia Balaya R, Soman SP, Nisar M, Babu S, John L, George A, Anto CK, Sanjeev D, Kandiyil MK, Raj SS, Awasthi K, Vinodchandra SS, Prasad TSK, Raju R. VirhostlncR: A comprehensive database to explore lncRNAs and their targets in viral infections. Comput Biol Med 2023; 164:107279. [PMID: 37572440 DOI: 10.1016/j.compbiomed.2023.107279] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 07/11/2023] [Accepted: 07/16/2023] [Indexed: 08/14/2023]
Abstract
Long non-coding-RNAs (lncRNAs) are an expanding set of cis-/trans-regulatory RNA genes that outnumber the protein-coding genes. Although being increasingly discovered, the functional role of the majority of lncRNAs in diverse biological conditions is undefined. Increasing evidence supports the critical role of lncRNAs in the emergence, regulation, and progression of various viral infections including influenza, hepatitis, coronavirus, and human immunodeficiency virus. Hence, the identification of signature lncRNAs would facilitate focused analysis of their functional roles accounting for their targets and regulatory mechanisms associated with infections. Towards this, we compiled 2803 lncRNAs identified to be modulated by 33 viral strains in various mammalian cell types and are provided through the resource named VirhostlncR (http://ciods.in/VirhostlncR/). The information on each of the viral strains, their multiplicity of infection, duration of infection, host cell name and cell types, fold change of lncRNA expression, and their specific identification methods are integrated into VirhostlncR. Based on the current datasets, we report 150 lncRNAs including differentiation antagonizing non-protein coding RNA (DANCR), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), maternally expressed gene 3 (MEG3), nuclear paraspeckle assembly transcript 1 (NEAT1), and plasmacytoma variant translocation 1 (PVT1) to be perturbed by two or more viruses. Analysis of viral protein interactions with human transcription factors (TFs) or TF-containing protein complexes identified that distinct viruses can transcriptionally regulate many of these lncRNAs through multiple protein complexes. Together, we believe that the current dataset will enable priority selection of lncRNAs for identification of their targets and serve as an effective platform for the analysis of noncoding RNA-mediated regulations in viral infections.
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Affiliation(s)
- Arun Kumar Sumaithangi Thatai
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Shruthi Ammankallu
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Rex Devasahayam Arokia Balaya
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Sreelakshmi Pathappillil Soman
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Mahammad Nisar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Sreeranjini Babu
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Anju George
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Christy Kallely Anto
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Diya Sanjeev
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Mrudula Kinarulla Kandiyil
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Sini S Raj
- Department of Computer Science, University of Kerala, Thiruvananthapuram, 695 581, Kerala, India.
| | - Kriti Awasthi
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - S S Vinodchandra
- Department of Computer Science, University of Kerala, Thiruvananthapuram, 695 581, Kerala, India.
| | - Thottethodi Subrahmanya Keshava Prasad
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Omics Analytics Pvt. Ltd., Yenepoya Incubator, Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Rajesh Raju
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India; Omics Analytics Pvt. Ltd., Yenepoya Incubator, Deralakatte, Mangalore, 575 018, Karnataka, India.
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16
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Wang W, Meng X, Xiang J, Shuai Y, Bedru HD, Li M. CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes. IEEE J Biomed Health Inform 2023; 27:4569-4578. [PMID: 37399160 DOI: 10.1109/jbhi.2023.3289490] [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: 07/05/2023]
Abstract
Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes.
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17
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Will I, Beckerson WC, de Bekker C. Using machine learning to predict protein-protein interactions between a zombie ant fungus and its carpenter ant host. Sci Rep 2023; 13:13821. [PMID: 37620441 PMCID: PMC10449854 DOI: 10.1038/s41598-023-40764-8] [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: 01/06/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023] Open
Abstract
Parasitic fungi produce proteins that modulate virulence, alter host physiology, and trigger host responses. These proteins, classified as a type of "effector," often act via protein-protein interactions (PPIs). The fungal parasite Ophiocordyceps camponoti-floridani (zombie ant fungus) manipulates Camponotus floridanus (carpenter ant) behavior to promote transmission. The most striking aspect of this behavioral change is a summit disease phenotype where infected hosts ascend and attach to an elevated position. Plausibly, interspecific PPIs drive aspects of Ophiocordyceps infection and host manipulation. Machine learning PPI predictions offer high-throughput methods to produce mechanistic hypotheses on how this behavioral manipulation occurs. Using D-SCRIPT to predict host-parasite PPIs, we found ca. 6000 interactions involving 2083 host proteins and 129 parasite proteins, which are encoded by genes upregulated during manipulated behavior. We identified multiple overrepresentations of functional annotations among these proteins. The strongest signals in the host highlighted neuromodulatory G-protein coupled receptors and oxidation-reduction processes. We also detected Camponotus structural and gene-regulatory proteins. In the parasite, we found enrichment of Ophiocordyceps proteases and frequent involvement of novel small secreted proteins with unknown functions. From these results, we provide new hypotheses on potential parasite effectors and host targets underlying zombie ant behavioral manipulation.
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Affiliation(s)
- Ian Will
- Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL, 32816, USA.
| | - William C Beckerson
- Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL, 32816, USA
| | - Charissa de Bekker
- Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL, 32816, USA.
- Department of Biology, Microbiology, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
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18
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Solano-González S, Castro-Vásquez R, Molina-Bravo R. Genomic Characterization and Functional Description of Beauveria bassiana Isolates from Latin America. J Fungi (Basel) 2023; 9:711. [PMID: 37504700 PMCID: PMC10381237 DOI: 10.3390/jof9070711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 07/29/2023] Open
Abstract
Beauveria bassiana is an entomopathogenic fungus used in agriculture as a biological controller worldwide. Despite being a well-studied organism, there are no genomic studies of B. bassiana isolates from Central American and Caribbean countries. This work characterized the functional potential of eight Neotropical isolates and provided an overview of their genomic characteristics, targeting genes associated with pathogenicity, the production of secondary metabolites, and the identification of CAZYmes as tools for future biotechnological applications. In addition, a comparison between these isolates and reference genomes was performed. Differences were observed according to geographical location and the lineages of the B. bassiana complex to which each isolate belonged.
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Affiliation(s)
- Stefany Solano-González
- Laboratorio de Bioinformática Aplicada (LABAP), Escuela de Ciencias Biológicas, Universidad Nacional, Heredia 40104, Costa Rica
| | - Ruth Castro-Vásquez
- Centro de Investigación en Biología Celular y Molecular, Universidad de Costa Rica, San José 11501, Costa Rica
| | - Ramón Molina-Bravo
- Biotecnología Vegetal y Recursos Genéticos para el Fitomejoramiento (BIOVERFI), Escuela de Ciencias Agrarias, Universidad Nacional, Heredia 40104, Costa Rica
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19
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Joshi A, Song HG, Yang SY, Lee JH. Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants. PLANTS (BASEL, SWITZERLAND) 2023; 12:2454. [PMID: 37447014 DOI: 10.3390/plants12132454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023]
Abstract
Modern plant pathology relies on bioinformatics approaches to create novel plant disease diagnostic tools. In recent years, a significant amount of biological data has been generated due to rapid developments in genomics and molecular biology techniques. The progress in the sequencing of agriculturally important crops has made it possible to develop a better understanding of plant-pathogen interactions and plant resistance. The availability of host-pathogen genome data offers effective assistance in retrieving, annotating, analyzing, and identifying the functional aspects for characterization at the gene and genome levels. Physical mapping facilitates the identification and isolation of several candidate resistance (R) genes from diverse plant species. A large number of genetic variations, such as disease-causing mutations in the genome, have been identified and characterized using bioinformatics tools, and these desirable mutations were exploited to develop disease resistance. Moreover, crop genome editing tools, namely the CRISPR (clustered regulatory interspaced short palindromic repeats)/Cas9 (CRISPR-associated) system, offer novel and efficient strategies for developing durable resistance. This review paper describes some aspects concerning the databases, tools, and techniques used to characterize resistance (R) genes for plant disease management.
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Affiliation(s)
- Alpana Joshi
- Department of Bioenvironmental Chemistry, College of Agriculture & Life Sciences, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Agriculture Technology & Agri-Informatics, Shobhit Institute of Engineering & Technology, Meerut 250110, India
| | - Hyung-Geun Song
- Department of Bioenvironmental Chemistry, College of Agriculture & Life Sciences, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Seo-Yeon Yang
- Department of Agricultural Chemistry, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Ji-Hoon Lee
- Department of Bioenvironmental Chemistry, College of Agriculture & Life Sciences, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Agricultural Chemistry, Jeonbuk National University, Jeonju 54896, Republic of Korea
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20
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Bishnoi R, Kaur S, Sandhu JS, Singla D. Genome engineering of disease susceptibility genes for enhancing resistance in plants. Funct Integr Genomics 2023; 23:207. [PMID: 37338599 DOI: 10.1007/s10142-023-01133-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/21/2023]
Abstract
Introgression of disease resistance genes (R-genes) to fight against an array of phytopathogens takes several years using conventional breeding approaches. Pathogens develop mechanism(s) to escape plants immune system by evolving new strains/races, thus making them susceptible to disease. Conversely, disruption of host susceptibility factors (or S-genes) provides opportunities for resistance breeding in crops. S-genes are often exploited by phytopathogens to promote their growth and infection. Therefore, identification and targeting of disease susceptibility genes (S-genes) are gaining more attention for the acquisition of resistance in plants. Genome engineering of S-genes results in targeted, transgene-free gene modification through CRISPR-Cas-mediated technology and has been reported in several agriculturally important crops. In this review, we discuss the defense mechanism in plants against phytopathogens, tug of war between R-genes and S-genes, in silico techniques for identification of host-target (S-) genes and pathogen effector molecule(s), CRISPR-Cas-mediated S-gene engineering, its applications, challenges, and future prospects.
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Affiliation(s)
- Ritika Bishnoi
- Bioinformatics Centre, School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India.
| | - Sehgeet Kaur
- Bioinformatics Centre, School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India
| | - Jagdeep Singh Sandhu
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India
| | - Deepak Singla
- Bioinformatics Centre, School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India.
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21
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Martins YC, Ziviani A, Cerqueira e Costa MDO, Cavalcanti MCR, Nicolás MF, de Vasconcelos ATR. PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets. BIOINFORMATICS ADVANCES 2023; 3:vbad067. [PMID: 37359724 PMCID: PMC10290227 DOI: 10.1093/bioadv/vbad067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/28/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023]
Abstract
Summary Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein-protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential host-pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare host-pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system. Availability and implementation https://github.com/YasCoMa/ppintegrator, https://github.com/YasCoMa/ppi_validation_process and https://github.com/YasCoMa/predprin.
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Affiliation(s)
- Yasmmin Côrtes Martins
- Bioinformatics Laboratory, National Laboratory for Scientific Computing, Petrópolis 25651-076, Brazil
| | - Artur Ziviani
- Data Extreme Laboratory (DEXL), National Laboratory for Scientific Computing, Petrópolis 25651-076, Brazil
| | | | | | - Marisa Fabiana Nicolás
- Bioinformatics Laboratory, National Laboratory for Scientific Computing, Petrópolis 25651-076, Brazil
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22
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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.
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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
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23
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Ma’ruf M, Fadli JC, Mahendra MR, Irham LM, Sulistyani N, Adikusuma W, Chong R, Septama AW. A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome. Genomics Inform 2023; 21:e26. [PMID: 37704211 PMCID: PMC10326529 DOI: 10.5808/gi.23010] [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: 02/09/2023] [Revised: 04/09/2023] [Accepted: 04/12/2023] [Indexed: 07/08/2023] Open
Abstract
Stevens-Johnson syndrome (SJS) produces a severe hypersensitivity reaction caused by Herpes simplex virus or mycoplasma infection, vaccination, systemic disease, or other agents. Several studies have investigated the genetic susceptibility involved in SJS. To provide further genetic insights into the pathogenesis of SJS, this study prioritized high-impact, SJS-associated pathogenic variants through integrating bioinformatic and population genetic data. First, we identified SJS-associated single nucleotide polymorphisms from the genome-wide association studies catalog, followed by genome annotation with HaploReg and variant validation with Ensembl. Subsequently, expression quantitative trait locus (eQTL) from GTEx identified human genetic variants with differential gene expression across human tissues. Our results indicate that two variants, namely rs2074494 and rs5010528, which are encoded by the HLA-C (human leukocyte antigen C) gene, were found to be differentially expressed in skin. The allele frequencies for rs2074494 and rs5010528 also appear to significantly differ across continents. We highlight the utility of these population-specific HLA-C genetic variants for genetic association studies, and aid in early prognosis and disease treatment of SJS.
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Affiliation(s)
- Muhammad Ma’ruf
- Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia
| | | | | | - Lalu Muhammad Irham
- Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia
- Center for Vaccine and Drugs, Research Organization for Health, National Research and Innovation Agency (BRIN), South Tangerang 15314, Indonesia
| | - Nanik Sulistyani
- Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia
| | - Wirawan Adikusuma
- Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram 83127, Indonesia
- Center for Vaccine and Drugs, Research Organization for Health, National Research and Innovation Agency (BRIN), South Tangerang 15314, Indonesia
| | - Rockie Chong
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, CA, USA
| | - Abdi Wira Septama
- Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram 83127, Indonesia
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24
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Kataria R, Kaur S, Kaundal R. Deciphering the complete human-monkeypox virus interactome: Identifying immune responses and potential drug targets. Front Immunol 2023; 14:1116988. [PMID: 37051239 PMCID: PMC10083500 DOI: 10.3389/fimmu.2023.1116988] [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: 12/06/2022] [Accepted: 02/22/2023] [Indexed: 03/29/2023] Open
Abstract
Monkeypox virus (MPXV) is a dsDNA virus, belonging to Poxviridae family. The outbreak of monkeypox disease in humans is critical in European and Western countries, owing to its origin in African regions. The highest number of cases of the disease were found in the United States, followed by Spain and Brazil. Understanding the complete infection mechanism of diverse MPXV strains and their interaction with humans is important for therapeutic drug development, and to avoid any future epidemics. Using computational systems biology, we deciphered the genome-wide protein-protein interactions (PPIs) between 22 MPXV strains and human proteome. Based on phylogenomics and disease severity, 3 different strains of MPXV: Zaire-96-I-16, MPXV-UK_P2, and MPXV_USA_2022_MA001 were selected for comparative functional analysis of the proteins involved in the interactions. On an average, we predicted around 92,880 non-redundant PPIs between human and MPXV proteomes, involving 8014 host and 116 pathogen proteins from the 3 strains. The gene ontology (GO) enrichment analysis revealed 10,624 common GO terms in which the host proteins of 3 strains were highly enriched. These include significant GO terms such as platelet activation (GO:0030168), GABA-A receptor complex (GO:1902711), and metalloendopeptidase activity (GO:0004222). The host proteins were also significantly enriched in calcium signaling pathway (hsa04020), MAPK signaling pathway (hsa04010), and inflammatory mediator regulation of TRP channels (hsa04750). These significantly enriched GO terms and KEGG pathways are known to be implicated in immunomodulatory and therapeutic role in humans during viral infection. The protein hubs analysis revealed that most of the MPXV proteins form hubs with the protein kinases and AGC kinase C-terminal domains. Furthermore, subcellular localization revealed that most of the human proteins were localized in cytoplasm (29.22%) and nucleus (26.79%). A few drugs including Fostamatinib, Tamoxifen and others were identified as potential drug candidates against the monkeypox virus disease. This study reports the genome-scale PPIs elucidation in human-monkeypox virus pathosystem, thus facilitating the research community with functional insights into the monkeypox disease infection mechanism and augment the drug development.
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Affiliation(s)
- Raghav Kataria
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Logan, United States
| | - Simardeep Kaur
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Logan, United States
- Bioinformatics Facility, Center for Integrated BioSystems, Logan, United States
- Division of Biochemistry, Indian Agricultural Research Institute (ICAR), New Delhi, India
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Logan, United States
- Bioinformatics Facility, Center for Integrated BioSystems, Logan, United States
- Department of Computer Science, College of Science, Utah State University, Logan, UT, United States
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25
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Unali G, Crivicich G, Pagani I, Abou‐Alezz M, Folchini F, Valeri E, Matafora V, Reisz JA, Giordano AMS, Cuccovillo I, Butta GM, Donnici L, D'Alessandro A, De Francesco R, Manganaro L, Cittaro D, Merelli I, Petrillo C, Bachi A, Vicenzi E, Kajaste‐Rudnitski A. Interferon‐inducible phospholipids govern
IFITM3
‐dependent endosomal antiviral immunity. EMBO J 2023; 42:e112234. [PMID: 36970857 PMCID: PMC10183820 DOI: 10.15252/embj.2022112234] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/27/2023] [Accepted: 03/06/2023] [Indexed: 03/29/2023] Open
Abstract
The interferon-induced transmembrane proteins (IFITM) are implicated in several biological processes, including antiviral defense, but their modes of action remain debated. Here, taking advantage of pseudotyped viral entry assays and replicating viruses, we uncover the requirement of host co-factors for endosomal antiviral inhibition through high-throughput proteomics and lipidomics in cellular models of IFITM restriction. Unlike plasma membrane (PM)-localized IFITM restriction that targets infectious SARS-CoV2 and other PM-fusing viral envelopes, inhibition of endosomal viral entry depends on lysines within the conserved IFITM intracellular loop. These residues recruit Phosphatidylinositol 3,4,5-trisphosphate (PIP3) that we show here to be required for endosomal IFITM activity. We identify PIP3 as an interferon-inducible phospholipid that acts as a rheostat for endosomal antiviral immunity. PIP3 levels correlated with the potency of endosomal IFITM restriction and exogenous PIP3 enhanced inhibition of endocytic viruses, including the recent SARS-CoV2 Omicron variant. Together, our results identify PIP3 as a critical regulator of endosomal IFITM restriction linking it to the Pi3K/Akt/mTORC pathway and elucidate cell-compartment-specific antiviral mechanisms with potential relevance for the development of broadly acting antiviral strategies.
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26
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Mostaffa NH, Suhaimi AH, Al-Idrus A. Interactomics in plant defence: progress and opportunities. Mol Biol Rep 2023; 50:4605-4618. [PMID: 36920596 DOI: 10.1007/s11033-023-08345-0] [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: 12/28/2022] [Accepted: 02/15/2023] [Indexed: 03/16/2023]
Abstract
Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.
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Affiliation(s)
- Nur Hikmah Mostaffa
- Programme of Genetics, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Ahmad Husaini Suhaimi
- Programme of Genetics, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Aisyafaznim Al-Idrus
- Programme of Genetics, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
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Palomba M, Rughetti A, Mignogna G, Castrignanò T, Rahimi H, Masuelli L, Napoletano C, Pinna V, Giorgi A, Santoro M, Schininà ME, Maras B, Mattiucci S. Proteomic characterization of extracellular vesicles released by third stage larvae of the zoonotic parasite Anisakis pegreffii (Nematoda: Anisakidae). Front Cell Infect Microbiol 2023; 13:1079991. [PMID: 37009516 PMCID: PMC10050594 DOI: 10.3389/fcimb.2023.1079991] [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: 10/25/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionAnisakis pegreffii is a sibling species within the A. simplex (s.l.) complex requiring marine homeothermic (mainly cetaceans) and heterothermic (crustaceans, fish, and cephalopods) organisms to complete its life cycle. It is also a zoonotic species, able to accidentally infect humans (anisakiasis). To investigate the molecular signals involved in this host-parasite interaction and pathogenesis, the proteomic composition of the extracellular vesicles (EVs) released by the third-stage larvae (L3) of A. pegreffii, was characterized.MethodsGenetically identified L3 of A. pegreffii were maintained for 24 h at 37°C and EVs were isolated by serial centrifugation and ultracentrifugation of culture media. Proteomic analysis was performed by Shotgun Analysis.Results and discussionEVs showed spherical shaped structure (size 65-295 nm). Proteomic results were blasted against the A. pegreffii specific transcriptomic database, and 153 unique proteins were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis predicted several proteins belonging to distinct metabolic pathways. The similarity search employing selected parasitic nematodes database revealed that proteins associated with A. pegreffii EVs might be involved in parasite survival and adaptation, as well as in pathogenic processes. Further, a possible link between the A. pegreffii EVs proteins versus those of human and cetaceans’ hosts, were predicted by using HPIDB database. The results, herein described, expand knowledge concerning the proteins possibly implied in the host-parasite interactions between this parasite and its natural and accidental hosts.
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Affiliation(s)
- Marialetizia Palomba
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Aurelia Rughetti
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Giuseppina Mignogna
- Department of Biochemistry Science, Sapienza University of Rome, Rome, Italy
| | - Tiziana Castrignanò
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Hassan Rahimi
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Laura Masuelli
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Chiara Napoletano
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Valentina Pinna
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Alessandra Giorgi
- Department of Biochemistry Science, Sapienza University of Rome, Rome, Italy
| | - Mario Santoro
- Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Naples, Italy
| | | | - Bruno Maras
- Department of Biochemistry Science, Sapienza University of Rome, Rome, Italy
| | - Simonetta Mattiucci
- Department of Public Health and Infectious Diseases, Section of Parasitology, Sapienza University of Rome, Rome, Italy
- *Correspondence: Simonetta Mattiucci,
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Ismail M, Bai B, Guo J, Bai Y, Sajid Z, Muhammad SA, Shaikh RS. Experimental Validation of MHC Class I and II Peptide-Based Potential Vaccine Candidates for Human Papilloma Virus Using Sprague-Dawly Models. Molecules 2023; 28:1687. [PMID: 36838675 PMCID: PMC9968051 DOI: 10.3390/molecules28041687] [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: 11/26/2022] [Revised: 01/09/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
Human papilloma virus (HPV) causes cervical and many other cancers. Recent trend in vaccine design is shifted toward epitope-based developments that are more specific, safe, and easy to produce. In this study, we predicted eight immunogenic peptides of CD4+ and CD8+ T-lymphocytes (MHC class I and II as M1 and M2) including early proteins (E2 and E6), major (L1) and minor capsid protein (L2). Male and female Sprague Dawly rats in groups were immunized with each synthetic peptide. L1M1, L1M2, L2M1, and L2M2 induced significant immunogenic response compared to E2M1, E2M2, E6M1 and E6M2. We observed optimal titer of IgG antibodies (>1.25 g/L), interferon-γ (>64 ng/L), and granzyme-B (>40 pg/mL) compared to control at second booster dose (240 µg/500 µL). The induction of peptide-specific IgG antibodies in immunized rats indicates the T-cell dependent B-lymphocyte activation. A substantial CD4+ and CD8+ cell count was observed at 240 µg/500 µL. In male and female rats, CD8+ cell count for L1 and L2 peptide is 3000 and 3118, and CD4+ is 3369 and 3484 respectively compared to control. In conclusion, we demonstrated that L1M1, L1M2, L2M1, L2M2 are likely to contain potential epitopes for induction of immune responses supporting the feasibility of peptide-based vaccine development for HPV.
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Affiliation(s)
- Mehreen Ismail
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan 60800, Pakistan
| | - Baogang Bai
- School of Information and Technology, Wenzhou Business College, Wenzhou 325015, China
- Engineering Research Center of Intelligent Medicine, Wenzhou 325000, China
- The 1st School of Medical, School of Information and Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325015, China
| | - Jinlei Guo
- School of Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang 453513, China
| | - Yuhui Bai
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zureesha Sajid
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan 60800, Pakistan
| | - Syed Aun Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan 60800, Pakistan
| | - Rehan Sadiq Shaikh
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan 60800, Pakistan
- Centre for Applied Molecular Biology, University of the Punjab, Lahore 54000, Pakistan
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29
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Duhan N, Kaundal R. HuCoPIA: An Atlas of Human vs. SARS-CoV-2 Interactome and the Comparative Analysis with Other Coronaviridae Family Viruses. Viruses 2023; 15:492. [PMID: 36851706 PMCID: PMC9962590 DOI: 10.3390/v15020492] [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: 11/08/2022] [Revised: 02/01/2023] [Accepted: 02/04/2023] [Indexed: 02/12/2023] Open
Abstract
SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains (SARS-CoV-2, SARS-CoV, and MERS) of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 (41,173), SARS-CoV (31,997), and MERS (26,862) interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.
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Affiliation(s)
- Naveen Duhan
- Department of Plants, Soils, and Climate/Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate/Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Department of Computer Science, College of Science, Utah State University, Logan, UT 84322, USA
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30
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Duan H, Zhang X, Figeys D. An emerging field: Post-translational modification in microbiome. Proteomics 2023; 23:e2100389. [PMID: 36239139 DOI: 10.1002/pmic.202100389] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/11/2022]
Abstract
Post-translational modifications (PTMs) play an essential role in most biological processes. PTMs on human proteins have been extensively studied. Studies on bacterial PTMs are emerging, which demonstrate that bacterial PTMs are different from human PTMs in their types, mechanisms and functions. Few PTM studies have been done on the microbiome. Here, we reviewed several studied PTMs in bacteria including phosphorylation, acetylation, succinylation, glycosylation, and proteases. We discussed the enzymes responsible for each PTM and their functions. We also summarized the current methods used to study microbiome PTMs and the observations demonstrating the roles of PTM in the microbe-microbe interactions within the microbiome and their interactions with the environment or host. Although new methods and tools for PTM studies are still needed, the existing technologies have made great progress enabling a deeper understanding of the functional regulation of the microbiome. Large-scale application of these microbiome-wide PTM studies will provide a better understanding of the microbiome and its roles in the development of human diseases.
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Affiliation(s)
- Haonan Duan
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.,Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Xu Zhang
- Center for Biologics Evaluation, Biologic and Radiopharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, Canada
| | - Daniel Figeys
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.,Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
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31
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Kori M, Turanli B, Arga KY. Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer. Front Oncol 2023; 13:1096081. [PMID: 36761959 PMCID: PMC9905826 DOI: 10.3389/fonc.2023.1096081] [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: 11/11/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023] Open
Abstract
Introduction Integrating interaction data with biological knowledge can be a critical approach for drug development or drug repurposing. In this context, host-pathogen-protein-protein interaction (HP-PPI) networks are useful instrument to uncover the phenomena underlying therapeutic effects in infectious diseases, including cervical cancer, which is almost exclusively due to human papillomavirus (HPV) infections. Cervical cancer is one of the second leading causes of death, and HPV16 and HPV18 are the most common subtypes worldwide. Given the limitations of traditionally used virus-directed drug therapies for infectious diseases and, at the same time, recent cancer statistics for cervical cancer cases, the need for innovative treatments becomes clear. Methods Accordingly, in this study, we emphasize the potential of host proteins as drug targets and identify promising host protein candidates for cervical cancer by considering potential differences between HPV subtypes (i.e., HPV16 and HPV18) within a novel bioinformatics framework that we have developed. Subsequently, subtype-specific HP-PPI networks were constructed to obtain host proteins. Using this framework, we next selected biologically significant host proteins. Using these prominent host proteins, we performed drug repurposing analysis. Finally, by following our framework we identify the most promising host-oriented drug candidates for cervical cancer. Results As a result of this framework, we discovered both previously associated and novel drug candidates, including interferon alfacon-1, pimecrolimus, and hyaluronan specifically for HPV16 and HPV18 subtypes, respectively. Discussion Consequently, with this study, we have provided valuable data for further experimental and clinical efforts and presented a novel bioinformatics framework that can be applied to any infectious disease.
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Affiliation(s)
- Medi Kori
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Türkiye,*Correspondence: Medi Kori,
| | - Beste Turanli
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Türkiye
| | - Kazim Yalcin Arga
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Türkiye,Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul, Türkiye
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Using a Network-Based Analysis Approach to Investigate the Involvement of S. aureus in the Pathogenesis of Granulomatosis with Polyangiitis. Int J Mol Sci 2023; 24:ijms24031822. [PMID: 36768148 PMCID: PMC9915048 DOI: 10.3390/ijms24031822] [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: 11/24/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Chronic nasal carriage of Staphylococcus aureus (SA) has been shown to be significantly higher in GPA patients when compared to healthy subjects, as well as being associated with increased endonasal activity and disease relapse. The aim of this study was to investigate SA involvement in GPA by applying a network-based analysis (NBA) approach to publicly available nasal transcriptomic data. Using these data, our NBA pipeline generated a proteinase 3 (PR3) positive ANCA associated vasculitis (AAV) disease network integrating differentially expressed genes, dysregulated transcription factors (TFs), disease-specific genes derived from GWAS studies, drug-target and protein-protein interactions. The PR3+ AAV disease network captured genes previously reported to be dysregulated in AAV associated. A subnetwork focussing on interactions between SA virulence factors and enriched biological processes revealed potential mechanisms for SA's involvement in PR3+ AAV. Immunosuppressant treatment reduced differential expression and absolute TF activities in this subnetwork for patients with inactive nasal disease but not active nasal disease symptoms at the time of sampling. The disease network generated identified the key molecular signatures and highlighted the associated biological processes in PR3+ AAV and revealed potential mechanisms for SA to affect these processes.
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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.
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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
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Iuchi H, Kawasaki J, Kubo K, Fukunaga T, Hokao K, Yokoyama G, Ichinose A, Suga K, Hamada M. Bioinformatics approaches for unveiling virus-host interactions. Comput Struct Biotechnol J 2023; 21:1774-1784. [PMID: 36874163 PMCID: PMC9969756 DOI: 10.1016/j.csbj.2023.02.044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.
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Affiliation(s)
- Hitoshi Iuchi
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan
| | - Junna Kawasaki
- Faculty of Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Kento Kubo
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Nishi Waseda, Shinjuku-ku, Tokyo 169-0051, Japan
| | - Koki Hokao
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Gentaro Yokoyama
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Akiko Ichinose
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kanta Suga
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Michiaki Hamada
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.,Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan
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Karpuzcu BA, Türk E, Ibrahim AH, Karabulut OC, Süzek BE. Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction. Methods Mol Biol 2023; 2690:401-417. [PMID: 37450162 DOI: 10.1007/978-1-0716-3327-4_31] [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] [Indexed: 07/18/2023]
Abstract
The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.
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Affiliation(s)
- Betül Asiye Karpuzcu
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Erdem Türk
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Ahmad Hassan Ibrahim
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Onur Can Karabulut
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Barış Ethem Süzek
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey.
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey.
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Ng TA, Rashid S, Kwoh CK. Virulence network of interacting domains of influenza a and mouse proteins. FRONTIERS IN BIOINFORMATICS 2023; 3:1123993. [PMID: 36875146 PMCID: PMC9982101 DOI: 10.3389/fbinf.2023.1123993] [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: 12/22/2022] [Accepted: 02/03/2023] [Indexed: 02/19/2023] Open
Abstract
There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.
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Affiliation(s)
- Teng Ann Ng
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Shamima Rashid
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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37
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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.
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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
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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.
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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:
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Zhou H, Astore C, Skolnick J. PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases. Sci Rep 2022; 12:20889. [PMID: 36463386 PMCID: PMC9719543 DOI: 10.1038/s41598-022-25412-x] [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: 09/15/2022] [Accepted: 11/29/2022] [Indexed: 12/04/2022] Open
Abstract
Infectious diseases are known to cause a wide variety of post-infection complications. However, it's been challenging to identify which diseases are most associated with a given pathogen infection. Using the recently developed LeMeDISCO approach that predicts comorbid diseases associated with a given set of putative mode of action (MOA) proteins and pathogen-human protein interactomes, we developed PHEVIR, an algorithm which predicts the corresponding human disease comorbidities of 312 viruses and 57 bacteria. These predictions provide an understanding of the molecular bases of complications and means of identifying appropriate drug targets to treat them. As an illustration of its power, PHEVIR is applied to identify putative driver pathogens and corresponding human MOA proteins for Type 2 diabetes, atherosclerosis, Alzheimer's disease, and inflammatory bowel disease. Additionally, we explore the origins of the oncogenicity/oncolyticity of certain pathogens and the relationship between heart disease and influenza. The full PHEVIR database is available at https://sites.gatech.edu/cssb/phevir/ .
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Affiliation(s)
- Hongyi Zhou
- grid.213917.f0000 0001 2097 4943Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332 USA
| | - Courtney Astore
- grid.213917.f0000 0001 2097 4943Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332 USA
| | - Jeffrey Skolnick
- grid.213917.f0000 0001 2097 4943Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332 USA
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Hao C, Dewar AE, West SA, Ghoul M. Gene transferability and sociality do not correlate with gene connectivity. Proc Biol Sci 2022; 289:20221819. [PMID: 36448285 PMCID: PMC9709509 DOI: 10.1098/rspb.2022.1819] [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] [Indexed: 12/03/2022] Open
Abstract
The connectivity of a gene, defined as the number of interactions a gene's product has with other genes' products, is a key characteristic of a gene. In prokaryotes, the complexity hypothesis predicts that genes which undergo more frequent horizontal transfer will be less connected than genes which are only very rarely transferred. We tested the role of horizontal gene transfer, and other potentially important factors, by examining the connectivity of chromosomal and plasmid genes, across 134 diverse prokaryotic species. We found that (i) genes on plasmids were less connected than genes on chromosomes; (ii) connectivity of plasmid genes was not correlated with plasmid mobility; and (iii) the sociality of genes (cooperative or private) was not correlated with gene connectivity.
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Affiliation(s)
- Chunhui Hao
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
| | - Anna E. Dewar
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
| | - Stuart A. West
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
| | - Melanie Ghoul
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
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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/.
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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
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The Innovative Informatics Approaches of High-Throughput Technologies in Livestock: Spearheading the Sustainability and Resiliency of Agrigenomics Research. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111893. [PMID: 36431028 PMCID: PMC9695872 DOI: 10.3390/life12111893] [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: 10/25/2022] [Revised: 11/09/2022] [Accepted: 11/14/2022] [Indexed: 11/17/2022]
Abstract
For more than a decade, next-generation sequencing (NGS) has been emerging as the mainstay of agrigenomics research. High-throughput technologies have made it feasible to facilitate research at the scale and cost required for using this data in livestock research. Scale frameworks of sequencing for agricultural and livestock improvement, management, and conservation are partly attributable to innovative informatics methodologies and advancements in sequencing practices. Genome-wide sequence-based investigations are often conducted worldwide, and several databases have been created to discover the connections between worldwide scientific accomplishments. Such studies are beginning to provide revolutionary insights into a new era of genomic prediction and selection capabilities of various domesticated livestock species. In this concise review, we provide selected examples of the current state of sequencing methods, many of which are already being used in animal genomic studies, and summarize the state of the positive attributes of genome-based research for cattle (Bos taurus), sheep (Ovis aries), pigs (Sus scrofa domesticus), horses (Equus caballus), chickens (Gallus gallus domesticus), and ducks (Anas platyrhyncos). This review also emphasizes the advantageous features of sequencing technologies in monitoring and detecting infectious zoonotic diseases. In the coming years, the continued advancement of sequencing technologies in livestock agrigenomics will significantly influence the sustained momentum toward regulatory approaches that encourage innovation to ensure continued access to a safe, abundant, and affordable food supplies for future generations.
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Islam J, Sarkar H, Hoque H, Hasan MN, Jewel GNA. In-silico approach of identifying novel therapeutic targets against Yersinia pestis using pan and subtractive genomic analysis. Comput Biol Chem 2022; 101:107784. [DOI: 10.1016/j.compbiolchem.2022.107784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/30/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022]
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Kori M, Arga KY. Human oncogenic viruses: an overview of protein biomarkers in viral cancers and their potential use in clinics. Expert Rev Anticancer Ther 2022; 22:1211-1224. [PMID: 36270027 DOI: 10.1080/14737140.2022.2139681] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Although the idea that carcinogenesis might be caused by viruses was first voiced about 100 years ago, today's data disappointingly show that we have not made much progress in preventing and/or treating viral cancers in a century. According to recent studies, infections are responsible for approximately 13% of cancer development in the world. Today, it is accepted and proven by many authorities that Epstein-Barr virus (EBV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), Human Herpesvirus 8 (HHV8), Human T-cell Lymphotropic virus 1 (HTLV1) and highly oncogenic Human Papillomaviruses (HPVs) cause or/and contribute to cancer development in humans. AREAS COVERED Considering the insufficient prevention and/or treatment strategies for viral cancers, in this review we present the current knowledge on protein biomarkers of oncogenic viruses. In addition, we aimed to decipher their potential for clinical use by evaluating whether the proposed biomarkers are expressed in body fluids, are druggable, and act as tumor suppressors or oncoproteins. EXPERT OPINION Consequently, we believe that this review will shed light on researchers and provide a guide to find remarkable solutions for the prevention and/or treatment of viral cancers.
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Affiliation(s)
- Medi Kori
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey.,Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul, Turkey
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Khan AA, Farooq F, Jain SK, Golinska P, Rai M. Comparative Host-Pathogen Interaction Analyses of SARS-CoV2 and Aspergillus fumigatus, and Pathogenesis of COVID-19-Associated Aspergillosis. MICROBIAL ECOLOGY 2022; 84:1236-1244. [PMID: 34738157 PMCID: PMC8568490 DOI: 10.1007/s00248-021-01913-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/25/2021] [Indexed: 05/03/2023]
Abstract
COVID-19 caused a global catastrophe with a large number of cases making it one of the major pandemics of the human history. The clinical presentations of the disease are continuously challenging healthcare workers with the variation of pandemic waves and viral variants. Recently, SARS-CoV2 patients have shown increased occurrence of invasive pulmonary aspergillosis infection even in the absence of traditional risk factors. The mechanism of COVID-19-associated aspergillosis is not completely understood and therefore, we performed this system biological study in order to identify mechanistic implications of aspergillosis susceptibility in COVID-19 patients and the important targets associated with this disease. We performed host-pathogen interaction (HPI) analysis of SARS-CoV2, and most common COVID-19-associated aspergillosis pathogen, Aspergillus fumigatus, using in silico approaches. The known host-pathogen interactions data of SARS-CoV2 was obtained from BIOGRID database. In addition, A. fumigatus host-pathogen interactions were predicted through homology modeling. The human targets interacting with both pathogens were separately analyzed for their involvement in aspergillosis. The aspergillosis human targets were screened from DisGeNet and GeneCards. The aspergillosis targets involved in both HPI were further analyzed for functional overrepresentation analysis using PANTHER. The results indicate that both pathogens interact with a number of aspergillosis targets and altogether they recruit more aspergillosis targets in host-pathogen interaction than alone. Common aspergillosis targets involved in HPI with both SARS-CoV2 and A. fumigatus can indicate strategies for the management of both conditions by modulating these common disease targets.
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Affiliation(s)
- Abdul Arif Khan
- Division of Microbiology, Indian Council of Medical Research-National AIDS Research Institute, Pune, Maharashtra, India.
| | - Fozia Farooq
- School of Studies in Microbiology, Vikram University, Ujjain, Madhya Pradesh, India
| | - Sudhir K Jain
- School of Studies in Microbiology, Vikram University, Ujjain, Madhya Pradesh, India
| | - Patrycja Golinska
- Department of Microbiology, Nicolaus Copernicus University, Torun, Poland
| | - Mahendra Rai
- Department of Microbiology, Nicolaus Copernicus University, Torun, Poland
- Department of Biotechnology, Sant Gadge Baba Amravati University, Amravati, Maharashtra, India
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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.
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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,
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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.
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Fang X, Yan P, Luo F, Han S, Lin T, Li S, Li S, Zhu T. Functional Identification of Arthrinium phaeospermum Effectors Related to Bambusa pervariabilis × Dendrocalamopsis grandis Shoot Blight. Biomolecules 2022; 12:biom12091264. [PMID: 36139102 PMCID: PMC9496123 DOI: 10.3390/biom12091264] [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: 08/03/2022] [Revised: 09/02/2022] [Accepted: 09/06/2022] [Indexed: 12/03/2022] Open
Abstract
The shoot blight of Bambusa pervariabilis × Dendrocalamopsis grandis caused by Arthrinium phaeospermum made bamboo die in a large area, resulting in serious ecological and economic losses. Dual RNA-seq was used to sequence and analyze the transcriptome data of A. phaeospermum and B. pervariabilis × D. grandis in the four periods after the pathogen infected the host and to screen the candidate effectors of the pathogen related to the infection. After the identification of the effectors by the tobacco transient expression system, the functions of these effectors were verified by gene knockout. Fifty-three differentially expressed candidate effectors were obtained by differential gene expression analysis and effector prediction. Among them, the effectors ApCE12 and ApCE22 can cause programmed cell death in tobacco. The disease index of B. pervariabilis × D. grandis inoculated with mutant ΔApCE12 and mutant ΔApCE22 strains were 52.5% and 47.5%, respectively, which was significantly lower than that of the wild-type strains (80%), the ApCE12 complementary strain (77.5%), and the ApCE22 complementary strain (75%). The tolerance of the mutant ΔApCE12 and mutant ΔApCE22 strains to H2O2 and NaCl stress was significantly lower than that of the wild-type strain and the ApCE12 complementary and ApCE22 complementary strains, but there was no difference in their tolerance to Congo red. Therefore, this study shows that the effectors ApCE12 and ApCE22 play an important role in A. phaeospermum virulence and response to H2O2 and NaCl stress.
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Affiliation(s)
- Xinmei Fang
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
- Faculty of Mathematics and Natural Sciences, University of Cologne, 50674 Köln, Germany
| | - Peng Yan
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
| | - Fengying Luo
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
| | - Shan Han
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
| | - Tiantian Lin
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
| | - Shuying Li
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
| | - Shujiang Li
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
- National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River, Chengdu 611130, China
- Correspondence: (S.L.); (T.Z.); Tel.: +86-17761264491 (T.Z.)
| | - Tianhui Zhu
- College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
- Correspondence: (S.L.); (T.Z.); Tel.: +86-17761264491 (T.Z.)
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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 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] [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. Deep learning approach (STEP) predicts virus protein to human host protein interactions It is based on recent deep protein sequence embeddings and Siamese neural network Prediction of PPIs of the JCV VP1 protein and of the SARS-CoV-2 spike protein Identify parts of sequences that most likely contribute to the PPI using explainable AI
The development of novel cell and tissue-specific therapies requires a profound knowledge about protein-protein interactions (PPIs). Identifying these PPIs with experimental approaches such as biochemical assays or yeast two-hybrid screens is cumbersome, costly, and at the same time difficult to scale. Computational approaches can help to prioritize huge amounts of possible PPIs by learning from biological sequences plus already known PPIs. In this work, we developed an approach that is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. We use this approach to train models by using protein sequence information and known PPIs. We apply the models to two use cases to predict virus protein to human host interactions. Altogether our work highlights the potential of deep sequence embedding techniques as well as explainable artificial intelligence methods for the analysis of biological sequence data.
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50
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Kumar N, Mishra B, Mukhtar MS. A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions. STAR Protoc 2022; 3:101608. [PMID: 35990739 PMCID: PMC9386103 DOI: 10.1016/j.xpro.2022.101608] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Investigating the complexity of host-pathogen interactions is challenging. Here, we outline a pipeline to identify important proteins and signaling molecules in human-viral interactomes. Firstly, we curate a comprehensive human interactome. Subsequently, we infer viral targets and transcriptome-specific human interactomes (VTTSHI) for papillomavirus and herpes viruses by integrating viral targets and transcriptome data. Finally, we reveal the common and shared nodes and pathways in viral pathogenesis following network topology and pathway enrichment analyses. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020). Protocol for integrative analysis of transcriptome and proteome network data Network subgroup enrichment of two host-pathogen interaction networks Preprocessing of data and heterogeneous network integration
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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