1
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Sonnert ND, Rosen CE, Ghazi AR, Franzosa EA, Duncan-Lowey B, González-Hernández JA, Huck JD, Yang Y, Dai Y, Rice TA, Nguyen MT, Song D, Cao Y, Martin AL, Bielecka AA, Fischer S, Guan C, Oh J, Huttenhower C, Ring AM, Palm NW. A host-microbiota interactome reveals extensive transkingdom connectivity. Nature 2024; 628:171-179. [PMID: 38509360 DOI: 10.1038/s41586-024-07162-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 02/05/2024] [Indexed: 03/22/2024]
Abstract
The myriad microorganisms that live in close association with humans have diverse effects on physiology, yet the molecular bases for these impacts remain mostly unknown1-3. Classical pathogens often invade host tissues and modulate immune responses through interactions with human extracellular and secreted proteins (the 'exoproteome'). Commensal microorganisms may also facilitate niche colonization and shape host biology by engaging host exoproteins; however, direct exoproteome-microbiota interactions remain largely unexplored. Here we developed and validated a novel technology, BASEHIT, that enables proteome-scale assessment of human exoproteome-microbiome interactions. Using BASEHIT, we interrogated more than 1.7 million potential interactions between 519 human-associated bacterial strains from diverse phylogenies and tissues of origin and 3,324 human exoproteins. The resulting interactome revealed an extensive network of transkingdom connectivity consisting of thousands of previously undescribed host-microorganism interactions involving 383 strains and 651 host proteins. Specific binding patterns within this network implied underlying biological logic; for example, conspecific strains exhibited shared exoprotein-binding patterns, and individual tissue isolates uniquely bound tissue-specific exoproteins. Furthermore, we observed dozens of unique and often strain-specific interactions with potential roles in niche colonization, tissue remodelling and immunomodulation, and found that strains with differing host interaction profiles had divergent interactions with host cells in vitro and effects on the host immune system in vivo. Overall, these studies expose a previously unexplored landscape of molecular-level host-microbiota interactions that may underlie causal effects of indigenous microorganisms on human health and disease.
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Affiliation(s)
- Nicole D Sonnert
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Microbial Pathogenesis, Yale School of Medicine, New Haven, CT, USA
| | - Connor E Rosen
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Andrew R Ghazi
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric A Franzosa
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | | | | | - John D Huck
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Yi Yang
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Yile Dai
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Tyler A Rice
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Mytien T Nguyen
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Deguang Song
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Yiyun Cao
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Anjelica L Martin
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Agata A Bielecka
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Suzanne Fischer
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Changhui Guan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Julia Oh
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Aaron M Ring
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
- Department of Pharmacology, Yale School of Medicine, New Haven, CT, USA.
| | - Noah W Palm
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
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2
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Chandrasekharan G, Unnikrishnan M. High throughput methods to study protein-protein interactions during host-pathogen interactions. Eur J Cell Biol 2024; 103:151393. [PMID: 38306772 DOI: 10.1016/j.ejcb.2024.151393] [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: 09/29/2023] [Revised: 01/18/2024] [Accepted: 01/21/2024] [Indexed: 02/04/2024] Open
Abstract
The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.
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Affiliation(s)
| | - Meera Unnikrishnan
- Division of Biomedical Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom.
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3
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Rodrigues CHM, Ascher DB. CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces. Proteins 2023. [PMID: 37870486 DOI: 10.1002/prot.26615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/24/2023]
Abstract
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted by the exponential growth of experimental structures, which has been greatly expanded by recent breakthroughs in protein structure prediction, most notably RosettaFold, and AlphaFold2. These advances have prompted the development of several computational approaches that leverage these data sources to explore potential biological interactions. However, most methods are generally limited to analysis of single types of interactions, such as protein-protein or protein-ligand interactions, and their complexity limits the usability to expert users. Here we report CSM-Potential2, a deep learning platform for the analysis of binding interfaces on protein structures. In addition to prediction of protein-protein interactions binding sites and classification of biological ligands, our new platform incorporates prediction of interactions with nucleic acids at the residue level and allows for ligand transplantation based on sequence and structure similarity to experimentally determined structures. We anticipate our platform to be a valuable resource that provides easy access to a range of state-of-the-art methods to expert and non-expert users for the study of biological interactions. Our tool is freely available as an easy-to-use web server and API available at https://biosig.lab.uq.edu.au/csm_potential.
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Affiliation(s)
- Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
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4
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Hu Y, Liu C, Yang J, Zhong M, Qian B, Chen J, Zhang Y, Song J. HMGB1 is involved in viral replication and the inflammatory response in coxsackievirus A16-infected 16HBE cells via proteomic analysis and identification. Virol J 2023; 20:178. [PMID: 37559147 PMCID: PMC10410909 DOI: 10.1186/s12985-023-02150-8] [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: 04/18/2023] [Accepted: 08/02/2023] [Indexed: 08/11/2023] Open
Abstract
Coxsackievirus A16 (CV-A16) is still an important pathogen that causes hand, foot and mouth disease (HFMD) in young children and infants worldwide. Previous studies indicated that CV-A16 infection is usually mild or self-limiting, but it was also found that CV-A16 infection can trigger severe neurological complications and even death. However, there are currently no vaccines or antiviral compounds available to either prevent or treat CV-A16 infection. Therefore, investigation of the virus‒host interaction and identification of host proteins that play a crucial regulatory role in the pathogenesis of CV-A16 infection may provide a novel strategy to develop antiviral drugs. Here, to increase our understanding of the interaction of CV-A16 with the host cell, we analyzed changes in the proteome of 16HBE cells in response to CV-A16 using tandem mass tag (TMT) in combination with LC‒MS/MS. There were 6615 proteins quantified, and 172 proteins showed a significant alteration during CV-A16 infection. These differentially regulated proteins were involved in fundamental biological processes and signaling pathways, including metabolic processes, cytokine‒cytokine receptor interactions, B-cell receptor signaling pathways, and neuroactive ligand‒receptor interactions. Further bioinformatics analysis revealed the characteristics of the protein domains and subcellular localization of these differentially expressed proteins. Then, to validate the proteomics data, 3 randomly selected proteins exhibited consistent changes in protein expression with the TMT results using Western blotting and immunofluorescence methods. Finally, among these differentially regulated proteins, we primarily focused on HMGB1 based on its potential effects on viral replication and virus infection-induced inflammatory responses. It was demonstrated that overexpression of HMGB1 could decrease viral replication and upregulate the release of inflammatory cytokines, but deletion of HMGB1 increased viral replication and downregulated the release of inflammatory cytokines. In conclusion, the results from this study have helped further elucidate the potential molecular pathogenesis of CV-A16 based on numerous protein changes and the functions of HMGB1 Found to be involved in the processes of viral replication and inflammatory response, which may facilitate the development of new antiviral therapies as well as innovative diagnostic methods.
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Affiliation(s)
- Yajie Hu
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of Yunnan Province, Kunming, China
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Chen Liu
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of Yunnan Province, Kunming, China
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Jinghui Yang
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
- Department of Pediatrics, The First People's Hospital of Yunnan Province, Kunming, China
| | - Mingmei Zhong
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of Yunnan Province, Kunming, China
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Baojiang Qian
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of Yunnan Province, Kunming, China
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Juan Chen
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of Yunnan Province, Kunming, China
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Yunhui Zhang
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of Yunnan Province, Kunming, China.
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
| | - Jie Song
- Institute of Medical Biology, Chinese Academy of Medical Science and Peking Union Medical College, Yunnan Key Laboratory of Vaccine Research and Development on Severe Infectious Diseases, Kunming, China.
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5
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A KK, Shayez Karim SM, Kumar M, Ravindranath Singh R. Prediction of transient and permanent protein interactions using AI methods. Bioinformation 2023; 19:749-753. [PMID: 37885791 PMCID: PMC10598364 DOI: 10.6026/97320630019749] [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: 06/01/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 10/28/2023] Open
Abstract
Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and for understanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent and transient categories, and 5 different Supervised Machine Learning models were developed with Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed using Tensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interface accessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The present method could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.
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Affiliation(s)
- Kiran Kumar A
- Department of Bioinformatics, Central University of South Bihar, Gaya, Bihar-824236, India
| | | | - Mayank Kumar
- Department of Bioinformatics, Central University of South Bihar, Gaya, Bihar-824236, India
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6
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Li XM, Huang S, Li XD. Photo-ANA enables profiling of host-bacteria protein interactions during infection. Nat Chem Biol 2023; 19:614-623. [PMID: 36702958 DOI: 10.1038/s41589-022-01245-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 12/16/2022] [Indexed: 01/27/2023]
Abstract
Bacterial pathogens rapidly change and adapt their proteome to cope with the environment in host cells and secrete effector proteins to hijack host targets and ensure their survival and proliferation during infection. Excessive host proteins make it difficult to profile pathogens' proteome dynamics by conventional proteomics. It is even more challenging to map pathogen-host protein-protein interactions in real time, given the low abundance of bacterial effectors and weak and transient interactions in which they may be involved. Here we report a method for selectively labeling bacterial proteomes using a bifunctional amino acid, photo-ANA, equipped with a bio-orthogonal handle and a photoreactive warhead, which enables simultaneous analysis of bacterial proteome reprogramming and pathogen-host protein interactions of Salmonella enterica serovar Typhimurium (S. Typhimurium) during infection. Using photo-ANA, we identified FLOT1/2 as host interactors of S. Typhimurium effector PipB2 in late-stage infection and globally profiled the extensive interactions between host proteins and pathogens during infection.
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Affiliation(s)
- Xiao-Meng Li
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Siyue Huang
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Xiang David Li
- Department of Chemistry, The University of Hong Kong, Hong Kong, China.
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7
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Khan M, Djamei A. Co-immunoprecipitation-Based Identification of Effector-Host Protein Interactions from Pathogen-Infected Plant Tissue. Methods Mol Biol 2023; 2690:87-100. [PMID: 37450139 DOI: 10.1007/978-1-0716-3327-4_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] [Indexed: 07/18/2023]
Abstract
Protein-protein interactions play an essential role in host-pathogen interactions. Phytopathogens secrete a cocktail of effector proteins to suppress plant immunity and reprogram host cell metabolism in their favor. Identification and characterization of effectors and their target protein complexes by co-immunoprecipitation can help to gain a deeper understanding of the functions of individual effectors during pathogenicity and can also provide new insights into the wiring of plant signaling pathways or metabolic complexes. Here we describe a detailed protocol to perform co-immunoprecipitation of effector-target protein complexes from plant extracts with an example of the Ustilago maydis/maize pathosystem for which we also provide a fungal protoplast transformation and maize seedling infection protocols.
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Affiliation(s)
- Mamoona Khan
- Department of Plant Pathology, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany.
| | - Armin Djamei
- Department of Plant Pathology, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany
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8
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Mun SJ, Cho E, Kim JS, Yang CS. Pathogen-derived peptides in drug targeting and its therapeutic approach. J Control Release 2022; 350:716-733. [PMID: 36030988 DOI: 10.1016/j.jconrel.2022.08.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/18/2022] [Accepted: 08/21/2022] [Indexed: 02/06/2023]
Abstract
Peptides, short stretches of amino acids or small proteins that occupy a strategic position between proteins and amino acids, are readily accessible by chemical and biological methods. With ideal properties for forming high-affinity and specific interactions with host target proteins, they have established an important niche in the drug development spectrum complementing small molecule and biological therapeutics. Among the most successful biomedicines in use today, peptide-based drugs show great promise. This, coupled with recent advances in synthetic and nanochemical biology, has led to the creation of tailor-made peptide therapeutics for improved biocompatibility. This review presents an overview of the latest research on pathogen-derived, host-cell-interacting peptides. It also highlights strategies for using peptide-based therapeutics that address cellular transport challenges through the introduction of nanoparticles that serve as platforms to facilitate the delivery of peptide biologics and therapeutics for treating various inflammatory diseases. Finally, this paper describes future perspectives, specific pathogen-based peptides that can enhance specificity, efficiency, and capacity in functional peptide-based therapeutics, which are in the spotlight as new treatment alternatives for various diseases, and also presents verified sequences and targets that can increase chemical and pharmacological value.
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Affiliation(s)
- Seok-Jun Mun
- Department of Bionano Technology, Hanyang University, Seoul 04673, Republic of Korea; Center for Bionano Intelligence Education and Research, Ansan 15588, Republic of Korea
| | - Euni Cho
- Department of Bionano Technology, Hanyang University, Seoul 04673, Republic of Korea; Center for Bionano Intelligence Education and Research, Ansan 15588, Republic of Korea
| | - Jae-Sung Kim
- Department of Bionano Technology, Hanyang University, Seoul 04673, Republic of Korea; Institute of Natural Science & Technology, Hanyang University, Ansan 15588, Republic of Korea
| | - Chul-Su Yang
- Center for Bionano Intelligence Education and Research, Ansan 15588, Republic of Korea; Department of Molecular and Life Science, Hanyang University, Ansan 15588, Republic of Korea.
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9
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Vidal-Veuthey B, González D, Cárdenas JP. Role of microbial secreted proteins in gut microbiota-host interactions. Front Cell Infect Microbiol 2022; 12:964710. [PMID: 35967863 PMCID: PMC9373040 DOI: 10.3389/fcimb.2022.964710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/06/2022] [Indexed: 11/30/2022] Open
Abstract
The mammalian gut microbiota comprises a variety of commensals including potential probiotics and pathobionts, influencing the host itself. Members of the microbiota can intervene with host physiology by several mechanisms, including the secretion of a relatively well-reported set of metabolic products. Another microbiota influence mechanism is the use of secreted proteins (i.e., the secretome), impacting both the host and other community members. While widely reported and studied in pathogens, this mechanism remains understood to a lesser extent in commensals, and this knowledge is increasing in recent years. In the following minireview, we assess the current literature covering different studies, concerning the functions of secretable proteins from members of the gut microbiota (including commensals, pathobionts, and probiotics). Their effect on host physiology and health, and how these effects can be harnessed by postbiotic products, are also discussed.
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Affiliation(s)
- Boris Vidal-Veuthey
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Huechuraba, Chile
| | - Dámariz González
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Huechuraba, Chile
| | - Juan P. Cárdenas
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Huechuraba, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- *Correspondence: Juan P. Cárdenas,
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10
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Rodrigues CHM, Pires DEV, Blundell TL, Ascher DB. Structural landscapes of PPI interfaces. Brief Bioinform 2022; 23:bbac165. [PMID: 35656714 PMCID: PMC9294409 DOI: 10.1093/bib/bbac165] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023] Open
Abstract
Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein-protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein-protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein-protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites.
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Affiliation(s)
- Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria
- School of Chemistry and Molecular Biosciences, Bio21 Institute, University of Queensland, Brisbane, Victoria
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria
- School of Chemistry and Molecular Biosciences, Bio21 Institute, University of Queensland, Brisbane, Victoria
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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11
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Rahmatbakhsh M, Moutaoufik MT, Gagarinova A, Babu M. HPiP: an R/Bioconductor package for predicting host-pathogen protein-protein interactions from protein sequences using ensemble machine learning approach. BIOINFORMATICS ADVANCES 2022; 2:vbac038. [PMID: 35669347 PMCID: PMC9154073 DOI: 10.1093/bioadv/vbac038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 04/27/2022] [Accepted: 05/17/2022] [Indexed: 01/26/2023]
Abstract
Motivation Despite arduous and time-consuming experimental efforts, protein-protein interactions (PPIs) for many pathogenic microbes with their human host are still unknown, limiting our understanding of the intricate interactions during infection and the identification of therapeutic targets. Since computational tools offer a promising alternative, we developed an R/Bioconductor package, HPiP (Host-Pathogen Interaction Prediction) software with a series of amino acid sequence property descriptors and an ensemble machine learning classifiers to predict the yet unmapped interactions between pathogen and host proteins. Results Using severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) or the novel SARS-CoV-2 coronavirus-human PPI training sets as a case study, we show that HPiP achieves a good performance with PPI predictions between SARS-CoV-2 and human proteins, which we confirmed experimentally in human monocyte THP-1 cells, and with several quality control metrics. HPiP also exhibited strong performance in accurately predicting the previously reported PPIs when tested against the sequences of pathogenic bacteria, Mycobacterium tuberculosis and human proteins. Collectively, our fully documented HPiP software will hasten the exploration of PPIs for a systems-level understanding of many understudied pathogens and uncover molecular targets for repurposing existing drugs. Availability and implementation HPiP is released as an open-source code under the MIT license that is freely available on GitHub (https://github.com/BabuLab-UofR/HPiP) as well as on Bioconductor (http://bioconductor.org/packages/devel/bioc/html/HPiP.html). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | | | - Alla Gagarinova
- Department of Biochemistry, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
- To whom correspondence should be addressed.
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12
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Tayal S, Bhatia V, Mehrotra T, Bhatnagar S. ImitateDB: A database for domain and motif mimicry incorporating host and pathogen protein interactions. Amino Acids 2022; 54:923-934. [PMID: 35487995 PMCID: PMC9054641 DOI: 10.1007/s00726-022-03163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/09/2022] [Indexed: 11/26/2022]
Abstract
Molecular mimicry of host proteins by pathogens constitutes a strategy to hijack the host pathways. At present, there is no dedicated resource for mimicked domains and motifs in the host-pathogen interactome. In this work, the experimental host-pathogen (HP) and host-host (HH) protein-protein interactions (PPIs) were collated. The domains and motifs of these proteins were annotated using CD Search and ScanProsite, respectively. Host and pathogen proteins with a shared host interactor and similar domain/motif constitute a mimicry pair exhibiting global structural similarity (domain mimicry pair; DMP) or local sequence motif similarity (motif mimicry pair; MMP). Mimicry pairs are likely to be co-expressed and co-localized. 1,97,607 DMPs and 32,67,568 MMPs were identified in 49,265 experimental HP-PPIs and organized in a web-based resource, ImitateDB ( http://imitatedb.sblab-nsit.net ) that can be easily queried. The results are externally integrated using hyperlinked domain PSSM ID, motif ID, protein ID and PubMed ID. Kinase, UL36, Smc and DEXDc were frequent DMP domains whereas protein kinase C phosphorylation, casein kinase 2 phosphorylation, glycosylation and myristoylation sites were frequent MMP motifs. Novel DMP domains SANT, Tudor, PhoX and MMP motif microbody C-terminal targeting signal, cornichon signature and lipocalin signature were proposed. ImitateDB is a novel resource for identifying mimicry in interacting host and pathogen proteins.
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Affiliation(s)
- Sonali Tayal
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India
| | - Venugopal Bhatia
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India
| | - Tanya Mehrotra
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India.
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India.
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13
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Abstract
Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
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14
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Uddin R, Khan K. A Comparative Computational Analysis Approach to Predict Significant
Protein-Protein Interactions of Human and Vancomycin Resistant Enterococcus
faecalis (VRE) to Prioritize Potential Drug Targets. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180818666211006125332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Various challenges exist in the treatment of infectious diseases due to the significant
rise in drug resistance, resulting in the failure of antibiotic treatment. As a consequence, a dire need
has arisen for the rethinking of the drug discovery cycle because of the challenge of drug resistance. The
underlying cause of the infectious diseases depends upon associations within the Host-pathogen Protein-
Protein Interactions (HP-PPIs) network, which represents a key to unlock new pathogenesis mechanisms.
Hence, the elucidation of significant PPIs is a promising approach for the identification of potential drug
targets.
Objective:
Identification of the most significant HP-PPIs and their partners, and targeting them to prioritize
potential new drug targets against Vancomycin-resistant Enterococcus faecalis (VRE).
Methods:
We applied a computational approach based on one of the emerging techniques i.e. Interolog
methodology to predict the significant Host-Pathogen PPIs. Structure-Based Studies were applied to
model shortlisted protein structures and validate them through PSIPRED, PROCHECK, VERIFY3D, and
ERRAT tools. Furthermore, 18,000 drug-like compounds from the ZINC library were docked against
these proteins to study protein-chemical interactions using the AutoDock based molecular docking method.
Results:
The study resulted in the identification of 118 PPIs for Enterococcus faecalis, and prioritized two
novel drug targets i.e. Exodeoxyribonuclease (ExoA) and ATP-dependent Clp protease proteolytic subunit
(ClpP). Consequently, the docking program ranked 2,670 and 3,154 compounds as potential binders
against Exodeoxyribonuclease and ATP-dependent Clp protease proteolytic subunit, respectively.
Conclusion:
Thereby, the current study enabled us to identify and prioritize potential PPIs in VRE and
their interacting proteins in human hosts along with the pool of novel drug candidates.
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Affiliation(s)
- Reaz Uddin
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological
Science, University of Karachi, Karachi, Pakistan
| | - Kanwal Khan
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological
Science, University of Karachi, Karachi, Pakistan
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15
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Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins. Processes (Basel) 2022. [DOI: 10.3390/pr10020291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted human proteins are important for improving both prognosis and treatment for the patient. Here, we introduce machine learning-based classifiers to discriminate between the two groups of human proteins. We used the sequence, network, and gene ontology features of human proteins. Among different classifiers and features, the deep neural network (DNN) classifier with amino acid composition (AAC), dipeptide composition (DC), and pseudo-amino acid composition (PAAC) (445 features) achieved the best area under the curve (AUC) value (0.939), F1-score (94.9%), and Matthews correlation coefficient (MCC) value (0.81). We found that each of the selected top 100 of the bacteria- and virus-targeted human proteins from a candidate pool of 1618 and 3916 proteins, respectively, were part of distinct enriched biological processes and pathways. Our proposed method will help to differentiate between the bacterial and viral infections based on the targeted human proteins on a global scale. Furthermore, identification of the crucial pathogen targets in the human proteome would help us to better understand the pathogen-specific infection strategies and develop novel therapeutics.
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16
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Li S, Zhou W, Li D, Pan T, Guo J, Zou H, Tian Z, Li K, Xu J, Li X, Li Y. Comprehensive characterization of human–virus protein-protein interactions reveals disease comorbidities and potential antiviral drugs. Comput Struct Biotechnol J 2022; 20:1244-1253. [PMID: 35356543 PMCID: PMC8924640 DOI: 10.1016/j.csbj.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/04/2022] [Accepted: 03/04/2022] [Indexed: 11/30/2022] Open
Abstract
Comprehensive collection of experimentally verified human–virus PPIs. Viral proteins interact with cell cycle and immune-related genes. Viral proteins are likely to interact with central human proteins in PPI network. Network analysis reveals associations between viral infections and human diseases. Potential anti-viral drugs are prioritized based on interactome analysis.
The protein-protein interactions (PPIs) between human and viruses play important roles in viral infection and host immune responses. Rapid accumulation of experimentally validated human–virus PPIs provides an unprecedented opportunity to investigate the regulatory pattern of viral infection. However, we are still lack of knowledge about the regulatory patterns of human–virus interactions. We collected 27,293 experimentally validated human–virus PPIs, covering 8 virus families, 140 viral proteins and 6059 human proteins. Functional enrichment analysis revealed that the viral interacting proteins were likely to be enriched in cell cycle and immune-related pathways. Moreover, we analysed the topological features of the viral interacting proteins and found that they were likely to locate in central regions of human PPI network. Based on network proximity analyses of diseases genes and human–virus interactions in the human interactome, we revealed the associations between complex diseases and viral infections. Network analysis also implicated potential antiviral drugs that were further validated by text mining. Finally, we presented the Human–Virus Protein-Protein Interaction database (HVPPI, http://bio-bigdata.hrbmu.edu.cn/HVPPI), that provides experimentally validated human–virus PPIs as well as seamlessly integrates online functional analysis tools. In summary, comprehensive understanding the regulatory pattern of human–virus interactome will provide novel insights into fundamental infectious mechanism discovery and new antiviral therapy development.
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Affiliation(s)
- Si Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
| | - Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Donghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tao Pan
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
| | - Jing Guo
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
| | - Haozhe Zou
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Zhanyu Tian
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
| | - Kongning Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Corresponding authors at: Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China.
| | - Xia Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Corresponding authors at: Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China.
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
- Corresponding authors at: Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China.
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17
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Rodrigues CHM, Pires DEV, Ascher DB. pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors. J Chem Inf Model 2021; 61:5438-5445. [PMID: 34719929 DOI: 10.1021/acs.jcim.1c01135] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.
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Affiliation(s)
- Carlos H M Rodrigues
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
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18
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Aspergillus fumigatus versus Genus Aspergillus: Conservation, Adaptive Evolution and Specific Virulence Genes. Microorganisms 2021; 9:microorganisms9102014. [PMID: 34683335 PMCID: PMC8539515 DOI: 10.3390/microorganisms9102014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 09/18/2021] [Accepted: 09/20/2021] [Indexed: 12/15/2022] Open
Abstract
Aspergillus is an important fungal genus containing economically important species, as well as pathogenic species of animals and plants. Using eighteen fungal species of the genus Aspergillus, we conducted a comprehensive investigation of conserved genes and their evolution. This also allows us to investigate the selection pressure driving the adaptive evolution in the pathogenic species A. fumigatus. Among single-copy orthologs (SCOs) for A. fumigatus and the closely related species A. fischeri, we identified 122 versus 50 positively selected genes (PSGs), respectively. Moreover, twenty conserved genes of unknown function were established to be positively selected and thus important for adaption. A. fumigatus PSGs interacting with human host proteins show over-representation of adaptive, symbiosis-related, immunomodulatory and virulence-related pathways, such as the TGF-β pathway, insulin receptor signaling, IL1 pathway and interfering with phagosomal GTPase signaling. Additionally, among the virulence factor coding genes, secretory and membrane protein-coding genes in multi-copy gene families, 212 genes underwent positive selection and also suggest increased adaptation, such as fungal immune evasion mechanisms (aspf2), siderophore biosynthesis (sidD), fumarylalanine production (sidE), stress tolerance (atfA) and thermotolerance (sodA). These genes presumably contribute to host adaptation strategies. Genes for the biosynthesis of gliotoxin are shared among all the close relatives of A. fumigatus as an ancient defense mechanism. Positive selection plays a crucial role in the adaptive evolution of A. fumigatus. The genome-wide profile of PSGs provides valuable targets for further research on the mechanisms of immune evasion, antimycotic targeting and understanding fundamental virulence processes.
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19
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Gupta SK, Ponte-Sucre A, Bencurova E, Dandekar T. An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens. Comput Struct Biotechnol J 2021; 19:5292-5308. [PMID: 34745452 PMCID: PMC8531761 DOI: 10.1016/j.csbj.2021.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 12/28/2022] Open
Abstract
Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.
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Affiliation(s)
- Shishir K Gupta
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
- Evolutionary Genomics Group, Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | - Alicia Ponte-Sucre
- Laboratorio de Fisiología Molecular, Instituto de Medicina Experimental, Escuela Luis Razetti, Universidad Central de Venezuela, Caracas, Venezuela
- Medical Mission Institute, Hermann-Schell-Str. 7, 97074 Würzburg, Germany
| | - Elena Bencurova
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
- EMBL Heidelberg, BioComputing Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
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20
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Zhu J, Eid FE, Tong L, Zhao W, Wang W, Heath LS, Kang L, Cui F. Characterization of protein-protein interactions between rice viruses and vector insects. INSECT SCIENCE 2021; 28:976-986. [PMID: 32537916 DOI: 10.1111/1744-7917.12840] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 06/09/2020] [Accepted: 06/10/2020] [Indexed: 06/11/2023]
Abstract
Planthoppers are the most notorious rice pests, because they transmit various rice viruses in a persistent-propagative manner. Protein-protein interactions (PPIs) between virus and vector are crucial for virus transmission by vector insects. However, the number of known PPIs for pairs of rice viruses and planthoppers is restricted by low throughput research methods. In this study, we applied DeNovo, a virus-host sequence-based PPI predictor, to predict potential PPIs at a genome-wide scale between three planthoppers and five rice viruses. PPIs were identified at two different confidence thresholds, referred to as low and high modes. The number of PPIs for the five planthopper-virus pairs ranged from 506 to 1985 in the low mode and from 1254 to 4286 in the high mode. After eliminating the "one-too-many" redundant interacting information, the PPIs with unique planthopper proteins were reduced to 343-724 in the low mode and 758-1671 in the high mode. Homologous analysis showed that 11 sets and 31 sets of homologous planthopper proteins were shared by all planthopper-virus interactions in the two modes, indicating that they are potential conserved vector factors essential for transmission of rice viruses. Ten PPIs between small brown planthopper and rice stripe virus (RSV) were verified using glutathione-S-transferase (GST)/His-pull down or co-immunoprecipitation assay. Five of the ten PPIs were proven positive, and three of the five SBPH proteins were confirmed to interact with RSV. The predicted PPIs provide new clues for further studies of the complicated relationship between rice viruses and their vector insects.
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Affiliation(s)
- Junjie Zhu
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China
| | | | - Lu Tong
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China
| | - Wan Zhao
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China
| | - Wei Wang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China
| | - Lenwood S Heath
- Department of Computer Science, Virginia Tech, Blacksburg, VA, United States
| | - Le Kang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China
| | - Feng Cui
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China
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21
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Tang X, Wippel HH, Chavez JD, Bruce JE. Crosslinking mass spectrometry: A link between structural biology and systems biology. Protein Sci 2021; 30:773-784. [PMID: 33594738 DOI: 10.1002/pro.4045] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 12/12/2022]
Abstract
Protein structure underpins functional roles in all biological processes; therefore, improved understanding of protein structures is of fundamental importance in nearly all biological and biomedical research areas. Traditional techniques such as X-ray crystallography and more recently, cryo-EM, can reveal structural features on isolated proteins/protein complexes at atomic resolution level and have become indispensable tools for structural biology. Crosslinking mass spectrometry (XL-MS), on the other hand, is an emerging technique capable of capturing transient and dynamic information on protein interactions and assemblies in their native environment. The combination of XL-MS with traditional techniques holds potential for bridging the gap between structural biology and systems biology approaches. Such a combination will enable visualization of protein structures and interactions within the crowded macromolecular environment in living systems that can dramatically increase understanding of biological functions. In this review, we first discuss general strategies of XL-MS and then survey recent examples to show how qualitative and quantitative XL-MS studies can be integrated with available protein structural data to better understand biological function at systems level.
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Affiliation(s)
- Xiaoting Tang
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Helisa H Wippel
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Juan D Chavez
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - James E Bruce
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
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22
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The Actin Cytoskeleton Mediates Transmission of " Candidatus Liberibacter solanacearum" by the Carrot Psyllid. Appl Environ Microbiol 2021; 87:AEM.02393-20. [PMID: 33188004 DOI: 10.1128/aem.02393-20] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/30/2020] [Indexed: 12/30/2022] Open
Abstract
Several vector-borne plant pathogens have evolved mechanisms to exploit and to hijack vector host cellular, molecular, and defense mechanisms for their transmission. In the past few years, Liberibacter species, which are transmitted by several psyllid vectors, have become an economically important group of pathogens that have devastated the citrus industry and caused tremendous losses to many other important crops worldwide. The molecular mechanisms underlying the interactions of Liberibacter species with their psyllid vectors are poorly studied. "Candidatus Liberibacter solanacearum," which is associated with important vegetable diseases, is transmitted by the carrot psyllid Bactericera trigonica in a persistent manner. Here, we elucidated the role of the B. trigonica Arp2/3 protein complex, which plays a major role in regulation of the actin cytoskeleton, in the transmission of "Ca Liberibacter solanacearum." "Ca Liberibacter solanacearum" colocalized with ArpC2, a key protein in this complex, and this colocalization was strongly associated with actin filaments. Silencing of the psyllid ArpC2 disrupted the colocalization and the dynamics of F-actin. Silencing of RhoGAP21 and Cdc42, which act in the signaling cascade leading to upregulation of Arp2/3 and F-actin bundling, showed similar results. On the other hand, silencing of ArpC5, another component of the complex, did not induce any significant effects on F-actin formation. Finally, ArpC2 silencing caused a 73.4% reduction in "Ca Liberibacter solanacearum" transmission by psyllids, strongly suggesting that transmission of "Ca Liberibacter solanacearum" by B. trigonica is cytoskeleton dependent and "Ca Liberibacter solanacearum" interacts with ArpC2 to exploit the intracellular actin nucleation process for transmission. Targeting this unique interaction could lead to the development of a novel strategy for the management of Liberibacter-associated diseases.IMPORTANCE Plant diseases caused by vector-borne pathogens are responsible for tremendous losses and threaten some of the most important agricultural crops. A good example is the citrus greening disease, which is caused by bacteria of the genus Liberibacter and is transmitted by psyllids; it has devastated the citrus industry in the United States, China, and Brazil. Here, we show that psyllid-transmitted "Candidatus Liberibacter solanacearum" employs the actin cytoskeleton of psyllid gut cells, specifically the ArpC2 protein in the Arp2/3 complex of this system, for movement and transmission in the vector. Silencing of ArpC2 dramatically influenced the interaction of "Ca Liberibacter solanacearum" with the cytoskeleton and decreased the bacterial transmission to plants. This system could be targeted to develop a novel approach for the control of Liberibacter-associated diseases.
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23
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Acharya D, Dutta TK. Elucidating the network features and evolutionary attributes of intra- and interspecific protein-protein interactions between human and pathogenic bacteria. Sci Rep 2021; 11:190. [PMID: 33420198 PMCID: PMC7794237 DOI: 10.1038/s41598-020-80549-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/09/2020] [Indexed: 01/08/2023] Open
Abstract
Host–pathogen interaction is one of the most powerful determinants involved in coevolutionary processes covering a broad range of biological phenomena at molecular, cellular, organismal and/or population level. The present study explored host–pathogen interaction from the perspective of human–bacteria protein–protein interaction based on large-scale interspecific and intraspecific interactome data for human and three pathogenic bacterial species, Bacillus anthracis, Francisella tularensis and Yersinia pestis. The network features revealed a preferential enrichment of intraspecific hubs and bottlenecks for both human and bacterial pathogens in the interspecific human–bacteria interaction. Analyses unveiled that these bacterial pathogens interact mostly with human party-hubs that may enable them to affect desired functional modules, leading to pathogenesis. Structural features of pathogen-interacting human proteins indicated an abundance of protein domains, providing opportunities for interspecific domain-domain interactions. Moreover, these interactions do not always occur with high-affinity, as we observed that bacteria-interacting human proteins are rich in protein-disorder content, which correlates positively with the number of interacting pathogen proteins, facilitating low-affinity interspecific interactions. Furthermore, functional analyses of pathogen-interacting human proteins revealed an enrichment in regulation of processes like metabolism, immune system, cellular localization and transport apart from divulging functional competence to bind enzyme/protein, nucleic acids and cell adhesion molecules, necessary for host-microbial cross-talk.
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Affiliation(s)
- Debarun Acharya
- Department of Microbiology, Bose Institute, P-1/12, CIT Scheme VII M, Kolkata, West Bengal, 700 054, India
| | - Tapan K Dutta
- Department of Microbiology, Bose Institute, P-1/12, CIT Scheme VII M, Kolkata, West Bengal, 700 054, India.
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24
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Terracciano R, Preianò M, Fregola A, Pelaia C, Montalcini T, Savino R. Mapping the SARS-CoV-2-Host Protein-Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics. Int J Mol Sci 2021; 22:E532. [PMID: 33430309 PMCID: PMC7825748 DOI: 10.3390/ijms22020532] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/02/2021] [Accepted: 01/04/2021] [Indexed: 12/12/2022] Open
Abstract
Protein-protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a "high resolution" view of virus-host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.
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Affiliation(s)
- Rosa Terracciano
- Department of Experimental and Clinical Medicine, University “Magna Græcia”, 88100 Catanzaro, Italy;
| | - Mariaimmacolata Preianò
- Department of Health Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; (M.P.); (A.F.)
| | - Annalisa Fregola
- Department of Health Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy; (M.P.); (A.F.)
| | - Corrado Pelaia
- Respiratory Medicine Unit, University “Magna Græcia”, 88100 Catanzaro, Italy;
| | - Tiziana Montalcini
- Department of Experimental and Clinical Medicine, University “Magna Græcia”, 88100 Catanzaro, Italy;
| | - Rocco Savino
- Department of Medical and Surgical Sciences, University “Magna Græcia”, 88100 Catanzaro, Italy
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Davies JP, Almasy KM, McDonald EF, Plate L. Comparative Multiplexed Interactomics of SARS-CoV-2 and Homologous Coronavirus Nonstructural Proteins Identifies Unique and Shared Host-Cell Dependencies. ACS Infect Dis 2020; 6:3174-3189. [PMID: 33263384 PMCID: PMC7724760 DOI: 10.1021/acsinfecdis.0c00500] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Indexed: 12/15/2022]
Abstract
Human coronaviruses (hCoVs) have become a threat to global health and society, as evident from the SARS outbreak in 2002 caused by SARS-CoV-1 and the most recent COVID-19 pandemic caused by SARS-CoV-2. Despite a high sequence similarity between SARS-CoV-1 and -2, each strain has a distinctive virulence. A better understanding of the basic molecular mechanisms mediating changes in virulence is needed. Here, we profile the virus-host protein-protein interactions of two hCoV nonstructural proteins (nsps) that are critical for virus replication. We use tandem mass tag-multiplexed quantitative proteomics to sensitively compare and contrast the interactomes of nsp2 and nsp4 from three betacoronavirus strains: SARS-CoV-1, SARS-CoV-2, and hCoV-OC43-an endemic strain associated with the common cold. This approach enables the identification of both unique and shared host cell protein binding partners and the ability to further compare the enrichment of common interactions across homologues from related strains. We identify common nsp2 interactors involved in endoplasmic reticulum (ER) Ca2+ signaling and mitochondria biogenesis. We also identify nsp4 interactors unique to each strain, such as E3 ubiquitin ligase complexes for SARS-CoV-1 and ER homeostasis factors for SARS-CoV-2. Common nsp4 interactors include N-linked glycosylation machinery, unfolded protein response associated proteins, and antiviral innate immune signaling factors. Both nsp2 and nsp4 interactors are strongly enriched in proteins localized at mitochondria-associated ER membranes suggesting a new functional role for modulating host processes, such as calcium homeostasis, at these organelle contact sites. Our results shed light on the role these hCoV proteins play in the infection cycle, as well as host factors that may mediate the divergent pathogenesis of OC43 from SARS strains. Our mass spectrometry workflow enables rapid and robust comparisons of multiple bait proteins, which can be applied to additional viral proteins. Furthermore, the identified common interactions may present new targets for exploration by host-directed antiviral therapeutics.
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Affiliation(s)
- Jonathan P. Davies
- Department of Biological Sciences, Immunology and Inflammation, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Nashville, TN, USA
| | - Katherine M. Almasy
- Department of Chemistry, Vanderbilt University, Immunology and Inflammation, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Nashville, TN, USA
| | - Eli F. McDonald
- Department of Chemistry, Vanderbilt University, Immunology and Inflammation, Nashville, TN, USA
| | - Lars Plate
- Department of Biological Sciences, Immunology and Inflammation, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Immunology and Inflammation, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Nashville, TN, USA
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26
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Hodgins-Davis A, O'Meara TR. Systems biology of host-Candida interactions: understanding how we shape each other. Curr Opin Microbiol 2020; 58:1-7. [PMID: 32485592 PMCID: PMC7704567 DOI: 10.1016/j.mib.2020.04.001] [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: 03/31/2020] [Revised: 04/21/2020] [Accepted: 04/23/2020] [Indexed: 11/24/2022]
Abstract
Candida albicans is both a member of the human mucosal microbiota and a common agent of invasive fungal disease. Systems biology approaches allow for analysis of the interactions between this fungus and its mammalian host. Framing these studies by considering how C. albicans and its host construct the niche the other occupies provides insight into how these interactions shape the ecosystems, behavior, and evolution of each organism. Here, we discuss recent work on multiscale systems biology approaches for examining C. albicans in relation to the host ecosystem to identify the emergent properties of the interactions and new variables that can be targeted for development of therapeutic strategies.
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Affiliation(s)
- Andrea Hodgins-Davis
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Teresa R O'Meara
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
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27
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Sauvage S, Hardouin J. Exoproteomics for Better Understanding Pseudomonas aeruginosa Virulence. Toxins (Basel) 2020; 12:E571. [PMID: 32899849 PMCID: PMC7551764 DOI: 10.3390/toxins12090571] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/25/2020] [Accepted: 09/01/2020] [Indexed: 12/12/2022] Open
Abstract
Pseudomonas aeruginosa is the most common human opportunistic pathogen associated with nosocomial diseases. In 2017, the World Health Organization has classified P. aeruginosa as a critical agent threatening human health, and for which the development of new treatments is urgently necessary. One interesting avenue is to target virulence factors to understand P. aeruginosa pathogenicity. Thus, characterising exoproteins of P. aeruginosa is a hot research topic and proteomics is a powerful approach that provides important information to gain insights on bacterial virulence. The aim of this review is to focus on the contribution of proteomics to the studies of P. aeruginosa exoproteins, highlighting its relevance in the discovery of virulence factors, post-translational modifications on exoproteins and host-pathogen relationships.
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Affiliation(s)
- Salomé Sauvage
- Polymers, Biopolymers, Surface Laboratory, UMR 6270 CNRS, University of Rouen, CEDEX, F-76821 Mont-Saint-Aignan, France;
- PISSARO Proteomics Facility, IRIB, F-76820 Mont-Saint-Aignan, France
| | - Julie Hardouin
- Polymers, Biopolymers, Surface Laboratory, UMR 6270 CNRS, University of Rouen, CEDEX, F-76821 Mont-Saint-Aignan, France;
- PISSARO Proteomics Facility, IRIB, F-76820 Mont-Saint-Aignan, France
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28
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Davies JP, Almasy KM, McDonald EF, Plate L. Comparative multiplexed interactomics of SARS-CoV-2 and homologous coronavirus non-structural proteins identifies unique and shared host-cell dependencies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.07.13.201517. [PMID: 32699849 PMCID: PMC7373130 DOI: 10.1101/2020.07.13.201517] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Human coronaviruses (hCoV) have become a threat to global health and society, as evident from the SARS outbreak in 2002 caused by SARS-CoV-1 and the most recent COVID-19 pandemic caused by SARS-CoV-2. Despite high sequence similarity between SARS-CoV-1 and -2, each strain has distinctive virulence. A better understanding of the basic molecular mechanisms mediating changes in virulence is needed. Here, we profile the virus-host protein-protein interactions of two hCoV non-structural proteins (nsps) that are critical for virus replication. We use tandem mass tag-multiplexed quantitative proteomics to sensitively compare and contrast the interactomes of nsp2 and nsp4 from three betacoronavirus strains: SARS-CoV-1, SARS-CoV-2, and hCoV-OC43 - an endemic strain associated with the common cold. This approach enables the identification of both unique and shared host cell protein binding partners and the ability to further compare the enrichment of common interactions across homologs from related strains. We identify common nsp2 interactors involved in endoplasmic reticulum (ER) Ca 2+ signaling and mitochondria biogenesis. We also identifiy nsp4 interactors unique to each strain, such as E3 ubiquitin ligase complexes for SARS-CoV-1 and ER homeostasis factors for SARS-CoV-2. Common nsp4 interactors include N -linked glycosylation machinery, unfolded protein response (UPR) associated proteins, and anti-viral innate immune signaling factors. Both nsp2 and nsp4 interactors are strongly enriched in proteins localized at mitochondrial-associated ER membranes suggesting a new functional role for modulating host processes, such as calcium homeostasis, at these organelle contact sites. Our results shed light on the role these hCoV proteins play in the infection cycle, as well as host factors that may mediate the divergent pathogenesis of OC43 from SARS strains. Our mass spectrometry workflow enables rapid and robust comparisons of multiple bait proteins, which can be applied to additional viral proteins. Furthermore, the identified common interactions may present new targets for exploration by host-directed anti-viral therapeutics.
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Affiliation(s)
- Jonathan P. Davies
- Department of Biological Sciences, Immunology and Inflammation, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Nashville, TN, USA
| | - Katherine M. Almasy
- Department of Chemistry, Vanderbilt University, Immunology and Inflammation, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Nashville, TN, USA
| | - Eli F. McDonald
- Department of Chemistry, Vanderbilt University, Immunology and Inflammation, Nashville, TN, USA
| | - Lars Plate
- Department of Biological Sciences, Immunology and Inflammation, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Immunology and Inflammation, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Nashville, TN, USA
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Khodadadi E, Zeinalzadeh E, Taghizadeh S, Mehramouz B, Kamounah FS, Khodadadi E, Ganbarov K, Yousefi B, Bastami M, Kafil HS. Proteomic Applications in Antimicrobial Resistance and Clinical Microbiology Studies. Infect Drug Resist 2020; 13:1785-1806. [PMID: 32606829 PMCID: PMC7305820 DOI: 10.2147/idr.s238446] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 05/23/2020] [Indexed: 12/11/2022] Open
Abstract
Sequences of the genomes of all-important bacterial pathogens of man, plants, and animals have been completed. Still, it is not enough to achieve complete information of all the mechanisms controlling the biological processes of an organism. Along with all advances in different proteomics technologies, proteomics has completed our knowledge of biological processes all around the world. Proteomics is a valuable technique to explain the complement of proteins in any organism. One of the fields that has been notably benefited from other systems approaches is bacterial pathogenesis. An emerging field is to use proteomics to examine the infectious agents in terms of, among many, the response the host and pathogen to the infection process, which leads to a deeper knowledge of the mechanisms of bacterial virulence. This trend also enables us to identify quantitative measurements for proteins extracted from microorganisms. The present review study is an attempt to summarize a variety of different proteomic techniques and advances. The significant applications in bacterial pathogenesis studies are also covered. Moreover, the areas where proteomics may lead the future studies are introduced.
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Affiliation(s)
- Ehsaneh Khodadadi
- Drug Applied Research Center, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Elham Zeinalzadeh
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.,Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sepehr Taghizadeh
- Drug Applied Research Center, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Bahareh Mehramouz
- Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fadhil S Kamounah
- Department of Chemistry, University of Copenhagen, Copenhagen, DK 2100, Denmark
| | - Ehsan Khodadadi
- Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | | | - Bahman Yousefi
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Milad Bastami
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Hossein Samadi Kafil
- Drug Applied Research Center, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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30
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Guven-Maiorov E, Hakouz A, Valjevac S, Keskin O, Tsai CJ, Gursoy A, Nussinov R. HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry. J Mol Biol 2020; 432:3395-3403. [PMID: 32061934 PMCID: PMC7261632 DOI: 10.1016/j.jmb.2020.01.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/28/2019] [Accepted: 01/14/2020] [Indexed: 02/07/2023]
Abstract
Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.
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Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Asma Hakouz
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Sukejna Valjevac
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA; Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
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31
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Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions. Med Microbiol Immunol 2020; 209:265-275. [PMID: 32072248 PMCID: PMC7223518 DOI: 10.1007/s00430-020-00663-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 01/30/2020] [Indexed: 01/01/2023]
Abstract
A central challenge in infection medicine is to determine the structure and function of host-pathogen protein-protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host-pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial-human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs.
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32
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Guven-Maiorov E, Tsai CJ, Nussinov R. Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry. Front Oncol 2019; 9:1236. [PMID: 31803618 PMCID: PMC6872517 DOI: 10.3389/fonc.2019.01236] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/28/2019] [Indexed: 01/17/2023] Open
Abstract
Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces—interfaces—of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.
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Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States.,Department of Human Genetics and Molecular Medicine, Sackler Institute of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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33
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Vijay R, Sims AC, Bloodsworth KJ, Kim YM, Moore RJ, Kyle JE, Nakayasu ES, Metz TO. Metabolite, Protein, and Lipid Extraction (MPLEx): A Method that Simultaneously Inactivates Middle East Respiratory Syndrome Coronavirus and Allows Analysis of Multiple Host Cell Components Following Infection. Methods Mol Biol 2019; 2099:173-194. [PMID: 31883096 PMCID: PMC7121680 DOI: 10.1007/978-1-0716-0211-9_14] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Mass spectrometry (MS)-based, integrated proteomics, metabolomics, and lipidomics (collectively, multi-omics) studies provide a very detailed snapshot of virus-induced changes to the host following infection and can lead to the identification of novel prophylactic and therapeutic targets for preventing or lessening disease severity. Multi-omics studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of biosafety level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins, and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of the three separate analytes, and their subsequent informatics pipelines.
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Affiliation(s)
- Rahul Vijay
- Department of Microbiology and Immunology, University of Iowa, Iowa City, IA USA
| | - Amy C Sims
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kent J Bloodsworth
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Young-Mo Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
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Lasso G, Mayer SV, Winkelmann ER, Chu T, Elliot O, Patino-Galindo JA, Park K, Rabadan R, Honig B, Shapira SD. A Structure-Informed Atlas of Human-Virus Interactions. Cell 2019; 178:1526-1541.e16. [PMID: 31474372 PMCID: PMC6736651 DOI: 10.1016/j.cell.2019.08.005] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/17/2019] [Accepted: 08/02/2019] [Indexed: 12/19/2022]
Abstract
While knowledge of protein-protein interactions (PPIs) is critical for understanding virus-host relationships, limitations on the scalability of high-throughput methods have hampered their identification beyond a number of well-studied viruses. Here, we implement an in silico computational framework (pathogen host interactome prediction using structure similarity [P-HIPSTer]) that employs structural information to predict ∼282,000 pan viral-human PPIs with an experimental validation rate of ∼76%. In addition to rediscovering known biology, P-HIPSTer has yielded a series of new findings: the discovery of shared and unique machinery employed across human-infecting viruses, a likely role for ZIKV-ESR1 interactions in modulating viral replication, the identification of PPIs that discriminate between human papilloma viruses (HPVs) with high and low oncogenic potential, and a structure-enabled history of evolutionary selective pressure imposed on the human proteome. Further, P-HIPSTer enables discovery of previously unappreciated cellular circuits that act on human-infecting viruses and provides insight into experimentally intractable viruses.
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Affiliation(s)
- Gorka Lasso
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Sandra V Mayer
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Evandro R Winkelmann
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Tim Chu
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
| | - Oliver Elliot
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
| | | | - Kernyu Park
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Raul Rabadan
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University Medical Center, New York, NY, USA; Howard Hughes Medical Institute, Columbia University Medical Center, New York, NY, USA.
| | - Sagi D Shapira
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA.
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35
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Futoma-Kołoch B, Bugla-Płoskońska G, Dudek B, Dorotkiewicz-Jach A, Drulis-Kawa Z, Gamian A. Outer Membrane Proteins of Salmonella as Potential Markers of Resistance to Serum, Antibiotics and Biocides. Curr Med Chem 2019; 26:1960-1978. [PMID: 30378478 DOI: 10.2174/0929867325666181031130851] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 07/13/2018] [Accepted: 08/14/2018] [Indexed: 01/05/2023]
Abstract
Salmonellosis continues to be a significant worldwide health problem. Despite rapid progress in identifying mechanisms of Salmonella virulence and resistance to chemicals, our knowledge of these mechanisms remains limited. Furthermore, it appears that the resistance to antibiotics can be amplified by ubiquitous usage of the disinfectants (biocides), both by industry and by ordinary households. Salmonella, as other Gram-negative bacteria possess outer membrane proteins (OMPs), which participate in maintaining cell integrity, adapting to environment, and interacting with infected host. Moreover, the OMPs may also contribute to resistance to antibacterials. This review summarizes the role of OMPs in Salmonella serum resistance, antibiotics resistance and cross-resistance to biocides. Although collected data do not allow to assign OMPs as markers of the Salmonella susceptibility to the above-mentioned factors, some of these proteins retain a dominant presence in certain types of resistance.
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Affiliation(s)
- Bożena Futoma-Kołoch
- Department of Microbiology, Institute of Genetics and Microbiology, University of Wrocław, Przybyszewskiego 63-77, 51-148 Wroclaw, Poland
| | - Gabriela Bugla-Płoskońska
- Department of Microbiology, Institute of Genetics and Microbiology, University of Wrocław, Przybyszewskiego 63-77, 51-148 Wroclaw, Poland
| | - Bartłomiej Dudek
- Department of Microbiology, Institute of Genetics and Microbiology, University of Wrocław, Przybyszewskiego 63-77, 51-148 Wroclaw, Poland
| | - Agata Dorotkiewicz-Jach
- Department of Pathogen Biology and Immunology, Institute of Genetics and Microbiology, University of Wrocław, Przybyszewskiego 63-77, 51-148 Wroclaw, Poland
| | - Zuzanna Drulis-Kawa
- Department of Pathogen Biology and Immunology, Institute of Genetics and Microbiology, University of Wrocław, Przybyszewskiego 63-77, 51-148 Wroclaw, Poland
| | - Andrzej Gamian
- Department of Immunology of Infectious Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Rudolfa Weigla 12, 53-114 Wroclaw, Poland
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36
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Rodrigues CHM, Myung Y, Pires DEV, Ascher DB. mCSM-PPI2: predicting the effects of mutations on protein-protein interactions. Nucleic Acids Res 2019; 47:W338-W344. [PMID: 31114883 PMCID: PMC6602427 DOI: 10.1093/nar/gkz383] [Citation(s) in RCA: 200] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 04/30/2019] [Accepted: 05/20/2019] [Indexed: 12/13/2022] Open
Abstract
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein-protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.
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Affiliation(s)
- Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Yoochan Myung
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Douglas E V Pires
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - David B Ascher
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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A quantitative Streptococcus pyogenes-human protein-protein interaction map reveals localization of opsonizing antibodies. Nat Commun 2019; 10:2727. [PMID: 31227708 PMCID: PMC6588558 DOI: 10.1038/s41467-019-10583-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 05/17/2019] [Indexed: 12/01/2022] Open
Abstract
A fundamental challenge in medical microbiology is to characterize the dynamic protein–protein interaction networks formed at the host–pathogen interface. Here, we generate a quantitative interaction map between the significant human pathogen, Streptococcus pyogenes, and proteins from human saliva and plasma obtained via complementary affinity-purification and bacterial-surface centered enrichment strategies and quantitative mass spectrometry. Perturbation of the network using immunoglobulin protease cleavage, mixtures of different concentrations of saliva and plasma, and different S. pyogenes serotypes and their isogenic mutants, reveals how changing microenvironments alter the interconnectivity of the interaction map. The importance of host immunoglobulins for the interaction with human complement proteins is demonstrated and potential protective epitopes of importance for phagocytosis of S. pyogenes cells are localized. The interaction map confirms several previously described protein–protein interactions; however, it also reveals a multitude of additional interactions, with possible implications for host–pathogen interactions involving other bacterial species. Characterizing host-pathogen protein interactions can help elucidate the molecular basis of bacterial infections. Here, the authors use an integrative proteomics approach to generate a quantitative map of protein interactions between Streptococcus pyogenes and human saliva and plasma.
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Uncovering complex molecular networks in host-pathogen interactions using systems biology. Emerg Top Life Sci 2019; 3:371-378. [PMID: 33523202 DOI: 10.1042/etls20180174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/12/2019] [Accepted: 04/25/2019] [Indexed: 12/26/2022]
Abstract
Interactions between pathogens and their hosts can induce complex changes in both host and pathogen states to privilege pathogen survival or host clearance of the pathogen. To determine the consequences of specific host-pathogen interactions, a variety of techniques in microbiology, cell biology, and immunology are available to researchers. Systems biology that enables unbiased measurements of transcriptomes, proteomes, and other biomolecules has become increasingly common in the study of host-pathogen interactions. These approaches can be used to generate novel hypotheses or to characterize the effects of particular perturbations across an entire biomolecular network. With proper experimental design and complementary data analysis tools, high-throughput omics techniques can provide novel insights into the mechanisms that underlie processes from phagocytosis to pathogen immune evasion. Here, we provide an overview of the suite of biochemical approaches for high-throughput analyses of host-pathogen interactions, analytical frameworks for understanding the resulting datasets, and a vision for the future of this exciting field.
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Eoh H, Jung JU. Bacterial Protein Reshapes Host Defense toward Antiviral Responses. Mol Cell 2019; 71:483-484. [PMID: 30118676 DOI: 10.1016/j.molcel.2018.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
In this issue of Molecular Cell, Penn et al. (2018) report the protein interactome between Mycobacterium tuberculosis (Mtb) secreted effectors and macrophage cytosolic proteins. This Resource reveals that the interaction of Mtb effector LpqN with host CBL counteracts antibacterial defense but causes a reciprocal enhancement of antiviral defense.
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Affiliation(s)
- Hyungjin Eoh
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
| | - Jae U Jung
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
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Rosani U, Young T, Bai CM, Alfaro AC, Venier P. Dual Analysis of Virus-Host Interactions: The Case of Ostreid herpesvirus 1 and the Cupped Oyster Crassostrea gigas. Evol Bioinform Online 2019; 15:1176934319831305. [PMID: 30828244 PMCID: PMC6388457 DOI: 10.1177/1176934319831305] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 01/14/2019] [Indexed: 12/20/2022] Open
Abstract
Dual analyses of the interactions between Ostreid herpesvirus 1 (OsHV-1) and the bivalve Crassostrea gigas during infection can unveil events critical to the onset and progression of this viral disease and can provide novel strategies for mitigating and preventing oyster mortality. Among the currently used “omics” technologies, dual transcriptomics (dual RNA-seq) coupled with the analysis of viral DNA in the host tissues has greatly advanced the knowledge of genes and pathways mostly contributing to host defense responses, expression profiles of annotated and unknown OsHV-1 open reading frames (ORFs), and viral genome variability. In addition to dual RNA-seq, proteomics and metabolomics analyses have the potential to add complementary information, needed to understand how a malacoherpesvirus can redirect and exploit the vital processes of its host. This review explores our current knowledge of “omics” technologies in the study of host-pathogen interactions and highlights relevant applications of these fields of expertise to the complex case of C gigas infections by OsHV-1, which currently threaten the mollusk production sector worldwide.
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Affiliation(s)
- Umberto Rosani
- Department of Biology, University of Padova, Padova, Italy
| | - Tim Young
- Aquaculture Biotechnology Research Group, School of Science, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Chang-Ming Bai
- Key Laboratory of Maricultural Organism Disease Control, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, China
| | - Andrea C Alfaro
- Aquaculture Biotechnology Research Group, School of Science, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Paola Venier
- Department of Biology, University of Padova, Padova, Italy
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Microbial Proteomics and Their Importance in Medical Microbiology. RECENT DEVELOPMENTS IN APPLIED MICROBIOLOGY AND BIOCHEMISTRY 2019. [PMCID: PMC7149639 DOI: 10.1016/b978-0-12-816328-3.00003-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Microbial infection is a leading cause of death around the world. Most of the infectious diseases are caused by drug-resistant microbes; this may lead to a delay in the administration of microbiologically effective therapy (Chen et al., 2017; Del Chierico et al., 2014). Therefore, exhaustive understanding of microbial physiologies, infection and defense systems, and survival strategies is of great interest in order to actively defeat microbial infection. Microbial proteomics provides complete information of microbial physiology and expression and function of the proteins that are involved in infection and also gives a clue in clinical diagnosis and antimicrobial therapy (Pérez-Llarena and Bou, 2016; Vranakis et al., 2014). Microbial proteomics helps to identify the proteins associated with microbial activity, microbial host-pathogen interactions, and antimicrobial resistant mechanism. Microbial activity of pathogens can be confirmed by using the 2-D gel-based and gel-free method with the combination of MALDI-TOF-LC-MS/MS. Proteomic analysis of microbial host-pathogen interaction reveals valuable information about the virulence of the pathogen and its resistance; it helps in better understanding of the infection and for developing strategies against microbial infections (Cheng et al., 2016). Fig. 3.1 schematically illustrates the proteomic analysis of the bacterial samples.
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Kánová E, Jiménez-Munguía I, Majerová P, Tkáčová Z, Bhide K, Mertinková P, Pulzová L, Kováč A, Bhide M. Deciphering the Interactome of Neisseria meningitidis With Human Brain Microvascular Endothelial Cells. Front Microbiol 2018; 9:2294. [PMID: 30319591 PMCID: PMC6168680 DOI: 10.3389/fmicb.2018.02294] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 09/07/2018] [Indexed: 11/16/2022] Open
Abstract
Neisseria meningitidis is able to translocate the blood-brain barrier and cause meningitis. Bacterial translocation is a crucial step in the onset of neuroinvasion that involves interactions between pathogen surface proteins and host cells receptors. In this study, we applied a systematic workflow to recover and identify proteins of N. meningitidis that may interact with human brain microvascular endothelial cells (hBMECs). Biotinylated proteome of N. meningitidis was incubated with hBMECs, interacting proteins were recovered by affinity purification and identified by SWATH-MS. Interactome of N. meningitidis comprised of 41 potentially surface exposed proteins. These were assigned into groups based on their probability to interact with hBMECs: high priority candidates (21 outer membrane proteins), medium priority candidates (14 inner membrane proteins) and low priority candidates (six secretory proteins). Ontology analysis provided information for 17 out of 41 surface proteins. Based on the series of bioinformatic analyses and literature review, five surface proteins (adhesin MafA1, major outer membrane protein P.IB, putative adhesin/invasion, putative lipoprotein and membrane lipoprotein) were selected and their recombinant forms were produced for experimental validation of interaction with hBMECs by ELISA and immunocytochemistry. All candidates showed interaction with hBMECs. In this study, we present a high-throughput approach to generate a dataset of plausible meningococcal ligands followed by systematic bioinformatic pipeline to categorize the proteins for experimental validation.
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Affiliation(s)
- Evelína Kánová
- Laboratory of Biomedical Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia
| | - Irene Jiménez-Munguía
- Laboratory of Biomedical Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia
| | - Petra Majerová
- Institute of Neuroimmunology of Slovak Academy of Sciences, Bratislava, Slovakia
| | - Zuzana Tkáčová
- Laboratory of Biomedical Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia
| | - Katarína Bhide
- Laboratory of Biomedical Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia
| | - Patrícia Mertinková
- Laboratory of Biomedical Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia
| | - Lucia Pulzová
- Laboratory of Biomedical Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia
| | - Andrej Kováč
- Institute of Neuroimmunology of Slovak Academy of Sciences, Bratislava, Slovakia
| | - Mangesh Bhide
- Laboratory of Biomedical Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia.,Institute of Neuroimmunology of Slovak Academy of Sciences, Bratislava, Slovakia
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Timmis K. Environmental microbiology - the next 20 years: bioconnectivity and meta'omics 2.0. Environ Microbiol 2018; 20:1949-1954. [PMID: 29750400 DOI: 10.1111/1462-2920.14236] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Kenneth Timmis
- Institute of Microbiology, Technical University Braunschweig, Braunschweig, Germany
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