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Delplace F, Huard-Chauveau C, Roux F, Roby D. The receptor MIK2 interacts with the kinase RKS1 to control quantitative disease resistance to Xanthomonas campestris. PLANT PHYSIOLOGY 2024; 197:kiae626. [PMID: 39577458 DOI: 10.1093/plphys/kiae626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 09/26/2024] [Accepted: 10/21/2024] [Indexed: 11/24/2024]
Abstract
Molecular mechanisms underlying qualitative resistance have been intensively studied. In contrast, although quantitative disease resistance (QDR) is a common, durable, and broad-spectrum form of immune responses in plants, only a few related functional analyses have been reported. The atypical kinase Resistance related kinase 1 (RKS1) is a major regulator of QDR to the bacterial pathogen Xanthomonas campestris (Xcc) and is positioned in a robust protein-protein decentralized network in Arabidopsis (Arabidopsis thaliana). Among the putative interactors of RKS1 found by yeast two-hybrid screening, we identified the receptor-like kinase MDIS1-interacting receptor-like kinase 2 (MIK2). Here, using multiple complementary strategies including protein-protein interaction tests, mutant analysis, and network reconstruction, we report that MIK2 is a component of RKS1-mediated QDR to Xcc. First, by co-localization experiments, co-immunoprecipitation (Co-IP), and bimolecular fluorescence complementation, we validated the physical interaction between RKS1 and MIK2 at the plasma membrane. Using mik2 mutants, we showed that MIK2 is required for QDR and contributes to resistance to the same level as RKS1. Interestingly, a catalytic mutant of MIK2 interacted with RKS1 but was unable to fully complement the mik2-1 mutant phenotype in response to Xcc. Finally, we investigated the potential role of the MIK2-RKS1 complex as a scaffolding component for the coordination of perception events by constructing a RKS1-MIK2 centered protein-protein interaction network. Eight mutants corresponding to seven RKs in this network showed a strong alteration in QDR to Xcc. Our findings provide insights into the molecular mechanisms underlying the perception events involved in QDR to Xcc.
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Affiliation(s)
- Florent Delplace
- Laboratoire des Interactions Plantes-Microbes Environnement (LIPME), INRAE, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Carine Huard-Chauveau
- Laboratoire des Interactions Plantes-Microbes Environnement (LIPME), INRAE, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Fabrice Roux
- Laboratoire des Interactions Plantes-Microbes Environnement (LIPME), INRAE, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Dominique Roby
- Laboratoire des Interactions Plantes-Microbes Environnement (LIPME), INRAE, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
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2
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Tang T, Li T, Li W, Cao X, Liu Y, Zeng X. Anti-symmetric framework for balanced learning of protein-protein interactions. Bioinformatics 2024; 40:btae603. [PMID: 39404784 PMCID: PMC11513017 DOI: 10.1093/bioinformatics/btae603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 09/13/2024] [Accepted: 10/12/2024] [Indexed: 10/29/2024] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) are essential for the regulation and facilitation of virtually all biological processes. Computational tools, particularly those based on deep learning, are preferred for the efficient prediction of PPIs. Despite recent progress, two challenges remain unresolved: (i) the imbalanced nature of PPI characteristics is often ignored and (ii) there exists a high computational cost associated with capturing long-range dependencies within protein data, typically exhibiting quadratic complexity relative to the length of the protein sequence. RESULT Here, we propose an anti-symmetric graph learning model, BaPPI, for the balanced prediction of PPIs and extrapolation of the involved patterns in PPI network. In BaPPI, the contextualized information of protein data is efficiently handled by an attention-free mechanism formed by recurrent convolution operator. The anti-symmetric graph convolutional network is employed to model the uneven distribution within PPI networks, aiming to learn a more robust and balanced representation of the relationships between proteins. Ultimately, the model is updated using asymmetric loss. The experimental results on classical baseline datasets demonstrate that BaPPI outperforms four state-of-the-art PPI prediction methods. In terms of Micro-F1, BaPPI exceeds the second-best method by 6.5% on SHS27K and 5.3% on SHS148K. Further analysis of the generalization ability and patterns of predicted PPIs also demonstrates our model's generalizability and robustness to the imbalanced nature of PPI datasets. AVAILABILITY AND IMPLEMENTATION The source code of this work is publicly available at https://github.com/ttan6729/BaPPI.
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Affiliation(s)
- Tao Tang
- School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Tianyang Li
- School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Weizhuo Li
- School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Xiaofeng Cao
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410086, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410086, China
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Yan W, Hu W, Song Y, Liu X, Zhou Z, Li W, Cao Z, Pei W, Zhou G, Hu G. Differential network analysis reveals the key role of the ECM-receptor pathway in α-particle-induced malignant transformation. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102260. [PMID: 39049874 PMCID: PMC11268105 DOI: 10.1016/j.omtn.2024.102260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 06/14/2024] [Indexed: 07/27/2024]
Abstract
Space particle radiation is a major environmental factor in spaceflight, and it is known to cause body damage and even trigger cancer, but with unknown molecular etiologies. To examine these causes, we developed a systems biology approach by focusing on the co-expression network analysis of transcriptomics profiles obtained from single high-dose (SE) and multiple low-dose (ME) α-particle radiation exposures of BEAS-2B human bronchial epithelial cells. First, the differential network and pathway analysis based on the global network and the core modules showed that genes in the ME group had higher enrichment for the extracellular matrix (ECM)-receptor interaction pathway. Then, collagen gene COL1A1 was screened as an important gene in the ME group assessed by network parameters and an expression study of lung adenocarcinoma samples. COL1A1 was found to promote the emergence of the neoplastic characteristics of BEAS-2B cells by both in vitro experimental analyses and in vivo immunohistochemical staining. These findings suggested that the degree of malignant transformation of cells in the ME group was greater than that of the SE, which may be caused by the dysregulation of the ECM-receptor pathway.
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Affiliation(s)
- Wenying Yan
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China
| | - Wentao Hu
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Yidan Song
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
| | - Xingyi Liu
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
| | - Ziyun Zhou
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China
| | - Wanshi Li
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Zhifei Cao
- Department of Pathology, the Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Weiwei Pei
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Guangming Zhou
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Guang Hu
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China
- Key Laboratory of Alkene-carbon Fibres-based Technology & Application for Detection of Major Infectious Diseases, Soochow University, Suzhou 215123, China
- Jiangsu Key Laboratory of Infection and Immunity, Soochow University, Suzhou 215123, China
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4
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Carrasco JL, Ambrós S, Gutiérrez PA, Elena SF. Adaptation of turnip mosaic virus to Arabidopsis thaliana involves rewiring of VPg-host proteome interactions. Virus Evol 2024; 10:veae055. [PMID: 39091990 PMCID: PMC11291303 DOI: 10.1093/ve/veae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/23/2024] [Accepted: 07/16/2024] [Indexed: 08/04/2024] Open
Abstract
The outcome of a viral infection depends on a complex interplay between the host physiology and the virus, mediated through numerous protein-protein interactions. In a previous study, we used high-throughput yeast two-hybrid (HT-Y2H) to identify proteins in Arabidopsis thaliana that bind to the proteins encoded by the turnip mosaic virus (TuMV) genome. Furthermore, after experimental evolution of TuMV lineages in plants with mutations in defense-related or proviral genes, most mutations observed in the evolved viruses affected the VPg cistron. Among these mutations, D113G was a convergent mutation selected in many lineages across different plant genotypes, including cpr5-2 with constitutive expression of systemic acquired resistance. In contrast, mutation R118H specifically emerged in the jin1 mutant with affected jasmonate signaling. Using the HT-Y2H system, we analyzed the impact of these two mutations on VPg's interaction with plant proteins. Interestingly, both mutations severely compromised the interaction of VPg with the translation initiation factor eIF(iso)4E, a crucial interactor for potyvirus infection. Moreover, mutation D113G, but not R118H, adversely affected the interaction with RHD1, a zinc-finger homeodomain transcription factor involved in regulating DNA demethylation. Our results suggest that RHD1 enhances plant tolerance to TuMV infection. We also discuss our findings in a broad virus evolution context.
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Affiliation(s)
- José L Carrasco
- Instituto de Biología Integrativa de Sistemas (CSIC—Universitat de València), Catedratico Agustin Escardino 9, Paterna, València 46182, Spain
| | - Silvia Ambrós
- Instituto de Biología Integrativa de Sistemas (CSIC—Universitat de València), Catedratico Agustin Escardino 9, Paterna, València 46182, Spain
| | - Pablo A Gutiérrez
- Laboratorio de Microbiología Industrial, Facultad de Ciencias, Universidad Nacional de Colombia, Carrera 65 Nro. 59A - 110, Medellín, Antioquia 050034, Colombia
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas (CSIC—Universitat de València), Catedratico Agustin Escardino 9, Paterna, València 46182, Spain
- The Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, United States
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5
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Pan J, Zhang Z, Li Y, Yu J, You Z, Li C, Wang S, Zhu M, Ren F, Zhang X, Sun Y, Wang S. A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts. Brief Bioinform 2024; 25:bbae119. [PMID: 38555472 PMCID: PMC10981679 DOI: 10.1093/bib/bbae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/23/2024] [Accepted: 03/02/2024] [Indexed: 04/02/2024] Open
Abstract
Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.
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Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhen Zhang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Ying Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Jiaoyang Yu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Chenyu Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shixu Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Minghui Zhu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Fengzhi Ren
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Xuexia Zhang
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
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6
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Watkins JM, Montes C, Clark NM, Song G, Oliveira CC, Mishra B, Brachova L, Seifert CM, Mitchell MS, Yang J, Braga Dos Reis PA, Urano D, Muktar MS, Walley JW, Jones AM. Phosphorylation Dynamics in a flg22-Induced, G Protein-Dependent Network Reveals the AtRGS1 Phosphatase. Mol Cell Proteomics 2024; 23:100705. [PMID: 38135118 PMCID: PMC10837098 DOI: 10.1016/j.mcpro.2023.100705] [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: 04/03/2023] [Revised: 11/22/2023] [Accepted: 12/19/2023] [Indexed: 12/24/2023] Open
Abstract
The microbe-associated molecular pattern flg22 is recognized in a flagellin-sensitive 2-dependent manner in root tip cells. Here, we show a rapid and massive change in protein abundance and phosphorylation state of the Arabidopsis root cell proteome in WT and a mutant deficient in heterotrimeric G-protein-coupled signaling. flg22-induced changes fall on proteins comprising a subset of this proteome, the heterotrimeric G protein interactome, and on highly-populated hubs of the immunity network. Approximately 95% of the phosphorylation changes in the heterotrimeric G-protein interactome depend, at least partially, on a functional G protein complex. One member of this interactome is ATBα, a substrate-recognition subunit of a protein phosphatase 2A complex and an interactor to Arabidopsis thaliana Regulator of G Signaling 1 protein (AtRGS1), a flg22-phosphorylated, 7-transmembrane spanning modulator of the nucleotide-binding state of the core G-protein complex. A null mutation of ATBα strongly increases basal endocytosis of AtRGS1. AtRGS1 steady-state protein level is lower in the atbα mutant in a proteasome-dependent manner. We propose that phosphorylation-dependent endocytosis of AtRGS1 is part of the mechanism to degrade AtRGS1, thus sustaining activation of the heterotrimeric G protein complex required for the regulation of system dynamics in innate immunity. The PP2A(ATBα) complex is a critical regulator of this signaling pathway.
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Affiliation(s)
- Justin M Watkins
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Christian Montes
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA
| | - Natalie M Clark
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA
| | - Gaoyuan Song
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA
| | - Celio Cabral Oliveira
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Biochemistry and Molecular Biology/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Bharat Mishra
- Department of Biology, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Libuse Brachova
- Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, USA
| | - Clara M Seifert
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Malek S Mitchell
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jing Yang
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Daisuke Urano
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - M Shahid Muktar
- Department of Biology, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Justin W Walley
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA.
| | - Alan M Jones
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
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7
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Fontes EPB. SERKs and NIKs: Coreceptors or signaling hubs in a complex crosstalk between growth and defense? CURRENT OPINION IN PLANT BIOLOGY 2024; 77:102447. [PMID: 37690927 DOI: 10.1016/j.pbi.2023.102447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/01/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023]
Abstract
SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASES (SERKs) and NUCLEAR SHUTTLE PROTEIN-INTERACTING KINASES (NIKs) belong to superfamily II of leucine-rich repeat receptor-like kinases, which share cytosolic kinase conservation and a similar ectodomain configuration. SERKs have been extensively demonstrated to function as coreceptors of receptor-like kinases, which sense biotic or developmental signals to initiate specific responses. NIKs, on the other hand, have emerged as downstream components in signaling cascades, not functioning as coreceptors but rather serving as hubs that converge information from both biotic and abiotic signals, resulting in a unified response. Like SERKs, NIKs play a crucial role as information spreaders in plant cells, forming hubs of high centrality. However, unlike SERKs, which function as coreceptors and assemble paired receptor-specific responses, NIKs employ a shared signaling circuit to transduce diverse biotic and abiotic signals into the same physiological response. Therefore, this review highlights the concept of signaling hubs that differ from coreceptors in signaling pathways.
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Affiliation(s)
- Elizabeth P B Fontes
- Biochemistry and Molecular Biology Department, Bioagro, Universidade Federal de Viçosa, 36570.000, Viçosa, MG, Brazil.
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8
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Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [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: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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9
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Derbyshire MC, Raffaele S. Till death do us pair: Co-evolution of plant-necrotroph interactions. CURRENT OPINION IN PLANT BIOLOGY 2023; 76:102457. [PMID: 37852141 DOI: 10.1016/j.pbi.2023.102457] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/18/2023] [Accepted: 08/29/2023] [Indexed: 10/20/2023]
Abstract
Plants use programmed cell death as a potent defense response against biotrophic pathogens that require living host cells to thrive. However, cell death can promote infection by necrotrophic pathogens. This discrepancy creates specific co-evolutionary dynamics in the interaction between plants and necrotrophs. Necrotrophic pathogens produce diverse cell death-inducing effectors that act redundantly on several plant targets and sometimes suppress plant immune responses as an additional function. Plants use surface receptors that recognize necrotrophic effectors to increase quantitative disease resistance, some of which evolved independently in several plant lineages. Co-evolution has shaped molecular mechanisms involved in plant-necrotroph interactions into robust systems, relying on degenerate and multifunctional modules, general-purpose components, and compartmentalized functioning.
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Affiliation(s)
- Mark C Derbyshire
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Bentley, Western Australia, Australia
| | - Sylvain Raffaele
- Université de Toulouse, INRAE, CNRS, Laboratoire des Interactions Plantes Micro-organismes Environnement (LIPME), 31326, Castanet-Tolosan, France.
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10
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Mukhtar MS, Mishra B, Athar M. Integrative systems biology framework discovers common gene regulatory signatures in multiple mechanistically distinct inflammatory skin diseases. RESEARCH SQUARE 2023:rs.3.rs-3611240. [PMID: 38014119 PMCID: PMC10680929 DOI: 10.21203/rs.3.rs-3611240/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
More than 20% of the population across the world is affected by non-communicable inflammatory skin diseases including psoriasis, atopic dermatitis, hidradenitis suppurativa, rosacea, etc. Many of these chronic diseases are painful and debilitating with limited effective therapeutic interventions. However, recent advances in psoriasis treatment have improved the effectiveness and provide better management of the disease. This study aims to identify common regulatory pathways and master regulators that regulate molecular pathogenesis. We designed an integrative systems biology framework to identify the significant regulators across several inflammatory skin diseases. With conventional transcriptome analysis, we identified 55 shared genes, which are enriched in several immune-associated pathways in eight inflammatory skin diseases. Next, we exploited the gene co-expression-, and protein-protein interaction-based networks to identify shared genes and protein components in different diseases with relevant functional implications. Additionally, the network analytics unravels 55 high-value proteins as significant regulators in molecular pathogenesis. We believe that these significant regulators should be explored with critical experimental approaches to identify the putative drug targets for more effective treatments. As an example, we identified IKZF1 as a shared significant master regulator in three inflammatory skin diseases, which can serve as a putative drug target with known disease-derived molecules for developing efficacious combinatorial treatments for hidradenitis suppurativa, atopic dermatitis, and rosacea. The proposed framework is very modular, which can indicate a significant path of molecular mechanism-based drug development from complex transcriptomics data and other multi-omics data.
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11
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Tayal S, Bhatnagar S. Role of molecular mimicry in the SARS-CoV-2-human interactome for pathogenesis of cardiovascular diseases: An update to ImitateDB. Comput Biol Chem 2023; 106:107919. [PMID: 37463554 DOI: 10.1016/j.compbiolchem.2023.107919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 06/13/2023] [Accepted: 07/06/2023] [Indexed: 07/20/2023]
Abstract
Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.
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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
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi 110078, India.
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12
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Jayaprakash A, Roy A, Thanmalagan RR, Arunachalam A, P T V L. Understanding the mechanism of pathogenicity through interactome studies between Arachis hypogaea L. and Aspergillus flavus. J Proteomics 2023; 287:104975. [PMID: 37482270 DOI: 10.1016/j.jprot.2023.104975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/28/2023] [Accepted: 07/15/2023] [Indexed: 07/25/2023]
Abstract
Aspergillus flavus (A. flavus) infects the peanut seeds during pre-and post-harvest stages, causing seed quality destruction for humans and livestock consumption. Even though many resistant varieties were developed, the molecular mechanism of defense interactions of peanut against A. flavus still needs further investigation. Hence, an interologous host-pathogen protein interaction (HPPI) network was constructed to understand the subcellular level interaction mechanism between peanut and A. flavus. Out of the top 10 hub proteins of both organisms, protein phosphatase 2C and cyclic nucleotide-binding/kinase domain-containing protein and different ribosomal proteins were identified as candidate proteins involved in defense. Functional annotation and subcellular localization based characterization of HPPI identified protein SGT1 homolog, calmodulin and Rac-like GTP-binding proteins to be involved in defense response against fungus. The relevance of HPPI in infectious conditions was assessed using two transcriptome data which identified the interplay of host kinase class R proteins, bHLH TFs and cell wall related proteins to impart resistance against pathogen infection. Further, the pathogenicity analysis identified glycogen phosphorylase and molecular chaperone and allergen Mod-E/Hsp90/Hsp1 as potential pathogen targets to enhance the host defense mechanism. Hence, the computationally predicted host-pathogen PPI network could provide valuable support for molecular biology experiments to understand the host-pathogen interaction. SIGNIFICANCE: Protein-protein interactions execute significant cellular interactions in an organism and are influenced majorly by stress conditions. Here we reported the host-pathogen protein-protein interaction between peanut and A. flavus, and a detailed network analysis based on function, subcellular localization, gene co-expression, and pathogenicity was performed. The network analysis identified key proteins such as host kinase class R proteins, calmodulin, SGT1 homolog, Rac-like GTP-binding proteins bHLH TFs and cell wall related to impart resistance against pathogen infection. We observed the interplay of defense related proteins and cell wall related proteins predominantly, which could be subjected to further studies. The network analysis described in this study could be applied to understand other host-pathogen systems generally.
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Affiliation(s)
- Aiswarya Jayaprakash
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Abhijeet Roy
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Raja Rajeswary Thanmalagan
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Annamalai Arunachalam
- Department of Food Science & Technology, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India
| | - Lakshmi P T V
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, R. V. Nagar Kalapet, Pondicherry 605014, India.
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Kumar N, Mishra BK, Liu J, Mohan B, Thingujam D, Pajerowska-Mukhtar KM, Mukhtar MS. Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators in Arabidopsis. Int J Mol Sci 2023; 24:ijms24087349. [PMID: 37108512 PMCID: PMC10139068 DOI: 10.3390/ijms24087349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Bharat K Mishra
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Jinbao Liu
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Binoop Mohan
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Doni Thingujam
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Karolina M Pajerowska-Mukhtar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
- Nutrition Obesity Research Center, University of Alabama at Birmingham, 1675 University Boulevard, Birmingham, AL 35294, USA
- Department of Surgery, University of Alabama at Birmingham, 1808 7th Ave S, Birmingham, AL 35294, USA
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14
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A binary interaction map between turnip mosaic virus and Arabidopsis thaliana proteomes. Commun Biol 2023; 6:28. [PMID: 36631662 PMCID: PMC9834402 DOI: 10.1038/s42003-023-04427-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 01/05/2023] [Indexed: 01/13/2023] Open
Abstract
Viruses are obligate intracellular parasites that have co-evolved with their hosts to establish an intricate network of protein-protein interactions. Here, we followed a high-throughput yeast two-hybrid screening to identify 378 novel protein-protein interactions between turnip mosaic virus (TuMV) and its natural host Arabidopsis thaliana. We identified the RNA-dependent RNA polymerase NIb as the viral protein with the largest number of contacts, including key salicylic acid-dependent transcription regulators. We verified a subset of 25 interactions in planta by bimolecular fluorescence complementation assays. We then constructed and analyzed a network comprising 399 TuMV-A. thaliana interactions together with intravirus and intrahost connections. In particular, we found that the host proteins targeted by TuMV are enriched in different aspects of plant responses to infections, are more connected and have an increased capacity to spread information throughout the cell proteome, display higher expression levels, and have been subject to stronger purifying selection than expected by chance. The proviral or antiviral role of ten host proteins was validated by characterizing the infection dynamics in the corresponding mutant plants, supporting a proviral role for the transcriptional regulator TGA1. Comparison with similar studies with animal viruses, highlights shared fundamental features in their mode of action.
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15
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Mohan B, Thingujam D, Pajerowska-Mukhtar KM. Cytotrap: An Innovative Approach for Protein-Protein Interaction Studies for Cytoplasmic Proteins. Methods Mol Biol 2023; 2690:9-22. [PMID: 37450133 DOI: 10.1007/978-1-0716-3327-4_2] [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 interaction mapping has gained immense importance in understanding protein functions in diverse biological pathways. There are various in vivo and in vitro techniques associated with the protein-protein interaction studies but generally, the focus is confined to understanding the protein interaction in the nucleus of the cell, and thus it limits the availability to explore protein interactions that are happening in the cytoplasm of the cell. Since posttranslational modification is a crucial step in signaling pathways and cellular protein interactions harnessing the cytoplasmic protein and evaluating the interaction in the cytoplasm, this protocol will provide more information about studying these types of protein interactions. Cytotrap is a type of yeast-two-hybrid system that differs in its ability to anchor along the membrane, thus directing the protein of interest to anchor along the membrane through the myristoylation signaling unit. The vector containing the target protein contains the myristoylation unit, called the prey, and the bait unit contains the protein of interest as a fusion with the hSos protein. In an event of interaction between the target and the protein of interest, the hSos protein unit will be localized to the membrane and the GDP/GTP exchange unit will trigger the activation of the Ras pathway that leads to the survival of the temperature-sensitive yeast strain at a higher temperature.
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Affiliation(s)
- Binoop Mohan
- Department of Biology, University of Alabama, Birmingham, AL, USA
| | - Doni Thingujam
- Department of Biology, University of Alabama, Birmingham, AL, USA
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16
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Lau V, Provart NJ. AGENT for Exploring and Analyzing Gene Regulatory Networks from Arabidopsis. Methods Mol Biol 2023; 2698:351-360. [PMID: 37682484 DOI: 10.1007/978-1-0716-3354-0_20] [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: 09/09/2023]
Abstract
Gene regulatory networks (GRNs) are important for determining how an organism develops and how it responds to external stimuli. In the case of Arabidopsis thaliana, several GRNs have been identified covering many important biological processes. We present AGENT, the Arabidopsis GEne Network Tool, for exploring and analyzing published GRNs. Using tools in AGENT, regulatory motifs such as feed-forward loops can be easily identified. Nodes with high centrality-and hence importance-can likewise be identified. Gene expression data can also be overlaid onto GRNs to help discover subnetworks acting in specific tissues or under certain conditions.
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Affiliation(s)
- Vincent Lau
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada
| | - Nicholas J Provart
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.
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17
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Fei W, Liu Y. Biotrophic Fungal Pathogens: a Critical Overview. Appl Biochem Biotechnol 2023; 195:1-16. [PMID: 35951248 DOI: 10.1007/s12010-022-04087-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 01/13/2023]
Abstract
Biotrophic fungi are one group of heterogeneous organisms and these fungi differ in their traits like mode of nutrition, types of reproduction, and dispersal systems. Generally, based on the nutritional mode, fungi are classified into three broad categories, viz. biotrophs, necrotrophs, and hemi-biotrophs. Biotrophs derive their nutrients and energy from living plant cells and survive within the interstitial space of the cells. Biotrophic fungi cause serious crop diseases but are highly challenging to investigate and develop a treatment strategy. Blumeria (Erysiphe) graminis, Uromyces fabae, Ustilago maydis, Cladosporium fulvum, Puccinia graminis, and Phytophthora infestans are some of the significant biotrophic fungi that affect mainly plants. One among the biotrophic fungus, Pneumocystis jirovecii (Taphrinomycotina subphylum of the Ascomycota) exclusively a human pathogen, can cause lung diseases such as "pneumocystis." Biotrophic fungus widely parasitizing Solanaceae family crops (Tomato and potato) has done massive damage to the crops and has led to economic impact worldwide. During infection and for nutrient absorption, biotrophs develops external appendages such as appressoria or haustoria. The hyphae or appressorium adheres to the plant cell wall and collapses the layers for their nutrient absorption. The pathogen also secretes effector molecules to escape from the plant defense mechanism. Later, plants activate their primary and secondary defense mechanisms; however, the pathogen induces virulence genes to escape the host immune responses. Obligate biotrophic fungi pathogenicity has not been fully understood at the molecular level because of the complex interaction, recognition, and signaling with the host. This review summarizes the mechanism of infection in the host, and immune response to emphasize the understanding of the biotrophic fungal biology and pathogenesis in crops. Thus, the detailed review will pave the way to design methods to overcome the resistance of biotrophic fungi and develop disease-free crops.
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Affiliation(s)
- Wang Fei
- Zhengzhou Yongfeng Bio-Fertilizer Co., Ltd, high-tech district, 6 Tsui Zhu Street, 863 Software Park, Building 9 1102, Henan Province, 450001, Zhengzhou City, China.
| | - Ye Liu
- Xiangtan Institute for Food and Drug Control, Xiangtan, China
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18
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Fakhar AZ, Liu J, Pajerowska-Mukhtar KM. Dynamic Enrichment for Evaluation of Protein Networks (DEEPN): A High Throughput Yeast Two-Hybrid (Y2H) Protocol to Evaluate Networks. Methods Mol Biol 2023; 2690:179-192. [PMID: 37450148 DOI: 10.1007/978-1-0716-3327-4_17] [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
Proteins are the building blocks of life, and a vast array of cellular processes is handled by protein-protein interactions (PPIs). The protein complexes formed via PPIs lead to tangled networks that, with their continuous remodeling, build up systematic functional units. Over the years, PPIs have become an area of interest for many researchers, leading to the development of multiple in vitro and in vivo methods to reveal these interactions. The yeast-two-hybrid (Y2H) system is a potent genetic way to map PPIs in both a micro- and high-throughput manner. Y2H is a technique that involves using modified yeast cells to identify protein-protein interactions. For Y2H, the yeast cells are engineered only to grow when there is a significant interaction between a specific protein with its interacting partner. PPIs are identified in the Y2H system by stimulating reporter genes in response to a restored transcription factor. However, Y2H results may be constrained by stringency requirements, as the limited number of colony screenings through this technique could result in the possible elimination of numerous genuine interactions. Therefore, DEEPN (dynamic enrichment for evaluation of protein networks) can be used, offering the potential to study the multiple static and transient protein interactions in a single Y2H experiment. DEEPN utilizes next-generation DNA sequencing (NGS) data in a high-throughput manner and subsequently applies computational analysis and statistical modeling to identify interacting partners. This protocol describes customized reagents and protocols through which DEEPN analysis can be utilized efficiently and cost-effectively.
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Affiliation(s)
| | - Jinbao Liu
- Department of Biology at University of Alabama, Birmingham, AL, USA
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19
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Abstract
As the protein-protein interaction (PPI) data increase exponentially, the development and usage of computational methods to analyze these datasets have become a new research horizon in systems biology. The PPI network analysis and visualization can help identify functional modules of the network, pathway genes involved in common cellular functions, and functional annotations of novel genes. Currently, a variety of tools are available for network graph visualization and analysis. Cytoscape, an open-source software tool, is one of them. It provides an interactive visualization interface along with other core features to import, navigate, filter, cluster, search, and export networks. It comes with hundreds of in-built Apps in App Manager to resolve research questions related to network visualization and integration. This chapter aims to illustrate the Cytoscape application to visualize and analyze the PPI network using Arabidopsis interactome-1 main (AI-1MAIN) PPI network dataset from Plant Interactome Database.
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Affiliation(s)
- Aqsa Majeed
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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20
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Kumar N, Mukhtar S. Building Protein-Protein Interaction Graph Database Using Neo4j. Methods Mol Biol 2023; 2690:469-479. [PMID: 37450167 DOI: 10.1007/978-1-0716-3327-4_36] [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
A cell's various components interact with each other in a coordinated manner to respond to environmental cues and intracellular signals. Compared to the other biological networks, the protein-protein interaction (PPI) is mostly responsible for maintaining signaling pathways. Increasing numbers of experimentally verified and predicted PPIs in plants demand a scalable platform to deal with large and complex datasets. Network/graph data can be organized and analyzed using different tools. This chapter uses Neo4j, a graph database management system, to store and analyze plant PPI networks. To make the graph database and analyze network centrality, we used Arabidopsis interactome-1 main (AI-1MAIN) PPI network.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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21
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Hasan M, Kumar N, Majeed A, Ahmad A, Mukhtar S. Protein-Protein Interaction Network Analysis Using NetworkX. Methods Mol Biol 2023; 2690:457-467. [PMID: 37450166 DOI: 10.1007/978-1-0716-3327-4_35] [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
In recent years, extracting information from biological data has become a particularly valuable way of gaining knowledge. Molecular interaction networks provide a framework for visualizing cellular processes, but their complexity frequently makes their interpretation difficult. Proteins are one of the primary determinants of biological function. Indeed, most biological activities in the living cells are functionally regulated by protein-protein interactions (PPIs). Thus, studying protein interactions is critical for understanding their roles within the cell. Exploring the PPI networks can open new avenues for future experimental studies and offer interspecies predictions for effective interaction mapping. In this chapter we will demonstrate how to construct, visualize, and analyze a protein-protein interaction network using NetworkX.
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Affiliation(s)
- Mehadi Hasan
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Aqsa Majeed
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Aftab Ahmad
- Department of Anesthesiology and Perioperative Medicine, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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22
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Diwan D, Pajerowska-Mukhtar KM. Preparation and Utilization of a Versatile GFP-Protein Trap-Like System for Protein Complex Immunoprecipitation in Plants. Methods Mol Biol 2023; 2690:59-68. [PMID: 37450136 DOI: 10.1007/978-1-0716-3327-4_5] [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 complex immunoprecipitation (co-IP) is an in vitro technique used to study protein-protein interaction between two or more proteins. This method relies on affinity purification of recombinant epitope-tagged proteins followed by western blotting detection using tag-specific antibodies for the confirmation of positive interaction. The traditional co-IP method relies on the use of porous beaded support with immobilized antibodies to precipitate protein complexes. However, this method is time-consuming, labor-intensive, and provides lower reproducibility and yield of protein complexes. Here, we describe the implementation of magnetic beads and high-affinity anti-green fluorescent protein (GFP) antibodies to develop an in vitro GFP-protein trap-like system. This highly reproducible system utilizes a combination of small sample size, versatile lysis buffer, and lower amounts of magnetic beads to obtain protein complexes and aggregates that are compatible with functional assays, Western blotting, and mass spectrometry. In addition to protein-protein interactions, this versatile method can be employed to study protein-nucleic acid interactions. This protocol also highlights troubleshooting and includes recommendations to optimize its application.
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Affiliation(s)
- Danish Diwan
- Department of Biology, University of Alabama, Birmingham, AL, USA
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23
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Guo B, Lin B, Huang Q, Li Z, Zhuo K, Liao J. A nematode effector inhibits plant immunity by preventing cytosolic free Ca 2+ rise. PLANT, CELL & ENVIRONMENT 2022; 45:3070-3085. [PMID: 35880644 DOI: 10.1111/pce.14406] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
The Meloidogyne enterolobii effector MeTCTP is a member of the translationally controlled tumour protein (TCTP) family, involved in M. enterolobii parasitism. In this study, we found that MeTCTP forms homodimers and, in this form, binds calcium ions (Ca2+ ). At the same time, Ca2+ could induce homodimerization of MeTCTP. We further identified that MeTCTP inhibits the increase of cytosolic free Ca2+ concentration ([Ca2+ ]cyt ) in plant cells and suppresses plant immune responses. This includes suppression of reactive oxygen species burst and cell necrosis, further promoting M. enterolobii parasitism. Our results have elucidated that the effector MeTCTP can directly target Ca2+ by its homodimeric form and prevent [Ca2+ ]cyt rise in plant roots, revealing a novel mechanism utilized by plant pathogens to suppress plant immunity.
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Affiliation(s)
- Bin Guo
- Laboratory of Plant Nematology, College of Plant Protection, South China Agricultural University, Guangzhou, China
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, South China Agricultural University, Guangzhou, China
| | - Borong Lin
- Laboratory of Plant Nematology, College of Plant Protection, South China Agricultural University, Guangzhou, China
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, South China Agricultural University, Guangzhou, China
| | - Qiuling Huang
- Laboratory of Plant Nematology, College of Plant Protection, South China Agricultural University, Guangzhou, China
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, South China Agricultural University, Guangzhou, China
| | - Zhiwen Li
- Laboratory of Plant Nematology, College of Plant Protection, South China Agricultural University, Guangzhou, China
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, South China Agricultural University, Guangzhou, China
| | - Kan Zhuo
- Laboratory of Plant Nematology, College of Plant Protection, South China Agricultural University, Guangzhou, China
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, South China Agricultural University, Guangzhou, China
| | - Jinling Liao
- Laboratory of Plant Nematology, College of Plant Protection, South China Agricultural University, Guangzhou, China
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, South China Agricultural University, Guangzhou, China
- Guangdong Vocational College of Ecological Engineering, Guangzhou, China
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24
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Spears BJ, McInturf SA, Collins C, Chlebowski M, Cseke LJ, Su J, Mendoza-Cózatl DG, Gassmann W. Class I TCP transcription factor AtTCP8 modulates key brassinosteroid-responsive genes. PLANT PHYSIOLOGY 2022; 190:1457-1473. [PMID: 35866682 PMCID: PMC9516767 DOI: 10.1093/plphys/kiac332] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 06/01/2022] [Indexed: 05/17/2023]
Abstract
The plant-specific TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR (TCP) transcription factor family is most closely associated with regulating plant developmental programs. Recently, TCPs were also shown to mediate host immune signaling, both as targets of pathogen virulence factors and as regulators of plant defense genes. However, comprehensive characterization of TCP gene targets is still lacking. Loss of function of the class I TCP gene AtTCP8 attenuates early immune signaling and, when combined with mutations in AtTCP14 and AtTCP15, additional layers of defense signaling in Arabidopsis (Arabidopsis thaliana). Here, we focus on TCP8, the most poorly characterized of the three to date. We used chromatin immunoprecipitation and RNA sequencing to identify TCP8-bound gene promoters and differentially regulated genes in the tcp8 mutant; these datasets were heavily enriched in signaling components for multiple phytohormone pathways, including brassinosteroids (BRs), auxin, and jasmonic acid. Using BR signaling as a representative example, we showed that TCP8 directly binds and activates the promoters of the key BR transcriptional regulatory genes BRASSINAZOLE-RESISTANT1 (BZR1) and BRASSINAZOLE-RESISTANT2 (BZR2/BES1). Furthermore, tcp8 mutant seedlings exhibited altered BR-responsive growth patterns and complementary reductions in BZR2 transcript levels, while TCP8 protein demonstrated BR-responsive changes in subnuclear localization and transcriptional activity. We conclude that one explanation for the substantial targeting of TCP8 alongside other TCP family members by pathogen effectors may lie in its role as a modulator of BR and other plant hormone signaling pathways.
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Affiliation(s)
| | - Samuel A McInturf
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
| | - Carina Collins
- Department of Biology, Marian University, Indianapolis, Indiana, USA
| | - Meghann Chlebowski
- Department of Biological Sciences, Butler University, Indianapolis, Indiana, USA
| | - Leland J Cseke
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
| | - Jianbin Su
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
| | - David G Mendoza-Cózatl
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
| | - Walter Gassmann
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
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25
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Xing B, Zheng Y, Zhang M, Liu X, Li L, Mou C, Wu Q, Guo H, Shao Q. Biocontrol: Endophytic bacteria could be crucial to fight soft rot disease in the rare medicinal herb, Anoectochilus roxburghii. Microb Biotechnol 2022; 15:2929-2941. [PMID: 36099393 PMCID: PMC9733646 DOI: 10.1111/1751-7915.14142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/21/2022] [Accepted: 08/26/2022] [Indexed: 12/14/2022] Open
Abstract
Microbial destabilization induced by pathogen infection has severely affected plant quality and output, such as Anoectochilus roxburghii, an economically important herb. Soft rot is the main disease that occurs during A. roxburghii culturing. However, the key members of pathogens and their interplay with non-detrimental microorganisms in diseased plants remain largely unsolved. Here, by utilizing a molecular ecological network approach, the interactions within bacterial communities in endophytic compartments and the surrounding soils during soft rot infection were investigated. Significant differences in bacterial diversity and community composition between healthy and diseased plants were observed, indicating that the endophytic communities were strongly influenced by pathogen invasion. Endophytic stem communities of the diseased plants were primarily derived from roots and the root endophytes were largely derived from rhizosphere soils, which depicts a possible pathogen migration image from soils to roots and finally the stems. Furthermore, interactions among microbial members indicated that pathogen invasion might be aided by positively correlated native microbial members, such as Enterobacter and Microbacterium, who may assist in colonization and multiplication through a mutualistic relationship in roots during the pathogen infection process. Our findings will help open new avenues for developing more accurate strategies for biological control of A. roxburghii bacterial soft rot disease.
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Affiliation(s)
- Bingcong Xing
- State Key Laboratory of Subtropical SilvicultureZhejiang A&F UniversityHangzhouChina,Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
| | - Ying Zheng
- State Key Laboratory of Subtropical SilvicultureZhejiang A&F UniversityHangzhouChina,Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
| | - Man Zhang
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
| | - Xinting Liu
- State Key Laboratory of Subtropical SilvicultureZhejiang A&F UniversityHangzhouChina,Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
| | - Lihong Li
- State Key Laboratory of Subtropical SilvicultureZhejiang A&F UniversityHangzhouChina,Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
| | - Chenhao Mou
- State Key Laboratory of Subtropical SilvicultureZhejiang A&F UniversityHangzhouChina,Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
| | - Qichao Wu
- State Key Laboratory of Subtropical SilvicultureZhejiang A&F UniversityHangzhouChina,Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
| | - Haipeng Guo
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro‐Products, School of Marine SciencesNingbo UniversityNingboChina
| | - Qingsong Shao
- State Key Laboratory of Subtropical SilvicultureZhejiang A&F UniversityHangzhouChina,Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese MedicineZhejiang A&F UniversityHangzhouChina
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26
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Kumar N, Mishra B, Mukhtar MS. A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions. STAR Protoc 2022; 3:101608. [PMID: 35990739 PMCID: PMC9386103 DOI: 10.1016/j.xpro.2022.101608] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Investigating the complexity of host-pathogen interactions is challenging. Here, we outline a pipeline to identify important proteins and signaling molecules in human-viral interactomes. Firstly, we curate a comprehensive human interactome. Subsequently, we infer viral targets and transcriptome-specific human interactomes (VTTSHI) for papillomavirus and herpes viruses by integrating viral targets and transcriptome data. Finally, we reveal the common and shared nodes and pathways in viral pathogenesis following network topology and pathway enrichment analyses. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020). Protocol for integrative analysis of transcriptome and proteome network data Network subgroup enrichment of two host-pathogen interaction networks Preprocessing of data and heterogeneous network integration
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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27
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Zhao C, Dong J, Deng L, Tan Y, Jiang W, Cai Z. Molecular network strategy in multi-omics and mass spectrometry imaging. Curr Opin Chem Biol 2022; 70:102199. [PMID: 36027696 DOI: 10.1016/j.cbpa.2022.102199] [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: 04/24/2022] [Revised: 06/01/2022] [Accepted: 07/10/2022] [Indexed: 11/30/2022]
Abstract
Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.
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Affiliation(s)
- Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, China
| | - Yawen Tan
- Department of Breast and Thyroid Surgery, Shenzhen Second People's Hospital, Shenzhen, China
| | - Wei Jiang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China.
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28
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Kumar R, Khatri A, Acharya V. Deep learning uncovers distinct behavior of rice network to pathogens response. iScience 2022; 25:104546. [PMID: 35754717 PMCID: PMC9218438 DOI: 10.1016/j.isci.2022.104546] [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: 01/20/2022] [Revised: 05/06/2022] [Accepted: 06/02/2022] [Indexed: 12/15/2022] Open
Abstract
Rice, apart from abiotic stress, is prone to attack from multiple pathogens. Predominantly, the two rice pathogens, bacterial Xanthomonas oryzae (Xoo) and hemibiotrophic fungus, Magnaporthe oryzae, are extensively well explored for more than the last decade. However, because of lack of holistic studies, we design a deep learning-based rice network model (DLNet) that has explored the quantitative differences resulting in the distinct rice network architecture. Validation studies on rice in response to biotic stresses show that DLNet outperforms other machine learning methods. The current finding indicates the compactness of the rice PTI network and the rise of independent modules in the rice ETI network, resulting in similar patterns of the plant immune response. The results also show more independent network modules and minimum structural disorderness in rice-M. oryzae as compared to the rice-Xoo model revealing the different adaptation strategies of the rice plant to evade pathogen effectors.
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Affiliation(s)
- Ravi Kumar
- Functional Genomics and Complex System Lab, Biotechnology Division, The Himalayan Centre for High-throughput Computational Biology (HiCHiCoB, A BIC Supported by DBT, India), CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Abhishek Khatri
- Functional Genomics and Complex System Lab, Biotechnology Division, The Himalayan Centre for High-throughput Computational Biology (HiCHiCoB, A BIC Supported by DBT, India), CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh, India
| | - Vishal Acharya
- Functional Genomics and Complex System Lab, Biotechnology Division, The Himalayan Centre for High-throughput Computational Biology (HiCHiCoB, A BIC Supported by DBT, India), CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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29
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Discovering driver nodes in chronic kidney disease-related networks using Trader as a newly developed algorithm. Comput Biol Med 2022; 148:105892. [PMID: 35932730 DOI: 10.1016/j.compbiomed.2022.105892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/04/2022] [Accepted: 07/16/2022] [Indexed: 11/18/2022]
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30
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Rintala TJ, Ghosh A, Fortino V. Network approaches for modeling the effect of drugs and diseases. Brief Bioinform 2022; 23:6608969. [PMID: 35704883 PMCID: PMC9294412 DOI: 10.1093/bib/bbac229] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/29/2022] [Accepted: 05/17/2021] [Indexed: 12/12/2022] Open
Abstract
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).
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Affiliation(s)
- T J Rintala
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - Arindam Ghosh
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - V Fortino
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
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31
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Mapuranga J, Zhang N, Zhang L, Chang J, Yang W. Infection Strategies and Pathogenicity of Biotrophic Plant Fungal Pathogens. Front Microbiol 2022; 13:799396. [PMID: 35722337 PMCID: PMC9201565 DOI: 10.3389/fmicb.2022.799396] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 04/19/2022] [Indexed: 01/01/2023] Open
Abstract
Biotrophic plant pathogenic fungi are widely distributed and are among the most damaging pathogenic organisms of agriculturally important crops responsible for significant losses in quality and yield. However, the pathogenesis of obligate parasitic pathogenic microorganisms is still under investigation because they cannot reproduce and complete their life cycle on an artificial medium. The successful lifestyle of biotrophic fungal pathogens depends on their ability to secrete effector proteins to manipulate or evade plant defense response. By integrating genomics, transcriptomics, and effectoromics, insights into how the adaptation of biotrophic plant fungal pathogens adapt to their host populations can be gained. Efficient tools to decipher the precise molecular mechanisms of rust–plant interactions, and standardized routines in genomics and functional pipelines have been established and will pave the way for comparative studies. Deciphering fungal pathogenesis not only allows us to better understand how fungal pathogens infect host plants but also provides valuable information for plant diseases control, including new strategies to prevent, delay, or inhibit fungal development. Our review provides a comprehensive overview of the efforts that have been made to decipher the effector proteins of biotrophic fungal pathogens and demonstrates how rapidly research in the field of obligate biotrophy has progressed.
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32
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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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33
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Mishra B, Kumar N, Shahid Mukhtar M. A Rice Protein Interaction Network Reveals High Centrality Nodes and Candidate Pathogen Effector Targets. Comput Struct Biotechnol J 2022; 20:2001-2012. [PMID: 35521542 PMCID: PMC9062363 DOI: 10.1016/j.csbj.2022.04.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/10/2022] [Accepted: 04/17/2022] [Indexed: 12/11/2022] Open
Abstract
Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein–protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.
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Hussain A, Asif N, Pirzada AR, Noureen A, Shaukat J, Burhan A, Zaynab M, Ali E, Imran K, Ameen A, Mahmood MA, Nazar A, Mukhtar MS. Genome wide study of cysteine rich receptor like proteins in Gossypium sp. Sci Rep 2022; 12:4885. [PMID: 35318409 PMCID: PMC8941122 DOI: 10.1038/s41598-022-08943-1] [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: 10/07/2021] [Accepted: 03/11/2022] [Indexed: 02/08/2023] Open
Abstract
Cysteine-rich receptor-like-kinases (CRKs), a transmembrane subfamily of receptor-like kinase, play crucial roles in plant adaptation. As such cotton is the major source of fiber for the textile industry, but environmental stresses are limiting its growth and production. Here, we have performed a deep computational analysis of CRKs in five Gossypium species, including G. arboreum (60 genes), G. raimondii (74 genes), G. herbaceum (65 genes), G. hirsutum (118 genes), and G. barbadense (120 genes). All identified CRKs were classified into 11 major classes and 43 subclasses with the finding of several novel CRK-associated domains including ALMT, FUSC_2, Cript, FYVE, and Pkinase. Of these, DUF26_DUF26_Pkinase_Tyr was common and had elevated expression under different biotic and abiotic stresses. Moreover, the 35 land plants comparison identified several new CRKs domain-architectures. Likewise, several SNPs and InDels were observed in CLCuD resistant G. hirsutum. The miRNA target side prediction and their expression profiling in different tissues predicted miR172 as a major CRK regulating miR. The expression profiling of CRKs identified multiple clusters with co-expression under certain stress conditions. The expression analysis under CLCuD highlighted the role of GhCRK057, GhCRK059, GhCRK058, and GhCRK081 in resistant accession. Overall, these results provided primary data for future potential functional analysis as well as a reference study for other agronomically important crops.
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Affiliation(s)
- Athar Hussain
- Genomics Lab, School of Food and Agricultural Sciences (SFAS), University of Management and Technology (UMT), Lahore, 54000, Pakistan.
| | - Naila Asif
- Department of Life Sciences, School of Science, University of Management and Technology (UMT), Lahore, 54000, Pakistan
| | - Abdul Rafay Pirzada
- Department of Life Sciences, School of Science, University of Management and Technology (UMT), Lahore, 54000, Pakistan
| | - Azka Noureen
- National Institute for Biotechnology and Genetic Engineering (NIBGE), College of Pakistan Institute of Engineering and Applied Sciences (PIEAS), Faisalabad, 38000, Pakistan.,PMAS-Arid Agriculture University Rawalpindi, Rawalpindi, 46300, Pakistan
| | - Javeria Shaukat
- Department of Life Sciences, School of Science, University of Management and Technology (UMT), Lahore, 54000, Pakistan
| | - Akif Burhan
- Department of Life Sciences, School of Science, University of Management and Technology (UMT), Lahore, 54000, Pakistan
| | - Madiha Zaynab
- Shenzhen Key Laboratory of Marine Bioresource & Eco-Environmental Sciences, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 51807, China
| | - Ejaz Ali
- Center of Excellence in Molecular Biology, University of Punjab, Lahore, 54000, Pakistan
| | - Koukab Imran
- Department of Life Sciences, School of Science, University of Management and Technology (UMT), Lahore, 54000, Pakistan
| | - Ayesha Ameen
- Office of Research Innovation and Commercialization, University of Management and Technology (UMT), Lahore, 54000, Pakistan
| | - Muhammad Arslan Mahmood
- National Institute for Biotechnology and Genetic Engineering (NIBGE), College of Pakistan Institute of Engineering and Applied Sciences (PIEAS), Faisalabad, 38000, Pakistan
| | - Aquib Nazar
- Department of Life Sciences, School of Science, University of Management and Technology (UMT), Lahore, 54000, Pakistan
| | - M Shahid Mukhtar
- Department of Biology, the University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA
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35
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Velásquez-Zapata V, Palacio-Rúa K, Cano LE, Gaviria-Rivera A. Assessment of genotyping markers in the molecular characterization of a population of clinical isolates of Fusarium in Colombia. BIOMEDICA : REVISTA DEL INSTITUTO NACIONAL DE SALUD 2022; 42:18-30. [PMID: 35471167 PMCID: PMC9059811 DOI: 10.7705/biomedica.5869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 08/06/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Fusarium is a very heterogeneous group of fungi, difficult to classify, with a wide range of living styles, acting as saprophytes, parasites of plants, or pathogens for humans and animals. Prevalence of clinical fusariosis and lack of effective treatments have increased the interest in the precise diagnosis, which implies a molecular characterization of Fusarium populations. OBJECTIVE We compared different genotyping markers in their assessment of the genetic variability and molecular identification of clinical isolates of Fusarium. MATERIALS AND METHODS We evaluated the performance of the fingerprinting produced by two random primers: M13, which amplifies a minisatellite sequence, and (GACA)4, which corresponds to a simple repetitive DNA sequence. Using the Hunter Gaston Discriminatory Index (HGDI), an analysis of molecular variance (AMOVA), and a Mantel test, the resolution of these markers was compared to the reference sequencing-based and PCR genotyping methods. RESULTS The highest HGDI value was associated with the M13 marker followed by (GACA)4. AMOVA and the Mantel tests supported a strong correlation between the M13 classification and the reference method given by the partial sequencing of the transcription elongation factor 1-alpha (TEF1-α) and rDNA 28S. CONCLUSION The strong correlation between the M13 classification and the sequencingbased reference together with its higher resolution demonstrates its adequacy for the characterization of Fusarium populations.
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Affiliation(s)
- Valeria Velásquez-Zapata
- Program in Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA; Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA.
| | - Katherine Palacio-Rúa
- Laboratorio Integrado de Medicina Especializada, Facultad de Medicina, IPS Universitaria, Universidad de Antioquia, Medellín, Colombia.
| | - Luz E Cano
- Grupo de Micología Médica y Experimental, Corporación para Investigaciones Biológicas, Medellín, Colombia; Escuela de Microbiología, Universidad de Antioquia, Medellín, Colombia.
| | - Adelaida Gaviria-Rivera
- Escuela de Biociencias, Facultad de Ciencias, Universidad Nacional de Colombia, Medellín, Colombia.
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Selecting Graph Metrics with Ecological Significance for Deepening Landscape Characterization: Review and Applications. LAND 2022. [DOI: 10.3390/land11030338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The usual approaches to describing and understanding ecological processes in a landscape use patch-mosaic models based on traditional landscape metrics. However, they do not consider that many of these processes cannot be observed without considering the multiple interactions between different land-use patches in the landscape. The objective of this research was to provide a synthetic overview of graph metrics that characterize landscapes based on patch-mosaic models and to analyze the ecological meaning of the metrics to propose a relevant selection explaining biodiversity patterns and ecological processes. First, we conducted a literature review of graph metrics applied in ecology. Second, a case study was used to explore the behavior of a group of selected graph metrics in actual differentiated landscapes located in a long-term socioecological research site in Brittany, France. Thirteen landscape-scale metrics and 10 local-scale metrics with ecological significance were analyzed. Metrics were grouped for landscape-scale and local-scale analysis. Many of the metrics were able to identify differences between the landscapes studied. Lastly, we discuss how graph metrics offer a new perspective for landscape analysis, describe the main characteristics related to their calculation and the type of information provided, and discuss their potential applications in different ecological contexts.
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Chai H, Gu Q, Hughes J, Robertson DL. In silico prediction of HIV-1-host molecular interactions and their directionality. PLoS Comput Biol 2022; 18:e1009720. [PMID: 35134057 PMCID: PMC8856524 DOI: 10.1371/journal.pcbi.1009720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 02/18/2022] [Accepted: 12/03/2021] [Indexed: 11/18/2022] Open
Abstract
Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/.
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Affiliation(s)
- Haiting Chai
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Quan Gu
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - David L. Robertson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- * E-mail:
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38
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Sharma N, Gadhave K, Kumar P, Giri R. Transactivation domain of Adenovirus Early Region 1A (E1A): Investigating folding dynamics and aggregation. Curr Res Struct Biol 2022; 4:29-40. [PMID: 35146445 PMCID: PMC8801969 DOI: 10.1016/j.crstbi.2022.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 12/05/2021] [Accepted: 01/04/2022] [Indexed: 02/06/2023] Open
Abstract
Transactivation domain of Adenovirus Early region 1A (E1A) oncoprotein is an intrinsically disordered molecular hub protein. It is involved in binding to different domains of human cell transcriptional co-activators such as retinoblastoma (pRb), CREB-binding protein (CBP), and its paralogue p300. The conserved region 1 (TAD) of E1A is known to undergo structural transitions and folds upon interaction with transcriptional adaptor zinc finger 2 (TAZ2). Previous reports on Taz2-E1A studies have suggested the formation of helical conformations of E1A-TAD. However, the folding behavior of the TAD region in isolation has not been studied in detail. Here, we have elucidated the folding behavior of E1A peptide at varied temperatures and solution conditions. Further, we have studied the effects of macromolecular crowding on E1A-TAD peptide. Additionally, we have also predicted the molecular recognition features of E1A using MoRF predictors. The predicted MoRFs are consistent with its structural transitions observed during TAZ2 interactions for transcriptional regulation in literature. Also, as a general rule of MoRFs, E1A undergoes helical transitions in alcohol and osmolyte solution. Finally, we studied the aggregation behavior of E1A, where we observed that the E1A could form amyloid-like aggregates that are cytotoxic to mammalian cells.
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Affiliation(s)
- Nitin Sharma
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175005, India
| | - Kundlik Gadhave
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175005, India
| | - Prateek Kumar
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175005, India
| | - Rajanish Giri
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175005, India
- BioX Center, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175005, India
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39
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Li C, Gao Z, Su B, Xu G, Lin X. Data analysis methods for defining biomarkers from omics data. Anal Bioanal Chem 2021; 414:235-250. [PMID: 34951658 DOI: 10.1007/s00216-021-03813-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 02/01/2023]
Abstract
Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.
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Affiliation(s)
- Chao Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, Liaoning, China
| | - Zhenbo Gao
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, Liaoning, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
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40
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Silva JMF, Nagata T, Melo FL, Elena SF. Heterogeneity in the Response of Different Subtypes of Drosophila melanogaster Midgut Cells to Viral Infections. Viruses 2021; 13:2284. [PMID: 34835089 PMCID: PMC8623525 DOI: 10.3390/v13112284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/05/2021] [Accepted: 11/13/2021] [Indexed: 11/29/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) offers the possibility to monitor both host and pathogens transcriptomes at the cellular level. Here, public scRNA-seq datasets from Drosophila melanogaster midgut cells were used to compare the differences in replication strategy and cellular responses between two fly picorna-like viruses, Thika virus (TV) and D. melanogaster Nora virus (DMelNV). TV exhibited lower levels of viral RNA accumulation but infected a higher number of cells compared to DMelNV. In both cases, viral RNA accumulation varied according to cell subtype. The cellular heat shock response to TV and DMelNV infection was cell-subtype- and virus-specific. Disruption of bottleneck genes at later stages of infection in the systemic response, as well as of translation-related genes in the cellular response to DMelNV in two cell subtypes, may affect the virus replication.
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Affiliation(s)
- João M. F. Silva
- Departamento de Biologia Celular, Universidade de Brasília, Brasília 70910-900, Brazil; (J.M.F.S.); (T.N.); (F.L.M.)
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC-Universitat de València, 46980 Paterna, València, Spain
| | - Tatsuya Nagata
- Departamento de Biologia Celular, Universidade de Brasília, Brasília 70910-900, Brazil; (J.M.F.S.); (T.N.); (F.L.M.)
| | - Fernando L. Melo
- Departamento de Biologia Celular, Universidade de Brasília, Brasília 70910-900, Brazil; (J.M.F.S.); (T.N.); (F.L.M.)
| | - Santiago F. Elena
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC-Universitat de València, 46980 Paterna, València, Spain
- The Santa Fe Institute, Santa Fe, NM 87501, USA
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41
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Singh N, Bhatnagar S. Machine Learning for Prediction of Drug Targets in Microbe Associated Cardiovascular Diseases by Incorporating Host-pathogen Interaction Network Parameters. Mol Inform 2021; 41:e2100115. [PMID: 34676983 DOI: 10.1002/minf.202100115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 10/01/2021] [Indexed: 12/20/2022]
Abstract
Host-pathogen interactions play a crucial role in invasion, infection, and induction of immune response in humans. In this work, four machine learning algorithms, namely Logistic regression, K-nearest neighbor, Support Vector Machine, and Random Forest were implemented for the classification of drug targets. The algorithms were trained using 3400 hosts and 3800 pathogen drug and non-drug target proteins as learning instances. For each protein, 68 pathogen and 73 host features were computed that included sequence, structure, biological and host-pathogen network centrality characteristics. The Random Forest classifier model achieved the best accuracy after 10-fold cross-validation. 99 % accuracy was achieved with a ROC-AUC score of 0.99±0.01 for both pathogen and host training sets. The Eigenvector Centrality of host-pathogen interactions and host-host interactions was the top feature in performing classification of pathogen and host targets respectively. Other features important for classification were the presence of catalytic and binding sites, low instability/aliphatic index, and cellular location. The Random Forest classifier was then used for prediction of drug targets involved in Microbe Associated Cardiovascular Diseases. 331 host and 743 pathogen proteins were predicted as drug targets by the random forest model and can be validated experimentally for therapeutic intervention in Microbe Associated Cardiovascular Diseases.
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Affiliation(s)
- Nirupma Singh
- Department of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India
| | - Sonika Bhatnagar
- Department of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India.,Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology Dwarka, New Delhi, 110078, India
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42
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Ceulemans E, Ibrahim HMM, De Coninck B, Goossens A. Pathogen Effectors: Exploiting the Promiscuity of Plant Signaling Hubs. TRENDS IN PLANT SCIENCE 2021; 26:780-795. [PMID: 33674173 DOI: 10.1016/j.tplants.2021.01.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/21/2021] [Accepted: 01/29/2021] [Indexed: 05/27/2023]
Abstract
Pathogens produce effectors to overcome plant immunity, thereby threatening crop yields and global food security. Large-scale interactomic studies have revealed that pathogens from different kingdoms of life target common plant proteins during infection, the so-called effector hubs. These hubs often play central roles in numerous plant processes through their ability to interact with multiple plant proteins. This ability arises partly from the presence of intrinsically disordered domains (IDDs) in their structure. Here, we highlight the role of the TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR (TCP) and JASMONATE-ZIM DOMAIN (JAZ) transcription regulator families as plant signaling and effector hubs. We consider different evolutionary hypotheses to rationalize the existence of diverse effectors sharing common targets and the possible role of IDDs in this interaction.
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Affiliation(s)
- Evi Ceulemans
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB, Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Heba M M Ibrahim
- Division of Crop Biotechnics, Department of Biosystems, Katholieke Universiteit (KU) Leuven, 3001 Leuven, Belgium
| | - Barbara De Coninck
- Division of Crop Biotechnics, Department of Biosystems, Katholieke Universiteit (KU) Leuven, 3001 Leuven, Belgium.
| | - Alain Goossens
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB, Center for Plant Systems Biology, 9052 Ghent, Belgium.
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43
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Mishra B, Kumar N, Mukhtar MS. Network biology to uncover functional and structural properties of the plant immune system. CURRENT OPINION IN PLANT BIOLOGY 2021; 62:102057. [PMID: 34102601 DOI: 10.1016/j.pbi.2021.102057] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/15/2021] [Accepted: 04/18/2021] [Indexed: 06/12/2023]
Abstract
In the last two decades, advances in network science have facilitated the discovery of important systems' entities in diverse biological networks. This graph-based technique has revealed numerous emergent properties of a system that enable us to understand several complex biological processes including plant immune systems. With the accumulation of multiomics data sets, the comprehensive understanding of plant-pathogen interactions can be achieved through the analyses and efficacious integration of multidimensional qualitative and quantitative relationships among the components of hosts and their microbes. This review highlights comparative network topology analyses in plant-pathogen co-expression networks and interactomes, outlines dynamic network modeling for cell-specific immune regulatory networks, and discusses the new frontiers of single-cell sequencing as well as multiomics data integration that are necessary for unraveling the intricacies of plant immune systems.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA.
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44
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Ayukawa Y, Asai S, Gan P, Tsushima A, Ichihashi Y, Shibata A, Komatsu K, Houterman PM, Rep M, Shirasu K, Arie T. A pair of effectors encoded on a conditionally dispensable chromosome of Fusarium oxysporum suppress host-specific immunity. Commun Biol 2021; 4:707. [PMID: 34108627 PMCID: PMC8190069 DOI: 10.1038/s42003-021-02245-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 05/18/2021] [Indexed: 11/29/2022] Open
Abstract
Many plant pathogenic fungi contain conditionally dispensable (CD) chromosomes that are associated with virulence, but not growth in vitro. Virulence-associated CD chromosomes carry genes encoding effectors and/or host-specific toxin biosynthesis enzymes that may contribute to determining host specificity. Fusarium oxysporum causes devastating diseases of more than 100 plant species. Among a large number of host-specific forms, F. oxysporum f. sp. conglutinans (Focn) can infect Brassicaceae plants including Arabidopsis (Arabidopsis thaliana) and cabbage. Here we show that Focn has multiple CD chromosomes. We identified specific CD chromosomes that are required for virulence on Arabidopsis, cabbage, or both, and describe a pair of effectors encoded on one of the CD chromosomes that is required for suppression of Arabidopsis-specific phytoalexin-based immunity. The effector pair is highly conserved in F. oxysporum isolates capable of infecting Arabidopsis, but not of other plants. This study provides insight into how host specificity of F. oxysporum may be determined by a pair of effector genes on a transmissible CD chromosome. Yu Ayukawa, Shuta Asai, et al. report the genome sequence of a Fusarium oxysporum isolate and demonstrate that it contains different conditionally dispensable chromosomes which are important to confer virulence on specific hosts, like Arabidopsis thaliana or cabbage. Altogether, these results provide further insight into the mechanisms underlying F. oxysporum pathogenicity.
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Affiliation(s)
- Yu Ayukawa
- Center for Sustainable Resource Science, RIKEN, Yokohama, Kanagawa, Japan.,Laboratory of Plant Pathology, Graduate School of Agriculture, Tokyo University of Agriculture and Technology (TUAT), Fuchu, Tokyo, Japan
| | - Shuta Asai
- Center for Sustainable Resource Science, RIKEN, Yokohama, Kanagawa, Japan. .,PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan.
| | - Pamela Gan
- Center for Sustainable Resource Science, RIKEN, Yokohama, Kanagawa, Japan
| | - Ayako Tsushima
- Center for Sustainable Resource Science, RIKEN, Yokohama, Kanagawa, Japan.,John Innes Centre, Norwich, UK
| | - Yasunori Ichihashi
- Center for Sustainable Resource Science, RIKEN, Yokohama, Kanagawa, Japan.,RIKEN BioResource Research Center, Tsukuba, Ibaraki, Japan
| | - Arisa Shibata
- Center for Sustainable Resource Science, RIKEN, Yokohama, Kanagawa, Japan
| | - Ken Komatsu
- Laboratory of Plant Pathology, Graduate School of Agriculture, Tokyo University of Agriculture and Technology (TUAT), Fuchu, Tokyo, Japan
| | - Petra M Houterman
- Molecular Plant Pathology, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Martijn Rep
- Molecular Plant Pathology, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Ken Shirasu
- Center for Sustainable Resource Science, RIKEN, Yokohama, Kanagawa, Japan
| | - Tsutomu Arie
- Laboratory of Plant Pathology, Graduate School of Agriculture, Tokyo University of Agriculture and Technology (TUAT), Fuchu, Tokyo, Japan.
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45
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Ambrós S, Gómez-Muñoz N, Giménez-Santamarina S, Sánchez-Vicente J, Navarro-López J, Martínez F, Daròs JA, Rodrigo G. Molecular signatures of silencing suppression degeneracy from a complex RNA virus. PLoS Comput Biol 2021; 17:e1009166. [PMID: 34181647 PMCID: PMC8270454 DOI: 10.1371/journal.pcbi.1009166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 07/09/2021] [Accepted: 06/09/2021] [Indexed: 11/21/2022] Open
Abstract
As genomic architectures become more complex, they begin to accumulate degenerate and redundant elements. However, analyses of the molecular mechanisms underlying these genetic architecture features remain scarce, especially in compact but sufficiently complex genomes. In the present study, we followed a proteomic approach together with a computational network analysis to reveal molecular signatures of protein function degeneracy from a plant virus (as virus-host protein-protein interactions). We employed affinity purification coupled to mass spectrometry to detect several host factors interacting with two proteins of Citrus tristeza virus (p20 and p25) that are known to function as RNA silencing suppressors, using an experimental system of transient expression in a model plant. The study was expanded by considering two different isolates of the virus, and some key interactions were confirmed by bimolecular fluorescence complementation assays. We found that p20 and p25 target a common set of plant proteins including chloroplastic proteins and translation factors. Moreover, we noted that even specific targets of each viral protein overlap in function. Notably, we identified argonaute proteins (key players in RNA silencing) as reliable targets of p20. Furthermore, we found that these viral proteins preferentially do not target hubs in the host protein interactome, but elements that can transfer information by bridging different parts of the interactome. Overall, our results demonstrate that two distinct proteins encoded in the same viral genome that overlap in function also overlap in their interactions with the cell proteome, thereby highlighting an overlooked connection from a degenerate viral system.
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Affiliation(s)
- Silvia Ambrós
- Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), Moncada, Spain
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), CSIC–Universitat Politècnica de València, València, Spain
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC–Universitat de València, Paterna, Spain
| | - Neus Gómez-Muñoz
- Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), Moncada, Spain
| | - Silvia Giménez-Santamarina
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), CSIC–Universitat Politècnica de València, València, Spain
| | - Javier Sánchez-Vicente
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), CSIC–Universitat Politècnica de València, València, Spain
| | - Josep Navarro-López
- Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), Moncada, Spain
| | - Fernando Martínez
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), CSIC–Universitat Politècnica de València, València, Spain
| | - José-Antonio Daròs
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), CSIC–Universitat Politècnica de València, València, Spain
| | - Guillermo Rodrigo
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), CSIC–Universitat Politècnica de València, València, Spain
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC–Universitat de València, Paterna, Spain
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46
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Plant virus-interactions: unraveling novel defense mechanisms under immune-suppressing pressure. Curr Opin Biotechnol 2021; 70:108-114. [PMID: 33866213 DOI: 10.1016/j.copbio.2021.03.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 03/18/2021] [Accepted: 03/24/2021] [Indexed: 11/22/2022]
Abstract
Plants have developed multilayered molecular defense strategies to combat pathogens. These defense layers have been predominantly identified and characterized in incompatible interactions, in which the plant immune system induces a rapid and efficient defense. Nevertheless, due to the constant evolutionary pressure between plants and pathogens for dominance, it is conceptually accepted that several mechanisms of plant defense may be hidden by the co-evolving immune-suppressing functions from pathogens. Recent studies focusing on begomovirus-host interactions have provided an in-depth view of how suppressed plant antiviral mechanisms can offer a more dynamic view of evolving pressures in the immune system also shared with nonviral pathogens. The emerging theme of crosstalk between host antiviral defenses and antibacterial immunity is also discussed. This interplay between immune responses allows bacteria and viruses to activate immunity against pathogens from a different kingdom, hence preventing multiple infections presumably to avoid competition.
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47
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Mishra B, Athar M, Mukhtar MS. Transcriptional circuitry atlas of genetic diverse unstimulated murine and human macrophages define disparity in population-wide innate immunity. Sci Rep 2021; 11:7373. [PMID: 33795737 PMCID: PMC8016976 DOI: 10.1038/s41598-021-86742-w] [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: 11/09/2020] [Accepted: 03/12/2021] [Indexed: 02/07/2023] Open
Abstract
Macrophages are ubiquitous custodians of tissues, which play decisive role in maintaining cellular homeostasis through regulatory immune responses. Within tissues, macrophage exhibit extremely heterogeneous population with varying functions orchestrated through regulatory response, which can be further exacerbated in diverse genetic backgrounds. Gene regulatory networks (GRNs) offer comprehensive understanding of cellular regulatory behavior by unfolding the transcription factors (TFs) and regulated target genes. RNA-Seq coupled with ATAC-Seq has revolutionized the regulome landscape influenced by gene expression modeling. Here, we employ an integrative multi-omics systems biology-based analysis and generated GRNs derived from the unstimulated bone marrow-derived macrophages of five inbred genetically defined murine strains, which are reported to be linked with most of the population-wide human genetic variants. Our probabilistic modeling of a basal hemostasis pan regulatory repertoire in diverse macrophages discovered 96 TFs targeting 6279 genes representing 468,291 interactions across five inbred murine strains. Subsequently, we identify core and distinctive GRN sub-networks in unstimulated macrophages to describe the system-wide conservation and dissimilarities, respectively across five murine strains. Our study concludes that discrepancies in unstimulated macrophage-specific regulatory networks not only drives the basal functional plasticity within genetic backgrounds, additionally aid in understanding the complexity of racial disparity among the human population during stress.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama At Birmingham, 464 Campbell Hall, 1300 University Boulevard, Alabama, 35294, USA
| | - Mohammad Athar
- UAB Research Center of Excellence in Arsenicals, Department of Dermatology, School of Medicine, University of Alabama At Birmingham, Alabama, 35294, USA.
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama At Birmingham, 464 Campbell Hall, 1300 University Boulevard, Alabama, 35294, USA. .,Nutrition Obesity Research Center, University of Alabama At Birmingham, 1675 University Blvd, Birmingham, AL, 35294, USA. .,Department of Surgery, University of Alabama At Birmingham, 1808 7th Ave S, Birmingham, AL, 35294, USA.
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48
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Wang Y, Zhou M, Zou Q, Xu L. Machine learning for phytopathology: from the molecular scale towards the network scale. Brief Bioinform 2021; 22:6204793. [PMID: 33787847 DOI: 10.1093/bib/bbab037] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/09/2021] [Accepted: 01/26/2021] [Indexed: 01/16/2023] Open
Abstract
With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant-pathogen interactions and discuss the applications and advances of machine learning in plant-pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein-protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.
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Affiliation(s)
- Yansu Wang
- Postdoctoral Innovation Practice Base, Shenzhen Polytechnic, China
| | | | - Quan Zou
- University of Electronic Science and Technology of China
| | - Lei Xu
- Shenzhen Polytechnic, China
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49
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Santamaria ME, Garcia A, Arnaiz A, Rosa‐Diaz I, Romero‐Hernandez G, Diaz I, Martinez M. Comparative transcriptomics reveals hidden issues in the plant response to arthropod herbivores. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2021; 63:312-326. [PMID: 33085192 PMCID: PMC7898633 DOI: 10.1111/jipb.13026] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 10/18/2020] [Indexed: 05/04/2023]
Abstract
Plants experience different abiotic/biotic stresses, which trigger their molecular machinery to cope with them. Besides general mechanisms prompted by many stresses, specific mechanisms have been introduced to optimize the response to individual threats. However, these key mechanisms are difficult to identify. Here, we introduce an in-depth species-specific transcriptomic analysis and conduct an extensive meta-analysis of the responses to related species to gain more knowledge about plant responses. The spider mite Tetranychus urticae was used as the individual species, several arthropod herbivores as the related species for meta-analysis, and Arabidopsis thaliana plants as the common host. The analysis of the transcriptomic data showed typical common responses to herbivory, such as jasmonate signaling or glucosinolate biosynthesis. Also, a specific set of genes likely involved in the particularities of the Arabidopsis-spider mite interaction was discovered. The new findings have determined a prominent role in this interaction of the jasmonate-induced pathways leading to the biosynthesis of anthocyanins and tocopherols. Therefore, tandem individual/general transcriptomic profiling has been revealed as an effective method to identify novel relevant processes and specificities in the plant response to environmental stresses.
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Affiliation(s)
- M. Estrella Santamaria
- Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y AlimentariaUniversidad Politécnica de MadridMadridSpain
| | - Alejandro Garcia
- Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y AlimentariaUniversidad Politécnica de MadridMadridSpain
| | - Ana Arnaiz
- Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y AlimentariaUniversidad Politécnica de MadridMadridSpain
| | - Irene Rosa‐Diaz
- Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y AlimentariaUniversidad Politécnica de MadridMadridSpain
| | - Gara Romero‐Hernandez
- Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y AlimentariaUniversidad Politécnica de MadridMadridSpain
| | - Isabel Diaz
- Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y AlimentariaUniversidad Politécnica de MadridMadridSpain
- Departamento de Biotecnología‐Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de BiosistemasUniversidad Politécnica de MadridMadridSpain
| | - Manuel Martinez
- Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y AlimentariaUniversidad Politécnica de MadridMadridSpain
- Departamento de Biotecnología‐Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de BiosistemasUniversidad Politécnica de MadridMadridSpain
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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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