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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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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: 5.5] [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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Feng L, Ma A, Song B, Yu S, Qi X. Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice. G3 (BETHESDA, MD.) 2021; 11:6369515. [PMID: 34515770 PMCID: PMC8527483 DOI: 10.1093/g3journal/jkab318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022]
Abstract
Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL-QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.
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Affiliation(s)
- Laibao Feng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Aimin Ma
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Song
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Sibin Yu
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Xiaoquan Qi
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
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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: 7.3] [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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Iqbal Z, Shariq Iqbal M, Singh SP, Buaboocha T. Ca 2+/Calmodulin Complex Triggers CAMTA Transcriptional Machinery Under Stress in Plants: Signaling Cascade and Molecular Regulation. FRONTIERS IN PLANT SCIENCE 2020; 11:598327. [PMID: 33343600 PMCID: PMC7744605 DOI: 10.3389/fpls.2020.598327] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 10/30/2020] [Indexed: 05/21/2023]
Abstract
Calcium (Ca2+) ion is a critical ubiquitous intracellular second messenger, acting as a lead currency for several distinct signal transduction pathways. Transient perturbations in free cytosolic Ca2+ ([Ca2+]cyt) concentrations are indispensable for the translation of signals into adaptive biological responses. The transient increase in [Ca2+]cyt levels is sensed by an array of Ca2+ sensor relay proteins such as calmodulin (CaM), eventually leading to conformational changes and activation of CaM. CaM, in a Ca2+-dependent manner, regulates several transcription factors (TFs) that are implicated in various molecular, physiological, and biochemical functions in cells. CAMTA (calmodulin-binding transcription activator) is one such member of the Ca2+-loaded CaM-dependent family of TFs. The present review focuses on Ca2+ as a second messenger, its interaction with CaM, and Ca2+/CaM-mediated CAMTA transcriptional regulation in plants. The review recapitulates the molecular and physiological functions of CAMTA in model plants and various crops, confirming its probable involvement in stress signaling pathways and overall plant development. Studying Ca2+/CaM-mediated CAMTA TF will help in answering key questions concerning signaling cascades and molecular regulation under stress conditions and plant growth, thus improving our knowledge for crop improvement.
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Affiliation(s)
- Zahra Iqbal
- Molecular Crop Research Unit, Department of Biochemistry, Chulalongkorn University, Bangkok, Thailand
| | - Mohammed Shariq Iqbal
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow, India
| | - Surendra Pratap Singh
- Plant Molecular Biology Laboratory, Department of Botany, Dayanand Anglo-Vedic (PG) College, Chhatrapati Shahu Ji Maharaj University, Kanpur, India
| | - Teerapong Buaboocha
- Molecular Crop Research Unit, Department of Biochemistry, Chulalongkorn University, Bangkok, Thailand
- Omics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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Yang X, Yang S, Qi H, Wang T, Li H, Zhang Z. PlaPPISite: a comprehensive resource for plant protein-protein interaction sites. BMC PLANT BIOLOGY 2020; 20:61. [PMID: 32028878 PMCID: PMC7006421 DOI: 10.1186/s12870-020-2254-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 01/16/2020] [Indexed: 05/02/2023]
Abstract
BACKGROUND Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants. DESCRIPTION PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page. CONCLUSION PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species.
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Affiliation(s)
- Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193 China
| | - Shiping Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193 China
| | - Huan Qi
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193 China
| | - Tianpeng Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193 China
| | - Hong Li
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, 570228 China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193 China
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Singh G, Singh V, Singh V. Construction and analysis of an interologous protein-protein interaction network of Camellia sinensis leaf (TeaLIPIN) from RNA-Seq data sets. PLANT CELL REPORTS 2019; 38:1249-1262. [PMID: 31197449 DOI: 10.1007/s00299-019-02440-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 06/04/2019] [Indexed: 06/09/2023]
Abstract
An interologous PPI network of tea leaf is designed by developing reference transcriptome assembly and using experimentally validated PPIs in plants. Key regulatory proteins are proposed and potential TFs are predicted. Worldwide, tea (Camellia sinensis) is the most consumed beverage primarily due to the taste, flavour, and aroma of its newly formed leaves; and has been used as an important ingredient in several traditional medicinal systems because of its antioxidant properties. For this medicinally and commercially important plant, design principles of gene-regulatory and protein-protein interaction (PPI) networks at sub-cellular level are largely un-characterized. In this work, we report a tea leaf interologous PPI network (TeaLIPIN) consisting of 11,208 nodes and 197,820 interactions. A reference transcriptome assembly was first developed from all the 44 samples of 6 publicly available leaf transcriptomes (1,567,288,290 raw reads). By inferring the high-confidence interactions among potential proteins coded by these transcripts using known experimental information about PPIs in 14 plants, an interologous PPI network was constructed and its modular architecture was explored. Comparing this network with 10,000 realizations of two types of corresponding random networks (Erdős-Rényi and Barabási-Albert models) and examining over three network centrality metrics, we predict 2750 bottleneck proteins (having p values < 0.01). 247 of these are deduced to have transcription factor domains by in-house developed HMM models of known plant TFs and these were also mapped to the draft tea genome for searching their probable loci of origin. Co-expression analysis of the TeaLIPIN proteins was also performed and top ranking modules are elaborated. We believe that the proposed novel methodology can easily be adopted to develop and explore the PPI interactomes in other plant species by making use of the available transcriptomic data.
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Affiliation(s)
- Gagandeep Singh
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, 176206, India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, 176206, India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, 176206, India.
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Wu X, Zhang Q, Wu Z, Tai F, Wang W. Subcellular locations of potential cell wall proteins in plants: predictors, databases and cross-referencing. Brief Bioinform 2019; 19:1130-1140. [PMID: 30481282 DOI: 10.1093/bib/bbx050] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Indexed: 01/21/2023] Open
Abstract
The cell wall is the most striking feature that distinguishes plant cells from animal cells. It plays an essential role in cell shape, stability, growth and protection. Despite being present in small amounts, cell wall proteins (CWPs) are crucial components of the cell wall. The cell wall proteome generally consists of sensu stricto CWPs, apoplast proteins and extracellular secreted proteins. Currently, there is a need for the bioinformatics analysis of a tremendous number of protein sequences that have been generated from genomic, transcriptomic and proteomics research. Compared with intracellular proteins, the location prediction of CWPs is challenging because many aspects of these proteins have not been experimentally characterized, and there are no CWP-trained, specific predictors available. By introducing the biological relevance (particularly molecular aspects) of the cell wall and CWPs, we critically evaluated the accuracy of 16 state-of-the-art predictors and classical predictors for the prediction of CWPs using an independent database of Arabidopsis and rice proteins. All experimentally verified CWPs and non-CWPs were retrieved from the UniProt Knowledgebase. Based on the evaluation, we currently recommend the predictors mGOASVM, HybridGO-Loc and FUEL-mLoc for CWPs. Furthermore, we outlined the public databases that can be used to cross-reference the subcellular location of CWPs. We illustrate a flowchart of the subcellular location prediction and a cross-reference of possible CWPs. Finally, we discuss challenges and perspectives in the bioinformatics analysis of CWPs. It is hoped that this article will provide practical guidance regarding CWPs for nonspecialists and provide insight for bioinformatics experts to develop computational tools for CWPs.
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Affiliation(s)
- Xiaolin Wu
- College of Life Sciences, Henan Agricultural University (HAU), China
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Yao H, Wang X, Chen P, Hai L, Jin K, Yao L, Mao C, Chen X. Predicted Arabidopsis Interactome Resource and Gene Set Linkage Analysis: A Transcriptomic Analysis Resource. PLANT PHYSIOLOGY 2018; 177. [PMID: 29530937 PMCID: PMC5933134 DOI: 10.1104/pp.18.00144] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis (Arabidopsis thaliana) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/.
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Affiliation(s)
- Heng Yao
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Xiaoxuan Wang
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Pengcheng Chen
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Ling Hai
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Kang Jin
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Lixia Yao
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905
| | - Chuanzao Mao
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Xin Chen
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou, People's Republic of China, 310058
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Ningthoujam SS, Talukdar AD, Sarker SD, Nahar L, Choudhury MD. Prediction of Medicinal Properties Using Mathematical Models and Computation, and Selection of Plant Materials. COMPUTATIONAL PHYTOCHEMISTRY 2018. [PMCID: PMC7149595 DOI: 10.1016/b978-0-12-812364-5.00002-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
In any phytochemical drug discovery programme, one of the major issues is the appropriate selection of target plant species that may provide lead for new drug discovery and development. Conducting research without any working hypotheses may produce serendipitous discoveries, but the chances of success are much slimmer than any information-based targeted approach. Therefore, the plant selection process is extremely important for ensuring success. In recent years, there have been significant amounts of work involving applications of various mathematical modelling and computational techniques to predict medicinal properties of plants, and thus to provide information-based selection of plant materials for further studies aiming at potential drug discovery and development. This chapter presents an overview of methods and processes involved in plant selection by utilizing various mathematical modelling and computational techniques.
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Affiliation(s)
| | | | | | - Lutfun Nahar
- Liverpool John Moores University, Liverpool, United Kingdom
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Zhang F, Liu S, Li L, Zuo K, Zhao L, Zhang L. Genome-Wide Inference of Protein-Protein Interaction Networks Identifies Crosstalk in Abscisic Acid Signaling. PLANT PHYSIOLOGY 2016; 171:1511-22. [PMID: 27208273 PMCID: PMC4902594 DOI: 10.1104/pp.16.00057] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 04/14/2016] [Indexed: 05/24/2023]
Abstract
Protein-protein interactions (PPIs) are essential to almost all cellular processes. To better understand the relationships of proteins in Arabidopsis (Arabidopsis thaliana), we have developed a genome-wide protein interaction network (AraPPINet) that is inferred from both three-dimensional structures and functional evidence and that encompasses 316,747 high-confidence interactions among 12,574 proteins. AraPPINet exhibited high predictive power for discovering protein interactions at a 50% true positive rate and for discriminating positive interactions from similar protein pairs at a 70% true positive rate. Experimental evaluation of a set of predicted PPIs demonstrated the ability of AraPPINet to identify novel protein interactions involved in a specific process at an approximately 100-fold greater accuracy than random protein-protein pairs in a test case of abscisic acid (ABA) signaling. Genetic analysis of an experimentally validated, predicted interaction between ARR1 and PYL1 uncovered cross talk between ABA and cytokinin signaling in the control of root growth. Therefore, we demonstrate the power of AraPPINet (http://netbio.sjtu.edu.cn/arappinet/) as a resource for discovering gene function in converging signaling pathways and complex traits in plants.
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Affiliation(s)
- Fangyuan Zhang
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shiwei Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ling Li
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kaijing Zuo
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lingxia Zhao
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lida Zhang
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract
Protein location and function can change dynamically depending on many factors, including environmental stress, disease state, age, developmental stage, and cell type. Here, we describe an integrative computational framework, called the conditional function predictor (CoFP; http://nbm.ajou.ac.kr/cofp/), for predicting changes in subcellular location and function on a proteome-wide scale. The essence of the CoFP approach is to cross-reference general knowledge about a protein and its known network of physical interactions, which typically pool measurements from diverse environments, against gene expression profiles that have been measured under specific conditions of interest. Using CoFP, we predict condition-specific subcellular locations, biological processes, and molecular functions of the yeast proteome under 18 specified conditions. In addition to highly accurate retrieval of previously known gold standard protein locations and functions, CoFP predicts previously unidentified condition-dependent locations and functions for nearly all yeast proteins. Many of these predictions can be confirmed using high-resolution cellular imaging. We show that, under DNA-damaging conditions, Tsr1, Caf120, Dip5, Skg6, Lte1, and Nnf2 change subcellular location and RNA polymerase I subunit A43, Ino2, and Ids2 show changes in DNA binding. Beyond specific predictions, this work reveals a global landscape of changing protein location and function, highlighting a surprising number of proteins that translocate from the mitochondria to the nucleus or from endoplasmic reticulum to Golgi apparatus under stress.
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13
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Keseler IM, Skrzypek M, Weerasinghe D, Chen AY, Fulcher C, Li GW, Lemmer KC, Mladinich KM, Chow ED, Sherlock G, Karp PD. Curation accuracy of model organism databases. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau058. [PMID: 24923819 PMCID: PMC4207230 DOI: 10.1093/database/bau058] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Manual extraction of information from the biomedical literature-or biocuration-is the central methodology used to construct many biological databases. For example, the UniProt protein database, the EcoCyc Escherichia coli database and the Candida Genome Database (CGD) are all based on biocuration. Biological databases are used extensively by life science researchers, as online encyclopedias, as aids in the interpretation of new experimental data and as golden standards for the development of new bioinformatics algorithms. Although manual curation has been assumed to be highly accurate, we are aware of only one previous study of biocuration accuracy. We assessed the accuracy of EcoCyc and CGD by manually selecting curated assertions within randomly chosen EcoCyc and CGD gene pages and by then validating that the data found in the referenced publications supported those assertions. A database assertion is considered to be in error if that assertion could not be found in the publication cited for that assertion. We identified 10 errors in the 633 facts that we validated across the two databases, for an overall error rate of 1.58%, and individual error rates of 1.82% for CGD and 1.40% for EcoCyc. These data suggest that manual curation of the experimental literature by Ph.D-level scientists is highly accurate. Database URL: http://ecocyc.org/, http://www.candidagenome.org//
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Affiliation(s)
- Ingrid M Keseler
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Marek Skrzypek
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Deepika Weerasinghe
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Albert Y Chen
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Carol Fulcher
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Gene-Wei Li
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Kimberly C Lemmer
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Katherine M Mladinich
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Edmond D Chow
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Gavin Sherlock
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
| | - Peter D Karp
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA
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14
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Li M, Guo S, Xu Y, Meng Q, Li G, Yang X. Glycine betaine-mediated potentiation of HSP gene expression involves calcium signaling pathways in tobacco exposed to NaCl stress. PHYSIOLOGIA PLANTARUM 2014; 150:63-75. [PMID: 23627631 DOI: 10.1111/ppl.12067] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2012] [Revised: 03/22/2013] [Accepted: 04/08/2013] [Indexed: 05/11/2023]
Abstract
Glycine betaine (GB) can enhance heat tolerance and the accumulation of heat-shock protein (HSP) in plants, but the effects of GB on HSP accumulation during salt stress were not previously known. To investigate the mechanism of how GB influences the expression of HSP, wild-type tobacco (Nicotiana tabacum) seedlings pretreated with exogenous GB and BADH-transgenic tobacco plants that accumulated GB in vivo were studied during NaCl stress. A transient Ca(2+) efflux was observed in the epidermal cells of the elongation zone of tobacco roots after NaCl treatment for 1-2 min. After 24 h of NaCl treatment, an influx of Ca(2+) was observed; a low concentration of GB significantly increased NaCl-induced Ca(2+) influx. GB increased the intracellular free calcium ion concentration and enhanced the expression of the calmodulin (CaM) and heat-shock transcription factor (HSF) genes resulting in potentiated levels of HSPs. Pharmacological experiments confirmed that Ca(2+) and CaM increased HSFs and HSPs gene expression, which coincided with increased the levels of HSP70 accumulation. These results suggest a mechanism by which GB acted as a cofactor in the NaCl induction of a Ca(2+) -permeable current. A possible regulatory model of Ca(2+) -CaM in the signal transduction pathway for induction of transcription and translation of the active HSPs is described.
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Affiliation(s)
- Meifang Li
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, Shandong Agricultural University, Taian, 271018, China; College of Life Science, Liaocheng University, Liaocheng, 252000, China
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15
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Garbutt CC, Bangalore PV, Kannar P, Mukhtar MS. Getting to the edge: protein dynamical networks as a new frontier in plant-microbe interactions. FRONTIERS IN PLANT SCIENCE 2014; 5:312. [PMID: 25071795 PMCID: PMC4074768 DOI: 10.3389/fpls.2014.00312] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 06/11/2014] [Indexed: 05/18/2023]
Abstract
A systems perspective on diverse phenotypes, mechanisms of infection, and responses to environmental stresses can lead to considerable advances in agriculture and medicine. A significant promise of systems biology within plants is the development of disease-resistant crop varieties, which would maximize yield output for food, clothing, building materials, and biofuel production. A systems or "-omics" perspective frames the next frontier in the search for enhanced knowledge of plant network biology. The functional understanding of network structure and dynamics is vital to expanding our knowledge of how the intercellular communication processes are executed. This review article will systematically discuss various levels of organization of systems biology beginning with the building blocks termed "-omes" and ending with complex transcriptional and protein-protein interaction networks. We will also highlight the prevailing computational modeling approaches of biological regulatory network dynamics. The latest developments in the "-omics" approach will be reviewed and discussed to underline and highlight novel technologies and research directions in plant network biology.
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Affiliation(s)
- Cassandra C. Garbutt
- Department of Biology, The University of Alabama at BirminghamBirmingham, AL, USA
| | - Purushotham V. Bangalore
- Department of Computer and Information Sciences, The University of Alabama at BirminghamBirmingham, AL, USA
| | - Pegah Kannar
- Department of Biology, The University of Alabama at BirminghamBirmingham, AL, USA
| | - M. S. Mukhtar
- Department of Biology, The University of Alabama at BirminghamBirmingham, AL, USA
- Nutrition Obesity Research Center, The University of Alabama at BirminghamBirmingham, AL, USA
- *Correspondence: M. S. Mukhtar, Department of Biology, The University of Alabama at Birmingham, Campbell Hall 369, 1300 University Boulevard, Birmingham, AL 35294-1170, USA e-mail:
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Lee K, Byun K, Hong W, Chuang HY, Pack CG, Bayarsaikhan E, Paek SH, Kim H, Shin HY, Ideker T, Lee B. Proteome-wide discovery of mislocated proteins in cancer. Genome Res 2013; 23:1283-94. [PMID: 23674306 PMCID: PMC3730102 DOI: 10.1101/gr.155499.113] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Several studies have sought systematically to identify protein subcellular locations, but an even larger task is to map which of these proteins conditionally relocates in disease (the mislocalizome). Here, we report an integrative computational framework for mapping conditional location and mislocation of proteins on a proteome-wide scale, called a conditional location predictor (CoLP). Using CoLP, we mapped the locations of over 10,000 proteins in normal human brain and in glioma. The prediction showed 0.9 accuracy using 100 location tests of 20 randomly selected proteins. Of the 10,000 proteins, over 150 have a strong likelihood of mislocation under glioma, which is striking considering that few mislocation events have been identified in this disease previously. Using immunofluorescence and Western blotting in both primary cells and tissues, we successfully experimentally confirmed 15 mislocations. The most common type of mislocation occurs between the endoplasmic reticulum and the nucleus; for example, for RNF138, TLX3, and NFRKB. In particular, we found that the gene for the mislocating protein GFRA4 had a nonsynonymous point mutation in exon 2. Moreover, redirection of GFRA4 to its normal location, the plasma membrane, led to marked reductions in phospho-STAT3 and proliferation of glioma cells. This framework has the potential to track changes in protein location in many human diseases.
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Affiliation(s)
- KiYoung Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-749, Korea.
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Braun P, Aubourg S, Van Leene J, De Jaeger G, Lurin C. Plant protein interactomes. ANNUAL REVIEW OF PLANT BIOLOGY 2013; 64:161-87. [PMID: 23330791 DOI: 10.1146/annurev-arplant-050312-120140] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.
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Affiliation(s)
- Pascal Braun
- Department of Plant Systems Biology, Center for Life and Food Sciences Weihenstephan, Technische Universität München (TUM), 85354 Freising-Weihenstephan, Germany.
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18
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Bussell JD, Behrens C, Ecke W, Eubel H. Arabidopsis peroxisome proteomics. FRONTIERS IN PLANT SCIENCE 2013; 4:101. [PMID: 23630535 PMCID: PMC3633942 DOI: 10.3389/fpls.2013.00101] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2012] [Accepted: 04/02/2013] [Indexed: 05/08/2023]
Abstract
The analytical depth of investigation of the peroxisomal proteome of the model plant Arabidopsis thaliana has not yet reached that of other major cellular organelles such as chloroplasts or mitochondria. This is primarily due to the difficulties associated with isolating and obtaining purified samples of peroxisomes from Arabidopsis. So far only a handful of research groups have been successful in obtaining such fractions. To make things worse, enriched peroxisome fractions frequently suffer from significant organellar contamination, lowering confidence in localization assignment of the identified proteins. As with other cellular compartments, identification of peroxisomal proteins forms the basis for investigations of the dynamics of the peroxisomal proteome. It is therefore not surprising that, in terms of functional analyses by proteomic means, peroxisomes are lagging considerably behind chloroplasts or mitochondria. Alternative strategies are needed to overcome the obstacle of hard-to-obtain organellar fractions. This will help to close the knowledge gap between peroxisomes and other organelles and provide a full picture of the physiological pathways shared between organelles. In this review, we briefly summarize the status quo and discuss some of the methodological alternatives to classic organelle proteomic approaches.
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Affiliation(s)
- John D. Bussell
- Australian Research Council Centre of Excellence in Plant Energy Biology, The University of Western AustraliaCrawley, WA, Australia
- *Correspondence: John D. Bussell, Australian Research Council Centre of Excellence in Plant Energy Biology, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. e-mail: ; Holger Eubel, Institute for Plant Genetics, Leibniz Universität Hannover, Herrenhäuser Str. 2, 30419 Hannover, Germany. e-mail:
| | - Christof Behrens
- Institute for Plant Genetics, Leibniz Universität HannoverHannover, Germany
| | - Wiebke Ecke
- Institute for Plant Genetics, Leibniz Universität HannoverHannover, Germany
| | - Holger Eubel
- Institute for Plant Genetics, Leibniz Universität HannoverHannover, Germany
- *Correspondence: John D. Bussell, Australian Research Council Centre of Excellence in Plant Energy Biology, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. e-mail: ; Holger Eubel, Institute for Plant Genetics, Leibniz Universität Hannover, Herrenhäuser Str. 2, 30419 Hannover, Germany. e-mail:
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Yang J, Osman K, Iqbal M, Stekel DJ, Luo Z, Armstrong SJ, Franklin FCH. Inferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2012; 3:297. [PMID: 23293649 PMCID: PMC3537189 DOI: 10.3389/fpls.2012.00297] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Accepted: 12/11/2012] [Indexed: 05/06/2023]
Abstract
Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein-protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain-domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.
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Affiliation(s)
- Jianhua Yang
- University of BirminghamBirmingham, UK
- *Correspondence: Jianhua Yang and F. Chris H. Franklin, University of Birmingham, B152TT Birmingham, UK. e-mail: ,
| | - Kim Osman
- University of BirminghamBirmingham, UK
| | | | | | - Zewei Luo
- University of BirminghamBirmingham, UK
| | | | - F. Chris H. Franklin
- University of BirminghamBirmingham, UK
- *Correspondence: Jianhua Yang and F. Chris H. Franklin, University of Birmingham, B152TT Birmingham, UK. e-mail: ,
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20
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Reddy ASN, Ben-Hur A, Day IS. Experimental and computational approaches for the study of calmodulin interactions. PHYTOCHEMISTRY 2011; 72:1007-19. [PMID: 21338992 DOI: 10.1016/j.phytochem.2010.12.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2010] [Revised: 11/10/2010] [Accepted: 12/28/2010] [Indexed: 05/22/2023]
Abstract
Ca(2+), a universal messenger in eukaryotes, plays a major role in signaling pathways that control many growth and developmental processes in plants as well as their responses to various biotic and abiotic stresses. Cellular changes in Ca(2+) in response to diverse signals are recognized by protein sensors that either have their activity modulated or that interact with other proteins and modulate their activity. Calmodulins (CaMs) and CaM-like proteins (CMLs) are Ca(2+) sensors that have no enzymatic activity of their own but upon binding Ca(2+) interact and modulate the activity of other proteins involved in a large number of plant processes. Protein-protein interactions play a key role in Ca(2+)/CaM-mediated in signaling pathways. In this review, using CaM as an example, we discuss various experimental approaches and computational tools to identify protein-protein interactions. During the last two decades hundreds of CaM-binding proteins in plants have been identified using a variety of approaches ranging from simple screening of expression libraries with labeled CaM to high-throughput screens using protein chips. However, the high-throughput methods have not been applied to the entire proteome of any plant system. Nevertheless, the data provided by these screens allows the development of computational tools to predict CaM-interacting proteins. Using all known binding sites of CaM, we developed a computational method that predicted over 700 high confidence CaM interactors in the Arabidopsis proteome. Most (>600) of these are not known to bind calmodulin, suggesting that there are likely many more CaM targets than previously known. Functional analyses of some of the experimentally identified Ca(2+) sensor target proteins have uncovered their precise role in Ca(2+)-mediated processes. Further studies on identifying novel targets of CaM and CMLs and generating their interaction network - "calcium sensor interactome" - will help us in understanding how Ca(2+) regulates a myriad of cellular and physiological processes.
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Affiliation(s)
- A S N Reddy
- Department of Biology, Program in Molecular Plant Biology, Program in Cell and Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA.
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21
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Reddy ASN, Ali GS, Celesnik H, Day IS. Coping with stresses: roles of calcium- and calcium/calmodulin-regulated gene expression. THE PLANT CELL 2011; 23:2010-32. [PMID: 21642548 PMCID: PMC3159525 DOI: 10.1105/tpc.111.084988] [Citation(s) in RCA: 416] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2011] [Revised: 05/02/2011] [Accepted: 05/16/2011] [Indexed: 05/18/2023]
Abstract
Abiotic and biotic stresses are major limiting factors of crop yields and cause billions of dollars of losses annually around the world. It is hoped that understanding at the molecular level how plants respond to adverse conditions and adapt to a changing environment will help in developing plants that can better cope with stresses. Acquisition of stress tolerance requires orchestration of a multitude of biochemical and physiological changes, and most of these depend on changes in gene expression. Research during the last two decades has established that different stresses cause signal-specific changes in cellular Ca(2+) level, which functions as a messenger in modulating diverse physiological processes that are important for stress adaptation. In recent years, many Ca(2+) and Ca(2+)/calmodulin (CaM) binding transcription factors (TFs) have been identified in plants. Functional analyses of some of these TFs indicate that they play key roles in stress signaling pathways. Here, we review recent progress in this area with emphasis on the roles of Ca(2+)- and Ca(2+)/CaM-regulated transcription in stress responses. We will discuss emerging paradigms in the field, highlight the areas that need further investigation, and present some promising novel high-throughput tools to address Ca(2+)-regulated transcriptional networks.
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Affiliation(s)
- Anireddy S N Reddy
- Department of Biology, Program in Molecular Plant Biology, Program in Cell and Molecular Biology, Colorado State University, Fort Collins, Colorado 80523, USA.
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22
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Lin M, Zhou X, Shen X, Mao C, Chen X. The predicted Arabidopsis interactome resource and network topology-based systems biology analyses. THE PLANT CELL 2011; 23:911-22. [PMID: 21441435 PMCID: PMC3082272 DOI: 10.1105/tpc.110.082529] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2010] [Revised: 12/30/2010] [Accepted: 03/10/2011] [Indexed: 05/17/2023]
Abstract
Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the predicted arabidopsis interactome resource (PAIR; ) presents 149,900 potential molecular interactions, which are expected to cover approximately 24% of the entire interactome with approximately 40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.
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Affiliation(s)
- Mingzhi Lin
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xi Zhou
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xueling Shen
- Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Chuanzao Mao
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xin Chen
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
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Abstract
The predicted Arabidopsis interactome resource (PAIR, http://www.cls.zju.edu.cn/pair/), comprised of 5990 experimentally reported molecular interactions in Arabidopsis thaliana together with 145 494 predicted interactions, is currently the most comprehensive data set of the Arabidopsis interactome with high reliability. PAIR predicts interactions by a fine-tuned support vector machine model that integrates indirect evidences for interaction, such as gene co-expressions, domain interactions, shared GO annotations, co-localizations, phylogenetic profile similarities and homologous interactions in other organisms (interologs). These predictions were expected to cover 24% of the entire Arabidopsis interactome, and their reliability was estimated to be 44%. Two independent example data sets were used to rigorously validate the prediction accuracy. PAIR features a user-friendly query interface, providing rich annotation on the relationships between two proteins. A graphical interaction network browser has also been integrated into the PAIR web interface to facilitate mining of specific pathways.
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Affiliation(s)
- Mingzhi Lin
- Department of Bioinformatics and Institute of Biochemistry, Zhejiang University, Hangzhou, PR China
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