1
|
Gong F, Cao D, Sun X, Li Z, Qu C, Fan Y, Cao Z, Zhao K, Zhao K, Qiu D, Li Z, Ren R, Ma X, Zhang X, Yin D. Homologous mapping yielded a comprehensive predicted protein-protein interaction network for peanut (Arachis hypogaea L.). BMC PLANT BIOLOGY 2024; 24:873. [PMID: 39304811 DOI: 10.1186/s12870-024-05580-w] [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: 01/23/2024] [Accepted: 09/09/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear. RESULTS We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut. CONCLUSION Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.
Collapse
Affiliation(s)
- Fangping Gong
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Di Cao
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Xiaojian Sun
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Zhuo Li
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Chengxin Qu
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Yi Fan
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Zenghui Cao
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Kai Zhao
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Kunkun Zhao
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Ding Qiu
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Zhongfeng Li
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Rui Ren
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Xingli Ma
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Xingguo Zhang
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China
| | - Dongmei Yin
- College of Agronomy, Henan Agricultural University, Zhengzhou, 450000, People's Republic of China.
| |
Collapse
|
2
|
Sun F, Deng Y, Ma X, Liu Y, Zhao L, Yu S, Zhang L. Structure-based prediction of protein-protein interaction network in rice. Genet Mol Biol 2024; 47:e20230068. [PMID: 38314883 PMCID: PMC10849033 DOI: 10.1590/1678-4685-gmb-2023-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/02/2023] [Indexed: 02/07/2024] Open
Abstract
Comprehensive protein-protein interaction (PPI) maps are critical for understanding the functional organization of the proteome, but challenging to produce experimentally. Here, we developed a computational method for predicting PPIs based on protein docking. Evaluation of performance on benchmark sets demonstrated the ability of the docking-based method to accurately identify PPIs using predicted protein structures. By employing the docking-based method, we constructed a structurally resolved PPI network consisting of 24,653 interactions between 2,131 proteins, which greatly extends the current knowledge on the rice protein-protein interactome. Moreover, we mapped the trait-associated single nucleotide polymorphisms (SNPs) to the structural interactome, and computationally identified 14 SNPs that had significant consequences on PPI network. The protein structural interactome map provided a resource to facilitate functional investigation of PPI-perturbing alleles associated with agronomically important traits in rice.
Collapse
Affiliation(s)
- Fangnan Sun
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Yaxin Deng
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Xiaosong Ma
- Shanghai Academy of Agricultural Sciences, Shanghai Agrobiological Gene Center, Shanghai, China
| | - Yuan Liu
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Lingxia Zhao
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| | - Shunwu Yu
- Shanghai Academy of Agricultural Sciences, Shanghai Agrobiological Gene Center, Shanghai, China
| | - Lida Zhang
- Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China
| |
Collapse
|
3
|
Zheng J, Yang X, Huang Y, Yang S, Wuchty S, Zhang Z. Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 114:984-994. [PMID: 36919205 DOI: 10.1111/tpj.16188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/20/2023] [Accepted: 03/09/2023] [Indexed: 05/27/2023]
Abstract
Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.
Collapse
Affiliation(s)
- Jingyan Zheng
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, 100034, China
| | - Yan Huang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, FL, 33146, USA
- Department of Biology, University of Miami, Miami, FL, 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, 33136, USA
- Institute of Data Science and Computing, University of Miami, Miami, FL, 33146, USA
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| |
Collapse
|
4
|
OUP accepted manuscript. Brief Funct Genomics 2022; 21:243-269. [DOI: 10.1093/bfgp/elac007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/14/2022] Open
|
5
|
Pazhamala LT, Kudapa H, Weckwerth W, Millar AH, Varshney RK. Systems biology for crop improvement. THE PLANT GENOME 2021; 14:e20098. [PMID: 33949787 DOI: 10.1002/tpg2.20098] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 03/09/2021] [Indexed: 05/19/2023]
Abstract
In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.
Collapse
Affiliation(s)
- Lekha T Pazhamala
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Himabindu Kudapa
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - A Harvey Millar
- ARC Centre of Excellence in Plant Energy Biology and School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
- State Agricultural Biotechnology Centre, Crop Research Innovation Centre, Food Futures Institute, Murdoch University, Murdoch, WA, Australia
| |
Collapse
|
6
|
Abstract
Plants encompass unparalleled multi-scale regenerative potential. Despite lacking specialized cells that are recruited to injured sites, and despite their cells being encased in rigid cell walls, plants exhibit a variety of regenerative responses ranging from the regeneration of specific cell types, tissues and organs, to the rebuilding of an entire organism. Over the years, extensive studies on embryo, shoot and root development in the model plant species Arabidopsis thaliana have provided insights into the mechanisms underlying plant regeneration. These studies highlight how Arabidopsis, with its wide array of refined molecular, genetic and cell biological tools, provides a perfect model to interrogate the cellular and molecular mechanisms of reprogramming during regeneration.
Collapse
Affiliation(s)
- Mabel Maria Mathew
- School of Biology, Indian Institute of Science Education and Research, Thiruvananthapuram, 695551, India
| | - Kalika Prasad
- School of Biology, Indian Institute of Science Education and Research, Thiruvananthapuram, 695551, India
| |
Collapse
|
7
|
Lan Y, Sun R, Ouyang J, Ding W, Kim MJ, Wu J, Li Y, Shi T. AtMAD: Arabidopsis thaliana multi-omics association database. Nucleic Acids Res 2021; 49:D1445-D1451. [PMID: 33219693 PMCID: PMC7778929 DOI: 10.1093/nar/gkaa1042] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/08/2020] [Accepted: 10/21/2020] [Indexed: 12/22/2022] Open
Abstract
Integration analysis of multi-omics data provides a comprehensive landscape for understanding biological systems and mechanisms. The abundance of high-quality multi-omics data (genomics, transcriptomics, methylomics and phenomics) for the model organism Arabidopsis thaliana enables scientists to study the genetic mechanism of many biological processes. However, no resource is available to provide comprehensive and systematic multi-omics associations for Arabidopsis. Here, we developed an Arabidopsis thaliana Multi-omics Association Database (AtMAD, http://www.megabionet.org/atmad), a public repository for large-scale measurements of associations between genome, transcriptome, methylome, pathway and phenotype in Arabidopsis, designed for facilitating identification of eQTL, emQTL, Pathway-mQTL, Phenotype-pathway, GWAS, TWAS and EWAS. Candidate variants/methylations/genes were identified in AtMAD for specific phenotypes or biological processes, many of them are supported by experimental evidence. Based on the multi-omics association strategy, we have identified 11 796 cis-eQTLs and 10 119 trans-eQTLs. Among them, 68 837 environment-eQTL associations and 149 622 GWAS-eQTL associations were identified and stored in AtMAD. For expression–methylation quantitative trait loci (emQTL), we identified 265 776 emQTLs and 122 344 pathway-mQTLs. For TWAS and EWAS, we obtained 62 754 significant phenotype-gene associations and 3 993 379 significant phenotype-methylation associations, respectively. Overall, the multi-omics associated network in AtMAD will provide new insights into exploring biological mechanisms of plants at multi-omics levels.
Collapse
Affiliation(s)
- Yiheng Lan
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China.,The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Ruikun Sun
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jian Ouyang
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Wubing Ding
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Min-Jun Kim
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China
| | - Jun Wu
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yuhua Li
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.,Big Data and Engineering Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| |
Collapse
|
8
|
Thanasomboon R, Kalapanulak S, Netrphan S, Saithong T. Exploring dynamic protein-protein interactions in cassava through the integrative interactome network. Sci Rep 2020; 10:6510. [PMID: 32300157 PMCID: PMC7162878 DOI: 10.1038/s41598-020-63536-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 04/01/2020] [Indexed: 01/01/2023] Open
Abstract
Protein-protein interactions (PPIs) play an essential role in cellular regulatory processes. Despite, in-depth studies to uncover the mystery of PPI-mediated regulations are still lacking. Here, an integrative interactome network (MePPI-Ux) was obtained by incorporating expression data into the improved genome-scale interactome network of cassava (MePPI-U). The MePPI-U, constructed by both interolog- and domain-based approaches, contained 3,638,916 interactions and 24,590 proteins (59% of proteins in the cassava AM560 genome version 6). After incorporating expression data as information of state, the MePPI-U rewired to represent condition-dependent PPIs (MePPI-Ux), enabling us to envisage dynamic PPIs (DPINs) that occur at specific conditions. The MePPI-Ux was exploited to demonstrate timely PPIs of cassava under various conditions, namely drought stress, brown streak virus (CBSV) infection, and starch biosynthesis in leaf/root tissues. MePPI-Uxdrought and MePPI-UxCBSV suggested involved PPIs in response to stress. MePPI-UxSB,leaf and MePPI-UxSB,root suggested the involvement of interactions among transcription factor proteins in modulating how leaf or root starch is synthesized. These findings deepened our knowledge of the regulatory roles of PPIs in cassava and would undeniably assist targeted breeding efforts to improve starch quality and quantity.
Collapse
Affiliation(s)
- Ratana Thanasomboon
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.,Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Saowalak Kalapanulak
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.,Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Supatcharee Netrphan
- National Center for Genetic Engineering and Biotechnology, Pathum Thani, 12120, Thailand
| | - Treenut Saithong
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand. .,Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Kumar M, Kesawat MS, Ali A, Lee SC, Gill SS, Kim HU. Integration of Abscisic Acid Signaling with Other Signaling Pathways in Plant Stress Responses and Development. PLANTS (BASEL, SWITZERLAND) 2019; 8:E592. [PMID: 31835863 PMCID: PMC6963649 DOI: 10.3390/plants8120592] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 11/26/2019] [Accepted: 12/10/2019] [Indexed: 12/30/2022]
Abstract
Plants are immobile and, to overcome harsh environmental conditions such as drought, salt, and cold, they have evolved complex signaling pathways. Abscisic acid (ABA), an isoprenoid phytohormone, is a critical signaling mediator that regulates diverse biological processes in various organisms. Significant progress has been made in the determination and characterization of key ABA-mediated molecular factors involved in different stress responses, including stomatal closure and developmental processes, such as seed germination and bud dormancy. Since ABA signaling is a complex signaling network that integrates with other signaling pathways, the dissection of its intricate regulatory network is necessary to understand the function of essential regulatory genes involved in ABA signaling. In the present review, we focus on two aspects of ABA signaling. First, we examine the perception of the stress signal (abiotic and biotic) and the response network of ABA signaling components that transduce the signal to the downstream pathway to respond to stress tolerance, regulation of stomata, and ABA signaling component ubiquitination. Second, ABA signaling in plant development processes, such as lateral root growth regulation, seed germination, and flowering time regulation is investigated. Examining such diverse signal integration dynamics could enhance our understanding of the underlying genetic, biochemical, and molecular mechanisms of ABA signaling networks in plants.
Collapse
Affiliation(s)
- Manu Kumar
- Department of Bioindustry and Bioresource Engineering, Plant Engineering Research Institute, Sejong University, Seoul 05006, Korea
| | | | - Asjad Ali
- Southern Cross Plant Science, Southern Cross University, East Lismore NSW 2480, Australia;
| | | | - Sarvajeet Singh Gill
- Stress Physiology and Molecular Biology Lab, Centre for Biotechnology, MD University, Rohtak 124001, India;
| | - Hyun Uk Kim
- Department of Bioindustry and Bioresource Engineering, Plant Engineering Research Institute, Sejong University, Seoul 05006, Korea
| |
Collapse
|
11
|
Shameer K, Naika MB, Shafi KM, Sowdhamini R. Decoding systems biology of plant stress for sustainable agriculture development and optimized food production. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2019; 145:19-39. [DOI: 10.1016/j.pbiomolbio.2018.12.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 10/23/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022]
|
12
|
Zhao J, Lei Y, Hong J, Zheng C, Zhang L. AraPPINet: An Updated Interactome for the Analysis of Hormone Signaling Crosstalk in Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2019; 10:870. [PMID: 31333706 PMCID: PMC6625390 DOI: 10.3389/fpls.2019.00870] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 06/18/2019] [Indexed: 05/29/2023]
Abstract
Protein-protein interactions (PPIs) play fundamental roles in various cellular processes. Here, we present a new version of computational interactome that contains more than 345,000 predicted PPIs involving about 51.2% of the Arabidopsis proteins. Compared to the earlier version, the updated AraPPINet displays a higher accuracy in predicting protein interactions through performance evaluation with independent datasets. In addition to the experimental verifications of the previous version, the new version has been subjected to further validation test that demonstrates its ability to discover novel PPIs involved in hormone signaling pathways. Moreover, network analysis shows that many overlapping proteins are significantly involved in the interactions which mediated the crosstalk among plant hormones. The new version of AraPPINet provides a more reliable interactome which would facilitate the understanding of crosstalk among hormone signaling pathways in plants.
Collapse
|
13
|
Subba P, Narayana Kotimoole C, Prasad TSK. Plant Proteome Databases and Bioinformatic Tools: An Expert Review and Comparative Insights. ACTA ACUST UNITED AC 2019; 23:190-206. [DOI: 10.1089/omi.2019.0024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Pratigya Subba
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Chinmaya Narayana Kotimoole
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Thottethodi Subrahmanya Keshava Prasad
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
- Institute of Bioinformatics, International Technology Park, Bangalore, India
| |
Collapse
|
14
|
Liu S, Lv Z, Liu Y, Li L, Zhang L. Network analysis of ABA-dependent and ABA-independent drought responsive genes in Arabidopsis thaliana. Genet Mol Biol 2018. [PMID: 30044467 DOI: 10.1590/1678-4685-gmb-2017-2229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023] Open
Abstract
Drought is one of the most severe abiotic factors restricting plant growth and yield. Numerous genes functioning in drought response are regulated by abscisic acid (ABA) dependent and independent pathways, but knowledge of interplay between the two pathways is still limited. Here, we integrated transcriptome sequencing and network analyses to explore interplays between ABA-dependent and ABA-independent pathways responding to drought stress in Arabidopsis thaliana. We identified 211 ABA-dependent differentially expressed genes (DEGs) and 1,118 ABA-independent DEGs under drought stress. Functional analysis showed that ABA-dependent DEGs were significantly enriched in expected biological processes in response to water deprivation and ABA stimulus, while ABA-independent DEGs were preferentially enriched in response to jasmonic acid (JA), salicylic acid (SA) and gibberellin (GA) stimuli. We found significantly enriched interactions between ABA-dependent and ABA-independent pathways with 94 genes acting as core interacting components by combining network analyses. A link between ABA and JA signaling mediated through a direct interaction of the ABA responsive elements-binding factor ABF3 with the basic helix-loop-helix transcription factor MYC2 was validated by yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays. Our study provides a systematic view of the interplay between ABA-dependent and ABA-independent pathways in response to drought stress.
Collapse
Affiliation(s)
- Shiwei Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zongyou Lv
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yihui Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Li
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Lida Zhang
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
15
|
Liu S, Lv Z, Liu Y, Li L, Zhang L. Network analysis of ABA-dependent and ABA-independent drought responsive genes in Arabidopsis thaliana. Genet Mol Biol 2018; 41:624-637. [PMID: 30044467 PMCID: PMC6136374 DOI: 10.1590/1678-4685-gmb-2017-0229] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 12/31/2017] [Indexed: 12/02/2022] Open
Abstract
Drought is one of the most severe abiotic factors restricting plant growth and yield. Numerous genes functioning in drought response are regulated by abscisic acid (ABA) dependent and independent pathways, but knowledge of interplay between the two pathways is still limited. Here, we integrated transcriptome sequencing and network analyses to explore interplays between ABA-dependent and ABA-independent pathways responding to drought stress in Arabidopsis thaliana. We identified 211 ABA-dependent differentially expressed genes (DEGs) and 1,118 ABA-independent DEGs under drought stress. Functional analysis showed that ABA-dependent DEGs were significantly enriched in expected biological processes in response to water deprivation and ABA stimulus, while ABA-independent DEGs were preferentially enriched in response to jasmonic acid (JA), salicylic acid (SA) and gibberellin (GA) stimuli. We found significantly enriched interactions between ABA-dependent and ABA-independent pathways with 94 genes acting as core interacting components by combining network analyses. A link between ABA and JA signaling mediated through a direct interaction of the ABA responsive elements-binding factor ABF3 with the basic helix-loop-helix transcription factor MYC2 was validated by yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays. Our study provides a systematic view of the interplay between ABA-dependent and ABA-independent pathways in response to drought stress.
Collapse
Affiliation(s)
- Shiwei Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zongyou Lv
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yihui Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Li
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Lida Zhang
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.,Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
16
|
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/.
Collapse
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
| |
Collapse
|
17
|
Vandereyken K, Van Leene J, De Coninck B, Cammue BPA. Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs. FRONTIERS IN PLANT SCIENCE 2018; 9:694. [PMID: 29922309 PMCID: PMC5996676 DOI: 10.3389/fpls.2018.00694] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/07/2018] [Indexed: 05/20/2023]
Abstract
Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.
Collapse
Affiliation(s)
- Katy Vandereyken
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Jelle Van Leene
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Barbara De Coninck
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Division of Crop Biotechnics, KU Leuven, Heverlee, Belgium
| | - Bruno P. A. Cammue
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- *Correspondence: Bruno P. A. Cammue
| |
Collapse
|
18
|
Prediction of cassava protein interactome based on interolog method. Sci Rep 2017; 7:17206. [PMID: 29222529 PMCID: PMC5722940 DOI: 10.1038/s41598-017-17633-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 11/28/2017] [Indexed: 12/20/2022] Open
Abstract
Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.
Collapse
|
19
|
Liu S, Liu Y, Zhao J, Cai S, Qian H, Zuo K, Zhao L, Zhang L. A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 90:177-188. [PMID: 28074633 DOI: 10.1111/tpj.13475] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 12/20/2016] [Accepted: 12/22/2016] [Indexed: 05/18/2023]
Abstract
Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice.
Collapse
Affiliation(s)
- Shiwei Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yihui Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jiawei Zhao
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shitao Cai
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hongmei Qian
- 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
- Key Laboratory of Urban Agriculture (South) Ministry of Agriculture, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| |
Collapse
|
20
|
Lv Q, Lan Y, Shi Y, Wang H, Pan X, Li P, Shi T. AtPID: a genome-scale resource for genotype-phenotype associations in Arabidopsis. Nucleic Acids Res 2016; 45:D1060-D1063. [PMID: 27899679 PMCID: PMC5210528 DOI: 10.1093/nar/gkw1029] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 10/16/2016] [Accepted: 11/08/2016] [Indexed: 01/01/2023] Open
Abstract
AtPID (Arabidopsis thalianaProtein Interactome Database, available at http://www.megabionet.org/atpid) is an integrated database resource for protein interaction network and functional annotation. In the past few years, we collected 5564 mutants with significant morphological alterations and manually curated them to 167 plant ontology (PO) morphology categories. These single/multiple-gene mutants were indexed and linked to 3919 genes. After integrated these genotype–phenotype associations with the comprehensive protein interaction network in AtPID, we developed a Naïve Bayes method and predicted 4457 novel high confidence gene-PO pairs with 1369 genes as the complement. Along with the accumulated novel data for protein interaction and functional annotation, and the updated visualization toolkits, we present a genome-scale resource for genotype–phenotype associations for Arabidopsis in AtPID 5.0. In our updated website, all the new genotype–phenotype associations from mutants, protein network, and the protein annotation information can be vividly displayed in a comprehensive network view, which will greatly enhance plant protein function and genotype–phenotype association studies in a systematical way.
Collapse
Affiliation(s)
- Qi Lv
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China.,School of Finance and Statistics, East China Normal University, Shanghai 200241, China
| | - Yiheng Lan
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yan Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Huan Wang
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Xia Pan
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Peng Li
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| |
Collapse
|
21
|
Sugimoto M. Metabolomic pathway visualization tool outsourcing editing function. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:7659-62. [PMID: 26738066 DOI: 10.1109/embc.2015.7320166] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Recent rapid improvements of measuring instrument enables us to perform various omics studies to simultaneous profile multiple molecules, which provides a holistic view of various molecular interactions, such as signal transaction, protein interactions, and metabolic pathways. Metabolomics is recently emerged omics that can identify and quantify low weight metabolites usually defined as organic molecules whose size is <; 1500 Da. In comparison to the other omics, the development of software tools to deal with metabolomic data is not matured. Conventional pathway drawing and visualization tool provide tool-specific unique functions, however, such user interface requires users to learn the usage and prevention for the use of these tools. Here, we developed a more generic pathway visualization tool. This tool incorporate pathway data yielded by common drawing tools, e.g. MS PowerPoint, and visualize the quantified values on the pathways. The statistical results also can be overlaid on each metabolite. The developed tools facilitate the interpreting metabolomic data in pathway forms.
Collapse
|
22
|
Reconstruction and Application of Protein-Protein Interaction Network. Int J Mol Sci 2016; 17:ijms17060907. [PMID: 27338356 PMCID: PMC4926441 DOI: 10.3390/ijms17060907] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Revised: 05/31/2016] [Accepted: 06/03/2016] [Indexed: 11/17/2022] Open
Abstract
The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms.
Collapse
|
23
|
Xing S, Wallmeroth N, Berendzen KW, Grefen C. Techniques for the Analysis of Protein-Protein Interactions in Vivo. PLANT PHYSIOLOGY 2016; 171:727-58. [PMID: 27208310 PMCID: PMC4902627 DOI: 10.1104/pp.16.00470] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 04/19/2016] [Indexed: 05/20/2023]
Abstract
Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique.
Collapse
Affiliation(s)
- Shuping Xing
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
| | - Niklas Wallmeroth
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
| | - Kenneth W Berendzen
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
| | - Christopher Grefen
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
| |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
Lv Q, Ma W, Liu H, Li J, Wang H, Lu F, Zhao C, Shi T. Genome-wide protein-protein interactions and protein function exploration in cyanobacteria. Sci Rep 2015; 5:15519. [PMID: 26490033 PMCID: PMC4614683 DOI: 10.1038/srep15519] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 09/21/2015] [Indexed: 11/10/2022] Open
Abstract
Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria.
Collapse
Affiliation(s)
- Qi Lv
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
| | - Weimin Ma
- College of Life and Environment Sciences, Shanghai Normal University, 100 Guilin Road, Shanghai, 200234, China
| | - Hui Liu
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
| | - Jiang Li
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
| | - Huan Wang
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
| | - Fang Lu
- College of Life and Environment Sciences, Shanghai Normal University, 100 Guilin Road, Shanghai, 200234, China
| | - Chen Zhao
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China.,The institute of plant physiology and ecology, Shanghai Institutes for Biological Sciences, Chinese Acedamy of Sciences, 300 Fenglin Road, Shanghai 200032, China
| |
Collapse
|
26
|
Dogra V, Bagler G, Sreenivasulu Y. Re-analysis of protein data reveals the germination pathway and up accumulation mechanism of cell wall hydrolases during the radicle protrusion step of seed germination in Podophyllum hexandrum- a high altitude plant. FRONTIERS IN PLANT SCIENCE 2015; 6:874. [PMID: 26579141 PMCID: PMC4620410 DOI: 10.3389/fpls.2015.00874] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 10/02/2015] [Indexed: 05/06/2023]
Abstract
Podophyllum hexandrum Royle is an important high-altitude plant of Himalayas with immense medicinal value. Earlier, it was reported that the cell wall hydrolases were up accumulated during radicle protrusion step of Podophyllum seed germination. In the present study, Podophyllum seed Germination protein interaction Network (PGN) was constructed by using the differentially accumulated protein (DAP) data set of Podophyllum during the radicle protrusion step of seed germination, with reference to Arabidopsis protein-protein interaction network (AtPIN). The developed PGN is comprised of a giant cluster with 1028 proteins having 10,519 interactions and a few small clusters with relevant gene ontological signatures. In this analysis, a germination pathway related cluster which is also central to the topology and information dynamics of PGN was obtained with a set of 60 key proteins. Among these, eight proteins which are known to be involved in signaling, metabolism, protein modification, cell wall modification, and cell cycle regulation processes were found commonly highlighted in both the proteomic and interactome analysis. The systems-level analysis of PGN identified the key proteins involved in radicle protrusion step of seed germination in Podophyllum.
Collapse
Affiliation(s)
- Vivek Dogra
- Biotechnology Division, Council of Scientific and Industrial Research-Institute of Himalayan Bioresource TechnologyPalampur, India
| | - Ganesh Bagler
- Centre for Biologically Inspired System Science, Indian Institute of Technology JodhpurJodhpur, India
- Ganesh Bagler
| | - Yelam Sreenivasulu
- Biotechnology Division, Council of Scientific and Industrial Research-Institute of Himalayan Bioresource TechnologyPalampur, India
- *Correspondence: Yelam Sreenivasulu ;
| |
Collapse
|
27
|
Sheth BP, Thaker VS. Plant systems biology: insights, advances and challenges. PLANTA 2014; 240:33-54. [PMID: 24671625 DOI: 10.1007/s00425-014-2059-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Accepted: 03/06/2014] [Indexed: 05/20/2023]
Abstract
Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.
Collapse
Affiliation(s)
- Bhavisha P Sheth
- Department of Biosciences, Centre for Advanced Studies in Plant Biotechnology and Genetic Engineering, Saurashtra University, Rajkot, 360005, Gujarat, India,
| | | |
Collapse
|
28
|
Choi D, Choi J, Kang B, Lee S, Cho YH, Hwang I, Hwang D. iNID: an analytical framework for identifying network models for interplays among developmental signaling in Arabidopsis. MOLECULAR PLANT 2014; 7:792-813. [PMID: 24380880 DOI: 10.1093/mp/sst173] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Integration of internal and external cues into developmental programs is indispensable for growth and development of plants, which involve complex interplays among signaling pathways activated by the internal and external factors (IEFs). However, decoding these complex interplays is still challenging. Here, we present a web-based platform that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID) in Arabidopsis. iNID provides a comprehensive resource of (1) transcriptomes previously collected under the conditions treated with a broad spectrum of IEFs and (2) protein and genetic interactome data in Arabidopsis. In addition, iNID provides an array of tools for identifying key regulators and network models related to interplays among IEFs using transcriptome and interactome data. To demonstrate the utility of iNID, we investigated the interplays of (1) phytohormones and light and (2) phytohormones and biotic stresses. The results revealed 34 potential regulators of the interplays, some of which have not been reported in association with the interplays, and also network models that delineate the involvement of the 34 regulators in the interplays, providing novel insights into the interplays collectively defined by phytohormones, light, and biotic stresses. We then experimentally verified that BME3 and TEM1, among the selected regulators, are involved in the auxin-brassinosteroid (BR)-blue light interplay. Therefore, iNID serves as a useful tool to provide a basis for understanding interplays among IEFs.
Collapse
Affiliation(s)
- Daeseok Choi
- School of Interdisciplinary Bioscience and Bioengineering, POSTECH, 790-784, Pohang, Republic of Korea
| | | | | | | | | | | | | |
Collapse
|
29
|
Jiménez-Gómez JM. Network types and their application in natural variation studies in plants. CURRENT OPINION IN PLANT BIOLOGY 2014; 18:80-86. [PMID: 24632305 DOI: 10.1016/j.pbi.2014.02.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2013] [Revised: 02/06/2014] [Accepted: 02/17/2014] [Indexed: 06/03/2023]
Abstract
We are in the age of data-driven biology. Not even a decade after the invention of high-throughput sequencing technologies, there are methods that accurately monitor DNA polymorphisms, transcription profiles, methylation states, transcription factor binding sites, chromatin compactness, nucleosome positions, dynamic histone marks, and so on. We are starting to generate comparable amounts of protein or metabolite data. A key issue is how are we going to make sense of all this information. Network analysis is the most promising method to integrate, query and display large amounts of data for human interpretation. This review shortly summarizes the basic types of networks, their properties and limitations. In addition, I introduce the application of networks to the study of the molecular mechanisms behind natural phenotypic variation.
Collapse
Affiliation(s)
- José M Jiménez-Gómez
- INRA - Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France; Max Planck Institute for Plant Breeding Research, Department of Plant Breeding and Genetics, Carl-von-Linné-Weg 10, 50829 Cologne, Germany.
| |
Collapse
|
30
|
Coego A, Brizuela E, Castillejo P, Ruíz S, Koncz C, del Pozo JC, Piñeiro M, Jarillo JA, Paz-Ares J, León J. The TRANSPLANTA collection of Arabidopsis lines: a resource for functional analysis of transcription factors based on their conditional overexpression. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2014; 77:944-53. [PMID: 24456507 DOI: 10.1111/tpj.12443] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Revised: 01/08/2014] [Accepted: 01/13/2014] [Indexed: 05/07/2023]
Abstract
Transcription factors (TFs) are key regulators of gene expression in all organisms. In eukaryotes, TFs are often represented by functionally redundant members of large gene families. Overexpression might prove a means to unveil the biological functions of redundant TFs; however, constitutive overexpression of TFs frequently causes severe developmental defects, preventing their functional characterization. Conditional overexpression strategies help to overcome this problem. Here, we report on the TRANSPLANTA collection of Arabidopsis lines, each expressing one of 949 TFs under the control of a β-estradiol-inducible promoter. Thus far, 1636 independent homozygous lines, representing an average of 2.6 lines for every TF, have been produced for the inducible expression of 634 TFs. Along with a GUS-GFP reporter, randomly selected TRANSPLANTA lines were tested and confirmed for conditional transgene expression upon β-estradiol treatment. As a proof of concept for the exploitation of this resource, β-estradiol-induced proliferation of root hairs, dark-induced senescence, anthocyanin accumulation and dwarfism were observed in lines conditionally expressing full-length cDNAs encoding RHD6, WRKY22, MYB123/TT2 and MYB26, respectively, in agreement with previously reported phenotypes conferred by these TFs. Further screening performed with other TRANSPLANTA lines allowed the identification of TFs involved in different plant biological processes, illustrating that the collection is a powerful resource for the functional characterization of TFs. For instance, ANAC058 and a TINY/AP2 TF were identified as modulators of ABA-mediated germination potential, and RAP2.10/DEAR4 was identified as a regulator of cell death in the hypocotyl-root transition zone. Seeds of TRANSPLANTA lines have been deposited at the Nottingham Arabidopsis Stock Centre for further distribution.
Collapse
Affiliation(s)
- Alberto Coego
- Instituto de Biología Molecular y Celular de Plantas, Valencia (CSIC-UPV), CPI, Edificio 8E, Av. Fausto Elio s/n, 46022, Valencia, Spain
| | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Sakata K, Komatsu S. Plant proteomics: from genome sequencing to proteome databases and repositories. Methods Mol Biol 2014; 1072:29-42. [PMID: 24136512 DOI: 10.1007/978-1-62703-631-3_3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Proteomic approaches are useful for the identification of functional proteins. These have been enhanced not only by the development of proteomic techniques but also in concert with genome sequencing. In this chapter, 30 databases and Web sites relating to plant proteomics are reviewed and recent technologies relating to data collection and annotation are surveyed.
Collapse
|
32
|
Xia Z, Zhai H, Lü S, Wu H, Zhang Y. Recent achievement in gene cloning and functional genomics in soybean. ScientificWorldJournal 2013; 2013:281367. [PMID: 24311973 PMCID: PMC3842071 DOI: 10.1155/2013/281367] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 09/18/2013] [Indexed: 11/18/2022] Open
Abstract
Soybean is a model plant for photoperiodism as well as for symbiotic nitrogen fixation. However, a rather low efficiency in soybean transformation hampers functional analysis of genes isolated from soybean. In comparison, rapid development and progress in flowering time and photoperiodic response have been achieved in Arabidopsis and rice. As the soybean genomic information has been released since 2008, gene cloning and functional genomic studies have been revived as indicated by successfully characterizing genes involved in maturity and nematode resistance. Here, we review some major achievements in the cloning of some important genes and some specific features at genetic or genomic levels revealed by the analysis of functional genomics of soybean.
Collapse
Affiliation(s)
- Zhengjun Xia
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
- Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Hong Zhai
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
| | - Shixiang Lü
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
| | - Hongyan Wu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
- Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Yupeng Zhang
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
| |
Collapse
|
33
|
De Bodt S, Inzé D. A guide to CORNET for the construction of coexpression and protein-protein interaction networks. Methods Mol Biol 2013; 1011:327-43. [PMID: 23616008 DOI: 10.1007/978-1-62703-414-2_26] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
To enable easy access and interpretation of heterogenous and scattered data, we have developed a user-friendly tool for data mining and integration in Arabidopsis thaliana, designated CORrelation NETworks (acronym CORNET), allowing browsing of microarray data, construction of coexpression and protein-protein interactions (PPIs), analysis of gene association and transcription factor (TF) regulatory networks, and exploration of diverse functional annotations. CORNET consists of three tools that can be used individually or in combination, namely, the coexpression tool, the PPI tool, and the TF tool. Different search options are implemented to enable the creation of networks centered around multiple input genes or proteins. Functional annotation resources are included to retrieve relevant literature, phenotypes, localization, gene ontology, plant ontology, and biological pathways. Networks and associated evidence of the majority of the currently available data types are visualized in Cytoscape. CORNET is available at https://bioinformatics.psb.ugent.be/cornet.
Collapse
|
34
|
Lu T, Dou Y, Zhang C. Fuzzy clustering of CPP family in plants with evolution and interaction analyses. BMC Bioinformatics 2013; 14 Suppl 13:S10. [PMID: 24268301 PMCID: PMC3849782 DOI: 10.1186/1471-2105-14-s13-s10] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Transcription factors have been studied intensively because they play an important role in gene expression regulation. However, the transcription factors in the CPP family (cystein-rich polycomb-like protein), compared with other transcription factor families, have not received sufficient attention, despite their wide prevalence in a broad spectrum of species, from plants to animals. The total number of known CPP transcription factors in plants is 111 from 16 plants, but only 2 of them have been studied so far, namely TSO1 and CPP1 in Arabidopsis thaliana and soybean, respectively. Methods In this work, to study their functions, we applied the fuzzy clustering method to all plant CPP transcription factors. The feature vector of each protein sequence for the fuzzy clustering method is encoded by the short length peptides and the combination of functional domain models. Results and conclusions With the fuzzy clustering method, all plant CPP transcription factors are grouped into two subfamilies. A systems approach, including Expressed Sequence Tag analysis, evolutionary analysis, protein-protein interaction network analysis and co-expression analysis, is employed to validate the clustering results, the results of which also indicates that the transcription factors from different subfamilies show uncorrelated responses.
Collapse
|
35
|
Rodgers-Melnick E, Culp M, DiFazio SP. Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS. BMC Genomics 2013; 14:608. [PMID: 24015873 PMCID: PMC3848842 DOI: 10.1186/1471-2164-14-608] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2012] [Accepted: 09/04/2013] [Indexed: 01/10/2023] Open
Abstract
Background The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms. Results In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants. Conclusion We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.
Collapse
Affiliation(s)
- Eli Rodgers-Melnick
- Department of Biology, West Virginia University, Morgantown, West Virginia, 26506, USA.
| | | | | |
Collapse
|
36
|
Oxley D, Ktistakis N, Farmaki T. Differential isolation and identification of PI(3)P and PI(3,5)P2 binding proteins from Arabidopsis thaliana using an agarose-phosphatidylinositol-phosphate affinity chromatography. J Proteomics 2013; 91:580-94. [PMID: 24007659 DOI: 10.1016/j.jprot.2013.08.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2013] [Revised: 07/25/2013] [Accepted: 08/20/2013] [Indexed: 12/13/2022]
Abstract
UNLABELLED A phosphatidylinositol-phosphate affinity chromatographic approach combined with mass spectrometry was used in order to identify novel PI(3)P and PI(3,5)P2 binding proteins from Arabidopsis thaliana suspension cell extracts. Most of the phosphatidylinositol-phosphate interacting candidates identified from this differential screening are characterized by lysine/arginine rich patches. Direct phosphoinositide binding was identified for important membrane trafficking regulators as well as protein quality control proteins such as the ATG18p orthologue involved in autophagosome formation and the lipid Sec14p like transfer protein. A pentatricopeptide repeat (PPR) containing protein was shown to directly bind to PI(3,5)P2 but not to PI(3)P. PIP chromatography performed using extracts obtained from high salt (0.4M and 1M NaCl) pretreated suspensions showed that the association of an S5-1 40S ribosomal protein with both PI(3)P and PI(3,5)P2 was abolished under salt stress whereas salinity stress induced an increase in the phosphoinositide association of the DUF538 domain containing protein SVB, associated with trichome size. Additional interacting candidates were co-purified with the phosphoinositide bound proteins. Binding of the COP9 signalosome, the heat shock proteins, and the identified 26S proteasomal subunits, is suggested as an indirect effect of their interaction with other proteins directly bound to the PI(3)P and the PI(3,5)P2 phosphoinositides. BIOLOGICAL SIGNIFICANCE PI(3,5)P2 is of special interest because of its low abundance. Furthermore, no endogenous levels have yet been detected in A. thaliana (although there is evidence for its existence in plants). Therefore the isolation of novel interacting candidates in vitro would be of a particular importance since the future study and localization of the respective endogenous proteins may indicate possible targeted compartments or tissues where PI(3,5)P2 could be enriched and thereafter identified. In addition, PI(3,5)P2 is a phosphoinositide extensively studied in mammalian and yeast systems. However, our knowledge of its role in plants as well as a list of its effectors from plants is very limited.
Collapse
Affiliation(s)
- David Oxley
- The Mass Spectrometry Group, Babraham Institute, Cambridge, CB2 4AT, UK
| | | | | |
Collapse
|
37
|
Li MW, Qi X, Ni M, Lam HM. Silicon era of carbon-based life: application of genomics and bioinformatics in crop stress research. Int J Mol Sci 2013; 14:11444-83. [PMID: 23759993 PMCID: PMC3709742 DOI: 10.3390/ijms140611444] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 05/07/2013] [Accepted: 05/17/2013] [Indexed: 01/25/2023] Open
Abstract
Abiotic and biotic stresses lead to massive reprogramming of different life processes and are the major limiting factors hampering crop productivity. Omics-based research platforms allow for a holistic and comprehensive survey on crop stress responses and hence may bring forth better crop improvement strategies. Since high-throughput approaches generate considerable amounts of data, bioinformatics tools will play an essential role in storing, retrieving, sharing, processing, and analyzing them. Genomic and functional genomic studies in crops still lag far behind similar studies in humans and other animals. In this review, we summarize some useful genomics and bioinformatics resources available to crop scientists. In addition, we also discuss the major challenges and advancements in the "-omics" studies, with an emphasis on their possible impacts on crop stress research and crop improvement.
Collapse
Affiliation(s)
- Man-Wah Li
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
| | - Xinpeng Qi
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
| | - Meng Ni
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
| | - Hon-Ming Lam
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
| |
Collapse
|
38
|
Sucaet Y, Wang Y, Li J, Wurtele ES. MetNet Online: a novel integrated resource for plant systems biology. BMC Bioinformatics 2012; 13:267. [PMID: 23066841 PMCID: PMC3483157 DOI: 10.1186/1471-2105-13-267] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2011] [Accepted: 08/10/2012] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Plants are important as foods, pharmaceuticals, biorenewable chemicals, fuel resources, bioremediation tools and general tools for recombinant technology. The study of plant biological pathways is advanced by easy access to integrated data sources. Today, various plant data sources are scattered throughout the web, making it increasingly complicated to build comprehensive datasets. RESULTS MetNet Online is a web-based portal that provides access to a regulatory and metabolic plant pathway database. The database and portal integrate Arabidopsis, soybean (Glycine max) and grapevine (Vitis vinifera) data. Pathways are enriched with known or predicted information on sub cellular location. MetNet Online enables pathways, interactions and entities to be browsed or searched by multiple categories such as sub cellular compartment, pathway ontology, and GO term. In addition to this, the "My MetNet" feature allows registered users to bookmark content and track, import and export customized lists of entities. Users can also construct custom networks using existing pathways and/or interactions as building blocks. CONCLUSION The site can be reached at http://www.metnetonline.org. Extensive video tutorials on how to use the site are available through http://www.metnetonline.org/tutorial/.
Collapse
Affiliation(s)
- Yves Sucaet
- Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA
- Interdepartmental Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA
| | - Yi Wang
- Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA
| | - Jie Li
- Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA
- Interdepartmental Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA
| | - Eve Syrkin Wurtele
- Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA
- Interdepartmental Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA
| |
Collapse
|
39
|
Bassel GW, Gaudinier A, Brady SM, Hennig L, Rhee SY, De Smet I. Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks. THE PLANT CELL 2012; 24:3859-75. [PMID: 23110892 PMCID: PMC3517224 DOI: 10.1105/tpc.112.100776] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2012] [Revised: 08/21/2012] [Accepted: 10/11/2012] [Indexed: 05/19/2023]
Abstract
Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach.
Collapse
Affiliation(s)
- George W. Bassel
- School of Biosciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
- Division of Plant and Crop Sciences, School of Biosciences and Centre for Plant Integrative Biology, University of Nottingham, Loughborough LE12 5RD, United Kingdom
| | - Allison Gaudinier
- Department of Plant Biology and Genome Center, University of California, Davis, California 95616
| | - Siobhan M. Brady
- Department of Plant Biology and Genome Center, University of California, Davis, California 95616
| | - Lars Hennig
- Department of Plant Biology and Forest Genetics, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, SE-75007 Uppsala, Sweden
| | - Seung Y. Rhee
- Department of Plant Biology, Carnegie Institution for Science, Stanford, California 94305
| | - Ive De Smet
- Division of Plant and Crop Sciences, School of Biosciences and Centre for Plant Integrative Biology, University of Nottingham, Loughborough LE12 5RD, United Kingdom
| |
Collapse
|
40
|
De Bodt S, Hollunder J, Nelissen H, Meulemeester N, Inzé D. CORNET 2.0: integrating plant coexpression, protein-protein interactions, regulatory interactions, gene associations and functional annotations. THE NEW PHYTOLOGIST 2012; 195:707-720. [PMID: 22651224 DOI: 10.1111/j.1469-8137.2012.04184.x] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
To enable easy access and interpretation of heterogeneous and scattered data, we have developed a user-friendly tool for data mining and integration in Arabidopsis, named CORNET. This tool allows the browsing of microarray data, the construction of coexpression and protein-protein interaction (PPI) networks and the exploration of diverse functional annotations. Here, we present the new functionalities of CORNET 2.0 for data integration in plants. First of all, CORNET allows the integration of regulatory interaction datasets accessible through the new transcription factor (TF) tool that can be used in combination with the coexpression tool or the PPI tool. In addition, we have extended the PPI tool to enable the analysis of gene-gene associations from AraNet as well as newly identified PPIs. Different search options are implemented to enable the construction of networks centered around multiple input genes or proteins. New functional annotation resources are included to retrieve relevant literature, phenotypes, plant ontology and biological pathways. We have also extended CORNET to attain the construction of coexpression and PPI networks in the crop species maize. Networks and associated evidence of the majority of currently available data types are visualized in Cytoscape. CORNET is available at https://bioinformatics.psb.ugent.be/cornet.
Collapse
Affiliation(s)
- Stefanie De Bodt
- Department of Plant Systems Biology, Flanders Institute for Biotechnology, and Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Jens Hollunder
- Department of Plant Systems Biology, Flanders Institute for Biotechnology, and Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Hilde Nelissen
- Department of Plant Systems Biology, Flanders Institute for Biotechnology, and Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Nick Meulemeester
- Department of Plant Systems Biology, Flanders Institute for Biotechnology, and Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Dirk Inzé
- Department of Plant Systems Biology, Flanders Institute for Biotechnology, and Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| |
Collapse
|
41
|
Tackling drought stress: receptor-like kinases present new approaches. THE PLANT CELL 2012; 24:2262-78. [PMID: 22693282 DOI: 10.1105/tpc.112.096677] [Citation(s) in RCA: 125] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Global climate change and a growing population require tackling the reduction in arable land and improving biomass production and seed yield per area under varying conditions. One of these conditions is suboptimal water availability. Here, we review some of the classical approaches to dealing with plant response to drought stress and we evaluate how research on RECEPTOR-LIKE KINASES (RLKs) can contribute to improving plant performance under drought stress. RLKs are considered as key regulators of plant architecture and growth behavior, but they also function in defense and stress responses. The available literature and analyses of available transcript profiling data indeed suggest that RLKs can play an important role in optimizing plant responses to drought stress. In addition, RLK pathways are ideal targets for nontransgenic approaches, such as synthetic molecules, providing a novel strategy to manipulate their activity and supporting translational studies from model species, such as Arabidopsis thaliana, to economically useful crops.
Collapse
|
42
|
Taking the next step: building an Arabidopsis information portal. THE PLANT CELL 2012; 24:2248-56. [PMID: 22751211 PMCID: PMC3406920 DOI: 10.1105/tpc.112.100669] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2012] [Revised: 06/05/2012] [Accepted: 06/13/2012] [Indexed: 05/20/2023]
Abstract
The Arabidopsis information portal (AIP), a resource expected to provide access to all community data and combine outputs into a single user-friendly interface, has emerged from community discussions over the last 23 months. These discussions began during two closely linked workshops in early 2010 that established the International Arabidopsis Informatics Consortium (IAIC). The design of the AIP will provide core functionality while remaining flexible to encourage multiple contributors and constant innovation. An IAIC-hosted Design Workshop in December 2011 proposed a structure for the AIP to provide a framework for the minimal components of a functional community portal while retaining flexibility to rapidly extend the resource to other species. We now invite broader participation in the AIP development process so that the resource can be implemented in a timely manner.
Collapse
|
43
|
Wang C, Marshall A, Zhang D, Wilson ZA. ANAP: an integrated knowledge base for Arabidopsis protein interaction network analysis. PLANT PHYSIOLOGY 2012; 158:1523-33. [PMID: 22345505 PMCID: PMC3320167 DOI: 10.1104/pp.111.192203] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Accepted: 02/12/2012] [Indexed: 05/18/2023]
Abstract
Protein interactions are fundamental to the molecular processes occurring within an organism and can be utilized in network biology to help organize, simplify, and understand biological complexity. Currently, there are more than 10 publicly available Arabidopsis (Arabidopsis thaliana) protein interaction databases. However, there are limitations with these databases, including different types of interaction evidence, a lack of defined standards for protein identifiers, differing levels of information, and, critically, a lack of integration between them. In this paper, we present an interactive bioinformatics Web tool, ANAP (Arabidopsis Network Analysis Pipeline), which serves to effectively integrate the different data sets and maximize access to available data. ANAP has been developed for Arabidopsis protein interaction integration and network-based study to facilitate functional protein network analysis. ANAP integrates 11 Arabidopsis protein interaction databases, comprising 201,699 unique protein interaction pairs, 15,208 identifiers (including 11,931 The Arabidopsis Information Resource Arabidopsis Genome Initiative codes), 89 interaction detection methods, 73 species that interact with Arabidopsis, and 6,161 references. ANAP can be used as a knowledge base for constructing protein interaction networks based on user input and supports both direct and indirect interaction analysis. It has an intuitive graphical interface allowing easy network visualization and provides extensive detailed evidence for each interaction. In addition, ANAP displays the gene and protein annotation in the generated interactive network with links to The Arabidopsis Information Resource, the AtGenExpress Visualization Tool, the Arabidopsis 1,001 Genomes GBrowse, the Protein Knowledgebase, the Kyoto Encyclopedia of Genes and Genomes, and the Ensembl Genome Browser to significantly aid functional network analysis. The tool is available open access at http://gmdd.shgmo.org/Computational-Biology/ANAP.
Collapse
|
44
|
Multiple organellar RNA editing factor (MORF) family proteins are required for RNA editing in mitochondria and plastids of plants. Proc Natl Acad Sci U S A 2012; 109:5104-9. [PMID: 22411807 DOI: 10.1073/pnas.1202452109] [Citation(s) in RCA: 214] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
RNA editing in plastids and mitochondria of flowering plants changes hundreds of selected cytidines to uridines, mostly in coding regions of mRNAs. Specific sequences around the editing sites are presumably recognized by up to 200 pentatricopeptide repeat (PPR) proteins. The here identified family of multiple organellar RNA editing factor (MORF) proteins provides additional components of the RNA editing machinery in both plant organelles. Two MORF proteins are required for editing in plastids; at least two are essential for editing in mitochondria. The loss of a MORF protein abolishes or lowers editing at multiple sites, many of which are addressed individually by PPR proteins. In plastids, both MORF proteins are required for complete editing at almost all sites, suggesting a heterodimeric complex. In yeast two-hybrid and pull-down assays, MORF proteins can connect to form hetero- and homodimers. Furthermore, MORF proteins interact selectively with PPR proteins, establishing a more complex editosome in plant organelles than previously thought.
Collapse
|
45
|
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.
Collapse
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: ,
| |
Collapse
|
46
|
Xu F, Zhao C, Li Y, Li J, Deng Y, Shi T. Exploring virus relationships based on virus-host protein-protein interaction network. BMC SYSTEMS BIOLOGY 2011; 5 Suppl 3:S11. [PMID: 22784617 PMCID: PMC3287566 DOI: 10.1186/1752-0509-5-s3-s11] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Currently, several systems have been proposed to classify viruses and indicate the relationships between different ones, though each system has its limitations because of the complexity of viral origins and their rapid evolution rate. We hereby propose a new method to explore the relationships between different viruses. Method A new method, which is based on the virus-host protein-protein interaction network, is proposed in this paper to categorize viruses. The distances between 114 human viruses, including 48 HIV-1 and HIV-2 viruses, are estimated according to the protein-protein interaction network between these viruses and humans. Conclusions/significance The results demonstrated that our method can disclose not only relationships consistent with the taxonomic results of currently used systems of classification but also the potential relationships that the current virus classification systems have not revealed. Moreover, the method points to a new direction where the functional relationships between viruses and hosts can be used to explore the virus relationships on a systematic level.
Collapse
Affiliation(s)
- Feng Xu
- The Center for Bioinformatics and Computational Biology and Institute of Biomedical Sciences, School of Life Science, East China Normal University, Shanghai, 200241, China
| | | | | | | | | | | |
Collapse
|
47
|
Mochida K, Shinozaki K. Advances in omics and bioinformatics tools for systems analyses of plant functions. PLANT & CELL PHYSIOLOGY 2011; 52:2017-38. [PMID: 22156726 PMCID: PMC3233218 DOI: 10.1093/pcp/pcr153] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.
Collapse
Affiliation(s)
- Keiichi Mochida
- RIKEN Biomass Engineering Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045 Japan.
| | | |
Collapse
|
48
|
Mochida K, Shinozaki K. Advances in omics and bioinformatics tools for systems analyses of plant functions. PLANT & CELL PHYSIOLOGY 2011. [PMID: 22156726 DOI: 10.1093/pcp/pc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.
Collapse
Affiliation(s)
- Keiichi Mochida
- RIKEN Biomass Engineering Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045 Japan.
| | | |
Collapse
|
49
|
Thakur S, Jha S, Chattoo BB. CastorDB: a comprehensive knowledge base for Ricinus communis. BMC Res Notes 2011; 4:356. [PMID: 21914200 PMCID: PMC3184282 DOI: 10.1186/1756-0500-4-356] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2011] [Accepted: 09/13/2011] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Ricinus communis is an industrially important non-edible oil seed crop, native to tropical and subtropical regions of the world. Although, R. communis genome was assembled in 4X draft by JCVI, and is predicted to contain 31,221 proteins, the function of most of the genes remains to be elucidated. A large amount of information of different aspects of the biology of R. communis is available, but most of the data are scattered one not easily accessible. Therefore a comprehensive resource on Castor, Castor DB, is required to facilitate research on this important plant. FINDINGS CastorDB is a specialized and comprehensive database for the oil seed plant R. communis, integrating information from several diverse resources. CastorDB contains information on gene and protein sequences, gene expression and gene ontology annotation of protein sequences obtained from a variety of repositories, as primary data. In addition, computational analysis was used to predict cellular localization, domains, pathways, protein-protein interactions, sumoylation sites and biochemical properties and has been included as derived data. This database has an intuitive user interface that prompts the user to explore various possible information resources available on a given gene or a protein. CONCLUSION CastorDB provides a user friendly comprehensive resource on castor with particular emphasis on its genome, transcriptome, and proteome and on protein domains, pathways, protein localization, presence of sumoylation sites, expression data and protein interacting partners.
Collapse
Affiliation(s)
- Shalabh Thakur
- Centre for Genome Research, Department of Microbiology and Biotechnology Centre, Faculty of Science, The M. S. University of Baroda, Vadodara-390002, India
| | - Sanjay Jha
- Department of Biotechnology, ASPEE College of Horticulture and Forestry, Navsari Agricultural University, Navsari, Gujarat-396450, India
| | - Bharat B Chattoo
- Centre for Genome Research, Department of Microbiology and Biotechnology Centre, Faculty of Science, The M. S. University of Baroda, Vadodara-390002, India
| |
Collapse
|
50
|
Agrawal GK, Bourguignon J, Rolland N, Ephritikhine G, Ferro M, Jaquinod M, Alexiou KG, Chardot T, Chakraborty N, Jolivet P, Doonan JH, Rakwal R. Plant organelle proteomics: collaborating for optimal cell function. MASS SPECTROMETRY REVIEWS 2011; 30:772-853. [PMID: 21038434 DOI: 10.1002/mas.20301] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2009] [Revised: 02/02/2010] [Accepted: 02/02/2010] [Indexed: 05/10/2023]
Abstract
Organelle proteomics describes the study of proteins present in organelle at a particular instance during the whole period of their life cycle in a cell. Organelles are specialized membrane bound structures within a cell that function by interacting with cytosolic and luminal soluble proteins making the protein composition of each organelle dynamic. Depending on organism, the total number of organelles within a cell varies, indicating their evolution with respect to protein number and function. For example, one of the striking differences between plant and animal cells is the plastids in plants. Organelles have their own proteins, and few organelles like mitochondria and chloroplast have their own genome to synthesize proteins for specific function and also require nuclear-encoded proteins. Enormous work has been performed on animal organelle proteomics. However, plant organelle proteomics has seen limited work mainly due to: (i) inter-plant and inter-tissue complexity, (ii) difficulties in isolation of subcellular compartments, and (iii) their enrichment and purity. Despite these concerns, the field of organelle proteomics is growing in plants, such as Arabidopsis, rice and maize. The available data are beginning to help better understand organelles and their distinct and/or overlapping functions in different plant tissues, organs or cell types, and more importantly, how protein components of organelles behave during development and with surrounding environments. Studies on organelles have provided a few good reviews, but none of them are comprehensive. Here, we present a comprehensive review on plant organelle proteomics starting from the significance of organelle in cells, to organelle isolation, to protein identification and to biology and beyond. To put together such a systematic, in-depth review and to translate acquired knowledge in a proper and adequate form, we join minds to provide discussion and viewpoints on the collaborative nature of organelles in cell, their proper function and evolution.
Collapse
Affiliation(s)
- Ganesh Kumar Agrawal
- Research Laboratory for Biotechnology and Biochemistry (RLABB), P.O. Box 13265, Sanepa, Kathmandu, Nepal.
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|