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Bulgakov VP. Chromatin modifications and memory in regulation of stress-related polyphenols: finding new ways to control flavonoid biosynthesis. Crit Rev Biotechnol 2024:1-17. [PMID: 38697923 DOI: 10.1080/07388551.2024.2336529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/18/2024] [Indexed: 05/05/2024]
Abstract
The influence of epigenetic factors on plant defense responses and the balance between growth and defense is becoming a central area in plant biology. It is believed that the biosynthesis of secondary metabolites can be regulated by epigenetic factors, but this is not associated with the formation of a "memory" to the previous biosynthetic status. This review shows that some epigenetic effects can result in epigenetic memory, which opens up new areas of research in secondary metabolites, in particular flavonoids. Plant-controlled chromatin modifications can lead to the generation of stress memory, a phenomenon through which information regarding past stress cues is retained, resulting in a modified response to recurring stress. How deeply are the mechanisms of chromatin modification and memory generation involved in the control of flavonoid biosynthesis? This article collects available information from the literature and interactome databases to address this issue. Visualization of the interaction of chromatin-modifying proteins with the flavonoid biosynthetic machinery is presented. Chromatin modifiers and "bookmarks" that may be involved in the regulation of flavonoid biosynthesis through memory have been identified. Through different mechanisms of chromatin modification, plants can harmonize flavonoid metabolism with: stress responses, developmental programs, light-dependent processes, flowering, and longevity programs. The available information points to the possibility of developing chromatin-modifying technologies to control flavonoid biosynthesis.
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Affiliation(s)
- Victor P Bulgakov
- Federal Scientific Center of the East Asia Terrestrial Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia
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2
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Zhang HB, Ding XB, Jin J, Guo WP, Yang QL, Chen PC, Yao H, Ruan L, Tao YT, Chen X. Predicted mouse interactome and network-based interpretation of differentially expressed genes. PLoS One 2022; 17:e0264174. [PMID: 35390003 PMCID: PMC8989236 DOI: 10.1371/journal.pone.0264174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 02/04/2022] [Indexed: 11/18/2022] Open
Abstract
The house mouse or Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database (MID, Version 1), which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn.
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Affiliation(s)
- Hai-Bo Zhang
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Xiao-Bao Ding
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Jie Jin
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Wen-Ping Guo
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Qiao-Lei Yang
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Peng-Cheng Chen
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Heng Yao
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Li Ruan
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Yu-Tian Tao
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
- * E-mail: (YTT); (XC)
| | - Xin Chen
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
- Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University, Hangzhou, China
- * E-mail: (YTT); (XC)
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3
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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
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4
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Zhang X, Li J, Pan BZ, Chen W, Chen M, Tang M, Xu ZF, Liu C. Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data. BMC Bioinformatics 2021; 22:409. [PMID: 34407772 PMCID: PMC8375076 DOI: 10.1186/s12859-021-04319-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically improved varieties of Jatropha. To extensively discover the potentially novel genes and pathways associated with the oil biosynthesis in J. curcas, new strategy other than homology alignment is on the demand. RESULTS In this study, we proposed a multi-step computational framework that integrates transcriptome and gene interactome data to predict functional pathways in non-model organisms in an extended process, and applied it to study oil biosynthesis pathway in J. curcas. Using homologous mapping against Arabidopsis and transcriptome profile analysis, we first constructed protein-protein interaction (PPI) and co-expression networks in J. curcas. Then, using the homologs of Arabidopsis oil-biosynthesis-related genes as seeds, we respectively applied two algorithm models, random walk with restart (RWR) in PPI network and negative binomial distribution (NBD) in co-expression network, to further extend oil-biosynthesis-related pathways and genes in J. curcas. At last, using k-nearest neighbors (KNN) algorithm, the predicted genes were further classified into different sub-pathways according to their possible functional roles. CONCLUSIONS Our method exhibited a highly efficient way of mining the extended oil biosynthesis pathway of J. curcas. Overall, 27 novel oil-biosynthesis-related gene candidates were predicted and further assigned to 5 sub-pathways. These findings can help better understanding of the oil biosynthesis pathway of J. curcas, as well as paving the way for the following J. curcas breeding application.
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Affiliation(s)
- Xuan Zhang
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Li
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Bang-Zhen Pan
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Wen Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Maosheng Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Mingyong Tang
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Zeng-Fu Xu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China. .,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
| | - Changning Liu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China. .,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
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Wang Y, Zhou M, Zou Q, Xu L. Machine learning for phytopathology: from the molecular scale towards the network scale. Brief Bioinform 2021; 22:6204793. [PMID: 33787847 DOI: 10.1093/bib/bbab037] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/09/2021] [Accepted: 01/26/2021] [Indexed: 01/16/2023] Open
Abstract
With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant-pathogen interactions and discuss the applications and advances of machine learning in plant-pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein-protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.
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Affiliation(s)
- Yansu Wang
- Postdoctoral Innovation Practice Base, Shenzhen Polytechnic, China
| | | | - Quan Zou
- University of Electronic Science and Technology of China
| | - Lei Xu
- Shenzhen Polytechnic, China
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6
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Guo WP, Ding XB, Jin J, Zhang HB, Yang QL, Chen PC, Yao H, Ruan LI, Tao YT, Chen X. HIR V2: a human interactome resource for the biological interpretation of differentially expressed genes via gene set linkage analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6156843. [PMID: 33677507 PMCID: PMC7937034 DOI: 10.1093/database/baab009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 02/09/2021] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
To facilitate biomedical studies of disease mechanisms, a high-quality interactome that connects functionally related genes is needed to help investigators formulate pathway hypotheses and to interpret the biological logic of a phenotype at the biological process level. Interactions in the updated version of the human interactome resource (HIR V2) were inferred from 36 mathematical characterizations of six types of data that suggest functional associations between genes. This update of the HIR consists of 88 069 pairs of genes (23.2% functional interactions of HIR V2 are in common with the previous version of HIR), representing functional associations that are of strengths similar to those between well-studied protein interactions. Among these functional interactions, 57% may represent protein interactions, which are expected to cover 32% of the true human protein interactome. The gene set linkage analysis (GSLA) tool is developed based on the high-quality HIR V2 to identify the potential functional impacts of the observed transcriptomic changes, helping to elucidate their biological significance and complementing the currently widely used enrichment-based gene set interpretation tools. A case study shows that the annotations reported by the HIR V2/GSLA system are more comprehensive and concise compared to those obtained by the widely used gene set annotation tools such as PANTHER and DAVID. The HIR V2 and GSLA are available at http://human.biomedtzc.cn.
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Affiliation(s)
- Wen-Ping Guo
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou City, Zhejiang Province, Taizhou 318000, China
| | - Xiao-Bao Ding
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou City, Zhejiang Province, Taizhou 318000, China
| | - Jie Jin
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou City, Zhejiang Province, Taizhou 318000, China
| | - Hai-Bo Zhang
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou City, Zhejiang Province, Taizhou 318000, China
| | - Qiao-Lei Yang
- Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, 866 Yuhangtang Road, Xihu District, Hangzhou City, Zhejiang Province, Hangzhou 310058, China
| | - Peng-Cheng Chen
- Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, 866 Yuhangtang Road, Xihu District, Hangzhou City, Zhejiang Province, Hangzhou 310058, China
| | - Heng Yao
- Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, 866 Yuhangtang Road, Xihu District, Hangzhou City, Zhejiang Province, Hangzhou 310058, China
| | - L I Ruan
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou City, Zhejiang Province, Taizhou 318000, China
| | - Yu-Tian Tao
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou City, Zhejiang Province, Taizhou 318000, China
| | - Xin Chen
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou City, Zhejiang Province, Taizhou 318000, China.,Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, 866 Yuhangtang Road, Xihu District, Hangzhou City, Zhejiang Province, Hangzhou 310058, China.,Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou City, Zhejiang Province, Hangzhou 310058, China
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Bulgakov VP, Koren OG. Basic Protein Modules Combining Abscisic Acid and Light Signaling in Arabidopsis. FRONTIERS IN PLANT SCIENCE 2021; 12:808960. [PMID: 35046987 PMCID: PMC8762054 DOI: 10.3389/fpls.2021.808960] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/23/2021] [Indexed: 05/02/2023]
Abstract
It is generally accepted that plants use the complex signaling system regulated by light and abscisic acid (ABA) signaling components to optimize growth and development in different situations. The role of ABA-light interactions is evident in the coupling of stress defense reactions with seed germination and root development, maintaining of stem cell identity and stem cell specification, stem elongation and leaf development, flowering and fruit formation, senescence, and shade avoidance. All these processes are regulated jointly by the ABA-light signaling system. Although a lot of work has been devoted to ABA-light signal interactions, there is still no systematic description of central signaling components and protein modules, which jointly regulate plant development. New data have emerged to promote understanding of how ABA and light signals are integrated at the molecular level, representing an extensively growing area of research. This work is intended to fill existing gaps by using literature data combined with bioinformatics analysis.
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Chen PC, Ruan L, Jin J, Tao YT, Ding XB, Zhang HB, Guo WP, Yang QL, Yao H, Chen X. Predicted functional interactome of Caenorhabditis elegans and a web tool for the functional interpretation of differentially expressed genes. Biol Direct 2020; 15:20. [PMID: 33076954 PMCID: PMC7574172 DOI: 10.1186/s13062-020-00271-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 09/23/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The nematode worm, Caenorhabditis elegans, is a saprophytic species that has been emerging as a standard model organism since the early 1960s. This species is useful in numerous fields, including developmental biology, neurobiology, and ageing. A high-quality comprehensive molecular interaction network is needed to facilitate molecular mechanism studies in C. elegans. RESULTS We present the predicted functional interactome of Caenorhabditis elegans (FIC), which integrates functional association data from 10 public databases to infer functional gene interactions on diverse functional perspectives. In this work, FIC includes 108,550 putative functional associations with balanced sensitivity and specificity, which are expected to cover 21.42% of all C. elegans protein interactions, and 29.25% of these associations may represent protein interactions. Based on FIC, we developed a gene set linkage analysis (GSLA) web tool to interpret potential functional impacts from a set of differentially expressed genes observed in transcriptome analyses. CONCLUSION We present the predicted C. elegans interactome database FIC, which is a high-quality database of predicted functional interactions among genes. The functional interactions in FIC serve as a good reference interactome for GSLA to annotate differentially expressed genes for their potential functional impacts. In a case study, the FIC/GSLA system shows more comprehensive and concise annotations compared to other widely used gene set annotation tools, including PANTHER and DAVID. FIC and its associated GSLA are available at the website http://worm.biomedtzc.cn .
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Affiliation(s)
- Peng-Cheng Chen
- Institute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China
| | - Li Ruan
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China
| | - Jie Jin
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China
| | - Yu-Tian Tao
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China
| | - Xiao-Bao Ding
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China
| | - Hai-Bo Zhang
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China
| | - Wen-Ping Guo
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China
| | - Qiao-Lei Yang
- Institute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China
| | - Heng Yao
- Institute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China
| | - Xin Chen
- Institute of Pharmaceutical Biotechnology of Zhejiang University School of Medicine and Department of Radiology of the First Affiliated Hospital, Hangzhou, 310058, China. .,Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, China. .,Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University, Hangzhou, 310058, China.
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James K, Olson PD. The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma. BMC Genomics 2020; 21:346. [PMID: 32380953 PMCID: PMC7204028 DOI: 10.1186/s12864-020-6710-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 03/30/2020] [Indexed: 12/14/2022] Open
Abstract
Background Reference genome and transcriptome assemblies of helminths have reached a level of completion whereby secondary analyses that rely on accurate gene estimation or syntenic relationships can be now conducted with a high level of confidence. Recent public release of the v.3 assembly of the mouse bile-duct tapeworm, Hymenolepis microstoma, provides chromosome-level characterisation of the genome and a stabilised set of protein coding gene models underpinned by bioinformatic and empirical data. However, interactome data have not been produced. Conserved protein-protein interactions in other organisms, termed interologs, can be used to transfer interactions between species, allowing systems-level analysis in non-model organisms. Results Here, we describe a probabilistic, integrated network of interologs for the H. microstoma proteome, based on conserved protein interactions found in eukaryote model species. Almost a third of the 10,139 gene models in the v.3 assembly could be assigned interaction data and assessment of the resulting network indicates that topologically-important proteins are related to essential cellular pathways, and that the network clusters into biologically meaningful components. Moreover, network parameters are similar to those of single-species interaction networks that we constructed in the same way for S. cerevisiae, C. elegans and H. sapiens, demonstrating that information-rich, system-level analyses can be conducted even on species separated by a large phylogenetic distance from the major model organisms from which most protein interaction evidence is based. Using the interolog network, we then focused on sub-networks of interactions assigned to discrete suites of genes of interest, including signalling components and transcription factors, germline multipotency genes, and genes differentially-expressed between larval and adult worms. Results show not only an expected bias toward highly-conserved proteins, such as components of intracellular signal transduction, but in some cases predicted interactions with transcription factors that aid in identifying their target genes. Conclusions With key helminth genomes now complete, systems-level analyses can provide an important predictive framework to guide basic and applied research on helminths and will become increasingly informative as new protein-protein interaction data accumulate.
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Affiliation(s)
- Katherine James
- Department of Applied Sciences, Northumbria University, Newcastle Upon Tyne, UK. .,Department of Life Sciences, The Natural History Museum, Cromwell Road, London, UK.
| | - Peter D Olson
- Department of Life Sciences, The Natural History Museum, Cromwell Road, London, UK
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10
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Abstract
Plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light, collectively called the shade avoidance syndrome (SAS). Plants also have extensive systems to defend themselves against pathogens and herbivores. Defense and shade avoidance are two fundamental components of plant survival and productivity, and there are often tradeoffs between growth and defense. Recently, MYC2, a major positive regulator of defense, was reported to inhibit elongation during shade avoidance. Here, we further investigate the role of MYC2 and the related MYC3 and MYC4 in shade avoidance, and we examine the relationship between MYC2/3/4 and the PIF family of light-regulated transcription factors. We demonstrate that MYC2/3/4 inhibit both elongation and flowering. Furthermore, using both genetic and transcriptomic analysis we find that MYCs and PIFs generally function independently in growth regulation. However, surprisingly, the pif4/5/7 triple mutant restored the petiole shade avoidance response of myc2 (jin1-2) and myc2/3/4 We theorize that increased petiole elongation in myc2/3/4 could be more due to resource tradeoffs or post-translational modifications rather than interactions with PIF4/5/7 affecting gene regulation.
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11
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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.
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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.
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12
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Tao YT, Ding XB, Jin J, Zhang HB, Guo WP, Ruan L, Yang QL, Chen PC, Yao H, Chen X. Predicted rat interactome database and gene set linkage analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5996022. [PMID: 33216897 PMCID: PMC7678787 DOI: 10.1093/database/baaa086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 08/21/2020] [Accepted: 09/10/2020] [Indexed: 11/13/2022]
Abstract
Rattus norvegicus, or the rat, has been widely used as animal models for a diversity of human diseases in the last 150 years. The rat, as a disease model, has the advantage of relatively large body size and highly similar physiology to humans. In drug discovery, rat models are routinely used in drug efficacy and toxicity assessments. To facilitate molecular pharmacology studies in rats, we present the predicted rat interactome database (PRID), which is a database of high-quality predicted functional gene interactions with balanced sensitivity and specificity. PRID integrates functional gene association data from 10 public databases and infers 305 939 putative functional associations, which are expected to include 13.02% of all rat protein interactions, and 52.59% of these function associations may represent protein interactions. This set of functional interactions may not only facilitate hypothesis formulation in molecular mechanism studies, but also serve as a reference interactome for users to perform gene set linkage analysis (GSLA), which is a web-based tool to infer the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that GSLA based on PRID may provide more precise and informative annotations for investigators to understand the physiological mechanisms underlying a phenotype and lead investigators to testable hypotheses for further studies. Widely used functional annotation tools such as Gene Ontology (GO) analysis, and Database for Annotation, Visualization and Integrated Discovery (DAVID) did not provide similar insights. Database URL: http://rat.biomedtzc.cn.
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Affiliation(s)
- Yu-Tian Tao
- Institute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China
| | - Xiao-Bao Ding
- Institute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China
| | - Jie Jin
- Institute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China
| | - Hai-Bo Zhang
- Institute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China
| | - Wen-Ping Guo
- Institute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China
| | - Li Ruan
- Institute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China
| | - Qiao-Lei Yang
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Peng-Cheng Chen
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Heng Yao
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Xin Chen
- Institute of Big data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, 1139 Shifu Avenue, Taizhou, 318000, China.,Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.,Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
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13
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Zhang X, Pan BZ, Chen M, Chen W, Li J, Xu ZF, Liu C. JCDB: a comprehensive knowledge base for Jatropha curcas, an emerging model for woody energy plants. BMC Genomics 2019; 20:958. [PMID: 31874631 PMCID: PMC6929279 DOI: 10.1186/s12864-019-6356-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 11/29/2019] [Indexed: 12/02/2022] Open
Abstract
Background Jatropha curcas is an oil-bearing plant, and has seeds with high oil content (~ 40%). Several advantages, such as easy genetic transformation and short generation duration, have led to the emergence of J. curcas as a model for woody energy plants. With the development of high-throughput sequencing, the genome of Jatropha curcas has been sequenced by different groups and a mass of transcriptome data was released. How to integrate and analyze these omics data is crucial for functional genomics research on J. curcas. Results By establishing pipelines for processing novel gene identification, gene function annotation, and gene network construction, we systematically integrated and analyzed a series of J. curcas transcriptome data. Based on these data, we constructed a J. curcas database (JCDB), which not only includes general gene information, gene functional annotation, gene interaction networks, and gene expression matrices but also provides tools for browsing, searching, and downloading data, as well as online BLAST, the JBrowse genome browser, ID conversion, heatmaps, and gene network analysis tools. Conclusions JCDB is the most comprehensive and well annotated knowledge base for J. curcas. We believe it will make a valuable contribution to the functional genomics study of J. curcas. The database is accessible at http://jcdb.xtbg.ac.cn.
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Affiliation(s)
- Xuan Zhang
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bang-Zhen Pan
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China
| | - Maosheng Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China
| | - Wen Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China
| | - Jing Li
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China
| | - Zeng-Fu Xu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China.
| | - Changning Liu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, Yunnan, 666303, China.
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14
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Ma X, Meng Y, Wang P, Tang Z, Wang H, Xie T. Bioinformatics-assisted, integrated omics studies on medicinal plants. Brief Bioinform 2019; 21:1857-1874. [PMID: 32706024 DOI: 10.1093/bib/bbz132] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 09/03/2019] [Accepted: 09/19/2019] [Indexed: 12/14/2022] Open
Abstract
The immense therapeutic and economic values of medicinal plants have attracted increasing attention from the worldwide researchers. It has been recognized that production of the authentic and high-quality herbal drugs became the prerequisite for maintaining the healthy development of the traditional medicine industry. To this end, intensive research efforts have been devoted to the basic studies, in order to pave a way for standardized authentication of the plant materials, and bioengineering of the metabolic pathways in the medicinal plants. In this paper, the recent advances of omics studies on the medicinal plants were summarized from several aspects, including phenomics and taxonomics, genomics, transcriptomics, proteomics and metabolomics. We proposed a multi-omics data-based workflow for medicinal plant research. It was emphasized that integration of the omics data was important for plant authentication and mechanistic studies on plant metabolism. Additionally, the computational tools for proper storage, efficient processing and high-throughput analyses of the omics data have been introduced into the workflow. According to the workflow, authentication of the medicinal plant materials should not only be performed at the phenomics level but also be implemented by genomic and metabolomic marker-based examination. On the other hand, functional genomics studies, transcriptional regulatory networks and protein-protein interactions will contribute greatly for deciphering the secondary metabolic pathways. Finally, we hope that our work could inspire further efforts on the bioinformatics-assisted, integrated omics studies on the medicinal plants.
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Affiliation(s)
- Xiaoxia Ma
- Hangzhou Normal University, Hangzhou 311121, P.R. China.,Holistic Integrative Pharmacy Institutes, Hangzhou Normal University, Hangzhou 311121, P.R. China.,Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province and Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, P.R. China.,College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, P.R. China
| | - Yijun Meng
- Hangzhou Normal University, Hangzhou 311121, P.R. China.,College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 311121, P.R. China
| | - Pu Wang
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, P.R. China
| | - Zhonghai Tang
- College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, P.R. China
| | - Huizhong Wang
- Hangzhou Normal University, Hangzhou 311121, P.R. China.,College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 311121, P.R. China
| | - Tian Xie
- Hangzhou Normal University, Hangzhou 311121, P.R. China.,Holistic Integrative Pharmacy Institutes, Hangzhou Normal University, Hangzhou 311121, P.R. China.,Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province and Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, P.R. China
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15
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Ding Z, Kihara D. Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. CURRENT PROTOCOLS IN PROTEIN SCIENCE 2018; 93:e62. [PMID: 29927082 PMCID: PMC6097941 DOI: 10.1002/cpps.62] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Ziyun Ding
- Department of Biological Science, Purdue University, West Lafayette, IN, 47907 USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN, 47907 USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907 USA
- Corresponding author: DK; , Phone: 1-765-496-2284 (DK)
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16
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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.
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17
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Kotlyar M, Rossos AEM, Jurisica I. Prediction of Protein-Protein Interactions. ACTA ACUST UNITED AC 2017; 60:8.2.1-8.2.14. [PMID: 29220074 DOI: 10.1002/cpbi.38] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The authors provide an overview of physical protein-protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field-highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Max Kotlyar
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Andrea E M Rossos
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Igor Jurisica
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Departments of Medical Biophysics and Computer Science, University of Toronto, Ontario, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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18
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Majewska M, Wysokińska H, Kuźma Ł, Szymczyk P. Eukaryotic and prokaryotic promoter databases as valuable tools in exploring the regulation of gene transcription: a comprehensive overview. Gene 2017; 644:38-48. [PMID: 29104165 DOI: 10.1016/j.gene.2017.10.079] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 07/26/2017] [Accepted: 10/27/2017] [Indexed: 01/02/2023]
Abstract
The complete exploration of the regulation of gene expression remains one of the top-priority goals for researchers. As the regulation is mainly controlled at the level of transcription by promoters, study on promoters and findings are of great importance. This review summarizes forty selected databases that centralize experimental and theoretical knowledge regarding the organization of promoters, interacting transcription factors (TFs) and microRNAs (miRNAs) in many eukaryotic and prokaryotic species. The presented databases offer researchers valuable support in elucidating the regulation of gene transcription.
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Affiliation(s)
- Małgorzata Majewska
- Department of Biology and Pharmaceutical Botany, Medical University of Lodz, 90-151 Lodz, Poland.
| | - Halina Wysokińska
- Department of Biology and Pharmaceutical Botany, Medical University of Lodz, 90-151 Lodz, Poland
| | - Łukasz Kuźma
- Department of Biology and Pharmaceutical Botany, Medical University of Lodz, 90-151 Lodz, Poland
| | - Piotr Szymczyk
- Department of Pharmaceutical Biotechnology, Medical University of Lodz, 90-151 Lodz, Poland
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19
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Qin J, Tang Z, Ma X, Meng Y. Investigating the regulatory roles of the microRNAs and the Argonaute 1-enriched small RNAs in plant metabolism. Gene 2017; 628:180-189. [PMID: 28698160 DOI: 10.1016/j.gene.2017.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 06/29/2017] [Accepted: 07/07/2017] [Indexed: 12/01/2022]
Abstract
The biological roles of small RNAs (sRNAs) in metabolic processes are emerging. However, a systemic study is needed to investigate the wide-spread involvement of the sRNAs in plant metabolism. By using the metabolism-related transcripts retrieved from the public database Plant Metabolic Network, and the publicly available sRNA high-throughput sequencing data, large-scale target identification was performed for microRNAs (miRNAs) and Argonaute 1 (AGO1)-enriched sRNAs in Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa). Based on the publicly available degradome sequencing data, 200 miRNA/sRNA-target pairs involving 19 miRNAs, 111 AGO1-enriched sRNAs and 58 target transcripts in Arabidopsis, and 151 pairs involving 62 miRNAs, 33 AGO1-enriched sRNAs and 69 target transcripts in rice were identified. After considering protein-protein interactions for the above identified target genes, a total of 251 pairs involving 21 miRNAs, 120 AGO1-enriched sRNAs and 75 target transcripts exist within the regulatory network of Arabidopsis, and 168 pairs involving 64 miRNAs, 38 AGO1-enriched sRNAs and 80 target transcripts exist in rice. Based on GO (Gene Ontology) term enrichment analysis, the targets within the networks of both plants are enriched in "metabolic process" and "catalytic activity", pointing to the high relevance of the established networks to metabolism. Several functionally conserved subnetworks were identified between the two plant species. Our study provides a basis for studies on metabolism-related sRNAs in plants.
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Affiliation(s)
- Jingping Qin
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, PR China
| | - Zhonghai Tang
- College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, PR China.
| | - Xiaoxia Ma
- College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 310036, PR China
| | - Yijun Meng
- College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 310036, PR China.
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20
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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.
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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
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21
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Chang JW, Zhou YQ, Ul Qamar MT, Chen LL, Ding YD. Prediction of Protein-Protein Interactions by Evidence Combining Methods. Int J Mol Sci 2016; 17:ijms17111946. [PMID: 27879651 PMCID: PMC5133940 DOI: 10.3390/ijms17111946] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/15/2016] [Accepted: 11/15/2016] [Indexed: 12/27/2022] Open
Abstract
Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.
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Affiliation(s)
- Ji-Wei Chang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yan-Qing Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Muhammad Tahir Ul Qamar
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ling-Ling Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yu-Duan Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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22
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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.
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23
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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: 77] [Impact Index Per Article: 9.6] [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.
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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
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24
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Yue J, Xu W, Ban R, Huang S, Miao M, Tang X, Liu G, Liu Y. PTIR: Predicted Tomato Interactome Resource. Sci Rep 2016; 6:25047. [PMID: 27121261 PMCID: PMC4848565 DOI: 10.1038/srep25047] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 04/08/2016] [Indexed: 01/18/2023] Open
Abstract
Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration.
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Affiliation(s)
- Junyang Yue
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Wei Xu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Rongjun Ban
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Shengxiong Huang
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Min Miao
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Xiaofeng Tang
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Guoqing Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Yongsheng Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
- Ministry of Education Key Laboratory for Bio-resource and Eco-environment, College of Life Science, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China
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Bulgakov VP, Avramenko TV, Tsitsiashvili GS. Critical analysis of protein signaling networks involved in the regulation of plant secondary metabolism: focus on anthocyanins. Crit Rev Biotechnol 2016; 37:685-700. [PMID: 26912350 DOI: 10.3109/07388551.2016.1141391] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Anthocyanin biosynthesis in Arabidopsis is a convenient and relatively simple model for investigating the basic principles of secondary metabolism regulation. In recent years, many publications have described links between anthocyanin biosynthesis and general defense reactions in plants as well as photomorphogenesis and hormonal signaling. These relationships are complex, and they cannot be understood intuitively. Upon observing the lacuna in the Arabidopsis interactome (an interaction map of the factors involved in the regulation of Arabidopsis secondary metabolism is not available), we attempted to connect various cellular processes that affect anthocyanin biosynthesis. In this review, we revealed the main signaling protein modules that regulate anthocyanin biosynthesis. To our knowledge, this is the first reconstruction of a network of proteins involved in plant secondary metabolism.
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Affiliation(s)
- Victor P Bulgakov
- a Institute of Biology and Soil Science, Far East Branch of the Russian Academy of Sciences , Vladivostok 690022 , Russia and.,b Far Eastern Federal University , Vladivostok 690950 , Russia , and
| | - Tatiana V Avramenko
- a Institute of Biology and Soil Science, Far East Branch of the Russian Academy of Sciences , Vladivostok 690022 , Russia and
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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.
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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
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27
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Chow CN, Zheng HQ, Wu NY, Chien CH, Huang HD, Lee TY, Chiang-Hsieh YF, Hou PF, Yang TY, Chang WC. PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants. Nucleic Acids Res 2015; 44:D1154-60. [PMID: 26476450 PMCID: PMC4702776 DOI: 10.1093/nar/gkv1035] [Citation(s) in RCA: 244] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 09/29/2015] [Indexed: 12/17/2022] Open
Abstract
Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks.
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Affiliation(s)
- Chi-Nga Chow
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Han-Qin Zheng
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Nai-Yun Wu
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Chia-Hung Chien
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Hsien-Da Huang
- Department of Biological Science and Technology, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li 320, Taiwan
| | - Yi-Fan Chiang-Hsieh
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Ping-Fu Hou
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan College of Biosciences and Biotechnology, Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Tien-Yi Yang
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Wen-Chi Chang
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
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Rodríguez-Cazorla E, Ripoll JJ, Andújar A, Bailey LJ, Martínez-Laborda A, Yanofsky MF, Vera A. K-homology nuclear ribonucleoproteins regulate floral organ identity and determinacy in arabidopsis. PLoS Genet 2015; 11:e1004983. [PMID: 25658099 PMCID: PMC4450054 DOI: 10.1371/journal.pgen.1004983] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Accepted: 01/05/2015] [Indexed: 12/20/2022] Open
Abstract
Post-transcriptional control is nowadays considered a main checking point for correct gene regulation during development, and RNA binding proteins actively participate in this process. Arabidopsis thaliana FLOWERING LOCUS WITH KH DOMAINS (FLK) and PEPPER (PEP) genes encode RNA-binding proteins that contain three K-homology (KH)-domain, the typical configuration of Poly(C)-binding ribonucleoproteins (PCBPs). We previously demonstrated that FLK and PEP interact to regulate FLOWERING LOCUS C (FLC), a central repressor of flowering time. Now we show that FLK and PEP also play an important role in the maintenance of the C-function during floral organ identity by post-transcriptionally regulating the MADS-box floral homeotic gene AGAMOUS (AG). Previous studies have indicated that the KH-domain containing protein HEN4, in concert with the CCCH-type RNA binding protein HUA1 and the RPR-type protein HUA2, facilitates maturation of the AG pre-mRNA. In this report we show that FLK and PEP genetically interact with HEN4, HUA1, and HUA2, and that the FLK and PEP proteins physically associate with HUA1 and HEN4. Taken together, these data suggest that HUA1, HEN4, PEP and FLK are components of the same post-transcriptional regulatory module that ensures normal processing of the AG pre-mRNA. Our data better delineates the roles of PEP in plant development and, for the first time, links FLK to a morphogenetic process. Unlike animals, angiosperms (flowering plants) lack a germline that is set-aside early in embryo development. Contrariwise, reproductive success relies on the formation of flowers during adult life, which provide the germ cells and the means for fertilization. Therefore, timing of flowering and flower organ morphogenesis are critical developmental operations that must be finely regulated and coordinated to complete reproduction. Arabidopsis thaliana FLOWERING LOCUS WITH KH DOMAINS (FLK) and PEPPER (PEP) encode two KH-domain RNA-binding proteins phylogenetically related to human proteins characterized by their high developmental versatility. FLK and PEP modulate the mRNA expression of the MADS-box gene FLOWERING LOCUS C, key in flowering control. In this work we have found that FLK and PEP also play a pivotal role in flower organogenesis by post-transcriptionally regulating the MADS-box floral organ identity gene AGAMOUS (AG). Interestingly, FLK and PEP physically interact with proteins involved in AG pre-mRNA processing to secure correct AG function in the floral meristem and flower. Taken together, our results reveal the existence of a post-transcriptional regulatory activity controlling key master genes for floral timing and flower morphogenesis, which might be instrumental for coordinating both developmental phases.
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Affiliation(s)
| | - Juan José Ripoll
- Division of Biological Sciences, Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California, United States of America
| | - Alfonso Andújar
- Área de Genética, Universidad Miguel Hernández, Campus de Sant Joan d’Alacant, Sant Joan d’Alacant, Alicante, Spain
| | - Lindsay J. Bailey
- Division of Biological Sciences, Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California, United States of America
| | - Antonio Martínez-Laborda
- Área de Genética, Universidad Miguel Hernández, Campus de Sant Joan d’Alacant, Sant Joan d’Alacant, Alicante, Spain
| | - Martin F. Yanofsky
- Division of Biological Sciences, Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California, United States of America
| | - Antonio Vera
- Área de Genética, Universidad Miguel Hernández, Campus de Sant Joan d’Alacant, Sant Joan d’Alacant, Alicante, Spain
- * E-mail:
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29
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Pajoro A, Biewers S, Dougali E, Leal Valentim F, Mendes MA, Porri A, Coupland G, Van de Peer Y, van Dijk ADJ, Colombo L, Davies B, Angenent GC. The (r)evolution of gene regulatory networks controlling Arabidopsis plant reproduction: a two-decade history. JOURNAL OF EXPERIMENTAL BOTANY 2014; 65:4731-45. [PMID: 24913630 DOI: 10.1093/jxb/eru233] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Successful plant reproduction relies on the perfect orchestration of singular processes that culminate in the product of reproduction: the seed. The floral transition, floral organ development, and fertilization are well-studied processes and the genetic regulation of the various steps is being increasingly unveiled. Initially, based predominantly on genetic studies, the regulatory pathways were considered to be linear, but recent genome-wide analyses, using high-throughput technologies, have begun to reveal a different scenario. Complex gene regulatory networks underlie these processes, including transcription factors, microRNAs, movable factors, hormones, and chromatin-modifying proteins. Here we review recent progress in understanding the networks that control the major steps in plant reproduction, showing how new advances in experimental and computational technologies have been instrumental. As these recent discoveries were obtained using the model species Arabidopsis thaliana, we will restrict this review to regulatory networks in this important model species. However, more fragmentary information obtained from other species reveals that both the developmental processes and the underlying regulatory networks are largely conserved, making this review also of interest to those studying other plant species.
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Affiliation(s)
- Alice Pajoro
- Plant Research International (PRI) Droevendaalseweg 1, 6708 PB Wageningen, The Netherlands Laboratory of Molecular Biology, Wageningen University, Droevendaalseweg 1, 6708 PB Wageningen, The Netherlands
| | - Sandra Biewers
- Centre for Plant Sciences, University of Leeds, Leeds LS2 9JT, UK
| | - Evangelia Dougali
- Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Ghent, Belgium
| | - Felipe Leal Valentim
- Plant Research International (PRI) Droevendaalseweg 1, 6708 PB Wageningen, The Netherlands
| | - Marta Adelina Mendes
- Dipartimento di BioScienze, Università degli Studi di Milano, Via Celoria 26, 20133, Milan, Italy
| | - Aimone Porri
- Max Planck Institute for Plant Breeding Research, Carl von Linne Weg 10, D-50829 Cologne, Germany
| | - George Coupland
- Max Planck Institute for Plant Breeding Research, Carl von Linne Weg 10, D-50829 Cologne, Germany
| | - Yves Van de Peer
- Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Ghent, Belgium Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria, 0028, South Africa
| | - Aalt D J van Dijk
- Plant Research International (PRI) Droevendaalseweg 1, 6708 PB Wageningen, The Netherlands Biometris, Wageningen University, Droevendaalseweg 1, 6708 PB Wageningen, The Netherlands
| | - Lucia Colombo
- Dipartimento di BioScienze, Università degli Studi di Milano, Via Celoria 26, 20133, Milan, Italy
| | - Brendan Davies
- Centre for Plant Sciences, University of Leeds, Leeds LS2 9JT, UK
| | - Gerco C Angenent
- Plant Research International (PRI) Droevendaalseweg 1, 6708 PB Wageningen, The Netherlands Laboratory of Molecular Biology, Wageningen University, Droevendaalseweg 1, 6708 PB Wageningen, The Netherlands
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30
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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.
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Affiliation(s)
- Bhavisha P Sheth
- Department of Biosciences, Centre for Advanced Studies in Plant Biotechnology and Genetic Engineering, Saurashtra University, Rajkot, 360005, Gujarat, India,
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31
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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.
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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.
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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.
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Affiliation(s)
- Eli Rodgers-Melnick
- Department of Biology, West Virginia University, Morgantown, West Virginia, 26506, USA.
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Braun P, Aubourg S, Van Leene J, De Jaeger G, Lurin C. Plant protein interactomes. ANNUAL REVIEW OF PLANT BIOLOGY 2013; 64:161-87. [PMID: 23330791 DOI: 10.1146/annurev-arplant-050312-120140] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.
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Affiliation(s)
- Pascal Braun
- Department of Plant Systems Biology, Center for Life and Food Sciences Weihenstephan, Technische Universität München (TUM), 85354 Freising-Weihenstephan, Germany.
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Abstract
Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.
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Affiliation(s)
- Casey S. Greene
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Olga G. Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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35
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Ng S, Giraud E, Duncan O, Law SR, Wang Y, Xu L, Narsai R, Carrie C, Walker H, Day DA, Blanco NE, Strand Å, Whelan J, Ivanova A. Cyclin-dependent kinase E1 (CDKE1) provides a cellular switch in plants between growth and stress responses. J Biol Chem 2012; 288:3449-59. [PMID: 23229550 DOI: 10.1074/jbc.m112.416727] [Citation(s) in RCA: 100] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Plants must deal effectively with unfavorable growth conditions that necessitate a coordinated response to integrate cellular signals with mitochondrial retrograde signals. A genetic screen was carried out to identify regulators of alternative oxidase (rao mutants), using AOX1a expression as a model system to study retrograde signaling in plants. Two independent rao1 mutant alleles identified CDKE1 as a central nuclear component integrating mitochondrial retrograde signals with energy signals under stress. CDKE1 is also necessary for responses to general cellular stresses, such as H(2)O(2) and cold that act, at least in part, via anterograde pathways, and integrates signals from central energy/stress sensing kinase signal transduction pathways within the nucleus. Together, these results place CDKE1 as a central kinase integrating diverse cellular signals and shed light on a mechanism by which plants can effectively switch between growth and stress responses.
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Affiliation(s)
- Sophia Ng
- Australian Research Council Centre of Excellence in Plant Energy Biology, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia
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Tzfadia O, Amar D, Bradbury LM, Wurtzel ET, Shamir R. The MORPH algorithm: ranking candidate genes for membership in Arabidopsis and tomato pathways. THE PLANT CELL 2012; 24:4389-406. [PMID: 23204403 PMCID: PMC3531841 DOI: 10.1105/tpc.112.104513] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 10/25/2012] [Accepted: 10/29/2012] [Indexed: 05/08/2023]
Abstract
Closing gaps in our current knowledge about biological pathways is a fundamental challenge. The development of novel computational methods along with high-throughput experimental data carries the promise to help in the challenge. We present an algorithm called MORPH (for module-guided ranking of candidate pathway genes) for revealing unknown genes in biological pathways. The method receives as input a set of known genes from the target pathway, a collection of expression profiles, and interaction and metabolic networks. Using machine learning techniques, MORPH selects the best combination of data and analysis method and outputs a ranking of candidate genes predicted to belong to the target pathway. We tested MORPH on 230 known pathways in Arabidopsis thaliana and 93 known pathways in tomato (Solanum lycopersicum) and obtained high-quality cross-validation results. In the photosynthesis light reactions, homogalacturonan biosynthesis, and chlorophyll biosynthetic pathways of Arabidopsis, genes ranked highly by MORPH were recently verified to be associated with these pathways. MORPH candidates ranked for the carotenoid pathway from Arabidopsis and tomato are derived from pathways that compete for common precursors or from pathways that are coregulated with or regulate the carotenoid biosynthetic pathway.
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Affiliation(s)
- Oren Tzfadia
- The Graduate School and University Center, The City University of New York, New York, New York 10016-4309
- Department of Biological Sciences, Lehman College, The City University of New York, Bronx, New York 10468
| | - David Amar
- Blavatnik School of Computer Science, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel
| | - Louis M.T. Bradbury
- Department of Biological Sciences, Lehman College, The City University of New York, Bronx, New York 10468
| | - Eleanore T. Wurtzel
- The Graduate School and University Center, The City University of New York, New York, New York 10016-4309
- Department of Biological Sciences, Lehman College, The City University of New York, Bronx, New York 10468
| | - Ron Shamir
- Blavatnik School of Computer Science, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel
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Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system. Microbiol Mol Biol Rev 2012; 76:331-82. [PMID: 22688816 DOI: 10.1128/mmbr.05021-11] [Citation(s) in RCA: 134] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.
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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: 49] [Impact Index Per Article: 4.1] [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.
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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
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Vadassery J, Reichelt M, Hause B, Gershenzon J, Boland W, Mithöfer A. CML42-mediated calcium signaling coordinates responses to Spodoptera herbivory and abiotic stresses in Arabidopsis. PLANT PHYSIOLOGY 2012; 159:1159-75. [PMID: 22570470 PMCID: PMC3387702 DOI: 10.1104/pp.112.198150] [Citation(s) in RCA: 183] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2012] [Accepted: 05/03/2012] [Indexed: 05/18/2023]
Abstract
In the interaction between Arabidopsis (Arabidopsis thaliana) and the generalist herbivorous insect Spodoptera littoralis, little is known about early events in defense signaling and their link to downstream phytohormone pathways. S. littoralis oral secretions induced both Ca²⁺ and phytohormone elevation in Arabidopsis. Plant gene expression induced by oral secretions revealed up-regulation of a gene encoding a calmodulin-like protein, CML42. Functional analysis of cml42 plants revealed more resistance to herbivory than in the wild type, because caterpillars gain less weight on the mutant, indicating that CML42 negatively regulates plant defense; cml42 also showed increased aliphatic glucosinolate content and hyperactivated transcript accumulation of the jasmonic acid (JA)-responsive genes VSP2 and Thi2.1 upon herbivory, which might contribute to increased resistance. CML42 up-regulation is negatively regulated by the jasmonate receptor Coronatine Insensitive1 (COI1), as loss of functional COI1 resulted in prolonged CML42 activation. CML42 thus acts as a negative regulator of plant defense by decreasing COI1-mediated JA sensitivity and the expression of JA-responsive genes and is independent of herbivory-induced JA biosynthesis. JA-induced Ca²⁺ elevation and root growth inhibition were more sensitive in cml42, also indicating higher JA perception. Our results indicate that CML42 acts as a crucial signaling component connecting Ca²⁺ and JA signaling. CML42 is localized to cytosol and nucleus. CML42 is also involved in abiotic stress responses, as kaempferol glycosides were down-regulated in cml42, and impaired in ultraviolet B resistance. Under drought stress, the level of abscisic acid accumulation was higher in cml42 plants. Thus, CML42 might serve as a Ca²⁺ sensor having multiple functions in insect herbivory defense and abiotic stress responses.
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Affiliation(s)
- Jyothilakshmi Vadassery
- Departments of Bioorganic Chemistry (J.V., W.B., A.M.) and Biochemistry (M.R., J.G.), Max Planck Institute for Chemical Ecology, 07745 Jena, Germany; and
- Department of Cell and Metabolic Biology, Leibniz Institute of Plant Biochemistry, D–06120 Halle/Saale, Germany (B.H.)
| | - Michael Reichelt
- Departments of Bioorganic Chemistry (J.V., W.B., A.M.) and Biochemistry (M.R., J.G.), Max Planck Institute for Chemical Ecology, 07745 Jena, Germany; and
- Department of Cell and Metabolic Biology, Leibniz Institute of Plant Biochemistry, D–06120 Halle/Saale, Germany (B.H.)
| | - Bettina Hause
- Departments of Bioorganic Chemistry (J.V., W.B., A.M.) and Biochemistry (M.R., J.G.), Max Planck Institute for Chemical Ecology, 07745 Jena, Germany; and
- Department of Cell and Metabolic Biology, Leibniz Institute of Plant Biochemistry, D–06120 Halle/Saale, Germany (B.H.)
| | - Jonathan Gershenzon
- Departments of Bioorganic Chemistry (J.V., W.B., A.M.) and Biochemistry (M.R., J.G.), Max Planck Institute for Chemical Ecology, 07745 Jena, Germany; and
- Department of Cell and Metabolic Biology, Leibniz Institute of Plant Biochemistry, D–06120 Halle/Saale, Germany (B.H.)
| | - Wilhelm Boland
- Departments of Bioorganic Chemistry (J.V., W.B., A.M.) and Biochemistry (M.R., J.G.), Max Planck Institute for Chemical Ecology, 07745 Jena, Germany; and
- Department of Cell and Metabolic Biology, Leibniz Institute of Plant Biochemistry, D–06120 Halle/Saale, Germany (B.H.)
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40
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Zhu P, Gu H, Jiao Y, Huang D, Chen M. Computational identification of protein-protein interactions in rice based on the predicted rice interactome network. GENOMICS PROTEOMICS & BIOINFORMATICS 2012; 9:128-37. [PMID: 22196356 PMCID: PMC5054448 DOI: 10.1016/s1672-0229(11)60016-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2011] [Accepted: 07/04/2011] [Indexed: 01/29/2023]
Abstract
Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box domain-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.
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Affiliation(s)
- Pengcheng Zhu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
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Abstract
The study of protein-protein interactions (PPIs) is essential to uncover unknown functions of proteins at the molecular level and to gain insight into complex cellular networks. Affinity purification and mass spectrometry (AP-MS), yeast two-hybrid, imaging approaches and numerous diverse databases have been developed as strategies to analyze PPIs. The past decade has seen an increase in the number of identified proteins with the development of MS and large-scale proteome analyses. Consequently, the false-positive protein identification rate has also increased. Therefore, the general consensus is to confirm PPI data using one or more independent approaches for an accurate evaluation. Furthermore, identifying minor PPIs is fundamental for understanding the functions of transient interactions and low-abundance proteins. Besides establishing PPI methodologies, we are now seeing the development of new methods and/or improvements in existing methods, which involve identifying minor proteins by MS, multidimensional protein identification technology or OFFGEL electrophoresis analyses, one-shot analysis with a long column or filter-aided sample preparation methods. These advanced techniques should allow thousands of proteins to be identified, whereas in-depth proteomic methods should permit the identification of transient binding or PPIs with weak affinity. Here, the current status of PPI analysis is reviewed and some advanced techniques are discussed briefly along with future challenges for plant proteomics.
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Affiliation(s)
- Yoichiro Fukao
- Plant Global Educational Project, Nara Institute of Science and Technology, Ikoma, Japan
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42
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Chen YA, Wen YC, Chang WC. AtPAN: an integrated system for reconstructing transcriptional regulatory networks in Arabidopsis thaliana. BMC Genomics 2012; 13:85. [PMID: 22397531 PMCID: PMC3314555 DOI: 10.1186/1471-2164-13-85] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2011] [Accepted: 03/08/2012] [Indexed: 11/25/2022] Open
Abstract
Background Construction of transcriptional regulatory networks (TRNs) is of priority concern in systems biology. Numerous high-throughput approaches, including microarray and next-generation sequencing, are extensively adopted to examine transcriptional expression patterns on the whole-genome scale; those data are helpful in reconstructing TRNs. Identifying transcription factor binding sites (TFBSs) in a gene promoter is the initial step in elucidating the transcriptional regulation mechanism. Since transcription factors usually co-regulate a common group of genes by forming regulatory modules with similar TFBSs. Therefore, the combinatorial interactions of transcription factors must be modeled to reconstruct the gene regulatory networks. Description For systems biology applications, this work develops a novel database called Arabidopsis thaliana Promoter Analysis Net (AtPAN), capable of detecting TFBSs and their corresponding transcription factors (TFs) in a promoter or a set of promoters in Arabidopsis. For further analysis, according to the microarray expression data and literature, the co-expressed TFs and their target genes can be retrieved from AtPAN. Additionally, proteins interacting with the co-expressed TFs are also incorporated to reconstruct co-expressed TRNs. Moreover, combinatorial TFs can be detected by the frequency of TFBSs co-occurrence in a group of gene promoters. In addition, TFBSs in the conserved regions between the two input sequences or homologous genes in Arabidopsis and rice are also provided in AtPAN. The output results also suggest conducting wet experiments in the future. Conclusions The AtPAN, which has a user-friendly input/output interface and provide graphical view of the TRNs. This novel and creative resource is freely available online at http://AtPAN.itps.ncku.edu.tw/.
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Affiliation(s)
- Yi-An Chen
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
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43
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Ching SLK, Gidda SK, Rochon A, van Cauwenberghe OR, Shelp BJ, Mullen RT. Glyoxylate reductase isoform 1 is localized in the cytosol and not peroxisomes in plant cells. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2012; 54:152-68. [PMID: 22309191 DOI: 10.1111/j.1744-7909.2012.01103.x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Glyoxylate reductase (GLYR) is a key enzyme in plant metabolism which catalyzes the detoxification of both photorespiratory glyoxylate and succinic semialdehdye, an intermediate of the γ-aminobutyrate (GABA) pathway. Two isoforms of GLYR exist in plants, GLYR1 and GLYR2, and while GLYR2 is known to be localized in plastids, GLYR1 has been reported to be localized in either peroxisomes or the cytosol. Here, we reappraised the intracellular localization of GLYR1 in Arabidopsis thaliana L. Heynh (ecotype Lansberg erecta) using both transiently-transformed suspension cells and stably-transformed plants, in combination with fluorescence microscopy. The results indicate that GLYR1 is localized exclusively to the cytosol regardless of the species, tissue and/or cell type, or exposure of plants to environmental stresses that would increase flux through the GABA pathway. Moreover, the C-terminal tripeptide sequence of GLYR1, -SRE, despite its resemblance to a type 1 peroxisomal targeting signal, is not sufficient for targeting to peroxisomes. Collectively, these results define the cytosol as the intracellular location of GLYR1 and provide not only important insight to the metabolic roles of GLYR1 and the compartmentation of the GABA and photorespiratory pathways in plant cells, but also serve as a useful reference for future studies of proteins proposed to be localized to peroxisomes and/or the cytosol.
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Affiliation(s)
- Steven L K Ching
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario N1G 2W1, Canada
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Yang J, Osman K, Iqbal M, Stekel DJ, Luo Z, Armstrong SJ, Franklin FCH. Inferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2012; 3:297. [PMID: 23293649 PMCID: PMC3537189 DOI: 10.3389/fpls.2012.00297] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Accepted: 12/11/2012] [Indexed: 05/06/2023]
Abstract
Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein-protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain-domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.
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Affiliation(s)
- Jianhua Yang
- University of BirminghamBirmingham, UK
- *Correspondence: Jianhua Yang and F. Chris H. Franklin, University of Birmingham, B152TT Birmingham, UK. e-mail: ,
| | - Kim Osman
- University of BirminghamBirmingham, UK
| | | | | | - Zewei Luo
- University of BirminghamBirmingham, UK
| | | | - F. Chris H. Franklin
- University of BirminghamBirmingham, UK
- *Correspondence: Jianhua Yang and F. Chris H. Franklin, University of Birmingham, B152TT Birmingham, UK. e-mail: ,
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45
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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.
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Affiliation(s)
- Keiichi Mochida
- RIKEN Biomass Engineering Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045 Japan.
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46
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Gaut B, Yang L, Takuno S, Eguiarte LE. The Patterns and Causes of Variation in Plant Nucleotide Substitution Rates. ANNUAL REVIEW OF ECOLOGY EVOLUTION AND SYSTEMATICS 2011. [DOI: 10.1146/annurev-ecolsys-102710-145119] [Citation(s) in RCA: 114] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Brandon Gaut
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697; , ,
| | - Liang Yang
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697; , ,
| | - Shohei Takuno
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697; , ,
| | - Luis E. Eguiarte
- Instituto de Ecología, Universidad Nacional Autónoma de México, CP 04510 Mexico City, Mexico;
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47
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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.
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Affiliation(s)
- Keiichi Mochida
- RIKEN Biomass Engineering Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045 Japan.
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48
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Gu H, Zhu P, Jiao Y, Meng Y, Chen M. PRIN: a predicted rice interactome network. BMC Bioinformatics 2011; 12:161. [PMID: 21575196 PMCID: PMC3118165 DOI: 10.1186/1471-2105-12-161] [Citation(s) in RCA: 131] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Accepted: 05/16/2011] [Indexed: 12/22/2022] Open
Abstract
Background Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. Results To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. Conclusions PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/.
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Affiliation(s)
- Haibin Gu
- Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
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Lin M, Zhou X, Shen X, Mao C, Chen X. The predicted Arabidopsis interactome resource and network topology-based systems biology analyses. THE PLANT CELL 2011; 23:911-22. [PMID: 21441435 PMCID: PMC3082272 DOI: 10.1105/tpc.110.082529] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2010] [Revised: 12/30/2010] [Accepted: 03/10/2011] [Indexed: 05/17/2023]
Abstract
Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the predicted arabidopsis interactome resource (PAIR; ) presents 149,900 potential molecular interactions, which are expected to cover approximately 24% of the entire interactome with approximately 40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.
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Affiliation(s)
- Mingzhi Lin
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xi Zhou
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xueling Shen
- Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Chuanzao Mao
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xin Chen
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
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