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Kang H, Huang T, Duan G, Meng Y, Chen X, He S, Xia Z, Zhou X, Chao J, Tang B, Wang Z, Zhu J, Du Z, Sun Y, Zhang S, Xiao J, Tian W, Wang W, Zhao W. TCOD: an integrated resource for tropical crops. Nucleic Acids Res 2024; 52:D1651-D1660. [PMID: 37843152 PMCID: PMC10767838 DOI: 10.1093/nar/gkad870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/25/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023] Open
Abstract
Tropical crops are vital for tropical agriculture, with resource scarcity, functional diversity and extensive market demand, providing considerable economic benefits for the world's tropical agriculture-producing countries. The rapid development of sequencing technology has promoted a milestone in tropical crop research, resulting in the generation of massive amount of data, which urgently needs an effective platform for data integration and sharing. However, the existing databases cannot fully satisfy researchers' requirements due to the relatively limited integration level and untimely update. Here, we present the Tropical Crop Omics Database (TCOD, https://ngdc.cncb.ac.cn/tcod), a comprehensive multi-omics data platform for tropical crops. TCOD integrates diverse omics data from 15 species, encompassing 34 chromosome-level de novo assemblies, 1 255 004 genes with functional annotations, 282 436 992 unique variants from 2048 WGS samples, 88 transcriptomic profiles from 1997 RNA-Seq samples and 13 381 germplasm items. Additionally, TCOD not only employs genes as a bridge to interconnect multi-omics data, enabling cross-species comparisons based on homology relationships, but also offers user-friendly online tools for efficient data mining and visualization. In short, TCOD integrates multi-species, multi-omics data and online tools, which will facilitate the research on genomic selective breeding and trait biology of tropical crops.
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Affiliation(s)
- Hailong Kang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianhao Huang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guangya Duan
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuyan Meng
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoning Chen
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuang He
- Sanya Nanfan Research Institute, Hainan University, Sanya 572025, China
| | - Zhiqiang Xia
- Sanya Nanfan Research Institute, Hainan University, Sanya 572025, China
| | - Xincheng Zhou
- Institute of Tropical Biosciences and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
| | - Jinquan Chao
- Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
| | - Bixia Tang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhonghuang Wang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junwei Zhu
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhenglin Du
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Yanlin Sun
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Sisi Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jingfa Xiao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weimin Tian
- Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
| | - Wenquan Wang
- Sanya Nanfan Research Institute, Hainan University, Sanya 572025, China
| | - Wenming Zhao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Roychowdhury R, Das SP, Gupta A, Parihar P, Chandrasekhar K, Sarker U, Kumar A, Ramrao DP, Sudhakar C. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses. Genes (Basel) 2023; 14:1281. [PMID: 37372461 DOI: 10.3390/genes14061281] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/03/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.
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Affiliation(s)
- Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO)-The Volcani Institute, Rishon Lezion 7505101, Israel
| | - Soumya Prakash Das
- School of Bioscience, Seacom Skills University, Bolpur 731236, West Bengal, India
| | - Amber Gupta
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, Faculty of Science, Maharaja Sayajirao University of Baroda, Vadodara 390002, Gujarat, India
| | - Parul Parihar
- Department of Biotechnology and Bioscience, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Kottakota Chandrasekhar
- Department of Plant Biochemistry and Biotechnology, Sri Krishnadevaraya College of Agricultural Sciences (SKCAS), Affiliated to Acharya N.G. Ranga Agricultural University (ANGRAU), Guntur 522034, Andhra Pradesh, India
| | - Umakanta Sarker
- Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Ajay Kumar
- Department of Botany, Maharshi Vishwamitra (M.V.) College, Buxar 802102, Bihar, India
| | - Devade Pandurang Ramrao
- Department of Biotechnology, Mizoram University, Pachhunga University College Campus, Aizawl 796001, Mizoram, India
| | - Chinta Sudhakar
- Plant Molecular Biology Laboratory, Department of Botany, Sri Krishnadevaraya University, Anantapur 515003, Andhra Pradesh, India
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Rodrigues Neto JC, Salgado FF, Braga ÍDO, Carvalho da Silva TL, Belo Silva VN, Leão AP, Ribeiro JADA, Abdelnur PV, Valadares LF, de Sousa CAF, Souza Júnior MT. Osmoprotectants play a major role in the Portulaca oleracea resistance to high levels of salinity stress-insights from a metabolomics and proteomics integrated approach. FRONTIERS IN PLANT SCIENCE 2023; 14:1187803. [PMID: 37384354 PMCID: PMC10296175 DOI: 10.3389/fpls.2023.1187803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/03/2023] [Indexed: 06/30/2023]
Abstract
Introduction Purslane (Portulaca oleracea L.) is a non-conventional food plant used extensively in folk medicine and classified as a multipurpose plant species, serving as a source of features of direct importance to the agricultural and agri-industrial sectors. This species is considered a suitable model to study the mechanisms behind resistance to several abiotic stresses including salinity. The recently achieved technological developments in high-throughput biology opened a new window of opportunity to gain additional insights on purslane resistance to salinity stress-a complex, multigenic, and still not well-understood trait. Only a few reports on single-omics analysis (SOA) of purslane are available, and only one multi-omics integration (MOI) analysis exists so far integrating distinct omics platforms (transcriptomics and metabolomics) to characterize the response of purslane plants to salinity stress. Methods The present study is a second step in building a robust database on the morpho-physiological and molecular responses purslane to salinity stress and its subsequent use in attempting to decode the genetics behind its resistance to this abiotic stress. Here, the characterization of the morpho-physiological responses of adult purslane plants to salinity stress and a metabolomics and proteomics integrative approach to study the changes at the molecular level in their leaves and roots is presented. Results and discussion Adult plants of the B1 purslane accession lost approximately 50% of the fresh and dry weight (from shoots and roots) whensubmitted to very high salinity stress (2.0 g of NaCl/100 g of the substrate). The resistance to very high levels of salinity stress increases as the purslane plant matures, and most of the absorbed sodium remains in the roots, with only a part (~12%) reaching the shoots. Crystal-like structures, constituted mainly by Na+, Cl-, and K+, were found in the leaf veins and intercellular space near the stoma, indicating that this species has a mechanism of salt exclusion operating on the leaves, which has its role in salt tolerance. The MOI approach showed that 41 metabolites were statistically significant on the leaves and 65 metabolites on the roots of adult purslane plants. The combination of the mummichog algorithm and metabolomics database comparison revealed that the glycine, serine, and threonine, amino sugar and nucleotide sugar, and glycolysis/gluconeogenesis pathways were the most significantly enriched pathways when considering the total number of occurrences in the leaves (with 14, 13, and 13, respectively) and roots (all with eight) of adult plants; and that purslane plants employ the adaptive mechanism of osmoprotection to mitigate the negative effect of very high levels of salinity stress; and that this mechanism is prevalent in the leaves. The multi-omics database built by our group underwent a screen for salt-responsive genes, which are now under further characterization for their potential to promote resistance to salinity stress when heterologously overexpressed in salt-sensitive plants.
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Affiliation(s)
| | | | | | | | | | - André Pereira Leão
- The Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, Brazil
| | | | | | | | | | - Manoel Teixeira Souza Júnior
- The Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, Brazil
- Graduate Program of Plant Biotechnology, Federal University of Lavras, Lavras, MG, Brazil
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Singh V, Gupta K, Singh S, Jain M, Garg R. Unravelling the molecular mechanism underlying drought stress response in chickpea via integrated multi-omics analysis. FRONTIERS IN PLANT SCIENCE 2023; 14:1156606. [PMID: 37287713 PMCID: PMC10242046 DOI: 10.3389/fpls.2023.1156606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/18/2023] [Indexed: 06/09/2023]
Abstract
Drought stress affects growth and productivity significantly in chickpea. An integrated multi-omics analysis can provide a better molecular-level understanding of drought stress tolerance. In the present study, comparative transcriptome, proteome and metabolome analyses of two chickpea genotypes with contrasting responses to drought stress, ICC 4958 (drought-tolerant, DT) and ICC 1882 (drought-sensitive, DS), was performed to gain insights into the molecular mechanisms underlying drought stress response/tolerance. Pathway enrichment analysis of differentially abundant transcripts and proteins suggested the involvement of glycolysis/gluconeogenesis, galactose metabolism, and starch and sucrose metabolism in the DT genotype. An integrated multi-omics analysis of transcriptome, proteome and metabolome data revealed co-expressed genes, proteins and metabolites involved in phosphatidylinositol signaling, glutathione metabolism and glycolysis/gluconeogenesis pathways, specifically in the DT genotype under drought. These stress-responsive pathways were coordinately regulated by the differentially abundant transcripts, proteins and metabolites to circumvent the drought stress response/tolerance in the DT genotype. The QTL-hotspot associated genes, proteins and transcription factors may further contribute to improved drought tolerance in the DT genotype. Altogether, the multi-omics approach provided an in-depth understanding of stress-responsive pathways and candidate genes involved in drought tolerance in chickpea.
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Affiliation(s)
- Vikram Singh
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Khushboo Gupta
- Department of Life Sciences, Shiv Nadar Institution of Eminence, Gautam Buddha Nagar, Uttar Pradesh, India
| | - Shubhangi Singh
- Department of Life Sciences, Shiv Nadar Institution of Eminence, Gautam Buddha Nagar, Uttar Pradesh, India
| | - Mukesh Jain
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Rohini Garg
- Department of Life Sciences, Shiv Nadar Institution of Eminence, Gautam Buddha Nagar, Uttar Pradesh, India
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