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Huo Q, Song R, Ma Z. Recent advances in exploring transcriptional regulatory landscape of crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1421503. [PMID: 38903438 PMCID: PMC11188431 DOI: 10.3389/fpls.2024.1421503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.
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Affiliation(s)
| | | | - Zeyang Ma
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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Guo C, Huang Z, Chen J, Yu G, Wang Y, Wang X. Identification of Novel Regulators of Leaf Senescence Using a Deep Learning Model. PLANTS (BASEL, SWITZERLAND) 2024; 13:1276. [PMID: 38732491 PMCID: PMC11085074 DOI: 10.3390/plants13091276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN's predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.
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Affiliation(s)
| | | | | | | | | | - Xu Wang
- Shanghai Collaborative Innovation Center of Agri-Seeds, Joint Center for Single Cell Biology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China; (C.G.); (Z.H.); (J.C.); (G.Y.); (Y.W.)
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Sferra G, Fantozzi D, Scippa GS, Trupiano D. Key Pathways and Genes of Arabidopsis thaliana and Arabidopsis halleri Roots under Cadmium Stress Responses: Differences and Similarities. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091793. [PMID: 37176850 PMCID: PMC10180823 DOI: 10.3390/plants12091793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023]
Abstract
Cadmium (Cd) is among the world's major health concerns, as it renders soils unsuitable and unsafe for food and feed production. Phytoremediation has the potential to remediate Cd-polluted soils, but efforts are still needed to develop a deep understanding of the processes underlying it. In this study, we performed a comprehensive analysis of the root response to Cd stress in A. thaliana, which can phytostabilize Cd, and in A. halleri, which is a Cd hyperaccumulator. Suitable RNA-seq data were analyzed by WGCNA to identify modules of co-expressed genes specifically associated with Cd presence. The results evidenced that the genes of the hyperaccumulator A. halleri mostly associated with the Cd presence are finely regulated (up- and downregulated) and related to a general response to chemical and other stimuli. Additionally, in the case of A. thaliana, which can phytostabilize metals, the genes upregulated during Cd stress are related to a general response to chemical and other stimuli, while downregulated genes are associated with functions which, affecting root growth and development, determine a deep modification of the organ both at the cellular and physiological levels. Furthermore, key genes of the Cd-associated modules were identified and confirmed by differentially expressed gene (DEG) detection and external knowledge. Together, key functions and genes shed light on differences and similarities among the strategies that the plants use to cope with Cd and may be considered as possible targets for future research.
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Affiliation(s)
- Gabriella Sferra
- Department of Biosciences and Territory, University of Molise, 86090 Pesche, Italy
| | - Daniele Fantozzi
- Department of Biosciences and Territory, University of Molise, 86090 Pesche, Italy
| | | | - Dalila Trupiano
- Department of Biosciences and Territory, University of Molise, 86090 Pesche, Italy
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Decoding transcriptional regulation via a human gene expression predictor. J Genet Genomics 2023; 50:305-317. [PMID: 36693565 DOI: 10.1016/j.jgg.2023.01.006] [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: 09/02/2022] [Revised: 01/04/2023] [Accepted: 01/10/2023] [Indexed: 01/22/2023]
Abstract
Transcription factors (TFs) regulate cellular activities by controlling gene expression, but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking. We construct a universal human gene expression predictor and utilize it to decode transcriptional regulation. Using the expression of 1613 TFs, the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions. The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues. Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development, immunity, metabolism, and stress response. A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages, and a portion of these TFs are conserved between humans and mice. The results demonstrate that our method is capable of delineating the TFs responsible for fate determination. Compared to other existing tools, our approach shows better performance in recovering the correct TF regulators. Thus, we present a novel approach that can be used to study human transcriptional regulation in general.
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Chen L, Dou P, Li L, Chen Y, Yang H. Transcriptome-wide analysis reveals core transcriptional regulators associated with culm development and variation in Dendrocalamus sinicus, the strongest woody bamboo in the world. Heliyon 2022; 8:e12600. [PMID: 36593818 PMCID: PMC9803789 DOI: 10.1016/j.heliyon.2022.e12600] [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: 03/15/2022] [Revised: 07/15/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Transcription factors (TFs) play indispensable roles in plant development and stress responses. As the largest woody bamboo species in the world, Dendrocalamus sinicus is endemic to Yunnan Province, China, and possesses two natural variants characterized by culm shape, namely straight or bent culms. Understanding the transcriptional regulation network of D. sinicus provides a unique opportunity to clarify the growth and development characteristics of woody bamboos. In this study, 10,236 TF transcripts belonging to 57 families were identified from transcriptome data of two variants at different developmental stages, from which we constructed a transcriptional regulatory network and unigene-coding protein-TFs interactive network of culm development for this attractive species. Gene function enrichment analysis revealed that hormone signaling and MAPK signaling pathways were two most enriched pathways in TF-regulated network. Based on PPI analysis, 50 genes interacting with nine TFs were screened as the core regulation components related to culm development. Of them, 18 synergistic genes of seven TFs, including nuclear cap-binding protein subunit 1, transcription factor GTE9-like, and ATP-dependent DNA helicase DDX11 isoform X1, involved in culm-shape variation. Most of these genes would interact with MYB, C3H, and ARF transcription factors. Six members with two each from ARF, C3H, and MYB transcription factor families and six key interacting genes (IAA3, IAA19, leucine-tRNA ligase, nuclear cap-binding protein subunit 1, elongation factor 2, and coiled-coil domain-containing protein 94) cooperate with these transcription factors were differentially expressed at development stage of young culms, and were validated by quantitative PCR. Our results represent a crucial step towards understanding the regulatory mechanisms of TFs involved in culm development and variation of D. sinicus.
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Affiliation(s)
- Lingna Chen
- Institute of Highland Forest Science, Chinese Academy of Forestry, Bailongsi, Panlong District, Kunming 650233, PR China,College of Life Science, Xinjiang Normal University, Xinyi Road, Shayibake District, Urumqi 830054, PR China
| | - Peitong Dou
- Institute of Highland Forest Science, Chinese Academy of Forestry, Bailongsi, Panlong District, Kunming 650233, PR China
| | - Lushuang Li
- Institute of Highland Forest Science, Chinese Academy of Forestry, Bailongsi, Panlong District, Kunming 650233, PR China
| | - Yongkun Chen
- College of Life Science, Xinjiang Normal University, Xinyi Road, Shayibake District, Urumqi 830054, PR China,Xinjiang Key Laboratory of Special Species Conservation and Regulatory Biology, Xinyi Road, Shayibake District, Urumqi 830054, PR China,Corresponding author.
| | - Hanqi Yang
- Institute of Highland Forest Science, Chinese Academy of Forestry, Bailongsi, Panlong District, Kunming 650233, PR China,Corresponding author.
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Zhang Y, Han E, Peng Y, Wang Y, Wang Y, Geng Z, Xu Y, Geng H, Qian Y, Ma S. Rice co-expression network analysis identifies gene modules associated with agronomic traits. PLANT PHYSIOLOGY 2022; 190:1526-1542. [PMID: 35866684 PMCID: PMC9516743 DOI: 10.1093/plphys/kiac339] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
Identifying trait-associated genes is critical for rice (Oryza sativa) improvement, which usually relies on map-based cloning, quantitative trait locus analysis, or genome-wide association studies. Here we show that trait-associated genes tend to form modules within rice gene co-expression networks, a feature that can be exploited to discover additional trait-associated genes using reverse genetics. We constructed a rice gene co-expression network based on the graphical Gaussian model using 8,456 RNA-seq transcriptomes, which assembled into 1,286 gene co-expression modules functioning in diverse pathways. A number of the modules were enriched with genes associated with agronomic traits, such as grain size, grain number, tiller number, grain quality, leaf angle, stem strength, and anthocyanin content, and these modules are considered to be trait-associated gene modules. These trait-associated gene modules can be used to dissect the genetic basis of rice agronomic traits and to facilitate the identification of trait genes. As an example, we identified a candidate gene, OCTOPUS-LIKE 1 (OsOPL1), a homolog of the Arabidopsis (Arabidopsis thaliana) OCTOPUS gene, from a grain size module and verified it as a regulator of grain size via functional studies. Thus, our network represents a valuable resource for studying trait-associated genes in rice.
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Affiliation(s)
- Yu Zhang
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Ershang Han
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Yuming Peng
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Yuzhou Wang
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yifan Wang
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Zhenxing Geng
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Yupu Xu
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Haiying Geng
- MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
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Ng JWX, Chua SK, Mutwil M. Feature importance network reveals novel functional relationships between biological features in Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2022; 13:944992. [PMID: 36212273 PMCID: PMC9539877 DOI: 10.3389/fpls.2022.944992] [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: 05/16/2022] [Accepted: 08/24/2022] [Indexed: 06/16/2023]
Abstract
Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it can find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools.
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Peng Y, Zuo W, Zhou H, Miao F, Zhang Y, Qin Y, Liu Y, Long Y, Ma S. EXPLICIT-Kinase: A gene expression predictor for dissecting the functions of the Arabidopsis kinome. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2022; 64:1374-1393. [PMID: 35446465 DOI: 10.1111/jipb.13267] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
Protein kinases regulate virtually all cellular processes, but it remains challenging to determine the functions of all protein kinases, collectively called the "kinome", in any species. We developed a computational approach called EXPLICIT-Kinase to predict the functions of the Arabidopsis kinome. Because the activities of many kinases can be regulated transcriptionally, their gene expression patterns provide clues to their functions. A universal gene expression predictor for Arabidopsis was constructed to predict the expression of 30,172 non-kinase genes based on the expression of 994 kinases. The model reconstituted highly accurate transcriptomes for diverse Arabidopsis samples. It identified the significant kinases as predictor kinases for predicting the expression of Arabidopsis genes and pathways. Strikingly, these predictor kinases were often regulators of related pathways, as exemplified by those involved in cytokinesis, tissue development, and stress responses. Comparative analyses revealed that portions of these predictor kinases are shared and conserved between Arabidopsis and maize. As an example, we identified a conserved predictor kinase, RAF6, from a stomatal movement module. We verified that RAF6 regulates stomatal closure. It can directly interact with SLAC1, a key anion channel for stomatal closure, and modulate its channel activity. Our approach enables a systematic dissection of the functions of the Arabidopsis kinome.
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Affiliation(s)
- Yuming Peng
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, 230027, China
| | - Wanzhu Zuo
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, 230027, China
| | - Hui Zhou
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, 475001, China
| | - Fenfen Miao
- State Key Laboratory for Plant Molecular Genetics, Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Yu Zhang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, 230027, China
| | - Yue Qin
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, 230027, China
| | - Yi Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, 230027, China
| | - Yu Long
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, 475001, China
| | - Shisong Ma
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, 230027, China
- School of Data Science, University of Science and Technology of China, Hefei, 230027, China
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Burks DJ, Sengupta S, De R, Mittler R, Azad RK. The Arabidopsis gene co-expression network. PLANT DIRECT 2022; 6:e396. [PMID: 35492683 PMCID: PMC9039629 DOI: 10.1002/pld3.396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/05/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Identifying genes that interact to confer a biological function to an organism is one of the main goals of functional genomics. High-throughput technologies for assessment and quantification of genome-wide gene expression patterns have enabled systems-level analyses to infer pathways or networks of genes involved in different functions under many different conditions. Here, we leveraged the publicly available, information-rich RNA-Seq datasets of the model plant Arabidopsis thaliana to construct a gene co-expression network, which was partitioned into clusters or modules that harbor genes correlated by expression. Gene ontology and pathway enrichment analyses were performed to assess functional terms and pathways that were enriched within the different gene modules. By interrogating the co-expression network for genes in different modules that associate with a gene of interest, diverse functional roles of the gene can be deciphered. By mapping genes differentially expressing under a certain condition in Arabidopsis onto the co-expression network, we demonstrate the ability of the network to uncover novel genes that are likely transcriptionally active but prone to be missed by standard statistical approaches due to their falling outside of the confidence zone of detection. To our knowledge, this is the first A. thaliana co-expression network constructed using the entire mRNA-Seq datasets (>20,000) available at the NCBI SRA database. The developed network can serve as a useful resource for the Arabidopsis research community to interrogate specific genes of interest within the network, retrieve the respective interactomes, decipher gene modules that are transcriptionally altered under certain condition or stage, and gain understanding of gene functions.
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Affiliation(s)
- David J. Burks
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
| | - Soham Sengupta
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
| | - Ronika De
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
| | - Ron Mittler
- The Division of Plant Sciences and Interdisciplinary Plant Group, College of Agriculture, Food and Natural ResourcesChristopher S. Bond Life Sciences Center University of MissouriColumbiaMissouriUSA
- Department of SurgeryUniversity of Missouri School of MedicineColumbiaMissouriUSA
| | - Rajeev K. Azad
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
- Department of MathematicsUniversity of North TexasDentonTexasUSA
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