Recent Advances in Exploring Transcriptional Regulatory Landscape of Crops

Overview

Journal Front Plant Sci

Date 2024 Jun 21

PMID 38903438

Authors

Qiang Huo

Rentao Song

Zeyang Ma

Affiliations

Soon will be listed here.

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.

References

Wang R, Cheng Y, Ke X, Zhang X, Zhang H, Huang J . Comparative analysis of salt responsive gene regulatory networks in rice and Arabidopsis. Comput Biol Chem. 2020; 85:107188. DOI: 10.1016/j.compbiolchem.2019.107188. View

Ambrosini G, Groux R, Bucher P . PWMScan: a fast tool for scanning entire genomes with a position-specific weight matrix. Bioinformatics. 2018; 34(14):2483-2484. PMC: 6041753. DOI: 10.1093/bioinformatics/bty127. View

Huang J, Vendramin S, Shi L, McGinnis K . Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data. Plant Physiol. 2017; 175(1):568-583. PMC: 5580776. DOI: 10.1104/pp.17.00825. View

Cassan O, Pimpare L, Dubos C, Gojon A, Bach L, Lebre S . A gene regulatory network in Arabidopsis roots reveals features and regulators of the plant response to elevated CO. New Phytol. 2023; 239(3):992-1004. DOI: 10.1111/nph.18788. View

Chow C, Yang C, Wu N, Wang H, Tseng K, Chiu Y . PlantPAN 4.0: updated database for identifying conserved non-coding sequences and exploring dynamic transcriptional regulation in plant promoters. Nucleic Acids Res. 2023; 52(D1):D1569-D1578. PMC: 10767843. DOI: 10.1093/nar/gkad945. View

Li Y, Pearl S, Jackson S . Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis. Trends Plant Sci. 2015; 20(10):664-675. DOI: 10.1016/j.tplants.2015.06.013. View

Brooks M, Cirrone J, Pasquino A, Alvarez J, Swift J, Mittal S . Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions. Nat Commun. 2019; 10(1):1569. PMC: 6451032. DOI: 10.1038/s41467-019-09522-1. View

Zhao J, Lei Y, Hong J, Zheng C, Zhang L . AraPPINet: An Updated Interactome for the Analysis of Hormone Signaling Crosstalk in . Front Plant Sci. 2019; 10:870. PMC: 6625390. DOI: 10.3389/fpls.2019.00870. View

He Q, Johnston J, Zeitlinger J . ChIP-nexus enables improved detection of in vivo transcription factor binding footprints. Nat Biotechnol. 2015; 33(4):395-401. PMC: 4390430. DOI: 10.1038/nbt.3121. View

10.

Gupta O, Deshmukh R, Kumar A, Singh S, Sharma P, Ram S . From gene to biomolecular networks: a review of evidences for understanding complex biological function in plants. Curr Opin Biotechnol. 2021; 74:66-74. DOI: 10.1016/j.copbio.2021.10.023. View

11.

von Mering C, Krause R, Snel B, Cornell M, Oliver S, Fields S . Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 2002; 417(6887):399-403. DOI: 10.1038/nature750. View

12.

Zuin J, Roth G, Zhan Y, Cramard J, Redolfi J, Piskadlo E . Nonlinear control of transcription through enhancer-promoter interactions. Nature. 2022; 604(7906):571-577. PMC: 9021019. DOI: 10.1038/s41586-022-04570-y. View

13.

Feng C, Song C, Song S, Zhang G, Yin M, Zhang Y . KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species. Nucleic Acids Res. 2023; 52(D1):D183-D193. PMC: 10767813. DOI: 10.1093/nar/gkad1016. View

14.

Xie Y, Jiang S, Li L, Yu X, Wang Y, Luo C . Single-Cell RNA Sequencing Efficiently Predicts Transcription Factor Targets in Plants. Front Plant Sci. 2021; 11:603302. PMC: 7793804. DOI: 10.3389/fpls.2020.603302. View

15.

Shi J, Zhao B, Zheng S, Zhang X, Wang X, Dong W . A phosphate starvation response-centered network regulates mycorrhizal symbiosis. Cell. 2021; 184(22):5527-5540.e18. DOI: 10.1016/j.cell.2021.09.030. View

16.

Kulkarni S, Vaneechoutte D, Van de Velde J, Vandepoele K . TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information. Nucleic Acids Res. 2017; 46(6):e31. PMC: 5888541. DOI: 10.1093/nar/gkx1279. View

17.

Lieberman-Aiden E, van Berkum N, Williams L, Imakaev M, Ragoczy T, Telling A . Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009; 326(5950):289-93. PMC: 2858594. DOI: 10.1126/science.1181369. View

18.

Wang J, Wang Y, Lu T, Yang X, Liu J, Dong Y . An Efficient and Universal Protoplast Isolation Protocol Suitable for Transient Gene Expression Analysis and Single-Cell RNA Sequencing. Int J Mol Sci. 2022; 23(7). PMC: 8998730. DOI: 10.3390/ijms23073419. View

19.

Xiang D, Quilichini T, Liu Z, Gao P, Pan Y, Li Q . The Transcriptional Landscape of Polyploid Wheats and Their Diploid Ancestors during Embryogenesis and Grain Development. Plant Cell. 2019; 31(12):2888-2911. PMC: 6925018. DOI: 10.1105/tpc.19.00397. View

20.

Sekula M, Gaskins J, Datta S . A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data. BMC Bioinformatics. 2020; 21(1):361. PMC: 7437941. DOI: 10.1186/s12859-020-03707-y. View