Identification of Potential Zinc Deficiency Responsive Genes and Regulatory Pathways in Rice by Weighted Gene Co-expression Network Analysis
Received date: 2021-12-09
Accepted date: 2022-04-24
Online published: 2022-09-02
Zinc (Zn) malnutrition is a major public health issue. Genetic biofortification of Zn in rice grain can alleviate global Zn malnutrition. Therefore, elucidating the genetic mechanisms regulating Zn deprivation response in rice is essential to identify elite genes useful for breeding high grain Zn rice varieties. Here, a meta-analysis of previous RNA-Seq studies involving Zn deficient conditions was conducted using the weighted gene co-expression network analysis (WGCNA) and other in silico prediction tools to identify modules (denoting cluster of genes with related expression pattern) of co-expressed genes, modular genes which are conserved differentially expressed genes (DEGs) across independent RNA-Seq studies, and the molecular pathways of the conserved modular DEGs. WGCNA identified 16 modules of co-expressed genes. Twenty-eight and five modular DEGs were conserved in leaf and crown, and root tissues across two independent RNA-Seq studies. Functional enrichment analysis showed that 24 of the 28 conserved modular DEGs from leaf and crown tissues significantly up-regulated 2 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 15 Gene Ontology (GO) terms, including the substrate- specific transmembrane transporter and the small molecule metabolic process. Further, the well-studied transcription factors (OsWOX11 and OsbHLH120), protein kinase (OsCDPK20 and OsMPK17), and miRNAs (OSA-MIR397A and OSA-MIR397B) were predicted to target some of the identified conserved modular DEGs. Out of the 24 conserved and up-regulated modular DEGs, 19 were yet to be experimentally validated as Zn deficiency responsive genes. Findings from this study provide a comprehensive insight on the molecular mechanisms of Zn deficiency response and may facilitate gene and pathway prioritization for improving Zn use efficiency and Zn biofortification in rice.
Blaise Pascal Muvunyi, Lu Xiang, Zhan Junhui, He Sang, Ye Guoyou . Identification of Potential Zinc Deficiency Responsive Genes and Regulatory Pathways in Rice by Weighted Gene Co-expression Network Analysis[J]. Rice Science, 2022 , 29(6) : 545 -558 . DOI: 10.1016/j.rsci.2022.04.002
| [1] | Ahmadi N, Cao T V, Frouin J, Norton G J, Price A H. 2021. Genomic prediction of arsenic tolerance and grain yield in rice: Contribution of trait-specific markers and multi-environment models. Rice Sci, 28(3): 268-278. |
| [2] | Akhtar S, Das J K, Ismail T, Wahid M, Saeed W, Bhutta Z A. 2021. Nutritional perspectives for the prevention and mitigation of COVID-19. Nutr Rev, 79(3): 289-300. |
| [3] | Ariel F D, Manavella P A, Dezar C A, Chan R L. 2007. The true story of the HD-Zip family. Trends Plant Sci, 12(9): 419-426. |
| [4] | Azodi C B, Pardo J, VanBuren R, de Los Campos G, Shiu S H. 2020. Transcriptome-based prediction of complex traits in maize. Plant Cell, 32(1): 139-151. |
| [5] | Balyan S, Kumar M, Mutum R D, Raghuvanshi U, Agarwal P, Mathur S, Raghuvanshi S. 2017. Identification of miRNA- mediated drought responsive multi-tiered regulatory network in drought tolerant rice, Nagina 22. Sci Rep, 7(1): 15446. |
| [6] | Banakar R, Alvarez Fernandez A, Díaz-Benito P, Abadia J, Capell T, Christou P. 2017. Phytosiderophores determine thresholds for iron and zinc accumulation in biofortified rice endosperm while inhibiting the accumulation of cadmium. J Exp Bot, 68(17): 4983-4995. |
| [7] | Bandyopadhyay T, Mehra P, Hairat S, Giri J. 2017. Morpho- physiological and transcriptome profiling reveal novel zinc deficiency-responsive genes in rice. Funct Integr Genomics, 17(5): 565-581. |
| [8] | Black M M. 1998. Zinc deficiency and child development. Am J Clin Nutr, 68: 464S-469S. |
| [9] | Bolger A M, Lohse M, Usadel B. 2014. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30(15): 2114-2120. |
| [10] | Chen X Y, Mei Q, Liang W F, Sun J, Wang X M, Zhou J, Wang J M, Zhou Y H, Zheng B S, Yang Y, Chen J P. 2020. Gene mapping, genome-wide transcriptome analysis, and WGCNA reveals the molecular mechanism for triggering programmed cell death in rice mutant pir1. Plants, 9(11): 1607. |
| [11] | Cheng S F, Zhou D X, Zhao Y. 2016. WUSCHEL-related homeobox gene WOX11 increases rice drought resistance by controlling root hair formation and root system development. Plant Signal Behav, 11(2): e1130198. |
| [12] | Childs K L, Davidson R M, Buell C R. 2011. Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS One, 6(7): e22196. |
| [13] | Cline M S, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico A R, Vailaya A, Wang P L, Adler A, Conklin B R, Hood L, Kuiper M, Sander C, Schmulevich I, Schwikowski B, Warner G J, Idker T, Bader G D. 2007. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc, 2: 2366-2382. |
| [14] | Dardenne M. 2002. Zinc and immune function. Eur J Clin Nutr, 56: S20-S23. |
| [15] | de Los Reyes B G, Mohanty B, Yun S J, Park M R, Lee D Y. 2015. Upstream regulatory architecture of rice genes: Summarizing the baseline towards genus-wide comparative analysis of regulatory networks and allele mining. Rice, 8: 14. |
| [16] | Du Z, Zhou X, Ling Y, Zhang Z H, Su Z. 2010. agriGO: A GO analysis toolkit for the agricultural community. Nucleic Acids Res, 38: W64-W70. |
| [17] | Gratz R, Manishankar P, Ivanov R, Köster P, Mohr I, Trofimov K, Steinhorst L, Meiser J, Mai H J, Drerup M, Arendt S, Holtkamp M, Karst U, Kudla J, Bauer P, Brumbarova T. 2019. CIPK11- dependent phosphorylation modulates FIT activity to promote Arabidopsis iron acquisition in response to calcium signaling. Dev Cell, 48(5): 726-740. |
| [18] | Guo R, Dhliwayo T, Mageto E K, Palacios-Rojas N, Lee M, Yu D S, Ruan Y Y, Zhang A, San Vicente F, Olsen M, Crossa J, Prasanna B M, Zhang L J, Zhang X C. 2020. Genomic prediction of kernel zinc concentration in multiple maize populations using genotyping-by-sequencing and repeat amplification sequencing markers. Front Plant Sci, 11: 534. |
| [19] | Hajiboland R. 2012. Effect of micronutrient deficiencies on plants stress responses. In: Ahmad P, Prasad M. Abiotic Stress Responses in Plants. New York, USA: Springer: 283-329. |
| [20] | Huang S, Sasaki A, Yamaji N, Okada H, Mitani-Ueno N, Ma J F. 2020. The ZIP transporter family member OsZIP9 contributes to root zinc uptake in rice under zinc-limited conditions. Plant Physiol, 183(3): 1224-1234. |
| [21] | Huizar M I, Arena R, Laddu D R. 2021. The global food syndemic: The impact of food insecurity, malnutrition and obesity on the healthspan amid the COVID-19 pandemic. Prog Cardiovasc Dis, 64: 105-107. |
| [22] | Impa S M, Morete M J, Ismail A M, Schulin R, Johnson-Beebout S E. 2013. Zn uptake, translocation and grain Zn loading in rice (Oryza sativa L.) genotypes selected for Zn deficiency tolerance and high grain Zn. J Exp Bot, 64(10): 2739-2751. |
| [23] | Ishimaru Y, Bashir K, Nishizawa N K. 2011. Zn uptake and translocation in rice plants. Rice, 4(1): 21-27. |
| [24] | Ishimaru Y, Suzuki M, Kobayashi T, Takahashi M, Nakanishi H, Mori S, Nishizawa N K. 2005. OsZIP4, a novel zinc-regulated zinc transporter in rice. J Exp Bot, 56: 3207-3214. |
| [25] | Kitomi Y, Kitano H, Inukai Y. 2011. Molecular mechanism of crown root initiation and the different mechanisms between crown root and radicle in rice. Plant Signal Behav, 6(9): 1270-1278. |
| [26] | Kumar Sarmah C, Samarasinghe S. 2010. Microarray data integration: Frameworks and a list of underlying issues. Curr Bioinform, 5(4): 280-289. |
| [27] | Langfelder P, Horvath S. 2008. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9: 559. |
| [28] | Lee S, Chiecko J C, Kim S A, Walker E L, Lee Y, Guerinot M L, An G. 2009. Disruption of OsYSL15 leads to iron inefficiency in rice plants. Plant Physiol, 150(2): 786-800. |
| [29] | Lee S, Kim S A, Lee J, Guerinot M L, An G. 2010. Zinc deficiency- inducible OsZIP8 encodes a plasma membrane-localized zinc transporter in rice. Mol Cells, 29(6): 551-558. |
| [30] | Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup. 2009. The sequence alignment/map format and SAMtools. Bioinformatics, 25(16): 2078-2079. |
| [31] | Liao Y, Smyth G K, Shi W. 2014. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7): 923-930. |
| [32] | Lu X, Liu S, Zhi S, Chen J, Ye G. 2021. Comparative transcriptome profile analysis of rice varieties with different tolerance to zinc deficiency. Plant Biol, 23(2): 375-390. |
| [33] | Luo J, Schumacher M, Scherer A, Sanoudou D, Megherbi D, Davison T, Shi T, Tong W, Shi L, Hong H, Zhao C, Elloumi F, Shi W, Thomas R, Lin S, Tillinghast G, Liu G, Zhou Y, Herman D, Li Y, Deng Y, Fang H, Bushel P, Woods M, Zhang J. 2010. A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. Pharmacogenomics J, 10(4): 278-291. |
| [34] | Lv Y M, Xu L, Dossa K, Zhou K, Zhu M D, Xie H J, Tang S J, Yu Y Y, Guo X Y, Zhou B. 2019. Identification of putative drought- responsive genes in rice using gene co-expression analysis. Bioinformation, 15(7): 480-489. |
| [35] | Maurya S, Vishwakarma A K, Dubey M, Shrivastava P, Shrivastava R, Chandel G. 2018. Developing gene-tagged molecular marker for functional analysis of OsZIP10 metal transporter gene in rice. Indian J Genet Plant Breed, 78(2): 180. |
| [36] | Meuwissen T H, Hayes B J, Goddard M E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4): 1819-1829. |
| [37] | Nanda A K, Wissuwa M. 2016. Rapid crown root development confers tolerance to zinc deficiency in rice. Front Plant Sci, 7: 428. |
| [38] | Nanda A K, Pujol V, Wissuwa M. 2017. Patterns of stress response and tolerance based on transcriptome profiling of rice crown tissue under zinc deficiency. J Exp Bot, 68(7): 1715-1729. |
| [39] | Nozoye T, Nagasaka S, Kobayashi T, Takahashi M, Sato Y, Sato Y, Uozumi N, Nakanishi H, Nishizawa N K. 2011. Phytosiderophore efflux transporters are crucial for iron acquisition in graminaceous plants. J Biol Chem, 286(7): 5446-5454. |
| [40] | Ogo Y, Itai R N, Nakanishi H, Inoue H, Kobayashi T, Suzuki M, Takahashi M, Mori S, Nishizawa N K. 2006. Isolation and characterization of IRO2, a novel iron-regulated bHLH transcription factor in graminaceous plants. J Exp Bot, 57(11): 2867-2878. |
| [41] | Olsen L I, Palmgren M G. 2014. Many rivers to cross: The journey of zinc from soil to seed. Front Plant Sci, 5: 30. |
| [42] | Pertea M, Kim D, Pertea G M, Leek J T, Salzberg S L. 2016. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc, 11(9): 1650-1667. |
| [43] | Ptashnyk M, Roose T, Jones D L, Kirk G J D. 2011. Enhanced zinc uptake by rice through phytosiderophore secretion: A modelling study. Plant Cell Environ, 34(12): 2038-2046. |
| [44] | Qi Y H, Wang S K, Shen C J, Zhang S N, Chen Y, Xu Y X, Liu Y, Wu Y R, Jiang D A. 2012. OsARF12, a transcription activator on auxin response gene, regulates root elongation and affects iron accumulation in rice (Oryza sativa). New Phytol, 193(1): 109-120. |
| [45] | Ramírez-González R H, Borrill P, Lang D, Harrington S A, Brinton J, Venturini L, Davey M, Jacobs J, van Ex F, Pasha A, Khedikar Y, Robinson S J, Cory A T, Florio T, Concia L, Juery C, Schoonbeek H, Steuernagel B, Xiang D, Ridout C J, Chalhoub B, Mayer K F X, Benhamed M, Latrasse D, Bendahmane A, Consortium I W G S, Wulff B B H, Appels R, Tiwari V, Datla R, Choulet F, Pozniak C J, Provart N J, Sharpe A G, Paux E, Spannagl M, Bräutigam A, Uauy C. 2018. The transcriptional landscape of polyploid wheat. Science, 361: eaar6089. |
| [46] | Rani Debi B, Taketa S, Ichii M. 2005. Cytokinin inhibits lateral root initiation but stimulates lateral root elongation in rice (Oryza sativa). J Plant Physiol, 162(5): 507-515. |
| [47] | Ritchie M E, Phipson B, Wu D, Hu Y F, Law C W, Shi W, Smyth G K. 2015. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 43(7): e47. |
| [48] | Schmittgen T D, Livak K J. 2008. Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc, 3(6): 1101-1108. |
| [49] | Sinclair S A, Krämer U. 2012. The zinc homeostasis network of land plants. Biochim Biophys Acta, 1823(9): 1553-1567. |
| [50] | Singh S P, Gruissem W, Bhullar N K. 2017. Single genetic locus improvement of iron, zinc and β-carotene content in rice grains. Sci Rep, 7(1): 6883. |
| [51] | Spindel J E, Begum H, Akdemir D, Collard B, Redoña E, Jannink J L, McCouch S. 2016. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity, 116(4): 395-408. |
| [52] | Suzuki M, Tsukamoto T, Inoue H, Watanabe S, Matsuhashi S, Takahashi M, Nakanishi H, Mori S, Nishizawa N K. 2008. Deoxymugineic acid increases Zn translocation in Zn-deficient rice plants. Plant Mol Biol, 66(6): 609-617. |
| [53] | Swamy B P M, Rahman M A, Inabangan-Asilo M A, Amparado A, Manito C, Chadha-Mohanty P, Reinke R, Slamet-Loedin I H. 2016. Advances in breeding for high grain zinc in rice. Rice, 9(1): 49. |
| [54] | Tan M P, Cheng D, Yang Y N, Zhang G Q, Qin M J, Chen J, Chen Y H, Jiang M Y. 2017. Co-expression network analysis of the transcriptomes of rice roots exposed to various cadmium stresses reveals universal cadmium-responsive genes. BMC Plant Biol, 17(1): 194. |
| [55] | Tian D G, Chen Z J, Lin Y, Chen Z Q, Bui K T, Wang Z H, Wang F. 2020. Weighted gene co-expression network coupled with a critical-time-point analysis during pathogenesis for predicting the molecular mechanism underlying blast resistance in rice. Rice, 13(1): 81. |
| [56] | van der Straeten D, Bhullar N K, de Steur H, Gruissem W, MacKenzie D, Pfeiffer W, Qaim M, Slamet-Loedin I, Strobbe S, Tohme J, Trijatmiko K R, Vanderschuren H, van Montagu M, Zhang C Y, Bouis H. 2020. Multiplying the efficiency and impact of biofortification through metabolic engineering. Nat Commun, 11(1): 5203. |
| [57] | Wang A J, Shu X Y, Niu X Y, Zhao W J, Ai P, Li P, Zheng A P. 2018. Comparison of gene co-networks analysis provide a systems view of rice (Oryza sativa L.) response to Tilletia horrida infection. PLoS One, 13(10): e0202309. |
| [58] | Wang Y, Frei M, Wissuwa M. 2008. An agar nutrient solution technique as a screening tool for tolerance to zinc deficiency and iron toxicity in rice. Soil Sci Plant Nutr, 54(5): 744-750. |
| [59] | White P J, Broadley M R. 2009. Biofortification of crops with seven mineral elements often lacking in human diets: Iron, zinc, copper, calcium, magnesium, selenium and iodine. New Phytol, 182(1): 49-84. |
| [60] | Wissuwa M, Ismail A M, Graham R D. 2008. Rice grain zinc concentrations as affected by genotype, native soil-zinc availability, and zinc fertilization. Plant Soil, 306: 37-48. |
| [61] | Xu Y, Ma K X, Zhao Y, Wang X, Zhou K, Yu G N, Li C, Li P C, Yang Z F, Xu C W, Xu S Z. 2021. Genomic selection: A breakthrough technology in rice breeding. Crop J, 9(3): 669-677. |
| [62] | Yang M, Li Y T, Liu Z H, Tian J J, Liang L M, Qiu Y, Wang G Y, Du Q Q, Cheng D, Cai H M, Shi L, Xu F S, Lian X M. 2020. A high activity zinc transporter OsZIP9 mediates zinc uptake in rice. Plant J, 103(5): 1695-1709. |
| [63] | Yang Z, Wu Y R, Li Y, Ling H Q, Chu C C. 2009. OsMT1a, a type 1 metallothionein, plays the pivotal role in zinc homeostasis and drought tolerance in rice. Plant Mol Biol, 70: 219-229. |
| [64] | Yao W, Li G W, Yu Y M, Ouyang Y D. 2018. funRiceGenes dataset for comprehensive understanding and application of rice functional genes. GigaScience, 7(1): 1-9. |
| [65] | Yi X, Du Z, Su Z. 2013. PlantGSEA: A gene set enrichment analysis toolkit for plant community. Nucleic Acids Res, 41: W98-W103. |
| [66] | Yu G C, Wang L G, Han Y Y, He Q Y. 2012. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS, 16(5): 284-287. |
| [67] | Zeng H Q, Wang G P, Hu X Y, Wang H Z, Du L Q, Zhu Y Y. 2014. Role of microRNAs in plant responses to nutrient stress. Plant Soil, 374: 1005-1021. |
| [68] | Zeng H Q, Zhang X, Ding M, Zhang X J, Zhu Y Y. 2019a. Transcriptome profiles of soybean leaves and roots in response to zinc deficiency. Physiol Plant, 167(3): 330-351. |
| [69] | Zeng H Q, Zhang X, Ding M, Zhu Y Y. 2019b. Integrated analyses of miRNAome and transcriptome reveal zinc deficiency responses in rice seedlings. BMC Plant Biol, 19(1): 585. |
| [70] | Zhang B, Horvath S. 2005. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol, 4: Article17. |
| [71] | Zhang Y Q, Parmigiani G, Johnson W E. 2020. ComBat-seq: Batch effect adjustment for RNA-seq count data. NAR Genom Bioinform, 2(3): lqaa078. |
| [72] | Zheng Y, Jiao C, Sun H H, Rosli H G, Pombo M A, Zhang P F, Banf M, Dai X B, Martin G B, Giovannoni J J, Zhao P X, Rhee S Y, Fei Z J. 2016. iTAK: A program for genome-wide prediction and classification of plant transcription factors, transcriptional regulators, and protein kinases. Mol Plant, 9(12): 1667-1670. |
| [73] | Zhou W, Koudijs K K M, Böhringer S. 2019. Influence of batch effect correction methods on drug induced differential gene expression profiles. BMC Bioinformatics, 20(1): 437. |
| [74] | Zhu M D, Xie H J, Wei X J, Dossa K, Yu Y Y, Hui S Z, Tang G H, Zeng X S, Yu Y H, Hu P S, Wang J L. 2019. WGCNA analysis of salt-responsive core transcriptome identifies novel hub genes in rice. Genes, 10(9): 719. |
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