Identification of potential Zinc deficiency responsive genes and regulatory pathways in rice genotypes by Weighted Gene Co-expression Network Analysis

Identification of potential Zinc deficiency responsive genes and regulatory pathways in rice genotypes by Weighted Gene Co-expression Network Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of potential Zinc deficiency responsive genes and regulatory pathways in rice genotypes by Weighted Gene Co-expression Network Analysis Blaise Pascal Muvunyi, Xiang Lu, Sang He, Guoyou Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-442740/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Zinc (Zn) malnutrition has been linked to serious health concerns in humans. Targeting genetic biofortification of rice grain Zn can efficiently improve the global Zn nutritional status. To genetically enhance rice grain Zn content, the genetic and molecular mechanisms of Zn deficiency response need to be elucidated. Here, Differential Gene Expression Analysis (DGEA) and Weighted Gene Coexpression Analysis (WGCNA) were established to identify modules of coexpressed genes, the most preserved genes and molecular pathways regulating Zn deficiency response in rice varieties. Results Twelve modules of coexpressed genes were obtained by WGCNA using 1649 differentially expressed genes (DEGs) from our DGEA and 2121 Zn genes from earlier studies. Three modules (TTA-M, TRA-M and CA-M) were judged the most relevant for Zn deficiency based on their richness in the well-recognized Zn deficiency responsive (ZDR) genes and molecular pathways. 96 (17%), 188 (47.6%) and 96 (24%) genes from TTA-M, TRA-M and CA-M modules respectively, were significantly expressed in the DGEA. These coexpressed DEGs (CoDEGs) were considered as the most preserved Zn deficiency responsive genes. Of the well-known ZDR genes, only OsZIP8 , OsZIP10 , OsMT1a and OsNAAT1 were preserved. Functional annotations for all CoDEGs from the identified ZDR modules showed that glutathione metabolism and biosynthesis of secondary metabolites were the most quickly upregulated molecular pathways. Lastly, a biology-informed gene-gene interaction network analysis (GGIN) indicated that CoDEGs including OsLSI3 , OsWOX11 , OsNRT1.1B , OsPSK5, OsSWN6 and OsMID1 strongly interact with the recognized ZDR genes in the ZDR modules. Curiously, these CoDEGs were previously validated for other economic traits in rice. Conclusion Findings from this study provide comprehensive insights into the molecular mechanisms of Zn deficiency response in rice and may facilitate gene and pathway prioritization, to enhance Zn use efficiency (ZUE) and Zn biofortification in rice. Plant Molecular Biology and Genetics Plant Physiology and Morphology Zinc deficiency Weighted Gene Coexpression Analysis Biofortification Rice Figures Figure 1 Figure 2 Figure 3 1. Background Zinc malnutrition is affecting about 1.2 billion of people and has been associated with detrimental health concerns including children stunting, weak immune system and brain development (Black, 1998 ; Dardenne, 2002 ). Regarding the current pandemic of COVID-19, Zn malnutrition will exacerbate infection rates (Akhtar et al.; Huizar et al., 2020; Van Der Straeten et al., 2020), underlining the need for Zn biofortification. Fortifying Zn in rice, a staple crop for nearly 3.5 billion of people, can significantly benefit the global Zn nutritional status (Van Der Straeten et al., 2020). Agronomic approaches to enhance grain Zn content through chemical fertilization for example, are not only unecomomical, but also ineffective, as Zn tends form insoluble complexes which limits its uptake from plant root tissues (Alloway, 2008 ). Favorably, natural genetic variations in rice germplasm are sufficient to attain Zn concentrations requirements of 28 ppm in the polished rice grain (Van Der Straeten et al., 2020). Zn efficient (ZE) genotypes have relatively higher ZUE and perform better than Zn inefficient (ZI) genotypes under low Zn soils or nutrient solutions (Nanda et al., 2017 ). Also, ZE genotypes exhibit higher growth of crown roots (Nanda and Wissuwa, 2016a ) and greater rates of the exudation of low-molecular weight organic acids (LMWOA) metal-chelators, such as nicotianamine synthase (NAs), mugineic acid (MAs) or 2′-deoxymugenic acid (DMAs) from the phytosiderophore family (Ishimaru et al., 2011 ; Ptashnyk et al., 2011 ; Widodo et al., 2010 ). Such traits have been linked to the loading of both Zn or Fe in rice grains (Banakar et al., 2017 ; Masuda et al., 2012 ). In addition to these LMWOAs, Zinc transporter genes mainly ZIP (zinc-regulated transporters and iron-regulated transporter proteins), HMAs (heavy metal ATPases) and MTPs (metal tolerance proteins) are essential for the uptake, distribution, and redistribution of Zn across different plant tissues under low Zn deficiency conditions (Ishimaru et al., 2005 ; Olsen and Palmgren, 2014 ). Further, transcription factor (TF) genes such as OsNAC, OsIRO2 , and OsIRO3 are also known to regulate the expressions of several Zn or Fe responsive genes (Banerjee and Chandel, 2011; Ogo et al., 2006 ). As to molecular pathways affected by Zn deficiency, previous transcriptomic studies have consistently shown that Zn deficiency responsive (ZDR) genes significantly enrich Zinc transmebrane activity, transmebrane transport activity, phenylpropanoid biosynthesis and glutathione metabolism functional gene sets (Bandyopadhyay et al., 2017 ; Nanda et al., 2017 ; Zeng et al., 2019b ). Nevertheless, the most conserved ZDR genes and molecular pathways are not known. Also, the interaction networks involving the known (experimentally validated) ZDR genes are yet to be investigated. Weighted gene co-expression network analysis (Langfelder and Horvath, 2008 ) is a systems biology approach that leverages on the extent of co-expression between genes to define their connectivity. Subsequently, it allows studying the universal network nature of a transcriptome (Zhao et al., 2010 ). For example, integrating transcriptomic analysis and WGCNA in rice enabled to find the global Cadmium (Cd)-regulated DEGs (Tan et al., 2017 ). Also, WGCNA was useful to functionally annotate the genome of rice (Childs et al., 2011 ) and construct the atlas of maize by integrating different omics data (Walley et al., 2016 ). In a transcriptome co-expression network, genes are represented by nodes which are linked together by edges based on the weighted co-expression across samples. The most connected genes within a network are assembled as modules, while the highly connected genes within a module are referred to hubs (Zhang and Horvath, 2005 ). Co-expression network architecture is inherent of cellular organization, with the functional modules build up from several interacting molecules (Hartwell et al., 1999). Here, DGEA and WGCNA were implemented to identify (1) functional modules of coexpressed genes in Zn-stressed rice genotypes (2) coexpressed DEGs (CoDEGs) and their preserved molecular functions, and (3) genes showing strong connectivity with the experimentally validated (herein referred to well-known or recognized) ZDR genes. Subsequently, modules of coexpressed genes were constructed by combining DEGs from our own samples including ZI (IR26 and IR64) and AUS (WCP22 and KALBOR026) genotypes, and the other publicly reported ZDR genes. The significance of a module for Zn deficiency response was dictated by the recognized ZDR functional annotations enriched by genes coexpressing in that module. Genes from the identified ZDR modules which are DEGs in our DGEA were judged as the most preserved, and so were their respective functional annotations. These functionally annotated CoDEGs are good candidates to track functional markers which can be incorporated into breeding schemes to improve rice grain Zn content. Lastly, gene-gene interaction network (GGIN) or protein-protein interaction network (PPIN) of the ZDR modules identified genes or proteins with strong connectivity with the well-recognized ZDR genes. We speculate that these identified hub genes could also be involved in the mechanistic of Zn stress response as their linked well-known ZDR genes. 2. Results Identification of differentially expressed genes. Gene expression analysis was performed for root and shoot tissues of two ZI (IR26 and IR64) and AUS (UCP122 and KALBOR026) rice genotypes using DESeq2 R package (Love et al., 2014). To obtain sufficient DEGs for the next stage of WGCNA, two approaches were used. A condition-based gene expression analysis (CEA) where gene expression for each ZI or AUS genotype was studied under Zn deficiency condition (Zn supply vs Zn deficiency), and a genotype-based expression analysis (GEA) by setting ZI genotypes as reference level for each of AUS genotype (IR26/IR64 vs UCP122/KALBOR026). Overall, 1649 unique genes ( Table 1 and Additional file1-10: Table S1-10) were differentially expressed (FDR = 2 or < 2). 1179 of the 1649 DEGs were novel ZDR genes, while 470 DEGs have been reported in previous studies (Fig. 1 a and Additional file 11: Table S11 ). Under CEA, the well-known ZDR genes such as ZIP ( OsZIP1, OsZIP5, OsZIP10 and OsZIP8 ), a phytosiderophores family gene ( OsNAAT1) , and a metallothionein gene ( OsMT1a) were induced by Zn deficiency treatment in ZI or AUS genotypes. Under low Zn supply conditions and GEA, the expressions ZIP genes and phytosiderophores family genes ( OsNAAT1 and OsNAS3 ) were significantly higher in the root sample of AUS genotypes than in the same tissue of IR26 genotype ( Additional file 9–10: Table S9-10 ). Functional annotation of differentially expressed genes. Functional annotations of DEGs (under CEA) by STRING tool version:11 (Mering et al., 2003 ) indicated that Zinc ion transmembrane transporter activity GO term was significantly enriched by DEGs upregulated by Zn deficiency treatment in both ZI and AUS genotypes. However, significant differences between UCP122 and other genotypes were detected in the enrichment of the DNA-binding transcription factor activity GO term. The latter was significantly upregulated in the root tissue of UCP122 yet downregulated in the same tissue of KALBOR026 and IR26 genotypes. Similarly, Zinc ion transmembrane transporter activity GO term was upregulated in root tissues of UCP122, while for all the other genotypes, it was only upregulated in crown tissues. Table 1 Number of DEGs obtained under CEA and GEA in AUS and ZI genotypes. Contrast levels Contrast categories Expressed genes (FDR = 2 log 2 fold < 2 Total Zn supply vs Zn Def (CEA) IR26_root 93 19 112 ZI IR26_crown 14 56 70 IR64_root 20 24 44 IR64_crown 35 276 311 Total non-redundant genes 145 312 457 UCP122_root 129 11 140 AUS UCP122_crown 4 10 14 KALBOR026_root 49 94 143 KALBOR026_crown 36 8 44 Total non-redundant genes 203 87 290 ZI vs AUS genotypes (GEA) Under Zn deficiency IR26 vs UCP122_crown 97 178 275 IR26 vs UCP122_root 155 387 526 IR26 vs KALBOR026 root 137 398 535 IR26 vs KALBOR026 crown 27 55 82 IR64 vs UCP122_crown 98 83 181 IR64 vs UCP122_root 46 123 169 IR64 vs KALBOR026 root 54 205 259 IR64 vs KALBOR026 crown 115 94 209 Total non-redundant genes 497 912 1419 Under Zn supply IR26 vs UCP122_crown 23 37 60 IR26 vs UCP122_root 58 119 177 IR26 vs KALBOR026 root 49 80 129 IR26 vs KALBOR026 crown 11 31 42 IR64 vs UCP122_crown 25 60 85 IR64 vs UCP122_root 27 157 184 IR64 vs KALBOR026 root 26 73 99 IR64 vs KALBOR026 crown 11 51 66 Total unique genes 129 294 413 Also, cellulose metabolic process was downregulated in crown tissues of all the genotypes but was not enriched by DEGs from the UCP122 genotype. These findings infer that UCP122 cultivar may have unique phenotype under low Zn supply conditions. All the significantly enriched functional gene sets are provided ( Table 2 and Additional file 12–15: Table S12-15) . Table 2 Functional annotations of DEGs in ZI and AUS genotypes after Zn deficiency treatment (under CEA). Samples Go term/ KEGG pathway_ID GO term/pathway description FDR ROOT_KALBOR026 GO:0015250 Water channel activity + 0.00087 GO:0015318 Inorganic molecular entity transmembrane transporter activity + 0.00087 GO:0015075 Ion transmembrane transporter activity + 0.0199 map00480 Glutathione metabolism + 0.0282 map00940 Phenylpropanoid biosynthesis + 0.0378 GO:0140110 Transcription regulator activity *0.00018 GO:0003700 DNA-binding transcription factor activity *0.00065 GO:0044249 Cellular biosynthetic process *0.0012 CROWN_KALBOR026 GO:0005385 Zinc ion transmembrane transporter activity + 3.45E-05 GO:0010333 Terpene synthase activity + 3.45E-05 GO:0022890 Inorganic cation transmembrane transporter activity + 0.00011 map00904 Diterpenoid biosynthesis + 0.0036 GO:0030243 Cellulose metabolic process *0.0052 GO:0071555 Cell wall organization *0.0067 ROOT_ UCP122 GO:0005488 Binding + 0.00019 GO:0003700 DNA-binding transcription factor activity + 0.0045 GO:0003824 Catalytic activity + 0.0045 GO:0005385 Zinc ion transmembrane transporter activity + 0.0134 GO:0030244 Cellulose biosynthetic process + 0.038 CROWN_ UCP122 NO significant annotations found ROOT_IR26 GO:0050667 Homocysteine metabolic process + 0.0057 GO:0008652 Cellular amino acid biosynthetic process + 0.031 GO:0009086 Methionine biosynthetic process + 0.031 map00940 Phenylpropanoid biosynthesis + 0.0044 map01110 Biosynthesis of secondary metabolites + 0.0044 map00130 Ubiquinone and other terpenoid-quinone biosynthesis + 0.0298 map00360 Phenylalanine metabolism + 0.0298 GO:0005488 Binding *0.0278 GO:0046872 Metal ion binding *0.0278 GO:0003700 DNA-binding transcription factor activity *0.0371 CROWN_IR26 GO:0005385 Zinc ion transmembrane transporter activity + 1.62E-06 GO:0006073 Cellular glucan metabolic process *1.70E-08 GO:0030243 Cellulose metabolic process *1.70E-08 GO:0071555 Cell wall organization *1.26E-07 ROOT_IR64 GO:0046872 Metal ion binding + 0.0142 map00940 Phenylpropanoid biosynthesis + 0.006 GO:0034219 Carbohydrate transmembrane transport *0.003 GO:0051119 Sugar transmembrane transporter activity *0.0026 CROWN_IR64 GO:0005385 Zinc ion transmembrane transporter activity + 1.62E-06 GO:0006073 Cellular glucan metabolic process *1.70E-08 GO:0030243 Cellulose metabolic process *1.70E-08 GO:0071555 Cell wall organization *1.26E-07 + : GO terms/KEGG pathways significantly (FDR < 0.05) enriched by upregulated genes. * : GO terms/KEGG pathways significantly (FDR < 0.05) enriched by downregulated genes. Construction of the global network of coexpressed modules of genes. To construct the global network of coexpressed genes, raw counts from nine Zn-deprived samples were obtained from https://www.ebi.ac.uk/ena , then processed and merged with eight Zn-deprived samples from our own study ( Fig. 1 b and Additional file 17: Table S17) . Samples from public datasets included ZE (Nipponbare, IR55179 and RIL46) and ZI (KP, IR64, IR74) genotypes. The selected genes for WGCNA were 1649 DEGs from this project and 2121 Zn responsive genes reported from seven previous studies (Fig. 1 a and Additional file 11: Table S11 ). We set the module minimum size and the threshold to merge correlated modules at 16 and 0.6, respectively. The merged raw counts from different projects were corrected for batch effects, tissue types and plant growth stage -mediated covariances. The expression variance was also stabilized as indicated in WGCNA tutorial https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html . These parameters resulted into 2076 genes which coexpressed across 13 different modules (Table 3 ), including a module of unassigned genes. All the modules were relabeled with short abbreviations for reference. Functional annotations of the identified modules. To associate modules to Zn deficiency responsiveness, GO terms and KEGG pathways enrichment by genes within each module were studied using STRING version:11 ( http://string-db.org/ ) (Mering et al., 2003 ). Apart from genes from P-M module, all the other modular genes significantly enriched at a minimum one GO term or KEGG pathway ( Table 3 ). Intriguingly, molecular functions and genes recognized for Zn uptake, distribution, and grain Zn/Fe content functions in rice, preferably coexpressed in TTA-M, TRA-M and CA-M modules (Fig. 2 a). Therefore, all the subsequent characterizations were implemented on these putative ZDR modules. Table 3 Functional annotations of coexpressed modules of genes. Module’s colour | Assigned label| Number of genes in a module Module preserved GO terms | preserved KEGG pathways (Bonferroni p -value < 0.05) Blue |TTA-M|555 * Transmembane transport activity ; Zinc ion transmembane transport ; iron ion homeostasis, etc. | Metabolic pathways ; Photosynthesis, etc. Midnightblue|TRA-M|395 * Transcription regulator activity; Zinc ion transmembrane transporter activity, etc. | Biosynthesis of secondary metabolites; alpha-Linolenic acid metabolism. Greenyellow|CA-M|393 * Catalytic activity; metal ion binding, etc. | Metabolic pathways; Phenylpropanoid biosynthesis, Glutathione metabolism, etc. Grey60 |AP-M|145 Apoplast | No significant KEGG annotations found. Royalblue |PB-M|217 No significant GO terms | phenylpropanoid biosynthesis; fatty acid metabolism, etc. Lightcyan|CG-M|65 Cell growth | glyoxylate and dicarboxylate metabolism; cutin, suberine and wax biosynthesis; biosynthesis of unsaturated fatty acids. Darkturquoise|PHEN-M|96 No significant GO annotations | Phenylpropanoid biosynthesis; phagosome; stilbenoid, diarylheptanoid and gingerol biosynthesis. Darkgreen|PHOT-M|41 Photosynthesis | Cyanoamino acid metabolism. Starch and sucrose metabolism; glycine, serine and threonine metabolism. Darkgrey|CA-M|35 Cold acclimation; photosynthesis; response to abiotic stress | porphyrin and chlorophyll metabolism. Black|RES-M|122 Response to external stimulus; response to extracellular stimulus | Amino sugar and nucleotide sugar metabolism, etc. Pink|DR-M|395 No significant GO annotation found | DNA replication, alpha-Linolenic acid metabolism, plant-pathogen interaction. Purple|P-M|79 No significant GO annotations | No significant KEGG annotations found. Grey|FAT-M|94 No significant GO annotation found | Fatty acid elongation. * Zn deficiency responsive modules (ZDR modules). Zn deficiency responsive modules are putative regulators of Zn deficiency response in rice. Many of the well-known ZDR genes and their molecular pathways were found in ZDR modules. Genes coexpressing in the TTA-M module included for example, Zinc ion transmembrane transporters ( OsZIP3, OsZIP8, OsHMA2 and OsHMA3 ), a MAs transporter ( OsZIFL4/OsTOM1) , transmembrane transporters ( OsYSL16, OsYSL18 and OsMTP4 ), metal-NA complexes transporters ( OsYSL4 and YSL6 ), metal binding ( OsMT4IC, OsMT1B and OsMT1a ) and iron homeostasis genes ( OsSHR1 and OsNRAMP6 ). Equally, functional annotations including Zinc ion transmembrane transporter activity ( OsMTP1 and OsZIP2 ), organic substance ( OsMCM4, OsMPK7, OsCIPK9, OsCESA6, OsCSLF6 , etc.), oxidoreductase activity ( OsCKX5, OsDAO , genes from CYP family genes, etc.), transmembrane transporter activity ( OsNRAMP6, OsMST6, OsABCG44 , etc.) and transcription regulator activity ( OsIRO2, OsERF67, OsONAC12, OsSWN6, OsDREB1C, OsJAZ TFs genes, etc.) were detected the TRA module. CA-M was another important module, with catalytic activity as its most enriched GO term. Genes including OsZIP10 and MAs biosynthesis genes ( OsSAMS1, OsSAMS2 , OsNAAT1 and OsDMAS1 coexpressed in the CA-M module. Collectively, these findings are suggestive of potential key roles of ZDR modules in the regulating Zn deficiency molecular mechanisms in rice. All the genes coexpressed across ZDR modules and their function annotations are provided ( Additional file 18–23: Table S18-23). Preserved Zn deficiency responsive genes and their molecular functions. To find the most preserved genes, we intersected ZDR module genes with DEGs from the DGEA (under CEA). 96 (17%), 188 (47.6%) and 96 (24%) genes from TTA-M, TRA-M and CA-M modules respectively, were responsive to Zn deficiency in our DGEA ( Fig. 2 b and Additional file 24–28: Table S24-28) . Among all the well-known ZDR genes, the most preserved were Zinc transmembrane transport genes OsZIP8 (TTA-M) and OsZIP10 (CA-M) which were significantly upregulated in crown tissues of all ZI genotypes and KALBOR026 genotype, a metallothionein gene OsMT1a (TTA-M) which was upregulated in the root tissue of UCP122 genotype, and a phytosiderophores family gene OsNAAT1 (CA-M) which upregulated in root and crown tissues of all AUS genotypes and IR64 genotypes ( Additional file 29–30:Table S29-30) . Enrichment analysis of all coexpressed DEGs (CoDEGs) showed that genes which upregulated in the root tissue of UCP122 genotype affected the highest number of functional sets vs genes from all the other sample genotypes. Of the 140 genes upregulated in root tissue of UCP122 genotype (Table 1 ) , 88 genes coexpressed in TRA module, and significantly enriched 77 GO terms including DNA-binding transcription factor activity, cellulose biosynthetic processes, catalytic activity, response to stimuli, etc (Fig. 2 c and Additional file 34: Table S34 ). Contrary, in the same module, five of the GO terms upregulated in the root sample of UCP122 genotype (including DNA-binding transcription factor activity, binding, sequence specific DNA binding, etc.) were rather downregulated in the root tissue of KALBOR26 genotype. This was consistent with our findings with the DGEA that suggested possible phenotypic differences between UCP122 and other varieties during Zn deficiency conditions. On the other hand, another set of CoDEGs from the TTA-M module upregulated the glutathione metabolism in root tissues of both AUS genotype. The other preserved and upregulated functional gene sets included the secondary metabolites biosynthesis (CA-M module) and transmembrane transport (TTA-M module) which were significantly enriched by CoDEGs from the root sample of IR26 and KALBOR026 genotypes, respectively ( Fig. 2 c ) . Identification of hub genes in Zn deficiency responsive modules. The Maximal Clique Centrality (MCC) algorithm (Chin et al., 2014 ) was applied to identify hub genes across ZDR modules. The first 30 hub genes as ranked by their scores from the strongest to weakest, are provided ( Additional file 35–37: Table S35-37 ). Among the well-known ZDR genes, only OsZIP3 and OsYLS18 ( both from TTA-M module) featured in the top 30 hub genes. To identify the most Zn deficiency relevant hub genes, we rather adopted a biology-informed approach where CoDEGs directly connected with at least three recognized ZDR genes, or proteins directly interacting with at least one well-known ZDR protein were considered as ZDR specific hub genes or proteins ( Additional file 38: Fig. S1-2) . The well-known ZDR genes or proteins for which the interactions were searched against were: OsZIP10 , OsNAAT1 , OsSAMS1 , OsSAM2 , OsDMAS1 and OsYSL10 (CA-M module); OsIRO2 , OsZIP2 , and OsNRAMP6 (TRA-M module); OsZIP3, OsZIP8, OsMT1a, OsHMA2, OsHMA3, OsYSL6, OsYSL14,OsYSL18, OsNRAMP4, OsNRAMP7 and OsTOM1 for the TTA-M module. Genes meeting the set criteria included OsbHLH120 , OsWOX11 , OsPSK5, OsNAC121 , OsPGIP1 , OsMID1 , OsMYB55 , OsJAmyb and OsMPH1 (CA-M module), OsSWN6, ONAC12, OsERF67, OsbHLH108 (TRA-M module), and OsHMA1, OsLIS3, OsGSTU12, OsNRT1.1B, OsMTI4C, OsMT1g, OsbZIP79 and OsTRX for TTA-M module ( Fig. 3 a-c and Additional file 18–20: Table S18-20) . Curiously, the https://funricegenes.github.io/ search database for functionally validated genes in rice, showed that several of the CoDEGs (Additional file 29–31: Table S29-31) linking up the recognized ZDR genes have established agronomic functions such as transport of essential nutrients, biotic and abiotic stress tolerance (Table 4 and Fig. 3 a-c). Table 4 Experimentally validated genes which are hub genes for the known ZDR genes. Module Gene name MSU_ID Gene functions (retrieved from https://funricegenes.github.io/ ) TTA-M OsNRT1.1B* LOC_Os10g40600 Key regulator of Nitrogen use efficiency in rice. Enhances of Selenium concentration in rice grains. OsLSI3* LOC_Os10g39980 Essential for Silicon distribution in vascular structures of rice. OsMT1g* LOC_Os12g38290 Confers multiple abiotic stress tolerance in rice. OsRNS4* LOC_Os11g05480 Improves salinity stress tolerance. OsTRX LOC_Os12g08730 Enhances carotenoid and chlorophyll content. CA-M OsBHL120 LOC_Os09g28210 A QTL for root thickness and lengths in upland rice. OsWOX11* LOC_Os07g48560 Enhances crown root development. Enhances drought stress tolerance. Improves potassium deficiency tolerance. Regulates cytokinin signaling pathway. OsMYB55 + LOC_Os05g48010 Regulates amino acid metabolism. Enhances heat stress tolerance and immunity. Regulates hormonal signaling. OsMPH1 + LOC_Os06g45890 Modulates plant height and enhances drought stress tolerance. Enhances Cadmium stress tolerance. OsMID1* + LOC_Os05g37060 Improves drought stress tolerance Negatively regulates Arsenic stress tolerance OsPSK5* LOC_Os12g05260 Target of osa-miR168a regulating seed vigor OsPGIP1* LOC_Os05g01380 Enhances sheath blight tolerance TRA-M OsSWN6* LOC_Os04g45340 Positively regulates the secondary wall biosynthetic. processes. * : Coexpressed in ZDR modules and expressed in our DGEA (CoDEGs). + : R2R3-type MYB transcription factor gene. 3. Discussion This study indicates that integrating WGCNA and prior biological knowledge on ZUE trait is an effective method to add value on previous findings from GWAS, qtl mapping, transgenic, miRNA and gene transcriptomic studies on Zn. This apporach enabled to establish modules of coregulated genes, determining conserved molecular functions, and predicting new putative Zn reponsive genes which may facilitate the improvement of Zn use efficiency and Zn biofortification in rice. The significance of Zinc deficiency responsive modules . Our results indicated that molecular pathways and genes known to play essential roles in Zn homeostasis and Zn loading into rice grain coexpressed in ZDR modules. These genes included for instance, ZIP genes ( OsZIP2, OsZIP3, OsZIP10 and OsZIP8 ), heavy metal ATPase genes ( OsHMA1, OsHMA2 and OsHMA3 ), mugeneic acid (MAs) or nicatiamine synthase (NAs) genes ( OsNAAT1, OsTOM1, OsDMAS1, OsSAMS1 , and OsSAM2 ), metallothionein ( MT ) protein coding gene ( OsMT1a ) and Zn/Fe transcription regulator genes ( OsIRO2 and OsIRO3 ). OsZIP8 gene is involved in Zn uptake and transport in rice (Lee et al., 2010 ). And the expression of OsZIP10 gene correlated with high iron (Lee et al., 2009 ) and Zn (Maurya et al., 2018 ) concentrations in rice seeds. Apart from ZIP genes, OsHMA2 and OsHMA3 have also been experimentally validated. OsHMA2 is a long-distance transporter of Zn and Cd (Yamaji et al., 2013), while OsHMA3 enhances Cd stress tolerance and the expression of other Zn transporter genes (Cai et al., 2019 ; Sasaki et al., 2014 ). MAs and NAs are metal chelators from the phytosiderophores family genes and play essential roles in the accumulation Zn/Fe into grain rice, while mitigating the loading of Cd (Banakar et al., 2017 ). Earlier studies demonstrated that OsTOM1 , a DMA efflux transporter gene, enhanced tolerance to both Zn (Ishimaru et al., 2011 ) and Fe (Nozoye et al., 2011 ) deficiencies in rice. Further, targeting MAs genes was proposed as a single strategy to improve both Zn/Fe in rice endosperm (Singh et al., 2017 ). MTs genes are also metal chelators that bind transition metals such as Zn (Sinclair and Krämer, 2012 ). Transgenic rice plants overxpressing OsMT1a showed enhanced expressions of Zn-induced CCCH zinc-finger TFs, ROS scavenging capacity, drought stress tolerance and Zn concentrations in rice tissues (Yang et al., 2009 ). Coexpression of all these Zn transporters, phytosiderophores family genes and metal chelators genes across TTA-M, TRA-M and CA-M modules suggests relevant functions of ZDR modules in the molecular mechanism Zn deficiency response. Conserved genes and molecular pathways across Zn deficiency responsive modules. By intersecting ZDR modules genes and DEGs from our study we obtained the most conserved transcriptionally active genes. The most significantly enriched functional annotations by these CoDEGs were glutathione metabolism, DNA binding transcription factor, transmembrane transport, and biosynthesis of secondary metabolites including phenylpropanoid and phenylalanine metabolism. Interestingly, these conserved molecular pathways were only enriched by genes expressed in root tissues, underscoring the crucial role of root tissue in Zn deficiency regulation (Fig. 2 c). Glutathione (GSH; γ-glutamyl-cysteinyl-glycine) is a low- molecular-weight (LMW) intracellular signalling molecule with indispensable antioxidant and abiotic stress tolerance roles (Noctor et al., 2012 ). The significant enrichment of glutathione metabolism by Zn stress induced genes was also reported by previous studies in rice (Bandyopadhyay et al., 2017 ; Zeng et al., 2019a). Zeng et al. (2019a) showed that Zn supply repressed the activity involved in glutathione metabolism genes, suggesting that glutathione has essential roles in Zn stress response. Further, it was shown that the antioxidant roles of GSH enhanced Cd stress tolerance in rice (Chen et al., 2010 ). In this study, a set of DEGs that upregulated in root tissues of both AUS genotypes and coexpressed in the TTA-M module, significantly enriched the glutathione metabolism pathway. An earlier study indicated that salt stress tolerant rice cultivar Pokkali had significantly greater levels of GSH compared to a sensitive variety, IR64 (El-Shabrawi et al., 2010 ). Whether the exudation of GSH or the expression of GSH pathway genes is linked to higher ZUE in ZE genotypes is worthy further investigations, for example, through transgenic or crop modelling approaches. Conversely, secondary metabolites KEGG pathways such as phenlypropanoid biosynthesis was preserved in a group CoDEGs from CA-M module which were upregulated in the root sample of IR26 genotype. The upregulation of phenlypropanoid biosynthesis pathways in rice tissues under low Zn supply was also reported in previous studies (Nanda et al., 2017 ; Zeng et al., 2019a). Phenylpropanoids constitute the major group of secondary metabolites in plants (Sharma et al., 2019 ). Phenylpropanoids can chelate metal ions to enhance the mobilization and uptake of essential elements suh as Zn, iron, manganese, potassium, calcium and magnesium (Seneviratne and Jayasinghearachchi, 2003 ). The high conservation of both glutathione and phenlypropanoid biosynthesis pathways in root tissues of AUS and ZI is another evidence for the vital roles of LMW root effluxes in enhancing Zn uptake from low Zn environments. Closest hub genes of the well-known ZDR genes are known stress responsive genes. Genes with strong connectivity with the validated ZDR genes were identified. Several of these genes or their homologuous were shown to play major roles in different morphogenetic events, biotic and abiotic stress responses by previous investigators. These included for example, a TF gene OsbHLH120 which interacted with validated ZDR genes in the CA-M module. OsbHLH120 is a QTL for root growth and thickness, and improves drought tolerance in upland rice (Li et al., 2015 ). The homologous gene of OsbHLH120 , OsIRO2 / OsbHLH56 , is a well-known TF gene that positively regulates the expression of several genes involved into Fe uptake in rice (Ogo et al., 2009 ; Ogo et al., 2007 ). Both TFs contain an homeodomain Leucine Zipper (HD- Zip 1) element known to mediate of hormonal responses and response to stimuli (Ariel et al., 2007 ). In contrast, another homologous gene, OsIRO3 , negatively regulates Fe uptake (Wang et al., 2020 ; Zheng et al., 2010 ) but interacted with OsDMAS1 in the PPIN (Additional file 38: Fig. S2c) . OsIRO3 has a similar binding site with OsIRO2 (5’-CACGTGG-3’). Whether the activity of OsbHLH120 under Zn/ Fe deficiency is similar to OsIRO2 or OsIRO3 requires further studies. Unknown targets of OsbHLH120/ OsIRO3 may contribute to Fe/Zn hypersensitivity. OsWOX11 was an other important hub gene for known ZDR genes. OsWOX11 is a regulator of cytokinin signalling pathway, crown and root hair development, and enhances drought stress tolerance (Cheng et al., 2016 ; Neogy et al., 2019 ). An ealier study associated crown development with Zn stress tolerance in ZE genotypes (Nanda and Wissuwa, 2016b). In rice, cytokinin inhibits crown root development, while auxin promotes that process (Debi et al., 2005 ; Kitomi et al., 2011 ). Here, a cytokining gene ( OsCKX5 ) from the ZDR modules was downregulated by Zn deficiency in root sample of KALBOR026 genotype (Additional file 31: Table S31) , constistent with the significant higher root biomass observed in KALBOR026 genotype relative to ZI genotypes (IR26 and IR64) (Lu et al., 2020 ). However, OsWOX11 and its homologous gene OsWOX6 only upregulated in root samples of a ZI genotype (IR26) (Additional file 31: Table S31) with less root biomass relative to AUS genotypes (Lu et al., 2020 ). OsWOX11 may have essential roles in Zn stress response, as its neigbour genes. However, more investigations are needed to illuminate the regulation of ctyokinin signalling by OsWOX11 during Zn deficiency conditions. On the other hand, in the TTA-M, OsNRT1.1B gene coexpressed and interacted with known ZDR genes such as OsZIP3, OsHMA2 and OsYSL18 ( Fig. 3 ) . OsNRT1.1B is a QTL for nitrate use efficiency divergence in rice cultivars (Hu et al., 2015 ) and enhances nitrogen uptake under low nitrogen conditions in cultivated rice (Fan et al., 2016 ). Also, OsNRT1.1B improves Selenium levels in rice grains (Zhang et al., 2019). A previous study indicated that OsNRT1.1B was induced by Zn stress conditions (Bandyopadhyay et al., 2017 ). Here, the expression of OsNRT1.1B was significantly repressed in root tissues of a Zn sensitive genotype, IR64 (Additional file 29: Table S29) . As a transporter gene, OsNRT1.1B could also have significant functions in the translocation of Zn in different rice tissues. Still in the TTA-M module, OsLSI3 linked up OsHMA3 , OsHMA2 , OsYSL14 and OsYSL18 . OsLSI3 and its homologue OsLIS2 involve in the distribution and redistribution of Arsenic across vascular structures in rice (Yamaji et al., 2015 ). OsLSI2 , is a silicon efflux transporter responsible for arsenic accumulation in rice grain (Ma et al., 2008 ). However, exogenous Silicon is known to enhance gas exchange capacity in rice plants under Zn stress (Song et al., 2014 ), and the uptake of essential micronutrients (Zn and Manganese) and macronutrients (Phosphorous, potassium, Calcium and Magnesium) in rice and other monocots plants under heavy metal stress conditions (Keller et al., 2015 ; Tripathi et al., 2013 ). On the other side, Silicon also reduced Zn uptake in root tissues of maize and cotton plants (Bokor et al., 2015 ). Here, OsLIS1 , OsLIS2 and OsLSI3 were significanlty upregulated in root samples of KALBOR026 relative to IR26 in the same tissue under Zn stress conditions (Additional file 10: Table S10) . OsLIS2 was also significantly induced by Zn stress relative to Zn supply conditions in root tissues of KALBOR026 genotype. It is likely that OsLIS2 / OsLIS3 have similar functions as their linked Zn/cd transporter genes. However, their specific roles in Zn homeostasis remain to be elucidated. Another gene connecting the well-known ZDR genes was OsHMA1 . It is presumed that OsHMA1 involves in Zn stress response or detoxification of Zn excess like its orthologue AtHMA1 (Takahashi et al., 2012). OsHMA1 transcripts accumulated by more than 7 folds under Zn-stress condition relative to Zn supply conditions (Suzuki et al., 2012 ). In this study, OsHMA1 was significantly upregulated in crown tissues of AUS (KALBOR026) and ZI (IR64) genotypes (Additional file 29: Table S29) . This suggests that OsHMA1 could also have similar functions as its homoguous genes OsHMA2 or OsHMA3 . Laslty, many of the validated R2R3 -type MYB TFs genes (Katiyar et al., 2012 ) linked up several known ZDR genes across the ZDR modules ( Fig. 3 c and Table 4 ) . These MYB TF genes were also reported in previous transcriptomic studies on Zn deficiency in rice, but none these has been experimentally validated for Zn deficiency functions. Considering that coexpressing and interacting genes are likely to share same functions, the identified hub genes which have been validated for the other economic traits in rice could also have key roles in enhancing Zn deficiency tolerance as their linked well-known ZDR genes. Thus, targeting these putative novel ZDR genes may benefit the concurrent improvement of several agronomic traits in rice. 4. Conclusions A comprehensive elucidation of functional pathways within the framework of co-expression network enabled to identify 3 key modules of coexpressed genes under Zn deficiency, 380 global conserved genes and their pathways, and key hub genes in the interaction networks of Zn responsive genes. Of the well-recognized Zn defiency responsive genes, only OsZIP8 , OsZIP10 , OsMT1a and OsNAAT1 were expressed in our samples and conserved in the identified key modules. OsZIP3 and OsYSL18 were the only recognized Zn responsive genes detected among the top hub genes in the key modules. Several other hub genes with strong interactions with the recognized Zinc defiency responsive genes were found. Results from this investigation may for instance facilitate genomic selection breeding schemes in targeting relevant molecular pathways and genes to enhance ZUE and Zn biofortification in rice. 5. Materials And Methods A comprehensive pipeline used from DGEA to hub genes identification is provided (Additional file 38: Fig. S3) . Sample description and data acquisition for DEG analysis. Procedures from sample preparation and Zn deficiency treatment to the generation of the Fragment Per Kilobase Millions (FPKM) data frame were previously performed in our laboratory, and are as described in a published study (Lu et al., 2020). Briefly, samples consisting of one-week old root or crown tissues of two ZI (IR26 and IR64) and two AUS (UCP122 and KALBOR026) genotypes were supplied (control) or deprived of Zn nutrient regime for one month (Zn stress treatment). The FPKM table as generated by our biological collaborators consisted of 36 columns covering the four genotypes, two tissues (root and crown) per each genotype, two replicates per tissue of each genotype and two treatment conditions (Zn supply or Zn deficiency). Two replicates per tissue for each genotype were treated as one independent biological sample under Zn supply or Zn deficiency conditions ( Additional file 16-17: Table S16-17 ). Differential gene expression analysis (DGEA) was only carried out for samples from our laboratory using the Bioconductor R package DEseq2 (Love et al., 2014). Genes were considered differentially expressed if passing the stringent criterions of fold-change ≥ 2 or ≤ -2, q _value ≤ 0.01, and false discovery rate-adjusted p value (FDR) < 0.01. Sample description, RNAseq data acquisition and processing for WGCNA The global gene co-expression network was built using the Weighted Gene Coexpression analysis (Langfelder and Horvath, 2008) with 17 independent biological samples obtained after collapsing related replicates by their averages (Additional file 17: Table S17) . Eight of the 17 samples were Zn-stressed samples from this study (as described in the section above), six others (IR55179, RIL46, Nipponbare, IR64, IR74 and KP) were from a study by (Nanda et al., 2017), and the remaining were from (Zeng et al., 2019b). Overall, nine genotype samples (IR55179, RIL46, IR64, IR74, IR26, NIPPONBARE, KP, UCP122 and KALBOR026) were used for WGCNA. These samples were representative of different varietal ecotypes including Japonica (NIPPONBARE and KP), Indica (IR55179, RIL46, IR64, IR74 and IR26) and AUS (UCP122 and KALBOR026), and different Zn use efficiency (ZU) levels. Only raw counts from Zn-stressed samples were retrieved from NCBI data repository and fed into RNAseq pipeline. The RNAseq pipeline first consisted of adapter removal and low-quality reads trimming by Trimmomatic tool (Bolger et al., 2014). Next, clean reads were mapped to the MSU7 rice reference genome using HISAT alignment tool (Anders et al., 2015). The annotation and reference genome files (MSU7) were obtained from Illumina’s iGenomes project ( support.illumina.com/sequencing/sequencing_software/igenome.html ). The poorly aligned reads were filtered out using the filter SAM tool version 0.1.19 (Li et al., 2009). The SAM tool generated a BAM file of quality reads which was fed into the subread tool (Liao et al., 2014) along with gene model annotation file. The resulting gene count matrix was converted to FPKM values and merged with the Zn deficiency FPKM table provided by our biological collaborators. All the used samples were pair-end sequenced libraries. Data quality processing for WGCNA The FPKM values from the above steps were rounded to their nearest integers and standardized using the rlog() function of DESeq2 R package. Rows with minimum counts less than 10 were also filtered out. As the only variation of interest was genotypic variation, effects from other factors such as tissue type, development stage, Zn stress treatment duration were corrected using removeBatchEffect() of limma R package. Samples’ PCA and clustering before and after controlling the unwanted variations are as illustrated in ( Additional file 38: Fig. S4a-b ). The resulting normalized FPKM values were finally filtered with their MAD (median and median absolute deviation) to reduce noises by removing low-expressed or non-varying genes (Langfelder and Horvath, 2008). Gene co-expression analysis and module construction Only Zn deficiency responsive gene loci comprising of genes reported in 7 previous studies using different methods (e.g., GWAS, qtl mapping, miRNA, transgenic and transcriptomic studies) and DEGs from this study were selected for network construction (Additional file 11: Table S11) . WGCNA (version 1.49) R package was used to assemble modules of coexpressed genes (Langfelder and Horvath, 2008). The scale free topology was used for choosing the softpower threshold (β) for the computation of adjacency matrix as a ij = |s ij | β ; where s ij is the correlation between gene i and gene j (Zhang et al., 2005). Soft thresholding refers to reducing low correlation continuously by powering the correlation between genes to that threshold using β parameter. The pickSoftThreshold() function was used to determine the β value. The soft power threshold of 13 was selected as the first power that surpasses the scale-free topology fit index of 0.85 ( Additional file 38: Fig. S5a ) (Ramírez-González et al., 2018). Next, a topological overlap matrix (TOM) was constructed from adjacency matrices using β value of 13 ( Additional file 38: Fig. S5b ). TOM enables to determine the network connectivity of a gene as defined by all its adjacencies with the rest of genes for network generation. Step by step procedure was finally established to construct a consensus network with parameters: minModuleSize = 16; dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM, deepSplit = 2, pamRespectsDendro = FALSE, minClusterSize = minModuleSize). After generation of the dynamic tree modules, modules were labeled to colors and merged with mergeCloseModules() function. Network visualization and hub genes identification Gene-gene and protein-protein network structures from ZDR modules were represented graphically using Cytoscape (v3.8) (Cline et al., 2007). Genes or proteins directly connected with at least three well-known ZDR genes or proteins were retained for a finer gene-gene interaction network (GGIN) visualization. The top hub genes in each ZDR modules were based on the Maximal Clique Centrality method implemented via the cytoHubba plugin (Chin et al., 2014) in Cytoscape (v3.8) software. Identification of transcription regulators, transcription factors, protein kinases genes Transcription factors were identified using the Plant Transcription Factor Database (PlantTFDB version 5.0 ( http://planttfdb.cbi.pku.edu.cn/index.php ) (Pérez-Rodríguez et al., 2010). Transcription regulators and protein kinases were predicted using iTAK version 1.6 software ( http://itak.feilab.net/cgi-bin/itak/index.cgi ) (Zheng et al., 2016). Functional annotation enrichment analysis Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways affected by the DEGs from this study, coexpressed genes across all the modules, and the differentially coexpressed genes across ZDR modules were identified using STRING tool version:11 ( http://string-db.org/ ) (Mering et al., 2003) at FDR < 0.05. List of Abbreviations ZUE : Zinc Use Efficiency DGEA : Differential Gene Expression Analysis ZDR : Zn Deficiency Responsive ZE : Zn Efficient ZI : Zn Inefficient WGCNA : Weighted Gene Coexpression Analysis DEGs : Differentially Expressed Genes CoDEGs : Coexpressed Differentially Expressed Genes MCC : Maximal Clique Centrality CEA : Condition-based gene Expression Analysis GEA : Genotype-based gene Expression Analysis LMWOA : Low-Molecular Weight Organic Acids GGIN : Gene-Gene Interaction Network PPIN : Protein-Protein Interaction Network NAs : Nicotianamine synthase MAs : Mugineic Acid DMAs : Deoxymugenic Acids ZIP : Zinc-regulated transporters and Iron-regulated transporter Proteins HMAs : Heavy Metal ATPases MTPs : Metal Tolerance Proteins ROS : Reactive Oxygen Species MT : Metallothionein TF : Transcription factor TFR : Transcription factor regulator Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and material All datasets generated for this study are included in the article/supplementary material, further inquiries can be directed to the first author. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Agricultural Science and Technology Innovation Program, National Key R&D Program of China (2020YFE0202300) and Shenzhen Science and Technology Projects (JCYJ20200109150650397). Author’s contributions Gouyou Ye conceived the project. Xiang Lu and Blaise Pascal MUVUNYI prepared the samples and collected data. Blaise Pascal MUVUNYI and Sang He analyzed the data. Blaise Pascal MUVUNYI drafted the manuscript. Sang He and Gouyou Ye revised the manuscript. All the authors have read, edited and approved the current version of the manuscript. Acknowledgements The authors wish to thank Dr.Liu Chen for revising the manuscript. References Akhtar S, Das JK, Ismail T, Wahid M, Saeed W, Bhutta ZA (2021) Nutritional perspectives for the prevention and mitigation of COVID-19. Nutr Rev 79(3):289-300 https://doi.org/10.1093/nutrit/nuaa063 Alloway BJ (2008) Zinc in soils and crop nutrition. http://www.topsoils.co.nz/wp-content/uploads/2014/09/Zinc-in-Soils-and-Crop-Nutrition-Brian-J.-Alloway.pdf .Accessed 15 Apr 2021 Anders S, Pyl PT, Huber W(2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166-169 https://doi.org/10.1093/bioinformatics/btu638 Ariel FD, Manavella PA, Dezar, CA, Chan RL (2007) The true story of the HD-Zip family. Trends in plant science (12)9:419-426 https://doi.org/10.1016/j.tplants.2007.08.003 Banakar R, Alvarez FA, 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. Journal of Experimental Botany 68(17):4983-4995 https://doi.org/10.1093/jxb/erx304 Bandyopadhyay T, Mehra P, Hairat S, Giri J (2017) Morpho-physiological and transcriptome profiling reveal novel zinc deficiency-responsive genes in rice. Fun & Int Gen (17):565-581 https://doi.org/10.1007/s10142-017-0556-x Banerjee S, Chandel G (2011a) Understanding the role of metal homeostasis related candidate genes in Fe/Zn uptake, transport and redistribution in rice using semi-quantitative RT-PCR. Plant Mol Biol Biotechnol 2:33–46 Black MM (1998) Zinc deficiency and child development. The American journal of clinical nutrition (68):464S-469S https://dx.doi.org/10.1093%2Fajcn%2F68.2.464S Bokor B, Bokorová S, Ondoš S, Švubová R, Lukačová Z, Hýblová M, Szemes T, Lux A (2015) Ionome and expression level of Si transporter genes (Lsi1, Lsi2, and Lsi6) affected by Zn and Si interaction in maize. Environmental Science, Research Pollution 22(9):6800-6811 https://doi.org/10.1007/s11356-014-3876-6 Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120 https://doi.org/10.1093/bioinformatics/btu170 Cai H, Huang S, Che J, Yamaji N, Ma JF (2019) The tonoplast-localized transporter OsHMA3 plays an important role in maintaining Zn homeostasis in rice. Journal of experimental botany 70(10):2717-2725 https://doi.org/10.1093/jxb/erl054 Chen F, Wang F, Wu F, Mao W, Zhang G, Zhou M (2010) Modulation of exogenous glutathione in antioxidant defense system against Cd stress in the two barley genotypes differing in Cd tolerance. Plant Physiology, Biochemistry 48(8):663-672 https://doi.org/10.1016/j.plaphy.2010.05.001 Cheng S, Zhou DX, Zhao Y (2016) WUSCHEL-related homeobox gene WOX11 increases rice drought resistance by controlling root hair formation and root system development. Plant Sign & Beh11(2):e1130198. https://doi.org/10.1080/15592324.2015.1130198 Childs KL, Davidson RM, Buell CR (2011) Gene coexpression network analysis as a source of functional annotation for rice genes. PloS one 6(7):e22196 https://doi.org/10.1371/journal.pone.0022196 Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC systems biology 8 Suppl 4(Suppl 4):S11-S11 https://doi.org/10.1186/1752-0509-8-S4-S11 Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B (2007) Integration of biological networks and gene expression datausing Cytoscape. Nat Protoc (2):2366–2382 https://doi.org/10.1038/nprot.2007.324 Dardenne M (2002) Zinc and immune function. European Journal of Clinical Nutrition (56):S20-S23 https://doi.org/10.1038/sj.ejcn.1601479 Debi BR, Taketa S, Ichii M (2005) Cytokinin inhibits lateral root initiation but stimulates lateral Root elongation in rice (Oryza sativa). Journal of plant physiology 162(5):507-515 https://doi.org/10.1016/j.jplph.2004.08.007 El-Shabrawi H, Kumar B, Kaul T, Reddy MK, Singla-Pareek SL, Sopory SK (2010) Redox homeostasis, antioxidant defense, and methylglyoxal detoxification as markers for salt tolerance in Pokkali rice. Protoplasma 245(1):85-96 https://doi.org/10.1007/s00709-0100144-6 Fan X, Feng H, Tan Y, Xu Y, Miao Q, Xu G (2016) A putative 6-transmembrane nitrate transporter OsNRT1.1b plays a key role in rice under low nitrogen. Integr Plant Biol 58(6):590-599. https://doi.org/10.1111/jipb.12382 Hartwell LH, Hopfield JJ, Leibler S, Murray AW (199) From molecular to modular cell biology. Nature 402(6761):C47-52 https://doi.org/10.1038/35011540 Hu B, Wang W, Ou S, Tang J, Li H, Che R, Zhang Z, Chai X, Wang H, Wang Y, Liang C, Liu L, Piao Z, Deng Q, Deng K, Xu C, Liang Y, Zhang L, Li L, Chu C (2015) Variation in NRT1.1B contributes to nitrate-use divergence between rice subspecies. Nat Genet 47(7): 834-838 https://doi.org/10.1038/ng.3337 Huizar MI, Arena R, Laddu DR (2021) The global food syndemic: the impact of food insecurity, malnutrition and obesity on the healthspan amid the COVID-19 pandemic. Progress in cardiovascular diseases 64:105–107 https://dx.doi.org/10.1016%2Fj.pcad.2020.07.002 Ishimaru Y, Bashir K, Nishizawa NK (2011) Zn Uptake and Translocation in Rice Plants. Rice 4:21–27 https://doi.org/10.1007/s12284-011-9061-3 Ishimaru Y, Suzuki M, Kobayashi T, Takahashi M, Nakanishi H, Mori S, Nishizawa NK (2005) OsZIP4, a novel zinc-regulated zinc transporter in rice. Journal of Experimental Botany 56(422):3207-3214 https://doi.org/10.1093/jxb/eri317 Katiyar A, Smita S, Lenka SK, Rajwanshi R, Chinnusamy V, Bansal KC (2012) Genome-wide classification and expression analysis of MYB transcription factor families in rice and Arabidopsis. BMC genomics (1):1-9 https://doi.org/10.1186/1471-2164-13-544 Keller C, Rizwan M, Davidian JC, Pokrovsky OS, Bovet N, Chaurand P, Meunier JD (2015) Effect of silicon on wheat seedlings (Triticum turgidum L.) grown in hydroponics and exposed to 0 to 30 µM Cu. Planta 241(4):847-860 https://doi.org/10.1007/s00425-014-2220-1 Kitomi Y, Hidemi K, Inukai Y (2011) Molecular mechanism of crown root initiation and the different mechanisms between crown root and radicle in rice. Plant signaling, behavior 6(9): 1276-1278 https://doi.org/10.4161/psb.6.9.16787 Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics 9(1):1-13 https://doi.org/10.1186/1471-2105-9-559 Lee S, Chiecko JC, Kim SA, Walker EL, LeeY, Guerinot ML, An G (2009) Disruption of OsYSL15 leads to iron inefficiency in rice plants. Plant physiology 150(2):786-800 https://doi.org/10.1104/pp.109.135418 Lee S, Kim SA, Lee J, Guerinot ML, An G (2010) Zinc deficiency-inducible OsZIP8 encodes a plasma membrane-localized zinc transporter in rice. Mol Cells (29):551-558 https://doi.org/10.1007/s10059-010-0069-0 Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078-2079 https://doi.org/10.1093/bioinformatics/btp352 Li J, Han Y, Liu L, Chen Y, Du Y, Zhang J, Sun H, Zhao Q (2015) qRT9, a quantitative trait locus controlling root thickness and root length in upland rice. Journal of Experimental Botany 66(9):2723-32 https://doi.org/10.1093/jxb/erv076 Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923-930 https://doi.org/10.1093/bioinformatics/btt656 Lu X, Liu S, Zhi S, Chen J, Ye G (2020) Comparative transcriptome profile analysis of rice varieties with different tolerance to zinc deficiency. Plant Biol 23(2):375-390 https://doi.org/10.1111/plb.13227 Ma JF, Yamaji N, Mitani N, Xu XY, Su YH, McGrath SP, Zhao FJ (2008) Transporters of arsenite in rice and their role in arsenic accumulation in rice grain. Proceedings of the National Academy of Sciences 105(29):9931-5 https://doi.org/10.1073/pnas.0802361105 Masuda H, Ishimaru Y, Aung MS, Kobayashi T, Kakei Y, Takahashi M, Higuchi K, Nakanishi H, Nishizawa NK (2012) Iron biofortification in rice by the introduction of multiple genes involved in iron nutrition. Sci Rep 2, 543 https://doi.org/10.1038/srep00543 Maurya S, Vishwakarma AK, 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 Journal of Genetics and Plant Breeding (The) 78(2): 180-186. https://doi.org/10.5958/0975-6906.2018.00023.8 Mering VC, Huynen M, Jaeggi, D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Research 31(1):258-261 https://doi.org/10.1093/nar/gkg034 Nanda AK, Pujol V, Wissuwa M (2017) Patterns of stress response and tolerance based on transcriptome profiling of rice crown tissue under zinc deficiency. Journal of Experimental Botany 68(7):1715-1729 https://doi.org/10.1093/jxb/erx039 Nanda AK, Wissuwa M (2016a) Rapid crown root development confers tolerance to zinc deficiency in rice. Frontiers in plant science 7, 428 https://doi.org/10.3389/fpls.2016.00428 Neogy A, Garg T, Kumar A, Dwivedi AK, Singh H, Singh U, Singh Z, Prasad K, Jain M, Yadav S (2019) Genome-Wide Transcript Profiling Reveals an Auxin-Responsive Transcription Factor, OsAP2/ERF-40, Promoting Rice Adventitious Root Development. Plant Cell Physiol 60(10):2343-2355 https://doi.org/10.1093/pcp/pcz132 Noctor G, Mhamdi A, Chaouch S, Han Y, Neukermans J, Marquez-Garcia B, Queval G, Foyer C H (2012) Glutathione in plants: an integrated overview. Plant Cell Environ 35(2):454-84 https://doi.org/10.1111/j.1365-3040.2011.02400.x Nozoye T, Nagasaka S, Kobayashi T, Takahashi M, Sato Y, Sato Y, Uozumi N, Nakanishi H, Nishizawa NK (2011) Phytosiderophore efflux transporters are crucial for iron acquisition in graminaceous plants. Journal of Biological Chemistry 286(7):5446-54. https://doi.org/10.1074/jbc.M110.18002 Ogo Y, Itai RN, Nakanishi H, Inoue H, Kobayashi T, Suzuki M, Takahashi M, Mori S, Nishizawa NK (2006) Isolation and characterization of IRO2, a novel iron-regulated bHLH transcription factor in graminaceous plants. Journal of Experimental Botany 57, 2867-78 https://doi.org/10.1093/jxb/erl054 Ogo Y, Itai RN, Kobayashi T, Nakanishi H, Mori S, Nishizawa NK (2009) Overexpression of OsIRO2 improves both iron uptake and translocation to seeds in rice. UC Davis: Department of Plant Sciences. https://escholarship.org/uc/item/2kd7m133 .Accessed 15 Apr 2021 Ogo Y, Nakanishi IR, Nakanishi H, Kobayashi T, Takahashi M, Mori S, Nishizawa NK (2007) The rice bHLH protein OsIRO2 is an essential regulator of the genes involved in Fe uptake under Fe‐deficient conditions. Plant journal 51(3):366-377 https://doi.org/10.1111/j.1365-313X.2007.03149.x Olsen LI, Palmgren MG (2014) Many rivers to cross: the journey of zinc from soil to seed. Frontiers in Plant Science 5, 30 https://doi.org/10.3389/fpls.2014.00030 Pérez-Rodríguez P, Riano-Pachon DM, Corrêa LG, Rensing SA, Kersten B, Mueller-Roeber B (2010) PlnTFDB: updated content and new features of the plant transcription factor database. Nucleic acids research 38(suppl_1):D822-7 https://doi.org/10.1093/nar/gkp805 Ptashnyk M, Roose T, Jones DL, Kirk G (2011) Enhanced zinc uptake by rice through phytosiderophore secretion: a modelling study. Plant Cell Environ 34(12):2038-2046 https://doi.org/10.1111/j.1365-3040.2011.02401.x Ramírez-González RH, Borrill P, Lang D, Harrington SA, Brinton J, Venturini L, Davey M, Jacobs J, Van Ex F, Pasha A (2018) The transcriptional landscape of polyploid wheat. Science 361(6403) https://doi.org/10.1126/science.aar6089 Sasaki A, Yamaji N, Ma JF (2014) Overexpression of OsHMA3 enhances Cd tolerance and expression of Zn transporter genes in rice. Journal of experimental botany 65(20):6013-6021 https://doi.org/10.1093/jxb/eru340 Seneviratne G, Jayasinghearachchi HS (2003) Mycelial colonization by bradyrhizobia and azorhizobia. Journal of biosciences 28(2):243-247 https://doi.org/10.1007/BF02706224 Sharma A, Shahzad B, Rehman A, Bhardwaj R, Landi M, Zheng B (2019) Response of phenylpropanoid pathway and the role of polyphenols in plants under abiotic stress. Molecules 24(13):2452 https://doi.org/10.3390/molecules24132452 Sinclair SA, Krämer U (2012) The zinc homeostasis network of land plants. Biochimica et Biophysica Acta -Molecular Cell Research 1823(9):1553-1567 https://doi.org/10.1016/j.bbamcr.2012.05.016 Singh SP, Gruissem W, Bhullar NK (2017) Single genetic locus improvement of iron, zinc and β-carotene content in rice grains. Scientific Reports 7(1):6883 https://doi.org/10.1038/s41598-017-07198-5 Sichul L, Jeff CC, Sun AK, Elsbeth LW, Youngsook L, Mary LG, Gynheung A (2009) Disruption of OsYSL15 leads to iron inefficiency in rice plants. Plant physiology 150(2):786-800 https://doi.org/10.1104/pp.109.135418 Song A, Li P, Fan F, Li Z, Liang Y (2014) The effect of silicon on photosynthesis and expression of its relevant genes in rice (Oryza sativa L.) under high-zinc stress. PLoS One 9(11):e113782 https://doi.org/10.1371/journal.pone.0113782 Suzuki M, Bashir K, Inoue H, Takahashi M, Nakanishi H, Nishizawa NK (2012) Accumulation of starch in Zn-deficient rice. Rice 5(1):1-8 https://doi.org/10.1186/1939-8433-5-9 Takahashi R, Bashir K, Ishimaru Y, Nishizawa NK, Nakanishi H (2012) The role of heavy-metal ATPases, HMAs, in zinc and cadmium transport in rice. Plant signaling & behavior 7(12):1605-1607 https://doi.org/10.4161/psb.22454 Tan M, Cheng D, Yang Y, Zhang G, Qin M, Chen J, Chen Y, Jiang M (2017) Co-expression network analysis of the transcriptomes of rice roots exposed to various cadmium stresses reveals universal cadmium-responsive genes. BMC Plant Biology 17(1):194 https://doi.org/10.1186/s12870-017-1143-y Tripathi P, Tripathi RD, Singh RP, Dwivedi S, Goutam D, Shri M, Trivedi PK, Chakrabarty D (2013) Silicon mediates arsenic tolerance in rice (Oryza sativa L.) through lowering of arsenic uptake and improved antioxidant defence system. Ecological engineering 52:96-103 https://doi.org/10.1016/j.ecoleng.2012.12.057 Van DSD, Bhullar NK, De SH, Gruissem W, MacKenzie D, Pfeiffer W, Qaim M, Slamet-Loedin I, Strobbe S, Tohme J, Trijatmiko KR, Vanderschuren H, Van MM, Zhang C, Bouis H (2020) Multiplying the efficiency and impact of biofortification through metabolic engineering. Nature Communications 11(1):5203 https://doi.org/10.1038/s41467-020-19020-4 Walley JW, Sartor RC, Shen Z, Schmitz RJ, Wu KJ, Urich MA, Nery JR, Smith LG, Schnable JC, Ecker JR (2016) Integration of omic networks in a developmental atlas of maize. Science 353(6301):814-818 https://doi.org/10.1126/science.aag1125 Wang W, Ye J, Ma Y, Wang T, Shou H, Zheng L (2020) OsIRO3 Plays an Essential Role in Iron Deficiency Responses and Regulates Iron Homeostasis in Rice. Plants (Basel) 9(9):1095 https://doi.org/10.3390/plants9091095 Widodo B, Broadley MR, Rose T, Frei M, Pariasca-Tanaka J, Yoshihashi T, Thomson M, Hammond JP, Aprile A, Close TJ, Ismail AM, Wissuwa M (2010) Response to zinc deficiency of two rice lines with contrasting tolerance is determined by root growth maintenance and organic acid exudation rates, and not by zinc-transporter activity. New Phytol 186(2):400-414 https://doi.org/10.1111/j.1469-8137.2009.03177.x Yamaji N, Sakurai G, Mitani-Ueno N, Ma JF (2015) Orchestration of three transporters and distinct vascular structures in node for intervascular transfer of silicon in rice. Proc Natl Acad Sci 112(36):11401-11406 https://doi.org/10.1073/pnas.1508987112 Yamaji Ni, Xia J, Mitani-Ueno N, Yokosho K, Ma JF (2013) Preferential delivery of zinc to developing tissues in rice is mediated by P-type heavy metal ATPase OsHMA2. Plant physiology 162(2):927-939 https://doi.org/10.1104/pp.113.216564 Yang Z, Wu Y, Li Y, Ling HQ, Chu C (2009) OsMT1a, a type 1 metallothionein, plays the pivotal role in zinc homeostasis and drought tolerance in rice. Plant molecular biology 70(1-2): 219-229 https://doi.org/10.1007/s11103-009-9466-1 Zeng H, Zhang X, Ding M, Zhu Y (2019b) Integrated analyses of miRNAome and transcriptome reveal zinc deficiency responses in rice seedlings. BMC Plant Biol 19(1):585 https://doi.org/10.1186/s12870-019-2203-2 Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1): Article17 https://doi.org/10.2202/1544-6115.1128 Zhang L, Hu B, Deng K, Gao X, Sun G, Zhang Z, Li P, Wang W, Li H, Zhang Z, Fu Z, Yang J, Gao S, Li L, Yu F, Li Y, Ling H, Chu C (NRT1.1B) improves selenium concentrations in rice grains by facilitating selenomethinone translocation. Plant Biotechnol 17(6):1058-1068 https://doi.org/10.1111/pbi.13037 Zhang Y, Yu C, Lin J, Liu J, Liu B, Wang J, Huang A, Li H, Zhao T (2017) OsMPH1 regulates plant height and improves grain yield in rice. PLoS One 12(7): e0180825 https://doi.org/10.1371/journal.pone.0180825 Zhao W, Langfelder P, Fuller T, Dong J, Li A, Hovarth S (2010) Weighted gene coexpression network analysis: state of the art. Journal of biopharmaceutical statistics 20(2):281-300 https://doi.org/10.1080/10543400903572753 Zheng L, Ying Y, Wang L, Wang F, Whelan J, Shou H (2010) Identification of a novel iron regulated basic helix-loop-helix protein involved in Fe homeostasis in Oryza sativa. BMC Plant Biol 10:166 https://doi.org/10.1186/1471-2229-10-166 Zheng Y, Jiao C, Sun H, Rosli HG, Pombo MA, Zhang P, Banf M, Dai X, Martin GB, Giovannoni J (2016) iTAK: a program for genome-wide prediction and classification of plant transcription factors, transcriptional regulators, and protein kinases. Molecular plant 9(12):1667-1670 https://doi.org/10.1016/j.molp.2016.09.014 Supplementary Files CaptionsforSupplementalTablesandFigures.docx Additionalfile1.xlsx Additionalfile10.xlsx Additionalfile3.xlsx Additionalfile11.xlsx Additionalfile12.xlsx Additionalfile13.xlsx Additionalfile14.xlsx Additionalfile15.xlsx Additionalfile16.xlsx Additionalfile17.xlsx Additionalfile18.xlsx Additionalfile19.xlsx Additionalfile2.xlsx Additionalfile20.xlsx Additionalfile21.xlsx Additionalfile22.xlsx Additionalfile23.xlsx Additionalfile24.xlsx Additionalfile25.xlsx Additionalfile26.xlsx Additionalfile27.xlsx Additionalfile28.xlsx Additionalfile29.xlsx Additionalfile30.xlsx Additionalfile31.xlsx Additionalfile32.xlsx Additionalfile33.xlsx Additionalfile34.xlsx Additionalfile35.xlsx Additionalfile36.xlsx Additionalfile37.xlsx Additionalfile38.docx Additionalfile4.xlsx Additionalfile5.xlsx Additionalfile6.xlsx Additionalfile7.xlsx Additionalfile8.xlsx Additionalfile9.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-442740","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":22868786,"identity":"b60fd544-fffb-46a4-899d-5baecd6680b7","order_by":0,"name":"Blaise Pascal Muvunyi","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Agricultural Genomes Institute at Shenzhen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Blaise","middleName":"Pascal","lastName":"Muvunyi","suffix":""},{"id":22868787,"identity":"1d172f06-5427-4ebb-aa88-152bcf915241","order_by":1,"name":"Xiang Lu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Agricultural Genomes Institute at Shenzhen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Lu","suffix":""},{"id":22868788,"identity":"34c0e88e-d273-4699-9201-ad1057a60ae5","order_by":2,"name":"Sang He","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Agricultural Genomes Institute at Shenzhen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sang","middleName":"","lastName":"He","suffix":""},{"id":22868789,"identity":"675c7c32-f454-4518-a6df-5f4791a4d242","order_by":3,"name":"Guoyou Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYBAC/gYeA4YPBgwMbERrkTjAY8A4gyQtBgxsCcw8JDnMgIH54GObgnv5fAzMxz5+IU4LY7NxjkGxZRsDW/JsGWK0GB5gbJPOMUgwYGPgMWaWIMqWA4ztvy1I1dLGzADVwviBGC0ShxmbJXtAWpjZkpmJ0cHA397Y+OHHnwQD+fbmw4w/iNLDjMQgMYJAgEhbRsEoGAWjYKQBAP3pJW0pXeRPAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9943-4815","institution":"Chinese Academy of Agricultural Sciences Agricultural Genomes Institute at Shenzhen","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guoyou","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2021-04-20 10:50:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-442740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-442740/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8457915,"identity":"873e84c7-0cc2-4d55-ac5c-ccbe07034871","added_by":"auto","created_at":"2021-04-26 16:24:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89636,"visible":true,"origin":"","legend":"Genes and sample genotypes used for WGNA. a A three-level venn diragram illustrating overlapping genes between DEGs from this study, coexpressed genes and reported Zn genes from GWAS, qtl mapping, transgenic, miRNA and gene transcriptomic studies. b Sample dendrogram and classification of 17 used samples into different ZUE categories. Height represents the average Euclidean distance between samples computed with hclust() function. A heat map of ZUE categories across all genotypes is illustrated underneath the sample dendrogram. Genotypes from the same category of ZUE are represented by red-colored bars. ZUE levels of AUS genotypes are still unknown. Therefore, no specific categories was attributed to AUS genotypes.","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-442740/v1/af39ff776cebcf97748f1f8e.png"},{"id":8457585,"identity":"5360fa01-999d-40fa-94b4-4916fc71dd98","added_by":"auto","created_at":"2021-04-26 16:21:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129309,"visible":true,"origin":"","legend":" Preserved genes and molecular pathways in ZDR modules. a Hierarchical dendrogram of coexpressed genes/nodes and visualization of ZDR modules. 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16:22:00","extension":"xlsx","order_by":37,"title":"","display":"","copyAsset":false,"role":"supplement","size":12147,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-442740/v1/0d0c578f017543cc16b024bc.xlsx"},{"id":8457583,"identity":"89e325b8-8f93-477d-979c-840f544ad6b6","added_by":"auto","created_at":"2021-04-26 16:21:57","extension":"xlsx","order_by":38,"title":"","display":"","copyAsset":false,"role":"supplement","size":26676,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-442740/v1/d04957615e060d4719ea9697.xlsx"},{"id":8457925,"identity":"f89924cc-7f2d-4951-a9f2-1135da5d4df9","added_by":"auto","created_at":"2021-04-26 16:24:59","extension":"xlsx","order_by":39,"title":"","display":"","copyAsset":false,"role":"supplement","size":46737,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-442740/v1/0fde26443174900aae6073ae.xlsx"}],"financialInterests":"","formattedTitle":"Identification of potential Zinc deficiency responsive genes and regulatory pathways in rice genotypes by Weighted Gene Co-expression Network Analysis","fulltext":[{"header":"1. Background","content":" \u003cp\u003eZinc malnutrition is affecting about 1.2\u0026nbsp;billion of people and has been associated with detrimental health concerns including children stunting, weak immune system and brain development (Black, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Dardenne, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Regarding the current pandemic of COVID-19, Zn malnutrition will exacerbate infection rates (Akhtar et al.; Huizar et al., 2020; Van Der Straeten et al., 2020), underlining the need for Zn biofortification.\u003c/p\u003e \u003cp\u003eFortifying Zn in rice, a staple crop for nearly 3.5\u0026nbsp;billion of people, can significantly benefit the global Zn nutritional status (Van Der Straeten et al., 2020). Agronomic approaches to enhance grain Zn content through chemical fertilization for example, are not only unecomomical, but also ineffective, as Zn tends form insoluble complexes which limits its uptake from plant root tissues (Alloway, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Favorably, natural genetic variations in rice germplasm are sufficient to attain Zn concentrations requirements of 28 ppm in the polished rice grain (Van Der Straeten et al., 2020). Zn efficient (ZE) genotypes have relatively higher ZUE and perform better than Zn inefficient (ZI) genotypes under low Zn soils or nutrient solutions (Nanda et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, ZE genotypes exhibit higher growth of crown roots (Nanda and Wissuwa, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e) and greater rates of the exudation of low-molecular weight organic acids (LMWOA) metal-chelators, such as nicotianamine synthase (NAs), mugineic acid (MAs) or 2\u0026prime;-deoxymugenic acid (DMAs) from the phytosiderophore family (Ishimaru et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ptashnyk et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Widodo et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Such traits have been linked to the loading of both Zn or Fe in rice grains (Banakar et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Masuda et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition to these LMWOAs, Zinc transporter genes mainly \u003cem\u003eZIP\u003c/em\u003e (zinc-regulated transporters and iron-regulated transporter proteins), \u003cem\u003eHMAs\u003c/em\u003e (heavy metal ATPases) and \u003cem\u003eMTPs\u003c/em\u003e (metal tolerance proteins) are essential for the uptake, distribution, and redistribution of Zn across different plant tissues under low Zn deficiency conditions (Ishimaru et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Olsen and Palmgren, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Further, transcription factor (TF) genes such as \u003cem\u003eOsNAC, OsIRO2\u003c/em\u003e, and \u003cem\u003eOsIRO3\u003c/em\u003e are also known to regulate the expressions of several Zn or Fe responsive genes (Banerjee and Chandel, 2011; Ogo et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). As to molecular pathways affected by Zn deficiency, previous transcriptomic studies have consistently shown that Zn deficiency responsive (ZDR) genes significantly enrich Zinc transmebrane activity, transmebrane transport activity, phenylpropanoid biosynthesis and glutathione metabolism functional gene sets (Bandyopadhyay et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nanda et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Nevertheless, the most conserved ZDR genes and molecular pathways are not known. Also, the interaction networks involving the known (experimentally validated) ZDR genes are yet to be investigated.\u003c/p\u003e \u003cp\u003eWeighted gene co-expression network analysis (Langfelder and Horvath, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) is a systems biology approach that leverages on the extent of co-expression between genes to define their connectivity. Subsequently, it allows studying the universal network nature of a transcriptome (Zhao et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, integrating transcriptomic analysis and WGCNA in rice enabled to find the global Cadmium (Cd)-regulated DEGs (Tan et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, WGCNA was useful to functionally annotate the genome of rice (Childs et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and construct the atlas of maize by integrating different omics data (Walley et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In a transcriptome co-expression network, genes are represented by nodes which are linked together by edges based on the weighted co-expression across samples. The most connected genes within a network are assembled as modules, while the highly connected genes within a module are referred to hubs (Zhang and Horvath, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Co-expression network architecture is inherent of cellular organization, with the functional modules build up from several interacting molecules (Hartwell et al., 1999).\u003c/p\u003e \u003cp\u003eHere, DGEA and WGCNA were implemented to identify (1) functional modules of coexpressed genes in Zn-stressed rice genotypes (2) coexpressed DEGs (CoDEGs) and their preserved molecular functions, and (3) genes showing strong connectivity with the experimentally validated (herein referred to well-known or recognized) ZDR genes. Subsequently, modules of coexpressed genes were constructed by combining DEGs from our own samples including ZI (IR26 and IR64) and AUS (WCP22 and KALBOR026) genotypes, and the other publicly reported ZDR genes. The significance of a module for Zn deficiency response was dictated by the recognized ZDR functional annotations enriched by genes coexpressing in that module. Genes from the identified ZDR modules which are DEGs in our DGEA were judged as the most preserved, and so were their respective functional annotations. These functionally annotated CoDEGs are good candidates to track functional markers which can be incorporated into breeding schemes to improve rice grain Zn content. Lastly, gene-gene interaction network (GGIN) or protein-protein interaction network (PPIN) of the ZDR modules identified genes or proteins with strong connectivity with the well-recognized ZDR genes. We speculate that these identified hub genes could also be involved in the mechanistic of Zn stress response as their linked well-known ZDR genes.\u003c/p\u003e "},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression analysis was performed for root and shoot tissues of two ZI (IR26 and IR64) and AUS (UCP122 and KALBOR026) rice genotypes using DESeq2 R package (Love et al., 2014). To obtain sufficient DEGs for the next stage of WGCNA, two approaches were used. A condition-based gene expression analysis (CEA) where gene expression for each ZI or AUS genotype was studied under Zn deficiency condition (Zn supply vs Zn deficiency), and a genotype-based expression analysis (GEA) by setting ZI genotypes as reference level for each of AUS genotype (IR26/IR64 vs UCP122/KALBOR026). Overall, 1649 unique genes \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cstrong\u003eand Additional file1-10: Table S1-10)\u003c/strong\u003e were differentially expressed (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01, log\u003csub\u003e2\u003c/sub\u003e fold\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2 or \u0026lt;\u0026thinsp;2). 1179 of the 1649 DEGs were novel ZDR genes, while 470 DEGs have been reported in previous studies (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea \u003cstrong\u003eand Additional file 11: Table S11\u003c/strong\u003e). Under CEA, the well-known ZDR genes such as \u003cem\u003eZIP\u003c/em\u003e (\u003cem\u003eOsZIP1, OsZIP5, OsZIP10\u003c/em\u003e and \u003cem\u003eOsZIP8\u003c/em\u003e), a phytosiderophores family gene (\u003cem\u003eOsNAAT1)\u003c/em\u003e, and a metallothionein gene (\u003cem\u003eOsMT1a)\u003c/em\u003e were induced by Zn deficiency treatment in ZI or AUS genotypes. Under low Zn supply conditions and GEA, the expressions \u003cem\u003eZIP\u003c/em\u003e genes and phytosiderophores family genes (\u003cem\u003eOsNAAT1\u003c/em\u003e and \u003cem\u003eOsNAS3\u003c/em\u003e) were significantly higher in the root sample of AUS genotypes than in the same tissue of IR26 genotype (\u003cstrong\u003eAdditional file 9\u0026ndash;10: Table S9-10\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotation of differentially expressed genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional annotations of DEGs (under CEA) by STRING tool version:11 (Mering et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) indicated that Zinc ion transmembrane transporter activity GO term was significantly enriched by DEGs upregulated by Zn deficiency treatment in both ZI and AUS genotypes. However, significant differences between UCP122 and other genotypes were detected in the enrichment of the DNA-binding transcription factor activity GO term. The latter was significantly upregulated in the root tissue of UCP122 yet downregulated in the same tissue of KALBOR026 and IR26 genotypes. Similarly, Zinc ion transmembrane transporter activity GO term was upregulated in root tissues of UCP122, while for all the other genotypes, it was only upregulated in crown tissues.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNumber of DEGs obtained under CEA and GEA in AUS and ZI genotypes.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eContrast levels\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eContrast categories\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eExpressed genes (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003elog\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;fold\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003elog\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;fold\u0026thinsp;\u0026lt;\u0026thinsp;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"10\" align=\"left\"\u003e\n\u003cp\u003eZn supply vs Zn Def\u003c/p\u003e\n\u003cp\u003e(CEA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e311\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal non-redundant genes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e145\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e312\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e457\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUCP122_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUCP122_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKALBOR026_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKALBOR026_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal non-redundant genes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e203\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e87\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e290\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"20\" align=\"left\"\u003e\n\u003cp\u003eZI vs AUS genotypes\u003c/p\u003e\n\u003cp\u003e(GEA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnder Zn deficiency\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs UCP122_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e275\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs UCP122_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e526\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs KALBOR026 root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e398\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e535\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs KALBOR026 crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs UCP122_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs UCP122_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs KALBOR026 root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e259\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs KALBOR026 crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e209\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal non-redundant genes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e497\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e912\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1419\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnder Zn supply\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs UCP122_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs UCP122_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs KALBOR026 root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR26 vs KALBOR026 crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs UCP122_crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs UCP122_root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs KALBOR026 root\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR64 vs KALBOR026 crown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal unique genes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e129\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e294\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e413\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAlso, cellulose metabolic process was downregulated in crown tissues of all the genotypes but was not enriched by DEGs from the UCP122 genotype. These findings infer that UCP122 cultivar may have unique phenotype under low Zn supply conditions. All the significantly enriched functional gene sets are provided \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003eand Additional file 12\u0026ndash;15: Table S12-15)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFunctional annotations of DEGs in ZI and AUS genotypes after Zn deficiency treatment (under CEA).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSamples\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGo term/\u003c/p\u003e\n\u003cp\u003eKEGG pathway_ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGO term/pathway description\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFDR\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eROOT_KALBOR026\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0015250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWater channel activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.00087\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0015318\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInorganic molecular entity transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.00087\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0015075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIon transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0199\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap00480\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlutathione metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0282\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap00940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhenylpropanoid biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0378\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0140110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTranscription regulator activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.00018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0003700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDNA-binding transcription factor activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.00065\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0044249\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellular biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.0012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCROWN_KALBOR026\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc ion transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e3.45E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0010333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTerpene synthase activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e3.45E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0022890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInorganic cation transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.00011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap00904\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiterpenoid biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030243\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellulose metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.0052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0071555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCell wall organization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.0067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eROOT_ UCP122\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBinding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.00019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0003700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDNA-binding transcription factor activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0045\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0003824\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCatalytic activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0045\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc ion transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0134\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellulose biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCROWN_ UCP122\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNO significant annotations found\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"10\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eROOT_IR26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0050667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHomocysteine metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0057\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0008652\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellular amino acid biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0009086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMethionine biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap00940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhenylpropanoid biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0044\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap01110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBiosynthesis of secondary metabolites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0044\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap00130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUbiquinone and other terpenoid-quinone biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0298\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap00360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhenylalanine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0298\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBinding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.0278\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0046872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetal ion binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.0278\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0003700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDNA-binding transcription factor activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.0371\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCROWN_IR26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc ion transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e1.62E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0006073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellular glucan metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*1.70E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030243\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellulose metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*1.70E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0071555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCell wall organization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*1.26E-07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eROOT_IR64\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0046872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetal ion binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.0142\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emap00940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhenylpropanoid biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0034219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarbohydrate transmembrane transport\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0051119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSugar transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*0.0026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCROWN_IR64\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc ion transmembrane transporter activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e1.62E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0006073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellular glucan metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*1.70E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030243\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellulose metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*1.70E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0071555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCell wall organization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*1.26E-07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e+ : GO terms/KEGG pathways significantly (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) enriched by upregulated genes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003cstrong\u003e*\u003c/strong\u003e : GO terms/KEGG pathways significantly (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) enriched by downregulated genes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the global network of coexpressed modules of genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo construct the global network of coexpressed genes, raw counts from nine Zn-deprived samples were obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/ena\u003c/span\u003e\u003c/span\u003e, then processed and merged with eight Zn-deprived samples from our own study \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb \u003cstrong\u003eand Additional file 17: Table S17)\u003c/strong\u003e. Samples from public datasets included ZE (Nipponbare, IR55179 and RIL46) and ZI (KP, IR64, IR74) genotypes. The selected genes for WGCNA were 1649 DEGs from this project and 2121 Zn responsive genes reported from seven previous studies (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea \u003cstrong\u003eand Additional file 11: Table S11\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eWe set the module minimum size and the threshold to merge correlated modules at 16 and 0.6, respectively. The merged raw counts from different projects were corrected for batch effects, tissue types and plant growth stage -mediated covariances. The expression variance was also stabilized as indicated in WGCNA tutorial \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html\u003c/span\u003e\u003c/span\u003e. These parameters resulted into 2076 genes which coexpressed across 13 different modules (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), including a module of unassigned genes. All the modules were relabeled with short abbreviations for reference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotations of the identified modules.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo associate modules to Zn deficiency responsiveness, GO terms and KEGG pathways enrichment by genes within each module were studied using STRING version:11 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003c/span\u003e) (Mering et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Apart from genes from P-M module, all the other modular genes significantly enriched at a minimum one GO term or KEGG pathway \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e Intriguingly, molecular functions and genes recognized for Zn uptake, distribution, and grain Zn/Fe content functions in rice, preferably coexpressed in TTA-M, TRA-M and CA-M modules (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Therefore, all the subsequent characterizations were implemented on these putative ZDR modules.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFunctional annotations of coexpressed modules of genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModule\u0026rsquo;s colour | Assigned label| Number of genes in a module\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModule preserved GO terms | preserved KEGG pathways\u003c/p\u003e\n\u003cp\u003e(Bonferroni \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlue |TTA-M|555 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTransmembane transport activity ; Zinc ion transmembane transport ; iron ion homeostasis, etc. \u003cstrong\u003e|\u003c/strong\u003e Metabolic pathways ; Photosynthesis, etc.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMidnightblue|TRA-M|395 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTranscription regulator activity; Zinc ion transmembrane transporter activity, etc. \u003cstrong\u003e|\u003c/strong\u003e Biosynthesis of secondary metabolites; alpha-Linolenic acid metabolism.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGreenyellow|CA-M|393 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCatalytic activity; metal ion binding, etc. \u003cstrong\u003e|\u003c/strong\u003e Metabolic pathways; Phenylpropanoid biosynthesis, Glutathione metabolism, etc.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrey60 |AP-M|145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApoplast | No significant KEGG annotations found.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRoyalblue |PB-M|217\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo significant GO terms \u003cstrong\u003e|\u003c/strong\u003e phenylpropanoid biosynthesis; fatty acid metabolism, etc.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLightcyan|CG-M|65\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCell growth \u003cstrong\u003e|\u003c/strong\u003e glyoxylate and dicarboxylate metabolism; cutin, suberine and wax biosynthesis; biosynthesis of unsaturated fatty acids.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDarkturquoise|PHEN-M|96\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo significant GO annotations \u003cstrong\u003e|\u003c/strong\u003e Phenylpropanoid biosynthesis; phagosome; stilbenoid, diarylheptanoid and gingerol biosynthesis.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDarkgreen|PHOT-M|41\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhotosynthesis \u003cstrong\u003e|\u003c/strong\u003e Cyanoamino acid metabolism.\u003c/p\u003e\n\u003cp\u003eStarch and sucrose metabolism; glycine, serine and threonine metabolism.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDarkgrey|CA-M|35\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCold acclimation; photosynthesis; response to abiotic stress \u003cstrong\u003e|\u003c/strong\u003e porphyrin and chlorophyll metabolism.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBlack|RES-M|122\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to external stimulus; response to extracellular stimulus \u003cstrong\u003e|\u003c/strong\u003e Amino sugar and nucleotide sugar metabolism, etc.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePink|DR-M|395\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo significant GO annotation found \u003cstrong\u003e|\u003c/strong\u003e DNA replication, alpha-Linolenic acid metabolism, plant-pathogen interaction.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePurple|P-M|79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo significant GO annotations \u003cstrong\u003e|\u003c/strong\u003e No significant KEGG annotations found.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrey|FAT-M|94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo significant GO annotation found \u003cstrong\u003e|\u003c/strong\u003e Fatty acid elongation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003e\u003cstrong\u003e*\u003c/strong\u003e Zn deficiency responsive modules (ZDR modules).\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eZn deficiency responsive modules are putative regulators of Zn deficiency response in rice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMany of the well-known ZDR genes and their molecular pathways were found in ZDR modules. Genes coexpressing in the TTA-M module included for example, Zinc ion transmembrane transporters (\u003cem\u003eOsZIP3, OsZIP8, OsHMA2\u003c/em\u003e and \u003cem\u003eOsHMA3\u003c/em\u003e), a MAs transporter (\u003cem\u003eOsZIFL4/OsTOM1)\u003c/em\u003e, transmembrane transporters (\u003cem\u003eOsYSL16, OsYSL18\u003c/em\u003e and \u003cem\u003eOsMTP4\u003c/em\u003e), metal-NA complexes transporters (\u003cem\u003eOsYSL4\u003c/em\u003e and \u003cem\u003eYSL6\u003c/em\u003e), metal binding (\u003cem\u003eOsMT4IC, OsMT1B\u003c/em\u003e and \u003cem\u003eOsMT1a\u003c/em\u003e) and iron homeostasis genes (\u003cem\u003eOsSHR1\u003c/em\u003e and \u003cem\u003eOsNRAMP6\u003c/em\u003e). Equally, functional annotations including Zinc ion transmembrane transporter activity (\u003cem\u003eOsMTP1\u003c/em\u003e and \u003cem\u003eOsZIP2\u003c/em\u003e), organic substance (\u003cem\u003eOsMCM4, OsMPK7, OsCIPK9, OsCESA6, OsCSLF6\u003c/em\u003e, etc.), oxidoreductase activity (\u003cem\u003eOsCKX5, OsDAO\u003c/em\u003e, genes from \u003cem\u003eCYP\u003c/em\u003e family genes, etc.), transmembrane transporter activity (\u003cem\u003eOsNRAMP6, OsMST6, OsABCG44\u003c/em\u003e, etc.) and transcription regulator activity (\u003cem\u003eOsIRO2, OsERF67, OsONAC12, OsSWN6, OsDREB1C, OsJAZ TFs\u003c/em\u003e genes, etc.) were detected the TRA module. CA-M was another important module, with catalytic activity as its most enriched GO term. Genes including \u003cem\u003eOsZIP10\u003c/em\u003e and MAs biosynthesis genes (\u003cem\u003eOsSAMS1, OsSAMS2\u003c/em\u003e, \u003cem\u003eOsNAAT1\u003c/em\u003e and \u003cem\u003eOsDMAS1\u003c/em\u003e coexpressed in the CA-M module. Collectively, these findings are suggestive of potential key roles of ZDR modules in the regulating Zn deficiency molecular mechanisms in rice. All the genes coexpressed across ZDR modules and their function annotations are provided (\u003cstrong\u003eAdditional file 18\u0026ndash;23: Table S18-23).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreserved Zn deficiency responsive genes and their molecular functions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo find the most preserved genes, we intersected ZDR module genes with DEGs from the DGEA (under CEA). 96 (17%), 188 (47.6%) and 96 (24%) genes from TTA-M, TRA-M and CA-M modules respectively, were responsive to Zn deficiency in our DGEA \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb \u003cstrong\u003eand Additional file 24\u0026ndash;28: Table S24-28)\u003c/strong\u003e. Among all the well-known ZDR genes, the most preserved were Zinc transmembrane transport genes \u003cem\u003eOsZIP8\u003c/em\u003e (TTA-M) and \u003cem\u003eOsZIP10\u003c/em\u003e (CA-M) which were significantly upregulated in crown tissues of all ZI genotypes and KALBOR026 genotype, a metallothionein gene \u003cem\u003eOsMT1a\u003c/em\u003e (TTA-M) which was upregulated in the root tissue of UCP122 genotype, and a phytosiderophores family gene \u003cem\u003eOsNAAT1\u003c/em\u003e (CA-M) which upregulated in root and crown tissues of all AUS genotypes and IR64 genotypes (\u003cstrong\u003eAdditional file 29\u0026ndash;30:Table S29-30)\u003c/strong\u003e. Enrichment analysis of all coexpressed DEGs (CoDEGs) showed that genes which upregulated in the root tissue of UCP122 genotype affected the highest number of functional sets vs genes from all the other sample genotypes.\u003c/p\u003e\n\u003cp\u003eOf the 140 genes upregulated in root tissue of UCP122 genotype (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e, 88 genes coexpressed in TRA module, and significantly enriched 77 GO terms including DNA-binding transcription factor activity, cellulose biosynthetic processes, catalytic activity, response to stimuli, etc (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec \u003cstrong\u003eand Additional file 34: Table S34\u003c/strong\u003e). Contrary, in the same module, five of the GO terms upregulated in the root sample of UCP122 genotype (including DNA-binding transcription factor activity, binding, sequence specific DNA binding, etc.) were rather downregulated in the root tissue of KALBOR26 genotype. This was consistent with our findings with the DGEA that suggested possible phenotypic differences between UCP122 and other varieties during Zn deficiency conditions. On the other hand, another set of CoDEGs from the TTA-M module upregulated the glutathione metabolism in root tissues of both AUS genotype. The other preserved and upregulated functional gene sets included the secondary metabolites biosynthesis (CA-M module) and transmembrane transport (TTA-M module) which were significantly enriched by CoDEGs from the root sample of IR26 and KALBOR026 genotypes, respectively \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of hub genes in Zn deficiency responsive modules.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Maximal Clique Centrality (MCC) algorithm (Chin et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) was applied to identify hub genes across ZDR modules. The first 30 hub genes as ranked by their scores from the strongest to weakest, are provided (\u003cstrong\u003eAdditional file 35\u0026ndash;37: Table S35-37\u003c/strong\u003e). Among the well-known ZDR genes, only \u003cem\u003eOsZIP3\u003c/em\u003e and \u003cem\u003eOsYLS18\u003c/em\u003e ( both from TTA-M module) featured in the top 30 hub genes. To identify the most Zn deficiency relevant hub genes, we rather adopted a biology-informed approach where CoDEGs directly connected with at least three recognized ZDR genes, or proteins directly interacting with at least one well-known ZDR protein were considered as ZDR specific hub genes or proteins (\u003cstrong\u003eAdditional file 38: Fig. S1-2)\u003c/strong\u003e. The well-known ZDR genes or proteins for which the interactions were searched against were: \u003cem\u003eOsZIP10\u003c/em\u003e, \u003cem\u003eOsNAAT1\u003c/em\u003e, \u003cem\u003eOsSAMS1\u003c/em\u003e, \u003cem\u003eOsSAM2\u003c/em\u003e, \u003cem\u003eOsDMAS1\u003c/em\u003e and \u003cem\u003eOsYSL10\u003c/em\u003e (CA-M module); \u003cem\u003eOsIRO2\u003c/em\u003e, \u003cem\u003eOsZIP2\u003c/em\u003e, and \u003cem\u003eOsNRAMP6\u003c/em\u003e (TRA-M module); \u003cem\u003eOsZIP3, OsZIP8, OsMT1a, OsHMA2, OsHMA3, OsYSL6, OsYSL14,OsYSL18, OsNRAMP4, OsNRAMP7\u003c/em\u003e and \u003cem\u003eOsTOM1\u003c/em\u003e for the TTA-M module. Genes meeting the set criteria included \u003cem\u003eOsbHLH120\u003c/em\u003e, \u003cem\u003eOsWOX11\u003c/em\u003e, \u003cem\u003eOsPSK5, OsNAC121\u003c/em\u003e, \u003cem\u003eOsPGIP1\u003c/em\u003e, \u003cem\u003eOsMID1\u003c/em\u003e, \u003cem\u003eOsMYB55\u003c/em\u003e, \u003cem\u003eOsJAmyb\u003c/em\u003e and \u003cem\u003eOsMPH1\u003c/em\u003e (CA-M module), \u003cem\u003eOsSWN6, ONAC12, OsERF67, OsbHLH108\u003c/em\u003e (TRA-M module), and \u003cem\u003eOsHMA1, OsLIS3, OsGSTU12, OsNRT1.1B, OsMTI4C, OsMT1g, OsbZIP79\u003c/em\u003e and \u003cem\u003eOsTRX\u003c/em\u003e for TTA-M module \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-c \u003cstrong\u003eand Additional file 18\u0026ndash;20: Table S18-20)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eCuriously, the \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://funricegenes.github.io/\u003c/span\u003e\u003c/span\u003e search database for functionally validated genes in rice, showed that several of the CoDEGs \u003cstrong\u003e(Additional file 29\u0026ndash;31: Table S29-31)\u003c/strong\u003e linking up the recognized ZDR genes have established agronomic functions such as transport of essential nutrients, biotic and abiotic stress tolerance (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cstrong\u003eand\u003c/strong\u003e Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-c).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eExperimentally validated genes which are hub genes for the known ZDR genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModule\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene name\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMSU_ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene functions (retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://funricegenes.github.io/\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTTA-M\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsNRT1.1B*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os10g40600\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKey regulator of Nitrogen use efficiency in rice.\u003c/p\u003e\n\u003cp\u003eEnhances of Selenium concentration in rice grains.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsLSI3*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os10g39980\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEssential for Silicon distribution in vascular structures of rice.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsMT1g*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os12g38290\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConfers multiple abiotic stress tolerance in rice.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsRNS4*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os11g05480\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImproves salinity stress tolerance.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsTRX\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os12g08730\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnhances carotenoid and chlorophyll content.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCA-M\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsBHL120\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os09g28210\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA QTL for root thickness and lengths in upland rice.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsWOX11*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os07g48560\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnhances crown root development.\u003c/p\u003e\n\u003cp\u003eEnhances drought stress tolerance.\u003c/p\u003e\n\u003cp\u003eImproves potassium deficiency tolerance.\u003c/p\u003e\n\u003cp\u003eRegulates cytokinin signaling pathway.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsMYB55\u003c/em\u003e\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003e+\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os05g48010\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulates amino acid metabolism.\u003c/p\u003e\n\u003cp\u003eEnhances heat stress tolerance and immunity.\u003c/p\u003e\n\u003cp\u003eRegulates hormonal signaling.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsMPH1\u003c/em\u003e\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003e+\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os06g45890\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModulates plant height and enhances drought stress tolerance.\u003c/p\u003e\n\u003cp\u003eEnhances Cadmium stress tolerance.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsMID1*\u003c/em\u003e\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003e+\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os05g37060\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImproves drought stress tolerance\u003c/p\u003e\n\u003cp\u003eNegatively regulates Arsenic stress tolerance\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsPSK5*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os12g05260\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTarget of osa-miR168a regulating seed vigor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsPGIP1*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os05g01380\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnhances sheath blight tolerance\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTRA-M\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsSWN6*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLOC_Os04g45340\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositively regulates the secondary wall biosynthetic. processes.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003cstrong\u003e*\u003c/strong\u003e : Coexpressed in ZDR modules and expressed in our DGEA (CoDEGs).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003e+\u003c/span\u003e :\u003c/sup\u003e \u003cem\u003eR2R3-type MYB\u003c/em\u003e transcription factor gene.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"3. Discussion","content":" \u003cp\u003eThis study indicates that integrating WGCNA and prior biological knowledge on ZUE trait is an effective method to add value on previous findings from GWAS, qtl mapping, transgenic, miRNA and gene transcriptomic studies on Zn. This apporach enabled to establish modules of coregulated genes, determining conserved molecular functions, and predicting new putative Zn reponsive genes which may facilitate the improvement of Zn use efficiency and Zn biofortification in rice.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe significance of Zinc deficiency responsive modules\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eOur results indicated that molecular pathways and genes known to play essential roles in Zn homeostasis and Zn loading into rice grain coexpressed in ZDR modules. These genes included for instance, \u003cem\u003eZIP\u003c/em\u003e genes (\u003cem\u003eOsZIP2, OsZIP3, OsZIP10\u003c/em\u003e and \u003cem\u003eOsZIP8\u003c/em\u003e), heavy metal ATPase genes (\u003cem\u003eOsHMA1, OsHMA2\u003c/em\u003e and \u003cem\u003eOsHMA3\u003c/em\u003e), mugeneic acid (MAs) or nicatiamine synthase (NAs) genes (\u003cem\u003eOsNAAT1, OsTOM1, OsDMAS1, OsSAMS1\u003c/em\u003e, and \u003cem\u003eOsSAM2\u003c/em\u003e), metallothionein (\u003cem\u003eMT\u003c/em\u003e) protein coding gene (\u003cem\u003eOsMT1a\u003c/em\u003e) and Zn/Fe transcription regulator genes (\u003cem\u003eOsIRO2\u003c/em\u003e and \u003cem\u003eOsIRO3\u003c/em\u003e). \u003cem\u003eOsZIP8\u003c/em\u003e gene is involved in Zn uptake and transport in rice (Lee et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). And the expression of \u003cem\u003eOsZIP10\u003c/em\u003e gene correlated with high iron (Lee et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Zn (Maurya et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) concentrations in rice seeds. Apart from \u003cem\u003eZIP\u003c/em\u003e genes, \u003cem\u003eOsHMA2\u003c/em\u003e and \u003cem\u003eOsHMA3\u003c/em\u003e have also been experimentally validated. \u003cem\u003eOsHMA2\u003c/em\u003e is a long-distance transporter of Zn and Cd (Yamaji et al., 2013), while \u003cem\u003eOsHMA3\u003c/em\u003e enhances Cd stress tolerance and the expression of other Zn transporter genes (Cai et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sasaki et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMAs and NAs are metal chelators from the phytosiderophores family genes and play essential roles in the accumulation Zn/Fe into grain rice, while mitigating the loading of Cd (Banakar et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Earlier studies demonstrated that \u003cem\u003eOsTOM1\u003c/em\u003e, a DMA efflux transporter gene, enhanced tolerance to both Zn (Ishimaru et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Fe (Nozoye et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) deficiencies in rice. Further, targeting MAs genes was proposed as a single strategy to improve both Zn/Fe in rice endosperm (Singh et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cem\u003eMTs\u003c/em\u003e genes are also metal chelators that bind transition metals such as Zn (Sinclair and Kr\u0026auml;mer, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Transgenic rice plants overxpressing \u003cem\u003eOsMT1a\u003c/em\u003e showed enhanced expressions of Zn-induced CCCH zinc-finger TFs, ROS scavenging capacity, drought stress tolerance and Zn concentrations in rice tissues (Yang et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoexpression of all these Zn transporters, phytosiderophores family genes and metal chelators genes across TTA-M, TRA-M and CA-M modules suggests relevant functions of ZDR modules in the molecular mechanism Zn deficiency response.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConserved genes and molecular pathways across Zn deficiency responsive modules.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBy intersecting ZDR modules genes and DEGs from our study we obtained the most conserved transcriptionally active genes. The most significantly enriched functional annotations by these CoDEGs were glutathione metabolism, DNA binding transcription factor, transmembrane transport, and biosynthesis of secondary metabolites including phenylpropanoid and phenylalanine metabolism. Interestingly, these conserved molecular pathways were only enriched by genes expressed in root tissues, underscoring the crucial role of root tissue in Zn deficiency regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Glutathione (GSH; γ-glutamyl-cysteinyl-glycine) is a low- molecular-weight (LMW) intracellular signalling molecule with indispensable antioxidant and abiotic stress tolerance roles (Noctor et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The significant enrichment of glutathione metabolism by Zn stress induced genes was also reported by previous studies in rice (Bandyopadhyay et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zeng et al., 2019a). Zeng et al. (2019a) showed that Zn supply repressed the activity involved in glutathione metabolism genes, suggesting that glutathione has essential roles in Zn stress response. Further, it was shown that the antioxidant roles of GSH enhanced Cd stress tolerance in rice (Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, a set of DEGs that upregulated in root tissues of both AUS genotypes and coexpressed in the TTA-M module, significantly enriched the glutathione metabolism pathway. An earlier study indicated that salt stress tolerant rice cultivar Pokkali had significantly greater levels of GSH compared to a sensitive variety, IR64 (El-Shabrawi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Whether the exudation of GSH or the expression of GSH pathway genes is linked to higher ZUE in ZE genotypes is worthy further investigations, for example, through transgenic or crop modelling approaches.\u003c/p\u003e \u003cp\u003eConversely, secondary metabolites KEGG pathways such as phenlypropanoid biosynthesis was preserved in a group CoDEGs from CA-M module which were upregulated in the root sample of IR26 genotype. The upregulation of phenlypropanoid biosynthesis pathways in rice tissues under low Zn supply was also reported in previous studies (Nanda et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zeng et al., 2019a). Phenylpropanoids constitute the major group of secondary metabolites in plants (Sharma et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Phenylpropanoids can chelate metal ions to enhance the mobilization and uptake of essential elements suh as Zn, iron, manganese, potassium, calcium and magnesium (Seneviratne and Jayasinghearachchi, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The high conservation of both glutathione and phenlypropanoid biosynthesis pathways in root tissues of AUS and ZI is another evidence for the vital roles of LMW root effluxes in enhancing Zn uptake from low Zn environments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClosest hub genes of the well-known ZDR genes are known stress responsive genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGenes with strong connectivity with the validated ZDR genes were identified. Several of these genes or their homologuous were shown to play major roles in different morphogenetic events, biotic and abiotic stress responses by previous investigators. These included for example, a TF gene \u003cem\u003eOsbHLH120\u003c/em\u003e which interacted with validated ZDR genes in the CA-M module. \u003cem\u003eOsbHLH120\u003c/em\u003e is a QTL for root growth and thickness, and improves drought tolerance in upland rice (Li et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The homologous gene of \u003cem\u003eOsbHLH120\u003c/em\u003e, \u003cem\u003eOsIRO2\u003c/em\u003e/\u003cem\u003eOsbHLH56\u003c/em\u003e, is a well-known TF gene that positively regulates the expression of several genes involved into Fe uptake in rice (Ogo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ogo et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Both TFs contain an homeodomain Leucine Zipper (HD- Zip 1) element known to mediate of hormonal responses and response to stimuli (Ariel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In contrast, another homologous gene, \u003cem\u003eOsIRO3\u003c/em\u003e, negatively regulates Fe uptake (Wang et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) but interacted with \u003cem\u003eOsDMAS1\u003c/em\u003e in the PPIN \u003cb\u003e(Additional file 38: Fig. S2c)\u003c/b\u003e. \u003cem\u003eOsIRO3\u003c/em\u003e has a similar binding site with \u003cem\u003eOsIRO2\u003c/em\u003e (5\u0026rsquo;-CACGTGG-3\u0026rsquo;). Whether the activity of \u003cem\u003eOsbHLH120\u003c/em\u003e under Zn/ Fe deficiency is similar to \u003cem\u003eOsIRO2\u003c/em\u003e or \u003cem\u003eOsIRO3\u003c/em\u003e requires further studies. Unknown targets of \u003cem\u003eOsbHLH120/ OsIRO3\u003c/em\u003e may contribute to Fe/Zn hypersensitivity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOsWOX11\u003c/em\u003e was an other important hub gene for known ZDR genes. \u003cem\u003eOsWOX11\u003c/em\u003e is a regulator of cytokinin signalling pathway, crown and root hair development, and enhances drought stress tolerance (Cheng et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Neogy et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An ealier study associated crown development with Zn stress tolerance in ZE genotypes (Nanda and Wissuwa, 2016b). In rice, cytokinin inhibits crown root development, while auxin promotes that process (Debi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kitomi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Here, a cytokining gene (\u003cem\u003eOsCKX5\u003c/em\u003e) from the ZDR modules was downregulated by Zn deficiency in root sample of KALBOR026 genotype \u003cb\u003e(Additional file 31: Table S31)\u003c/b\u003e, constistent with the significant higher root biomass observed in KALBOR026 genotype relative to ZI genotypes (IR26 and IR64) (Lu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, \u003cem\u003eOsWOX11\u003c/em\u003e and its homologous gene \u003cem\u003eOsWOX6\u003c/em\u003e only upregulated in root samples of a ZI genotype (IR26) \u003cb\u003e(Additional file 31: Table S31)\u003c/b\u003e with less root biomass relative to AUS genotypes (Lu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eOsWOX11\u003c/em\u003e may have essential roles in Zn stress response, as its neigbour genes. However, more investigations are needed to illuminate the regulation of ctyokinin signalling by \u003cem\u003eOsWOX11\u003c/em\u003e during Zn deficiency conditions.\u003c/p\u003e \u003cp\u003eOn the other hand, in the TTA-M, \u003cem\u003eOsNRT1.1B\u003c/em\u003e gene coexpressed and interacted with known ZDR genes such as \u003cem\u003eOsZIP3, OsHMA2\u003c/em\u003e and \u003cem\u003eOsYSL18\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. \u003cem\u003eOsNRT1.1B\u003c/em\u003e is a QTL for nitrate use efficiency divergence in rice cultivars (Hu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and enhances nitrogen uptake under low nitrogen conditions in cultivated rice (Fan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Also, \u003cem\u003eOsNRT1.1B\u003c/em\u003e improves Selenium levels in rice grains (Zhang et al., 2019). A previous study indicated that \u003cem\u003eOsNRT1.1B\u003c/em\u003e was induced by Zn stress conditions (Bandyopadhyay et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Here, the expression of \u003cem\u003eOsNRT1.1B\u003c/em\u003e was significantly repressed in root tissues of a Zn sensitive genotype, IR64 \u003cb\u003e(Additional file 29: Table S29)\u003c/b\u003e. As a transporter gene, \u003cem\u003eOsNRT1.1B\u003c/em\u003e could also have significant functions in the translocation of Zn in different rice tissues.\u003c/p\u003e \u003cp\u003eStill in the TTA-M module, \u003cem\u003eOsLSI3\u003c/em\u003e linked up \u003cem\u003eOsHMA3\u003c/em\u003e, \u003cem\u003eOsHMA2\u003c/em\u003e, \u003cem\u003eOsYSL14\u003c/em\u003e and \u003cem\u003eOsYSL18\u003c/em\u003e. \u003cem\u003eOsLSI3\u003c/em\u003e and its homologue \u003cem\u003eOsLIS2\u003c/em\u003e involve in the distribution and redistribution of Arsenic across vascular structures in rice (Yamaji et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cem\u003eOsLSI2\u003c/em\u003e, is a silicon efflux transporter responsible for arsenic accumulation in rice grain (Ma et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, exogenous Silicon is known to enhance gas exchange capacity in rice plants under Zn stress (Song et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and the uptake of essential micronutrients (Zn and Manganese) and macronutrients (Phosphorous, potassium, Calcium and Magnesium) in rice and other monocots plants under heavy metal stress conditions (Keller et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tripathi et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). On the other side, Silicon also reduced Zn uptake in root tissues of maize and cotton plants (Bokor et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Here, \u003cem\u003eOsLIS1\u003c/em\u003e, \u003cem\u003eOsLIS2\u003c/em\u003e and \u003cem\u003eOsLSI3\u003c/em\u003e were significanlty upregulated in root samples of KALBOR026 relative to IR26 in the same tissue under Zn stress conditions \u003cb\u003e(Additional file 10: Table S10)\u003c/b\u003e. \u003cem\u003eOsLIS2\u003c/em\u003e was also significantly induced by Zn stress relative to Zn supply conditions in root tissues of KALBOR026 genotype. It is likely that \u003cem\u003eOsLIS2\u003c/em\u003e /\u003cem\u003eOsLIS3\u003c/em\u003e have similar functions as their linked Zn/cd transporter genes. However, their specific roles in Zn homeostasis remain to be elucidated.\u003c/p\u003e \u003cp\u003eAnother gene connecting the well-known ZDR genes was \u003cem\u003eOsHMA1\u003c/em\u003e. It is presumed that \u003cem\u003eOsHMA1\u003c/em\u003e involves in Zn stress response or detoxification of Zn excess like its orthologue \u003cem\u003eAtHMA1\u003c/em\u003e (Takahashi et al., 2012). \u003cem\u003eOsHMA1\u003c/em\u003e transcripts accumulated by more than 7 folds under Zn-stress condition relative to Zn supply conditions (Suzuki et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003eOsHMA1\u003c/em\u003e was significantly upregulated in crown tissues of AUS (KALBOR026) and ZI (IR64) genotypes \u003cb\u003e(Additional file 29: Table S29)\u003c/b\u003e. This suggests that \u003cem\u003eOsHMA1\u003c/em\u003e could also have similar functions as its homoguous genes \u003cem\u003eOsHMA2\u003c/em\u003e or \u003cem\u003eOsHMA3\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eLaslty, many of the validated \u003cem\u003eR2R3\u003c/em\u003e-type \u003cem\u003eMYB\u003c/em\u003e TFs genes (Katiyar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) linked up several known ZDR genes across the ZDR modules \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These \u003cem\u003eMYB\u003c/em\u003e TF genes were also reported in previous transcriptomic studies on Zn deficiency in rice, but none these has been experimentally validated for Zn deficiency functions. Considering that coexpressing and interacting genes are likely to share same functions, the identified hub genes which have been validated for the other economic traits in rice could also have key roles in enhancing Zn deficiency tolerance as their linked well-known ZDR genes. Thus, targeting these putative novel ZDR genes may benefit the concurrent improvement of several agronomic traits in rice.\u003c/p\u003e "},{"header":"4. Conclusions","content":" \u003cp\u003eA comprehensive elucidation of functional pathways within the framework of co-expression network enabled to identify 3 key modules of coexpressed genes under Zn deficiency, 380 global conserved genes and their pathways, and key hub genes in the interaction networks of Zn responsive genes. Of the well-recognized Zn defiency responsive genes, only \u003cem\u003eOsZIP8\u003c/em\u003e, \u003cem\u003eOsZIP10\u003c/em\u003e, \u003cem\u003eOsMT1a\u003c/em\u003e and OsNAAT1 were expressed in our samples and conserved in the identified key modules. \u003cem\u003eOsZIP3\u003c/em\u003e and \u003cem\u003eOsYSL18\u003c/em\u003e were the only recognized Zn responsive genes detected among the top hub genes in the key modules. Several other hub genes with strong interactions with the recognized Zinc defiency responsive genes were found. Results from this investigation may for instance facilitate genomic selection breeding schemes in targeting relevant molecular pathways and genes to enhance ZUE and Zn biofortification in rice.\u003c/p\u003e "},{"header":"5. Materials And Methods","content":"\u003cp\u003eA comprehensive pipeline used from DGEA to hub genes identification is provided \u003cstrong\u003e(Additional file 38: Fig. S3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample description and data acquisition for DEG analysis. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProcedures from sample preparation and Zn deficiency treatment to the generation of the Fragment Per Kilobase Millions (FPKM) data frame were previously performed in our laboratory, and are as\u0026nbsp; described in a published study (Lu et al., 2020). Briefly, samples consisting of one-week old root or crown tissues of two ZI (IR26 and IR64) and two AUS (UCP122 and KALBOR026) genotypes were supplied (control) or deprived of\u0026nbsp; Zn nutrient regime for one month (Zn stress treatment). The FPKM table as generated by our biological collaborators consisted of 36 columns covering the four genotypes, two tissues (root and crown) per each genotype, two replicates per tissue of each genotype and\u0026nbsp; two treatment conditions (Zn supply or Zn deficiency). Two replicates per tissue for each genotype were treated as one independent biological sample under Zn supply or Zn deficiency conditions (\u003cstrong\u003eAdditional file 16-17:\u003c/strong\u003e \u003cstrong\u003eTable S16-17\u003c/strong\u003e). Differential gene expression analysis (DGEA) was only carried out for samples from our laboratory using the Bioconductor R package DEseq2 (Love et al., 2014). Genes were considered differentially expressed if passing the stringent criterions of fold-change \u0026ge; 2 or \u0026le;\u0026thinsp;-2,\u0026nbsp; \u003cem\u003eq\u003c/em\u003e_value \u0026le; 0.01, and false discovery rate-adjusted \u003cem\u003ep \u003c/em\u003evalue (FDR) \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample description, RNAseq data acquisition and processing for WGCNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe global gene co-expression network was built using the Weighted Gene Coexpression analysis (Langfelder and Horvath, 2008) with 17 independent biological samples obtained after collapsing related replicates by their averages\u003cstrong\u003e (Additional file 17:\u003c/strong\u003e \u003cstrong\u003eTable S17)\u003c/strong\u003e. Eight of the 17 samples were Zn-stressed samples from this study (as described in the section above), six others (IR55179, RIL46, Nipponbare, IR64, IR74 and KP) were from a study by (Nanda et al., 2017), and the remaining were from (Zeng et al., 2019b). Overall, nine genotype samples (IR55179, RIL46, IR64, IR74, IR26, NIPPONBARE, KP, UCP122 and KALBOR026) were used for WGCNA. These samples were representative of different varietal ecotypes including \u003cem\u003eJaponica\u003c/em\u003e (NIPPONBARE and KP), \u003cem\u003eIndica\u003c/em\u003e (IR55179, RIL46, IR64, IR74 and IR26) and \u003cem\u003eAUS\u003c/em\u003e (UCP122 and KALBOR026), and different Zn use efficiency (ZU) levels. Only raw counts from Zn-stressed\u0026nbsp; samples were retrieved from NCBI data repository and fed into RNAseq pipeline.\u003c/p\u003e\n\u003cp\u003eThe RNAseq pipeline first consisted of adapter removal and low-quality reads trimming by Trimmomatic tool (Bolger et al., 2014). Next, clean reads were mapped to the MSU7 rice reference genome using HISAT alignment tool (Anders et al., 2015). The annotation and reference genome files (MSU7) were obtained from Illumina\u0026rsquo;s iGenomes project (\u003ca href=\"http://support.illumina.com/sequencing/sequencing_software/igenome.html\"\u003esupport.illumina.com/sequencing/sequencing_software/igenome.html\u003c/a\u003e). The poorly aligned reads were filtered out using the filter SAM tool version 0.1.19 (Li et al., 2009). The SAM tool generated\u0026nbsp; a BAM file of quality reads which was fed into\u0026nbsp; the subread tool (Liao et al., 2014) along with gene model annotation file. The resulting gene count matrix was converted to FPKM values and merged with the Zn deficiency FPKM table provided by our biological collaborators.\u0026nbsp; All the used samples were pair-end sequenced libraries. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData quality processing for WGCNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FPKM values from the above steps were rounded to their nearest integers and standardized using the rlog() function of\u0026nbsp; DESeq2 R package. Rows with minimum counts less than 10 were also filtered out. As the only variation of interest was genotypic variation, effects from other factors such as tissue type, development stage, Zn stress treatment duration were corrected using removeBatchEffect() of limma R package. Samples\u0026rsquo; PCA and clustering before and after controlling the unwanted variations are as illustrated in (\u003cstrong\u003eAdditional file 38: Fig. S4a-b\u003c/strong\u003e). The resulting\u0026nbsp; normalized FPKM values were finally filtered with their MAD (median and median absolute deviation) to reduce noises by removing low-expressed or non-varying genes (Langfelder and Horvath, 2008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene co-expression analysis and module construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly Zn deficiency responsive gene loci comprising of genes reported in 7 previous studies using different methods (e.g., GWAS, qtl mapping, miRNA, transgenic and transcriptomic studies)\u0026nbsp; and DEGs from this study were selected for network construction \u003cstrong\u003e(Additional file 11: Table S11)\u003c/strong\u003e. WGCNA (version 1.49) R package was used to assemble modules of coexpressed genes (Langfelder and Horvath, 2008). The scale free topology was used for choosing the softpower threshold (\u0026beta;) for the computation of adjacency matrix as a\u003csub\u003eij\u003c/sub\u003e\u0026nbsp;= |s\u003csub\u003eij\u003c/sub\u003e|\u003csup\u003e\u0026beta;\u003c/sup\u003e; where s\u003csub\u003eij\u003c/sub\u003e\u0026nbsp;is the correlation between gene\u0026nbsp;i\u0026nbsp;and gene\u0026nbsp;j (Zhang et al., 2005). Soft thresholding refers to reducing low correlation continuously by powering the correlation between genes to that threshold using \u0026beta; parameter. The pickSoftThreshold() function was used to determine the \u0026beta; value. The soft power threshold of 13 was selected as the first power that surpasses the scale-free topology fit index of 0.85 (\u003cstrong\u003eAdditional file 38: Fig. S5a\u003c/strong\u003e) (Ram\u0026iacute;rez-Gonz\u0026aacute;lez et al., 2018). Next, a topological overlap matrix (TOM) was constructed from adjacency matrices using\u0026nbsp; \u0026beta; value of 13 (\u003cstrong\u003eAdditional file 38: Fig. S5b\u003c/strong\u003e). TOM enables to determine the network connectivity of a gene as defined by all its adjacencies with the rest of genes for network generation. Step by step procedure was finally established to construct a consensus network with parameters: minModuleSize = 16; dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM, deepSplit = 2, pamRespectsDendro = FALSE, minClusterSize = minModuleSize). After generation of the dynamic tree modules, modules were labeled to colors and merged with mergeCloseModules() function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork visualization and hub genes identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene-gene and protein-protein network structures from ZDR modules were represented graphically using Cytoscape (v3.8) (Cline et al., 2007). Genes or proteins directly connected with at least three well-known ZDR genes or proteins were retained for a finer gene-gene interaction network (GGIN) visualization. The top hub genes in each ZDR modules were based on the Maximal Clique Centrality method implemented via the cytoHubba plugin (Chin et al., 2014) in Cytoscape (v3.8) software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of transcription regulators, transcription factors, protein kinases genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscription factors were identified using the Plant Transcription Factor Database (PlantTFDB version 5.0 (\u003ca href=\"http://planttfdb.cbi.pku.edu.cn/index.php\"\u003ehttp://planttfdb.cbi.pku.edu.cn/index.php\u003c/a\u003e) (P\u0026eacute;rez-Rodr\u0026iacute;guez et al., 2010). Transcription regulators and protein kinases were predicted using iTAK version 1.6 software\u003c/p\u003e\n\u003cp\u003e(\u003ca href=\"http://itak.feilab.net/cgi-bin/itak/index.cgi\"\u003ehttp://itak.feilab.net/cgi-bin/itak/index.cgi\u003c/a\u003e) (Zheng et al., 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotation enrichment analysis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG)\u0026nbsp; pathways affected by the DEGs from this study, coexpressed genes across all the modules, and the\u0026nbsp; differentially coexpressed \u0026nbsp;genes across ZDR modules were identified using STRING \u0026nbsp;tool\u0026nbsp; version:11 (\u003ca href=\"http://string-db.org/\"\u003ehttp://string-db.org/\u003c/a\u003e) (Mering et al., 2003) at FDR \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"List of Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eZUE\u003c/strong\u003e: Zinc Use Efficiency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDGEA\u003c/strong\u003e: Differential Gene Expression Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZDR\u003c/strong\u003e : Zn Deficiency Responsive\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZE\u003c/strong\u003e :\u0026nbsp; Zn Efficient\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZI \u003c/strong\u003e: Zn Inefficient\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCNA \u003c/strong\u003e: Weighted Gene Coexpression Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs\u003c/strong\u003e : Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoDEGs\u003c/strong\u003e : Coexpressed Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMCC\u003c/strong\u003e: Maximal Clique Centrality\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCEA\u003c/strong\u003e: Condition-based gene Expression Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEA \u003c/strong\u003e: Genotype-based gene Expression Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLMWOA \u003c/strong\u003e: Low-Molecular Weight Organic Acids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGGIN \u003c/strong\u003e: Gene-Gene Interaction Network\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPIN \u003c/strong\u003e: Protein-Protein Interaction Network\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAs \u003c/strong\u003e: Nicotianamine synthase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAs \u003c/strong\u003e:\u0026nbsp; Mugineic Acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDMAs \u003c/strong\u003e: Deoxymugenic Acids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZIP \u003c/strong\u003e: Zinc-regulated transporters and Iron-regulated transporter Proteins\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHMAs \u003c/strong\u003e: Heavy Metal ATPases\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMTPs \u003c/strong\u003e: Metal Tolerance Proteins\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROS \u003c/strong\u003e: Reactive Oxygen Species\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMT \u003c/strong\u003e: Metallothionein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTF\u003c/strong\u003e : Transcription factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTFR\u003c/strong\u003e : Transcription factor regulator\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets generated for this study are included in the article/supplementary material, further inquiries can be directed to the first author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Agricultural Science and Technology Innovation Program, National Key R\u0026amp;D Program of China (2020YFE0202300) and Shenzhen Science and Technology Projects (JCYJ20200109150650397).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGouyou Ye conceived the project. Xiang Lu and Blaise Pascal MUVUNYI prepared the samples and collected data. Blaise Pascal MUVUNYI and Sang He analyzed the data. Blaise Pascal MUVUNYI drafted the manuscript. Sang He and Gouyou Ye revised the manuscript. All the authors have read, edited and approved the current version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank Dr.Liu Chen for revising the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAkhtar S, Das JK, Ismail T, Wahid M, Saeed W, Bhutta ZA (2021) Nutritional perspectives for\u003c/p\u003e\n\u003cp\u003ethe prevention and mitigation of COVID-19. Nutr Rev 79(3):289-300\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1093/nutrit/nuaa063\"\u003ehttps://doi.org/10.1093/nutrit/nuaa063\u003c/a\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlloway BJ (2008) Zinc in soils and crop nutrition.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"http://www.topsoils.co.nz/wp-content/uploads/2014/09/Zinc-in-Soils-and-Crop-Nutrition-Brian-J.-Alloway.pdf\"\u003ehttp://www.topsoils.co.nz/wp-content/uploads/2014/09/Zinc-in-Soils-and-Crop-Nutrition-Brian-J.-Alloway.pdf\u003c/a\u003e .Accessed 15 Apr 2021\u003c/p\u003e\n\u003cp\u003eAnders S, Pyl PT, Huber W(2015) HTSeq\u0026mdash;a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166-169\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1093/bioinformatics/btu638\"\u003ehttps://doi.org/10.1093/bioinformatics/btu638\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAriel FD, Manavella PA, Dezar, CA, Chan RL (2007) The true story of the HD-Zip family. Trends in plant science (12)9:419-426 \u003ca href=\"https://doi.org/10.1016/j.tplants.2007.08.003\"\u003ehttps://doi.org/10.1016/j.tplants.2007.08.003\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBanakar R, Alvarez FA, D\u0026iacute;az-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. Journal of Experimental Botany 68(17):4983-4995 \u003ca href=\"https://doi.org/10.1093/jxb/erx304\"\u003ehttps://doi.org/10.1093/jxb/erx304\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBandyopadhyay T, Mehra P, Hairat S, Giri J (2017) Morpho-physiological and transcriptome profiling reveal novel zinc deficiency-responsive genes in rice. Fun \u0026amp; Int Gen (17):565-581 \u003ca href=\"https://doi.org/10.1007/s10142-017-0556-x\"\u003ehttps://doi.org/10.1007/s10142-017-0556-x\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBanerjee S, Chandel G (2011a) Understanding the role of metal homeostasis related candidate genes in Fe/Zn uptake, transport and redistribution in rice using semi-quantitative RT-PCR. Plant Mol Biol Biotechnol 2:33\u0026ndash;46 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBlack MM (1998) Zinc deficiency and child development. The American journal of clinical nutrition (68):464S-469S \u003ca href=\"https://dx.doi.org/10.1093%2Fajcn%2F68.2.464S\"\u003ehttps://dx.doi.org/10.1093%2Fajcn%2F68.2.464S\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBokor B, Bokorov\u0026aacute; S, Ondo\u0026scaron; S, \u0026Scaron;vubov\u0026aacute; R, Lukačov\u0026aacute; Z, H\u0026yacute;blov\u0026aacute; M, Szemes T, Lux A (2015) Ionome and expression level of Si transporter genes (Lsi1, Lsi2, and Lsi6) affected by Zn and Si interaction in maize. Environmental Science, Research Pollution 22(9):6800-6811 \u003ca href=\"https://doi.org/10.1007/s11356-014-3876-6\"\u003ehttps://doi.org/10.1007/s11356-014-3876-6\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114\u0026ndash;2120 \u003ca href=\"https://doi.org/10.1093/bioinformatics/btu170\"\u003ehttps://doi.org/10.1093/bioinformatics/btu170\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCai H, Huang S, Che J, Yamaji N, Ma JF (2019) The tonoplast-localized transporter OsHMA3 plays an important role in maintaining Zn homeostasis in rice.\u0026nbsp;Journal of experimental botany\u0026nbsp;70(10):2717-2725 \u003ca href=\"https://doi.org/10.1093/jxb/erl054\"\u003ehttps://doi.org/10.1093/jxb/erl054\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eChen F, Wang F, Wu F, Mao W, \u0026nbsp;Zhang G, Zhou M (2010) Modulation of exogenous glutathione in antioxidant defense system against Cd stress in the two barley genotypes differing in Cd tolerance. Plant Physiology, Biochemistry 48(8):663-672 \u003ca href=\"https://doi.org/10.1016/j.plaphy.2010.05.001\"\u003ehttps://doi.org/10.1016/j.plaphy.2010.05.001\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCheng S, Zhou DX, Zhao Y (2016) WUSCHEL-related homeobox gene WOX11 increases rice drought resistance by controlling root hair formation and root system \u0026nbsp;development. Plant Sign \u0026amp; Beh11(2):e1130198. \u003ca href=\"https://doi.org/10.1080/15592324.2015.1130198\"\u003ehttps://doi.org/10.1080/15592324.2015.1130198\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eChilds KL, Davidson RM, Buell CR (2011) Gene coexpression network analysis as a source of functional annotation for rice genes. PloS one 6(7):e22196 \u003ca href=\"https://doi.org/10.1371/journal.pone.0022196\"\u003ehttps://doi.org/10.1371/journal.pone.0022196\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eChin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and \u0026nbsp;sub-networks from complex interactome. BMC systems biology 8 Suppl 4(Suppl 4):S11-S11\u003ca href=\"https://doi.org/10.1186/1752-0509-8-S4-S11\"\u003ehttps://doi.org/10.1186/1752-0509-8-S4-S11\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, \u0026nbsp;Creech M, Gross B (2007) Integration of biological networks and gene expression datausing Cytoscape. Nat Protoc (2):2366\u0026ndash;2382\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1038/nprot.2007.324\"\u003ehttps://doi.org/10.1038/nprot.2007.324\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDardenne M (2002) Zinc and immune function. European Journal of Clinical Nutrition (56):S20-S23 \u003ca href=\"https://doi.org/10.1038/sj.ejcn.1601479\"\u003ehttps://doi.org/10.1038/sj.ejcn.1601479\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDebi BR, Taketa S, Ichii M (2005) Cytokinin inhibits lateral root initiation but stimulates lateral Root elongation in rice (Oryza sativa). Journal of plant physiology 162(5):507-515 \u003ca href=\"https://doi.org/10.1016/j.jplph.2004.08.007\"\u003ehttps://doi.org/10.1016/j.jplph.2004.08.007\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eEl-Shabrawi H, Kumar B, Kaul T, Reddy MK, Singla-Pareek SL, Sopory SK (2010) Redox homeostasis, antioxidant defense, and methylglyoxal detoxification as markers for salt tolerance in Pokkali rice. Protoplasma 245(1):85-96\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1007/s00709-0100144-6\"\u003ehttps://doi.org/10.1007/s00709-0100144-6\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFan X, Feng H, Tan Y, Xu Y, Miao Q, Xu G (2016) A putative 6-transmembrane nitrate transporter OsNRT1.1b plays a key role in rice under low nitrogen. Integr Plant Biol 58(6):590-599. \u003ca href=\"https://doi.org/10.1111/jipb.12382\"\u003ehttps://doi.org/10.1111/jipb.12382\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHartwell LH, Hopfield JJ, Leibler S, Murray AW (199) From molecular to modular cell biology. Nature 402(6761):C47-52 \u003ca href=\"https://doi.org/10.1038/35011540\"\u003ehttps://doi.org/10.1038/35011540\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHu B, Wang W, Ou S, Tang J, Li H, Che R, Zhang Z, Chai X, Wang H, Wang Y, Liang C, Liu L, Piao Z, Deng Q, Deng K, Xu C, Liang Y, Zhang L, Li L, Chu C (2015) Variation in NRT1.1B contributes to nitrate-use divergence between rice subspecies. Nat Genet 47(7): 834-838 \u0026nbsp;\u003ca href=\"https://doi.org/10.1038/ng.3337\"\u003ehttps://doi.org/10.1038/ng.3337\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHuizar MI, Arena R, Laddu DR (2021) The global food syndemic: the impact of food insecurity, malnutrition and obesity on the healthspan amid the COVID-19 pandemic. Progress in cardiovascular diseases 64:105\u0026ndash;107 \u003ca href=\"https://dx.doi.org/10.1016%2Fj.pcad.2020.07.002\"\u003ehttps://dx.doi.org/10.1016%2Fj.pcad.2020.07.002\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIshimaru Y, Bashir K, Nishizawa NK (2011) Zn Uptake and Translocation in Rice Plants. Rice 4:21\u0026ndash;27 \u003ca href=\"https://doi.org/10.1007/s12284-011-9061-3\"\u003ehttps://doi.org/10.1007/s12284-011-9061-3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIshimaru Y, Suzuki M, Kobayashi T, Takahashi M, Nakanishi H, Mori S, Nishizawa NK (2005) OsZIP4, a novel zinc-regulated zinc transporter in rice. Journal of Experimental Botany 56(422):3207-3214 \u003ca href=\"https://doi.org/10.1093/jxb/eri317\"\u003ehttps://doi.org/10.1093/jxb/eri317\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eKatiyar A, Smita S, Lenka SK, Rajwanshi R, Chinnusamy V, Bansal KC (2012) Genome-wide classification and expression analysis of MYB transcription factor families in rice and Arabidopsis.\u0026nbsp; BMC genomics (1):1-9 \u003ca href=\"https://doi.org/10.1186/1471-2164-13-544\"\u003ehttps://doi.org/10.1186/1471-2164-13-544\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eKeller C, Rizwan M, Davidian JC, Pokrovsky OS, Bovet N, Chaurand P, Meunier JD (2015) Effect of silicon on wheat seedlings (Triticum turgidum L.) grown in hydroponics and exposed to 0 to 30 \u0026micro;M Cu. Planta 241(4):847-860 \u003ca href=\"https://doi.org/10.1007/s00425-014-2220-1\"\u003ehttps://doi.org/10.1007/s00425-014-2220-1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eKitomi Y, Hidemi K, Inukai Y (2011) Molecular mechanism of crown root initiation and the different mechanisms between crown root and radicle in rice. Plant signaling, behavior 6(9): 1276-1278 \u003ca href=\"https://doi.org/10.4161/psb.6.9.16787\"\u003ehttps://doi.org/10.4161/psb.6.9.16787\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLangfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. \u0026nbsp;BMC bioinformatics 9(1):1-13 \u003ca href=\"https://doi.org/10.1186/1471-2105-9-559\"\u003ehttps://doi.org/10.1186/1471-2105-9-559\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLee S, Chiecko JC, Kim SA, Walker EL, LeeY, Guerinot ML, An G (2009) Disruption of OsYSL15 leads to iron inefficiency in rice plants.\u0026nbsp;Plant physiology\u0026nbsp;150(2):786-800\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1104/pp.109.135418\"\u003ehttps://doi.org/10.1104/pp.109.135418\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLee S, Kim SA, Lee J, Guerinot ML, An G (2010) Zinc deficiency-inducible OsZIP8 encodes a plasma membrane-localized zinc transporter in rice. Mol Cells (29):551-558\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1007/s10059-010-0069-0\"\u003ehttps://doi.org/10.1007/s10059-010-0069-0\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078-2079 \u003ca href=\"https://doi.org/10.1093/bioinformatics/btp352\"\u003ehttps://doi.org/10.1093/bioinformatics/btp352\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLi J, Han Y, Liu L, Chen Y, Du Y, Zhang J, Sun H, Zhao Q (2015) qRT9, a quantitative trait locus controlling root thickness and root length in upland rice. Journal of Experimental Botany 66(9):2723-32 \u003ca href=\"https://doi.org/10.1093/jxb/erv076\"\u003ehttps://doi.org/10.1093/jxb/erv076\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLiao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923-930 \u003ca href=\"https://doi.org/10.1093/bioinformatics/btt656\"\u003ehttps://doi.org/10.1093/bioinformatics/btt656\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLu X, Liu S, Zhi S, Chen J, Ye G (2020) Comparative transcriptome profile analysis of rice varieties with different tolerance to zinc deficiency. Plant Biol 23(2):375-390 \u003ca href=\"https://doi.org/10.1111/plb.13227\"\u003ehttps://doi.org/10.1111/plb.13227\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMa JF, Yamaji N, Mitani N, Xu XY, Su YH, McGrath SP, Zhao FJ (2008) Transporters of arsenite in rice and their role in arsenic accumulation in rice grain. Proceedings of the National Academy of Sciences 105(29):9931-5 \u003ca href=\"https://doi.org/10.1073/pnas.0802361105\"\u003ehttps://doi.org/10.1073/pnas.0802361105\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMasuda H, Ishimaru Y, Aung MS, Kobayashi T, Kakei Y, Takahashi M, Higuchi K, Nakanishi H, Nishizawa NK (2012) Iron biofortification in rice by the introduction of multiple genes involved in iron nutrition. Sci Rep 2, 543 \u003ca href=\"https://doi.org/10.1038/srep00543\"\u003ehttps://doi.org/10.1038/srep00543\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMaurya S, Vishwakarma AK, Dubey M, Shrivastava P, Shrivastava R, Chandel G (2018) Developing gene-tagged molecular marker for functional analysis of OsZIP10 metal transporter gene in rice.\u0026nbsp;Indian Journal of Genetics and Plant Breeding (The)\u0026nbsp;78(2): 180-186. \u003ca href=\"https://doi.org/10.5958/0975-6906.2018.00023.8\"\u003ehttps://doi.org/10.5958/0975-6906.2018.00023.8\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMering VC, Huynen M, Jaeggi, D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Research 31(1):258-261 \u003ca href=\"https://doi.org/10.1093/nar/gkg034\"\u003ehttps://doi.org/10.1093/nar/gkg034\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNanda AK, Pujol V, Wissuwa M (2017) Patterns of stress response and tolerance based on transcriptome profiling of rice crown tissue under zinc deficiency. Journal of Experimental Botany 68(7):1715-1729 \u003ca href=\"https://doi.org/10.1093/jxb/erx039\"\u003ehttps://doi.org/10.1093/jxb/erx039\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNanda AK, Wissuwa M (2016a) Rapid crown root development confers tolerance to zinc deficiency in rice. Frontiers in plant science 7, 428 \u003ca href=\"https://doi.org/10.3389/fpls.2016.00428\"\u003ehttps://doi.org/10.3389/fpls.2016.00428\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNeogy A, Garg T, Kumar A, Dwivedi AK, Singh H, Singh U, Singh Z, Prasad K, Jain M, Yadav S (2019) Genome-Wide Transcript Profiling Reveals an Auxin-Responsive Transcription Factor, OsAP2/ERF-40, Promoting Rice Adventitious Root Development. Plant Cell Physiol 60(10):2343-2355 \u003ca href=\"https://doi.org/10.1093/pcp/pcz132\"\u003ehttps://doi.org/10.1093/pcp/pcz132\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNoctor G, Mhamdi A, Chaouch S, Han Y, Neukermans J, Marquez-Garcia B, Queval G, Foyer C H (2012) Glutathione in plants: an integrated overview. Plant Cell Environ 35(2):454-84 \u003ca href=\"https://doi.org/10.1111/j.1365-3040.2011.02400.x\"\u003ehttps://doi.org/10.1111/j.1365-3040.2011.02400.x\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNozoye T, Nagasaka S, Kobayashi T, Takahashi M, Sato Y, Sato Y, Uozumi N, Nakanishi H, Nishizawa NK (2011) Phytosiderophore efflux transporters are crucial for iron acquisition in graminaceous plants. Journal of Biological Chemistry 286(7):5446-54.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1074/jbc.M110.180026\"\u003ehttps://doi.org/10.1074/jbc.M110.18002\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOgo Y, Itai RN, Nakanishi H, Inoue H, Kobayashi T, Suzuki M, Takahashi M, \u0026nbsp;Mori S, Nishizawa NK (2006) Isolation and characterization of IRO2, a novel iron-regulated bHLH transcription factor in graminaceous plants. \u0026nbsp;Journal of Experimental Botany 57, 2867-78 \u003ca href=\"https://doi.org/10.1093/jxb/erl054\"\u003ehttps://doi.org/10.1093/jxb/erl054\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOgo Y, Itai RN, Kobayashi T, Nakanishi H, Mori S, Nishizawa NK (2009) Overexpression of OsIRO2 improves both iron uptake and translocation to seeds in rice. UC Davis: Department of Plant Sciences. \u003ca href=\"https://escholarship.org/uc/item/2kd7m133\"\u003ehttps://escholarship.org/uc/item/2kd7m133\u003c/a\u003e .Accessed 15 Apr 2021\u003c/p\u003e\n\u003cp\u003eOgo Y, Nakanishi IR, Nakanishi H, Kobayashi T, Takahashi M, Mori S, Nishizawa NK (2007) The rice bHLH protein OsIRO2 is an essential regulator of the genes involved in Fe uptake under Fe‐deficient conditions. Plant journal 51(3):366-377\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1111/j.1365-313X.2007.03149.x\"\u003ehttps://doi.org/10.1111/j.1365-313X.2007.03149.x\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOlsen LI, Palmgren MG (2014) Many rivers to cross: the journey of zinc from soil to seed.\u0026nbsp; Frontiers in Plant Science 5, 30 \u003ca href=\"https://doi.org/10.3389/fpls.2014.00030\"\u003ehttps://doi.org/10.3389/fpls.2014.00030\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP\u0026eacute;rez-Rodr\u0026iacute;guez P, Riano-Pachon DM, Corr\u0026ecirc;a LG, Rensing SA, Kersten B, Mueller-Roeber B (2010) PlnTFDB: updated content and new features of the plant transcription factor database. Nucleic acids research 38(suppl_1):D822-7 \u003ca href=\"https://doi.org/10.1093/nar/gkp805\"\u003ehttps://doi.org/10.1093/nar/gkp805\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePtashnyk M, Roose T, Jones DL, Kirk G (2011) Enhanced zinc uptake by rice through phytosiderophore secretion: a modelling study. Plant Cell Environ 34(12):2038-2046\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1111/j.1365-3040.2011.02401.x\"\u003ehttps://doi.org/10.1111/j.1365-3040.2011.02401.x\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRam\u0026iacute;rez-Gonz\u0026aacute;lez RH, Borrill P, Lang D, Harrington SA, Brinton J, Venturini L, Davey M, Jacobs J, Van Ex F, Pasha A (2018) The transcriptional landscape of polyploid wheat. Science 361(6403) \u003ca href=\"https://doi.org/10.1126/science.aar6089\"\u003ehttps://doi.org/10.1126/science.aar6089\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSasaki A, Yamaji N, Ma JF (2014) Overexpression of OsHMA3 enhances Cd tolerance and expression of Zn transporter genes in rice.\u0026nbsp;Journal of experimental botany\u0026nbsp;65(20):6013-6021 \u003ca href=\"https://doi.org/10.1093/jxb/eru340\"\u003ehttps://doi.org/10.1093/jxb/eru340\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSeneviratne G, Jayasinghearachchi HS (2003) Mycelial colonization by bradyrhizobia and azorhizobia. Journal of biosciences 28(2):243-247 \u003ca href=\"https://doi.org/10.1007/BF02706224\"\u003ehttps://doi.org/10.1007/BF02706224\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSharma A, Shahzad B, Rehman A, Bhardwaj R, Landi M, Zheng B (2019) Response of phenylpropanoid pathway and the role of polyphenols in plants under abiotic stress. Molecules 24(13):2452 \u003ca href=\"https://doi.org/10.3390/molecules24132452\"\u003ehttps://doi.org/10.3390/molecules24132452\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSinclair SA, Kr\u0026auml;mer U (2012) The zinc homeostasis network of land plants. Biochimica et Biophysica Acta -Molecular Cell Research 1823(9):1553-1567\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1016/j.bbamcr.2012.05.016\"\u003ehttps://doi.org/10.1016/j.bbamcr.2012.05.016\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingh SP, Gruissem W, Bhullar NK (2017) Single genetic locus improvement of iron, zinc and \u0026beta;-carotene content in rice grains. Scientific Reports 7(1):6883\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1038/s41598-017-07198-5\"\u003ehttps://doi.org/10.1038/s41598-017-07198-5\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSichul L, Jeff CC, Sun AK, Elsbeth LW, Youngsook L, Mary LG, Gynheung A (2009) Disruption of OsYSL15 leads to iron inefficiency in rice plants. Plant physiology 150(2):786-800 \u003ca href=\"https://doi.org/10.1104/pp.109.135418\"\u003ehttps://doi.org/10.1104/pp.109.135418\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Song A, Li P, Fan F, Li Z, Liang Y (2014) The effect of silicon on photosynthesis and expression of its relevant genes in rice (Oryza sativa L.) under high-zinc stress. PLoS One 9(11):e113782 \u003ca href=\"https://doi.org/10.1371/journal.pone.0113782\"\u003ehttps://doi.org/10.1371/journal.pone.0113782\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSuzuki M, Bashir K, Inoue H, Takahashi M, Nakanishi H, Nishizawa NK (2012) Accumulation of starch in Zn-deficient rice. Rice 5(1):1-8 \u003ca href=\"https://doi.org/10.1186/1939-8433-5-9\"\u003ehttps://doi.org/10.1186/1939-8433-5-9\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTakahashi R, Bashir K, Ishimaru Y, Nishizawa NK, Nakanishi H (2012) The role of heavy-metal ATPases, HMAs, in zinc and cadmium transport in rice. Plant signaling \u0026amp;amp; behavior 7(12):1605-1607 \u003ca href=\"https://doi.org/10.4161/psb.22454\"\u003ehttps://doi.org/10.4161/psb.22454\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTan M, Cheng D, Yang Y, Zhang G, Qin M, Chen J, Chen Y, Jiang M (2017) Co-expression network analysis of the transcriptomes of rice roots exposed to various cadmium stresses reveals universal cadmium-responsive genes. BMC Plant Biology 17(1):194\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1186/s12870-017-1143-y\"\u003ehttps://doi.org/10.1186/s12870-017-1143-y\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTripathi P, Tripathi RD, Singh RP, Dwivedi S, Goutam D, Shri M, Trivedi PK, Chakrabarty D (2013) Silicon mediates arsenic tolerance in rice (Oryza sativa L.) through lowering of arsenic uptake and improved antioxidant defence system. Ecological engineering 52:96-103 \u003ca href=\"https://doi.org/10.1016/j.ecoleng.2012.12.057\"\u003ehttps://doi.org/10.1016/j.ecoleng.2012.12.057\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVan DSD, Bhullar NK, De SH, Gruissem W, MacKenzie D, Pfeiffer W, Qaim M, Slamet-Loedin I, Strobbe S, Tohme J, Trijatmiko KR, Vanderschuren H, Van MM, Zhang C, Bouis H (2020) Multiplying the efficiency and impact of biofortification through metabolic engineering. Nature Communications 11(1):5203 \u003ca href=\"https://doi.org/10.1038/s41467-020-19020-4\"\u003ehttps://doi.org/10.1038/s41467-020-19020-4\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWalley JW, Sartor RC, Shen Z, Schmitz RJ, Wu KJ, Urich MA, Nery JR, Smith LG, Schnable JC, Ecker JR (2016) Integration of omic networks in a developmental atlas of maize. Science 353(6301):814-818\u0026nbsp; \u003ca href=\"https://doi.org/10.1126/science.aag1125\"\u003ehttps://doi.org/10.1126/science.aag1125\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWang W, Ye J, Ma Y, Wang T, Shou H, Zheng L (2020) OsIRO3 Plays an Essential Role in Iron Deficiency Responses and Regulates Iron Homeostasis in Rice. Plants (Basel) 9(9):1095\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.3390/plants9091095\"\u003ehttps://doi.org/10.3390/plants9091095\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWidodo B, Broadley MR, Rose T, Frei M, Pariasca-Tanaka J, Yoshihashi T, Thomson M, Hammond JP, Aprile A, Close TJ, Ismail AM, Wissuwa M (2010) Response to zinc deficiency of two rice lines with contrasting tolerance is determined by root growth maintenance and organic acid exudation rates, and not by zinc-transporter activity. New Phytol 186(2):400-414\u0026nbsp; \u003ca href=\"https://doi.org/10.1111/j.1469-8137.2009.03177.x\"\u003ehttps://doi.org/10.1111/j.1469-8137.2009.03177.x\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYamaji N, Sakurai G, Mitani-Ueno N, Ma JF (2015) Orchestration of three transporters and distinct vascular structures in node for intervascular transfer of silicon in rice. Proc Natl Acad Sci 112(36):11401-11406\u0026nbsp; \u003ca href=\"https://doi.org/10.1073/pnas.1508987112\"\u003ehttps://doi.org/10.1073/pnas.1508987112\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Yamaji Ni, Xia J, Mitani-Ueno N, Yokosho K, Ma JF (2013) Preferential delivery of zinc to developing tissues in rice is mediated by P-type heavy metal ATPase OsHMA2. Plant physiology 162(2):927-939 \u003ca href=\"https://doi.org/10.1104/pp.113.216564\"\u003ehttps://doi.org/10.1104/pp.113.216564\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYang Z, Wu Y, \u0026nbsp;Li Y, Ling HQ, Chu C (2009) OsMT1a, a type 1 metallothionein, plays the pivotal role in zinc homeostasis and drought tolerance in rice. Plant molecular biology 70(1-2): 219-229\u0026nbsp; \u003ca href=\"https://doi.org/10.1007/s11103-009-9466-1\"\u003ehttps://doi.org/10.1007/s11103-009-9466-1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eZeng H, Zhang X, Ding M, Zhu Y (2019b) Integrated analyses of miRNAome and transcriptome reveal zinc deficiency responses in rice seedlings. BMC Plant Biol 19(1):585 \u003ca href=\"https://doi.org/10.1186/s12870-019-2203-2\"\u003ehttps://doi.org/10.1186/s12870-019-2203-2\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eZhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1): Article17 \u003ca href=\"https://doi.org/10.2202/1544-6115.1128\"\u003ehttps://doi.org/10.2202/1544-6115.1128\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eZhang L, Hu B, Deng K, Gao X, Sun G, Zhang Z, Li P, Wang W, Li H, Zhang Z, Fu Z, Yang J, Gao S, Li L, Yu F, Li Y, Ling H, Chu C (NRT1.1B) improves selenium concentrations in rice grains by facilitating selenomethinone translocation. Plant Biotechnol\u0026nbsp; 17(6):1058-1068 \u0026nbsp;\u003ca href=\"https://doi.org/10.1111/pbi.13037\"\u003ehttps://doi.org/10.1111/pbi.13037\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eZhang Y, Yu C, Lin J, Liu J, Liu B, Wang J, Huang A, Li H, Zhao T (2017) OsMPH1 regulates plant height and improves grain yield in rice. PLoS One 12(7): e0180825\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003ca href=\"https://doi.org/10.1371/journal.pone.0180825\"\u003ehttps://doi.org/10.1371/journal.pone.0180825\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eZhao W, Langfelder P, Fuller T, Dong J, Li A, Hovarth S (2010) Weighted gene coexpression network analysis: state of the art. Journal of biopharmaceutical statistics 20(2):281-300 \u003ca href=\"https://doi.org/10.1080/10543400903572753\"\u003ehttps://doi.org/10.1080/10543400903572753\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eZheng L, Ying Y, Wang L, Wang F, Whelan J, Shou H (2010) Identification of a novel iron regulated basic helix-loop-helix protein involved in Fe homeostasis in Oryza sativa. BMC Plant Biol 10:166\u0026nbsp; \u003ca href=\"https://doi.org/10.1186/1471-2229-10-166\"\u003ehttps://doi.org/10.1186/1471-2229-10-166\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eZheng Y, Jiao C, Sun H, Rosli HG, Pombo MA, Zhang P, Banf M, Dai X, Martin GB, Giovannoni J (2016) iTAK: a program for genome-wide prediction and classification of plant transcription factors, transcriptional regulators, and protein kinases. Molecular plant 9(12):1667-1670 \u003ca href=\"https://doi.org/10.1016/j.molp.2016.09.014\"\u003ehttps://doi.org/10.1016/j.molp.2016.09.014\u003c/a\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Zinc deficiency, Weighted Gene Coexpression Analysis, Biofortification, Rice ","lastPublishedDoi":"10.21203/rs.3.rs-442740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-442740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eZinc (Zn) malnutrition has been linked to serious health concerns in humans. Targeting genetic biofortification of rice grain Zn can efficiently improve the global Zn nutritional status. To genetically enhance rice grain Zn content, the genetic and molecular mechanisms of Zn deficiency response need to be elucidated. Here, Differential Gene Expression Analysis (DGEA) and Weighted Gene Coexpression Analysis (WGCNA) were established to identify modules of coexpressed genes, the most preserved genes and molecular pathways regulating Zn deficiency response in rice varieties. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTwelve modules of coexpressed genes were obtained by WGCNA using 1649 differentially expressed genes (DEGs) from our DGEA and 2121 Zn genes from earlier studies. Three modules (TTA-M, TRA-M and CA-M) were judged the most relevant for Zn deficiency based on their richness in the well-recognized Zn deficiency responsive (ZDR) genes and molecular pathways. 96 (17%), 188 (47.6%) and 96 (24%) genes from TTA-M, TRA-M and CA-M modules respectively, were significantly expressed in the DGEA. These coexpressed DEGs (CoDEGs) were considered as the most preserved Zn deficiency responsive genes. Of the well-known ZDR genes, only \u003cem\u003eOsZIP8\u003c/em\u003e, \u003cem\u003eOsZIP10\u003c/em\u003e, \u003cem\u003eOsMT1a\u003c/em\u003e and \u003cem\u003eOsNAAT1\u003c/em\u003e were preserved. Functional annotations for all CoDEGs from the identified ZDR modules showed that glutathione metabolism and biosynthesis of secondary metabolites were the most quickly upregulated molecular pathways. Lastly, a biology-informed gene-gene interaction network analysis (GGIN) indicated that CoDEGs including \u003cem\u003eOsLSI3\u003c/em\u003e, \u003cem\u003eOsWOX11\u003c/em\u003e, \u003cem\u003eOsNRT1.1B\u003c/em\u003e, \u003cem\u003eOsPSK5, OsSWN6 \u003c/em\u003eand \u003cem\u003eOsMID1\u003c/em\u003e strongly interact with the recognized ZDR genes in the ZDR modules. Curiously, these CoDEGs were previously validated for other economic traits in rice.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFindings from this study provide comprehensive insights into the molecular mechanisms of Zn deficiency response in rice and may facilitate gene and pathway prioritization, to enhance Zn use efficiency (ZUE) and Zn biofortification in rice. \u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Identification of potential Zinc deficiency responsive genes and regulatory pathways in rice genotypes by Weighted Gene Co-expression Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-26 16:21:51","doi":"10.21203/rs.3.rs-442740/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50b8220f-0f10-4bd6-8a6d-c4996d8c07a0","owner":[],"postedDate":"April 26th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":3905381,"name":"Plant Molecular Biology and Genetics"},{"id":3905382,"name":"Plant Physiology and Morphology"}],"tags":[],"updatedAt":"2021-05-14T08:54:42+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-26 16:21:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-442740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-442740","identity":"rs-442740","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}