Next Article in Journal
Combination of ACY-241 and JQ1 Synergistically Suppresses Metastasis of HNSCC via Regulation of MMP-2 and MMP-9
Next Article in Special Issue
Varying Atmospheric CO2 Mediates the Cold-Induced CBF-Dependent Signaling Pathway and Freezing Tolerance in Arabidopsis
Previous Article in Journal
Functional and Pharmacological Comparison of Human, Mouse, and Rat Organic Cation Transporter 1 toward Drug and Pesticide Interaction
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 21
Issue 18
10.3390/ijms21186872
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Systematic Analysis of Cold Stress Response and Diurnal Rhythm Using Transcriptome Data in Rice Reveals the Molecular Networks Related to Various Biological Processes

by
Woo-Jong Hong
1,†,
Xu Jiang
1,†,
Hye Ryun Ahn
1,
Juyoung Choi
2,
Seong-Ryong Kim
2,* and
Ki-Hong Jung
1,*
1
Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin 17104, Korea
2
Department of Life Science, Sogang University, Seoul 04107, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2020, 21(18), 6872; https://doi.org/10.3390/ijms21186872
Submission received: 20 August 2020 / Revised: 14 September 2020 / Accepted: 17 September 2020 / Published: 19 September 2020
(This article belongs to the Special Issue Plant Responses and Tolerance to Temperature Changes)
Browse Figures
Versions Notes

Abstract

:
Rice (Oryza sativa L.), a staple crop plant that is a major source of calories for approximately 50% of the human population, exhibits various physiological responses against temperature stress. These responses are known mechanisms of flexible adaptation through crosstalk with the intrinsic circadian clock. However, the molecular regulatory network underlining this crosstalk remains poorly understood. Therefore, we performed systematic transcriptome data analyses to identify the genes involved in both cold stress responses and diurnal rhythmic patterns. Here, we first identified cold-regulated genes and then identified diurnal rhythmic genes from those (119 cold-upregulated and 346 cold-downregulated genes). We defined cold-responsive diurnal rhythmic genes as CD genes. We further analyzed the functional features of these CD genes through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and performed a literature search to identify functionally characterized CD genes. Subsequently, we found that light-harvesting complex proteins involved in photosynthesis strongly associate with the crosstalk. Furthermore, we constructed a protein–protein interaction network encompassing four hub genes and analyzed the roles of the Stay-Green (SGR) gene in regulating crosstalk with sgr mutants. We predict that these findings will provide new insights in understanding the environmental stress response of crop plants against climate change.

1. Introduction

Rice (Oryza sativa L.) is a model crop plant and a staple crop across the globe, particularly Asia. Owing to dramatic global climate change and population growth, it has become necessary to increase the production of staple food sources [1,2]. Because rice is a cold-sensitive crop, cold stress encompassing chilling (0–20 °C) and freezing temperature (below 0 °C) is considered a major factor that leads to drastic reduction in rice productivity [3,4,5]. Therefore, various studies on enhancing cold stress tolerance have been reported, with the identification of important factors affecting cold stress response in rice [6]. However, there is evidence that the cold stress response is associated with various biological processes, including biological clocks, disease resistance, metabolism, hormonal balance, and aging [7,8,9]. This complexity is not only a major obstacle to increasing cold stress tolerance but also to the regional adaptation of resistant varieties.
The circadian clock caused the diurnal rhythmic expression of the genes to cope with the earth’s rotation and daily repeated environmental fluctuations. Organisms have evolved adaptive mechanisms associated with the light/dark environmental alternations and temperature changes, which manifests as a profound change in metabolism, physiology, and behavior occurring between the day and night cycles in most organisms [10,11]. Circadian clock greatly affect plants, particularly regarding synchronizing biological processes and increasing the efficiency of photosynthesis owing to the non-motile feature of these organisms [12,13,14]. It is involved in various physiological regulatory roles, including flowering time, by controlling the expression and photostability of CONSTANS proteins, which induce FLOWERING LOCUS proteins for flowering [15].
With the recent introduction of high-throughput technology such as microarrays, several reports provided insights into the molecular components of the crosstalk between the circadian clock and cold stress response [16]. There are reports on Arabidopsis that the disruption of the circadian clock changes cold-response-associated transcriptomes, and the knockout of the three pseudo-response regulator genes (PRR), PRR5, PRR7, and PRR9, affects the expression of stress response genes [17,18]. Several cold-related and circadian rhythm genes have been reported in rice. A serine-threonine protein kinase without lysine kinase, OsWNK1, exhibited differential expression under various abiotic stressors, including cold, and also exhibited circadian rhythm [19]. Aquaporin in rice roots is responsible for cold stress-induced acclimation and a cold-induced MYB transcription factor, CMYB1, exhibited a circadian rhythm expression pattern in rice leaves [20,21]. However, the crosstalk between cold stress response and the circadian clock in rice remains unclear from a systems biology approach.
To understand the interplay between the circadian clock and cold stress response in rice, we performed a systemic analysis using two large transcriptome datasets related to the diurnal rhythm and abiotic stress such as drought, salt, cold, and submergence. We identified cold-regulated genes and then identified cold-responsive diurnal rhythmic genes (hereafter CD genes; 119 and 346 genes for up/down-regulation, respectively). Next, we analyzed the functional features of these CD genes through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, after a literature search for functionally characterized genes. Furthermore, we constructed a protein–protein interaction (PPI) network and validated it using the stay-green (sgr) mutant for the regulation of the crosstalk between the cold-response and circadian clock. Based on these results, we proposed that a hypothetical molecular network mediates interplay among various biological processes, including the circadian clock, cold stress response, hormone signaling, and senescence. Our systems biology approach regarding the cold stress response and circadian clock identified some details of the molecular network which could be used to improve the productivity and regional adaptation of rice.

2. Results

2.1. Genome-wide Identification of Cold Stress Response Genes Exhibiting Diurnal Rhythm Expression Patterns Using Meta-Expression Datasets

For systematic analyses aimed at revealing the unknown molecular mechanisms of cold stress response and circadian rhythm, we identified candidate genes responsive to cold stress and diurnal rhythm expression patterns (hereafter cold-responsive diurnal rhythmic genes; CD genes). We identified rice genes unique to cold stress response using a meta-expression dataset consisting of four abiotic stress responses such as drought, salt, cold, and submergence. Consequently, we identified 885 and 572 cold-response genes that were up/downregulated at least 2-fold, respectively (Table S1). Next, we then analyzed the diurnal expression patterns of these genes using Agilent 44K array data of rice leaves under natural field conditions, encompassing the vegetative, reproductive, and ripening stages, at 2-h intervals for 2 days in each developmental stage [22]. Finally, we identified 119 and 346 up/downregulated CD genes, respectively, exhibiting diurnal rhythm expression patterns (Figure 1, Table S2). These genes were used for further analyses.

2.2. Literature Analysis to Identify Characterized Gene Functions Associated with CD Genes

To explore the CD genes’ functional roles, we retrieved information of functionally characterized genes from the Overview of functionally characterized Genes in Rice Online database (OGRO, http://qtaro.abr.affrc.go.jp/ogro/table) [23]. Regarding upregulated CD genes, the functions of 14 of these have been reported. Four genes (basic leucine zipper 52 (OsBzip52), rice carbon catabolite repressor 4-associated factor 1B, mitogen-activated protein kinase 5 (OsMAPK5), and dehydration-responsive element-binding protein 1A)) are related to cold stress tolerance [24,25,26,27]. One (calcium-dependent protein kinase 18) is related to blast resistance [28]. One (Stress-responsive NAC1) is related to drought tolerance [29]. Three (OsBZR1, OsDWARF, and TIFY11b) are related to dwarfism [30,31,32]. Two (Ethylene response 2 and rice Dof daily fluctuations 1 (OsRdd1)) are related to flowering time [33,34]. Two (Jumonji C domain-containing protein 6 and Multi-floret spikelet1) are related to floral organ identity [35,36]. Lastly, one (SGR) is related to leaf senescence [37]. Furthermore, we identified 13 downregulated CD genes with known functions from the OGRO database. Two genes (histone deacetylase 701 and OsTFX1) are related to bacterial blight resistance [38,39]. Two (OsLSD1 and stromal-derived factor-2-1) are related to blast resistance [40,41]. One (calcineurin B-like protein-interacting protein kinase 12) is related to drought tolerance [42]. Three (Cytokinin-responsive GATA transcription factor 1, histone deacetylase 702, and ascorbate peroxidase 2) are related to dwarfism [43,44,45]. One (waxy) is related to seed amylose content [46]. One (OsCDT3) is related to aluminum tolerance [47]. One (days to heading on chromosome 8) is related to flowering time [48]. Two (faded green leaf and zebra-necrosis) are related to chlorophyll biosynthesis [49,50]. This result means that cold-upregulated genes are more significant targets for cold stress tolerance than downregulated genes. Moreover, this indicated that the CD genes are not only related to abiotic stress, including low temperature, but also to various other processes such as biotic resistance, senescence, and development (Table 1).

2.3. GO and KEGG Enrichment Analyses of the CD Genes Reveal that Photosynthesis and Light Harvesting Are Closely Related to the Cold Stress Response and Circadian Clock

To further explore the biological significance of genes involved in the crosstalk between circadian clock and the cold stress response, we conducted GO and KEGG enrichment analyses. In total, 21 GO terms from the biological process were enriched for up/downregulated CD genes with the criteria of hypergeometric p-value of < 0.05 and fold-enrichment value of > 2 (Figure 2A, Table S3). The seven GO terms of upregulated CD genes were as follows: response to deep water (192.3-fold enrichment value, GO:0030912); polysaccharide catabolic process (17.0, GO:0000272); protein amino acid dephosphorylation (10.6, GO:0006470); response to stress (8.5, GO:0006950); multicellular organismal development (5.7, GO:0007275); transcription (2.7, GO:0006350); and regulation of transcription (2.5, GO: 0045449). GO enrichment analysis of the downregulated CD genes revealed that 14 biological processes were overrepresented. These processes included: the cytokinin metabolic process (27.5-fold enrichment value, GO:0009690); photosynthesis and light harvesting (26.8, GO:0009765); RNA splicing (11.7, GO:0008380); rRNA processing (10.3, GO:0006364); RNA processing (9.2, GO:0006396); cellular protein metabolic process (8.9, GO:0044267); tetracycline transport (7.4, GO:0015904); ciliary or flagellar motility (7.2, GO:0001539); response to antibiotics (6.3, GO:0046677); protein folding (6.0, GO:0006457); response to stress (5.9, GO:0006950); cellular amino acid biosynthetic process (5.6, GO:0008652); metal ion transport (4.7, GO:0030001); and type 1 hypersensitivity (2.7, GO:0016068).
In addition to GO enrichment, we performed KEGG enrichment analysis to identify the metabolic pathways involved in cold response and the circadian clock mechanism. Interestingly, only one pathway, photosynthesis-antenna proteins, was enriched, which was consistent with the enriched GO term in downregulated CD genes (Figure 2B). Using the KEGG mapper software, we identified three light-harvesting proteins involved in this pathway (Figure 2C) [51]. These results were consistent regarding photosynthesis and the light harvesting process mainly associated with the cold stress response and circadian clock, which was also confirmed with the MapMan software (3.6.0 RC1; Figure S1A, Table S4). Along with the photosynthesis process, GO and MapMan results also implied a variety of potential biological processes involved in this crosstalk (Figure S1B).

2.4. Construction of Protein-protein Interactions of CD Genes Reveals the Molecular Network Interplay with Various Biological Processes

To further explore the molecular mechanisms regulating crosstalk between the circadian clock and cold stress response, we constructed a hypothetical protein–protein interaction (PPI) network of up/downregulated CD genes using a rice interaction viewer (http://bar.utoronto.ca/interactions/cgi-bin/rice_interactions_viewer.cgi) [52]. This viewer generated a network of CD genes comprising 764 nodes and 1340 edges (Figure S2). However, owing to its complexity, we reconstructed the network to only show the interactions between genes for which some information was available. The reconstructed network comprised 208 nodes and 248 edges (Figure 3, Table S5), including both up- and downregulated CD genes.
Among the upregulated CD genes, seven were present in the network nodes, two of which were functionally characterized. Brassinozole-resistant1 (OsBZR1) is a key transcription regulator in rice with activity regulated by 14-3-3 interactions. The other gene is a mitogen-activated protein kinase in rice (OsMAPK5) that was reported as an ABA-induced regulator that can modulate disease resistance and abiotic stress tolerance [26,30]. Among the downregulated CD genes, two known genes were identified in the network nodes: the waxy gene in rice (Wx), which is associated with the amylose content in rice endosperms, and the rice histone deacetylase gene (OsHDA702), which is associated with plant growth, particularly in the root [44,46]. Among the interactors, 42 known genes were identified, five of which were related to flowering time, including phytochromes A, B, and C (OsPhyA, OsPhyB, OsPhyC); four were related to biotic stress resistance, including calcium-dependent protein kinase 10 (CPK10), somatic embryogenesis receptor-like kinase 1 (SERK1), WRKY62, and disease resistance-responsive gene 8 (DR8); four were related to dwarfism, including decreased DNA methylation (DDM1a), dwarf 1 (D1), pyruvate dehydrogenase kinase 1 (PDK1), and HDA704; and three were related to chloroplast development, including virescent3 (V3), thioredoxin m (Trxm), and spo0B-associated GTP-binding protein (ObgC). Flowering time control gene in rice (rFCA) and pseudo-response regulator (OsPRR37) in rice were also identified. Other identified genes are listed in Table 2.

2.5. Validation of the Cold Stress Response and Circadian Clock Network with a Case Study Using the Stay-green (sgr) Mutant

Among the functionally characterized genes exhibiting crosstalk between cold stress response and circadian clock, sgr mutant maintains greenness during leaf senescence [37]. Under cold stress for 4 days and recovery for 4 days, we found that the sgr mutant exhibited more cold stress tolerant phenotype than the control plant, Dongjin rice (DJ) (Figure 4A). Moreover, we confirmed that this tolerant phenotype is not originated from the developmental effect using reproducible experiment with Hwacheong-wx rice in the same genetic background with sgr mutant (Figure S3). Upregulation of the SGR gene under cold treatment and its diurnal expression patterns were confirmed by qRT-PCR (Figure 4B). The expression patterns of the four hub genes in our PPI network, i.e., OsMAPK5 (LOC_Os03g17700), OsSnRK1a (LOC_Os05g45420), OsPhyB (LOC_Os03g19590), and OsHDA702 (LOC_Os06g38470) (Figure 4C) were then examined. All four genes were upregulated in the sgr mutants compared with DJ after cold treatment for 4 days. Two genes were also significantly changed. Consistent with the results of our PPI network analysis, sgr mutants also exhibited altered expression levels of four hub genes in response to cold stress. These results suggest that several biological processes, including cold response and senescence, are linked through the hub genes predicted by our PPI network model.

3. Discussion

Cold threatens the normal growth and yield requirements of various major crops [92]. The circadian clock rhythmically controls the behavior and physiological activities of plants for improved environmental adaptation [16,93]. The interplays between the circadian clock, light-quality, and cold-response have been studied intensively in the model plant, Arabidopsis thaliana [13,94]. Circadian clock components such as CIRCADIAN CLOCK ASSOCIATED 1, TIMING OF CAB EXPRESSION1, LATE ELONGATED HYPOCOTYL and Pseudo-Response Regulators (PRR) genes mutually interact with temperature signals [95]. Moreover, the photoreceptors: ZEITLUPE, phytochromes, and cryptochromes which are regulated by GIGANTEA protein, integrate light-quality information with circadian clock [96,97,98,99]. These clock components have a role in regulating the expression of C-repeat binding factors that bind to the promoter of the cold-regulated genes [100]. In rice, the circadian clock genes have been reported using ortholog search, but it has been mainly studied in depth related to the agronomic traits especially, flowering time [101]. The research of the trait along with the geographical distribution of the rice suggested the relationship between the circadian clock and cold stress [67], but, still, there have been limited reports to shape the molecular network as in the arabidopsis.
Therefore, it is of great developmental significance to explore the possible correlations between cold stress response and circadian clock through systematic analysis in rice.
We first identified 465 CD genes, including 119 upregulated and 346 downregulated genes. These were identified through global transcriptome analyses using public resources, which provided a more extensive scope of utilities that could possibly enhance cold tolerance in rice. These genes will serve as the basis for a series of studies to follow.
To understand the meaningful biological processes underlying the crosstalk between cold stress response and circadian clock in rice, we performed GO and KEGG enrichment analyses. Based on the visualized results, we found that photosynthesis and light harvesting are key biological processes in rice associated with cold stress response. Previous studies also suggested the importance of photosynthesis in cold stress response and circadian clock. For instance, several genes of Zea mays cold-induced genes associated with photosynthesis during long-term cold treatment in maize seedlings [102]. Transgenic rice plants overexpressing choline oxidase A exhibited expanded Fv/Fm when exposed to cold stress [103]. Besides, some researchers indicated that photosynthesis is coupled with diurnal rhythms through light harvesting and CO2 fixation rates by ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) [104]. However, research on photosynthesis and the crosstalk effects of cold stress and circadian clock remains limited. We also found that the GO term ‘polysaccharide catabolic process’ was highly enriched in the upregulated CD genes. This could be explained by the energy supply required for plants under cold stress.
Our network analysis suggests a hypothetical molecular mechanism underlying the diverse phenomenon related to crosstalk between cold stress response and circadian clock regulation. In this network, OsPhyA, OsPhyB, OsPhyC, and OsPRR37 are key components associated with the regulation of circadian clock. These components interact with both OsMAPK5, a component of Ca2+ signaling and modulator of disease resistance that is upregulated in response to cold and exhibits diurnal rhythmic expression, and OsHDA702, a component of DNA remodeling that is downregulated in response to cold and exhibits rhythmic expression [44,105]. OsSnRK1a has roles in regulating various metabolism-related processes in plants and is also related to ABA signaling [106,107]. Moreover, D1/RGA1 is related to these four hub genes. Studies of daikoku 1 (d1) mutant using microarray analysis revealed that D1/RGA1 has a role in regulating multiple abiotic stresses, including drought, cold, heat, and salinity [108]. Angel et al. (2016) also revealed decreased drought sensitivity in the erect leaves of d1 mutant owing to the increased photo-avoidance and more effective light harvesting [109,110]. More interestingly, sgr mutant with cold tolerant phenotype is exhibited as erect leaves which is similar to d1 mutant, and the expression level of four hub genes are all upregulated in sgr mutant relative to wild type. Thus, the identification of these components implies that cold-induced SGR and four hub genes (OsMAPK5, OsSnRK1a, OsHDA702, and OsPhyB) might coregulate the physiological response to cold in a circadian clock-dependent manner.
Overall, we hypothesize that the interconnection between different physiological responses, such as cold, and circadian clock is achieved by manipulating the expression of the hub genes (OsMAPK5, OsSnRK1a, OsHDA702, and OsPhyB). Cumulatively, we constructed a framework diagram of multiple elements that are interconnected through the hub genes (Figure 5). More experimental evidence is required to validate this hypothesis; however, we anticipate that our data will serve as a bridge for integrating initial studies on cold stress and circadian clock.

4. Materials and Methods

4.1. Collection of Microarray Data and Meta-expression Analysis

To understand the crosstalk between cold stress and circadian clock regulation, we used two different meta-expression datasets: Affymetrix array gene expression dataset for abiotic stresses (E-MEXP-2401, GSE16108, GSE18930, GSE21651, GSE24048, GSE25176, GSE26280, GSE33204, GSE37940, GSE38023, and GSE6901) and a diurnal dataset comprising Agilent 44K array gene expression data from 202 leaf samples collected from nine developmental stages (GSE36040). As previously described [111,112], public transcriptome data were downloaded from the GEO database [113]. After collecting the expression datasets, data were normalized with the Affy and Limma packages using the R programming language [114,115]. After normalization, intensity values were converted to log2 scale, and the log2 fold-change values in response to cold stress (compared with their values in an untreated control) were calculated. Cold responsive genes were defined as those with a ≥ 1.5 or ≤−1.5 log2 fold change, and a p-value of ≤ 0.05. For the selected genes, log2 fold-change data were visualized with those in other abiotic stress treatments using the MeV software [116]. KMC clustering in MeV was used to identify cold-induced or cold-repressed diurnal rhythmic genes.

4.2. Literature Search for Functionally Characterized Genes

The OGRO database was searched to determine the functionally characterized cold-induced diurnal rhythmic genes [23]. Detailed information is presented in Table 1.

4.3. GO and KEGG Enrichment Analyses

The rice oligonucleotide array database was used to retrieve GO information for our candidate genes [117]. Fold-enrichment values were obtained by dividing the query number by the query expected value. Significant GO terms were defined as those with a fold-enrichment value of >2 and a hypergeometric p-value of <0.05. The ClusterProfiler software package was then used to perform KEGG enrichment analysis [118]. To use the enrichKEGG function in this package, we used input data containing clustering information and the ID of the rice annotation project database. Additionally, we selected an adjusted cutoff p-value of <0.05, thereby selecting the organism code data and filtering the results. To visualize these results, we used the dotplot function of R studio (version: 1.2.5042) and modified the graph using the ggplot2 software package (version: 3.3.0). Illustrator software was used to refine the presentation.

4.4. MapMan Analysis

We used the MapMan toolkit (3.6.0 RC1) to functionally classify our candidate genes [119]. We assigned different colors for diurnal rhythmic genes that were up/downregulated (up, red; down, green) in response to cold stress and visualized all candidate genes simultaneously. In our analysis, we used metabolism and regulation overviews. Detailed information regarding the MapMan analysis is presented in Table S4.

4.5. Protein–Protein Interaction Network Construction

A hypothetical protein–protein interaction network was generated to investigate crosstalk between the cold stress response and circadian rhythm. A network file of the queried genes (up/down-CD genes) was obtained from the rice interaction viewer (http://bar.utoronto.ca/interactions/cgi-bin/rice_interactions_viewer.cgi). Next, a base file containing interaction information was constructed. A data file containing additional information on network nodes was also generated. These data were visualized using the Cytoscape software (3.3.0) [120,121]. We used a p-value threshold of <0.05 and enabled the “fusion option”, which merges nodes with similar information to simplify our network.

4.6. Plant Material and Stress Treatment

Japonica rice (Oryza sativa L.) (cv. Dongjin and Hwacheong-wx) seeds and sgr defective mutants (cv. Hwacheong-wx) were independently germinated on Murashige and Skoog (MS) medium for one week in an incubator at 28 °C/22 °C (day/night). To simulate cold stress, we exposed 7-day-old seedlings to 4 ± 1°C for 4 days in a fridge and allowed them to recover for 4 days. For RNA extraction, we collected the leaves of Dongjin and sgr at two time points: 7-day-old seedlings before cold treatment and after 4 days of cold treatment. For confirming diurnal rhythm, we germinated and incubated seeds of japonica rice, cv. Dongjin, on MS medium for a week and transplant the seedlings to a growth chamber (28 °C/25 °C day/night, 14/10 hrs light/dark, and 80% humidity). We then sampled the leaves every two hours in the growth chamber for a day.

4.7. RNA Extraction and Quantitative RT-PCR (qRT-PCR) Analysis

Leaf samples from Dongjin and sgr before and after cold treatment (four days) were immediately frozen in liquid nitrogen. After total RNA was isolated using the RNAiso (Takara Bio, Shiga, Japan), first-strand cDNA was synthesized using the SuPrimeScript RT Premix (with oligo (dT), 2×) (GeNet Bio, Daegu, Korea). The synthesized cDNAs were amplified using SYBR Green I (GeNet Bio, Korea), and qRT-PCR was performed on the Rotor Gene Q instrument system (Qiagen, Hidden, Germany). To normalize the amplified transcripts, we used a primer pair for rice ubiquitin 5 (OsUbi5/Os01g22490) [122,123]. All the primers for this analysis are summarized in Table S6.

Supplementary Materials

Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/18/6872/s1.

Author Contributions

K.-H.J. and S.-R.K. designed this work; W.-J.H., X.J., J.C., and H.R.A. performed experiments and analyzed the data; W.-J.H., and X.J. generated figures and tables; W.-J.H., X.J., S.-R.K. and K.-H.J. wrote manuscript. All authors have read and agreed to publish the manuscript.

Funding

This work was supported by grants from the Next-Generation BioGreen 21 Program (PJ01325901 and PJ01366401 to K.-H.J., and PJ01329501 to S.-R.K.), the Collaborative Genome Program of the Korea Institute of Marine Science and Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (MOF) (No. 2018043004 to K.-H.J.) and the Global Ph.D. Fellowship Program supported by the National Research Foundation (NRF) (NRF-2018H1A2A1060336 to W.-J.H.).

Acknowledgments

We thank Nam-Chon Paek in Seoul National University for kindly providing the sgr mutant.

Conflicts of Interest

The authors declare no conflict of interests.

References

  1. Ray, D.K.; Ramankutty, N.; Mueller, N.D.; West, P.C.; Foley, J.A. Recent Patterns of Crop Yield Growth and Stagnation. Nature Commun. 2012, 3, 1–7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Godfray, H.C.J.; Beddington, J.R.; Crute, I.R.; Haddad, L.; Lawrence, D.; Muir, J.F.; Pretty, J.; Robinson, S.; Thomas, S.M.; Toulmin, C. Food Security: The Challenge of Feeding 9 Billion People. Science 2010, 327, 812–818. [Google Scholar] [CrossRef] [Green Version]
  3. Chinnusamy, V.; Zhu, J.; Zhu, J. Cold Stress Regulation of Gene Expression in Plants. Trends Plant Sci. 2007, 12, 444–451. [Google Scholar] [CrossRef] [PubMed]
  4. Cruz, R.P.d.; Sperotto, R.A.; Cargnelutti, D.; Adamski, J.M.; de FreitasTerra, T.; Fett, J.P. Avoiding Damage and Achieving Cold Tolerance in Rice Plants. Food Energy Secur. 2013, 2, 96–119. [Google Scholar] [CrossRef]
  5. Kuroki, M.; Saito, K.; Matsuba, S.; Yokogami, N.; Shimizu, H.; Ando, I.; Sato, Y. A Quantitative Trait Locus for Cold Tolerance at the Booting Stage on Rice Chromosome 8. Theor. Appl. Genet. 2007, 115, 593–600. [Google Scholar] [CrossRef]
  6. Zhang, Q.; Chen, Q.; Wang, S.; Hong, Y.; Wang, Z. Rice and Cold Stress: Methods for its Evaluation and Summary of Cold Tolerance-Related Quantitative Trait Loci. Rice 2014, 7, 24. [Google Scholar] [CrossRef] [Green Version]
  7. Kinmonth-Schultz, H.A.; Golembeski, G.S.; Imaizumi, T. Circadian Clock-Regulated Physiological Outputs: Dynamic Responses in Nature. Semin. Cell Dev. Biol. 2013, 24, 407–413. [Google Scholar] [CrossRef] [Green Version]
  8. Fujita, M.; Fujita, Y.; Noutoshi, Y.; Takahashi, F.; Narusaka, Y.; Yamaguchi-Shinozaki, K.; Shinozaki, K. Crosstalk between Abiotic and Biotic Stress Responses: A Current View from the Points of Convergence in the Stress Signaling Networks. Curr. Opin. Plant Biol. 2006, 9, 436–442. [Google Scholar] [CrossRef]
  9. Atkinson, N.J.; Urwin, P.E. The Interaction of Plant Biotic and Abiotic Stresses: From Genes to the Field. J. Exp. Bot. 2012, 63, 3523–3543. [Google Scholar] [CrossRef] [Green Version]
  10. McClung, C.R. Plant Circadian Rhythms. Plant Cell 2006, 18, 792–803. [Google Scholar] [CrossRef] [Green Version]
  11. Bell-Pedersen, D.; Cassone, V.M.; Earnest, D.J.; Golden, S.S.; Hardin, P.E.; Thomas, T.L.; Zoran, M.J. Circadian Rhythms from Multiple Oscillators: Lessons from Diverse Organisms. Nat. Rev. Genet. 2005, 6, 544–556. [Google Scholar] [CrossRef] [PubMed]
  12. Mas, P.; Yanovsky, M.J. Time for Circadian Rhythms: Plants Get Synchronized. Curr. Opin. Plant Biol. 2009, 12, 574–579. [Google Scholar] [CrossRef] [PubMed]
  13. Harmer, S.L. The Circadian System in Higher Plants. Annu. Rev. Plant Biol. 2009, 60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Dodd, A.N.; Salathia, N.; Hall, A.; Kévei, E.; Tóth, R.; Nagy, F.; Hibberd, J.M.; Millar, A.J.; Webb, A.A. Plant Circadian Clocks Increase Photosynthesis, Growth, Survival, and Competitive Advantage. Science 2005, 309, 630–633. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Turck, F.; Fornara, F.; Coupland, G. Regulation and Identity of Florigen: FLOWERING LOCUS T Moves Center Stage. Annu.Rev. Plant Biol. 2008, 59, 573–594. [Google Scholar] [CrossRef] [Green Version]
  16. Grundy, J.; Stoker, C.; Carré, I.A. Circadian Regulation of Abiotic Stress Tolerance in Plants. Front. Plant Sci. 2015, 6, 648. [Google Scholar] [CrossRef]
  17. Bieniawska, Z.; Espinoza, C.; Schlereth, A.; Sulpice, R.; Hincha, D.K.; Hannah, M.A. Disruption of the Arabidopsis Circadian Clock is Responsible for Extensive Variation in the Cold-Responsive Transcriptome. Plant Physiol. 2008, 147, 263–279. [Google Scholar] [CrossRef] [Green Version]
  18. Nakamichi, N.; Kusano, M.; Fukushima, A.; Kita, M.; Ito, S.; Yamashino, T.; Saito, K.; Sakakibara, H.; Mizuno, T. Transcript Profiling of an Arabidopsis PSEUDO RESPONSE REGULATOR Arrhythmic Triple Mutant Reveals a Role for the Circadian Clock in Cold Stress Response. Plant Cell Physiol. 2009, 50, 447–462. [Google Scholar] [CrossRef] [Green Version]
  19. Kumar, K.; Rao, K.P.; Biswas, D.K.; Sinha, A.K. Rice WNK1 is Regulated by Abiotic Stress and Involved in Internal Circadian Rhythm. Plant Signal. Behav. 2011, 6, 316–320. [Google Scholar] [CrossRef] [Green Version]
  20. Ahamed, A.; Murai-Hatano, M.; Ishikawa-Sakurai, J.; Hayashi, H.; Kawamura, Y.; Uemura, M. Cold Stress-Induced Acclimation in Rice is Mediated by Root-Specific Aquaporins. Plant Cell Physiol. 2012, 53, 1445–1456. [Google Scholar] [CrossRef]
  21. Duan, M.; Huang, P.; Yuan, X.; Chen, H.; Huang, J.; Zhang, H. CMYB1 Encoding a MYB Transcriptional Activator is Involved in Abiotic Stress and Circadian Rhythm in Rice. Sci. World J. 2014, 2014, 178038. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Sato, Y.; Takehisa, H.; Kamatsuki, K.; Minami, H.; Namiki, N.; Ikawa, H.; Ohyanagi, H.; Sugimoto, K.; Antonio, B.A.; Nagamura, Y. RiceXPro Version 3.0: Expanding the Informatics Resource for Rice Transcriptome. Nucleic Acids Res. 2013, 41, D1206–D1213. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Yamamoto, E.; Yonemaru, J.; Yamamoto, T.; Yano, M. OGRO: The Overview of Functionally Characterized Genes in Rice Online Database. Rice 2012, 5, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Liu, C.; Wu, Y.; Wang, X. bZIP Transcription Factor OsbZIP52/RISBZ5: A Potential Negative Regulator of Cold and Drought Stress Response in Rice. Planta 2012, 235, 1157–1169. [Google Scholar] [CrossRef]
  25. Chou, W.; Huang, L.; Fang, J.; Yeh, C.; Hong, C.; Wu, S.; Lu, C. Divergence of the Expression and Subcellular Localization of CCR4-Associated Factor 1 (CAF1) Deadenylase Proteins in Oryza Sativa. Plant Mol. Biol. 2014, 85, 443–458. [Google Scholar] [CrossRef]
  26. Xiong, L.; Yang, Y. Disease Resistance and Abiotic Stress Tolerance in Rice are Inversely Modulated by an Abscisic Acid–inducible Mitogen-Activated Protein Kinase. Plant Cell 2003, 15, 745–759. [Google Scholar] [CrossRef] [Green Version]
  27. Ito, Y.; Katsura, K.; Maruyama, K.; Taji, T.; Kobayashi, M.; Seki, M.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Functional Analysis of Rice DREB1/CBF-Type Transcription Factors Involved in Cold-Responsive Gene Expression in Transgenic Rice. Plant Cell Physiol. 2006, 47, 141–153. [Google Scholar] [CrossRef] [Green Version]
  28. Xie, K.; Chen, J.; Wang, Q.; Yang, Y. Direct Phosphorylation and Activation of a Mitogen-Activated Protein Kinase by a Calcium-Dependent Protein Kinase in Rice. Plant Cell 2014, 26, 3077–3089. [Google Scholar] [CrossRef] [Green Version]
  29. Hu, H.; Dai, M.; Yao, J.; Xiao, B.; Li, X.; Zhang, Q.; Xiong, L. Overexpressing a NAM, ATAF, and CUC (NAC) Transcription Factor Enhances Drought Resistance and Salt Tolerance in Rice. Proc. Natl. Acad. Sci. USA. 2006, 103, 12987–12992. [Google Scholar] [CrossRef] [Green Version]
  30. Bai, M.; Zhang, L.; Gampala, S.S.; Zhu, S.; Song, W.; Chong, K.; Wang, Z. Functions of OsBZR1 and 14-3-3 Proteins in Brassinosteroid Signaling in Rice. Proc. Natl. Acad. Sci. USA. 2007, 104, 13839–13844. [Google Scholar] [CrossRef] [Green Version]
  31. Hong, Z.; Ueguchi-Tanaka, M.; Shimizu-Sato, S.; Inukai, Y.; Fujioka, S.; Shimada, Y.; Takatsuto, S.; Agetsuma, M.; Yoshida, S.; Watanabe, Y. Loss-of-function of a Rice Brassinosteroid Biosynthetic Enzyme, C-6 Oxidase, Prevents the Organized Arrangement and Polar Elongation of Cells in the Leaves and Stem. Plant J. 2002, 32, 495–508. [Google Scholar] [CrossRef] [PubMed]
  32. Hakata, M.; Kuroda, M.; Ohsumi, A.; Hirose, T.; Nakamura, H.; Muramatsu, M.; Ichikawa, H.; Yamakawa, H. Overexpression of a Rice TIFY Gene Increases Grain Size through Enhanced Accumulation of Carbohydrates in the Stem. Biosci. Biotechnol. Biochem. 2012, 76, 2129–2134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Wuriyanghan, H.; Zhang, B.; Cao, W.; Ma, B.; Lei, G.; Liu, Y.; Wei, W.; Wu, H.; Chen, L.; Chen, H. The Ethylene Receptor ETR2 Delays Floral Transition and Affects Starch Accumulation in Rice. Plant Cell 2009, 21, 1473–1494. [Google Scholar] [CrossRef] [Green Version]
  34. Iwamoto, M.; Higo, K.; Takano, M. Circadian Clock-and Phytochrome-regulated Dof-like Gene, Rdd1, is Associated with Grain Size in Rice. Plant Cell Environ. 2009, 32, 592–603. [Google Scholar] [CrossRef] [PubMed]
  35. Sun, Q.; Zhou, D. Rice jmjC Domain-Containing Gene JMJ706 Encodes H3K9 Demethylase Required for Floral Organ Development. Proc. Natl. Acad. Sci. USA 2008, 105, 13679–13684. [Google Scholar] [CrossRef] [Green Version]
  36. Ren, D.; Li, Y.; Zhao, F.; Sang, X.; Shi, J.; Wang, N.; Guo, S.; Ling, Y.; Zhang, C.; Yang, Z. MULTI-FLORET SPIKELET1, which Encodes an AP2/ERF Protein, Determines Spikelet Meristem Fate and Sterile Lemma Identity in Rice. Plant Physiol. 2013, 162, 872–884. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Park, S.; Yu, J.; Park, J.; Li, J.; Yoo, S.; Lee, N.; Lee, S.; Jeong, S.; Seo, H.S.; Koh, H. The Senescence-Induced Staygreen Protein Regulates Chlorophyll Degradation. Plant Cell 2007, 19, 1649–1664. [Google Scholar] [CrossRef] [Green Version]
  38. Ding, B.; del Rosario Bellizzi, M.; Ning, Y.; Meyers, B.C.; Wang, G. HDT701, a Histone H4 Deacetylase, Negatively Regulates Plant Innate Immunity by Modulating Histone H4 Acetylation of Defense-Related Genes in Rice. Plant Cell 2012, 24, 3783–3794. [Google Scholar] [CrossRef] [Green Version]
  39. Sugio, A.; Yang, B.; Zhu, T.; White, F.F. Two Type III Effector Genes of Xanthomonas Oryzae Pv. Oryzae Control the Induction of the Host Genes OsTFIIAγ1 and OsTFX1 during Bacterial Blight of Rice. Proc. Natl. Acad. Sci. USA 2007, 104, 10720–10725. [Google Scholar] [CrossRef] [Green Version]
  40. Wang, L.; Pei, Z.; Tian, Y.; He, C. OsLSD1, a Rice Zinc Finger Protein, Regulates Programmed Cell Death and Callus Differentiation. Mol. Plant Microbe Interact. 2005, 18, 375–384. [Google Scholar] [CrossRef] [Green Version]
  41. Park, C.; Sharma, R.; Lefebvre, B.; Canlas, P.E.; Ronald, P.C. The Endoplasmic Reticulum-Quality Control Component SDF2 is Essential for XA21-Mediated Immunity in Rice. Plant Sci. 2013, 210, 53–60. [Google Scholar] [CrossRef] [PubMed]
  42. Xiang, Y.; Huang, Y.; Xiong, L. Characterization of Stress-Responsive CIPK Genes in Rice for Stress Tolerance Improvement. Plant Physiol. 2007, 144, 1416–1428. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Hudson, D.; Guevara, D.R.; Hand, A.J.; Xu, Z.; Hao, L.; Chen, X.; Zhu, T.; Bi, Y.; Rothstein, S.J. Rice Cytokinin GATA Transcription Factor1 Regulates Chloroplast Development and Plant Architecture. Plant Physiol. 2013, 162, 132–144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Hu, Y.; Qin, F.; Huang, L.; Sun, Q.; Li, C.; Zhao, Y.; Zhou, D. Rice Histone Deacetylase Genes Display Specific Expression Patterns and Developmental Functions. Biochem. Biophys. Res. Commun. 2009, 388, 266–271. [Google Scholar]
  45. Rosa, S.B.; Caverzan, A.; Teixeira, F.K.; Lazzarotto, F.; Silveira, J.A.; Ferreira-Silva, S.L.; Abreu-Neto, J.; Margis, R.; Margis-Pinheiro, M. Cytosolic APx Knockdown Indicates an Ambiguous Redox Responses in Rice. Phytochemistry 2010, 71, 548–558. [Google Scholar] [CrossRef]
  46. Wang, Z.; Zheng, F.; Shen, G.; Gao, J.; Snustad, D.P.; Li, M.; Zhang, J.; Hong, M. The Amylose Content in Rice Endosperm is Related to the Post-transcriptional Regulation of the Waxy Gene. Plant J. 1995, 7, 613–622. [Google Scholar] [CrossRef]
  47. Xia, J.; Yamaji, N.; Ma, J.F. A Plasma Membrane-localized Small Peptide is Involved in Rice Aluminum Tolerance. Plant J. 2013, 76, 345–355. [Google Scholar] [CrossRef]
  48. Wei, X.; Xu, J.; Guo, H.; Jiang, L.; Chen, S.; Yu, C.; Zhou, Z.; Hu, P.; Zhai, H.; Wan, J. DTH8 Suppresses Flowering in Rice, Influencing Plant Height and Yield Potential Simultaneously. Plant Physiol. 2010, 153, 1747–1758. [Google Scholar] [CrossRef] [Green Version]
  49. Sakuraba, Y.; Rahman, M.L.; Cho, S.; Kim, Y.; Koh, H.; Yoo, S.; Paek, N. The Rice Faded Green Leaf Locus Encodes Protochlorophyllide Oxidoreductase B and is Essential for Chlorophyll Synthesis Under High Light Conditions. Plant J. 2013, 74, 122–133. [Google Scholar] [CrossRef]
  50. Li, J.; Pandeya, D.; Nath, K.; Zulfugarov, I.S.; Yoo, S.; Zhang, H.; Yoo, J.; Cho, S.; Koh, H.; Kim, D. ZEBRA-NECROSIS, a Thylakoid-bound Protein, is Critical for the Photoprotection of Developing Chloroplasts during Early Leaf Development. Plant J. 2010, 62, 713–725. [Google Scholar] [CrossRef]
  51. Kanehisa, M.; Sato, Y. KEGG Mapper for Inferring Cellular Functions from Protein Sequences. Protein Sci. 2020, 29, 28–35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Ho, C.; Wu, Y.; Shen, H.; Provart, N.J.; Geisler, M. A Predicted Protein Interactome for Rice. Rice 2012, 5, 15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Hirose, F.; Inagaki, N.; Hanada, A.; Yamaguchi, S.; Kamiya, Y.; Miyao, A.; Hirochika, H.; Takano, M. Cryptochrome and Phytochrome Cooperatively but Independently Reduce Active Gibberellin Content in Rice Seedlings Under Light Irradiation. Plant Cell Physiol. 2012, 53, 1570–1582. [Google Scholar] [CrossRef] [PubMed]
  54. Huang, C.F.; Yamaji, N.; Mitani, N.; Yano, M.; Nagamura, Y.; Ma, J.F. A Bacterial-Type ABC Transporter is Involved in Aluminum Tolerance in Rice. Plant Cell 2009, 21, 655–667. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Yang, W.; Ren, S.; Zhang, X.; Gao, M.; Ye, S.; Qi, Y.; Zheng, Y.; Wang, J.; Zeng, L.; Li, Q. BENT UPPERMOST INTERNODE1 Encodes the Class II Formin FH5 Crucial for Actin Organization and Rice Development. Plant Cell 2011, 23, 661–680. [Google Scholar] [CrossRef] [Green Version]
  56. Sunohara, H.; Kawai, T.; Shimizu-Sato, S.; Sato, Y.; Sato, K.; Kitano, H. A Dominant Mutation of TWISTED DWARF 1 Encoding an A-Tubulin Protein Causes Severe Dwarfism and Right Helical Growth in Rice. Genes Genet. Syst. 2009, 84, 209–218. [Google Scholar] [CrossRef] [Green Version]
  57. Yoo, S.; Cho, S.; Sugimoto, H.; Li, J.; Kusumi, K.; Koh, H.; Iba, K.; Paek, N. Rice Virescent3 and Stripe1 Encoding the Large and Small Subunits of Ribonucleotide Reductase are Required for Chloroplast Biogenesis during Early Leaf Development. Plant Physiol. 2009, 150, 388–401. [Google Scholar] [CrossRef] [Green Version]
  58. Chi, Y.H.; Moon, J.C.; Park, J.H.; Kim, H.; Zulfugarov, I.S.; Fanata, W.I.; Jang, H.H.; Lee, J.R.; Lee, Y.M.; Kim, S.T. Abnormal Chloroplast Development and Growth Inhibition in Rice Thioredoxin M Knock-Down Plants. Plant Physiol. 2008, 148, 808–817. [Google Scholar] [CrossRef] [Green Version]
  59. Bang, W.Y.; Chen, J.; Jeong, I.S.; Kim, S.W.; Kim, C.W.; Jung, H.S.; Lee, K.H.; Kweon, H.; Yoko, I.; Shiina, T. Functional Characterization of ObgC in Ribosome Biogenesis during Chloroplast Development. Plant J. 2012, 71, 122–134. [Google Scholar] [CrossRef]
  60. Park, J.; Jin, P.; Yoon, J.; Yang, J.; Jeong, H.J.; Ranathunge, K.; Schreiber, L.; Franke, R.; Lee, I.; An, G. Mutation in Wilted Dwarf and Lethal 1 (WDL1) Causes Abnormal Cuticle Formation and Rapid Water Loss in Rice. Plant Mol. Biol. 2010, 74, 91–103. [Google Scholar] [CrossRef]
  61. Higo, H.; Tahir, M.; Takashima, K.; Miura, A.; Watanabe, K.; Tagiri, A.; Ugaki, M.; Ishikawa, R.; Eiguchi, M.; Kurata, N. DDM1 (Decrease in DNA Methylation) Genes in Rice (Oryza Sativa). Mol. Genet. Genomics 2012, 287, 785–792. [Google Scholar] [CrossRef]
  62. Ashikari, M.; Wu, J.; Yano, M.; Sasaki, T.; Yoshimura, A. Rice Gibberellin-Insensitive Dwarf Mutant Gene Dwarf 1 Encodes the A-Subunit of GTP-Binding Protein. Proc. Natl. Acad. Sci. USA 1999, 96, 10284–10289. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Jan, A.; Nakamura, H.; Handa, H.; Ichikawa, H.; Matsumoto, H.; Komatsu, S. Gibberellin Regulates Mitochondrial Pyruvate Dehydrogenase Activity in Rice. Plant Cell Physiol. 2006, 47, 244–253. [Google Scholar] [CrossRef] [PubMed]
  64. Yang, W.; Kong, Z.; Omo-Ikerodah, E.; Xu, W.; Li, Q.; Xue, Y. Calcineurin B-Like Interacting Protein Kinase OsCIPK23 Functions in Pollination and Drought Stress Responses in Rice (Oryza sativa L.). J. Genet. Genomics 2008, 35, 531–543, S1–S2. [Google Scholar] [CrossRef]
  65. Cui, R.; Han, J.; Zhao, S.; Su, K.; Wu, F.; Du, X.; Xu, Q.; Chong, K.; Theißen, G.; Meng, Z. Functional Conservation and Diversification of Class E Floral Homeotic Genes in Rice (Oryza sativa). Plant J. 2010, 61, 767–781. [Google Scholar] [CrossRef] [PubMed]
  66. Hong, F.; Attia, K.; Wei, C.; Li, K.; He, G.; Su, W.; Zhang, Q.; Qian, X.; Yang, J. Overexpression of the R FCA RNA Recognition Motif Affects Morphologies Modifications in Rice (Oryza sativa L.). Biosci. Rep. 2007, 27, 225–234. [Google Scholar] [CrossRef] [PubMed]
  67. Koo, B.; Yoo, S.; Park, J.; Kwon, C.; Lee, B.; An, G.; Zhang, Z.; Li, J.; Li, Z.; Paek, N. Natural Variation in OsPRR37 Regulates Heading Date and Contributes to Rice Cultivation at a Wide Range of Latitudes. Mol. Plant 2013, 6, 1877–1888. [Google Scholar] [CrossRef] [Green Version]
  68. Takano, M.; Inagaki, N.; Xie, X.; Yuzurihara, N.; Hihara, F.; Ishizuka, T.; Yano, M.; Nishimura, M.; Miyao, A.; Hirochika, H. Distinct and Cooperative Functions of Phytochromes A, B, and C in the Control of Deetiolation and Flowering in Rice. Plant Cell 2005, 17, 3311–3325. [Google Scholar] [CrossRef] [Green Version]
  69. Nallamilli, B.R.R.; Zhang, J.; Mujahid, H.; Malone, B.M.; Bridges, S.M.; Peng, Z. Polycomb Group Gene OsFIE2 Regulates Rice (Oryza Sativa) Seed Development and Grain Filling Via a Mechanism Distinct from Arabidopsis. PLoS Genet. 2013, 9, e1003322. [Google Scholar] [CrossRef] [Green Version]
  70. Yamagata, Y.; Yamamoto, E.; Aya, K.; Win, K.T.; Doi, K.; Ito, T.; Kanamori, H.; Wu, J.; Matsumoto, T.; Matsuoka, M. Mitochondrial Gene in the Nuclear Genome Induces Reproductive Barrier in Rice. Proc. Natl. Acad. Sci. USA 2010, 107, 1494–1499. [Google Scholar] [CrossRef] [Green Version]
  71. Tanaka, N.; Itoh, H.; Sentoku, N.; Kojima, M.; Sakakibara, H.; Izawa, T.; Itoh, J.; Nagato, Y. The COP1 Ortholog PPS Regulates the Juvenile–adult and Vegetative–reproductive Phase Changes in Rice. Plant Cell 2011, 23, 2143–2154. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Wang, M.; Tang, D.; Luo, Q.; Jin, Y.; Shen, Y.; Wang, K.; Cheng, Z. BRK1, a Bub1-Related Kinase, is Essential for Generating Proper Tension between Homologous Kinetochores at Metaphase I of Rice Meiosis. Plant Cell 2012, 24, 4961–4973. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Tsukiyama, T.; Teramoto, S.; Yasuda, K.; Horibata, A.; Mori, N.; Okumoto, Y.; Teraishi, M.; Saito, H.; Onishi, A.; Tamura, K. Loss-of-Function of a Ubiquitin-Related Modifier Promotes the Mobilization of the Active MITE mPing. Mol. Plant 2013, 6, 790–801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Dreni, L.; Jacchia, S.; Fornara, F.; Fornari, M.; Ouwerkerk, P.B.; An, G.; Colombo, L.; Kater, M.M. The D-lineage MADS-box Gene OsMADS13 Controls Ovule Identity in Rice. Plant J. 2007, 52, 690–699. [Google Scholar] [CrossRef]
  75. Saito, H.; Okumoto, Y.; Yoshitake, Y.; Inoue, H.; Yuan, Q.; Teraishi, M.; Tsukiyama, T.; Nishida, H.; Tanisaka, T. Complete Loss of Photoperiodic Response in the Rice Mutant Line X61 is Caused by Deficiency of Phytochrome Chromophore Biosynthesis Gene. Theor. Appl. Genet. 2011, 122, 109–118. [Google Scholar] [CrossRef]
  76. Chen, R.; Zhao, X.; Shao, Z.; Wei, Z.; Wang, Y.; Zhu, L.; Zhao, J.; Sun, M.; He, R.; He, G. Rice UDP-Glucose Pyrophosphorylase1 is Essential for Pollen Callose Deposition and its Cosuppression Results in a New Type of Thermosensitive Genic Male Sterility. Plant Cell 2007, 19, 847–861. [Google Scholar] [CrossRef] [Green Version]
  77. Han, M.; Jung, K.; Yi, G.; An, G. Rice Importin Β1 Gene Affects Pollen Tube Elongation. Mol. Cells 2011, 31, 523–530. [Google Scholar] [CrossRef] [Green Version]
  78. Wang, W.; Xia, H.; Yang, X.; Xu, T.; Si, H.J.; Cai, X.X.; Wang, F.; Su, J.; Snow, A.A.; Lu, B. A Novel 5-enolpyruvoylshikimate-3-phosphate (EPSP) Synthase Transgene for Glyphosate Resistance Stimulates Growth and Fecundity in Weedy Rice (O Ryza Sativa) without Herbicide. New Phytol. 2014, 202, 679–688. [Google Scholar] [CrossRef] [Green Version]
  79. Wang, Y.; Ren, Y.; Liu, X.; Jiang, L.; Chen, L.; Han, X.; Jin, M.; Liu, S.; Liu, F.; Lv, J. OsRab5a Regulates Endomembrane Organization and Storage Protein Trafficking in Rice Endosperm Cells. Plant J. 2010, 64, 812–824. [Google Scholar] [CrossRef]
  80. Lieberherr, D.; Thao, N.P.; Nakashima, A.; Umemura, K.; Kawasaki, T.; Shimamoto, K. A Sphingolipid Elicitor-Inducible Mitogen-Activated Protein Kinase is Regulated by the Small GTPase OsRac1 and Heterotrimeric G-Protein in Rice. Plant Physiol. 2005, 138, 1644–1652. [Google Scholar] [CrossRef] [Green Version]
  81. Fu, L.; Yu, X.; An, C. Overexpression of Constitutively Active OsCPK10 Increases Arabidopsis Resistance Against Pseudomonas Syringae Pv. Tomato and Rice Resistance Against Magnaporthe Grisea. Plant Physiol. Biochem. 2013, 73, 202–210. [Google Scholar] [CrossRef] [PubMed]
  82. Hu, H.; Xiong, L.; Yang, Y. Rice SERK1 Gene Positively Regulates Somatic Embryogenesis of Cultured Cell and Host Defense Response Against Fungal Infection. Planta 2005, 222, 107–117. [Google Scholar] [CrossRef] [PubMed]
  83. Peng, Y.; Bartley, L.E.; Chen, X.; Dardick, C.; Chern, M.; Ruan, R.; Canlas, P.E.; Ronald, P.C. OsWRKY62 is a Negative Regulator of Basal and Xa21-Mediated Defense Against Xanthomonas Oryzae Pv. Oryzae in Rice. Mol. Plant 2008, 1, 446–458. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Zhou, J.; Jiao, F.; Wu, Z.; Li, Y.; Wang, X.; He, X.; Zhong, W.; Wu, P. OsPHR2 is Involved in Phosphate-Starvation Signaling and Excessive Phosphate Accumulation in Shoots of Plants. Plant Physiol. 2008, 146, 1673–1686. [Google Scholar] [CrossRef] [Green Version]
  85. Qin, C.; Li, Y.; Gan, J.; Wang, W.; Zhang, H.; Liu, Y.; Wu, P. OsDGL1, a Homolog of an Oligosaccharyltransferase Complex Subunit, is Involved in N-Glycosylation and Root Development in Rice. Plant Cell Physiol. 2013, 54, 129–137. [Google Scholar] [CrossRef]
  86. Zang, A.; Xu, X.; Neill, S.; Cai, W. Overexpression of OsRAN2 in Rice and Arabidopsis Renders Transgenic Plants Hypersensitive to Salinity and Osmotic Stress. J. Exp. Bot. 2010, 61, 777–789. [Google Scholar] [CrossRef] [Green Version]
  87. Fang, Y.; Xie, K.; Hou, X.; Hu, H.; Xiong, L. Systematic Analysis of GT Factor Family of Rice Reveals a Novel Subfamily Involved in Stress Responses. Mol. Genet. Genomics 2010, 283, 157–169. [Google Scholar] [CrossRef]
  88. Lu, C.; Lin, C.; Lee, K.; Chen, J.; Huang, L.; Ho, S.; Liu, H.; Hsing, Y.; Yu, S. The SnRK1A Protein Kinase Plays a Key Role in Sugar Signaling during Germination and Seedling Growth of Rice. Plant Cell 2007, 19, 2484–2499. [Google Scholar] [CrossRef] [Green Version]
  89. Han, X.; Wang, Y.; Liu, X.; Jiang, L.; Ren, Y.; Liu, F.; Peng, C.; Li, J.; Jin, X.; Wu, F. The Failure to Express a Protein Disulphide Isomerase-Like Protein Results in a Floury Endosperm and an Endoplasmic Reticulum Stress Response in Rice. J. Exp. Bot. 2012, 63, 121–130. [Google Scholar] [CrossRef]
  90. Sheoran, I.S.; Koonjul, P.; Attieh, J.; Saini, H.S. Water-Stress-Induced Inhibition of A-Tubulin Gene Expression during Growth, and its Implications for Reproductive Success in Rice. Plant Physiol. Biochem. 2014, 80, 291–299. [Google Scholar] [CrossRef]
  91. Wang, G.; Ding, X.; Yuan, M.; Qiu, D.; Li, X.; Xu, C.; Wang, S. Dual Function of Rice OsDR8 Gene in Disease Resistance and Thiamine Accumulation. Plant Mol. Biol. 2006, 60, 437–449. [Google Scholar] [CrossRef]
  92. Heidarvand, L.; Amiri, R.M. What Happens in Plant Molecular Responses to Cold Stress? Acta Physiol. Plant 2010, 32, 419–431. [Google Scholar] [CrossRef]
  93. Webb, A.A. The Physiology of Circadian Rhythms in Plants. New Phytol. 2003, 160, 281–303. [Google Scholar] [CrossRef] [Green Version]
  94. Gil, K.; Park, C. Thermal Adaptation and Plasticity of the Plant Circadian Clock. New Phytol. 2019, 221, 1215–1229. [Google Scholar] [CrossRef] [Green Version]
  95. Gould, P.D.; Locke, J.C.; Larue, C.; Southern, M.M.; Davis, S.J.; Hanano, S.; Moyle, R.; Milich, R.; Putterill, J.; Millar, A.J. The Molecular Basis of Temperature Compensation in the Arabidopsis Circadian Clock. Plant Cell 2006, 18, 1177–1187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Crepy, M.; Yanovsky, M.J.; Casal, J.J. Blue Rhythms between GIGANTEA and Phytochromes. Plant Signal. Behav. 2007, 2, 530–532. [Google Scholar] [CrossRef] [Green Version]
  97. Huq, E.; Tepperman, J.M.; Quail, P.H. GIGANTEA is a Nuclear Protein Involved in Phytochrome Signaling in Arabidopsis. Proc. Natl. Acad. Sci. USA 2000, 97, 9789–9794. [Google Scholar] [CrossRef] [Green Version]
  98. Gould, P.D.; Ugarte, N.; Domijan, M.; Costa, M.; Foreman, J.; MacGregor, D.; Rose, K.; Griffiths, J.; Millar, A.J.; Finkenstädt, B. Network Balance Via CRY Signalling Controls the Arabidopsis Circadian Clock Over Ambient Temperatures. Mol. Syst. Biol. 2013, 9, 650. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  99. Kim, W.; Fujiwara, S.; Suh, S.; Kim, J.; Kim, Y.; Han, L.; David, K.; Putterill, J.; Nam, H.G.; Somers, D.E. ZEITLUPE is a Circadian Photoreceptor Stabilized by GIGANTEA in Blue Light. Nature 2007, 449, 356–360. [Google Scholar] [CrossRef]
  100. Miura, K.; Furumoto, T. Cold Signaling and Cold Response in Plants. Int. J. Mol. Sci. 2013, 14, 5312–5337. [Google Scholar] [CrossRef] [Green Version]
  101. Bendix, C.; Marshall, C.M.; Harmon, F.G. Circadian Clock Genes Universally Control Key Agricultural Traits. Mol Plant. 2015, 8, 1135–1152. [Google Scholar] [CrossRef] [Green Version]
  102. Nguyen, H.T.; Leipner, J.; Stamp, P.; Guerra-Peraza, O. Low Temperature Stress in Maize (Zea mays L.) Induces Genes Involved in Photosynthesis and Signal Transduction as Studied by Suppression Subtractive Hybridization. Plant Physiol. Biochem. 2009, 47, 116–122. [Google Scholar] [CrossRef]
  103. Kathuria, H.; Giri, J.; Nataraja, K.N.; Murata, N.; Udayakumar, M.; Tyagi, A.K. Glycinebetaine-induced Water-stress Tolerance in codA-expressing Transgenic Indica Rice is Associated with Up-regulation of several Stress Responsive Genes. Plant Biotech. J. 2009, 7, 512–526. [Google Scholar] [CrossRef] [PubMed]
  104. Dodd, A.N.; Kusakina, J.; Hall, A.; Gould, P.D.; Hanaoka, M. The Circadian Regulation of Photosynthesis. Photosynthesis Res. 2014, 119, 181–190. [Google Scholar] [CrossRef] [PubMed]
  105. Xie, G.; Kato, H.; Imai, R. Biochemical Identification of the OsMKK6–OsMPK3 Signalling Pathway for Chilling Stress Tolerance in Rice. Biochem. J. 2012, 443, 95–102. [Google Scholar] [CrossRef] [PubMed]
  106. Jossier, M.; Bouly, J.; Meimoun, P.; Arjmand, A.; Lessard, P.; Hawley, S.; Grahame Hardie, D.; Thomas, M. SnRK1 (SNF1-related Kinase 1) has a Central Role in Sugar and ABA Signalling in Arabidopsis Thaliana. Plant J. 2009, 59, 316–328. [Google Scholar] [CrossRef] [PubMed]
  107. Polge, C.; Thomas, M. SNF1/AMPK/SnRK1 Kinases, Global Regulators at the Heart of Energy Control? Trends Plant Sci. 2007, 12, 20–28. [Google Scholar] [CrossRef]
  108. Jangam, A.P.; Pathak, R.R.; Raghuram, N. Microarray Analysis of Rice D1 (RGA1) Mutant Reveals the Potential Role of G-Protein Alpha Subunit in Regulating Multiple Abiotic Stresses such as Drought, Salinity, Heat, and Cold. Front. Plant Sci. 2016, 7, 11. [Google Scholar] [CrossRef] [PubMed]
  109. Ferrero-Serrano, Á.; Assmann, S.M. The A-Subunit of the Rice Heterotrimeric G Protein, RGA1, Regulates Drought Tolerance during the Vegetative Phase in the Dwarf Rice Mutant D1. J. Exp. Bot. 2016, 67, 3433–3443. [Google Scholar] [CrossRef] [Green Version]
  110. Ferrero-Serrano, Á.; Su, Z.; Assmann, S.M. Illuminating the Role of the Gα Heterotrimeric G Protein Subunit, RGA1, in Regulating Photoprotection and Photoavoidance in Rice. Plant Cell Environ. 2018, 41, 451–468. [Google Scholar] [CrossRef]
  111. Kumar, M.; Gho, Y.; Jung, K.; Kim, S. Genome-Wide Identification and Analysis of Genes, Conserved between Japonica and Indica Rice Cultivars, that Respond to Low-Temperature Stress at the Vegetative Growth Stage. Front. Plant Sci. 2017, 8, 1120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  112. Kim, S.; Lee, S.; Jeong, H.; An, G.; Jeon, J.; Jung, K. Crosstalk between Diurnal Rhythm and Water Stress Reveals an Altered Primary Carbon Flux into Soluble Sugars in Drought-Treated Rice Leaves. Sci. Reports 2017, 7, 1–18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M. NCBI GEO: Archive for Functional Genomics Data Sets—update. Nucleic Acids Res. 2012, 41, D991–D995. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. Gautier, L.; Cope, L.; Bolstad, B.M.; Irizarry, R.A. Affy—analysis of Affymetrix GeneChip Data at the Probe Level. Bioinformatics 2004, 20, 307–315. [Google Scholar] [CrossRef]
  115. Smyth, G.K. Limma: Linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor; Anonymous, Ed.; Springer: New York, NY, USA, 2005; pp. 397–420. [Google Scholar]
  116. Howe, E.; Holton, K.; Nair, S.; Schlauch, D.; Sinha, R.; Quackenbush, J. Mev: Multiexperiment viewer. In Biomedical Informatics for Cancer Research; Anonymous, Ed.; Springer: New York, NY, USA, 2010; pp. 267–277. [Google Scholar]
  117. Cao, P.; Jung, K.; Choi, D.; Hwang, D.; Zhu, J.; Ronald, P.C. The Rice Oligonucleotide Array Database: An Atlas of Rice Gene Expression. Rice 2012, 5, 17. [Google Scholar] [CrossRef] [Green Version]
  118. Yu, G.; Wang, L.; Han, Y.; He, Q. clusterProfiler: An R Package for Comparing Biological Themes among Gene Clusters. Omics 2012, 16, 284–287. [Google Scholar] [CrossRef]
  119. Thimm, O.; Bläsing, O.; Gibon, Y.; Nagel, A.; Meyer, S.; Krüger, P.; Selbig, J.; Müller, L.A.; Rhee, S.Y.; Stitt, M. MAPMAN: A User-driven Tool to Display Genomics Data Sets Onto Diagrams of Metabolic Pathways and Other Biological Processes. Plant J. 2004, 37, 914–939. [Google Scholar] [CrossRef]
  120. Su, G.; Morris, J.H.; Demchak, B.; Bader, G.D. Biological Network Exploration with Cytoscape 3. Curr. Protoc. Bioinform. 2014, 47, 8.13. 1–8.13. 24. [Google Scholar] [CrossRef] [Green Version]
  121. Li, Y.; Luo, C.; Chen, Y.; Xiao, X.; Fu, C.; Yang, Y. Transcriptome-based Discovery of AP2/ERF tRanscription Factors and Expression Profiles Under Herbivore Stress Conditions in Bamboo (Bambusa emeiensis). J. Plant Biol. 2019, 62, 297–306. [Google Scholar] [CrossRef]
  122. Jain, M.; Nijhawan, A.; Tyagi, A.K.; Khurana, J.P. Validation of Housekeeping Genes as Internal Control for Studying Gene Expression in Rice by Quantitative Real-Time PCR. Biochem. Biophys. Res. Commun. 2006, 345, 646–651. [Google Scholar] [CrossRef]
  123. Kim, E.J.; Kim, Y.J.; Hong, W.J.; Lee, C.; Jeon, J.S.; Jung, K.H. Genome-wide Analysis of Root Hair Preferred RBOH Genes Suggests that Three RBOH Genes are Associated with Auxin-mediated Root Hair Development in Rice. J. Plant Biol. 2019, 62, 229–238. [Google Scholar] [CrossRef]
Ijms 21 06872 g001 550
Figure 1. Heat map of cold-regulated genes with diurnal rhythmic expressions. (A) Overview of the process that identified 885 cold-upregulated genes, 119 of which were shown to have diurnal rhythmic expression. (B) Overview of the process that identified 572 cold-downregulated genes, 346 of which were shown to have diurnal rhythm expression. Cold-responsive diurnal rhythm genes are indicated in red. DAT means days after transplanting [22].
Figure 1. Heat map of cold-regulated genes with diurnal rhythmic expressions. (A) Overview of the process that identified 885 cold-upregulated genes, 119 of which were shown to have diurnal rhythmic expression. (B) Overview of the process that identified 572 cold-downregulated genes, 346 of which were shown to have diurnal rhythm expression. Cold-responsive diurnal rhythm genes are indicated in red. DAT means days after transplanting [22].
Ijms 21 06872 g001
Ijms 21 06872 g002 550
Figure 2. Functional classification of the CD genes via GO and KEGG analysis. (A) GO enrichment within the biological process category; terms were identified for 465 candidate genes. Dot colors indicate fold-enrichment values (blue color indicates 2-fold, which is the minimum cut off to select significant fold-enrichment value, and red color indicates fold-enrichment values greater than 2), and dot size indicates statistical significance (−log10(hyper p-values) are used, with higher values having greater significance). (B) KEGG enrichment analysis of candidate genes. Enriched KEGG pathways are indicated, with dot size representing the ratio of selected genes to total genes in the pathway, and dot color illustrating adjusted p-values. (C) Overview of the light-harvesting chlorophyll protein complex associated with downregulated CD genes visualized with the KEGG mapper webtool (http://www.genome.jp/kegg/mapper.html). Red colored boxes indicate mapped subunits of light-harvesting chlorophyll protein complex.
Figure 2. Functional classification of the CD genes via GO and KEGG analysis. (A) GO enrichment within the biological process category; terms were identified for 465 candidate genes. Dot colors indicate fold-enrichment values (blue color indicates 2-fold, which is the minimum cut off to select significant fold-enrichment value, and red color indicates fold-enrichment values greater than 2), and dot size indicates statistical significance (−log10(hyper p-values) are used, with higher values having greater significance). (B) KEGG enrichment analysis of candidate genes. Enriched KEGG pathways are indicated, with dot size representing the ratio of selected genes to total genes in the pathway, and dot color illustrating adjusted p-values. (C) Overview of the light-harvesting chlorophyll protein complex associated with downregulated CD genes visualized with the KEGG mapper webtool (http://www.genome.jp/kegg/mapper.html). Red colored boxes indicate mapped subunits of light-harvesting chlorophyll protein complex.
Ijms 21 06872 g002
Ijms 21 06872 g003 550
Figure 3. Hypothetical protein–protein interaction (PPI) network associated with CD genes. The PPI network constructed from the Rice Interactions Viewer by querying all CD genes. The network was simplified by removing all genes that were not functionally characterized. The full network is shown in Figure S2.
Figure 3. Hypothetical protein–protein interaction (PPI) network associated with CD genes. The PPI network constructed from the Rice Interactions Viewer by querying all CD genes. The network was simplified by removing all genes that were not functionally characterized. The full network is shown in Figure S2.
Ijms 21 06872 g003
Ijms 21 06872 g004 550
Figure 4. Phenotypes of the sgr mutant compared with wild-type controls under cold stress, and the role of SGR in the regulatory network. (A) Phenotypic comparison of DJ and sgr during cold stress. Bar = 2 cm. (B) Validation of the differential expression patterns of the SGR gene under cold stress and diurnal rhythm. OsGI, rice gigantea gene used as a diurnal rhythm marker gene showing a peak in the evening. Y-axis colors indicate relative expression value of SGR (red) and OsGI (black) to OsUbi5, respectively. (C) Expression levels of the cold-stress hub genes OsMAPK5, OsSnRK1a, OsPhyB, and OsHDA702 in the leaves of DJ and sgr mutants after 4 days of cold treatment. DJ, Dongjin; sgr, SGR defective mutant. * p < 0.05; ** p < 0.01.
Figure 4. Phenotypes of the sgr mutant compared with wild-type controls under cold stress, and the role of SGR in the regulatory network. (A) Phenotypic comparison of DJ and sgr during cold stress. Bar = 2 cm. (B) Validation of the differential expression patterns of the SGR gene under cold stress and diurnal rhythm. OsGI, rice gigantea gene used as a diurnal rhythm marker gene showing a peak in the evening. Y-axis colors indicate relative expression value of SGR (red) and OsGI (black) to OsUbi5, respectively. (C) Expression levels of the cold-stress hub genes OsMAPK5, OsSnRK1a, OsPhyB, and OsHDA702 in the leaves of DJ and sgr mutants after 4 days of cold treatment. DJ, Dongjin; sgr, SGR defective mutant. * p < 0.05; ** p < 0.01.
Ijms 21 06872 g004
Ijms 21 06872 g005 550
Figure 5. Hypothetical model of the crosstalk among different biological processes mediated by the cold stress hub genes. Different biological processes are thought to be linked via OsMAPK5, OsSnRK1a, OsPhyB, and OsHDA702 as intermediates. The arrow means confirmed interplay with our experimental validation and dashed one means postulated interplay from the literature study.
Figure 5. Hypothetical model of the crosstalk among different biological processes mediated by the cold stress hub genes. Different biological processes are thought to be linked via OsMAPK5, OsSnRK1a, OsPhyB, and OsHDA702 as intermediates. The arrow means confirmed interplay with our experimental validation and dashed one means postulated interplay from the literature study.
Ijms 21 06872 g005
Table
Table 1. List of functionally characterized cold-responsive diurnal rhythmic genes (CD genes) in rice.
Table 1. List of functionally characterized cold-responsive diurnal rhythmic genes (CD genes) in rice.
Locus_IDGene_SymbolCharacter_MinorMethod 1Detailed FunctionsDOI 2
Upregulated cold-responsive diurnal rhythmic genes (Upregulated CD genes)
LOC_Os07g22710CPK18Blast resistanceKnockdownBinds and phosphorylates MPK5, regulating resistant to blast (Magnaporthe oryzae)[28]
LOC_Os06g45140OsbZIP52/RISBZ5Cold toleranceOverexpressionCold and drought tolerance[24]
LOC_Os04g58810OsCAF1BCold toleranceOthersDrought tolerance [25]
LOC_Os09g35030OsDREB1ACold toleranceOverexpressionCold, drought and salinity tolerance.[27]
LOC_Os03g17700OsMAPK5Cold toleranceKnockdown OverexpressionResistance to Magnaporthe grisea and Burkholderia glumae. Cold, drought and salinity tolerance.[26]
LOC_Os03g60080SNAC1Drought toleranceOverexpressionDrought and salinity tolerance. Stomatal control.[29]
LOC_Os07g39220OsBZR1DwarfismKnockdownDwarfism. Leaf angle. Brassinosteroid sensitivity.[30]
LOC_Os03g40540OsDWARFDwarfMutantDwarfism. Brassinosteroid biosynthesis.[31]
LOC_Os03g08330TIFY11bDwarfOverexpressionGrain size. Plant height[32]
LOC_Os04g08740Etr2FloweringMutantFlowering time. Ethylene sensitivity. Stem starch content.[33]
LOC_Os01g15900Rdd1FloweringKnockdown OverexpressionGrain length and width. 1000-grain weight. Flowering time.[34]
LOC_Os10g42690Jmj6Panicle flowerMutantNumber and morphology of floral organ.[35]
LOC_Os05g41760MSF1Panicle flowerMutantSpikelet determinacy. Floral organ development.[36]
LOC_Os09g36200SGRSource activityMutantLeaf senescence. Chlorophyll degradation.[37]
Downregulated cold-responsive diurnal rhythmic genes (Downregulated CD genes)
LOC_Os05g51830HDT701Bacterial blight resistanceKnockdown OverexpressionResistance to Magnaporthe oryzae and Xanthomonas oryzae pv oryzae.[38]
LOC_Os09g29820OsTFX1Bacterial blight resistanceOverexpressionResistance to Xanthomonas oryzae pv. oryzae.[39]
LOC_Os08g06280OsLSD1Blast resistanceKnockdown OverexpressionLesion mimic. Resistance to Magnaporthe grisea.[40]
LOC_Os08g17680SDF2-1Blast resistanceKnockdownXA21-mediated resistance to Xanthomonas oryzae pv. oryzae[41]
LOC_Os01g55450OsCIPK12Drought toleranceOverexpressionDrought tolerance.[42]
LOC_Os02g12790Cga1DwarfKnockdown OverexpressionDwarfism. Tillering. Chlorophyll content. Grain filling rate.[43]
LOC_Os06g38470HDA702DwarfKnockdownElongated uppermost internode. Fertility.[44]
LOC_Os07g49400OsApx2DwarfKnockdownAluminum tolerance. Dwarfism.[45]
LOC_Os06g04200WxEating qualityNatural variationSeed amylose content.[46]
LOC_Os08g07740DTH8FloweringNatural variationFlowering time under long day condition.[48]
LOC_Os01g08300OsCDT3Other soil stress toleranceKnockdownAluminum tolerance.[47]
LOC_Os10g35370FglSource activityMutantChlorophyll synthesis under high light conditions.[49]
LOC_Os06g02580ZnSource activityMutantChloroplast biosynthesis.[50]
1 Methods used to characterize the function of the genes. 2 Digital Object Identifier.
Table
Table 2. List of functionally characterized genes in the hypothetical PPI network.
Table 2. List of functionally characterized genes in the hypothetical PPI network.
Locus_IDGene_SymbolCharacter_MinorMethods 1Detailed FunctionsDOI 2
LOC_Os04g37920Cry1bShoot seedlingMutantLeaf sheath elongation during seedling stage. Gibberellin metabolism.[53]
LOC_Os06g48060Star1Other soil stress toleranceMutantAluminum tolerance.[54]
LOC_Os07g40510Bui1DwarfMutantCell division and expansion. Actin organization.[55]
LOC_Os11g14220Tid1Culm leafMutantCell division and expansion. Twisted growth. Microtubule arrangement.[56]
LOC_Os06g07210V3Source activityMutantChloroplast development during seedling stage.[57]
LOC_Os12g08730OstrxmSource activityKnockdownChloroplast development. Growth retardation[58]
LOC_Os07g47300ObgCSource activityMutantChloroplast development. Plastid ribosome biogenesis[59]
LOC_Os11g48070Wdl1Drought toleranceMutantCuticle formation. Drought tolerance.[60]
LOC_Os09g27060OsDDM1aDwarfKnockdownDwarfism, DNA methylation[61]
LOC_Os05g26890D1DwarfMutantDwarfism.[62]
LOC_Os07g44330OsPDK1DwarfKnockdownDwarfism.[63]
LOC_Os07g06980HDA704Culm leafKnockdownDwarfism. Twisted flag leaf.[44]
LOC_Os07g05620OsCIPK23Salinity toleranceKnockdown OverexpressionFertility. Salinity tolerance.[64]
LOC_Os06g06750OsMADS5Panicle flowerKnockdownFloral organ formation.[65]
LOC_Os09g03610rFCAFloweringOverexpressionFlowering time.[66]
LOC_Os07g49460OsPRR37FloweringNatural variationFlowering time.[67]
LOC_Os03g51030OsPhyAFloweringMutantFlowering time. Deetiolation response. Sensitivity to red and far-red light.[68]
LOC_Os03g19590OsPhyBFloweringMutantFlowering time. Deetiolation response. Sensitivity to red and far-red light.[68]
LOC_Os03g54084OsPhyCFloweringMutantFlowering time. Deetiolation response. Sensitivity to red and far-red light.[68]
LOC_Os08g04270OsFIE2Germination dormancyKnockdownGrain size. Grain filling rate. Seed dormancy.[69]
LOC_Os04g25540S28SterilityNatural variationHybrid sterility between Oryza sativa and Oryza glaberrima. Pollen development. Interaction with S27[70]
LOC_Os02g53140PpsDwarfMutantJuvenile to adult phase change. Flowering time independent of daylength. Dwarfism.[71]
LOC_Os07g32480BRK1SterilityMutantMeiosis.[72]
LOC_Os07g28280Rurm1OthersMutantMobilization of the Active MITE mPing[73]
LOC_Os12g10540OsMADS13Panicle flowerMutantOvule identity.[74]
LOC_Os01g72090Se13FloweringMutantPhotoperiodic response.[75]
LOC_Os09g38030Ugp1DwarfKnockdownPollen callose deposition. Dwarfism.[76]
LOC_Os05g28510OsImpβ1SterilityMutantPollen tube elongation.[77]
LOC_Os06g04280EpspsPanicle flowerOthersproduction of panicle[78]
LOC_Os12g43550Gpa1Eating qualityMutantPro-gultelin content in seed. Floury endosperm.[79]
LOC_Os06g06090OsMAPK6OthersKnockdownRegulation of stress response genes.[80]
LOC_Os03g57450OsCPK10Blast resistanceOverexpressionResistance to Magnaporthe grisea.[81]
LOC_Os04g38480OsSERK1Blast resistanceOverexpressionResistance to Magnaporthe grisea.[82]
LOC_Os09g25070OsWRKY62Bacterial blight resistanceOverexpressionResistance to Xanthomonas oryzae pv. oryzae.[83]
LOC_Os07g25710OsPHR2Other soil stress toleranceOverexpressionResponse to phosphate starvation.[84]
LOC_Os07g10830OsDGL1RootMutantRoot development.[85]
LOC_Os05g49890OsRan2Other stress resistanceKnockdown OverexpressionSalinity and osmotic stress tolerance. ABA sensitivity.[86]
LOC_Os02g33770OsGTgamma-1Salinity toleranceMutantSalinity tolerance.[87]
LOC_Os05g45420OsSnRK1aGermination dormancyMutantSeed germination. Seedling growth.[88]
LOC_Os11g09280PDIL1Eating qualityMutantStarch biosynthesis.[89]
LOC_Os03g51600Rip-3 (a-tubulin)Panicle flowerOtherssuppress panicle elongation during water deficit[90]
LOC_Os07g34570OsDR8Bacterial blight resistanceKnockdownThiamine mediated resistance to Xanthomonas oryzae and Magnaporthe grisea.[91]
1 Methods used to characterize the function of the genes. 2 Digital Object Identifier.

Share and Cite

MDPI and ACS Style

Hong, W.-J.; Jiang, X.; Ahn, H.R.; Choi, J.; Kim, S.-R.; Jung, K.-H. Systematic Analysis of Cold Stress Response and Diurnal Rhythm Using Transcriptome Data in Rice Reveals the Molecular Networks Related to Various Biological Processes. Int. J. Mol. Sci. 2020, 21, 6872. https://doi.org/10.3390/ijms21186872

AMA Style

Hong W-J, Jiang X, Ahn HR, Choi J, Kim S-R, Jung K-H. Systematic Analysis of Cold Stress Response and Diurnal Rhythm Using Transcriptome Data in Rice Reveals the Molecular Networks Related to Various Biological Processes. International Journal of Molecular Sciences. 2020; 21(18):6872. https://doi.org/10.3390/ijms21186872

Chicago/Turabian Style

Hong, Woo-Jong, Xu Jiang, Hye Ryun Ahn, Juyoung Choi, Seong-Ryong Kim, and Ki-Hong Jung. 2020. "Systematic Analysis of Cold Stress Response and Diurnal Rhythm Using Transcriptome Data in Rice Reveals the Molecular Networks Related to Various Biological Processes" International Journal of Molecular Sciences 21, no. 18: 6872. https://doi.org/10.3390/ijms21186872

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop