Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on Multi-Plex Biological Networks
Abstract
:1. Introduction
2. Results
2.1. Performance Prediction Analysis of the RWR and the RWMRP on Various Biological Molecular Networks
2.1.1. Performance Prediction Analysis of the RWR on Various Biological Molecular Networks
2.1.2. Performance Prediction Analysis of the RWMRP on Various Biological Molecular Networks
2.1.3. The Multiple Biological Molecular Networks Exhibit Superior Performance
2.1.4. Enhanced Predictive Performance of RWMRP Compared to RWR
2.2. Obtaining Potential Drought Stress-Responsive Genes Based on RWMRP
2.3. Enrichment Analysis of Potential Genes for Drought Stress
2.4. Obtaining Candidate Genes for Drought Stress Based on Association Analysis
2.5. The Results of qRT-PCR
3. Discussion
4. Materials and Methods
4.1. Data Sets and Biological Molecular Network
4.1.1. Protein–Protein Interaction Network
4.1.2. Gene Co-Expression Network
4.1.3. Gene Pathway Networks
4.1.4. Multiple Biological Molecular Networks
4.1.5. Known Genes for Abiotic Stress-Responsive
4.2. Random Walk with Restart on Multi-Plex Networks
4.3. Multiple Probability Restart Random Walk on Multi-Plex Networks
Algorithm 1: Random Walk with Multi-Restart probability on Multi-Plex Network |
Input: Restart probability vector , Random walk probability , Transition Matrix N, Initial probability vector , iterations iter. |
Output: A vector that displays the degree of association between all nodes in the network and the starting node |
1: |
2: |
3: ; |
4: : |
5: |
6: |
7: |
8: |
4.4. Leave-One-Out Cross-Validation Strategy
Algorithm 2: Leave-Out Cross-Validation Strategy |
Input: Abiotic stress-related gene set: _. |
Output: Ranking set for left-out genes: ___ |
1: function LOOCV_S |
2: ___ |
3: for to length do |
4: ___ = |
5: |
6: |
7: |
8: |
9: end for |
10: return |
11: end function |
4.5. Association Analysis
4.6. qRT-PCR Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
RWMRP | Random Ralk with Multi-Restart Probability |
RWR | Random Walk with Restart |
WGCNA | Weighted Gene Co-expression Network Analysis |
GWAS | Genome-Wide Association Studies |
LOOCV | Leave-One-Out Cross-Validation |
GO | Gene Ontology |
CC | Cellular Component |
BP | Biological Process |
MF | Molecular Function |
References
- Singh, V.; Gupta, K.; Singh, S.; Jain, M.; Garg, R. Unravelling the molecular mechanism underlying drought stress response in chickpea via integrated multi-omics analysis. Front. Plant Sci. 2023, 14, 1156606. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Yin, T. Analysis of topology properties in different tissues of poplar based on gene co-expression networks. Tree Genet. Genomes 2020, 16, 6. [Google Scholar] [CrossRef]
- Yin, L.; Cai, Z.; Zhu, B.; Xu, C. Identification of key pathways and genes in the dynamic progression of HCC based on WGCNA. Genes 2018, 9, 92. [Google Scholar] [CrossRef]
- Guo, H.; Li, T.; Wang, Z. Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection. BMC Genom. 2023, 24, 759. [Google Scholar] [CrossRef] [PubMed]
- Qu, X.; Wu, S.; Gao, J.; Qin, Z.; Zhou, Z.; Liu, J. Weighted gene co expression network analysis (WGCNA) with key pathways and hub-genes related to micro RNAs in ischemic stroke. IET Syst. Biol. 2021, 15, 93–100. [Google Scholar] [CrossRef] [PubMed]
- Uffelmann, E.; Huang, Q.Q.; Munung, N.S.; De Vries, J.; Okada, Y.; Martin, A.R.; Martin, H.C.; Lappalainen, T.; Posthuma, D. Genome-wide association studies. Nat. Rev. Methods Prim. 2021, 1, 59. [Google Scholar] [CrossRef]
- Li, J.; Chen, L.; Wang, S.; Zhang, Y.; Kong, X.; Huang, T.; Cai, Y.D. A computational method using the random walk with restart algorithm for identifying novel epigenetic factors. Mol. Genet. Genom. 2018, 293, 293–301. [Google Scholar] [CrossRef] [PubMed]
- Chen, X.; You, Z.H.; Yan, G.Y.; Gong, D.W. IRWRLDA: Improved random walk with restart for lncRNA-disease association prediction. Oncotarget 2016, 7, 57919. [Google Scholar] [CrossRef] [PubMed]
- Zhu, L.; Zhang, H.; Cao, D.; Xu, Y.; Li, L.; Ning, Z.; Zhu, L. Drought Stress-Related Gene Identification in Rice by Random Walk with Restart on Multiplex Biological Networks. Agriculture 2023, 13, 53. [Google Scholar] [CrossRef]
- Li, Y.; Patra, J.C. Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network. Bioinformatics 2010, 26, 1219–1224. [Google Scholar] [CrossRef]
- Valdeolivas, A.; Tichit, L.; Navarro, C.; Perrin, S.; Odelin, G.; Levy, N.; Cau, P.; Remy, E.; Baudot, A. Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics 2019, 35, 497–505. [Google Scholar] [CrossRef]
- Wang, Y.; Lyu, L.; Yang, Y. Joint non-negative matrix factorization for link prediction in temporal networks using graph regularization and eigenvector centrality. J. Phys. Soc. Jpn. 2022, 91, 034004. [Google Scholar] [CrossRef]
- Jian, H.; Wang, H.; Qiu, X.; Yan, H.; Ma, L. Identification and Validation of Reference Genes for qRT-PCR Analysis of Petal-Color-Related Genes in Rosa praelucens. Genes 2024, 15, 277. [Google Scholar] [CrossRef] [PubMed]
- The Gene Ontology resource: Enriching a GOld mine. Nucleic Acids Res. 2021, 49, D325–D334. [CrossRef] [PubMed]
- Zia, R.; Nawaz, M.S.; Siddique, M.J.; Hakim, S.; Imran, A. Plant survival under drought stress: Implications, adaptive responses, and integrated rhizosphere management strategy for stress mitigation. Microbiol. Res. 2021, 242, 126626. [Google Scholar] [CrossRef]
- Stra, A.; Almarwaey, L.O.; Alagoz, Y.; Moreno, J.C.; Al-Babili, S. Carotenoid metabolism: New insights and synthetic approaches. Front. Plant Sci. 2023, 13, 1072061. [Google Scholar] [CrossRef]
- Swapnil, P.; Meena, M.; Singh, S.K.; Dhuldhaj, U.P.; Marwal, A. Vital roles of carotenoids in plants and humans to deteriorate stress with its structure, biosynthesis, metabolic engineering and functional aspects. Curr. Plant Biol. 2021, 26, 100203. [Google Scholar] [CrossRef]
- Kim, J.J.; Kim, Y.S.; Park, S.I.; Mok, J.E.; Kim, Y.H.; Park, H.M.; Kim, I.S.; Yoon, H.S. Cytosolic monodehydroascorbate reductase gene affects stress adaptation and grain yield under paddy field conditions in Oryza sativa L. japonica. Mol. Breed. 2017, 37, 118. [Google Scholar] [CrossRef]
- Katagiri, S.; Kamiyama, Y.; Yamashita, K.; Iizumi, S.; Suzuki, R.; Aoi, Y.; Takahashi, F.; Kasahara, H.; Kinoshita, T.; Umezawa, T. Accumulation of Phosphorylated SnRK2 Substrate 1 Promotes Drought Escape in Arabidopsis. Plant Cell Physiol. 2024, 65, 259–268. [Google Scholar] [CrossRef] [PubMed]
- Han, X.; Yang, Y. Phospholipids in salt stress response. Plants 2021, 10, 2204. [Google Scholar] [CrossRef]
- Zhang, H.; Mao, X.; Jing, R.; Xie, H.; Chang, X. Cloning of wheat TaPK7 gene and its expression analysis under various stress conditions. J. Wheat Crop. 2008, 28, 177–182. (In Chinses) [Google Scholar]
- Moreno, J.C.; Martinez-Jaime, S.; Kosmacz, M.; Sokolowska, E.M.; Schulz, P.; Fischer, A.; Luzarowska, U.; Havaux, M.; Skirycz, A. A multi-OMICs approach sheds light on the higher yield phenotype and enhanced abiotic stress tolerance in tobacco lines expressing the carrot lycopene β-cyclase1 gene. Front. Plant Sci. 2021, 12, 624365. [Google Scholar] [CrossRef]
- Kerbler, S.M.L.; Armijos-Jaramillo, V.; Lunn, J.E.; Vicente, R. The trehalose 6-phosphate phosphatase family in plants. Physiol. Plant. 2023, 175, e14096. [Google Scholar] [CrossRef] [PubMed]
- Kobayashi, Y.; Yamamoto, S.; Minami, H.; Kagaya, Y.; Hattori, T. Differential activation of the rice sucrose nonfermenting1—Related protein kinase2 family by hyperosmotic stress and abscisic acid. Plant Cell 2004, 16, 1163–1177. [Google Scholar] [CrossRef] [PubMed]
- Zhang, S.; Han, Y.; Zeng, Q.; Wang, C.; Wang, H.; Zhang, J.; Cai, M.; Lu, J.; Chen, T. Whole-Transcriptome Sequencing Reveals the Global Molecular Responses and NAC Transcription Factors Involved in Drought Stress in Dendrobium catenatum. Antioxidants 2024, 13, 94. [Google Scholar] [CrossRef] [PubMed]
- Shahzad, Z.; Tournaire-Roux, C.; Canut, M.; Adamo, M.; Roeder, J.; Verdoucq, L.; Martinière, A.; Amtmann, A.; Santoni, V.; Grill, E.; et al. Protein kinase SnRK2. 4 is a key regulator of aquaporins and root hydraulics in Arabidopsis. Plant J. 2024, 117, 264–279. [Google Scholar] [CrossRef] [PubMed]
- Yao, P.; Sun, L.; Dekomah, S.; Bi, Z.; Sun, C.; Mao, J.; Zhang, C.; Qin, T.; Wang, Y.; Liu, Y.; et al. Evolutionary Analysis of StSnRK2 Family Genes and Their Overexpression in Transgenic Tobacco Improve Drought Tolerance. Int. J. Mol. Sci. 2023, 24, 1000. [Google Scholar] [CrossRef] [PubMed]
- Tang, D.; Qian, H.; Yu, S.; Cao, Y.; Liao, Z.; Zhao, L.; Sun, X.; Huang, D.; Tang, K. cDNA cloning and characterization of a new stress-responsive gene BoRS1 from Brassica oleracea var. acephala. Physiol. Plant. 2004, 121, 578–585. [Google Scholar] [CrossRef]
- You, J.; Chan, Z. ROS regulation during abiotic stress responses in crop plants. Front. Plant Sci. 2015, 6, 165102. [Google Scholar] [CrossRef]
- Mei, W.; Chen, W.; Wang, Y.; Liu, Z.; Dong, Y.; Zhang, G.; Deng, H.; Liu, X.; Lu, X.; Wang, F.; et al. Exogenous kinetin modulates ROS homeostasis to affect heat tolerance in rice seedlings. Int. J. Mol. Sci. 2023, 24, 6252. [Google Scholar] [CrossRef] [PubMed]
- Pyngrope, S.; Bhoomika, K.; Dubey, R. Reactive oxygen species, ascorbate–glutathione pool, and enzymes of their metabolism in drought-sensitive and tolerant indica rice (Oryza sativa L.) seedlings subjected to progressing levels of water deficit. Protoplasma 2013, 250, 585–600. [Google Scholar] [CrossRef] [PubMed]
- Filyushin, M.; Arkhestova, D.; Kochieva, E.; Shchennikova, A. Characteristics of a Novel Monodehydroascorbate Reductase Gene in Corn (Zea mays L.) and Its Role in the Response to Stress. Russ. J. Plant Physiol. 2024, 71, 22. [Google Scholar] [CrossRef]
- Sharma, A.; Kaur, A.; Singh, K.; Upadhyay, S.K. Modulation in gene expression and enzyme activity suggested the roles of monodehydroascorbate reductase in development and stress response in bread wheat. Plant Sci. 2024, 338, 111902. [Google Scholar]
- Ezquerro, M.; Burbano, E.; Rodriguez-Concepcion, M. Overlapping and specialized roles of tomato phytoene synthase isoforms PSY1 and PSY2 in carotenoid and ABA production. bioRxiv 2023, 193, 2021–2036. [Google Scholar]
- Wu, Y.; Wang, S.; Wang, W.; Wang, X.; Dong, Y.; Han, C.; Wang, Y. Cloning and functional characterization of MhPSY1 gene from Malus halliana in apple calli and Arabidopsis thaliana. Plant Cell Tissue Organ Cult. (PCTOC) 2023, 154, 249–259. [Google Scholar] [CrossRef]
- Qin, Y.; Shen, X.; Wang, N.; Ding, X. Characterization of a novel cyclase-like gene family involved in controlling stress tolerance in rice. J. Plant Physiol. 2015, 181, 30–41. [Google Scholar] [CrossRef]
- Davison, P.; Hunter, C.; Horton, P. Overexpression of β-carotene hydroxylase enhances stress tolerance in Arabidopsis. Nature 2002, 418, 203–206. [Google Scholar] [CrossRef]
- D’Ambrosio, C.; Giorio, G.; Marino, I.; Merendino, A.; Petrozza, A.; Salfi, L.; Stigliani, A.L.; Cellini, F. Virtually complete conversion of lycopene into β-carotene in fruits of tomato plants transformed with the tomato lycopene β-cyclase (tlcy-b) cDNA. Plant Sci. 2004, 166, 207–214. [Google Scholar] [CrossRef]
- Kang, C.; Zhai, H.; Xue, L.; Zhao, N.; He, S.; Liu, Q. A lycopene β-cyclase gene, IbLCYB2, enhances carotenoid contents and abiotic stress tolerance in transgenic sweetpotato. Plant Sci. 2018, 272, 243–254. [Google Scholar] [CrossRef]
- Lin, Q.; Yang, J.; Wang, Q.; Zhu, H.; Chen, Z.; Dao, Y.; Wang, K. Overexpression of the trehalose-6-phosphate phosphatase family gene AtTPPF improves the drought tolerance of Arabidopsis thaliana. BMC Plant Biol. 2019, 19, 381. [Google Scholar] [CrossRef]
- Lin, Q.; Wang, S.; Dao, Y.; Wang, J.; Wang, K. Arabidopsis thaliana trehalose-6-phosphate phosphatase gene TPPI enhances drought tolerance by regulating stomatal apertures. J. Exp. Bot. 2020, 71, 4285–4297. [Google Scholar] [CrossRef]
- Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef] [PubMed]
- Gu, H.; Zhu, P.; Jiao, Y.; Meng, Y.; Chen, M. PRIN: A predicted rice interactome network. BMC Bioinform. 2011, 12, 161. [Google Scholar] [CrossRef] [PubMed]
- Liu, S.; Liu, Y.; Zhao, J.; Cai, S.; Qian, H.; Zuo, K.; Zhao, L.; Zhang, L. A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa). Plant J. 2017, 90, 177–188. [Google Scholar] [CrossRef] [PubMed]
- Sakai, H.; Lee, S.S.; Tanaka, T.; Numa, H.; Kim, J.; Kawahara, Y.; Wakimoto, H.; Yang, C.C.; Iwamoto, M.; Abe, T.; et al. Rice Annotation Project Database (RAP-DB): An integrative and interactive database for rice genomics. Plant Cell Physiol. 2013, 54, e6. [Google Scholar] [CrossRef]
- Zhang, Y.; Han, E.; Peng, Y.; Wang, Y.; Wang, Y.; Geng, Z.; Xu, Y.; Geng, H.; Qian, Y.; Ma, S. Rice co-expression network analysis identifies gene modules associated with agronomic traits. Plant Physiol. 2022, 190, 1526–1542. [Google Scholar] [CrossRef]
- Kanehisa, M.; Sato, Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020, 29, 28–35. [Google Scholar] [CrossRef]
- Zhang, J.D.; Wiemann, S. KEGGgraph: A graph approach to KEGG PATHWAY in R and bioconductor. Bioinformatics 2009, 25, 1470–1471. [Google Scholar] [CrossRef]
- Cheng, J.; Dekkers, J.C.; Fernando, R.L. Cross-validation of best linear unbiased predictions of breeding values using an efficient leave-one-out strategy. J. Anim. Breed. Genet. 2021, 138, 519–527. [Google Scholar] [CrossRef]
- Wang, G.; Yang, X.; Xue, X. Transgenic Tobacco with the BADH Gene Shows Enhanced Photosynthesis Resistance to Drought Stress Induced by PEG-6000. Agronomy 2024, 14, 690. [Google Scholar] [CrossRef]
GO Term | Number of Genes | Ontology | Description | p-Value |
---|---|---|---|---|
GO:0008152 | 213 | BP | Metabolic process | 4.85 × |
GO:0009605 | 14 | BP | Response to environmental stimulus | 2.22 × |
GO:0009266 | 7 | BP | Response to temperature stimulus | 3.86 × |
GO:0006950 | 39 | BP | Response to abiotic stress | 1.19 × |
GO:0009737 | 7 | BP | Response to abscisic acid | 3.75 × |
GO:0004674 | 16 | MF | Protein serine/threonine kinase activity | 8.26 × |
GO:0046905 | 2 | MF | 15-cis-phytoene synthase activity | 1.28 × |
GO:0003825 | 2 | MF | Alpha, alpha-trehalose-phosphate synthase (UDP-forming) activity | 1.17 × |
GO:0016688 | 2 | MF | L-ascorbate peroxidase activity | 1.49 × |
GO:0005737 | 139 | CC | Cytoplasm | 5.32 × |
GO:0043229 | 124 | CC | Intracellular organelle | 6.66 × |
GO:0043231 | 120 | CC | Intracellular membrane-bounded | 2.66 × |
Candidate Genes | Name in STRING | Annotation |
---|---|---|
Os07g0204900 | OS07T0204900-01 | Zeta-carotene desaturase; Catalyzes the conversion of zeta-carotene to lycopene via the intermediary of neurosporene. It carries out two consecutive desaturations (introduction of double bonds) at positions C-7 and C-7′ |
Os09g0397300 | OS09T0397300-01 | Os09g0397300 protein; Putative trehalose-6-phosphate synthase/phosphatase |
Os02g0190600 | OS02T0190600-00 | Putative lycopene beta-cyclase |
Os04g0432000 | SAPK7; OsJ_14855 | Serine/threonine-protein kinase SAPK7 |
Os09g0516500 | OS09T0516500-00 | NAD dependent epimerase/dehydratase family protein |
Os06g0729000 | PSY1 | cDNA clone: J023057D05, full insert sequence; Phytoene synthase-like |
Os03g0405000 | OS03T0405000-01 | Reticulon-like protein; NAD dependent epimerase/dehydratase family protein |
Os02g0707000 | OS02T0707000-00 | Monodehydroascorbate reductase, putative, expressed |
Os02g0707100 | MDAR2 | Monodehydroascorbate reductase 2, peroxisomal; Catalyzes the conversion of monodehydroascorbate to ascorbate, oxidizing NADH in the process. Ascorbate is a major antioxidant against reactive oxygen species (ROS) and nitric oxide (NO) |
Os12g0541300 | OS12T0541300-01 | Respiratory burst oxidase, putative, expressed |
Os05g0528000 | OsJ_19284 | Putative cytochrome b245 beta chain |
Os05g0543400 | OsJ_19411 | cDNA clone: J023072A14, full insert sequence; Os05g0543400 protein; Putative farnesyl pyrophosphate synthase; cDNA clone: J023072A14, full insert sequence; cDNA clone: J033069I20, full insert sequence; Belongs to the FPP/GGPP synthase family |
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Liu, J.; Zhu, L.; Cao, D.; Zhu, X.; Zhang, H.; Zhang, Y.; Liu, J. Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on Multi-Plex Biological Networks. Int. J. Mol. Sci. 2024, 25, 9216. https://doi.org/10.3390/ijms25179216
Liu J, Zhu L, Cao D, Zhu X, Zhang H, Zhang Y, Liu J. Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on Multi-Plex Biological Networks. International Journal of Molecular Sciences. 2024; 25(17):9216. https://doi.org/10.3390/ijms25179216
Chicago/Turabian StyleLiu, Jiacheng, Liu Zhu, Dan Cao, Xinghui Zhu, Hongyan Zhang, Yinqiong Zhang, and Jing Liu. 2024. "Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on Multi-Plex Biological Networks" International Journal of Molecular Sciences 25, no. 17: 9216. https://doi.org/10.3390/ijms25179216