Skip to main content
Log in

Yield-associated putative gene regulatory networks in Oryza sativa L. subsp. indica and their association with high-yielding genotypes

  • Original Article
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

Background

With the increase in population and economies of developing countries in Asia and Africa, the research towards securing future food demands is an imminent need. Among japonica and indica genotypes, indica rice varieties are largely cultivated across the globe. However, our present understanding of yield-contributing gene information stems mainly from japonica and studies on the yield potential of indica genotypes are limited.

Methods and results

In the present study, yield contributing orthologous genes previously characterized from japonica varieties were identified in the indica genome and analysed with binGO tool for GO biological processes categorization. Transcription factor binding site enrichment analysis in the promoters of yield-related genes of indica was performed with MEME-AME tool that revealed putative common TF regulators are enriched in flower development, two-component signalling and water deprivation biological processes. Gene regulatory networks revealed important TF-target interactions that might govern yield-related traits. Some of the identified candidate genes were validated by qRT-PCR analysis for their expression and association with yield-related traits among 16 widely cultivated popular indica genotypes. Further, SNP-metabolite-trait association analysis was performed using high-yielding indica variety Rasi. This resulted in the identification of putative SNP variations in TF regulators and targeted yield genes significantly linked with metabolite accumulation.

Conclusions

The study suggests some of the high yielding indica genotypes such as Ravi003, Rasi and Kavya could be used as potential donors in breeding programs based on yield gene expression analysis and SNP-metabolites associations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

All the supporting data of the article has been provided in the supplementary files.

References

  1. Gravois KA, Helms RS (1992) Path analysis of rice yield and yield components as affected by seeding rate. Agron J 84(1):1–4

    Article  Google Scholar 

  2. Casanova D, Goudriaan J, Forner MC, Withagen JCM (2002) Rice yield prediction from yield components and limiting factors. Eur J Agron 17(1):41–61

    Article  Google Scholar 

  3. Chung SO, Sudduth KA, Chang YC (2005) Path analysis of factors limiting crop yield in rice paddy and upland corn fields. Biosyst Eng 30(108):45–55

    Google Scholar 

  4. Zahid MA, Akhter M, Sabar M, Manzoor Z, Awan T (2006) Correlation and path analysis studies of yield and economic traits in Basmati rice (Oryza sativa L). Asian J Plant Sci 5(4):643–645

    Article  Google Scholar 

  5. Chandra BS, Reddy TD, Ansari NA, Kumar SS (2009) Correlation and path analysis for yield and yield components in rice (Oryza sativa L). Agric Sci Digest 29(1):45–47

    Google Scholar 

  6. Huang M, Zou YB, Jiang P, Bing XIA, Md I, Ao HJ (2011) Relationship between grain yield and yield components in super hybrid rice. Agric Sci China 10(10):1537–1544

    Article  Google Scholar 

  7. Xing Y, Zhang Q (2010) Genetic and molecular bases of rice yield. Annu Rev Plant Biol 61:421–442

    Article  CAS  PubMed  Google Scholar 

  8. Jiang J, Xing F, Zeng X, Zou Q (2018) RicyerDB: a database for collecting rice yield-related genes with biological analysis. Int J Biol Sci 14(8):965

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ying JZ, Chen YY, Zhang HW (2014) Functional characterization of genes/QTLs for increasing rice yield potential. In: Yan W, Bao J (eds) Rice germplasm, genetics and improvement, vol 177. Intech Open, London. https://doi.org/10.5772/56820

    Chapter  Google Scholar 

  10. Kim SR, Ramos J, Ashikari M, Virk PS, Torres EA, Nissila E, Hechanova SL, Mauleon R, Jena KK (2016) Development and validation of allele-specific SNP/indel markers for eight yield-enhancing genes using whole-genome sequencing strategy to increase yield potential of rice Oryza sativa L. Rice 9(1):12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Miura K, Ikeda M, Matsubara A, Song XJ, Ito M, Asano K, Matsuoka M, Kitano H, Ashikari M (2010) OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet 42(6):545–549

    Article  CAS  PubMed  Google Scholar 

  12. Vemireddy LR, Kadambari G, Reddy GE, Kola VSR, Ramireddy E, Puram VRR, Badri T, Eslavath SN, Bollineni SN, Naik BJ, Chintala S, Pottepalem R, Akkareddy S, Nagireddy R, Reddy BR, Lekala SP, Navajeeth K, Siddiq EA (2019) Uncovering of natural allelic variants of key yield contributing genes by targeted resequencing in rice (Oryza sativa L). Sci Rep 9(1):8192

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Wang Y, Zhai L, Chen K, Shen C, Liang Y, Wang C, Zhao X, Wang S, Xu J (2020) Natural sequence variations and combinations of GNP1 and NAL1 determine the grain number per panicle in rice. Rice 13:14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li R, Li M, Ashraf U, Liu S, Zhang J (2019) Exploring the relationships between yield and yield-related traits for rice varieties released in China from 1978 to 2017. Front Plant Sci 10:543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang J, Lin Z, Zhang X, Liu H, Zhou L, Zhong S, Li Y, Zhu C, Lin Z (2019) krn1, a major quantitative trait locus for kernel row number in maize. New Phytol 223(3):1634–1646

    Article  CAS  PubMed  Google Scholar 

  16. Chen Z, Shen Z, Xu L, Zhao D, Zou Q (2020) Regulator network analysis of rice and maize yield-related genes. Front Cell Dev Biol 8:1483

    Google Scholar 

  17. Yan J, Tan BC (2019) Maize biology: from functional genomics to breeding application. J Integr Plant Biol 61(6):654

    Article  PubMed  Google Scholar 

  18. Petrillo E, Godoy Herz MA, Barta A, Kalyna M, Kornblihtt AR (2014) Let there be light: regulation of gene expression in plants. RNA Biol 11:1215–1220

    Article  PubMed  Google Scholar 

  19. Win KT, Kubo T, Miyazaki Y, Doi K, Yamagata Y, Yoshimura A (2009) Identification of two loci causing F1 pollen sterility in inter-and intraspecific crosses of rice. Breed Sci 59(4):411–418

    Article  CAS  Google Scholar 

  20. Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles ER, Qian Q, Kitano H, Matsuoka M (2005) Cytokinin oxidase regulates rice grain production. Science 309:741–745

    Article  CAS  PubMed  Google Scholar 

  21. Xue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q (2008) Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat Genet 40(6):761–767

    Article  CAS  PubMed  Google Scholar 

  22. Mishra SS, Behera PK, Kumar V, Lenka SK, Panda D (2018) Physiological characterization and allelic diversity of selected drought tolerant traditional rice (Oryza sativa L) landraces of Koraput. India Physiol Mol Biol Plants 24(6):1035–1046

    Article  CAS  PubMed  Google Scholar 

  23. Takano-Kai N, Jiang H, Powell A, McCouch S, Takamure I, Furuya N, Doi K, Yoshimura A (2013) Multiple and independent origins of short seeded alleles of GS3 in rice. Breed Sci 63(1):77–85

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhao M, Sun J, Xiao Z, Cheng F, Xu H, Tang L, Chen W, Xu Z, Xu Q (2016) Variations in DENSE AND ERECT PANICLE 1 (DEP1) contribute to the diversity of the panicle trait in high-yielding japonica rice varieties in northern China. Breed Sci 66(4):599–605

    Article  PubMed  PubMed Central  Google Scholar 

  25. Asano K, Takashi T, Miura K, Qian Q, Kitano H, Matsuoka M, Ashikari M (2007) Genetic and molecular analysis of utility of sd1 alleles in rice breeding. Breed Sci 57(1):53–58

    Article  CAS  Google Scholar 

  26. O’Malley RC, Huang SSC, Song L, Lewsey MG, Bartlett A, Nery JR, Galli M, Gallavotti A, Ecker JR (2016) Cistrome and epicistrome features shape the regulatory DNA landscape. Cell 165(5):1280–1292

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Franco-Zorrilla JM (1860) Solano R (2017) Identification of plant transcription factor target sequences. Biochim Biophys Acta Gene Regul Mech 1:21–30

    Google Scholar 

  28. Chen CJ, Chen H, Zhang Y, Thomas HR, Frank MH, He YH, Xia R (2020) TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol Plant Pathol 13:1194–1202

    CAS  Google Scholar 

  29. Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. J Bioinform 21(16):3448–3449

    Article  CAS  Google Scholar 

  30. Merico D, Isserlin R, Stueker O, Emili A, Bader GD (2010) Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5:e13984

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Shaik H, Yugander A, Balachiranjeevi CH, Pranathi K, Anila M, Mahadevaswamy HK, Kousik BVN, Dilip Kumar T, Ashok Reddy G, Bhaskar S, Abhilash Kumar V, Harika G, Rekha G, Laha GS, Viraktamath BC, Balachandran SM, Neeraja CN, Sheshu Madhav M, Mangrauthia SK, Bhadana VP, Sundaram RM (2014) Development of durable bacterial blight resistant lines of samba mahsuri possessing Xa33, Xa21, Xa13 & Xa5. Progr Res 9:1224–1227

    Google Scholar 

  32. Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H, Zhang W (2014) Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46(7):714–721

    Article  CAS  PubMed  Google Scholar 

  33. Nutan KK, Rathore RS, Tripathi AK, Mishra M, Pareek A, Singla-Pareek SL (2020) Integrating the dynamics of yield traits in rice in response to environmental changes. J Exp Bot 71(2):490–506

    Article  CAS  PubMed  Google Scholar 

  34. Sugiyama SH, Yasui Y, Ohmori S, Tanaka W, Hirano HY (2019) Rice flower development revisited: regulation of carpel specification and flower meristem determinacy. Plant Cell Physiol 60(6):1284–1295

    Article  CAS  PubMed  Google Scholar 

  35. Ya-Jiao P, Wang D, Ling-Hua Z, Bin-Ying F, Zhi-Kang L (2009) Differential expressions of two-component element genes in rice under drought stress. Acta Agron Sin 35(9):1628–1636

    Google Scholar 

  36. Dansana PK, Kothari KS, Vij S, Tyagi AK (2014) OsiSAP1 overexpression improves water-deficit stress tolerance in transgenic rice by affecting expression of endogenous stress-related genes. Plant Cell Rep 33(9):1425–1440

    Article  CAS  PubMed  Google Scholar 

  37. Moldenhauer K, Nathan S (2004) 1-Rice growth and development. In: Slaton N (ed) Rice Production Handbook. University of Arkansas, Arkansas

    Google Scholar 

  38. Sakamoto T, Matsuoka M (2008) Identifying and exploiting grain yield genes in rice. Curr Opin Plant Biol 11(2):209–214

    Article  CAS  PubMed  Google Scholar 

  39. Huang R, Jiang L, Zheng J, Wang T, Wang H, Huang Y, Hong Z (2013) Genetic bases of rice grain shape: so many genes, so little known. Trends Plant Sci 18(4):218–226

    Article  CAS  PubMed  Google Scholar 

  40. Li J, Chen F, Li Y, Li P, Wang Y, Mi G, Yuan L (2019) ZmRAP27, an AP2 transcription factor, is involved in maize brace roots development. Front Plant Sci 10:820

    Article  PubMed  PubMed Central  Google Scholar 

  41. Guo N, Gu M, Hu J, Qu H, Xu G (2020) Rice OsLHT1 functions in leaf-to-panicle nitrogen allocation for grain yield and quality. Front Plant Sci 11:1150

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wei J, Wang A, Li R, Qu H, Jia Z (2018) Metabolome-wide association studies for agronomic traits of rice. Heredity 120(4):342–355

    Article  CAS  PubMed  Google Scholar 

  43. Donde R, Mohapatra S, Baksh SY, Padhy B, Mukherjee M, Roy S, Chattopadhyay K, Anandan A, Swain P, Sahoo KK, Singh ON (2020) Identification of QTLs for high grain yield and component traits in new plant types of rice. PLoS ONE 15(7):pe0227785

    Article  CAS  Google Scholar 

  44. Itoh JI, Nonomura KI, Ikeda K, Yamaki S, Inukai Y, Yamagishi H, Kitano H, Nagato Y (2005) Rice plant development: from zygote to spikelet. Plant Cell Physiol 46(1):23–47

    Article  CAS  PubMed  Google Scholar 

  45. Gu H, Zhu P, Jiao Y, Meng Y, Chen M (2011) PRIN: a predicted rice interactome network. BMC Bioinform 12(1):1–3

    Article  Google Scholar 

  46. Lu H, Bai Y, Ren H, Campbell DE (2010) Integrated emergy, energy and economic evaluation of rice and vegetable production systems in alluvial paddy fields: implications for agricultural policy in China. J Environ Manag 91(12):2727–2735

    Article  Google Scholar 

  47. Li M, Li X, Zhou Z, Wu P, Fang M, Pan X, Lin Q, Luo W, Wu G, Li H (2016) Reassessment of the four yield-related genes Gn1a, DEP1, GS3, and IPA1 in rice using a CRISPR/Cas9 system. Front Plant Sci 30(7):377

    Google Scholar 

  48. Tsago Y, Chen Z, Cao H, Sunusi M, Khan AU, Shi C, Jin X (2020) Rice gene, OsCKX2-2, regulates inflorescence and grain size by increasing endogenous cytokinin content. Plant Growth Regul 92(2):283–294

    Article  CAS  Google Scholar 

  49. Zhou S, Zhang YK, Kremling KA, Ding Y, Bennett JS, Bae JS, Kim DK, Ackerman HH, Kolomiets MV, Schmelz EA, Schroeder FC (2019) Ethylene signaling regulates natural variation in the abundance of antifungal acetylated diferuloylsucroses and Fusarium graminearum resistance in maize seedling roots. New Phytol 221(4):2096–2111

    Article  CAS  PubMed  Google Scholar 

  50. Vavilova V, Konopatskaia I, Kuznetsova AE, Blinov A, Goncharov NP (2017) DEP1 gene in wheat species with normal, compactoid and compact spikes. BMC Genet 18(1):61–70

    Google Scholar 

  51. Mahesh HB, Shirke MD, Singh S, Rajamani A, Hittalmani S, Wang GL, Gowda M (2016) Indica rice genome assembly, annotation and mining of blast disease resistance genes. BMC Genom 17(1):1–2

    Article  CAS  Google Scholar 

  52. Su Z, Ma X, Guo H, Sukiran NL, Guo B, Assmann SM, Ma H (2013) Flower development under drought stress: morphological and transcriptomic analyses reveal acute responses and long-term acclimation in Arabidopsis. Plant Cell 25(10):3785–3807

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Nagar P, Kumar A, Jain M, Kumari S, Mustafiz A (2020) Genome-wide analysis and transcript profiling of PSKR gene family members in Oryza sativa. PLoS ONE 15(7):e0236349

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Bassard JE, Ullmann P, Bernier F, Werck-Reichhart D (2010) Phenolamides: bridging polyamines to the phenolic metabolism. Phytochemistry 71:1808–1824

    Article  CAS  PubMed  Google Scholar 

  55. Dong X, Gao Y, Chen W, Wang W, Gong L, Liu X, Luo J (2015) Spatiotemporal distribution of phenolamides and the genetics of natural variation of hydroxycinnamoyl spermidine in rice. Mol Plant 8(1):111–121

    Article  CAS  PubMed  Google Scholar 

  56. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

LRV gratefully acknowledge ICAR for funding the Extramural Project (F.No.:CS/18 (8)/2015-O&P dated 12–082016). This research was supported by the Department of Biotechnology (DBT), Govt. of India (Sanction Order: No. BT/PR5493/AGIII/103/848/2012) awarded to LRV. VS and KVS is supported by IISER Tirupati institutional postdoctoral research fellowship. E.R. acknowledges IISER Tirupati for research support. Aparna to Bayer fellowship.

Author information

Authors and Affiliations

Authors

Contributions

LRV and ER: conceived the experiment and prepared the manuscript. AE, VS, VS, LP, and SA: conducted the experiment. MLK: assisted in field work and VS: has carried out data analysis.

Corresponding authors

Correspondence to Eswarayya Ramireddy or Lakshminarayana R. Vemireddy.

Ethics declarations

Conflict of interest

Authors declare that they do not have any conflict of interest.

Ethical approval

This is an observational study. The Research Ethics Committee of the University has confirmed that no ethical approval is required.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent to publish

As there is no personal data of the authors in the article, no ethical approval is required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eragam, A., Shukla, V., Kola, V.S. et al. Yield-associated putative gene regulatory networks in Oryza sativa L. subsp. indica and their association with high-yielding genotypes. Mol Biol Rep 49, 7649–7663 (2022). https://doi.org/10.1007/s11033-022-07581-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-022-07581-0

Keywords

Navigation