Orthologous Revelation between Elaeis guineensis, Arabidopsis thaliana and Solanum lycopersicum

Öz

Oil palms are important commodity crop in Malaysia as major contributor to agriculture sector. Thus, the need for better yield production is urgent to accommodate rising local and global demand while reducing the land expansion for oil palm plantation. This can be achieved by identifying the agronomical important traits in oil palm using comparative genomic approach. In this study, gene related to plant height, fruit development and fruit ripening in oil palm were predicted by comparing Elaeis guineensis genome sequence with Arabidopsis thaliana and Solanum lycopersicum genome sequence. The model plant chosen are based on its special attribute such as completely sequenced and fleshy fruit model. The analysis begun with orthology analysis using InParanoid, and SonicParanoid. There are 9,624 orthologous genes identified common among species selected. The reformatted orthologous genes were then annotated with Gene Ontology (GO) using Blast2GO program. The process of annotation include blast with local database (DIAMOND), mapping, annotation and project statistical analysis. 100% of the orthologous gene blasted has sequence of significant similarity but only 61.79% of the sequence has GO assignment. By using the annotated orthologous genes generated, only small amount of gene associated with trait of interest was predicted i.e. gibberellins (GAs) 10 genes, bassinosteroids (BRs) 14 genes, auxin (9 genes), fruit development (8 genes) and fruit ripeing (4 genes). These agronomical important genes could be utilized in genetic engineering and molecular breeding to improve the production of palm oils.

Anahtar Kelimeler

• comparative genomic
• ortholog
• SonicParanoid

Kaynakça

1. Abdullah, R., & Wahid, M. B., World palm oil supply, demand, price, and prospects: focus on Malaysian and Indonesian palm oil industry. Malaysian Palm Oil Board Press. Malaysia. 2010.
2. Samsulrizal, N. H., & Yusof, N. Y., In Silico Prediction of Cell Wall Remodeling Genes in Tomato, Banana, Melon and Grape. International Journal of Life Sciences and Biotechnology, 2019. 2(2), 108-121.
3. Cosentino, S., & Iwasaki, W., SonicParanoid: fast, accurate and easy orthology inference. Bioinformatics, 2019. 35(1), 149-151.
4. Remm, M., Storm, C. E., & Sonnhammer, E. L., Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. Journal of molecular biology, 2001. 314(5), 1041-1052.
5. Steinegger, M., & Söding, J., MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature biotechnology, 2017. 35(11), 1026-1028.
6. Somero, G. N., The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. Journal of Experimental Biology, 2010. 213(6), 912-920.
7. Buchfink, B., Xie, C., & Huson, D. H., Fast and sensitive protein alignment using DIAMOND. Nature methods, 2015. 12(1), 59.
8. The Gene Ontology Consortium., The gene ontology resource: 20 years and still GOing strong. Nucleic acids research, 2018. 47(D1), D330-D338.

9. Lewis, S. E., The Vision and Challenges of the Gene Ontology. The Gene Ontology Handbook, 2017. 291.
10. Gyawali, A., et al., Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs. BMC plant biology, 2019. 19(1), 412.
11. Wang, Y., & Jiao, Y., Auxin and above-ground meristems. Journal of experimental botany, 2017. 69(2), 147-154.
12. Handa, A. K., et al., Fruit development and ripening: a molecular perspective. In Plant biotechnology and agriculture. Academic Press, 2012. pp. 405-424.
13. Gillaspy, G., Ben-David, H., & Gruissem, W., Fruits: a developmental perspective. The Plant Cell, 1993. 5(10), 1439.
14. Ezura, H., & Hiwasa-Tanase, K., Fruit development. In Plant Developmental Biology-Biotechnological Perspectives, Springer, Berlin, Heidelberg. 2010. pp. 301-318.
15. Carrari, F., et al., Integrated analysis of metabolite and transcript levels reveals the metabolic shifts that underlie tomato fruit development and highlight regulatory aspects of metabolic network behavior. Plant Physiology, 2006. 142(4), pp.1380-1396.
16. Mounet, F., et al., Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development. Plant Physiology, 2009. 149(3), 1505-1528.
17. Srivastava, A., et al., Maturity and ripening-stage specific modulation of tomato (Solanum lycopersicum) fruit transcriptome. GM crops, 2010. 1(4), 237-249.
18. Manning, K., et al., A naturally occurring epigenetic mutation in a gene encoding an SBP-box transcription factor inhibits tomato fruit ripening. Nature genetics, 2006. 38(8), 948.
19. Busi, M. V., et al., MADS-box genes expressed during tomato seed and fruit development. Plant molecular biology, 2003. 52(4), 801-815.
20. Giovannoni, J. J., Fruit ripening mutants yield insights into ripening control. Current opinion in plant biology, 2007. 10(3), 283-289.