Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice

Version 1 : Received: 12 April 2021 / Approved: 13 April 2021 / Online: 13 April 2021 (10:52:26 CEST)

How to cite: Lahiri, A.; Rastogi, K.; Datta, A.; Septiningsih, E.M. Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice. Preprints 2021, 2021040344 (doi: 10.20944/preprints202104.0344.v1). Lahiri, A.; Rastogi, K.; Datta, A.; Septiningsih, E.M. Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice. Preprints 2021, 2021040344 (doi: 10.20944/preprints202104.0344.v1).

Abstract

Lysine is the first limiting essential amino acid in rice because it is present in the lowest quantity compared to all the other amino acids. Amino acids are the building block of proteins and play an essential role in maintaining the human body’s healthy functioning. Rice is a staple food for large proportion of the global population, thus increasing the lysine content in rice will improve its nutritional value. In this paper, we studied the lysine biosynthesis pathway in rice (Oryza Sativa) to identify the regulators of the lysine reporter gene LYSA (LOC_Os02g24354). Genetically intervening at the regulators has the potential to increase the overall lysine content in rice. We modeled the lysine biosynthesis pathway in rice seedlings under normal and saline (NaCl) stress conditions using Bayesian networks. We estimated the model parameters using experimental data and identified the gene DAPF(LOC_Os12g37960) as a positive regulator of the lysine reporter gene LYSA under both normal and saline stress conditions. Based on this analysis, we conclude that the gene DAPF is a potent candidate for genetic intervention. Upregulating DAPF using methods such as CRISPR-Cas9 has the potential to upregulate the lysine reporter gene LYSA and increase the overall lysine content in rice.

Subject Areas

Lysine; Rice; Amino Acids; Saline Stress; Abiotic Stress; Gene Regulatory Network; Bayesian Network; Parameter Estimation; Inference; RNA Seq

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