In silico Lactochassis: In Silico Prediction of Essential Genes in Lacticaseibacillus casei: A Step towards Genome Minimisation

Abstract

Introduction: Synthetic biology using minimal-genome engineering has been proposed as the best way to optimize probiotic chassis. A minimal genome presents a significant advantage of enhanced production of heterologous proteins. This research article presents a comprehensive computational biology study for bacterial gene essentiality and genome reduction design within Lacticaseibacillus casei ATCC 393. Methods: This study used a computational biology approach to identify the essential genes of L.casei ATCC 393. Essential genes were identified using DELetion design by Essentiality Analy-sis Tool (DELEATv0.1), Gene Essentiality Prediction Tool for Complete-Genome Based on Orthology and Phylogeny (Geptop2), the Database of Essential Genes (DEG), and Alignable Tight Genomic Clusters-Clusters of Orthologous Genes (ATGC-COG). The criteria for identifica-tion of essential genes included phyletic retention (essential orthologs), codon usage, G + C con-tent, length, hydrophobicity score, and essential genomic elements, such as protein-coding genes and noncoding RNAs, among other factors. Results: Using a consensus approach, 633 putative essential genes were identified. In addition, 145 genes associated with probiotic attributes, such as the production of bacteriocins, bile and ac-id resistance, immune modulation, and adherence to host gut epithelia, were identified. Discussion: The directed evolution by serial passage was initiated by streaking L. casei ATCC 393 as part of the test phase of the Design-Build-Test-Learn (DBTL) cycle. The survival rate data were calculated from mean 0D600 nm readings. The data revealed a significant difference in sur-vival rates between E1 and E2 from day 1 to day 38 (V = 224, p = 0.00745), indicating that fac-tors, possibly inherent to the isolates themselves or subtle variations in the environment, may be influencing the results. Overall, the significant differences suggest that survival rates were affect-ed by specific NaCl concentrations. Lower survival rates were observed at 50 g/L and 71g/L compared to other concentrations. Conclusion: The in-silico analysis yielded valuable insights into the essential genes of L. casei ATCC 393. Further, it contributes to understanding the fundamental genetic makeup of L. casei ATCC 393 and its potential as a probiotic chassis for various applications, including the devel-opment of novel biotherapeutics.