The systems approach to the prespore-specific activation of sigma factor SigF in Bacillus subtilis

Enzymatic deamination of the epigenetic nucleoside N6-methyladenosine regulates gene expression

N⁶-methyladenosine (m⁶A) modification is the most extensively studied epigenetic modification due to its crucial role in regulating an array of biological processes. Herein, Bsu06560, formerly annotated as an adenine deaminase derived from Bacillus subtilis 168, was recognized as the first enzyme capable of metabolizing the epigenetic nucleoside N⁶-methyladenosine. A model of Bsu06560 was constructed, and several critical residues were putatively identified via mutational screening. Two mutants, F91L and Q150W, provided a superiorly enhanced conversion ratio of adenosine and N⁶-methyladenosine. The CRISPR-Cas9 system generated Bsu06560-knockout, F91L, and Q150W mutations from the B. subtilis 168 genome. Transcriptional profiling revealed a higher global gene expression level in BS-F91L and BS-Q150W strains with enhanced N⁶-methyladenosine deaminase activity. The differentially expressed genes were categorized using GO, COG, KEGG and verified through RT-qPCR. This study assessed the crucial roles of Bsu06560 in regulating adenosine and N⁶-methyladenosine metabolism, which influence a myriad of biological processes. This is the first systematic research to identify and functionally annotate an enzyme capable of metabolizing N⁶-methyladenosine and highlight its significant roles in regulation of bacterial metabolism. Besides, this study provides a novel method for controlling gene expression through the mutations of critical residues.

Modeling and analysis of biopathways dynamics

Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here we review quantitative models of biochemical networks based on ordinary differential equations (ODEs). We mainly focus on the parameter estimation and sensitivity analysis problems and survey the current methods for tackling them. In this context we also highlight a recently developed probabilistic approximation technique using which these two problems can be considerably simplified.