Exploring the Respective Contributions of DNA Polymerase Proofreading and Mismatch Repair in the Shaping of Spontaneous Mutation Rates.
The spontaneous mutation rate is a crucial parameter in molecular evolution which is maintained very low. To better characterize how proofreading activity of the DNA polymerase and Mismatch repair (MMR) which are ubiquitous in all kingdoms of life shape a mutational landscape we built B. subtilis 168-derived strains allowing conditional inactivation of either one or both of these two error reparation mechanisms. In practice, we used an IPTG-inducible promoter to control the expression of mutant alleles selected for their ability to displace by competition their functional counterparts. The first allele, denoted here mutL*, has a mutation in the ATP hydrolysis active site of MutL. The second allele, denoted here polC* encodes an exonuclease-deficient variant of PolC. Fluctuation tests and Mutation Accumulation experiments confirmed extremely high mutation rates, upon IPTG-induction, in the strain that combine these two deficient alleles in a synthetic operon (mutL*//polC*). The purpose of this transcritomic study was to better characterize this inducible system. Analysis of the data did not reveal specific transcriptional responses of the bacterium to IPTG addition and extreme mutations rates. Overall design: To investigate the potential impact on mutation rate on the transcriptional profile of B. subtilis, we performed RNA-Seq a WT-like chassis strain (CS), and three strains that are deficient in a reparation system after addition of IPTG : an MMR-deficient strain (mutL*), a proofreading-deficient strain (polC*) and a MMR- and proofreading-deficient strain (mutL*//polC*). CS with and without IPTG, as well as mutLpolC without IPTG, were used to 1) control the absence of impact of IPTG on transcription 2) provide a transcriptional profile of B. subtilis with a WT mutation rate as a point of reference. Two biological replicates per strain x condition were made. We then performed differential expression analysis using the data from the RNA-Seq.