Characterization of Mycobacterium smegmatis SigF regulon

Mycobacterium smegmatis SigF is a group III sigma factor. Its ortholog in M. tuberculosis is reported to have role in regulation and function of cell wall components. In present study we have created an M. smegmatis ΔsigF mutant by allele exchange method. M. smegmatis sigF mutant shows non pigmented phenotype and is more sensitive to hydrogen peroxide generated oxidative stress. DNA microarray analysis of M. smegmatis wild type and ΔsigF mutant suggests that SigF in this species controls the expression of several energy and central intermediary metabolism genes along with regulation of carotenoid biosynthesis. Gene expression patterns of M.smegmatis wild type and ΔsigF mutant strains were compared at two growth stages i.e. log (OD600 ~1.4) and stationary (OD600 ~3.0). M. smegmatis strains were grown in DifcoTM MiddleBrook 7H9 broth base (BD Biosciences, Sparks, MD, USA) with 0.2% glycerol (v/v) and 0.05% Tween-80 (Sigma-Aldrich, St Louis, MO, USA) supplemented with 10% albumen dextrose catalase (BD Biosciences) (v/v) at 37 0C with continuous shaking. Total RNA was isolated using Trizole (Invitrogen) method and labelled with cyanine 3 (Cy3) as per Agilent 1-color labelling protocol (Version 5.5, February 2007). Six hundred nanograms of each Cy3 labelled samples were fragmented and hybridized. Fragmentation of labeled cRNA and hybridization were performed using Gene Expression Hybridization kit (Agilent Technologies). Hybridization was carried out in Agilent’s Surehyb Chambers at 65 0C for 16 h. The hybridized slides were washed using Agilent Gene Expression wash buffers and scanned using the Agilent Microarray Scanner G Model G2565BA at 5 micron resolution. Feature extracted data was analysed using GeneSpring GX v 7.3.1 software from Agilent. Normalization of the data was done in GeneSpring GX using the recommended one color Per Chip and Per Gene Data Transformation. Set measurements less than 0.01 to 0.01 per chip, normalize to 50th percentile per gene, and normalize to specific samples.