Identificação e caracterização funcional de genes de origem metagenômica associados à resistência ao estresse em bactérias

Abstract

Industrial biotechnology has developed rapidly as a platform for producing goods beneficial to society attributable to the use of cell factories, with the use of renewable resources with minimum impact to the environment as one of its major advantages. Industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial or enzymatic bioprocesses; therefore, improved technologies for strain engineering and optimization will be needed. The genetic potential of bacteria living in severe settings, such as those with high salinity, low pH, and other factors, is exploited using metagenomic techniques. Building strong, efficient microbes can benefit from understanding the molecular mechanisms they employ to survive under these conditions. In order to build synthetic gene circuits that can increase bacterial resistance to various stress situations, we addressed the identification of new genes in metagenomic databases using an in silico approach. We recovered information from harsh environment metagenomes to identify genes encoding chaperones and other proteins (such as proteases, nucleic-acid-binding proteins, etc.) that provide resistance to stress conditions. The most relevant sequences were chosen using Hidden Markov Model (HMM) profiles, which were then categorized based on their protein sequence identity and literature review. Ten protein sequences including the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ were selected and functionally characterized in Escherichia coli under six stress conditions: high temperature, acidity, oxidative stress, osmotic stress, UV radiation and p-coumaric acid.Further characterization allowed us to identify five proteins that responded under at least two stress conditions compared with control cultures. These findings demonstrated that metagenomic techniques and bioinformatic tools can prospect novel genes that could potentially increase bacteria's resilience to stressful situations.