Purpose: In this study, an integrative approach was followed to select molecular biomarkers of bacterial resistance to further predict the acid inactivation of Bacillus weihenstephanensis.
Methods: Combining gene expression and survival ability, potential biomarkers of acid resistance of B. weihenstephanensis were identified. RT-qPCR gene expression of 31 genes was quantified (i) during exposure to sublethal conditions and correlated to a subsequent acid-resistance (3D values i.e. the time necessary to lose 99.9% of the bacterial population) and (ii) throughout bacterial inactivation and correlated to an instant first bacterial decrease, i.e. the time necessary to lose 90% of the bacterial population at time t. An individual gene selection was made, and a selection of gene set was also made using a Partial Least Square (PLS) regression. The main advantage of this method is to take gene expression interactions into account.
Results: In sublethal conditions, 4 genes exhibited a linear correlation between their expression and subsequent bacterial resistance. They could be selected as direct biomarkers. While 9 genes, named long-acting biomarkers, showed an up-regulation during short adaptation time and were correlated to an increased acid-resistance over time. It highlighted the importance of non-linear correlation particularly when focusing at transcriptional level. In lethal conditions a set of 8 genes were selected to predict acid-resistance. Using this PLS model, the bacterial resistance of two independent samples was predicted at 2.1 h and 3.3 h whereas the resistances were observed at 2.8 h and 3.7 h, respectively. Further investigations are running to validate the mathematical model in process like conditions.
Significance: This study underlines the possibility to integrate the bacterial physiology state, using molecular biomarkers, into bacterial behavior modeling and thereby further improve microbial risk assessment.