Mechanisms of adaptation in antibiotic resistant bacteria; Genomic landscape of starvation-recovery transitions in bacterial populations
Mechanisms of adaptation in antibiotic resistant bacteria
Mutations responsible for clinically significant, high levels of antibiotic resistance are almost invariably associated with reduced growth rate in laboratory strains, under standard conditions. Despite its apparent relevance for the development of anti-infectious therapeutics as well as for general bacterial physiology, regulation and adaptation, the phenomenon of growth retardation has not been understood or even adequately studied. Recently we began investigating metabolic consequences of the acquired antibiotic resistance using topologically constrained metabolic modelling with the goal of identifying key modulators of the phenotypic variation.
Genomic landscape of starvation-recovery transitions in bacterial populations
Life cycle of bacteria can be viewed as ever repeating phases of starvation and recovery. Genes whose products contribute to optimal entry, survival and exit from starvation, but do not influence exponential growth, are the primary biological focus of this study. Identification of such genes and their relative contribution to fitness under chemically controlled, starvation-recovery conditions will be used to model internally consistent genotype-phenotype relationships. Using high-throughput sequencing methods in conjunction with saturation transposon mutagenesis we can quantify the effect of every non-essential gene in the Escherichia coli genome on the starvation-related fitness. However, the reliability of fitness measurements depends on the ability to statistically model the high-throughput sequencing data. While intensive research into the best statistical practices in the analysis of transcriptomics data has been pursued in the bioinformatics literature, much less is known about optimal analysis strategies for transposon-saturation data sets. We develop and test experimental designs and statistical approaches to the basic analysis of such data.
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