Global discovery of adaptive mutations.

Although modern DNA sequencing enables rapid identification of genetic variation, characterizing the phenotypic consequences of individual mutations remains a labor-intensive task. Here we describe array-based discovery of adaptive mutations (ADAM), a technology that searches an entire bacterial genome for mutations that contribute to selectable phenotypic variation between an evolved strain and its parent. We found that ADAM identified adaptive mutations in laboratory-evolved Escherichia coli strains with high sensitivity and specificity

Genetic Architecture of Intrinsic Antibiotic Susceptibility

BACKGROUND:Antibiotic exposure rapidly selects for more resistant bacterial strains, and both a drug's chemical structure and a bacterium's cellular network affect the types of mutations acquired. METHODOLOGY/PRINCIPAL FINDINGS:To better characterize the genetic determinants of antibiotic susceptibility, we exposed a transposon-mutagenized library of Escherichia coli to each of 17 antibiotics that encompass a wide range of drug classes and mechanisms of action. Propagating the library for multiple generations with drug concentrations that moderately inhibited the growth of the isogenic parental strain caused the abundance of strains with even minor fitness advantages or disadvantages to change measurably and reproducibly. Using a microarray-based genetic footprinting strategy, we then determined the quantitative contribution of each gene to E. coli's intrinsic antibiotic susceptibility. We found both loci whose removal increased general antibiotic tolerance as well as pathways whose down-regulation increased tolerance to specific drugs and drug classes. The beneficial mutations identified span multiple pathways, and we identified pairs of mutations that individually provide only minor decreases in antibiotic susceptibility but that combine to provide higher tolerance. CONCLUSIONS/SIGNIFICANCE:Our results illustrate that a wide-range of mutations can modulate the activity of many cellular resistance processes and demonstrate that E. coli has a large mutational target size for increasing antibiotic tolerance. Furthermore, the work suggests that clinical levels of antibiotic resistance might develop through the sequential accumulation of chromosomal mutations of small individual effect

Fitness Landscape of Antibiotic Tolerance in Pseudomonas aeruginosa Biofilms

Bacteria in biofilms have higher antibiotic tolerance than their planktonic counterparts. A major outstanding question is the degree to which the biofilm-specific cellular state and its constituent genetic determinants contribute to this hyper-tolerant phenotype. Here, we used genome-wide functional profiling of a complex, heterogeneous mutant population of Pseudomonas aeruginosa MPAO1 in biofilm and planktonic growth conditions with and without tobramycin to systematically quantify the contribution of each locus to antibiotic tolerance under these two states. We identified large sets of mutations that contribute to antibiotic tolerance predominantly in the biofilm or planktonic setting only, offering global insights into the differences and similarities between biofilm and planktonic antibiotic tolerance. Our mixed population-based experimental design recapitulated the complexity of natural biofilms and, unlike previous studies, revealed clinically observed behaviors including the emergence of quorum sensing-deficient mutants. Our study revealed a substantial contribution of the cellular state to the antibiotic tolerance of biofilms, providing a rational foundation for the development of novel therapeutics against P. aeruginosa biofilm-associated infections

Bacterial Adaptation through Loss of Function

The metabolic capabilities and regulatory networks of bacteria have been optimized by evolution in response to selective pressures present in each species' native ecological niche. In a new environment, however, the same bacteria may grow poorly due to regulatory constraints or biochemical deficiencies. Adaptation to such conditions can proceed through the acquisition of new cellular functionality due to gain of function mutations or via modulation of cellular networks. Using selection experiments on transposon-mutagenized libraries of bacteria, we illustrate that even under conditions of extreme nutrient limitation, substantial adaptation can be achieved solely through loss of function mutations, which rewire the metabolism of the cell without gain of enzymatic or sensory function. A systematic analysis of similar experiments under more than 100 conditions reveals that adaptive loss of function mutations exist for many environmental challenges. Drawing on a wealth of examples from published articles, we detail the range of mechanisms through which loss-of-function mutations can generate such beneficial regulatory changes, without the need for rare, specific mutations to fine-tune enzymatic activities or network connections. The high rate at which loss-of-function mutations occur suggests that null mutations play an underappreciated role in the early stages of adaption of bacterial populations to new environments

Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli

We have designed an experimental/computational framework for studying complex phenotypes in bacteria.Our framework relies on whole-genome fitness profiling coupled with a module-level analysis to discover pathways that directly affect fitness.As a proof-of-principle, we studied ethanol tolerance in Escherichia coli and we identified key pathways that contribute to this phenotype.We then validated our findings through genetic manipulations, gene-expression profiling, metabolite-level measurements, and stable-isotope labeling

Transcriptional Profiling of Caulobacter crescentus during Growth on Complex and Minimal Media

Microarray analysis was used to examine gene expression in the freshwater oligotrophic bacterium Caulobacter crescentus during growth on three standard laboratory media, including peptone-yeast extract medium (PYE) and minimal salts medium with glucose or xylose as the carbon source. Nearly 400 genes (approximately 10% of the genome) varied significantly in expression between at least two of these media. The differentially expressed genes included many encoding transport systems, most notably diverse TonB-dependent outer membrane channels of unknown substrate specificity. Amino acid degradation pathways constituted the largest class of genes induced in PYE. In contrast, many of the genes upregulated in minimal media encoded enzymes for synthesis of amino acids, including incorporation of ammonia and sulfate into glutamate and cysteine. Glucose availability induced expression of genes encoding enzymes of the Entner-Doudoroff pathway, which was demonstrated here through mutational analysis to be essential in C. crescentus for growth on glucose. Xylose induced expression of genes encoding several hydrolytic exoenzymes as well as an operon that may encode a novel pathway for xylose catabolism. A conserved DNA motif upstream of many xylose-induced genes was identified and shown to confer xylose-specific expression. Xylose is an abundant component of xylan in plant cell walls, and the microarray data suggest that in addition to serving as a carbon source for growth of C. crescentus, this pentose may be interpreted as a signal to produce enzymes associated with plant polymer degradation

Cytokinesis signals truncation of the PodJ polarity factor by a cell cycle-regulated protease

We demonstrate that successive cleavage events involving regulated intramembrane proteolysis (Rip) occur as a function of time during the Caulobacter cell cycle. The proteolytic substrate PodJ(L) is a polar factor that recruits proteins required for polar organelle biogenesis to the correct cell pole at a defined time in the cell cycle. We have identified a periplasmic protease (PerP) that initiates the proteolytic sequence by truncating PodJ(L) to a form with altered activity (PodJ(S)). Expression of perP is regulated by a signal transduction system that activates cell type-specific transcription programs and conversion of PodJ(L) to PodJ(S) in response to the completion of cytokinesis. PodJ(S), sequestered to the progeny swarmer cell, is subsequently released from the polar membrane by the membrane metalloprotease MmpA for degradation during the swarmer-to-stalked cell transition. This sequence of proteolytic events contributes to the asymmetric localization of PodJ isoforms to the appropriate cell pole. Thus, temporal activation of the PerP protease and spatial restriction of the polar PodJ(L) substrate cooperatively control the cell cycle-dependent onset of Rip

A regulatory network adapted for an organism's native habitat may perform poorly in a new environment.

<p>The hypothetical organism's fitness (shading) depends only on the concentration of two environmental factors. The area enclosed by the red dotted line indicates the typical range of these parameters in the native environment. ‘X’ indicates the parameter values in a new environment. (<b>A</b>) Fitness of the wild-type organism, which is tuned to be optimal in the native environment. Even if an organism's genome encodes proteins that would confer high fitness in a new environment, its regulatory network might limit the actual fitness achieved. (<b>B</b>) Fitness of a mutated network that might result from a single regulatory null mutation. While not optimal, the mutated network may be advantageous in a new environment by breaking the previous mapping of environment to phenotype. (<b>C</b>) Extended evolution in the new environment will rewire the organism's regulatory network to allow the cell to optimize the use of its genetic resources (even in the absence of new genes).</p

Null mutations increase fitness through varied mechanisms.

<p>(<b>A</b>) In a hypothetical cellular network, E1–E4 are enzymes, M1–M5 are metabolites, S is a structural protein, R1–R3 are regulatory proteins, and H is a housekeeping protein that inhibits translation and promotes degradation of some mRNAs. Dotted lines indicate other activities of the indicated proteins. The fitness of cells depends only on the levels of S, M2, and M5. (<b>B,C</b>) Optimal concentrations of S, M2, and M5 in the native environment (<b>B</b>) and a novel environment to which the cells might need to adapt (<b>C</b>). Null mutations adaptive in the novel environment are marked in panel (<b>A</b>) with an orange ‘x’.</p