Environmental dependency of gene knockouts on phenotype microarray analysis in Escherichia coli

High-Throughput Screening Method Using Escherichia coli Keio Mutants for Assessing Primary Damage Mechanism of Antimicrobials

The Escherichia coli Keio mutant collection has been a tool for assessing the role of specific genes and determining their role in E. coli physiology and uncovering novel functions. In this work, specific mutants in the DNA repair pathways and oxidative stress response were evaluated to identify the primary targets of silver nanoparticles (NPs) and their mechanism of action. The results presented in this work suggest that NPs mainly target DNA via double-strand breaks and base modifications since the recA, uvrC, mutL, and nfo mutants rendered the most susceptible phenotype, rather than involving the oxidative stress response. Concomitantly, during the establishment of the control conditions for each mutant, the katG and sodA mutants showed a hypersensitive phenotype to mitomycin C, an alkylating agent. Thus, we propose that KatG catalase plays a key role as a cellular chaperone, as reported previously for the filamentous fungus Neurospora crassa, a large subunit catalase. The Keio collection mutants may also be a key tool for assessing the resistance mechanism to metallic NPs by using their potential to identify novel pathways involved in the resistance to NPs.

Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments

Biolog Phenotype Microarray (PM) is a technology allowing simultaneous screening of the metabolic behaviour of bacteria under a large number of different conditions. Bacteria may often undergo several cycles of metabolic activity during a Biolog experiment. We introduce a novel algorithm to identify these metabolic cycles in PM experimental data, thus increasing the potential of PM technology in microbiology. Our method is based on a statistical decomposition of the time-series measurements into a set of growth models. We show that the method is robust to measurement noise and captures accurately the biologically relevant signals from the data. Our implementation is made freely available as a part of an R package for PM data analysis and can be found at www.helsinki.fi/bsg/software/Biolog_Decomposition.

Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations

Genetic and epigenetic changes in cancer cells are typically divided into “drivers” and “passengers”. Drug development strategies target driver mutations, but inter- and intra-tumoral heterogeneity usually results in emergence of resistance. Here we model intratumoral evolution in the context of a fecundity/survivorship trade-off. Simulations demonstrate the fitness value, of any genetic change is not fixed but dependent on evolutionary triage governed by initial cell properties, current selection forces, and prior genotypic/phenotypic trajectories. We demonstrate spatial variations in molecular properties of tumor cells are the result of changes in environmental selection forces such as blood flow. Simulated therapies targeting fitness-increasing (driver) mutations usually decrease the tumor burden but almost inevitably fail due to population heterogeneity. An alternative strategy targets gene mutations that are never observed. Because up or down regulation of these genes unconditionally reduces cellular fitness, they are eliminated by evolutionary triage but can be exploited for targeted therapy.

GenoBase: comprehensive resource database of Escherichia coli K-12

Comprehensive experimental resources, such as ORFeome clone libraries and deletion mutant collections, are fundamental tools for elucidation of gene function. Data sets by omics analysis using these resources provide key information for functional analysis, modeling and simulation both in individual and systematic approaches. With the long-term goal of complete understanding of a cell, we have over the past decade created a variety of clone and mutant sets for functional genomics studies of Escherichia coli K-12. We have made these experimental resources freely available to the academic community worldwide. Accordingly, these resources have now been used in numerous investigations of a multitude of cell processes. Quality control is extremely important for evaluating results generated by these resources. Because the annotation has been changed since 2005, which we originally used for the construction, we have updated these genomic resources accordingly. Here, we describe GenoBase (http://ecoli.naist.jp/GB/), which contains key information about comprehensive experimental resources of E. coli K-12, their quality control and several omics data sets generated using these resources.

Detecting functional groups of Arabidopsis mutants by metabolic profiling and evaluation of pleiotropic responses

Metabolic profiles and fingerprints of Arabidopsis thaliana plants with various defects in plastidic sugar metabolism or photosynthesis were analyzed to elucidate if the genetic mutations can be traced by comparing their metabolic status. Using a platform of chromatographic and spectrometric tools data from untargeted full MS scans as well as from selected metabolites including major carbohydrates, phosphorylated intermediates, carboxylates, free amino acids, major antioxidants, and plastidic pigments were evaluated. Our key observations are that by multivariate statistical analysis each mutant can be separated by a unique metabolic signature. Closely related mutants come close. Thus metabolic profiles of sugar mutants are different but more similar than those of photosynthesis mutants. All mutants show pleiotropic responses mirrored in their metabolic status. These pleiotropic responses are typical and can be used for separating and grouping of the mutants. Our findings show that metabolite fingerprints can be taken to classify mutants and hence may be used to sort genes into functional groups.