A complete collection of single‐gene deletion mutants of Acinetobacter baylyi ADP1

We have constructed a collection of single‐gene deletion mutants for all dispensable genes of the soil bacterium Acinetobacter baylyi ADP1. A total of 2594 deletion mutants were obtained, whereas 499 (16%) were not, and are therefore candidate essential genes for life on minimal medium. This essentiality data set is 88% consistent with the Escherichia coli data set inferred from the Keio mutant collection profiled for growth on minimal medium, while 80% of the orthologous genes described as essential in Pseudomonas aeruginosa are also essential in ADP1. Several strategies were undertaken to investigate ADP1 metabolism by (1) searching for discrepancies between our essentiality data and current metabolic knowledge, (2) comparing this essentiality data set to those from other organisms, (3) systematic phenotyping of the mutant collection on a variety of carbon sources (quinate, 2‐3 butanediol, glucose, etc.). This collection provides a new resource for the study of gene function by forward and reverse genetic approaches and constitutes a robust experimental data source for systems biology approaches.     

Influence of phospholipid long chain polyunsaturated fatty acid composition on neonatal rat cardiomyocyte function in physiological conditions and during glucose-free hypoxia-reoxygenation

There is evidence that dietary polyunsaturated fatty acids (PUFA) may protect against cardiovascular diseases, but the involvement of the cardiac muscle cell in this beneficial action remain largely unknown. The present study compared the respective influence of n-3 and n-6 PUFA on the function of cultured neonatal rat cardiomyocytes (CM). Cells were grown for 4 days in media enriched either n-3 (eicosapentaenoic acid, EPA and docosahexaenoic acid, DHA) or n-6 (arachidonic acid, AA) PUFA. The PUFA n-6/n-3 ratio in the phospholipids was close to 1 and 20 in the n-3 and n-6 cells, respectively. The transmembrane potentials were recorded using microelectrodes and the contractions were monitored with a photoelectric device. In physiological conditions, the increase of n-6 PUFA level in the phospholipids resulted in a significant decrease in the maximal rate of initial depolarization (–16%). In opposition, the action potential amplitude and duration were not altered, and the cell contractio n outline was not affected. Ischemia was simulated in vitro using a substrate-free, hypoxia-reoxygenation procedure in a specially designed gas-flow chamber. The progressive loss of electrical activity induced by the substrate-free, hypoxic treatment was affected by the n-6/n-3 ratio, since the n-6 rich CM displayed a slower depression of the AP amplitude and duration parameters. Conversely, the recovery of the resting potential (MDP) during reoxygenation was faster in n-3 CM, whereas the recovery of the contraction parameters was unaffected by the fatty acid composition of the cells. These results suggested that, in physiological conditions, the modification of long chain PUFA balance in the phospholipids of cardiac muscle cells may modulate the initial AP upstroke, which is governed by sodium channels. Moreover, the presence of n-3 PUFA appeared to accelerate the electrical depression during substrate-free hypoxia but in turn to allow a faster recovery upon reoxygenation. (Mol Cell Biochem 175: 253–262, 1997)     

Genome-scale models of bacterial metabolism: reconstruction and applications

Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities.     

Dependence on the phospholipid polyunsaturated fatty acids of the oxidative injury of isolated cardiomyocytes

We investigated the influence of PUFA in phospholipids (PL) on the functional characteristics of cultured cardiomyocytes (CM) in basal conditions and during free radical (FR) stress provoked either by the xanthine/xanthine oxidase (X/XO) system or by a (9Z, 11E, 13 (S), 15Z)-13-hydroperoxyoctadecatrienoic acid (13-HpOTrE). CM were grown in media containing either n - 3 (eicosapentaenoic acid, EPA, and docosahexaenoic acid, DHA) or n - 6 (arachidonic acid, AA). These two groups of CM displayed different PUFA n - 6/n - 3 ratio in PL. However, their basal electromechanical characteristics were similar. The X/XO system drastically altered CM functions, without difference between the two groups of CM. 13-HpOTrE caused a moderate and reversible depression in action potential parameters, which was dependent upon the PL PUFA, since the n - 3-enriched CM exhibited an earlier functional depression but faster recovery. Thus, the peroxidative damage of CM depended on a cross relationship between FR species and the PL PUFA composition.     

Core and panmetabolism in Escherichia coli

Escherichia coli exhibits a wide range of lifestyles encompassing commensalism and various pathogenic behaviors which its highly dynamic genome contributes to develop. How environmental and host factors shape the genetic structure of E. coli strains remains, however, largely unknown. Following a previous study of E. coli genomic diversity, we investigated its diversity at the metabolic level by building and analyzing the genome-scale metabolic networks of 29 E. coli strains (8 commensal and 21 pathogenic strains, including 6 Shigella strains). Using a tailor-made reconstruction strategy, we significantly improved the completeness and accuracy of the metabolic networks over default automatic reconstruction processes. Among the 1,545 reactions forming E. coli panmetabolism, 885 reactions were common to all strains. This high proportion of core reactions (57%) was found to be in sharp contrast to the low proportion (13%) of core genes in the E. coli pangenome, suggesting less diversity of metabolic functions compared to that of all gene functions. Core reactions were significantly overrepresented among biosynthetic reactions compared to the more variable degradation processes. Differences between metabolic networks were found to follow E. coli phylogeny rather than pathogenic phenotypes, except for Shigella networks, which were significantly more distant from the others. This suggests that most metabolic changes in non-Shigella strains were not driven by their pathogenic phenotypes. Using a supervised method, we were yet able to identify small sets of reactions related to pathogenicity or commensalism. The quality of our reconstructed networks also makes them reliable bases for building metabolic models.     

High‐throughput metabolomics predicts drug–target relationships for eukaryotic proteins

Chemical probes are important tools for understanding biological systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high‐throughput metabolomics and overexpression to predict drug–target interactions. The metabolome profiles of yeast treated with 1,280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug–target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug–target relationships for eukaryotic proteins using high‐throughput metabolomics.