Comparative and Functional Genomics of Rhodococcus opacus PD630 for Biofuels Development

The Actinomycetales bacteria Rhodococcus opacus PD630 and Rhodococcus jostii RHA1 bioconvert a diverse range of organic substrates through lipid biosynthesis into large quantities of energy-rich triacylglycerols (TAGs). To describe the genetic basis of the Rhodococcus oleaginous metabolism, we sequenced and performed comparative analysis of the 9.27 Mb R. opacus PD630 genome. Metabolic-reconstruction assigned 2017 enzymatic reactions to the 8632 R. opacus PD630 genes we identified. Of these, 261 genes were implicated in the R. opacus PD630 TAGs cycle by metabolic reconstruction and gene family analysis. Rhodococcus synthesizes uncommon straight-chain odd-carbon fatty acids in high abundance and stores them as TAGs. We have identified these to be pentadecanoic, heptadecanoic, and cis-heptadecenoic acids. To identify bioconversion pathways, we screened R. opacus PD630, R. jostii RHA1, Ralstonia eutropha H16, and C. glutamicum 13032 for growth on 190 compounds. The results of the catabolic screen, phylogenetic analysis of the TAGs cycle enzymes, and metabolic product characterizations were integrated into a working model of prokaryotic oleaginy.Cambridge-MIT InstituteMassachusetts Institute of Technology. (Seed Grant program)Shell Oil CompanyNational Institute of Allergy and Infectious Diseases (U.S.)United States. National Institutes of HealthNational Institutes of Health. Department of Health and Human Services (Contract No. HHSN272200900006C

Silymarin protects liver against toxic effects of anti-tuberculosis drugs in experimental animals

<p>Abstract</p> <p>Background</p> <p>The first line anti-tuberculosis drugs isoniazid (INH), rifampicin (RIF) and pyrazinamide (PZA) continues to be the effective drugs in the treatment of tuberculosis, however, the use of these drugs is associated with toxic reactions in tissues, particularly in the liver, leading to hepatitis. Silymarin, a standard plant extract with strong antioxidant activity obtained from <it>S. marianum</it>, is known to be an effective agent for liver protection and liver regeneration. The aim of this study was to investigate the protective actions of silymarin against hepatotoxicity caused by different combinations of anti-tuberculosis drugs.</p> <p>Methods</p> <p>Male Wistar albino rats weighing 250–300 g were used to form 6 study groups, each group consisting of 10 rats. Animals were treated with intra-peritoneal injection of isoniazid (50 mg/kg) and rifampicin (100 mg/kg); and intra-gastric administration of pyrazinamid (350 mg/kg) and silymarin (200 mg/kg). Hepatotoxicity was induced by a combination of drugs with INH+RIF and INH+RIF+PZA. Hepatoprotective effect of silymarin was investigated by co-administration of silymarin together with the drugs. Serum biochemical tests for liver functions and histopathological examination of livers were carried out to demonstrate the protection of liver against anti-tuberculosis drugs by silymarin.</p> <p>Results</p> <p>Treatment of rats with INH+RIF or INH+RIF+PZA induced hepatotoxicity as evidenced by biochemical measurements: serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP) activities and the levels of total bilirubin were elevated, and the levels of albumin and total protein were decreased in drugs-treated animals. Histopathological changes were also observed in livers of animals that received drugs. Simultaneous administration of silymarin significantly decreased the biochemical and histological changes induced by the drugs.</p> <p>Conclusion</p> <p>The active components of silymarin had protective effects against hepatotoxic actions of drugs used in the chemotherapy of tuberculosis in animal models. Since no significant toxicity of silymarin is reported in human studies, this plant extract can be used as a dietary supplement by patients taking anti-tuberculosis medications.</p

Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data. Author Summary The ability of cells to survive and grow depends on their ability to metabolize nutrients and create products vital for cell function. This is done through a complex network of reactions controlled by many genes. Changes in cellular metabolism play a role in a wide variety of diseases. However, despite the availability of genome sequences and of genome-scale expression data, which give information about which genes are present and how active they are, our ability to use these data to understand changes in cellular metabolism has been limited. We present a new approach to this problem, linking gene expression data with models of cellular metabolism. We apply the method to predict the effects of drugs and agents on Mycobacterium tuberculosis (M. tb). Virulence, growth in human hosts, and drug resistance are all related to changes in M. tb's metabolism. We predict the effects of a variety of conditions on the production of mycolic acids, essential cell wall components. Our method successfully identifies seven of the eight known mycolic acid inhibitors in a compendium of 235 conditions, and identifies the top anti-TB drugs in this dataset. We anticipate that the method will have a range of applications in metabolic engineering, the characterization of disease states, and drug discovery.: National Institute of Allergy and Infectious Disease; National Institutes of Health (HHSN 26620040000IC); Department of Health and Human Services (HHSN266200400001C) National Institue of Allergy and Infectious Diseases (1U19AI076217, R01 071155, 014334-001); the Bill & Melinda Gates Foundation Dedicated Tuberculosis Gene Expression Database; the Ellison Medical Foundation (ID-SS-0693-04); Burroughs Wellcome Fun

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here

Management of multidrug resistant Gram-negative bacilli infections in solid organ transplant recipients: SET/GESITRA-SEIMC/REIPI recommendations

Solid organ transplant (SOT) recipients are especially at risk of developing infections by multidrug resistant (MDR) Gram-negative bacilli (GNB), as they are frequently exposed to antibiotics and the healthcare setting, and are regulary subject to invasive procedures. Nevertheless, no recommendations concerning prevention and treatment are available. A panel of experts revised the available evidence; this document summarizes their recommendations: (1) it is important to characterize the isolate´s phenotypic and genotypic resistance profile; (2) overall, donor colonization should not constitute a contraindication to transplantation, although active infected kidney and lung grafts should be avoided; (3) recipient colonization is associated with an increased risk of infection, but is not a contraindication to transplantation; (4) different surgical prophylaxis regimens are not recommended for patients colonized with carbapenem-resistant GNB; (5) timely detection of carriers, contact isolation precautions, hand hygiene compliance and antibiotic control policies are important preventive measures; (6) there is not sufficient data to recommend intestinal decolonization; (7) colonized lung transplant recipients could benefit from prophylactic inhaled antibiotics, specially for Pseudomonas aeruginosa; (8) colonized SOT recipients should receive an empirical treatment which includes active antibiotics, and directed therapy should be adjusted according to susceptibility study results and the severity of the infection.J.T.S. holds a research contract from the Fundación para la Formación e Investigación de los Profesionales de la Salud de Extremadura (FundeSalud), Instituto de Salud Carlos III. M.F.R. holds a clinical research contract “Juan Rodés” (JR14/00036) from the Spanish Ministry of Economy and Competitiveness, Instituto de Salud Carlos III