A data-driven approach for exploiting enzyme promiscuity as a means to predict novel biochemical reactions

Systems metabolic engineering has been widely used to produce chemicals of high commercial value from low cost substrates. But this process has challenges for some applications, such as harnessing lignocellulosic biomass for biofuel and biochemical production, due to our limited metabolic knowledgebase. With current advances in protein engineering, it is possible to exploit substrate promiscuity of enzymes to enable novel biochemical reactions. Nevertheless, performing experiments to determine what substrates an enzyme can act on can be time consuming and it is not always clear what potential substrates to test. So, the current work aims to employ machine learning approaches for identifying novel substrates and in turn, predicting novel reactions that are more promising than the putative reactions predicted simply based on compound similarity measures (e.g., Tanimoto coefficient). A highly accurate (up to 88.3%) machine learning model was developed to identify candidate substrates for alcohol dehydrogenase (ADH) using a dataset consisting of 23 metabolites (with 8 of them being known positives) and 46 chemo-informatics based molecular descriptors (e.g., topology, stereochemistry, and electronic features). In addition, support vector regression proved to be a useful method for estimating enzyme kinetics (characterized by Michaelis-Menten constants, Km and Vmax) for a variety of oxidoreductases that are typically found in biofuel biosynthesis pathways. Such machine learning methods can be applied to other classes of enzymes and hence, used as a tool to expand the knowledgebase of metabolic reactions paving the way for next generation of metabolic/ pathway engineering. Please click Additional Files below to see the full abstract

Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets

<p>Abstract</p> <p>Background:</p> <p><it>Mycobacterium tuberculosis </it>continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them.</p> <p>Results:</p> <p>We completed a bottom up reconstruction of the metabolic network of <it>Mycobacterium tuberculosis </it>H37Rv. This functional <it>in silico </it>bacterium, <it>iNJ</it>661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium <it>in silico </it>on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints.</p> <p>Conclusion:</p> <p>Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between <it>in vitro </it>and <it>in silico </it>or <it>in vivo </it>and <it>in silico </it>results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.</p

Quantum-chemical investigation of the structure and the antioxidant properties of α-lipoic acid and its metabolites

Quantum-chemical computations were used to investigate the structure–antioxidant parameter relationships of α-lipoic acid and its natural metabolites bisnorlipoic acid and tetranorlipoic acid in their oxidized and reduced forms. The enantiomers of lipoic and dihydrolipoic acid were optimized using the B3LYP/6-311+G(3df,2p), B3LYP/aug-cc-pVDZ and MP2(full)/6-31+G(d,p) levels of theory as isolated molecules and in the presence of water. The geometries of the metabolites and the values of their antioxidant parameters (proton affinity, bond dissociation enthalpy, adiabatic ionization potential, spin density, and the highest occupied molecular orbital energy) were calculated at the B3LYP/6-311+G(3df,2p) level of theory. The results obtained reveal similarities between these structures: a pentatomic, nonaromatic ring is present in the oxidized forms, while an unbranched aliphatic chain (as found in saturated fatty acids) is present in both the oxidized and the reduced forms. Analysis of the spin density and the highest occupied molecular orbital energy revealed that the SH groups exhibited the greatest electron-donating activities. The values obtained for the proton affinity, bond dissociation enthalpy and adiabatic ionization potential indicate that the preferred antioxidant mechanisms for α-lipoic acid and its metabolites are sequential proton loss electron transfer in polar media and hydrogen atom transfer in vacuum

?2-Microglobulin Amyloid Fibril-Induced Membrane Disruption Is Enhanced by Endosomal Lipids and Acidic pH

Although the molecular mechanisms underlying the pathology of amyloidoses are not well understood, the interaction between amyloid proteins and cell membranes is thought to play a role in several amyloid diseases. Amyloid fibrils of ?2-microglobulin (?2m), associated with dialysis-related amyloidosis (DRA), have been shown to cause disruption of anionic lipid bilayers in vitro. However, the effect of lipid composition and the chemical environment in which ?2m-lipid interactions occur have not been investigated previously. Here we examine membrane damage resulting from the interaction of ?2m monomers and fibrils with lipid bilayers. Using dye release, tryptophan fluorescence quenching and fluorescence confocal microscopy assays we investigate the effect of anionic lipid composition and pH on the susceptibility of liposomes to fibril-induced membrane damage. We show that ?2m fibril-induced membrane disruption is modulated by anionic lipid composition and is enhanced by acidic pH. Most strikingly, the greatest degree of membrane disruption is observed for liposomes containing bis(monoacylglycero)phosphate (BMP) at acidic pH, conditions likely to reflect those encountered in the endocytic pathway. The results suggest that the interaction between ?2m fibrils and membranes of endosomal origin may play a role in the molecular mechanism of ?2m amyloid-associated osteoarticular tissue destruction in DRA

The Environmental Dependence of Inbreeding Depression in a Wild Bird Population

BACKGROUND: Inbreeding depression occurs when the offspring produced as a result of matings between relatives show reduced fitness, and is generally understood as a consequence of the elevated expression of deleterious recessive alleles. How inbreeding depression varies across environments is of importance for the evolution of inbreeding avoidance behaviour, and for understanding extinction risks in small populations. However, inbreeding-by-environment (IxE) interactions have rarely been investigated in wild populations. METHODOLOGY/PRINCIPAL FINDINGS: We analysed 41 years of breeding events from a wild great tit (Parus major) population and used 11 measures of the environment to categorise environments as relatively good or poor, testing whether these measures influenced inbreeding depression. Although inbreeding always, and environmental quality often, significantly affected reproductive success, there was little evidence for statistically significant I x E interactions at the level of individual analyses. However, point estimates of the effect of the environment on inbreeding depression were sometimes considerable, and we show that variation in the magnitude of the I x E interaction across environments is consistent with the expectation that this interaction is more marked across environmental axes with a closer link to overall fitness, with the environmental dependence of inbreeding depression being elevated under such conditions. Hence, our analyses provide evidence for an environmental dependence of the inbreeding x environment interaction: effectively an I x E x E. CONCLUSIONS/SIGNIFICANCE: Overall, our analyses suggest that I x E interactions may be substantial in wild populations, when measured across relevant environmental contrasts, although their detection for single traits may require very large samples, or high rates of inbreeding

Expression of an Epitope-Tagged Virulence Protein in Rickettsia parkeri Using Transposon Insertion

Despite recent advances in our ability to genetically manipulate Rickettsia, little has been done to employ genetic tools to study the expression and localization of Rickettsia virulence proteins. Using a mariner-based Himar1 transposition system, we expressed an epitope-tagged variant of the actin polymerizing protein RickA under the control of its native promoter in Rickettsia parkeri, allowing the detection of RickA using commercially-available antibodies. Native RickA and epitope-tagged RickA exhibited similar levels of expression and were specifically localized to bacteria. To further facilitate protein expression in Rickettsia, we also developed a plasmid for Rickettsia insertion and expression (pRIE), containing a variant Himar1 transposon with enhanced flexibility for gene insertion, and used it to generate R. parkeri strains expressing diverse fluorescent proteins. Expression of epitope-tagged proteins in Rickettsia will expand our ability to assess the regulation and function of important virulence factors

Epilysin (matrix metalloproteinase-28) contributes to airway epithelial cell survival

MMP28 is constitutively expressed by epithelial cells in many tissues, including the respiratory epithelium in the lung and keratinocytes in the skin. This constitutive expression suggests that MMP28 may serve a role in epithelial cell homeostasis. In an effort to determine its function in epithelial cell biology, we generated cell lines expressing wild-type or catalytically-inactive mutant MMP28 in two pulmonary epithelial cell lines, A549 and BEAS-2B. We observed that over-expression of MMP28 provided protection against apoptosis induced by either serum-deprivation or treatment with a protein kinase inhibitor, staurosporine. Furthermore, we observed increased caspase-3/7 activity in influenza-infected lungs from Mmp28-/- mice compared to wild-type mice, and this activity localized to the airway epithelium but was not associated with a change in viral load. Thus, we have identified a novel role of MMP28 in promoting epithelial cell survival in the lung

Identification of novel associations and localization of signals in idiopathic inflammatory myopathies using genome-wide imputation

Objective: The idiopathic inflammatory myopathies (IIMs) are heterogeneous diseases thought to be initiated by immune activation in genetically predisposed individuals. We imputed variants from the ImmunoChip array using a large reference panel to fine-map associations and identify novel associations in IIM. Methods: We analyzed 2,565 Caucasian IIM patient samples collected through the Myositis Genetics Consortium (MYOGEN) and 10,260 ethnically matched control samples. We imputed 1,648,116 variants from the ImmunoChip array using the Haplotype Reference Consortium panel and conducted association analysis on IIM and clinical and serologic subgroups. Results: The HLA locus was consistently the most significantly associated region. Four non-HLA regions reached genome-wide significance, SDK2 and LINC00924 (both novel) and STAT4 in the whole IIM cohort, with evidence of independent variants in STAT4, and NAB1 in the polymyositis (PM) subgroup. We also found suggestive evidence of association with loci previously associated with other autoimmune rheumatic diseases (TEC and LTBR). We identified more significant associations than those previously reported in IIM for STAT4 and DGKQ in the total cohort, for NAB1 and FAM167A-BLK loci in PM, and for CCR5 in inclusion body myositis. We found enrichment of variants among DNase I hypersensitivity sites and histone marks associated with active transcription within blood cells. Conclusion: We found novel and strong associations in IIM and PM and localized signals to single genes and immune cell types

Sometimes You Cannot Have It All: Party Switching and Affiliation Motivations as Substitutes

Existing research on when legislators switch parties reports inconsistent results about motivations for switching (e.g., office, ideology, and votes). I treat the motivations for party switching as substitutes and argue that many of the inconsistencies that persist can be explained by modelling the interactive effects between these motivations. For example, scholars differ in terms of whether they find that electoral considerations are an important determinant of party switching. The conflicting findings on the independent effects of electoral considerations are explained here by demonstrating that these effects are conditional on the level of office benefits a legislators enjoys, as well as the ideological distance between the legislator and party. More generally, the empirical analysis provides strong support for the substitution effect hypothesis. Thus, modelling interactive effects increases our understanding of party switching