A novel data mining method to identify assay-specific signatures in functional genomic studies

BACKGROUND: The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies. RESULTS: The proposed method (PM) is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data – a study of classification of human tissue samples and a study of E. coli cultures with different medium formulations. CONCLUSION: We have developed a PCA-based method that effectively identifies assay-specific signatures in ranked groups of genes from the full data set in a more efficient and simplistic procedure than current approaches. Although this work demonstrates the ability of the PM to identify assay-specific signatures in DNA microarray experiments, this approach could be useful in areas such as proteomics and metabolomics

Metabolic flux analysis of fermentative carbon metabolism in Escherichia coli

Recently, the production of various chemicals and fuels via microbial fermentation has gained momentum. Development of efficient bio-processes requires a system-scale understanding of the metabolic network of biocatalysts. In this context, we evaluated the roles of metabolic pathways and enzymes during fermentation of various substrates in E. coli using metabolic flux analysis (MFA), which is a powerful and efficient tool for comprehensive investigation of a biological system. The combination of substrates studied covered the full range of oxidation states of common carbon sources. During fermentation, pyruvate is a key precursor metabolite and a prominent intermediate for the synthesis of most fermentation products in E. coli. Under fermentative conditions, pyruvate is primarily dissimilated via pyruvate formate lyase (PFL), and pyruvate dehydrogenase (PDH) exhibits negligible activity with unknown physiological role. However, we found that the activity of PDH was required for efficient fermentative growth of E. coli. PDH was even able to support fermentative growth on glucuronate in a strain devoid of PFL. MFA indicated that a deletion of PDH leads to more than 10 fold increase in flux through oxidative pentose pathway. These results were supported by the 13C labeling based flux analysis. Subsequently, the hypothesized the role of PDH: to efficiently generate CO 2, assisting cell growth during glucose fermentation and to efficiently generate reducing equivalents aiding cell growth during glucuronate fermentation. In silico flux analysis was used to design further genetic modifications and supplementation experiments, which were instrumental in verifying our hypothesis. On the other hand, the fermentation of glycerol by E. coli, had been unknown until recently. In this study, we identified the factors facilitating this process in E. coli. Nuclear magnetic resonance (NMR) analysis of fermentation samples identified ethanol and 1,2-propanediol (1,2-PDO) as products of glycerol fermentation. Employing 13C tracer experiments, we demonstrated that majority of the fermentation products and about 20% of the biomass building blocks originate from glycerol. In silico flux analysis was instrumental in elucidating the role of active pathways during glycerol fermentation: redox-balanced pathway to ethanol generates energy for cell growth, and the redox consuming pathway synthesizing 1,2-PDO facilitates cell growth by enabling redox balance

Anaerobic Fermentation of Glycerol in Paenibacillus macerans: Metabolic Pathways and Environmental Determinants▿

Paenibacillus macerans is one of the species with the broadest metabolic capabilities in the genus Paenibacillus, able to ferment hexoses, deoxyhexoses, pentoses, cellulose, and hemicellulose. However, little is known about glycerol metabolism in this organism, and some studies have reported that glycerol is not fermented. Despite these reports, we found that several P. macerans strains are capable of anaerobic fermentation of glycerol. One of these strains, P. macerans N234A, grew fermentatively on glycerol at a maximum specific growth rate of 0.40 h−1 and was chosen for further characterization. The use of [U-13C]glycerol and further analysis of extracellular metabolites and proteinogenic amino acids via nuclear magnetic resonance (NMR) spectroscopy allowed identification of ethanol, formate, acetate, succinate, and 1,2-propanediol (1,2-PDO) as fermentation products and demonstrated that glycerol is incorporated into cellular components. A medium formulation with low concentrations of potassium and phosphate, cultivation at acidic pH, and the use of a CO2-enriched atmosphere stimulated glycerol fermentation and are proposed to be environmental determinants of this process. The pathways involved in glycerol utilization and synthesis of fermentation products were identified using NMR spectroscopy in combination with enzyme assays. Based on these studies, the synthesis of ethanol and 1,2-PDO is proposed to be a metabolic determinant of glycerol fermentation in P. macerans N234A. Conversion of glycerol to ethanol fulfills energy requirements by generating one molecule of ATP per molecule of ethanol synthesized. Conversion of glycerol to 1,2-PDO results in the consumption of reducing equivalents, thus facilitating redox balance. Given the availability, low price, and high degree of reduction of glycerol, the high metabolic rates exhibited by P. macerans N234A are of paramount importance for the production of fuels and chemicals

Approaches and Recent Developments for the Commercial Production of Semi-synthetic Artemisinin

The antimalarial drug artemisinin is a natural product produced by the plant Artemisia annua. Extracts of A. annua have been used in Chinese herbal medicine for over two millennia. Following the re-discovery of A. annua extract as an effective antimalarial, and the isolation and structural elucidation of artemisinin as the active agent, it was recommended as the first-line treatment for uncomplicated malaria in combination with another effective antimalarial drug (Artemisinin Combination Therapy) by the World Health Organization (WHO) in 2002. Following the WHO recommendation, the availability and price of artemisinin fluctuated greatly, ranging from supply shortfalls in some years to oversupply in others. To alleviate these supply and price issues, a second source of artemisinin was sought, resulting in an effort to produce artemisinic acid, a late-stage chemical precursor of artemisinin, by yeast fermentation, followed by chemical conversion to artemisinin (i.e., semi-synthesis). Engineering to enable production of artemisinic acid in yeast relied on the discovery of A. annua genes encoding artemisinic acid biosynthetic enzymes, and synthetic biology to engineer yeast metabolism. The progress of this effort, which resulted in semi-synthetic artemisinin entering commercial production in 2013, is reviewed with an emphasis on recent publications and opportunities for further development. Aspects of both the biology of artemisinin production in A. annua, and yeast strain engineering are discussed, as are recent developments in the chemical conversion of artemisinic acid to artemisinin

Metabolic analysis of wild-type Escherichia coli and a Pyruvate Dehydrogenase Complex (PDHC)-deficient derivative reveals the role of PDHC in the fermentative metabolism of glucose

Pyruvate is located at a metabolic junction of assimilatory and dissimilatory pathways and represents a switch point between respiratory and fermentative metabolism. In Escherichia coli, the pyruvate dehydrogenase complex (PDHC) and pyruvate formate-lyase are considered the primary routes of pyruvate conversion to acetyl-CoA for aerobic respiration and anaerobic fermentation, respectively. During glucose fermentation, the in vivo activity of PDHC has been reported as either very low or undetectable, and the role of this enzyme remains unknown. In this study, a comprehensive characterization of wild-type E. coli MG1655 and a PDHC-deficient derivative (Pdh) led to the identification of the role of PDHC in the anaerobic fermentation of glucose. The metabolism of these strains was investigated by using a mixture of 13C-labeled and -unlabeled glucose followed by the analysis of the labeling pattern in protein-bound amino acids via two-dimensional 13C, 1H NMR spectroscopy. Metabolite balancing, biosynthetic 13C labeling of proteinogenic amino acids, and isotopomer balancing all indicated a large increase in the flux of the oxidative branch of the pentose phosphate pathway (ox-PPP) in response to the PDHC deficiency. Because both ox-PPP and PDHC generate CO 2 and the calculated CO 2 evolution rate was significantly reduced in Pdh, it was hypothesized that the role of PDHC is to provide CO 2 for cell growth. The similarly negative impact of either PDHC or ox-PPP deficiencies, and an even more pronounced impairment of cell growth in a strain lacking both ox-PPP and PDHC, provided further support for this hypothesis. The three strains exhibited similar phenotypes in the presence of an external source of CO 2, thus confirming the role of PDHC. Activation of formate hydrogen-lyase (which converts formate to CO 2 and H 2) rendered the PDHC deficiency silent, but its negative impact reappeared in a strain lacking both PDHC and formate hydrogen-lyase. A stoichiometric analysis of CO 2 generation via PDHC and ox-PPP revealed that the PDHC route is more carbon- and energy-efficient, in agreement with its beneficial role in cell growth.This research was originally published in Journal of Biological Chemistry. Abhishek Murarka, James M. Clomburg, Sean Moran, Jacqueline V. Shanks, and Ramon Gonzalez.JMetabolic Analysis of Wild-type Escherichia coli and a Pyruvate Dehydrogenase Complex (PDHC)-deficient Derivative Reveals the Role of PDHC in the Fermentative Metabolism of Glucose. Journal of Biological Chemistry. 2010;Vol.:285, pp.31548-31558.© the American Society for Biochemistry and Molecular Biology.</p

A novel data mining method to identify assay-specific signatures in functional genomic studies

Background: The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies. Results: The proposed method (PM) is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data - a study of classification of human tissue samples and a study of E coli cultures with different medium formulations. Conclusion: We have developed a PCA-based method that effectively identifies assay-specific signatures in ranked groups of genes from the full data set in a more efficient and simplistic procedure than current approaches. Although this work demonstrates the ability of the PM to identify assay-specific signatures in DNA microarray experiments, this approach could be useful in areas such as proteomics and metabolomics.This article is from BMC Bioinformatics 7 (2006): article no. 377, doi: 10.1186/1471-2105-7-377.</p