Computational identification of altered metabolism using gene expression and metabolic pathways

Understanding altered metabolism is an important issue because altered metabolism is often revealed as a cause or an effect in pathogenesis. It has also been shown to be an important factor in the manipulation of an organism's metabolism in metabolic engineering. Unfortunately, it is not yet possible to measure the concentration levels of all metabolites in the genome‐wide scale of a metabolic network; consequently, a method that infers the alteration of metabolism is beneficial. The present study proposes a computational method that identifies genome‐wide altered metabolism by analyzing functional units of KEGG pathways. As control of a metabolic pathway is accomplished by altering the activity of at least one rate‐determining step enzyme, not all gene expressions of enzymes in the pathway demonstrate significant changes even if the pathway is altered. Therefore, we measure the alteration levels of a metabolic pathway by selectively observing expression levels of significantly changed genes in a pathway. The proposed method was applied to two strains of Saccharomyces cerevisiae gene expression profiles measured in very high‐gravity (VHG) fermentation. The method identified altered metabolic pathways whose properties are related to ethanol and osmotic stress responses which had been known to be observed in VHG fermentation because of the high sugar concentration in growth media and high ethanol concentration in fermentation products. With the identified altered pathways, the proposed method achieved best accuracy and sensitivity rates for the Red Star (RS) strain compared to other three related studies (gene‐set enrichment analysis (GSEA), significance analysis of microarray to gene set (SAM‐GS), reporter metabolite), and for the CEN.PK 113‐7D (CEN) strain, the proposed method and the GSEA method showed comparably similar performances. Biotechnol. Bioeng. 2009;103: 835–843. © 2009 Wiley Periodicals, Inc.     

Computational identification of gene over-expression targets for metabolic engineering of taxadiene production

Taxadiene is the first dedicated intermediate in the biosynthetic pathway of the anticancer compound Taxol. Recent studies have taken advantage of heterologous hosts to produce taxadiene and other isoprenoid compounds, and such ventures now offer research opportunities that take advantage of the engineering tools associated with the surrogate host. In this study, metabolic engineering was applied in the context of over-expression targets predicted to improve taxadiene production. Identified targets included genes both within and outside of the isoprenoid precursor pathway. These targets were then tested for experimental over-expression in a heterologous Escherichia coli host designed to support isoprenoid biosynthesis. Results confirmed the computationally predicted improvements and indicated a synergy between targets within the expected isoprenoid precursor pathway and those outside this pathway. The presented algorithm is broadly applicable to other host systems and/or product choices.     

Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information

MOTIVATION: The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS: We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.     

Computational integration of homolog and pathway gene module expression reveals general stemness signatures

The stemness hypothesis states that all stem cells use common mechanisms to regulate self-renewal and multi-lineage potential. However, gene expression meta-analyses at the single gene level have failed to identify a significant number of genes selectively expressed by a broad range of stem cell types. We hypothesized that stemness may be regulated by modules of homologs. While the expression of any single gene within a module may vary from one stem cell type to the next, it is possible that the expression of the module as a whole is required so that the expression of different, yet functionally-synonymous, homologs is needed in different stem cells. Thus, we developed a computational method to test for stem cell-specific gene expression patterns from a comprehensive collection of 49 murine datasets covering 12 different stem cell types. We identified 40 individual genes and 224 stemness modules with reproducible and specific up-regulation across multiple stem cell types. The stemness modules included families regulating chromatin remodeling, DNA repair, and Wnt signaling. Strikingly, the majority of modules represent evolutionarily related homologs. Moreover, a score based on the discovered modules could accurately distinguish stem cell-like populations from other cell types in both normal and cancer tissues. This scoring system revealed that both mouse and human metastatic populations exhibit higher stemness indices than non-metastatic populations, providing further evidence for a stem cell-driven component underlying the transformation to metastatic disease.     

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

Site-specific integration and expression of an anti-malarial gene in transgenic Anopheles gambiae significantly reduces Plasmodium infections

Diseases transmitted by mosquitoes have a devastating impact on global health and this is worsening due to difficulties with existing control measures and climate change. Genetically modified mosquitoes that are refractory to disease transmission are seen as having great potential in the delivery of novel control strategies. Historically the genetic modification of insects has relied upon transposable elements which have many limitations despite their successful use. To circumvent these limitations the Streptomyces phage phiC31 integrase system has been successfully adapted for site-specific transgene integration in insects. Here, we present the first site-specific transformation of Anopheles gambiae, the principal vector of human malaria. Mosquitoes were initially engineered to incorporate the phiC31 targeting site at a defined genomic location. A second phase of genetic modification then achieved site-specific integration of Vida3, a synthetic anti-malarial gene. Expression of Vida3, specifically in the midgut of bloodfed females, offered consistent and significant protection against Plasmodium yoelii nigeriensis, reducing average parasite intensity by 85%. Similar protection was observed against Plasmodium falciparum in some experiments, although protection was inconsistent. In the fight against malaria, it is imperative to establish a broad repertoire of both anti-malarial effector genes and tissue-specific promoters for their expression, enabling those offering maximum effect with minimum fitness cost to be identified. In the future, this technology will allow effective comparisons and informed choices to be made, potentially leading to complete transmission blockade.     

Detecting microRNA activity from gene expression data

Background  

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.     

Bayesian normalization and identification for differential gene expression data.

Commonly accepted intensity-dependent normalization in spotted microarray studies takes account of measurement errors in the differential expression ratio but ignores measurement errors in the total intensity, although the definitions imply the same measurement error components are involved in both statistics. Furthermore, identification of differentially expressed genes is usually considered separately following normalization, which is statistically problematic. By incorporating the measurement errors in both total intensities and differential expression ratios, we propose a measurement-error model for intensity-dependent normalization and identification of differentially expressed genes. This model is also flexible enough to incorporate intra-array and inter-array effects. A Bayesian framework is proposed for the analysis of the proposed measurement-error model to avoid the potential risk of using the common two-step procedure. We also propose a Bayesian identification of differentially expressed genes to control the false discovery rate instead of the ad hoc thresholding of the posterior odds ratio. The simulation study and an application to real microarray data demonstrate promising results.     

Microarray data integration for genome-wide analysis of human tissue-selective gene expression

Background

Microarray gene expression data are accumulating in public databases. The expression profiles contain valuable information for understanding human gene expression patterns. However, the effective use of public microarray data requires integrating the expression profiles from heterogeneous sources.

Results

In this study, we have compiled a compendium of microarray expression profiles of various human tissue samples. The microarray raw data generated in different research laboratories have been obtained and combined into a single dataset after data normalization and transformation. To demonstrate the usefulness of the integrated microarray data for studying human gene expression patterns, we have analyzed the dataset to identify potential tissue-selective genes. A new method has been proposed for genome-wide identification of tissue-selective gene targets using both microarray intensity values and detection calls. The candidate genes for brain, liver and testis-selective expression have been examined, and the results suggest that our approach can select some interesting gene targets for further experimental studies.

Conclusion

A computational approach has been developed in this study for combining microarray expression profiles from heterogeneous sources. The integrated microarray data can be used to investigate tissue-selective expression patterns of human genes.

     

Entamoeba histolytica: an ecto-phosphatase activity regulated by oxidation-reduction reactions

In this work, an ecto-phosphatase activity of Entamoeba histolytica was characterized using intact cells. This activity presented the following biochemical characteristics: (i) it hydrolyzes p-NPP with V(max) of 8.00+/-0.22 nmol p-NP x h(-1) x 10(-5) cells and K(m) of 2.68+/-0.25 mM; (ii) it is inhibited by acid phosphatase inhibitors, such as sodium molybdate (K(i)=1.70+/-0.24 microM) and sodium fluoride (K(i)=0.25+/-0.02 mM); (iii) it also showed high sensitivity to phosphotyrosine phosphatase inhibitors, such as sodium orthovanadate (K(i)=1.07+/-0.14 microM), bpV-PHEN (K(i)=0.38+/-0.02 microM) and mpV-PIC (K(i)=0.39+/-0.04 microM). Zn(2+), an oxidizing agent, decreased the enzymatic activity in 50%. DTT and GSH, two reducing agents, enhanced the activity twofold. The non-invasive E. histolytica and free-living E. moshkovskii were less efficient in hydrolyzing p-NPP than the pathogenic E. histolytica suggesting that this enzyme could represent a virulence marker for this cell.