Genome-wide screen for modulation of hepatic apolipoprotein A-I (ApoA-I) secretion

Control of plasma cholesterol levels is a major therapeutic strategy for management of coronary artery disease (CAD). Although reducing LDL cholesterol (LDL-c) levels decreases morbidity and mortality, this therapeutic intervention only translates into a 25-40% reduction in cardiovascular events. Epidemiological studies have shown that a high LDL-c level is not the only risk factor for CAD; low HDL cholesterol (HDL-c) is an independent risk factor for CAD. Apolipoprotein A-I (ApoA-I) is the major protein component of HDL-c that mediates reverse cholesterol transport from tissues to the liver for excretion. Therefore, increasing ApoA-I levels is an attractive strategy for HDL-c elevation. Using genome-wide siRNA screening, targets that regulate hepatocyte ApoA-I secretion were identified through transfection of 21,789 siRNAs into hepatocytes whereby cell supernatants were assayed for ApoA-I. Approximately 800 genes were identified and triaged using a convergence of information, including genetic associations with HDL-c levels, tissue-specific gene expression, druggability assessments, and pathway analysis. Fifty-nine genes were selected for reconfirmation; 40 genes were confirmed. Here we describe the siRNA screening strategy, assay implementation and validation, data triaging, and example genes of interest. The genes of interest include known and novel genes encoding secreted enzymes, proteases, G-protein-coupled receptors, metabolic enzymes, ion transporters, and proteins of unknown function. Repression of farnesyltransferase (FNTA) by siRNA and the enzyme inhibitor manumycin A caused elevation of ApoA-I secretion from hepatocytes and from transgenic mice expressing hApoA-I and cholesterol ester transfer protein transgenes. In total, this work underscores the power of functional genetic assessment to identify new therapeutic targets.

DNA microarray data integration by ortholog gene analysis reveals potential molecular mechanisms of estrogen-dependent growth of human uterine fibroids

Background

Uterine fibroids or leiomyoma are a common benign smooth muscle tumor. The tumor growth is well known to be estrogen-dependent. However, the molecular mechanisms of its estrogen-dependency is not well understood.

Methods

Differentially expressed genes in human uterine fibroids were either retrieved from published papers or from our own statistical analysis of downloaded array data. Probes for the same genes on different Affymetrix chips were mapped based on probe comparison information provided by Affymetrix. Genes identified by two or three array studies were submitted for ortholog analysis. Human and rat ortholog genes were identified by using ortholog gene databases, HomoloGene and TOGA and were confirmed by synteny analysis with MultiContigView tool in the Ensembl genome browser.

Results

By integrated analysis of three recently published DNA microarray studies with human tissue, thirty-eight genes were found to be differentially expressed in the same direction in fibroid compared to adjacent uterine myometrium by at least two research groups. Among these genes, twelve with rat orthologs were identified as estrogen-regulated from our array study investigating uterine expression in ovariectomized rats treated with estrogen. Functional and pathway analyses of the twelve genes suggested multiple molecular mechanisms for estrogen-dependent cell survival and tumor growth. Firstly, estrogen increased expression of the anti-apoptotic PCP4 gene and suppressed the expression of growth inhibitory receptors PTGER3 and TGFBR2. Secondly, estrogen may antagonize PPARγ signaling, thought to inhibit fibroid growth and survival, at two points in the PPAR pathway: 1) through increased ANXA1 gene expression which can inhibit phospholipase A2 activity and in turn decrease arachidonic acid synthesis, and 2) by decreasing L-PGDS expression which would reduce synthesis of PGJ2, an endogenous ligand for PPARγ. Lastly, estrogen affects retinoic acid (RA) synthesis and mobilization by regulating expression of CRABP2 and ALDH1A1. RA has been shown to play a significant role in the development of uterine fibroids in an animal model.

Conclusion

Integrated analysis of multiple array datasets revealed twelve human and rat ortholog genes that were differentially expressed in human uterine fibroids and transcriptionally responsive to estrogen in the rat uterus. Functional and pathway analysis of these genes suggest multiple potential molecular mechanisms for the poorly understood estrogen-dependent growth of uterine fibroids. Fully understanding the exact molecular interactions among these gene products requires further study to validate their roles in uterine fibroids. This work provides new avenues of study which could influence the future direction of therapeutic intervention for the disease.

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems' blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.

Identification of genes expressed during Xenopus laevis limb regeneration by using subtractive hybridization

Suppression polymerase chain reaction-based subtractive hybridization was used to identify genes that are expressed during Xenopus laevis hindlimb regeneration. Subtractions were done by using RNAs extracted from the regeneration-competent stage (stage 53) and regeneration-incompetent stage (stage 59) of limb development. Forward and reverse subtractions were done between stage 53 7-day blastema and stage 53 contralateral limb (competent stage), stage 59 7-day pseudoblastema and stage 59 contralateral limb (incompetent stage), and stage 53 7-day blastema and stage 59 7-day pseudoblastema. Several thousand clones were analyzed from the various subtracted libraries, either by random selection and sequencing (1,920) or by screening subtracted cDNA clones (6,150), arrayed on nylon membranes, with tissue-specific probes. Several hundred clones were identified from the array screens whose expression levels were at least twofold higher in experimental tissue vs. control tissue (e.g., blastema vs. limb) and selected for sequencing. In addition, primers were designed to assay several of the randomly selected clones and used to assess the level of expression of these genes during regeneration and normal limb development. Approximately half of the selected clones were differentially expressed, as expected, including several that demonstrate blastema-specific enhancement of expression. Three distinct categories of expression were identified in our screens: (1) clones that are expressed in both regeneration-competent blastemas and -incompetent pseudoblastemas, (2) clones that are expressed at highest levels in regeneration-competent blastemas, and (3) clones that are expressed at highest levels in regeneration-incompetent pseudoblastemas. Characterizing the role of each of these three categories of genes will be important in furthering our understanding of the process of tissue regeneration.

An intelligent biological information management system

MOTIVATION

As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to sift through these vast volumes of heterogeneous data and analysis tools. In this paper we present a general model for an information management system that is adaptable and scalable, followed by a detailed design and implementation of one component of the model. The prototype, called BioSifter, was applied to problems in the bioinformatics area.

RESULTS

BioSifter was tested using 500 documents obtained from PubMed database on two biological problems related to genetic polymorphism and extracorporal shockwave lithotripsy. The results indicate that BioSifter is a powerful tool for biological researchers to automatically retrieve relevant text documents from biological literature based on their interest profile. The results also indicate that the first stage of information management process, i.e. data to information transformation, significantly reduces the size of the information space. The filtered data obtained through BioSifter is relevant as well as much smaller in dimension compared to all the retrieved data. This would in turn significantly reduce the complexity associated with the next level transformation, i.e. information to knowledge.