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Biomedical subjects

Amir Ben-Dor

Publications and source records attributed to Amir Ben-Dor.

9 recordsLinked to original sources
PubMed2003

Novel role for the potent endogenous inotrope apelin in human cardiac dysfunction.

BACKGROUND: Apelin is among the most potent stimulators of cardiac contractility known. However, no physiological or pathological role for apelin-angiotensin receptor-like 1 (APJ) signaling has ever been described. METHODS AND RESULTS: We performed transcriptional profiling using a spotted cDNA microarray with 12 814 unique clones on paired samples of left ventricle obtained before and after placement of a left ventricular assist device in 11 patients. The significance analysis of microarrays and a novel rank consistency score designed to exploit the paired structure of the data confirmed that natriuretic peptides were among the most significantly downregulated genes after offloading. The most significantly upregulated gene was the G-protein-coupled receptor APJ, the specific receptor for apelin. We demonstrate here using immunoassay and immunohistochemical techniques that apelin is localized primarily in the endothelium of the coronary arteries and is found at a higher concentration in cardiac tissue after mechanical offloading. These findings imply an important paracrine signaling pathway in the heart. We additionally extend the clinical significance of this work by reporting for the first time circulating human apelin levels and demonstrating increases in the plasma level of apelin in patients with left ventricular dysfunction. CONCLUSIONS: The apelin-APJ signaling pathway emerges as an important novel mediator of cardiovascular control.

Adolescent↗
PubMed2003

Identification of endothelial cell genes by combined database mining and microarray analysis.

Vascular endothelial cells maintain the interface between the systemic circulation and soft tissues and mediate critical processes such as inflammation in a vascular bed-selective fashion. To expand our understanding of the genetic pathways that underlie these specific functions, we have focused on the identification of novel genes that are differentially expressed in all endothelial cells, as well as restricted groups of this cell type. Virtual subtraction was conducted employing gene expression data deposited in public databases and 384 genes identified. These genes were spotted on custom microarrays, along with 288 genes identified through subtraction cloning from TGF-beta-stimulated endothelial cells. Arrays were evaluated with RNA samples representing endothelial cells cultured from four vascular sources and five non-endothelial cell types. These studies identified 64 pan-endothelial markers that were differentially expressed with at least a threefold difference (range 3- to 55-fold). In addition, differences in gene expression profiles among endothelial cells from different vascular beds were identified. Validation of these findings was performed by RNA blot expression studies, and a number of the novel genes were shown to be expressed under angiogenic conditions in the developing mouse embryo. The combined tools of database mining and transcriptional profiling thus provide expanded knowledge of endothelial cell gene expression and endothelial cell biology.

Adult↗
PubMed2003

Molecular classification of familial non-BRCA1/BRCA2 breast cancer.

In the decade since their discovery, the two major breast cancer susceptibility genes BRCA1 and BRCA2, have been shown conclusively to be involved in a significant fraction of families segregating breast and ovarian cancer. However, it has become equally clear that a large proportion of families segregating breast cancer alone are not caused by mutations in BRCA1 or BRCA2. Unfortunately, despite intensive effort, the identification of additional breast cancer predisposition genes has so far been unsuccessful, presumably because of genetic heterogeneity, low penetrance, or recessive/polygenic mechanisms. These non-BRCA1/2 breast cancer families (termed BRCAx families) comprise a histopathologically heterogeneous group, further supporting their origin from multiple genetic events. Accordingly, the identification of a method to successfully subdivide BRCAx families into recognizable groups could be of considerable value to further genetic analysis. We have previously shown that global gene expression analysis can identify unique and distinct expression profiles in breast tumors from BRCA1 and BRCA2 mutation carriers. Here we show that gene expression profiling can discover novel classes among BRCAx tumors, and differentiate them from BRCA1 and BRCA2 tumors. Moreover, microarray-based comparative genomic hybridization (CGH) to cDNA arrays revealed specific somatic genetic alterations within the BRCAx subgroups. These findings illustrate that, when gene expression-based classifications are used, BRCAx families can be grouped into homogeneous subsets, thereby potentially increasing the power of conventional genetic analysis.

Adult↗
PubMed2003

Discovering local structure in gene expression data: the order-preserving submatrix problem.

This paper concerns the discovery of patterns in gene expression matrices, in which each element gives the expression level of a given gene in a given experiment. Most existing methods for pattern discovery in such matrices are based on clustering genes by comparing their expression levels in all experiments, or clustering experiments by comparing their expression levels for all genes. Our work goes beyond such global approaches by looking for local patterns that manifest themselves when we focus simultaneously on a subset G of the genes and a subset T of the experiments. Specifically, we look for order-preserving submatrices (OPSMs), in which the expression levels of all genes induce the same linear ordering of the experiments (we show that the OPSM search problem is NP-hard in the worst case). Such a pattern might arise, for example, if the experiments in T represent distinct stages in the progress of a disease or in a cellular process and the expression levels of all genes in G vary across the stages in the same way. We define a probabilistic model in which an OPSM is hidden within an otherwise random matrix. Guided by this model, we develop an efficient algorithm for finding the hidden OPSM in the random matrix. In data generated according to the model, the algorithm recovers the hidden OPSM with a very high success rate. Application of the methods to breast cancer data seem to reveal significant local patterns.

Algorithms↗
PubMed2003

The restriction scaffold problem.

Most shotgun sequencing projects undergo a long and costly phase of finishing, in which a partial assembly forms several contigs whose order, orientation, and relative distance is unknown. We propose here a new technique that supplements the shotgun assembly data by experimentally simple and commonly used complete restriction digests of the target. By computationally combining information from the contig sequences and the fragment sizes measured for several different enzymes, we seek to form a "scaffold" on which the contigs will be placed in their correct orientation, order, and distance. We give a heuristic search algorithm for solving the problem and report on promising preliminary simulation results. The key to the success of the search scheme is the very rapid solution of two time-critical subproblems that are solved to optimality in linear time. Our simulations indicate that with noise levels of some 3% relative error in measuring fragment sizes, using six enzymes, most datasets of 13 contigs spanning 300kb can be correctly ordered, and the remaining ones have most of their pairs of neighboring contigs correct. Hence, the technique has a potential to provide real help to finishing. Even without closing all gaps, the ability to order and orient the contigs correctly makes the partial assembly both more accessible and more useful for biologists.

Computational Biology↗
PubMed2003

Methods for analysis and visualization of SNP genotype data for complex diseases.

SNP markers are becoming central for studying genetic determinants of complex diseases. Large SNP data collected in such studies call for the development of specialized analysis tools. We present methods for selecting sets of SNPs that can be associated to sample properties in case/control studies. We also describe how scoring and selection can be statistically tested. This is done at the single locus as well as at the set level.

Bayes Theorem↗
PubMed2002

Gene expression analysis reveals matrilysin as a key regulator of pulmonary fibrosis in mice and humans.

Pulmonary fibrosis is a progressive and largely untreatable group of disorders that affects up to 100,000 people on any given day in the United States. To elucidate the molecular mechanisms that lead to end-stage human pulmonary fibrosis we analyzed samples from patients with histologically proven pulmonary fibrosis (usual interstitial pneumonia) by using oligonucleotide microarrays. Gene expression patterns clearly distinguished normal from fibrotic lungs. Many of the genes that were significantly increased in fibrotic lungs encoded proteins associated with extracellular matrix formation and degradation and proteins expressed in smooth muscle. Using a combined set of scoring systems we determined that matrilysin (matrix metalloproteinase 7), a metalloprotease not previously associated with pulmonary fibrosis, was the most informative increased gene in our data set. Immunohistochemisry demonstrated increased expression of matrilysin protein in fibrotic lungs. Furthermore, matrilysin knockout mice were dramatically protected from pulmonary fibrosis in response to intratracheal bleomycin. Our results identify matrilysin as a mediator of pulmonary fibrosis and a potential therapeutic target. They also illustrate the power of global gene expression analysis of human tissue samples to identify molecular pathways involved in clinical disease.

Animals↗