Biomedical subjects

Anya Tsalenko

Publications and source records attributed to Anya Tsalenko.

14 recordsLinked to original sources

PubMed2006

Network analysis of human in-stent restenosis.

BACKGROUND: Recent successes in the treatment of in-stent restenosis (ISR) by drug-eluting stents belie the challenges still faced in certain lesions and patient groups. We analyzed human coronary atheroma in de novo and restenotic disease to identify targets of therapy that might avoid these limitations. METHODS AND RESULTS: We recruited 89 patients who underwent coronary atherectomy for de novo atherosclerosis (n=55) or in-stent restenosis (ISR) of a bare metal stent (n=34). Samples were fixed for histology, and gene expression was assessed with a dual-dye 22,000 oligonucleotide microarray. Histological analysis revealed significantly greater cellularity and significantly fewer inflammatory infiltrates and lipid pools in the ISR group. Gene ontology analysis demonstrated the prominence of cell proliferation programs in ISR and inflammation/immune programs in de novo restenosis. Network analysis, which combines semantic mining of the published literature with the expression signature of ISR, revealed gene expression modules suggested as candidates for selective inhibition of restenotic disease. Two modules are presented in more detail, the procollagen type 1 alpha2 gene and the ADAM17/tumor necrosis factor-alpha converting enzyme gene. We tested our contention that this method is capable of identifying successful targets of therapy by comparing mean significance scores for networks generated from subsets of the published literature containing the terms "sirolimus" or "paclitaxel." In addition, we generated 2 large networks with sirolimus and paclitaxel at their centers. Both analyses revealed higher mean values for sirolimus, suggesting that this agent has a broader suppressive action against ISR than paclitaxel. CONCLUSIONS: Comprehensive histological and gene network analysis of human ISR reveals potential targets for directed abrogation of restenotic disease and recapitulates the results of clinical trials of existing agents.

ADAM Proteins↗

PubMed2006

Genetic variation in putative regulatory loci controlling gene expression in breast cancer.

Candidate single-nucleotide polymorphisms (SNPs) were analyzed for associations to an unselected whole genome pool of tumor mRNA transcripts in 50 unrelated patients with breast cancer. SNPs were selected from 203 candidate genes of the reactive oxygen species pathway. We describe a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort, which revealed significant associations between subsets of SNPs and transcripts, shedding light on the underlying biology. We identified SNPs in EGF, IL1A, MAPK8, XPC, SOD2, and ALOX12 that are associated with the expression patterns of a significant number of transcripts, indicating the presence of regulatory SNPs in these genes. SNPs were found to act in trans in a total of 115 genes. SNPs in 43 of these 115 genes were found to act both in cis and in trans. Finally, subsets of SNPs that share significantly many common associations with a set of transcripts (biclusters) were identified. The subsets of transcripts that are significantly associated with the same set of SNPs or to a single SNP were shown to be functionally coherent in Gene Ontology and pathway analyses and coexpressed in other independent data sets, suggesting that many of the observed associations are within the same functional pathways. To our knowledge, this article is the first study to correlate SNP genotype data in the germ line with somatic gene expression data in breast tumors. It provides the statistical framework for further genotype expression correlation studies in cancer data sets.

Breast Neoplasms↗

PubMed2006

Molecular signatures determining coronary artery and saphenous vein smooth muscle cell phenotypes: distinct responses to stimuli.

OBJECTIVE: Phenotypic differences between vascular smooth muscle cell (VSMC) subtypes lead to diverse pathological processes including atherosclerosis, postangioplasty restenosis and vein graft disease. To better understand the molecular mechanisms underlying functional differences among distinct SMC subtypes, we compared gene expression profiles and functional responses to oxidized low-density lipoprotein (OxLDL) and platelet-derived growth factor (PDGF) between cultured SMCs from human coronary artery (CASM) and saphenous vein (SVSM). METHODS AND RESULTS: OxLDL and PDGF elicited markedly different functional responses and expression profiles between the 2 SMC subtypes. In CASM, OxLDL inhibited cell proliferation and migration and modified gene expression of chemokines (CXCL10, CXCL11 and CXCL12), proinflammatory cytokines (IL-1, IL-6, and IL-18), insulin-like growth factor binding proteins (IGFBPs), and both endothelial and smooth muscle marker genes. In SVSM, OxLDL promoted proliferation partially via IGF1 signaling, activated NF-kappaB and phosphatidylinositol signaling pathways, and upregulated prostaglandin (PG) receptors and synthases. In untreated cells, alpha-chemokines, proinflammatory cytokines, and genes associated with apoptosis, inflammation, and lipid biosynthesis were higher in CASM, whereas some beta-chemokines, metalloproteinase inhibitors, and IGFBPs were higher in SVSM. Interestingly, the basal expression levels of these genes seemed closely related to their responses to OxLDL and PDGF. In summary, our results suggest dramatic differences in gene expression patterns and functional responses to OxLDL and PDGF between venous and arterial SMCs, with venous SMCs having stronger proliferative/migratory responses to stimuli but also higher expression of atheroprotective genes at baseline. CONCLUSIONS: These results reveal molecular signatures that define the distinct phenotypes characteristics of coronary artery and saphenous vein SMC subtypes.

Atherosclerosis↗

PubMed2006

Multilocus analysis of SNP and metabolic data within a given pathway.

BACKGROUND: Complex traits, which are under the influence of multiple and possibly interacting genes, have become a subject of new statistical methodological research. One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common multifactorial diseases and their association to different quantitative phenotypic traits. RESULTS: Two types of data from the same metabolic pathway were used in the analysis: categorical measurements of 18 SNPs; and quantitative measurements of plasma levels of several steroids and their precursors. Using the combinatorial partitioning method we tested various thresholds for each metabolic trait and each individual SNP locus. One SNP in CYP19, 3UTR, two SNPs in CYP1B1 (R48G and A119S) and one in CYP1A1 (T461N) were significantly differently distributed between the high and low level metabolic groups. The leave one out cross validation method showed that 6 SNPs in concert make 65% correct prediction of phenotype. Further we used pattern recognition, computing the p-value by Monte Carlo simulation to identify sets of SNPs and physiological characteristics such as age and weight that contribute to a given metabolic level. Since the SNPs detected by both methods reside either in the same gene (CYP1B1) or in 3 different genes in immediate vicinity on chromosome 15 (CYP19, CYP11 and CYP1A1) we investigated the possibility that they form intragenic and intergenic haplotypes, which may jointly account for a higher activity in the pathway. We identified such haplotypes associated with metabolic levels. CONCLUSION: The methods reported here may enable to study multiple low-penetrance genetic factors that together determine various quantitative phenotypic traits. Our preliminary data suggest that several genes coding for proteins involved in a common pathway, that happen to be located on common chromosomal areas and may form intragenic haplotypes, together account for a higher activity of the whole pathway.

Aged↗

PubMed2006

Transcriptional profiling of reporter genes used for molecular imaging of embryonic stem cell transplantation.

Stem cell therapy offers exciting promise for treatment of ischemic heart disease. Recent advances in molecular imaging techniques now allow investigators to monitor cell fate noninvasively and repetitively. Here we examine the effects of a triple-fusion reporter gene on embryonic stem (ES) cell transcriptional profiles. Murine ES cells were stably transfected with a self-inactivating lentiviral vector carrying a triple-fusion (TF) construct consisting of fluorescence, bioluminescence, and positron emission tomography (PET) reporter genes. Fluorescence-activated cell sorting (FACS) analysis allowed isolation of stably transfected populations. Microarray studies comparing gene expression in nontransduced control ES cells vs. stably transduced ES cells expressing triple fusion (ES-TF) revealed some increases in transcriptional variability. Annotation analysis showed that ES-TF cells downregulated cell cycling, cell death, and protein and nucleic acid metabolism genes while upregulating homeostatic and anti-apoptosis genes. Despite these transcriptional changes, expression of the TF reporter gene had no significant effects on ES cell viability, proliferation, and differentiation capability. Importantly, transplantation studies in murine myocardium demonstrated the feasibility of tracking ES-TF cells in living subjects using bioluminescence and PET imaging. Taken together, this is the first study to analyze in detail the effects of reporter genes on molecular imaging of ES cells.

Animals↗

PubMed2006

Experimental validation of data mined single nucleotide polymorphisms from several databases and consecutive dbSNP builds.

Rapid development in the annotation of human genetic variation has increased the numbers of single nucleotide polymorphisms (SNPs) in candidate genes by several orders of magnitude. The selection of both useful target SNPs for disease-gene association studies and SNPs associated with the treatment response is therefore an increasingly challenging task. We describe a workflow for selecting SNPs based on their putative function and frequency in candidate genes extracted from PubMed resources. The annotation of each SNP and its frequency in a Caucasian population was assessed in several databases. Approximately 4000 SNPs were identified from an initial 233 candidate genes. In a case study, we performed actual genotyping of 1030 of these SNPs in 213 genes and obtained 710 successfully genotyped SNPs. Using the flow-chart outlined here, only 87 SNPs were monomorphic (approximately 12%). This study reports the frequency of SNPs in a Caucasian population, selected in silico, using a candidate gene approach and validated by actually genotyping 193 individuals. The selected genotypes represent a valuable set of verified candidate SNPs for pharmacogenetic studies in Caucasian populations.

Breast Neoplasms↗

PubMed2006

Analysis of SNP-expression association matrices.

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 50 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Algorithms↗

PubMed2005

Differences in vascular bed disease susceptibility reflect differences in gene expression response to atherogenic stimuli.

Atherosclerosis occurs predominantly in arteries and only rarely in veins. The goal of this study was to test whether differences in the molecular responses of venous and arterial endothelial cells (ECs) to atherosclerotic stimuli might contribute to vascular bed differences in susceptibility to atherosclerosis. We compared gene expression profiles of primary cultured ECs from human saphenous vein (SVEC) and coronary artery (CAEC) exposed to atherogenic stimuli. In addition to identifying differentially expressed genes, we applied statistical analysis of gene ontology and pathway annotation terms to identify signaling differences related to cell type and stimulus. Differential gene expression of untreated venous and arterial endothelial cells yielded 285 genes more highly expressed in untreated SVEC (P<0.005 and fold change >1.5). These genes represented various atherosclerosis-related pathways including responses to proliferation, oxidoreductase activity, antiinflammatory responses, cell growth, and hemostasis functions. Moreover, stimulation with oxidized LDL induced dramatically greater gene expression responses in CAEC compared with SVEC, relating to adhesion, proliferation, and apoptosis pathways. In contrast, interleukin 1beta and tumor necrosis factor alpha activated similar gene expression responses in both CAEC and SVEC. The differences in functional response and gene expression were further validated by an in vitro proliferation assay and in vivo immunostaining of alphabeta-crystallin protein. Our results strongly suggest that different inherent gene expression programs in arterial versus venous endothelial cells contribute to differences in atherosclerotic disease susceptibility.

Atherosclerosis↗

PubMed2005

Marek's disease virus Meq transforms chicken cells via the v-Jun transcriptional cascade: a converging transforming pathway for avian oncoviruses.

Marek's disease virus (MDV) is a highly pathogenic and oncogenic herpesvirus of chickens. MDV encodes a basic leucine zipper (bZIP) protein, Meq (MDV EcoQ). The bZIP domain of Meq shares homology with Jun/Fos, whereas the transactivation/repressor domain is entirely different. Increasing evidence suggests that Meq is the oncoprotein of MDV. Direct evidence that Meq transforms chicken cells and the underlying mechanism, however, remain completely unknown. Taking advantage of the DF-1 chicken embryo fibroblast transformation system, a well established model for studying avian sarcoma and leukemia oncogenes, we probed the transformation properties and pathways of Meq. We found that Meq transforms DF-1, with a cell morphology akin to v-Jun and v-Ski transformed cells, and protects DF-1 from apoptosis, and the transformed cells are tumorigenic in chorioallantoic membrane assay. Significantly, using microarray and RT-PCR analyses, we have identified up-regulated genes such as JTAP-1, JAC, and HB-EGF, which belong to the v-Jun transforming pathway. In addition, c-Jun was found to form stable dimers with Meq and colocalize with it in the transformed cells. RNA interference to Meq and c-Jun down-modulated the expression of these genes and reduced the growth of the transformed DF-1, suggesting that Meq transforms chicken cells by pirating the Jun pathway. These data suggest that avian herpesvirus and retrovirus oncogenes use a similar strategy in transformation and oncogenesis.

Animals↗

PubMed2005

Pathway analysis of coronary atherosclerosis.

Large-scale gene expression studies provide significant insight into genes differentially regulated in disease processes such as cancer. However, these investigations offer limited understanding of multisystem, multicellular diseases such as atherosclerosis. A systems biology approach that accounts for gene interactions, incorporates nontranscriptionally regulated genes, and integrates prior knowledge offers many advantages. We performed a comprehensive gene level assessment of coronary atherosclerosis using 51 coronary artery segments isolated from the explanted hearts of 22 cardiac transplant patients. After histological grading of vascular segments according to American Heart Association guidelines, isolated RNA was hybridized onto a customized 22-K oligonucleotide microarray, and significance analysis of microarrays and gene ontology analyses were performed to identify significant gene expression profiles. Our studies revealed that loss of differentiated smooth muscle cell gene expression is the primary expression signature of disease progression in atherosclerosis. Furthermore, we provide insight into the severe form of coronary artery disease associated with diabetes, reporting an overabundance of immune and inflammatory signals in diabetics. We present a novel approach to pathway development based on connectivity, determined by language parsing of the published literature, and ranking, determined by the significance of differentially regulated genes in the network. In doing this, we identify highly connected "nexus" genes that are attractive candidates for therapeutic targeting and followup studies. Our use of pathway techniques to study atherosclerosis as an integrated network of gene interactions expands on traditional microarray analysis methods and emphasizes the significant advantages of a systems-based approach to analyzing complex disease.

Adult↗

PubMed2005

Analysis of SNP-expression association matrices.

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 49 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Algorithms↗

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

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↗