Development of a novel proteomic approach for mitochondrial proteomics from cardiac tissue from patients with atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting approximately 2.2 million Americans. Because several studies have suggested that changes in mitochondrial function and morphology may contribute to AF, we developed a novel proteomic workflow focused on the identification of differentially expressed mitochondrial proteins in AF patients. Right human atrial tissue was collected from 20 patients, 10 with and 10 without AF, and the tissue was subjected to hydrostatic pressure cycling-based lysis followed by label-free mass spectrometric (MS) analysis of mitochondrial enriched isolates. Approximately 5% of the 700 proteins identified by MS analysis were differentially expressed between the AF and non-AF samples. We chose four differentially abundant proteins for further verification using reverse phase protein microarray analysis based on their known importance in energy production and regulatory association with atrial ion channels: four and a half LIM, destrin, heat shock protein 2, and chaperonin-containing TCP1. These initial study results provide evidence that a workflow to identify AF-related proteins that combines a powerful upfront tissue cell lysis with high resolution MS for discovery and protein array technology for verification may be an effective strategy for discovering candidate markers in highly fibrous tissue samples.

Hepatic gene expression in patients with obesity-related non-alcoholic steatohepatitis.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is among the most common causes of chronic liver disease. NAFLD includes a spectrum of clinicopathologic syndromes that includes non-alcoholic steatohepatitis (NASH) that has potential for progression. The pathogenesis of NASH is poorly characterized. AIM: This study was designed to identify differences in hepatic gene expression in patients with NASH and to relate such differences to their clinical characteristics. DESIGN: Consecutive patients undergoing bariatric surgery were prospectively recruited. Extensive clinical data and two liver biopsy specimens were obtained at the time of enrollment. A single hepatopathologist reviewed and classified the liver biopsies. Patients with excessive alcohol use and other causes of liver disease were excluded. A group of 29 NASH patients, 12 with steatosis alone, seven obese controls and six non-obese controls were selected for further investigation. Customized cDNA microarrays containing 5220 relevant genes were designed specifically for this study. Microarray experiments were run in triplicate for each sample and a selected group of genes were confirmed using real-time PCR. OUTCOME MEASURE: Differential hepatic gene expressions in patients with NASH as compared with controls. RESULTS: Thirty-four genes with significant differential expression were identified in patients with NASH when compared with non-obese controls. Moreover, 19 of these genes showed no significant expression differences in obese vs. non-obese controls, suggesting a stronger association of these genes to NASH. CONCLUSIONS: Several differentially expressed genes in patients with NASH are related to lipid metabolism and extracellular matrix remodeling. Additionally, genes related to liver regeneration, apoptosis, and the detoxification process were differentially expressed. These findings may help clarify the molecular pathogenesis of NASH and identify potential targets for therapeutic intervention.

Microarrays in cancer research.

Microarray technology has presented the scientific community with a compelling approach that allows for simultaneous evaluation of all cellular processes at once. Cancer, being one of the most challenging diseases due to its polygenic nature, presents itself as a perfect candidate for evaluation by this approach. Several recent articles have provided significant insight into the strengths and limitations of microarrays. Nevertheless, there are strong indications that this approach will provide new molecular markers that could be used in diagnosis and prognosis of cancers. To achieve these goals it is essential that there is a seamless integration of clinical and molecular biological data that allows us to elucidate genes and pathways involved in various cancers. To this effect we are currently evaluating gene expression profiles in human brain, ovarian, breast and hematopoetic, lung, colorectal, head and neck and biliary tract cancers. To address the issues we have a joint team of scientists, doctors and computer scientists from two Virginia Universities and a major healthcare provider. The study has been divided into several focus groups that include; Tissue Bank Clinical & Pathology Laboratory Data, Chip Fabrication, QA/QC, Tissue Devitalization, Database Design and Data Analysis, using multiple microarray platforms. Currently over 300 consenting patients have been enrolled in the study with the largest number being that of breast cancer patients. Clinical data on each patient is being compiled into a secure and interactive relational database and integration of these data elements will be accomplished by a common programming interface. This clinical database contains several key parameters on each patient including demographic (risk factors, nutrition, co-morbidity, familial history), histopathology (non genetic predictors), tumor, treatment and follow-up information. Gene expression data derived from the tissue samples will be linked to this database, which allows us to query the data at multiple levels. The challenge of tissue acquisition and processing is of paramount importance to the success of this venture. A tissue devitalization timeline protocol was devised to ensure sample and RNA integrity. Stringent protocols are being employed to ascertain accurate tumor homogeneity, by serial dissection of each tumor sample at 10 microM frozen sections followed by histopathological evaluation. The multiple platforms being utilized in this study include Affimetrix, Oligo-Chips and custom-designed cDNA arrays. Selected RNA samples will be evaluated on each platform between the groups. Analysis steps will involve normalization and standardization of gene expression data followed by hierarchical clustering to determine co-regulation profiles. The aim of this conjoint effort is to elucidate pathways and genes involved in various cancers, resistance mechanisms, molecular markers for diagnosis and prognosis.