Chronic rosiglitazone therapy normalizes expression of ACE1, SCD1 and other genes in the kidney of obese Zucker rats as determined by microarray analysis.

Thiazolidinediones increase tissue insulin sensitivity and are protective against worsening of nephropathy and hypertension in diabetes. Mechanisms underlying protection at the renal level likely involve a variety of unknown changes in gene expression. We examined kidney gene expression in obese and lean Zucker rats in response to rosiglitazone (Avandia), a peroxisome proliferator activated receptor (gamma-subtype) agonist. Lean and obese Zucker rats were treated with either control chow or chow with added rosiglitazone (3 mg/kg x bw) for 12 weeks (n = 3/group). Total kidney mRNA expression was evaluated using the Affymetrix Rat Genome 230 2.0 GeneChip. 903 probe sets were significantly (P < 0.05) altered with at least 1.5-fold changes between groups. In untreated obese rats, 300 probe sets were increased and 244 decreased, relative to lean. Increased genes included the beta-subunit of the epithelial sodium channel (ENaC), the thiazide-sensitive Na-Cl cotransporter, and aquaporin 3. Decreased genes included angiotensin converting enzyme, type 1 (ACE1). FatiGO analysis showed that the highest number of altered genes between lean and obese belonged to the categories: ion binding, hydrolase activity, and protein binding. RGZ increased expression of uncoupling protein 1 (UCP1), CD36, and fatty acid binding protein 4 (FAbp4) in both lean and obese rats. In obese rats, 33 genes were normalized by RGZ (no longer different from lean) including ACE1, fatty acid synthase (Fasn), and stearoyl-coenzyme A desaturase (SCD1). Ingenuity Pathways System analysis of genes upregulated by RGZ in obese rats revealed two major nodes affected: PPAR-gamma and tumor necrosis factor alpha (TNF-alpha).

Peak selection from MALDI-TOF mass spectra using ant colony optimization.

MOTIVATION: Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks. RESULTS: The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method. AVAILABILITY: Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.