Effect of Brain Tumor Presence During Radiation on Tissue Toxicity: Transcriptomic and Metabolic Changes.

PURPOSE: Radiation therapy (RT) causes functional and transcriptomic changes in the brain; however, most studies have been carried out in normal rodent brains. Here, the long-term effect of irradiation and tumor presence during radiation was investigated. METHODS AND MATERIALS: Male Wistar rats ∼7 weeks old were divided into 3 groups: sham implant, RT+sham implant, and RT+tumor implant (C6 glioma). Hypofractionated irradiation (8 or 6 Gy/day for 5 days) was localized to a 1-cm strip of cranium starting 5 days after implantation, resulting in complete tumor regression and prolonged survival. Biopsy of tissue was performed in the implant area 65 days after implantation. RNA was hybridized to GeneChip Rat Exon 1.0 ST array. Data were analyzed using significant analysis of microarrays and ingenuity pathway analysis. 1H magnetic resonance spectroscopy (1H-MRS) imaging was performed in the implantation site 65 to 70 days after implantation using a 9.4 T Biospec magnetic resonance imaging scanner with a quadrature rat brain array. Immunohistochemical staining for astrogliosis, HMG-CoA synthase 2, γ-aminobutyric acid (GABA) and taurine was performed at ∼65 days after implantation. RESULTS: Eighty-four genes had a false discovery rate <3.5%. We compared RT+tumor implant with RT+sham implant animals. The tumor presence affected networks associated with cancer/cell morphology/tissue morphology. 1H-MRS showed significant reduction in taurine levels (P<.04) at the implantation site in both groups. However, the RT+tumor group also showed significant increase in levels of neurotransmitter GABA (P=.02). Hippocampal taurine levels were only significantly reduced in the RT+tumor group (P=.03). HMG-CoA synthase 2, GABA and taurine levels were confirmed using staining. Glial fibrillary acidic protein staining demonstrated a significant increase in inflammation that was heightened in the RT+tumor group. CONCLUSIONS: Our data indicate that tumor presence during radiation significantly affects long-term functional transcriptomics landscape and neurotransmitter levels at the tumor implantation site/normal tissue, accompanied by increased inflammation (astrogliosis).

Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics.

Liver cirrhosis is a worldwide health problem. Reliable, noninvasive methods for early detection of liver cirrhosis are not available. Using a three-step approach, we classified sera from rats with liver cirrhosis following different treatment insults. The approach consisted of: (i) protein profiling using surface-enhanced laser desorption/ionization (SELDI) technology; (ii) selection of a statistically significant serum biomarker set using machine learning algorithms; and (iii) identification of selected serum biomarkers by peptide sequencing. We generated serum protein profiles from three groups of rats: (i) normal (n=8), (ii) thioacetamide-induced liver cirrhosis (n=22), and (iii) bile duct ligation-induced liver fibrosis (n=5) using a weak cation exchanger surface. Profiling data were further analyzed by a recursive support vector machine algorithm to select a panel of statistically significant biomarkers for class prediction. Sensitivity and specificity of classification using the selected protein marker set were higher than 92%. A consistently down-regulated 3495 Da protein in cirrhosis samples was one of the selected significant biomarkers. This 3495 Da protein was purified on-chip and trypsin digested. Further structural characterization of this biomarkers candidate was done by using cross-platform matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) peptide mass fingerprinting (PMF) and matrix-assisted laser desorption/ionization time of flight/time of flight (MALDI-TOF/TOF) tandem mass spectrometry (MS/MS). Combined data from PMF and MS/MS spectra of two tryptic peptides suggested that this 3495 Da protein shared homology to a histidine-rich glycoprotein. These results demonstrated a novel approach to discovery of new biomarkers for early detection of liver cirrhosis and classification of liver diseases.