Jeff Chou

Altered Gene Expression and DNA Damage in Peripheral Blood Cells from Friedreich's Ataxia Patients: Cellular Model of Pathology

The neurodegenerative disease Friedreichs ataxia (FRDA) is the most common autosomal-recessively inherited ataxia and is caused by a GAA triplet repeat expansion in the first intron of the frataxin gene. In this disease, transcription of frataxin, a mitochondrial protein involved in iron homeostasis, is impaired, resulting in a significant reduction in mRNA and protein levels. Global gene expression analysis was performed in peripheral blood samples from FRDA patients as compared to controls, which suggested altered expression patterns pertaining to genotoxic stress. We then confirmed the presence of genotoxic DNA damage by using a gene-specific quantitative PCR assay and discovered an increase in both mitochondrial and nuclear DNA damage in the blood of these patients (p<0.0001, respectively). Additionally, frataxin mRNA levels correlated with age of onset of disease and displayed unique sets of gene alterations involved in immune response, oxidative phosphorylation, and protein synthesis. Many of the key pathways observed by transcription profiling were downregulated, and we believe these data suggest that patients with prolonged frataxin deficiency undergo a systemic survival response to chronic genotoxic stress and consequent DNA damage detectable in blood. In conclusion, our results yield insight into the nature and progression of FRDA, as well as possible therapeutic approaches. Furthermore, the identification of potential biomarkers, including the DNA damage found in peripheral blood, may have predictive value in future clinical trials.

Profiles of global gene expression in ionizing-radiation-damaged human diploid fibroblasts reveal synchronization behind the G1 checkpoint in a G0-like state of quiescence.

Cell cycle arrest and stereotypic transcriptional responses to DNA damage induced by ionizing radiation (IR) were quantified in telomerase-expressing human diploid fibroblasts. Analysis of cytotoxicity demonstrated that 1.5 Gy IR inactivated colony formation by 40–45% in three fibroblast lines; this dose was used in all subsequent analyses. Fibroblasts exhibited > 90% arrest of progression from G2 to M at 2 hr post-IR and a similarly severe arrest of progression from G1 to S at 6 and 12 hr post-IR. Normal rates of DNA synthesis and mitosis 6 and 12 hr post-IR caused the S and M compartments to empty by > 70% at 24 hr. Global gene expression was analyzed in IR-treated cells. A microarray analysis algorithm, EPIG, identified nine IR-responsive patterns of gene expression that were common to the three fibroblast lines, including a dominant p53-dependent G1 checkpoint response. Many p53 target genes, such as CDKN1A, GADD45, BTG2, and PLK3, were significantly up-regulated at 2 hr post-IR. Many genes whose expression is regulated by E2F family transcription factors, including CDK2, CCNE1, CDC6, CDC2, MCM2, were significantly down-regulated at 24 hr post-IR. Numerous genes that participate in DNA metabolism were also markedly repressed in arrested fibroblasts apparently as a result of cell synchronization behind the G1 checkpoint. However, cluster and principal component analyses of gene expression revealed a profile 24 hr post-IR with similarity to that of G0 growth quiescence. The results reveal a highly stereotypic pattern of response to IR in human diploid fibroblasts that reflects primarily synchronization behind the G1 checkpoint but with prominent induction of additional markers of G0 quiescence such as GAS1.

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

BackgroundA common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.ResultsThrough evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratios, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV) or ionizing radiation (IR)-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying biological processes affected by IR- and/or UV- induced DNA damage.ConclusionEPIG competed with CLICK and performed better than CAST in extracting patterns from simulated data. EPIG extracted more biological informative patterns and co-expressed genes from both C. elegans and IR/UV-treated human fibroblasts. Using Gene Ontology analysis of the genes in the patterns extracted by EPIG, several key biological categories related to p53-dependent cell cycle control were revealed from the IR/UV data. Among them were mitotic cell cycle, DNA replication, DNA repair, cell cycle checkpoint, and G0-like status transition. EPIG can be applied to data sets from a variety of experimental designs.

Identification of genes implicated in methapyrilene-induced hepatotoxicity by comparing differential gene expression in target and nontarget tissue.

Background Toxicogenomics experiments often reveal thousands of transcript alterations that are related to multiple processes, making it difficult to identify key gene changes that are related to the toxicity of interest. Objectives The objective of this study was to compare gene expression changes in a nontarget tissue to the target tissue for toxicity to help identify toxicity-related genes. Methods Male rats were given the hepatotoxicant methapyrilene at two dose levels, with livers and kidneys removed 24 hr after one, three, and seven doses for gene expression analysis. To identify gene changes likely to be related to toxicity, we analyzed genes on the basis of their temporal pattern of change using a program developed at the National Institute of Environmental Health Sciences, termed “EPIG” (extracting gene expression patterns and identifying co-expressed genes). Results High-dose methapyrilene elicited hepatic damage that increased in severity with the number of doses, whereas no treatment-related lesions were observed in the kidney. High-dose methapyrilene elicited thousands of gene changes in the liver at each time point, whereas many fewer gene changes were observed in the kidney. EPIG analysis identified patterns of gene expression correlated to the observed toxicity, including genes associated with endoplasmic reticulum stress and the unfolded protein response. Conclusions By factoring in dose level, number of doses, and tissue into the analysis of gene expression elicited by methapyrilene, we were able to identify genes likely to not be implicated in toxicity, thereby allowing us to focus on a subset of genes to identify toxicity-related processes.

Systematic variation normalization in microarray data to get gene expression comparison unbiased.

Normalization removes or minimizes the biases of systematic variation that exists in experimental data sets. This study presents a systematic variation normalization (SVN) procedure for removing systematic variation in two channel microarray gene expression data. Based on an analysis of how systematic variation contributes to variability in microarray data sets, our normalization procedure includes background subtraction determined from the distribution of pixel intensity values from each data acquisition channel and log conversion, linear or non-linear regression, restoration or transformation, and multiarray normalization. In the case when a non-linear regression is required, an empirical polynomial approximation approach is used. Either the high terminated points or their averaged values in the distributions of the pixel intensity values observed in control channels may be used for rescaling multiarray datasets. These pre-processing steps remove systematic variation in the data attributable to variability in microarray slides, assay-batches, the array process, or experimenters. Biologically meaningful comparisons of gene expression patterns between control and test channels or among multiple arrays are therefore unbiased using normalized but not unnormalized datasets.

Identification of primary transcriptional regulation of cell cycle-regulated genes upon DNA damage

The changes in global gene expression in response to DNA damage may derive from either direct induction or repression by transcriptional regulation or indirectly by synchronization of cells to specific cell cycle phases, such as G1 or G2. We developed a model that successfully estimated the expression levels of >400 cell-cycle-regulated genes in normal human fibroblasts based on the proportions of cells in each phase of the cell cycle. By isolating effects on the gene expression associated with the cell cycle phase redistribution after genotoxin treatment, the direct transcriptional target genes were distinguished from genes for which expression changed secondary to cell synchronization. Application of this model to ionizing radiation (IR)-treated normal human fibroblasts identified 150 of 406 cycle-regulated genes as putative direct transcriptional targets of IR-induced DNA damage. Changes in expression of these genes after IR treatment derived from both direct transcriptional regulation and cell cycle synchronization.

Toxicogenomics: transcription profiling for toxicology assessment.

Toxicogenomics, the application of transcription profiling to toxicology, has been widely used for elucidating the molecular and cellular actions of chemicals and other environmental stressors on biological systems, predicting toxicity before any functional damages, and classification of known or new toxicants based on signatures of gene expression. The success of a toxicogenomics study depends upon close collaboration among experts in different fields, including a toxicologist or biologist, a bioinformatician, statistician, physician and, sometimes, mathematician. This review is focused on toxicogenomics studies, including transcription profiling technology, experimental design, significant gene extraction, toxicological results interpretation, potential pathway identification, database input and the applications of toxicogenomics in various fields of toxicological study.

Ataxia telangiectasia-mutated dependent DNA damage checkpoint functions regulate gene expression in human fibroblasts.

The relationships between profiles of global gene expression and DNA damage checkpoint functions were studied in cells from patients with ataxia telangiectasia (AT). Three telomerase-expressing AT fibroblast lines displayed the expected hypersensitivity to ionizing radiation (IR) and defects in DNA damage checkpoints. Profiles of global gene expression in AT cells were determined at 2, 6, and 24 h after treatment with 1.5-Gy IR or sham treatment and were compared with those previously recognized in normal human fibroblasts. Under basal conditions, 160 genes or expressed sequence tags were differentially expressed in AT and normal fibroblasts, and these were associated by gene ontology with insulin-like growth factor binding and regulation of cell growth. On DNA damage, 1,091 gene mRNAs were changed in at least two of the three AT cell lines. When compared with the 1,811 genes changed in normal human fibroblasts after the same treatment, 715 were found in both AT and normal fibroblasts, including most genes categorized by gene ontology into cell cycle, cell growth, and DNA damage response pathways. However, the IR-induced changes in these 715 genes in AT cells usually were delayed or attenuated in comparison with normal cells. The reduced change in DNA damage response genes and the attenuated repression of cell cycle–regulated genes may account for the defects in cell cycle checkpoint function in AT cells. (Mol Cancer Res 2007;5(8):813–22)

Genomic-Derived Markers for Early Detection of Calcineurin Inhibitor Immunosuppressant–Mediated Nephrotoxicity

Calcineurin inhibitor (CI) therapy has been associated with chronic nephrotoxicity, which limits its long-term utility for suppression of allograft rejection. In order to understand the mechanisms of the toxicity, we analyzed gene expression changes that underlie the development of CI immunosuppressant-mediated nephrotoxicity in male Sprague-Dawley rats dosed daily with cyclosporine (CsA; 2.5 or 25 mg/kg/day), FK506 (0.6 or 6 mg/kg/day), or rapamycin (1 or 10 mg/kg/day) for 1, 7, 14, or 28 days. A significant increase in blood urea nitrogen was observed in animals treated with CsA (high) or FK506 (high) for 14 and 28 days. Histopathological examination revealed tubular basophilia and mineralization in animals given CsA (high) or FK506 (low and high). We identified a group of genes whose expression in rat kidney is correlated with CI-induced kidney injury. Among these genes are two genes, Slc12a3 and kidney-specific Wnk1 (KS-Wnk1), that are known to be involved in sodium transport in the distal nephrons and could potentially be involved in the mechanism of CI-induced nephrotoxicity. The downregulation of NCC (the Na-Cl cotransporter coded by Slc12a3) in rat kidney following CI treatment was confirmed by immunohistochemical staining, and the downregulation of KS-Wnk1 was confirmed by quantitative real-time-polymerase chain reaction (qRT-PCR). We hypothesize that decreased expression of Slc12a3 and KS-Wnk1 could alter the sodium chloride reabsorption in the distal tubules and contribute to the prolonged activation of the renin-angiotensin system, a demonstrated contributor to the development of CI-induced nephrotoxicity in both animal models and clinical settings. Therefore, if validated as biomarkers in humans, SLC12A3 and KS-WNK1 could potentially be useful in the early detection and reduction of CI-related nephrotoxicity in immunosuppressed transplant patients when monitoring the health of kidney xenographs in clinical practice.

An assessment of prognostic immunity markers in breast cancer

Tumor-infiltrating lymphocytes (TIL) and immunity gene signatures have been reported to be significantly prognostic in breast cancer but have not yet been applied for calculation of risk of recurrence in clinical assays. A compact set of 17 immunity genes was derived herein from an Affymetrix-derived gene expression dataset including 1951 patients (AFFY1951). The 17 immunity genes demonstrated significant prognostic stratification of estrogen receptor (ER)-negative breast cancer patients with high proliferation gene expression. Further analysis of blood and breast cancer single-cell RNA-seq datasets revealed that the 17 immunity genes were derived from TIL that were inactive in the blood and became active in tumor tissue. Expression of the 17 immunity genes was significantly (pu2009<u20092.2E-16, nu2009=u200991) correlated with TILs percentage on H&E in triple negative breast cancer. To demonstrate the impact of tumor immunity genes on prognosis, we built a Cox model to incorporate breast cancer subtypes, proliferation score and immunity score (72 gene panel) with significant prediction of outcomes (pu2009<u20090.0001, nu2009=u20091951). The 72 gene panel and its risk evaluation model were validated in two other published gene expression datasets including Illumina beads array data METABRIC (pu2009<u20090.0001, nu2009=u20091997) and whole transcriptomic mRNA-seq data TCGA (pu2009=u20090.00019, nu2009=u2009996) and in our own targeted RNA-seq data TARGETSEQ (pu2009<u20090.0001, nu2009=u2009303). Further examination of the 72 gene panel in single cell RNA-seq of tumors demonstrated tumor heterogeneity with more than two subtypes observed in each tumor. In conclusion, immunity gene expression was an important parameter for prognosis and should be incorporated into current multi-gene assays to improve assessment of risk of distant metastasis in breast cancer.Genomic testing: Tumor-infiltrating immune cells express detectable gene signatureThe elevated expression of 17 immunity-related genes is associated with better outcomes among women with aggressive forms of estrogen receptor–negative breast cancer. Zhiyuan Hu from the University of North Carolina at Chapel Hill, USA, and colleagues identified the 17-gene set by analyzing a larger expression dataset from close to 2,000 patients. Single-cell sequencing revealed that the genes were turned on in a group of cancer-fighting immune cells known as tumor-infiltrating lymphocytes, but were inactive in circulating blood cells. The researchers incorporated the immunity-related genes into a larger panel of genes involved in proliferation, invasion and other relevant biological processes. The resulting 72-gene test was an accurate predictor of the risk for developing distant metastases. The findings suggest that immunity-related genes should be incorporated into current multi-gene prognostic assays for women with breast cancer.