Regulation and consequences of differential gene expression in diabetic kidney disease

DIN (diabetic nephropathy) is the leading cause of end-stage renal disease worldwide and develops in 25-40% of patients with Type 1 or Type 2 diabetes mellitus. Elevated blood glucose over long periods together with glomerular hypertension leads to progressive glomerulosclerosis and tubulointerstitial fibrosis in susceptible individuals. Central to the pathology of DIN are cytokines and growth factors such as TGF-beta (transforming growth factor beta) superfamily members, including BMPs (bone morphogenetic protein) and TGF-beta 1, which play key roles in fibrogenic responses of the kidney, including podocyte loss, mesangial cell hypertrophy, matrix accumulation and tubulointerstitial fibrosis. Many of these responses can be mimicked in in vitro models of cells cultured in high glucose. We have applied differential gene expression technologies to identify novel genes expressed in in vitro and in vivo models of DN and, importantly, in human renal tissue. By mining these datasets and probing the regulation of expression and actions of specific molecules, we have identified novel roles for molecules such as Gremlin, IHG-1 (induced in high glucose-1) and CTGF (connective tissue growth factor) in DIN and potential regulators of their bioactions.

cudaMap: a GPU accelerated program for gene expression connectivity mapping

Background: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take >2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping.

Results: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance.

Conclusion: Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

BACKGROUND: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.

METHODS: In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.

RESULTS: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.

CONCLUSION: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.

High glucose decreases intracellular glutathione concentrations and upregulates inducible nitric oxide synthase gene expression in intestinal epithelial cells

Diabetes is associated with oxidative stress and increased levels of inflammatory cytokines. The aim of the study was to assess the effects of inflammatory cytokines and oxidative stress associated with raised glucose levels on inducible nitric oxide synthase (iNOS) promoter activity in intestinal epithelial cells. High glucose (25 mmol/l) conditions reduced glutathione (GSH) levels in the human intestinal epithelial cell line, DLD-1. Addition of the antioxidant alpha-lipoic acid resulted in the restoration of GSH levels to normal. Upregulation of basal iNOS promoter activity was observed when cells were incubated in high glucose alone. This effect was significantly reduced by the addition of the antioxidant, alpha-lipoic acid and completely blocked with inhibition of NFkappa B activity. Cytokine stimulation [interleukin-1 beta, tumor necrosis factor-alpha, interferon-gamma] induced iNOS promoter activity in all conditions and this was accompanied by an increase in nitric oxide (NO) production. Inhibition of NFkappa-B activity decreased but did not completely inhibit cytokine-induced iNOS promoter activity and subsequent NO production. In conclusion, high glucose-induced iNOS promoter activity is mediated in part through intracellular GSH and NFkappa-B.

The Role of Thymidylate Synthase Induction in Modulating p53-regulated Gene Expression in Response to 5-Fluorouracil and Antifolates

Thymidylate synthase (TS) is a critical target for chemotherapeutic agents such as 5-fluorouracil (5-FU) and antifolates such as tomudex (TDX),multitargeted antifolate, and ZD9331. Using the MCF-7 breast cancer line, we have developed p53 wild-type (M7TS90) and null (M7TS90-E6) isogenic lines with inducible TS expression (approximately 6-fold induction compared with control after 48 h). In the M7TS90 line, inducible TS expression resulted in a moderate approximately 3-fold increase in 5-FU IC-50(72 h) dose and a dramatic >20-fold increase in the IC-50(72 h) doses of TDX, multitargeted antifolate, and ZD9331. S-phase cell cycle arrest and apoptosis induced by the antifolates were abrogated by TS induction. In contrast, cell cycle arrest and apoptosis induced by 5-FU was unaffected by TS expression levels. Inactivation of p53 significantly increased resistance to 5-FU and the antifolates with IC-50(72 h) doses for 5-FU and TDX of >100 and >10 microM, respectively, in the M7TS90-E6 cell line. Furthermore, p53 inactivation completely abrogated the cell cycle arrest and apoptosis induced by 5-FU. The antifolates induced S-phase arrest in the p53 null cell line; however, the induction of apoptosis by these agents was significantly reduced compared with p53 wild-type cells. Both inducible TS expression and the addition of exogenous thymidine (10 microM) blocked p53 and p21 induction by the antifolates but not by 5-FU in the M7TS90 cell line. Similarly, inducible TS expression and exogenous thymidine abrogated antifolate but not 5-FU-mediated up-regulation of Fas/CD95 in M7TS90 cells. Our results indicate that in M7TS90 cells, inducible TS expression modulates p53 and p53 target gene expression in response to TS-targeted antifolate therapies but not to 5-FU.

Glucocorticoid induced muscle atrophy is associated with upregulation of myostatin gene expression

The mechanisms by which excessive glucocorticoids cause muscular atrophy remain unclear. We previously demonstrated that dexamethasone increases the expression of myostatin, a negative regulator of skeletal muscle mass, in vitro. In the present study, we tested the hypothesis that dexamethasone-induced muscle loss is associated with increased myostatin expression in vivo. Daily administration (60, 600, 1,200 micro g/kg body wt) of dexamethasone for 5 days resulted in rapid, dose-dependent loss of body weight (-4.0, -13.4, -17.2%, respectively, P <0.05 for each comparison), and muscle atrophy (6.3, 15.0, 16.6% below controls, respectively). These changes were associated with dose-dependent, marked induction of intramuscular myostatin mRNA (66.3, 450, 527.6% increase above controls, P <0.05 for each comparison) and protein expression (0.0, 260.5, 318.4% increase above controls, P <0.05). We found that the effect of dexamethasone on body weight and muscle loss and upregulation of intramuscular myostatin expression was time dependent. When dexamethasone treatment (600 micro g. kg-1. day-1) was extended from 5 to 10 days, the rate of body weight loss was markedly reduced to approximately 2% within this extended period. The concentrations of intramuscular myosin heavy chain type II in dexamethasone-treated rats were significantly lower (-43% after 5-day treatment, -14% after 10-day treatment) than their respective corresponding controls. The intramuscular myostatin concentration in rats treated with dexamethasone for 10 days returned to basal level. Concurrent treatment with RU-486 blocked dexamethasone-induced myostatin expression and significantly attenuated body loss and muscle atrophy. We propose that dexamethasone-induced muscle loss is mediated, at least in part, by the upregulation of myostatin expression through a glucocorticoid receptor-mediated pathway.