RNA microarray analysis in prenatal mouse cochlea reveals novel IGF-I target genes: implication of MEF2 and FOXM1 transcription factors

Background: Insulin-like growth factor-I (IGF-I) provides pivotal cell survival and differentiation signals during inner ear development throughout evolution. Homozygous mutations of human IGF1 cause syndromic sensorineural deafness, decreased intrauterine and postnatal growth rates, and mental retardation. In the mouse, deficits in IGF-I result in profound hearing loss associated with reduced survival, differentiation and maturation of auditory neurons. Nevertheless, little is known about the molecular basis of IGF-I activity in hearing and deafness. Methodology/Principal Findings: A combination of quantitative RT-PCR, subcellular fractionation and Western blotting, along with in situ hybridization studies show IGF-I and its high affinity receptor to be strongly expressed in the embryonic and postnatal mouse cochlea. The expression of both proteins decreases after birth and in the cochlea of E18.5 embryonic Igf1(-/-) null mice, the balance of the main IGF related signalling pathways is altered, with lower activation of Akt and ERK1/2 and stronger activation of p38 kinase. By comparing the Igf1(-/-) and Igf1(+/+) transcriptomes in E18.5 mouse cochleae using RNA microchips and validating their results, we demonstrate the up-regulation of the FoxM1 transcription factor and the misexpression of the neural progenitor transcription factors Six6 and Mash1 associated with the loss of IGF-I. Parallel, in silico promoter analysis of the genes modulated in conjunction with the loss of IGF-I revealed the possible involvement of MEF2 in cochlear development. E18.5 Igf1(+/+) mouse auditory ganglion neurons showed intense MEF2A and MEF2D nuclear staining and MEF2A was also evident in the organ of Corti. At P15, MEF2A and MEF2D expression were shown in neurons and sensory cells. In the absence of IGF-I, nuclear levels of MEF2 were diminished, indicating less transcriptional MEF2 activity. By contrast, there was an increase in the nuclear accumulation of FoxM1 and a corresponding decrease in the nuclear cyclin-dependent kinase inhibitor p27(Kip1). Conclusions/Significance: We have defined the spatiotemporal expression of elements involved in IGF signalling during inner ear development and reveal novel regulatory mechanisms that are modulated by IGF-I in promoting sensory cell and neural survival and differentiation. These data will help us to understand the molecular bases of human sensorineural deafness associated to deficits in IGF-I

Networks of intergenic long-range enhancers and snpRNAs drive castration-resistant phenotype of prostate cancer and contribute to pathogenesis of multiple common human disorders

Biological and mechanistic relevance of intergenic disease-associated genetic loci (IDAGL) containing highly statistically significant disease-linked SNPs remains unknown. Here we present the experimental and clinical evidence revealing important role of IDAGL in human diseases. Targeted RT-PCR screen coupled with sequencing of purified PCR products detects widespread transcription at multiple intergenic disease-associated genomic loci (IDAGL) and identifies 96 small non-coding trans-regulatory RNAs of ~ 100-300 nt in length containing SNPs associated with 21 common human disorders (snpRNAs). Functionality of snpRNAs is supported by multiple independent lines of experimental evidence demonstrating their cell-type-specific expression and evolutionary conservation of sequences, genomic coordinates, and biological effects. Analysis of chromatin state signatures, expression profiling experiments using microarray and Q-PCR technologies, and luciferase reporter assays indicate that many IDAGL are Polycomb-regulated long-range enhancers. Expression of snpRNAs in human and mouse cells markedly affects cellular behavior and induces allele-specific clinically-relevant phenotypic changes: NLRP1-locus snpRNAs exert regulatory effects on monocyte/macrophage trans-differentiation, induce prostate cancer (PC) susceptibility snpRNAs, and transform low-malignancy hormone-dependent human PC cells into highly malignant androgen-independent PC. Q-PCR analysis and luciferase reporter assays demonstrate that snpRNA sequences represent allele-specific “decoy” targets of microRNAs which function as SNP-allele-specific modifiers of microRNA expression and activity. We demonstrate that trans-acting RNA molecules facilitating androgen depletion-independent growth (ADIG) in vitro and castration-resistant (CR) phenotype in vivo of PC contain intergenic 8q24-locus SNP variants which were recently linked with increased risk of developing PC. Expression level of 8q24-locus PC susceptibility snpRNAs is regulated by NLRP1-locus snpRNAs, which are transcribed from the intergenic long-range enhancer sequence located in 17p13 region at ~ 30 kb distance from the NLRP1 gene. Q-PCR analysis of clinical PC samples reveals markedly increased snpRNA expression levels in tumor tissues compared to the adjacent normal prostate [122-fold and 45-fold in Gleason 7 tumors (p = 0.03); 370-fold and 127-fold in Gleason 8 tumors (p = 0.0001); for NLRP1-locus and 8q24-locus SnpRNAs, respectively]. Highly concordant expression profiles of the NLRP1-locus snpRNAs and 8q24 CR-locus snpRNAs (r = 0.896; p < 0.0001) in clinical PC samples and experimental evidence of trans-regulatory effects of NLRP1-locus snpRNAs on expression of 8q24-locus SnpRNAs indicate that ADIG and CR phenotype of human PC cells can be triggered by RNA molecules transcribed from the NLRP1-locus intergenic enhancer and down-stream activation of the 8q24-locus snpRNAs. Our results define the intergenic NLRP1 and 8q24 regions as regulatory loci of ADIG and CR phenotype of human PC, reveal previously unknown molecular links between the innate immunity/inflammasome system and development of hormone-independent PC, and identify novel diagnostic and therapeutic targets exploration of which should be highly beneficial for clinical management of PC

Predictive response-relevant clustering of expression data provides insights into disease processes

This article describes and illustrates a novel method of microarray data analysis that couples model-based clustering and binary classification to form clusters of ;response-relevant' genes; that is, genes that are informative when discriminating between the different values of the response. Predictions are subsequently made using an appropriate statistical summary of each gene cluster, which we call the ;meta-covariate' representation of the cluster, in a probit regression model. We first illustrate this method by analysing a leukaemia expression dataset, before focusing closely on the meta-covariate analysis of a renal gene expression dataset in a rat model of salt-sensitive hypertension. We explore the biological insights provided by our analysis of these data. In particular, we identify a highly influential cluster of 13 genes-including three transcription factors (Arntl, Bhlhe41 and Npas2)-that is implicated as being protective against hypertension in response to increased dietary sodium. Functional and canonical pathway analysis of this cluster using Ingenuity Pathway Analysis implicated transcriptional activation and circadian rhythm signalling, respectively. Although we illustrate our method using only expression data, the method is applicable to any high-dimensional datasets

Identifying gene locus associations with promyelocytic leukemia nuclear bodies using immuno-TRAP.

Important insights into nuclear function would arise if gene loci physically interacting with particular subnuclear domains could be readily identified. Immunofluorescence microscopy combined with fluorescence in situ hybridization (immuno-FISH), the method that would typically be used in such a study, is limited by spatial resolution and requires prior assumptions for selecting genes to probe. Our new technique, immuno-TRAP, overcomes these limitations. Using promyelocytic leukemia nuclear bodies (PML NBs) as a model, we used immuno-TRAP to determine if specific genes localize within molecular dimensions with these bodies. Although we confirmed a TP53 gene-PML NB association, immuno-TRAP allowed us to uncover novel locus-PML NB associations, including the ABCA7 and TFF1 loci and, most surprisingly, the PML locus itself. These associations were cell type specific and reflected the cell's physiological state. Combined with microarrays or deep sequencing, immuno-TRAP provides powerful opportunities for identifying gene locus associations with potentially any nuclear subcompartment

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

Viral expression and molecular profiling in liver tissue versus microdissected hepatocytes in hepatitis B virus - associated hepatocellular carcinoma.

Background: The molecular mechanisms whereby hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) remain elusive. We used genomic and molecular techniques to investigate host-virus interactions by studying multiple areas of the same liver from patients with HCC. Methods: We compared the gene signature of whole liver tissue (WLT) versus laser capture-microdissected (LCM) hepatocytes along with the intrahepatic expression of HBV. Gene expression profiling was performed on up to 17 WLT specimens obtained at various distances from the tumor center from individual livers of 11 patients with HCC and on selected LCM samples. HBV markers in liver and serum were determined by real-time polymerase chain reaction (PCR)and confocal immunofluorescence. Results: Analysis of 5 areas of the liver showed a sharp change in gene expression between the immediate perilesional area and tumor periphery that correlated with a significant decrease in the intrahepatic expression of HB surface antigen (HBsAg). The tumor was characterized by a large preponderance of down-regulated genes, mostly involved in the metabolism of lipids and fatty acids, glucose, amino acids and drugs, with down-regulation of pathways involved in the activation of PXR/RXR and PPARα/RXRα nuclear receptors, comprising PGC-1α and FOXO1, two key regulators critically involved not only in the metabolic functions of the liver but also in the life cycle of HBV, acting as essential transcription factors for viral gene expression. These findings were confirmed by gene expression of microdissected hepatocytes. Moreover, LCM of malignant hepatocytes also revealed up-regulation of unique genes associated with cancer and signaling Pathways, including two novel HCC-associated cancer testis antigen genes, NUF2 and TTK. Conclusions: Integrated gene expression profiling of whole liver tissue with that of microdissected hepatocytes demonstrated that HBV-associated HCC is characterized by a metabolism switch-off and by a significant reduction in HBsAg. LCM proved to be a critical tool to validate gene signatures associated with HCC and to identify genes that may play a role in hepatocarcinogenesis, opening new perspectives for the discovery of novel diagnostic markers and therapeutic targets