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

Translation efficiency is a determinant of the magnitude of miRNA-mediated repression

Abstract MicroRNAs are well known regulators of mRNA stability and translation. However, the magnitude of both translational repression and mRNA decay induced by miRNA binding varies greatly between miRNA targets. This can be the result of cis and trans factors that affect miRNA binding or action. We set out to address this issue by studying how various mRNA characteristics affect miRNA-mediated repression. Using a dual luciferase reporter system, we systematically analyzed the ability of selected mRNA elements to modulate miRNA-mediated repression. We found that changing the 3′UTR of a miRNA-targeted reporter modulates translational repression by affecting the translation efficiency. This 3′UTR dependent modulation can be further altered by changing the codon-optimality or 5′UTR of the luciferase reporter. We observed maximal repression with intermediate codon optimality and weak repression with very high or low codon optimality. Analysis of ribosome profiling and RNA-seq data for endogenous miRNA targets revealed translation efficiency as a key determinant of the magnitude of miRNA-mediated translational repression. Messages with high translation efficiency were more robustly repressed. Together our results reveal modulation of miRNA-mediated repression by characteristics and features of the 5′UTR, CDS and 3′UTR

Model-based clustering with data correction for removing artifacts in gene expression data

The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.Comment: 28 page

Inferring causal relations from multivariate time series : a fast method for large-scale gene expression data

Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation

MicroRNAs in the stressed heart: Sorting the signal from the noise

The short noncoding RNAs, known as microRNAs, are of undisputed importance in cellular signaling during differentiation and development, and during adaptive and maladaptive responses of adult tissues, including those that comprise the heart. Cardiac microRNAs are regulated by hemodynamic overload resulting from exercise or hypertension, in the response of surviving myocardium to myocardial infarction, and in response to environmental or systemic disruptions to homeostasis, such as those arising from diabetes. A large body of work has explored microRNA responses in both physiological and pathological contexts but there is still much to learn about their integrated actions on individual mRNAs and signaling pathways. This review will highlight key studies of microRNA regulation in cardiac stress and suggest possible approaches for more precise identification of microRNA targets, with a view to exploiting the resulting data for therapeutic purposes

Cell-based gene therapy for mending infarcted hearts

The goal of this study was to analyse the efficiency of a combinatorial cell/growth factor therapy to improve function of infarcted murine hearts. The Insulin-like Growth Factor-1 (IGF-1) isoform, IGF-1Ea, has been shown to reduce scar formation and decrease cell death after MI. The present study utilized P19Cl6-derived, IGF-1Ea over-expressing cardiomyocytes to achieve its goal. The P19Cl6 cells were stably transduced with IGF-1Ea using a lentiviral vector and investigated first in vitro for their feasibility for in vivo cell therapy. The engineered pluripotent cells over-expressing IGF-1Ea survived better to hypoxia-induced injury than the control cells. The cells maintained their pluripotency and efficient differentiation capacity towards ventricular cardiomyocyte lineage, generating large quantities of cardiomyocytes optimal for the transplantation study. The generated cardiomyocytes were functionally active and exhibited a mature phenotype. Transplantation of the cardiomyocytes into allogeneic wild type murine infarcted hearts conferred a tendency for maintenance of function at short-term time point. At long-term however, this effect was lost, returning to the level of the control infarcted hearts. Cell tracing assessment revealed engraftment of both IGF-1Ea- and empty-cells, although the cells failed to couple with the recipient tissue. Scar size and capillary density analyses revealed no significant difference between the cells transplanted compared to the saline treated hearts, corroborating with the long-term functional data. Interestingly, the IGF- 1Ea-cell transplanted hearts expressed significantly higher amount of VEGFa compared to the controls, albeit no change in capillary density. Further investigation revealed that the enhanced VEGFa expression in IGF-1Ea-cells transplanted hearts was associated with reduced hypertrophy, marked by reduced cell cross-sectional area at the border-zone, aSK and bMHC expression compared to the control hearts. Nonetheless, modulation of hypertrophic response and transplantation of IGF-1Ea-cells were not able to confer lasting functional preservation, possibly due to lack of sufficient engraftment and coupling of the transplanted cells

Protein Delivery of an Artificial Transcription Factor Restores Widespread Ube3a Expression in an Angelman Syndrome Mouse Brain.

Angelman syndrome (AS) is a neurological genetic disorder caused by loss of expression of the maternal copy of UBE3A in the brain. Due to brain-specific genetic imprinting at this locus, the paternal UBE3A is silenced by a long antisense transcript. Inhibition of the antisense transcript could lead to unsilencing of paternal UBE3A, thus providing a therapeutic approach for AS. However, widespread delivery of gene regulators to the brain remains challenging. Here, we report an engineered zinc finger-based artificial transcription factor (ATF) that, when injected i.p. or s.c., crossed the blood-brain barrier and increased Ube3a expression in the brain of an adult mouse model of AS. The factor displayed widespread distribution throughout the brain. Immunohistochemistry of both the hippocampus and cerebellum revealed an increase in Ube3a upon treatment. An ATF containing an alternative DNA-binding domain did not activate Ube3a. We believe this to be the first report of an injectable engineered zinc finger protein that can cause widespread activation of an endogenous gene in the brain. These observations have important implications for the study and treatment of AS and other neurological disorders

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues