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Retinitis pigmentosa (RP) is a genetically heterogeneous inherited retinal dystrophy. To date, over 80 genes have been implicated in RP. However, the disease demonstrates significant locus and allelic heterogeneity not entirely captured by current testing platforms. The purpose of the present study was to characterize the underlying mutation in a patient with RP without a molecular diagnosis after initial genetic testing. Whole-exome sequencing of the affected proband was performed. Candidate gene mutations were selected based on adherence to expected genetic inheritance pattern and predicted pathogenicity. Sanger sequencing of MERTK was completed on the patient’s unaffected mother, affected brother, and unaffected sister to determine genetic phase. Eight sequence variants were identified in the proband in known RP-associated genes. Sequence analysis revealed that the proband was a compound heterozygote with two independent mutations in MERTK, a novel nonsense mutation (c.2179C > T) and a previously reported missense variant (c.2530C > T). The proband’s affected brother also had both mutations. Predicted phase was confirmed in unaffected family members. Our study identifies a novel nonsense mutation in MERTK in a family with RP and no prior molecular diagnosis. The present study also demonstrates the clinical value of exome sequencing in determining the genetic basis of Mendelian diseases when standard genetic testing is unsuccessful.

// Hong-Jian Jin 1 , Segun Jung 1 , Auditi R. DebRoy 1 , Ramana V. Davuluri 1 1 Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA Correspondence to: Ramana V. Davuluri, email: ramana.davuluri@northwestern.edu Keywords: SNP, prostate cancer, transcription factor, CRISPR/Cas9, eQTL Received: March 23, 2016 Accepted: May 23, 2016 Published: July 09, 2016 ABSTRACT Prostate cancer (PCa) is the second most common solid tumor for cancer related deaths in American men. Genome wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with the increased risk of PCa. Because most of the susceptibility SNPs are located in noncoding regions, little is known about their functional mechanisms. We hypothesize that functional SNPs reside in cell type-specific regulatory elements that mediate the binding of critical transcription factors (TFs), which in turn result in changes in target gene expression. Using PCa-specific functional genomics data, here we identify 38 regulatory candidate SNPs and their target genes in PCa. Through risk analysis by incorporating gene expression and clinical data, we identify 6 target genes (ZG16B, ANKRD5, RERE, FAM96B, NAALADL2 and GTPBP10) as significant predictors of PCa biochemical recurrence. In addition, 5 SNPs (rs2659051, rs10936845, rs9925556, rs6057110 and rs2742624) are selected for experimental validation using Chromatin immunoprecipitation (ChIP), dual-luciferase reporter assay in LNCaP cells, showing allele-specific enhancer activity. Furthermore, we delete the rs2742624-containing region using CRISPR/Cas9 genome editing and observe the drastic downregulation of its target gene UPK3A. Taken together, our results illustrate that this new methodology can be applied to identify regulatory SNPs and their target genes that likely impact PCa risk. We suggest that similar studies can be performed to characterize regulatory variants in other diseases.

The foot-and-mouth-disease virus (FMDV) utilizes non-canonical translation initiation for viral protein synthesis, by forming a specific RNA structure called internal ribosome entry site (IRES). Domain 3 in FMDV IRES is phylogenetically conserved and highly structured; it contains four-way junctions where intramolecular RNA-RNA interactions serve as a scaffold for the RNA to fold for efficient IRES activity. Although the 3D structure of domain 3 is crucial to exploring and deciphering the initiation mechanism of translation, little is known. Here, we employ a combination of various modeling approaches to propose candidate tertiary structures for the apical region of domain 3, thought to be crucial for IRES function. We begin by modeling junction topology candidates and build atomic 3D models consistent with available experimental data. We then investigate each of the four candidate 3D structures by molecular dynamics simulations to determine the most energetically favorable configurations and to analyze specific tertiary interactions. Only one model emerges as viable containing not only the specific binding site for the GNRA tetraloop but also helical arrangements which enhance the stability of domain 3. These collective findings, together with available experimental data, suggest a plausible theoretical tertiary structure of the apical region in FMDV IRES domain 3.

Background Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. Results We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. Conclusions The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms.

Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs), also known as generic variants, associated with disease susceptibility. Prostate cancer (PCa) is a highly heritable disease. GWAS studies have so far reported more than 70 SNPs that are associated with PCa risk. However, most of these SNPs are located in the noncoding genomic regions that little are known about their functional roles. Here we describe an informatics system that performs an integrative analysis of ChIP-seq, RNA-seq, SNP array and clinical data for identifying candidate regulatory SNPs (rSNPs) that could alter transcription factor (TF) binding sites and neighboring gene regulation. By applying the informatics framework on HOXB13 TF in PCa, we identified 213 rSNPs that include a recently discovered rSNP (rs339331) and identified a novel candidate rSNP (rs1476161) associated with the PCa risk. We confirmed rs1476161 by performing the HOXB13 knockout experiment. The expression level the target gene, AURKB, was decreased by about 2-fold in HOXB13-silencing cells compared to the control cells. This indicates the involvement of HOXB13 in altering AURKB gene expression, suggesting a critical role of rs1476161 in allele-specific gene regulation. Taken together, the results demonstrate the feasibility of our system in searching for candidate rSNPs associated with PCa risk.