Identification and Functional Prediction of Long Non-Coding RNAs in Dilated Cardiomyopathy by Bioinformatics Analysis

Dilated cardiomyopathy (DCM) is a relatively common cause of heart failure and the leading cause of heart transplantation. Aberrant changes in long non-coding RNAs (lncRNAs) are involved in DCM disorder; however, the detailed mechanisms underlying DCM initiation and progression require further investigation, and new molecular targets are needed. Here, we obtained lncRNA-expression profiles associated with DCM and non-failing hearts through microarray probe-sequence re-annotation. Weighted gene co-expression network analysis revealed a module highly associated with DCM status. Then eight hub lncRNAs in this module (FGD5-AS1, AC009113.1, WDFY3-AS2, NIFK-AS1, ZNF571-AS1, MIR100HG, AC079089.1, and EIF3J-AS1) were identified. All hub lncRNAs except ZNF571-AS1 were predicted as localizing to the cytoplasm. As a possible mechanism of DCM pathogenesis, we predicted that these hub lncRNAs might exert functions by acting as competing endogenous RNAs (ceRNAs). Furthermore, we found that the above results can be essentially reproduced in an independent external dataset. We observed the localization of hub lncRNAs by RNA-FISH in human aortic smooth muscle cells and confirmed the upregulation of the hub lncRNAs in DCM patients through quantitative RT-PCR. In conclusion, these findings identified eight candidate lncRNAs associated with DCM disease and revealed their potential involvement in DCM partly through ceRNA crosstalk. Our results facilitate the discovery of therapeutic targets and enhance the understanding of DCM pathogenesis.

Evaluation of mitochondrial DNA copy number estimation techniques

Mitochondrial DNA copy number (mtDNA-CN), a measure of the number of mitochondrial genomes per cell, is a minimally invasive proxy measure for mitochondrial function and has been associated with several aging-related diseases. Although quantitative real-time PCR (qPCR) is the current gold standard method for measuring mtDNA-CN, mtDNA-CN can also be measured from genotyping microarray probe intensities and DNA sequencing read counts. To conduct a comprehensive examination on the performance of these methods, we use known mtDNA-CN correlates (age, sex, white blood cell count, Duffy locus genotype, incident cardiovascular disease) to evaluate mtDNA-CN calculated from qPCR, two microarray platforms, as well as whole genome (WGS) and whole exome sequence (WES) data across 1,085 participants from the Atherosclerosis Risk in Communities (ARIC) study and 3,489 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). We observe mtDNA-CN derived from WGS data is significantly more associated with known correlates compared to all other methods (p < 0.001). Additionally, mtDNA-CN measured from WGS is on average more significantly associated with traits by 5.6 orders of magnitude and has effect size estimates 5.8 times more extreme than the current gold standard of qPCR. We further investigated the role of DNA extraction method on mtDNA-CN estimate reproducibility and found mtDNA-CN estimated from cell lysate is significantly less variable than traditional phenol-chloroform-isoamyl alcohol (p = 5.44×10-4) and silica-based column selection (p = 2.82×10-7). In conclusion, we recommend the field moves towards more accurate methods for mtDNA-CN, as well as re-analyze trait associations as more WGS data becomes available from larger initiatives such as TOPMed.

Cleaning genotype data from Diversity Outbred mice

Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in quantitative traits. Here we illustrate approaches to quality control and cleaning of array-based genotyping data for multiparent populations (experimental crosses derived from more than two founder strains), using MegaMUGA array data from a set of 291 from Diversity Outbred (DO) mice. Our approach employs data visualizations that can reveal problems at the level of individual mice or with individual SNP markers. We find that the proportion of missing genotypes for each mouse is an effective indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to confirm the sex of each mouse, and we use the proportion of matching SNP genotypes between pairs of mice to detect sample duplicates. We use a hidden Markov model (HMM) reconstruction of the founder haplotype mosaic across each mouse genome to estimate the number of crossovers and to identify potential genotyping errors. To evaluate marker quality, we find that missing data and genotyping error rates are the most effective diagnostics. We also examine the SNP genotype frequencies with markers grouped according to their minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele-specific probe intensities can reveal the underlying cause of incorrect genotype calls. The decision to include or exclude low-quality samples can have a significant impact on the mapping results for a given study. We find that the impact of low-quality markers on a given study is often minimal, but reporting problematic markers can improve the utility of the genotyping array across many studies.

A Tool to Build Up-To-Date Gene Annotations for Affymetrix Microarrays

Genome-wide expression profiling and genotyping is widely applied in functional genomics research, ranging from stem cell studies to cancer, in drug response studies, and in clinical diagnostics. The Affymetrix GeneChip microarrays represent the most popular platform for such assays. Nevertheless, due to rapid and continuous improvement of the knowledge about the genome, the definition of many of the genes and transcripts change, and new genes are discovered. Thus the original probe information is out-dated for a number of Affymetrix platforms, and needs to be re-defined. It has been demonstrated, that accurate probe set definition improves both coverage of the gene expression analysis and its statistical power. Therefore we developed a method that incorporates the most recent genome annotations into the annotation of the microarray probe sets, using tools from the next generation sequencing. Additionally our method allows to quickly build project specific gene annotation models, as well as for comparison of microarray to RNAseq data.

Respiratory Tularemia: Francisella Tularensis and Microarray Probe Designing

Background:Francisella tularensis(F. tularensis) is the etiological microorganism for tularemia. There are different forms of tularemia such as respiratory tularemia. Respiratory tularemia is the most severe form of tularemia with a high rate of mortality; if not treated. Therefore, traditional microbiological tools and Polymerase Chain Reaction (PCR) are not useful for a rapid, reliable, accurate, sensitive and specific diagnosis. But, DNA microarray technology does. DNA microarray technology needs to appropriate microarray probe designing.Objective:The main goal of this original article was to design suitable long oligo microarray probes for detection and identification ofF. tularensis.Method:For performing this research, the complete genomes ofF. tularensissubsp.tularensisFSC198,F. tularensissubsp.holarcticaLVS,F. tularensissubsp.mediasiatica,F. tularensissubsp.novicida(F. novicidaU112), andF. philomiragiasubsp.philomiragiaATCC 25017 were studiedviaNCBI BLAST tool, GView and PanSeq Servers and finally the microarray probes were produced and processedviaAlleleID 7.7 software and Oligoanalyzer tool, respectively.Results:In thisin silicoinvestigation, a number of long oligo microarray probes were designed for detecting and identifyingF. tularensis. Among these probes, 15 probes were recognized as the best candidates for microarray chip designing.Conclusion:Calibrated microarray probes reduce the biasis of DNA microarray technology as an advanced, rapid, accurate and cost-effective molecular diagnostic tool with high specificity and sensitivity. Professional microarray probe designing provides us with much more facility and flexibility regarding preparation of a microarray diagnostic chip.