Urmo Võsa

Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression

Recently it has become clear that only a small percentage (7%) of disease-associated single nucleotide polymorphisms (SNPs) are located in protein-coding regions, while the remaining 93% are located in gene regulatory regions or in intergenic regions. Thus, the understanding of how genetic variations control the expression of non-coding RNAs (in a tissue-dependent manner) has far-reaching implications. We tested the association of SNPs with expression levels (eQTLs) of large intergenic non-coding RNAs (lincRNAs), using genome-wide gene expression and genotype data from five different tissues. We identified 112 cis-regulated lincRNAs, of which 45% could be replicated in an independent dataset. We observed that 75% of the SNPs affecting lincRNA expression (lincRNA cis-eQTLs) were specific to lincRNA alone and did not affect the expression of neighboring protein-coding genes. We show that this specific genotype-lincRNA expression correlation is tissue-dependent and that many of these lincRNA cis-eQTL SNPs are also associated with complex traits and diseases.

Meta-analysis of microRNA expression in lung cancer†

The prognostic and diagnostic value of microRNA (miRNA) expression aberrations in lung cancer has been studied intensely in recent years. However, due to the application of different technological platforms and small sample size, the miRNA expression profiling efforts have led to inconsistent results between the studies. We performed a comprehensive meta‐analysis of 20 published miRNA expression studies in lung cancer, including a total of 598 tumor and 528 non‐cancerous control samples. Using a recently published robust rank aggregation method, we identified a statistically significant miRNA meta‐signature of seven upregulated (miR‐21, miR‐210, miR‐182, miR‐31, miR‐200b, miR‐205 and miR‐183) and eight downregulated (miR‐126‐3p, miR‐30a, miR‐30d, miR‐486‐5p, miR‐451a, miR‐126‐5p, miR‐143 and miR‐145) miRNAs. We conducted a gene set enrichment analysis to identify pathways that are most strongly affected by altered expression of these miRNAs. We found that meta‐signature miRNAs cooperatively target functionally related and biologically relevant genes in signaling and developmental pathways. We have shown that such meta‐analysis approach is suitable and effective solution for identification of statistically significant miRNA meta‐signature by combining several miRNA expression studies. This method allows the analysis of data produced by different technological platforms that cannot be otherwise directly compared or in the case when raw data are unavailable.

Identification of miR-374a as a prognostic marker for survival in patients with early-stage nonsmall cell lung cancer†

Lung cancer is one of the deadliest types of cancer proven by the poor survival and high relapse rates after surgery. Recently discovered microRNAs (miRNAs), small noncoding RNA molecules, play a crucial role in modulating gene expression networks and are directly involved in the progression of a number of human cancers. In this study, we analyzed the expression profile of 858 miRNAs in 38 Estonian nonsmall cell lung cancer (NSCLC) samples (Stage I and II) and 27 adjacent nontumorous tissue samples using Illumina miRNA arrays. We found that 39 miRNAs were up‐regulated and 33 down‐regulated significantly in tumors compared with normal lung tissue. We observed aberrant expression of several well‐characterized tumorigenesis‐related miRNAs, as well as a number of miRNAs whose function is currently unknown. We show that low expression of miR‐374a in early‐stage NSCLC is associated with poor patient survival. The combinatorial effect of the up‐ and down‐regulated miRNAs is predicted to most significantly affect pathways associated with cell migration, differentiation and growth, and several signaling pathways that contribute to tumorigenesis. In conclusion, our results demonstrate that expression of miR‐374a at early stages of NSCLC progression can serve as a prognostic marker for patient risk stratification and may be a promising therapeutic target for the treatment of lung cancer.

Methylation Markers of Early-Stage Non-Small Cell Lung Cancer

Background Despite of intense research in early cancer detection, there is a lack of biomarkers for the reliable detection of malignant tumors, including non-small cell lung cancer (NSCLC). DNA methylation changes are common and relatively stable in various types of cancers, and may be used as diagnostic or prognostic biomarkers. Methods We performed DNA methylation profiling of samples from 48 patients with stage I NSCLC and 18 matching cancer-free lung samples using microarrays that cover the promoter regions of more than 14,500 genes. We correlated DNA methylation changes with gene expression levels and performed survival analysis. Results We observed hypermethylation of 496 CpGs in 379 genes and hypomethylation of 373 CpGs in 335 genes in NSCLC. Compared to adenocarcinoma samples, squamous cell carcinoma samples had 263 CpGs in 223 hypermethylated genes and 513 CpGs in 436 hypomethylated genes. 378 of 869 (43.5%) CpG sites discriminating the NSCLC and control samples showed an inverse correlation between CpG site methylation and gene expression levels. As a result of a survival analysis, we found 10 CpGs in 10 genes, in which the methylation level differs in different survival groups. Conclusions We have identified a set of genes with altered methylation in NSCLC and found that a minority of them showed an inverse correlation with gene expression levels. We also found a set of genes that associated with the survival of the patients. These newly-identified marker candidates for the molecular screening of NSCLC will need further analysis in order to determine their clinical utility.

Endothelial TLR4 and the microbiome drive cerebral cavernous malformations

Cerebral cavernous malformations (CCMs) are a cause of stroke and seizure for which no effective medical therapies yet exist. CCMs arise from the loss of an adaptor complex that negatively regulates MEKK3–KLF2/4 signalling in brain endothelial cells, but upstream activators of this disease pathway have yet to be identified. Here we identify endothelial Toll-like receptor 4 (TLR4) and the gut microbiome as critical stimulants of CCM formation. Activation of TLR4 by Gram-negative bacteria or lipopolysaccharide accelerates CCM formation, and genetic or pharmacologic blockade of TLR4 signalling prevents CCM formation in mice. Polymorphisms that increase expression of the TLR4 gene or the gene encoding its co-receptor CD14 are associated with higher CCM lesion burden in humans. Germ-free mice are protected from CCM formation, and a single course of antibiotics permanently alters CCM susceptibility in mice. These studies identify unexpected roles for the microbiome and innate immune signalling in the pathogenesis of a cerebrovascular disease, as well as strategies for its treatment.

Gene co-expression analysis for functional classification and gene–disease predictions

Abstract Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Whole-exome sequencing identifies a polymorphism in the BMP5 gene associated with SSRI treatment response in major depression

Although antidepressants are widely used in the pharmacotherapy of major depressive disorder (MDD), their efficacy is still insufficient as approximately one-third of the patients do not fully recover even after several treatment trials. Inter-individual genetic differences are thought to contribute to the variability in antidepressant response; however, current findings from pharmacogenetic studies are uncertain or not clearly replicated. Here we report the first application of full exome sequencing for the analysis of pharmacogenomics on antidepressant treatment. After 12 weeks of treatment with the selective serotonin re-uptake inhibitor escitalopram, we selected five clear responders and five clear non-responders for exome sequencing. By comparing the allele counts of previously known single nucleotide polymorphisms and novel polymorphisms we selected 38 markers for further genotyping in two independent patient samples treated with escitalopram (n=116 and n=394). The A allele, carried by approximately 30% of the patients with MDD, of rs41271330 in the bone morphogenetic protein (BMP5) gene showed strong association with worse treatment response in both sample sets (p=0.001), indicating that this is an promising pharmacogenetic marker for prediction of antidepressant therapeutic outcome.

Altered Gene Expression Associated with microRNA Binding Site Polymorphisms

Allele-specific gene expression associated with genetic variation in regulatory regions can play an important role in the development of complex traits. We hypothesized that polymorphisms in microRNA (miRNA) response elements (MRE-SNPs) that either disrupt a miRNA binding site or create a new miRNA binding site can affect the allele-specific expression of target genes. By integrating public expression quantitative trait locus (eQTL) data, miRNA binding site predictions, small RNA sequencing, and Argonaute crosslinking immunoprecipitation (AGO-CLIP) datasets, we identified genetic variants that can affect gene expression by modulating miRNA binding efficiency. We also identified MRE-SNPs located in regions associated with complex traits, indicating possible causative mechanisms associated with these loci. The results of this study expand the current understanding of gene expression regulation and help to interpret the mechanisms underlying eQTL effects.

Meta-signature of human endometrial receptivity: a meta-analysis and validation study of transcriptomic biomarkers

Previous transcriptome studies of the human endometrium have revealed hundreds of simultaneously up- and down-regulated genes that are involved in endometrial receptivity. However, the overlap between the studies is relatively small, and we are still searching for potential diagnostic biomarkers. Here we perform a meta-analysis of endometrial-receptivity associated genes on 164 endometrial samples (76 from ‘pre-receptive’ and 88 from mid-secretory, ‘receptive’ phase endometria) using a robust rank aggregation (RRA) method, followed by enrichment analysis, and regulatory microRNA prediction. We identify a meta-signature of endometrial receptivity involving 57 mRNA genes as putative receptivity markers, where 39 of these we confirm experimentally using RNA-sequencing method in two separate datasets. The meta-signature genes highlight the importance of immune responses, the complement cascade pathway and the involvement of exosomes in mid-secretory endometrial functions. Bioinformatic prediction identifies 348 microRNAs that could regulate 30 endometrial-receptivity associated genes, and we confirm experimentally the decreased expression of 19 microRNAs with 11 corresponding up-regulated meta-signature genes in our validation experiments. The 57 identified meta-signature genes and involved pathways, together with their regulatory microRNAs could serve as promising and sought-after biomarkers of endometrial receptivity, fertility and infertility.

Metagenes Associated with Survival in Non-Small Cell Lung Cancer

NSCLC (non-small cell lung cancer) comprises about 80% of all lung cancer cases worldwide. Surgery is most effective treatment for patients with early-stage disease. However, 30%–55% of these patients develop recurrence within 5 years. Therefore, markers that can be used to accurately classify early-stage NSCLC patients into different prognostic groups may be helpful in selecting patients who should receive specific therapies. A previously published dataset was used to evaluate gene expression profiles of different NSCLC subtypes. A moderated two-sample t-test was used to identify differentially expressed genes between all tumor samples and cancer-free control tissue, between SCC samples and AC/BC samples and between stage I tumor samples and all other tumor samples. Gene expression microarray measurements were validated using qRT-PCR. Bayesian regression analysis and Kaplan-Meier survival analysis were performed to determine metagenes associated with survival. We identified 599 genes which were down-regulated and 402 genes which were up-regulated in NSCLC compared to the normal lung tissue and 112 genes which were up-regulated and 101 genes which were down-regulated in AC/BC compared to the SCC. Further, for stage Ib patients the metagenes potentially associated with survival were identified. Genes that expressed differently between normal lung tissue and cancer showed enrichment in gene ontology terms which were associated with mitosis and proliferation. Bayesian regression and Kaplan-Meier analysis showed that gene-expression patterns and metagene profiles can be applied to predict the probability of different survival outcomes in NSCLC patients.