Small and long regulatory RNAs in the immune system and immune diseases

Cellular differentiation is regulated on the level of gene expression and it is known that dysregulation of gene expression can lead to deficiencies in differentiation that contribute to a variety of diseases, particularly of the immune system. Until recently, it was thought that the dysregulation was governed by changes in the binding or activity of a class of proteins called transcription factors. However, the discovery of micro-RNAs and recent descriptions of long noncoding RNAs have given enormous momentum to a whole new field of biology: the regulatory RNAs. In this review, we describe these two classes of regulatory RNAs and summarize what is known about how they regulate aspects of the adaptive and innate immune systems. Finally, we describe what is known about the involvement of micro-RNAs and long noncoding RNAs in three different autoimmune diseases (celiac disease, inflammatory bowel disease, and multiple sclerosis)

Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

Background: RNA-sequencing (RNA-seq) is a powerful technique for the identification of genetic variants that affect gene-expression levels, either through expression quantitative trait locus (eQTL) mapping or through allele-specific expression (ASE) analysis. Given increasing numbers of RNA-seq samples in the public domain, we here studied to what extent eQTLs and ASE effects can be identified when using public RNA-seq data while deriving the genotypes from the RNA-sequencing reads themselves. Methods: We downloaded the raw reads for all available human RNA-seq datasets. Using these reads we performed gene expression quantification. All samples were jointly normalized and subjected to a strict quality control. We also derived genotypes using the RNA-seq reads and used imputation to infer non-coding variants. This allowed us to perform eQTL mapping and ASE analyses jointly on all samples that passed quality control. Our results were validated using samples for which DNA-seq genotypes were available. Results: 4,978 public human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though these data originated from many different laboratories, samples reflecting the same cell type clustered together, suggesting that technical biases due to different sequencing protocols are limited. In a joint analysis on the 1,262 samples with high quality genotypes, we identified cis-eQTLs effects for 8,034 unique genes (at a false discovery rate Conclusions: By deriving and imputing genotypes from RNA-seq data, it is possible to identify both eQTLs and ASE effects. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become especially relevant for studying the effects of tissue-specific and rare pathogenic genetic variants to aid clinical interpretation of exome and genome sequencing

Evidence for a Minimal Eukaryotic Phosphoproteome?

BACKGROUND: Reversible phosphorylation catalysed by kinases is probably the most important regulatory mechanism in eukaryotes. METHODOLOGY/PRINCIPAL FINDINGS: We studied the in vitro phosphorylation of peptide arrays exhibiting the majority of PhosphoBase-deposited protein sequences, by factors in cell lysates from representatives of various branches of the eukaryotic species. We derived a set of substrates from the PhosphoBase whose phosphorylation by cellular extracts is common to the divergent members of different kingdoms and thus may be considered a minimal eukaryotic phosphoproteome. The protein kinases (or kinome) responsible for phosphorylation of these substrates are involved in a variety of processes such as transcription, translation, and cytoskeletal reorganisation. CONCLUSIONS/SIGNIFICANCE: These results indicate that the divergence in eukaryotic kinases is not reflected at the level of substrate phosphorylation, revealing the presence of a limited common substrate space for kinases in eukaryotes and suggests the presence of a set of kinase substrates and regulatory mechanisms in an ancestral eukaryote that has since remained constant in eukaryotic life

Systems genetics:From GWAS to disease pathways

AbstractMost common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function

Pleiotropic effects of lipid genes on plasma glucose, HbA1c, and HOMA-IR levels

Dyslipidemia is strongly associated with raised plasma glucose levels and insulin resistance (IR), and genome-wide association studies have identified 95 loci that explain a substantial proportion of the variance in blood lipids. However, the loci's effects on glucose-related traits are largely unknown. We have studied these lipid loci and tested their association collectively and individually with fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and IR in two independent cohorts: 10,995 subjects from LifeLines Cohort Study and 2,438 subjects from Prevention of Renal and Vascular Endstage Disease (PREVEND) study. In contrast to the positive relationship between dyslipidemia and glucose traits, the genetic predisposition to dyslipidemia showed a pleiotropic lowering effect on glucose traits. Specifically, the genetic risk score related to higher triglyceride level was correlated with lower levels of FPG (P = 9.6 × 10(-10) and P = 0.03 in LifeLines and PREVEND, respectively), HbA1c (P = 4.2 × 10(-7) in LifeLines), and HOMA of estimated IR (P = 6.2 × 10(-4) in PREVEND), after adjusting for blood lipid levels. At the single nucleotide polymorphism level, 15 lipid loci showed a pleiotropic association with glucose traits (P < 0.01), of which eight (CETP, MLXIPL, PLTP, GCKR, APOB, APOE-C1-C2, CYP7A1, and TIMD4) had opposite allelic directions of effect on dyslipidemia and glucose levels. Our findings suggest a complex genetic regulation and metabolic interplay between lipids and glucose

Resolving variants of unknown significance through reanalysis of 4,978 public RNA-seq samples

<p>Presentation given at the European Society of Human Genetics (ESHG) 2014 conference.</p