Huntington's disease blood and brain show a common gene expression pattern and share an immune signature with Alzheimer's disease.

There is widespread transcriptional dysregulation in Huntington's disease (HD) brain, but analysis is inevitably limited by advanced disease and postmortem changes. However, mutant HTT is ubiquitously expressed and acts systemically, meaning blood, which is readily available and contains cells that are dysfunctional in HD, could act as a surrogate for brain tissue. We conducted an RNA-Seq transcriptomic analysis using whole blood from two HD cohorts, and performed gene set enrichment analysis using public databases and weighted correlation network analysis modules from HD and control brain datasets.

Meta-analysis of polycystic kidney disease expression profiles defines strong involvement of injury repair processes.

Polycystic kidney disease (PKD) is a major cause of end-stage renal disease. The disease mechanisms are not well understood and the pathogenesis toward renal failure remains elusive. In this study, we present the first RNASeq analysis of a -mutant mouse model in a combined meta-analysis with other published PKD expression profiles.

Identifying a gene expression signature of cluster headache in blood.

Cluster headache is a relatively rare headache disorder, typically characterized by multiple daily, short-lasting attacks of excruciating, unilateral (peri-)orbital or temporal pain associated with autonomic symptoms and restlessness. To better understand the pathophysiology of cluster headache, we used RNA sequencing to identify differentially expressed genes and pathways in whole blood of patients with episodic (n = 19) or chronic (n = 20) cluster headache in comparison with headache-free controls (n = 20). Gene expression data were analysed by gene and by module of co-expressed genes with particular attention to previously implicated disease pathways including hypocretin dysregulation.

Disease variants alter transcription factor levels and methylation of their binding sites.

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions.

Identification of context-dependent expression quantitative trait loci in whole blood.

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease.

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer.

Gene coexpression network analysis is a powerful "data-driven" approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise "meta-analysis" framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types.

Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms.

Background: Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages.

Results: Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile.

Integration of targeted metabolomics and transcriptomics identifies deregulation of phosphatidylcholine metabolism in Huntington's disease peripheral blood samples.

Introduction: Metabolic changes have been frequently associated with Huntington's disease (HD). At the same time peripheral blood represents a minimally invasive sampling avenue with little distress to Huntington's disease patients especially when brain or other tissue samples are difficult to collect.

Objectives: We investigated the levels of 163 metabolites in HD patient and control serum samples in order to identify disease related changes.

Blood lipids influence DNA methylation in circulating cells.

Background: Cells can be primed by external stimuli to obtain a long-term epigenetic memory. We hypothesize that long-term exposure to elevated blood lipids can prime circulating immune cells through changes in DNA methylation, a process that may contribute to the development of atherosclerosis. To interrogate the causal relationship between triglyceride, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels and genome-wide DNA methylation while excluding confounding and pleiotropy, we perform a stepwise Mendelian randomization analysis in whole blood of 3296 individuals.

Consistency of biological networks inferred from microarray and sequencing data.

Background: Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples.

Results: Our analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network.

The Implicitome: A Resource for Rationalizing Gene-Disease Associations.

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome).

Molecular signatures of age-associated chronic degeneration of shoulder muscles.

Chronic muscle diseases are highly prevalent in the elderly causing severe mobility limitations, pain and frailty. The intrinsic molecular mechanisms are poorly understood due to multifactorial causes, slow progression with age and variations between individuals. Understanding the underlying molecular mechanisms could lead to new treatment options which are currently limited.

Comparative mass spectrometric and immunoassay-based proteome analysis in serum of Duchenne muscular dystrophy patients.

Purpose: Duchenne muscular dystrophy (DMD) is a severe and fatal neuromuscular disease. With the current developments on novel therapeutic strategies for DMD, the need to carefully monitor disease progression or regression upon treatment using molecular markers has become urgent.

Experimental Design: 2D LC protein fractionation was performed on patient serum samples, followed by LC-MS/MS-based identifications with label-free quantifications.

Non-sequential and multi-step splicing of the dystrophin transcript.

The dystrophin protein encoding DMD gene is the longest human gene. The 2.2 Mb long human dystrophin transcript takes 16 hours to be transcribed and is co-transcriptionally spliced.

Assessing the translational landscape of myogenic differentiation by ribosome profiling.

The formation of skeletal muscles is associated with drastic changes in protein requirements known to be safeguarded by tight control of gene transcription and mRNA processing. The contribution of regulation of mRNA translation during myogenesis has not been studied so far. We monitored translation during myogenic differentiation of C2C12 myoblasts, using a simplified protocol for ribosome footprint profiling.

SplicePie: a novel analytical approach for the detection of alternative, non-sequential and recursive splicing.

Alternative splicing is a powerful mechanism present in eukaryotic cells to obtain a wide range of transcripts and protein isoforms from a relatively small number of genes. The mechanisms regulating (alternative) splicing and the paradigm of consecutive splicing have recently been challenged, especially for genes with a large number of introns. RNA-Seq, a powerful technology using deep sequencing in order to determine transcript structure and expression levels, is usually performed on mature mRNA, therefore not allowing detailed analysis of splicing progression.

Gateways to the FANTOM5 promoter level mammalian expression atlas.

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.

Alternative mRNA transcription, processing, and translation: insights from RNA sequencing.

The human transcriptome comprises >80,000 protein-coding transcripts and the estimated number of proteins synthesized from these transcripts is in the range of 250,000 to 1 million. These transcripts and proteins are encoded by less than 20,000 genes, suggesting extensive regulation at the transcriptional, post-transcriptional, and translational level. Here we review how RNA sequencing (RNA-seq) technologies have increased our understanding of the mechanisms that give rise to alternative transcripts and their alternative translation.

Huntington's disease biomarker progression profile identified by transcriptome sequencing in peripheral blood.

With several therapeutic approaches in development for Huntington's disease, there is a need for easily accessible biomarkers to monitor disease progression and therapy response. We performed next-generation sequencing-based transcriptome analysis of total RNA from peripheral blood of 91 mutation carriers (27 presymptomatic and, 64 symptomatic) and 33 controls. Transcriptome analysis by DeepSAGE identified 167 genes significantly associated with clinical total motor score in Huntington's disease patients.

Determining the quality and complexity of next-generation sequencing data without a reference genome.

We describe an open-source kPAL package that facilitates an alignment-free assessment of the quality and comparability of sequencing datasets by analyzing k-mer frequencies. We show that kPAL can detect technical artefacts such as high duplication rates, library chimeras, contamination and differences in library preparation protocols. kPAL also successfully captures the complexity and diversity of microbiomes and provides a powerful means to study changes in microbial communities.

Validation of genetic modifiers for Duchenne muscular dystrophy: a multicentre study assessing SPP1 and LTBP4 variants.

Objective: Duchenne muscular dystrophy (DMD) is characterised by progressive muscle weakness. It has recently been reported that single nucleotide polymorphisms (SNPs) located in the SPP1 and LTBP4 loci can account for some of the inter-individual variability observed in the clinical disease course. The validation of genetic association in large independent cohorts is a key process for rare diseases in order to qualify prognostic biomarkers and stratify patients in clinical trials.