Blood and Biomarkers in Huntington's Disease

The work described in this thesis presents part of a framework that can be used to extract detailed disease biological information from peripheral tissue. This framework is based on the central dogma of biology “DNA to RNA to protein” and on a systems biology approach that aims to produce synergetic data whose disease pathological, prognostic and predictive value is greater than the sum of the individual experiment results. HD patients are often characterized by a multifaceted clinical profile, consisting of several symptoms and variable disease progression rates. Therefore, a systems approach such as the one described above is expected to be the most effective in identifying potential treatments and predictive biomarkers that will be most informative for the different patient subpopulations. LUMC / Geneeskund

Transcriptional correlates of the pathological phenotype in a Huntington’s disease mouse model

Huntington disease (HD) is a fatal neurodegenerative disorder without a cure that is caused by an aberrant expansion of CAG repeats in exon 1 of the huntingtin (HTT) gene. Although a negative correlation between the number of CAG repeats and the age of disease onset is established, additional factors may contribute to the high heterogeneity of the complex manifestation of symptoms among patients. This variability is also observed in mouse models, even under controlled genetic and environmental conditions. To better understand this phenomenon, we analysed the R6/1 strain in search of potential correlates between pathological motor/cognitive phenotypical traits and transcriptional alterations. HD-related genes (e.g., Penk, Plk5, Itpka), despite being downregulated across the examined brain areas (the prefrontal cortex, striatum, hippocampus and cerebellum), exhibited tissue-specific correlations with particular phenotypical traits that were attributable to the contribution of the brain region to that trait (e.g., striatum and rotarod performance, cerebellum and feet clasping). Focusing on the striatum, we determined that the transcriptional dysregulation associated with HD was partially exacerbated in mice that showed poor overall phenotypical scores, especially in genes with relevant roles in striatal functioning (e.g., Pde10a, Drd1, Drd2, Ppp1r1b). However, we also observed transcripts associated with relatively better outcomes, such as Nfya (CCAAT-binding transcription factor NF-Y subunit A) plus others related to neuronal development, apoptosis and differentiation. In this study, we demonstrated that altered brain transcription can be related to the manifestation of HD-like symptoms in mouse models and that this can be extrapolated to the highly heterogeneous population of HD patients

WHOLE BLOOD SAGE DIGITAL GENE EXPRESSION PROFILING FROM HUNTINGTON'S DISEASE PATIENTS

Stress-related psychiatric disorders across the life spa

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

Contains fulltext : 153019.pdf (publisher's version ) (Open Access)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. Relative to previous studies, this yielded novel genes and confirmed previously identified genes, such as H2AFY, an overlap in results that has proven difficult in the past. Pathway analysis showed enrichment of genes of the immune system and target genes of miRNAs, which are downregulated in Huntington's disease models. Using a highly parallelized microfluidics array chip (Fluidigm), we validated 12 of the top 20 significant genes in our discovery cohort and 7 in a second independent cohort. The five genes (PROK2, ZNF238, AQP9, CYSTM1 and ANXA3) that were validated independently in both cohorts present a candidate biomarker panel for stage determination and therapeutic readout in Huntington's disease. Finally we suggest a first empiric formula predicting total motor score from the expression levels of our biomarker panel. Our data support the view that peripheral blood is a useful source to identify biomarkers for Huntington's disease and monitor disease progression in future clinical trials

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. Additionally, we integrated the metabolomics data with our previously published next generation sequencing-based gene expression data from the same patients in order to interconnect the metabolomics changes with transcriptional alterations. Methods: This analysis was performed using targeted metabolomics and flow injection electrospray ionization tandem mass spectrometry in 133 serum samples from 97 Huntington’s disease patients (29 pre-symptomatic and 68 symptomatic) and 36 controls. Results: By comparing HD mutation carriers with controls we identified 3 metabolites significantly changed in HD (serine and threonine and one phosphatidylcholine—PC ae C36:0) and an additional 8 phosphatidylcholines (PC aa C38:6, PC aa C36:0, PC ae C38:0, PC aa C38:0, PC ae C38:6, PC ae C42:0, PC aa C36:5 and PC ae C36:0) that exhibited a significant association with disease severity. Using workflow based exploitation of pathway databases and by integrating our metabolomics data with our gene expression data from the same patients we identified 4 deregulated phosphatidylcholine metabolism related genes (ALDH1B1, MBOAT1, MTRR and PLB1) that showed significant association with the changes in metabolite concentrations. Conclusion: Our results support the notion that phosphatidylcholine metabolism is deregulated in HD blood and that these metabolite alterations are associated with specific gene expression changes