Common disease signatures from gene expression analysis in Huntington’s disease human blood and brain

Mina E, van Roon-Mom W, Hettne K, van Zwet E, Goeman J, Neri C, ’t Hoen PAC, Mons B, Roos M

Orphanet Journal of Rare Diseases 11(1):97, August 2016
DOI: 10.1186/s13023-016-0475-2


Background: Huntington’s disease (HD) is a devastating brain disorder with no effective treatment or cure available. The scarcity of brain tissue makes it hard to study changes in the brain and impossible to perform longitudinal studies. However, peripheral pathology in HD suggests that it is possible to study the disease using peripheral tissue as a monitoring tool for disease progression and/or efficacy of novel therapies. In this study, we investigated if blood can be used to monitor disease severity and progression in brain. Since previous attempts using only gene expression proved unsuccessful, we compared blood and brain Huntington’s disease signatures in a functional context. Methods: Microarray HD gene expression profiles from three brain regions were compared to the transcriptome of HD blood generated by next generation sequencing. The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: “biological process”, “cellular component”, “molecular function” and “disease or syndrome”. Results: We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology. The HD signatures in caudate nucleus and BA4 exhibited the highest similarity with blood, irrespective of the category of semantic annotations used. BA9 exhibited an intermediate similarity, while cerebellum had the least similarity. We present two signatures that were shared between blood and brain: immune response and spinocerebellar ataxias. Conclusions: Our results demonstrate that HD blood exhibits dysregulation that is similar to brain at a functional level, but not necessarily at the level of individual genes. We report two common signatures that can be used to monitor the pathology in brain of HD patients in a non-invasive manner. Our results are an exemplar of how signals in blood data can be used to represent brain disorders. Our methodology can be used to study disease specific signatures in diseases where heterogeneous tissues are involved in the pathology.

Lay summary

Huntington’s disease is a serious brain disorder for which no effective treatment exists. It is difficult to study because of the scarcity of available brain tissue and samples cannot be collected repeatedly form one patient. However, as Huntington’s disease affects also other organs, it is possible to use peripheral tissue for the studies and monitoring disease progression and/or drug efficacy. The authors of the article checked if blood could be used to monitor disease severity and progression in the brain. They measured the abundance of RNAs encoding various genes and compared it between blood and three different brain regions. Even though individual genes showed no correlations between blood and brain, similarities were visible when analysing gene categories, such as genes with the same cellular localisation, molecular function or associated with the same biological processes or diseases. The researchers discovered that dysregulations shared between blood and brain concerned genes involved in immune response and spinocerebellar ataxias.

These results demonstrate how blood can be used for non-invasive monitoring of those brain disorders that affect various tissues. It also shows that looking at gene categories allows for a better assessment than analysing individual genes. This finding is particularly relevant for the research on rare diseases that affect organs, from which collecting biopsies is impossible and samples are scarce.


Further info

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