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Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson's disease revealed by deep sequencing analysis

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Motivation: Most computational tools for small non-coding RNAs (sRNA) sequencing data analysis focus in microRNAs (miRNAs), overlooking other types of sRNAs that show multi-mapping hits. Here, we have developed a pipeline to non-redundantly quantify all types of sRNAs, and extract patterns of expression in biologically deﬁned groups. We have used our tool to characterize and proﬁle sRNAs in post-mortem brain samples of control individuals and Parkinson's disease (PD) cases at early-premotor and late-symptomatic stages. Results: Clusters of co-expressed sRNAs mapping onto tRNAs signiﬁcantly separated premotor and motor cases from controls. A similar result was obtained using a matrix of miRNAs slightly varying in sequence (isomiRs). The present framework revealed sRNA alterations at premotor stages of PD, which might reﬂect initial pathogenic perturbations. This tool may be useful to discover sRNA expression patterns linked to different biological conditions.

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Articles publicats en revistes (Patologia i Terapèutica Experimental)

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Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson's disease revealed by deep sequencing analysis