TRNA-derived fragments as sex-dependent circulating candidate biomarkers for Parkinson's disease.


Journal

Parkinsonism & related disorders
ISSN: 1873-5126
Titre abrégé: Parkinsonism Relat Disord
Pays: England
ID NLM: 9513583

Informations de publication

Date de publication:
08 2019
Historique:
received: 04 12 2018
revised: 21 05 2019
accepted: 23 05 2019
pubmed: 14 8 2019
medline: 1 7 2020
entrez: 13 8 2019
Statut: ppublish

Résumé

Parkinson's Disease (PD) is diagnosed clinically. Reliable non-invasive PD biomarkers are actively sought. Transfer RNAs produce short non-coding RNAs, the tRNA-derived fragments (tRF). tRF have been shown to play diverse roles, including in amyotrophic lateral sclerosis, and the response to ischemic stroke. Rich tRF populations are being reported in biofluids. We explored the possibility that tRF can serve as non-invasive biomarkers for PD. We collected existing RNA-seq samples and re-analyzed a total of 254 legacy datasets from 3 previous studies, from male and female PD patients and controls that belong to three categories: prefrontal cortex samples from 29 patients and 33 controls; cerebrospinal fluid (CSF) samples from 63 patients and 64 controls; and, serum samples from 34 patients and 31 controls. First, we identified tRF exhaustively and deterministically in every dataset. Second, we determined tRF that are differentially abundant (DA) between PD and control samples, using uncorrected t-tests. Lastly, we assessed all the DA tRF from the previous step with Partial Least Squares - Discriminant Analysis (PLS-DA) to stringently sub-select tRF that can distinguish PD patients from controls. We show that PLS-DA identified tRF from prefrontal cortex, CSF, and serum that can distinguish PD patients from controls. A handful of identified tRF were previously investigated in neurological contexts. Signatures built from relatively few tRF suffice to distinguish PD from control in each category of samples with high sensitivity (89-100%) and specificity (79-98%). tRF-based signatures are promising candidates that warrant further evaluation as non-invasive PD biomarkers.

Identifiants

pubmed: 31402278
pii: S1353-8020(19)30256-1
doi: 10.1016/j.parkreldis.2019.05.035
pii:
doi:

Substances chimiques

Biomarkers 0
RNA, Transfer 9014-25-9

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

203-209

Informations de copyright

Copyright © 2019. Published by Elsevier Ltd.

Auteurs

Rogan Magee (R)

Computational Medicine Center, Jefferson Alumni Hall #M81, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA.

Eric Londin (E)

Computational Medicine Center, Jefferson Alumni Hall #M81, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA.

Isidore Rigoutsos (I)

Computational Medicine Center, Jefferson Alumni Hall #M81, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA. Electronic address: isidore.rigoutsos@jefferson.edu.

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Classifications MeSH