Rogan Magee1, Eric Londin1, Isidore Rigoutsos2. 1. Computational Medicine Center, Jefferson Alumni Hall #M81, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA. 2. 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.
Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS: 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%). CONCLUSION: tRF-based signatures are promising candidates that warrant further evaluation as non-invasive PD biomarkers.
INTRODUCTION: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. METHODS: We collected existing RNA-seq samples and re-analyzed a total of 254 legacy datasets from 3 previous studies, from male and female PDpatients 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 PDpatients from controls. RESULTS: We show that PLS-DA identified tRF from prefrontal cortex, CSF, and serum that can distinguish PDpatients 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%). CONCLUSION: tRF-based signatures are promising candidates that warrant further evaluation as non-invasive PD biomarkers.
Authors: Shubhra Acharya; Antonio Salgado-Somoza; Francesca Maria Stefanizzi; Andrew I Lumley; Lu Zhang; Enrico Glaab; Patrick May; Yvan Devaux Journal: Int J Mol Sci Date: 2020-09-06 Impact factor: 5.923