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Evaluation of RNA Blood Biomarkers in the Parkinson's Disease Biomarkers Program

By:
Jose A. Santiago, Virginie Bottero and Judith A. Potashkin
Evaluation of RNA Blood Biomarkers in the Parkinson's Disease Biomarkers Program

There is a high misdiagnosis rate between Parkinson’s disease (PD) and atypical parkinsonian disorders (APD), such as progressive supranuclear palsy (PSP), the second most common parkinsonian syndrome. In our earlier studies, we identified and replicated RNA blood biomarkers in several independent cohorts, however, replication in a cohort that includes PSP patients has not yet been performed. To this end, we evaluated the diagnostic potential of nine previously identified RNA biomarkers using quantitative PCR assays in 138 blood samples at baseline from PD, PSP and healthy controls (HCs) nested in the PD Biomarkers Program. Linear discriminant analysis showed that COPZ1 and PTPN1 distinguished PD from PSP patients with 62.5% accuracy. Five biomarkers, PTPN1, COPZ1, FAXDC2, SLC14A1s and NAMPT were useful for distinguishing PSP from controls with 69% accuracy. Several biomarkers correlated with clinical features in PD patients. SLC14A1-s correlated with Unified Parkinson’s Disease Rating Scale total and part III scores. In addition, COPZ1, PTPN1 and MLST8, correlated with Montreal Cognitive Assessment (MoCA). Interestingly, COPZ1, EFTUD2 and PTPN1 were downregulated in cognitively impaired (CI) compared to normal subjects. Linear discriminant analysis showed that age, PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 distinguished CI from normal subjects with 65.9% accuracy. These results suggest that COPZ1 and PTPN1 are useful for distinguishing PD from PSP patients. In addition, the combination of PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 is a useful signature for cognitive impairment. Evaluation of these biomarkers in a larger study will be a key to advancing these biomarkers into the clinic.

Introduction

Parkinson’s disease (PD) is a neurodegenerative disease characterized by the selective loss of dopamine neurons in the substantia nigra pars compacta. Deterioration of the dopaminergic system leads to severe motor symptoms including resting tremor, rigidity, bradykinesia and postural instability. Current treatments for PD afford symptomatic relief, but a disease modifying or neuroprotective agent capable of halting the progression of the disease is not yet available. The lack of a robust biomarker with high sensitivity and specificity has limited the progress towards the development of effective therapeutics for PD. In this context, biomarkers would offer great advantages in clinical trials testing drugs and neuroprotective agents. For example, biomarkers could facilitate the selection and stratification of study participants, monitor disease progression and inform about target selection (Santiago and Potashkin, 2014a; Gwinn et al., 2017).

Distinguishing PD from atypical parkinsonian disorders (APD) is an unmet goal for currently proposed biomarker studies. The overlap in symptoms and pathological features between PD and APD makes these diseases very difficult to distinguish early in the disease process where therapeutic intervention may be more beneficial (Rajput and Rajput, 2014; Santiago and Potashkin, 2014c). Progressive supranuclear palsy (PSP), for example, is frequently misdiagnosed as PD. Deposition of fibrillar aggregates of four-repeat Tau protein in the brainstem and cerebral cortex is a pathological hallmark of PSP (Dickson et al., 2010). Clinical symptoms in PSP patients include prominent hypokinesia, oculo-motor and balance disturbances (Dickson et al., 2010). In contrast, PD is characterized by the accumulation of alpha synuclein (SNCA) in the substantia nigra pars compacta. PD patients exhibit classical motor symptoms that include rigidity, tremor and bradykinesia (Ascherio and Schwarzschild, 2016). To date, diagnosis of PD and PSP patients is based on the assessment of motor symptoms and response to dopaminergic therapy (Ascherio and Schwarzschild, 2016). The problem with this approach is that PSP and PD patients manifest similarities at early stages of the disease and both respond to dopaminergic treatment, which makes the diagnosis very challenging (Dickson et al., 2010). The high misdiagnosis rate between PD and PSP reaching approximately 30% heightens the urgency for the identification of highly specific biomarkers capable of distinguishing these diseases (Rajput and Rajput, 2014; Santiago and Potashkin, 2014c).

Although substantial progress has been made in the discovery of blood biomarkers for PD (Khoo et al., 2012; Ciaramella et al., 2013; Qiang et al., 2013; Santiago and Potashkin, 2013a, 2014a, 2015b, 2017; Santiago et al., 2014, 2016; Alieva et al., 2015; Calligaris et al., 2015; Locascio et al., 2015; Swanson et al., 2015), very few studies have addressed the misdiagnosis problem between PD and PSP. Our first studies identified a splice-variant specific signature capable of distinguishing PD from healthy and APD (Potashkin et al., 2012). This biomarker signature was composed of 13 splice variants including fatty acid hydroxylase domain containing 2 (FAXDC2; C5ORF4), coatomer protein complex subunit zeta 1 (COPZ1), microtubule-actin crosslinking factor 1 (MACF1), wntless WNT ligand secretion mediator (WLS), proteoglycan 3, pro eosinophil major basic protein 2 (PRG3), zinc finger protein 160 (ZNF160), elongation factor Tu GTP binding domain containing 2 (EFTUD2), mitogen-activated protein 4 kinase 1 (MAP4K1), membrane palmitoylated protein 1 (MPP1), pyruvate kinase M2 (PKM2), solute carrier family 14 member 1 (SLC14A1-s), SLC14A1-l and zinc finger protein 134 (ZNF134; Potashkin et al., 2012). Seven out these 13 biomarkers, including FAXDC2 (C5ORF4), COPZ1, MACF1, WLS, PRG3, ZNF160 and EFTUD2, replicated in a second independent cohort of participants that included PD and healthy controls (HCs), but not APD patients (Santiago et al., 2013). Another promising study used a network approach integrating our microarray data (Potashkin et al., 2012) in order to identify protein tyrosine phosphatase, non-receptor type 1 (PTPN1) as a potential biomarker for PSP (Santiago and Potashkin, 2014c). PTPN1 was capable of distinguishing PD from PSP patients with 86% overall diagnostic accuracy. Nonetheless, these markers have not been replicated in an independent set of participants that includes PSP patients. In this study, we tested a subset of nine RNA biomarkers in blood samples obtained from the PD Biomarkers Program (PDBP). We hypothesized that these RNA markers from our previous studies could be helpful for the differential diagnosis between PD and PSP and may be informative of clinical features including disease progression and cognition.

Materials and Methods

Study Participants

Samples used in this study were obtained from PDBP, a consortium of clinical sites funded by the National Institute of Neurological Diseases and Stroke (NINDS, National Institutes of Health (NIH), United States). The consortium projects focus on the development of clinical and laboratory-based biomarkers for PD diagnosis, progression and prognosis. RNA samples used in this study were obtained from Penn State Milton S. Hershey Medical Center and University of Florida College of Medicine. The Institutional Review Boards (IRB) of each PDBP center and the Rosalind Franklin University of Medicine and Sciences approved the study protocol. Written informed consent was obtained from all participants before inclusion in the study. PD patients were recruited and evaluated by movement disorder specialists using establish criteria (Rosenthal et al., 2016). Disease severity was assessed using the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III and Hoehn & Yahr scale. Inclusion and exclusion criteria were the following: PD patients had a history of adequate response to dopaminergic therapy and history of asymmetrical symptom onset. HC had no history of neurological disorder; PSP patients were over 40 years old with vertical gaze palsy and/or slow vertical gaze/postural instability during first year of diagnosis. Additional inclusion and exclusion criteria have been published elsewhere (Rosenthal et al., 2016). The demographic and clinical characteristics of the study participants selected for this study are listed in Table 1.

Author Contributions

JS and JP: conceived and designed the experiments. JS and VB: performed experiments and wrote the article. JS, VB and JP: analyzed the data.

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Funding

This study was funded by the National Institute of Neurological Disorders and Stroke (NINDS) Grant No. U01NS097037 to JP. Data and biospecimens used in preparation of this manuscript were obtained from the PD Biomarkers Program (PDBP) Consortium, part of the National Institute of Neurological Disorders and Stroke at the National Institutes of Health. Investigators include: Roger Albin, Roy Alcalay, Alberto Ascherio, DuBois Bowman, Alice Chen-Plotkin, Ted Dawson, Richard Dewey, Dwight German, Xuemei Huang, Judith Potashkin, Rachel Saunders-Pullman, Liana Rosenthal, Clemens Scherzer, David Vaillancourt, Vladislav Petyuk, David Walt, Andy West and Jing Zhang. The PDBP Investigators have not participated in reviewing the data analysis or content of the manuscript.

 

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 

Acknowledgments

We are grateful to the participants and clinicians who participated in the PDBP study.

 

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2018.00157/full#supplementary-material

References

Santiago, J. A., Littlefield, A. M., and Potashkin, J. A. (2016). Integrative transcriptomic meta-analysis of Parkinson’s disease and depression identifies NAMPT as a potential blood biomarker for de novo Parkinson’s disease. Sci. Rep. 6:34579. doi: 10.1038/srep34579

Santiago, J. A., and Potashkin, J. A. (2013a). Integrative network analysis unveils convergent molecular pathways in Parkinson’s disease and diabetes. PLoS One 8:e83940. doi: 10.1371/journal.pone.0083940

Wen, M. C., Chan, L. L., Tan, L. C. S., and Tan, E. K. (2017). Mild cognitive impairment in Parkinson’s disease: a distinct clinical entity? Transl. Neurodegener. 6:24. doi: 10.1186/s40035-017-0094-4