Evaluation of RNA Blood Biomarkers in Individuals at Risk of Parkinson's Disease

The Role of PKM2 in Multiple Signaling Pathways Related to Neurological Diseases

Pyruvate kinase M2 (PKM2) is a key rate-limiting enzyme in glycolysis. It is well known that PKM2 plays a vital role in the proliferation of tumor cells. However, PKM2 can also exert its biological functions by mediating multiple signaling pathways in neurological diseases, such as Alzheimer's disease (AD), cognitive dysfunction, ischemic stroke, post-stroke depression, cerebral small-vessel disease, hypoxic-ischemic encephalopathy, traumatic brain injury, spinal cord injury, Parkinson's disease (PD), epilepsy, neuropathic pain, and autoimmune diseases. In these diseases, PKM2 can exert various biological functions, including regulation of glycolysis, inflammatory responses, apoptosis, proliferation of cells, oxidative stress, mitochondrial dysfunction, or pathological autoimmune responses. Moreover, the complexity of PKM2's biological characteristics determines the diversity of its biological functions. However, the role of PKM2 is not entirely the same in different diseases or cells, which is related to its oligomerization, subcellular localization, and post-translational modifications. This article will focus on the biological characteristics of PKM2, the regulation of PKM2 expression, and the biological role of PKM2 in neurological diseases. With this review, we hope to have a better understanding of the molecular mechanisms of PKM2, which may help researchers develop therapeutic strategies in clinic.

Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering

The second most common progressive neurodegenerative disorder, Parkinson’s disease (PD), is characterized by a broad spectrum of symptoms that are associated with its progression. Several studies have attempted to classify PD according to its clinical manifestations and establish objective biomarkers for early diagnosis and for predicting the prognosis of the disease. Recent comprehensive research on the classification of PD using clinical phenotypes has included factors such as dominance, severity, and prognosis of motor and non-motor symptoms and biomarkers. Additionally, neuroimaging studies have attempted to reveal the pathological substrate for motor symptoms. Genetic and transcriptomic studies have contributed to our understanding of the underlying molecular pathogenic mechanisms and provided a basis for classifying PD. Moreover, an understanding of the heterogeneity of clinical manifestations in PD is required for a personalized medicine approach. Herein, we discuss the possible subtypes of PD based on clinical features, neuroimaging, and biomarkers for developing personalized medicine for PD. In addition, we conduct a preliminary clustering using gait features for subtyping PD. We believe that subtyping may facilitate the development of therapeutic strategies for PD.

Rehabilitative Impact of Exercise Training on Human Skeletal Muscle Transcriptional Programs in Parkinson's Disease

Parkinson's disease (PD) is the most common motor neurodegenerative disease, and neuromuscular function deficits associated with PD contribute to disability. Targeting these symptoms, our laboratory has previously evaluated 16-week high-intensity resistance exercise as rehabilitative training (RT) in individuals with PD. We reported significant improvements in muscle mass, neuromuscular function (strength, power, and motor unit activation), indices of neuromuscular junction integrity, total and motor scores on the unified Parkinson's disease rating scale (UPDRS), and total and sub-scores on the 39-item PD Quality of Life Questionnaire (PDQ-39), supporting the use of RT to reverse symptoms. Our objective was to identify transcriptional networks that may contribute to RT-induced neuromuscular remodeling in PD. We generated transcriptome-wide skeletal muscle RNA-sequencing in 5 participants with PD [4M/1F, 67 ± 2 years, Hoehn and Yahr stages 2 (n = 3) and 3 (n = 2)] before and after 16-week high intensity RT to identify transcriptional networks that may in part underpin RT-induced neuromuscular remodeling in PD. Following RT, 304 genes were significantly upregulated, notably related to remodeling and nervous system/muscle development. Additionally, 402 genes, primarily negative regulators of muscle adaptation, were downregulated. We applied the recently developed Pathway-Level Information ExtractoR (PLIER) method to reveal coordinated gene programs (as latent variables, LVs) that differed in skeletal muscle among young (YA) and old (OA) healthy adults and PD (n = 12 per cohort) at baseline and in PD pre- vs. post-RT. Notably, one LV associated with angiogenesis, axon guidance, and muscle remodeling was significantly lower in PD than YA at baseline and was significantly increased by exercise. A different LV annotated to denervation, autophagy, and apoptosis was increased in both PD and OA relative to YA and was also reduced by 16-week RT in PD. Thus, this analysis identified two novel skeletal muscle transcriptional programs that are dysregulated by PD and aging, respectively. Notably, RT has a normalizing effect on both programs in individuals with PD. These results identify potential molecular transducers of the RT-induced improvements in neuromuscular remodeling and motor function that may aid in optimizing exercise rehabilitation strategies for individuals with PD.

PTPRC Expression in Blood is Downregulated in Parkinson's and Progressive Supranuclear Palsy Disorders

BACKGROUND

Parkinson's disease (PD) shares pathological and clinical features with progressive supranuclear palsy (PSP) patients making the diagnosis challenging. Distinguishing PD from PSP is crucial given differences in disease course, treatment and clinical management.

OBJECTIVE

Although some progress has been made in the discovery of biomarkers for PD and PSP, there is an urgent need to identify additional biomarkers capable of distinguishing between these diseases.

METHODS

In this study, we tested the phosphatases DUSP8 and PTPRC for their diagnostic potential using quantitative PCR assays, in blood of 138 samples from participants nested in the Parkinson's Disease Biomarkers Program.

RESULTS

Relative abundance of PTPRC mRNA was downregulated in PSP patients compared to PD and healthy controls, whereas there was no significant difference in the expression of DUSP8. Interestingly, PTPRC mRNA correlated with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score and MDS-UPDRS- part III, thus indicating it might be useful as part of a biosignature to stratify patients according to disease severity and progression.

CONCLUSIONS

Collectively, these results suggest that PTPRC expression may be useful for distinguishing PD from PSP patients as part of a biosignature. Evaluation of PTPRC along with additional biomarkers in a larger and well-characterized longitudinal study is warranted.

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

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.

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.

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.