Henrike Knacke

Bio: Henrike Knacke is an academic researcher. The author has contributed to research in topics: Parkinson's disease. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

A blood marker for Parkinson’s Disease: Neuronal exosome-derived α-synuclein

Abstract: To date, no reliable clinically applicable biomarker has been established for Parkinson’s disease (PD). Our results indicate that a long hoped blood test for Parkinson’s disease may be realized. We here assess the potential of pathological α-synuclein originating from neuron-derived exosomes from blood plasma as a possible biomarker. Following the isolation of neuron-derived exosomes from plasma of PD patients and non-PD individuals immunoblot analyses were performed to detect exosomal α-synuclein. Under native conditions significantly increased signals of disease-associated α-synuclein forms in neuron-derived exosomes were measured in all individuals with PD and clearly distinguished PD samples from controls. By performing a protein misfolding cyclic amplification assay these aggregates could be amplified and seeding could be demonstrated. Moreover, the aggregates exhibited β-sheet-rich structures and showed a fibrillary appearance. Our study demonstrates that the detection of pathological α-synuclein conformers from neuron-derived exosomes from plasma samples has the potential of a promising blood-biomarker of PD.

Validation of the MDS Clinical Diagnostic Criteria for Parkinson’s Disease (S3.001)

Abstract: Objective: To validate the International Parkinson Disease and Movement Disorders society (MDS) diagnostic criteria against a gold-standard expert clinical diagnosis and to compare concordance/accuracy of the MDS criteria to 1988 United Kingdom brain bank criteria. Background: In 2015, the MDS published the new clinical diagnostic criteria for Parkinson’s disease (PD). These criteria aimed to codify/reproduce the expert clinical diagnostic process, to help standardize diagnosis in research and clinical settings. Their accuracy compared to expert clinical diagnosis has not been tested. Design/Methods: From 8 centers, we recruited 626 patients with parkinsonism (434 with PD and 192 with other causes, diagnosed by an expert treating physician). A second, less experienced neurologist evaluated the presence/absence of each individual item from the diagnostic criteria. The overall accuracy/concordance rate, sensitivity, and specificity of diagnostic criteria were calculated. Results: Of 434 patients diagnosed with PD, 94.5% met MDS criteria for probable PD (i.e. a 5.5% false-negative rate). Of 192 non-PD patients, 88.5% were identified as non-PD by the criteria (i.e. a 11.5% false-positive rate). The overall accuracy for probable PD was 92.6%. For the clinically-established PD category, 59.3% of PD patients and only 1.6% of non-PD patients met MDS criteria. Compared to MDS probable PD criteria, the UK brain bank criteria had significantly lower sensitivity (89.2%, p=0.008), specificity (79.2%, p=0.018), and overall accuracy (86.4%, p Conclusions: The MDS criteria demonstrated high sensitivity and specificity compared to gold-standard expert diagnosis. To meet the unmet gap of specific diagnostic criteria for early PD, we suggest additional ‘Clinically-Established Early PD’ criteria to be used for clinical trials of early PD. Study Supported by: Michael J. Fox Foundation Disclosure: Dr. Postuma has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Biotie, Roche/Prothena, Teva Neurosciences, Jazz Pharmaceuticals, Novartis Canada, Theranexus, GE HealthCare, . Dr. Poewe has nothing to disclose. Dr. Litvan has received personal compensation for serving onthe scientific steering committee of the Biotie/Parkinson Study Group clinical trial Dr. Lewis has nothing to disclose. Dr. Lang has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Abbvie, Acorda, Avanir Pharmaceuticals, Biogen, Bristol Myers Squibb, Sun Pharma, Cipla, Intekrin, Merck, Medichem, Medtronic, Teva, UCB, and Sunovion, . Dr. Halliday has nothing to disclose. Dr. Goetz has nothing to disclose. Dr. Chan has nothing to disclose. Dr. Slow has nothing to disclose. Dr. Seppi has nothing to disclose. Dr. Schaeffer has nothing to disclose. Dr. Berg has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with UCB Pharma GmbH, Lundbeck, Prexton Therapeutics, GE-Healthcare. Dr. Rios Romenets has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with NIA, Genentech/Roche. Dr. Rios Romenets has received research support from NIA, Genentech/Roche. Dr. Mi has nothing to disclose. Dr. Maetzler has nothing to disclose. Dr. Li has nothing to disclose. Dr. Heim has nothing to disclose. Dr. Bledsoe has nothing to disclose.

Novel diagnostic biomarkers related to immune infiltration in Parkinson’s disease by bioinformatics analysis

Abstract: Background Parkinson’s disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers. Materials and methods Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the “limma” package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein–protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers. Results A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package “UpSet.” We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905). Conclusion By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.