Biology subjects

Marek, K.

Publications and source records attributed to Marek, K..

3 recordsLinked to original sources

bioRxiv2026

Potential nematicides identified through targeted in vivo screening of a 5-HT-gated chloride channel, MOD-1

New approaches to mitigate the reduction of crop yields by plant parasitic nematodes are needed in the face of increasing concerns of the impact of nematicides on precious ecosystems. One approach is to target receptors in the parasitic nematode that are vital for their survival that less widely expressed in non-target organisms. Nematodes express a phylogenetically restricted 5-HT-gated chloride channel, MOD-1, activation of which causes paralysis in Caenorhabditis elegans. We show that MOD-1 is expressed in the motor nervous system of the plant parasitic nematode Globodera pallida and its functional characterisation is validated by 5-HT activation when reconstituted in Xenopus laevis oocytes. To evaluate MOD-1 as a nematicide target we utilised a previously described platform called PhaGeM4 for PharmacoGenetic targeting of M4 neurone in which MOD-1 is expressed in transgenic C. elegans and nematode development in the face of MOD-1 chemical modulation is tracked. We screened Pathogen Box, a chemical library of 400 diverse drug-like molecules, using PhaGeM4. This identified 10 putative hits for C. elegans MOD-1. These hits were pursued through a sequential, iterative pipeline encompassing mod-1 dependent C. elegans motility and G.pallida motility assays in combination with pharmacological interrogation of G. pallida mod-1 in PhaGeM4. This approach highlights 3 compounds with a mod-1 dependent action (quipazine, our benchmark compound; MMV687251, a vancomycin-like compound; MMV688774, an antifungal with common name posaconazole) and one compound that acts through an undetermined target (MMV002816, also known as the antifilarial drug, diethylcarbamazine). Each of these compounds had a significant inhibitory effect on G. pallida J2 root invasion. Overall, this lends confidence that the PhaGeM4 screening platform can delivery new chemical leads for crop protection and highlights four new chemistries of interest. More generally, this approach could be applied to other ligand-gated ion channels of interest as targets.

pharmacology and toxicology↗

bioRxiv2022

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

BackgroundThe clinical manifestations of Parkinsons disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need. Methods and FindingsWe used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinsons Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinsons Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 {+/-} 0.01 for the slower progressing group (PDvec1), 0.87 {+/-} 0.03 for moderate progressors, and 0.95 {+/-} 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner. ConclusionsOur data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.

bioinformatics↗

bioRxiv2019

Cross-Sectional and Longitudinal Validation of Serum Neurofilament Light Chain (NfL) as a Biomarker of Parkinson’s Disease Progression

ObjectiveTo assess neurofilament light chain (NfL), as a biomarker for Parkinsons disease (PD). MethodsWe quantified NfL in (1) longitudinal CSF samples from PD, other cognate/neurodegenerative disorders (OND), and healthy controls (HC); (2) a cross-sectional cohort with paired CSF and serum samples from participants with PD, OND, and HC, and (3) a large longitudinal validation cohort with serum samples from PD, OND, HC, prodromal conditions, and mutation carriers. ResultsIn the longitudinal discovery cohort (1) NfL in CSF was highest in OND and higher in PD vs. HC across all visits (p<0.05) but did not change longitudinally. In the cross-sectional cohort (2) paired CSF and serum NfL samples were highly correlated (Spearmans rank [Formula]; p<10^-6). In the large validation cohort (3) mean baseline serum NfL was higher in PD (13{+/-}7.2pg/ml) vs. HC (12{+/-}6.7pg/ml; p=0.0336) and was highest in OND (18{+/-}7pg/ml; p=0.0351). Serum NfL increased longitudinally in PD vs. HC (p<0.01). Longitudinal motor scores were positively longitudinally associated with NfL, whereas some cognitive scores showed a negative longitudinal association with NfL. ConclusionsNfL levels in serum samples are increased in PD vs. HC, increase significantly over time, and correlate with clinical measures of PD severity. Although the specificity of NfL in PD is low and more specific biomarkers are needed, serum NfL is the first blood-based biomarker candidate that could support disease stratification (PD vs. OND), track clinical progression, and possibly assess responsiveness to neuroprotective treatments. NfL as a biomarker of response to neuroprotective interventions remains to be determined. Funding sources for studyPPMI is sponsored by the Michael J. Fox Foundation for Parkinsons Research (MJFF) and is co-funded by MJFF, Abbvie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Eli Lilly & Co., F. Hoffman-La Roche, Ltd., GE Healthcare, Genentech, GlaxoSmithKline, Lundbeck, Merck, MesoScale, Piramal, Pfizer and UCB. The funders had no role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, in the preparation, review, or approval of the manuscript or in the decision to submit the manuscript for publication. Financial Disclosure/Conflict of Interest concerning the research related to the manuscriptBrit Mollenhauer, Douglas Galasko, Tatiana Foroud, Lana M. Chahine, Christopher S. Coffey, Andrew B. Singleton, Tanya Simuni, Daniel Weintraub, John Seibyl, Arthur W. Toga, and Caroline M. Tanner received funding from The Michael J. Fox Foundation for Parkinsons Research. Mohammed Dakna, Tzu-Ying Liu, Henrik Zetterberg, Sebastian Schade, Roland G. Gera, Wenting Wang, Feng Gao, Niels Kruse, Mark Frasier, Jesse M. Cedarbaum, Samantha J. Hutten, Claudia Trenkwalder, and Danielle Graham report no disclosures.

neuroscience↗