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Your search keyword '"Schymanski, Emma L"' showing total 223 results
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223 results on '"Schymanski, Emma L"'

1. Beyond target chemicals: updating the NORMAN prioritisation scheme to support the EU chemicals strategy with semi-quantitative suspect/non-target screening data

Author

Dulio, Valeria, Alygizakis, Nikiforos, Ng, Kelsey, Schymanski, Emma L., Andres, Sandrine, Vorkamp, Katrin, Hollender, Juliane, Finckh, Saskia, Aalizadeh, Reza, Ahrens, Lutz, Bouhoulle, Elodie, Čirka, Ľuboš, Derksen, Anja, Deviller, Geneviève, Duffek, Anja, Esperanza, Mar, Fischer, Stellan, Fu, Qiuguo, Gago-Ferrero, Pablo, Haglund, Peter, Junghans, Marion, Kools, Stefan A. E., Koschorreck, Jan, Lopez, Benjamin, Lopez de Alda, Miren, Mascolo, Giuseppe, Miège, Cécile, Osté, Leonard, O’Toole, Simon, Rostkowski, Pawel, Schulze, Tobias, Sims, Kerry, Six, Laetitia, Slobodnik, Jaroslav, Staub, Pierre-François, Stroomberg, Gerard, Thomaidis, Nikolaos S., Togola, Anne, Tomasi, Giorgio, and von der Ohe, Peter C.

Published
2024
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5. Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Author

Bittremieux, Wout, Avalon, Nicole E., Thomas, Sydney P., Kakhkhorov, Sarvar A., Aksenov, Alexander A., Gomes, Paulo Wender P., Aceves, Christine M., Caraballo-Rodríguez, Andrés Mauricio, Gauglitz, Julia M., Gerwick, William H., Huan, Tao, Jarmusch, Alan K., Kaddurah-Daouk, Rima F., Kang, Kyo Bin, Kim, Hyun Woo, Kondić, Todor, Mannochio-Russo, Helena, Meehan, Michael J., Melnik, Alexey V., Nothias, Louis-Felix, O’Donovan, Claire, Panitchpakdi, Morgan, Petras, Daniel, Schmid, Robin, Schymanski, Emma L., van der Hooft, Justin J. J., Weldon, Kelly C., Yang, Heejung, Xing, Shipei, Zemlin, Jasmine, Wang, Mingxun, and Dorrestein, Pieter C.

Published
2023
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6. NORMAN guidance on suspect and non-target screening in environmental monitoring

Author

Hollender, Juliane, Schymanski, Emma L., Ahrens, Lutz, Alygizakis, Nikiforos, Béen, Frederic, Bijlsma, Lubertus, Brunner, Andrea M., Celma, Alberto, Fildier, Aurelie, Fu, Qiuguo, Gago-Ferrero, Pablo, Gil-Solsona, Ruben, Haglund, Peter, Hansen, Martin, Kaserzon, Sarit, Kruve, Anneli, Lamoree, Marja, Margoum, Christelle, Meijer, Jeroen, Merel, Sylvain, Rauert, Cassandra, Rostkowski, Pawel, Samanipour, Saer, Schulze, Bastian, Schulze, Tobias, Singh, Randolph R., Slobodnik, Jaroslav, Steininger-Mairinger, Teresa, Thomaidis, Nikolaos S., Togola, Anne, Vorkamp, Katrin, Vulliet, Emmanuelle, Zhu, Linyan, and Krauss, Martin

Published
2023
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9. Harmonized quality assurance/quality control provisions to assess completeness and robustness of MS1 data preprocessing for LC-HRMS-based suspect screening and non-targeted analysis

Author

Lennon, Sarah, Chaker, Jade, Price, Elliott J., Hollender, Juliane, Huber, Carolin, Schulze, Tobias, Ahrens, Lutz, Béen, Frederic, Creusot, Nicolas, Debrauwer, Laurent, Dervilly, Gaud, Gabriel, Catherine, Guérin, Thierry, Habchi, Baninia, Jamin, Emilien L., Klánová, Jana, Kosjek, Tina, Le Bizec, Bruno, Meijer, Jeroen, Mol, Hans, Nijssen, Rosalie, Oberacher, Herbert, Papaioannou, Nafsika, Parinet, Julien, Sarigiannis, Dimosthenis, Stravs, Michael A., Tkalec, Žiga, Schymanski, Emma L., Lamoree, Marja, Antignac, Jean-Philippe, and David, Arthur

Published
2024
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14. Expanding the Use of Spectral Libraries in Proteomics

Author

Deutsch, Eric W, Perez-Riverol, Yasset, Chalkley, Robert J, Wilhelm, Mathias, Tate, Stephen, Sachsenberg, Timo, Walzer, Mathias, Käll, Lukas, Delanghe, Bernard, Böcker, Sebastian, Schymanski, Emma L, Wilmes, Paul, Dorfer, Viktoria, Kuster, Bernhard, Volders, Pieter-Jan, Jehmlich, Nico, Vissers, Johannes PC, Wolan, Dennis W, Wang, Ana Y, Mendoza, Luis, Shofstahl, Jim, Dowsey, Andrew W, Griss, Johannes, Salek, Reza M, Neumann, Steffen, Binz, Pierre-Alain, Lam, Henry, Vizcaíno, Juan Antonio, Bandeira, Nuno, and Röst, Hannes

Subjects
Animals, Databases, Protein, Humans, Peptide Library, Proteomics, Tandem Mass Spectrometry, Workflow, mass spectrometry, spectral libraries, standards, formats, Dagstuhl Seminar, meeting report, Proteomics Standards Initiative, Chemical Sciences, Biological Sciences, Biochemistry & Molecular Biology
Abstract

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Published
2018

15. The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Author

Mohammed Taha, Hiba, Aalizadeh, Reza, Alygizakis, Nikiforos, Antignac, Jean-Philippe, Arp, Hans Peter H., Bade, Richard, Baker, Nancy, Belova, Lidia, Bijlsma, Lubertus, Bolton, Evan E., Brack, Werner, Celma, Alberto, Chen, Wen-Ling, Cheng, Tiejun, Chirsir, Parviel, Čirka, Ľuboš, D’Agostino, Lisa A., Djoumbou Feunang, Yannick, Dulio, Valeria, Fischer, Stellan, Gago-Ferrero, Pablo, Galani, Aikaterini, Geueke, Birgit, Głowacka, Natalia, Glüge, Juliane, Groh, Ksenia, Grosse, Sylvia, Haglund, Peter, Hakkinen, Pertti J., Hale, Sarah E., Hernandez, Felix, Janssen, Elisabeth M.-L., Jonkers, Tim, Kiefer, Karin, Kirchner, Michal, Koschorreck, Jan, Krauss, Martin, Krier, Jessy, Lamoree, Marja H., Letzel, Marion, Letzel, Thomas, Li, Qingliang, Little, James, Liu, Yanna, Lunderberg, David M., Martin, Jonathan W., McEachran, Andrew D., McLean, John A., Meier, Christiane, Meijer, Jeroen, Menger, Frank, Merino, Carla, Muncke, Jane, Muschket, Matthias, Neumann, Michael, Neveu, Vanessa, Ng, Kelsey, Oberacher, Herbert, O’Brien, Jake, Oswald, Peter, Oswaldova, Martina, Picache, Jaqueline A., Postigo, Cristina, Ramirez, Noelia, Reemtsma, Thorsten, Renaud, Justin, Rostkowski, Pawel, Rüdel, Heinz, Salek, Reza M., Samanipour, Saer, Scheringer, Martin, Schliebner, Ivo, Schulz, Wolfgang, Schulze, Tobias, Sengl, Manfred, Shoemaker, Benjamin A., Sims, Kerry, Singer, Heinz, Singh, Randolph R., Sumarah, Mark, Thiessen, Paul A., Thomas, Kevin V., Torres, Sonia, Trier, Xenia, van Wezel, Annemarie P., Vermeulen, Roel C. H., Vlaanderen, Jelle J., von der Ohe, Peter C., Wang, Zhanyun, Williams, Antony J., Willighagen, Egon L., Wishart, David S., Zhang, Jian, Thomaidis, Nikolaos S., Hollender, Juliane, Slobodnik, Jaroslav, and Schymanski, Emma L.

Published
2022
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19. Critical Assessment of Small Molecule Identification 2016: automated methods

Author

Schymanski, Emma L, Ruttkies, Christoph, Krauss, Martin, Brouard, Céline, Kind, Tobias, Dührkop, Kai, Allen, Felicity, Vaniya, Arpana, Verdegem, Dries, Böcker, Sebastian, Rousu, Juho, Shen, Huibin, Tsugawa, Hiroshi, Sajed, Tanvir, Fiehn, Oliver, Ghesquière, Bart, and Neumann, Steffen

Subjects
Analytical Chemistry, Chemical Sciences, Compound identification, In silico fragmentation, High resolution mass spectrometry, Metabolomics, Structure elucidation, Macromolecular and Materials Chemistry, Analytical chemistry, Macromolecular and materials chemistry
Abstract

BACKGROUND:The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. RESULTS:The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification-In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification-Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. CONCLUSIONS:The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .

Published
2017

24. Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods

Author

Abate-Pella, Daniel, Freund, Dana M, Ma, Yan, Simón-Manso, Yamil, Hollender, Juliane, Broeckling, Corey D, Huhman, David V, Krokhin, Oleg V, Stoll, Dwight R, Hegeman, Adrian D, Kind, Tobias, Fiehn, Oliver, Schymanski, Emma L, Prenni, Jessica E, Sumner, Lloyd W, and Boswell, Paul G

Subjects
Chromatography, High Pressure Liquid, Mass Spectrometry, Reproducibility of Results, Retention projection, Retention prediction, Liquid chromatography-mass spectrometry, Retention library, Retention database, Multi-laboratory study, Liquid chromatography–mass spectrometry, Chemical Sciences, Engineering, Technology, Analytical Chemistry
Abstract

Identification of small molecules by liquid chromatography-mass spectrometry (LC-MS) can be greatly improved if the chromatographic retention information is used along with mass spectral information to narrow down the lists of candidates. Linear retention indexing remains the standard for sharing retention data across labs, but it is unreliable because it cannot properly account for differences in the experimental conditions used by various labs, even when the differences are relatively small and unintentional. On the other hand, an approach called "retention projection" properly accounts for many intentional differences in experimental conditions, and when combined with a "back-calculation" methodology described recently, it also accounts for unintentional differences. In this study, the accuracy of this methodology is compared with linear retention indexing across eight different labs. When each lab ran a test mixture under a range of multi-segment gradients and flow rates they selected independently, retention projections averaged 22-fold more accurate for uncharged compounds because they properly accounted for these intentional differences, which were more pronounced in steep gradients. When each lab ran the test mixture under nominally the same conditions, which is the ideal situation to reproduce linear retention indices, retention projections still averaged 2-fold more accurate because they properly accounted for many unintentional differences between the LC systems. To the best of our knowledge, this is the most successful study to date aiming to calculate (or even just to reproduce) LC gradient retention across labs, and it is the only study in which retention was reliably calculated under various multi-segment gradients and flow rates chosen independently by labs.

Published
2015

27. Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding.

Author

Foreman, Amy L., Warth, Benedikt, Hessel, Ellen V. S., Price, Elliott J., Schymanski, Emma L., Cantelli, Gaia, Parkinson, Helen, Hecht, Helge, Klánová, Jana, Vlaanderen, Jelle, Hilscherova, Klara, Vrijheid, Martine, Vineis, Paolo, Araujo, Rita, Barouki, Robert, Vermeulen, Roel, Lanone, Sophie, Brunak, Søren, Sebert, Sylvain, and Karjalainen, Tuomo

Published
2024
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28. CLN3 deficiency leads to neurological and metabolic perturbations during early development.

Author

Heins-Marroquin, Ursula, Singh, Randolph R., Perathoner, Simon, Gavotto, Floriane, Ruiz, Carla Merino, Patraskaki, Myrto, Gomez-Giro, Gemma, Borgmann, Felix Kleine, Meyer, Melanie, Carpentier, Anaıs, Warmoes, Marc O., Jager, Christian, Mittelbronn, Michel, Schwamborn, Jens C., Cordero-Maldonado, Maria Lorena, Crawford, Alexander D., Schymanski, Emma L., and Linster, Carole L.

Published
2024
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29. The NORMAN Association and the European Partnership for Chemicals Risk Assessment (PARC): let’s cooperate!

Author

Dulio, Valeria, Koschorreck, Jan, van Bavel, Bert, van den Brink, Paul, Hollender, Juliane, Munthe, John, Schlabach, Martin, Aalizadeh, Reza, Agerstrand, Marlene, Ahrens, Lutz, Allan, Ian, Alygizakis, Nikiforos, Barcelo’, Damia’, Bohlin-Nizzetto, Pernilla, Boutroup, Susanne, Brack, Werner, Bressy, Adèle, Christensen, Jan H., Cirka, Lubos, Covaci, Adrian, Derksen, Anja, Deviller, Geneviève, Dingemans, Milou M. L., Engwall, Magnus, Fatta-Kassinos, Despo, Gago-Ferrero, Pablo, Hernández, Félix, Herzke, Dorte, Hilscherová, Klára, Hollert, Henner, Junghans, Marion, Kasprzyk-Hordern, Barbara, Keiter, Steffen, Kools, Stefan A. E., Kruve, Anneli, Lambropoulou, Dimitra, Lamoree, Marja, Leonards, Pim, Lopez, Benjamin, López de Alda, Miren, Lundy, Lian, Makovinská, Jarmila, Marigómez, Ionan, Martin, Jonathan W., McHugh, Brendan, Miège, Cécile, O’Toole, Simon, Perkola, Noora, Polesello, Stefano, Posthuma, Leo, Rodriguez-Mozaz, Sara, Roessink, Ivo, Rostkowski, Pawel, Ruedel, Heinz, Samanipour, Saer, Schulze, Tobias, Schymanski, Emma L., Sengl, Manfred, Tarábek, Peter, Ten Hulscher, Dorien, Thomaidis, Nikolaos, Togola, Anne, Valsecchi, Sara, van Leeuwen, Stefan, von der Ohe, Peter, Vorkamp, Katrin, Vrana, Branislav, and Slobodnik, Jaroslav

Published
2020
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34. Future water quality monitoring: improving the balance between exposure and toxicity assessments of real-world pollutant mixtures

Author

Altenburger, Rolf, Brack, Werner, Burgess, Robert M., Busch, Wibke, Escher, Beate I., Focks, Andreas, Mark Hewitt, L., Jacobsen, Bo N., de Alda, Miren López, Ait-Aissa, Selim, Backhaus, Thomas, Ginebreda, Antoni, Hilscherová, Klára, Hollender, Juliane, Hollert, Henner, Neale, Peta A., Schulze, Tobias, Schymanski, Emma L., Teodorovic, Ivana, Tindall, Andrew J., de Aragão Umbuzeiro, Gisela, Vrana, Branislav, Zonja, Bozo, and Krauss, Martin

Published
2019
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40. Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis

Author

Schymanski, Emma L., Singer, Heinz P., Slobodnik, Jaroslav, Ipolyi, Ildiko M., Oswald, Peter, Krauss, Martin, Schulze, Tobias, Haglund, Peter, Letzel, Thomas, Grosse, Sylvia, Thomaidis, Nikolaos S., Bletsou, Anna, Zwiener, Christian, Ibáñez, María, Portolés, Tania, de Boer, Ronald, Reid, Malcolm J., Onghena, Matthias, Kunkel, Uwe, Schulz, Wolfgang, Guillon, Amélie, Noyon, Naïke, Leroy, Gaëla, Bados, Philippe, Bogialli, Sara, Stipaničev, Draženka, Rostkowski, Pawel, and Hollender, Juliane

Published
2015
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44. Matching structures to mass spectra using fragmentation patterns: are the results as good as they look?

Author

Schymanski, Emma L., Meringer, Markus, and Brack, Werner

Subjects
Spectra (Spectroscopy) -- Research, Mass spectrometry -- Methods, Isomerism -- Properties, Chemistry, University of Bayreuth
Abstract

Three programs were assessed for their ability to predict mass spectral fragmentation patterns for all constitutional isomers of an experimental low-resolution electron impact mass spectrum (EI-MS), given the molecular formula, and use this information to identify the 'correct structure'. MOLGEN 3.5 was used to generate the structures, while all spectra were extracted from the NIST database. The commercial programs Mass Frontier and ACD MS Manager, as well as MOLGEN-MSF (developed by the University of Bayreuth) were used to generate mass spectral fragments. MOLGEN-MSF was used to generate 'match values' to compare the different programs and their ability to identify the 'correct structure'. Although high match values could be achieved with certain settings, the ranking of the correct structure relative to other constitutional isomers was not significantly better than the results published previously and in some cases significantly worse. Furthermore, all programs showed bias toward specific structures, which changed significantly with minor changes to the program settings. Thus, advances in mass spectral fragment prediction have not necessarily improved computer aided structure elucidation (CASE) from EI-MS and indicate that caution must be used when confirming the identity of a compound only based on the match between its predicted fragments and the mass spectrum.

Published
2009

47. Molecular mechanisms defining penetrance of LRRK2-associated Parkinson's disease.

Author

Trinh, Joanne, Schymanski, Emma L., Smajic, Semra, Kasten, Meike, Sammler, Esther, and Grünewald, Anne

Abstract

Mutations in Leucine-rich repeat kinase 2 (LRRK2) are the most frequent cause of dominantly inherited Parkinson's disease (PD). LRRK2 mutations, among which p.G2019S is the most frequent, are inherited with reduced penetrance. Interestingly, the disease risk associated with LRRK2 G2019S can vary dramatically depending on the ethnic background of the carrier. While this would suggest a genetic component in the definition of LRRK2-PD penetrance, only few variants have been shown to modify the age at onset of patients harbouring LRRK2 mutations, and the exact cellular pathways controlling the transition from a healthy to a diseased state currently remain elusive. In light of this knowledge gap, recent studies also explored environmental and lifestyle factors as potential modifiers of LRRK2-PD. In this article, we (i) describe the clinical characteristics of LRRK2 mutation carriers, (ii) review known genes linked to LRRK2-PD onset and (iii) summarize the cellular functions of LRRK2 with particular emphasis on potential penetrance-related molecular mechanisms. This section covers LRRK2's involvement in Rab GTPase and immune signalling as well as in the regulation of mitochondrial homeostasis and dynamics. Additionally, we explored the literature with regard to (iv) lifestyle and (v) environmental factors that may influence the penetrance of LRRK2 mutations, with a view towards further exposomics studies. Finally, based on this comprehensive overview, we propose potential future in vivo, in vitro and in silico studies that could provide a better understanding of the processes triggering PD in individuals with LRRK2 mutations. [ABSTRACT FROM AUTHOR]

Published
2022
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48. Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy.

Author

Frigerio, Gianfranco, Moruzzi, Camilla, Mercadante, Rosa, Schymanski, Emma L., and Fustinoni, Silvia

Subjects
QUALITY control, LIQUID chromatography-mass spectrometry
Abstract

Pooled quality controls (QCs) are usually implemented within untargeted methods to improve the quality of datasets by removing features either not detected or not reproducible. However, this approach can be limiting in exposomics studies conducted on groups of exposed and nonexposed subjects, as compounds present at low levels only in exposed subjects can be diluted and thus not detected in the pooled QC. The aim of this work is to develop and apply an untargeted workflow for human biomonitoring in urine samples, implementing a novel separated approach for preparing pooled quality controls. An LC-MS/MS workflow was developed and applied to a case study of smoking and non-smoking subjects. Three different pooled quality controls were prepared: mixing an aliquot from every sample (QC-T), only from non-smokers (QC-NS), and only from smokers (QC-S). The feature tables were filtered using QC-T (T-feature list), QC-S, and QC-NS, separately. The last two feature lists were merged (SNS-feature list). A higher number of features was obtained with the SNS-feature list than the T-feature list, resulting in identification of a higher number of biologically significant compounds. The separated pooled QC strategy implemented can improve the nontargeted human biomonitoring for groups of exposed and nonexposed subjects. [ABSTRACT FROM AUTHOR]

Published
2022
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49. LIPAD (LRRK2/Luebeck International Parkinson's Disease) Study Protocol: Deep Phenotyping of an International Genetic Cohort.

Author

Usnich, Tatiana, Vollstedt, Eva-Juliane, Schell, Nathalie, Skrahina, Volha, Bogdanovic, Xenia, Gaber, Hanaa, Förster, Toni M., Heuer, Andreas, Koleva-Alazeh, Natalia, Csoti, Ilona, Basak, Ayse Nazli, Ertan, Sibel, Genc, Gencer, Bauer, Peter, Lohmann, Katja, Grünewald, Anne, Schymanski, Emma L., Trinh, Joanne, Schaake, Susen, and Berg, Daniela

Subjects
PARKINSON'S disease, DARDARIN, RESEARCH protocols, MONTREAL Cognitive Assessment, BLOOD collection, DYSAUTONOMIA
Abstract

Background: Pathogenic variants in the Leucine-rich repeat kinase 2 (LRRK2) gene are the most common known monogenic cause of Parkinson's disease (PD). LRRK2 -linked PD is clinically indistinguishable from idiopathic PD and inherited in an autosomal dominant fashion with reduced penetrance and variable expressivity that differ across ethnicities and geographic regions. Objective: To systematically assess clinical signs and symptoms including non-motor features, comorbidities, medication and environmental factors in PD patients, unaffected LRRK2 pathogenic variant carriers, and controls. A further focus is to enable the investigation of modifiers of penetrance and expressivity of LRRK2 pathogenic variants using genetic and environmental data. Methods: Eligible participants are invited for a personal or online examination which comprises completion of a detailed eCRF and collection of blood samples (to obtain DNA, RNA, serum/plasma, immune cells), urine as well as household dust. We plan to enroll 1,000 participants internationally: 300 with LRRK2 -linked PD, 200 with LRRK2 pathogenic variants but without PD, 100 PD patients with pathogenic variants in the GBA or PRKN genes, 200 patients with idiopathic PD, and 200 healthy persons without pathogenic variants. Results: The eCRF consists of an investigator-rated (1 h) and a self-rated (1.5 h) part. The first part includes the Movement Disorder Society Unified Parkinson's Disease Rating, Hoehn &Yahr, and Schwab & England Scales, the Brief Smell Identification Test, and Montreal Cognitive Assessment. The self-rating part consists of a PD risk factor, food frequency, autonomic dysfunction, and quality of life questionnaires, the Pittsburgh Sleep Quality Inventory, and the Epworth Sleepiness as well as the Hospital Anxiety and Depression Scales. The first 15 centers have been initiated and the first 150 participants enrolled (as of March 25th, 2021). Conclusions: LIPAD is a large-scale international scientific effort focusing on deep phenotyping of LRRK2 -linked PD and healthy pathogenic variant carriers, including the comparison with additional relatively frequent genetic forms of PD, with a future perspective to identify genetic and environmental modifiers of penetrance and expressivity Clinical Trial Registration: ClinicalTrials.gov, NCT04214509. [ABSTRACT FROM AUTHOR]

Published
2021
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