Literature DB >> 31305828

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges.

Emma L Schymanski1, Nancy C Baker, Antony J Williams, Randolph R Singh, Jean-Pierre Trezzi, Paul Wilmes, Pierre L Kolber, Rejko Kruger, Nicole Paczia, Carole L Linster, Rudi Balling.   

Abstract

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases.

Entities:  

Year:  2019        PMID: 31305828     DOI: 10.1039/c9em00068b

Source DB:  PubMed          Journal:  Environ Sci Process Impacts        ISSN: 2050-7887            Impact factor:   4.238


  4 in total

1.  Discovering pesticides and their TPs in Luxembourg waters using open cheminformatics approaches.

Authors:  Jessy Krier; Randolph R Singh; Todor Kondić; Adelene Lai; Philippe Diderich; Jian Zhang; Paul A Thiessen; Evan E Bolton; Emma L Schymanski
Journal:  Environ Int       Date:  2021-09-21       Impact factor: 9.621

2.  Machine learning for identification of silylated derivatives from mass spectra.

Authors:  Milka Ljoncheva; Tomaž Stepišnik; Tina Kosjek; Sašo Džeroski
Journal:  J Cheminform       Date:  2022-09-15       Impact factor: 8.489

3.  Studying the Parkinson's disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study.

Authors:  Begoña Talavera Andújar; Dagny Aurich; Velma T E Aho; Randolph R Singh; Tiejun Cheng; Leonid Zaslavsky; Evan E Bolton; Brit Mollenhauer; Paul Wilmes; Emma L Schymanski
Journal:  Anal Bioanal Chem       Date:  2022-07-13       Impact factor: 4.478

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

Authors:  Tatiana Usnich; Eva-Juliane Vollstedt; Nathalie Schell; Volha Skrahina; Xenia Bogdanovic; Hanaa Gaber; Toni M Förster; Andreas Heuer; Natalia Koleva-Alazeh; Ilona Csoti; Ayse Nazli Basak; Sibel Ertan; Gencer Genc; Peter Bauer; Katja Lohmann; Anne Grünewald; Emma L Schymanski; Joanne Trinh; Susen Schaake; Daniela Berg; Doreen Gruber; Stuart H Isaacson; Andrea A Kühn; Brit Mollenhauer; David J Pedrosa; Kathrin Reetz; Esther M Sammler; Enza Maria Valente; Franco Valzania; Jens Volkmann; Simone Zittel; Norbert Brüggemann; Meike Kasten; Arndt Rolfs; Christine Klein
Journal:  Front Neurol       Date:  2021-08-09       Impact factor: 4.003

  4 in total

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