The NORMAN Suspect List Exchange (NORMAN-SLE): Facilitating European and Worldwide Collaboration on Suspect Screening in High Resolution Mass Spectrometry

Abstract

Background: The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for “suspect screening” lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results: The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per-and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of >40,000 unique views, >50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA’s CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101). Conclusions: The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the “one chemical, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Efficient PFAS prioritization in non-target HRMS data: systematic evaluation of the novel MD/C-m/C approach

“…To perform a data evaluation with a robust amount of organic chemicals, raw data was downloaded from the PubChem Classification Browser and from the Supporting Information of Koch et al 2007 [22-24] and preprocessed in three individual datasets which are PFAS, organic compounds (OCs), and NOM compounds. From PubChem, the EPA DSSTox dataset (245,545 compounds) [25], the NORMAN Suspect List Exchange (113,737 compounds) [26] and from the "PFAS and Fluorinated Compounds in PubChem Tree" PFAS with parts larger than CF 2 or CF 3 that fall into the OECD definition (224,017 compounds) were downloaded as CSV and TXT files [27,28]. The EPA DSSTox dataset includes any kind of toxic substances while the NORMAN database includes emerging environmental contaminants.…”

An algorithm to classify homologous series within compound datasets

“…OngLai was applied to three different datasets, NOR-MAN-SLE [47,48] used in environmental chemistry, PubChemLite [49,59] used in exposomics/ metabolomics, and COCONUT [50,51] in natural products research, respectively. All the datasets are openly available (see Additional file 1 Sect.…”

“…These collections were chosen to highlight the prevalence of homologous compounds in such varied research domains as well as to demonstrate the broad applicability of OngLai. The first of these three collections, the NORMAN Suspect List Exchange (NORMAN-SLE) [47], comprises synthetic chemicals suspected to be present in the environment such as pesticides, pharmaceuticals, surfactants, food-contact chemicals, and those used in industrial applications, like PFAS [48]. The NORMAN-SLE contains 99 so-called 'suspect' lists of chemicals hosted by the NORMAN Network, which are used for suspect screening mass spectrometry data generated from measuring environmental samples [47].…”

“…The first of these three collections, the NORMAN Suspect List Exchange (NORMAN-SLE) [47], comprises synthetic chemicals suspected to be present in the environment such as pesticides, pharmaceuticals, surfactants, food-contact chemicals, and those used in industrial applications, like PFAS [48]. The NORMAN-SLE contains 99 so-called 'suspect' lists of chemicals hosted by the NORMAN Network, which are used for suspect screening mass spectrometry data generated from measuring environmental samples [47]. The second collection, PubChemLite for Exposomics (PubChemLite), is a subset of PubChem that aims to capture the chemical space relevant for exposomics [49], the study of exposures to chemicals over time.…”

Metabolomics Reveals Strain-Specific Cyanopeptide Profiles and Their Production Dynamics in Microcystis aeruginosa and M. flos-aquae

“…This indicates that our current notion of cyanobacterial natural product diversity is likely grossly underestimated. Advancements in liquid chromatography-tandem mass spectrometry (LC-MS/MS) platforms and open-source bioinformatic tools provide opportunities to better describe microbial natural product diversity [18][19][20][21]. From cyanobacteria cultures and bloom events, metabolomic analysis has demonstrated variable co-production of multiple cyanopeptide groups and distinct profiles, even within the same species [22][23][24][25].…”