Release of live baitfish by recreational anglers drives fish pathogen introduction risk

Emerging diseases of wildlife are an existential threat to biodiversity, and human-mediated movements of live animals are a primary vector of their spread. Wildlife disease risk analyses offer an appealing alternative to precautionary approaches because they allow for explicit quantification of uncertainties and consideration of tradeoffs. Such considerations become particularly important in high-frequency invasion pathways with hundreds of thousands of individual vectors, where even low pathogen prevalence can lead to substantial risk. The purpose of this study was to examine the landscape-level dynamics of human behavior-mediated pathogen introduction risk in the context of a high-frequency invasion pathway. One such pathway is the use and release of live fish used as bait by recreational anglers. We used a stochastic risk assessment model parameterized by angler survey data from Minnesota, USA, to simulate one year of fishing in Minnesota and estimate the total number of risky trips for each of three pathogens: viral hemorrhagic septicemia virus, the microsporidian parasite Ovipleistophora ovariae, and the Asian fish tapeworm Schizocotyle acheilognathi. We assessed the number of introductions under four scenarios: current/baseline conditions, outbreak conditions (increased pathogen prevalence), source-focused control measures (decreased pathogen prevalence), and angler-focused control measures (decreased rates of release). We found that hundreds of thousands of introduction events can occur per year, even for regulated pathogens at low pathogen prevalence. Reducing the rate of illegal baitfish release had significant impact on risky trips in scenarios where a high number of anglers were involved, but was less impactful in circumstances with limited outbreaks and fewer affected anglers. In contrast, reducing pathogen prevalence in the source populations of baitfish had relatively little impact. In order to make meaningful changes in pathogen introduction risk, managers should focus efforts on containing local outbreaks and reducing illegal baitfish release to reduce pathogen introduction risk. Our study also demonstrates the risk associated with high-frequency invasion pathways and the importance of incorporating human behaviors into wildlife disease models and risk assessments

"MS-Ready" structures for non-targeted high-resolution mass spectrometry screening studies.

Chemical database searching has become a fixture in many non-targeted identification workflows based on high-resolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workflow has been developed to produce structural representations observed using HRMS ("MS-Ready structures") and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA's Chemistry Dashboard ( https://comptox.epa.gov/dashboard/ ). This article describes the workflow for the generation and linking of ~ 700,000 MS-Ready structures (derived from ~ 760,000 original structures) as well as download, search and export capabilities to serve structure identification using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/

Resolving taxonomic uncertainty in vulnerable elasmobranchs : are the Madeira skate (Raja maderensis) and the thornback ray (Raja clavata) distinct species?

Skates and rays constitute the most speciose group of chondrichthyan fishes, yet are characterised by remarkable levels of morphological and ecological conservatism. They can be challenging to identify, which makes monitoring species compositions for fisheries management purposes problematic. Owing to their slow growth and low fecundity, skates are vulnerable to exploitation and species exhibiting endemism or limited ranges are considered to be the most at risk. The Madeira skate Raja maderensis is endemic and classified as ‘Data Deficient’ by the IUCN, yet its taxonomic distinctiveness from the morphologically similar and more wide-ranging thornback ray Raja clavate is unresolved. This study evaluated the sequence divergence of both the variable control region and cytochrome oxidase I ‘DNA barcode’ gene of the mitochondrial genome to elucidate the genetic differentiation of specimens identified as R. maderensis and R. clavate collected across much of their geographic ranges. Genetic evidence was insufficient to support the different species designations. However regardless of putative species identification, individuals occupying waters around the Azores and North African Seamounts represent an evolutionarily significant unit worthy of special consideration for conservation management

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

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 substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-.network.com/nds/SLE/)

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

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 substance, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/)

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

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.The NORMAN-SLE project has received funding from the NORMAN Association via its joint proposal of activities. HMT and ELS are supported by the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. ELS, PC, SEH, HPHA, ZW acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036756, project ZeroPM: Zero pollution of persistent, mobile substances. The work of EEB, TC, QL, BAS, PAT, and JZ was supported by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health (NIH). JOB is the recipient of an NHMRC Emerging Leadership Fellowship (EL1 2009209). KVT and JOB acknowledge the support of the Australian Research Council (DP190102476). The Queensland Alliance for Environmental Health Sciences, The University of Queensland, gratefully acknowledges the financial support of the Queensland Department of Health. NR is supported by a Miguel Servet contract (CP19/00060) from the Instituto de Salud Carlos III, co-financed by the European Union through Fondo Europeo de Desarrollo Regional (FEDER). MM and TR gratefully acknowledge financial support by the German Ministry for Education and Research (BMBF, Bonn) through the project “Persistente mobile organische Chemikalien in der aquatischen Umwelt (PROTECT)” (FKz: 02WRS1495 A/B/E). LiB acknowledges funding through a Research Foundation Flanders (FWO) fellowship (11G1821N). JAP and JMcL acknowledge financial support from the NIH for CCSCompendium (S50 CCSCOMPEND) via grants NIH NIGMS R01GM092218 and NIH NCI 1R03CA222452-01, as well as the Vanderbilt Chemical Biology Interface training program (5T32GM065086-16), plus use of resources of the Center for Innovative Technology (CIT) at Vanderbilt University. TJ was (partly) supported by the Dutch Research Council (NWO), project number 15747. UFZ (TS, MaK, WB) received funding from SOLUTIONS project (European Union’s Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. 603437). TS, MaK, WB, JPA, RCHV, JJV, JeM and MHL acknowledge HBM4EU (European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 733032). TS acknowledges funding from NFDI4Chem—Chemistry Consortium in the NFDI (supported by the DFG under project number 441958208). TS, MaK, WB and EMLJ acknowledge NaToxAq (European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 722493). S36 and S63 (HPHA, SEH, MN, IS) were funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) Project No. (FKZ) 3716 67 416 0, updates to S36 (HPHA, SEH, MN, IS) by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0. MiK acknowledges financial support from the EU Cohesion Funds within the project Monitoring and assessment of water body status (No. 310011A366 Phase III). The work related to S60 and S82 was funded by the Swiss Federal Office for the Environment (FOEN), KK and JH acknowledge the input of Kathrin Fenner’s group (Eawag) in compiling transformation products from European pesticides registration dossiers. DSW and YDF were supported by the Canadian Institutes of Health Research and Genome Canada. The work related to S49, S48 and S77 was funded by the MAVA foundation; for S77 also the Valery Foundation (KG, JaM, BG). DML acknowledges National Science Foundation Grant RUI-1306074. YL acknowledges the National Natural Science Foundation of China (Grant No. 22193051 and 21906177), and the Chinese Postdoctoral Science Foundation (Grant No. 2019M650863). WLC acknowledges research project 108C002871 supported by the Environmental Protection Administration, Executive Yuan, R.O.C. Taiwan (Taiwan EPA). JG acknowledges funding from the Swiss Federal Office for the Environment. AJW was funded by the U.S. Environmental Protection Agency. LuB, AC and FH acknowledge the financial support of the Generalitat Valenciana (Research Group of Excellence, Prometeo 2019/040). KN (S89) acknowledges the PhD fellowship through Marie Skłodowska-Curie grant agreement No. 859891 (MSCA-ETN). Exposome-Explorer (S34) was funded by the European Commission projects EXPOsOMICS FP7-KBBE-2012 [308610]; NutriTech FP7-KBBE-2011-5 [289511]; Joint Programming Initiative FOODBALL 2014–17. CP acknowledges grant RYC2020-028901-I funded by MCIN/AEI/1.0.13039/501100011033 and “ESF investing in your future”, and August T Larsson Guest Researcher Programme from the Swedish University of Agricultural Sciences. The work of ML, MaSe, SG, TL and WS creating and filling the STOFF-IDENT database (S2) mostly sponsored by the German Federal Ministry of Education and Research within the RiSKWa program (funding codes 02WRS1273 and 02WRS1354). XT acknowledges The National Food Institute, Technical University of Denmark. MaSch acknowledges funding by the RECETOX research infrastructure (the Czech Ministry of Education, Youth and Sports, LM2018121), the CETOCOEN PLUS project (CZ.02.1.01/0.0/0.0/15_003/0000469), and the CETOCOEN EXCELLENCE Teaming 2 project supported by the Czech ministry of Education, Youth and Sports (No CZ.02.1.01/0.0/0.0/17_043/0009632).Peer reviewe

Revisiting Five Years of CASMI Contests with EPA Identification Tools

Software applications for high resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) continue to enhance chemical identification capabilities. Given the variety of available applications, determining the most fit-for-purpose tools and workflows can be difficult. The Critical Assessment of Small Molecule Identification (CASMI) contests were initiated in 2012 to provide a means to evaluate compound identification tools on a standardized set of blinded tandem mass spectrometry (MS/MS) data. Five CASMI contests have resulted in recommendations, publications, and invaluable datasets for practitioners of HRMS-based screening studies. The US Environmental Protection Agency’s (EPA) CompTox Chemicals Dashboard is now recognized as a valuable resource for compound identification in NTA studies. However, this application was too new and immature in functionality to participate in the five previous CASMI contests. In this work, we performed compound identification on all five CASMI contest datasets using Dashboard tools and data in order to critically evaluate Dashboard performance relative to that of other applications. CASMI data was accessed via the CASMI webpage and processed for use in our spectral matching and identification workflow. Relative to applications used by former contest participants, our tools, data, and workflow performed well, placing more challenge compounds in the top five of ranked candidates than did the winners of three contest years and tying in a fourth. In addition, we conducted an in-depth review of the CASMI structure sets and made these reviewed sets available via the Dashboard. Our results suggest that Dashboard data and tools would enhance chemical identification capabilities for practitioners of HRMS-based NTA

“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies

Abstract Chemical database searching has become a fixture in many non-targeted identification workflows based on high-resolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workflow has been developed to produce structural representations observed using HRMS (“MS-Ready structures”) and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA’s Chemistry Dashboard (https://comptox.epa.gov/dashboard/). This article describes the workflow for the generation and linking of ~ 700,000 MS-Ready structures (derived from ~ 760,000 original structures) as well as download, search and export capabilities to serve structure identification using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/