The loop structure and the RNA helicase p72/DDX17 influence the processing efficiency of the mice miR-132

miRNAs are small RNAs that are key regulators of gene expression in eukaryotic organisms. The processing of miRNAs is regulated by structural characteristics of the RNA and is also tightly controlled by auxiliary protein factors. Among them, RNA binding proteins play crucial roles to facilitate or inhibit miRNA maturation and can be controlled in a cell, tissue and species-specific manners or in response to environmental stimuli. In this study we dissect the molecular mechanism that promotes the overexpression of miR-132 in mice over its related, co-transcribed and co-regulated miRNA, miR-212. We have shown that the loop structure of miR-132 is a key determinant for its efficient processing in cells. We have also identified a range of RNA binding proteins that recognize the loop of miR-132 and influence both miR-132 and miR-212 processing. The DEAD box helicase p72/DDX17 was identified as a factor that facilitates the specific processing of miR-132

Making use of apex predator sample collections: an integrated workflow for quality assured sample processing, analysis and digital sample freezing of archived samples

Using monitoring data from apex predators for chemicals risk assessment can provide important information on bioaccumulating as well as biomagnifying chemicals in food webs. A survey among European institutions involved in chemical risk assessment on their experiences with apex predator data in chemical risk assessment revealed great interest in using such data. However, the respondents indicated that constraints were related to expected high costs, lack of standardisation and harmonised quality criteria for exposure assessment, data access, and regulatory acceptance/application. During the Life APEX project, we demonstrated that European sample collections (i.e. environmental specimen banks (ESBs), research collection (RCs), natural history museums (NHMs)) archive a large variety of biological samples that can be readily used for chemical analysis once appropriate quality assurance/control (QA/QC) measures have been developed and implemented. We therefore issued a second survey on sampling, processing and archiving procedures in European sample collections to derive key quality QA/QC criteria for chemical analysis. The survey revealed great differences in QA/QC measures between ESBs, NHMs and RCs. Whereas basic information such as sampling location, date and biometric data were mostly available across institutions, protocols to accompany the sampling strategy with respect to chemical analysis were only available for ESBs. For RCs, the applied QA/QC measures vary with the respective research question, whereas NHMs are generally less aware of e.g. chemical cross-contamination issues. Based on the survey we derived key indicators for assessing the quality of biota samples that can be easily implemented in online databases. Furthermore, we provide a QA/QC workflow not only for sampling and processing but also for the chemical analysis of biota samples. We focussed on comprehensive analytical techniques such as non-target screening and provided insights into subsequent storage of high-resolution chromatograms in online databases (i.e. digital sample freezing platform) to ultimately support chemicals risk assessment

Consideration of the bioavailability of metal/metalloid species in freshwaters: experiences regarding the implementation of biotic ligand model-based approaches in risk assessment frameworks

After the scientific development of Biotic Ligand Models (BLMs) in recent decades these models are now considered suitable for implementation in regulatory risk assessment of metals in freshwater bodies. The approach has been developed over several years and has been described in many peer-reviewed publications. The original complex BLMs have been applied in prospective risk assessment reports for metals and metal compounds and are also recommended as suitable concepts for the evaluation of monitoring data in the context of the European Water Framework Directive. Currently, several user-friendly BLM-based bioavailability software tools are available for assessing the aquatic toxicity of a limited number of metals (mainly copper, nickel, and zinc). These tools need only a basic set of water parameters as input (e.g., pH, hardness, dissolved organic matter and dissolved metal concentration). Such tools seem appropriate to foster the implementation in routine water quality assessments. This work aims to review the existing bioavailability-based regulatory approaches and the application of available BLM-based bioavailability tools for this purpose. Advantages and possible drawbacks of these tools (e.g., feasibility, boundaries of validity) are discussed, and recommendations for further implementation are given

Organotin compounds in surface sediments of the Southern Baltic coastal zone: a study on the main factors for their accumulation and degradation

Abstract Sediment samples were collected in the Gulf of Gdańsk, and the Vistula and Szczecin Lagoons—all located in the coastal zone of the Southern Baltic Sea—just after the total ban on using harmful organotins in antifouling paints on ships came into force, to assess their butyltin and phenyltin contamination extent. Altogether, 26 sampling stations were chosen to account for different potential exposure to organotin pollution and environmental conditions: from shallow and well-oxygenated waters, shipping routes and river mouths, to deep and anoxic sites. Additionally, the organic carbon content, pigment content, and grain size of all the sediment samples were determined, and some parameters of the nearbottom water (oxygen content, salinity, temperature) were measured as well. Total concentrations of butyltin compounds ranged between 2 and 182 ng Sn g−1 d.w., whereas phenyltins were below the detection limit. Sediments from the Gulf of Gdańsk and Vistula Lagoon were found moderately contaminated with tributyltin, whereas those from the Szczecin Lagoon were ranked as highly contaminated. Butyltin degradation indices prove a recent tributyltin input into the sediments adjacent to sites used for dumping for dredged harbor materials and for anchorage in the Gulf of Gdańsk (where two big international ports are located), and into those collected in the Szczecin Lagoon. Essential factors affecting the degradation and distribution of organotins, based on significant correlations between butyltins and environmental variables, were found in the study area

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