From effect-directed anaysis to metabolomic assessment: How do the main emerging contaminants released into the Adour estuary affect glass eels (Anguilla anguilla)?

168 p.Since countless xenobiotic compounds are being found in the environment, ecotoxicology faces an astounding challenge in identifying toxicants. The combination of high-throughput in vivo/in vitro bioassays with high-resolution chemical analysis is an effective way to elucidate the cause-effect relationship. However, these combined strategies imply an enormous workload that can hinder their implementation in routine analysis. The first aim of this thesis was to develop a new high throughput screening method to implement the sea urchin embryo test in effect-directed analysis. This way, we developed a novel predictive expert system, the SETApp, which can be used to automatically quantify the two endpoints of the sea urchin embryo test from a given image set. We demonstrated that chemometrics, and especially multivariate linear classification models, can be successfully implemented in bioassay automation to avoid the cumbersome measurement of embryo sizes and malformation levels. In addition, we have also shown the efficiency of this HTS in a very demanding scenario, the EDA of Bayonne's (Basque Country, France) Pont de l'aveugle WWTP effluent. This EDA study concluded that the SETApp is an efficient, fast, cost-effective, and reproducible tool that can approach EDA to routine analysis.On the other hand, the presence of these contaminants of emerging concern (CECs) in the aquatic environment directly impacts water-living organisms and can alter their living functions. These compounds are often metabolized and excreted, but they can also be accumulated and spread through the food chain. The metabolized contaminants can also lead to the formation of new compounds with unknown toxicity and bioaccumulation potential. In the second study of this work, we studied the occurrence, bioconcentration, and biotransformation of CECs in glass eels (Anguilla anguilla) using UHPLC-HRMS.Finally, in our third study, we focused on the impact assessment of the selected emerging contaminants in glass eels by means of metabolomics. This approach not only allowed us to evaluate the toxicity of these contaminants but also to gain insight into the metabolic differences between migrant and non-migrant glass eels

Metabolomics to study the sublethal effects of diazepam and irbesartan on glass eels (Anguilla anguilla)

Since glass eels are continuously exposed to contamination throughout their migratory journey in estuaries, to a certain extent the fall in the population of this endangered species might be attributed to this exposure, which is especially acute in estuaries under high urban pressure. In this work, metabolomics was used to address the main objective of this study, to evaluate the effects of two pharmaceuticals previously identified as potential concerning chemicals for fish (diazepam and irbesartan) on glass eels. An exposure experiment to diazepam, irbesartan and their mixture was carried out over 7 days followed by 7 days of depuration phase. After exposure, glass eels were individually sacrificed using a lethal bath of anesthesia, and then an unbiased sample extraction method was used to extract separately the polar metabolome and the lipidome. The polar metabolome was submitted to targeted and non-targeted analysis, whereas for the lipidome only the non-targeted analysis was carried out. A combined strategy using partial least squares discriminant analysis and univariate and multivariate statistical analysis (ANOVA, ASCA, t-test, and fold-change analysis) was used to identify the metabolites altered in the exposed groups with respect to the control group. The results of the polar metabolome analysis revealed that glass eels exposed to the diazepam-irbesartan mixture were the most impacted ones, with altered levels for 11 metabolites, some of them belonging to the energetic metabolism, which was confirmed to be sensitive to these contaminants. Additionally, the dysregulation of the levels of twelve lipids, most of them with energetic and structural functions, was also found after exposure to the mixture, which might be related to oxidative stress, inflammation, or alteration of the energetic metabolism.Authors acknowledge financial support from the Agencia Estatal de Investigación (AEI) of Spain and the European Regional Development Fund through CTM2017–84763-C3–1-R and CTM2020–117686RB-C31 projects and the Basque Government through the financial support as a consolidated group of the Basque Research System (IT1446–22). Naroa Lopez-Herguedas is grateful to the Spanish Ministry of Economy, Industry and Competitivity for her predoctoral scholarship FPI 2018. Iker Alvarez-Mora is grateful to the University of the Basque Country and the Université de Pau et des Pays de L' Adour for his cotutelle predoctoral scholarship

SETApp: A machine learning and image analysis based application to automate the sea urchin embryo test

[EN] Since countless xenobiotic compounds are being found in the environment, ecotoxicology faces an astounding challenge in identifying toxicants. The combination of high-throughput in vivo/in vitro bioassays with high-resolution chemical analysis is an effective way to elucidate the cause-effect relationship. However, these combined strategies imply an enormous workload that can hinder their implementation in routine analysis. The purpose of this study was to develop a new high throughput screening method that could be used as a predictive expert system that automatically quantifies the size increase and malformation of the larvae and, thus, eases the application of the sea urchin embryo test in complex toxicant identification pipelines such as effect-directed analysis. For this task, a training set of 242 images was used to calibrate the size-increase and malformation level of the larvae. Two classification models based on partial least squares discriminant analysis (PLS-DA) were built and compared. Moreover, Hierarchical PLS-DA shows a high proficiency in classifying the larvae, achieving a prediction accuracy of 84 % in validation. The scripts built along the work were compiled in a user-friendly standalone app (SETApp) freely accessible at https://github.com/UPV-EHU-IBeA/SETApp. The SETApp was tested in a real case scenario to fulfill the tedious requirements of a WWTP effect-directed analysis.Authors gratefully acknowledge financial support from the Agencia Estatal de Investigación (AEI) of Spain and the European Regional Development Fund through project CTM2017–84763-C3–1-R and the Basque Government through the financial support as a consolidated group of the Basque Research System (IT1213–19). Iker Alvarez is grateful to the University of the Basque Country and the Université de Pau et des Pays de L′ Adour for his cotutelle predoctoral scholarship

Effect-directed analysis of a hospital effluent sample using A-YES for the identification of endocrine disrupting compounds

An effect-directed analysis (EDA) approach was used to identify the compounds responsible for endocrine disruption in a hospital effluent (Basque Country). In order to facilitate the identification of the potentially toxic substances, a sample was collected using an automated onsite large volume solid phase extraction (LV-SPE) system. Then, it was fractionated with a two-step orthogonal chromatographic separation and tested for estrogenic effects with a recombinant yeast (A-YES) in-vitro bioassay. The fractionation method was optimized and validated for 184 compounds, and its application to the hospital effluent sample allowed reducing the number of unknowns from 292 in the raw sample to 35 after suspect analysis of the bioactive fractions. Among those, 7 of them were confirmed with chemical standards. In addition, target analysis of the raw sample confirmed the presence of mestranol, estrone and dodemorph in the fractions showing estrogenic activity. Predictive estrogenic activity modelling using quantitative structure-activity relationships indicated that the hormones mestranol (5840 ng/L) and estrone (128 ng/L), the plasticiser bisphenol A (9219 ng/L) and the preservative butylparaben (1224 ng/L) were the main contributors of the potential toxicity. Derived bioanalytical equivalents (BEQs) pointed mestranol and estrone as the main contributors (56 % and 43 %, respectively) of the 50 % of the sample's explained total estrogenic activity.Authors acknowledge financial support from the Agencia Estatal de Investigación (AEI) of Spain and the European Regional Development Fund through CTM2017-84763-C3-1-R and CTM2020-117686RB-C31 projects and the Basque Government through the financial support as a consolidated group of the Basque Research System (IT1213-19). The authors are grateful to the Consorcio de Aguas de Bilbao and especially to Iñigo González. Naroa Lopez is grateful to the Spanish Ministry of Economy, Industry and Competitivity for her predoctoral scholarship FPI 2018. Belen González-Gaya and Leire Mijangos acknowledge the University of the Basque Country for their postdoctoral scholarships (FPI 2018). Iker Alvarez-Mora is grateful to the University of the Basque Country and the Université de Pau et des Pays de L' Adour for his cotutelle predoctoral scholarship. Finally, the authors acknowledge support from the AEI and the Ministry of Science, Innovation and Universities (MICIU) to support the Thematic Network of Excellence (NET4SEA) on emerging contaminants in marine settings (CTM2017-90890-REDT, MICIU/AEI/FEDER, EU)

Suspect Screening of Chemicals in Hospital Wastewaters Using Effect-Directed Analysis Approach as Prioritization Strategy

The increasing number of contaminants in the environment has pushed water monitoring programs to find out the most hazardous known and unknown chemicals in the environment. Sample treatment-simplification methods and non-target screening approaches can help researchers to not overlook potential chemicals present in complex aqueous samples. In this work, an effect-directed analysis (EDA) protocol using the sea urchin embryo test (SET) as a toxicological in vivo bioassay was used as simplified strategy to identify potential unknown chemicals present in a very complex aqueous matrix such as hospital effluent. The SET bioassay was used for the first time here to evaluate potential toxic fractions in hospital effluent, which were obtained after a two-step fractionation using C18 and aminopropyl chromatographic semi-preparative columns. The unknown compounds present in the toxic fractions were identified by means of liquid chromatography coupled to a Q Exactive Orbitrap high-resolution mass spectrometer (LC-HRMS) and using a suspect analysis approach. The results were complemented by gas chromatography-mass spectrometry analysis (GC-MS) in order to identify the widest range of chemical compounds present in the sample and the toxic fractions. Using EDA as sample treatment simplification method, the number of unknown chemicals (>446 features) detected in the raw sample was narrowed down to 94 potential toxic candidates identified in the significantly toxic fractions. Among them, the presence of 25 compounds was confirmed with available chemical standards including 14 pharmaceuticals, a personal care product, six pesticides and four industrial products. The observations found in this work emphasize the difficulties in identifying potential toxicity drivers in complex water samples, as in the case of hospital wastewater.Authors acknowledge financial support from the Agencia Estatal de Investigación (AEI) of Spain and the European Regional Development Fund through CTM2017-84763-C3-1-R and CTM2020-11686RB-C31 projects and the Basque Government through the financial support as a consolidated group of the Basque Research System (IT1446-22). The authors are grateful to the Consorcio de Aguas de Bilbao and especially to Iñigo González. Naroa Lopez-Herguedas is grateful to the Spanish Ministry of Economy, Industry and Competitivity for her predoctoral scholarship FPI 2018 (PRE2018-086493)

Genetic landscape of 6089 inherited retinal dystrophies affected cases in Spain and their therapeutic and extended epidemiological implications

Inherited retinal diseases (IRDs), defined by dysfunction or progressive loss of photoreceptors, are disorders characterized by elevated heterogeneity, both at the clinical and genetic levels. Our main goal was to address the genetic landscape of IRD in the largest cohort of Spanish patients reported to date. A retrospective hospital-based cross-sectional study was carried out on 6089 IRD affected individuals (from 4403 unrelated families), referred for genetic testing from all the Spanish autonomous communities. Clinical, demographic and familiar data were collected from each patient, including family pedigree, age of appearance of visual symptoms, presence of any systemic findings and geographical origin. Genetic studies were performed to the 3951 families with available DNA using different molecular techniques. Overall, 53.2% (2100/3951) of the studied families were genetically characterized, and 1549 different likely causative variants in 142 genes were identified. The most common phenotype encountered is retinitis pigmentosa (RP) (55.6% of families, 2447/4403). The most recurrently mutated genes were PRPH2, ABCA4 and RS1 in autosomal dominant (AD), autosomal recessive (AR) and X-linked (XL) NON-RP cases, respectively; RHO, USH2A and RPGR in AD, AR and XL for non-syndromic RP; and USH2A and MYO7A in syndromic IRD. Pathogenic variants c.3386G > T (p.Arg1129Leu) in ABCA4 and c.2276G > T (p.Cys759Phe) in USH2A were the most frequent variants identified. Our study provides the general landscape for IRD in Spain, reporting the largest cohort ever presented. Our results have important implications for genetic diagnosis, counselling and new therapeutic strategies to both the Spanish population and other related populations.This work was supported by the Instituto de Salud Carlos III (ISCIII) of the Spanish Ministry of Health (FIS; PI16/00425 and PI19/00321), Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER, 06/07/0036), IIS-FJD BioBank (PT13/0010/0012), Comunidad de Madrid (CAM, RAREGenomics Project, B2017/BMD-3721), European Regional Development Fund (FEDER), the Organización Nacional de Ciegos Españoles (ONCE), Fundación Ramón Areces, Fundación Conchita Rábago and the University Chair UAM-IIS-FJD of Genomic Medicine. Irene Perea-Romero is supported by a PhD fellowship from the predoctoral Program from ISCIII (FI17/00192). Ionut F. Iancu is supported by a grant from the Comunidad de Madrid (CAM, PEJ-2017-AI/BMD7256). Marta del Pozo-Valero is supported by a PhD grant from the Fundación Conchita Rábago. Berta Almoguera is supported by a Juan Rodes program from ISCIII (JR17/00020). Pablo Minguez is supported by a Miguel Servet program from ISCIII (CP16/00116). Marta Corton is supported by a Miguel Servet program from ISCIII (CPII17/00006). The funders played no role in study design, data collection, data analysis, manuscript preparation and/or publication decisions

Sample preparation for suspect screening of persistent, mobile and toxic substances and their phase II metabolites in human urine by mixed-mode liquid chromatography

Persistent, mobile and toxic substances have drawn attention nowadays due to their particular properties, but they are overlooked in human monitorization works, limiting the knowledge of the human exposome. In that sense, human urine is an interesting matrix since not only parent compounds are eliminated, but also their phase II metabolites that could act as biomarkers. In this work, 11 sample preparation procedures involving preconcentration were tested to ensure maximum analytical coverage in human urine using mixed-mode liquid chromatography coupled with high-resolution tandem mass spectrometry. The optimized procedure consisted of a combination of solid-phase extraction and salt-assisted liquid-liquid extraction and it was employed for suspect screening. Additionally, a non-discriminatory dilute-and-shoot approach was also evaluated. After evaluating the workflow in terms of limits of identification and type II errors (i.e., false negatives), a pooled urine sample was analysed. From a list of 1450 suspects and in-silico simulated 1568 phase II metabolites (i.e. sulphates, glucuronides, and glycines), 44 and 14 substances were annotated, respectively. Most of the screened suspects were diverse industrial chemicals, but biocides, natural products and pharmaceuticals were also detected. Lastly, the complementarity of the sample preparation procedures, columns, and analysis conditions was assessed. As a result, dilute-and-shoot and the Acclaim Trinity P1 column at pH =3 (positive ionization) and pH =7 (negative ionization) allowed the maximum coverage since almost 70 % of the total suspects could be screened using those conditions.The authors acknowledge financial support from the State Research Agency of the Ministry of Science and Innovation, Government of Spain (project PID2020-117686RB-C31) and the Basque Government as a consolidated group of the Basque Research System (IT-1446-22). M. Musatadi and I. Baciero-Hernandez also acknowledge the Basque Government for their predoctoral fellowship. Lastly, I. Alvarez-Mora acknowledges the Basque Government for his postdoctoral fellowship as well

NIR-hyperspectral imaging and machine learning for non-invasive chemotype classification in Cannabis sativa L

The current public acceptance rate towards medical cannabis feasibility has led to a worldwide increase in this plant species production. Nevertheless, the currently transforming legal framework does not prevent the originally unlawful knowledge around cannabis breeding, which lacks quality control regulations or standards for correct manufacturing processes, a fact that could subsequently lead to uncontrolled and even harmful crop products. In this line, the objective of this work was to develop a non-invasive methodology for cannabis chemotype classification in different cultivars during the plant cultivation process, in order to keep undoubtful production control over cannabis crops. Hence, hyperspectral imaging (HSI), coupled with various multivariate data analysis approaches, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), enabled the non-invasive in-situ analysis of the plants. Hence, two PLS-DA classification models were trained with the plant spectral data for three chemotypes, based on the cannabinoid content of the plant inflorescences, with the difference between both approaches being the regard of the stem part of the plant as a bias. Thus, obtained sensitivity and specificity values in the inflorescences were 0.845/0.845 for Chemotype I, 0.954/0.920 for Chemotype II, and 0.888/0.925 for Chemotype III. At last, a hierarchical PLS-DA, which considered the stem as a bias, presented an overall 94.7 % trueness in the external validation of 57 different plant individuals, divided as 92.3 % trueness for chemotype I, 100.0 % trueness for chemotype II and 88.9 % trueness for chemotype III. Based on these results, the proof of concept for comprehensive agricultural control of cannabis crops through a non-invasive analytical technique was demonstrated, a previously unproven fact. Therefore, this work could further pave the way for non-invasive technology development for horticultural quality control in medical cannabis productions, as this emerging industry will require strict control over the cannabis chemotypes, with the strong advantage of avoiding destructive and time-consuming analytical techniques such as chromatography.This work was financially supported by the Education Department of the Basque Country as a consolidated group of the Basque Research System (IT1213-19)

Evaluating membrane bioreactor treatment for the elimination of emerging contaminants using different analytical methods

Since wastewater treatment plants (WWTPs) were not originally designed to eliminate contaminants of emerging concern (CECs), alternative strategies like membrane bioreactor (MBR) technology are gaining importance in achieving effective CEC removal and minimising their environmental impact. In this study, composite wastewater samples were collected from the biggest WWTP in the Basque Country (Galindo, Biscay) and the performance of two secondary treatments (i.e. conventional activated sludge treatment, CAS, and MBR) was assessed. The combination of a suspect screening approach using liquid chromatography tandem high-resolution mass spectrometry (LC-HRMS) and multitarget analysis by gas chromatography-mass spectrometry (GC-MS) allowed the detection of approximately 200 compounds in the WWTP effluents. The estimated removal efficiencies (REs) revealed that only 16 micropollutants exhibited enhanced removal by MBR treatment (RE >70% or 40 – 60%). The environmental risk posed by the non-eliminated compounds after both treatments remained similar, being anthracene, clarithromycin, bis(2-ethylhexyl) phthalate (DEHP) and dilantin the most concerning pollutants (RQ >1). The Microtox® bioassay confirmed the MBR’s efficiency in removing baseline toxicity, while suggesting a similar performance of CAS treatment. These minimal differences between treatments call into question the worthiness of MBR treatment and emphasise the need to seek more efficient alternative treatment methods.Authors acknowledge financial support from the Agencia Estatal de Investigación (AEI) of Spain through project PID2020–117686RB-C31 and the Basque Government through the financial support as a consolidated group of the Basque Research System (IT1446–22) and the PA23/06 research project (Agriculture 2022–2026 Program). The authors are grateful to the Consorcio de Aguas de Bilbao, especially to Iñigo González and Alberto Ciriza for providing us with the effluent samples, and to the Laboratorio de Saneamiento and PROSIMED Ingenieros for the analyses of physicochemical parameters of the samples. Naroa Lopez-Herguedas is grateful to the Spanish Ministry of Science, Innovation and Universities for her predoctoral scholarship FPI 2018. Dennis Bilbao is grateful to the University of The Basque Country for his predoctoral fellowship. I. Alvarez-Mora is grateful to the Basque Government for his postdoctoral fellowship

Predicting severe pneumonia in the emergency department: a global study of the Pediatric Emergency Research Networks (PERN)—study protocol

Introduction Pneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.Methods and analysis This study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7–14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.Ethics and dissemination This study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children’s Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation