Suspect and Non-Target Analysis of polar organic compounds in biota using LC-HRMS

Pablo Gago-Ferrero Contact: [email protected] Introduction

Emerging Pollutants (EPs)  Pharmaceuticals  Personal care products  Flame retardants  Food additives  Disinfection by-products  Pesticides +  Metabolites &  Transformation Products (TPs) aquatic environment

& Biota 2 Introduction

Challenges in the analysis of organic contaminants in biota

 Sample preparation (lipid content, trace level, sample size)  Thousands of organic contaminants with very different physicochemical properties  Investigation of new (unknown) contaminants potentially dangerous for the ecosystems (and human health)

 Metabolites

3 Target screening

Target screening

• Known EP  Well-established analytical (quantitative) • Reference standards methods for many priority contaminants available  Good limits of detection • Unequivocal identification  High accuracy  Reliable quantification

4 Why Suspect / non-target?

 Target screening is biased due to preselection of substances  Most organic constitutes of environmental samples are not identified!  Potential chemical stressors may be omitted  Most of the labs analyse the same substances  Reference standards are necessary for all compounds

5 Suspect & non-target: Where to put our efforts?

Thousands of chromatographic peaks in one sample

Impossible & Pointless to identify all of them

Smart use Suspect & Non target strategies

Define Research question &Prioritization Suspect screening

Classical micropollutants for which their presence in biota has been widely assessed and target methodologies are easily available Very long list of compounds without a clear purpose

 Metabolites of previously detected parent compounds  Regulatory data base & market data  Compounds potentially present due to close industrial activity Non-Target screening: Prioritization strategies

Intensity-based prioritization

 Long time series prioritization (prioritized features whose intensities varied substantially over the time course of the sampling campaign)  Geographical series prioritization  Effect-directed analysis (EDA), for identifying predominant toxicants in complex environmental mixtures combining effect testing and fractionation  In source fragmentation flagging  Metabolites Case study I: Metabolites of venlafaxine in Biota

Objective L.E. Santos • Suspect screening analysis for the assessment of the metabolization of venlafaxine by fish (Argyrosomus regius)

• Fish exposure at 20 µg L-1 of VLF via water for 28 days Sample analysis in a HPLC-LTQ-Orbitrap Velos in Data Dependant •Sample treatment using ultrasound assisted extraction Acquisition mode (DDA) with fragmentation of the most intense ions Case study I: Metabolites of venlafaxine in Biota

Software data processing using Compound Discoverer Identification of tentative VLF metabolites via compound 2.0 connected to Mass Frontier 7.0 software. exact mass and MS/MS ion fragments.

• Selection of 5 (phase I) and 10 (phase II) possible chemical transformations. Suspect list (Compound Prediction) • Max. number of combined transformations: 3 { • Nº compounds predicted: 1527 Methods: Suspect screening performance

 Confirmation with corresponding reference standard

Modified from Gago-Ferrero et al. 2015; Env. Sci. Tech 49(20) 1433 11 Case study I: Metabolites of venlafaxine in Biota

Proposed metabolic pathway of venlafaxine in fish tissues

• 10 VLF metabolites were tentatively identified • 2 VLF Phase II metabolites were identified in the fish liver * * * ** • Oxidation, demethylation and conjugation are the main reactions * involved • All VLF metabolites were identified in liver (*), except MET275 ** ** • 7 VLF metabolites were identified in brain (*)

*

* * * * * Confirmed Case study II: Ecometabolomics in fluvial biofilm

Objectives A. Serra-Compte • Identify biomarkers of drought stress and pharmaceutical exposure Biofilm  Sensitive to river changes • Identify metabolic pathways affected by stress  Rapid interaction with dissolved • Relate metabolome changes with changes in the biofilm structural substances parameters  Short life cycle & Bioaccumulation capacity

Pharmaceutical exposure Drought

Ecometabolomics

Serra-Compte et al. 2018; Sci. Tot. Environ 618: 1382-1388 Case study II: Ecometabolomics in fluvial biofilm

Biofilm exposure experiment in Experimental Streams Facility

Conc Compound (ng/L) - Pharmaceutical exposure (P) Ibuprofen 404 - Dry period (D) (7 days) Diclofenac 366 Carbamazepine 124 - Dry period + pharm exposure (D+P) Sulfamethoxazole 699 - Control (C) Erithromycin 169 Metoprolol 1845 Atenolol 117 Gemfibrozil 140 Hydrochlorothiazide 1135

Analytical Workflow

LC-LTQ Orbitrap Velos Acquisition: 100-700 m/z, Data Treatment:

Extraction: PLE SIEVE software: deconvolution and allingment Biofilm Extracts Clean-up: SPE Case study II: Ecometabolomics in fluvial biofilm Metabolomics workflow

First injection: LC-HRMS Orbitrap analysis Full scan mode •Chemometrics analysis •Background subtraction •Prioritization (databases: Human •Component detection •Identification of features that Metabolome DataBase (HMDB), contributed to the separation between chemspider, Plant metabolomics) • Peak alignment groups 67 potential metabolites 1978 features ( + and – ESI) 664 features ( + and – ESI)

Second injection: LC-HRMS Orbitrap analysis MSMS (data dependent)

• • Tentative identification •Confirmation Metabolic routes altered based on the metabolites identified 9 Metabolites identified 6 confirmed with standard •(KEGG databases) Case study II: Ecometabolomics in fluvial biofilm

Biofilm exposure experiment in Experimental Streams Facility

42 days exposure experiment

- Pharmaceutical exposure (P) - Dry period (D) (7 days) - Dry period + pharm exposure (D+P) - Control (C)

Principal component analysis (PCA) of significant metabolites of biofilm exposed to the treatments

Serra-Compte et al. 2018; Sci. Tot. Environ 618: 1382-1388 Case study II: Ecometabolomics in fluvial biofilm

Potential chemical markers of stress

Tentative Compoud Family Drought PhACs Drought + PhACs biomarkers Saturated Behenic acid ↑ ↓ – fatty acid Saturated Lignoceric acid ↑ ↓ – fatty acid Saturated Palmitic acid – ↓ – fatty acid Unsaturated Stearidonic acid ↑ – ↑ Potential biomarker of fatty acid drought stress Unsaturated Alpha linolenic acid ↑ – - fatty acid Unsaturated Palmitoleic acid ↓ – ↓ fatty acid LPA (0:0/16:0) Glycerophospholipid – ↓ ↓ Potential biomarker of pharm 16-Oxohexadecanoic Oxo fatty acid ↑ – ↑ stress Azelaic Acid Carboxylic acid ↑ ↓ ↑

Serra-Compte et al. 2018; Sci. Tot. Environ 618: 1382-1388 Acknowledgements  Spanish Ministry of Economy and Competitiveness: SCARCE (CSD2009-00065) and PLAS-MED (CTM2017-89701-C3-2-R)

 GLOBAQUA-xxx

 ECsafeSEAFOOD FP7/2007-2013

 NANOTRANSFER (ERA SIINN PCIN-2015-182-C02-02)

 Ramon y Cajal program (RYC-2014-16707)

 Economy and Knowledge Department of the Catalan Government (Consolidated Research Group ICRA- ENV 2017 SGR 1124)