# Metabolomics insight into the influence of environmental factors in responses of freshwater biofilms to the model herbicide diuron

Nicolas Creusot, Betty Chaumet, Mélissa Eon, Nicolas Mazzella, Aurélie Moreira, Soizic Morin

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## ► To cite this version:

Nicolas Creusot, Betty Chaumet, Mélissa Eon, Nicolas Mazzella, Aurélie Moreira, et al.. Metabolomics insight into the influence of environmental factors in responses of freshwater biofilms to the model herbicide diuron. Environmental Science and Pollution Research, 2022, 29 (20), pp.29332-29347. 10.1007/s11356-021-17072-7 . hal-03691889

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1 METABOLOMICS INSIGHT INTO THE INFLUENCE OF ENVIRONMENTAL FACTORS IN

2 RESPONSES OF FRESHWATER BIOFILMS TO THE MODEL HERBICIDE DIURON

3 Nicolas CREUSOT¹,², Betty CHAUMET¹, Mélissa EON¹, Nicolas MAZZELLA¹, Aurélie MOREIRA¹,

4 Soizic MORIN¹

5

6

7 ¹ INRAE, UR EABX, 50 avenue de Verdun, Gazinet, F-33612 Cestas Cedex

8 ² Plateforme Bordeaux Metabolome, F-33140 Villenave d'Ornon

9

10 Corresponding author

11 nicolas.creusot@inrae.fr

12
13 Abstract

14 Freshwater biofilms have been increasingly used during the last decade in ecotoxicology due to their ecological
15 relevance to assess the effect(s) of environmental stress at the community level. Despite growing knowledge about
16 the effect of various stressors on the structure and the function of these microbial communities, a strong research
17 effort is still required to better understand their response to chemical stress and the influence of environmental
18 stressors in this response. To tackle this challenge, untargeted metabolomics is an approach of choice because of
19 its capacity to give an integrative picture of the exposure to multiple stress and associated effect as well as
20 identifying the molecular pathways involved in these responses. In this context, the present study aimed to explore
21 the use of an untargeted metabolomics approach to unravel at the molecular/biochemical level the response of the
22 whole biofilm to chemical stress and the influence of various environmental factors in this response. To this end,
23 archived high-resolution mass spectrometry data from previous experiments at our laboratory on the effect of the
24 model photosynthesis inhibitor diuron on freshwater biofilm were investigated by using innovative solutions for
25 OMICS data (e.g., DRomics) and more usual chemometric approaches (multivariate and univariate statistical
26 analyses). The results showed a faster (1 min) and more sensitive response of the metabolome to diuron than usual
27 functional descriptors, including photosynthesis. Also, the metabolomics response to diuron resulted from
28 metabolites following various trends (increasing, decreasing, U/Bell shape) along increasing concentration and
29 time. This metabolomics response was influenced by the temperature, photoperiod and flow. A focus on a plant-
30 specific omega-3 (eicosapentaenoic acid) playing a key role in the trophic chain highlighted the potential relevance
31 of metabolomics approach to establish the link between molecular alteration and ecosystem structure/functioning
32 impairment but also how complex is the response and the influence of all the tested factors on this response at the
33 metabolomics level. Altogether, our results underline that more fundamental researches are needed to decipher the
34 metabolomics response of freshwater biofilm to chemical stress and its link with physiological, structural and
35 functional responses toward the unravelling of adverse outcome pathways (AOP) for key ecosystem functions
36 (e.g. primary production).

37

38
39 **Keywords**

40 Untargeted Metabolomics, freshwater microbial communities; chemical stress; confounding factors, chemometric

41 methods; herbicide diuron

42
43 Declarations

44 Ethics approval and consent to participate : Not applicable

45 Consent for publication : Not applicable

46 Availability of data and materials

47 Although most of the data generated or analysed during this study are included in the manuscript and the
48 supplementary material, the full datasets are available from the corresponding author on reasonable request.

49

50 Competing interests

51 The authors declare no conflict of interest.

52

53 Funding

54 This work was funded by the French National Research Agency (ANR) in the framework of the Investments for
55 the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-45).

56

57 Authors' contributions

58 Conceptualization: [Nicolas Creusot, Betty Chaumet, Nicolas Mazzella, Soizic Morin]; Formal analysis and
59 investigation: [Nicolas Creusot, Betty Chaumet, Mélissa Eon, Aurélie Moreira, Soizic Morin]; Methodology:
60 [Nicolas Creusot, Betty Chaumet, Nicolas Mazzella, Soizic Morin]; Funding acquisition: [Nicolas Mazzella,
61 Soizic Morin]; Supervision: [Nicolas Mazzella, Soizic Morin] ; Validation [Nicolas Creusot, Betty Chaumet];
62 Writing - original draft preparation: [Nicolas Creusot]; Writing - review and editing: [Nicolas Creusot, Betty
63 Chaumet, Nicolas Mazzella, Soizic Morin]

64

65 Acknowledgements

66 This study was carried out with financial support from the French National Research Agency (ANR) in the
67 framework of the Investments for the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-
68 45).

69
## 1. Introduction

Facing increasing pollution of aquatic ecosystems, one major challenge in ecotoxicology is to better understand the link between the exposure and the effect at high levels of biological organization in a multi-stress context, in order to gain knowledge on the sustainability of these ecosystems and associated services (Ghiglione et al. 2016, Shahsavari et al. 2017). In this context, freshwater biofilms are increasingly used because of their diversity and their role in key ecosystem functions (biogeochemical cycles, primary production, overall detoxification) (Guasch et al. 2016, Shahsavari et al. 2017). Also, by impacting such communities, chemical stress (as others) might impair ecosystem functions and associated services that are of major concern in the “Eco/One-health” context. Despite growing knowledge about the effect of various stressors on the structure and the functions of microbial communities, a strong research effort is still required to better understand the response of these communities to chemical stress (Sabater 2017). Indeed, most of the currently used descriptors only focus on one component of the biofilm (autotrophic vs heterotrophic) without taking into account the full response at the whole biofilm level (Guasch et al. 2017). Thus there is a need to get a more comprehensive picture of the global response of the entire biofilm. In addition, some of the current physiological and structural descriptors are not sensitive enough to early detect exposure and/or ecological response to chemical stress raising the need to develop/discover more sensitive methods at the molecular level (Forbes & Galic 2016, Sabater 2017). Finally, there is also a critical need to better understand the influence of environmental stressors in the response and tolerance to chemical stress in order to gain knowledge on the resilience of these ecosystems in the global change context. Indeed, despite growing evidence about the influence of temperature (Chaumet et al. 2020, Fadhlaoui et al. 2020, Pesce et al. 2018), light (Chaumet et al. 2020, Guasch et al. 2016), nutrients (Guasch et al. 2004, Tlili et al. 2010) and flow (Chaumet et al. 2019b, Corcoll et al. 2019, Graba et al. 2013) on the response and tolerance of biofilms to chemical stress the underlying mechanisms remained poorly understood. This is as well of particular concern regarding the discovery of relevant biomarkers (i.e characterization of confounding factors).

To tackle these challenges, the high-resolution mass spectrometry (HRMS) based-untargeted metabolomics is a cutting-edge approach of choice regarding the chemical stress since its ability to simultaneously assess the exposure (xeno-metabolome, parent and (bio)transformation products) and the effect (endometabolome, summation of all metabolites related to a particular biochemical process), as well as identify signalling pathways
associated to these responses (Al-Salhi et al. 2012, David et al. 2014). This approach has demonstrated its
relevance to investigate the response of aquatic organisms and populations to environmental stressors and the
discovery of biomarkers (Bedia et al. 2018, Booij et al. 2015, Wang et al. 2019). In addition, higher sensitivity of
metabolomics markers than usual descriptors has been recently shown (Gauthier et al. 2019, Serra-Compte et al.
2018, Wang et al. 2019). Also, Pomfret et al. (2019) have recently highlighted that the metabolome meets many
bioindicator criteria and has the potential to be a priori predictive. For instance, Taylor et al. (2018) have shown
that early-response metabolic biomarkers can predict chronic reproductive individual fitness and thus population
response in daphnids. At the community level, metabolomics is widely used in microbial ecology for biomarker
discovery and diagnostics since it is a powerful approach to characterize ecosystem phenotypic traits (at the macro-
scale) resulting from the network of interactions occurring between the members of the microbial communities (at
the micro-scale) (Abram 2015, Douglas 2020). Therefore, the implementation of metabolomics analyses on
freshwater biofilms in parallel of usual physiological, structural and functional microbial community descriptors
could provide a way to uncover adverse outcome pathways (AOP) of chemical stress for key ecosystem functions
and thus identify biomarkers allowing the sensitive, specific and early detection of such impairment. However,
despite its relevance, metabolomics approach is still very limited in microbial ecotoxicology in comparison to
ecology (i.e. eco-metabolomics), raising a research opportunity to explore (Riedl et al. 2015, Serra-Compte et al.
2018, Zhang et al. 2020). To this end, beyond usual chemo-metric methodologies supporting the identification of
relevant features (i.e. metabolites), promising approaches have been recently proposed to aggregate metabolomics
fingerprints towards quantitative assessment of the dose-dependent metabolomics response to support ecological
risk assessment of chemicals (Metabolic Effect Level Index, MELI; DRomics) (Larras et al. 2018, Riedl et al.
2015). Nevertheless, there is still an overall need for additional empirical evidence to determine the metabolic
shifts that generate ecologically relevant changes and demonstrate its causal diagnostic capacity (Pomfret et al.
2019). In particular, investigation of the underlying processes driving metabolome variability becomes necessary
in order to develop transitive principles and enhance predictive capabilities across ecosystems (Danczak et al.
2020).
Among chemical contaminants, the model herbicide diuron is a Water Framework Directive (WFD) priority
substance (2000/06/EC) broadly detected in European aquatic systems (Loos et al. 2009) and that has been widely
124 used as a model photosynthesis inhibitor in ecotoxicology. Indeed, as an inhibitor of photosystem II (i.e. blocker
125 of the electron transfer through interaction with the plastoquinone binding site), diuron has been shown to strongly
126 reduce photosynthetic activity in both microalgae and freshwater biofilms (Moisset et al. 2015, Tlili et al. 2008).
127 In addition, investigations of the molecular mechanisms have revealed that diuron causes oxidative stress that
128 might impair several central metabolic pathways (citric acid cycle, amino acid metabolism) in microalgae (Booij
129 et al. 2014). In biofilms, several studies have shown that both short and long exposure to diuron and its metabolites
130 (DCPMU and 3,4-DCA) can affect the community structure (biomass, algal composition), its function (primary
131 production) and its further tolerance, as well as the EPS composition and protein content on the long term (Pesce
132 et al. 2010, Sgier et al. 2018, Tlili et al. 2008). More recently, we have highlighted that environmental factors
133 (light, temperature, duration of exposure and flow conditions) may affect the effect of diuron on freshwater
134 biofilms (Chaumet 2018, Chaumet et al. 2020, Chaumet et al. 2019a, Chaumet et al. 2019b, Morin et al. 2018)
135 although the underlying molecular/biochemical mechanisms remain unknown.

136 In this context, the present study aims to explore the use of an untargeted metabolomics approach on freshwater
137 biofilms to provide an integrative picture of the molecular/biochemical response at the whole community level
138 (i.e. molecular phenotype) to diuron and to enhance understanding of the influence of various environmental
139 confounding factors on this response. To this end, archived HRMS data from previous investigations in our
140 laboratory consisting of chronic exposure of biofilms to the model herbicide diuron were investigated (Chaumet
141 et al. 2020, Chaumet et al. 2019a-b, Chaumet et al. 2018). More specifically, we aimed to address the following
142 questions: (i) How sensitive is the aggregated metabolomics dose-response relationship of the whole biofilm to
143 diuron exposure in comparison to usual descriptors, in particular to the photosynthesis (the main diuron target)? ;
144 (ii) What are the potential metabolic pathways modulated by diuron prior the photosynthesis disruption? ; (iii)
145 How fast is the metabolomics response and does this response changes along with chronic exposure? ; (iv) What
146 is the influence of light, temperature and flow on the metabolomics response to diuron? Regarding the scientific
147 literature, we could hypothesize that the metabolomics response to diuron and the tested confounding factors
148 would be fast and sensitive and involve energy metabolism related to photosynthesis alteration by the model
149 herbicide.

150
## 2. Material and methods

### 2.1. Chemical and reagents

Diuron was purchased at Dr. Ehrenstorfer GmbH (purity: 99%) and prepared in acetonitrile (ACN) (UHPLC grade, Biosolve) to obtain a concentration of 200 mg/L. UPLC eluents were prepared with water (UHPLC grade, Biosolve) supplemented with ammonium acetate at 10 mM (Sigma Aldrich) and 0.1% formic acid (purity: 99%, Biosolve), and methanol (UHPLC grade, Biosolve) supplemented with 0.1 formic acid.

### 2.2. Field sampling and laboratory exposure

In the present study, we investigated HRMS data acquired following previous exposure of biofilms to diuron reported in Chaumet (2018), Chaumet et al. (2020), Chaumet et al. (2019a), Chaumet et al. (2019b) and summarized in Table 1. Briefly, for all these previous investigations, biofilms were sampled through one month colonization on glass slides (L: 26cm, l: 6 cm) in 2016 and 2017 in the hypereutrophic Gazinet-Cestas pond (South West France, geographical coordinates: 44°46'30.1"N, 0°41'44.3"W). All the experiments were conducted in indoor artificial channels (L: 50 cm, l: 9.2 cm, H: 5 cm) supplied with recirculating Dauta medium (Dauta 1982) at a pH of 7.5-8 in a thermo/light-regulated room. Prior biofilm exposure, the channels were contaminated with diuron (at whished concentration) for equilibration during two hours. To this end, the stock solution was first diluted in ACN and then in water in order to reach the attempted concentration in the channels depending on the experiments (Table 1). In accordance with previous experiments in our laboratory (Ornany et al., unpublished data), the maximum ACN concentration was 0.25% for all the tested conditions, including the control conditions (i.e. only with 0.25% of ACN). Then, following each sampling, glass slides with biofilms were randomly place horizontally at the bottom of the channels (with no acclimation period).

### 2.3. Laboratory sampling and sample preparation

As described in Chaumet et al (2019a,b, 2020) and Chaumet (2018), for all tested parameters, biofilms were sampled prior the exposure in channels (T0) and along these exposures as described in Table 1. In these experiments, we evaluated the influence of exposure duration, light, temperature and flow on the response of freshwater biofilm to the diuron. At each sampling date, biofilms were scrapped from the glass slides with a razor
blade and resuspended in beakers with Dauta medium. Suspensions were slightly homogenized using magnetic agitation (150 rpm) for 30 min at 20°C. A part of the suspension were used for the determination of physiological/structural descriptors (total organic carbon, polysaccharide, proteins content, enzymatic and photosynthetic activity) as described in section 1 of the supporting information. The other part were filtrated on previously-weighted ashed GF/F filters (Ø 90 mm, 0.7 µm, Whatman) allowing for further dry weight determination and organic extraction for in quantification of diuron into the biofilm and in the case of this study the implementation of untargeted metabolomics approach.

185

## 2.4. Biofilm organic extraction

Following GF/F filtration, filters with biofilm were freeze-dried, weighed (for dry weigh determination) further extracted by Pressurized Liquid Extraction as described in Chaumet et al. (2019a), Chaumet et al. (2019b) and (ASE200, Dionex). Briefly, ASE cells were filled with cellulose filter at the bottom, dried biofilm filter and Fontainebleau sand to fulfil the cells. Extractions were performed at 70 bar and at 100°C by using acetone as a carrier solvent. The acetone extracts were then evaporated until dryness under nitrogen flow prior to their resuspension in 10 mL of ultra-pure water for further purification through solid-phase extraction on HR-X Chromabond cartridges. To this end, the cartridges were first conditioned with 5 mL of methanol and 5 mL of ultra-pure water. The samples were then loaded and percolated. After rinsing with MeOH:H20 (80:20, v:v) and 30 min drying of the cartridges, the analytes were eluted with 5 mL of acetonitrile. These eluates were evaporated by using a rotary evaporator (SpeedVac, ThermoFisher Scientific) and resuspended in 1 mL of ultrapure water prior to their storage at -20°C waiting for their analyses.

It is noticed that the experiments were not initially designed for metabolomics analyses. Thus, the samples were not quenched during the sampling while the extraction method might be not the best one for metabolomics since it is based on acetone and SPE purification that might give a partial view of the metabolome. Despite these limitations, recent studies reported such methodology to implement metabolomics in various organisms, including freshwater biofilm (Serra-Compte et al. 2018).

203

## 2.5. High-resolution mass spectrometry data acquisition
Samples have been analyzed on an UPLC-ESI-TOF system (Ultra-Performance Liquid Chromatography — Time of Flight Mass Spectrometry, Xevo G2-S ToF, Waters) as described in Chaumet et al. (2019a,b, and 2020). Briefly, 20µL of extract have been injected and separated at a flow rate of 450 µL/min on an ACQUITY BEHC18 column (50 x 2.1 mm x 1.7µM, Waters) at 45°C according to the following gradient: 0-0.1 min: 98% of A; 0.1-3.60: from 98% to 1% of A: 3.6-3.8: 1% of A; 3.8-6 min: from 1% to 98% of A 1% (A: 10 mM ammonium acetate buffer supplemented with 0.1 formic acid; B: MeOH supplemented with 0.1 formic acid).

The electrospray source has been operated in positive mode with the following parameters: Source capillary voltage at 0.7 kV; cone voltage at 30V; Nitrogen as nebulizer at a flow rate of 50L.h⁻¹ and at 150°C and desolvation gaz at flow rate of 1200L.h⁻¹ and 600°C. Data were acquired in the 50 to 1200 m/z range with a scan every 0.2s in MSe mode consisting in low (CE=6ev) vs high collision (CE=10-30eV) energy acquisition. The Leucine Enkephalin was used as lock-mass at 556.2771 (28,000 of full width at half maximum (FWHM) resolution) allowing m/z correction during the run.

217

## 2.6. Untargeted metabolomics data pre-processing

In the present study, we used the HRMS data acquired in Chaumet (2018) and Chaumet et al. (2019a, b and 2020), to implement an untargeted metabolomics approach. To do so, the FUNC003 (LockMass signal) was suppressed from the raw data prior to data conversion into mzXML files by using MS-Convert (ProteoWizard) based on CWT algorithm in Peak-Picking filter. MzXML files were pre-processed by using W4M (https://workflow4metabolomics.usegalaxy.fr/) based on XCMS scripts as described in Table S1. The obtained peak list was first filtrated for missing signals (NA>75%), and then processed for missing values imputation (half minimum value) and for batch-effects and signal-drift correction by using the all_loess_pool function based on QC samples. The features were finally filtrated against the blank signals (Sample/Blank>10) and normalized by using the biofilm dry-weigh. These feature lists were further analysed through the calculation of the MELI (Metabolic Effect Level Index) and their processing in DRomics (i.e. benchmark responses) and MetaboAnalyst 5.0 (i.e. statistical and functional analyses).

230

## 2.7. MELI calculation
The MELI is an aggregated quantitative parameter of the metabolomics response that reflects the average x-fold change (increase or decrease) of all the features (defined by a m/z value, a retention time and an intensity) of the fingerprint. It was calculated as proposed by Riedl et al. (2015). Briefly, as a first step the enhanced metabolic change (MC) is calculated for each feature as follows:

$$MC_{i,T,t} = e^{|\ln(A_{i,T,t})|} - e^{|\ln(A_{i,c,t})|} \qquad (1)$$

Where $A_T$ is the ratio between the abundance of a single m/z feature (i) to the mean abundance of this feature in the control (i.e. non-exposed biofilm) at time t, and $A_c$ is the relative metabolomics change of the control (by definition equal to 1). Here, absolute metabolomics changes are considered (no distinction between increasing or decreasing signals). All the $MC_{i,T,t}$ are then summed as the $MC_{total}$ corresponding to the accumulated metabolomics change as followed

$$MC_{total,T,t} = \sum_{i=1}^{i=n} MC_{i,T,t} \qquad (2)$$

The $MC_{total, T,t}$ was divided by the number of m/z features to give the MELI.

## 2.8. Concentration and time trends analysis

Characterization of concentration and time dependency response for metabolomics dataset were established by using the DRomics tool (Shiny version, https://lbbe.univ-lyon1.fr/-dromics-.html) allowing to encompass both monophasic and biphasic trends of each feature and further determination of benchmark dose (BMD) summarized as empirical cumulative distribution function (ECDF). In this study, beyond the concentration-response, we also processed the metabolomics as time-series data by using this framework in order to calculate Benchmark time (BMTs). First (step1), the data were log-2 transformed. Second (step2), the relevant features (i.e. those displaying a significant change of signal along the concentration gradient or the time) were identified using a quadratic trend test with a False Discovery Rate (Benjamin-Hochberg test) of 0.001 because of the high number of metabolites, as suggested by Larras et al. (2018). Third (step 3), concentration/time response modelling was performed for each of the selected features based on nominal concentrations/time. All these curves were manually curated to avoid further miscalculation and in case of false modelling. In particular for U-shape and bell-shaped curves, the features for which the modelled curves did not fit the experimental values were removed from the dataset before reimplementation of step 1-3. Overall, only a small part of the features was removed (<5%). Fourth (step 4), the
model with the best fit was used to derive a benchmark dose/time (BMD-T<sub>1SD</sub>, BMT-T<sub>1SD</sub>) for each feature, as recommended by EFSA (Committee 2017). The BMD/T<sub>1SD</sub> values are the concentration/time corresponding to a Benchmark Response (BMR-zSD) defined as follows: BMR-zSD = y<sub>0</sub> +/- z*SD, where y<sub>0</sub> is the mean control response, and SD is the residual standard deviation of the considered concentration-response model and z is the factor of SD (z fixed at 1 by default). These BMD/T values give an indication of the concentration/time leading to a significant level of change compared to the control (non-exposed in case of concentration-response, initial metabolic status in case of time series). In this study, such values were also calculated for the photosynthetic activities (data from Chaumet et al. (2019a) and Chaumet (2018) by using the anchoring continuous data of DRomics (in step 1) in order to highlight how much of the metabolome was altered prior alteration of the photosynthesis.

269

## 2.9. Multivariate and univariate statistical analyses

In parallel to the trends analyses, chemometric tools were implemented as well in order to describe the whole metabolomics datasets and to highlight the influence of the various tested conditions on the metabolomics fingerprints. This was performed by using the statistical function of MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/). For this purpose, the data were uploaded as peak lists (i.e. table with m/z value and corresponding intensity in each sample). In case of more than 5000 features were listed, data were filtrated by interquartile range (IQR) prior to other processing. In all cases, data were normalized consisting in sample quantile normalization and log transformation. Multivariate analyses were then implemented on the whole datasets of the various experiments as Partial Least Squares –Discriminant Analysis (PLS-DA) and also as Orthogonal PLS-DA (OPLS-DA) for the more complex dataset (i.e. influence of exposure duration, influence of temperature and photoperiod). Univariate analyses were also performed as ANOVA in order to identify significant differences in features intensity (p-value = 0.01 with FDR adjusted method) between various experimental conditions. Those results are presented as box plots or volcano plots in the manuscript. Finally, non-parametric Mann-Whitney tests were also performed to highlight significant differences in MELI, photosynthesis inhibition and bioconcentration of diuron following exposure to 5 and 50 µgL of diuron (n=4, p<0.05).

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284

285
286 **2.10. Functional and pathway analyses**

287 Following the identification of the more relevant features by trends analysis or through chemometric multivariate
288 approach, a functional analysis was performed in MetaboAnalyst 5.0 in order to annotate pathways and metabolites
289 (i.e. Level 3 based on mono-isotopic mass). To this end, filtrated metabolomics datasets were uploaded as peak
290 lists with a mass tolerance of 5 ppm and further normalized (i.e. sample quantile normalization of the log2
291 transformed data). The Mummichog algorithm (Li et al. 2013) was used in order to enhance the annotation with a
292 p-value cutoff of 1.0E-5 based on the more common adducts (i.e. [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+,
293 [M]+,). Several KEGG pathway libraries were investigated in order to cover the diversity of the biofilm
294 composition including *Saccharomyces cerevisiae* (fungi), *Chlorella vulgaris* (alga), *Bacillus subtilis* (bacteria) and
295 *Synechococcus elongatus* (cyanobacteria).

296

297 **2.11. Quality Assurance and Quality Control**

298 All along the experiments implemented in Chaumet (2018) and Chaumet et al. (2019a, 2019b, 2020), temperature
299 (water and room), pH, light and nutrient concentrations have been measured throughout the experiment. The
300 temperature was assessed every 10 minutes by using data loggers (Tinytag, Radiospare). Dissolved nutrients
301 (nitrates, phosphates) were analysed in triplicates by ion chromatography (COMPACT 881 IC Pro, Metrohm,
302 Switzerland) coupled with a conductivity detector (Metrohm 850 IC, Switzerland). A light-meter (LI-COR Li-
303 250, USA) was used to control incident light intensity reaching the slides. Regarding the biofilm extraction,
304 procedural blanks were prepared and analysed in parallel with the samples to control any external contamination.
305 Regarding HRMS-analysis, quality controls (QC) consisting in a solution of diuron (10 µg/L) were injected at
306 regular intervals in order to check for signal drift.

307

308 **3. Results and discussion**

309 **3.1. The metabolomics response of the biofilm to the diuron is sensitive and concentration-dependent**

310 From the experiments reported in Chaumet et al. (2019a) (#4 in Table 1), the metabolomics response to serial
311 dilution of diuron (1, 5, 10, 25, 75; 500 µg/L) following 2 hours of exposure was assessed in the present study.
312 First, the response of the 6245 isolated features from W4M pre-processing was aggregated along increasing
concentrations of diuron through the MELI calculation and compared to the inhibition of the photosynthetic activity in the same biofilm reported in Chaumet et al. (2019a) (Figure S1). The results highlighted the higher sensitivity of the metabolomics response in comparison to the inhibition of photosynthesis despite this function was the main diuron target. To go further, the metabolomics data were processed in DRomics tool allowing to isolate 532 signals (FDR=0.001) that fitted dose-response curve models (Hill, linear, exponential, Gauss-probit, log-Gauss-probit) with various trends (i.e. monophasic, biphasic, U-Shape, Bell) (Figure S2 and Figure S3). Indeed, as reported in previous studies implementing similar models on aggregated OMICs responses following exposure of microalgae to various contaminants (Larras et al. 2018, Larras et al. 2020, Smetanová et al. 2015), most of the signals did not follow a monophasic response (i.e. 4% monophasic decreasing pattern, 18% monophasic increasing pattern, 41% non-monophasic hyperbolic/bell pattern, non-monophasic 37% U-shape patterns). Thus, our results confirmed the relevance of the benchmark approach englobing various dose-response models to provide a full understanding of the metabolome modulation. Also, the cumulative distribution of the BMD<sub>ISD</sub> value for the metabolites showed the higher sensitivity of the metabolome to diuron (i.e. responses at lower concentrations) than photosynthesis that is the specific target of the investigated PSII inhibitor (Figure 1). Indeed, at the BMD<sub>ISD</sub> value of the photosynthesis inhibition (1.13 µg/L), already around 45% (ECDF 0.45) of the metabolites have been altered (i.e. increasing or decreasing level). This result confirmed the higher sensitivity of the molecular response in comparison to physiological endpoints and its relevance for the early detection of potential impairment of such process (Kluender et al. 2008, Pino-Otín et al. 2019). Since photosynthesis is directly linked to autotrophs growth and primary production, its long terms disruption might lead to impairment of biofilm structure and function and further at the ecosystem level (Dokulil & Qian 2021). Thus, the early detection and further prediction of such disruption is of major concern. Overall, our results indicate that the establishment of aggregated metabolomics responses in freshwater biofilms appears as particularly powerful and relevant for the early (i.e. molecular response prior response at the individual level) and sensitive detection of potential impairment of biofilm functioning. Nevertheless, more investigations are needed to confirm that such metabolomics response to short term exposure at low concentration is able to predict long term disruption (i.e. not reversible) of photosynthesis or other processes and associated ecosystem structure and functions (e.g. primary production).
As illustrated in Figure 1, DRomics allows unravelling the contribution of increasing and decreasing trend responses (including both monophasic and biphasic shapes) as well as the sensitivity of each feature/metabolite to the metabolomics responses of the whole biofilm. Thus, the detected features showed sensitivity differences to diuron. Indeed, if half of the signals are sensitive to an environmental concentration (< 1µg/L), some others are altered at a higher concentration (100-200 µg/L). This likely suggested that the features responsive to low concentrations could be those evolved in defence (i.e. biotransformation) or acclimation mechanisms while those responding at higher doses could be those associated to the adverse effect of diuron (Riedl et al. 2015). This discrepancy between low and high concentration responsiveness was also highlighted in terms of type of responses since the majority of signals showed a strong decreasing pattern between 10-100 µg/L (signal in blue in Figure 1 and Figure S3).

Further pathway annotation (KEGG libraries) revealed potential alteration of α-linolenic acid metabolism, sphingolipid metabolism, brassinosteroid biosynthesis, fatty acids biosynthesis and xenobiotic metabolism pathways at low concentration (below 0.1 µg/L according to BMD<sub>1SD</sub> derivation from DRomics) (Table S2).

Contradictory to Booing et al. (2015) studying microalgae response to diuron and Kluender et al. (2008) investigated the microalgae response to the herbicide prometryn, no effect was noted on citric acid cycle (TCA), amino acid metabolism. This could be related to the global response of the biofilm that can hide specific responses of one of its components. Nevertheless, there was as well no clear evidence of the modulation of the energy metabolism although this could be attempted regarding the mode of action of the diuron and the scientific literature (Tlili et al. 2008, Pesce et al. 2010). Thus, our results underline likely that some cautions have also to be taken regarding the annotation of our dataset since these HRMS data were initially not acquired for the assessment of the comprehensive metabolomics response. For instance, the absence of quenching at the sampling step as well as the extraction based on acetone and further SPE cleaning might lead to an incomplete metabolomics fingerprint of the biofilm in comparison to dedicated methodology allowing to englobe more metabolites classes (Barnes et al. 2016, Gong et al. 2017, Salem et al. 2017). In addition, the application of a very low FDR factor (0.001) in DRomics (as recommended by Larras et al. (2018)) to identify the metabolites following quadratic trends drastically reduced the number of features (From 6245 to 532) whereas higher FDR might be a better compromise. For instance, Larras et al. (2020) used recently an FDR of 0.1 to filter metabolomics data (224 metabolites). Despite
these limitations, the observed alteration of fatty acids has been previously reported in biofilms or their components following exposure to pharmaceuticals or pesticides contributing to strengthening the relevance of their potential use as biomarkers (DeForest et al. 2016, Demailly et al. 2019, Gauthier et al. 2019, Serra-Compte et al. 2018) although further investigations are still needed to fully characterize this potency (i.e. quantitative relationship, predictive power). Overall, in the candidate pathways, the intensity level of the metabolites increased while others decreased along with increasing concentration of diuron (Table S2). This confirmed the complexity of the metabolomics response, highlighting potential feedback control in some pathways and the contribution of the same metabolites to different pathways (Chaves & Oyarzún 2019). This is even more complex in biofilms in which the metabolomics fingerprint results from the response of the different components (algae, bacteria, fungi) -sharing some similar pathways that can react with discrepancy to the diuron exposure- but also from the interaction between these components (e.g. cross-feeding interaction) (Douglas 2020).
Altogether, our results highlighted the need for more fundamental research to decipher the metabolomics response of freshwater biofilm to the chemical stress and its link with structural, physiological and functional responses. In particular, this requires the investigation of the metabolomics response (i.e. endometabolome) of individual components (e.g. alga, bacteria, fungi) and of their interaction (i.e. exometabolome) in the biofilm as a whole.
Indeed, the metabolomics is a cutting edge approach to better understand the metabolites exchange among microorganisms and how these interactions influence the diversity and functions of microbial communities through the investigation of the exometabolome (Douglas et al. 2020). Such investigations will further support the establishment of a quantitative relationship between the early molecular/biochemical response of the biofilms to short term exposure (e.g. pulse) to chemical stress and the long-term alteration of ecosystem functions supported by these microbial communities. These will help to unravel AOPs and allow the development of predictive biomarkers/bioindicators able to distinguish between different types of stressors, as reported in other systems (Bahamonde et al. 2016, Davis et al. 2017, Lankadurai et al. 2013, Pomfret et al. 2019, Taylor et al. 2018). To do so, it will be also required to enhance our knowledge about the fluctuation of basal metabolomics status according to the structure of the biofilms related to its maturation over time and under various environmental conditions.

391

## 3.2. The biofilm metabolomics fingerprint changes along exposure time to diuron
Based on the experiments implemented by Chaumet (2018) (#1 in table 1), the metabolomics response to diuron was assessed over an exposure of 21 days. In fact, it could be expected that the metabolomics fingerprint of the whole biofilm changes over a longer exposure to the herbicide according to changes in the structure and the functioning of the biofilm. Thus, the time dependency of the metabolomics response to diuron was characterized through MELI calculation and further time-trend analysis by using DRomics.

The MELI calculation revealed a very fast increase of the metabolomics response in the first fifteen minutes followed by a more stable response till the end of the exposure (Figure S4a). Interestingly the pattern of the MELI followed those of both the diuron sorption and the photosynthesis inhibition (i.e. fast increase and then stable) (Figure S4b and c) while no effect was noted for total protein and polysaccharides content and heterotrophic enzymatic activity (Figure S5). Once again, our data highlighted the higher sensitivity of the metabolomics response to diuron than the photosynthesis inhibition since the two tested diuron concentrations led to almost a similar metabolomics response while there is a strong discrepancy in the response of photosynthesis. Surprisingly, significant stronger metabolic responses were noted with 5µg/L of diuron at 6h and 336h (Mann-Whitney test, n=4, p<0.05) that could be related to various sensitivity of metabolites/pathways to increasing concentration of diuron as illustrated in Figure 1. Overall such a pattern of MELI is quite similar to this reported by Riedl et al. (2015) showing a stable response of *Myriophyllum spicatum* (macrophyte) along a 14 days exposure to isoproturon, another PSII inhibitor. Even if such stability of the MELI might reflect potential acclimation to the contaminant (i.e. reach a new metabolomics homeostasis), this might hide a more complex response to the herbicide along with exposure duration.

To address this question, a time trends analysis was further performed by using DRomics allowing to compare the time dependency response of the non-exposed biofilm vs those exposed to 5 µg/L and 50 µg/L of diuron. As shown in Figure 2, the ECDF of the BMT<sub>ISD</sub> clearly differed between the experimental conditions. Indeed, if there is a fast change in the metabolome for all the conditions, this shift is stronger following exposure to diuron. This response was even different depending on the tested concentration meaning that a higher proportion of metabolites seemed to be altered with higher diuron concentration. This result was different from what was noted with MELI at 2h and 336h (Figure S4a). This discrepancy might be explained by the fact that the MELI was calculated based on all the features (almost 6608) while the benchmarked time response (ECDF distribution of the BMT<sub>ISD</sub>) was
based on drastically selected features (FDR=0.001) following a quadratic trend over the exposure (1632 features in controls, 590 features with 5 µg/L of diuron, 554 features with 50 µg/L of diuron). Beyond the FDR criteria, it could be assumed that the quadratic trend might be too restrictive to provide an accurate picture of the time-resolved metabolic response (pers. comm. with the authors of DRomics). In fact, even if decreasing FDR (and so increasing number of features) made closer the distribution of the exposed conditions, the conditions 50 µg/L seemed to be still the earlier altered (data not shown). As for the concentration-response, BMT<sub>ISD</sub> values were calculated for photosynthesis inhibition and plotted against the ECDF distribution, confirming the higher sensitivity of the metabolomics response noted with the MELI (i.e. 50% of the metabolites change before a significant alteration of the photosynthesis) (Figure 2). To the best of our knowledge, this study is the first to report the use of DRomics to investigate time-dependency of Omics response. Even some current limitations have been highlighted (i.e. exclusion of metabolites with a non-quadratic trend over exposure duration), the DRomics tool is very promising in order to unravel the metabolomics response over time through directing the annotation toward the metabolites with various specific trends (Figure S6).

These shifts in the metabolomics fingerprints along the 21 days exposure were further highlighted through the implementation of OPLS-DA on the whole dataset (Figure 3). This showed clear discrimination between each exposure time as well as discrimination between exposure conditions at each time, even clearer through dedicated PLS-DA at each exposure time (Figure S7). In particular, in Fig 3, the discrimination between T0 condition and all the other experimental times (15min, 6h, 10days, 14 days, 21 days) in controls likely highlighted a change of the metabolic fingerprint over time according to biofilms structure and functioning due to its maturation, as well as, potential acclimation/adaption of the biofilms to the exposure conditions. Since biofilm was investigated in the present study as a biological model allowing to assess potential alteration of ecosystems functions, metabolomics responses after longer exposure duration appeared more relevant to highlight such potential impairment. As shown in Figure 4, a high number of metabolites were significantly down- or up-regulated following 21 days of exposure to 50µg/L (i.e. 1816 among the 6608 features) (Figure 4a). If most of them were up-regulated (1611 features), some were also down-regulated (205 features). Among these down-regulated metabolites following 21 days of exposure to diuron, we identified the eicosapentaenoic acid, a plant-specific fatty acid (Figure 4b). This fatty acid is an omega-3 which overall play a major role in the sustainability of the trophic chain and so ecosystem services
since they are hardly produced by higher trophic levels (because of high metabolic cost) (de Carvalho & Caramujo 2018). Thus, such down-regulation could endanger the entire ecosystem although further investigations are still needed to provide quantitative evidence of this causal relationship. In addition, this fatty acid, as others, has been described as antibacterial or as growth inhibitor of some algae species (i.e. involved in allelopathic interactions) (Allen et al. 2015, Gubelit & Grossart 2020). Hence, their downregulation might trigger shifts in community composition and so its functioning and associated ecosystem functions supported by these microbial communities. This would need as well further investigation to confirm such assumptions.

454

### 3.3. Temperature and photoperiod change the metabolomics response of the biofilm to diuron

From the experiments of Chaumet et al. (2020) (#2 in table 1), the effect of light and temperature on the metabolomics response to the diuron was investigated here. Since both parameters were described as environmental factors able to affect the response of biofilms (or their components) to chemical stress by targeting photosynthesis and metabolic processes (Chalifour et al. 2014, Guasch et al. 2016), it could be expected that such influence can be characterized through the investigation of the metabolome.

As a first step, MELI calculation showed no influence of temperature nor photoperiod on the metabolomics response, in line with our previous findings (Figure S8a). Indeed, we have shown only limited (not significant) influence of temperature and photoperiod on photosynthesis inhibition induced by diuron and its sorption (Figure S8b, c) and these environmental factors did not change the (non-)sensitivity of heterotrophic enzymatic activities, protein and polysaccharide contents of biofilms exposed to this herbicide (Chaumet et al. 2020). However, according to our results and the literature we previously assumed that metabolism would be boosted at the highest temperature tested (26°C) while it would down-regulated the lowest temperature (10°C) and with shorter light exposure in order to reduce their energy needs under winter conditions. The MELI did not allow to detect such phenomena probably because of the full aggregation of up-regulated and down-regulated pathways not only involved in energetic metabolism. Nevertheless, as shown in Figure 5, following 72h exposure to diuron, the metabolic fingerprints showed clear discrepancies between the tested conditions (i.e. control vs diuron at 5 µg/l and diuron at 50 µg/L) for all the tested combinations of temperature and photoperiod. Thus, these results highlighted that both temperature and photoperiod (according to season) change the metabolome of the whole
community but also change its response to chemical stress. Further characterization of the eicosapentaenoic acid showed interesting patterns (Figure S9). Indeed, the intensity level of this omega-3 decreased with increasing temperature and light in the control in accordance to the literature (de Carvalho & Caramujo 2018, Fadhlaoui et al. 2020, Hill et al. 2011) whereas in biofilms exposed to diuron the response was totally different. At 5 µg/L a very high level of the fatty acid was observed at higher temperature and longer light exposure while lower intensity was observed for the other conditions. At 50 µg/L, a higher level of this omega-3 was noted at 26°C than at 10°C while increasing light exposure (from 10:14 to 16:8) led to the down-regulation of this fatty acid.

Altogether, these discrepancies highlighted the complex interaction between exposure to diuron, temperature and light at the metabolome level. Also, our results demonstrated how much the response to chemical stress might be changed by environmental factors as previously reported in the literature, in particular regarding light conditions and temperature (Fadhlaoui et al. 2020, Guasch et al. 2016, Larras et al. 2013). Even such results might be explained by the strong influence of both parameters on biofilm structure and function related to their role in physiological and biochemical processes (Mora et al. 2016), our results observed after only 72h showed how fast these interactions can happen at the metabolomics level and so can be investigated.

### 3.4. Flow influences the response of the biofilm metabolome to diuron

On the basis of the experiments of Chaumet et al. (2019b) (#3 in Table 1), the influence of the flow on the metabolic response to the diuron was assessed in the present study. Beyond temperature and light, flow regime is susceptible to change the sensitivity to chemical stress by affecting the chemical bioavailability as well as the structure and functioning of the biofilm as previously reported in the literature (Guasch et al. 2016). Thus, it could be assumed that such a shift in structure and function of the biofilm would lead to changes in the metabolome.

In the present study, MELI calculation did not show a significant influence of flow on the metabolomics response confirming our previous findings (Figure S10a). Indeed, in Chaumet et al. (2019b), we reported that flow influences the structure of the biofilm (i.e. thicker at no flow) leading to higher sorption of the diuron whereas photosynthetic inhibition by the herbicides was not affected by flow conditions (Figure S10b and c). Nevertheless, the statistical comparison of the metabolomics fingerprints (i.e. PLS-DA) revealed marked differences between the tested conditions highlighting the influence of flow on the metabolome as well as its influence on the
metabolomics response to the diuron (Figure 6). This was even visible after 1 min of exposure (Figure 6a).

Interestingly, the pattern of discrimination of exposed vs non-exposed biofilm for each flow condition changed along exposure time. Indeed, after short exposure time (1 min), this pattern was quite similar whereas for longer exposure time (2h), the discrimination was stronger in low flow condition (LF) than with normal flow (NF) (Figure 6a vs 6b). Altogether these results highlighted the combined influence of flow and exposure to diuron on the metabolome. In particular, this stronger discrimination in NF conditions could be due to higher sorption of diuron increasing after longer exposure, as previously reported (Figure S10c). Controversy, the eicosapentaenoic acid showed an opposite pattern with a stronger difference between exposed vs non-exposed in NF than in LF condition while an overall strong down-regulation of the omega-3 was noted in NF conditions (Figure 7). The strong decrease of this fatty acid following exposure to diuron in NF is similar to the one reported at 21 days in the time dependency experiments (Figure 4b), reinforcing our findings in both cases. Regarding the difference observed between NF and LF, it is in line with the usual decrease of polyunsaturated fatty acids (PUFAs) content reported under environmental stress conditions (de Carvalho &Caramujo 2018, DeForest et al. 2016, Fadhlaoui et al. 2020, Serra-Compte et al. 2018). Since this alteration appeared fastly, it is likely that it was linked to a direct metabolic response (i.e. fatty acids biosynthesis) rather than the consequence of a change in the biofilm assemblage.

516

## 517 Conclusions

This study aimed to explore the use of an untargeted metabolomics approach to unravel the response of freshwater biofilms to chemical stress and the influence of environmental parameters in this response. By using archives of HRMS data from our previous experiments, we highlighted the influence of exposure duration, light, temperature and flow on the metabolomics response of freshwater biofilm to the model herbicide diuron. Even though the dataset had some limitations to draw accurate conclusions on the metabolomics pathways involved in these interactions, our results already highlighted how fast and sensitive is the metabolomics response to chemical stress and to its modulation by environmental factors compared to usual descriptors (e.g. photosynthesis) as well as their complementarity to link the molecular/biochemical response to structural/functional impairment at the community level. For instance, by focusing on a plant-specific omega-3 (eicosapentaenoic acid) -playing a key role in the
sustainability of the trophic chain- detected among the thousands of metabolites, this study showed the potential
relevance of the metabolomics approach to detect critical impairment for the ecosystems.

Nevertheless, this study highlighted how complex is the metabolomics response of the whole biofilm including
metabolites following various trends along with increasing concentrations and exposure durations likely associated
with the response of its individual components (i.e. endometabolome) and their interaction (i.e. exometabolome).

Altogether, our results underline that a lot more fundamental researches are critically needed to decipher the
metabolomics responses of the whole community and their link with structural, physiological and functional
responses under chemical stress as well as the influence of environmental factors on these responses. These
researches should focus on the in-depth characterization of: (i) the quantitative relationship between aggregate
metabolomics and single metabolite "dose" responses to chemical stress and physiological processes (e.g.
photosynthesis) and functions (e.g. primary production) in both single components and biofilm as a whole
following various controlled exposure scenarios (acute, pulse, chronic etc.); (ii) the acclimation/adaptation,
tolerance and recovery of biofilms to chemical stress at both the metabolomics, structural and functional levels
following these exposures; (iii) the "natural" fluctuation of the endometabolome and exometabolome anchored to
biofilm composition and functioning related to cross-feeding interaction under various environmental conditions
(i.e. confounding factors) under both controlled and field conditions. Altogether, these researches will unravel
AOP towards the discovery of metabolomics biomarkers/bioindicators able to early detect and further predict long
terms effects on ecosystem functions (i.e. biomonitoring purpose) and able to distinguish between different types
of stressors (i.e. causal diagnostic purpose), that is of crucial concern in the global change context. To do so, the
combination of metabolomics and metagenomics would be of great support to link the microbial structure
(composition) and functions. Also, the present study has contributed to demonstrate the usefulness of DRomics to
define aggregate metabolomics dose-response at the whole community level and to prioritize the annotation
towards features with specific trends (e.g. focusing only on increasing signals along increasing contaminant
concentration or exposure duration).

Finally, in terms of practical consideration, even the untargeted metabolomics approach appears currently as a
resource-intensive method, the above-described researches would further support the development of a cost-
effective targeted method towards the identified biomarkers that could be routinely implemented in field-based
554 monitoring programs for regulatory environmental risk assessment. Moreover, the implementation of untargeted
555 metabolomics, as a second step of a tiered approach, will still be of great help as a retrospective diagnostic tool to
556 better understand the mechanisms underlying the adverse effects measured on the field by using the current low
557 resource biomonitoring approach. Overall, the increasing use of metabolomics (i.e. both targeted and untargeted)
558 in environmental (and human) biomonitoring will be supported by ongoing actions at the French National
559 Infrastructure for Metabolomics (METABOHUB, ANR 11-INBS-0010) related to methodological developments
560 towards the high-throughput use of metabolomics for large cohorts characterization.

561

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<table><caption>Table 1. Experimental setups implemented to assess the influence of environmental parameters on the response of aquatic biofilms to diuron</caption><thead><tr><th rowspan="2">Tested parameter</th><th rowspan="2">Sampling</th><th colspan="6">Experimental conditions</th><th rowspan="2">Ref</th></tr><tr><th>[Diuron] (µg/L)</th><th>Total Exposure duration</th><th>Sampling time</th><th>T°C</th><th>Photoperiod (L:light, D: dark)</th><th>Flow</th><th># Reps</th></tr></thead><tbody><tr><td>#1 Exposure duration</td><td>09-11 2016</td><td>Ctl, 5, 50</td><td>21d</td><td>T0, 15', 6h, 10d, 14d, 21d</td><td>20°C</td><td>24L</td><td>2cm.s<sup>-1</sup></td><td>4</td><td>a</td></tr><tr><td>#2 T°C /photoperiod</td><td>01-02 2017</td><td>Ctl, 5, 50</td><td>3d</td><td>T0, 1d, 3d</td><td>10°C vs 26°C</td><td>16L:8D vs 10L:14D</td><td>2cm.s<sup>-1</sup></td><td>4</td><td>b</td></tr><tr><td>#3 Flow</td><td>03-04 2017</td><td>Ctl, 5, 50</td><td>120'</td><td>T0, 1', 2', 5', 15', 30', 120'</td><td>20°C</td><td>24L</td><td>2cm.s<sup>-1</sup> vs &lt;&lt;1cm.s<sup>-1</sup></td><td>3/4</td><td>c</td></tr></tbody></table>

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<table><tr><td>#4<br>Concentration</td><td>07-2017</td><td>Ctl, 1, 5, 10,<br>25, 50,<br>75,500</td><td>2h</td><td>2h</td><td>20°C</td><td>24L</td><td>2cm.s<sup>-1</sup></td><td>4</td><td>d</td></tr></table>

736 a, Chaumet (2018); b, Chaumet et al. (2020); c, Chaumet et al. (2019b) d, Chaumet et al. (2019a)

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<figure><img src="image_3.png" /><figcaption>**Figure 1.** Cumulative distribution of the BMD<sub>1SD</sub> values for the metabolomics response and BMD<sub>1SD</sub> value of photosynthetic inhibition. Metabolites signal intensity along the concentration gradient is represented by the color gradient (from blue = down-regulation compared to control (non-exposed) biofilm, to red = up-regulation compared to control).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Cumulative distribution of time-dependent benchmark metabolomics and photosynthetic responses (BMT<sub>1SD</sub>) to diuron in control and exposed biofilms. Metabolomics responses are reported as continuous lines (control: green; 5 µg/L: yellow; 50 µg/L: pink) while photosynthesis is reported as dot lines (5 µg/L: yellow; 50 µg/L: pink). BMT<sub>1SD</sub> correspond to a significant change in the response compared to the control (i.e. non-exposed biofilm at T0).</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>**Figure 3.** Score plot from OPLS-DA of metabolomics responses along exposure time in non-exposed biofilm (Ctrl), and biofilm exposed to 5 µg/L (5D) and to 50 µg/L (50D). Colored ellipses around each group show the 95% confidence regions of the groups under the assumption of normally distributed data. Black empty ellipses highlight how exposure times are discriminated.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Down- and up-regulation of the whole metabolome (a) and of the eicosapentaenoic acid (b) in the biofilms exposed to diuron at 50 µg/L during 72h in comparison to the non-exposed biofilm. Results are expressed as volcano plot (a) displaying significant changes (blue: down-regulation; red: up-regulation) and non-significant (grey) intensity changes following ANOVA (Significant threshold: FDR corrected p-value=0.01; Log FC=2; one dot is one metabolite) and as box plot (b) displaying significant change following ANOVA (FDR corrected p-value=0.01)</figcaption></figure>

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<figure><img src="image_7.png" /><figcaption>**Figure 5.** Score plot from OPLS-DA of metabolomics responses in non-exposed biofilm (Ctl), and biofilm exposed to 5 µg/L (5D) and to 50 µg/L (50D) under various combination of temperature and photoperiod corresponding to seasons: S1, 26°C and 16L:8D; S2, 26°C and 10L:14D; S3, 10°C and 16L/8D; S4, 10°C and 10L:14D. Colored ellipses around each group show the 95% confidence regions of the groups under the assumption of normally distributed data. Black empty ellipses highlight how seasonal conditions are discriminated.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Score plots from PLD-DA of metabolomics responses in non-exposed biofilm (CtI), and biofilm exposed to 5 µg/L (D5) and to 50 µg/L (D50) under various flow conditions after T0 vs 1 min of exposure (a) and 2h of exposure (b). Colored ellipses around each group show the 95% confidence regions of the groups under the assumption of normally distributed data. Black empty ellipses highlight how flow conditions are discriminated. NF: Normal flow ; LF: Low flow.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 7.** Distribution of intensity level of the eicosapentaenoic acid in control (CtI) and biofilms exposed to 5µg/L (D5) and 50 µg/L (D50) according flow conditions after 2h of exposure. Results are expressed as boxplot and diuron-exposed conditions are considered as significantly different from the controls following ANOVA (p-value=0.01 with FDR correction)</figcaption></figure>
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