(Mahamoud Ahmed et al., 2020) were applied to both genes. To construct standard plasmids, *czcA* and *cusA* fragments were amplified from *Burkholderia* sp. S9I1 (accession no. KP081480) and *Cupriavidus metallidurans* CH34, respectively, and cloned into the pGEM-T vector. Gene copy numbers were established using standard curves prepared with 10-fold serial dilutions of standard plasmids. All runs were performed in an iCycler iQ system (Bio-Rad) associated with iCycler Optical System Interface software (version 2.3, Bio-Rad).

The abundance of selected antibiotic resistance gene targets (*sul1*, *strA*, *strB*, *ermB*, *ermF*, *aadA*, and *bla*<sub>OXA-20</sub>) and the class 1 integron (*intI1*) in sediment DNA was quantified by qPCR with SYBR green or Taqman probe chemistry. All procedures, primers, quality control and standards for quantification were exactly as described in Lau *et al.* (2020). The identities of the quantified gene targets were verified on the basis of hybridization when using TaqMan chemistry, or melting behavior when using SYBR green. Gene abundances were expressed as a ratio with the total 16S rRNA gene copies.

## Data Analyses

All statistical analyses were performed with the R free software (version 3.4.3, R Core Team, 2018). After confirming normality of the residuals (Shapiro-Wilk test; Royston, 1982) and data homoscedasticity (Fligner-Killeen test; Conover et al., 1981), significant differences between sites and dates in the enzymatic activities, metabolic potentials, bacterial abundance, OTU richness and Shannon diversity index and genetic resistance potential were sought by analysis of variance (ANOVA) and further analyzed with a *post hoc* Tukey test. Differences were considered statistically significant when the *p*-value was below 0.05.

For bacteria and archaeal diversity, Bray-Curtis similarities (BC) between samples were calculated, and distances between samples were represented using a Principal Coordinates Analysis (PCoA) with R software (Vegan package). Functional-based community parameters (enzymatic and metabolic activities, metals and antibiotic resistance genes) were analyzed using a Principal Component Analysis (PCA). The significance of the sample groupings on the ordinations was tested using ANAnalysis Of SIMilarity (ANOSIM). The relationship between community composition and functional-based structures (based on PCoA and PCA 2-dimension projections, respectively) were evaluated using a Procrustean analysis (using R software, Vegan package). Procrustean analyses superimpose, scale and rotate one data matrix upon the other until an optimal fit is found (Jackson and Harvey, 1993; Peres-Neto and Jackson, 2001; Heino et al., 2004). Estimated residuals between original values and the derived best fit solution give the m12 statistic, with a low m12 statistic indicating a good level of correspondence between data matrices (Paavola et al., 2006). The PROTEST permutation procedure (999 permutations) was used to assess the statistical significance of the Procrustean fit between the two matrices (Peres-Neto and Jackson, 2001; Paavola et al., 2006). Vectors of environmental variable data were fitted onto the procrustean analysis plot using the envfit function of the vegan package. This function calculates the goodness-of-fit values (R²) for environmental

variables onto the procrustean analysis plot and the significance of each correlation was tested based on 999 random permutations (Oksanen et al., 2020).

## RESULTS

### Environmental Conditions and Sediment Contamination at the Sampling Sites

The main sampling site characteristics are presented in **Table 1**. Surface sediments were collected at a sampling depth ranging from 22 m (site 32) to 95 m (site 29), and the median grain size varied from 8 µm (sites 6 and 21) to 35 µm (site 53). Sediments from the sites 53 and 78 had the highest and lowest organic matter content (13.6 and 2.7%, respectively) and total organic carbon (7.0 and 0.7%, respectively). These two sites were also characterized by lower carbonate content (about 14.4 and 19.7%, respectively) than the six other sites (i.e., 5, 6, 21, 29, 32, and 36) where it varied between 31.2% (site 29) and 48.9% (site 36). Total phosphorus concentrations were 4.5- to 7-fold higher in the site 53 (3900 mg kg⁻¹ dw) than in the seven other sites (659 to 849 mg kg⁻¹ dw). Sediments from the site 53 contained high total concentrations of metals (about 1193 mg kg⁻¹ dw) compared to the seven other ones, where it varied from 170 mg kg⁻¹ dw (site 36) to 322 mg kg⁻¹ dw (site 29). The high total concentration of metals in the site 53 was mainly due to zinc and copper (Zn and Cu, 645 and 281 mg kg⁻¹ dw, respectively; **Supplementary Table 1**). The sediments from site 78 contained high concentrations of titanium (Ti; 1736 mg kg⁻¹ dw) while for the other sites Ti concentrations ranged between 37 and 405 mg kg⁻¹ dw. The site 53 was also characterized by higher values for the sum of the seven indicator PCB (Σ7PCBi) (387 µg Σ7PCBi kg⁻¹ dw): more than 400-fold higher than those observed in the site 78 (0.9 µg Σ7PCBi kg⁻¹ dw) and 36- to 75-fold higher than those observed in the six other sites (5.2–10.5 µg Σ7PCBi kg⁻¹ dw) (**Supplementary Table 2**). Sediments from sites 53 and 36 contained higher concentrations of PAH (25262 µg kg⁻¹ dw and 13581 µg kg⁻¹ dw, respectively) compared to the six other ones, where it varied from 774 µg kg⁻¹ dw (site 78) to 2263 µg kg⁻¹ dw (site 32) (**Supplementary Table 3**).

### Functional Potential

A set of potential microbial activities involved in the C cycle (β-glu, respiration, and methanogenesis), the N cycle (LAP and denitrification) and the P cycle (Pase) was measured (**Figure 2**). The results reflected a high spatial heterogeneity between the sampling sites. With the exception of the LAP activity (**Figure 2B**), sediment microbial communities from the site 53 exhibited the highest functional potential, both in October 2017 and May 2018. Methanogenesis activity (**Figure 2F**) was only detected in this site and β-glu (**Figure 2A**), Pase (**Figure 2C**), aerobic respiration (**Figure 2D**) and denitrification (**Figure 2E**) were also significantly higher than in the other sampling sites (*p* < 0.05). Significant but less marked spatial differences were also observed between the seven other sites (i.e., 5, 6, 21, 29, 32, 36, and 78) for the three enzymatic activities, which were
<figure><img src="image_129.png" /><figcaption>**FIGURE 2** | Dot plots of the values obtained for (A) beta-glucosidase, (B) leucine-aminopeptidase, (C) phosphatase, (D) respiration, (E) denitrification, and (F) methanogenesis benthic microbial activities for the triplicate samples from the 8 studied sites in Lake Geneva in October 2017 (empty symbols) and May 2018 (plain symbols). For each sampling site (and date if applicable – October 2017, italic letter and May 2018, roman letter), different letters indicate significant differences between treatments (*P* < 0.05, Tukey test).</figcaption></figure>

generally the highest in sites 5 and 6 and the lowest in sites 21 and 36 (Figures 2A–C). In contrast, no significant difference was observed between these seven sites for both aerobic respiration (Figure 2D) and denitrification (Figure 2E).

Seasonal variations between the two sampling periods were less pronounced in the four sites that have been sampled in 2017 and 2018 (i.e., 29, 32, 53, and 78). Significant differences (*p* < 0.05) between October 2017 and May 2018 were only observed in site 53 for denitrification (Figure 2D) and methanogenesis (Figure 2E), with the highest values measured in October 2017.

Microbial Community Structure

The mean bacterial abundance in the sample sediments ranged between 0.55 × 10⁶ to 8.03 × 10⁶ cells g⁻¹ dw (Figure 3A). A high spatial variability was observed in October 2017, when the bacterial community from site 78 exhibited the highest abundance (about 8.0 × 10⁶ cells g⁻¹ dw), which was about twice higher than the one measured in site 53, and threefold higher than those measured in sites 29 and 32. In these four sites, the bacterial abundance was reduced by about two- to fourfold between October 2017 and May 2018. In May 2018, abundances were more stable between the different sites, ranging from 0.55 × 10⁶ at site 36 to 2.80 × 10⁶ cells g⁻¹ dw at site 78.

High-throughput amplicon sequencing of the 16S rRNA gene revealed no temporal difference in the number of OTUs affiliated to the archaea and bacteria domains (Figure 3B) and the resulting Shannon-Wiener diversity index (Figure 3C) between October

2017 and May 2018. Based on these two parameters, communities from site 53, and to a lesser extent, from site 78, exhibited the lowest diversity, whereas these two parameters were relatively similar between the six other sites. According to the sampling date and the sample replicate, the number of OTUs varied from 503 to 626 in site 53, from 947 to 1048 in site 78, and from 1067 to 1311 in the six other sites. The mean Shannon-Wiener diversity index was close to 4.0 (±0.2) in site 53, 4.9 (±0.1) in site 78, and between 5.4 and 5.8 in the six other sites.

At the Phylum (or Class for Proteobacteria) level, the microbial community was dominated by members of the Gammaproteobacteria (25.2 ± 7.0% of the reads), followed by members of the Bacteroidetes (19.4 ± 6.9%), Deltaproteobacteria (13.1 ± 5.4%), Nitrospirae (9.3 ± 5.9%), and Acidobacteria (6.8 ± 1.9%) (Figure 4). Community composition at site 53 was especially dominated by members of the Bacteroidetes (32%), Deltaproteobacteria (22%) and Gammaproteobacteria (20%), whereas at site 78, community composition was dominated by members of the Gammaproteobacteria (27%), Cyanobacteria (24%) and Bacteroidetes (23%). Site 5 community composition was characterized by the most important proportion of members of the Gammaproteobacteria (42%). Members of the Nitrospirae were present at low abundances at sites 53 (0.30%), 78 (4.23%) and 5 (8.05%), whereas they ranged between 11% (sites 29 and 32) and 18% (site 36). Among the less abundant groups, Actinobacteria, Chloroflexi, Nitrospinae, and other Archaea and Bacteria did not exhibit any temporal or spatial trends. For the main archaeal Phylum, Crenarchaeota peaked up to 4% at site
<figure><img src="image_130.png" /><figcaption>**FIGURE 3** | Dot plots of the (A) bacterial abundance, (B) observed OTU richness, and (C) Shannon diversity index for the triplicate samples from the 8 studied sites in Lake Geneva in October 2017 (open symbols) and May 2018 (closed symbols). For each sampling site (and date if applicable – October 2017, italic letter and May 2018, roman letter), different letters indicate significant differences between treatments (*P* < 0.05, Tukey test).</figcaption></figure>

<figure><img src="image_131.png" /><figcaption>**FIGURE 4** | Microbial community composition for the triplicate samples from the 8 studied sites in Lake Geneva in October 2017 and May 2018. The 15 more abundant phylum (or class for Proteobacteria) are represented and the remaining phylum (or class for Proteobacteria) are grouped under "Other Archaea" or "Other Bacteria". Data is presented as relative abundance of the read counts.</figcaption></figure>

36, but remained below 1.5% at other sites, Euryarchaeota were generally below 2% except at site 53 where they represented 7% of the community in October 2017 and 3% in May 2018, and Thaumarchaeota represented less than 0.5% of the community at sites 5 and 53 but between 2 and 10% elsewhere. Finally, Verrumicrobia were below 1% in every samples but at site 53 where they accounted for 5 and 6% of the community in October 2017 and May 2018, respectively.

Metal and Antibiotic Resistance Potential

Two distinct sets of quantitative PCRs were performed to,
respectively, quantify two genes involved in microbial resistance

to metals (cusA and czcA, Figures 5A,B) and eight genes
conferring antibiotic resistance (sull, strA, strB, ermB, ermF,
aadA, and blaOXA20), and the class 1 integron integrase intI1
(Figures 5C-J).

Both metal resistance genes were detected in every site and
the two sampling dates (Figures 5A,B). The cusA gene had
the highest relative abundance with mean cusA to 16S rRNA
gene relative abundances varying between about 0.49 × 10⁻³ to
210 × 10⁻³ depending on the sampling site and date (Figure 5A).
No significant temporal variation was observed for this gene
between the two sampling dates with the exception of sites 29
and 32 where mean values were about twofold higher (p < 0.05)
<figure><img src="image_132.png" /><figcaption>**FIGURE 5** | Dotplots of the gene abundance ratio to 16S rRNA gene abundance of (A) *cusA*, (B) *czcA*, (C) *sul1*, (D) *strA*, (E) *strB*, (F) *intl1*, (G) *ermB*, (H) *ermF*, (I) *aadA*, and (J) *bla*<sub>OXA-20</sub> genes for the triplicate samples from the eight sites in Lake Geneva in October 2017 (open symbols) and May 2018 (closed symbols). For each sampling site (and date if applicable – October 2017, italic letter and May 2018, roman letter), different letters indicate significant differences between treatments (*P* < 0.05, Tukey test).</figcaption></figure>

in October 2017 than in May 2018. Spatial differences among the sites varied according to the sampling date. In October 2017, the relative abundance of *cusA* was significantly higher in sites 29 and 32 than in sites 53 and 78 (*p* < 0.05). In May 2018, the highest relative abundance was observed in site 5 (204 × 10<sup>-3</sup> ± 14 × 10<sup>-3</sup>) with values significantly higher than those recorded in sites 21, 32, 36, 53, and 78 (*p* < 0.05). The mean relative abundances of *czcA* to 16S rRNA varied between 1.42 × 10<sup>-3</sup> and 15.3 × 10<sup>-3</sup> with relatively high variations among replicates and no significant difference between sampling sites or between sampling dates (Figure 5B).

The eight genes for antibiotic resistance (Figures 5C-J) were less represented than *cusA* and *czcA*. The class 1 integron marker *intl1* was detected in all samples with the highest relative abundances with mean values varying between 98.7 × 10<sup>-6</sup> and 11.2 × 10<sup>-3</sup> (Figure 5F). The genes *sul1*, *strA*, and *aadA* (Figures 5C,D,I) were also detected in all the samples but their highest relative abundance value was lower than 1 × 10<sup>-3</sup>. The detection of *strB*, *ermB*, *ermF*, and *bla*<sub>OXA-20</sub> (Figures 5E,G,H,J) genes was variable according to sampling site, date and sample replicate, with mean relative abundances always lower than 1 × 10<sup>-4</sup>. In addition to the difference among the measured genes, the results showed a high spatial heterogeneity among the sampling sites. The general tendency showed that sediment microbial communities from site 53, and to a lesser extent those from site 78, were characterized by higher relative abundances of the eight genes than the communities from the six other sites (i.e., 5, 6, 21, 29, 32, and 36). Indeed, sediment microbial communities collected in site 53 were characterized by the highest relative abundances of seven of the eight measured genes. The only exception was for the gene *ermF*, which was about threefold more represented in site 78 than in site 53 (Figure 5H). This gene was under the detection limit in the other sampling sites. At site 53, the other seven genes relative abundances were about 3- to 28-fold more abundant than in site 78 and about 30- to

2000-fold more abundant than at the 6 other sites. Moreover, no significant difference in the relative abundance of the eight genes conferring antibiotic resistance was observed in those sites (i.e., 5, 6, 21, 29, 32, and 36) (Figures 5C-J). No temporal variation was observed between the two sampling dates with the exception of site 53 where relative abundances were significantly higher in October 2017 than in May 2018 for the genes *sul1* (Figure 5C), *strA* (Figure 5D), *strB* (Figure 5E), *intl1* (Figure 5F), and *aadA* (Figure 5I).

## Environmental Drivers of Microbial Structure and Functional-Based Parameters

The 2-dimensional representation of the procrustean analysis of 16S rRNA gene-based bacterial community structure (PCoA, the first 2 axes explained 74% of the variance) and functional-based community parameters (PCA, the first 2 axes explained 76% of the variance) (Figure 6) showed that sites 53 and 78 were distinct from each other, and from the six other sites (ANOSIM; *p* < 0.0001 for PCoA, and *p* < 0.01 for PCA), with very limited differences between the two sampling dates. Among these six sites, communities from site 5 were also slightly distinct both according to their structure and their functional parameters. The phylogenetic structure and the functional and resistance pattern of the communities displayed a highly significant level of concordance (Procrustean analysis, residual sum of squares = 0.340; m12 = 0.812; *p* < 0.001), as indicated by the relatively limited length of the colored arrows on the 2-dimension projection.

The sediment physico-chemical characteristics that were correlated with the microbial community ordinations (Figure 4 and Supplementary Table 4) were organic toxicants (PCB and PAH), phosphorus concentrations and median grain size, which were higher at site 53, some metals, which were high at sites 78
<figure><img src="image_133.png" /></figure>

**FIGURE 6** | Two-dimensional representation of the procrustean analysis of 16S rRNA gene-based bacterial community structure (PCoA analysis of Bray-Curtis distances, origin of colored arrows) and functional-based community parameters (PCA analysis of microbial activities, metals and antibiotic resistance gene relative abundances, arrival of colored arrows) for the triplicate samples from the 8 studied sites in Lake Geneva in October 2017 (empty symbols) and May 2018 (plain symbols). Black arrows are environmental variables showing significant correlations with the sample 2-dimensional projections.

(As and Cr) and 53 (sum of Co, Ni, Cu, Zn, Mo, Ag, Cd, Sn, Pb, and Hg concentrations) and carbonate, which was lower at sites 53 and 78 (Table 1). Site 78 microbial community ordination was also significantly correlated to Ti concentration.

DISCUSSION

This study investigated *in situ* the possible influence of the
anthropogenic contamination of Lake Geneva surface sediments
on benthic microbial communities by assessing spatiotemporal
changes in their structure, their functional potential and their
genetic potential for resistance to selected metals and antibiotics.
Our results provide evidence that the multiple contaminants
in sediments of Lake Geneva alter the diversity of microbial
communities with consequences for the important ecological
functions they provide. The magnitude of these effects depends
on the types and the concentrations of contaminants in the
sediments. All contaminant concentrations measured in the
present study are in agreement with previous findings (Haller
et al., 2011; Gascon Diez et al., 2014; Masson and Tercier-
Waeber, 2014; Gascón Díez et al., 2017; Loizeau et al., 2017). In
addition, our results also revealed high concentrations of PAH
(>13.5 µg kg⁻¹ dw) in sediments collected at site 36. Such a
contamination was not reported in Loizeau et al. (2017) because
organic toxicants (PAH and PCB) were not analyzed in this
study site (and also not in sites 5 and 21). Our results suggest
that the multi-contamination (PAH, PCB, metals, phosphorus,
organic matter, and possibly microbial and genetic material) of

sediments observed in the Vidy Bay (site 53) is a strong driver
of sediment microbial community structure and diversity, the
latter being significantly lower than that at other sites Some
phyla were significantly less represented in the Vidy Bay, such
as Planctomycetes, the *Nitrospira* genus from the phylum of
Nitrospirae, whereas Euryarchaeota were mainly detected in this
highly contaminated site. This archaeal phylum includes both the
archaeal sulfide reducers as well as the methanogens (Chrencik
and Marsh, 2012). Potential methanogenesis activity was only
detected at this site, consistent with the observed abundance
of methanogenic archeae, belonging to the *Methanobacterium*,
*Methanolinea*, *Methanoregula*, *Methanosaeta*, *Methanosarcina*
genera and to the *Methanomassiliicoccaceae* family.

In the present study, the Verrucomicrobia phylum was
also more represented in the Vidy Bay than in the other
sites, in contrast to previous findings (Haller et al., 2011).
However, this is in line with other observations where this
phylum was more abundant in very urbanized rivers than in
suburban rivers (Lin et al., 2019). The urban contamination
is also probably responsible for the observed higher relative
abundance of members of the Bacteroidetes phylum, which
includes bacteria from the human and animal gastrointestinal
tract, frequently detected in wastewater, and generally considered
as fecal indicators in the environment (Gomez-Donate et al.,
2016; Su et al., 2017; Niestępski et al., 2020). In the Vidy Bay,
fecal indicator bacteria and other activated sludge bacteria from
local WWTP inputs were previously detected (Haller et al.,
2009; Sauvain et al., 2014). Bacteroidetes are known to harbor
a large variety of metal- or antibiotic-resistance genes that can
favor their establishment and development in contaminated
sediments (Chen et al., 2018; Niestępski et al., 2020). The
higher abundance of gene markers associated with resistance
to various classes of antibiotics (*sul1*, *strA*, *strB*, *ermB*, *ermF*,
and *bla*<sub>OXA-20</sub>) and of the class 1 integron (*intI1*) detected
in the Vidy Bay sediment is in accordance with the high
occurrence of multiresistant bacteria and antibiotic-resistance
genes previously reported at this site (Czekalski et al., 2012, 2014).
Reporting in 2011 a 200-fold increase of the relative abundances
of six antibiotic-resistance genes (including *sul1*) as compared
to sediment sampled in the center of the lake, Czekalski et al.
(2014) hypothesized that the increasing abundance of antibiotic-
resistance genes was mainly due to direct inputs from wastewater
effluents rather than *in situ* development of antibiotic-resistant
bacteria in the lake sediment following their chronic exposure
to contaminants. In the present study, given the high metal
concentrations observed in the surface sediment from the Vidy
Bay, the hypothesis of co-selection of antibiotic resistance by
this kind of contaminant cannot be excluded (Poole, 2017; Silva
et al., 2021). Although pharmaceutical residues in the collected
sediments were not analyzed it is more than probable that
site 53 sediment were contaminated by antibiotics (Morasch
et al., 2010; Bonvin et al., 2011, 2013), potentially favoring the
development of antibiotic resistance in sediment communities in
response to antibiotic selection pressure (Bengtsson-Palme and
Larsson, 2016). In contrast, the analysis of the metal-resistance
genes *cusA* (involved in copper resistance) and *czcA* (involved
in cobalt-zinc-cadmium resistance) did not show higher relative
abundances in the Vidy Bay, even if this site had the highest concentrations of Cu (about 281 mg kg⁻¹ dw), Zn (about 645 mg kg⁻¹ dw) and Cd (about 1.5 mg kg⁻¹ dw), which were 3- to 12-fold higher than the contamination with these three metals in the seven other stations. Significant increases in the relative abundance of the *cusA* and *czcA* genes in contaminated sediments compared to uncontaminated ones has been previously reported by Besaury et al. (2013) and Roosa et al. (2014). In both cases, however, the level of metal contamination was very high (total Cu > 1400 mg kg⁻¹, Besaury et al., 2013; total Zn > 3000 mg kg⁻¹ and Cd > 35 mg kg⁻¹, Roosa et al., 2014), as was the potential metal bioavailability. Altogether, these results questions whether the qPCR quantification of these metal resistance genes is sensitive enough when contamination gradients are weak, or whether metal bioavailability in the Vidy Bay is poor and causes limited selection pressure.

More surprisingly, a notable and sometimes significant increase in the relative abundance of antibiotic-resistance genes was observed in the site 78 in comparison to the other sites other than the Vidy Bay one. This was especially the case for the *ermB* and *ermF* genes, responsible for erythromycin resistance, which were only (*ermF*) or mainly (*ermB*) detected in site 53 and 78. It cannot be excluded that the surface sediment from the site 78 has been directly contaminated by antibiotic-resistant bacteria and/or antibiotics coming from the upper section of the Rhône River, which has been shown to be an important source of contamination of Lake Geneva with pharmaceutical substances (Loizeau et al., 2013). In this site, benthic microbial richness and diversity index was impacted, being 10 to 20% lower than those observed in the other sites (excluding 53) and community composition was also affected. The relative abundance of Bacteroidetes members could also confirm inputs from sewage water located in the upper Rhône section (see related discussion above for site 53). Cyanobacteria were also significantly more represented at site 78 than the other sites, showing that this kind of microorganisms was particularly adapted to the local site characteristics. Given the water column height at this site and associated limited light penetration, it is unlikely that these microorganisms are part of a microbial mat developing at the sediment - water interface, but nutrients flowing in from the Rhône River might promote their planktonic growth following which they are deposited into sediment. Previous studies (Loizeau et al., 2012; Silva et al., 2019) found that sediment accumulation was 5- to 10-fold more rapid at this site compared to other areas of the lake.

Despite their geographical distance and their notable differences in depth and other physico-chemical characteristics, such as total organic carbon or grain size, the sediment communities collected in the less contaminated sites (i.e., 5, 6, 21, 29, and 32) and at site 36, only contaminated by high concentrations of PAH, exhibited relatively comparable structural and functional properties. All these sampling sites (as well as sites 53 and 78) had a depth below 100 m out of the 309 m of maximal depth for Lake Geneva. Extrapolation of the probe sensor data collected for the environmental survey of Lake Geneva at the deepest site indicated that water temperature at the sediment interface varied between 6 and 14°C in October

2017 and was about 6-7°C in Mai 2018, and that dissolved oxygen concentrations increased from between 6 and 8 mg L⁻¹ in October 2017 and between 9 and 12 mg L⁻¹ in May 2018 (Rimet et al., 2020), because of the first 201 m water column partial turnover on March 6, 2018 (CIPEL, 2019). Even if this data should be interpreted with care since it might not perfectly reflect the local conditions prevailing at sampling sites, whatever the measured parameter, no significant difference was observed between the sediment microbial communities collected in the shallowest (22 m; site 32) and deepest (95 m, site 29) sites. As revealed by the multivariate analysis (Figure 6), it confirms that depth, and indirectly related environmental parameters such as dissolved oxygen, light intensity, temperature, and nutrient availability (Böer et al., 2009), was not a major driver of benthic microbial community structure and functions in Lake Geneva, in contrast to other studies that linked microbial community structure changes with water depth (Edlund et al., 2006; Hewson et al., 2007; Zhang et al., 2014; Ding et al., 2015; Ruuskanen et al., 2018; Wu et al., 2019). The only observed differences between sites 5, 6, 21, 29, 32, and 36 concerned the enzymatic activities (β-glu, LAP, and Pase), which were higher at site 5, and to a lesser extent, at site 6. This difference could be explained, at least partly, by a higher amount of available substrates, as suggested by the higher organic matter and total organic carbon contents measured in these samples. Based on these results, it can be argued that the contamination of the sediment with high concentrations of PAH at site 36 (>13.5 mg ΣPAH kg⁻¹ dw) exerts no or very limited effects on the functional potential, the structure and the composition of the microbial communities, which were almost similar to those from sites characterized by 6- to 12-fold lower PAH concentrations. The limited effects of chronic PAH contamination on sediment microbial community diversity and functions has likewise been observed in coastal ecosystems (Hernandez-Raquet et al., 2006; Païssé et al., 2008; Zhang et al., 2008; Jeanbille et al., 2016), in contrast to the more important influence of other contaminants such as metals (Sun et al., 2012, 2013). The small effect of PAHs could be due to their limited toxicity (Païssé et al., 2010) and/or their limited bioavailability because of their ready sorption to sediments (Kraaij et al., 2002; Ghosh et al., 2003).

Despite the evident negative effects of the anthropogenic contamination on the diversity of sediment microbial communities of sites 53 and 78, no inhibition in their functional potential was observed. On the contrary, communities from site 53 exhibited the highest potential for most of the measured activities (i.e., β-glu, Pase, aerobic respiration, denitrification and methanogenesis). It suggests that the observed shifts in their community composition reflects a long-term selection of benthic microbial species that are strongly adapted to their environment, allowing them to benefit from the regular inputs of nutrients and organic substrates that can be used as energy sources, thus making them key players in benthic biogeochemical cycles (Huang et al., 2019, Steinsberger et al., 2019). This long-term adaptation hypothesis can be supported by the very limited temporal variations that were observed in the present study between the two sampling dates. Indeed, at the four sites sampled twice (i.e., 29, 32, 53, and 78), sediment community diversity and
composition were very similar in October 2017 and May 2018, as illustrated in the multivariate analysis (Figure 6). It suggests, that, the structural characteristics of the surface sediment microbial communities of Lake Geneva are relatively stable over time, unlike the case for shallower lakes (Peng et al., 2017; Wan et al., 2017; Zhang et al., 2019).

## CONCLUSION

Using a whole lake approach, the present study highlighted that surface sediment microbial communities were impacted by anthropogenic load of contaminants including toxicants, organic matter, nutrients, and microorganisms in wastewater discharge affecting their diversity, enzymatic and metabolic functional potential as well as their genetic potential resistance to antimicrobial agents. These forcing factors appeared to yield to a more important structuration of the microbial parameters studied than the classical environmental factors generally controlling microbial diversity in lakes.

Given the ecological role of microbial communities in lake sediments, further research to better understand the consequences of anthropogenic contamination on microbial communities should focus on larger scale approaches, including more sites to cover a finer gradient of contamination in order to distinguish the relative importance of the different type of contaminants, integrating the full range of water depths from the littoral to the deeper areas, and considering a more important depth of the sediment compartment to take into account the full range of metabolic activities carried out. Together this would improve the ecotoxicological assessment of sediments in lake ecosystems to understand the consequences of multiple stress at the community level on microbial key activities and functions.

## DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in the NCBI at https://www.ncbi.nlm.nih.gov/sra/PRJNA742879.

## REFERENCES

* Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990). Basic local alignment search tool. *J. Mol. Biol.* 215, 403–410. doi: 10.1016/S0022-2836(05)80360-2

* Bedard, D. L. (2008). A case study for microbial biodegradation: anaerobic bacterial reductive dechlorination of polychlorinated biphenyls – from sediment to defined medium. *Annu. Rev. Microbiol.* 62, 253–270. doi: 10.1146/annurev.micro.62.081307.162733

* Bengtsson-Palme, J., and Larsson, D. G. (2016). Concentrations of antibiotics predicted to select for resistant bacteria: proposed limits for environmental regulation. *Environ. Int.* 86, 140–149. doi: 10.1016/j.envint.2015.10.015

* Besaury, L., Bodilis, J., Delgas, F., Andrade, S., De la Iglesia, R., Ouddane, B., et al. (2013). Abundance and diversity of copper resistance genes *cusA* and *copA* in microbial communities in relation to the impact of copper on Chilean marine sediments. *Mar. Pollut. Bull.* 67, 16–25. doi: 10.1016/j.marpolbul.2012.12.007

## AUTHOR CONTRIBUTIONS

BF and SP conceived and designed the study. EL, J-LL, BF, and SP organized and performed the sampling. EL, CB, PB, AT, and ET were responsible for the microbial analyses. J-LL, EN, and BF were responsible for the physico-chemical analyses. EL conducted the bioinformatic and biostatistical analyses. EL and SP drafted the first version of the manuscript. All co-authors analyzed and interpreted the data, contributed to subsequent revisions to the manuscript, and approved its final submitted version.

## FUNDING

This study was partly funded by a grant of the Auvergne-Rhône-Alpes Region (CMIRA COOPERA 2016–2018 Project) and by the European Cross-Border Cooperation Program (Interreg France-Switzerland 2014–2020, SYNAQUA).

## ACKNOWLEDGMENTS

We are grateful to all the staff who have contributed to the sampling campaigns: the boat skippers, Philippe Arpagaus and Jean-Christophe Hustache, and the colleagues who collected and prepared the samples, Anaïs Charton, Christina Lüthi, Bernard Motte, Amélie Roinat, Régis Vivien, and Sandrine Vix. We thank all the persons who realized microbial and physico-chemical analyses: Anaïs Charton, Bernard Motte, Christophe Rosy, Bernadette Volat, Bettina Wagner, and Yuan-Ching Tien. We also thank OLA-IS, AnaEE-France, INRAE of Thonon-les-Bains, CIPEL for providing probe data at site SHL2.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.738629/full#supplementary-material

* Böer, S. I., Hedtkamp, S. I. C., Van Beusekom, J. E. E., Fuhrman, J. A., Boetius, A., and Ramette, A. (2009). Time- and sediment depth-related variations in bacterial diversity and community structure in subtidal sands. *ISME J.* 3, 780–791. doi: 10.1038/ismej.2009.29

* Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. *Bioinformatics* 30, 2114–2120. doi: 10.1093/bioinformatics/btu170

* Bonvin, F., Razmi, A. M., Barry, D. A., and Kohn, T. (2013). A coupled hydrodynamic-photolysis model to simulate the fate of micropollutants in Vidy Bay. *Environ. Sci. Technol.* 47, 9207–9216. doi: 10.1021/es401294c

* Bonvin, F., Rutler, R., Chèvre, N., Halder, J., and Kohn, T. (2011). Spatial and temporal presence of a wastewater-derived micropollutant plume in Lake Geneva. *Environ. Sci. Technol.* 45, 4702–4709. doi: 10.1021/es2003588

* Burrus, D., Thomas, R. L., Dominik, B., Vernet, J.-P., and Dominik, J. (1990). Characteristics of suspended sediment in the upper Rhone River, Switzerland, including the particulate forms of phosphorus. *Hydrol. Process.* 4, 85–92. doi: 10.1002/hyp.3360040108
Cébron, A., Norini, M. P., Beguiristain, T., and Leyval, C. (2008). Real-Time PCR quantification of PAH-ring hydroxylating dioxygenase (PAH-RHDA) genes from Gram positive and Gram negative bacteria in soil and sediment samples. *J. Microbiol. Methods* 73, 148–159. doi: 10.1016/j.mimet.2008.01.009

Chen, Y., Jiang, Y., Huang, H., and Mou, L. (2018). Long-term and high-concentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments. *Sci. Total Environ.* 637–638, 1400–1412. doi: 10.1016/j.scitotenv.2018.05.109

Chrencik, B. J., and Marsh, T. L. (2012). "Contributions of methanogenic Archaebacteria in community-driven anaerobic chromate reduction by Yellowstone National Park hot spring microorganisms," in *Microbes in Applied Research: Current Advances and Challenges*, ed. A. Méndez-Vilas (Singapore: World Scientific), 60–64. doi: 10.1142/9789814405041_0012

CIPEL (2018). *Rapports sur les études et Recherches Entreprises dans le Bassin Lémanique, Campagne 2017*. Nyon: Commission Internationale Pour la Protection des Eaux du Léman, 196.

CIPEL (2019). *Rapports sur les études et Recherches Entreprises dans le Bassin Lémanique, Campagne 2018*. Nyon: Commission internationale pour la protection des eaux du Léman, 305.

Codling, G., Sturchio, N. C., Rockne, K. J., Li, A., Peng, H., Tse, T. J., et al. (2018). Spatial and temporal trends in poly- and per-fluorinated compounds in the Laurentian Great Lakes Erie, Ontario and St. Clair. *Environ. Pollut.* 237, 396–405. doi: 10.1016/j.envpol.2018.02.013

Conover, W. J., Johnson, M. E., and Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. *Am. Stat.* 35, 351–361. doi: 10.2307/1268225

Czekalski, N., Berthold, T., Caucci, S., Egl, A., and Bürgmann, H. (2012). Increased levels of multiresistant bacteria and resistance genes after wastewater treatment and their dissemination into Lake Geneva, Switzerland. *Front. Microbiol.* 3:106. doi: 10.3389/fmicb.2012.00106

Czekalski, N., Gascón Díez, E., and Bürgmann, H. (2014). Wastewater as a point source of antibiotic-resistance genes in the sediment of a freshwater lake. *ISME J.* 8, 1381–1390. doi: 10.1038/ismej.2014.8

Dean, W. E. (1974). Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition; comparison with other methods. *J. Sediment. Res.* 44, 242–248. doi: 10.1306/74D729D2-D2B21-11D7-8648000102C1865D

Diepens, N. J., Dimitrov, M. R., Koelmans, A. A., and Smidt, H. (2015). Molecular assessment of bacterial community dynamics and functional end points during sediment bioaccumulation tests. *Environ. Sci. Technol.* 49, 13586–13595. doi: 10.1021/acs.est.5b02992

Ding, X., Peng, X. J., Jin, B. S., Xiao, M., Chen, J. K., Li, B., et al. (2015). Spatial distribution of bacterial communities driven by multiple environmental factors in a beach wetland of the largest freshwater lake in China. *Front. Microbiol.* 6:129. doi: 10.3389/fmicb.2015.00129

Edlund, A., Soule, T., Sjöling, S., and Jansson, J. K. (2006). Microbial community structure in polluted Baltic Sea sediments. *Environ. Microbiol.* 8, 223–232. doi: 10.1111/j.1462-2920.2005.00887.x

Escudié, F., Auer, L., Bernard, M., Mariadassou, M., Cauquil, L., Vidal, K., et al. (2018). FROGS: find, rapidly, OTUs with Galaxy solution. *Bioinformatics* 34, 1287–1294. doi: 10.1093/bioinformatics/btx791

Foulquier, A., Volat, B., Neyra, M., Bornette, G., and Montuelle, B. (2013). Long-term impact of hydrological regime on structure and functions of microbial communities in riverine wetland sediments. *FEMS Microbiol. Ecol.* 85, 211–226. doi: 10.1111/1574-6941.12112

Frossard, A., Gerull, L., Mutz, M., and Gessner, M. O. (2012). Disconnect of microbial structure and function: enzyme activities and bacterial communities in nascent stream corridors. *ISME J.* 6, 680–691. doi: 10.1038/ismej.2011.134

Gascon Díez, E., Bravo, A. G., à Porta, N., Masson, M., Graham, N. D., Stoll, S., et al. (2014). Influence of a wastewater treatment plant on mercury contamination and sediment characteristics in Vidy Bay (Lake Geneva, Switzerland). *Aquat. Sci.* 76, 21–32. doi: 10.1007/s00027-013-0325-4

Gascón Díez, E., Corella, J. P., Adatte, T., Thevenon, F., and Loizeau, J.-L. (2017). High-resolution reconstruction of the 20th century history of trace metals, major elements, and organic matter in sediments in a contaminated area of Lake Geneva, Switzerland. *Appl. Geochem.* 78, 1–11. doi: 10.1016/j.apgeochem.2016.12.007

Ghosh, U., Zimmerman, J. R., and Luthy, R. G. (2003). PCB and PAH speciation among particle types in contaminated harbor sediments and effects on PAH bioavailability. *Environ. Sci. Technol.* 37, 2209–2217. doi: 10.1021/es020833k

Golovko, O., Rehrl, A. L., Köhler, S., and Ahrens, L. (2020). Organic micropollutants in water and sediment from Lake Målaren, Sweden. *Chemosphere* 258:127293. doi: 10.1016/j.chemosphere.2020.127293

Gomez-Donate, M., Casanovas-Massana, A., Muniesa, M., and Blanch, A. R. (2016). Development of new host-specific *Bacteroides* qPCRs for the identification of fecal contamination sources in water. *Microbiologyopen* 5, 83–94. doi: 10.1002/mbo.313

Grandjean, D., De Alencastro, L. F., and Tarradellas, J. (1990). Micropollutants organiques dans les effluents des stations d'épuration, les eaux du Rhône et les eaux du Léman. *Rapp. Comm. int prot. eaux Léman contre pollut. Campagne* 1989, 199–212.

Haglund, A. L., Lantz, P., Törnblom, E., and Tranvik, L. (2003). Depth distribution of active bacteria and bacterial activity in lake sediment. *FEMS Microbiol. Ecol.* 46, 31–38. doi: 10.1016/S0168-6496(03)00190-9

Haller, L., Poté, J., Loizeau, J.-L., and Wildi, W. (2009). Distribution and survival of faecal pathogen bacteria in the sediments of the Bay of Vidy, Lake Geneva, Switzerland. *Ecol. Indic.* 9, 540–547. doi: 10.1016/j.ecolind.2008.08.001

Haller, L., Tonolla, M., Zopfi, J., Peduzzi, R., Wildi, W., and Poté, J. (2011). Composition of bacterial and archaeal communities in freshwater sediments with different contamination levels (Lake Geneva, Switzerland). *Water Res.* 45, 1213–1228. doi: 10.1016/j.watres.2010.11.018

Harwood, J. E., Van Steenderen, R. A., and Kühn, A. L. (1969). A comparison of some methods for total phosphate analyses. *Water Res.* 3, 425–432. doi: 10.1016/0043-1354(69)90004-9

Heino, J., Louhi, P., and Muotka, T. (2004). Identifying the scales of variability in stream macroinvertebrate abundance, functional composition and assemblage structure. *Freshw. Biol.* 49, 1230–1239. doi: 10.1111/j.1365-2427.2004.01259.x

Hernandez-Raquet, G., Budzinski, H., Caumette, P., Dabert, P., Le Ménach, K., Muyzer, G., et al. (2006). Molecular diversity studies of bacterial communities of oil polluted microbial mats from the Etang de Berre (France). *FEMS Microbiol. Ecol.* 58, 550–562. doi: 10.1111/j.1574-6941.2006.00187.x

Hewson, I., Jacobson-Meyers, M. E., and Fuhrman, J. A. (2007). Diversity and biogeography of bacterial assemblages in surface sediments across the San Pedro Basin, Southern California Borderlands. *Environ. Microbiol.* 9, 923–933. doi: 10.1111/j.1462-2920.2006.01214.x

Hoerger, C. C., Akhtman, Y., Martelletti, L., Rutler, R., Bonvin, F., Grange, A., et al. (2014). Spatial extent and ecotoxicological risk assessment of a micropollutant-contaminated wastewater plume in Lake Geneva. *Aquat. Sci.* 76, 7–19. doi: 10.1007/s00027-013-0315-6

Huang, C., Chen, Z., Gao, Y., Luo, Y., Huang, T., Zhu, A., et al. (2019). Enhanced mineralization of sedimentary organic carbon induced by excess carbon from phytoplankton in a eutrophic plateau lake. *J. Soils Sediments* 19, 2613–2623. doi: 10.1007/s11368-019-02261-2

Jackson, D., and Harvey, H. (1993). Fish and benthic invertebrates: community concordance and community-environment relationships. *Can. J. Fish. Aquat. Sci.* 50, 2641–2651. doi: 10.1139/f93-287

Jeanbille, M., Gury, J., Duran, R., Tronczynski, J., Ghiglione, J. F., Agogue, H., et al. (2016). Chronic polyaromatic hydrocarbon (PAH) contamination is a marginal driver for community diversity and prokaryotic predicted functioning in coastal sediments. *Front. Microbiol.* 7:1303. doi: 10.3389/fmicb.2016.01303

Jin, X., Ma, Y., Kong, Z., Kou, W., and Wu, L. (2019). The variation of sediment bacterial community in response to anthropogenic disturbances of Poyang Lake, China. *Wetlands* 39, 63–73. doi: 10.1007/s13157-017-0909-1

Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., and Schloss, P. D. (2013). Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the misec illumina sequencing platform. *Appl. Environ. Microbiol.* 79, 5112–5120. doi: 10.1128/AEM.01043-13

Kraaij, R., Seinen, W., Tolls, J., Cornelissen, G., and Belfroid, A. C. (2002). Direct evidence of sequestration in sediments affecting the bioavailability of hydrophobic organic chemicals to benthic deposit-feeders. *Environ. Sci. Technol.* 36, 3525–3529. doi: 10.1021/es0102787

Krzmarzick, M. J., McNamara, P. J., Crary, B. B., and Novak, P. J. (2013). Abundance and diversity of organohalide-respiring bacteria in lake sediments across a geographical sulfur gradient. *FEMS Microbiol. Ecol.* 84, 248–258. doi: 10.1111/1574-6941.12059
Larras, F., Rimet, F., Gregorio, V., Bérard, A., Leboulanger, C., Montuelle, B., et al. (2016). Pollution-induced community tolerance (PICT) as a tool for monitoring Lake Geneva long-term in situ ecotoxic restoration from herbicide contamination. *Environ. Sci. Pollut. Res.* 23, 4301–4311. doi: 10.1007/s11356-015-5302-0

Lau, C. H., Tien, Y. C., Stedtfeld, R. D., and Topp, E. (2020). Impacts of multi-year field exposure of agricultural soil to macrolide antibiotics on the abundance of antibiotic resistance genes and selected mobile genetic elements. *Sci. Total Environ.* 727:138520. doi: 10.1016/j.scitotenv.2020.138520

Lécrivain, N., Aurenche, V., Cottin, N., Frossard, V., and Clément, B. (2018). Multi-contamination (heavy metals, polychlorinated biphenyls and polycyclic aromatic hydrocarbons) of littoral sediments and the associated ecological risk assessment in a large lake in France (Lake Bourget). *Sci. Total Environ.* 619–620, 854–865. doi: 10.1016/j.scitotenv.2017.11.151

Lécrivain, N., Frossard, V., Naffrechoux, E., and Clément, B. (2019). Looking at organic pollutants (OPs) signatures in littoral sediments to assess the influence of a local urban source at the whole lake scale. *Polycycl. Aromat. Compd.* 41, 950–962. doi: 10.1080/10406638.2019.1631195

Lin, X., Gao, D., Lu, K., and Li, X. (2019). Bacterial community shifts driven by nitrogen pollution in river sediments of a highly urbanized city. *Int. J. Environ. Res. Public Health* 16:3794. doi: 10.3390/ijerph16203794

Loizeau, J.-L., Arbouille, D., Santiago, S., and Vernet, J.-P. (1994). Evaluation of a wide-range laser diffraction grain-size analyzer for use with sediments. *Sedimentology* 41, 353–361. doi: 10.1111/j.1365-3091.1994.tb01410.x

Loizeau, J.-L., Edder, P., De Alencastro, L. F., Corvi, C., and Ramseier Gentile, S. (2013). Lake Geneva pollution by micropollutants – a review of 40 years of study [La contamination du Léman par les micropollutants Revue de 40 ans d'études]. *Arch. Sci.* 66, 117–136.

Loizeau, J.-L., Girardclos, S., and Dominik, J. (2012). Taux d'accumulation de sédiments récents et bilan de la matière particulaire dans le Léman (Suisse-France). *Arch. Sci.* 65, 81–92.

Loizeau, J. L., Makri, S., Arpagaus, P., Ferrari, B., Casado-Martinez, C., Benejam, T., et al. (2017). Micropollutants métalliques et organiques dans les sediments superficiel du Léman. Metallic and organic micropollutants in the surface sediments of Lake Geneva. *Rapp. Comm. Int. Prot. Eaux Léman contre Pollut. Campagne* 2016, 153–207. pdf,

Magoè, T., and Salzberg, S. L. (2011). FLASH: fast length adjustment of short reads to improve genome assemblies. *Bioinformatics* 27, 2957–2963. doi: 10.1093/bioinformatics/btr507

Mahamoud Ahmed, A., Lyauyte, E., Bonnineau, C., Dabrin, A., and Pesce, S. (2018). Environmental concentrations of copper, alone or in mixture with arsenic, can impact river sediment microbial community structure and functions. *Front. Microbiol.* 9:1852. doi: 10.3389/fmicb.2018.01852

Mahamoud Ahmed, A., Tardy, V., Bonnineau, C., Billard, P., Pesce, S., and Lyauyte, E. (2020). Changes in sediment microbial diversity following chronic copper-exposure induce community copper-tolerance without increasing sensitivity to arsenic. *J. Hazard. Mater.* 391:122197. doi: 10.1016/j.jhazmat.2020.122197

Mahé, F., Rognes, T., Quince, C., de Vargas, C., and Dunthorn, M. (2015). Swarm v2: highly scalable and high-resolution amplicon clustering. *Peer J.* 3:e1420. doi: 10.7717/peerj.1420

Marvin, C., Painter, S., Williams, D., Richardson, V., Rossmann, R., and Van Hoof, P. (2004). Spatial and temporal trends in surface water and sediment contamination in the Laurentian Great Lakes. *Environ. Pollut.* 129, 131–144. doi: 10.1016/j.envpol.2003.09.029

Masson, M., and Tercier-Waeber, M.-L. (2014). Trace metal speciation at the sediment-water interface of Vidy Bay: influence of contrasting sediment characteristics. *Aquat. Sci.* 76, 47–58. doi: 10.1007/s00027-013-0323-6

Morasch, B., Bonvin, F., Reiser, H., Grandjean, D., de Alencastro, L. F., Perazzolo, C., et al. (2010). Occurrence and fate of micropollutants in the Vidy Bay of Lake Geneva, Switzerland. Part II: micropollutant removal between wastewater and raw drinking water. *Environ. Toxicol. Chem.* 29, 1658–1668. doi: 10.1002/etc.222

Murphy, J., and Riley, J. P. (1962). A modified single solution method for the determination of phosphate in natural waters. *Anal. Chim. Acta* 27, 31–36. doi: 10.1016/S0003-2670(00)88444-5

Mwanamoki, P., Devarajan, N., Thevenon, F., Birane, N., De Alencastro, L., Grandjean, D., et al. (2014). Trace metals and persistent organic pollutants in sediments from river-reservoir systems in Democratic Republic of Congo

(DRC): spatial distribution and potential ecotoxicological effects. *Chemosphere* 111, 485–492. doi: 10.1016/j.chemosphere.2014.04.083

Niestępski, S., Harnisz, M., Ciesielski, S., Korzeniewska, E., and Osińska, A. (2020). Environmental fate of Bacteroidetes, with particular emphasis on *Bacteroides fragilis* group bacteria and their specific antibiotic resistance genes, in activated sludge wastewater treatment plants. *J. Hazard. Mater.* 394:122544. doi: 10.1016/j.jhazmat.2020.122544

Office Fédéral de l'environnement [OFEV] (1998). *Ordonnance du 1er Juillet 1998 sur les Atteintes Portées aux Sols (OSol).* N° RS 814.12. Berne: OFEV.

Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., McGlin, D., Minchin, P. R., et al. (2020). *vegan: Community Ecology Package. R Package Version 2.5-7.* Available online at: https://CRAN.R-project.org/package=vegan

Paavola, R., Muotka, T., Virtanen, R., Heino, J., Jackson, D., and Maki-Petäys, A. (2006). Spatial scale affects community concordance among fishes, benthic macroinvertebrates, and bryophytes in streams. *Ecol. Appl.* 16, 368–379. doi: 10.1890/03-5410

Paissé, S., Coulon, F., Goïn-Urriza, M., Peperzak, L., McGenity, T. J., and Duran, R. (2008). Structure of bacterial communities along a hydrocarbon contamination gradient in a coastal sediment. *FEMS Microbiol. Ecol.* 66, 295–305. doi: 10.1111/j.1574-6941.2008.00589.x

Paissé, S., Goïn-Urriza, M., Coulon, F., and Duran, R. (2010). How a bacterial community originating from a contaminated coastal sediment responds to an oil input. *Microb. Ecol.* 60, 394–405. doi: 10.1007/s00248-010-9721-7

Pearman, J. K., Biessy, L., Thomson-Laing, G., Waters, S., Vandergoes, M. J., Howarth, J. D., et al. (2020). Local factors drive bacterial and microeukaryotic community composition in lake surface sediment collected across an altitudinal gradient. *FEMS Microbiol. Ecol.* 96:fiaa070. doi: 10.1093/femsec/fiaa070

Peng, Y., Yue, D., Xiao, L., and Qian, X. (2017). Temporal variation and co-occurrence patterns of bacterial communities in eutrophic Lake Taihu, China. *Geomicrobiol. J.* 35, 186–197. doi: 10.1080/01490451.2017.1348406

Perazzolo, C., Morasch, B., Kohn, T., Magnet, A., Thonney, D., and Chèvre, N. (2010). Occurrence and fate of micropollutants in the Vidy Bay of Lake Geneva, Switzerland. Part I: priority list for environmental risk assessment of pharmaceuticals. *Environ. Toxicol. Chem.* 29, 1649–1657. doi: 10.1002/etc.221

Peres-Neto, P. R., and Jackson, D. A. (2001). How well do multivariate data sets match? Evaluating the association of multivariate biological data sets: comparing the robustness of Mantel test and a Procrustean superimposition approach. *Oecologia* 129, 169–178. doi: 10.1007/s004420100720

Pesce, S., Perceval, O., Bonnineau, C., Casado-Martinez, C., Dabrin, A., Lyauyte, E., et al. (2018). Looking at biological community level to improve ecotoxicological assessment of freshwater sediments: report on a first French-Swiss workshop. *Environ. Sci. Pollut. Res.* 25, 970–974. doi: 10.1007/s11356-017-0620-z

Poole, K. (2017). At the nexus of antibiotics and metals: the impact of Cu and Zn on antibiotic activity and resistance. *Trends Microbiol.* 25, 820–832. doi: 10.1016/j.tim.2017.04.010

Poté, J., Haller, L., Loizeau, J. L., Bravo, A. G., Sastre, V., and Wildi, W. (2008). Effects of a sewage treatment plant outlet pipe extension on the distribution of contaminants in the sediments of the Bay of Vidy, Lake Geneva, Switzerland. *Biorens. Technol.* 99, 7122–7131. doi: 10.1016/j.biortech.2007.12.075

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2013). The SILVA ribosomal RNA gene database project: improved data processing and webbased tools. *Nucleic Acids Res.* 41, 590–596.

R Core Team (2018). *R: A Language and Environment for Statistical Computing*. Vienna: R Foundation for Statistical Computing.

Rimet, F., Anneville, O., Barbet, D., Chardon, C., Crépin, L., Domaizon, I., et al. (2020). The Observatory on LAkes (OLA) database: sixty years of environmental data accessible to the public: the Observatory on LAkes (OLA) database. *J. Limnol.* 79, 164–178. doi: 10.4081/jlimnol.2020.1944

Rognes, T., Flouri, T., Nichols, B., Quince, C., and Mahé, F. (2016). VSEARCH: a versatile open source tool for metagenomics. *Peer J.* 4:e2584. doi: 10.7717/peerj.2584

Roosa, S., Wattiez, R., Prygiel, E., Lesven, L., Billon, G., and Gillan, D. C. (2014). Bacterial metal resistance genes and metal bioavailability in contaminated sediments. *Environ. Pollut.* 189, 143–151. doi: 10.1016/j.envpol.2014.02.031

Roos-Baraclough, F., Givelet, N., Martinez-Cortizas, A., Goodsite, M. E., Biester, H., and Shotyk, W. (2002). An analytical protocol for the determination of total
mercury concentrations in solid peat samples. *Sci. Total Environ.* 292, 129–139. doi: 10.1016/s0048-9697(02)00035-9

Royston, J. P. (1982). An extension of Shapiro and Wilk's W test for normality to large samples. *J. R. Stat. Soc. C Appl.* 31, 115–124. doi: 10.2307/2347973

Ruuskanen, M. O., St Pierre, K. A., St Louis, V. L., Aris-Brosou, S., and Poulain, A. J. (2018). Physicochemical drivers of microbial community structure in sediments of Lake Hazen, Nunavut, Canada. *Front. Microbiol.* 9:1138. doi: 10.3389/fmicb. 2018.01138

Sauvain, L., Bueche, M., Junier, T., Masson, M., Wunderlin, T., Kohler-Milleret, R., et al. (2014). Bacterial communities in trace metal contaminated lake sediments are dominated by endospore-forming bacteria. *Aquat. Sci.* 76, 33–46. doi: 10. 1007/s00027-013-0313-8

Schallenberg, M., and Kalff, J. (1993). The ecology of sediment bacteria in lakes and comparisons with other aquatic ecosystems. *Ecology* 74, 919–934. doi: 10.2307/1940816

Schultz, P., and Urban, N. R. (2008). Effects of bacterial dynamics on organic matter decomposition and nutrient release from sediments: a modeling study. *Ecol. Modell.* 210, 1–14. doi: 10.1016/j.ecolmodel.2007.06.026

Schwefel, R., Gaudard, A., Wüest, A., and Bouffard, D. (2016). Effects of climate change on deepwater oxygen and winter mixing in a deep lake (Lake Geneva): comparing observational findings and modeling. *Water Resour. Res.* 52, 8811– 8826. doi: 10.1002/2016WR019194

Sedláček, J., Tolaszová, J., Kříženecká, S., Bábek, O., and Zimová, K. (2020). Regional contamination history revealed in coal-mining-impacted Oxbow Lake sediments. *Water Air Soil Pollut.* 231:208. doi: 10.1007/s11270-020-04583-1

Silva, I., Tacão, M., and Henriques, I. (2021). Selection of antibiotic resistance by metals in a riverine bacterial community. *Chemosphere* 263:127936. doi: 10.1016/j.chemosphere.2020.127936

Silva, T. A., Stutenbecker, L., Bakker, M., Costa, A., Schlunegger, F., Lane, S., et al. (2019). The sediment budget and dynamics of a delta-canyon-lobe system over the Anthropocene timescale: the Rhone River Delta, Lake Geneva (Switzerland/France). *Sedimentology* 66, 838–858. doi: 10.1111/sed.12519

Steinsberger, T., Schmid, M., Wüest, A., Schwefel, R., Wehrli, B., and Müller, B. (2019). Organic carbon mass accumulation rate regulates the flux of reduced substances from the sediments of deep lakes. *Biogeosciences* 14, 3275–3285. doi: 10.5194/bg-14-3275-2017

Su, J. Q., An, X. L., Li, B., Chen, Q. L., Gillings, M. R., Chen, H., et al. (2017). Metagenomics of urban sewage identifies an extensively shared antibiotic resistome in China. *Microbiome* 5:84. doi: 10.1186/s40168-017-0298-y

Sun, M. Y., Dafforn, K. A., Brown, M. V., and Johnston, E. L. (2012). Bacterial communities are sensitive indicators of contaminant stress. *Mar. Pollut. Bull.* 64, 1029–1038. doi: 10.1016/j.marpolbul.2012.01.035

Sun, M. Y., Dafforn, K. A., Johnston, E. L., and Brown, M. V. (2013). Core sediment bacteria drive community response to anthropogenic contamination over multiple environmental gradients. *Environ. Microbiol.* 15, 2517–2531. doi: 10.1111/1462-2920.12133

Szakova, I., Kolihova, D., Miholova, D., and Mader, P. (2004). Single-purpose atomic absorption spectrometer AMA-254 for mercury determination and its performance in analysis of agricultural and environmental materials. *Chem. Pap.* 58, 311–315.

Vadeboncoeur, Y., Vander Zanden, M. J., and Lodge, D. M. (2002). Putting the lake back together: reintegrating benthic pathways into lake food web models: lake ecologists tend to focus their research on pelagic energy pathways, but,

from algae to fish, benthic organisms form an integral part of lake food webs. *BioScience* 52, 44–54. doi: 10.1641/0006-3568

Wan, Y., Bai, Y., He, J., Zhang, Y., Li, R., and Ruan, X. (2017). Temporal and spatial variations of aquatic environmental characteristics and sediment bacterial community in five regions of Lake Taihu. *Aquat. Ecol.* 51, 343–358. doi: 10.1007/s10452-017-9621-8

Wang, Y., and Qian, P. Y. (2009). Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies. *PLoS One* 4:e7401. doi: 10.1371/journal.pone.0007401

Widenfalk, A., Bertilsson, S., Sundh, I., and Goedkoop, W. (2008). Effects of pesticides on community composition and activity of sediment microbes- responses at various levels of microbial community organization. *Environ. Pollut.* 152, 576–584. doi: 10.1016/j.envpol.2007.07.003

Widenfalk, A., Svensson, J. M., and Goedkoop, W. (2004). Effects of the pesticides captan, deltamethrin, isoproturon, and pirimicarb on the microbial community of a freshwater sediment. *Environ. Toxicol. Chem.* 23, 1920–1927. doi: 10.1897/ 03-345

Wu, K., Zhao, W., Wang, Q., Yang, X., Zhu, L., Shen, J., et al. (2019). The relative abundance of benthic bacterial phyla along a water-depth gradient in a plateau lake: physical, chemical, and biotic drivers. *Front. Microbiol.* 10:1521. doi: 10.3389/fmicb.2019.01521

Xu, Y., Wu, Y., Han, J., and Li, P. (2017). The current status of heavy metal in lake sediments from China: pollution and ecological risk assessment. *Ecol. Evolut.* 7, 5454–5466. doi: 10.1002/ece3.3124

Zhang, L., Cheng, Y., Gao, G., and Jiang, J. (2019). Spatial-temporal variation of bacterial communities in sediments in Lake Chaohu, a large, shallow eutrophic lake in China. *Int. J. Environ. Res. Public Health* 16:3966. doi: 10.3390/ ijerph16203966

Zhang, W., Ki, J.-S., and Qian, P.-Y. (2008). Microbial diversity in polluted harbor sediments I: bacterial community assessment based on four clone libraries of 16S rDNA. *Estuar. Coast. Shelf Sci.* 76, 668–681. doi: 10.1016/j.ecss.2007. 07.040

Zhang, X. Z., Xie, J. J., and Sun, F. L. (2014). Effects of three polycyclic aromatic hydrocarbons on sediment bacterial community. *Curr. Microbiol.* 68, 756–762. doi: 10.1007/s00284-014-0535-6

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<figure><img src="image_134.png" /></figure>

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# Driving Factors of Geosmin Appearance in a Mediterranean River Basin: The Ter River Case

Carmen Espinosa¹,², Meritxell Abril¹, Èlia Bretxa², Marta Jutglar², Sergio Ponsá¹, Núria Sellarès², Lídia Vendrell-Puigmitjà¹, Laia Llenas¹, Marc Ordeix² and Lorenzo Proia¹*

¹ BETA Technological Center, University of Vic - Central University of Catalonia (UVic-UCC), Vic, Spain, ² CERM, Center for the Study of Mediterranean Rivers, University of Vic - Central University of Catalonia (UVic-UCC), Manlleu, Spain

OPEN ACCESS

**Edited by:**
Soizic Morin,
INRAE Nouvelle-Aquitaine Bordeaux,
France

**Reviewed by:**
Jingrang Lu,
Office of Research and Development,
United States Environmental
Protection Agency (EPA),
United States
Leda Giannuzzi,
Consejo Nacional de Investigaciones
Científicas y Técnicas (CONICET),
Argentina

*Correspondence:
Lorenzo Proia
lorenzo.proia@uvic.cat

**Specialty section:**
This article was submitted to
Microbiotechnology,
a section of the journal
Frontiers in Microbiology

**Received:** 15 July 2021
**Accepted:** 20 September 2021
**Published:** 01 November 2021

**Citation:**
Espinosa C, Abril M, Bretxa È, Jutglar M, Ponsá S, Sellarès N, Vendrell-Puigmitjà L, Llenas L, Ordeix M and Proia L (2021) Driving Factors of Geosmin Appearance in a Mediterranean River Basin: The Ter River Case.
Front. Microbiol. 12:741750.
doi: 10.3389/fmicb.2021.741750

In recent decades, human activity coupled with climate change has led to a deterioration in the quality of surface freshwater. This has been related to an increase in the appearance of algal blooms, which can produce organic compounds that can be toxic or can affect the organoleptic characteristics of the water, such as its taste and odor. Among these latter compounds is geosmin, a metabolite produced by certain cyanobacteria that confers an earthy taste to water and which can be detected by humans at very low concentrations (nanogram per liter). The difficulty and cost of both monitoring the presence of this compound and its treatment is a problem for drinking water treatment companies, as the appearance of geosmin affects consumer confidence in the quality of the drinking water they supply. In this field study, the evaluation of four sampling sites with different physicochemical conditions located in the upper part of the Ter River basin, a Mediterranean river located in Catalonia (NE Spain), has been carried out, with the aim of identifying the main triggers of geosmin episodes. The results, obtained from 1 year of sampling, have made it possible to find out that: (i) land uses with a higher percentage of agricultural and industrial activity are related to high nutrient conditions in river water, (ii) these higher nutrient concentrations favor the development of benthic cyanobacteria, (iii) in late winter-early spring, when these cyanobacteria are subjected to both an imbalance of the dissolved inorganic nitrogen and soluble reactive phosphorus ratio, guided by a phosphorus concentration increase, and to cold-mild temperatures close to 10°C, they produce and release geosmin, and (iv) 1-2 weeks after cyanobacteria reach a high relative presence in the whole biofilm, an increase in geosmin concentration in water is observed, probably associated with the cyanobacteria detachment from cobbles and consequent cell lysis. These results could serve as a guide for drinking water treatment companies, indicating under what conditions they can expect the appearance of geosmin episodes and implement the appropriate treatment before it reaches consumers' tap.

Keywords: geosmin, field study, cyanobacteria, biofilm, global change, Mediterranean river
# INTRODUCTION

In the last decades, increasing pressures generated by human activities conjointly with global change trend led to worsened water quality in freshwater ecosystems, giving rise to several ecological issues. One of these problems, mainly caused by eutrophication and temperature rise, is the uncontrolled and unpredictable growth of algal blooms, frequently associated with organic compounds production, which can be toxic or can alter the water organoleptic characteristics, such as the taste and odor compounds (T&Os) (Clercin and Druschel, 2019). The presence of natural toxins in water often leads to bathing or consumption prohibition, whereas T&Os are a problem for drinking water treatment plants (DWTPs) due to the negative impact they have on the user's perception of the drinking water quality (Ding et al., 2014).

Regarding human activities, their huge increase in the last century significantly affected rivers' basin morphology and water quality (Rubio-Gracia et al., 2017). Particularly, basins' land uses generally shifted from forestry-dominated to agricultural, livestock, and industrial. The increase of intensive agricultural activities in the catchments has been associated with higher nutrients and pesticides concentrations in river waters, which also receive a wide range of organic and inorganic chemical stressors, such as heavy metals, from industrial and urban areas (Drury et al., 2013; Argudo et al., 2020). The effects on water quality induced by the shift of land uses are acting conjointly with the alterations associated with climate change. Climatic conditions have changed in recent decades, being observed specifically in the Mediterranean regions, a clear decrease of the average rainfall, which is becoming much more sporadic and intense, and a relevant increase of the mean air temperature (Intergovernmental Panel on Climate Change [IPCC], 2019). These consequences of climate change take a greater relevance in rivers affected by water scarcity, such as those of the Mediterranean area, where there can be prolonged periods with a very low river flow, which can lead to a higher nutrients concentration, among other parameters (e.g., salts, heavy metals, and pesticides), due to the decreased dilution capacity of the system (Karaouzas et al., 2018). These conditions can trigger the appearance of algal blooms, with a large number of cases described worldwide (Clercin and Druschel, 2019; Ho et al., 2019), and that can finally lead to the appearance of T&O compounds (Watson et al., 2016).

Among the T&O compounds produced by microorganisms, geosmin has been described as the most common in freshwater ecosystems. This metabolite is mainly produced by certain cyanobacteria and actinomycetes, being the firsts associated with geosmin episodes in freshwaters, while actinomycetes usually have a terrestrial origen (Lukassen et al., 2019). Some of the main geosmin-producing cyanobacteria identified are *Oscillatoria* sp., *Dolichospermum* sp., *Lynghya* sp., and *Symploca* sp. (Smith et al., 2009). For a long time, due to the methodology of routine sampling, only the odorous potential of pelagic cyanobacterial taxa has been considered. However, recent studies suggested that most of the geosmin producers are benthic instead of pelagic cyanobacteria taxa (Jähnichen et al., 2011).

Most of the studies on drivers of geosmin appearance in the field have been carried out in reservoirs and lakes (Dzialowski et al., 2009; Harris et al., 2016), whereas only a few studies have investigated geosmin appearance in rivers and streams (Vilalta, 2004). Two of the main important factors associated with geosmin episodes in reservoirs have been described to be the excess of nutrient loads and alterations in its stoichiometric balance. In particular, it has been suggested that increasing nutrient concentrations and lower nitrogen to phosphorus ratio (TN:TP) can promote cyanobacteria growth and dominance in freshwater ecosystems (Olsen et al., 2016; Espinosa et al., 2021), giving rise to the appearance of geosmin episodes. Harris et al. (2016) found out that low TN:TP ratio conditions (<30:1 by mass) favor geosmin episodes in reservoirs, which could be related to an increase in cyanobacterial biovolumes at lower TN:TP ratios. In the Llobregat River (Catalonia, NE Spain), Vilalta (2004) found similar results, being geosmin concentration higher at TN:TP values close to 10:1, comparing with TN:TP = 94:1, when no geosmin was detected. From experiments carried out mainly under laboratory conditions, the appearance of geosmin has also been related to other factors such as light availability and temperature. Depending on the geosmin producers, the value of these factors differed, but in general, it has been described that low light availability together with low water temperatures favored intracellular geosmin formation (Zhang et al., 2009; Wang and Li, 2015; Alghanmi et al., 2018). Water flow also influence microbial production of geosmin, being its presence is higher under low water flow conditions (Jüttner and Watson, 2007; Espinosa et al., 2020).

As comment before, although there are studies on drivers of geosmin in reservoirs and lakes, very few have approached this topic in rivers. Geosmin occurrence can be a problem for the companies that exploit rivers to provide drinking water to the surrounding populations, as they do not know under which conditions is produced, and thus, they are unable to predict geosmin episodes. Small companies (water treated ≈ 1,500 L/day, drinking water users ≈ 22,000) cannot incorporate regular monitoring of geosmin concentrations in collected river waters, as analyses are complex and time-consuming, and they need specific equipment often not available in their labs. Moreover, the cost of the analytics can be high and make it difficult for the concerned companies to contract the geosmin analysis in external laboratories as a routine. The low capacity to predict geosmin presence in water leads to the reception of consumer complaints and economic losses associated with the decrease in water consumption supplied by the DWTPs. In that sense, there is a growing need to investigate and understand the drivers associated with the production of geosmin in rivers, helping DWTPs to be prepared for possible geosmin episodes and avoiding the possible costs associated with geosmin analysis *per se*.

In the Osona region (Catalonia, NE Spain), most of DWTPs collect water from the upper section of the Ter River to supply the nearby cities and villages (around 150.00 inhabitants). In recent years, they have suffered several geosmin episodes that have led to customers' complaints due to the inability to apply the required treatment on time. This situation has generated the need for a better understanding of the environmental factors
associated with geosmin appearance. The main objective of this study was to determine the main triggers of geosmin episodes in the Ter River. To this aim, a 1-year field monitoring (2019) was carried out, analyzing a wide set of physicochemical and biological parameters at four sampling sites distributed along the upper part of the Ter River basin.

# MATERIALS AND METHODS

## Study Site

The Ter River is located in the NE of Catalonia (Spain) (Figure 1). It is characterized by Pyrenean, pre-Pyrenean, and humid continental Mediterranean climate in the upper regions of its catchment (El Ripollès and Osona) and pre-coastal Mediterranean and coastal Mediterranean climates in the lower regions (Meteocat, 2019). The Ter River is affected by environmental fluctuations typical of the Mediterranean climate, with a higher probability of precipitation during spring and autumn and dry and warm summers. In the Ter River basin (208 km-long and 3,010 km² of catchment area), several anthropogenic activities drastically affect water flow and quality. The most relevant impacts are: (i) the presence of small and frequent hydropower weirs, which significantly reduce river flow, (ii) livestock farming and intensive agriculture, leading to an increase of nutrients concentration in fields and in surface and groundwater, and (iii) a large reservoirs system, which supplies energy and raw water for drinking, agriculture, and industrial purposes to Barcelona city and Costa Brava area, which is dramatically affecting the river connectivity, and clearly dividing the catchment in two different areas: upstream and downstream reservoirs system. In this work, the upper part of the Ter River basin (upstream of the large reservoirs system) has been studied (Figure 1).

The river basin area evaluated in this study is included in the regions of El Ripollès and Osona, which cover a surface of around 2,000 km². El Ripollès region is located at the head of the Ter River basin, starting in the Pyrenees, and it is characterized by coniferous forest, natural grasslands, broad-leaved forest, and moors and heathlands (CORINE Land Cover System, 2018). Located downstream, the Osona region is affected by strong anthropogenic pressures, being the main land uses related to non-irrigated arable fields, industrial and commercial units, and continuous and discontinuous urban fabrics (CORINE Land Cover System, 2018).

Four sampling sites (Figure 1) were chosen to perform a 1-year field study to identify the driving factors triggering geosmin episodes in the upper part of the Ter River basin. The most upstream sampling site (T1) was located 10.8 km downstream from the source of the Ter River in the Pyrenees, at Vilallonga de Ter municipality. T1 was considered as a reference sampling site, with the best water quality. The next sampling site was located along the Ter River, downstream the municipality of Ripoll (T2) (41 km downstream from the source). At this site, the Ter River has already received the input of the Freser tributary and some WWTPs effluents. The third sampling site was located at the Colonia de Borgonyà, few kilometers upstream of Torelló

municipality (T3) and 66.3 km downstream from the source. This site was selected because it is one of the collection points of the drinking water company "Aigües d'Osona S.A.," which supplies drinking water to Torelló municipality and surroundings. The last sampling site along the Ter River (T4) was located in Gurb municipality, 100 m upstream to the collection point of "Aigües de Vic S.A.," the drinking water company that supplies the city of Vic and surroundings, and 81.8 km downstream Ter River source.

## Sampling Procedure and Physicochemical and Biological Analysis

During winter (January–March) and spring (April–June) seasons, weekly or biweekly field samplings were carried out, whereas from July to November (summer and autumn), monthly sampling campaigns were done. The higher sampling intensity during winter and spring was chosen because of the higher probability of geosmin occurrence during these seasons (Vilalta, 2004; Personal communication from drinking water companies). The nomenclature used in this study to identify the different sampling days includes the letter of the season (W = winter, Sp = spring, Su = summer, and A = autumn) followed by the number of the sampling day in this season, being, for example, W6, the name given to the sixth day of sampling in winter.

## Water Samples

The following physicochemical parameters were measured in situ with specific probes: temperature, dissolved oxygen concentration, and oxygen saturation (YSI professional plus, YSI Incorporated, United States), pH (XS pH7+ DHS), and electrical conductivity (XS COND 7+). Water samples were taken and filtered through 0.2-µm nylon membranes filters (Merck Millipore) before the analysis of soluble reactive phosphorus (SRP), N-NH₄⁺, N-NO₂⁻, and N-NO₃⁻. The volume filtered for SRP and N-NH₄⁺ was 10 ml and for N-NO₂⁻ and N-NO₃⁻ was 50 ml, and the analyses were performed following the protocols established by Murphy and Riley (1962); Reardon et al. (1966), and Rand et al. (1976), respectively. The dissolved inorganic nitrogen (DIN):SRP ratio was calculated and determined as DIN divided by SRP in molar quantities. DIN concentration was determined as the sum of ammonium (N-NH₄⁺), nitrite (N-NO₂⁻), and nitrate (N-NO₃⁻) concentrations. Furthermore, 1 L of water was taken for the analysis of turbidity, suspended solids, and organic matter. Water turbidity was measured using a turbidimeter (HI 98713, HANNA Instruments). The organic matter present in water samples was estimated from the absorbance values measured at 254 nm using a spectrophotometer (NanoPhotometer™ P-360, INTEM), and the suspended solids were obtained following APHA (2005) and using a Forced air oven, MEMMERT IFE500. All samples were stored at -20°C until analysis.

A 1-L opaque glass bottle was used to collect the water sample for geosmin quantification. Bottles were stored at 4°C in dark conditions until analysis, which was performed within 48 h after collection to avoid degradation and volatilization. The protocol followed for geosmin analysis in water was described in Espinosa et al. (2021). Briefly, to analyze geosmin concentration, 50 ml of each water sample and 10 g of NaCl were added to a 100-ml
<figure><img src="image_135.png" /><figcaption>**FIGURE 1** | Ter River basin and sampling sites located in upper part (El Ripollès and Osona regions): T1 = Ter at Vilallonga de Ter municipality, T2 = Ter downstream Ripoll municipality, T3 = Ter at Colónia de Borgonyà (Sant Vicenç de Torelló municipality), and T4 = Ter at Gurb municipality.</figcaption></figure>

opaque reaction vial and heated at 60°C for 25 min in agitation to favor geosmin volatilization. To extract the geosmin, a 65-µm polydimethylsiloxane/divinylbenzene fiber was used, and the separation and analysis of the extracted volatile compound were performed in a gas chromatography-mass spectrometry instrument (ISQ-TRACE GC ULTRA). The analytical detection limit was 2.5 ng/L, the analytical quantification limit was 8 ng/L, and the precision of the method was evaluated with the relative standard deviation (RSD ≤ 20%).

## Biofilm Samples

Each sampling day, three cobbles were randomly taken from each sampling site to evaluate *in situ* the biofilm photosynthetic efficiency and the phototrophic community composition. The community photosynthetic efficiency (Yeff) and the minimum fluorescence yield (F₀) (that can be used as an estimation of algal biomass) were measured with an amplitude modulated fluorimetry (Mini-PAM fluorometer Walz, Effeltrich, Germany), and the phototrophic community composition was evaluated with a BenthoTorch (bbe Moldaenke, Schwentinenta, DK). After that, each cobble was scrapped in 60 ml of water from the same sampling site to obtain a biofilm suspension. Aliquots of this suspension were used to analyze chlorophyll-a (Chl-a), performed as described by Jeffrey and Humphrey (1975), and ash-free dry mass as described in Espinosa et al. (2020). The Margalef index was also calculated as the quotient between the carotenoid/Chl-a ratio, being values obtained from the spectrophotometric reading of the sample at 430 nm (carotenoids and accessory pigments concentration) and 665 nm (Chl-a concentration), to obtain information about the maturity of the populations (Elosegui and Sabater, 2009). These samples were stored at -20°C until analysis.

## Data Treatment

The Kolmogorov-Smirnov test was performed to verify that the variables fulfilled the conditions of normal distribution, and if they did not, they were logarithmically transformed. Physicochemical and biological data were analyzed using an analysis of variance (ANOVA) using the “aov” function (“devtools” package) in RStudio software (version 3.6.0) being the sampling site, the season, and its interaction the factors evaluated. Significant results were tested *post hoc* with a Bonferroni test. Pearson correlation coefficient tests were carried out to explore the relationship between variables. Statistical significance was set at *p* < 0.05 for all tests performed. A redundancy discriminant analysis (RDA) was carried out to explore the potential relationship between the independent variables or potential drivers and the response variables (“vegan” and “tidyr” R packages). They were considered as potential drivers for all the water analytics except for geosmin concentration, which was considered as a response variable together with all the biofilm analytics. Forecasting models to predict the geosmin appearance according to the physicochemical data collected were generated by means of multiple regression analysis (MLR) and random forest (RF) models. The primary purpose of using a linear model was to provide a baseline against which to compare the non-linear RF model, an ensemble machine learning method that constructs a non-linear function based on an ensemble of simpler decision tree models (Kehoe et al., 2015). These models were tested for their ability to predict geosmin at different time delays, not just current conditions, to understand at what time scale geosmin can be predicted and which are the important predictors at each time lag. Models were calibrated for 11 different forecasting time lags ranging from the current level (i.e., no time lag, *t* = 0) up to 10 weeks in advance—in a 1-week increment. Linear (regression)
and non-linear models (RF) were calibrated and validated (70-30%) on randomized subsets of the total dataset. The R function "lm" was used for the MLR, whereas RF models were developed with the "randomForest" package. The performance of the models was assessed by the multiple determination coefficient or R² adjusted. The relative importance of the different predictors at different time lags was evaluated by using the "importance" function ("randomForest" package).

# RESULTS

## Geosmin Concentration

The presence and concentration of geosmin in water varied throughout the year (2019), in which the present study was carried out (Figure 2). Statistically, geosmin presence in water was significantly affected by the sampling site (ANOVA, *F* = 10.378, *p* < 0.001), the season (*F* = 9.007, *p* < 0.001), and the interaction between site and season (*F* = 2.243, *p* < 0.05). Sampling site T4 was statistically different from the others (Bonferroni test: *p* < 0.001), being the one with the highest geosmin concentration, especially in spring (249 ± 33 ng/L) compared with the other seasons (*p* < 0.01).

## Physicochemical Parameters

Some of the evaluated physicochemical variables (Table 1) significantly changed depending on the sampling site, the season, and its interaction. The lowest values reported for most of the parameters evaluated were found at sampling site T1, mainly highlighting pH, electrical conductivity, temperature, and nutrient concentrations (ANOVA, *p* < 0.05). In contrast, sampling site T4 showed opposite results to T1, with very high values, especially for the nitrogen forms, whose concentration increased up to four times compared with T1, on average.

Seasonality also had an effect in some of the physicochemical parameters evaluated, being in autumn when lower pH values and dissolved oxygen were recorded, together with higher nitrites concentration (Bonferroni test, *p* < 0.01 in all cases). Phosphate concentration and turbidity presented the highest values in spring (*p* < 0.001), when, in contrast, the DIN:SRP ratio showed lower results compared with summer and autumn (*p* < 0.01).

Geosmin concentration for all sampling sites and seasons was positively correlated with pH, electrical conductivity, turbidity, and phosphate concentration (Pearson's correlation, *p* < 0.01 in all cases) and negatively correlated with the DIN:SRP ratio (Pearson's correlation, *p* < 0.05).

Considering that the geosmin peak was measured at T4, a specific analysis was carried on the sub-dataset regarding this sampling site. The analysis of correlations revealed that geosmin concentration was positively correlated with phosphorus concentration (Pearson's correlation: *r* = 0.789, *p* < 0.01) and had a negative correlation with the DIN:SRP ratio (*r* = -0.868, *p* < 0.01). During spring, when the highest geosmin peak was detected, the DIN:SRP ratio was significantly lower (47:1 ± 16:1) compared with the other seasons. On the contrary, autumn was the season showing the highest DIN:SRP

ratio (221:1 ± 34:1), due to the important decrease of phosphorus concentration (19 ± 11 µg P-PO₄³⁻ µg/L).

## Biological Parameters

Biological parameters data showed significant differences depending on the sampling site, the season, and the interaction between both factors (Table 2). Sampling site T4 showed the highest values of Chl-*a* concentration and cyanobacteria presence, being significantly different from the rest of the sampling sites (Bonferroni test, *p* < 0.01). Seasonality also affected the biological parameters evaluated. Phototrophic community presented higher biomass values (estimated as microgram Chl-*a* per square centimeters) in summer (*p* < 0.01), whereas the *F*₀ value was higher in autumn (*p* < 0.05), and Chl-*a* concentration and diatoms relative abundance had higher values in winter (*p* < 0.01). Correlation analysis revealed that diatom abundance was negatively correlated with geosmin concentration (*r* = -0.28, *p* < 0.05), but no significant correlation was found between geosmin and cyanobacteria presence.

Evaluating the sampling site with the highest geosmin concentration (T4), it was found that geosmin concentration was negatively correlated with the *F*₀ (Pearson's correlation: *r* = -0.681, *p* < 0.01), diatoms biomass (*r* = -0.661, *p* < 0.01), Margalef index (*r* = -0.643, *p* < 0.01), and Chl-*a* concentration (*r* = -0.533; *p* < 0.05).

## Geosmin Drivers

An RDA was performed with the objective to identify the relationship between the potential drivers (all the physicochemical variables except geosmin) and the response variables (all the biological variables together with geosmin) measured at the different sampling sites (Figure 3). The independent variables in the first two RDA dimensions (RDA1 and RDA2) explained 89% of the total variance in the distribution of the response parameters.

This ordination clearly separates upstream (T1 and T2) from the downstream sites (T3 and T4) along the first axis, which explains the 68.8% of variability, and is mainly driven by increasing nutrient concentrations along the Ter River gradient. The DIN:SRP relationship also influences the distribution of the sampling sites, with T1 being the site with the lowest mean values. Geosmin concentration was mainly affected by the concentration of phosphates and nitrites, the turbidity, pH, and DIN:SRP ratio values, being in agreement with the Pearson correlations found (all of them positively correlated with geosmin concentration except DIN:SRP ratio). The presence of cyanobacteria was favored by lower values of DIN:SRP, in contrast to diatoms, which presented higher values under high nitrate concentration conditions. This last situation also favored high Chl-*a* values and biofilms with higher photosynthetic efficiency (Yeff).

Considering that the highest geosmin concentrations were found at the T4 sampling site, a specific RDA was performed for this sub-dataset (Figure 4). The potential drivers for RDA1 and RDA2 explain 83.3% of the total variance in the distribution of response parameters in T4. This analysis clearly shows the effect of seasonality in the appearance of geosmin at this sampling site. In particular, the first axis, which explains the 69.71%
<figure><img src="image_136.png" /><figcaption>**FIGURE 2** | Mean value and standard deviation of geosmin concentration in water for different sampling sites (T1, T2, T3, and T4) in upper part of Ter River basin (NE Catalonia, Spain) at each sampling day.</figcaption></figure>

<table><caption>**TABLE 1** | Mean value and standard deviation of physicochemical variables evaluated for different sampling sites (T1–T4) in upper Ter River basin (NE Catalonia, Spain) in 2019: in winter (W), spring (Sp), summer (Su), and autumn (A).</caption><thead><tr><th></th><th>pH</th><th>EC<br>(µS/cm)</th><th>Temp.<br>(°C)</th><th>DO<br>(mg/L)</th><th>Sat.<br>(%)</th><th>N-NH<sub>4</sub><sup>+</sup><br>(µg/L)</th><th>N-NO<sub>2</sub><sup>-</sup><br>(µg/L)</th><th>N-NO<sub>3</sub><sup>-</sup><br>(mg/L)</th><th>P-PO<sub>4</sub><sup>3-</sup><br>(µg/L)</th><th>DIN:SRP</th><th>SS<br>(mg/L)</th><th>Turbidity<br>(NTU)</th><th>OM<br>(A<sub>254 nm</sub>)</th></tr></thead><tbody><tr><td>T1 W</td><td>7.8 ± 0.6</td><td>153 ± 32</td><td>4.6 ± 1.3</td><td>12.1 ± 1.0</td><td>99 ± 16</td><td>55 ± 40</td><td>2 ± 1</td><td>0.37 ± 0.07</td><td>66 ± 68</td><td>27 ± 19</td><td>29 ± 19</td><td>13 ± 11</td><td>0.40 ± 0.34</td></tr><tr><td>Sp</td><td>7.9 ± 0.3</td><td>149 ± 37</td><td>7.4 ± 1.6</td><td>11.6 ± 1.0</td><td>105 ± 11</td><td>26 ± 13</td><td>3 ± 2</td><td>0.42 ± 0.06</td><td>96 ± 94</td><td>19 ± 14</td><td>8 ± 4</td><td>12 ± 10</td><td>0.23 ± 0.35</td></tr><tr><td>Su</td><td>8.1 ± 0.4</td><td>150 ± 24</td><td>12.9 ± 3.0</td><td>10.1 ± 1.0</td><td>98 ± 10</td><td>45 ± 34</td><td>9 ± 10</td><td>0.30 ± 0.20</td><td>36 ± 50</td><td>31 ± 20</td><td>6 ± 5</td><td>10 ± 7</td><td>0.30 ± 0.39</td></tr><tr><td>A</td><td>7.8 ± 0.4</td><td>148 ± 30</td><td>7.0 ± 2.9</td><td>11.9 ± 2.0</td><td>100 ± 18</td><td>21 ± 14</td><td>3 ± 5</td><td>0.33 ± 0.22</td><td>18 ± 10</td><td>60 ± 41</td><td>13 ± 14</td><td>10 ± 7</td><td>0.34 ± 0.44</td></tr><tr><td>T2 W</td><td>8.3 ± 0.4</td><td>250 ± 46</td><td>6.2 ± 2.1</td><td>12.7 ± 1.1</td><td>106 ± 10</td><td>79 ± 35</td><td>7 ± 5</td><td>0.61 ± 0.16</td><td>54 ± 40</td><td>42 ± 43</td><td>16 ± 11</td><td>23 ± 9</td><td>0.23 ± 0.26</td></tr><tr><td>Sp</td><td>8.3 ± 0.4</td><td>291 ± 61</td><td>9.5 ± 1.2</td><td>11.6 ± 0.7</td><td>108 ± 8</td><td>54 ± 27</td><td>9 ± 8</td><td>0.61 ± 0.14</td><td>48 ± 25</td><td>37 ± 24</td><td>23 ± 14</td><td>20 ± 8</td><td>0.13 ± 0.15</td></tr><tr><td>Su</td><td>8.2 ± 0.5</td><td>293 ± 67</td><td>16.0 ± 3.4</td><td>10.2 ± 1.6</td><td>104 ± 12</td><td>61 ± 33</td><td>20 ± 26</td><td>0.78 ± 0.54</td><td>29 ± 28</td><td>74 ± 44</td><td>14 ± 13</td><td>21 ± 8</td><td>0.12 ± 0.09</td></tr><tr><td>A</td><td>8.2 ± 0.5</td><td>324 ± 67</td><td>7.8 ± 4.4</td><td>12.8 ± 1.8</td><td>109 ± 13</td><td>45 ± 29</td><td>14 ± 16</td><td>0.48 ± 0.14</td><td>34 ± 25</td><td>69 ± 55</td><td>20 ± 17</td><td>22 ± 27</td><td>0.28 ± 0.34</td></tr><tr><td>T3 W</td><td>8.4 ± 0.3</td><td>343 ± 87</td><td>7.5 ± 2.4</td><td>12.9 ± 1.1</td><td>108 ± 9</td><td>80 ± 74</td><td>8 ± 5</td><td>0.75 ± 0.19</td><td>73 ± 57</td><td>50 ± 52</td><td>27 ± 16</td><td>18 ± 13</td><td>0.41 ± 0.29</td></tr><tr><td>Sp</td><td>8.4 ± 0.1</td><td>302 ± 85</td><td>12.0 ± 2.5</td><td>10.8 ± 1.2</td><td>103 ± 10</td><td>75 ± 70</td><td>10 ± 6</td><td>0.67 ± 0.29</td><td>73 ± 55</td><td>31 ± 18</td><td>29 ± 26</td><td>19 ± 15</td><td>0.33 ± 0.33</td></tr><tr><td>Su</td><td>8.5 ± 0.2</td><td>338 ± 91</td><td>17.2 ± 5.7</td><td>11.2 ± 3.1</td><td>107 ± 8</td><td>78 ± 38</td><td>19 ± 23</td><td>0.77 ± 0.50</td><td>82 ± 100</td><td>39 ± 24</td><td>19 ± 9</td><td>12 ± 5</td><td>0.35 ± 0.33</td></tr><tr><td>A</td><td>8.4 ± 0.2</td><td>366 ± 76</td><td>9.7 ± 5.4</td><td>12.7 ± 2.1</td><td>111 ± 12</td><td>92 ± 119</td><td>36 ± 74</td><td>0.73 ± 0.45</td><td>80 ± 113</td><td>71 ± 56</td><td>27 ± 21</td><td>12 ± 9</td><td>0.35 ± 0.35</td></tr><tr><td>T4 W</td><td>8.4 ± 0.3</td><td>388 ± 70</td><td>7.4 ± 2.4</td><td>12.3 ± 0.7</td><td>104 ± 8</td><td>119 ± 49</td><td>14 ± 10</td><td>1.48 ± 0.68</td><td>87 ± 87</td><td>75 ± 61</td><td>31 ± 20</td><td>21 ± 15</td><td>0.35 ± 0.20</td></tr><tr><td>Sp</td><td>8.3 ± 0.2</td><td>326 ± 75</td><td>13.6 ± 1.7</td><td>10.4 ± 2.0</td><td>101 ± 13</td><td>122 ± 90</td><td>19 ± 9</td><td>1.15 ± 0.42</td><td>79 ± 39</td><td>54 ± 38</td><td>34 ± 29</td><td>26 ± 21</td><td>0.35 ± 0.30</td></tr><tr><td>Su</td><td>8.7 ± 0.5</td><td>374 ± 66</td><td>21.2 ± 4.3</td><td>9.5 ± 1.4</td><td>106 ± 12</td><td>93 ± 33</td><td>26 ± 21</td><td>1.23 ± 0.88</td><td>102 ± 76</td><td>54 ± 53</td><td>40 ± 5</td><td>18 ± 7</td><td>0.19 ± 0.15</td></tr><tr><td>A</td><td>8.4 ± 0.4</td><td>405 ± 90</td><td>9.7 ± 5.4</td><td>11.9 ± 3.0</td><td>108 ± 15</td><td>75 ± 67</td><td>17 ± 9</td><td>1.60 ± 0.92</td><td>57 ± 47</td><td>150 ± 129</td><td>19 ± 20</td><td>11 ± 9</td><td>0.26 ± 0.17</td></tr></tbody></table>

OM, organic matter; SS, suspended solids.

of the variability, separates winter and autumn from spring and summer samplings and is mainly driven by increasing phosphorus and nitrites concentrations and decreasing DIN:SRP values that again seem to be related to cyanobacteria abundance and geosmin concentration. More specifically, on the first sampling day of winter (W1), the tendency was to move from high DIN:SRP ratio values (187:1) toward lower values (48:1

in W6). This gradual decrease of DIN:SRP ratio was mainly guided by a relevant increase of phosphorus concentration (from 32 ± 3.5 to 70 – 100 ± 10.4 µg P-PO₄³⁻/L) conjointly with a decrease in nitrate concentration (from 2.98 ± 0.02 to 1.26 ± 0.18 mg N-NO₃⁻/L) and coincide with the first detection (W4) and gradual increase of geosmin concentration during late winter–early spring. During this period, water temperature
<table><caption>**TABLE 2** | Mean value and standard deviation of biological variables evaluated for different sampling sites (T1–T4) in upper Ter River basin (NE Catalonia, Spain) in 2019, in winter (W), spring (Sp), summer (Su), and autumn (A).</caption><thead><tr><th></th><th></th><th>F<sub>0</sub></th><th>Yeff</th><th>Cyanobacteria<br>(µg/cm<sup>2</sup>)</th><th>Green<br>algae<br>(µg/cm<sup>2</sup>)</th><th>Diatoms<br>(µg/cm<sup>2</sup>)</th><th>Cyanobacteria<br>(%)</th><th>Green<br>algae (%)</th><th>Diatoms (%)</th><th>Chl-a<br>(µg/cm<sup>2</sup>)</th><th>MI</th><th>AFDM<br>(g/m<sup>2</sup>)</th></tr></thead><tbody><tr><td rowspan="4">T1</td><td>W</td><td>113 ± 43</td><td>322 ± 92</td><td>0.16 ± 0.08</td><td>0.02 ± 0.05</td><td>3.67 ± 2.80</td><td>7.9 ± 7.5</td><td>0.0 ± 0.0</td><td>89.9 ± 12.0</td><td>3.9 ± 3.9</td><td>2.0 ± 0.2</td><td>44 ± 59</td></tr><tr><td>Sp</td><td>93 ± 54</td><td>350 ± 63</td><td>0.18 ± 0.07</td><td>0.19 ± 0.14</td><td>1.09 ± 0.33</td><td>13.3 ± 6.8</td><td>0.0 ± 0.0</td><td>73.9 ± 12.0</td><td>1.0 ± 0.8</td><td>1.9 ± 0.1</td><td>74 ± 69</td></tr><tr><td>Su</td><td>89 ± 30</td><td>368 ± 132</td><td>0.91 ± 1.03</td><td>0.00 ± 0.00</td><td>0.99 ± 0.39</td><td>38.2 ± 25.7</td><td>0.0 ± 0.0</td><td>61.8 ± 25.7</td><td>0.6</td><td>2.1</td><td>46 ± 61</td></tr><tr><td>A</td><td>165</td><td>277</td><td>0.54 ± 0.61</td><td>0.03 ± 0.05</td><td>1.82 ± 0.30</td><td>19.8 ± 18.8</td><td>0.0 ± 0.0</td><td>79.3 ± 20.4</td><td>3.2 ± 1.8</td><td>2.0 ± 0.2</td><td>21 ± 18</td></tr><tr><td rowspan="4">T2</td><td>W</td><td>160 ± 117</td><td>489 ± 80</td><td>0.45 ± 0.33</td><td>0.01 ± 0.03</td><td>3.21 ± 2.42</td><td>11.4 ± 5.9</td><td>0.0 ± 0.0</td><td>87.0 ± 3.3</td><td>23.9 ± 21.7</td><td>2.2 ± 0.1</td><td>299 ± 271</td></tr><tr><td>Sp</td><td>127 ± 58</td><td>435 ± 145</td><td>0.49 ± 0.27</td><td>0.45 ± 0.31</td><td>1.49 ± 0.62</td><td>19.5 ± 5.9</td><td>0.0 ± 0.0</td><td>62.2 ± 8.9</td><td>2.5 ± 1.6</td><td>2.0 ± 0.2</td><td>140 ± 111</td></tr><tr><td>Su</td><td>133 ± 80</td><td>452 ± 278</td><td>1.02 ± 0.35</td><td>0.00 ± 0.00</td><td>1.74 ± 0.99</td><td>39.5 ± 21.9</td><td>0.0 ± 0.0</td><td>60.5 ± 21.9</td><td>11.1</td><td>2.5</td><td>17 ± 1</td></tr><tr><td>A</td><td>227</td><td>630</td><td>0.42 ± 0.36</td><td>0.00 ± 0.00</td><td>2.32 ± 1.43</td><td>14.5 ± 5.9</td><td>0.0 ± 0.0</td><td>85.5 ± 5.9</td><td>7.0 ± 8.0</td><td>2.3 ± 0.1</td><td>48 ± 41</td></tr><tr><td rowspan="4">T3</td><td>W</td><td>189 ± 64</td><td>317 ± 106</td><td>0.78 ± 0.48</td><td>0.00 ± 0.00</td><td>3.59 ± 2.32</td><td>18.8 ± 6.2</td><td>0.0 ± 0.0</td><td>81.2 ± 6.2</td><td>18.0 ± 17.3</td><td>2.4 ± 0.1</td><td>134 ± 133</td></tr><tr><td>Sp</td><td>149 ± 43</td><td>355 ± 134</td><td>0.58 ± 0.26</td><td>0.03 ± 0.04</td><td>1.74 ± 0.53</td><td>24.8 ± 6.7</td><td>0.0 ± 0.0</td><td>74.0 ± 6.4</td><td>7.2 ± 3.1</td><td>2.3 ± 0.1</td><td>196 ± 208</td></tr><tr><td>Su</td><td>299 ± 108</td><td>501 ± 111</td><td>1.66 ± 1.12</td><td>0.11 ± 0.16</td><td>2.38 ± 0.34</td><td>37.7 ± 15.2</td><td>0.0 ± 0.0</td><td>58.8 ± 10.3</td><td>8.1</td><td>2.3</td><td>11.2 ± 1.9</td></tr><tr><td>A</td><td>335</td><td>530</td><td>1.05 ± 0.25</td><td>0.00 ± 0.00</td><td>2.98 ± 0.71</td><td>26.2 ± 4.3</td><td>0.0 ± 0.0</td><td>73.8 ± 4.3</td><td>12.3 ± 2.7</td><td>2.3 ± 0.2</td><td>81 ± 74</td></tr><tr><td rowspan="4">T4</td><td>W</td><td>219 ± 115</td><td>473 ± 106</td><td>0.91 ± 0.63</td><td>0.00 ± 0.00</td><td>4.19 ± 3.35</td><td>21.3 ± 13.7</td><td>0.0 ± 0.0</td><td>78.7 ± 13.7</td><td>31.2 ± 25.4</td><td>2.2 ± 0.2</td><td>269 ± 288</td></tr><tr><td>Sp</td><td>160 ± 50</td><td>467 ± 118</td><td>0.86 ± 0.64</td><td>0.02 ± 0.05</td><td>1.80 ± 0.41</td><td>28.9 ± 11.1</td><td>0.0 ± 0.0</td><td>70.3 ± 11.6</td><td>10.3 ± 6.2</td><td>1.8 ± 0.3</td><td>140 ± 102</td></tr><tr><td>Su</td><td>206 ± 0</td><td>570 ± 48</td><td>1.26 ± 0.31</td><td>0.02 ± 0.03</td><td>1.83 ± 0.02</td><td>40.2 ± 6.2</td><td>0.0 ± 0.0</td><td>59.0 ± 5.1</td><td>11.0</td><td>3.0</td><td>7</td></tr><tr><td>A</td><td>318</td><td>499</td><td>0.80 ± 0.68</td><td>0.00 ± 0.00</td><td>5.10 ± 2.41</td><td>15.1 ± 14.8</td><td>0.0 ± 0.0</td><td>84.4 ± 14.8</td><td>12.9 ± 4.4</td><td>2.4 ± 0.2</td><td>48 ± 22</td></tr></tbody></table>

AFDM, ash-free dry mass; MI, Margalef index.

<figure><img src="image_137.png" /><figcaption>**FIGURE 3** | Redundancy discriminant analysis showing response parameters distribution based on drivers evaluated in four different sampling sites in upper Ter River basin (NE Catalonia, Spain) in 2019. Axes 1 and 2 combined explain 89% of variance. Drivers: pH, temperature (Temp), electrical conductivity (EC), dissolved oxygen (DO), turbidity, nitrite (N-NO₂⁻), nitrate (N-NO₃⁻), ammonium (N-NH₄⁺), and phosphate concentration (P-PO₄³⁻), and nitrogen to phosphorus ratio (DIN:SRP). Response variables: geosmin, minimum fluorescence yield (F₀), photosynthetic efficiency (Yeff), chlorophyll-a concentration (Chla), ash free dry mass (AFDM), Margalef index (MI), cyanobacteria (Cb), diatoms (Dt) and green algae (GA) biomass, and relative abundance of cyanobacteria (R_Cb) and diatoms (R_Dt).</figcaption></figure>

increases from 4.2°C in W1 to 9.5°C in W6, keeping with average values of 13.6 ± 1.7°C in spring. The same physicochemical parameters related to geosmin concentration are those that seem

to be linked to the cyanobacteria presence in the biofilm (both absolute concentration and relative abundance), with higher values at the end of winter and in summer. Conversely, the
<figure><img src="image_138.png" /><figcaption>**FIGURE 4** | Redundancy discriminant analysis showing biological parameters distribution at T4 sampling site based on physicochemical variables evaluated in upper Ter River basin (NE Catalonia, Spain) in 2019. Axes 1 and 2 combined explain 83.3% of variance. Seasons are winter (W), spring (Sp), summer (Su), and autumn (Au). Drivers: pH, temperature (Temp), electrical conductivity (EC), dissolved oxygen (DO), turbidity, nitrite (N-NO₂⁻), nitrate (N-NO₃⁻), ammonium (N-NH₄⁺), and phosphate concentration (P-PO₄³⁻), and nitrogen to phosphorus ratio (DIN:SRP). Response variables: geosmin, minimum fluorescence yield (F₀), photosynthetic efficiency (Yeff), chlorophyll-a concentration (Chla), ash free dry mass (ADFM), Margalef index (MI), cyanobacteria (Cb), diatoms (Dt) and green algae (GA) biomass, and relative abundance of cyanobacteria (R_Cb) and diatoms (R_Dt).</figcaption></figure>

highest concentration of Chl-a and diatoms occur in autumn and early winter, driven by higher values of the DIN:SRP ratio, nitrates concentration, and dissolved oxygen.

An MLR and an RF model between the physicochemical variables (drivers) and geosmin concentration was performed at different time lags (weeks) to identify which factors are the best predictors of geosmin concentration.

Both RF and MLR modeling techniques led to good model fits, but the RF was the better of the two at each time lag (based on R² adjusted) (Table 3). At t = 0 weeks (w), the R² adj. provided by the RF is 0.70, increasing up to 0.81 at t = 2w, and decreasing below 0.5 at 8 weeks. A similar pattern is found for the MLR, but in this case, the higher predictive accuracy is 0.62 (t = 2w) and decreases below 0.5 at 6 weeks. The analysis of relative predictor importance revealed patterns associated with the time lag of

prediction (Figure 5 and Supplementary Figure 1). Before lag 4 weeks (t = 5-t = 10w), nitrates concentration and temperature are the parameters with higher relative importance within the RF model (≈10-20%). From that moment on, the phosphorus concentration, the DIN:SRP ratio, and the turbidity value begin to gain relevance, being at t = 2w when the phosphorus concentration reaches its maximum (28.3%). At this time, there is also a decrease in the relative importance of nitrate concentration, which coincides with an increase in the weight of the DIN:SRP ratio within the model, which is one of the main predictors of geosmin concentration among time-lags 0-2w (22.4-15.2%). At t = 0 and t = 1 week, turbidity is the parameter with the higher relative importance (26.1 and 23.8%, respectively).

DISCUSSION

This study, carried out in the upper part of the Ter River basin, suggested the physicochemical parameters that could trigger geosmin appearance in a river where benthic cyanobacteria are the main geosmin producers.

Geosmin Episodes in the Upper Ter River Basin

During the study, geosmin concentration varied depending on the sampling site and the season (Figure 2). Sampling sites

<table><caption>**TABLE 3** | Predictive accuracy as measured by R² adjusted for different time lags (in weeks), for multiple linear regression (MLR) and random forest (RF) models in upper Ter River basin (NE Catalonia, Spain) in 2019.</caption><thead><tr><th rowspan="2">R<sup>2</sup> adj.</th><th colspan="10">Time lag (weeks)</th></tr><tr><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th></tr></thead><tbody><tr><th>MLR</th><td>0.61</td><td>0.58</td><td>0.62</td><td>0.55</td><td>0.49</td><td>0.50</td><td>0.47</td><td>0.45</td><td>0.36</td><td>0.35</td><td>0.30</td></tr><tr><th>RF</th><td>0.70</td><td>0.73</td><td>0.81</td><td>0.77</td><td>0.74</td><td>0.68</td><td>0.57</td><td>0.52</td><td>0.46</td><td>0.46</td><td>0.43</td></tr></tbody></table>
<figure><img src="image_139.png" /><figcaption>**FIGURE 5** | Relative importance (in %) of main predictors [temperature, in degrees Celsius; nitrates concentration, in N-NO₃⁻ mg/L; phosphate concentration (SRP), in P-PO₄³⁻ mg/L; DIN:SRP ratio, in mol:mol; and turbidity, in NTU] at different time lags (in weeks) in random forest model.</figcaption></figure>

differed mainly in the nutrient's concentration, being higher in T3 and T4, located downstream. The nutrient concentration pattern measured along the upper river Ter could be explained by nearby land uses, which in the Osona region (where T3 and T4 are located) are predominantly related to agriculture (35% of the total area) and urban and industrial development (CORINE Land Cover System, 2018), whereas El Ripollès region, where T1 and T2 sampling sites were located, was characterized by a high percentage of forest and pastures (between 31.2 and 42.5%). The lower levels of point and non-point pollution sources, related to dominant land-use together with the river continuum concept, could help to explain the lower nutrient concentrations found at these sampling sites (Table 1). Furthermore, forests and pastures are environments that can contribute to reducing surface erosion and soil sediment runoff, which are among the main diffuse sources of phosphorus to freshwater rivers. Ongley et al. (2010) reported that agriculture contributes >50% of the total nutrient load, and the fraction of this total load is highly dependent on the proportion of agriculture in the watershed (Shi et al., 2017). Furthermore, agricultural activities are used to have a marked seasonality, with spring being the season that, due to a greater number of rains and higher temperatures, favors better growth and development of crops. This agrees with what is observed in the agricultural activity of Osona, where most of the crops are cereals (53.7%) and forages (43.1%), whose sowing time is in late winter and spring (mainly in March) (Departament d'Agricultura, Generalitat de Catalunya). This makes agricultural land uses have a greater impact on water quality in winter and spring, when planting occurs and crops are fertilized. Moreover, in spring, a larger amount of rainfall could cause fertilizer used in the crops to overflow into the river leaches, directly or

through the contribution of groundwater, thereby leading to the deterioration of river water quality. Specifically, ammonium and phosphorus can be easily absorbed by soil particles, then being transported to streams and rivers in events of soil erosion and runoff (Withers and Jarvie, 2008). In the opposite way, nitrate is highly soluble and mobile, and when there is an excess of nitrates, it is leached to groundwater and reaches the rivers through underground flows (Grizzetti et al., 2011). The difference in the mobilization dynamic of nitrogen and phosphorus from the surface can give rise to changes in the DIN:SRP ratio.

Both the concentration of nutrients and the DIN:SRP ratio are factors related to the development of certain cyanobacteria, being described that high nutrient concentrations favor the appearance of cyanobacterial blooms (Dodds and Smith, 2016; Lee et al., 2017), in many cases related to geosmin production (Ding et al., 2014). Similar results were observed in this field study, where higher nutrient concentration may have generated favorable conditions for the cyanobacterial development within biofilm communities (0.91 ± 0.58 µg/cm² in T4 compared with 0.33 ± 0.44 µg/cm² in T1, mean annual value). Some studies have described that a high nitrogen concentration is necessary for cyanobacteria blooms to occur (>0.8 mgTN/L, Xu et al., 2015; ≈0.1 mgN-NH₄⁺/L, ≈1.1 mgN-NO₃⁻/L, Espinosa et al., 2021). Perkins et al. (2019) pointed out that the ammonium concentration was key for stimulating cyanobacteria development and production of T&O compounds, specifically revealing that metabolites were associated with high ammonium relative to nitrate. In the opposite way, a study performed by Harris et al. (2016) suggested that relatively low NO₃⁻:NH₃ ratios provide conditions that favor the production of cyanobacterial metabolites. A previous study
by Jankowiak et al. (2019) described that phosphorus availability
excess may stimulate cyanobacteria blooms, and some studies
have pointed out that total phosphorus (TP, both organic and
inorganic forms) concentration had to be between 20 and
100 µg TP/L to control the growth of cyanobacteria (Sharma
et al., 2011; Li et al., 2018). Moreover, Graham et al. (2004)
identified the dominance of cyanobacteria often greatest when
the total nitrogen (TN):TP ratio was low (<29:1 by mass),
similar to the results found by Harris et al. (2016) in a
study carried out in four North American reservoirs. Vilalta
et al. (2003), in a study performed in the Llobregat River
(Catalonia, NE Spain), suggested that an unbalanced proportion
between nitrogen and phosphorus had an effect on benthic
geosmin production, being its appearance favored under low
TN:TP ratios (TN:TP = 10:1). The study carried out under
laboratory conditions by Espinosa et al. (2021) showed that
high nutrient concentration, together with a low DIN:SRP ratio
(DIN:SRP = 4:1), triggered the production and release of geosmin
by the Oscillatoria cyanobacterium present in the biofilm. This
difference in the results could be due to factors related to the study
system, such as stratification, redox potential, and resuspension
of nutrients in reservoirs. However, it could be pointed out
that a minimum of nitrogen should be necessary to favor
cyanobacteria development. Nevertheless, to trigger geosmin
production, an increase in the phosphorus concentration must
occur, leading to an imbalance in the DIN:SRP ratio or TN:TP
ratio. A similar situation has been observed in this study, as
both cyanobacteria abundance and geosmin concentration was
favored by high nutrient concentrations and lower DIN:SRP
ratio, led by an increase of phosphorus concentration. The
effect was clearly observed at T4, the sampling site with
higher nutrient concentrations, where in late winter-early spring,
there was a pronounced decrease of the DIN:SRP ratio value
(Figure 4), associated with an important episode of geosmin. The
interaction between DIN:SRP ratio and nutrient concentration
seems to be an important driver favoring the production
and release of this metabolite from benthic cyanobacteria
in the Ter River.

Mismatch Between Cyanobacteria
Abundance and Geosmin Concentration

Although cyanobacteria presence and geosmin production seem
to be favored by the same physicochemical factors, in this
field study, no significant correlation was found between both
parameters. This is somehow surprising, as these microorganisms
are described as the main geosmin producers in freshwater
ecosystems. One reason could be that an identification of the
biofilm community at the genus level was not carried out in
this study, and, as previously discussed, not all cyanobacteria are
geosmin producers. On the other hand, it could be explained
by the relative dynamics of geosmin production and release
associated with the cyanobacteria life cycle. In fact, it has been
described under controlled conditions that geosmin production
mainly occurs during the growth phase, and its release to water
is the direct consequence of biomass decomposition and/or cell
lysis (Kim et al., 2018).

Although no biofilm community identification was carried
out throughout the study, biofilm samples were taken from the
T4 sampling site in late March for a parallel study carried out
under laboratory conditions. These samples were characterized,
and the cyanobacterium *Oscillatoria* sp. was identified as the
main geosmin producer (Espinosa et al., 2021). Furthermore, a
visual difference detected *in situ* was the presence of floating
cyanobacterial mats coming from the biofilm in winter, whereas
in summer, these mats were not observed (personal observation).

Regarding the mismatch found between cyanobacteria and geosmin in late winter-early spring, it could be explained by the cyanobacterial life cycle itself. Different studies performed by Hu et al. (2001, 2003) evaluating different cyanobacterial species explained that intracellular geosmin concentration increased in proportion to biomass. In addition to intracellular accumulation, it was observed that the concentration of geosmin in water began to increase. Once these cyanobacteria reached the stationary phase, there was a rapid decrease in intracellular concentration with a corresponding rapid increase of geosmin release, indicating that cell lysis and decomposition of geosmin producers may result in large spikes of these compounds in water supplies. Similar results were observed by Cai et al. (2017); Alghanmi et al. (2018), and Espinosa et al. (2021), supporting the idea that the majority of geosmin is normally retained with cyanobacterial cells during their growth, and release to the medium occur as a consequence of lysis and cellular decomposition. Different studies have pointed out that, depending on the cyanobacterial strain, the growth phase differs. Kruskopf and Du Plessis (2006) observed that *Oscillatoria simplicissima* reached the fast growth phase after 8 days, whereas Espinosa et al. (2021) found out the maximum *Oscillatoria* sp. presence at 16 days, and Jindal et al. (2011) described that *Oscillatoria formosa* could grow exponentially for 24 days before starting the stationary phase. In this field study, the relative abundance of cyanobacteria reached its peak after 1-2 weeks of gradual increase and 1 week later started to decrease, whereas geosmin in the water started reaching its peak 1 week later. This would confirm what was observed in several studies reporting geosmin release to water as a consequence of cyanobacterial biomass decomposition and/or cell lysis.

Despite the lack of correlation between cyanobacteria
abundance and the geosmin concentration, the trends that have
been observed, such as the ones shown in **Figure 6**, can help
make a hypothesis about the geosmin drivers in the upper river
Ter. This figure shows a notable increase in the cyanobacteria
abundance during the first months of the year, reaching almost
50% of the biofilm community at the beginning of March and
decreasing strongly 15 days later, coinciding with the geosmin
peak (Figure 6A). Similar behavior was observed at the end
of April. Nevertheless, this trend only occurs in winter and
spring. In summer, cyanobacteria presence was also high, but
geosmin was not detected, indicating that the factors favoring
its production are not stable throughout the year and that a
set of specific conditions have to co-occur to trigger geosmin
production by benthic cyanobacteria. Some of the factors that
differ between these two moments were the DIN:SRP ratio and
the temperature, which presented significantly lower values in
<figure><img src="image_140.png" /></figure>

<figure><img src="image_141.png" /><figcaption>FIGURE 6 | (A) Mean value and standard deviation of geosmin concentration (nanogram per liter), and cyanobacteria relative abundance (%) and (B) mean value and standard deviation of geosmin concentration (nanogram per liter) and DIN:SRP ratio (mol/mol), water temperature (°C) in T4 sampling site for different sampling in 2019, being Jan = January, Feb = February, Mar = March, Apr = April, May = May, Jul = July, Sep = September, Oct = October, Nov = November, and Dec = December. Blue boxes indicate time when cyanobacteria relative abundance in biofilm was high.</figcaption></figure>

winter-spring than in summer (Figure 6B). The study performed by Alghanmi et al. (2018) pointed out that many cyanobacteria grow better under 25°C conditions, but this does not imply higher geosmin production. In fact, some studies have found higher geosmin concentration and production yield at 10°C compared with higher temperatures (25 and 35°C), indicating that lower temperatures could stimulate geosmin production and favor the accumulation of geosmin in cells (Zhang et al., 2009; Wang and Li, 2015). This would agree with our study, where higher geosmin concentration was observed at lower temperatures (close to 10°C), whereas at higher temperatures (20-25°C), geosmin levels were below the detection limit (Figure 6B). Moreover, considering that low light conditions have been described to favor intracellular geosmin production in river biofilms (Espinosa et al., 2020), the increased light availability occurring in summer may be an additional limiting factor for microbial geosmin production in the Ter River, as higher light incidence prevents gaseous vacuoles formation and geosmin production (Li et al., 2012). In fact, different studies have shown that at temperatures ≈20-25°C, higher light intensity hinders the production of geosmin by cyanobacteria (Oh et al., 2017; Alghanmi et al., 2018).

## Change in the Relative Predictor Importance

The change in the physicochemical conditions at the different sampling sites and seasons could explain the change in the relative importance of the parameters included in the models developed at different time lags. As shown in Figure 5, nitrate concentration and temperature are more relevant in predicting geosmin between lags 5 and 10 (1-2 and a half months in advance), which could indicate the need for high basal nitrate conditions (Xu et al., 2015; Espinosa et al., 2021). However, for a geosmin episode to occur, it seems that a gradual increase in the phosphorus concentration, whose maximum importance as a geosmin driver within the model

is reached in lag 2-w, is needed to generate conditions that would favor the development of cyanobacteria biomass. The increase in phosphorus concentration generates an imbalance in the DIN:SRP ratio, whose values decrease as its relative importance in the model increases, up to its maximum at $t = 0$w (22.4%), indicating that this ratio must be kept low throughout the geosmin episode. These results agree with what was demonstrated by Espinosa et al. (2021) in a study performed under controlled conditions with biofilm communities collected from Ter River, in which cyanobacterial development (*Oscillatoria* sp.) and geosmin production were favored by higher nutrient concentration (both nitrogen and phosphorus) together with lower DIN:SRP ratio (4:1 compared with 64:1). Another parameter that has presented relatively high importance in the models is turbidity. This could have different explanations: the first one is that the lower incidence of light generated by greater turbidity promotes the development of low-light organisms, such as geosmin-producing cyanobacteria (Espinosa et al., 2020). In fact, other studies evaluating the light incidence with the Secchi Disk method have found a negative relationship between light penetration and geosmin concentration, supporting the idea that low light availability conditions may favor the development of geosmin-producing cyanobacteria (Dzialowski et al., 2009; Parr, 2014). Another explanation is that turbidity has been shown to be correlated with phosphorus concentration, mainly associated with the runoff process (Schilling et al., 2017), which has also been observed in this study. Finally, when the benthic geosmin-cyanobacteria producers are detached from the substrata, which coincide with the cells lysis and consequent geosmin release to the water column, it also releases different material, which can generate an increase in the turbidity value (i.e., solids trapped within thick biofilms in slow flow waters). This last point could explain why turbidity is the principal predictor at times 0 and 1.

The models developed with the database generated in the Ter River in 2019 have made it possible to know with a precision of 0.70-0.81 (considering 1 as the maximum possible value)
the geosmin concentration up to a month-month and a half in advance. In addition, the results generated by the models indicate that the RF algorithm offers a great option for the evaluation of long-term ecological data sets. This model has also made it possible to identify the related parameters in each lag and the necessary changes of physicochemical parameters that should occur to increase the possibility of a geosmin episode being triggered. Knowing the relative importance of geosmin drivers, as evidenced by this study, drinking water treatment companies have the possibility of advancing to geosmin episodes based on the monitoring of easier and cheaper variables. In this way, they can have enough time to implement the required treatment and prevent geosmin from reaching the consumer's tap, avoiding complaints from users by being able to offer quality drinking water continuously.

## CONCLUSION

Overall, this field study showed that factors directly and indirectly related to both global change and anthropogenic factors could be potential drivers of geosmin occurrence in Mediterranean rivers.

River stretches, which land uses of the surrounding areas favoring higher nutrient concentrations, are more susceptible to be affected by cyanobacterial blooms and geosmin episodes. For example, industrial and agricultural watersheds could lead to higher nutrient concentrations in river waters, which can favor certain cyanobacteria development (such as *Oscillatoria* sp.). Furthermore, agricultural watersheds are used to experience an increase of phosphorous concentration associated with planting and fertilization periods that may generate a DIN:SRP ratio decrease especially during late winter-early spring. This situation can favor that certain cyanobacterium would start to produce geosmin, which would be released into the water between 7 and 15 days after the cyanobacteria peak in biofilms, associated with the organism degradation or cell lysis.

These results could help to drinking water companies in the forecasting and management of geosmin episodes, being able to understand which ecological conditions are more prompt to favor

the appearance of geosmin in the water collected from surface waters and thus allowing them to implement more targeted treatment regimens before geosmin reach the consumer's tap.

## DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## AUTHOR CONTRIBUTIONS

All authors made substantial contributions to the conception or design of the work, or the acquisition, analysis, or interpretation of data for the work.

## FUNDING

We would like to acknowledge the water utilities Aigües de Vic S.A. and Aigües d'Osona S.A. (Catalonia, Spain) for their funding support in the study presented in this publication. The research leading to these results have received funding from the European Research Council under the European Union's Horizon 2020 Research and Innovation Program—H2020 INTCATCH (grant agreement n°689341). LP have received funding from the Post-doctoral Fellowships Programme Beatriu de Pinós, funded by the Secretary of Universities and Research (Government of Catalonia) and by the Horizon 2020 Programme of Research and Innovation of the European Union under the Marie Skłodowska-Curie Grant Agreement No. 801370.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.741750/full#supplementary-material

## REFERENCES

Alghanmi, H. A., Alkam, F. M., and Al-Taee, M. M. (2018). Effect of light and temperature on new cyanobacteria producers for geosmin and 2-methylisoborneol. *J. Appl. Phycol.* 30, 319–328. doi: 10.1007/s10811-017-1233-0

APHA (2005). *Standard Methods for the Examination of Water and Wastewater.* 21st Edn. Washington DC: American Public Health Association/American Water Works Association/Water Environment Federation.

Argudo, M., Gich, F., Bonet, B., Espinosa, C., Gutiérrez, M., and Guasch, H. (2020). Responses of resident (DNA) and active (RNA) microbial communities in fluvial biofilms under different polluted scenarios. *Chemosphere* 242:125108. doi: 10.1016/j.chemosphere.2019.125108

Cai, F., Yu, G., Zhang, K., Chen, Y., Li, Q., Yang, Y., et al. (2017). Geosmin production and polyphasic characterization of Oscillatoria limosa Agardh ex Gomont isolated from the open canal of a large drinking water system in Tianjin City, China. *Harmful Algae* 69, 28–37. doi: 10.1016/j.hal.2017.09.006

Clercin, N. A., and Druschel, G. K. (2019). Influence of environmental factors on the production of MIB and Geosmin metabolites by bacteria in a eutrophic reservoir. *Water Resour. Res.* 55, 5413–5430. doi: 10.1029/2018wr023651

CORINE Land Cover System (2018). *Copernicus Global Land Service. Providing Bio-Geophysical Products of Global Land Surface.*

Ding, Z., Peng, S., Jin, Y., Xuan, Z., Chen, X., and Yin, L. (2014). Geographical and seasonal patterns of geosmin and 2-methylisoborneol in environmental water in Jiangsu Province of China. *J. Anal. Methods Chem.* 2014:743924.

Dodds, W. K., and Smith, V. H. (2016). Nitrogen, phosphorus, and eutrophication in streams. *Inland Waters* 6, 155–164. doi: 10.5268/iw-6.2.909

Drury, B., Rosi-Marshall, E., and Kelly, J. J. (2013). Wastewater treatment effluent reduces the abundance and diversity of benthic bacterial communities in urban and suburban rivers. *Appl. Environ. Microbiol.* 79, 1897–1905. doi: 10.1128/aem.03527-12

Dzialowski, A. R., Smith, V. H., Huggins, D. G., deNoyelles, F., Lim, N. C., Baker, D. S., et al. (2009). Development of predictive models for geosmin-related taste and odor in Kansas, USA, drinking water reservoirs. *Water Res.* 43, 2829–2840. doi: 10.1016/j.watres.2009.04.001

Elosegui, A., and Sabater, S. (eds) (2009). *Conceptos y Técnicas en Ecología Fluvial*. Bilbao: Fundación BBVa.

Espinosa, C., Abril, M., Guasch, H., Pou, N., Proia, L., Ricart, M., et al. (2020). Water flow and light availability influence on intracellular geosmin production
in river biofilms. *Front. Microbiol.* 10:3002. doi: 10.3389/fmicb.2019.03002

Espinosa, C., Abril, M., Ponsá, S., Ricart, M., Vendrell-Puigmitjà, L., Ordeix, M., et al. (2021). Effects of the interaction between nutrient concentration and DIN: SRP ratio on geosmin production by freshwater biofilms. *Sci. Total Environ.* 768:14447. doi: 10.1016/j.scitotenv.2020.14447

Graham, J. L., Jones, J. R., Jones, S. B., Downing, J. A., and Clevenger, T. E. (2004). Environmental factors influencing microcystin distribution and concentration in the Midwestern United States. *Water Res.* 38, 4395–4404.

Grizzetti, B., Bouraoui, F., Billen, G., van Grinsven, H., Cardoso, A. C., Thieu, V., et al. (2011). *Nitrogen as a Threat to European Water Quality*. Cambridge: Cambridge University Press.

Harris, T., Smith, V., Graham, J., Van de Waal, D., Tedesco, L., and Clercin, N. (2016). Combined effects of nitrogen to phosphorus and nitrate to ammonia ratios on cyanobacterial metabolite concentrations in eutrophic Midwestern USA reservoirs. *Inland Waters* 6, 199–210. doi: 10.5268/iw-6.2.938

Ho, J. C., Michalak, A. M., and Pahlevan, N. (2019). Widespread global increase in intense lake phytoplankton blooms since the 1980s. *Nature* 574, 667–670. doi: 10.1038/s41586-019-1648-7

Hu, Q., Fortuna, A., Sommerfeld, M. R., and Westerhoff, P. K. (2003). "Physiological studies of MIB and geosmin-producing cyanobacteria isolated from the Phoenix drinking water supply system," in *Poster at the CAP LTER 5th Annual Poster Symposium* (Tempe: Arizona State University), 1–4.

Hu, Q., Sommerfeld, M., Lowry, D., Dempster, T., Westerhoff, P., Baker, L., et al. (2001). Production and release of geosmin by the cyanobacterium Oscillatoria splendida isolated from a Phoenix water source. *J. Phycol.* 37, 25–26.

Intergovernmental Panel on Climate Change [IPCC] (2019). *2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories*. Geneva: Intergovernmental Panel on Climate Change.

Jähnichen, S., Jäschke, K., Wieland, F., Packroff, G., and Benndorf, J. (2011). Spatiotemporal distribution of cell-bound and dissolved geosmin in Wahnbach reservoir: causes and potential odour nuisances in raw water. *Water Res.* 45, 4973–4982. doi: 10.1016/j.watres.2011.06.043

Jankowiak, J., Hattenrath-Lehmann, T., Kramer, B. J., Ladds, M., and Gobler, C. J. (2019). Deciphering the effects of nitrogen, phosphorus, and temperature on cyanobacterial bloom intensification, diversity, and toxicity in western Lake Erie. *Limnol. Oceanogr.* 64, 1347–1370. doi: 10.1002/lno.11120

Jeffrey, S. W., and Humphrey, G. F. (1975). New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. *Biochem. Physiol. Plants* 167, 191–194. doi: 10.1016/s0015-3796(17)30778-3

Jindal, N., Singh, D. P., and Khattar, J. I. S. (2011). Kinetics and physico-chemical characterization of exopolysaccharides produced by the cyanobacterium *Oscillatoria formosa*. *World J. Microbiol. Biotechnol.* 27, 2139–2146.

Jüttner, F., and Watson, S. B. (2007). Biochemical and ecological control of geosmin and 2-methylisoborneol in source waters. *Appl. Environ. Microbiol.* 73, 4395–4406. doi: 10.1128/aem.02250-06

Karaouzas, I., Theodoropoulos, C., Vardakas, L., Kalogianni, E., and Skoulikidis, N. (2018). A review of the effects of pollution and water scarcity on the stream biota of an intermittent Mediterranean basin. *River Res. Appl.* 34, 291–299. doi: 10.1002/rra.3254

Kehoe, M. J., Chun, K. P., and Baulch, H. M. (2015). Who smells? Forecasting taste and odor in a drinking water reservoir. *Environ. Sci. Technol.* 49, 10984–10992. doi: 10.1021/acs.est.5b00979

Kim, K., Park, C., Yoon, Y., and Hwang, S. J. (2018). Harmful cyanobacterial material production in the North Han river (South Korea): genetic potential and temperature-dependent properties. *Int. J. Environ. Res. Public Health* 15:444.

Kruskopf, M., and Du Plessis, S. (2006). Growth and filament length of the bloom forming *Oscillatoria simplicissima* (Oscillatoriales, Cyanophyta) in varying N and P concentrations. *Hydrobiologia* 556, 357–362. doi: 10.1007/s10750-005-1061-0

Lee, J., Rai, P. K., Jeon, Y. J., Kim, K. H., and Kwon, E. E. (2017). The role of algae and cyanobacteria in the production and release of odorants in water. *Environ. Pollut.* 227, 252–262. doi: 10.1016/j.envpol.2017.04.058

Li, J., Hansson, L. A., and Persson, K. M. (2018). Nutrient control to prevent the occurrence of cyanobacterial blooms in a eutrophic lake in Southern Sweden, used for drinking water supply. *Water* 10:919.

Li, Z., Hobson, P., An, W., Burch, M. D., House, J., and Yang, M. (2012). Earthy odor compounds production and loss in three cyanobacterial cultures. *Water Res.* 46, 5165–5173. doi: 10.1016/j.watres.2012.06.008

Lukassen, M. B., de Jonge, N., Bjerregaard, S. M., Podduturi, R., Jørgensen, N. O., Petersen, M. A., et al. (2019). Microbial production of the off-flavor geosmin in tilapia production in Brazilian water reservoirs: importance of bacteria in the intestine and other fish-associated environments. *Front. Microbiol.* 10:2447. doi: 10.3389/fmicb.2019.02447

Meteoatoc (2019). *Servei Meteorològic de Catalunya. Bolletí Climàtic Anual del 2019*. Barcelona: Generalitat de Catalunya. Departament de Territori i Sostenibilitat.

Murphy, J., and Riley, J. P. (1962). A modified single solution method for the determination of phosphate in natural waters. *Anal. Chim. Acta* 27, 31–36. doi: 10.1016/s0003-2670(00)88444-5

Oh, H. S., Lee, C. S., Srivastava, A., Oh, H. M., and Ahn, C. Y. (2017). Effects of environmental factors on cyanobacterial production of odorous compounds: geosmin and 2-methylisoborneol. *J. Microbiol. Biotechnol.* 27, 1316–1323. doi: 10.4014/jmb.1702.02069

Olsen, B. K., Chislock, M. F., and Wilson, A. E. (2016). Eutrophication mediates a common off-flavor compound, 2-methylisoborneol, in a drinking water reservoir. *Water Res.* 92, 228–234. doi: 10.1016/j.watres.2016.01.058

Ongley, E. D., Xiaolan, Z., and Tao, Y. (2010). Current status of agricultural and rural non-point source pollution assessment in China. *Environ. Pollut.* 158, 1159–1168. doi: 10.1016/j.envpol.2009.10.047

Parr, G. (2014). *Development of Predictive Geosmin Models in Northern Colorado Lakes, Reservoirs, and Rivers 2000-2019-CSU*. Ph.D. thesis. Fort Collins, CO: Colorado State University.

Perkins, R. G., Slavin, E. I., Andrade, T. M. C., Blenkinsopp, C., Pearson, P., Froggatt, T., et al. (2019). Managing taste and odour metabolite production in drinking water reservoirs: the importance of ammonium as a key nutrient trigger. *J. Environ. Manag.* 244, 276–284. doi: 10.1016/j.jenvman.2019.04.123

Rand, M. C., Greenberg, A. E., and Taras, M. J. (1976). *Standard Methods for the Examination of Water and Wastewater*. Washington, DC: American Public Health Association.

Reardon, J., Foreman, J. A., and Searcy, R. L. (1966). New reactants for the colorimetric determination of ammonia. *Clin. Chim. Acta* 14, 403–405. doi: 10.1016/0009-8981(66)90120-3

Rubio-Gracia, F., García-Berthou, E., Zamora, L., Martí, E., Almeida, D., Espinosa, C., et al. (2017). Combined effects of hydrologic alteration and cyprinid fish in mediating biogeochemical processes in a Mediterranean stream. *Sci. Total Environ.* 601–602, 1217–1225. doi: 10.1016/j.scitotenv.2017.05.287

Schilling, K. E., Kim, S. W., and Jones, C. S. (2017). Use of water quality surrogates to estimate total phosphorus concentrations in Iowa rivers. *J. Hydrol. Reg. Stud.* 12, 111–121. doi: 10.1016/j.ejrh.2017.04.006

Sharma, N. K., Choudhary, K. K., Bajpai, R., and Rai, A. K. (2011). "Freshwater cyanobacterial (bluegreen algae) blooms: causes, consequences and control," in *Impact, Monitoring and Management of Environmental Pollution*, ed. A. Al-Nemr (Hauppauge, NY: Nova Science Publishers), 73–95.

Shi, P., Zhang, Y., Li, Z., Li, P., and Xu, G. (2017). Influence of land use and land cover patterns on seasonal water quality at multi-spatial scales. *Catena* 151, 182–190. doi: 10.1016/j.catena.2016.12.017

Smith, V. H., DeNoyelles, F., Pan, S., Sieber-Denlinger, J., Randtke, S. J., Strasser, V. A., et al. (2009). Managing taste and odor problems in a eutrophic drinking water reservoir. *Lake Reserv. Manag.* 18, 319–323. doi: 10.1080/07438140209353938

Vilalta, E. (2004). *Structure and Function in Fluvial Biofilms: Implications in River DOC Dynamics and Nuisance Metabolite Production*. Ph.D. thesis. Barcelona: University of Barcelona.

Vilalta, E., Guasch, H., Muñoz, I., Navarro, E., Romaní, A. M., Valero, F., et al. (2003). Ecological factors that co-occur with geosmin production by benthic cyanobacteria. The case of the Llobregat River. *Algol. Stud.* 109, 579–592. doi: 10.1127/1864-1318/2003/0109-0579

Wang, Z., and Li, R. (2015). Effects of light and temperature on the odor production of 2-methylisoborneol-producing pseudanabaena sp. and geosmin-producing *Anabaena ucrainica* (cyanobacteria). *Biochem. Syst. Ecol.* 58, 219–226. doi: 10.1016/j.bse.2014.12.013

Watson, S. B., Monis, P., Baker, P., and Giglio, S. (2016). Biochemistry and genetics of taste- and odor-producing cyanobacteria. *Harmful Algae* 54, 112–127. doi: 10.1016/j.hal.2015.11.008
Withers, P. J. A., and Jarvie, H. P. (2008). Delivery and cycling of phosphorus in rivers: a review. *Sci. Total Environ.* 400, 379–395.

Xu, H., Paerl, H. W., Qin, B., Zhu, G., Hall, N. S., and Wu, Y. (2015). Determining critical nutrient thresholds needed to control harmful cyanobacterial blooms in eutrophic Lake Taihu, China. *Environ. Sci. Technol.* 49, 1051–1059. doi: 10.1021/es503744q

Zhang, T., Li, L., Song, L., and Chen, W. (2009). Effects of temperature and light on the growth and geosmin production of Lyngbya kuetzingii (Cyanophyta). *J. Appl. Phycol.* 21, 279–285. doi: 10.1007/s10811-008-9363-z

**Conflict of Interest:** The authors declare that this study received funding from Aigües de Vic S.A. and Aigües d'Osona S.A. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

The handling editor SM declared a past co-authorship with several of the authors CE, MA, SP, LV-P, LL, LP.

**Publisher's Note:** All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2021 Espinosa, Abril, Bretxa, Jutglar, Ponsà, Sellarès, Vendrell-Puigmitjà, Llenas, Ordeix and Proia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_142.png" /></figure>

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# Microbial Transformation of Chlordecone and Two Transformation Products Formed During *in situ* Chemical Reduction

Jennifer Hellal¹*, Pierre-Loïc Saaidi², Sébastien Bristeau¹, Marc Crampon¹, Delphine Muselet², Oriane Della-Negra², Aourell Mauffret¹, Christophe Mouvet¹ and Catherine Joulian¹

¹ BRGM, Orléans, France, ² UMR 8030 Génomique Métabolique, CEA, Institut de Biologie François Jacob, Genoscope, Université d'Evry Val d'Essonne, Université Paris-Saclay, Evry, France

**OPEN ACCESS**

**Edited by:**
Fabrice Martin-Laurent,
Institut National de la Recherche
Agronomique (INRA), France

**Reviewed by:**
Joaquim Vila,
University of Barcelona, Spain
Giovanni Pilloni,
ExxonMobil Research
and Engineering, United States

*Correspondence:
Jennifer Hellal
j.hellal@brgm.fr

**Specialty section:**
This article was submitted to
Microbiotechnology,
a section of the journal
Frontiers in Microbiology

**Received:** 15 July 2021
**Accepted:** 30 September 2021
**Published:** 04 November 2021

**Citation:** Hellal J, Saaidi P-L, Bristeau S, Crampon M, Muselet D, Della-Negra O, Mauffret A, Mouvet C and Joulian C (2021) Microbial Transformation of Chlordecone and Two Transformation Products Formed During *in situ* Chemical Reduction. *Front. Microbiol.* 12:742039.
doi: 10.3389/fmicb.2021.742039

Chlordecone (CLD) is a very persistent synthetic organochlorine pesticide found in the French West Indies. Recently published work has demonstrated the potential of zero-valent iron to dechlorinate CLD by *in situ* chemical reduction (ISCR) in soils under water-saturated conditions, forming mono- to penta-dechlorinated CLD transformation products. These transformation products are more mobile than CLD and less toxic; however, nothing is known about their further degradation, although increasing evidence of CLD biodegradation by bacteria is being found. The present study began with the enrichment from wastewater sludge of a CLD-transforming community which was then inoculated into fresh media in the presence of either CLD or two of the main ISCR transformation products, 10-monohydroCLD (-1CI-CLD) and tri-hydroCLD (-3CI-CLD). Carried out in triplicate batches and incubated at 38°C under anoxic conditions and in the dark, the cultures were sampled regularly during 3 months and analyzed for CLD, -1CI-CLD, -3CI-CLD, and possible transformation products by gas chromatography coupled to mass spectrometry. All batches showed a decrease in the amended substrates (CLD or hydroCLD). CLD degradation occurred with concomitant formation of a nine-carbon compound (pentachloroindene) and two sulfur-containing transformation products (chlordeethiol, CLD-SH; methyl chlordecsulfide, CLD-SCH₃), demonstrating competing transformation pathways. In contrast, -1CI-CLD and -3CI-CLD only underwent a sequential reductive sulfidation/S-methylation process resulting in -1CI-CLD-SH and -1CI-CLD-SCH₃ on the one hand, and -3CI-CLD-SH, -3CI-CLD-SCH₃ on the other hand. Some sulfur-containing transformation products have been reported previously with single bacterial strains, but never in the presence of a complex microbial community. At the end of the experiment, bacterial and archaeal populations were investigated by 16S rRNA gene amplicon sequencing. The observed diversity was mostly similar in the CLD and -1CI-CLD conditions to the inoculum with a dominant archaea genus, *Methanobacterium*, and four OTU affiliated to bacteria, identified at the family (*Spirochaetaceae*) or genus level (*Desulfovibrio*, *Aminobacterium*,
and *Soehngenia*). On the other hand, in the -3Cl-CLD condition, although the same OTU were found, *Clostridium* sensu stricto 7, *Candidatus Cloacimonas*, and *Proteiniphilum* were also present at > 2% sequences. Presence of methanogens and sulfate-reducing bacteria could contribute to sulfidation and S-methylation biotransformations. Overall, these results contribute to increasing our knowledge on the biodegradability of CLD and its transformation products, helping to progress toward effective remediation solutions.

**Keywords: chlordecone (CLD), West Indies, ISCR, bacteria, biotransformation**

INTRODUCTION

Soils, surface water, and groundwater in Martinique and Guadeloupe (French West Indies, FWI) are contaminated with chlordecone [CLD; C₁₀Cl₁₀O or C₁₀Cl₁₀H₂O₂ in hydrated form; CAS number 143-50-0; decachlorooctahydro-1,3,2-metheno-2H-cyclobuta(c,d)pentalen-2-one], a very persistent synthetic organochlorine pesticide. CLD was widely used (3 kg·ha⁻¹ per year; an estimated total of 300 t) in the FWI, officially from 1972 to 1978 and from 1981 to 1993, to protect banana plantations against the banana weevil (*Cosmopolites sordidus*). CLD is a highly hydrophobic organochlorine pesticide (Kow between 4.5 and 5.4), strongly adsorbed to the soil (log Koc = 4.1–4.2) and poorly soluble (2 mg·l⁻¹ at 25°C) (UNEP, 2007; U.S. Environmental Protection Agency, 2012). In Martinique, 80% of soil analyses carried out on plots that had historically been cultivated with bananas between 1970 and 1993 (18,000 ha) show CLD contamination. Its persistence in the soils of the FWI has been estimated at several decades in nitisols, centuries in ferralsols, and half a millennium in andosols. CLD builds up in food chains, and long-term chronic exposure to CLD through food and drinking water can have serious consequences on human health. Indeed, CLD is a potential carcinogen and causes liver tumors in laboratory rats and mice (Reuber, 1979). It is a reproductive and developmental toxicant, an endocrine disruptor, and a neurotoxicant depending on the exposure doses (Dallaire et al., 2012; Boucher et al., 2013; Costet et al., 2015). It is also suspected of increasing the risk of preterm birth and delaying development in young children. Epidemiological studies have made it possible to link its chronic exposure to an increase in the occurrence of prostate cancer and its recurrence (Kadhel et al., 2014; Cordier et al., 2015; Emeville et al., 2015; Multigner et al., 2016; Brureau et al., 2019). Although preventive measures such as prior authorization for cultivation, the ban on fishing, the closure of aquaculture farms, or the control of foodstuffs on display have been installed to avoid transfer to local populations, pollution of the West Indian environment remains a major problem.

Following a study conducted by the French Geological Survey (BRGM, Colombano et al., 2009), a degradation treatment based on *in situ* chemical reduction (ISCR) was identified as a promising depollution technique to treat contaminated soils on a large scale. ISCR using zero-valent iron (ZVI) or nano-ZVI is a promising depollution method used for chlorinated organic pollutants in soils or contaminated aquifers (Li et al., 2021). From 2010 to 2016, a succession of projects tested this remediation method in the laboratory and in the field (Dictor

et al., 2011; Belghit et al., 2015; Mouvet et al., 2017, 2020) and showed that ISCR leads to the formation of several dechlorination products, reducing the concentration of CLD by 70% in two of the three main types of West Indian soils (nitisols and ferralsols). This allowed plants to be cultivated with levels of contamination falling below the authorized maximum residue limit for CLD (20 µg·kg⁻¹ wet weight) unlike untreated soils (Mouvet et al., 2017, 2020). The main degradation products formed during ISCR are hydrochlordecones that have lost one or more chlorine atoms, especially -1Cl-CLD (10-monohydrochlordecone) and -3Cl-CLD (trihydrochlordecone). Additional toxicity research has shown that these major transformation products are neither mutagenic nor genotoxic and have lower proangiogenic properties than CLD (Legeay et al., 2017). On the other hand, they are more mobile and can therefore reach groundwater more easily (Ollivier et al., 2020a,b). However, to date we do not have information on the biodegradability of these transformation products and still few elements on the biodegradation of the CLD parent molecule.

Despite the consensus at the time, which admitted that CLD was non-biodegradable (Cabidoche et al., 2009), Dolfing et al. (2012) argued that the bibliographic, thermodynamic, and experimental data suggested that the molecule is not completely refractory to a microbial attack under reducing conditions. Since then, with the progress made in analytical chemistry, several studies from independent laboratories have demonstrated a microbial degradation of CLD with opening of the bishomocubane cage and loss of a C₁ fragment. In each case, CLD was incubated in laboratory conditions with either a bacterial consortia or a bacterial strain isolated from one of these consortia (i.e., *Citrobacter* sp. 86 and *Desulfovibrio* sp. 86) (Chaussonnerie et al., 2016; Chevallier et al., 2019; Lomheim et al., 2020). Recently, Della-Negra et al. (2020) have also shown that a change in incubation conditions of *Desulfovibrio* sp. 86 could also give rise to chlordethiol, the sulfur analog of chlordecone alcohol. Taken together, these results have enabled to classify the identified transformation products resulting from CLD biotransformation into seven families. However, it should be noted that these experimental results were all obtained under artificial anaerobic conditions, which do not correspond to the real redox state of FWI soils. Additionally, several of these studies (Chevallier et al., 2019; Della-Negra et al., 2020; Lomheim et al., 2020) also highlight the presence of these seven families of transformation products in the West Indian environment, reviving the idea that the microbial biodegradation of CLD under natural conditions remains possible.
In the present study, the enrichment of a new microbial
consortium from wastewater sludge capable of biotransforming
4 mg·l⁻¹ of CLD within a few weeks under reductive laboratory-
controlled conditions into the same nine-carbon compound
(pentachloroindene, C₉Cl₅H₃) as previously identified in
Chevallier et al. (2019) is presented. Its ability to transform either
CLD, -1Cl-CLD, or -3Cl-CLD, two of the major transformation
products derived from ISCR, was established, and further
characterization of the transformation products formed during
the incubation reveal the sulfidation and possible methylation of
the three parent molecules.

# MATERIALS AND METHODS

## Chemical

CLD hydrate (98% purity) was purchased from LGC Standards (Molsheim, France). 10-Monohydrochlordecone (-1Cl-CLD) (97% purity) and trihydrochlordecone (-3Cl-CLD) (97% purity) were obtained from Alpha Chimica (Chatenay-Malabry, France). ¹³C₁₀-chlordecone (CLD-¹³C₁₀) at 100 mg·l⁻¹ in nonane is commercialized by LGC Standards (Molsheim, France). 2,4,5,6,7-pentachloroindene C₉Cl₅H₃, chlordecthiol, 10-monohydrochlordecthiols, and methyl chlordecsulfide were synthesized and purified according to the literature (Chevallier et al., 2019; Della-Negra et al., 2020). Chemical products used for microbiological media were purchased from VWR (France). Analytic-grade organic solvents (acetone, cyclohexane, and dichloromethane) were purchased from Fisher Scientific (Illkirch, France). The individual standards were prepared from solid standards dissolved in acetone (200 mg·l⁻¹) and stored at -18°C.

## Consortium Enrichment

Before performing the experiment presented in this paper,
enrichments were carried out using three samples: (1) sludge
from a wastewater treatment plant (WWTP), collected in
September 2014 from a settling tank at the Orléans-La-Source
WWTP, (2) a nitisol soil in Martinique, collected in 2012, and (3)
sediment from the Bourget lake, collected in 2012. The hypothesis
behind using WWTP sludge and Bourget lake sediment was
that bacteria found in these environments are likely to have
been exposed to a large range of organic molecules, which
could make them able to degrade CLD. Moreover, Orndorff and
Colwell (1980) and Chaussonnerie et al. (2016) had previously
isolated bacteria capable of transforming CLD from a WWTP.
The hypothesis behind using soil from a contaminated field
in Martinique was that bacteria exposed to CLD could be
able to degrade it.

It took several years to enrich a CLD-transforming microbial
community from these samples, following two steps. The first
step lasted 32 months and consisted of fed batches set up in
anaerobic conditions. The second step consisted of reinoculating
the consortia in a liquid fresh medium. Fed batches consisted
of either 150 ml of wastewater sludge and 50 ml of culture
medium or 10 g of soil and 120 ml culture medium. The
culture medium that was used is similar to the OECD 311

directive and composed of 2/3 Solution A (KH₂PO₄ 0.27 g·l⁻¹; Na₂HPO₄·12H₂O 1.12 g·l⁻¹; NH₄Cl 0.53 g·l⁻¹, 1 ml of an oligo-element solution: SL9—see DSMZ 1257; pH 7) and 1/3 Solution B (CaCl₂·2H₂O 0.075 g·l⁻¹; MgCl₂·6H₂O 0.1 g·l⁻¹). Batches were supplemented with sodium acetate and sodium lactate (8 mM each) as a carbon source and Na₂S and L-cysteine (0.4 mM each) to induce reductive conditions. Abiotic controls were established in the same conditions except that batches were autoclaved (121°C, 40 min) three times at 24-h intervals followed by the addition of sodium azide (200 mg·l⁻¹). CLD was added to all batches in order to obtain concentrations of 2 mg·l⁻¹ using a concentrated solution prepared in acetone. Finally, blank controls consisted only of culture medium supplemented with the carbon sources and reducing agents. All batches were flushed with nitrogen to eliminate O₂ and incubated under agitation (80 rpm) in the dark at 30°C for 20 months and then at 38°C for the following 16 months. Temperature was raised as little degradation was observed at 30°C. During the first 32 months of enrichment, the batches were sampled (15 ml) and analyzed eight times (at months 0, 2, 7, 19, 20, 25, 28, and 32) to analyze chlordecone and its known degradation products by GC-MS (see below). At four of these sampling dates, T7, T20, T25, and T28, fresh culture medium, carbon sources, and CLD were added to the batches. When this occurred, GC-MS analyses were carried out before and after the feeding. Results from these fed batches showed an accumulation of CLD transformation product 2,4,5,6,7-pentachloroindene (C₉Cl₅H₃), especially in the batches containing WWTP sludge compared to the controls and led us to conclude that a bacterial biotransformation of CLD had occurred.

Based on these results, it was decided to pursue to the second
step of the enrichments using only the WWTP batches.

The second step was carried out to further enrich a CLD-
transforming microbial community. This step consisted
of reinoculating WWTP fed batches in fresh culture
medium (as described for step 1 and supplemented with
sodium acetate and sodium lactate, 4 mM each, and the
same amounts of reducers) to confirm the production
of C₉Cl₅H₃. Finally, after four reinoculations, the CLD-
transforming consortium was used to inoculate the
kinetic experiment presented in the present paper and
described below.

## Experimental Setup for the Kinetic Experiment

The experiment was carried out in 500-ml glass Schott bottles
with PTFE hermetic caps (Q-Series Cap, GL45, Omnifit®) to
minimize adsorption to materials and ensure anoxic conditions.
Batches consisted of 460 ml fresh culture media (as described
above) inoculated with 40 ml inoculum (around 8%) and
supplemented with sodium acetate and sodium lactate (8 mM
each) and 0.4 mM Na₂S. Controls were identical except for
the inoculum. Three conditions were set up in biotic and
control batches: the first received CLD (condition 1), the
second 10-monohydrochlordecone (-1Cl-CLD) (condition 2),
and the third trihydrochlordecone (-3Cl-CLD) (condition 3).
Initial concentrations of these substances were 4 mg·l⁻¹ in the batches. All conditions for controls and biotic assays were carried out in triplicates. Samples were collected for analysis of transformation products, Eh measurements, and DNA extraction at the beginning of the experiment and then after 8, 19, 29, 43, 57, and 90 days. A further sample was collected after 8 months to elucidate the fate of -1Cl-CLD and -3Cl-CLD. As this was not sufficient to conclude, fresh transfer cultures were made of conditions 2 and 3 by inoculating them into fresh media in the same conditions as the initial experiment; these transfer experiments are further referred to as condition 4 (transfer of condition 2 with -1Cl-CLD) and condition 5 (transfer of condition 3 with -3Cl-CLD).

# GC-MS Methods

## GC-MS Method 1

GC/MS/MS was achieved with a Bruker GC450 gas chromatograph, 1177 injector, automated sampler Combi Pal (CTC), and a triple-quadrupole mass spectrometer 300-MS. The instrument was equipped with a non-polar 30 m × 0.25 mm × 0.25 µm Rtx-1 column (Restek, Lisses, France) and a split/splitless injector. One microliter was injected in splitless mode. Mass spectrometry and a GC program have been described elsewhere (Bristeau et al., 2014).

## GC-MS Method 2

A Thermo Fisher Focus GC coupled to a single-quadrupole mass spectrometer (Thermo Fisher DSQ II) was used. The instrument was equipped with a non-polar 30 m × 0.25 mm × 0.25 µm DB-5MS column (Agilent J&W) and a split/splitless injector. Three microliters were injected in splitless mode. Ionization conditions and the GC program have been described elsewhere (Chevallier et al., 2019). Duplicate injections of all samples were performed.

# GC-MS Monitoring

## Step 1 Enrichment Batches

The 15-ml samples were extracted with dichloromethane and then analyzed by GC-MS (GC-MS method 1), according to the following protocol: an internal standard (0.3 ml of 4 mg·l⁻¹ CLD-¹³C in acetone) was first added to the sample to correct possible biases during extraction and analysis of CLD and its transformation products. After 6 h (at room temperature), 100 ml of dichloromethane was added and the flasks were shaken for 16 h on a ping-pong shaking table at a speed of 150 rpm. The samples were then centrifuged for 10 min at 2,500 rpm if necessary (in the presence of particles). Then, 10 ml of the organic phase was evaporated and transferred to cyclohexane for a final volume of around 1 ml. The extract was stored at -18°C before analysis by GC-MS. The recovery rates for this analysis protocol were estimated prior to the experiment and measured 103 ± 4% for CLD and 118 ± 2% for -3Cl-CLD.

Pentachloroindene was used as an identification standard. Its limited amount prevented us from developing a satisfactory quantification method. Instead, estimation of its relative

abundance was provided by calculating the relative peak area of each chlorinated species (Mouvet et al., 2017).

# Step 2 Batches and Kinetic Experiment

As there were no solid particles in the reinoculations or the batches set up to compare the transformability of CLD, -1Cl-CLD, and -3Cl-CLD, the extraction protocol was slightly modified: only 5 ml dichloromethane were added for 10 ml of culture medium. After 6 h agitation, 1.5 ml of organic phase were recovered and evaporated and the extract was retrieved in 1 ml cyclohexane. The recovery rates for this analysis protocol were estimated prior to the experiment and were 103 ± 5% for CLD, 74 ± 9% for -1Cl-CLD, and 72 ± 9% for -3Cl-CLD. As previously, C₉Cl₅H₃ results are shown as the area of the peaks whereas -1Cl-CLD, -3Cl-CLD, and CLD are shown in percentage of the initial amount (for examples of chromatograms, see Supplementary Figure 1).

# Further Characterization of Transformation Products

The initial analytical protocols allowed us to demonstrate the appearance of pentachloroindene during the biodegradation of CLD. However, it did not permit to detect any other transformation products, e.g., lower chlorinated polychloroindene or hydrochlorodecone analogs (-4Cl-CLD, -5Cl-CLD) in -1Cl-CLD and -3Cl-CLD conditions. A search for other transformation products was conducted using a protocol adapted from Chevallier et al. (2019) and Della-Negra et al. (2020). Sampling for the kinetic experiment and the transfer experiment was carried out after 8 months of incubation and 90 days of incubation, respectively. Two milliliters of each biotic and abiotic experiment were collected, acidified to pH 3 with HCl, and stored at -18°C. After defrosting, 750 µl of the aqueous samples were extracted three times with 250 µl dichloromethane. The combined organic layers were evaporated under a N₂ flux, dissolved in 100 µl chloroform, and analyzed using GC-MS method 2. The unambiguous presence of chlordethiol (CLD-SH), 10-monohydrochlordechiols (-1Cl-CLD-SH), and methyl chordecsulfide (CLD-SCH₃) was demonstrated in biotic incubations (Figure 2 and Supplementary Figures 2, 3). Identification was achieved using synthetic standards already available for these compounds (Della-Negra et al., 2020).

The structure of the other transformation products resulting from the biotransformation of -1Cl-CLD and -3Cl-CLD were determined from their EI-mass spectra and their GC retention times (Supplementary Figures 3–7).

We first focused on the two signals barely separable at retention times 18.93 min and 19.03 min present in -3Cl-CLD conditions only (Figure 2 entries 3 and 5). They both shared the same EI mass spectrum (data not shown), thus suggesting a pair of diastereoisomers. Their two major isotopic patterns centered at m/z 167 and 202 strongly resemble the predominant EI fragments at m/z 201 and 236 both present in the -1Cl-CLD-SH and CLD-SH mass spectra, which were previously assigned to [C₅Cl₃SH₂]⁺ and [C₅Cl₄SH₂]⁺• (Della-Negra et al., 2020). The
observed downward shift of 34 units suggested the replacement of a Cl atom by an H atom in the studied structure. Due to the rather low intensity of the two GC-MS signals at 18.93 and 19.03 min, no expected molecular ion could be detected. Instead, the highest fragment centered at m/z 371 showed an isotopic distribution compatible with the ion $[C_{10}Cl_7H_4]^{+}$. The analogous ions, namely, $[C_{10}Cl_9H_2]^{+}$ and $[C_{10}Cl_{10}H]^{+}$, present in -1Cl-CLD-SH and CLD-SH mass spectra, respectively, were indeed originated from the loss of HS by the molecular ions $[C_{10}Cl_9H_3S]^{+\bullet}$ and $[C_{10}Cl_{10}H_2S]^{+\bullet}$ (Della-Negra et al., 2020). Lastly, the retention times 18.93 and 19.03 min were significantly higher than that of starting material -3Cl-CLD (15.84 min) as in the case of both -1Cl-CLD-SH/-1Cl-CLD (22.89-22.97 min vs. 19.12 min) and CLD-SH/CLD (24.47 min vs. 20.46 min). Based on these observations and the context of the paralleled experiments carried out with CLD, -1Cl-CLD, and -3Cl-CLD, we assigned the trihydrochlordethiol structure (-3Cl-CLD-SH; **Figure 2**) to these compounds.

We applied the same methodology to the two other couples of signals at retention times 24.33 min/24.45 min and 20.44 min/20.58 min formed in -1Cl-CLD and -3Cl-CLD conditions, respectively. The first two signals showed the exact same mass spectrum, again suggesting a pair of diastereoisomers. The three key isotopic patterns centered at m/z 203, 213, and 250 were identical to those visible in the mass spectrum of CLD-SCH₃ (**Supplementary Figure 7**) and previously assigned to $[C_5Cl_4H]^{+}$, $[C_6Cl_3SH_4]^{+}$, and $C_6Cl_4SH_4]^{+}$, respectively (Della-Negra et al., 2020). These ions strongly suggested the presence of a methylthioether moiety connected to the C5-fragment characteristic of the bishomocubane ring. An interesting difference with CLD-SCH₃ lies in the absence of ion $[C_5Cl_5]^{+}$ that was in favor of a lower chlorinated methylchlordecsulfide congener. Lastly, the highest isotopic pattern of the mass spectrum centered at m/z 488 matched perfectly with the simulation of the molecular ion $[C_{11}Cl_9H_5S]^{+\bullet}$. We therefore ended up with the methyl monohydrochlordecsulfide structure for these transformation products (-1Cl-CLD-SCH₃; **Figure 2**). Most of the previous features remained in the mass spectrum associated with the last couple of signals at 20.44 min/20.58 min (**Supplementary Figure 4**), except that they were shifted down by 34, 68, or 102 units, depending on the fragments (**Supplementary Figure 7**). We thus concluded a common methyl trihydrochlordecsulfide structure (-3Cl-CLD-SCH₃; **Figure 2**).

## Microbial Community Monitoring

### DNA Extraction

Ten milliliters of each microcosm were sampled at each sampling time and filtered through 0.22-µm sterile membrane filters (Ø 2.5 cm, Millipore, Bedford, MA, United States). Membranes were stored at -20°C until DNA extraction. DNA was extracted using the FastDNA® Spin Kit for Soil and the FastPrep® instrument according to the manufacturer's instructions (MP Biomedicals, Santa Ana, CA, United States), with minor modifications (30 s lysis at a speed setting of 5.0 m·s⁻¹, subsequent centrifugation of cell debris

for 25 min). Extracted total DNA was quantified using the QuantiFluor dsDNA Sample Kit and the Quantus fluorimeter according to the manufacturer's instructions (Promega, United States).

## Capillary Electrophoresis Single-Strand Conformation Polymorphism Fingerprinting

CE-SSCP (capillary electrophoresis single-strand conformation polymorphism) bacterial community fingerprinting was performed at each sampling date. The V3 region of the 16S rRNA gene was amplified by PCR from DNA extracts with forward primer w49 (5'-ACGGTCCAGACTCCTACGGG-3') and 5' FAM-labeled reverse primer w34 (5'-TTACCGCGGCTGCTGGCAC-3'), by 30-s hybridization at 61°C, and 30 s elongation at 72°C for 28 cycles as described previously (Delbès et al., 2001). One microliter of diluted PCR product (5- to 100-fold in nuclease-free water) was then added to a mixture of 18.6 µl of deionized formamide and 0.4 µl of GeneScan 600 LIZ internal DNA standard (Life Technologies, United States). To obtain single-strand DNA, samples were heat-denatured for 10 min at 95°C and immediately cooled on ice. CE-SSCP analyses were performed on an ABI Prism 310 genetic analyzer using a 47-cm-long capillary, a non-denaturing 5.6% CAP polymer (Life Technologies, United States), and the following electrophoresis conditions: run temperature 32°C, sample injection for 5 s at 15 kV, and data collection for 35 min at 12 kV. Alignment of the profiles using an internal DNA standard and assignment of peak positions were performed with Bionumerics software (Applied Maths, Sint-Martens-Latem, Belgium). Profiles are shown using the "gel view" option.

## Illumina Sequencing

At the end of the kinetic experiment (90 days of incubation), bacterial and archaeal diversity was determined by 16S rRNA gene metabarcoding. A portion of the 16S rRNA gene (V4-V5 region) was amplified using the barcoded, universal primer set (515WF/918WR) (Wang et al., 2009). PCR reactions were performed using the AccuStart II PCR ToughMix Kit, followed by cleaning (HighPrep PCR beads, MokaScience, La Madeleine Cedex, France). Pooled triplicates were submitted for sequencing on an Illumina MiSeq instrument at GeT-PlaGe (Auzeville, France). Fastq sequences were processed using FROGS (Escudié et al., 2018) based on the Galaxy analysis platform (Afgan et al., 2016). Sequences were demultiplexed and dereplicated, sequence quality was checked, oligonucleotides were removed from sequences, and sequences were filtered on additional criteria. Sequences were removed from the data set, if they exhibited ambiguous bases or did not match expectations in amplicon size. Remaining sequences were clustered into OTUs based on the iterative Swarm algorithm, then chimeras and singletons (OTUs containing only one sequence) were removed. Bacterial double affiliation was performed by blasting OTUs against the SILVA 138.1 database (Quast et al., 2013). OTUs with affiliation < 100% at the phylum level were removed from the
data set. OTUs at lower taxonomic ranks than the phylum level were considered as “unidentified” below when the RDP bootstrap value was < 0.70. 16S rRNA gene amplicon sequencing data have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under the study accession number PRJEB47148.

Diversity indexes (Chao1, Inverse Simpsons, and Shannon) were calculated based on the Illumina sequence data.

# RESULTS

## Evolution of Chlordecone, -1Cl- Chlordecone, -3Cl- Chlordecone, and Redox Potential

Reductive conditions were maintained during the duration of the experiment between -150 and -250 mV due to the addition of Na₂S (Supplementary Figure 8).

In the biotic incubations, concentrations of CLD, -1Cl-CLD, and -3Cl-CLD decreased over time (Figure 1). Less than 10% of the initially added CLD and -1Cl-CLD were found after 90 days of incubation whereas around 30% of -3Cl-CLD persisted in the batches. In the CLD conditions, a concomitant increase of C₉Cl₅H₃ was observed through the increase in the size of the peak area (Figure 1). This degradation pathway with ring opening and dechlorination steps has been well documented for anaerobic microbiological degradation of CLD (Chaussonnerie et al., 2016; Chevallier et al., 2019; Della-Negra et al., 2020; Lomheim et al., 2020).

In abiotic conditions, the concentrations of CLD, -1Cl-CLD, and -3Cl-CLD decreased only slightly over time (Figure 1).

## Further Characterization of Transformation Products

All biotic incubations with CLD, -1Cl-CLD, and -3Cl-CLD led to a significant decrease in substrate concentration. This is most likely due to a microbiological transformation of all three compounds. In several past studies, the decrease of CLD could not be correlated with the formation of any transformation products and questions remained on its exact fate (degradation and/or sorption) (Sakakibara et al., 2011; Fernández-Bayo et al., 2013; Merlin et al., 2014; Amba Esegni et al., 2019). However, recent work by Lomheim et al. (2021) monitored the transformation of CLD into a suite of progressively more dechlorinated products, including a fully dechlorinated carboxylated indene product in anaerobic microcosms constructed with soil from Guadeloupe, suggesting that complete dechlorination of CLD is possible. For the endpoint sampling of the experiments (8 months), we therefore decided to change the extraction protocol and the analytical method used for monitoring. Acidification of the samples prior to extraction was applied as reported in Chevallier et al. (2019), and a longer gas chromatographic method was employed. GC-MS analysis revealed a series of polychlorinated compounds not only in -1Cl-CLD and -3Cl-CLD conditions but also for the CLD incubations. Comparison of the EI mass spectra and the GC retention times with the library of CLD transformation products

that we have built over the years (Chaussonnerie et al., 2016; Chevallier et al., 2019; Della-Negra et al., 2020) allowed us to unambiguously identify CLD-SH, CLD-SCH₃, and -1Cl-CLD-SH (as a pair of diastereoisomers). A more detailed analysis of the mass spectra of the other unknown compounds (see section "Further Characterization of Transformation Products") enabled us to conclude the formation of -1Cl-CLD-SCH₃, -3Cl-CLD-SH, and -3Cl-CLD-SCH₃ (Figure 2, entries 2 and 3).

Additional LC-MS analysis did not allow us to detect any polychloroindene carboxylic acids (data not shown). This series of polar transformation products also named as family C have been described in other CLD microbial degradation experiments (Chevallier et al., 2019; Lomheim et al., 2020, 2021). They differ from their polychloroindene congeners due to the presence of a carboxylic acid function that makes them only detectable using LC-MS instrumentation.

The presence of small peaks of CLD-SCH₃ among the transformation products produced in the -1Cl-CLD and -3Cl-CLD was unexpected (Figure 2, entries 2 and 3). We hypothesized that this CLD-SCH₃ came from the initial inoculum used (8% v/v in the kinetic experiment) that probably already contained CLD-SCH₃ (not monitored at that time). To check this hypothesis, biotic incubations 2 and 3 were reinoculated into fresh medium (8% V/V), and after 90 days, GC-MS analysis showed only a production of -1Cl-CLD-SH and -3Cl-CLD-SH (entries 4 and 5; Figure 2 and Supplementary Figure 5) and no detectable amounts of CLD-SCH₃, emphasizing that its presence in incubations 2 and 3 came from the initial inoculum.

## Evolution of Bacterial Diversity Fingerprint During the Incubation

CE-SSCP profiles of bacterial diversity obtained during the incubation showed that the CLD and -1Cl-CLD conditions (except replicate 3 for -1Cl-CLD) maintained a similar diversity fingerprint over time with two to three dominant bacterial strains and showed similarities with the inoculated consortium. On the contrary, the third replicate for -1Cl-CLD differed from the inoculum, evolving more toward the profiles found in the -3Cl-CLD condition where community fingerprints differed and suggested conditions which favored the development of a larger diversity (Supplementary Figure 9).

At T6, after 90 days of incubation, bacterial and archaeal diversity was assessed with 16S rRNA gene metabarcoding (Figure 3). Results are consistent with the CE-SSCP diversity fingerprints with similar communities composed of three to four dominant bacterial OTUs in the inoculum, CLD, and -1Cl-CLD, belonging to the genera *Desulfovibrio*, *Soehngenia*, *Aminobacterium*, and to an unknown genus of *Spirochaetaceae*. Moreover, in the CLD and -1Cl-CLD [replicates (1) and (2)] and the inoculum, the main OTU that was found belongs to an archaeal genus, *Methanobacterium*, which represented up to 60% of the sequences. On the other hand, in the -3Cl-CLD condition and replicate (3) of the -1Cl-CLD condition, the relative abundance of this *Methanobacterium* is reduced and two other OTUs were present in significant relative
<figure><img src="image_143.png" /><figcaption>**FIGURE 1** | Evolution of CLD (A), -1Cl-CLD (B), and -3Cl-CLD (C) over time in biotic (full lines) and abiotic (dotted lines) incubations expressed as the remaining percentage in the solution and of the peak area measured for C<sub>9</sub>Cl<sub>5</sub>H<sub>3</sub>, which increased over time in the batches containing CLD. Data represent the average of three replicates, and error bars are the standard deviation.</figcaption></figure>

abundances, *Candidatus Cloacimonas* and *Clostridium* as well as *Proteiniphilum*. It is possible that tolerance/resistance to CLD and -1Cl-CLD positively selected *Methanobacterium* whereas in less stressful conditions with -3Cl-CLD, other bacteria were more competitive.

An nMDS ordination (Figure 4), carried out on the OTU relative abundance data, highlights the similarities between the inoculum, CLD, and -1Cl-CLD [replicates (1) and (2)] and their difference with the -3Cl-CLD diversity. However, the ANOSIM

test carried out on these data did not have significant differences between conditions (p > 0.001).

Finally, diversity indexes (Supplementary Figure 10) all show higher values for the -3Cl-CLD conditions, which corroborates the hypothesis that this condition was less toxic to the bacteria present in the consortium and that cells that had not grown in the inoculum, CLD, and -1Cl-CLD conditions were able to develop. Further work on the resistance/tolerance and toxicity of these compounds would be required to further understand this.
<figure><img src="image_144.png" /><figcaption>**FIGURE 2** | General overview of the transformation products resulting from CLD, -1Cl-CLD, and -3Cl-CLD incubations. Entries 1–3 correspond to the kinetic experiment conditions after 8 months while entries 4 and 5 refer to the transfer experiment (after 90 days). The size of the circles gives a qualitative overview of the intensity of the GC-MS signals observed (mean of two injections). Analyses were conducted using GC-MS method 2. Complete data are found in **Supplementary Table 1**.</figcaption></figure>

<figure><img src="image_145.png" /><figcaption>**FIGURE 3** | Histogram of the bacterial and archaeal genus represented by > 1% of the sequences and identified to the genus level. Multi-affiliated (MA) and unknown genera (UG) are given with their associated family (f).</figcaption></figure>

DISCUSSION

This work aimed to study the biodegradability potential
of two major transformation products produced during

ISCR CLD remediation in soils, namely, -1Cl-CLD and
-3Cl-CLD, by a microbial consortium enriched from
wastewater sludge and able to transform CLD into a
nine-carbon compound (pentachloroindene, C₉Cl₅H₃) and
<figure><img src="image_146.png" /><figcaption>**FIGURE 4** | nMDS ordination plot of bacterial community diversity in the different incubation conditions and the inoculum.</figcaption></figure>

identify both the transformation products and the dominant genera involved.

There are only a few references of microbial transformation of CLD. Jablonski et al. (1996) is one of the earliest references with evidence of CLD biotransformation by the methanogenic archaeon *Methanosarcina thermophila* to polar and non-polar products using ¹⁴C-CLD. Other references testify to the formation of CLD derivatives in anaerobic or aerobic conditions (Fernández-Bayo et al., 2013; Merlin et al., 2014) but without detecting significant amounts of metabolites, as was found by Chaussonnerie et al. (2016) and Lomheim et al. (2021). During the 3-months-long incubation, CLD decreased concomitantly to the appearance of C₉Cl₅H₃ (B family) as in the first enrichment steps. CLD has previously been shown to form C₉Cl₅H₃ in similar conditions, in the presence of either bacterial consortia or isolated strains such as *Citrobacter* sp. 86 (Chaussonnerie et al., 2016) and *Desulfovibrio* sp. 86 (Della-Negra et al., 2020). Although the exact metabolic pathways have yet to be elucidated, recent work by Barbance et al. (2020) shows the involvement of cobalt-containing corrinoids in the microbial degradation of CLD, which could also be a possibility in the present study.

Since Chaussonnerie et al. (2016), several new families of degradation products derived from CLD have been identified by LC-HRMS (Chevallier et al., 2019; Lomheim et al., 2020, 2021). However, these families were not discovered at the end of the experiment in the present work (data not shown), suggesting either their further degradation in the incubation conditions or the inability of the enriched consortium to transform CLD to these compounds.

In the incubation conditions with either -1Cl-CLD or -3Cl-CLD, these compounds both appeared to be removed from the media. Further analysis of the transformation products did not reveal compounds from families B, C, D, and E (Chevallier et al., 2019); however, it did reveal the presence of sulfur derivatives,

namely, thiols and methylthioethers (family F, Della-Negra et al., 2020). Reinoculation experiments (Figure 2, entries 4 and 5) led to an initial formation of thiols when the incubation period was shortened compared to the kinetic experiment, indicating a probable sequence mechanism of reductive sulfidation—S-methylation (Figure 2, entries 1–3). Reinvestigation of CLD biotic incubations using a modified analytical protocol showed the same type of sulfured transformation products (CLD-SH and CLD-SCH₃). This proves that the present microbial enrichment can induce both the biodegradation of CLD (formation of pentachloroindene coming from the loss of carbon, oxygen, and chlorine atoms) and the biotransformation (conversion of the gem-diol to thiols and methylthioethers) of CLD, the two pathways being in competition in the applied incubation conditions. It is worth mentioning that neither -1Cl-CLD nor -3Cl-CLD appeared to be degraded, only biotransformed. This suggests that the position of the H atom(s) of -1Cl-CLD and -3Cl-CLD may inhibit the ring opening of the bishomocubane core, common to CLD and its two lower chlorinated congeners.

On the one hand, introduction of a sulfur atom on the chlordecone scaffolding has previously been demonstrated in presence of the sulfate-reducing bacterium *Desulfovibrio* sp. 86 (Della-Negra et al., 2020). To be effective, the biotransformation required an electron acceptor containing sulfur or hydrogen sulfide and a confined atmosphere. Microbiological reductive sulfidation is not limited to chlordecone since other carbonyl derivatives have been transformed into their corresponding thiol congeners using the same conditions with *Desulfovibrio* sp. 86 (Della-Negra et al., 2021). The authors suggested that two microbiological mechanisms may occur since aldehydes were converted during the growth phase while chlordecone, 10-monohydrochlordecone, and other ketones were biotransformed during the stationary phase. The present incubation conditions
that fulfill the requirements reported by Della-Negra et al. (2021) (presence of H₂S, confined atmosphere) and the presence of OTU affiliated to the sulfate-reducing genus *Desulfovibrio* are in favor of an analogous biotransformation. As in previous studies (Della-Negra et al., 2020, 2021), no transient thioketones could be detected. These intermediate compounds are observed in the chemical version of reductive sulfidation where a two-step process (initial S/O exchange followed by a reductive step) has been proven (Ozturk et al., 2010; Della-Negra et al., 2021). Moreover, the presence of OTU affiliated to the sulfate-reducing genus *Desulfovibrio* is in favor of this hypothesis in the present study.

On the other hand, while methylated thiols (methyl chlordecsulfides) were significantly formed after a switch in the incubation condition in the work of Della-Negra et al. (2020) using *Desulfovibrio* sp. 86, the present microbial community can induce both reductive sulfidation and methylation of the intermediate thiols in a one-pot manner. The absence of methyl chlordecsulfides in the transfer experiment after 90 days (Figure 2 entries 4 and 5; Supplementary Figure 5) and the systematic presence of both thiols and their methylated analogous in the kinetic experiment (Figure 2 entries 1–3; Supplementary Figures 2–5) are indicative of a probable sequential path. Another explanation would be the modification of the consortia contents, a hypothesis that is less probable as it would occur with both -1Cl-CLD and -3Cl-CLD substrates. In fermentative conditions, *Desulfovibrio* sp. 86 was able to methylate (Della-Negra et al., 2020), but no explanation has yet been proposed for this. In the present study, it is possible that the length of the experiment (8 months) enabled these reactions, although other explanations could exist.

Indeed, the OTU with the most relative sequence abundance (around 50%) was affiliated to *Methanobacterium*, a strictly anaerobic methane-producing archaeal genus that uses H₂ as an electron donor. Further work is required to evidence the role of archaea in CLD biodegradation/biotransformation, but several hypotheses can be drawn. For instance, methylated-thiol-coenzyme M methyltransferase, a key enzyme involved in the methanogenesis (Ferry and Kastead, 2007), probably produced by this archaea in the present enrichment is capable of methylating thiols *via* an activated methylated corrinoid. A possible cometabolic methyl transferase activity *via* such methyl donor could take place. *Methanobacterium* could not only be involved in the methylation of CLD-SH and congeners but also induce the ring opening of the bishomocubane ring of CLD *via* the corrinoids it produced. Indeed, corrinoids, such as those required in methanogenesis, play a key role in the ring opening of CLD as demonstrated by *Citrobacter* sp. 86 mutants (Barbance et al., 2020). Cofactor F430 also required in the methanogenic metabolism was found to abiotically transform CLD in the presence of a reducing reagent (Jablonski et al., 1996). The dominance of *Methanobacterium* in incubations where CLD and -1Cl-CLD and -3Cl-CLD transformation products are being degraded is stressing that methanogenic conditions are adequate for the biotransformation of such complex molecules. This statement is in line with previously reported CLD biotransformation in such conditions by the methanogenic

archaeon *Methanosarcina thermophila* (Jablonski et al., 1996). In the later study, CLD dehalogenation was suggested to occur while we rather expect sulfidation then methylation by *Methanobacterium*. Thus, our study strongly suggests that different pathways for CLD (and derivates) transformation might exist among methanogenic genera, such as sulfidation then methylation by the genus *Methanobacterium* found in our study or dehalogenation by the genus *Methanosarcina*, as suggested by Jablonski et al. (1996). This is further supported by the absence of a source on sulfur in the incubations with *Methanosarcina*.

Based on existing literature where, either from a thermodynamic point of view (Dolfing et al., 2012) or from an experimental point of view, there are no clear reasons why bacteria should not be able to respire CLD, reductive dehalogenation stands out as the best existing option to degrade CLD. However, while the redox potential in the present experiment was mostly comprised between -150 and -250 mV, no reductive dehalogenation, i.e., the direct replacement of a chlorine by a hydrogen atom, has been observed. This differs from the recent successful microbiological transformations of CLD that consistently reported the formation of hydrochlordecones, albeit in varying proportions. Indeed, only 10-monohydrochlordecone was reported as a minor transformation product by Chaussonnerie et al. (2016), Chevallier et al. (2019), and Della-Negra et al. (2020), while Lomheim et al. (2020, 2021) demonstrated the formation of a significant amount of several hydrochlordecones. However, in these studies the redox potential was not monitored so we cannot compare our experimental conditions to theirs.

In the work of Della-Negra et al. (2020), no formation of hydrochlordecones was observed when reductive sulfidation occurred. In the case of the single bacterium *Desulfovibrio* sp. 86, the observed inhibition of reductive dechlorination and ring opening dechlorination when reductive sulfidation takes place has been investigated (Della-Negra et al., 2020) and H₂S formed by sulfate-reducing bacteria was shown to be responsible for such an inhibition. Using several biotic and abiotic additional experiments, the authors concluded that the corrinoids, known to be involved in CLD biodegradation, are probably deactivated due to the formation of a cobalt-sulfur covalent bond in the presence of H₂S that accumulates in the confined atmosphere. Of course, the composition of the microbial consortia may also play a critical role.

Microbial diversity in the present experiment was lowest in the CLD and -1Cl-CLD conditions whereas it increased compared to the inoculum in the -3Cl-CLD condition and in one of the replicates (3) of the -1Cl-CLD incubations. Where diversity was lowest, it was dominated by known gram-negative genera (*Desulfovibrio*, *Aminobacterium*, *Soehngenia*, and *Spirochaetaceae*) and the archaeal *Methanobacterium* genus. Indeed, at high exposures of CLD, gram-negative bacteria have been shown to be more resistant to CLD (Mahaffey et al., 1982). To date, only gram-negative bacteria (*Pseudomonas aeruginosa* (Orndorff and Colwell, 1980), *P. putida*, *P. maltophilia*, *P. vesicularis* (George and Claxton, 1988), *Citrobacter* sp. 86, *Citrobacter* sp. 92 (Chaussonnerie et al., 2016), and
*Desulfovibrio* sp. 86 (Della-Negra et al., 2020) as well as an archeon (*Methanosarcina thermophila*, Jablonski et al., 1996) have been reported to partially or completely transform CLD. All microbial degradations have been performed at elevated concentrations of CLD (equal or above 10 mg·l⁻¹) that demonstrate a strong resistance of these microorganisms, which corroborates our observations. When diversity increased in our experiments, the representation of gram-positive genera increased (*Candidatus Cloacimonas*, *Clostridium*), possibly due to the reduced toxicity of -3Cl-CLD toward these bacteria, and the relative abundance of *Methanobacterium* and *Desulfovibrio* decreased, possibly explaining the incomplete transformation of -3Cl-CLD to -3Cl-CLD-SH and -3Cl-CLD-SCH₃ in these incubations. Furthermore, the increase in the diversity indexes in -3Cl-CLD conditions also corroborates this hypothesis. Further work on the resistance/tolerance and toxicity of these compounds would be required to further understand this.

Identification of CLD transformation products and CLD-degrading microbial communities in soils and sediments of the French West Indies (FWI) demonstrates that natural transformation of CLD already takes place in these two types of environmental compartments (Chevallier et al., 2019; Della-Negra et al., 2020; Lomheim et al., 2020). Moreover, it has also been demonstrated that the microbial potential to further transform CLD with as many as nine chlorine atoms removed is present in these environmental compartments (Lomheim et al., 2020). However, more than 25 years after its use was forbidden, CLD pollution in the FWI is still a public health issue, implying that the mechanisms of natural CLD transformation are not particularly efficient. The present work is the fourth, after Orndorff and Colwell (1980), Chaussonnerie et al. (2016) and Lomheim et al. (2020), to report on enrichments from wastewater sludge capable of inducing CLD transformation. Anaerobic reactors of water treatment plants would thus appear as a suitable place to observe an even more effective CLD degradation. To date, only one study aimed at searching CLD transformation products in FWI raw wastewater (Devault et al., 2018). However, it failed to detect both CLD and the targeted transformation products (chlordecol and 8-monohydrochlordecone). Since sampling was carried out at the entry of the treatment plant and that chlordecol and 8-monohydrochlordecone have not been yet linked to microbial anaerobic transformation of CLD, these results should not discourage further investigation of this presumably efficient industrial degradation process.

Finally, we are aware that the experimental setup presented here is carried out in optimized laboratory conditions where Eh is maintained negative with very reducing conditions, theoretically favorable for dehalogenation. Thus, the conditions are not those found naturally in agricultural soils in the West Indies except when an ISCR treatment is applied (Mouvet et al., 2020) and the zero-valent iron combined with water saturation and soil compaction induces negative redox conditions and leads to the transformation of CLD to hydroCLDs such as the -1-Cl-CLD and -3-Cl-CLD used in this study and that would subsequently find themselves also in these conditions. In previous work, chlordecthiol (the S-containing derivative from CLD) was detected in mangrove sediments from Martinique Island

(Della-Negra et al., 2020). This environmental compartment shows favorable conditions (sulfate-reducing bacteria and anoxic conditions) for the reductive sulfidation. One can imagine that S-methylation of such thiol would occur in the presence of the numerous sulfate-reducing microorganisms. Thus, we would expect to find similar several S-containing derivatives in the FWI indies environmental provided that ISCR treatment is to be set up and dedicated analytical methods are applied (no such additional analytical procedure was used in the work published by Mouvet et al. (2020).

## DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ebi.ac.uk/ena browser/view/PRJEB47148.

## AUTHOR CONTRIBUTIONS

JH, CJ, AM, and MC designed the experiment. JH, DM, OD-N, P-LS, SB, and MC carried out the experiment and the analysis (chemical and microbial). JH, CJ, SB, MC, and P-LS analyzed the data. JH and P-LS drafted the manuscript. JH, AM, and CM carried out the funding acquisition. All authors have read and agreed to the published version of the manuscript, contributed to reviewing and editing the manuscript.

## FUNDING

We gratefully acknowledge the financial support provided to the PIVOTS project by the Région Centre-Val de Loire (ARD 2020 program and CPER 2015-2020) and the French Ministry of Higher Education and Research (CPER 2015-2020 and public service subsidy to the Bureau de Recherches Géologiques et Minière). This operation was cofunded by the European Union. Europe was committed to the Centre-Val de Loire region with the European Regional Development Fund. Additional support was provided by Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), the Center National de la Recherche Scientifique (CNRS), and the University Evry Val d'Essonne (UEVE). OD-N was supported by the "IDI 2017" project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02.

## ACKNOWLEDGMENTS

We thank Mickaël Charron for his technical support during the experiment.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.742039/full#supplementary-material