confirmed that the factors of sampling date or material chemical
composition can significantly explain the changes of bacterial
community structure within each group, with higher values
found between the primo-colonization or the growing phase
compared to the mature biofilm influenced by the diatom bloom
(R² > 0.37, p < 0.01). Smaller but significant differences were
found between substrate types (R² = 0.07 between PLA and
glass and R² = 0.11 and 0.14 between PE and PLA or glass,
respectively; p < 0.01), indicating that the bacterial community
is more similar between PLA and glass than with PE. However,
no significant difference between material size or shape could
be found (p > 0.05), which was also supported by dispersion
analyses (p > 0.05) (**Supplementary Table 2**).

Samples from the primo-colonization (D3) and the growing
phases of the biofilms (D10) grouped in separated clusters
for PLA and glass (dissimilarity 59%), and also for PE but
with less dissimilarity among samples (48%). A clear shift in
bacterial community was observed when the biofilms became
mature (within D30 – dissimilarity 66%) followed by a drastic
change after the diatom bloom (within D66 – dissimilarity 77%),
whatever the material type (PE, PLA, glass) and size. All the
above cited clusters were significantly different when using the
SIMPROF tests (p < 0.05).

The time was the main factor driving the bacterial
communities, the material type showed also different patterns.
At each sampling date, PE formed always a specific cluster with
all the size fractions. This was also the case for PLA and glass,
except for the primo-colonization (D3) where they grouped
in a same cluster.

Interestingly, the Mantel tests showed a significant relation
between temporal changes in the seawater communities and
in the biofilms growing on the different material types
(**Supplementary Table 3**). Permutating correspondence between
seawater and biofilms communities showed high correlation
(Spearman rank = 0.84; *p* < 0.05) within dates (D3, D10,
D66 seawater samples compared to biofilms at the same dates)
that decreased drastically when permutating dates and for
D66 in particular (Spearman rank < 0.5; *p* < 0.05). Shared
ASVs between seawater and biofilms were <25% for PE,
<17% for PLA and glass, but these ASVs were abundant
in the samples since they represented >48% of the tags
in all cases, and maximum reached at 62% for PE on D3
(**Supplementary Figure 2**). SIMPER analyses based on presence
and absence data showed high dissimilarity among each substrate
alongside the temporal evolution (61, 65, 63, and 60% for
PE, PLA, glass, and seawater, respectively), while the shared
ASVs and shared tags remained constant, suggesting that the
bacterial community from the biofilm and seawater shift in
the same direction.

Taxonomic Composition

Taxonomic analyses confirmed the specificity of the community
structures formed on the different materials compared to
seawater, the latter being dominated by Alphaproteobacteria and
composed mainly of Gammaproteobacteria, Cyanobacteria,
Bacteroidetes, and Actinobacteria throughout the
experimentation (Figure 5).

The distinct phases of biofilm formation were also clearly
visible. The primo-colonization phase (D3) was dominated by
Gammaproteobacteria (61 ± 0%, 61 ± 12%, and 71% for PE,
PLA, and glass, respectively) and Alphaproteobacteria (31 ± 2%,
31 ± 9%, and 23% for PE, PLA and glass, respectively), dominant
family were Alteromonadaceae from Gammaproteobacteria and
Rhodobacteraceae from Alphaproteobacteria. The growing phase
resulted in a decrease of Gammaproteobacteria (27 ± 18%,
29 ± 15%, and 40 ± 8% for PE, PLA, and glass, respectively)
and a concomitant increase of Alphaproteobacteria (53 ± 17%,
64 ± 15%, and 51 ± 5% for PE, PLA, and glass, respectively).
The main change for the maturation phase (D30) was the
increase of Planctomycetes (8 ± 1%, 4 ± 2%, and 6 ± 2% for
PE, PLA and glass, respectively) and of Bacteroidetes mainly
for glass but not for PE and PLA (5 ± 2% for glass). At
this stage, Gammaproteobacteria (39 ± 9%, 22 ± 5%, and
25 ± 5%) became as abundant or even less abundant as
Alphaproteobacteria (34 ± 6%, 58 ± 5%, and 54 ± 4% for PE,
PLA, and glass, respectively).

The mature biofilm changed drastically in the presence of
diatoms (D66) with a continuous increase of Planctomycetes
(20 ± 5%, 10 ± 5%, and 16 ± 3% for PE, PLA, and glass,
respectively) and Bacteroidetes (16 ± 2%, 12 ± 3%, and 18 ± 3%
for PE, PLA, and glass, respectively). The presence of diatoms
at this stage was confirmed when looking at the eukaryote
sequences that were initially removed for the bacterial diversity
analysis. Note that the number of eukaryotic sequences increased
at the end of the experiment (D66), especially for glass where
eukaryotic sequences could reach until 19% of the total reads per
sample, but also for PE and PLA where they can reach until 12
and 9%, respectively (data not shown). Interestingly, eukaryotic
sequences were always <4% before the D66 on these materials
and represented <0.7% all along the experiment in seawater.
More than 50% and until 86% of the eukaryotic sequences
belonged to the diatom *Pseudo-nitzschia* sp. on D66 for PE,
PLA, and glass, whereas the sequences of *Pseudo-nitzschia* sp. in
seawater remained relative very low (<10%).

We followed in particular 21 dominant bacterial ASVs that accounted for >5% of the sequences in each substrate for individual sampling date (Figure 6). Among those top 21 ASVs, 5 were unique for PE, PLA or glass that were not visible in seawater (Figure 6). We found the SAR11surface_2 and ASV of Rhodobacteraceae more abundant in seawater compared to biofilms on the different material types.

During the primary colonization phase (D3), one ASV belonging to Alteromonadaceae was abundant in all material types, and distinction could be made between *Neptuniibacter* sp. and *Thalassobius* sp. that were more abundant on PE, whereas ASV of Cellvibrionaceae were more abundant in PLA, and *Alteromonas* sp. in glass samples. During the growing phase (D10), *Thalassobius* sp. were more represented on both PE and glass, while *Ponticaulis* sp. and *Oleibacter* sp. were more found for PLA. Changes between the maturation phase (D30) and the diatom bloom event (D66) varied according to the material types. The ASV of SSI-B-06-26 was more abundant on PE at D30 and ASV of Gammaproteobacteria and *Portibacter* sp. increased drastically at D66. *Ponticaulis* sp. dominated PLA at D30, while it
<figure><img src="image_22.png" /><figcaption>**FIGURE 6** | Bubble plot showing the relative abundance of the majority ASVs (> 5%). Each dot corresponds to the average abundance among different size (18 mm, 3 mm, IR and RE). The closest taxonomic affiliation of each ASV was shown on the left of the panel. Black filled color indicates significant difference between seawater and different materials (PE, PLA, and glass) considering all sampling date (Welch's ANOVA test).</figcaption></figure>

switched to ASV of Gammaproteobacteria at D66. Less changes were found for glass where *Sphingobium* sp. remain abundant for glass both at D30 and D66.

DISCUSSION

Our results are providing the first complete description of
bacterial abundance, diversity and activity during the three
classical colonization phases (primo-colonization, growing and
maturation phases) of biofilm formation on plastics. We also
showed that the mature biofilm can be drastically modified
by a phytoplankton bloom, thus indicating that environmental
conditions may have a crucial role on shaping the plastisphere.
Our experimental design enable us to evaluate the relative
influence of plastic composition, size or shape as well as the
importance of phytoplankton-bacteria interactions in shaping
the plastisphere.

Effect of Size and Shape of the Substrate
and Its Chemical Composition on the
Plastisphere

Raw measured values of abundance were found to differ between diverse size (meso-plastics of 18 mm diameter, LMP of 3 mm diameter and SMP of 100 µm diameter) and shapes (irregular IR and regular RE) for a given material. However, once the abundance was expressed per unit surface as in **Figure 2**, the differences disappeared between mesoplastics, LMP and SMP of RE or IR shapes. These results show, for the first time, that the

apparent size effect on raw data is only due to the difference in
specific surface (i.e., surface to volume ratio) for different particle
shapes. For a same mass of material, an irregular surface has a
larger available surface than a regular one, which therefore leads
to a higher abundance hence a higher activity. In the present
experiments, the excess surface is typically around 1.5 fold higher
for IR shape compared to RE shape.

Besides, there was also no effect on the bacterial diversity
from the different material size or shape. This result is in
accordance with the only one study focusing on the effect of
plastic size on bacterial diversity from coastal seawater (Brest,
France), where no size effect was found between SMP (0.3 to
1 mm) and LMP (1 to 5 mm) (Frère et al., 2018). By contrast,
our data showed dissimilarity in bacterial heterotrophic activities
between different plastic size and shape. For instance, bacterial
activity from PE in 3 mm (slight curly) was higher than other
that of 18 mm, and bacterial activity from IR shape was higher
than that of RE shape. This could be explained by very large
roughness at the typical scale of a bacteria, one could expect in
addition, a different packing of bacteria or a different biofilm
structure - due for example to adhesion differences - which
could induce differences in density of bacteria and/or in their
heterotrophic production. In this study though, the roughness
of all samples (including IR beads) was of the order of 100 nm
over 100 × 100 squared microns areas, so that such effects -if
any- would not be visible for the bacterial abundance or diversity.
Other studies on roughness at a very small scale would need
to be undertaken if one wants to conclude on this aspect. The
present experiments also seem to show that in the present
case, the temporal dynamic of biofilm formation together with the material type were always more important factors than the material size and shape for shaping the bacterial abundance, diversity and activity. However, it has been previously shown that the bacterial spatial position of natural plastisphere on particles is not totally even distributed and hence could depend on the size of the particles (Schlundt et al., 2019). Further studies with particle size less than the minimum size used in this study (100 µm) would probably be necessary to clarify this point.

During the three phases of its development, the biofilm evolved in a fairly different way on the two polymers studied (PE and PLA) and glass. PLA and glass were more similar in terms of bacterial activity and diversity compared to PE, while PE and PLA had similar and higher bacterial abundance compared to glass. In general, PE showed drastic differences compared to glass, while PLA showed intermediate values between them. The result is also pointing out that the bacterial activity could be not correlated to its abundance on plastisphere, which is similar to that found in seawater (Campbell et al., 2011). Our results are in accordance with another demonstration of significant influence of material type (PE, PLA, and Glass) on bacterial diversity (Kirstein et al., 2018). Our results tend to confirm the role of wetting properties: polymer surfaces being far more hydrophobic than glass. Attachment to surface are indeed supposed to be mediated via specific and non-specific interactions, both depending on surface hydrophobicity/hydrophilicity, roughness, charge and functional groups (Caruso, 2020). The role of hardness cannot, however, be excluded since it was recently shown to also be a key factor compared with other physicochemical properties (Cai et al., 2019).

Other factors may impact the plastisphere, such as the buoyancy: HDPE (0.95 g cm⁻³) was floating, whereas PLA (1.25 g cm⁻³) settled on tank bottom in our experimental conditions, which could lead to a different oxygenation of water or a different exposure to bacteria (composition, light intensity, contact frequency, exposition to air versus water). The buoyancy may explain the small but significantly higher average activity on PE compared to PLA, which differed also in term of community structure. Even though PLA is a compostable polymer and degradable in human body (Pillai and Sharma, 2010), it is known that PLA, like PE, is not biodegradable in marine environment (Karamanlioglu et al., 2017), and certainly not in the relatively short time frame of this experiment. Colonization, heterotrophic activity as well as the specificity of the species observed are therefore certainly not related to any biodegradation of the polymers. It seems more relevant to attribute the colonization, activity and specialization of bacteria to the formation of a conditioning film and subsequently the chemical composition of extracellular polymeric substance which could also be surface properties dependent.

## Presence of Three Following Phases of the Biofilm Development in All Substrates

Confocal microscopy was a powerful tool to follow the biofilm formation on the various samples tested in our

study. For all studied material, the triplicate samples out of all the sampling dates highlighted three classical successive phases (primo-colonization, growth, and maturation) of biofilm formation. Such succession has already been observed on natural (rocks and algae) or artificial surfaces (glass, acryl, steel, and plastics) immerged in seawater (Caruso, 2020), such as PE sheets and dolly ropes during long-term exposure experiment in the North Sea (De Tender et al., 2017) or non-biodegradable and biodegradable plastics in the Mediterranean sea (Dussud et al., 2018a).

First, the primo-colonization designates the pioneer bacteria that shape the first layer of initial biofilm (Lorite et al., 2011). After 3 days of immersion in natural seawater, we observed that the primo-colonizers presented a higher abundance and heterotrophic activity (³H leucine incorporation) on PE compared to PLA and glass. MiSeq 16S rRNA sequencing revealed that bacterial richness was already high after 3 days with a minimum average Chao1 estimation of 265 ASVs whatever the material types. We observed distinct but closed bacterial communities in PE, PLA, and glass, dominated by Gammaproteobacteria (61, 61, and 71% for PE, PLA, and glass, respectively) and Alphaproteobacteria (31, 31, and 23% for PE, PLA, and glass, respectively). Previous studies showed that the *Roseobacter* clade and *Alteromonas* were the main bacterial primary colonizers (Dang and Lovell, 2000; Salta et al., 2013; Dang and Lovellc, 2016). From our results, we reported that *Thalassobius* sp. of the Roseobacter clade could be also the primary colonizer on plastisphere. *Thalassobius* sp. was also found on 3-day-old polymethyl methacrylate (PMMA) panel immersed the Arabian Gulf with relatively low abundance compared to our study (Abed et al., 2019) as well as on PE plastisphere from North Atlantic (Zettler et al., 2013). *Alteromonas* sp. was extremely abundant on glass samples compared to PE and PLA, where the three materials occupied different ASVs from Alteromonadaceae (Figure 5). *Neptuniibacter* sp. was also one of the main taxa revealed in our study, but also on 7-day-old colonized poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) from the Banyuls Bay, France (Dussud et al., 2018a). Even though it is considered that the *Neptuniibacter* sp. is an hydrocarbonoclastic bacteria (Dombrowski et al., 2016), we showed here that it might be just a primary colonizer, while not participating to the plastic degradation.

Second, the growing phase of the biofilm is described growth by secondary species, which induce modifications in the properties of the substratum (Lorite et al., 2011). After 10 days immersion in seawater, we have seen an increase in bacterial abundance and heterotrophic activity, together with significant changes in bacterial community structure for all material types. In particular, Alphaproteobacteria became more abundant (53, 64, and 51% for PE, PLA and glass, respectively) compared to Gammaproteobacteria (27, 29, and 40% for PE, PLA, and glass, respectively). *Thalassobius* sp. was still abundant in the growing phase after the primo-colonization phase. In addition, we firstly reported the *Ponticaulis* sp. as an important group of primary colonizers on plastisphere, strikingly on PLA. Besides, previous study also showed that *Ponticaulis* sp. was one of the main
colonizers for the metallic alloys (Procópio, 2020). It suggested that the *Oleibacter* sp. could be one of the pioneer bacteria for plastic colonization within hours (Pollet et al., 2018), while we showed that it was more obvious on PLA in the growing phase after 10 days.

Third, the “maturation phase” occurs through diverse, competitive or synergistic interactions between cells, with either further recruitment or loss of species (Lorite et al., 2011). This has led to a stabilization of bacterial abundance and heterotrophic bacterial production, together with a drastic shift in bacterial community structure in all material types. PE still presented significantly higher abundance and activity compared to glass, whereas PLA showed similar bacterial abundance compared to PE and similar bacterial activity compared to glass. At this stage, Gammaproteobacteria (39, 22, and 25%) were as abundant or even less abundant as Alphaproteobacteria (34, 58, and 54% for PE, PLA, and glass, respectively). Bacteroidetes and Planctomycetes were also found as secondary colonizers in other studies (Dang and Lovellc, 2016; Pinto et al., 2019). We observed noticeable increases of Planctomycetes (8, 4, and 6% for PE, PLA, and glass, respectively) and also Bacteroidetes (mainly for glass (5%) but not for PE and PLA). Interestingly, we observed a similar evenness associated to an increase of richness during the growing and maturation phases, which is characteristic abundant and heterogeneous resources and nutrients offered by plastic particles compared to the nutrient-depleted oceanic deserts (Zhou et al., 2002).

During this study, we found that the Roseobacter and Alteromonas were important clades for whatever the three-colonization phases, while the two clades were also found as bacterial “phycosphere” (bacterial taxa colonizing on phytoplankton) (Seymour et al., 2017). Thus, we suspect that the plastic surface and phytoplankton surface could have some similar trait to be the environmental cue for these two clades. Another long term study identified the consistent presence of *Polaribacter* sp., *Kangiella* sp., *Lacinutrix* sp. during a 44 weeks experiment using plastic sheet and dolly rope emerged at an offshore station in the North Sea (De Tender et al., 2017). These taxonomic groups were dissimilar from the persistent taxa observed in our study, probably because of difference between the plastic substrates and the environmental conditions.

## Influence of Phytoplankton Bloom on the Mature Biofilm

In our study, the diatoms were presented in seawater during the entire course of the experiment course, while a diatom bloom was observed at D66 on plastisphere. It suggested that a bacterial biofilm would be a prerequisite for the diatom bloom on plastisphere. Most of the microalgae sequences on plastisphere belonged to *Pseudo-Nitzschia* sp., which was in accordance to the observation of their typical morphotype on D66 with confocal microscopy technique on all material surfaces. The diatoms were more observed on the film other than small microplastics, suggesting that the rigidity morphology of diatoms require more flatter surface area for the colonization comparing to bacteria. *Pseudo-nitzschia* is a global distributed diatom genus in the

marine environment (Lelong et al., 2012). It has not only been reported in the Mediterranean Sea of marine observatory stations in the Banyuls Bay (France), but also in the 150 km-south Blanes Bay (Spain) where the phytoplanktonic bloom in seawater were consistently attributable to chromophytes, the most abundant taxa being *Pseudo-nitzschia calliantha* (Mura and Agustí, 1996; Charles et al., 2005).

Diatoms have been found as omnipresent and sometimes dominant colonizers on plastic debris (Oberbeckmann et al., 2014; Maso et al., 2016; Michels et al., 2018; Kettner et al., 2019). Morphological identification by microscopy together with new chloroplast databases from bacterial amplicon surveys (Decelle et al., 2015) included *Mastogloia*, *Cyclotella*, *Pleurosigma*, *Amphora*, and *Pseudo-Nitzschia* genera in the Arabian Gulf, Grenada Island, Atlantic, and Pacific gyres (Amaral-Zettler et al., 2020). *Pseudo-nitzschia* spp. has been identified as the dominant diatoms on 10-day-old biofilm developed on polystyrene Petri dishes immersed at the low intertidal zone, Hong Kong (China) (Chiu et al., 2008). To our knowledge, it is the first time that *Pseudo-nitzschia* spp. were identified as dominant phototrophs on plastic debris on Mediterranean plastisphere. The diatoms event happened on plastisphere could be also related to the diatom bloom events happened on Banyuls Bay (Supplementary Figure 1C).

Interaction between phytoplankton and bacteria are known to play key roles in mediating biogeochemical cycling and the food web structure in the ocean, including the microbial loop (Mayali, 2018). Diatom blooms are also known to be one of the main drivers of the temporal dynamics of bacterial abundance, diversity and activity in the Mediterranean Sea and elsewhere (Ghiglione et al., 2005; Lambert et al., 2019), with consistent taxonomic association between specific bacteria and diatom taxa (Behringer et al., 2018). Our results confirm that such association exist also within the biofilms associated with plastic, as it has been observed in other studies (Amaral-Zettler et al., 2020). We found co-associated bacterial epibions on the mature biofilms at D66 that were related to the specific biofilm of each polymer type. For example, we found common colonizers of diatom detritus, such as *Portibacter* sp. (Crenn et al., 2018), and *Sphingobium* sp. (Ramanan et al., 2015) and Rhodobacteraceae (previously mostly assigned as Roseobacter clade) (Simon et al., 2017). The interaction between diatoms and bacteria within the mature biofilms was accompanied with a drastic increase of bacterial heterotrophic activities in PE and PLA. This is a typical response of nutrient recycling heterotrophs to primary producing photoautotrophs, where bacterial activity per cell increases drastically together with changes in community structure (Mayali, 2018). Our results showed that the diversity and activity of the mature biofilms on plastic can be rapidly and drastically changed due to phytoplanktonic growth on plastics, whatever the polymer type, size or shape. To our knowledge, only one study so far measuring chlorophyll *a* and net primary production in the North Pacific gyre showed that microplastic particles were creating net autotrophic hot-spots in the oligotrophic ocean (Bryant et al., 2016). In parallel, another unique study in the Mediterranean Sea revealed higher
bacterial heterotrophic activity on plastic compared to the surrounding seawater (Dussud et al., 2018a).

During this study, we cannot really compare the bacterial activity or growth rate between plastisphere vs. our seawater samples, because of the potentially lower bacterial abundance numeration on seawater samples. Instead, we propose the following scenario: the bacterial growth rate on the primo-colonization and growing phase should be higher than that of seawater, at least on PE samples, as previous study showed that the Roseobacter and Alteromonadaceae have relative high growth rate compared to the bulk bacterial community (Ferrera et al., 2011). The bacterial activity or growth rate on the maturation phase in the marine environment could be higher than seawater considering that autotroph microbes such as diatoms were omnipresent on plastics. Further works coupling both primary and heterotrophic production measurements are needed to determine the bacterial activity difference between plastisphere and seawater, but also test if the microscale algal-bacterial interactions on the large amount of plastic floating in sea surface have consequences on ecosystem functioning and/or biogeochemical cycling.

## Concluding Remarks

Here we prove that phytoplankton-bacteria interactions may greatly modify the plastisphere, which can no longer be considered as a vector of a durable and stable community, but rather a vector of communities interacting with their environment and subjected to changes. We showed that phytoplankton-bacteria interactions do not influence the abundance of the mature biofilm formed on plastics, but may drastically impact the diversity and the heterotrophic activities of the plastisphere. These results may have consequences in the further evaluation of the functional role of the plastisphere, such as its contribution to the biogeochemical cycles of elements. Another major finding of our study was that the size and the shape of the plastics showed little influence on the plastisphere abundance, diversity and activity, which is in accordance with the few studies dealing with this aspect but focusing on the diversity only. Hydrophobicity, topography, roughness, crystallinity, and surface charge have been previously found to influence the early stage of plastic colonization, whereas the plastisphere directly sampled at sea was found to be rather driven by geographical location or seasons, which are typically related to environmental conditions. Our results, together with the growing literature in this field, are opening the description of the complex and fascinating interactions between the plastisphere and its plastic support, but also with the surrounding environment that may greatly influence its spatial and temporal dynamics.

## REFERENCES

Abed, R. M. M., Al Fahdi, D., and Muthukrishnan, T. (2019). Short-term succession of marine microbial fouling communities and the identification of primary and secondary colonizers. *Biofouling* 35, 526–540. doi: 10.1080/08927014.2019.1622004

## 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.ncbi.nlm.nih.gov/genbank/, PRJNA663787.

## AUTHOR CONTRIBUTIONS

JC, A-LM, and J-FG: conception and design of study. JC, JJ, and VB: acquisition of data. JC, MG, PF, A-LM, and J-FG: analysis and interpretation of data. SB, AT, A-LM, and J-FG: provision of equipment. JC and J-FG: drafting the manuscript. PF, MG, SB, AT, and A-LM: revising the manuscript critically for important intellectual content. JC, JJ, PC, MP-P, VB, MG, PF, SB, AT, A-LM, and J-FG: approval version of the manuscript to be published. All authors contributed to the article and approved the submitted version.

## FUNDING

This work was supported by the European project JRA-ASSEMBLE +, the French National Research Agency (project ANR-OXOMAR), the Tara Ocean Foundation (project Microplastic 2019), and by the CNRS (project PEPS'I-PHABIO). This work was part of the Ph.D. thesis of JC supported by the China Scholarship Council (CSC; NO, 201706330086).

## ACKNOWLEDGMENTS

We are grateful to Valerie Bayo, Charlene Odobel, and Caroline Pandin for their kind laboratory supports, David Pecqueur for the support on Confocal microscopy, Philippe Catala for the advice on flow cytometry data and to Guigui PA and VF for insightful comments on the manuscript. We also thank A. Kirkman for her careful reading and corrections of the manuscript.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2020.610231/full#supplementary-material

Abràmoff, M. D., Magalhães, P. J., and Ram, S. J. (2004). Image processing with imageJ. *Biophotonics Int.* 11, 36–41.

Amaral-Zettler, L. A., Zettler, E. R., and Mincer, T. J. (2020). Ecology of the plastisphere. *Nat. Rev. Microbiol.* 18, 139–151. doi: 10.1038/s41579-019-0308-0

Amaral-Zettler, L. A., Zettler, E. R., Slikas, B., Boyd, G. D., Melvin, D. W., Morrall, C. E., et al. (2015). The biogeography of the Plastisphere:
implications for policy. *Front. Ecol. Environ.* 13, 541–546. doi: 10.1890/150017

Anderson, M. J., and Walsh, D. C. I. (2013). PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? *Ecol. Monogr.* 83, 557–574. doi: 10.1890/12-2010.1

Andrady, A. (2003). *Plastics and the Environment*. Hoboken, NJ: John Wiley & Sons.

Barnes, D. K. A., Galgani, F., Thompson, R. C., and Barlaz, M. (2009). Accumulation and fragmentation of plastic debris in global environments. *Philos. Trans. R. Soc. B Biol. Sci.* 364, 1985–1998. doi: 10.1098/rstb.2008.0205

Basili, M., Quero, G. M., Giovannelli, D., Manini, E., Vignaroli, C., Avio, C. G., et al. (2020). Major role of surrounding environment in shaping biofilm community composition on marine plastic debris. *Front. Mar. Sci.* 7:2622. doi: 10.3389/fmars.2020.00262

Behringer, G., Ochsenkühn, M. A., Fei, C., Fanning, J., Koester, J. A., and Amin, S. A. (2018). Bacterial communities of diatoms display strong conservation across strains and time. *Front. Microbiol.* 9:659. doi: 10.3389/fmicb.2018.00659

Blanchet, M., Pringault, O., Panagiotopoulos, C., Lefèvre, D., Charrière, B., Ghiglione, J. F., et al. (2017). When riverine dissolved organic matter (DOM) meets labile DOM in coastal waters: changes in bacterial community activity and composition. *Aquat. Sci.* 79, 27–43. doi: 10.1007/s00027-016-0477-0

Bryant, J. A., Clemente, T. M., Viviani, D. A., Fong, A. A., Thomas, K. A., Kemp, P., et al. (2016). Diversity and activity of communities inhabiting plastic debris in the north pacific gyre. *mSystems* 1:e00024-16.

Cai, L., Wu, D., Xia, J., Shi, H., and Kim, H. (2019). Influence of physicochemical surface properties on the adhesion of bacteria onto four types of plastics. *Sci. Total Environ.* 671, 1101–1107. doi: 10.1016/j.scitotenv.2019.03.434

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., and Holmes, S. P. (2016). DADA2: high resolution sample inference from Illumina amplicon data. *Nat. Methods* 13, 581–583. doi: 10.1038/nmeth.3869

Campbell, B. J., Yu, L., Heidelberg, J. F., and Kirchman, D. L. (2011). Activity of abundant and rare bacteria in a coastal ocean. *Proc. Natl. Acad. Sci. U.S.A.* 108, 12776–12781. doi: 10.1073/pnas.1101405108

Caruso, G. (2020). Microbial colonization in marine environments: overview of current knowledge and emerging research topics. *J. Mar. Sci. Eng.* 8, 1–22.

Céa, B., Lefèvre, D., Chirurgien, L., Raimbault, P., Garcia, N., Charrière, B., et al. (2015). An annual survey of bacterial production, respiration and ectoenzyme activity in coastal NW Mediterranean waters: temperature and resource controls. *Environ. Sci. Pollut. Res. Int.* 22, 13654–13668. doi: 10.1007/s11356-014-3500-9

Charles, F., Lantoine, F., Brugel, S., Chrétiennot-Dinet, M. J., Quiroga, I., and Rivière, B. (2005). Seasonal survey of the phytoplankton biomass, composition and production in a littoral NW Mediterranean site, with special emphasis on the picoplanktonic contribution. *Estuar. Coast. Shelf Sci.* 65, 199–212. doi: 10.1016/j.ecss.2005.06.006

Chiu, J. M. Y., Zhang, R., Wang, H., Thiyagarajan, V., and Qian, P. Y. (2008). Nutrient effects on intertidal community: from bacteria to invertebrates. *Mar. Ecol. Prog. Ser.* 358, 41–50. doi: 10.3354/meps07310

Chubarenko, I., Bagaev, A., Zobkov, M., and Esiukova, E. (2016). On some physical and dynamical properties of microplastic particles in marine environment. *Mar. Pollut. Bull.* 108, 105–112. doi: 10.1016/j.marpolbul.2016.04.048

Cózar, A., Sanz-Martín, M., Martí, E., González-Gordillo, J. I., Ubeda, B., Gálvez, J. Á., et al. (2015). Plastic accumulation in the Mediterranean Sea. *PLoS One* 10:e0121762. doi: 10.1371/journal.pone.0121762

Crenn, K., Duffieux, D., and Jeanthon, C. (2018). Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short- and long-term associations. *Front. Microbiol.* 9:2879. doi: 10.3389/fmicb.2018.02879

Dang, H., and Lovell, C. R. (2000). Bacterial primary colonization and early succession on surfaces in marine waters as determined by amplified rRNA gene restriction analysis and sequence analysis of 16S rRNA genes. *Appl. Environ. Microbiol.* 66, 467–475. doi: 10.1128/aem.66.2.467-475. 2000

Dang, H., and Lovell, C. R. (2016). Microbial surface colonization and biofilm development in marine environments. *Am. Soc. Microbiol.* 80, 91–138. doi: 10.1128/mmbr.00037-15

De Tender, C., Devriese, L. I., Haegeman, A., Maes, S., Vangeyte, J., Cattrijsse, A., et al. (2017). Temporal dynamics of bacterial and fungal colonization on plastic debris in the North Sea. *Environ. Sci. Technol.* 51, 7350–7360. doi: 10.1021/acs.est.7b00697

Decelle, J., Romac, S., Stern, R. F., Bendif, E. M., Zingone, A., Audic, S., et al. (2015). PhytoREF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. *Mol. Ecol. Resour.* 15, 1435–1445. doi: 10.1111/1755-0998.12401

Dombrowski, N., Donaho, J. A., Gutierrez, T., Seitz, K. W., Teske, A. P., and Baker, B. J. (2016). Reconstructing metabolic pathways of hydrocarbon-degrading bacteria from the Deepwater Horizon oil spill. *Nat. Microbiol.* 1:16057.

Dussud, C., and Ghiglione, J. (2014). *Bacterial Degradation of Synthetic Plastics*. (Monaco: CIESM), 43–48.

Dussud, C., Hudec, C., George, M., Fabre, P., Higgs, P., Bruzaud, S., et al. (2018a). Colonization of non-biodegradable and biodegradable plastics by marine microorganisms. *Front. Microbiol.* 9:1571. doi: 10.3389/fmicb.2018.01571

Dussud, C., Meistertzheim, A. L., Conan, P., Pujo-Pay, M., George, M., Fabre, P., et al. (2018b). Evidence of niche partitioning among bacteria living on plastics, organic particles and surrounding seawaters. *Environ. Pollut.* 236, 807–816. doi: 10.1016/j.envpol.2017.12.027

Eriksen, M., Lebreton, L. C. M., Carson, H. S., Thiel, M., Moore, C. J., Borerro, J. C., et al. (2014). Plastic Pollution in the World's Oceans: more than 5 Trillion Plastic Pieces Weighing over 250,000 Tons Afloat at Sea. *PLoS One* 9:e111913. doi: 10.1371/journal.pone.0111913

Ferrera, I., Gasol, J. M., Sebastián, M., Hojerová, E., and Kobižek, M. (2011). Comparison of growth rates of aerobic anoxygenic phototrophic bacteria and other bacterioplankton groups in coastal mediterranean waters. *Appl. Environ. Microbiol.* 77, 7451–7458. doi: 10.1128/aem.00208-11

Frère, L., Maignien, L., Chalopin, M., Huvet, A., Rinnert, E., Morrison, H., et al. (2018). Microplastic bacterial communities in the Bay of Brest: influence of polymer type and size. *Environ. Pollut.* 242, 614–625. doi: 10.1016/j.envpol. 2018.07.023

Fukuda, R., Ogawa, H., Nagata, T., and Koike, I. (1998). Direct determination of carbon and nitrogen contents of natural bacterial assemblages in marine environments. *Appl. Environ. Microbiol.* 64, 3352–3358. doi: 10.1128/aem.64.9.3352-3358.1998

Ghiglione, J. F., Larcher, M., and Lebaron, P. (2005). Spatial and temporal scales of variation in bacterioplankton community structure in the NW Mediterranean Sea. *Aquat. Microb. Ecol.* 40, 229–240. doi: 10.3354/ame040229

Ghiglione, J. F., and Laudet, V. (2020). Marine life cycle: a polluted terra ignota is unveiled. *Curr. Biol.* 30, R130–R133.

Ghiglione, J. F., Philippot, L., Normand, P., Lensi, R., and Potier, P. (1999). Disruption of narG, the gene encoding the catalytic subunit of respiratory nitrate reductase, also affects nitrite respiration in *Pseudomonas fluorescens* YT101. *J. Bacteriol.* 181, 5099–5102. doi: 10.1128/jb.181.16.5099-5102.1999

Grömping, U. (2006). Relative importance for linear regression in R: the package relaimpo. *J. Stat. Softw.* 17, 1–27. doi: 10.3758/s13428-010-0043-y

Hadfield, M. G. (2011). Biofilms and marine invertebrate larvae: what bacteria produce that larvae use to choose settlement sites. *Ann. Rev. Mar. Sci.* 3, 453–470. doi: 10.1146/annurev-marine-120709-142753

Harrison, J. P., Boardman, C., O'Callaghan, K., Delort, A. M., and Song, J. (2018). Biodegradability standards for carrier bags and plastic films in aquatic environments: a critical review. *R. Soc. Open Sci.* 5:171792. doi: 10.1098/rsos.171792

Jacquin, J., Cheng, J., Odobel, C., Pandin, C., Conan, P., Pujo-Pay, M., et al. (2019). Microbial ecotoxicology of marine plastic debris: a review on colonization and biodegradation by the “plastisphere.” *Front. Microbiol.* 10:865. doi: 10.3389/fmicb.2019.00865

Kane, I. A., Clare, M. A., Miramontes, E., Wogelius, R., Rothwell, J. J., Garreau, P., et al. (2020). Seafloor microplastic hotspots controlled by deep-sea circulation. *Science* 5899:eaba5899.

Karamanlioglu, M., Preziosi, R., and Robson, G. D. (2017). Abiotic and biotic environmental degradation of the bioplastic polymer poly(lactic acid): a review. *Polym. Degrad. Stab.* 137, 122–130. doi: 10.1016/j.polymdegradstab.2017.01.009

Kettner, M. T., Oberbeckmann, S., Labrenz, M., and Grossart, H. P. (2019). The eukaryotic life on microplastics in brackish ecosystems. *Front. Microbiol.* 10:538. doi: 10.3389/fmicb.2019.00538
Kirchman, D. (2001). Measuring bacterial biomass production and growth rates from leucine incorporation in natural aquatic environments. *Methods Microbiol.* 30, 227-237. doi: 10.1016/s0580-9517(01)30047-8

Kirstein, I. V., Kirmizi, S., Wichels, A., Garin-Fernandez, A., Erler, R., Löder, M., et al. (2016). Dangerous hitchhikers? Evidence for potentially pathogenic Vibrio spp. on microplastic particles. *Mar. Environ. Res.* 120, 1-8. doi: 10.1016/j.marenvres.2016.07.004

Kirstein, I. V., Wichels, A., Krohne, G., and Gerdts, G. (2018). Mature biofilm communities on synthetic polymers in seawater - Specific or general? *Mar. Environ. Res.* 142, 147-154. doi: 10.1016/j.marenvres.2018.09.028

Kooi, M., Van Nes, E. H., Scheffer, M., and Koelmans, A. A. (2017). Ups and downs in the ocean: effects of biofouling on vertical transport of microplastics. *Environ. Sci. Technol.* 51, 7963-7971. doi: 10.1021/acs.est.b604702

Kowalski, N., Reichardt, A. M., and Waniek, J. J. (2016). Sinking rates of microplastics and potential implications of their alteration by physical, biological, and chemical factors. *Mar. Pollut. Bull.* 109, 310-319. doi: 10.1016/j.marpolbul.2016.05.064

Lambert, S., Tragin, M., Lozano, J. C., Ghiglione, J. F., Vaulot, D., Bouget, F. Y., et al. (2019). Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. *ISME J.* 13, 388-401. doi: 10.1038/s41396-018-0281-z

Lelong, A., Hégaret, H., Soudant, P., and Bates, S. S. (2012). Pseudo-nitzschia (Bacillariophyceae) species, domoic acid and amnesic shellfish poisoning: revisiting previous paradigms. *Phycologia* 51, 168-216. doi: 10.2216/11-37.1

Long, M., Moriceau, B., Gallinari, M., Lambert, C., Huvet, A., Raffray, J., et al. (2015). Interactions between microplastics and phytoplankton aggregates: impact on their respective fates. *Mar. Chem.* 175, 39-46. doi: 10.1016/j.marchem.2015.04.003

Lorite, G. S., Rodrigues, C. M., de Souza, A. A., Kranz, C., Mizaikoff, B., and Cotta, M. A. (2011). The role of conditioning film formation and surface chemical changes on *Xylella fastidiosa* adhesion and biofilm evolution. *J. Colloid Interface Sci.* 359, 289-295. doi: 10.1016/j.jcis.2011.03.066

Lusher, A. L., Tirelli, V., O'Connor, I., and Officer, R. (2015). Microplastics in Arctic polar waters: the first reported values of particles in surface and sub-surface samples. *Sci. Rep.* 5:14947.

Marty, F., Ghiglione, J. F., Paisse, S., Gueuné, H., Quillet, L., van Loosdrechta, M. C. M., et al. (2012). Evaluation and optimization of nucleic acid extraction methods for the molecular analysis of bacterial communities associated with corroded carbon steel. *Biofouling* 28, 363-380. doi: 10.1080/08927014.2012.672644

Maso, M., Fortuño, J. M., De Juan, S., and Demestre, M. (2016). Microfouling communities from pelagic and benthic marine plastic debris sampled across Mediterranean coastal waters. *Sci. Mar.* 80, 117-127. doi: 10.3989/scimar.04281.10a

Mayali, X. (2018). Editorial: metabolic interactions between bacteria and phytoplankton. *Front. Microbiol.* 9:e00727. doi: 10.3389/fmicb.2018.00727

McMurdie, P. J., and Holmes, S. (2013). Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. *PLoS One* 8:e00612-17. doi: 10.1371/journal.pone.0061217

Mével, G., Vernet, M., Goutx, M., and Ghiglione, J. F. (2008). Seasonal to hour variation scales in abundance and production of total and particle-attached bacteria in the open NW Mediterranean Sea (0-1000 m). *Biogeosciences* 5, 1573-1586. doi: 10.5194/bg-5-1573-2008

Michels, J., Stippkugel, A., Lenz, M., Wirtz, K., and Engel, A. (2018). Rapid aggregation of biofilm-covered microplastics with marine biogenic particles. *Proc. R. Soc. B Biol. Sci.* 285:20181203. doi: 10.1098/rspb.2018.1203

Mura, M. P., and Agustí, S. (1996). Growth rates of diatoms from coastal Antarctic waters estimated by in situ dialysis incubation. *Mar. Ecol. Prog. Ser.* 144, 237-245. doi: 10.3354/meps144237

Neëas, D., and Klapetek, P. (2012). Gwyddion: an open-source software for SPM data analysis. *Cent. Eur. J. Phys.* 10, 181-188.

Oberbeckmann, S., Kreikemeyer, B., and Labrenz, M. (2018). Environmental factors support the formation of specific bacterial assemblages on microplastics. *Front. Microbiol.* 8:2709. doi: 10.3389/fmicb.2017.02709

Oberbeckmann, S., Loeder, M. G. J., Gerdts, G., and Mark Osborn, A. (2014). Spatial and seasonal variation in diversity and structure of microbial biofilms on marine plastics in Northern European waters. *FEMS Microbiol. Ecol.* 49, 478-492. doi: 10.1111/1574-6941.12409

Oberbeckmann, S., Loeder, M. G. J., and Labrenz, M. (2015). Marine microplastic-associated biofilms - a review. *Environ. Chem.* 12, 551-562. doi: 10.1071/en15069

O'Donnell, P. B., and McGinity, J. W. (1997). Preparation of microspheres by the solvent evaporation technique. *Adv. Drug Deliv. Rev.* 28, 25-42. doi: 10.1016/s0169-409x(97)00049-5

Ogonowski, M., Motiei, A., Ininbergs, K., Hell, E., Gerdes, Z., Udekuwu, K. I., et al. (2018). Evidence for selective bacterial community structuring on microplastics. *Environ. Microbiol.* 20, 2796-2808. doi: 10.1111/1462-2920.14120

Oksanen, J., Kindt, R., Legendre, P., O'Hara, B., Stevens, M. H. H., Oksanen, M. J., et al. (2007). The vegan package. *Comm. Ecol. Packag.* 10:719.

Parada, A. E., Needham, D. M., and Fuhrman, J. A. (2016). Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. *Environ. Microbiol.* 18, 1403-1414. doi: 10.1111/1462-2920.13023

Pillai, C. K. S., and Sharma, C. P. (2010). Review paper: absorbable polymeric surgical sutures: chemistry, production, properties, biodegradability, and performance. *J. Biomater. Appl.* 25, 291-366. doi: 10.1177/0885328210384890

Pinto, M., Langer, T. M., Hüffer, T., Hofmann, T., and Herndl, G. J. (2019). The composition of bacterial communities associated with plastic biofilms differs between different polymers and stages of biofilm succession. *PLoS One* 14:e0217165. doi: 10.1371/journal.pone.0217165

Pollet, T., Berdjeb, L., Garnier, C., Durrieu, G., Le Poupon, C., Misson, B., et al. (2018). Prokaryotic community successions and interactions in marine biofilms: the key role of *Flavobacteriia*. *FEMS Microbiol. Ecol.* 94:fiy083.

Procópio, L. (2020). Microbial community profiles grown on 1020 carbon steel surfaces in seawater-isolated microcosm. *Ann. Microbiol.* 70:13.

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 web-based tools. *Nucleic Acids Res.* 41, 590-596.

Ramanan, R., Kang, Z., Kim, B. H., Cho, D. H., Jin, L., Oh, H. M., et al. (2015). Phycosphere bacterial diversity in green algae reveals an apparent similarity across habitats. *Algal Res.* 8, 140-144. doi: 10.1016/j.algal.2015.02.003

Rummel, C. D., Jahnke, A., Gorokhova, E., Kühnel, D., and Schmitt-Jansen, M. (2017). Impacts of biofilm formation on the fate and potential effects of microplastic in the aquatic environment. *Environ. Sci. Technol. Lett.* 4, 258-267. doi: 10.1021/acs.estlett.7b00164

Sabev, H. A., Barratt, S. R., Greenhalgh, M., Handley, P. S., and Robson, G. D. (eds) (2006). "Biodegradation and biodeterioration of man-made polymeric materials," in *Fungi in Biogeochemical Cycles*, (Cambridge: Cambridge University Press), 212-235. doi: 10.1017/cbo9780511550522.010

Salta, M., Wharton, J. A., Blache, Y., Stokes, K. R., and Briand, J. F. (2013). Marine biofilms on artificial surfaces: structure and dynamics. *Environ. Microbiol.* 15, 2879-2893.

Schlundt, C., Mark Welch, J. L., Knochel, A. M., Zettler, E. R., and Amaral-Zettler, L. A. (2019). Spatial structure in the "Plastisphere": molecular resources for imaging microscopic communities on plastic marine debris. *Mol. Ecol. Resour.* 620-634. doi: 10.1111/1755-0998.13119

Schwarz, A. E., Lighthart, T. N., Boukris, E., and van Harmelen, T. (2019). Sources, transport, and accumulation of different types of plastic litter in aquatic environments: a review study. *Mar. Pollut. Bull.* 143, 92-100. doi: 10.1016/j.marpolbul.2019.04.029

Severin, T., Kessouri, F., Rembauville, M., Sánchez-Pérez, E., Oriol, L., Caparros, J., et al. (2017). Open-ocean convection process: a driver of the winter nutrient supply and the spring phytoplankton distribution in the Northwestern Mediterranean Sea. *J. Geophys. Res. Ocean* 122, 4587-4601. doi: 10.1002/2016jc012664
Seymour, J. R., Amin, S. A., Raina, J. B., and Stocker, R. (2017). Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. *Nat. Microbiol.* 2:17065.

Simon, M., Scheuner, C., Meier-Kolthoff, J. P., Brinkhoff, T., Wagner-Döbler, I., Ulbrich, M., et al. (2017). Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. *ISME J.* 11, 1483–1499. doi: 10.1038/ismej.2016.198

Thompson, R. C., Moore, C. J., Saal, F. S. V., and Swan, S. H. (2009). Plastics, the environment and human health: current consensus and future trends. *Philos. Trans. R. Soc. B Biol. Sci.* 364, 2153–2166. doi: 10.1098/rstb.2009.0053

Van Sebille, E., Wilcox, C., Lebreton, L., Maximenko, N., Hardesty, B. D., Van Franeker, J. A., et al. (2015). A global inventory of small floating plastic debris. *Environ. Res. Lett.* 10:124006. doi: 10.1088/1748-9326/10/12/124006

Wang, W., Gao, H., Jin, S., Li, R., and Na, G. (2019). The ecotoxicological effects of microplastics on aquatic food web, from primary producer to human: a review. *Ecotoxicol. Environ. Saf.* 173, 110–117. doi: 10.1016/j.ecoenv.2019.01.113

Wickham, H. (2016). *ggplot2* - *Elegant Graphics for Data Analysis*, 2nd Edn. New York, NY: Springer-Verlag.

Yeh, Y. C., McNichol, J. C., Needham, D. M., Fichot, E. B., and Fuhrman, J. A. (2019). Comprehensive single-PCR 16S and 18S rRNA community analysis

validated with mock communities and denoising algorithms. *bioRxiv* 1, 1. doi: 10.1101/866731

Zettler, E. R., Mincer, T. J., and Amaral-Zettler, L. A. (2013). Life in the “plastisphere”: microbial communities on plastic marine debris. *Environ. Sci. Technol.* 47, 7137–7146. doi: 10.1021/es401288x

Zhou, J., Xia, B., Treves, D. S., Wu, L., Marsh, T. L., Neill, R. V. O., et al. (2002). Spatial and resource factors influencing high microbial diversity in soil. *Appl. Environ. Microbiol.* 68, 326–334. doi: 10.1128/aem.68.1.326-334.2002

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Cheng, Jacquin, Conan, Pujo-Pay, Barbe, George, Fabre, Bruzaud, Ter Halle, Meistertzheim and Ghiglione. 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_23.png" /></figure>

Check for
updates

# Variability of the Atmospheric PM₁₀ Microbiome in Three Climatic Regions of France

Abdoulaye Samaké<fnref n="1" />, Jean M. F. Martins<fnref n="1" />*, Aurélie Bonin², Gaëlle Uzu<fnref n="1" />, Pierre Taberlet², Sébastien Conil³, Olivier Favez⁴, Alexandre Thomasson⁵, Benjamin Chazeau⁶, Nicolas Marchand⁶ and Jean-Luc Jaffrezo<fnref n="1" />

## OPEN ACCESS

**Edited by:**

Dimitrios Georgios Karpouzas,
University of Thessaly, Greece

**Reviewed by:**

Naresh Singhal,
The University of Auckland,
New Zealand
Savvas Genitsaris,
International Hellenic University,
Greece

*Correspondence:

Jean M. F. Martins
jean.martins@univ-grenoble-alpes.fr

**Specialty section:**

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

Received: 26 June 2020

Accepted: 10 December 2020

Published: 13 January 2021

**Citation:**

Samaké A, Martins JMF, Bonin A, Uzu G, Taberlet P, Conil S, Favez O, Thomasson A, Chazeau B, Marchand N and Jaffrezo J-L (2021) Variability of the Atmospheric PM₁₀ Microbiome in Three Climatic Regions of France. *Front. Microbiol.* 11:576750.
doi: 10.3389/fmicb.2020.576750

Primary Biogenic Organic Aerosols (PBOA) were recently shown to be produced by only a few types of microorganisms, emitted by the surrounding vegetation in the case of a regionally homogeneous field site. This study presents the first comprehensive description of the structure and main sources of airborne microbial communities associated with temporal trends in Sugar Compounds (SC) concentrations of PM₁₀ in 3 sites under a climatic gradient in France. By combining sugar chemistry and DNA Metabarcoding approaches, we intended to identify PM₁₀-associated microbial communities and their main sources at three sampling-sites in France, under different climates, during the summer of 2018. This study accounted also for the interannual variability in summer airborne microbial community structure (bacteria and fungi only) associated with PM₁₀-SC concentrations during a 2 consecutive years' survey at one site. Our results showed that temporal changes in PM₁₀-SC in the three sites are associated with the abundance of only a few specific taxa of airborne fungi and bacterial. These taxa differ significantly between the 3 climatic regions studied. The microbial communities structure associated with SC concentrations of PM₁₀ during a consecutive 2-year study remained stable in the rural area. Atmospheric concentration levels of PM₁₀-SC species varied significantly between the 3 study sites, but with no clear difference according to site typology (rural vs. urban), suggesting that SC emissions are related to regional rather than local climatic characteristics. The overall microbial beta diversity in PM₁₀ samples is significantly different from that of the main vegetation around the urban sites studied. This indicates that the airborne microorganisms at these urban sites are not solely from the immediate surrounding vegetation, which contrasts with observations at the scale of a regionally homogeneous rural site in 2017. These

<footnote n="1">¹ University Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), Grenoble, France, ² University Grenoble Alpes, CNRS, LECA (UMR 5553), BP 53, Grenoble, France, ³ ANDRA DRD/OPE Observatoire Pérenne de l'Environnement, Bure, France, ⁴ INERIS, Parc Technologique Alata, BP 2, Verneuil-en-Halatte, France, ⁵ AtmoAuvergne-Rhônes Alpes, Grenoble, France, ⁶ Aix Marseille Univ, CNRS, LCE, Marseille, France</footnote>
results improve our understanding of the spatial behavior of tracers of PBOA emission sources, which need to be better characterized to further implement this important mass fraction of Organic Matter (OM) in Chemical Transport models (CTM).

**Keywords:** airborne microorganisms, bioaerosol, regional sources, bacteria, fungi, sugar compounds, DNA metabarcoding, climatic gradient

INTRODUCTION

Primary biogenic organic aerosols (PBOAs) are basically a
subgroup of atmospheric organic particles that are directly
introduced from the biosphere to the atmosphere (Després et al.,
2012). PBOAs include many types of biological particles, notably
including living and non-living organisms (e.g., bacteria, viruses,
green algae, microbial fragments, etc.), dispersal units (e.g., plant
pollen, fungal and bacterial spores, etc.) and other types of
biological materials (e.g., plant and pollen debris, etc.) (Forde
et al., 2019; Huffman et al., 2020). They constitute a major
fraction of the total concentration of organic matter (OM) in
the atmosphere (Coz et al., 2010; Bozzetti et al., 2016). For
instance, it has recently been shown that the contribution of
PBOA to the mass load of organic aerosols in PM₁₀ is comparable
to that of secondary aerosol collected at a rural background
site in Switzerland in both winter and summer (Bozzetti et al.,
2016). PBOAs are subject of increasing research interest, not least
because of the growing evidence of their adverse effects on human
health and agricultural issues (e.g., allergic asthma, aspergillosis,
rhinitis, damage to food crops, etc.) and their influence on the
hydrological cycle and climate by acting as condensation or
ice nuclei in mixed-phase clouds (Després et al., 2012; Forde
et al., 2019). These various impacts are likely to be effective on a
regional scale due to the transport of PBOA-containing air masses
(Després et al., 2012; Yu et al., 2016).

Although recent studies have revealed useful information
on the size, segregation, and abundance of specific PBOA
components, quantitative studies on the overall contribution to
OM and their predominant atmospheric emission processes are
still relatively scarce (Bozzetti et al., 2016; Yu et al., 2016). As
a result, the estimation of global emissions of terrestrial PBOAs
into the atmosphere is still relatively poorly constrained, ranging
from 50 to 1,000 Tg y⁻¹ (Boucher et al., 2005; Jaenicke, 2005;
Yu et al., 2016), indicating that considerable uncertainties in the
modeling of their physico-chemical influences in a climate system
still remain (Bozzetti et al., 2016; Yu et al., 2016). Consequently,
the need for more studies to better quantify the atmospheric
loading of PBOAs on regional and global scales has been
highlighted in recent studies (Yu et al., 2016; Helin et al., 2017).
Such information would certainly allow better parameterization
of source-resolved chemical transport models (CTM), which are
still generally unable to accurately simulate the relevant fractions
of OM (Heald et al., 2011; Ciarelli et al., 2016).

Primary sugar compounds (SC, defined here as sugar alcohols
and primary saccharides), emitted continuously from biological
sources, are one of the main water-soluble organic compounds
present in atmospheric aerosols (Medeiros and Simoneit, 2007;
Li et al., 2018; Yan et al., 2019). Due to their ubiquity and

abundance, specific SCs have been used as relevant markers
to describe sources and estimate the contributions of PBOAs
to the OM mass in the atmosphere (Zhu et al., 2015; Kang
et al., 2018; Samaké et al., 2019a). For example, glucose and
trehalose (also called mycose) are the most common primary
saccharides in vascular plants and are an important source
of carbon for soil microorganisms (e.g., bacteria, fungi, etc.)
(Medeiros and Simoneit, 2007; Pietrogrande et al., 2014).
Although other sources of glucose in atmospheric aerosols have
been suggested in a few previous studies, such as biomass
combustion and marine emissions (Yang et al., 2012; Zhu et al.,
2015), these two chemical compounds have been used mainly
as generic markers for soil biota emitted to the atmosphere
from natural soil suspension and agricultural soils (Medeiros
and Simoneit, 2007; Pietrogrande et al., 2014). Similarly, sugar
alcohols, particularly mannitol and arabitol, have long been
proposed as markers for airborne fungi, and have been used to
estimate their contribution to the mass of PBOAs in various
studies around the world (Bauer et al., 2008; Golly et al.,
2018; Li et al., 2018). Sugar alcohols, especially arabitol and
mannitol are together an important fraction of the dry weight
of fungi as they are both intracellular osmoprotectant and
common storage carbohydrates (Medeiros and Simoneit, 2007;
Pietrogrande et al., 2014).

Our recent study on daily (24 h) filter samples of particulate
matter with aerodynamic diameter below 10 µm (PM₁₀)
collected simultaneously at 16 sites, grouped by sets of increasing
spatial scales in France, revealed very synchronous temporal
trends in SC concentrations and species ratio at city scale and
scales up to 200 km (Samaké et al., 2019b). Such a pattern
indicates that the processes responsible for the evolution of
SC concentrations in PM₁₀ show spatial homogeneity over
typical areas of at least tens of kilometers, which could
most probably be attributed to a very dynamic assemblage
in the airborne microbiome that is strongly influenced by
the local environment (e.g., meteorological conditions, land
use, etc.) (Forde et al., 2019). Nevertheless, little is known
about the identity of airborne microbial communities (i.e.,
community composition and diversity) associated with the
temporal dynamics of atmospheric SC concentrations. In this
context, our recent taxonomic analyses conducted in summer
2017 at a rural agricultural site in France showed that the daily
dynamics in PM₁₀ SC concentrations are clearly determined
by the abundance of only a few specific taxa of airborne
fungi and bacteria measured at that study site (Samaké et al.,
2020). As a follow-up, the present study aimed to use the
DNA metabarcoding approach (Taberlet et al., 2012) to identify
PM₁₀-associated microbial communities and their main sources
at three climatically very different sampling sites in France
during the summer of 2018. Our main objectives were 2-fold: (i) to examine the interannual variability of the structure of summer airborne microbial community (bacteria and fungi only) associated with PM₁₀ SC concentrations during a consecutive 2-year survey, (ii) to determine whether the airborne microbial community structure associated with PM₁₀ SC concentrations differs between 3 different French climatic conditions in order to test the hypothesis of local rather than regional emissions of PBOA and associate SC. The results of this study can provide insight into the structure of airborne microbiomes reflecting the spatiotemporal dynamics of PM₁₀ SC concentrations, which is essential to improve the modeling accuracy of PBOA emission processes in CTM models.

# MATERIALS AND METHODS

## Site Description and Sampling Strategy

Samples of PM₁₀ aerosols were collected at three representative sites in different geographic regions of France. The sampling sites were selected to cover several main types of environmental conditions in terms of site topography, climate, land use and cover (Figure 1). Two urban background sites (Grenoble and Marseille) and one rural background site (OPE) were selected for this study. These sites are respectively located in alpine (Grenoble, 45°09'41" N, 5°44'07" E, 220 m a.s.l.), mediterranean (Marseille, 43°18'20" N, 5°23'40" E, 64 m a.s.l.) and continental (OPE, 48°56'2" N; 5°5' E, 392 m a.s.l.) areas of France. The detailed geographical characteristics of the sampling sites have been described previously (Favez et al., 2010; Salameh et al., 2015; Samaké et al., 2020) and will only be briefly described here. In short, the city of Grenoble is by far the most densely populated urban area in the French Alps and is located at the junction of three mountain ranges (Favez et al., 2010). Marseille is the second most populated city in France and displays a large port area that extends over almost 70 km of the Mediterranean coastline (Salameh et al., 2015). The two sampling sites are part of French regional air quality monitoring networks. Finally, the OPE is a specific long-term multi-disciplinary observatory, with no major sources of pollution nearby (Samaké et al., 2020). The sampling site itself is located in the middle of an intensive agricultural area and is surrounded by field crops (several tens of kilometers in all directions) (Samaké et al., 2020).

Two seasonal aerosol sampling campaigns were conducted during the summers of 2017 and 2018 at the study sites. PM₁₀ aerosol samples were collected according to the methodology described previously (Samaké et al., 2019b) using a high-volume sampler (DA80, Digitel; 24 h at 30 m³ h⁻<fnref n="1" />). In brief, PM₁₀ samples were collected onto quartz fiber filters (Tissue-quartz PALL QAT-UP 2500 150 mm diameter) pre-heated 6 h at 500°C to remove potential trace of organic contaminants. One 150 mm filter is obtained over a 24 h cycle (starting at 9 am UTC to 9 am UTC the next day) and no replicate is done according to a national procedure validated for atmospheric aerosols sampling: a filtration at 30 m³/h for 24 h produces a homogenous deposition on the surface area within 5%. Then the filter is kept to freezer prior analysis. More details can be found

in the recommendations: (EN 16450:2017¹; EN 12341:2014²). All the analyses are done on the same filter after punching some parts directed toward different analysis or extraction before their analysis. The summer 2018 monitoring campaign was achieved simultaneously at the three sites and samples were taken at different frequencies: 1 day out of 6 for the OPE site from 14/05 to 30/08 (16 samples), every 3 days for Grenoble from 23/06 to 31/08 (24 samples) and on a daily basis for the Marseille sites from 05/07 to 25/07 (20 samples).

At the OPE site, a daily collection of filters has already been carried out during the summer 2017 (June to August) (Samaké et al., 2020), allowing to assess the inter-annual variation in the microbial composition of PM₁₀. Field blank filters (about 10% of the samples) were obtained by setting the filters in the sampler without air flow during the sampling period. They were handled as real samples for quality assurance purposes. DNA extraction and PCR controls were also included. The dataset presented here has been corrected from negative controls.

A previous source-tracking analysis indicated that airborne microbial communities are most likely the result of high intakes from nearby sources, whereas intakes from distant sources are low and diluted (Tignat-Perrier et al., 2019). Since our previous study showed that the local vegetation was the main sources of airborne microbial communities in relation to SC concentrations at the OPE site (Samaké et al., 2020), leaf samples of the main vegetation type within 100 meters of each aerosol sampler were collected at the same time as the PM₁₀ sampling. After collection, the samples were stored in airtight containers (sterile bottles, Schott, GL45, 100 ml) containing 15 g of silica gel. More details on sampling procedures and storage conditions are reported elsewhere (Samaké et al., 2020). Approximately 10 to 15 vegetation samples were analyzed per site, which is probably sufficient to conclude about local influence but is clearly insufficient to have an exhaustive image of the microbial communities from regions beyond the immediate surroundings of the urban sites. Due to unfortunate circumstances, leaf samples collected at the OPE site were lost during transport to the laboratory. This is not, however, a prohibitive factor for this study as we had already been able to observe stable SC concentrations trends over 7 consecutive years at this site.

## Chemical Analyses

Details on the chemical analysis of aerosol are presented elsewhere (Golly et al., 2018; Samaké et al., 2019a,b). Briefly, each aerosol sample was analyzed for various chemical species using subsampled fractions of the collection filters and a wide range of analytical methods. In particular, SCs (i.e., arabinol, mannitol, trehalose, and glucose) were systematically analyzed in PM₁₀ samples by high-performance liquid chromatography with pulsed amperometric detection (Waked et al., 2014; Golly et al., 2018). Elemental and organic carbon (EC, OC) were analyzed

<footnote n="1">¹Ambient Air—Automated Measuring Systems for the Measurement of The Concentration of Particulate Matter (PM₁₀; PM₂.₅); Technical Report EN 16450:2017; CEN: Paris, France, 2017.</footnote>

<footnote n="2">²Ambient Air—Standard Gravimetric Measurement Method for the Determination of the PM₁₀ or PM₂.₅ Mass Concentration of Suspended Particulate Matter; Technical Report EN 12341:2014; CEN: Paris, France, 2014.</footnote>
<figure><img src="image_24.png" /><figcaption>**FIGURE 1** | Geographical location of the sampling sites in France. The green dot indicates a rural background site in an area of intensive agriculture, while the light and dark beige dots correspond to urban background sites in Mediterranean and Alpine environments, respectively.</figcaption></figure>

using a Sunset Lab. instrument and following the EUSSAR2 thermo-optical protocol (Cavalli et al., 2010).

# Biological Analyses

## DNA Extraction

Aerosol samples pre-treatment and DNA extraction experiments were performed according to an optimized protocol, as presented elsewhere (Samaké et al., 2020). Briefly, 2 filter punches of a diameter of 38 mm each were extracted with the DNeasy PowerWater kit (QIAGEN, Germantown, MD) according to the supplier's instructions, with the following modifications (Samaké et al., 2020): Elution of biological materials on the polyethersulphone membrane disc filter (PES, Supor® 47 mm 200, 0.2 µm, PALL), 30 min sonication at 65°C in a thermal water bath (EMAG, Emmi-60 HC, Germany; 50% of efficiency), 5 min bead-beating before and after sonication, and the DNA was finally eluted with 50 µL EB buffer.

Leave samples were dried in contact of about 30 g of silica gel. To extract DNA from epiphytic or endophytic microorganisms, aliquots of leaf samples (approx. 25–30 mg) were crushed using a Tissue-Lyser (QIAGEN, Germany). DNA was extracted with the DNeasy Plant Mini Kit (QIAGEN, Germany) according to the original manufacturer's protocol, with some minor modifications as already detailed elsewhere (Samaké et al., 2020).

# PCR Amplification and Sequencing

The PCR amplification procedures and materials used in this study were similar to those described in our recent work (Samaké et al., 2020). Briefly, the hypervariable V4 region of the bacterial 16S rRNA gene was amplified using the Bact02 primer (Forward 5'-KGCCAGCMGCCGCGGTAA-3' and Reverse 3'-GGACTACCMGGGTATCTAA-5'). The nuclear ribosomal internal transcribed spacer region 1 (ITS1) of the fungal gene was amplified with the Fung02 primer (Forward 5'-GGAAGTAAAAGTCGTAACAAGG-3' and Reverse 3'-CAAGAGATCCGTTGYTGAAAGTK-5'). The expected length of the produced reads was of 250 base pairs (bp) for each set of primers. Eight-nucleotide tags were added to both primer ends to uniquely identify each sample, ensuring that each PCR replicate is labeled with a unique combination of forward and reverse tags. The tag sequences were created with the oligotag command in the OBITools software (Boyer et al., 2016), so that all pairwise tag combinations were differentiated by at least five different base pairs (Taberlet et al., 2018).

Four independent PCR replicates were performed for each DNA extract. Amplification of the bacterial 16S rDNA gene and fungal ITS1 region was performed in a 20 µL PCR reaction mixture containing 10 µL of AmpliTaq Gold 360 Master Mix (Applied Biosystems, Foster City, CA, United States),
0.16 µL of 20 mg ml⁻¹ bovine serum albumin (BSA; Roche Diagnostics, Basel, Switzerland), 0.2 µM of each primer, and 2 µL of diluted DNA extract. The DNA extracts from the air samples were diluted eight times, while DNA extracts from the leaves were diluted four times. The thermal cycling program was as follows: an initial activation of DNA polymerase for 10 min at 95°C; 40 denaturation cycles of 30 s at 95°C, 30 s annealing at 53°C and 56°C for Bacteria and Fungi, respectively, 90 s elongation at 72°C; and a final elongation at 72°C for 7 min. Approximately 10% of the PCR products were randomly selected and controlled using a QIAxel Advance device (QIAGEN, Hilden, Germany) equipped with a high-resolution separation cartridge.

After amplification, PCR products from the same marker were pooled in equal volumes and purified with the MinElute kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. Both pools were sent to Fasteris SA (Geneva, Switzerland) for library preparation and MiSeq Illumina 2 × 250 bp paired-end sequencing. The two sequencing libraries (one per marker) were prepared and libraries as are presented elsewhere (Samaké et al., 2020), which aims to limit the formation of chimeras. Negatives of DNA extraction and of PCR, as well as unused tag combinations were included in the sequencing experiment to monitor for potential false positives due to tag jumps and contamination (Schnell et al., 2015).

## Bioinformatics Analyses of Raw Sequences

The raw read sequences were processed separately for each library using the OBITools software suite (Boyer et al., 2016) and the detailed workflow has recently been presented elsewhere (Samaké et al., 2020). Note that all raw reads (i.e., 2017 and 2018) have been processed simultaneously. In short, the raw paired-ends were assembled and the low-quality sequences (Fastq average quality score < 40) were rejected. The aligned sequences were then assigned to the corresponding PCR replicates allowing zero and two mismatches on the tags and primers, respectively. Strictly identical sequences were de-replicated and a base filtration step was performed to select sequences within the expected range length (i.e., longer than 65 or 39 bp for fungi and bacteria, respectively, excluding tags and primers), without ambiguous nucleotides, and observed at least 10 times in at least one PCR replicate.

The remaining unique sequences were then clustered into Molecular Taxonomic Units (MOTUs) at a 97% threshold using Sumatra and Sumaclust algortihms (Mercier et al., 2013). Taxonomy assignments of each MOTU were made to a reference set of full-length metabarcodes using the open reference *ecotag* program (Boyer et al., 2016). The reference database was built with the *ecoPCR* program (Ficetola et al., 2010) for each library based on the version 113 of the EMBL database. The resulting datasets were then processed with the open source software R (R studio interface, version 3.4.1) to filter out chimeric sequences, potential contaminants and failed PCR replicates. Finally, the remaining PCR replicates were summed per sample. The final datasets, as well as the OBITools commands applied, have been uploaded to the DRYAD repository (doi: 10.5061/dryad.dv41ns1wf).

## Statistical Analyses

All statistical analyses were carried out with R (R Core Team, 2017). First of all, raw MOTU abundances were rarefied at the same sequencing depth as before any statistical analyses to cope with the heterogeneity of the number of sequences per sample. The rarefaction and extrapolation curves were obtained with the *iNext 2.0-12* package (Hsieh et al., 2016), to study the gain in species richness as we increased the sequencing depth for each sample. The alpha diversity estimator was calculated using the *phyloseq* package 1.22-3 (McMurdie and Holmes, 2013) to estimate the Chao1 index and Shannon diversity. The similarity of microbial communities was represented by non-metric multidimensional scale (NMDS) using the Morisita-Horn similarity distance metric. The NMDS ordination analyses were performed with the *metaMDS* function within the *vegan* package (Oksanen et al., 2020) with the number of random starts set to 999. The samples were also visualized using hierarchical cluster analysis based on the same dissimilarity index. An analysis of similarities (ANOSIM) was performed to assess whether sample categories contained significantly different microbial communities. An analysis of homogeneity of variance was performed with the *betadisper* function in the *vegan* package to test whether airborne microbial compositions differed in their dispersion over time. The null hypothesis was that the mean dispersion within a group was the same across all groups. The relationship between airborne microbial communities and PM₁₀ SC species was assessed using Kendall's rank correlation.

# RESULTS AND DISCUSSION

## Richness and Diversity Indexes of Microbial Communities

The structures of airborne bacterial and fungal communities (the fraction of micro-eucaryotes was not considered here) were obtained for the 121 samples collected, consisting of 96 aerosols and 25 leaf samples. After paired-end assembly of reads, sample assignment, and stringent quality filtering (including chimera and contaminant filtering), 4,678,172 and 8,721,309 sequences were obtained in total for the bacterial and fungal dataset, respectively. These sequences represented 3,460 bacterial and 1,490 fungal MOTUs in all collected samples. The rarefaction curves of MOTU richness revealed common logarithmic shapes approaching a plateau for each sample (Supplementary Figure S1), indicating that most bacterial and fungal species were detected in our samples. Alpha-diversity estimators, including Chao1 and Shannon indices, were calculated to estimate the mean richness and biodiversity of microbial communities in PM₁₀ samples analyzed at each study site. MOTU tables were refined to 2,468 and 8,072 bacterial and fungal sequence reads respectively, prior to diversity analyses. As shown in Supplementary Figure S2, the microbial diversity and richness of PM₁₀ differed significantly at each sampling site (*p* < 0.05), confirming that the diversity and composition of atmospheric bacteria and fungi in aerosol samples are most likely determined by the surrounding landscape and their
<figure><img src="image_25.png" /><figcaption>**FIGURE 2** | Relative abundance of the top 20 fungi (A) and bacteria (B) detected in PM₁₀ samples at all sites. The samples are grouped by site location. X-axis corresponds to each individual daily sample ranged in chronological order.</figcaption></figure>

corresponding local climatic conditions. On average, the Chao1 richness estimators and Shannon diversity indices of the bacterial MOTU in Marseille were the highest ($p < 0.05$), while that of the OPE site was the lowest ($p < 0.05$). Chao1 values of airborne fungi were highest in Grenoble and lowest at the OPE site ($p < 0.05$), while the Shannon index values remained similar at the different study sites.

We also compared the microbial diversity of PM₁₀ during two consecutive summer periods at the rural OPE site. Samples from summer 2017 had the highest richness (number of MOTUs) in bacterial and fungal MOTUs ($p < 0.05$), while, the summer 2018 samples presented approximately the highest bacterial and fungal MOTU diversity (Richness and Evenness).

## Microbial Community in Airborne PM₁₀
Airborne Bacterial Community Composition

In total, the airborne bacterial microbiome was divided into 24 microbial phyla, 54 classes, 124 orders, and 275 families for all

PM₁₀ samples. Despite very different geographical conditions, the average bacterial sequence reads (mean ± SD) were dominated in the three sites by Proteobacteria (48.7 ± 10.7%), followed by Bacteroidetes (18.9 ± 6.1%), Actinobacteria (16.5 ± 5.1%), Firmicutes (9.2 ± 7.4%), Cyanobacteria (1.9 ± 3.2%), with less than 3% of the total bacterial sequence reads unclassified. These phyla have been commonly observed in the atmosphere (Maron et al., 2005; Liu et al., 2019; Wei et al., 2020). Proteobacteria constitute a major taxonomic group among prokaryotes, and have previously been reported as the most abundant bacterial phylum in air samples from central northern France (Maron et al., 2005). At the class level, the predominant Bacteria are Alphaproteobacteria (25.5 ± 6.4%), Actinobacteria (15.8 ± 4.9%), Gammaproteobacteria (10.6 ± 7.3%), Betaproteobacteria (8.0 ± 4.9%), Cytophagia (6.4 ± 2.5%), Bacilli (6.2 ± 4.6%), Sphingobacteriia (6.0 ± 3.0%), Flavobacteriia (4.0 ± 2.3%), Clostridia (2.5 ± 3.6%), and Deltaproteobacteria (2.3 ± 4.7%). Up to 650 genera were detected in all aerosol
<figure><img src="image_26.png" /><figcaption>**FIGURE 3** | Non-metric multi-dimensional scale ordination plots (NMDS) of airborne microbial community composition over two consecutive summer periods at the OPE site. NDMS plots are constructed from a Horn distance matrix of MOTUs abundances for fungi (A) and bacteria (B), respectively. The data sets are rarefied at the same sequencing depth. The stress values indicate an adequate two-dimensional picture of the sample distribution. The ellipses represent 95% confidence intervals for the cluster centroids.</figcaption></figure>

samples, although many sequences (24.5 ± 6.7%) could not be taxonomically attributed at the genus level. The most abundant genera (>2%, see **Figure 2B**) are *Sphingomonas* (13.9 ± 7.3%), followed by *Massilia* (7.4 ± 5.0%), *Curtobacterium* (5.0 ± 2.6%), *Hymenobacter* (3.3 ± 1.9%), *Pedobacter* (3.1 ± 2.0%), *Pseudomonas* (2.3 ± 2.4%), *Chryseobacterium* (2.1 ± 1.8%), and *Methylobacterium* (1.9 ± 1.0%).

## Airborne Fungal Community Composition

In all aerosol samples, fungal communities were characterized by 3 phyla, 24 classes, 73 orders, and 212 families. The fungal sequence reads in air samples of the three sites are consistently dominated by the Ascomycota phylum (76.5 ± 19.1%), followed by the Basidiomycota (23.4 ± 19.0%), which are known to actively eject spores into the atmosphere along with aqueous jets or droplets containing a mixture of inorganic solutes and carbohydrates into the atmosphere (Després et al., 2007). The remaining sequences are affiliated to Mucoromycota (<0.03%) and to unclassified sequences (approximately 0.03%). This is consistent with the results of previous studies also indicating that the subkingdom of Dikarya (Ascomycota and Basidiomycota) accounts for 98% of known species in the biological kingdom of Eumycota (i.e., fungi) in the atmosphere (James et al., 2006; Fröhlich-Nowoisky et al., 2009). Among these observed phylotypes, the predominant classes (>1%) are Dothideomycetes (69.4 ± 19.9%), followed by Agaricomycetes (18.1 ± 18.9%), Eurotiomycetes (5.7 ± 8.1%), Sordariomycetes (5.5 ± 6.3%), Leotiomycetes (3.7 ± 3.9%), Tremellomycetes (3.2 ± 5.8%), and Microbotryomycetes (1.1 ± 2.3%). The predominant orders are Pleosporales (3.2 ± 5.8%) and Capnodiales (3.2 ± 5.8%), which belong to Ascomycota. Similarly, the dominant orders in Basidiomycota are Polyporales (10.2 ± 10.5%), followed by Russulales (3.2 ± 6.8%) and Tremellales (2.0 ± 2.6%). At the genus level, about 528 taxa

are characterized in all air samples (**Figure 2A**), of which *Cladosporium* (24.0 ± 12.2%), *Epicoccum* (15.7 ± 11.2%), *Alternaria* (13.7 ± 8.0%), *Coriolopsis* (4.1 ± 7.1%), *Aspergillus* (2.5 ± 5.8%), and *Peniophora* (1.9 ± 5.0%) are the most abundant communities.

## Interannual Variation in the Airborne Microbe Composition at the OPE Site

To explore and visualize the variation of airborne microbial communities in PM₁₀ collected during two consecutive summer periods at the OPE site, we performed an NMDS ordination analysis based on Morishita-Horn dissimilarity distance matrices. As shown in **Figure 3**, the airborne bacterial and fungal communities in the aerosol samples collected in summer 2017 and 2018 are closely grouped. An unsupervised hierarchical cluster analysis shows a pattern similar to that observed on the NMDS ordination, where the airborne bacterial and fungal MOTUs in PM₁₀ are clustered together regardless of the sampling year. It indicates that the structure of the predominant airborne bacterial and fungal community has remained stable over the years at the OPE site (**Supplementary Figure S3**). This result is logical since agricultural practices (types of crops, harvests, etc.) have been shown to drive airborne microbial communities (Mhuireach et al., 2016; Wei et al., 2019). Although crops around the OPE observatory are subject to a 3-year rotation system, overall crops in the vicinity do not vary significantly from 1 year to the next (Samaké et al., 2019b). However, the microbial communities observed in summer 2018 are dispersively distributed with longer distances between samples (Betadisper, *p* < 0.05) than in summer 2017, for both fungi and bacteria. This result could be, at least partially, attributed to the lower aerosol sampling frequency applied in 2018.
<figure><img src="image_27.png" /><figcaption>**FIGURE 4** | Venn diagrams showing the numbers of fungal (A) and bacterial (B) MOTUs shared between study sites. Only those MOTUs detected at least 10 times in each sample were used to construct the Venn diagrams. Comparison of the composition of PM₁₀ samples by geographical sites on a NMDS scaling ordination (C,D). The NDMS plots are constructed from a Horn distance matrix of MOTUs abundances for fungi (A) and bacteria (B), respectively. The data sets are rarefied at the same sequencing depth. The stress values indicate an adequate two-dimensional picture of samples distribution. The ellipses represent 95% confidence intervals for the cluster centroids.</figcaption></figure>

<table><caption>**TABLE 1** | Abundances (mean ± standard deviation) of four major primary sugar compounds measured at each study site over two campaigns (2017 and 2018).</caption><thead><tr><th rowspan="2">Compounds</th><th colspan="2">OPE</th><th colspan="2">Grenoble</th><th colspan="2">Marseille</th></tr><tr><th>Annual</th><th>Summer</th><th>Annual</th><th>Summer</th><th>Annual</th><th>Summer</th></tr></thead><tbody><tr><td><strong>2017</strong></td><td>(n = 107)</td><td>(n = 64)</td><td>(n = 123)</td><td>(n = 31)</td><td>(n = 66)</td><td>(n = 15)</td></tr><tr><td>Arabitol</td><td>46.2 ± 71.8</td><td>69.4 ± 85.0</td><td>19.6 ± 21.2</td><td>39.1 ± 27.3</td><td>8.0 ± 7.8</td><td>12.0 ± 6.8</td></tr><tr><td>Mannitol</td><td>46.0 ± 65.9</td><td>69.2 ± 76.4</td><td>21.5 ± 22.7</td><td>43.9 ± 22.1</td><td>6.9 ± 6.1</td><td>11.6 ± 6.7</td></tr><tr><td>Glucose</td><td>36.1 ± 33.8</td><td>48.3 ± 37.5</td><td>26.8 ± 22.0</td><td>44.5 ± 25.6</td><td>11.7 ± 7.4</td><td>15.8 ± 8.1</td></tr><tr><td>Trehalose</td><td>34.6 ± 47.5</td><td>53.7 ± 53.1</td><td>10.9 ± 15.4</td><td>24.0 ± 19.8</td><td>NA</td><td>NA</td></tr><tr><td><strong>2018</strong></td><td>(n = 57)</td><td>(n = 16)</td><td>(n = 186)</td><td>(n = 71)</td><td>(n = 21)</td><td></td></tr><tr><td>Arabitol</td><td>16.4 ± 19.4</td><td>37.9 ± 25.3</td><td>24.8 ± 23.4</td><td>38.5 ± 25.1</td><td>NA</td><td>151.2 ± 81.5</td></tr><tr><td>Mannitol</td><td>20.8 ± 20.8</td><td>37.3 ± 24.0</td><td>25.8 ± 22.0</td><td>40.5 ± 19.2</td><td>NA</td><td>164.6 ± 67.4</td></tr><tr><td>Glucose</td><td>25.6 ± 30.3</td><td>36.3 ± 37.8</td><td>33.1 ± 27.9</td><td>37.7 ± 19.7</td><td>NA</td><td>155.8 ± 128.7</td></tr><tr><td>Trehalose</td><td>4.5 ± 10.3</td><td>8.4 ± 15.5</td><td>9.2 ± 14.6</td><td>12.9 ± 15.7</td><td>NA</td><td>85.9 ± 43.7</td></tr></tbody></table>

In 2018, data for the Marseille site were only available in July. NA denotes not measured. Summer period corresponds to June–July–August.
<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

**FIGURE 5** | Heatmaps of Kendall's rank correlation between SCs and abundance of airborne fungal communities at the study sites: (A–C) correspond to the OPE, Marseille and Grenoble sites, respectively. Only the 24 most abundant fungal genera (relative abundance ≥1) are indicated. Asterisks indicate significant correlations (\*\*0.001 < *p* < 0.01, \*0.01 < *p* < 0.05).

## Spatial Variation in the Composition of Airborne Microbes

A Venn diagram was constructed to analyze the overlap of MOTUs on the pairs of sites (Figures 4A,B). A total of 177 fungal MOTUs are shared between the urban background sites of Marseille and Grenoble, whereas the rural OPE site shared only 16 and 86 MOTUs respectively with the Marseille and Grenoble sites. Similarly, 369 bacterial MOTUs are shared between the Marseille and Grenoble sites, whereas the bacterial community of OPE shared only 173 and 179 MOTUs with the Marseille and Grenoble site, respectively. Summer PM₁₀ samples from the urban sites shared the highest proportion of microbial MOTUs, suggesting that the Marseille and Grenoble sites tend to have more similar airborne bacterial and fungal community compositions. This

suggests that agricultural practices have a different influence on the composition of the local microbiome in the air than urbanization activities.

To study the overall similarity and disparity of microbial community between sites, a NMDS ordination analysis based on the Horn distance matrix was performed (Figures 4C,D). Figures 4C,D show that, in general, PM₁₀ samples from the same location are closely grouped together. Further similarity analysis (ANOSIM, overall *R* = 0.28 and *R* = 0.34 with *p* < 0.01 for fungal and bacterial communities, respectively) confirmed the significant difference between the 3 distinct clusters by geographic locations. These results indicate that the bacterial and fungal communities in PM₁₀ samples are grouped by geographical region, although some taxa are common to all three sites.
<figure><img src="image_31.png" /></figure>

Spatial and Temporal Variability in Concentrations of
Primary Sugar Compounds

A large database on concentrations of PM₁₀ SC at study sites has been acquired previously at many sites all over France (Samaké et al., 2019a,b), including the three sites of this study. It has already been shown that arabitol, mannitol, glucose, and trehalose are generally the main SC species in all urban and rural areas investigated in France (Samaké et al., 2019a). Their mean annual and summer concentrations at the 3 sites of the present study for the years 2017 and 2018 are shown in

**Table 1.** This table shows that the concentration of individual SC species at the Grenoble site remained almost constant throughout the 2 years of sampling, indicating a reproducibility in the concentration trends of SC species. At the OPE site, mean concentrations of individual SC species in 2018 are generally lower than in 2017. However, it should be noted that this result may have been biased by the difference in sampling frequency used in summer 2018 (1 every 6 days) compared to 2017 (daily). This is important because the source contribution is episodic in nature (e.g., harvesting)
<figure><img src="image_32.png" /></figure>

(Samaké et al., 2020), therefore the most intense episodes were not necessarily collected.

As also shown in **Table 1**, the ambient concentration of the main SC species varies significantly between the sites, but with no clear difference according to the site typology (rural vs. urban). This suggests that SC emissions are less site specific but more related with regional climatic characteristics.

## Relationship Between Primary Sugar Compounds and Dominant Airborne Microbial MOTUs

The structure of airborne PM₁₀ microbial taxa associated with the temporal trends in atmospheric SC concentration levels may vary in different geographical areas. Therefore, we calculated the Kendall's rank correlation between the dominant SC species (i.e., arabitol, mannitol, glucose, trehalose) and the abundance of airborne microbial community at genus level. This analysis reveals variable relationships among sampling sites (Figures 5A–C, 6A–C). At the OPE site, the temporal dynamics of SC species is mainly better correlated (p < 0.05) with the fungal genera *Alternaria*, *Cladosporium*, *Dioszegia*, *Itersonilia*, *Microdochium*, and *Sporobolomyces* (Figure 5A). This observation is consistent with that made in summer 2017 at the same site (Samaké et al., 2020). Similarly, at the Marseille site (Figure 5B), time series of SC species are positively correlated (p < 0.05) only with *Cladosporium*, and to a lesser degree (not significant) with the fungal genus *Alternaria*, whereas they are best correlated (p < 0.05) with the abundances of fungal genera *Hyphodermella*, *Phanerochaete*, *Peniophora*, and *Stereum* at the Grenoble site (Figure 5C).

As for airborne fungi, we also found that the temporal dynamics of concentration levels of SC species are positively correlated with the abundances of the bacterial genera *Massilia*

(p < 0.05) and *Pseudomonas* (p < 0.05) at the OPE site (Figure 6A), whereas it is better correlated (p < 0.05) with the abundances of the bacterial genera *Sphingomonas*, *Curtobacterium*, *Streptomyces*, *Pedobacter*, and *Halospirulina* at the Marseille site (Figure 6A). Some bacterial genera, notably *Melittangium*, also showed a strong positive correlation (p < 0.05) with PM₁₀ SC species at the Grenoble site (Figure 6C). These results clearly show site-specific microbiome signatures measured in the PM₁₀ of the 3 sites, with overlaps of only few fungal and bacterial genera. Overall, these observed regional trends probably result from ecosystems heterogeneity, such as site topography, microclimate, vegetation structure, around our study sites. A recent study has shown that the atmospheric microbiome composition is indeed primarily structured by the surrounding landscape types (Tignat-Perrier et al., 2019).

## Potential Sources of Airborne Microbial Communities at Study Sites

Vegetation structure has been proposed as one of the main sources of airborne microorganisms in urban background areas (Genitsaris et al., 2017). A recent study has shown that the episodic daily fluctuations in PM₁₀ SC concentrations at the Grenoble site are very often well synchronized with those of cellulose (Samaké et al., 2019b), a suitable molecular tracer of atmospheric plant biomass (Puxbaum and Tenze-Kunit, 2003). The latter study in Grenoble (Samaké et al., 2019b) also reported maximum atmospheric concentrations of SCs when the vegetation density indicator (leaf area index) was at its maximum in late summer. Therefore, to determine whether the atmospheric microbial genera most positively correlated with SC species were derived from surrounding vegetation, the overall microbial beta diversity in leaf samples of the main
<figure><img src="image_33.png" /></figure>

vegetation around the two urban stations was analyzed. For the Grenoble site, the NMDS ordination analysis (Figure 7) shows that the overall beta diversities are very similar within the same habitat (PM₁₀ or plant) and are very different from a habitat to another (ANOSIM, R = 0.89 and 0.97, *p* < 0.01 for fungal and bacterial communities, respectively). These results are also confirmed by an unsupervised hierarchical cluster analysis, which reveals a pattern similar to that observed in the NMDS ordinate (Figure 8), where airborne PM₁₀ taxa are grouped separately from those in leaf samples. Similar results were also obtained for the Marseille site (Supplementary Figure S4). These results suggest that the airborne microorganisms at the urban sites studied here do not originate only from the immediate surrounding vegetation. To validate this suggestion, specific samplings of PM₁₀ and vegetation should be conducted beyond all immediate surroundings of the urban sites, although this appears a complicate, if not impossible, task, being the large number of potential and heterogeneous sources. In this study, the collection of vegetation samples was clearly not

exhaustive and microorganisms typical of each site may have
been missed, and do not appear in our analysis. Although
not fully supported by direct evidence, these results clearly
invalidate, however, our initial hypothesis of specific local
emissions of PBOA and SC at urban sites. In other words,
airborne microbes in urban areas are probably of allochthonous
origin, as already suggested in other contexts (Genitsaris
et al., 2017; Tignat-Perrier et al., 2019), with influence on
emissions within a radius of several tens of kilometers
(Tignat-Perrier et al., 2019).

CONCLUSION

This study presents the first comprehensive description of the
structure and main sources of airborne microbial communities
associated with temporal trends in PM₁₀ SC concentration levels
at 3 French sites with different climatic conditions. The following
main conclusions could be drawn from the results obtained:
* Temporal trends of SC in PM₁₀ in the three sites are associated with the abundance of only a few specific taxa of airborne fungal and bacterial. These microbial taxa differ significantly between the 3 different climatic zones studied.

* The structure of summer airborne microbial community associated with PM₁₀ SC concentrations during a consecutive 2 year survey remains stable at one site in an agricultural area.

* Atmospheric concentration levels of PM₁₀ SC species vary significantly between the 3 study sites, but without clear differences according to site typology (rural vs urban), suggesting that SC emissions are not only local, around the site, but also related to regional climatic characteristics.

* The overall microbial beta diversity in PM₁₀ samples are significantly different from that of the main vegetation around the urban sites studied. This indicates that the airborne microorganisms at the urban sites studied here do not originate only from the immediate surrounding vegetation, and are likely to be of allochthonous origin, although no direct evidence could be provided, given the great difficulty in comprehensively sampling the wide variety of potential sources at long distances. The hypothesis of only specific local emissions in urban areas is however ruled out as already suggested in recent studies. This is a different conclusion to that obtained for our rural site in 2017 with homogeneous agricultural practices on a large scale.

Overall, these results improve our understanding of the spatial behavior of tracers of PBOA emission sources in general, which is imperative for further implementation of this large OM mass fraction into CTM models. However, contributions from remote sources in urban areas still need to be validated through specific sampling. Similarly, direct validation of the important SC contents of some species among the dominant microbial taxa identified in each site of this study still needs to be performed, after growth under controlled laboratory conditions. This will allow to decipher the environmental conditions controlling the profile and concentration levels of the SC species produced by the dominant microorganisms, relevant in PBOA emissions.

## 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: doi.org/10.5061/dryad.dv41ns1wf.

## REFERENCES

Bauer, H., Claeys, M., Vermeylen, R., Schueller, E., Weinke, G., Berger, A., et al. (2008). Arabitol and mannitol as tracers for the quantification of airborne fungal spores. *Atmos. Environ.* 42, 588–593. doi: 10.1016/j.atmosenv.2007.10.013

Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., et al. (2005). “Clouds and Aerosols,” in *Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change*, eds D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, et al. (Cambridge: Cambridge University Press), 571–892.

## AUTHOR CONTRIBUTIONS

GU, J-LJ, and JM supervised the thesis of AS. GU and J-LJ designed the atmospheric chemistry part of the Mobil’Air program. AT and SC supervised the sampling at Grenoble and ANDRA sites, respectively. BC and NM supervised the sampling at Marseille site. AS, AB, and JM designed the microbiological aspects of the study. AS and AB performed the experiments. AB performed the bioinformatic analyses. AS performed statistical analyses and wrote the original manuscript. All authors revised the manuscript.

## FUNDING

This work was supported by the French National Research Agency in the framework of the “Investissements d’avenir” program (ANR-15-IDEX-02), for the Mobil’Air program. It also received support from the program CAREMBIOS funded by LEFE and EC2CO-Microbien national CNRS programs, from the program QAMECS funded by ADEME (convention 1662C0029), and from the French reference laboratory for air quality monitoring (LCSQA) for the chemical analyses of the filter collected in Grenoble as part of the CARA program. Chemical and microbiological analysis on the Air-O-Sol facility at IGE was made possible with the funding of some of the equipment by the Labex OSUG@2020 (ANR10 LABX56). The Ph.D. of AS was funded by the government of Mali.

## ACKNOWLEDGMENTS

We acknowledge the work of many engineers in the lab at the Institut des Géosciences de l'Environnement for the analyses (A. Crouzet, A. Vella, C. Vérin, and E. Vince). We would like to kindly thank the dedicated efforts of many other people at the sampling sites.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2020.576750/full#supplementary-material

Boyer, F., Mercier, C., Bonin, A., Le Bras, Y., Taberlet, P., and Coissac, E. (2016). OBITOOLS: a UNIX -inspired software package for DNA metabarcoding. *Mol. Ecol. Resour.* 16, 176–182. doi: 10.1111/1755-0998.12428

Bozzetti, C., Daellenbach, K. R., Hueglin, C., Fermo, P., Sciare, J., Kasper-Giebl, A., et al. (2016). Size-resolved identification, characterization, and quantification of primary biological organic aerosol at a european rural site. *Environ. Sci. Technol.* 50, 3425–3434. doi: 10.1021/acs.est.5b05960
Cavalli, F., Viana, M., Yttri, K. E., Genberg, J., and Putaud, J.-P. (2010). Toward a standardised thermal-optical protocol for measuring atmospheric organic and elemental carbon: the EUSAAR protocol. *Atmos. Meas. Tech.* 3, 79-89. doi: 10.5194/amt-3-79-2010

Ciarelli, G., Aksoyoglu, S., Crippa, M., Jimenez, J.-L., Nemitz, E., Sellegrìe, K., et al. (2016). Evaluation of European air quality modelled by CAMx including the volatilitybasis set scheme. *Atmos. Chem. Phys.* 16, 10313-10332. doi: 10.5194/acp-16-10313-2016

Coz, E., Artiñano, B., Clark, L. M., Hernandez, M., Robinson, A. L., Casuccio, G. S., et al. (2010). Characterization of fine primary biogenic organic aerosol in an urban area in the northeastern United States. *Atmos. Environ.* 44, 3952-3962. doi: 10.1016/j.atmosenv.2010.07.007

Després, V. R., Alex Huffman, J., Burrows, S. M., Hoose, C., Safatov, A. S., Buryak, G., et al. (2012). Primary biological aerosol particles in the atmosphere: a review. *Tellus B* 64:15598.

Després, V. R., Nowoisky, J. F., Klose, M., Conrad, R., Andreae, M. O., and Pöschl, U. (2007). Characterization of primary biogenic aerosol particles in urban, rural, and high-alpine air by DNA sequence and restriction fragment analysis of ribosomal RNA genes. *Biogeosciences* 4, 1127-1141. doi: 10.5194/bg-4-1127-2007

Favez, O., Haddad, I. E., Piot, C., Boreave, A., Abidi, E., and Marchand, N. (2010). Inter-comparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France). *Atmos. Chem. Phys.* 10, 5295-5314. doi: 10.5194/acp-10-5295-2010

Ficetola, G., Coissac, E., Zundel, S., Riaz, T., Shehzad, W., Bessière, J., et al. (2010). An In silico approach for the evaluation of DNA barcodes. *BMC Genom.* 11:434. doi: 10.1186/1471-2164-11-434

Forde, E., Gallagher, M., Foot, V., Sarda-Esteve, R., Crawford, I., Kaye, P., et al. (2019). Characterisation and source identification of biofluorescent aerosol emissions over winter and summer periods in the United Kingdom. *Atmos. Chem. Phys.* 19, 1665-1684. doi: 10.5194/acp-19-1665-2019

Fröhlich-Nowoisky, J., Pickersgill, D. A., Després, V. R., and Pöschl, U. (2009). High diversity of fungi in air particulate matter. *Proc. Natl. Acad. Sci. U.S.A.* 106, 12814-12819. doi: 10.1073/pnas.0811003106

Genitsaris, S., Stefanidou, N., Katsiapi, M., Kormas, K. A., Sommer, U., and Moustaka-Gouni, M. (2017). Variability of airborne bacteria in an urban Mediterranean area (Thessaloniki, Greece). *Atmos. Environ.* 157, 101-110. doi: 10.1016/j.atmosenv.2017.03.018

Golly, B., Waked, A., Weber, S., Samake, A., Jacob, V., Conil, S., et al. (2018). Organic markers and OC source apportionment for seasonal variations of PM2.5 at 5 rural sites in France. *Atmos. Environ.* 198, 142-157. doi: 10.1016/j.atmosenv.2018.10.027

Heald, C. L., Coe, H., Jimenez, J. L., Weber, R. J., Bahreini, R., Middlebrook, A. M., et al. (2011). Exploring the vertical profile of atmospheric organic aerosol: comparing 17 aircraft field campaigns with a global model. *Atmos. Chem. Phys.* 11, 12673-12696. doi: 10.5194/acp-11-12673-2011

Helin, A., Siëtiö, O.-M., Heinonsalo, J., Bäck, J., Riekkola, M.-L., and Parshintsev, J. (2017). Characterization of free amino acids, bacteria and fungi in size-segregated atmospheric aerosols in boreal forest: seasonal patterns, abundances and size distributions. *Atmos. Chem. Phys.* 17, 13089-13101. doi: 10.5194/acp-17-13089-2017

Hsieh, T. C., Ma, K. H., and Chao, A. (2016). iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). *Methods Ecol. Evol.* 7, 1451-1456. doi: 10.1111/2041-210X.12613

Huffman, J. A., Perring, A. E., Savage, N. J., Clot, B., Crouzy, B., Tummon, F., et al. (2020). Real-time sensing of bioaerosols: review and current perspectives. *Aerosol Sci. Technol.* 54, 465-495. doi: 10.1080/02786826.2019.1664724

Jaenicke, R. (2005). Abundance of cellular material and proteins in the atmosphere. *Science* 308, 73-73. doi: 10.1126/science.1106335

James, T. Y., Kauff, F., Schoch, C. L., Matheny, P. B., Hofstetter, V., Cox, C. J., et al. (2006). Reconstructing the early evolution of Fungi using a six-gene phylogeny. *Nature* 443, 818-822. doi: 10.1038/nature05110

Kang, M., Ren, L., Ren, H., Zhao, Y., Kawamura, K., Zhang, H., et al. (2018). Primary biogenic and anthropogenic sources of organic aerosols in Beijing, China: insights from saccharides and n-alkanes. *Environ. Pollut.* 243, 1579-1587. doi: 10.1016/j.envpol.2018.09.118

Li, L., Ren, L., Ren, H., Yue, S., Xie, Q., Zhao, W., et al. (2018). Molecular characterization and seasonal variation in primary and secondary organic aerosols in Beijing, China. *J. Geophys. Res. Atmos.* 123, 12394-12412. doi: 10.1029/2018JD028527

Liu, H., Hu, Z., Zhou, M., Hu, J., Yao, X., Zhang, H., et al. (2019). The distribution variance of airborne microorganisms in urban and rural environments. *Environ. Pollut.* 247, 898-906. doi: 10.1016/j.envpol.2019.01.090

Maron, P.-A., Lejon, D. P. H., Carvalho, E., Bizet, K., Lemanceau, P., Ranjard, L., et al. (2005). Assessing genetic structure and diversity of airborne bacterial communities by DNA fingerprinting and 16S rDNA clone library. *Atmos. Environ.* 39, 3687-3695. doi: 10.1016/j.atmosenv.2005.03.002

McMurdie, P. J., and Holmes, S. (2013). Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. *PLoS One* 8:e61217. doi: 10.1371/journal.pone.0061217

Medeiros, P. M., and Simoneit, B. R. T. (2007). Analysis of sugars in environmental samples by gas chromatography-mass spectrometry. *J. Chromatogr. A* 1141, 271-278. doi: 10.1016/j.chroma.2006.12.017

Mercier, C., Boyer, F., Kopylova, E., Taberlet, P., Bonin, A., and Coissac, E. (2013). SUMATRA and SUMACLUST: fast and exact comparison and clustering of sequences. *Programs Abstr. SeqBio Workshop* 14, 27-28.

Mhuireach, G., Johnson, B. R., Altrichter, A. E., Ladau, J., Meadow, J. F., Pollard, K. S., et al. (2016). Urban greenness influences airborne bacterial community composition. *Sci. Total Environ.* 571, 680-687. doi: 10.1016/j.scitotenv.2016.07.037

Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., et al. (2020). *Vegan: Community Ecology Package*. Available online at: https://cran.r-project.org/web/packages/vegan/index.html (accessed February 10, 2020).

Pietrogrande, M. C., Bacco, D., Visentin, M., Ferrari, S., and Casali, P. (2014). Polar organic marker compounds in atmospheric aerosol in the Po Valley during the Supersito campaigns — Part 2: seasonal variations of sugars. *Atmos. Environ.* 97, 215-225. doi: 10.1016/j.atmosenv.2014.07.056

Puxbaum, H., and Tenze-Kunit, M. (2003). Size distribution and seasonal variation of atmospheric cellulose. *Atmos. Environ.* 37, 3693-3699. doi: 10.1016/s1352-2310(03)00451-5

R Core Team (2017). *R: A Language and Environment for Statistical Computing*. Available online at: https://www.R-project.org/

Salameh, D., Detournay, A., Pey, J., Pérez, N., Liguori, F., Saraga, D., et al. (2015). PM2.5 chemical composition in five European Mediterranean cities: a 1-year study. *Atmos. Res.* 155, 102-117. doi: 10.1016/j.atmosres.2014.12.001

Samaké, A., Bonin, A., Jaffrezo, J.-L., Taberlet, P., Weber, S., Uzu, G., et al. (2020). High levels of primary biogenic organic aerosols are driven by only a few plant-associated microbial taxa. *Atmos. Chem. Phys.* 20, 5609-5628. doi: 10.5194/acp-20-5609-2020

Samaké, A., Jaffrezo, J.-L., Favez, O., Weber, S., Jacob, V., Albinet, A., et al. (2019a). Polyols and glucose particulate species as tracers of primary biogenic organic aerosols at 28 French sites. *Atmos. Chem. Phys.* 19, 3357-3374. doi: 10.5194/acp-19-3357-2019

Samaké, A., Jaffrezo, J.-L., Favez, O., Weber, S., Jacob, V., Canete, T., et al. (2019b). Arabitol, mannitol, and glucose as tracers of primary biogenic organic aerosol: the influence of environmental factors on ambient air concentrations and spatial distribution over France. *Atmos. Chem. Phys.* 19, 11013-11030. doi: 10.5194/acp-19-11013-2019

Schnell, I. B., Bohmann, K., and Gilbert, M. T. P. (2015). Tag jumps illuminated - reducing sequence-to-sample misidentifications in metabarcoding studies. *Mol. Ecol. Resour.* 15, 1289-1303. doi: 10.1111/1755-0998.12402

Taberlet, P., Bonin, A., Zinger, L., and Coissac, E. (2018). *Environmental DNA: For Biodiversity Research and Monitoring*. Oxford: Oxford University Press.

Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C., and Willerslev, E. (2012). Towards next-generation biodiversity assessment using DNA metabarcoding: next-generation dna metabarcoding. *Mol. Ecol.* 21, 2045-2050. doi: 10.1111/j.1365-294X.2012.05470.x

Tignat-Perrier, R., Dommergue, A., Thollot, A., Keuschnig, C., Magand, O., Vogel, T. M., et al. (2019). Global airborne microbial communities controlled by surrounding landscapes and wind conditions. *Sci. Rep.* 9:14441. doi: 10.1038/s41598-019-51073-4

Waked, A., Favez, O., Alleman, L. Y., Piot, C., Petit, J.-E., Delaunay, T., et al. (2014). Source apportionment of PM10 in a north-western Europe regional
urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions. *Atmos. Chem. Phys.* 14, 3325–3346. doi: 10.5194/acp-14-3325-2014

Wei, M., Liu, H., Chen, J., Xu, C., Li, J., Xu, P., et al. (2020). Effects of aerosol pollution on PM2.5-associated bacteria in typical inland and coastal cities of northern China during the winter heating season. *Environ. Pollut.* 262:114188. doi: 10.1016/j.envpol.2020.114188

Wei, M., Xu, C., Xu, X., Zhu, C., Li, J., and Lv, G. (2019). Characteristics of atmospheric bacterial and fungal communities in PM2.5 following biomass burning disturbance in a rural area of North China Plain. *Sci. Total Environ.* 651, 2727–2739. doi: 10.1016/j.scitotenv.2018.09.399

Yan, C., Sullivan, A. P., Cheng, Y., Zheng, M., Zhang, Y., Zhu, T., et al. (2019). Characterization of saccharides and associated usage in determining biogenic and biomass burning aerosols in atmospheric fine particulate matter in the North China Plain. *Sci. Total Environ.* 650, 2939–2950. doi: 10.1016/j.scitotenv.2018.09.325

Yang, Y., Chan, C., Tao, J., Lin, M., Engling, G., Zhang, Z., et al. (2012). Observation of elevated fungal tracers due to biomass burning in the Sichuan Basin at Chengdu City, China. *Sci. Total Environ.* 431, 68–77. doi: 10.1016/j.scitotenv.2012.05.033

Yu, X., Wang, Z., Zhang, M., Kuhn, U., Xie, Z., Cheng, Y., et al. (2016). Ambient measurement of fluorescent aerosol particles with a WIBS in the Yangtze River Delta of China: potential impacts of combustion-related aerosol particles. *Atmos. Chem. Phys.* 16, 11337–11348. doi: 10.5194/acp-16-11337-2016

Zhu, C., Kawamura, K., and Kunwar, B. (2015). Organic tracers of primary biological aerosol particles at subtropical Okinawa Island in the western North Pacific Rim: organic biomarkers in the north pacific. *J. Geophys. Res. Atmos.* 120, 5504–5523. doi: 10.1002/2015jd023611

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Samaké, Martins, Bonin, Uzu, Taberlet, Conil, Favez, Thomasson, Chazeau, Marchand and Jaffrezo. 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_34.png" /></figure>

Check for
updates

# Graphene-Based Nanomaterials Modulate Internal Biofilm Interactions and Microbial Diversity

Lauris Evariste<fnref n="1" />*, Paul Braylé<fnref n="1" />, Florence Mouchet<fnref n="1" />, Jérôme Silvestre<fnref n="1" />, Laury Gauthier<fnref n="1" />, Emmanuel Flahaut<fnref n="2" />, Eric Pinelli<fnref n="1" /> and Maïalen Barret<fnref n="1" />

Graphene-based nanomaterials (GBMs), such as graphene oxide (GO) and reduced graphene oxide (rGO), possess unique properties triggering high expectations for the development of new technological applications and are forecasted to be produced at industrial-scale. This raises the question of potential adverse outcomes on living organisms and especially toward microorganisms constituting the basis of the trophic chain in ecosystems. However, investigations on GBMs toxicity were performed on various microorganisms using single species that are helpful to determine toxicity mechanisms but fail to predict the consequences of the observed effects at a larger organization scale. Thus, this study focuses on the ecotoxicological assessment of GO and rGO toward a biofilm composed of the diatom *Nitzschia palea* associated to a bacterial consortium. After 48 and 144 h of exposure to these GBMs at 0, 0.1, 1, and 10 mg.L⁻<fnref n="1" />, their effects on the diatom physiology, the structure, and the metabolism of bacterial communities were measured through the use of flow cytometry, 16S amplicon sequencing, and Biolog ecoplates, respectively. The exposure to both of these GBMs stimulated the diatom growth. Besides, GO exerted strong bacterial growth inhibition as from 1 mg.L⁻<fnref n="1" />, influenced the taxonomic composition of diatom-associated bacterial consortium, and increased transiently the bacterial activity related to carbon cycling, with weak toxicity toward the diatom. On the contrary, rGO was shown to exert a weaker toxicity toward the bacterial consortium, whereas it influenced more strongly the diatom physiology. When compared to the results from the literature using single species tests, our study suggests that diatoms benefited from diatom-bacteria interactions and that the biofilm was able to maintain or recover its carbon-related metabolic activities when exposed to GBMs.

**Keywords:** microbial ecotoxicology, graphene, freshwater, biofilm, diatom, metabarcoding

**OPEN ACCESS**

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

**Reviewed by:**
Mariana Carmen Chifriuc,
University of Bucharest, Romania
Lingzhan Miao,
Hohai University, China

**\*Correspondence:**
Lauris Evariste
lauris.evariste@gmail.com

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

**Received:** 30 October 2020

**Accepted:** 28 February 2021

**Published:** 26 March 2021

**Citation:**
Evariste L, Braylé P, Mouchet F, Silvestre J, Gauthier L, Flahaut E, Pinelli E and Barret M (2021) *Graphene-Based Nanomaterials Modulate Internal Biofilm Interactions and Microbial Diversity*. *Front. Microbiol.* 12:623853.
doi: 10.3389/fmicb.2021.623853

## INTRODUCTION

Two-dimensional nanomaterials derived from graphene possess unique properties such as high surface area, electrical and thermal conductivity, mechanical strength, and optical transmittance that are currently being explored for the development of new applications in multiple area including composite improvement, energy storage, electronics, medicine, or water

<footnote n="1">¹Laboratoire d'écologie fonctionnelle et environnement, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France,</footnote>

<footnote n="2">²CIRIMAT, Université de Toulouse, CNRS, INPT, UPS, UMR CNRS-UPS-INP N°5085, Université Toulouse 3 Paul Sabatier, Bât. CIRIMAT, Toulouse, France</footnote>
purification (Perreault et al., 2015; Dasari Shareena et al., 2018; Mohan et al., 2018; Nag et al., 2018). Among these graphene-based nanomaterials (GBMs), graphene oxide (GO), and reduced graphene oxide (rGO) appear as very attractive due to their ease of synthesis, their high stability after dispersion in various solvents and the possibility for surface functionalization (Smith et al., 2019). rGO, which carries a lower amount of oxygen-containing functions compared to GO (Lavin-Lopez et al., 2017), constitutes a good compromise between GO and graphene, especially for electrical conductivity properties, leading to its use for the development of electrochemical sensors (Rowley-Neale et al., 2018; Tarcan et al., 2020). As the production of high quality graphene suffers from high energy consumption and cost, GO and rGO constitute major products in the graphene market (Lin et al., 2019). For these reasons, these GBMs are forecasted to be mass-produced and are thus likely to be released in the environment during their whole life cycle, from the production to the recycling (Mottier et al., 2017). However, the monitoring of environmental pollutions by GBMs is not possible yet due to technical limitations for their detection at current low concentrations in complex matrices (Goodwin et al., 2018). Nevertheless, despite the lack of modeling data about their expected environmental concentrations, it is estimated that with increasing needs, GBMs could reach concentrations between 1 and 1,000 µg/L in aqueous environment (Zhang et al., 2017), with an accumulation trend in sediment (Sun et al., 2016; Avant et al., 2019). This requires to carefully evaluate the potential impact of these materials on environmental health, in order to contribute to the development of this nanotechnology in a safety and sustainable way ( Fadeel et al., 2018).

Previous studies investigated the toxicity of GBMs toward various aquatic organisms including vertebrates (Clemente et al., 2019; Evariste et al., 2019; Paital et al., 2019) or invertebrates (Castro et al., 2018; Lv et al., 2018), while the most abundant literature concerns the effects on bacteria and microalgae (Han et al., 2019; Kumar et al., 2019; Tashan et al., 2019; Saxena et al., 2020). Studying the effects of GBMs on these microorganisms is essential since they play crucial roles in aquatic ecosystems. Indeed, microalgae ensure primary production through photosynthesis while bacterial heterotrophic activities contribute to organic matter and nutrient cycling (Paerl and Pinckney, 1996; Scala and Bowler, 2001). Moreover, these microorganisms are at the basis of the trophic chain in the environment and act as a resource supplier for many primary consumers. Thus, impairment of these communities by GBMs exposure could indirectly affect organisms from higher trophic levels (Evariste et al., 2020). The vast majority of the studies available on bacteria and algae were performed on isolated single strains (e.g., *Escherichia coli*, *Staphylococcus aureus*, and *Chlorella* sp.) and highlighted antibacterial activities, and algal growth inhibitory effects of GBMs that were both associated to oxidative stress and membrane injuries (Ouyang et al., 2015; Ji et al., 2016; Zhao et al., 2017). Although these effects are well-documented in free-living cells, data concerning GBMs toxicity toward microorganisms living in complex biofilms remain scarce and inconsistent despite the fact that environmental

biofilms are recognized to bind and accumulate nanoparticles (Ikuma et al., 2015). Biofilm lifestyle confers ecological advantages over free-living cells as it includes social cooperation as well as enhanced resource capture and resistance to antimicrobials for the organisms embedded in a matrix of extracellular polymeric substances (EPS; Flemming et al., 2016).

The main literature available focus on the effects of metallic nanoparticles toward biofilms, while studies focusing on carbon-based nanomaterials remains scarce (González et al., 2015; Lawrence et al., 2016; Hou et al., 2017; Miao et al., 2019). Specifically, the effects of GBMs toward biofilms were monitored on single bacterial strains in order for the development of antimicrobial treatments to avoid the formation of pathogenic biofilms for biomedical purposes (Han et al., 2019; Liu et al., 2019; Cacaci et al., 2020; Cao et al., 2021). In this context, studies indicated that GO-coated surfaces could either promote or inhibit biofilm formation by *E. coli* and *S. aureus* (Ruiz et al., 2011; Guo et al., 2017; Yadav et al., 2017). However, these studies were performed under conditions that are not fully relevant within environmental-based contexts that are not sufficiently investigated (Jastrzębska and Olszyna, 2015; Montagner et al., 2016). Thus, understanding the toxicological effects of GBMs toward more complex communities is crucial to better characterize the ecotoxic potential of these nanomaterials and to further determine the possible consequences of their presence in freshwater environments on the ecosystem functioning. The aim of this study was to investigate the toxicity of a commercial GO and its reduced form toward a complex assembly composed by the diatom *Nitzschia palea* associated to a bacterial consortium. This biofilm was exposed under controlled conditions to GBMs at concentrations ranging from 0.1 to 10 mg.L⁻¹ to determine the effects on diatom physiology using flow cytometry, as well as on bacterial community structure and activity using high throughput 16S sequencing and community-level physiological profiles, respectively.

# MATERIALS AND METHODS

## Graphene-Based Nanomaterials

Graphene oxide was provided by Antolin Group and prepared by oxidizing Grupo Antolin Carbon Nanofibers (GANF®; Grupo Antolín, Burgos, Spain) using the Hummer's method (Hummers and Offeman, 1958; Lobato et al., 2016). We thermally reduced it at 200°C in H₂ atmosphere into rGO, as previously described (Evariste et al., 2019). GBMs were stored as dry powder in the dark and dispersions were prepared extemporaneously in order to avoid any possible change of material characteristics. Full characterization of the tested materials was detailed in previous work (Evariste et al., 2019) and characterization data are summarized in **Table 1**.

## Complex Biofilm Model and Exposure Procedure

The experimental model for complex biofilms was composed of an association between an axenic strain of *N. palea* CPCC-160
<table><caption>**TABLE 1** | Physico-chemical characteristics of graphene oxide (GO) and reduced graphene oxide (rGO) used in the study.</caption><thead><tr><th></th><th>Graphene Oxide</th><th>Reduced Graphene Oxide</th></tr></thead><tbody><tr><td>Carbon content (at. %)</td><td>69.0 ± 0.4</td><td>83.8 ± 0.5</td></tr><tr><td>Oxygen content (at. %)</td><td>31.0 ± 0.4</td><td>16.2 ± 0.3</td></tr><tr><td>Csp2 graphene (at. %)</td><td>35.5</td><td>64.5</td></tr><tr><td>C—OH/C—O—C (at. %)</td><td>24.7</td><td>7.8</td></tr><tr><td>C=O (at. %)</td><td>2.5</td><td>5.8</td></tr><tr><td>O=C—O (at. %)</td><td>5.3</td><td>1.3</td></tr><tr><td>Sat. (at. %)</td><td>1.4</td><td>4.5</td></tr><tr><td>Number of layers (HRTEM)</td><td>1–5</td><td>1–5</td></tr><tr><td>Lateral size (TEM; µm)</td><td>0.2–8</td><td>0.2–8</td></tr><tr><td>Specific surface area (BET; m².g⁻¹)</td><td>228 ± 7</td><td>16 ± 0.5</td></tr></tbody></table>

TEM, transmission electron microscope; HRTEM, high resolution TEM; BET, Brunauer-Emett-Teller; at. %, atomic %.

provided by the Canadian Phycological Culture Center (University of Waterloo, Waterloo, ON, Canada) and a bacterial consortium isolated from water filters of the freshwater Museum-Aquarium of Nancy (France). After sampling, the consortium was suspended in 50% glycerol and stored at -80°C until use.

Before the beginning of the exposure to GBMs, microorganisms were sequentially introduced in culture Flasks (Falcon 355,001, 600 ml - 150 cm²) as follows. *Nitzschia palea* was cultured as previously described in a modified CHU no. 10 basic medium, called SPE medium (SPE; 6.4 < pH < 6.6; Garacci et al., 2017). Standard growth conditions consisted in an incubation at 22 ± 1°C on a rotary shaker (50 rpm) in a culture room. An illumination of 50 µmol m⁻² s⁻¹ with a day/night period of 14/10 h, respectively, was applied. Two days prior to exposure (T~_48h~), diatoms were transferred at a density of 5 × 10⁴ cells/ml in a flask containing 50 ml of SPE medium. After 24 h of growth in standard conditions (T~_24h~), diatoms reached a concentration of 9 × 10⁴ cells/ml. Glycerol was removed from the bacterial consortium after centrifugation and the consortium was suspended in 100 ml of SPE medium. Thus, at T~_24h~, flasks containing diatoms were inoculated with the bacterial consortium to reach the concentration of 3 × 10⁴ bacterial cells/ml, leading to the ratio of 3 diatoms per bacterial cells in each flask. After 24 h, flasks were contaminated with GBMs (T0). For this purpose, nanomaterials were dispersed in SPE medium through the use of an ultrasonic bath for 10 min and autoclaved. Dilutions of the GBMs stock dispersions were carried out under axenic conditions in order to avoid contamination. Intermediary dispersions were prepared at 0.2, 2 and 20 mg.L⁻¹. A volume of 50 ml of GBMs-contaminated SPE media was added in the flasks to reach a final concentration of 0.1, 1, or 10 mg.L⁻¹ of GBMs while uncontaminated medium was added in the control groups (T0). Exposures were performed over 144 h under standard conditions as previously indicated.

Biofilm was sampled after 48 and 144 h of exposure with the different concentrations of GBMs (n = 3 per time and GBM concentration). For each sampling, the biofilm was gently scrapped and flask content was homogenized and divided into two fractions of 50 ml. The first fraction was used for diatom

and bacteria counts as well as analysis of the diatom physiological parameters using flow cytometry. In addition, this fraction was also used to perform community level physiological profiles (CLPP) analysis. The second 50 ml fraction was filtrated at 0.45 µm (Whatman® Nuclepore™) to collect the whole microorganism community and investigate the microbial community structure. Filters were stored individually in sterile tubes at -80°C prior to further processing.

## Flow Cytometry Analysis

Flow cytometric (FCM) analysis of the diatom *N. palea* and bacterial counts were performed using a Beckman-Coulter Cytoflex flow cytometer equipped with a 488 nm laser and data were collected and analyzed using Cytexpert v. 2.2.0.97 software.

## Microorganisms Counts and Growth Rate Calculation

For *N. palea* counts, unstained algae were gated based on their forward scatter parameters (FSC-A) and chlorophyll fluorescence (690/50 nm). Bacteria were accounted using SYTO9 dye (Invitrogen). Samples were incubated with 5 µM of the probe for 15 min in the dark at room temperature. Bacteria were detected and enumerated based on the fluorescence emitted by SYTO9-positive events (525/40 nm) and side scatter parameters (SSC-H). Normalized growth rates of the diatoms and bacteria were calculated as follows:

$$Growth rate = \left( \frac{Cs - Mic}{Mic} \right)$$

$$Normalized growth rate (\%) = \left( \frac{Growth rate}{MCtGr} \right) \times 100$$

Cs corresponds to the organism concentration at sampling time, Mic is the mean initial concentration of organisms (at T~_48h~ for diatoms and at T~_24h~ for bacteria) and MCtGr is the mean growth rate in the control group at sampling time.

## Diatom Physiological Parameters

The relative chlorophyll a content of *N. palea* was determined through the measurement of natural chlorophyll a fluorescence emitted at 690/50 nm. The mean fluorescence intensity (MFI in arbitrary unit) collected is expressed as a percentage of the negative control.

Diatom viability was evaluated using SYTOX® Green. After incubation with the probe for 10 min at a final concentration of 0.5 µM, cell suspensions were analyzed by flow cytometry to measure the fluorescence emitted at 525/40 nm. Diatoms with injured or permeable membrane are positive to the green fluorescence-emitting probe bound to DNA. Results are expressed as viability percentage (100% - percentage of SYTOX-positive cells).

Neutral lipid relative content of the diatoms was evaluated using BODIPY (4,4-difluoro-1,3,5,7,7-tetramethyl-4-bora-3a,4a-diaza-s-indacene; 505/515). Algae were incubated for 1.5 min with the lipophilic dye at a final concentration of 1 µg.ml⁻¹
before FCM. The BODIPY fluorescence emitted by the stained diatoms was collected using a 530/30 nm band-pass filter. The MFI measured is presented as a percentage of the negative control.

The intracellular reactive oxygen species (ROS) produced by the diatoms was measured using 2',7'-dicholorofluorescindiacetate (DCFH-DA), a marker of oxidative stress. Samples were stained for 30 min with the probe at a final concentration of 10 µM prior running flow cytometry measurements. Diatoms with elevated intracellular ROS are positively stained by the probe (Thamatrakoln et al., 2012). The results are presented as a percentage of the diatom population emitting probe-related fluorescence.

## Analysis of Community Level Physiological Profiles

BIOLOG® EcoPlates, consisting of 96-well plates containing a triplicate of 31 different carbon sources and a control with no carbon source, were used to analyze the CLPP of the microorganism communities. Samples were diluted 100 times with fresh SPE medium and 120 µl of the diluted suspension was transferred into each well of the plate. After inoculation, EcoPlates were incubated in aerobic conditions at 22 ± 1°C in darkness for 144 h. Optical density (OD) at 590 nm was measured immediately after microorganism plating (Tin) and was monitored daily over the 144 h with a CLARIOStar plate reader (BMG Labtech). Over these 144 h, OD increased linearly (r² = 0.94) and did not reached a plateau phase. For each substrate, absorbance was corrected by subtracting the absorbance of the control well containing media only. Negative values of the corrected readings were set to zero. The average well color development (AWCD) of substrate utilization was calculated across all wells per plate as follows:

$$AWCD = (\sum OD_i)/31,$$

where ODi represent the corrected optical density of the ith well. AWCD was also calculated for each guild of carbon sources, grouped into (1) carbohydrates, (2) carboxylic acids, (3) amino acids, (4) amines and amides, and (5) polymers as defined by Weber and Legge (2009). To compensate the influence of the microorganism density on the AWCD measurement, corrected OD values were calculated per bacteria by dividing the OD value per the number of bacteria introduced in each well at Tin. The normalized carbon source utilization data were also subjected to principal components analysis (PCA). All the results reported refer to the 144 h time point.

## Analysis of Bacterial Community Structure: DNA Extraction, PCR, Sequencing, and Data Processing

After cutting filters into pieces, total DNA was extracted using the QIAGEN DNeasy PowerSoil kit following manufacturer's instructions. Extraction controls were performed using unused filters to ensure the absence of DNA contamination. The DNA extracts quantity and quality were analyzed using a NanoDrop 2000 UV spectrophotometer (Thermo Scientific). The V4-V5 region of 16S rRNA gene was targeted for *Archaea*

and Bacteria using 515F (5'-GTGYAGCMGCCGCGGTA-3') and 928R (5'-CCCCGYCAATTCMTTTTRAGT-3') primers set (Wang and Qian, 2009). PCR reactions were run in a final volume of 50 µl containing: 37.5 µl of PCR water, 5 µl of 10X PCR buffer, 2 µl of DNA extract, 2 µl of both primer, 1 µl of dNTP (2.5 mM) and 0.5 µl of Taq DNA polymerase (5 U/µl - Sigma Aldrich). The following PCR protocol was applied: 94°C for 120 s, 30 cycles of 94°C for 60 s, 65°C for 40 s, 72°C 30 s, and 72°C for 10 min. Sequencing of the resulting amplicons was performed using Illumina MiSeq technology (2 × 250 pb) by the Get_PlaGe platform (Genotoul, Toulouse, France). Bioinformatic analysis was performed using Find Rapidly Operational Taxonomic Units Galaxy Solution (FROGS) pipeline on Galaxy (Escudié et al., 2018). Briefly, sequences with mismatch in the primers were excluded and PCR primers were trimmed. Reads were clustered into operational taxonomic units (OTUs) using the Swarm clustering method (Mahé et al., 2014). Chimera was removed and filters were applied to remove singletons and keep OTUs present in at least two samples. OTUs were assigned at different taxonomic levels (from Kingdom to species) using RDP classifier and NCBI Blast+ against Silva 138 database (pintail 80; Quast et al., 2012). Amplicons affiliated to the diatom chloroplast and mitochondria were removed from the dataset prior to data analysis.

## Statistical Analysis

As interactive effects were observed between the exposure duration and the contaminant concentrations using two-ways ANOVA, one-way ANOVA was performed at each sampling time to compare the effects induced by the different concentrations of contaminant. Thus, data related to microorganism growth rates, diatom physiological parameters, and community physiological profiles were analyzed using one-way ANOVA when assumptions of normality and homogeneity of variance were met. Otherwise, data were transformed to meet these assumptions and data were analyzed using Minitab 16 Statistical software. Concentrations leading to 50% of bacterial growth inhibition (IC₅₀) were determined using non-linear Hill regression in Graphpad Prism software.

Sequencing data analyses for OTUs counts, alpha diversity indexes, and Weighted Unifrac Distances calculations as well as multidimensional scaling (MDS) plot were carried out using the R package "Phyloseq" (McMurdie and Holmes, 2013). Differential abundance of bacterial genera between exposed conditions compared to the control group was examined using "Deseq2" R package (Love et al., 2014). PERMANOVA was performed using Adonis function from the "vegan" R package (Oksanen et al., 2015).

## RESULTS

### Effects on Microorganism Growth Rates

Exposure to the GBMs led to a transitory growth stimulation of the diatom. At T₄₈h, the growth rate calculated in the control
group reached a value of 5.4 ± 0.95 (Supplementary Figure S1). Except after exposure to GO at 0.1 mg.L⁻¹, a significant growth stimulation was observed in all GBMs-containing conditions (ANOVA *p* < 0.001), reaching 268 ± 31% of the control group value after exposure to 10 mg.L⁻¹ of rGO (Figure 1A). After 144 h of exposure, none of the growth rates calculated in the exposure conditions were significantly different from the control group (Figure 1B). In addition, growth rates of the diatom calculated after 48 h of exposure to GBMs reached values similar to the control group at T₁₄₄h (12.7 ± 2.6; t-test; *p* = 0.604; Supplementary Figure S1).

For the bacterial compartment, exposure to GO led to a dose dependant growth inhibition from 1 mg.L⁻¹, while only a slight inhibition was noticed after exposure to rGO at 10 mg.L⁻¹ (Figure 1C). The calculated concentrations leading to a growth inhibition of 50% (IC₅₀) were 2.18 mg.L⁻¹ and 13.25 mg.L⁻¹ after 48 and 144 h of exposure to GO, respectively. Contrary to the recovery observed for diatoms at 144 h, bacterial growth rates were still significantly different from the control at 1 and 10 mg.L⁻¹ of GO to at 10 mg.L⁻¹ of rGO (Figure 1D).

## Effects on *N. palea* Physiology

The relative chlorophyll content of the diatom remained unchanged after 48 h of exposure to the GBMs (Figure 2A)

while a higher relative chlorophyll content could be measured in the diatoms exposed to GO at 10 mg.L⁻¹ during 144 h (Figure 2B). On the contrary, exposure to rGO led to a slight decrease of the chlorophyll content after 144 h of contact with rGO (Figure 2B).

Graphene oxide exposure did not lead to any significant change in the lipid content of the diatom while in rGO containing conditions, an accumulation of lipids was measured, with a more marked effect after 48 h of exposure (Figures 2C,D).

Measurement of oxidative stress in the diatom indicated a significant oxidative stress after 48 h of exposure to rGO at 10 mg.L⁻¹ (Figure 2E) while no DCF-positive cells were observed at T₁₄₄h (data not shown). Monitoring of the diatom viability highlighted a significantly higher percentage of alive diatoms after 48 h of exposure to GO at 1 and 10 mg.L⁻¹ (ANOVA, *p* = 0.027, Figure 2F). At T₁₄₄h, no differences were observed between the different GBMs-containing conditions and the control group in the diatom viability that reached that 99 ± 0.64% (data not shown).

## Effects of GBMs Exposure on Community Level Physiological Profiles

The community level physiological profile of the biofilm was monitored using Biolog® Ecoplate after 48 and 144 h

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

**FIGURE 1** | Normalized growth rate of the diatom *Nitzschia palea* calculated after 48 h (A) and 144 h (B) of exposure to graphene-based nanomaterials (GBMs). Normalized growth rate of the bacterial consortium calculated after 48 h (C) and 144 h (D) of exposure to GBMs. ANOVA was followed by Tukey test. Letters indicate significant differences between the tested conditions.
<figure><img src="image_39.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_44.png" /></figure>

**FIGURE 2** | Physiological parameters of the diatom *N. palea* following exposure to GBMs. Normalized chlorophyll content measured after 48 h (A) and 144 h of exposure (B), normalized lipid content measured after 48 h (C) and 144 h (D), oxidative stress (E) and viability (F) following 48 h of exposure to GBMs. Values are presented as mean ± SD. ANOVA (*p* < 0.05) was followed by Tukey test. Letters indicate significant differences between the tested conditions.

of exposure to the different concentrations of GBMs. As indicated by the normalized AWCD values as well as the PCA results, a dose-dependent increase of the overall utilization of carbon sources was noticed after 48 h of exposure to GO while the substrate utilization did not differ from the control group after exposure to rGO (**Figure 3A; Supplementary Figures S2A,B**). In the case of GO exposure, this increase of normalized AWCD values resulted from an

increase of the utilization of all the different guilds of carbon sources (**Supplementary Figure S3A**). At T₁₄₄h, the normalized AWCD values were significantly lower in the control group compared to T₄₈h (*t*-test, *p* = 0.016) and values were not significantly different from the control group after exposure to GBMs (**Figure 3B**). However, according to PCA results, carbon sources utilization appears to be different from the control in every GO-containing condition
A

<figure><img src="image_45.png" /></figure>

B

<figure><img src="image_46.png" /><figcaption>**FIGURE 3** | Normalized average well color development (AWCD) values measured after 48 h (A) and 144 h (B) of exposure to GBMs. ANOVA followed by Tukey test. Letters indicate significant differences between the tested conditions.</figcaption></figure>

(Supplementary Figure S2C). This is associated to an increased utilization of the polymers guild (Supplementary Figure S3B). Similarly, PCA results suggest changes in the CLPP after 144 h of exposure to rGO at 10 mg.L⁻¹ compared to unexposed biofilm (Supplementary Figure S2D). In this condition, carbon sources 4-hydroxy benzoic acid, itaconic acid, and D-xylose appeared to be utilized by the microbial consortium while it was not the case in other conditions. The highest average AWCD values measured in the control group at both 48 and 144 h was for the carbohydrate and amino acid guilds while after 144 h of exposure to GO at 1 and 10 mg.L⁻¹, the utilization of the substrates from the polymers guild is favored.

## Effects on Bacterial Community Structure

After 48 h of exposure, the Shannon indexes calculated for the bacterial communities were similar in the GBMs containing conditions compared to the control group (ANOVA *p* = 0.305; **Figure 4A**) and the bacterial community structure was not significantly affected as revealed by MDS and PERMANOVA analysis using weighted UniFrac distances (PERMANOVA, *p* = 0.221; **Figure 4B**). Between 48 and 144 h, the trajectory of bacterial communities in control conditions resulted in a decrease of Shannon index (**Figure 4B**). However, after 144 h of exposure to GO at 10 mg.L⁻¹, the Shannon index was significantly higher than in the control (ANOVA, *p* < 0.001; **Figure 4C**) when compared to the control group. At T₁₄₄h, the exposure to GBMs significantly affected the bacterial community structure (PERMANOVA: *p* = 0.001) and MDS analysis revealed that the effects were more marked after exposure to GO at 10 mg.L⁻¹ (**Figure 4D**).

At T₄₈h in the control group, over 98% of the bacteria constituting the biofilm belonged to the phyla Proteobacteria and Bacteroidota, accounting for 60.7 ± 2.0% and 38 ± 1.5%

of the total bacteria, respectively (Figure 5). At T₁₄₄h, these two phyla represented over 99% of the whole community but the relative abundance of the phylum Proteobacteria decreased to 37.0 ± 4.9% while it increased to 62.5 ± 4.7% for the phylum Bacteroidota (Figure 5). At T₄₈h, the relative abundances of these two phyla were not affected by the GBMs exposure (Bacteroidota: ANOVA, *p* = 0.954; Proteobacteria: ANOVA, *p* = 0.944). After 144 h of exposure to the different concentrations of GBMs, only the exposure to GO at 10 mg.L⁻¹ led to significant changes in the phyla distribution. In this condition, the relative abundance of the phylum Bacteroidota was significantly lower (51.8 ± 3.5%) compared to the one observed in the control group (ANOVA, *p* = 0.001 followed by Tuckey test), while the relative abundance of the phylum Proteobacteria was significantly higher (47.8 ± 3.4%; ANOVA, *p* = 0.002 followed by Tuckey test).

At T₄₈h, OTUs assigned to the phylum Bacteroidota were mainly members of the orders *Chitinophagales* and *Sphingobacteriales* (Supplementary Figure S4A). Members from the order *Burkholderiales* and *Rhodobacterales* predominated among OTUs affiliated to the phylum Proteobacteria (Supplementary Figure S4B). While *Burkholderiales* and *Rhodobacterales* members still predominated over other Proteobacteria after 144 h of incubation, the relative abundance of OTUs affiliated to the Order *Xanthomonadales* increased over time in all conditions (Supplementary Figure S4B). Statistical analysis of the OTUs relative abundances in the biofilm indicated that four OTUs from the phylum Proteobacteria were differentially abundant upon exposure to GBMs, with a significance threshold fixed at *p* = 0.01. All four OTUs were more abundant after exposure to 10 mg.L⁻¹ of GO during 144 h than in control condition. Two of these OTUs, with a log2-fold change of 6.79 ± 1.2 and 5.82 ± 1.1, were affiliated to the genus *Acidovorax* while the two others with a log2-fold
A

<figure><img src="image_47.png" /></figure>

C

<figure><img src="image_48.png" /></figure>

B

<figure><img src="image_49.png" /></figure>

D

<figure><img src="image_50.png" /><figcaption>**FIGURE 4** | Effects of exposure to GBMs on bacterial communities from the biofilm. Shannon evenness index following 48 h (A) or 144 h of exposure to GBMs (C) are compared between the exposure conditions. ANOVA followed by Tukey test. Letters indicate significant differences between the tested conditions. Multidimensional scaling (MDS) plot of bacterial communities based on unweighted Unifrac distances after 48 h (B) and 144 h (D) of exposure to the different conditions.</figcaption></figure>

change of 1.81 ± 0.4 and 1.95 ± 0.2 belonged to the genus *Silanimonas* (Table 2). In the control group, the relative abundance of the two *Acidovorax* members (OTUs 29 and 18) decreased between T₄₈h and T₁₄₄h while it increased for the two *Silanimonas* sp. (OTUs 9 and 61; **Figure 6**). Upon exposure to GO at 10 mg·L⁻¹, these OTUs followed different trajectories compared to the control conditions. Thus, the increases of the former OTUs were enhanced while the decreases were less marked over time for the latter OTUs (**Figure 6**). For example, the OTU 9 increased from 0.18 ± 0.02% at T₄₈h to 1.7 ± 0.2% at

T₁₄₄h in the control group whereas it reached 6.2 ± 1.2% following exposure to GO at 10 mg·L⁻¹.

DISCUSSION

Due to the multiple crucial roles played by microbial communities in the environment, studying the effects of GBMs toward these communities is essential to better assess the ecosystemic consequences of a contamination of the environment by these
<figure><img src="image_51.png" /><figcaption>**FIGURE 5** | Relative abundance of bacterial phyla from the biofilm after 48 h (T<sub>48h</sub>) and 144 h (T<sub>144h</sub>) of exposure to GO or rGO at concentrations ranging from 0 to 10 mg·L<sup>-1</sup>.</figcaption></figure>

<table><caption>**TABLE 2** | Operational taxonomic units (OTUs) differentially abundant (p < 0.01) at 10 mg·L<sup>-1</sup> of GO compared to the control group after 144 h of exposure.</caption><thead><tr><th>OTU</th><th>Log2-fold change</th><th>Phylum</th><th>Class</th><th>Order</th><th>Family</th><th>Genus</th></tr></thead><tbody><tr><td>18</td><td>6.79</td><td>Proteobacteria</td><td>Gammaproteobacteria</td><td>Burkholderiales</td><td>Comamonadaceae</td><td>Acidovorax</td></tr><tr><td>29</td><td>5.82</td><td>Proteobacteria</td><td>Gammaproteobacteria</td><td>Burkholderiales</td><td>Comamonadaceae</td><td>Acidovorax</td></tr><tr><td>61</td><td>1.81</td><td>Proteobacteria</td><td>Gammaproteobacteria</td><td>Xanthomonadales</td><td>Xanthomonadaceae</td><td>Silanimonas</td></tr><tr><td>9</td><td>1.95</td><td>Proteobacteria</td><td>Gammaproteobacteria</td><td>Xanthomonadales</td><td>Xanthomonadaceae</td><td>Silanimonas</td></tr></tbody></table>

Positive log2-fold change values indicate enriched OTUs in the exposure condition.

<figure><img src="image_52.png" /><figcaption>**FIGURE 6** | Heatmap showing the relative abundances of the discriminant OTUs identified by Deseq analysis. The dendrogram is based on Bray-Curtis distances metric and hierarchical clustering of OTUs using the complete method.</figcaption></figure>

For this purpose, many studies evaluated their ecotoxic potential through the use of single-species-based assays. These tests are relevant to determine the toxicological effects

and pathways associated to GBMs exposure but they are not realistic and fail to inform the consequences that could occur when interacting species are exposed. As literature about GBMs
impact on complex communities is scarce for the aquatic environment, this study aims to fill the gap on the subject.

## Consequences of the GBMs Exposure on the Algal Compartment

In our study, exposure to the different GBMs concentrations led to a growth stimulation of the diatom *N. palea*, reaching the maximum growth rate earlier compared to the control group, the growth rate of the latter being comparable to previous studies (Garacci et al., 2017). These results are contradictory with the vast majority of single-species-based studies which indicated that exposure to engineered nanoparticles led to algal growth inhibition associated to oxidative stress induced by cellular damages (Chen et al., 2019; Saxena et al., 2020). For example, this was observed for green pelagic algae such as *Raphidocelis subcapitata*, *Chlorella pyrenoidosa*, or *Scenedesmus obliquus* after exposure to GO or rGO (Du et al., 2016; Zhao et al., 2017, 2019; Malina et al., 2019). It was also indicated that the exposure of the freshwater diatom *N. palea* to few layer graphene (FLG) increased the production of EPS substances which mitigated its toxicity (Garacci et al., 2017, 2019). Despite the difference of sensitivity of the different algal species to GBMs, the concentrations leading to 50% of algal growth inhibitions all ranged from 20 to over 150 mg.L⁻¹, which is approximately one order of magnitude higher than the concentrations tested in our study (Nogueira et al., 2015; Du et al., 2016; Zhao et al., 2017). In our work, the similar trends observed on the increase of the diatom growth following exposure to GO or rGO suggest that this response may not be influenced by specific physico-chemical characteristics such as oxidation level. In addition, the weak oxidative stress measured in the diatom is consistent with the absence of growth impairment and highlights the protective effect associated to the biofilm lifestyle. Growth inhibitory effects were also hypothesized to be associated to indirect ecotoxic effects through shading as well as to nutrient depletion by GBMs (Garacci et al., 2017; Zhao et al., 2017). As the algal growth increased, these two hypotheses are unlikely under the exposure conditions used in this study or they could have been counterbalanced by favorable interactions with bacteria.

According to the FCM analyses performed, we observed that exposure to GO led to an increase of chlorophyll content and to a decrease of lipid content in the diatom while opposite results were observed following rGO exposure. Although few studies monitored changes in algal physiological parameters, the results obtained in the latter case are in line with studies from the literature focusing on the effects associated to low-oxygen content GBMs exposure. Indeed, a significant reduction of chlorophyll level and an under-expression of genes associated to chlorophyll biosynthesis resulted from an exposure to rGO or few layer graphene in the algae *Scenedesmus obliquus* or *N. palea*, respectively (Du et al., 2016; Garacci et al., 2019). In addition, exposure to graphene nanosheets was shown to favor lipid accumulation in *Chlorella pyrenoidosa* (Khanra et al., 2018). However, in the case of GO exposure, our results are contradictory with the single-species-based literature that usually reports a decrease of chlorophyll pigments (Tang et al., 2015; Hu et al., 2016; Hazeem et al., 2017;

Malina et al., 2019). *Nitzschia palea* is a mixotrophic diatom able to grow using autotrophic metabolism (e.g., photosynthesis) using an inorganic carbon source (CO₂) and/or through a heterotrophic metabolism using organic carbon sources (Villanova et al., 2017). It was reported that mixotrophic algae accumulated chlorophyll when cultured in autotrophy, while this content decreased and lipids were accumulated by the algae under heterotrophic conditions (Cheirsilp and Torpee, 2012; da Silva Ferreira and Sant'Anna, 2017). Thus, our observations suggest that in *N. palea*, when it is associated to a bacterial consortium, autotrophic and heterotrophic metabolism were differentially balanced under GO or rGO exposure. In any case, these changes of the energy acquisition pathways in the diatom did not alter growth performance following 144 h of exposure to GBMs.

## Consequences of the GBMs Exposure on the Bacterial Compartment

The effects of GBMs toward bacteria have been mainly investigated through the exposure of single bacterial strains. The effects toward bacterial communities were studied in soil (Du et al., 2015; Xiong et al., 2018; Forstner et al., 2019) or in activated sludge (Ahmed and Rodrigues, 2013; Guo et al., 2018; Yujie et al., 2020) but data remain scarce in aquatic ecosystems (Evariste et al., 2020). Given the important bacterial growth inhibition and the relatively weak consequences on microbial composition noticed after 48 h of exposure to GO, we can suggest that the effects of this nanomaterial could be associated to a mechanism that impacts in a similar manner most bacterial species found in this biofilm. Previous studies suggested that the membrane composition, especially in gram-negative bacteria, as well as bacterial shape could constitute a criteria of resistance to GBMs (Kang et al., 2009; Akhavan and Ghaderi, 2010; Pulingam et al., 2019; Sengupta et al., 2019). Despite that the four OTUs benefiting from exposure to high GO concentrations are Gram-negative bacteria, it is more likely that their metabolic capacities are involved in the greater tolerance to GO. Indeed, it was recently reported that bacteria from the genus *Acidovorax* sp. were tolerant to GO concentrations up to 95 mg.L⁻¹ in granulated sludge treating wastewater (Kedves et al., 2020). This genus is also well-known to be able to degrade organic matter in wastewater treatment plants (Schulze et al., 1999) as well as aromatical compounds like phenanthrene (Singleton et al., 2018) or biphenyl (Ohtsubo et al., 2012). Similarly, *Silanimonas* spp. are categoricized as benzene-degrading species (Mosmeri et al., 2019). Thus, we can hypothesize that these graphene-tolerant bacteria benefited from the presence of GBMs in the media (i) by outcompeting other less tolerant species and (ii) because they could be able to degrade or modify the GO structure.

According to the literature, bactericidal activities of GO and rGO toward planktonic bacteria indicated generally stronger bactericidal effects of GO compared to rGO as observed in our study (Han et al., 2019). However, results concerning antibacterial effects toward bacterial biofilms are more contradictory. Indeed, strong bactericidal effects of GO were previously observed like in our study (Mejías Carpio et al., 2012; Yadav et al., 2017; Giulio et al., 2018; Pandit et al., 2018; Song et al., 2018) while others indicated that GO enhanced biofilm formation and that
rGO exerted strong inhibitory effects (Ruiz et al., 2011; Guo et al., 2017). The interstudy discrepancies may be associated to several causes influencing the biological responses. Among the previously cited studies, when available, the characterization data indicate a wide variability within the range of oxygen content of the tested GO (from 27.8 to over 36 atomic %), with different distribution of the types of oxygen-containing functions. In addition, depending on the method applied, the reduction of these materials lead to the production of a wide variety of rGO. This underline the need to provide detailed characterization data and the use of a classification framework to facilitate interstudy comparisons (Wick et al., 2014). Among other possible causes, it was indicated that the effects of GBMs may vary depending on the medium composition and/or on the biofilm maturity (Hui et al., 2014; Guo et al., 2017; Fallatah et al., 2019). The results obtained in our study are in line with the latter assumption as the IC₅₀ values increased between T₄₈h and T₁₄₄h, indicating that the effects of GO on bacterial growth were mitigated over time with biofilm maturation. This suggests that bacterial growth was delayed by GO exposure and that bacterial communities were recovering. However, after 144 h of exposure, the presence of GBMs in the media resulted in bacterial communities with diverging community structure trajectories compared to the control group. Further research would be needed to determine if bacterial communities are able to fully recover and to assess the time duration necessary for full recovery.

In addition to the effects on bacterial growth and diversity, exposure to GBMs was shown to influence microbial activities and could potentially lead to disturbances of carbon or nitrogen cycles (Zhou et al., 2019; Yilihamu et al., 2020). For example, previous studies indicated that exposure to GO or rGO could enhance the anaerobic ammonium oxidation activity that is involved in nitrogen removal in ecosystems (Wang et al., 2013; Yin et al., 2015; Tomaszewski et al., 2019). Extracellular enzymatic activities involved in carbon cycling were also shown to be lowered by short term exposure to GO (Chung et al., 2015). The results obtained in our study using Biolog Ecoplates suggested that the effects of GBMs exposure on the community level physiological profiles were transitory. Indeed, the activity levels of the microbial communities were similar to the control group in the different tested conditions after 144 h of exposure. However, this method does not allow to determine if the increased activity is associated to an increase of intra- or extracellular metabolism. The recovery of carbon-related metabolic activities despite the changes occurring in microbial community composition could be explained by functional redundancy between the species composing the biofilm. Further experiment using other omics tools would be needed to examine the metabolic pathways that are influenced by GBMs exposure and to determine if the metabolism of compounds involved in algae-bacteria mutualistic interactions are modified (Cooper and Smith, 2015).

## Consequences of the Diatom-Bacteria Interactions on the Response to GBMs Exposure

In the case of mixed-species biofilms containing phototropic microorganisms like diatoms as in our study case, the EPS

produced can be used by heterotrophic bacteria as carbon and energy sources (Mühlenbruch et al., 2018). In return, bacteria may benefit diatoms by providing sources of nutrients such as vitamins or nitrogen but are also able to reduce algal oxidative stress through the production of enzymes such as catalase (Hünken et al., 2008; Amin et al., 2012; Natrah et al., 2014). Diatom-associated microbiota from environmental samples was described to mainly belong to the phylum Proteobacteria and Bacteroidota (ex phylum Bacteroidetes) that are involved in EPS degradations (Schäfer et al., 2002; Amin et al., 2012; Bohórquez et al., 2017). Thus, the composition of the bacterial compartment in the biofilm of our study is consistent with the data from the literature. The heterotrophic bacteria remineralize the organic matter produced by the diatom, ensuring an efficient nutrient cycling (Christie-Oleza et al., 2017). The functioning of biofilms relies on this mutual benefit between the algae and the bacterial consortium. In the present study, diatom and bacteria growth were not correlated across our experimental conditions, demonstrating an interplay between multiple/complex interactions and GBMs direct effects.

It has been previously shown that the exposure to carbon-based nanomaterials such as carbon nanotubes and FLG increased the EPS excretion by *N. palea* (Verneuil et al., 2015; Garacci et al., 2017). This mechanism was suggested to constitute a strategy allowing toxicity mitigation for the algae. However, this could have several consequences for the bacterial compartment. Indeed, the embedment of GBMs into the biofilm EPS could increase contacts with bacteria and favor membrane impairments (Dizaj et al., 2015), which could explain the strong bacterial growth inhibition that we observed in presence of GO. The differential effects observed between GO and rGO on bacterial growth might be associated to the oxidation level of the materials. Indeed, the GBMs were shown to be able to interact with natural organic matter including polysaccharides (Chowdhury et al., 2014). As these interactions depend on the nature of oxygen-containing functional groups (Saya et al., 2021), the interaction dynamics between EPS and GO or rGO could be different, influencing the contact with bacterial cells embedded in EPS. In addition to the increase in EPS excretion, the composition and the quality of the EPS produced by the diatoms could be modified by the presence of GBMs. This was previously observed after exposure of bacteria or algae to metallic nanoparticles (Hou et al., 2015, 2017; You et al., 2015; Chen et al., 2019). Moreover, it was suggested that the energy balance between growth and EPS production could be modified following exposure to nanoparticles that is likely to occur in the case of the growth stimulation observed in our study (Taylor et al., 2016). As it was indicated that the EPS composition could affect the bacterial communities in biofilms (Haynes et al., 2007; Bohórquez et al., 2017), we can hypothesize that the changes observed in the bacterial communities could be associated to indirect effects associated to modifications of the EPS quantity and quality.

As discussed in paragraph “Consequences of the GBMs Exposure on the Algal Compartment,” the growth stimulation observed in the diatom is more likely to be due to indirect effects. We can hypothesize that the overproduction of EPS associated to the
presence of GBMs might benefit *in fine* to the diatoms through
the increase of metabolite quantities produced by bacterial activity.
In addition, the results obtained from the community level
physiological profiles indicated an activation of heterotrophic
metabolism of the biofilm following short term exposure to GO.
This metabolic increase allowed to compensate the loss of activity
that can be expected due to the bacterial growth inhibition
measured in presence of GO. Indeed, considering the AWCD
values for the whole biofilm (data not shown) instead of AWCD
per bacterial cell as previously presented, the overall carbon-
related metabolic activities are maintained at the level of the
control group until 1 mg.L⁻¹ and a slight decrease is noticed at
10 mg.L⁻¹ of GO. This should allow to maintain a baseline carbon
and nutrient cycling between the algae and the bacteria.

CONCLUSION

In this study, we observed that the biological responses of
mixed microbial communities facing exposure to GBMs were
complex and contradictory to the results of single-species
experiments found in the literature. Here and according to
the obtained data, we suggest that the biofilm responses were
mainly associated to indirect effects initiated by the
overproduction of EPS by the diatom. This potentially increased
the amount of carbon available for bacterial metabolism and
further benefit to the diatom. However, the oxidized form of
graphene was shown to strongly impact bacterial biomass. The
activities of carbon sources utilization were maintained in most
conditions except at 10 mg.L⁻¹ of GO that is unlikely to occur
under a scenario of chronic release into the environment.
However, changes associated to the diatom physiology and
changes in bacterial community composition might lead to
modifications of the biofilm biochemical properties and
potentially affect higher trophic level organisms feeding on it.
Thus, future research on the environmental risk associated to
GBMs should increase environmental relevance of the bioassays.

REFERENCES

Ahmed, F., and Rodrigues, D. F. (2013). Investigation of acute effects of graphene oxide on wastewater microbial community: a case study. *J. Hazard. Mater.* 256-257, 33-39. doi: 10.1016/j.jhazmat.2013.03.064

Akhavan, O., and Ghaderi, E. (2010). Toxicity of graphene and graphene oxide nanowalls against bacteria. *ACS Nano* 4, 5731-5736. doi: 10.1021/nn101390x

Amin, S. A., Parker, M. S., and Armbrust, E. V. (2012). Interactions between diatoms and bacteria. *Microbiol. Mol. Biol. Rev.* 76, 667-684. doi: 10.1128/MMBR.00007-12

Avant, B., Bouchard, D., Chang, X., Hsieh, H.-S., Acrey, B., Han, Y., et al. (2019). Environmental fate of multiwalled carbon nanotubes and graphene oxide across different aquatic ecosystems. *NanoImpact* 13, 1-12. doi: 10.1016/j.impact.2018.11.001

Bohórquez, J., McGenity, T. J., Papaspyrou, S., García-Robledo, E., Corzo, A., and Underwood, G. J. C. (2017). Different types of diatom-derived extracellular polymeric substances drive changes in heterotrophic bacterial communities from intertidal sediments. *Front. Microbiol.* 8:245. doi: 10.3389/fmicb.2017.00245

Cacaci, M., Martini, C., Guarino, C., Torelli, R., Bugli, F., and Sanguinetti, M. (2020). Graphene oxide coatings as tools to prevent microbial biofilm

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are publicly
available. These data can be found in NCBI under accession
number PRJNA674395.

AUTHOR CONTRIBUTIONS

LE: writing original draft - investigation - formal analysis.
PB: formal analysis - investigation. FM and LG: project
administration - funding acquisition. JS: resources -
methodology. EF: formal analysis - funding acquisition -
review and editing. EP: supervision - review and editing.
MB: writing, review and editing - methodology - supervision.
All authors contributed to the article and approved the
submitted version.

FUNDING

The authors thank the European Union's Horizon 2020 research
and innovation programme under grant agreement no 785219.

ACKNOWLEDGMENTS

We thank the Genotoul bioinformatics platform Toulouse Midi-
Pyrenees and Sigenae group for providing computing and
storage resources through Galaxy.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.623853/
full#supplementary-material

formation on medical device. *Adv. Exp. Med. Biol.* 1282, 21-35. doi: 10.1007/5584_2019_434

Cao, G., Yan, J., Ning, X., Zhang, Q., Wu, Q., Bi, L., et al. (2021). Antibacterial and antibiofilm properties of graphene and its derivatives. *Colloids Surf. B: Biointerfaces* 200:111588. doi: 10.1016/j.col surfb.2021.111588

Castro, V. L., Clemente, Z., Jonsson, C., Silva, M., Vallim, J. H., de Medeiros, A. M. Z., et al. (2018). Nanoecotoxicity assessment of graphene oxide and its relationship with humic acid: nanoecotoxicity of graphene oxide and humic acid. *Environ. Toxicol. Chem.* 37, 1998-2012. doi: 10.1002/etc.4145

Cheirsilp, B., and Torpee, S. (2012). Enhanced growth and lipid production of microalgae under mixotrophic culture condition: effect of light intensity, glucose concentration and fed-batch cultivation. *Bioresour. Technol.* 110, 510-516. doi: 10.1016/j.biortech.2012.01.125

Chen, F., Xiao, Z., Yue, L., Wang, J., Feng, Y., Zhu, X., et al. (2019). Algae response to engineered nanoparticles: current understanding, mechanisms and implications. *Environ. Sci. Nano* 6, 1026-1042. doi: 10.1039/C8EN01368C

Chowdhury, I., Duch, M. C., Mansukhani, N. D., Hersam, M. C., and Bouchard, D. (2014). Interactions of graphene oxide nanomaterials with natural organic matter and metal oxide surfaces. *Environ. Sci. Technol.* 48, 9382-9390. doi: 10.1021/es5020828

Christie-Oleza, J. A., Sousoni, D., Lloyd, M., Armengaud, J., and Scanlan, D. J. (2017). Nutrient recycling facilitates long-term stability of marine microbial
phototroph-heterotroph interactions. *Nat. Microbiol.* 2, 1-10. doi: 10.1038/nmicrobiol.2017.100

Chung, H., Kim, M. J., Ko, K., Kim, J. H., Kwon, H., Hong, L., et al. (2015). Effects of graphene oxides on soil enzyme activity and microbial biomass. *Nat. Total Environ.* 514, 307-313. doi: 10.1016/j.scitotenv.2015.01.077

Clemente, Z., Silva, G. H., de Souza Nunes, M. C., Martinez, D. S. T., Maurer-Morelli, C. V., Thomaz, A. A., et al. (2019). Exploring the mechanisms of graphene oxide behavioral and morphological changes in zebrafish. *Environ. Sci. Pollut. Res.* 26, 3058-30523. doi: 10.1007/s11356-019-05870-z

Cooper, M. B., and Smith, A. G. (2015). Exploring mutualistic interactions between microalgae and bacteria in the omics age. *Curr. Opin. Plant Biol.* 26, 147-153. doi: 10.1016/j.pbi.2015.07.003

Dasari Shareena, T. P., McShan, D., Dasmahapatra, A. K., and Tchounwou, P. B. (2018). A review on graphene-based nanomaterials in biomedical applications and risks in environment and health. *Nano-Micro Lett.* 10:53. doi: 10.1007/s40820-018-0206-4

da Silva Ferreira, V., and Sant'Anna, C. (2017). Impact of culture conditions on the chlorophyll content of microalgae for biotechnological applications. *World J. Microbiol. Biotechnol.* 33:20. doi: 10.1007/s11274-016-2181-6

Dizaj, S. M., Mennati, A., Jafari, S., Khezri, K., and Adibkia, K. (2015). Antimicrobial activity of carbon-based nanoparticles. *Adv. Pharm. Bull.* 5, 19-23. doi: 10.5681/apb.2015.003

Du, J., Hu, X., and Zhou, Q. (2015). Graphene oxide regulates the bacterial community and exhibits property changes in soil. *RSC Adv.* 5, 27009-27017. doi: 10.1039/C5RA01045D

Du, S., Zhang, P., Zhang, R., Lu, Q., Liu, L., Bao, X., et al. (2016). Reduced graphene oxide induces cytotoxicity and inhibits photosynthetic performance of the green alga *Scenedesmus obliquus*. *Chemosphere* 164, 499-507. doi: 10.1016/j.chemosphere.2016.08.138

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

Evariste, L., Lagier, L., Gonzalez, P., Mottier, A., Mouchet, F., Cadarsi, S., et al. (2019). Thermal reduction of graphene oxide mitigates its in vivo genotoxicity toward *Xenopus laevis* tadpoles. *Nano* 9:584. doi: 10.3390/nano9040584

Evariste, L., Mottier, A., Lagier, L., Cadarsi, S., Barret, M., Sarrieu, C., et al. (2020). Assessment of graphene oxide ecotoxicity at several trophic levels using aquatic microcosms. *Carbon* 156, 261-271. doi: 10.1016/j.carbon.2019.09.051

Fadeel, B., Bussy, C., Merino, S., Vázquez, E., Flahaut, E., Mouchet, F., et al. (2018). Safety assessment of graphene-based materials: focus on human health and the environment. *ACS Nano* 12, 10582-10620. doi: 10.1021/acsnano.8b04758

Fallatah, H., Elhaneid, M., Ali-Boucetta, H., Overton, T. W., El Kadri, H., and Gkatzionis, K. (2019). Antibacterial effect of graphene oxide (GO) nanoparticles against pseudomonas putida biofilm of variable age. *Environ. Sci. Pollut. Res.* 26, 25057-25070. doi: 10.1007/s11356-019-05688-9

Flemming, H.-C., Wingender, J., Szewzyk, U., Steinberg, P., Rice, S. A., and Kjelleberg, S. (2016). Biofilms: an emergent form of bacterial life. *Nat. Rev. Microbiol.* 14, 563-575. doi: 10.1038/nrmicro.2016.94

Forstner, C., Orton, T. G., Skarshewski, A., Wang, P., Kopittke, P. M., and Dennis, P. G. (2019). Effects of graphene oxide and graphite on soil bacterial and fungal diversity. *bioRxiv* 530485. doi: 10.1101/530485

Garacci, M., Barret, M., Folgoas, C., Flahaut, E., Chimowa, G., Bertucci, A., et al. (2019). Transcriptomic response of the benthic freshwater diatom *Nitzschia palea* exposed to few layer graphene. *Environ. Sci. Nano* 6, 1363-1381. doi: 10.1016/j.carbon.2016.11.033

Garacci, M., Barret, M., Mouchet, F., Sarrieu, C., Lonchambon, P., Flahaut, E., et al. (2017). Few layer graphene sticking by biofilm of freshwater diatom *Nitzschia palea* as a mitigation to its ecotoxicity. *Carbon* 113, 139-150. doi: 10.1016/j.carbon.2016.11.033

Giulio, M. D., Zappacosta, R., Lodovico, S. D., Campli, E. D., Siani, G., Fontana, A., et al. (2018). Antimicrobial and antibiofilm efficacy of graphene oxide against chronic wound microorganisms. *Antimicrob. Agents Chemother.* 62, e00547-e00518. doi: 10.1128/AAC.00547-18

González, A. G., Mombo, S., Leflaive, J., Lamy, A., Pokrovsky, O. S., and Rols, J.-L. (2015). Silver nanoparticles impact phototrophic biofilm communities to a considerably higher degree than ionic silver. *Environ. Sci. Pollut. Res.* 22, 8412-8424. doi: 10.1007/s11356-014-3978-1

Goodwin, D. G., Adeleye, A. S., Sung, L., Ho, K. T., Burgess, R. M., and Petersen, E. J. (2018). Detection and quantification of Graphene-family nanomaterials in the environment. *Environ. Sci. Technol.* 52, 4491-4513. doi: 10.1021/acs.est.7b04938

Guo, C., Wang, Y., Luo, Y., Chen, X., Lin, Y., and Liu, X. (2018). Effect of graphene oxide on the bioactivities of nitrifying and denitrifying bacteria in aerobic granular sludge. *Ecotoxicol. Environ. Saf.* 156, 287-293. doi: 10.1016/j.ecoenv.2018.03.036

Guo, Z., Xie, C., Zhang, P., Zhang, J., Wang, G., He, X., et al. (2017). Toxicity and transformation of graphene oxide and reduced graphene oxide in bacteria biofilm. *Sci. Total Environ.* 580, 1300-1308. doi: 10.1016/j.scitotenv.2016.12.093

Han, W., Wu, Z., Li, Y., and Wang, Y. (2019). Graphene family nanomaterials (GFNs)—promising materials for antimicrobial coating and film: a review. *Chem. Eng. J.* 358, 1022-1037. doi: 10.1016/j.cej.2018.10.106

Haynes, K., Hofmann, T. A., Smith, C. J., Ball, A. S., Underwood, G. J. C., and Osborn, A. M. (2007). Diatom-derived carbohydrates as factors affecting bacterial community composition in estuarine sediments. *Appl. Environ. Microbiol.* 73, 6112-6124. doi: 10.1128/AEM.00551-07

Hazeem, L. J., Bououdina, M., Dewaily, E., Slomiany, C., Barras, A., Coffinier, Y., et al. (2017). Toxicity effect of graphene oxide on growth and photosynthetic pigment of the marine alga Picochlorum sp. during different growth stages. *Environ. Sci. Pollut. Res. Int.* 24, 4144-4152. doi: 10.1007/s11356-016-8174-z

Hou, J., Miao, L., Wang, C., Wang, P., Ao, Y., and Lv, B. (2015). Effect of CuO nanoparticles on the production and composition of extracellular polymeric substances and physicochemical stability of activated sludge flocs. *Bioresour. Technol.* 176, 65-70. doi: 10.1016/j.biortech.2014.11.020

Hou, J., Yang, Y., Wang, P., Wang, C., Miao, L., Wang, X., et al. (2017). Effects of CeO₂, CuO, and ZnO nanoparticles on physiological features of Microcystis aeruginosa and the production and composition of extracellular polymeric substances. *Environ. Sci. Pollut. Res.* 24, 226-235. doi: 10.1007/s11356-016-7387-5

Hu, X., Gao, Y., and Fang, Z. (2016). Integrating metabolic analysis with biological endpoints provides insight into nanotoxicological mechanisms of graphene oxide: from effect onset to cessation. *Carbon* 109, 65-73. doi: 10.1016/j.carbon.2016.07.068

Hui, L., Piao, J.-G., Auletta, J., Hu, K., Zhu, Y., Meyer, T., et al. (2014). Availability of the basal planes of graphene oxide determines whether it is antibacterial. *ACS Appl. Mater. Interfaces* 6, 13183-13190. doi: 10.1021/am503070z

Hummers, W. S. Jr., and Offeman, R. E. (1958). Preparation of graphitic oxide. *J. Am. Chem. Soc.* 80, 1339-1339. doi: 10.1021/ja01539a017

Hünken, M., Harder, J., and Kirst, G. O. (2008). Epiphytic bacteria on the Antarctic ice diatom Amphiprora kufferathii Manguin cleave hydrogen peroxide produced during algal photosynthesis. *Plant Biol. (Stuttg.)* 10, 519-526. doi: 10.1111/j.1438-6777.2008.00040.x

Ikuma, K., Decho, A. W., and Lau, B. L. T. (2015). When nanoparticles meet biofilms—interactions guiding the environmental fate and accumulation of nanoparticles. *Front. Microbiol.* 6:591. doi: 10.3389/fmicb.2015.00591

Jastrzębska, A. M., and Olszyna, A. R. (2015). The ecotoxicity of graphene family materials: current status, knowledge gaps and future needs. *J. Nanopart. Res.* 17:40. doi: 10.1007/s11051-014-2817-0

Ji, H., Sun, H., and Qu, X. (2016). Antibacterial applications of graphene-based nanomaterials: recent achievements and challenges. *Adv. Drug Deliv. Rev.* 105, 176-189. doi: 10.1016/j addressed.2016.04.009

Kang, S., Mauter, M. S., and Elimelech, M. (2009). Microbial cytotoxicity of carbon-based nanomaterials: implications for river water and wastewater effluent. *Environ. Sci. Technol.* 43, 2648-2653. doi: 10.1021/es8031506

Kedves, A., Sánta, L., Balázs, M., Kesserű, P., Kiss, I., Rónavári, A., et al. (2020). Chronic responses of aerobic granules to the presence of graphene oxide in sequencing batch reactors. *J. Hazard. Mater.* 389:121905. doi: 10.1016/j.jhazmat.2019.121905

Khanra, A., Sangam, S., Shakeel, A., Suhag, D., Mistry, S., Rai, M. P., et al. (2018). Sustainable growth and lipid production from *Chlorella pyrenoidosa* using N-doped carbon nanosheets: unravelling the role of graphitic nitrogen. *ACS Sustain. Chem. Eng.* 6, 774-780. doi: 10.1021/acssuschemeng.7b03103

Kumar, P., Huo, P., Zhang, R., and Liu, B. (2019). Antibacterial properties of graphene-based nanomaterials. *Nano* 9:737. doi: 10.3390/nano9050737

Lavin-Lopez, M. P., Paton-Carrero, A., Sanchez-Silva, L., Valverde, J. L., and Romero, A. (2017). Influence of the reduction strategy in the synthesis of reduced graphene oxide. *Adv. Powder Technol.* 28, 3195-3203. doi: 10.1016/j.apt.2017.09.032
Lawrence, J. R., Waiser, M. J., Swerhone, G. D. W., Roy, J., Tumber, V., Paule, A., et al. (2016). Effects of fullerene (C60), multi-wall carbon nanotubes (MWCNT), single wall carbon nanotubes (SWCNT) and hydroxyl and carboxyl modified single wall carbon nanotubes on riverine microbial communities. *Environ. Sci. Pollut. Res.* 23, 10090-10102. doi: 10.1007/s11356-016-6244-x

Lin, L., Peng, H., and Liu, Z. (2019). Synthesis challenges for graphene industry. *Nat. Mater.* 18, 520–524. doi: 10.1038/s41563-019-0341-4

Liu, Y., Shi, L., Su, L., van der Mei, H. C., Jutte, P. C., Ren, Y., et al. (2019). Nanotechnology-based antimicrobials and delivery systems for biofilm-infection control. *Chem. Soc. Rev.* 48, 428–446. doi: 10.1039/C7CS00807D

Lobato, B., Merino, C., Barranco, V., and Centeno, T. A. (2016). Large-scale conversion of helical-ribbon carbon nanofibers to a variety of graphene-related materials. *RSC Adv.* 6, 57514–57520. doi: 10.1039/C6RA08865A

Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. *Genome Biol.* 15:550. doi: 10.1186/s13059-014-0550-8

Lv, X., Yang, Y., Tao, Y., Jiang, Y., Chen, B., Zhu, X., et al. (2018). A mechanism study on toxicity of graphene oxide to *Daphnia magna*: direct link between bioaccumulation and oxidative stress. *Environ. Pollut.* 234, 953–959. doi: 10.1016/j.envpol.2017.12.034

Mahé, F., Rognes, T., Quince, C., de Vargas, C., and Dunthorn, M. (2014). Swarm: robust and fast clustering method for amplicon-based studies. *PeerJ* 2:e593. doi: 10.7717/peerj.593

Malina, T., Maršálková, E., Holá, K., Tuček, J., Scheibe, M., Zbořil, R., et al. (2019). Toxicity of graphene oxide against algae and cyanobacteria: nanoblade-morphology-induced mechanical injury and self-protection mechanism. *Carbon* 155, 386–396. doi: 10.1016/j.carbon.2019.08.086

McMurdie, P. J., and Holmes, S. (2013). Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. *PLoS One* 8:e61217. doi: 10.1371/journal.pone.0061217

Mejías Carpio, I. E., Santos, C. M., Wei, X., and Rodrigues, D. F. (2012). Toxicity of a polymer-graphene oxide composite against bacterial planktonic cells, biofilms, and mammalian cells. *Nanoscale* 4, 4746–4756. doi: 10.1039/c2nr30774j

Miao, L., Wang, P., Hou, J., Yao, Y., Liu, Z., and Liu, S. (2019). Low concentrations of copper oxide nanoparticles alter microbial community structure and function of sediment biofilms. *Sci. Total Environ.* 653, 705–713. doi: 10.1016/j.scitotenv.2018.10.354

Mohan, V. B., Lau, K., Hui, D., and Bhattacharyya, D. (2018). Graphene-based materials and their composites: a review on production, applications and product limitations. *Compos. Part B Eng.* 142, 200–220. doi: 10.1016/j.compositesb.2018.01.013

Montagner, A., Bosi, S., Tenori, E., Bidussi, M., Alshatwi, A. A., Tretiach, M., et al. (2016). Ecotoxicological effects of graphene-based materials. *2D Mater.* 4:012001. doi: 10.1088/2053-1583/4/1/012001

Mosmeri, H., Gholami, F., Shavandi, M., Dastgheib, S. M. M., and Alaie, E. (2019). Bioremediation of benzene-contaminated groundwater by calcium peroxide (CaO₂) nanoparticles: continuous-flow and biodiversity studies. *J. Hazard. Mater.* 371, 183–190. doi: 10.1016/j.jhazmat.2019.02.071

Mottier, A., Mouchet, F., Pinelli, É., Gauthier, L., and Flahaut, E. (2017). Environmental impact of engineered carbon nanoparticles: from releases to effects on the aquatic biota. *Curr. Opin. Biotechnol.* 46, 1–6. doi: 10.1016/j.copbio.2016.11.024

Mühlenbruch, M., Grossart, H.-P., Eigemann, F., and Voss, M. (2018). Mini-review: phytoplankton-derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. *Environ. Microbiol.* 20, 2671–2685. doi: 10.1111/1462-2920.14302

Nag, A., Mitra, A., and Mukhopadhyay, S. C. (2018). Graphene and its sensor-based applications: a review. *Sensors Actuators A Phys.* 270, 177–194. doi: 10.1016/j.sna.2017.12.028

Natrah, F. M. I., Bossier, P., Sorgeloos, P., Yusoff, F. M., and Defoirdt, T. (2014). Significance of microalgal-bacterial interactions for aquaculture. *Rev. Aquac.* 6, 48–61. doi: 10.1111/raq.12024

Nogueira, P. F. M., Nakabayashi, D., and Zucolotto, V. (2015). The effects of graphene oxide on green algae *Raphidocelis subcapitata*. *Aquat. Toxicol.* 166, 29–35. doi: 10.1016/j.aquatox.2015.07.001

Ohtsubo, Y., Maruyama, F., Mitsui, H., Nagata, Y., and Tsuda, M. (2012). Complete genome sequence of Acidovorax sp. strain KKS102, a polychlorinated-biphenyl degrader. *J. Bacteriol.* 194, 6970–6971. doi: 10.1128/JB.01848-12

Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'hara, R. B., et al. (2015). Package 'vegan.' Community Ecol. Package Version 2.

Ouyang, S., Hu, X., and Zhou, Q. (2015). Envelopment-internalization synergistic effects and metabolic mechanisms of graphene oxide on single-cell *Chlorella vulgaris* are dependent on the nanomaterial particle size. *ACS Appl. Mater. Interfaces* 7, 18104–18112. doi: 10.1021/acsami.5b05328

Paerl, H. W., and Pinckney, J. L. (1996). A mini-review of microbial consortia: their roles in aquatic production and biogeochemical cycling. *Microb. Ecol.* 31, 225–247. doi: 10.1007/BF00171569

Paital, B., Guru, D., Mohapatra, P., Panda, B., Parida, N., Rath, S., et al. (2019). Ecotoxic impact assessment of graphene oxide on lipid peroxidation at mitochondrial level and redox modulation in fresh water fish *Anabas testudineus*. *Chemosphere* 224, 796–804. doi: 10.1016/j.chemosphere.2019.02.156

Pandit, S., Cao, Z., Mokkapati, V. R. S. S., Celauro, E., Yurgens, A., Lovmar, M., et al. (2018). Vertically aligned graphene coating is bactericidal and prevents the formation of bacterial biofilms. *Adv. Mater. Interfaces* 5:1701331. doi: 10.1002/admi.201701331

Perreault, F., Fonseca de Faria, A., and Elimelech, M. (2015). Environmental applications of graphene-based nanomaterials. *Chem. Soc. Rev.* 44, 5861–5896. doi: 10.1039/C5CS00021A

Pulingam, T., Thong, K. L., Ali, M. E., Appaturi, J. N., Dinshaw, I. J., Ong, Z. Y., et al. (2019). Graphene oxide exhibits differential mechanistic action towards gram-positive and gram-negative bacteria. *Colloids Surf. B: Biointerfaces* 181, 6–15. doi: 10.1016/j.colsurf.2019.05.023

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2012). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. *Nucleic Acids Res.* 41, D590–D596. doi: 10.1093/nar/gks1219

Rowley-Neale, S. J., Randviir, E. P., Abo Dena, A. S., and Banks, C. E. (2018). An overview of recent applications of reduced graphene oxide as a basis of electroanalytical sensing platforms. *Appl. Mater. Today* 10, 218–226. doi: 10.1016/j.apmt.2017.11.010

Ruiz, O. N., Fernando, K. A. S., Wang, B., Brown, N. A., Luo, P. G., McNamara, N. D., et al. (2011). Graphene oxide: a nonspecific enhancer of cellular growth. *ACS Nano* 5, 8100–8107. doi: 10.1021/nn20699t

Saxena, P., Sangela, V., Ranjan, S., Dutta, V., Dasgupta, N., Phulwaria, M., et al. (2020). Aquatic nanotoxicology: impact of carbon nanomaterials on algal flora. *Energy Ecol. Environ.* 5, 240–252. doi: 10.1007/s40974-020-00151-9

Saya, L., Gautam, D., Malik, V., Singh, W. R., and Hooda, S. (2021). Natural polysaccharide based graphene oxide nanocomposites for removal of dyes from wastewater: a review. *J. Chem. Eng. Data* 66, 11–37. doi: 10.1021/acs.jced.0c00743

Scala, S., and Bowler, C. (2001). Molecular insights into the novel aspects of diatom biology. *Cell. Mol. Life Sci.* 58, 1666–1673. doi: 10.1007/PL00000804

Schäfer, H., Abbas, B., Witte, H., and Muyzer, G. (2002). Genetic diversity of "satellite" bacteria present in cultures of marine diatoms. *FEMS Microbiol. Ecol.* 42, 25–35. doi: 10.1111/j.1574-6941.2002.tb00992.x

Schulze, R., Spring, S., Amann, R., Huber, I., Ludwig, W., Schleifer, K.-H., et al. (1999). Genotypic diversity of Acidovorax strains isolated from activated sludge and description of *Acidovorax defluvii* sp. nov. *Syst. Appl. Microbiol.* 22, 205–214. doi: 10.1016/S0723-2020(99)80067-8

Sengupta, I., Bhattacharya, P., Talukdar, M., Neogi, S., Pal, S. K., and Chakraborty, S. (2019). Bactericidal effect of graphene oxide and reduced graphene oxide: influence of shape of bacteria. *Colloid Interface Sci. Commun.* 28, 60–68. doi: 10.1016/j.colcom.2018.12.001

Singleton, D. R., Lee, J., Dickey, A. N., Stroud, A., Scholl, E. H., Wright, F. A., et al. (2018). Polyphasic characterization of four soil-derived phenanthrene-degrading Acidovorax strains and proposal of Acidovorax carolinensis sp. nov. *Syst. Appl. Microbiol.* 41, 460–472. doi: 10.1016/j.syapm.2018.06.001

Smith, A. T., LaChance, A. M., Zeng, S., Liu, B., and Sun, L. (2019). Synthesis, properties, and applications of graphene oxide/reduced graphene oxide and their nanocomposites. *Nano Mater. Sci.* 1, 31–47. doi: 10.1016/j.nanoms.2019.02.004

Song, C., Yang, C.-M., Sun, X.-F., Xia, P.-F., Qin, J., Guo, B.-B., et al. (2018). Influences of graphene oxide on biofilm formation of gram-negative and gram-positive bacteria. *Environ. Sci. Pollut. Res. Int.* 25, 2853–2860. doi: 10.1007/s11356-017-0616-8

Sun, T. Y., Bornhöft, N. A., Hungerbühler, K., and Nowack, B. (2016). Dynamic probabilistic modeling of environmental emissions of engineered nanomaterials. *Environ. Sci. Technol.* 50, 4701–4711. doi: 10.1021/acs.est.5b05828

Tang, Y., Tian, J., Li, S., Xue, C., Xue, Z., Yin, D., et al. (2015). Combined effects of graphene oxide and cd on the photosynthetic capacity and survival
of *Microcystis aeruginosa*. *Sci. Total Environ.* 532, 154–161. doi: 10.1016/j.scitotenv.2015.05.081

Tarcan, R., Todor-Boer, O., Petrovai, I., Leordean, C., Astilean, S., and Botiz, I. (2020). Reduced graphene oxide today. *J. Mater. Chem. C* 8, 1198–1224. doi: 10.1039/C9TC04916A

Tashan, H., Khosravi-Darani, K., Yazdian, F., Omidi, M., Sheikhpour, M., Farahani, M., et al. (2019). Antibacterial properties of graphene based nanomaterials: an emphasis on molecular mechanisms, surface engineering and size of sheets. *Mini-Rev. Org. Chem.* 16, 159–172. doi: 10.2174/1570193X15666180712120309

Taylor, C., Matzke, M., Kroll, A., Read, D. S., Svendsen, C., and Crossley, A. (2016). Toxic interactions of different silver forms with freshwater green algae and cyanobacteria and their effects on mechanistic endpoints and the production of extracellular polymeric substances. *Environ. Sci. Nano* 3, 396–408. doi: 10.1039/C5EN00183H

Thamatrakoln, K., Korenovska, O., Niheu, A. K., and Bidle, K. D. (2012). Whole-genome expression analysis reveals a role for death-related genes in stress acclimation of the diatom *Thalassiosira pseudonana*. *Environ. Microbiol.* 14, 67–81. doi: 10.1111/j.1462-2920.2011.02468.x

Tomaszewski, M., Cema, G., and Ziemblińska-Buczyńska, A. (2019). Short-term effects of reduced graphene oxide on the anammox biomass activity at low temperatures. *Sci. Total Environ.* 646, 206–211. doi: 10.1016/j.scitotenv.2018.07.283

Verneuil, L., Silvestre, J., Randrianjatovo, I., Marcato-Romain, C.-E., Girbal-Neuhauser, E., Mouchet, F., et al. (2015). Double walled carbon nanotubes promote the overproduction of extracellular protein-like polymers in *Nitzschia palea*: an adhesive response for an adaptive issue. *Carbon* 88, 113–125. doi: 10.1016/j.carbon.2015.02.053

Villanova, V., Fortunato, A. E., Singh, D., Bo, D. D., Conte, M., Obata, T., et al. (2017). Investigating mixotrophic metabolism in the model diatom *Phaeodactylum tricornutum*. *Philos. Trans. R. Soc. B Biol. Sci.* 372:20160404. doi: 10.1098/rstb.2016.0404

Wang, A., Pu, K., Dong, B., Liu, Y., Zhang, L., Zhang, Z., et al. (2013). Role of surface charge and oxidative stress in cytotoxicity and genotoxicity of graphene oxide towards human lung fibroblast cells: toxicity of graphene oxide to HLF cells. *J. Appl. Toxicol.* 33, 1156–1164. doi: 10.1002/jat.2877

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

Weber, K. P., and Legge, R. L. (2009). One-dimensional metric for tracking bacterial community divergence using sole carbon source utilization patterns. *J. Microbiol. Methods* 79, 55–61. doi: 10.1016/j.mimet.2009.07.020

Wick, P., Louw-Gaume, A. E., Kucki, M., Krug, H. F., Kostarelos, K., Fadeel, B., et al. (2014). Classification framework for graphene-based materials. *Angew. Chem. Int. Ed. Eng.* 53, 7714–7718. doi: 10.1002/anie.201403335

Xiong, T., Yuan, X., Wang, H., Leng, L., Li, H., Wu, Z., et al. (2018). Implication of graphene oxide in Cd-contaminated soil: a case study of bacterial communities. *J. Environ. Manag.* 205, 99–106. doi: 10.1016/j.jenvman.2017.09.067

Yadav, N., Dubey, A., Shukla, S., Saini, C. P., Gupta, G., Priyadarshini, R., et al. (2017). Graphene oxide-coated surface: inhibition of bacterial biofilm formation due to specific surface-interface interactions. *ACS Omega* 2, 3070–3082. doi: 10.1021/acsomega.7b00371

Yilihamu, A., Ouyang, B., Ouyang, P., Bai, Y., Zhang, Q., Shi, M., et al. (2020). Interaction between graphene oxide and nitrogen-fixing bacterium *Azotobacter chroococcum*: transformation, toxicity and nitrogen fixation. *Carbon* 160, 5–13. doi: 10.1016/j.carbon.2020.01.014

Yin, X., Qiao, S., Yu, C., Tian, T., and Zhou, J. (2015). Effects of reduced graphene oxide on the activities of anammox biomass and key enzymes. *Chem. Eng. J.* 276, 106–112. doi: 10.1016/j.cej.2015.04.073

You, G., Hou, J., Xu, Y., Wang, C., Wang, P., Miao, L., et al. (2015). Effects of CeO2 nanoparticles on production and physicochemical characteristics of extracellular polymeric substances in biofilms in sequencing batch biofilm reactor. *Bioresour. Technol.* 194, 91–98. doi: 10.1016/j.biortech.2015.07.006

Yujie, S., Jian, L., Yu, J., Xu, S., Wenning, Y., Zhenghua, L., et al. (2020). Effect of graphene oxide on the ammonia removal and bacterial community in a simulated wastewater treatment process. *J. Environ. Eng.* 146:04020097. doi: 10.1061/(ASCE)EE.1943-7870.0001781

Zhang, X., Zhou, Q., Zou, W., and Hu, X. (2017). Molecular mechanisms of developmental toxicity induced by graphene oxide at predicted environmental concentrations. *Environ. Sci. Technol.* 51, 7861–7871. doi: 10.1021/acs.est.7b01922

Zhao, J., Cao, X., Wang, Z., Dai, Y., and Xing, B. (2017). Mechanistic understanding toward the toxicity of graphene-family materials to freshwater algae. *Water Res.* 111, 18–27. doi: 10.1016/j.watres.2016.12.037

Zhao, J., Li, Y., Cao, X., Guo, C., Xu, L., Wang, Z., et al. (2019). Humic acid mitigated toxicity of graphene-family materials to algae through reducing oxidative stress and heteroaggregation. *Environ. Sci. Nano* 6, 1909–1920. doi: 10.1039/C9EN00067D

Zhou, N., Zhao, Z., Wang, H., Chen, X., Wang, M., He, S., et al. (2019). The effects of graphene oxide on nitrification and N₂O emission: dose and exposure time dependent. *Environ. Pollut.* 252, 960–966. doi: 10.1016/j.envpol.2019.06.009

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Evariste, Braylé, Mouchet, Silvestre, Gauthier, Flahaut, Pinelli and Barret. 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_53.png" /></figure>

Check for
updates

# Antibiotrophy: Key Function for Antibiotic-Resistant Bacteria to Colonize Soils—Case of Sulfamethazine-Degrading *Microbacterium* sp. C448

Loren Billet<fnref n="1" />,²*, Stéphane Pesce², Nadine Rouard<fnref n="1" />, Aymé Spor<fnref n="1" />, Laurianne Paris³, Martin Leremboure³, Arnaud Mounier<fnref n="1" />, Pascale Besse-Hoggan³, Fabrice Martin-Laurent<fnref n="1" /> and Marion Devers-Lamrani<fnref n="1" />

**OPEN ACCESS**

**Edited by:**
Ales Lapanje,
Institut Jožef Stefan (IJS), Slovenia

**Reviewed by:**
Mariusz Cycoń,
Medical University of Silesia, Poland
Pratyosh Shukla,
Banaras Hindu University, India

*Correspondence:
Loren Billet
loren.billet@gmail.com

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

Received: 17 December 2020

Accepted: 22 February 2021

Published: 26 March 2021

**Citation:**
Billet L, Pesce S, Rouard N, Spor A, Paris L, Leremboure M, Mounier A, Besse-Hoggan P, Martin-Laurent F and Devers-Lamrani M (2021) Antibiotrophy: Key Function for Antibiotic-Resistant Bacteria to Colonize Soils—Case of Sulfamethazine-Degrading Microbacterium sp. C448.

Front. Microbiol. 12:643087.
doi: 10.3389/fmicb.2021.643087

Chronic and repeated exposure of environmental bacterial communities to anthropogenic antibiotics have recently driven some antibiotic-resistant bacteria to acquire catabolic functions, enabling them to use antibiotics as nutritive sources (antibiotrophy). Antibiotrophy might confer a selective advantage facilitating the implantation and dispersion of antibiotrophs in contaminated environments. A microcosm experiment was conducted to test this hypothesis in an agroecosystem context. The sulfonamide-degrading and resistant bacterium *Microbacterium* sp. C448 was inoculated in four different soil types with and without added sulfamethazine and/or swine manure. After 1 month of incubation, *Microbacterium* sp. (and its antibiotrophic gene *sada*) was detected only in the sulfamethazine-treated soils, suggesting a low competitiveness of the strain without antibiotic selection pressure. In the absence of manure and despite the presence of *Microbacterium* sp. C448, only one of the four sulfamethazine-treated soils exhibited mineralization capacities, which were low (inferior to 5.5 ± 0.3%). By contrast, manure addition significantly enhanced sulfamethazine mineralization in all the soil types (at least double, comprised between 5.6 ± 0.7% and 19.5 ± 1.2%). These results, which confirm that the presence of functional genes does not necessarily ensure functionality, suggest that sulfamethazine does not necessarily confer a selective advantage on the degrading strain as a nutritional source. 16S rDNA sequencing analyses strongly suggest that sulfamethazine released trophic niches by biocidal action. Accordingly, manure-originating bacteria and/or *Microbacterium* sp. C448 could gain access to low-competition or competition-free ecological niches. However, simultaneous inputs of manure and of the strain could induce competition detrimental for *Microbacterium* sp. C448, forcing it to use sulfamethazine as a

<footnote n="1">¹ AgroSup Dijon, INRAE, Université de Bourgogne Franche-Comté, Agroécologie, Dijon, France, ² INRAE, UR RiverLy, Villeurbanne, France, ³ Université Clermont Auvergne, CNRS, Sigma Clermont, Institut de Chimie de Clermont-Ferrand, Clermont-Ferrand, France</footnote>