Manon Ternois

Maxence Mougon

Emmanuel Flahaut

Audrey

Audrey Dussutour
email: audrey.dussutour@univ-tlse3.fr

Slime moulds response to carbon nanotubes exposure: from 2 internalisation to behaviour 3 4

Keywords: Slime mould, Carbon nanotubes, Ecotoxicology, Behaviour

come

Slime moulds response to carbon nanotubes exposure:

from internalisation to behaviour

following exposure, DWCNTs were almost entirely excreted from the slime moulds. Lastly, we uncovered that DWCNTs exposure altered the migration speed, the pseudopods formation and the expansion rate of the slime moulds. Our results extend our current knowledge of CNTs cytotoxicity and introduce P. polycephalum as an ideal organism to nanotoxicology.

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MovieS1_MS_Ternois et_al.mp4

Introduction 32

Recent years have witnessed a growing interest in nanomaterials due to their specific electrical, 33 optical, thermal and magnetic properties that enhance the performance of the final product (Guinée 34 et al., 2017). Nanomaterials may be of different nature including metals, oxides, or carbon-based. 35 Carbon nanotubes (CNTs) were brought to the forefront in 1991 by Sumio Iijima and since then they 36 have revolutionised several fields of research with their extraordinary properties and applications (De 37 Volder et al., 2013). They are incorporated in a wide variety of consumer products (Guinée et al., 2017) 38 and used in many fields of applications such as biomedical engineering, energy production and storage, 39 nanoelectronics, mechanical engineering, and so forth [START_REF] Gabaudan | Double-walled 523 carbon nanotubes, a performing additive to enhance capacity retention of antimony anode in 524 potassium-ion batteries[END_REF][START_REF] Simon | Overview of carbon nanotubes for biomedical applications[END_REF]. 40

CNTs can be single-walled (SWCNTs), multi-walled (MWCNTs) or, at the interface such as the double-41 walled carbon nanotubes (DWCNTs). DWCNTs offer several advantages in comparison to both SWCNTs 42 and MWCNTs. They show enhanced properties thanks to an outer wall protecting the inner tube. 43

Hence, DWCNTs can be functionalised without degrading their intrinsic mechanical properties which 44 makes them the perfect compromise for many industrial applications [START_REF] Flahaut | Gram-scale CCVD synthesis of double-walled 519 carbon nanotubes[END_REF]Simon et 45 al., 2019).They also offer higher stability under chemical, mechanical and thermal treatments and a 46 longer lifetime than MWCNTs [START_REF] Green | Properties and application of double-walled carbon nanotubes 542 sorted by outer-wall electronic type[END_REF]. Lastly, they enhance electron transfer and thus 47 provide better electrochemical behaviour than SWCNTs [START_REF] Pumera | Electrochemical properties of double wall carbon nanotube electrodes[END_REF].

48

Despite the attractive properties of CNTs for numerous applications, some studies have raised 49 concerns regarding the potential risks of these nanomaterials to the environment (Francis & Devasena, 50 2018). CNTs are released in the environment throughout their life cycle by accidental release during 51 production, transport, storage, uses of the final product [START_REF] Mouchet | Assessment of the potential in 622 vivo ecotoxicity of Double-Walled Carbon Nanotubes (DWNTs) in water, using the amphibian 623 Ambystoma mexicanum[END_REF], and as a waste from 52 sewage treatment or incineration plants [START_REF] Petersen | Potential release pathways, 646 environmental fate, and ecological risks of carbon nanotubes[END_REF]. The fate of CNTs and their impact 53 depends on their transport, diffusion, degradation, bioaccumulation, transformation and persistence. 54

Hence, to evaluate the potential risks associated with CNTs, it is primordial to quantify their presence 55 in each compartment of the environment. However, it is difficult to measure precisely the quantity of 56

CNTs in environmental matrices, and models are often used to predict environmental concentrations 57 (T. Y. [START_REF] Sun | Dynamic Probabilistic Modeling of 694 Environmental Emissions of Engineered Nanomaterials[END_REF]. Soil and water are the main environmental reservoirs for CNTs [START_REF] Liné | Carbon nanotubes: Impacts and behaviour in the terrestrial 595 ecosystem -A review[END_REF]. 58

Using a dynamic probabilistic material flow model, predicted CNTs concentration was estimated to be 59 ca. 3.6.10 -4 mg•L -1 in surface waters and approximatively 3.5.10 -2 mg•kg -1 in natural and urban soil (T. 60 Y. [START_REF] Sun | Dynamic Probabilistic Modeling of 694 Environmental Emissions of Engineered Nanomaterials[END_REF].

61

Single cell organisms play an important role in nutrients cycling and a change in their activity could 62 indicate a response to an environmental stressor [START_REF] Chung | The effect of multi-walled carbon nanotubes 509 on soil microbial activity[END_REF]. Several studies indicate that 63

MWCNTs and SWCNTs present in soils repress enzymatic activities of bacteria, reduce microbial 64 biomass [START_REF] Chung | The effect of multi-walled carbon nanotubes 509 on soil microbial activity[END_REF][START_REF] Ge | Long-532 term effects of multiwalled carbon nanotubes and graphene on microbial communities in dry 533 soil[END_REF][START_REF] Jin | High concentrations of single-568 walled carbon nanotubes lower soil enzyme activity and microbial biomass[END_REF] and alter soil microbial community 65 composition [START_REF] Jin | Single-walled carbon 565 nanotubes alter soil microbial community composition[END_REF][START_REF] Kerfahi | Effects of Functionalized and Raw 577 Multi-Walled Carbon Nanotubes on Soil Effects of Functionalized and Raw Multi-Walled Carbon 578 Nanotubes on Soil Bacterial Community Composition[END_REF]. It was also shown in soil bacteria that SWCNTs inhibit 66 growth, reduce viability [START_REF] Arias | Inactivation of bacterial pathogens by carbon nanotubes in 490 suspensions[END_REF], disrupt wall and membrane [START_REF] Kang | Single-walled carbon nanotubes exhibit 575 strong antimicrobial activity[END_REF]Liu et al., 67 2009), induce stress response [START_REF] Kang | Antibacterial effects of carbon 571 nanotubes: size does matter![END_REF] and affect denitrification activity (Zheng et 68 al., 2014). CNTs are hydrophobic, but stable CNTs suspension can persist for over one month in natural 69 surface water when stabilised by natural organic matter [START_REF] Hyung | Natural organic matter stabilizes carbon 559 nanotubes in the aqueous phase[END_REF]. Hence negative impacts 70 of CNTs on aquatic single cell organisms have also been observed. Among these single cell organisms, 71 ciliates and unicellular algae have been the most studied to assess CNTs adverse effects (reviewed in 72 [START_REF] Jackson | Bioaccumulation and ecotoxicity of carbon nanotubes[END_REF]. SWCNTs and MWCNTs can be readily ingested by various ciliates (Tetrahymena 73 thermophila, Tetrahymena thermophila pyriformis, Pseudocohnilembus persalinus and Stylonychia 74 mytilus) and can alter the motility, induce growth inhibition, cause cell aggregation, decrease viability, 75 impair predation and alter the intracellular trafficking of vesicles [START_REF] Chan | Interactions between Single-499 Walled Carbon Nanotubes (SWNT) and Ciliates: SWNT Interfere with Ciliate Ecological Functions 500 and Ciliates Transport/Transform SWNT[END_REF][START_REF] Ghafari | 535 Impact of carbon nanotubes on the ingestion and digestion of bacteria by ciliated protozoa[END_REF]76 Guo et al., 2008;[START_REF] Mortimer | Bioaccumulation of 618 Multiwall Carbon Nanotubes in Tetrahymena thermophila by Direct Feeding or Trophic 619 Transfer[END_REF][START_REF] Weijie | TiO2 nanoparticles 703 and multi-walled carbon nanotubes monitoring and bioremediation potential using ciliates 704 Pseudocohnilembus persalinus[END_REF][START_REF] Zhu | The interaction and toxicity of multi-walled carbon 716 nanotubes with Stylonychia mytilus[END_REF]. Similarly to protozoa, both tertiolecta) are sensitive to CNTs and exhibit reduced growth and stress response when exposed (Long 79 et al., 2012;[START_REF] Schwab | Are 673 carbon nanotube effects on green algae caused by shading and agglomeration?[END_REF][START_REF] Wei | Cytotoxicity effects of water 700 dispersible oxidized multiwalled carbon nanotubes on marine alga, Dunaliella tertiolecta[END_REF].

80

Due to its comparably simple structure in relation to its behavioural complexity and due to the ease 81 with which it can be cultivated and manipulated, the acellular slime mould P. polycephalum presents 82

itself as an ideal model system to determine the cytotoxicity of CNTs in single cell organisms. P.

83

polycephalum is a remarkable organism belonging to the Myxomycetes and the Amoebozoa. It is a 84 phagotrophic organism commonly observed in association with decaying plant material in terrestrial 85 ecosystems. P. polycephalum, as a major component of the detritus food chain is essential in nutrient 86 cycling. Bacteria and fungi are the primary natural food source of P. polycephalum, and these are 87 phagocytized [START_REF] Aldrich | Cell biology of Physarum and Didymium V1: organisms, nucleus, and cell cycle 482[END_REF]. Food particles are first captured in flask-like invaginations of the 88 membrane and then isolated in vacuoles which are released into the cytoplasm. P. polycephalum can 89 also take up droplets of medium by pinocytosis [START_REF] Aldrich | Cell biology of Physarum and Didymium V1: organisms, nucleus, and cell cycle 482[END_REF]. The vegetative state of P polycephalum 90 is a large mobile polynucleated cell also called plasmodium. It can extend to up to hundreds of square 91 centimetres (Figure 1A) and be severed into viable and structurally similar yet smaller plasmodia. Upon 92 contact, these plasmodia can fuse with each other to form a unique plasmodium [START_REF] Aldrich | Cell biology of Physarum and Didymium V1: organisms, nucleus, and cell cycle 482[END_REF]. Lastly, 93 a starving plasmodium can encapsulate and enter a dormant stage called sclerotium (Figure 1C).

94

Both P. polycephalum's motion and behaviour rely on a network of interconnected veins (Figure 1B). 95

These veins contract and relax periodically, causing the cytoplasm to flow back and forth, a 96 phenomenon called "shuttle streaming" [START_REF] Matsumoto | Locomotive mechanism of Physarum plasmodia 606 based on spatiotemporal analysis of protoplasmic streaming[END_REF]. The veins are porous, allowing 97 respiratory gases, molecules, nuclei and organelles to be exchanged with the surrounding cytoplasm 98 [START_REF] Oettmeier | Form follows function: Ultrastructure of different 633 morphotypes of Physarum polycephalum[END_REF]. This open network distributes the cytoplasm throughout the cell body and 99 allows to maintain homeostasis. This network is also responsible for the plasmodium migration at a 100 speed of up to 4 cm per hour, through the interplay of intracellular flow and rhythmic vein contractions 101 [START_REF] Alim | Random network 484 peristalsis in Physarum polycephalum organizes fluid flows across an individual[END_REF][START_REF] Matsumoto | Locomotive mechanism of Physarum plasmodia 606 based on spatiotemporal analysis of protoplasmic streaming[END_REF][START_REF] Alim | Random network 484 peristalsis in Physarum polycephalum organizes fluid flows across an individual[END_REF]. These contractions produce a pressure 102 gradient that pushes the cytoplasm towards the cell periphery where local cytoskeletal reorganisation 103 leads to the formation of fan shaped leading fronts. These fronts extend and retract in synchrony with 104 the shuttle streaming of the cytoplasm [START_REF] Lewis | Coordination of contractility, adhesion 581 and flow in migrating Physarum amoebae[END_REF][START_REF] Matsumoto | Locomotive mechanism of Physarum plasmodia 606 based on spatiotemporal analysis of protoplasmic streaming[END_REF]. The frequency and 105 the amplitude of the contractions and by extension the shape, size and motion of the plasmodium 106 depend on the external cues encountered in the environment [START_REF] Matsumoto | Propagation of phase wave in relation to tactic 609 responses by the plasmodium of Physarum polycephalum[END_REF]Ridgway & 107 Durham, 1976;[START_REF] Shimizu | Relationship between Intracellular Period Modulation and External 679 Environment Change in Physarum Plasmodium[END_REF]. It has been shown that slime moulds respond to a variety of 108 cues such as chemicals [START_REF] Aldrich | Cell biology of Physarum and Didymium V1: organisms, nucleus, and cell cycle 482[END_REF], light [START_REF] Hato | Phototaxis in true slime mold Physarum 551 polycephalum[END_REF][START_REF] Marwan | Photomovement[END_REF], temperature (Wolf et 109 al., 1997), humidity [START_REF] Rakoczy | The myxomycete Physarum nudum as a model organism for photobiological 651 studies[END_REF], etc. P. polycephalum exhibits a rich repertoire of complex 110 behaviours which makes it a model organism to study problem-solving in unicellular systems 111 [START_REF] Adamatzky | Thirty seven things to do with live slime mould[END_REF][START_REF] Oettmeier | Physarum polycephalum -A new take on a classic 630 model system[END_REF][START_REF] Reid | Information integration and multiattribute 653 decision making in non-neuronal organisms[END_REF][START_REF] Smith-Ferguson | Who needs a brain? Slime moulds, behavioural ecology 687 and minimal cognition[END_REF]112 Vallverdú et al., 2018).

113

Although the importance of slime moulds to the environment is well acknowledged, only a single study 114 was found on exposure of such organisms to carbon nanotubes (CNTs) [START_REF] Gizzie | On modulating the Physarum polycephalum 538 plasmodium's electrical resistance, resting membrane potential and capacitance by application 539 of nanoparticles and nanostructures[END_REF]. The 115 objective of [START_REF] Gizzie | On modulating the Physarum polycephalum 538 plasmodium's electrical resistance, resting membrane potential and capacitance by application 539 of nanoparticles and nanostructures[END_REF] study was not to measure the impact of CNTs on P. polycephalum but 116 to hybridise the organism with nanomaterials. In contrast, the main objective of our work was to 117 measure the impact of CNTs exposure on P. polycephalum behaviour. First, we verified that CNTs could 118 be internalised into the cell using various routes of exposure. CNTs were either mixed with food, spread 119 in the environment or administrated by topical application. Being a phagotrophic organisms, we 120 expected the slime molds to readily internalize CNTs. Second, we evaluated if CNTs accumulated over 121 time when cells were continuously exposed to CNTs for 6 weeks. Bioaccumulation of CNTs is a major 122 issue, as it means that CNTs could move up along the food chain. Third, we measured the persistence Magnesia (MgO) was used as support material for in situ generated catalytic nanoparticles of cobalt 139 and molybdenum [START_REF] Flahaut | Gram-scale CCVD synthesis of double-walled 519 carbon nanotubes[END_REF]. CCVD is a widely used method due to its capacity to produce 140

CNTs with a high yield and possibly also a high purity with easily controllable reaction conditions to 141 tune the desired type of carbon nanotubes [START_REF] Yeoh | Synthesis of high purity multi-walled 709 carbon nanotubes over Co-Mo / MgO catalyst by the catalytic chemical vapor deposition of 710 methane[END_REF]. First, the catalytic powder was placed 142 in a ceramic container which was positioned in a tube furnace. The aim of the first experiment was to find out how DWCNTs may be internalised within the slime 182 mould. Circular slime moulds (14 mm Ø) were placed in the centre of a Petri dish (55 mm Ø) previously 183 filled with 10 mL of 1 wt. % agar gel. 100 µL of DWCNT (50 mg•L -1 ) were either dropped on rolled oats 184 (Food treatment, n=20), directly on top of the slime mould (Topical treatment, n=20) or in the slime 185 mould environment (Environment treatment, n=22). For the environment treatment, 2 drops of 50 µL 186 were put on either side of the slime mould and spread out with a brush to cover the entire gel as evenly 187 as possible. All the petri dishes were stored in a thermoregulated chamber (25°C) for 16 hours. The 188 slime moulds were then observed with a binocular microscope (Leica S9, 600 lp/mm, pi=0.4µm 189 equipped with a MC170 HD camera) to quantify the DWCNTs internalisation. The DWCNTs that we 190 observed using the binocular microscope were much larger than bundles and correspond to 191 agglomerates. 8 to 10 pictures of each slime mould were taken (depending on the size of the slime oats). We used food as a route of exposure as we demonstrated in the first experiment that it was the 206 most efficient method for DWCNTS internalisation. For 9 slime moulds chosen randomly, 12 pictures 207 were taken the first week and the last week with a binocular microscope (Leica S9, 600 lp/mm, 208 pi=0.4µm) and camera MC170 HD Leica. Static DWCNTs agglomerates were counted in four 0.5 mm 2 209 zone chosen randomly on each picture leading to a total of 432 counts for each treatment, using the 210 software ImageJ, to find out if there was an increase in the number of CNT agglomerates over time 211 (bioaccumulation). We also measured the area of the agglomerates using the software ImageJ to 212 investigate a potential deagglomeration through time.

213

Persistence of pristine DWCNTs into the slime mould 214

When environmental conditions deteriorate, slime moulds enter a dormant state called sclerotium 215 until conditions become favourable again. During the sclerotisation process, slime moulds lose 50% of 216 their total protein content together with 40% of their DNA and 65% of their RNA [START_REF] Aldrich | Cell biology of Physarum and Didymium V1: organisms, nucleus, and cell cycle 482[END_REF]. Slime 217 mould can be easily re-activated from sclerotia if placed in favourable conditions. The aim of the third 218 experiment was to examine if the DWCNTs internalized after a chronic exposure were excreted by the 219 slime molds before entering the dormant stage. 240 samples of the slime moulds used in the second 220 experiment were successfully turned into a dormant state (N=20 for each treatment every week for 6 221 weeks). The transition from plasmodia to sclerotia was initiated by placing the slime moulds on a moist 222 filter paper for 4 days to dry. Six months after entering the dormant state, 60 samples were chosen 223 randomly and were reactivated to test the viability of the sclerotia (5 samples per treatment and per 224 week). To this end, the sclerotia were soaked in water and placed in petri dishes (55 mm Ø) on agar 225 gel (1%). Once revived, slime moulds start to explore the agar gel, usually 24h after the reactivation of 226 the sclerotia.

227

Then, 20 sclerotia were re-activated one year later for each treatment (10 samples from week 1 and 228 10 samples from week 6). One sclerotium out of 20 could not be re-activated in each treatment. The 229 day following the reactivation, 120 sec videos of 3 veins per slime mould were recorded with a 230 binocular microscope and camera MC170 HD Leica to quantify circulating DWCNTs agglomerates. Ten 231 pictures per slime mould were also taken. DWCNTs agglomerates were counted in 5 zones of 0.5 mm² 232 chosen randomly on each picture leading to a total of 912 counts. As we used sample from the slime 233 moulds tested in the bioaccumulation protocol, we were able to compare the number of static 234

DWCNTs agglomerates in the slime mould before and after entering the dormant state. 1F). The dishes were placed in a thermoregulated chamber and pictures were 266 taken every 5 min for 36 hours with a digital Canon 70D camera. For each slime mould, we measured 267 the time of appearance of the first pseudopod, which is an indicator of slime mould healing process, 268 as well as the expansion rate. Expansion rate was computed as the difference between the final surface 269 (the area of the slime moulds when it reached a distance of 15 mm from its original location) and the 270 initial surface (the area of the slime mould when we started the experiment) divided by the time to 271 reach the final surface. The pictures were analysed using the software Image J. internalisation. The number of static DWCNTs agglomerates was considerably high when the slime 290 mould encountered the DWCNTs while exploring their environment, five times higher than when 291

DWCNTs were presented together with food (p<0.001, Figure 2A, Table S1) and fifty times higher than 292

when DWCNTs were dropped directly on the slime mould (p<0.001, Figure 2A, Table S1). The number 293 of circulating DWCNTs agglomerates was also significantly higher when food or the environment were 294 the routes of exposure when compared to topical application (p<0.001, p<0.001 and p=0.513 for Food 295 vs Topical, Environment vs Topical and Food vs Environment respectively, Figure 2B, Table S2). When 296 the slime moulds were exposed to DWCNTs by topical application, black streaks were observed on the 297 surface of the organism where the DWCNTs suspension was dropped. This clearly indicates that a 298 significant proportion of DWCNTs was not internalised. These observations were later confirmed by 299 picture and video analyses revealing that there were almost no static or circulating DWCNTs 300 agglomerates in the slime mould. Here, the aim was to evaluate if DWCNTs accumulated within the slime mould when DWCNTs were 310 added daily to the food for an extended period of time (6 weeks). Surprisingly despite daily exposure 311 to DWCNTs, slime mould did not accumulate DWCNTs agglomerates over time. Both the number and 312 the area of DWCNTs agglomerates remained constant between the first and the last week of exposure 313 (p=0.213 and p=0.639, Table S3 and Table S4, Figure 3A and Figure 3B). S5, Figure 3C) regardless of the extent of exposure 323

to DWCNTs (p=0.272, Table S5, Figure 3C). We noticed that the filter papers holding the sclerotia were 324 covered with black streaks which appeared to be DWCNTs agglomerates when observed with a 325 binocular microscope. These observations provide an evidence that the DWCNTs agglomerates 326 internalised by slime moulds were later excreted before entering the dormant state. S6 andS7, Figure 4A and4B). 24 hours after a single exposure to 342 DWCNTS static and circulating agglomerates were spotted in half of the pictures and the videos, 343 respectively. 79 hours after the exposure only few static DWCNTs agglomerates were spotted on 2 344 pictures out of 420 and a single occurrence of circulating DWCNTs agglomerates was recorded out of 345 60 videos. As expected, the number of static agglomerates is highly correlated to the number of 346 circulating ones (p<0.001, Figure 4C). The area of the static agglomerates did not change through time 347

(time effect p=0.714, Table S8) Here, the objective was to investigate if a single exposure to DWCNTs altered the slime moulds 362 migration speed depending on the route of exposure. When the DWCNTs were associated to the food, 363 the slime moulds were significantly slower than the controls (p=0.001, Table S9, Figure 5A). In contrast, 364 we observed no difference in speed between the slime moulds exposed to DWCNTs and the controls 365 when they were exposed via the environment or via a topical application (p=0.626 and p=0.685, 366 respectively, Table S10 andS11 In the last experiment, the aim was to examine the effect of DWCNTs daily exposure on performance. 375

348
We used two proxies of performance: the appearance of the first pseudopod and the expansion rate. 376

The delay in the appearance of the first pseudopod was longer when the organisms were exposed to 377 the DWCNTs when compared to the controls (treatment effect p<0.001, Table S12, Figure 6A, Figure 378 S2). Similarly, the expansion rate was slower for the slime mould exposed to the DWCNTs when 379 compared to the controls (treatment effect p=0.026, Table S13, Figure 6B, Figure S3).

391

Deciphering the mechanisms responsible for CNTs internalisation into live cells is essential both from 392 an ecological point of view but also to design CNT-based delivery systems. CNTs toxicity or efficacy 393 depends on their ability to cross cell boundaries and membranes. In slime moulds, we demonstrated 394 that, as in other grazing protists [START_REF] Ghafari | 535 Impact of carbon nanotubes on the ingestion and digestion of bacteria by ciliated protozoa[END_REF] spreading out the CNTs in the environment or 395 presenting them together with food were the most effective routes of exposure. As P. polycephalum 396 continuously explores its environment in search for food, it phagocytes microorganisms and 397 continuously secretes a thick extracellular slime [START_REF] Reid | Slime mold uses an externalized spatial 656 "memory" to navigate in complex environments[END_REF]. Hence slime moulds can either 398 internalise CNTs through phagocytosis while feeding or trapped CNTs in their own mucus while 399 crawling on the substrate. Topical application of CNTs was the least effective route of exposure. The 400 presence of black streaks where the CNTs were deposited indicate that the CNTs were not all passively 401 internalised through membrane pores. In slime moulds, objects smaller than 1 µm can be endocytosed 402 within vesicles [START_REF] Gizzie | On modulating the Physarum polycephalum 538 plasmodium's electrical resistance, resting membrane potential and capacitance by application 539 of nanoparticles and nanostructures[END_REF][START_REF] Mayne | On the 614 internalisation, intraplasmodial carriage and excretion of metallic nanoparticles in the slime 615 mould, Physarum polycephalum[END_REF][START_REF] Mayne | Toward Hybrid Nanostructure-Slime Mould Devices[END_REF]. For instance, Mayne 403 et al. (2011) showed that magnetite nanoparticles can be internalised by endocytosis in slime moulds.

404

CNTs are persistent in the environment due to their low biodegradation, so the assessment of their 405 potential for bioaccumulation is of major concern [START_REF] Bjorkl | Increasing evidence indicates low bioaccumulation of 492 carbon nanotubes[END_REF] when exposed to CNTs, eliminate these nanoparticles quickly. For instance, mussels, zebrafish, 409 oligochaete, lugworms and earthworms exposed to CNTs for more than two weeks eliminate the 410 nanoparticles within a couple of days [START_REF] Cheng | 505 Acute and long-term effects after single loading of functionalized multi-walled carbon 506 nanotubes into zebrafish (Danio rerio)[END_REF][START_REF] Galloway | Sublethal toxicity of 526 nano-titanium dioxide and carbon nanotubes in a sediment dwelling marine polychaete[END_REF][START_REF] Hanna | Deposition of carbon nanotubes by a marine 548 suspension feeder revealed by chemical and isotopic tracers[END_REF]S. 411 Li et al., 2013;[START_REF] Petersen | Ecological uptake and depuration of carbon 642 nanotubes by Lumbriculus variegatus[END_REF]. In the excretion experiment, we showed that there was a drop in 412 the number of static and circulating CNTs agglomerates after 24 hours, and the majority of CNTs were 413 excreted from the cell at 48 hours post exposure. Therefore, the egestion of CNTs from the cell 414 occurred rapidly which explains why only few CNTs agglomerates persisted throughout the 415 sclerotisation process which last at least 72h in average. We noticed significant black streaks on the 416 filter paper where the slime had migrated before going into dormancy. These observations corroborate 417 that slime moulds egested most of the CNTs back in the environment before entering a dormant state. 418

The egestion of CNTs raises the possibility that these CNTs might have been ingested again when the 419 slime explored the filter paper upon awakening. We attempted to quantify the CNTs agglomerates 420 excreted by the slime moulds in the environment. We noticed that the number of agglomerates 421 detected in the environment were very low (Figure S4). However, our counting methods was rather 422 rough, as agglomerates smaller than 5 m could not be detected with our binocular microscope.

423

CNTs have been shown to impact many microorganism [START_REF] Chen | 502 Understanding the influence of carbon nanomaterials on microbial communities[END_REF] and slime moulds are no 424 exception [START_REF] Gizzie | On modulating the Physarum polycephalum 538 plasmodium's electrical resistance, resting membrane potential and capacitance by application 539 of nanoparticles and nanostructures[END_REF]. In our experiment, we observed that both a single and a chronic 425 exposure to CNTs affected the slime moulds behaviour. We noticed that slime moved more slowly 426 when exposed to CNTs while foraging (Figure 5A) or exploring (Figure 6). These differences could result 427 from a defect in the motor or growing systems as movement and growth are intimately connected in 428 slime moulds. We can propose various processes that might explain our results based on in vitro and 429

in vivo studies performed on various eukaryotic cell lines. First, direct contact with CNTs might have 430 damaged the cell membrane and altered its function [START_REF] Kang | Physicochemical determinants of multiwalled carbon 573 nanotube bacterial cytotoxicity[END_REF]. This could explain 431 the delay in forming the first pseudopod (Figure 6A). Second, internalisation of CNTs might have led to 432 the production of reactive oxygen species which in turn might have altered the structure and function 433 of the cells [START_REF] Andersen | Complement: 488 alive and kicking nanomedicines[END_REF][START_REF] Chen | 502 Understanding the influence of carbon nanomaterials on microbial communities[END_REF][START_REF] Müller | Oxidative 626 stress and inflammation response after nanoparticle exposure: differences between human 627 lung cell monocultures and an advanced three-dimensional model of the human epithelial 628 airways[END_REF][START_REF] Pacurari | Cell 636 permeability, migration, and reactive oxygen species induced by multiwalled carbon nanotubes 637 in human microvascular endothelial cells[END_REF]Singh et 434 al., 2014;[START_REF] Srivastava | 689 Multi-walled carbon nanotubes induce oxidative stress and apoptosis in human lung cancer cell 690 line-A549[END_REF]. Last, once inside the cell, CNTs might have interacted with 435 intracellular filaments: actin [START_REF] Holt | Carbon nanotubes 557 reorganize actin structures in cells and ex vivo[END_REF][START_REF] Holt | Altered Cell Mechanics from the Inside: Dispersed Single Wall 554 Carbon Nanotubes Integrate with and Restructure Actin[END_REF][START_REF] Shams | 676 Actin reorganization through dynamic interactions with single-wall carbon nanotubes[END_REF], DNA (X. Li, Peng, & Qu, 2006;436 Sargent et al., 2012;[START_REF] Singh | Carbon nanotube-684 induced loss of multicellular chirality on micropatterned substrate is mediated by oxidative 685 stress[END_REF] and tubulin polymers (microtubules) (X. Li, Peng, & Qu, 2006;437 X. Li, Peng, Ren, et al., 2006;[START_REF] Pampaloni | Microtubule architecture: inspiration for novel carbon nanotube-640 based biomimetic materials[END_REF] leading to migratory defects, DNA breakage 438 and mitotic blockage. Interestingly, the deleterious effects produced by SWCNTs and MWCNT reported 439 in the literature differ. SWCNTs interact mostly with DNA [START_REF] Dong | 516 Carbon nanotube uptake changes the biomechanical properties of human lung epithelial cells in 517 a time-dependent manner[END_REF][START_REF] García-Hevia | 529 Nanotube interactions with microtubules: Implications for cancer medicine[END_REF]440 X. Li, Peng, & Qu, 2006;[START_REF] Rodriguez-Fernandez | 662 Multiwalled carbon nanotubes display microtubule biomimetic properties in vivo, enhancing 663 microtubule assembly and stabilization[END_REF][START_REF] Sargent | Single-walled carbon nanotube-induced 666 mitotic disruption[END_REF][START_REF] Singh | Carbon nanotube-684 induced loss of multicellular chirality on micropatterned substrate is mediated by oxidative 685 stress[END_REF], while 441

MWCNTs interfere with actin and microtubules [START_REF] Dong | 516 Carbon nanotube uptake changes the biomechanical properties of human lung epithelial cells in 517 a time-dependent manner[END_REF][START_REF] García-Hevia | 529 Nanotube interactions with microtubules: Implications for cancer medicine[END_REF]Rodriguez-442 Fernandez et al., 2012). However, the reason for such differences is difficult to explain because authors The interaction between living organisms and CNTs is a critical issue. Here we showed that CNTs are 452 taken up by slime moulds and could therefore move up the food chain. In addition, we demonstrated 453 that the internalisation of CNTs altered the behaviour of slime moulds and by extend could impede 454 their adaptability to the environment. Our results extend our current knowledge on CNTs and their 455 effects on cellular systems and introduce the slime mould P. polycephalum as a new model system to 456 study CNTs toxicity. Slime moulds offer multifaceted advantages. First, being giant cells, they allowed 457 us to observe internalised CNTs agglomerates in vivo with little equipment. Second, being single cell 458 organisms, they enable us to measure the impact of CNTs at the cell level on multiple parameters: 459 morphology, motility and differentiation. Third, slime moulds exhibit primitive forms of cognition (such 460 as learning, memory, anticipation, and decision making) and thus provide an ideal model to measure 461 the impact of CNTs on behaviour [START_REF] Vallverdú | Slime mould: The fundamental mechanisms of biological cognition[END_REF]. Lastly, slime moulds display many features of 462 animal cells such as a high molecular complexity and a large diversity of signalling molecules (Schaap 463 et al., 2016)

Figure 1 :

 1 the cell when cells were exposed to CNTs for 6 weeks. If the CNTs remain into the cell, 124 they might be transferred to the next generation. Fourth, we estimated the excretion rate of CNTs 125 when cells were exposed only once to CNTs. Last, we measured the effect of CNTs exposure on 126 behaviour including movement initiation and migration speed. 127 128 Pictures of P. polycephalum. These pictures illustrate (A) a macroplasmodium; the vegetative 129 phase of P. polycephalum, (B) a magnified vein network, (C) a sclerotium, the dormant stage of P. 130 polycephalum, (D) DWCNTs agglomerates internalised in a slime mould (E) two slime moulds migrating 131 on a bridge and (F) one slime mould exploring an agar gel in a petri dish. we used DWCNTs as they can be considered as a general model for carbon nanotubes, 136 representing both thin and flexible SWCNTs and multiwall CNTs. 137 We used the CCVD (catalytic chemical vapor deposition) technique to synthesise the DWCNTs. 138

  count the number of static DWCNTs agglomerates (black spots, Figure1). For each picture 193 several zones of 0.5 mm² were delimited randomly and DWCNTs were counted in each zone. Data 194 acquisition was performed using the software ImageJ. As we could not preclude that some static 195 agglomerates were trapped in the extracellular slime layer surrounding the cell and not actually inside 196 the slime mould, we also tracked circulating DWCNTs agglomerates within the organism. To this end, 197 five veins per slime mould were video monitored for 100 seconds and the number of DWCNTs 198 agglomerates circulating within the veins were counted (Movie S1). 199 4. Bioaccumulation of pristine DWCNTs into the slime mould 200 Beyond internalising DWCNTs, do the slime moulds accumulate the DWCNTs? The aim of the second 201 experiment was to investigate if chronic exposure to DWCNTs led to bioaccumulation. 20 slime moulds 202 were exposed daily to either a CMC solution (Control group N=20) or DWCNTs suspension (CNT group 203 N=20), for 6 weeks. Slime moulds were reared in Petri dishes (55 mm Ø) and 1 mL of DWCNTs 204 suspension (50 mg•L -1 ) or CMC was spread on the food given daily to the slime moulds (1 g of rolled 205

Figure 2 .

 2 Internalisation of pristine DWCNTs by slime moulds. Number of static (A) and circulating (B) 303 DWCNTs agglomerates as a function of the route of exposure: environment, food or topical. A total of 304 2342 pictures and 310 videos were analysed. The boxes extend from lower to upper quartile values 305 and vertical black lines extend to most extreme data point. The horizontal black lines indicate median 306 values and the dots represent the mean. ***p<0.001 of pristine DWCNTs into the slime mould 309

Figure 3 .

 3 Bioaccumulation and persistence of pristine DWCNTs into the slime mould. (A) Number of 329 static DWCNTs agglomerates as a function of the number of weeks of exposure. (N= 416 and N=368 330 pictures analysed for week 1 and week 6 respectively); (B) Area of the static agglomerates as a function 331 of the number of weeks of exposure N= 317 and N=297 pictures analysed for week 1 and week 6 332 respectively); (C) Number of static DWCNTs agglomerates before and after entering a dormant state 333 (N= 864 and N= 1687 pictures analysed before and after entering the formant state respectively). The 334 boxes extend from lower to upper quartile values and vertical black lines extend to most extreme data 335 point. The horizontal black lines indicate median values and the dots represent the mean. ***p<0.001 of pristine DWCNTs from the slime mould 338 Knowing that slime moulds did not accumulate DWCNTs and could excrete them in the environment, 339 we examined the duration and dynamics of excretion of DWCNTs for slime moulds exposed only once 340 to DWCNTs. Both the number of static and circulating DWCNTs agglomerates dropped drastically after 341 24h (Time effect p<0.001, Table

Figure 4 :

 4 Excretion of pristine DWCNTs from the slime mould. Number of static (per 0.5 mm2) (A) and 352 circulating (per 100sec) (B) DWCNTs agglomerates observed for slime moulds exposed once to 353 DWNCTs. N= 1688 pictures and N=352 videos analysed. Error bars are Confidence intervals. (C) 354 Correlation between the number of static aggregates and the number of circulating ones (N=12 355 plasmodia). The red line corresponds to the regression line (p< 0.001, R 2 =0.77). (D) Area of the static 356 agglomerates as a function of time (N= 280 agglomerates in total). The boxes extend from lower to 357 upper quartile values and vertical black lines extend to most extreme data point. The horizontal black 358 lines indicate median values and the dots represent the mean. 359 360 5. Migration speed following a single exposure to DWCNTs 361

Figure 5 :

 5 Migration speed following a single exposure to DWCNTs as a function of the route of 369 exposure: environment, food or topical. 80 slime moulds per treatment and per route of exposure 370 were observed. The boxes extend from lower to upper quartile values and vertical black lines extend 371 to most extreme data point. The horizontal black lines indicate median values and the dots represent 372 the mean. 373 6. First pseudopod and expansion rate following a chronic exposure to DWCNTs 374

Figure 6 :

 6 Behaviour analyses. (A) Latency to form a first pseudopod following a chronic exposure to 383 DWCNTs. (B) Expansion rate following a chronic exposure to DWCNTs. 40 slime moulds were observed 384 per treatment and per week for a total of 480 slime moulds. The boxes extend from lower to upper 385 quartile values and vertical black lines extend to most extreme data point. The horizontal black lines 386 indicate median values and the dots represent the mean. *p<0.05; ***p<0.our results indicate that slime moulds can internalise DWCNTs and that DWCNTS 390 exposure affects behavioural performances.

  443 have worked with CNTs exhibiting different morphologies (bundling, length) and may simply have 444 focused on different endpoints. Movement in slime moulds, as mentioned earlier, relies on a driving 445 force generated by the actin and myosin filaments surrounding the veins. Any change in actin filaments 446 induced by CNTs might have induced a reorganisation of the cell cytoskeleton at larger scale and 447 altered cell migration. Slime mould growth is associated to synchronous mitosis occurring every 8 to 448 10 hours. Knowing that microtubules are responsible for DNA segregation during mitosis, any 449 alteration of the microtubule cytoskeleton might have compromised cell growth.

Figure 1 :Figure 2 .Figure 3 .Figure 4 :Figure 5 :Figure 6 :

 123456 Figure 1: Pictures of P. polycephalum. These pictures illustrate (A) a macroplasmodium; the vegetative phase of P. polycephalum, (B) a magnified vein network, (C) a sclerotium, the dormant stage of P. polycephalum, (D) DWCNTs agglomerates internalised in a slime mould (E) two slime moulds migrating on a bridge and (F) one slime mould exploring an agar gel in a petri dish. 222x148mm (300 x 300 DPI)

Figure S1 :

 S1 Figure S1: Figure associated to the methods section. CNT characterization (A) TEM image of the DWCNTs. (B) CNT powder Raman scattering spectrum obtained using a 633 nm wavelength laser. (C) The weight loss profile obtained from TGA analysis in air atmosphere (1°/min). (D) Table summarizing the physicochemical characteristics (TW = triple walled, DW = double walled, SW = single walled).

Figure S2 :

 S2 Figure S2: Figure associated to Figure 6 (A). Latency to form a first pseudopod following a chronic exposure to DWCNTs. 40 slime molds were observed per treatment and per week for a total of 480 slime moulds. The boxes extend from lower to upper quartile values and vertical black lines extend to most extreme data point. The horizontal black lines indicate median values and the dots represent the mean.

Figure S3 :

 S3 Figure S3: Figure associated to Figure 6 (B). Expansion rate following a chronic exposure to DWCNTs. 40 slime molds were observed per treatment and per week for a total of 480 slime moulds. The boxes extend from lower to upper quartile values and vertical black lines extend to most extreme data point. The horizontal black lines indicate median values and the dots represent the mean

  236The aim of the fourth experiment was to evaluate the excretion rate of DWCNTs by the slime mould. 237Slime moulds were exposed once to DWCNTs using food as a route of exposure. Circular slime moulds 238 (10 mm Ø) were placed in a square Petri dish (120 mm wide) previously filled with 10 mL of 1 wt. % 239 agar gel. Few oat flakes soaked in a DWCNTs suspension (volume 40 µL) were placed 5 mm away from 240 the slime mould. We let the slime moulds feed and explore for 24h. Any food residues remaining after 241 24hours were discarded as they may provide a niche in which microorganisms can rapidly grow. As 242 they explore, slime moulds leave behind a thick mat of non-living, translucent, extracellular slime. To 243 prevents internalisation of DWCNTs that had been already excreted and left in the extracellular slime 244 we replaced the agar gel after 48 hours so that the slime mould never explored the same area twice. 245 Slime moulds were observed with a binocular microscope and camera MC170 HDLeica 24, 31, 48, 55, 246 72 and 79 h post exposure. 5 to 10 pictures were taken for each slime mould sample and DWCNTs 247 agglomerates were counted using ImageJ software. 5 veins per slime mould were video monitored for 248 100 seconds to quantify circulating DWCNTs. We also measured the area of the static agglomerates 249 using the software ImageJ to investigate a potential deagglomeration through time.

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235 6. Excretion of pristine DWCNTs from the slime mould 250 7. Migration speed following a single exposure to DWCNTs 251 The aim of the fifth experiment was to investigate how a single exposure to DWCNTs affects the slime 252 mould behaviour. In this experiment a circular slime mould (10 mm Ø) had to migrate on a bridge (35 253 mm long, 100 mm wide) made of 10% w/v powdered oat-agar (Figure 1E). Slime mould were first 254 gently placed in contact with the bridge and then exposed to DWCNTs suspension (CNT group) or CMC 255 only (Control group). Three routes of exposure were tested: a drop of DWCNTs suspension (20 µL) was 256 either placed directly on the slime mould (Topical treatment), on few oat flakes resting on the slime 257 mould (Food treatment) or on the bridge (Environment treatment). Hence, 80 slime mould were tested 258 for each treatment and each group, leading to total of 480 assays. After one night's incubation (ca. 15 259 h), the distance travelled (in mm) on each bridge was measured with a ruler. 260 8. First pseudopod and expansion rate following a chronic exposure to DWCNTs 261 The aim of the sixth experiment was to investigate how a chronic exposure to DWCNTs affects the 262 slime mould performance. Twenty slime moulds were exposed chronically to DWCNTs or CMC control 263 treatment (see bioaccumulation protocol) for 6 weeks. Each week, a circular sample of each slime 264 mould (Ø 13 mm) was introduced in the centre of a petri dish (Ø 55 mm) containing a layer of agar (1% 265 in tap water) (Figure

  DWCNTs did not accumulate over time, a certain quantity of DWCNTs were retained in 317 the slime moulds. Thus, we next examined if DWCNTs altered the induction of the dormant state and 318 the reactivation of the slime mould and if they persisted throughout the dormant state. All the slime 319 moulds could enter a dormant state and be re-activated after 6 months regardless of the treatment. 320 After one year, only 2 out of 40 slime moulds could not be re-activated, one for each treatment. Hence, 321 the DWCNTs did not alter the dormancy process. The number of static DWCNTs agglomerates dropped 322 drastically after the dormant state (p<0.001, Table

	314
	315	3. Persistence of pristine DWCNTs into the slime mould
	316	Even though

  . Despite a daily exposure to CNTs , no bioaccumulation was observed within the cell of P. polycephalum. Hence, 407 slime moulds were internalising and eliminating CNTs continuously for 6 weeks. Most living organisms, 408

	406	via food for 6 weeks
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  . As such, they could constitute an interesting model to test CNTs application in drug 464 delivery and medical imaging in future studies. SMARTCELL and the University of Toulouse and the Region Occitanie. A.D. and E.F. 469 acknowledge support by the MITI (CNRS) NTCELL grant.

	465 466 467 468 474 475 476 477	Acknowledgments: M.T. was supported by a grant from the "Agence Nationale de la recherche" reference no. ANR-17-CE02-0019-01-D. and E.F. wrote the manuscript. A.D. and E.F. secured funding. All authors gave final approval for publication. F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F F o r o r P e P e e r e r R e R v e v i e i e w w O O n O n O n O n O n O n l n y
		l l l l l l
		y y y y y y

470 471 Authors' contributions. M.T., A.D. and E.F. conceived and designed the experiments. E.F. carried out 472 chemical synthesis of the CNTs sample. M.T and M.M performed the experiments and collected the 473 data. M.T and A.D. performed the statistical analyses. M.T and A.D. produced the figures. M.T., A.

Table S1 : Statistics associated to Figure 2 (A).

 S1 To assess the difference in internalization of DWCNTs between the three routes of exposure; we used a generalized linear mixed model (function glmer, error family: Poisson, Package lme4). The model was fitted by specifying the fixed effect: treatment (categorical predictor, routes of exposure) and the random effect: Replicate (slime mould identity). Pairwise comparisons were conducted using the package emmeans (method= pairwise).

				Internalisation: Number of static agglomerates Internalisation: Number of circulating agglomerates
	Predictors Predictors		Incidence Rate Ratios Incidence Rate Ratios	CI CI	Z value Z value	p p	df df
	Intercept Intercept			3.06 0.43	1.69 -5.56 0.25 -0.75 -2.98 3.68	<0.001 2338.00 0.003 306.00
	Manon Ternois 1,2 , Maxence Mougon 1,2 , Emmanuel Flahaut 2* and Audrey Dussutour 1* † [Environment] vs [Food] 0.17 0.08 -0.38 -4.42 <0.001 2338.00 [Environment] vs [Food] 1.53 0.72 -3.27 1.10 0.271 306.00
	[Environment] vs [Topical] [Environment] vs [Topical]		0.01 0.10	0.01 -0.03 -10.12 <0.001 2338.00 0.03 -0.29 -4.25 <0.001 306.00
	Random Effects Random Effects
	σ 2 σ 2			1.46 1.56
	τ 00 Replicate τ 00 Replicate	F F		1.10 1.14
	ICC N Replicate ICC N Replicate	o r o r	0.43 48 0.42 62
	P e e r 2342 Marginal R 2 / Conditional R 2 Observations 0.543 / 0.740 Internalisation: Number of static agglomerates Pairwise comparisons Observations 310 P r e e Marginal R 2 / Conditional R 2 0.345 / 0.622
	Predictors [Environment] vs [Food] [Environment] vs [Topical] [Food] vs [Topical] Predictors [Environment] vs [Food] [Environment] vs [Topical] [Food] vs [Topical]	R e v i e w Estimate SE z.ratio 1.76 0.397 4.42 4.36 0.430 10.12 2.60 0.395 -6.58 Internalisation: Number of circulating agglomerates p <0.001 2338.00 df <0.001 2338.00 <0.001 2338.00 R Pairwise comparisons e Estimate SE Z.ratio p df v -0.43 0.387 -1.10 0.513 2338.00 i e 2.32 0.546 4.24 <0.001 2338.00 w 2.75 0.547 5.02 <0.001 2338.00
					O n l O n l
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Table S2 : Statistics associated to Figure 2 (B).

 S2 To assess the difference in internalization of DWCNTs between the three routes of exposure; we used a generalized linear mixed model (function glmer, error family: Poisson, Package lme4). The model was fitted by specifying the fixed effect: treatment (categorical predictor, routes of exposure) and the random effect: Replicate (slime mould). Pairwise comparisons were conducted using the package emmeans (method= pairwise).

			Accumulation: Number of static agglomerates
	Predictors		Incidence Rate Ratios	CI	Z value	p	df
	Intercept		19.01	18.01 -20.07 106.82 <0.001 781.00
	[Time] (Week)		0.98	0.95 -1.01	-1.25	0.213 781.00
	Random Effects
	σ 2		0.05
	τ 00 Replicate		0.01
	ICC		0.10
	N Replicate	F	9
	Observations Marginal R 2 / Conditional R 2 o r 784 0.002 / 0.101
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Table S3 : Statistics associated to Figure 3 (A).

 S3 To assess accumulation of DWCNTs throughout the weeks; we used a generalized linear mixed model (function glmer, error family: Poisson, Package lme4). The model was fitted by specifying the fixed effect: Time (categorical predictor: Week1 vs Week 6) and the random effect: Replicate (slime mould identity).

				Persistence: Number of static agglomerates
	Predictors	Incidence Rate Ratios	CI	Statistic	p	df
	Intercept			0.40	0.31 -0.50	-7.92 <0.001 2507.00
	[Time] (after/before dormancy)		47.83	42.85 -53.39 68.95 <0.001 2507.00
	[Week]			1.02	0.99 -1.05	1.10	0.272 2507.00
	Random Effects
	σ 2		0.48
	τ 00 Replicate		0.24
	ICC	F	0.33
	N Replicate Observations	o r	20 2511
	P Marginal R 2 / Conditional R 2 0.817 / 0.878
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	Predictors Intercept [Week]	Estimates -0.01 -0.04	Area Statistic p -0.21 -0.19 -0.10 0.920 606.00 CI df -0.20 -0.12 -0.47 0.639 606.00 e v i e w
	Random Effects σ 2 τ 00 Replicate ICC N Replicate	0.95 0.06 0.06 9		O n l y
	Observations	610
	Marginal R 2 / Conditional R 2 0.000 / 0.063
	Table S4: Statistics associated to Figure 3 (B).
	To assess the difference in agglomerates area throughout the weeks; we used a linear model
	(function lmer, Package lme4). We used linear mixed model (function lmer, error family:
	Gaussian, Package lme4). The model was fitted by specifying the fixed effects: week
	(continuous predictor, week 1 to week 6) and the random effect: Replicate (slime mould
	identity). The dependent variable (area) was normalized using the function bestNormalize
	(Package bestNormalize).

Table S5 : Statistics associated to Figure 3

 S5

		Excretion: Number of static agglomerates
	Predictors	Incidence Rate Ratios	CI	Z value	p	df
	Intercept	0.32	0.05 -1.99 -1.22	0.223	1625.00
	[Time] (Hours)	0.93	0.89 -0.96 -3.92 <0.001 1625.00
	Random Effects
	σ 2	5.62
	τ 00 Replicate	4.52
	ICC	0.45
	N Replicate	72
	Observations	1628
	Marginal R 2 / Conditional R 2	0.177 / 0.544

(C).

To assess persistence of DWCNTs after the dormancy period; we used a generalized linear mixed model (function glmer, error family: Poisson, Package lme4). The model was fitted by specifying the fixed effects: Time (categorical predictor: After vs Before dormancy), Week (categorical factor: Week 1 vs Week 6) and the random effect: Replicate (slime mould identity).

Table S6 : Statistics associated to Figure 4

 S6

			Excretion: Number of circulating agglomerates
	Predictors		Incidence Rate Ratios	CI	Z value	p	df
	Intercept		2.76	0.54 -14.23	1.21	0.225	349.00
	[Time] (Hours)		0.91	0.88 -0.95	-4.74 <0.001 349.00
	Random Effects
	σ 2		4.72
	τ 00 Replicate		2.40
	ICC N Replicate Observations	F o r	0.34 72 352
	P Marginal R 2 / Conditional R 2 0.329 / 0.555
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(A).

To assess excretion of static DWCNTs agglomerates throughout time; we used a generalized linear mixed model (function glmer, error family: Poisson, Package lme4). The model was fitted by specifying the fixed effect: Time (continuous predictor) and the random effect: Replicate (slime mould identity).

Table S7 : Statistics associated to Figure 4

 S7

			Migration speed -Route of Exposure: Food
	Predictors	Estimates	CI	Statistic	p	df
	Intercept	0.25	0.03 -0.46	2.29	0.024	158.00
	[Treatment]	-0.50	-0.80 --0.19	-3.23	0.001	158.00
	Observations	160
	R 2 / R 2 adjusted	0.062 / 0.056
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	Predictors Intercept [Week]	Estimates 0.13 -0.01	Area Statistic p -0.72 -0.99 0.30 0.760 275.00 CI df -0.04 -0.02 -0.37 0.714 275.00 e v i e w
	Random Effects σ 2 τ 00 Replicate	0.94 0.07		O n l
	ICC	0.07		y
	N Replicate	17
	Observations	279
	Marginal R 2 / Conditional R 2 0.001 / 0.071
	Table S8: Statistics associated to Figure 4 (D).
	To assess the difference in agglomerates area through time; we used a linear model (function
	lmer, Package lme4). We used linear mixed model (function lmer, error family: Gaussian,

(B).

To assess excretion of circulating DWCNTs agglomerates throughout time; we used a generalized linear mixed model (function glmer, error family: Poisson, Package lme4). The model was fitted by specifying the fixed effect: Time (continuous predictor) and the random effect: Replicate (slime mould identity). Package lme4). The model was fitted by specifying the fixed effects: week (continuous predictor, week 1 to week 6) and the random effect: Replicate (slime mould identity). The dependent variable (area) was normalized using the function bestNormalize (Package bestNormalize).

Table S9 : Statistics associated to Figure 5 (A).

 S9 To assess the difference in migration speed between the two treatments when the route of exposure was food; we used a linear model (function lm, Package lme4). The model was fitted by specifying the fixed effect: treatment (categorical predictor, NTC vs Control). The dependent variable (migration speed) was normalized using the function bestNormalize (Package bestNormalize).

		Migration speed -Route of Exposure: Environment
	Predictors	Estimates	CI	Statistic	p	df
	Intercept	0.04	-0.18 -0.26	0.35	0.726	158.00
	[NTC vs Control]	-0.08	-0.39 -0.23	-0.49	0.626	158.00
	Observations	160
	R 2 / R 2 adjusted	0.002 / -0.005

Table S10 : Statistics associated to Figure 5 (B).

 S10 To assess the difference in migration speed between the two treatments when the route of exposure was Environment; we used a linear model (function lm, Package lme4). The model was fitted by specifying the fixed effect: treatment (categorical predictor, NTC vs Control). The dependent variable (migration speed) was normalized using the function bestNormalize (Package bestNormalize).

		Migration speed -Route of Exposure: Topical
	Predictors	Estimates	CI	Statistic	p	df
	Intercept	0.03	-0.19 -0.25	0.29	0.774	158.00
	[NTC vs Control]	-0.06	-0.38 -0.25	-0.41	0.685	158.00
	Observations	160
	R 2 / R 2 adjusted	0.001 / -0.005
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Table S11 :

 S11 Statistics associated to Figure 5 (C).To assess the difference in migration speed between the two treatments when the route of exposure was Topical; we used a linear model (function lm, Package lme4). The model was fitted by specifying the fixed effect: treatment (categorical predictor, NTC vs Control). The dependent variable (migration speed) was normalized using the function bestNormalize (Package bestNormalize).

			Latency first pseudopod
	Predictors	Estimates	CI	Statistic	p	df
	Intercept	0.04	-0.22 -0.31	0.32	0.747	474.00
	[Treatment: NTC vs Control]	1.08	0.70 -1.46	5.58	<0.001	474.00
	[Week]	-0.10	-0.16 --0.03	-2.82	0.005	474.00
	[Treatment*Week]	-0.14	-0.24 --0.05	-2.98	0.003	474.00
	Random Effects
	σ 2	0.80
	τ 00 Replicate	0.03
	ICC	0.03
	N Replicate	40
	Observations	480
	Marginal R 2 / Conditional R 2	0.179 / 0.206

Table S12 : Statistics associated to Figure 6 (A).

 S12 To assess the difference in latency to the first pseudopod between the two treatments; we used a linear mixed model (function lmer, error family: Gaussian, Package lme4). The model was fitted by specifying the fixed effects: treatment (categorical predictor, NTC vs Control), week (continuous predictor, week 1 to week 6) and the random effect: Replicate (slime mould identity). The dependent variable (migration speed) was normalized using the function bestNormalize (Package bestNormalize).

					Expansion rate
	Predictors			Estimates	CI	Statistic	p	df
	Intercept			-0.40	-0.67 --0.12	-2.83	0.005	474.00
	[Treatment: NTC vs Control]		-0.44	-0.83 --0.05	-2.22	0.026	474.00
	[Week]			0.16	0.09 -0.23	4.63	<0.001	474.00
	[Treatment*Week]		0.03	-0.07 -0.12	0.52	0.601	474.00
	Random Effects σ 2	F		0.87
	τ 00 Replicate ICC	o r	0.02 0.02
	N Replicate	P	40
	Observations Marginal R 2 / Conditional R 2	480 e r e 0.121 / 0.137
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Table S13 : Statistics associated to Figure 6 (B).

 S13 To assess the difference in expansion rate between the two treatments; we used linear mixed model (function lmer, error family: Gaussian, Package lme4). The model was fitted by specifying the fixed effects: treatment (categorical predictor, NTC vs Control), week (continuous predictor, week 1 to week 6) and the random effect: Replicate (slime mould identity). The dependent variable (expansion rate) was normalized using the function bestNormalize (Package bestNormalize).

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URL: http://mc.manuscriptcentral.com/tnan

Research Centre on Animal Cognition (CRCA), Centre for Integrative Biology (CBI), Toulouse University, CNRS, UPS, Toulouse 31062, France. 1 Centre Interuniversitaire de Recherche et d'Ingénierie des Matériaux (CIRIMAT), Toulouse University, CNRS, UPS, Toulouse 31062, France.