Shiteng Mi

Alaa Fahs

Gábor Molnár

William Nicolazzi
email: william.nicolazzi@lcc-toulouse.fr

Azzedine Bousseksou
email: azzedine@bousseksou@lcc-toulouse.fr

Molecular mechanics simulations of lattice dynamical properties of the Spin Crossover complex [Fe(pyrazine)][Ni(CN)4]

An empirical molecular mechanics force field has been built for the [Fe(pyrazine)][Ni(CN)4] spin crossover complex using Raman spectroscopic and nuclear inelastic scattering data. Based on this force field, molecular dynamics simulations were conducted to investigate the lattice dynamical properties of the complex in the high-spin and low-spin states. The extracted thermodynamic and mechanical parameters match well with the corresponding experimental data. Most importantly, the force field can successfully reproduce the low-frequency acoustic part of the partial densities of vibrational states, which is vital for the understanding the thermodynamic analysis of the spin crossover phenomenon in crystalline solids and which would be difficult to tackle by quantum mechanical methods.

Introduction

The metal-organic framework compounds [Fe(pyrazine)][M(CN)4] (M=Ni, Pd, Pt) exhibit a change in their electronic configurations between the low spin (LS) and high spin (HS) states accompanied by drastic modifications of electronic, magnetic, optical, vibrational and mechanical properties. Since the first report of this family of spin crossover (SCO) complexes by Real et al. [1], they have been extensively studied for their appealing room temperature spin transition, which is also associated with a large hysteresis [2][3][4][5][6][7][8][9]. They were also used to investigate finite size [10][11][12][13][14][15][16] and matrix effects [17] on the SCO phenomenon at the nanometric scale. Moreover, the adsorption of guest molecules in [Fe(pyrazine)][M(CN)4] has also aroused great interests due to the relatively large accessible volume for guests and the strong interplay between the guest adsorption and SCO phenomena [18][19][20][21][22][23][24][25][26].

From the onset of the work on this appealing family of complexes, much attention has been paid to their lattice dynamics, since it is well accepted that the knowledge of vibrational and mechanical properties have paramount importance in the understanding of the various physico-chemical phenomena governing the spin state change, in particular at the solid state [27,28]. In particular, Raman scattering and infrared (IR) absorption spectroscopic experiments at variable temperature(s) [29,30] and pressure(s) [31] allowed for a detailed assignment of the optical modes and for an estimation of their contributions to the vibrational entropy change, which is known to be the primary driving force of the thermally induced SCO. Remarkably, solid-state NMR and neutronscattering experiments performed on the compound [Fe(pyrazine)][Pt(CN)4] revealed also a ca. 10% rotational entropy contribution of the pyrazine ligands [32], which was also substantiated later using molecular dynamics (MD) simulations [33,34]. More subtle details on the dynamics and ordering of pyrazine ligands, including the interplay with guest molecules, were recently assessed using combined inelastic neutron scattering and neutron diffraction experiments as well as density functional theory (DFT) calculations [35]. In addition, nuclear inelastic scattering (NIS) experiments were carried out to study the partial (Fe) vibrational density of states (vDOS) of As mentioned above, in the field of numerical simulations, DFT methods have been successfully employed to simulate the vDOS of [Fe(pyrazine)][Pt(CN)4] [35,38]. In addition, a combination of force field parametrization through DFT calculations with atomistic molecular dynamics simulations has been employed to investigate time-resolved lattice dynamics of multinuclear chain-like triazole Fe (II) complexes [39]. Although such quantum mechanics calculations can reach a high accuracy, the computational time can be quickly unacceptable. A direct consequence of this technical limitation is that, in general, only zone-center (-point) phonon frequencies are reported and most studies limit their focus to optic modes only. However, acoustic phonons are known to play a critical role in driving collective phenomena (phase transitions) in SCO materials [40] and, generally speaking, low-frequency phonons have key importance in governing the HS/LS phase stability through both entropic and elastic contributions [40]. Hence, in order to fully understand the SCO behavior, contributions from all phonons must be considered, which would imply mapping the entire Brillouin zone. Such detailed investigation of the phonon spectrum, including phonon dispersion curves, remains at present a remarkable challenge on SCO materials with complex structures -both experimentally (inelastic neutron scattering) and computationally (quantum mechanics).

In the interim, research has been focused on techniques, which give direct access to the low-frequency phonon DOS without the necessity of measuring full dispersion relations.

Experimentally this was achieved using NIS [36][37][38]. The vDOS of simplified model systems was also assessed using MD simulations [41,42]. However, such model systems just catch the main features of the octahedral coordination of SCO materialsall the more that the parametrization of the force field is based on experimental data collected for specific compounds.

In the present study, a new force field of [Fe(pyrazine)][Ni(CN)4] for the LS and HS states has been constructed from Raman and NIS spectroscopic data. This force field, including simple harmonic and Lennard-Jones type interactions, is thus designed to investigate lattice dynamical properties of bulk [Fe(pyrazine)][Ni(CN)4] in the two spin states. Notably, we show that it can be used to predict the spin-state dependent values of sound velocity, vibrational entropy and other properties, which stem primarily from the low-frequency part of the vDOS.

Computational approach

Model and method

We study a three-dimensional (3D) network of [Fe(pyrazine)][Ni(CN)4] [1,43].

According to the experimental data [1,43], the size of the LS and HS tetragonal unit cells is 7.013×7.013×6.776 Å 3 and 7.257×7.257×7.256 Å 3 , respectively. As shown in Fig. 1(a), all the atoms are bonded by pair harmonic potentials with different force constants. The bond lengths based on the experimental data [24] and force constants are displayed in Table 1. It should be noted that, the Fe-NN-C and Fe-Ncycle bond lengths are set as 1.944 and 1.973 Å for the LS structure (resp. 2.117 and 2.213 Å for the HS structure). It brings out an increase of about 14.7% of the unit cell volume [24]. For a sake of simplification, all the bond lengths in pyrazine are set to be 1.415 Å.

In order to stabilize the structure, as indicated in Fig. 1 (b) and (c), 3-body harmonic angular potentials (angles 1, 2 … 7) are introduced in the force field. Based on the crystallographic space group P4/mmm [24], angles 1, 4 and 6 are set as 90°; angles 2 and 3 are set as 180°, while angles 5 and 7 are set as 120° for simplification. Furthermore, we also introduce 4-body improper potentials (Ⅰ and Ⅱ in Fig. 1 (b) and(c)) and a dihedral potential (Ⅲ in Fig. 1 (c)) in order to keep atoms in the same plane. Such 4-body potentials are much weaker than bond potentials and angular potentials, while they play an important role in the low frequency vibrations. The corresponding improper angles Ⅰ and Ⅱ are both set as 180°.

According to the above setting, the total potential energy (Epotential) can be described in the usual way [44] as:

𝐸 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 = ∑ 1 2 𝐾 𝑏 [(𝑏 -𝑏 0 ) 2 -(𝑏 𝑐 -𝑏 0 ) 2 ] 𝑏𝑜𝑛𝑑𝑠 + ∑ 1 2 𝐾 𝜃 (𝜃 -𝜃 𝑜 ) 2 𝑎𝑛𝑔𝑙𝑒𝑠 + ∑ 1 2 𝐾 𝜑 (𝜑 -𝜑 𝑜 ) 2 𝑖𝑚𝑝𝑟𝑜𝑝𝑒𝑟𝑠 + ∑ 1 2 𝐾 ∅ (1 -cos ∅) 𝑑𝑖ℎ𝑒𝑑𝑟𝑎𝑙𝑠 + 𝜖 𝑖𝑗 [( 𝜎 𝑖𝑗 𝑟 𝑖𝑗 ) 12 -2 ( 𝜎 𝑖𝑗 𝑟 𝑖𝑗 ) 6 ], (1)
where 𝐾 𝑏 , 𝐾 𝜃 , 𝐾 𝜑 and 𝐾 ∅ stand for the force constants of the 2-body bond potential, 3-body angular potential, 4-body improper potential and 4-body dihedral potential, respectively. 𝑏, 𝜃, 𝜑 and ∅ are the bond length, angle, improper angle and dihedral angle, respectively, and the subscript 'o' refers to their equilibrium value. The zerocrossing 𝑏 𝑐 can be roughly estimated by 0.84𝑏 𝑜 [45]. To describe the non-bonding interaction, a 12-6 Lennard-Jones potential (last term of Eq. ( 1)) is selected in this work. rij is the instantaneous distance between atoms i and j, 𝜎 𝑖𝑗 is the van der Waals bond length and 𝜖 𝑖𝑗 corresponds to the well depth of this anharmonic potential.

We now turn to define the force constants of harmonic potentials and establish relations with experimentally accessible physical quantities. Based on Raman spectroscopy data of [Fe(pyrazine)][Ni(CN)4] [30], the bond potential force constants can be obtained from the harmonic oscillator approximation [46]:

𝐾 𝑏 = 4𝜋 2 𝑐 2 𝜇 𝑁 𝑜 𝜔 𝑒 2 , (2)
where 𝑁 𝑜 is the Avogadro number, c is the velocity of light and 𝜔 𝑒 is the harmonic frequency from Raman experiment. 𝜇 is the reduced mass, defined by:

𝜇 = 𝑚 𝑖 𝑚 𝑗 𝑚 𝑖 +𝑚 𝑗 , (3)
where mi and mj are the masses of atoms i and j, respectively.

To our best knowledge, there is no effective method in literature to obtain angular, dihedral and improper force constants from experimental data. Thus the harmonic angular force constants of [Fe(pyrazine)][Ni(CN)4] are first estimated by [44] 𝐾 𝜃 = 664.12

𝑍 𝑖 * 𝑍 𝑘 * 𝑟 𝑖𝑘 5 [3𝑟 𝑖𝑗 𝑟 𝑗𝑘 (1 -cos 2 𝜃 𝑜 ) -𝑟 𝑖𝑘 2 cos 𝜃 𝑜 ], (4)
where 𝑍 𝑖 * and 𝑍 𝑘 * are the effective charges of the atoms i and j, respectively. rik is the distance between the atoms i and k. These force constants are further adjusted to fit the partial vDOS from NIS experiments. The optimized angular, dihedral and improper force constants are summarized in Table 2.

For the non-bonding potential, which models the long-range interaction within the structure, the parameters in reference [44] are directly used in this work. It should be pointed out that the non-bonding interaction energies are only calculated between atoms separated by more than two bonds to avoid non-physical strong short-range interactions.

Indeed, at very short distances, long-range interactions could combine with pairwise harmonic potential, leading to an unphysical increase of the bond rigidity and causing an instability of the structure resulting in its collapse. The parameters of the nonbonding potential are shown in Table 3, wherein 𝜎 𝑖𝑗 is obtained by:

𝜎 𝑖𝑗 = √ 𝜎 𝑖 × 𝜎 𝑗 , (5)
Where 𝜎 𝑖 and 𝜎 𝑗 are the atomic van der Waals distances of the atoms i and j, respectively. And 𝜖 𝑖𝑗 is obtained by:

𝜖 𝑖𝑗 = √ 𝜖 𝑖 × 𝜖 𝑗 , (6)
Where 𝜖 𝑖 and 𝜖 𝑖 are the atomic van der Waals energies of the atoms i and j, respectively. Finally, 𝜋-stacking have not been considered in the force field since the averaged pyrazine-pyrazine distance is approximatively 7 Å, which is large enough to avoid strong interactions between pyrazine rings, since the interaction energies described by the Lennard-Jones potential are close to zero at such distance. structures are initially optimized at 0 K through the conjugate gradient method [48].

Molecular dynamics simulations

MD
Then, the temperatures are fixed at 100 K and 300 K for the LS and HS states, respectively, using the deterministic Nose-Hoover thermostat [49,50]. Each structure is relaxed under canonical ensembles (NVT) for 150000 timesteps and the velocity Verlet algorithm is used to integrate the equations of motion [51]. Finally, the velocity autocorrelation function (𝛾(𝜏)) is collected during 4000 timesteps and the partial vDOS (g ̃(𝐸) ) of Fe is derived through a suitable Fourier transformation as described in reference [41].

Results and discussion

The parameters of the force field were obtained by comparison with experimental vibrational spectra of [Fe(pyrazine)][Ni(CN)4] and by empirical methods.

In a first step, the force constants of harmonic bond potentials, as displayed in Table 1, were derived from Raman spectra using equation (2). For this latter, no adjustment of the experimental values has been realized since, within the scope of the present study, the harmonic pairwise potential defined by the first term of equation ( 1))

gives a relevant description of the stretching process of chemical bonds [46]. Obviously, The force constants of harmonic angular potential are initially roughly estimated by the method described in section 2. The values are then further adjusted by trial-anderror to fit the partial vDOS from NIS experiment. Table 2 displays the optimized force constants as well as the first estimates. As expected, large differences between the LS and HS states can be found for angles related to the FeN6 octahedra. Yet, for a better match with the experimental vDOS a spin-state dependence of the force-field of the [Ni(CN)4] 2-moiety had to be also implemented. Table 2 gather also the parameters of the dihedral and improper potentials. Generally speaking, the force constants of improper potentials are lower than those of bonding and angular potentials.

The optimized partial vDOS of Fe is compared with the experimental vDOS spectra [36,37] in Fig. 2. Overall, the shape of the vDOS spectrum in the LS state is substantially different from that of the HS state -primarily due to the structural differences of the FeN6 octahedra between the two spin states. In comparison with a previous work [42], in which a simple cubic SCO model structure was investigated, the asymmetry of the vDOS curves between the LS and the HS states is retrieved since the redshift) of the HS peak (resp. LS peaks) can be denoted, i.e. the effect of the SCO is less marked in the simulations. In addition, the width of these peaks is sharper in the simulated spectra, most likely due to the harmonic character of the pairwise potentials and the lack of disorder and defects (e.g. guest molecules) in the theoretical study.

The vDOS is of fundamental importance for the investigation of lattice dynamics. Its knowledge provides informations on the Debye sound velocity (𝑣 𝐷 ), the vibrational entropy (s), the mean force constant (⟨𝐶⟩) and the vibrational internal energy (𝑢) [52],

according to the following equations, which are mainly related to the area under the vDOS curves. In particular the expression of the Debye sound velocity is:

𝑣 𝐷 = lim 𝐸→0 ( 𝑚 ρ 1 2𝜋 2 ħ 3 𝐸 2 𝑔 ̃(𝐸) ) 1 3 ⁄ , (7)
where 𝑚 ̃ is the average mass of oscillating atoms, ħ the Planck constant, E the energy, 𝑔 ̃(𝐸) the density of vibrational states, 𝜌 = 𝑁〈𝑀〉 𝑉 the mass density, N the number of atoms in the primitive cell, V the volume of the primitive cell, and 〈𝑀〉 the average mass of the primitive cell atoms. The vibrational entropy can be expressed as:

𝑠 = 3𝑘 𝐵 ∫ 𝑔 ̃(𝐸) [ 𝛽𝐸 2 𝑒 𝛽𝐸 +1 𝑒 𝛽𝐸 -1 -𝑙𝑛 (𝑒 𝛽𝐸 2 -𝑒 -𝛽𝐸 2 )] +∞ 0 𝑑𝐸, (8)
where 𝑘 𝐵 is the Boltzmann constant, T the temperature and 𝛽 = 1/(𝑘 𝐵 𝑇). Finally, the mean force constant ⟨𝐶⟩ and the vibrational internal energy 𝑢 can be written as follows:

⟨𝐶⟩ = 𝑚 ħ2 ∫ 𝑔 ̃(𝐸) ∞ 0 𝐸 2 𝑑𝐸, (9)
u= 3 2 ∫ 𝑔 ̃(𝐸)𝐸 +∞ 0 𝑒 𝛽𝐸 +1 𝑒 𝛽𝐸 -1 𝑑𝐸, (10)
The computational results are summarized in Table 4 together with the experimental data taken from ref. [37].

To characterize the lattice rigidity, the Debye sound velocity was extracted from the low frequency part (acoustic modes) of the vDOS, where the conditions for the application of continuum mechanics and linear elastic theory are fulfilled (large wavelength in comparison with lattice parameters). It results in the validity of the Debye approximation [52], which can be inferred from a nearly constant value for the ratio g ̃(𝐸)/E 2 at low energies as shown in Fig. (3). When going from the LS to the HS state, the Debye sound velocity downshifts from 2083 to 1820 m/s. The decrease of 𝑣 𝐷 upon the LS to HS transition indicates that the HS phase is softer than the LS phase, as it can be expected. We note the good match with the experimental observations (Table 4) providing confidence in the parametrization of the force field with respect to the critical region of acoustic modes.

Conclusions

Compared to ab initio simulation, this new force field is quite simplified by the use of harmonic interactions and less heavy in terms of computational implementations and computational times. Nevertheless, it appears to be able to reasonably simulate the whole vDOS spectra, from the low (acoustic) to the high (optical) energies with an acceptable accuracy. This numerical approach constitutes thus a good compromise for the investigations of lattice dynamics of SCO materials, between qualitative spring-ball models, providing only general trends, and ab initio calculations, providing quantitative vibrational properties of SCO solids only at a very high computational cost.

Undoubtedly, the success of the force field parametrization in this work mainly comes from a sufficiently large set of accurate experimental data and a legitime question is how generally applicable is the proposed parametrization. In this context, considerable progress have been achieved in the parametrization of force-field applied to materials where experimental input is less rich than in the present case, based on ab-initio calculations [53].

A very important point is the ability of this approach to quantitatively model the acoustic part of the vDOS, which is not easily accessible with ab initio calculations or experiments. The simulation of the mechanical properties and the collective vibrational modes is essential to grasp cooperative mechanisms at the origin phase transitions in these materials. In addition, the knowledge of thermo-mechanical quantities is essential for the study of size reduction effects on the switching properties of SCO materials. The study of size effects represents thus an immediate prospect for the present work.

[

  Fe(pyrazine)][Ni(CN)4] [36,37] and [Fe(pyrazine)][Pt(CN)4] [38], completed with DFT calculations of the vibrational spectra. Through the analysis of the vDOS, a number of thermodynamical and thermoelastic quantities could be assessed, such as the vibrational internal energy and entropy, the mean force constant or the Young's modulus.

  calculations of [Fe(pyrazine)][Ni(CN)4] were conducted through the wellestablished software of Large-scale Atomic/Molecular Massively Parallel Simulator package (LAMMPS) [47]. [Fe(pyrazine)][Ni(CN)4] structures with 20×20×20 unitcells are built in the LS and HS states, which corresponds to simulation box volumes of 140.26×140.26×135.52 Å 3 and 145.15×145.15×145.12 Å 3 , respectively. In each simulation, periodic boundary conditions are applied along the x, y and z directions in order to simulate a bulk material and to avoid the contributions of surface effects. Both

  Fe-N force constants display a huge change at the spin transition, but one can note changes of all force constants related to bonds in the 2D sheet, formed by [Ni(CN)4] 2- and Fe 2+ ions. Nevertheless, we shall stress that the changes in the force field of the [Ni(CN)4] 2-moiety remain just a work hypothesis as other effects may also account for the Raman frequency shifts (e.g. mode coupling).

  anisotropy of the structural changes upon the SCO in the compound [Fe(pyrazine)][Ni(CN)4] is considered in this force field. In Fig.2(b), several peaks, identified as Fe-ligand stretching vibrational modes can be observed in the optical part of the vDOS spectra, above ca. 20 meV, which are also well recognizable in the experimental vDOS. Notably, an intense, broad peak is observed for the HS structure, located around 35 meV, while several peaks appear in the LS state at 28, 33, 40.5, 42, 54 and 60 meV. Although the vibrational frequency values are not accurate, they are close to the experimental observation (< ± 5 meV). In particular, a blueshift (resp.

  The vibrational entropy, whose definition takes mainly into account the low frequency part of the spectrum, increases upon a LS to HS transition in coherence with the overall softening of the HS lattice. The numerical and the experimental values of 𝑠 almost coincide, which reinforces our belief that the force field is able to model the low frequency vDOS of SCO materials. The mean force constant and vibrational internal energy have been also extracted from the vDOS for both spin states. These two quantities characterize the optical modes since according to their definitions, the main contribution to ⟨𝐶⟩ and 𝑢 come from the high-energy part of the vDOS. The extracted values for 𝑢 match well the experimental observations. However, some discrepancy can be observed for the mean force constants ⟨𝐶⟩ . The redshift of the calculated optical modes in the LS state may explain the lower numerical value for ⟨𝐶⟩ in comparison with experimental results. In the HS state, however, the computed blueshift should result in a stiffening of the local environment of iron. Yet, the vibrational modes located between 5 -20 meV (corresponding to a complicated combination of inter-and intra-molecular vibrational modes) apparently give nonnegligible contributions, leading to a lower numerical value of the mean force constant if compared with the extracted experimental value. Nevertheless, the numerical ratio ( 69 is close to the experimental one (

Fig 1 .

 1 Fig 1. (a) Schematic representation of the [Fe(pyrazine)][Ni(CN)4] structure. (b -c) Schematic representation of the force field with the different interactions considered in this work. The lines between two atoms stand for the chemical bonds, which are painted in the color of the corresponding atoms.

Fig 2 .

 2 Fig 2. Partial (Fe) densities of vibrational states in the LS and HS states obtained (a) from NIS experiments [37] and (b) from MD simulations performed in this work.

Fig 3 .

 3 Fig 3. g ̃(𝐸)/E 2 as a function of E calculated for the LS and HS states. The dashed lines are the linear fits at lowest energies -showing the validity range of the Debye approximation.

Table 1

 1 Harmonic bond-stretching force constants and bond lengths for the LS and HS states used in the simulations.

	Bond	LS		HS
		Force constants	Bond lengths	Force constants	Bond lengths (Å)
		(kcal/mol•Å 2 )	(Å)	(kcal/mol•Å 2 )
	C-N	2512	1.158	2392	1.158
	C-Ni	220	1.857	180	1.857
	Fe-NN-C	128	1.944	50	2.117
	Fe-Npz	96	1.973	28	2.213
	Cpz-Cpz	866	1.415	866	1.415
	Cpz-Npz	780	1.415	780	1.415

Table 3

 3 Parameters for the nonbonding potential used in the simulations.

	Element	εi (kJ/mol)	σi (Å)
	Ni	0.015	2.834
	Fe	0.013	2.912
	C	0.105	3.815
	N	0.069	3.660

Table 4

 4 Comparison of Debye sound velocity (𝑣 𝐷 ), mean force constant (⟨𝐶⟩), vibrational entropy (s) and vibrational internal energy (u) of the LS and HS forms obtained in the present study and from NIS experiments[37].

		LS		HS
		This work	NIS	This work	NIS
	𝑣 𝐷 (m/s)	2083	2063 ± 27	1820	1933 ± 20
	⟨𝐶⟩(N/m)	292	331 ± 10	173	204 ± 10
	s (kB)	0.99	0.90	3.92	3.57
	u (meV)	53.3	56.0	84.8	85.8

Acknowledgments

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement N° 101019522). SM thanks the China Scholarship Council for a PhD grant.


Maxime Fuster
email: maxime.fuster@ojce.fr

Hermine Billard

Gisèle Bronner

Télesphore Sime-Ngando

Jonathan Colombet

Occurrence and seasonal dynamics of ALNs in freshwater lakes are influenced by their biological environment

Keywords:

Aster Like Nanoparticles (ALNs) are femtoentities, recently discovered in different aquatic environments, whose intrinsic nature and ecological features remain to be determined. In this study, we investigate the in-situ temporal dynamics of ALNs during one year in 3 different lakes, in relation with the physico-chemical and biological environment. ALN abundances in investigated lakes showed a marked seasonal dynamic (from no detectable to 4.28 ± 0.75 x 10 6 ALNs.mL -1 ), with characteristic peaks in spring. We recorded correlation between ALNs and some prokaryotic phyla suggesting a broad and non-specific relationship. From their seasonal dynamics and potential link with prokaryotes, we conclude that ALNs represent an important ecological actor in the functioning of aquatic ecosystems.

1/ INTRODUCTION

The ecological importance of planktonic communities closely depends on their diversity and the associated spatio-temporal dynamics, in response to environmental changes. In the past, the ecological importance of picoplankton (0.2-2 µm) [START_REF] Fogg | Review Lecture -Picoplankton[END_REF][START_REF] Callieri | Freshwater autotrophic picoplankton: a review[END_REF] and nanoplankton (2-20 µm) [3,[START_REF] Watson | Relationships between Nannoplankton and Lake Trophic Status[END_REF] making up the bulk of the so-called microbial loop has been studied extensively. Their roles in the flow of energy and matter on a global scale has become evident [START_REF] Pomeroy | The Microbial Loop[END_REF]. Highlighting the importance of viruses resulted in the inclusion of femtoplankton (< 0.2 µm) in ecological models [6][START_REF] Fuhrman | Marine viruses and their biogeochemical and ecological effects[END_REF][START_REF] Sime-Ngando | Virus et prophages dans les écosystèmes aquatiques[END_REF]. Viruses have long been considered as the main, if not the only, compartment of femtoplankton, mainly because of their interactions within the microbial loop where they shunt microbial particles into dissolved matter, i.e. the 'viral shunt' [START_REF] Jmcn | Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1[END_REF][START_REF] Jover | The elemental composition of virus particles: implications for marine biogeochemical cycles[END_REF]. However, in the recent years, the discovery of an unsuspected diversity of femtoplankton entities as Candidate Phyla

Radiation

(CPR), Diapherotrites-Parvarchaeota-Aenigmarchaeota-Nanoarchaeota-Nanohaloarchaeota (DPANN), Bio-Mineral Organic Particles (BMOPs), Extracellular Vesicles (EVs), and Aster Like Nanoparticles (ALNs), leads to a necessary reconsideration of the ecological role of femtoplankton compartment, at least through the study of the diversity and dynamics of their representatives (Colombet et al., [START_REF] Colombet | Femtoplankton: What's New?[END_REF]). Attempts to evaluate the quantitative and qualitative importance of CPR / DPANN [START_REF] Brown | Unusual biology across a group comprising more than 15% of domain Bacteria[END_REF]13], BMOPs [START_REF] Wu | Formation and characteristics of biomimetic mineralo-organic particles in natural surface water[END_REF], EVs [START_REF] Biller | Bacterial Vesicles in Marine Ecosystems[END_REF] underscores this necessity. Likewise, pioneering reports on ALNs also showed that these entities might have important ecological functions [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF][START_REF] Fuster | Trophic conditions influence widespread distribution of Aster-Like nanoparticles within aquatic environments[END_REF]. ALNs are original aster-like entities composed of C, O, Ca, N, K with distinct morphotypes depending on the number of arms (4, 11 and 20 arms) present and their budding (Fig 1). Their size (on average from 110 to 430 nm) and volume (less than 1.4x10 -3 µm 3 ) lower than the minimum required for life expression (Theoretical Minimal Cell Volume, i.e. 0.008 µm 3 ) [START_REF](US) Steering Group for the Workshop on Size Limits of Very Small Microorganisms[END_REF], together with their organic nature, their sensitivity to different biocidal treatments, and their capacity to change their numbers in the absence of potential hosts [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF], question their exact nature. The presence of nucleic acids remains to be proven [START_REF] Colombet | Femtoplankton: What's New?[END_REF][START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF]. However, (i) their ability to colonize different continental and coastal aquatic environments according to their trophic status [START_REF] Fuster | Trophic conditions influence widespread distribution of Aster-Like nanoparticles within aquatic environments[END_REF], (ii) their capabilities to produce bloom situations, with abundance peaks reaching up to 9.0 ± 0.5 × 10 7

ALNs.mL -1 [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF], and (iii) their putative functional interactions with prokaryotes [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF][START_REF] Fuster | Trophic conditions influence widespread distribution of Aster-Like nanoparticles within aquatic environments[END_REF] strongly suggest that they are potentially significant actors in the flow of energy and matter transiting in aquatic systems. To understand the quantitative and functional importance of ALNs in the environment and their exact nature, it seems to be crucial to realize ecological studies integrating temporal variations of ALNs in various ecosystems considering possible environmental forcing parameters.

For this purpose, we monitored the in-situ dynamic of ALNs as well as the physico-chemical and biological parameters in three lakes of the Massif Central, France, during a one-year period.

Our results indicate that ALNs are new dynamic players in the ecology of aquatic systems, with potential links with microbial phyla. 2°48'43''E ; 680 m altitude ; surface area 10.5 ha ; maximum depth 4.5 m). These lakes are in the French Massif Central (within a 120 km area). Fargette is a hyper eutrophic lake while St.

2/ MATERIALS & METHODS

2.1/ Study sites and sample collections

Gervais and Lapeyrouse are eutrophic lakes with a significant human presence. Five liters of water was collected at the surface (0-40cm) of the deepest point of the lake, using a sampling bottle (5L-Niskin-type, Bionef, Fr). St. Gervais has the particularity of having been emptied

during November, December 2018 and January 2019 and returned to its maximum depth in December 2019. During the empty period, samples were taken from a permanent puddle at the center of the lake supplied by the upstream source flowing into the lake. Monitoring of the number of Aster Like Nanoparticles (ALNs) in this upstream source was performed during all the survey, in addition to the lake. Samples of virus-like particles (VLPs), prokaryotes and Aster Like Nanoparticles (ALNs) were fixed with 1% (v/v) formaldehyde and stored at 4

2.2/ Abiotic parameters measurements

Dissolved oxygen content (mg.L -1 ), temperature (°Celsius) and conductivity (µSiemens.cm -1 )

were measured in-situ with a submersible probe (ProDSS YSI, Yellow Springs, Ohio, USA), while pH was measured in the laboratory (probe Thermo Scientific, Orion Star A111, Waltham, MA). Organic phosphorus, chlorides and total organic nitrogen were analyzed by UV-VIS spectrophotometry (NF ISO 15923-1).

2.3/ Biotic parameters measurements

2.3.1/ Phytoplanktonic communities

The biomasses of different groups of the Nanophytoplankton (i.e. Chlorophyceae, cyanobacteria, Cryptophyceae and diatoms) were inferred from in situ measurements of chlorophyll a, phycoerythrin and phycocyanin (μg.L -1 ) using a submersible spectrofluorometric probe (BBE FluoroProbe, Moldaenke GmbH, DE). Counts of picophytoplankton populations were determined by flow cytometry as described elsewhere [START_REF] Marie | An improved protocol for flow cytometry analysis of phytoplankton cultures and natural samples[END_REF] using a BD FACSCalibur (BD Sciences, San Jose, CA) equipped with an air-cooled laser, delivering 15 mW at 488 nm with the standard filter set-up.

2.3.2/ VLP, Prokaryote, Heterotrophic eukaryote and ALN counts

Counts of VLPs, prokaryotes and heterotrophic eukaryotes (HE) were performed in triplicates by flow cytometry according to Brussaard [START_REF] Brussaard | Optimization of procedures for counting viruses by flow cytometry[END_REF] and Christaki et al., [START_REF] Christaki | Optimized routine flow cytometric enumeration of heterotrophic flagellates using SYBR Green I[END_REF] respectively using a BD FACSAria Fusion SORP (BD Sciences, San Jose, CA) equipped with an air-cooled laser, delivering 50 mW at 488 nm with 502 longpass, and 530/30 bandpass filter set-up. ALN (and their different forms) detection and quantification were realized by transmission electron microscopy (TEM) using a Jeol JEM 2100-Plus microscope (JEOL, Akishima, Tokyo, Japan) operating at 80 kV and x50,000 magnifications as described in Colombet et al., [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF].

ALN/prokaryote ratios were determined in triplicate following uranyl salt contrasting of particles centrifuged at 18,000xg; 20 min; 14°C directly onto 400-mesh electron microscopy copper grids covered with carbon-coated Formvar film (Pelanne Instruments, Toulouse, France). The number of ALN results from the multiplication of this ratio-fold by prokaryote concentration obtained by cytometry.

2.3.3/ Diversity of microbial communities Nucleic acid extractions and amplifications

For each unfixed sample, microbial communities were collected on a 0.2 µm (Millipore) polycarbonate filter (until saturation, pressure < 25 kPa) and stored at -20°C until DNA extraction. The filters were covered with a lysing buffer (lysozyme 2 mg.ml -1 , SDS 0.5%, Proteinase K 100 µg.mL -1 and RNase A 8.33 µg.mL -1 in TE buffer pH 8) at 37°C for 90 minutes. A CTAB 10% / NaCl 5M solution was added, and the samples were incubated at 65°C for 30 minutes. The nucleic acids were extracted with phenol-chloroform-isoamyl alcohol (25:24:1) ; the aqueous phase containing the nucleic acids was recovered and purified by adding chloroform-isoamyl alcohol (24:1). The nucleic acids were then precipitated with a mixture of glycogen 5mg.mL -1 , sodium acetate 3M and ethanol 100% overnight at -20°C. The DNA pellet was rinsed with ethanol (70%), dried and dissolved in the TE buffer. DNA was then purified using a commercial kit (NucleoSpin® gDNA Clean-up, Macherey-Nagel).

For archae, amplification of the V4-V6 region of the small subunit rDNA was amplified using the universal archae 519F and 1017R primers [START_REF] Klindworth | Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies[END_REF] (Tab s1). For bacteria, the V4-V5 region of the bacterial small subunit rDNA was amplified using the universal bacteria 515F and 928R

primers [START_REF] Wang | Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies[END_REF] (Tab s1). In addition, the ITS region of fungi was amplified using common fungi primers ITS7f and ITS4r [START_REF] Ihrmark | New primers to amplify the fungal ITS2 region--evaluation by 454-sequencing of artificial and natural communities[END_REF] (Tab s1). Each PCR was performed in a total volume of 50µL containing 1x final reaction buffer, 2 mM MgCl2, 0.2mM dNTP, 100 µg.mL -1 BSA, 0.2 µM of each primer, 0.025 U.µL -1 PROMEGA GoTaq HotsStart G2 and 5 µL of DNA. The different PCR programs are detailed in the supplemental (Tab s1).

Amplicon analysis and taxonomic affiliation

Bacterial sequencing data were processed with the PANAM2 pipeline [START_REF] Taib | Phylogenetic Affiliation of SSU rRNA Genes Generated by Massively Parallel Sequencing: New Insights into the Freshwater Protist Diversity[END_REF]. Briefly, sequence reads were assembled with Vsearch [START_REF] Kim | Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes[END_REF], excluding reads <200pb, with ambiguous calls (N)

or having mismatch in the forward/reverse primer. Demultiplexed amplicons were clustered into OTUs using Vsearch at a 95% id threshold. Representative sequence of OTUs were compared against the SILVA SSURef 115 database restricted to sequences with length > 1,200 pb, quality score > 75% and a pintail value > 50. Archaea and Fungi sequencing data were analysed through the FROGs pipeline [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. Sequence reads were assembled with Vsearch [START_REF] Kim | Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes[END_REF],

excluding reads < 200pb, with ambiguous calls (N) or having mismatch in the forward/reverse primer. Demultiplexed amplicons were clustered into OTUs using Swarm [START_REF] Mahé | Swarm v3: towards tera-scale amplicon clustering[END_REF] with distance (d) = 1. OTUs sequences were compared against the SILVA_132_16s database for archaea and Unite_Fungi_8,0 database for fungi. Link between the log-transformed abundance of ALNs and prokaryotes was tested by linear regression using data obtained in the 3 lakes and those obtained from a previous study in Colombet et al., [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF]. Cross effects of measured parameters were tested by the omega-square method ω2 [START_REF] Olejnik | Measures of Effect Size for Comparative Studies: Applications, Interpretations, and Limitations[END_REF].

2.4/ Data analysis

3/ RESULTS

3.1/ Environmental parameters

3.1.2/ Standing stocks of Viruses like particles, prokaryotes, phytoplankton and heterotrophic eukaryotes

The variations of biological environment (Viruses Like Particles (VLPs), prokaryotes, phytoplankton and heterotrophic eukaryotes (HE)) between lakes and seasons are listed in

3.1.3/ Composition of communities

Metabarcoding of bacterial, archaeal, and fungal communities revealed the predominance of the same phyla, whatever the lake and the season (

3.2/ Aster-Like Nanoparticles distribution

As for productivity indicators (P, TOC, TN) and prokaryotes concentrations, significant differences between ALN anbundances (p < 0.01) were recorded between lakes (Table 1

-A).

As the above-mentioned parameters, the highest and lowest annual mean abundance of Aster Like Nanoparticles (ALNs) were recorded in lake Fargette (6.12 ± 11.7 x10 5 ALNs.mL -1 ) and Lapeyrouse (0.83 ± 1.99 x10 5 ALNs.mL -1 ). St. Gervais had an intermediate average value (2.32 ± 2.60 x10 5 ALNs.mL -1 ) (

3.3/ Interaction between Aster-like Nanoparticles and their environment

There was no significant correlation between ALNs and any of the measured physico-chemical parameters (Table 2). In contrast, significant correlations (p ≤ 0.05) were recorded between ALNs and their biotic environment, primarily with VLPs (r 2 = 0.47) and prokaryotes (r 2 = 0.32) (Table 2).

Linear regression of data including data obtained from a former study in lake Fargette No significant correlations were observed between ALNs and archaeal OTUs during the sampling period in the three lakes (Table 3). On the other hand, ALNs were significantly correlated (p < 0.01) with 5 bacterial phyla (Acidobacteria, Cyanobacteria, Firmicutes, Planctomycetes, Proteobacteria) and 2 fungal phyla (Ascomycota and Chytridiomycota) (Table 3).

4/ DISCUSSION

Here we report the seasonal fluctuations of Aster Like Nanoparticles (ALNs) concentrations in 3 different lakes. These entities were first described in 2019 [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF] and colonize different continental and coastal environments, reaching important concentrations in the order of 10 7

ALNs.mL -1 [START_REF] Fuster | Trophic conditions influence widespread distribution of Aster-Like nanoparticles within aquatic environments[END_REF]. We recently demonstrated that their distribution could be influenced by the trophic state of the environment [START_REF] Fuster | Trophic conditions influence widespread distribution of Aster-Like nanoparticles within aquatic environments[END_REF]. These pioneering works result in a partial understanding of the ecology of ALNs. Information on their spatial and seasonal dynamics in relation to their environment are crucial and could allow us to (i) reveal their environmental forcing factors, (ii) understand their contribution to the flow of matter and energy and (iii) perhaps open a window on their exact nature.

The seasonal variations of ALN abundances were similar in the three lakes which were in a restricted geographical area (French Massif Central) and were apparently driven more by differences in biological parameters. This is in agreement with our previous study [START_REF] Fuster | Trophic conditions influence widespread distribution of Aster-Like nanoparticles within aquatic environments[END_REF] were ALNs increased in environments with high microbial density and nutrients content, such as Lake Fargette. The study of the temporal dynamics of ALNs revealed a marked seasonality.

The seasonal patterns of ALNs in the three lakes was relatively similar, with characteristic peaks in spring. The ALNs communities exhibited a rapid growth capacity with a high amplitude of abundance variations (from undetectable to 4.28 ± 0.74 x 10 6 ALNs.mL -1 between two consecutive time point in lake Fargette for example). ALNs are therefore a rapid mobilization and sequestration factor of their constituent elements. Colombet and colleagues [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF] showed that Ca is a preponderant constitutive element of ALNs. It is therefore very likely that ALNs have a key role in the aquatic Ca cycle. Considering the biomass dynamics of ALNs can therefore be fundamental in understanding the flow of elements through aquatic ecosystems, as has been demonstrated for viruses for example [START_REF] Sime-Ngando | Virus et prophages dans les écosystèmes aquatiques[END_REF].

Seasonal variations in the form of bloom are a recurring pattern in aquatic microbial and viral communities [START_REF] Sime-Ngando | Virus et prophages dans les écosystèmes aquatiques[END_REF][START_REF] Weinbauer | Ecology of prokaryotic viruses[END_REF][START_REF] Diao | Succession of Bacterial Communities in a Seasonally Stratified Lake with an Anoxic and Sulfidic Hypolimnion[END_REF][START_REF] Lymer | Temporal variation in freshwater viral and bacterial community composition[END_REF]. All the models showing this kind of dynamics (free or symbiotic), have a prominent role in the functioning of the ecosystem. It is therefore undoubtedly that ALNs are intrinsic actors in the ecology of aquatic systems. However, the understanding of their ecology is intimately linked to the understanding of their control factors.

Different factors can explain temporal variations of microbial entities, including geographic and environmental factors [START_REF] Yannarell | Geographic and Environmental Sources of Variation in Lake Bacterial Community Composition[END_REF], abiotic and biotic factors, bottom-up and top-down factors [START_REF] Muylaert | Relationship between Bacterial Community Composition and Bottom-Up versus Top-Down Variables in Four Eutrophic Shallow Lakes[END_REF],

or the presence/absence of hosts for the life cycle such as for viruses [START_REF] Tijdens | Population Dynamics and Diversity of Viruses, Bacteria and Phytoplankton in a Shallow Eutrophic Lake[END_REF]. The case of lake Saint Gervais, which was emptied for several weeks, makes it possible to specify here that the ALNs are aboriginal to the lake. Indeed, during the drying phase and until the peak of ALNs, we carried out regular sampling in the tributaries feeding the lake without ever detecting the presence of ALNs. The controlling factors of ALNs are thus intrinsic to the lake. No abiotic factors seem to have an impact on the variations of ALNs abundance in lake ecosystems.

It's interesting to note that with equal temperature between spring and fall, we recorded a significant development of ALNs in spring. The development of ALNs could therefore be more associated with the seasonal succession of biological communities forced by the interaction of physico-chemical environmental variables rather than with a single physico-chemical variable.

Among biotic factors measured, correlations with prokaryotes and VLPs were identified. We further explored these putative links to the microbial environment by integrating data previously acquired in Fargette Lake [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF]. The linear regression model used allows us to highlight a strong link between ALNs and prokaryotes. The ω2 allowed us to estimate that 47 % of the variation in number of ALNs could be explained by the prokaryotes. These results support the observations described in Colombet et al., [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF] where one can clearly distinguish a close physical interaction between ALNs and prokaryotes. These data raise the hypothesis that ALNs could have strong interactions with prokaryotes. One of the particularities of ALNs is their pleomorphism. Each form (see description in Colombet et al., [START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF]) has a particular seasonal dynamic (Fig 2-B). In the hypothesis that they are dependent on each other within a developmental cycle, it is likely that a partner, possibly prokaryotic, may intervene at certain stages and regulate the dynamics of the different forms. 11-arms forms could be a resistant form.

To identify more precisely which microbial communities may be involved in these interactions with ALNs, we analyzed the covariation of the presence of ALNs with those of the different bacterial, archaeal, and fungal phyla identified in the three lakes. ALNs appeared strongly positively correlated (r ² > 0,7, p < 0.05) with some fungal (Ascomycota and Chytridiomycota) and bacterial (Acidobacteria, Cyanobacteria, Firmicutes, Planctomycetes, Proteobacteria) OTUs in lakes. Ascomycota is the most common fungal phylum in aquatic environments [START_REF] Jones | Classification of marine Ascomycota, Basidiomycota, Blastocladiomycota and Chytridiomycota[END_REF] and Chytridiomycota is a phylum with a huge implication in the regulation of aquatic organisms, especially influencing directly or indirectly the microbial loop [START_REF] Jobard | Diversity and functions of microscopic fungi: a missing component in pelagic food webs[END_REF][START_REF] Gleason | The ecology of chytrids in aquatic ecosystems: roles in food web dynamics[END_REF]. Although we cannot rule out a direct relationship, it is more likely that fungi interact indirectly with ALNs via a third-party microbe. The positive correlations between ALNs and prokaryotes seem to target a large heterogeneity of bacterial phyla. Acidobacteria, Cyanobacteria, Firmicutes, Planctomycetes, and Proteobacteria represent most lake bacteria [START_REF] Newton | A Guide to the Natural History of Freshwater Lake Bacteria[END_REF]. Thus, although we cannot formally infer a direct relationship, these data suggest that the spectrum of possible prokaryotic partners for ALNs may be broad and non-specific.

The data acquired here allow a better understanding of the ecology of ALNs, namely, a pronounced seasonal dynamic in the form of spring "bloom" that can be controlled by the microbial and probably prokaryotic environment. The hypothesis that ALNs have strong interactions with prokaryotes is emerging but will have to be verified in the future as well as

  Differences in physico-chemical and biological variables between seasons (Spring: March-May, Summer: June-August, Autumn: September-November, Winter: December-February) and lakes were tested by Kruskal-Wallis and Dunn tests. To investigate the potential proximity between our sampling points, function of lakes or seasons, principal component analysis (PCA) was used with the FactoMineR package. Potential relationships among all variables were tested by linear pair-wise correlations (Pearson correlation analyses). Potential relationships between the presence/absence of ALNs and OTUs were tested by pairwise correlations (Spearman correlation analyses).

  Fig s3). Bacterial communities in the three lakes were dominated by Proteobacteria (30%), Bacteroïdetes (15%) and unclassified bacteria (13%) (Fig s3). Euryarchaeota (43%), Nanoarchaeota (40%) and Crenarchaeota (11%) were the major phyla for Archaea (Fig s3). While the fungus community is dominated by unclassified fungi (40%), major known phyla correspond to Chytridiomycota (27%) and Basidiomycota (17%) (Fig s3). The mean percentage of the relative abundance of these major phyla presents important temporal variations specific to each lake. For Lake Fargette, we can notice a peak of Proteobacteria in March and April (More than 60% of total abundance), Bacteroidetes in May (50%) and the dominance of unclassified fungi in April (98%) (Fig s3). In St. Gervais, Bacteroidetes (40%) increase in March, Euryarchaeota in February and April (50%), and Basidiomycota in December (90%) (Fig s3). For Lapeyrouse lake, Bacteroidetes increase in October and May (35 and 40 % respectively) (Fig s3).

4 x 10 5

 5 to 7.0 x 10 5 for St. Gervais and from no detectable to 10 5 ALNs.mL -1 for Lapeyrouse) (Fig 2-A), immediately followed by a decline in April in the three lakes. A second increasing phase was recorded from May until July, especially in St. Gervais. The mean distribution combined from the three lakes of different forms (Fig 1) varied with time (Fig 2-B). The 4-10 arms forms were recorded preferentially from October (15%) to November (27%) and showed highest values from March (45%) to July (35%) during and following the spring peaks of ALNs (Fig 2-B). The 11 arms forms dominated the ALNs communities in October (71%), December (100%), January (87%), April (43%) and May (61%) mainly when the lowest concentrations of ALNs were recorded, whereas the 11 budding arms form dominated from June (53%) to September (63%) (Fig 2-B). The 20 arms were detected from October (13%) to November (13%) and had highest values from February (35%) to April (15%) concomitantly with the spring peaks of ALNs (Fig 2-B). During the ALN peaks of (March 2019), the contributions of the 4 different forms (4-10, 11, 11 budding and 20 arms) were at 45, 25, 21 and 9%, respectively (Fig 2-B). When the abundances of ALNs were particularly low, i.e., in December and January (Fig 2-A), only the 11-arm form was represented, with absence or very low proportion of the budding form. All parameters and ALNs were integrated in a principal component analysis (Fig 3). 54.1% of the variance is explained by the first 2 axes. This analysis showed a pronounced inter-lake variability, mainly due to the biological variables. Like the patterns of ALN abundance, Fargette Lake was clearly distinguished from Lake Lapeyrouse with St. Gervais in intermediary (Fig 3-A). The summer/winter seasonal discrimination with spring and autumn in between is classic for temperate zone lake systems (Fig 3-B). The remarkable point of Fargette in March stands out clearly from this analysis which coincides with the bloom of ALNs.

(

  Colombet et al.,[START_REF] Colombet | Discovery of High Abundances of Aster-Like Nanoparticles in Pelagic Environments: Characterization and Dynamics[END_REF]), revealed a significant relation between ALNs and prokaryotes (Fig 4). The regression line linking number of ALNs as a function of number of prokaryotes appears highly significant (ALN concentration = -3 + 1.2 x prokaryotes concentration, R² = 0.43, ω 2 = 0.43, one way ANOVA (F (1,62) = 49.14, p < 0.001)) (Fig 4). As revealed by ω 2 = 0.43, one of the two parameters can explain 43 % of the distribution of the other (Fig 4).

  the possibility of a development cycle linking the different morphotypes. The functionality of ALNs is certain for the transfer of their constitutive elements within the microbial network but their role on other microbial components (neutrality, mutualism, parasitism...) remains to be determined. Through their dynamics, ALNs are undoubtedly new actors to consider in the functioning of aquatic ecosystems. More generally, our data highlight the seasonal importance of overlooked femtoplankton in aquatic ecosystems and their potential roles in the ecosystem functioning.Author's contribution MF, HB and JC designed and performed the experiments. MF and GB analyze genomic datas. MF, HB, GB, TSN and JC wrote the manuscript. All authors have read, commented, and approved the final version of the manuscript.

Figure 1 .

 1 Figure 1. Transmission electron microscopy (TEM) micrographs of different morphotypes of aster-like nanoparticles (ALNs). A-C, 4-10 arms forms. D-F, 11 arms forms and their budding variants G-I. J-L 20 arms forms. Scale Bars = 100 nm

  Figure 2. Dynamics of Aster Like Nanoparticles (ALNs) concentrations (A) and their different forms (B). Total ALNs concentration in the 3 lakes during the 12 months of sampling (A). Dynamics of the different forms during this period (B)*. ND = No Detectable. * = data are obtained from mean of the 3 lakes

(

  Figure 3. Principal Component Analysis of individuals (sampling points) in function of lakes (A) or seasons (B).

F

  Figure S3. Variation of the different bacterial, archaeal and fungal phyla in the 3 lakes during the 12months of sampling period.

Table 1 -

 1 B). As expected, temperature was inversely correlated to oxygen (r 2 =-0.77, p < 0.01).The summer and autumn warming periods of the lakes corresponded to oxygen depletion while the cold spring and winter periods corresponded to a rise in oxygenation of lake waters (

	3.1.1/ Physico-chemical environment of the studied sites
	Absolute values and seasonal variations in oxygen content, temperature and pH were
	characteristics of freshwater lakes in temperate regions. The three lakes showed similar average
	values (p > 0.05, Table 1-A) with marked statistical differences (p < 0.01) between seasons
	(

Table 1 -

 1

B). pH was stable during spring, summer and autumn and decreased significantly in winter (p < 0.01) (Table

1

-B).

The availability of mineral elements (K, Mg, Na, Ca) showed different distributions between lakes (Table

1-A

). K and Mg had similar distributions with significant differences (p < 0.01) between the three lakes, with highest and lowest values recorded respectively in Fargette and St. Gervais (Table

1

-A). Na concentration was significantly lower in Fargette compared to St.

Gervais and Lapeyrouse (p < 0.01). Ca was significantly lower in St. Gervais compared to Fargette and Lapeyrouse (p < 0.01) (Table

1

-A).

The main chemical factors that discriminated the three lakes appeared to be productivity indicators such as Phosphorus (P), Total Organic Carbon (TOC) or Total Nitrogen (TN). P, TOC and TN were significantly higher in Fargette compared to Lapeyrouse while St. Gervais displayed intermediate values (p < 0.01) (Table

1-A

). At the seasonal scale, no significant variation (p > 0.05) was recorded for the three lakes in the availability of mineral elements, P, TOC and TN (Table

1

-B).

Table 1 and

 1 Figs s1-s2. The average numbers of VLPs were similar for the three lakes (p > 0.05) (Table1-A). Prokaryote and HE numbers followed the spatio-temporal dynamic of the productivity indicators (P, TOC and TN) with highest average recorded in Lake Fargette compared to Lapeyrouse (p < 0.01) and no significant difference with lake St. Gervais (p > 0.05). Lapeyrouse had higher content in nanophytoplankton and lower content in picophytoplankton compared to Fargette and St. Gervais (p < 0.01).

	At

the seasonal scale, no significant variations (p > 0.05) were recorded for the three lakes (Table

1-

B), excepted for HE. HE dynamic was significantly affected by seasons (p < 0.01) with a maximum number in autumns and minimal number in winter. Detailed lake-specific monthly variations for each biological parameter are presented in Figs s1-s2.

Table 1

 1

-A

). ALN dynamic was significantly affected by seasons (p < 0.01) with remarkable spring peaks in the three lakes, recorded in March 2019 (Table

1

-B). In these lakes, ALN abundances fluctuated from undetectable to a low range from October 2018 to February 2019 before a strong increase towards the main peaks recorded in March 2019 (from no detectable to 10 6 for Fargette, 2.

Table 1 . Mean ± SD of physico-chemical and biological characteristics according to the lakes (A) or the seasons (B)*.

 1

	(A)	Fargette	St. Gervais	Lapeyrouse
	O2 ns (mg.L -1 )	8.46 ± 3.45	9.36 ± 3.02	9.86 ± 2.07
	Temperature ns (°C)	12.63 ± 7.13	12.79 ± 8.04	13.32 ± 7.72
	pH ns	7.54 ± 0.67	7.88 ± 0.83	7.53 ± 0.56
	K (mg.L -1 )	31.15 ± 5.53 a	2.78 ± 0.51 b	5.11 ± 0.49 c
	Mg (mg.L -1 )	4.84 ± 0.52 a	3.22 ± 1.13 b	4.23 ± 0.48 c
	Na (mg.L -1 )	8.98 ± 2.27 a	12.52 ± 1.29 b	13.04 ± 1.41 b
	Ca (mg.L -1 )	22.94 ± 2.44 a	17.61 ± 6.08 b	24.06 ± 2.75 a
	P (mg.L -1 )	0.23 ± 0.06 a	0.14 ± 0.12 ab	0.06 ± 0.02 b
	T.O.C. (mg.L -1 )	23.92 ± 4.34 a	10.78 ± 3.70 b	10.67 ± 1.05 b
	T.N. (mg.L -1 )	4.73 ± 2.65 a	3.13 ± 1.89 ab	1.75 ± 0.67 b
	VLP ns (VLPs.mL -1 )	1.31 ± 0.70 x10 8	1.08 ± 0.88 x 10 8	0.78 ± 0.66 x 10 8
	Prokaryotes (cells.mL -1 )	2.73 ± 1.77 x10 7 a	1.59 ± 0.92 x 10 7 a	0.39 ± 0.36 x 10 7 b
	Nanophytoplankton (µg.L -1 )	178.20 ± 98.80 a	106.27 ± 70.84 a	222.44 ± 13.13 b
	Picophytoplankton (cells.mL -1 )	4.62 ± 2.01 x 10 5 a	4.97 ± 6.43 x 10 5 a	0.76 ± 0.93 x 10 5 b
	Heterotrophic eukaryotes	2.34 ± 1.46 x10 4 a	2.08 ± 1.56 x10 4 ab	1.23 ± 0.77 x10 4 b
	(cells.mL -1 )
	ALN (ALNs.mL -1 )	6.12 ± 11.7 x10 5 a	2.32 ± 2.60 x10 5 ab	0.83 ± 1.99 x10 5 b
	(B)	Spring	Summer	Autumn	Winter
	O2 (mg.L -1 )	10.22 ± 2.80 bc	6.29 ± 1.31 a	8.46 ± 1.82 ac	11.94 ± 1.93 b
	Temperature (°C)	12.70 ± 3.18 a	22.39 ± 2.54 b	11.87 ± 6.68 a	4.70 ± 1.36 c
	pH	8.31 ± 0.44 a	7.69 ± 0.44 a	7.60 ± 0.83 ab	7.01 ± 0.29 b
	K ns (mg.L -1 )	11.75 ± 12.18	14.94 ± 16.23	13.97 ± 15.02	11.40 ± 11.84
	Mg ns (mg.L -1 )	3.97 ± 0.95	4.45 ± 0.87	4.37 ± 1.15	3.59 ± 0.96

Acknowledgments This study benefited from equipment and technical assistance of CYSTEM platform UCA-PARTNER (University of Clermont Auvergne UCA), Clermont-Ferrand, France) and the Laboratoire Microorganismes: Genome et Environment (LMGE, UMR6023 CNRS-UCA, Clermont-Ferrand, France). The authors thanks Thomas Ruiz (LMGE) for helpful comments and discussions on the manuscript.

Funding Information MF was supported by a PhD fellowship from the CPER 2015-2020 SYMBIOSE challenge program (French Ministry of Research, UCA, CNRS, INRA, Auvergne-Rhône-Alpes Region, FEDER). This study is a contribution to the "C NO LIMIT" project funded by the Interdisciplinary Mission of the French National Center of Scientific Research (CNRS) Program X-life, 2018 edition. This study is a contribution to the "NANOPOULPE'' project funded by the Interdisciplinary Mission of the French National Center of Scientific Research (CNRS) Program Origines, 2020. This research was also financed by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25).

Conflicts of interest:

The authors declare that they have no conflict of interest. ----------------72°c. 10mn 95°c 3mn ------x32------98°c 20s 68°c 30s 72°c 30s - ----------------72°c. 5mn 95°c 3mn ------x32------98°c 20s 57°c 30s 72°c 30s - ----------------72°c


Pierre Cadeau
email: pierre.cadeau@gmail.com

Didier Jézéquel
email: jezequel@ipgp.fr

Alexis Groleau
email: groleau@ipgp.fr

Andrea Di Muro
email: dimuro@ipgp.fr

Magali Ader
email: ader@ipgp.fr

in the Comoros archipelago (Mozambique channel) / La crise sismo-volcanique de 2018-2021 de Mayotte dans l'archipel des Comores (Canal du Mozambique) Impact of the seismo-volcanic crisis offshore Mayotte on the Dziani Dzaha Lake

Keywords: Dziani Dzaha Lake, Mayotte, Seismo-volcanic crisis, Carbon isotopes, Nitrogen isotopes, Volcanic CO 2 degassing

Since May 2018, an unexpected long and intense seismic crisis started offshore Mayotte (Indian Ocean, France). This ongoing seismic sequence is associated with the birth of a newly discovered submarine volcano 50 km east of Petite Terre. Here, we investigate the indirect impact of this crisis on the stability of an atypical ecosystem located in Mayotte, the Dziani Dzaha Lake. This lacustrine system presented physical, chemical and biogeochemical characteristics that were distinct from those classically observed in modern lakes or seawater, e.g. high salinity (up to 70 psu), lack of nitrate, sulfate content below 3 mM, and permanent water column anoxia below ca. 1.5 m depth (2012-2017 period). Based on three surveys conducted in 2020 and 2021, we documented an episode of water column oxygenation, a significant pH decrease and an impressive change in the carbon isotope signatures. These preliminary data suggest that the functioning of biogeochemical cycles in the Dziani Dzaha Lake is impacted by increased CO 2 inputs and the changes in the lake mixing dynamics, which is an indirect consequence of the ongoing seismo-volcanic crisis.

Introduction

Mayotte Island is one of the four main volcanic islands of the Comoros Archipelago located in the the Comoros Archipelago were known for their diffuse and moderate seismicity with few historically felt events. The ongoing seismic sequence is thus unexpected and remarkable with regard to its duration and intensity (i.e. earthquakes of magnitude 5.9 on 15 May 2018). After the first two months of intense seismicity at the beginning of the crisis, seismicity still persisted a long time with numerous small earthquakes [Bertil et al., 2019], and ground deformation was observed [Lemoine et al., 2020]. A new submarine volcano was discovered at 50 km east of Petite Terre [Feuillet et al., 2019[Feuillet et al., , 2021]], and significant subsidence of Mayotte Island (up to 20 cm) was attributed to the drainage of a deep magma chamber near the coast [Foix et al., 2021]. This long-term seismic sequence and the associated new active volcano offshore Mayotte led the scientific community to investigate further the seismo-volcanic hazard in this area [Feuillet et al., 2019, Cesca et al., 2020, Darnet et al., 2020, Lemoine et al., 2020, Berthod et al., 2021, Foix et al., 2021, Saurel et al., 2022]. This seismo-volcanic crisis has been monitored since the beginning of May 2018; numerous studies were performed and many others are in progress to gain further information about the deep volcanic system, to better constrain the seismo-volcanic hazard for Mayotte and its population (e.g. seismicity, slope instability, tsunami, island subsidence or even new volcanic activity onshore).

On the Petite Terre Island in Mayotte, one of the latest eruptive events proposed at around 7 and 4 ky is probably at the origin of the Dziani Dzaha Lake formation [Zinke et al., 2003]. For the last decade, this atypical lacustrine system has been extensively studied [Leboulanger et al., 2017, Cellamare et al., 2018, Gérard et al., 2018, Milesi et al., 2019, Aucher et al., 2020, Jovovic et al., 2020, Sala et al., 2022]. Based on its water chemistry, the lake was most likely initially filled with seawater [Sarazin et al., 2021], and has since evolved as a closed system. Before the seismovolcanic crisis, the physical, chemical and biological features of this ecosystem were far from those observed in a modern oceanic or lacustrine environment (details in Study site section). The ongoing seismo-volcanic crisis offshore Mayotte could have a significant impact on the stability and the functioning of this ecosystem through the reactivation of fractures in the crater/basement, the infiltration of fresh/sea water and/or the increase in magmatic bubbling and degassing through its waters [Liuzzo et al., 2021].

In order to investigate the impact of the perturbations related to the growth of the submarine volcano offshore Mayotte on this lacustrine system, we conducted three field trips in 2020 and 2021 on the Dziani Dzaha Lake. We documented the evolution of the physical and chemical properties in the water column (e.g. [O 2 ], pH, salinity, T), as well as the carbon and nitrogen isotopic signatures of dissolved inorganic carbon (DIC) and suspended particulate matter (SPM)) within it (i.e. δ 15 N SPM , δ 13 C DIC , δ 13 C SPM ). The comparison of these results with those obtained before the seismo-volcanic crisis illustrates the response of this lacustrine system to this kind of geological perturbations.

Study site

The Dziani Dzaha Lake is a shallow maar lake located on the Petite Terre Island of Mayotte (Figure 1). This lake is small (i.e. 2.36 × 10 5 m 2 ), close to sea level and separated from the nearby seashore by a 220-m-thick crater wall [Rouwet et al., 2021]. The lake watershed is restricted to the volcanic crater (i.e. ∼1 km in diameter and 50-100 m height). The average depth of the Dziani Dzaha Lake is about 3 m with a narrow depression reaching 18 m depth (Figure 1), likely related to the chimney of the phreatomagmatic eruption at the origin of this lake. Magmatic gases bubble through the water column in several locations [Milesi et al., 2020, Liuzzo et al., 2021] mostly in shallow areas of the lake, with one bubbling site in the central part for which a strong increase in the bubbling activity has been observed since 2020 compared to our last observations from 2012 to 2017 (Figure 1).

From 2010 to 2017, i.e. before the beginning of the seismo-volcanic crisis in May 2018, the lake water column showed an atypical combination of physical and chemical features for a modern lacustrine system [e.g. Sarazin et al., 2021]. Salinity measured was between 35 and 70 psu (practical salinity unit), nearly twice the usual salinity of seawater. The alkalinity was also very high, with values ranging from ca. 0.1 to 0.2 M (nearly 100 times the alkalinity of seawater), and pH ranged between 9 and 9.5. The water column was periodically stratified with a sharp halocline at ca. 2 m depth about half of the year, driven by the salinity decrease in surface waters as a result of increasing precipitation during the rainy season. A permanent chemocline was also present at ca. 14 m depth in the narrow depression [Sarazin et al., 2021]. Importantly, the lake was permanently anoxic below ca. 1.5 m depth despite seasonal variations in its water column structure, and was strongly euxinic (sulfides H 2 S/HS -concentration ranging from 2 to 6 mM) below 2 m depth when the halocline is present (during the stratified period), or only below a 14 m depth when it is not [during the non-stratified period, Sarazin et al., 2021]. Details on the evolution of the physical and chemical parameters according to stratification are available in previous works [e.g. Leboulanger et al., 2017, Cadeau et al., 2020, Sarazin et al., 2021]. All our field trips done since the beginning of the crisis were conducted during nonstratified periods (i.e. from August to December). Before the crisis, these non-stratified periods were characterized by the absence of halocline (except at ca. 14 m depth). As a result, most physical, chemical and biological parameters were constant with depth down to the deep chemocline at 14 m depth, except for the dissolved oxygen that was only present down to a maximum of 1.5 m depth depending on the photosynthetic activity. The fact that O 2 saturation values fluctuate widely (from 0 to 350%) is related to large nycthemeral fluctuations in the balance between the activities of photosynthesis (which produces O 2 ) and respiration (which consumes it) in this biologically very active lacustrine system [e.g. Leboulanger et al., 2017, Cadeau et al., 2020]. The salinity was close to 60 psu, the alkalinity close to 0.14 M, the pH close to 9.2, the [SO 2- 4 ] content close to 3 mM, [H 2 S] content close to 0.1 mM, and a relatively homogeneous diversity of cyanobacteria, bacteria, and archaea was observed along the water column [Hugoni et al., 2018]. Below the 14 m deep chemocline, the salinity and alkalinity increased to 70 psu and 0.2 M, respectively, the pH decreased to a value close to 9, no SO 2- 4 was observed while H 2 S accumulated up to 6 mM. Our previous work has demonstrated that the characteristics of non-stratified seasons have remained stable since 2010 [e.g. Leboulanger et al., 2017, Hugoni et al., 2018, Bernard et al., 2019, Sarazin et al., 2021].

Materials and methods

The isotope signatures in this study were acquired on samples from surveys conducted in November 2020, September 2021 and December 2021. A nomenclature consistent with previous publications was used to identify each sample taken during the different surveys. For example, for the sample "DZ21-9 2m," "DZ" indicates the Dziani Dzaha Lake, "21-9" indicates the year (2021) and month (September) of the survey, "2m" refers to sampling depth of the water column station called "18m" as shown on Figure 1.

Water samples were collected using a horizontal 1.2 L Niskin bottle along a vertical profile at the 18 m depth station for each survey. For each sampling depth, samples were taken in 500 mL plastic bottles. Each sample was then filtered onto precombusted Whatman GF/F glass fiber filters (0.7 µm) and quartz Millipore AQFA (size cut-off ca. 3 µm) for δ 13 C POC and δ 15 N PON analysis, and a 12 mL Labco Exetainer glass tube was filled with the filtrate and fixed with 0.2 mL of saturated HgCl 2 solution for DIC and δ 13 C DIC analyses. The GF/F filters with SPM were decarbonated by exposure to concentrated HCl fumes in a desiccator for 12 h to avoid loss of material.

Physical and chemical in situ measurements

In situ profiling were performed with a CTD multiparameter EXO2 probe (temperature, conductivity/salinity, pH, ORP, O 2 , turbidity, chlorophyll, phycocyanine (PC) and fDOM) and a turbidity AQUAlogger 210 TYPT Aquatec probe. The pH electrode was calibrated using pH 4.00, 7.01 and 10.00 NBS-NIST buffers; O 2 optode was calibrated at 100% saturation in wet air; salinity sensor was calibrated with a 34.271 psu seawater. Other sensors have not been calibrated and the corresponding parameters are used as relative values; nevertheless, most of these sensors offer quite stable response (especially T, turb., chl., PC, fDOM), at the exception of oxidationreduction potential (ORP) electrode which can only be used as relative values. In addition, two O 2 NKE SDOT optodes were deployed at 0.25 m depth from 10/09 to 13/12 2021 near the central gas bubbling zone (at ca. 1-3 m from the outlet, respectively), and a water level probe (OTT Orpheus Mini) was installed in September 2021 in the NO part of the lake.

Carbon and nitrogen isotope measurements

DIC and δ 13 C DIC were measured using an AP2003 mass spectrometer. Phosphoric acid (100% H 3 PO 4 ) Figure 2. Vertical profiles of physical and chemical parameters recorded during each survey including salinity (psu), temperature (°C), pH and dissolved oxygen (%). The grey profiles correspond to previous data obtained before 2018 from Cadeau et al. [2022].

was injected into 12 mL Labco Exetainer tubes that were then flushed with helium. For each sample, 0.1 mL water was sub-sampled and transferred into the acidified Labco Exetainer tubes. The samples were agitated for 15-24 h so that CO 2 in the water was in equilibrium with the CO 2 in the headspace. The CO 2 in the headspace gas was then separated by gas chromatography with helium as a carrier gas and analyzed using the AP2003 mass spectrometer. Solutions presenting a range of DIC were prepared using sodium hydrogen carbonate for DIC calibration. Three internal carbonate standards with known isotopic compositions (Across, Merck and Rennes II) were used for isotopic composition calibration. The standard carbonate powder was loaded into 12 mL Labco Exetainer tubes with 0.1 mL distilled water before being flushed with helium and then analyzed in the same way as DIC. DIC values are expressed in mol•L -1 with a reproducibility of ±0.01 mol•L -1 , and δ 13 C DIC values are expressed in h relative to Vienna Pee Dee Belemnite (VPDB) with a reproducibility of ±0.1h (1σ).

The δ 13 C POC and δ 15 N PON of the decarbonated filters were measured using a Flash EA1112 elemental analyzer coupled to a Thermo Finnigan Deltaplus XP mass spectrometer via a Conflo IV interface (Thermo Fisher Scientific, Waltham, MA, USA). The decarbonated samples were loaded into tin capsules and heated to 1200 °C in a combustion tube with a mixture of chromium oxide and silver cobalt oxides. The combustion gases were carried by helium through a reduction column and a gas chromatography column to separate CO 2 from N 2 and from the other gases. CO 2 and N 2 were successively injected into the mass spectrometer for isotopic analysis. For the isotope calibration, four internal standards of organic-rich soil or sediment were analyzed in the same way. δ 13 C POC values are expressed as h relative to VPDB with a reproducibility of ±0.2h (1σ) and δ 15 N PON are expressed as h relative to air with a reproducibility of ±0.3h (1σ).

Results

Physical and chemical features of the water column

In September 2020, salinity was constant down to 14 m depth with an average value close to 55.6 psu and increased below this depth up to 59.7 psu (Figure 2, Table 1). pH was constant down to 14 m depth with an average value of 9.1 and decreased below this depth down to 8.6. Temperature decreased in surface waters from 34 °C to 30.3 °C at 1.5 m depth, and remained constant with depth down to the lake bottom. In September 2021, salinity, pH and temperature significantly decreased in the water column.

Figure 1 .

 1 Figure 1. Location and bathymetric maps of the Dziani Dzaha Lake on Mayotte Islands (Indian Ocean).(A) Mayotte Islands in Western Indian Ocean, (B) Dziani Dzaha Lake in Petite Terre Island on Mayotte, (C) Picture of the lake taken in October 2014, and (D) Bathymetric map of the lake, with blue circle representing the water column sampling location and the red circles representing the identified bubbling areas at the water-air interface (modified fromLeboulanger et al. [2017] andCadeau et al. [2022]).

Table 1 .

 1 Salinity (psu), temperature (°C), pH and dissolved oxygen (%) measured in November 2020, September 2021 and December 2021 in the Dziani Dzaha Lake

	Sample (name) Depth (cm) Temperature (°C)	pH	[O 2 ] (%) Salinity (psu)
	DZ20-11	0.25	34	9.07		55.6
	DZ20-11	1.5	30.3	9.09		55.6
	DZ20-11	2.5	30.5	9.095		55.6
	DZ20-11	5	30.4	9.093		55.6
	DZ20-11	11	30.5	9.083		55.8
	DZ20-11	14	30.3	9.038		57.3
	DZ20-11	16	30.7	9.009	0.2	58.1
	DZ20-11	17	29.8	8.610		59.7
	DZ21-9	0.25	32.2	8.6	327	55.1
	DZ21-9	0.50	30.4	8.6	212	55.8
	DZ21-9	0.75	29.5	8.6	149	55.6
	DZ21-9	1.01	29.1	8.6	128	55.3
	DZ21-9	1.25	29.0	8.6	116	55.2
	DZ21-9	1.50	28.9	8.6	100	55.2
	DZ21-9	1.75	28.8	8.6	93	55.1

PierreCadeau et al.

 2) that are related to the high photosynthetic activity similar to before the volcanic crisis. Surprisingly however, the deeper part of 60.2

Salinity and pH values were still constant with depth throughout the whole water column but with average values close to 55 psu and 8.5, respectively. Temperature decreased from 32 °C in surface water to 28.2 °C at about 2 m depth, and then remained constant with depth down to the lake bottom. Dissolved oxygen was present throughout the whole water column. Surface waters showed very high O 2 saturation values (i.e. up to 327%, Figure 2) that are related to the high photosynthetic activity similar to before the volcanic crisis. Surprisingly however, the deeper part of 60.2

Salinity and pH values were still constant with depth throughout the whole water column but with average values close to 55 psu and 8.5, respectively. Temperature decreased from 32 °C in surface water to 28.2 °C at about 2 m depth, and then remained constant with depth down to the lake bottom. Dissolved oxygen was present throughout the whole water column. Surface waters showed very high O 2 saturation values (i.e. up to 327%, Figure 2) that are related to the high photosynthetic activity similar to before the volcanic crisis. Surprisingly however, the deeper part of 60.2

Salinity and pH values were still constant with depth throughout the whole water column but with average values close to 55 psu and 8.5, respectively. Temperature decreased from 32 °C in surface water to 28.2 °C at about 2 m depth, and then remained constant with depth down to the lake bottom. Dissolved oxygen was present throughout the whole water column. Surface waters showed very high O 2 saturation values (i.e. up to 327%, Figure 2) that are related to the high photosynthetic activity similar to before the volcanic crisis. Surprisingly however, the deeper part of 60.2

Salinity and pH values were still constant with depth throughout the whole water column but with average values close to 55 psu and 8.5, respectively. Temperature decreased from 32 °C in surface water to 28.2 °C at about 2 m depth, and then remained constant with depth down to the lake bottom. Dissolved oxygen was present throughout the whole water column. Surface waters showed very high O 2 saturation values (i.e. up to 327%, Figure 2) that are related to the high photosynthetic activity similar to before the volcanic crisis. Surprisingly however, the deeper part of 60.2

Salinity and pH values were still constant with depth throughout the whole water column but with average values close to 55 psu and 8.5, respectively. Temperature decreased from 32 °C in surface water to 28.2 °C at about 2 m depth, and then remained constant with depth down to the lake bottom. Dissolved oxygen was present throughout the whole water column. Surface waters showed very high O 2 saturation values (i.e. up to 327%, Figure 2) that are related to the high photosynthetic activity similar to before the volcanic crisis. Surprisingly however, the deeper part of 60.2

Salinity and pH values were still constant with depth throughout the whole water column but with average values close to 55 psu and 8.5, respectively. Temperature decreased from 32 °C in surface water to 28.2 °C at about 2 m depth, and then remained constant with depth down to the lake bottom. Dissolved oxygen was present throughout the whole water column. Surface waters showed very high O 2 saturation values (i.e. up to 327%, Figure 2) that are related to the high photosynthetic activity similar to before the volcanic crisis. Surprisingly however, the deeper part of 60.2

Salinity and pH values were still constant with depth throughout the whole water column but with average values close to 55 psu and 8.5, respectively. Temperature decreased from 32 °C in surface water to 28.2 °C at about 2 m depth, and then remained con-


Cyril Kurylo

Cervin Guyomar

Sarah Djebali

Sylvain Foissac

Keywords: And Gene Assembly, Deconvolution, Analysis - Assembly, Deconvolution, Analysis

Integrates RNA-seq "short" reads into a reference annotation -Identifies new genes and transcripts -Predicts transcript coding/non-coding status -Filters transcripts based on cross-replicates consistency -Merges results from many samples into a single new annotation -Quantifies gene expression on reference and new annotations -Runs on various computational environments -Open-source pipeline written in NextFlow

Portable

Modular Scalable Reproducible

Introducing the TAGADA pipeline Application to pig and chicken transcriptomes: the GENE-SWitCH project sarah.djebali@inserm.fr cervin.guyomar@inrae.fr sylvain.foissac@inrae.fr cyril.kurylo@gmail.com

@

An RNA-seq data analysis pipeline to improve genome annotations with new genes and transcripts

The regulatory GENomE of SWine and CHicken -Part of the FAANG international community -2 species: pig (Sus scrofa) and chicken (Gallus gallus) -4 animals per species: 2 males and 2 females -3 development stages: 2 embryonic and newborn -8 omics assays (including RNA-seq) -7 tissues

908 51,171 24,356 34,712 Expressed genes 21,130 42,516 16,637 30,957 Transcripts 63,041 197,396 39,288 138,272 Expressed transcripts 40,396 171,103 24,973 124,874

						Chicken
		Pig		Chicken
		ENSEMBL	TAGADA	ENSEMBL	TAGADA
	Genes 31,FEELnc coding transcripts -	3,152	-	3,636	Pig
	FEELnc non-coding transcripts	-	50,105	-	40,197
	Improving the reference annotation ENSEMBL v102 using TAGADA v2.1.0 with RNA-seq samples
	(~150M 2x150 bp x 84 libraries) on a Kubernetes cluster. New (non-)coding transcripts are predicted
	by FEELnc.
	Gene expression quantification: hierarchical clustering of samples from each species based on TPM

TAGADA in a nutshell

-is a containerized NextFlow pipeline that integrates RNA-seq data into an existing genome annotation -tripled the number of annotated transcripts in pig and chicken by finding new transcripts of coding and non-coding genes -generated expression profiles that led to meaningful sample hierarchical clustering -is available at: https://github.com/FAANG/analysis-TAGADA expression values of reference genes. Figures are automatically generated by TAGADA during quality control.

This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the grant agreement n° 817998 ¹ GenPhySE,