Olivier Etrillard

Robin Gerzaguet

Laurent Feichter

Malo Mabon

Antoine Courtay

Olivier Berder

LOLA SDR: Low power low latency software defined radio for broadcast audio applications

Keywords: Software Defined Radio, microcontroller, audio processing, low power, low latency

This brief proposes a new low power, low latency and low cost reconfigurable architecture for software defined radio. Due to their flexibility and reconfigurability, software defined radios are now massively used as wideband transceivers, channel sounders or network gateways. However, they often struggle to meet the desired requirements in terms of energy consumption and throughput. In this brief, we present a new architecture capable of tackling these challenges, by combining an off-the-shelf generic radio component with a low power microcontroller associated to a Fourier transform coprocessor. To prove the benefit of our approach, after describing the key assets of the architecture, we derive a complete physical layer dedicated to audio broadcast applications. This chain is capable of streaming High Definition audio stream in real time with low power (437 mW) and very low latency (854 µs). We show that our processing chain can be flawlessly run on our architecture paving the way for larger adoption of a new generation of low power low latency software defined radio architectures.

I. INTRODUCTION

Since the first definition of Software Defined Radio (SDR) done by Mitola [START_REF] Mitola | Software radios: Survey, critical evaluation and future directions[END_REF] thirty years ago, many studies carried in various domains show the benefits and the numerous challenges this topic has to tackle. The initial purpose was quite simple: shift as many parts as possible into digital domain and limit the Radio Frequency (RF) analog parts to the antenna and the analog-to-digital converters. If nowadays the myth of a pure digital architecture fizzled out, the adoption of the SDR has been massive and the scope of its use has largely grown.

Many SDR architectures have been proposed: some based on General Purpose Processors (GPP), Digital Signal Processors (DSP) or on specific hardware such as Field Programmable Gate Arrays (FPGA) [START_REF] Dardaillon | Software defined radio architecture survey for cognitive testbeds[END_REF]. More recently, the advances in hardware integration and efficient software methodologies [START_REF] Lavaud | AbstractSDRs: Bring down the two-language barrier with Julia Language for efficient SDR prototyping[END_REF] allow the emergence of embedded SDR architectures based on System on Chip (SoC) that combines hardware and software computational resources. This paves the way for high performance SDR applications such as wideband channel sounding, reconfigurable cellular base stations, etc.

SDR are highly appealing for low power scenarios, especially for Internet Of Things (IoT) deployment. Indeed, the low cost and the reconfigurability of these devices are key assets for volume deployment, easy maintenance and extensive reuse. However, as SDR are wideband receivers, use generic components and are often piloted by greedy processing units, it seems illusional to use them as end-devices. Hence, most of SDR integrations for IoT scenarios have used the flexible radios on the gateway side where energy constraints are greatly alleviated [START_REF] Lin | Wireless IoT Platform Based on SDR Technology[END_REF]. Standalone embedded SDRs are based on SoC [START_REF] Garg | Spectral coexistence of candidate waveforms and DME in air-to-ground communications: Analysis via hardware software co-design on Zynq SoC[END_REF], [START_REF] Kumar | Software-Defined Radio Transceiver Design Using FPGA-Based System-on-Chip Embedded Platform With Adaptive Digital Predistortion[END_REF] or custom multiprocessor architectures based on Network On Chips (NoC) [START_REF] Clermidy | MAGALI: A Network-on-Chip based multi-core system-on-chip for MIMO 4G SDR[END_REF]. If it allows portability and flexibility, it also leads to energy consumption several orders of magnitude higher than the requirements for IoT end-devices [START_REF] Akeela | Efficient co-design partitioning of WLANs on SoC-based SDRs[END_REF]. On the other side, there are also very cheap SDR such as RTL-SDR [START_REF] Stewart | A low-cost desktop software defined radio design environment using matlab, simulink, and the rtlsdr[END_REF], but these devices have limited bandwidth, limited targetable carrier frequencies, and require processing to be done on an external computing unit.

We strongly believe that the recent advances in very low power hardware architectures, with limited but efficient embedded computational resources, will change the game. Some preliminary works have tended to prove that SDR can be used as low cost end-device. TinySDR [START_REF] Hessar | TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds[END_REF] is based on a low power FPGA and is dedicated to low power wide area transmissions. Marmote is an FPGA based SDR [START_REF] Szilvási | Marmote SDR: Experimental platform for low-power wireless protocol stack research[END_REF] focuses on protocol study. The SDR presented in [START_REF] Cai | Lowpower SDR design on an FPGA for intersatellite communications[END_REF] addresses satellite communications and embeds an FPGA. These proposals are promising but however fail in two key aspects: i) the reconfigurability is limited as processing is only done through hardware description (e.g. FPGA) instead of a software approach ii) and the cost is not sufficiently reduced and dominated by the FGPA. Very recently, a similar architecture based on microcontroller has been proposed for LORA communications [START_REF] Xhonneux | Implementing a LoRa Software-Defined Radio on a General-Purpose ULP Microcontroller[END_REF]. The computational complexity of this architecture is however very limited and can only address low datarate applications. This is the reason we introduce LOLA SDR for Low pOwer Low lAtency reconfigurable Software Defined Radio. Leveraging generic radio components and efficient low power microcontroller (MCU), this architecture is capable of addressing low power and low latency while being able to run flawlessly in real time a complex audio broadcast processing chain that follows High Definition (HD audio) requirements. The core contribution of the brief is the proposal of the architecture and how the different blocks are mapped together. The second contribution of the brief is related to the physical layer chain and how we fit the processing chain with the architecture. The final contribution is a show-off where we demonstrate that our platform can run flawlessly our application in real time, with a very low latency and with a reduced computational power.

The brief is divided into four main parts. In Section II, we introduce the proposed SDR architecture and its key components and justify why it is suitable for low power flexible applications. In Section III, we describe the proposed processing chain dedicated to audio broadcast and justify the technical choices and parameters. Section IV assesses the key performance indicators that are the latency, the power consumption and demonstrate the viability of our approach through trials and a comparison with a Zynq based implementation. Section V eventually draws some conclusions. The proposed architecture for the LOLA SDR is depicted on Figure 1 and is composed of several modules described hereafter:

II. PROPOSED ARCHITECTURE

• An agile front-end receiver (depicted in red) is used for transmitting and receiving signals. The stages are composed of wideband configurable analog to digital converters (and digital to analog converters), band-filters, power amplifiers and antennas. Depending on the target applications, large tunable RF boards or specific frontends can be chosen. For the former, it will increase the flexibility and the agility (more targetable frequency bands) at the price of increased cost, energy consumption and slight degradation of the noise figure. For the latter, the specialization of the SDR will be enhanced, leading to potential better noise figure, reduced cost and reduced energy consumption which is interesting for some IoT scenarios where only a few bands can be targeted. • A microcontroller unit (MCU), depicted in green is the core processing unit of the LOLA SDR. This is a strong difference with classical processing units proposed in the literature and we strongly believe that using MCU offers several advantages to address low power efficient SDR. Some modern MCU have two cores that can be used as independent processing units. Besides, their energy consumption is magnitude lower than the one of GPP and the numerous interfaces can be used to map external devices, peripherals or coprocessors. Finally, their clock speed is on the same order of magnitude as the one required by modern SDR applications. • The specific coprocessor is depicted in blue. Some modern MCU are built with specific hardware components such as encryption engines or Fast Fourier Transform (FFT). Using an MCU with coprocessors offers two key advantages: the operations are performed with high efficiency and the MCU can perform other tasks during the coprocessor use. In our LOLA show-off (see Section IV) we have used an MCU with an FFT coprocessor which greatly alleviates the receiver timing constraints. This is also a strong novelty of our architecture. • The RAM (in purple) is located in the MCU. The Direct Memory Access (DMA) used in LOLA architecture aims to ease the sample acquisition by the radio front end that can be then processed by the MCU or its coprocessors. • A small FPGA is depicted in orange and has two objectives: the first is to provide glue logic for the different components and interfaces. Depending on the size of the FPGA, some coprocessors can also be synthesized on the FPGA and mapped on the MCU. In our example, we have used a tiny FPGA on which we have synthesized three small blocks of channel coding (see Section III). The use of FPGA is a key enabler to specialize our architecture through specific hardware description. The use of a small FPGA still ensures low cost and low energy consumption. • Additional analog and digital audio connectivity with I2S interface is also present. A USB link is also used for programming purposes, and to obtain performance feedback (see Section IV). The proposed architecture is built to have a promising tradeoff between simplicity and low cost of the modules with still computational capacity illustrated by the double core MCU and the coprocessors. We detail in the next section an audio broadcast processing chain that uses this architecture and which is capable of meeting the HD audio requirements.

III. AUDIO BROADCAST PROCESSING CHAIN

A. Physical layer properties and parameters

The processing chain is built in order to ensure low power, low latency and high resilience of the audio processing stack. The proposed physical layer starts with an HD audio stream and uses Orthogonal Frequency Division Multiplexing (OFDM) as it allows to perform the channel equalization in frequency domain.

The proposed chain depends on several parameters that can be split into two categories: the defined parameters (see Table I) and the obtained parameters. For the former, the choice is either technological (for instance based on audio stack) or practical (implementation tricks). For the latter, the values are derived from the defined parameters: ).

• We apply a Hamming [START_REF] Clermidy | MAGALI: A Network-on-Chip based multi-core system-on-chip for MIMO 4G SDR[END_REF][START_REF] Lin | Wireless IoT Platform Based on SDR Technology[END_REF] encoder to protect the audio data. The output of the encoder is then interleaved to enhance error resistance against burst of errors that can typically happen in case of a strong fading channel.

The output size of the Hamming encoder is defined as

2 (1+ log 2 (na×Na) )) × Ho Hi = 448 = N o .
The rate of the output of the Forward Error Correcting (FEC) is = F a × 448 240 = 4.3008 MBits/s. • The number of payload subcarriers is No n = 224. The number of allocated subcarriers (payload + pilots) is set to have 65% of the spectral allocation (similar ratio as it is done in Long Term Evolution [START_REF]3GPP, Technical Specification Group Radio Access Network[END_REF]), leading to a total number of 336. Based on this the number of pilot subcarriers is 336-224 = 112. In practice, to be sure that the last subcarrier on the upper corner is a pilot one (and to ease the channel interpolation) one pilot is added at the corner. We thus have 113 pilots and 337 total allocated subcarriers.

• The binary encoded data is then modulated with a Quadrature Phase Shift Keying (QPSK) modulator that is then mapped in the frequency domain. The rate of the output of the QPSK mapper (complex samples) is F a × 224 240 = 2.1504 MS/s. • We apply an IFFT on this buffer using the coprocessor.

The input rate of the IFFT is equal to F a × N F F T (na×Na) = 4.915 MS/s. It means that an IFFT should be done for each 240 bits of data, so every na×Na F a = 104.16 µs.

• To ensure channel circularity, we insert a Cyclic Prefix (CP) of a duration of 6.9µs. The output rate of the transmitter is then calculated as F e =F a × N F F T +N CP (na×Na) =

MS/s

The processing blocks have been carefully chosen as they offer appealing trade-off between obtained performance (that match the desired indicators) and their relative simplicity: the OFDM waveform uses the coprocessor while the FEC and the CRC can be easily implemented on the FPGA. The choice of QPSK, albeit offering a low spectral efficiency, allows implementation tricks on the MCU side that strongly alleviate the timing constraints and ensures strong noise resistance.

B. Receiver side

To receive and decode the audio stream, the processing is done in two steps: i) an initialization phase with fine time/frequency synchronization and then ii) the decoding phase where the audio stream is decoded in real time with low latency.

The initialization step is computationally intensive as it aims to finely synchronize the receiver in time and frequency domains. This synchronization is done with a correlation between the received buffer in time domain and a pre-calculated sequence. This sequence s p [n] is defined as the Inverse Fourier Transform of the scattered pilots (set to 1) mapped in the frequency domain.

s p [n] = p∈Sp e 2jπp×n N F F T , (1)
where S p denotes the indexes of pilot subcarriers. The synchronisation index p is obtained from the maximum of Γ d,sp [n], the correlation between the received sequence d[n] and the pilot sequence s p [n]. The phase of this maximum gives the Carrier Frequency Offset (CFO) δ as depicted in Eq. 2.

p = Argmax n Γ d,sp [n] , δ = F e × Γ d,sp [p] 2πN F F T (2)
In a second step the CFO estimation δ is used to tune the receiver local oscillator. The low residual carrier frequency offset is handled in the channel equalization step. We depict the receiver blocks of the decoding part on Figure 2. Each color corresponds to a different computational domain and follows the same pattern as Figure 1. The purpose of the chain to be able to track the multipath channel variations and to monitor the performance with the CRC check while efficiency uses all the hardware and software resources. For each received symbol, we will have to provide the audio packet (240 bits) and one CRC flag. In purple are depicted the Direct Memory Access (DMA) mechanics: the DMA takes sample from the RF front-end and align the buffer with the estimated delay p. As the time and frequency localization of the received symbols may slightly vary, the second core of the microcontroller is dedicated to time/frequency lock as depicted in dark green. A correlation is computed on samples at the neighborhood of the previously locked time synchronization index p. If a mismatch is detected, a retro-action is done on the DMA to keep the received buffer perfectly synchronized. In blue is depicted the OFDM demodulator on the FFT coprocessor. In green, the main processing steps done on the first core of the MCU. First, the data in frequency domain is separated between zeros (dropped), pilots and data. The channel is then estimated directly from the pilots (as their value in frequency domain have been set to 1). The equalization and the QPSK demodulation are performed in a joint manner: for each payload subcarrier, the bits after decision are identical to the most significant bits of the output of the complex channel estimation ĉ. It also allows us to pack the bits after decision to reduce the memory footprint. The de-interleaver is done on the FPGA as it only corresponds to flip in bits ordering. The hamming74 decoder is also implemented on the FPGA as it corresponds to simple xor operations, such as the CRC check, implemented in a parallel approach [START_REF] Sprachmann | Automatic generation of parallel CRC circuits[END_REF]. These are depicted in orange on Figure 2. The last step corresponds to the audio rendering with I2S interface and the jack output.

IV. BENCHMARKS AND EXPERIMENTS A. Board synthesis discussion

For our show-off, we have chosen to use a wideband radio front-end, the AD9361 from Analog Devices as it has a wide carrier frequency range. It also allows us to have a very flexible (in both carrier and sampling frequencies) and to be able to configure all the digital stages (automatic gain control policy, digital filter coefficients, etc). We have integrated a Lattice Semiconductor FPGA MachXO, a 26 dBm power amplifier SE5004L from Skyworks and a dual Cortex-M33 microcontroller from NXP : the LPC55S69. This $4 MCU, to be compared to a $50 Zynq (see comparison in Table II), integrates an FFT coprocessor.

The board use the proposed processing chain at a carrier frequency of 5.5 GHz as it is a large licence free band with less traffic. The power supply is provided by an external battery of 37Wh. We depict on Table IV-A a summary of the FPGA resources used in terms of number of registers, slices and Look Up Table (LUT). It is shown that only a part of the FPGA is used with half of the resources still available and with slightly more resources used on the Rx side.

B. Measurements in controlled environment

We validated the technical choices by measurements in an anechoic chamber as depicted in Figure 3. We put the Fig. 3. Setup for validation in the anechoic chamber transmitter and the receiver at a distance of 3.8 meters. At the transmitter side, we used a variable attenuator HMC624 from Analog Devices. We consider three scenarios: Line Of Sight (LOS) and two scenarios with a plate to have constructive (Plate-1) and destructive (Plate-2) interference. We compute on Figure 4, the Packet Error Rate versus the attenuation level. It demonstrates the good performance of the reception stage and also proves the capacity of our receiver to synchronize and detect the OFDM symbol even at low signal-to-noise ratio.

-32 -30 -28 -26 -24 -22 -20 -18 10 -4 10 -3

C. Energy consumption and latency

We expose in Table IV the latency of the Tx and Rx stacks. It shows that the complete latency is 854 µs and is equally shared between Tx and Rx. The latencies are function of the audio word duration (10.4 µs) and the audio packet duration (104.1µs).

In table V, we derive the power consumption of the Rx chain as most of the processing is done on the Rx side. The consumption is split among the different processing parts. The overall consumption of the LOLA SDR Rx part is 437 mW which is magnitude lower than the Zynq based architecture.

D. Comparison with Zynq based implementation

In this part, we compare the performance obtained with our board to an implementation done on a Zynq based SDR architecture. We have implemented the same physical layer chain as described in Section III on a USRP E310 from Ettus Research [START_REF]Universal Software radio platform (USRP)[END_REF]. This dual core Zynq architecture has the same RF board as our LOLA board and the processing was run on the custom Linux provided by Ettus. We depict on Table II some key performance indicators such as the energy consumption, the obtained PER in the LOS case at -20dBm and architecture properties. It shows that the decoding performance is very similar but that we have damatically reduced the power consumption by a factor 7.5.

V. CONCLUSION

In this brief we present the LOLA SDR, a new SDR architecture dedicated to low cost, low power and low latency applications. The proposed architecture leverages new generation of low power microcontrollers that embed calculation capacities as well as coprocessors. By incorporating such component on a board with a generic wideband radio transceiver and low power FPGA, a very flexible yet efficient SDR architecture can be synthesized. We have demonstrated the benefit of our approach by proposing a showcase of an audio broadcast application: the complete processing chain is capable of continuously handling a high definition audio stream in real time with a very low latency and good decoding performance. The energy consumption is of order of magnitude lower than state-of-theart SDR architectures with equivalent computational capacity. It is also worth mentioning that the cost is also greatly alleviated. LOLA architecture paves the way for an area of flexible, efficient, low cost, low power SDR platform that can be used in various applications.

VI. ACKNOWLEGMENT

The research leading to these results received funding from the French Brittany Region and Lannion Tregor Communauté (APP-PME HAD-OC).

Fig. 1 .

 1 Fig. 1. LOLA SDR architecture

Fig. 4 .

 4 Fig. 4. Packet Error Rate versus attenuation for the three scenarios.

TABLE I DEFINED

 I PARAMETERS, VALUE AND ASSOCIATED TYPE. CS STANDS FOR COMPLEX SYMBOLS

	Definition	Name	Value
	Audio sampling frequency	Fa	96 kHz
	Audio word size	na	24 bits
	Number of audio words per packet	Na	10
	Hamming FEC input size	H i	4 bits
	Hamming FEC output size	Ho	7 bits
	Bits per symbol	n	2
	FFT size	N F F T	512 CS
	CP size	N CP	36 CS

  The audio rate is 2.304 Mbits/s and is the product of the audio frequency and audio word size (F a × n a )• The packet encapsulation aims to append a Cyclic Redundancy Check (CRC) to ensure the validity at the receiver side of a group of N a = 10 audio words. The audio packet size is 240 bits (n a × N a ) • The CRC size is computed to have the output of the CRC as a power of 2 (for efficient memory store). The desired output size is 2 (1+ log 2 (Na×na) )) , the next closest power of 2 based on the audio packet size. It leads to a CRC size of 256n a × N a = 16. A generator polynomial 0x8005 is used. • The output size of the CRC encoder is thus 256 bits (2 (1+ log 2 (na×Na) ))

	Radio							µC			FPGA
	Radio & ADC	DMA Memory (512 + 36) CS	Apply sync (512 + 36) CS	Remove CP & FFT 512 CS	Payload masking 224 CS	Channel Equalization 224 CS	448 bits QPSK Demod-ulation	448 bits De-interleaver	256 bits Hamming Decoder	240 bits removal CRC	Audio packet
	δ		p		113 CS						16 bits
			Time / Freq synchro		Pilot masking	Channel Estimation	ĉ				idation CRC val-	Audio Flag
	Fig. 2. Proposed receiver chain. CS stands for complex symbols

•

TABLE II COMPARISON

 II BETWEEN LOLA SDR AND USRP E310

			RF Board	Processing unit	Frequency (MHz)	Operating System	PER @-20dB	Rx Energy consumption
	LOLA SDR	AD-9361		LPC55S69	150 MHz	Bare metal	3.9e-3	437 mW
	USRP E310	AD-9361		Zynq 7010	866 MHz	Yocto Linux	4e-3	3.25 W
			TABLE III
		SUMMARY OF USED FPGA RESOURCES
		Tx Resources	Tx %	Rx Resources	Rx %
	Registers	488/2352		21%	632/2352		27%
	Slices	413/1056		39%	513/1056		49%
	LUTs	817/2112		39%	1011 / 2112		48%


Marina Chauvet
email: marina.chauvet@uca.fr

Didier Debroas

Anne Moné

Aurore Dubuffet

Cécile Lepère

Temporal variations of Microsporidia diversity and discovery of new host-parasite interactions in a lake ecosystem Running title: Microsporidia diversity and new hosts in lakes

In this study, we highlighted for the first time temporal variations of microsporidian diversity in a lake ecosystem over one year. Through two complementary approaches, co-occurrence network and in situ hybridization (with a newly designed probe specific to Microsporidia), we identified new hosts of Microsporidia among community at the base of the food webs.

Introduction

Microsporidia are a diverse phylum of unicellular eukaryotes related to Fungi [START_REF] Karpov | Morphology, phylogeny, and ecology of the aphelids (Aphelidea, Opisthokonta) and proposal for the new superphylum Opisthosporidia[END_REF].

They are obligate intracellular parasites known to date to parasitize a broad range of hosts among the animal kingdom as well as some protists [START_REF] Murareanu | Generation of a Microsporidia Species Attribute Database and Analysis of the Extensive Ecological and Phenotypic Diversity of Microsporidia[END_REF]. Many species parasitizing metazoan hosts have been described, with 16 metazoan phyla described as microsporidian hosts [START_REF] Murareanu | Generation of a Microsporidia Species Attribute Database and Analysis of the Extensive Ecological and Phenotypic Diversity of Microsporidia[END_REF]. Microsporidia include the canonical Microsporidia, which share common features (including the presence of a polar tube, highly reduced mitochondria (mitosomes) and a compact genome), and microsporidia-like organisms [START_REF] Bass | Clarifying the Relationships between Microsporidia and Cryptomycota[END_REF][START_REF] Wadi | Evolution of microsporidia: An extremely successful group of eukaryotic intracellular parasites[END_REF][START_REF] Park | Revisiting the phylogeny of microsporidia[END_REF]. These parasites infect hosts in all environments, but 50% of the known genera infect aquatic hosts [START_REF] Stentiford | Microsporidia: diverse, dynamic, and emergent pathogens in aquatic systems[END_REF]. The environmental diversity of Microsporidia is nevertheless clearly underestimated since these species have been described mostly after the search of infectious agents in organisms of interest (edible fish and crustaceans, auxiliary insects, domestic insects (silkworms, bees)), pests (crop pests, disease-carrying insects), bioindicators (daphnids, amphipods), and humans. Moreover, Microsporidia are generally absent from studies focusing on the diversity of protists carried out by high-throughput sequencing, due to the use of primers not adapted to the microsporidian phylum [START_REF] Hadziavdic | Characterization of the 18S rRNA Gene for Designing Universal Eukaryote Specific Primers[END_REF][START_REF] Hugerth | Systematic design of 18S rRNA gene primers for determining eukaryotic diversity in microbial consortia[END_REF]. In aquatic ecosystems, high-throughput sequencing studies yet suggest the presence of numerous parasites with many sequences affiliated especially to alveolates and Fungi (e.g. [START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF].

Recent studies using microsporidian-specific primers suggest that a large amount of the environmental microsporidian diversity remains to be described [START_REF] Ardila-Garcia | Microsporidian diversity in soil, sand, and compost of the Pacific Northwest[END_REF][START_REF] Williams | Group-specific environmental sequencing reveals high levels of ecological heterogeneity across the microsporidian radiation[END_REF][START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF]. Moreover, although Microsporidia have a very wide host spectrum, they have rarely been described as parasites of protists and microzooplankton. In marine ecosystems, they are notably described as hyperparasites of gregarines (e.g. [START_REF] Mikhailov | Genomic Survey of a Hyperparasitic Microsporidian Amphiamblys sp. (Metchnikovellidae)[END_REF]. In freshwater ecosystems, infections by Microsporidia have been registered in ciliates [START_REF] Lutz | Über Pebrine und verwandte Mikrosporidien. Zweite Mitteilung[END_REF][START_REF] Krüger | Über die Microsporidien-Infektion von Campanella umbellaria (Ciliata Peritricha)[END_REF][START_REF] Görtz | Infections of Stentor roeseli and S. polymorphus (Ciliophora, Heterotrichida) by microsporidia[END_REF][START_REF] Fokin | Euplotespora binucleata n. gen., n. sp. (Protozoa: Microsporidia), a parasite infecting the hypotrichous ciliate Euplotes woodruffi, with observations on microsporidian infections in ciliophora[END_REF][START_REF] Yakovleva | The first case of microsporidiosis in Paramecium[END_REF], gregarines [START_REF] Rühl | Ein Mikrosporidier, Geusia gamocysti n. gen., n. sp. als Hyperparsit bei Gamocystis ephemerae[END_REF][START_REF] Codreanu-Bălcescu | L'ultrastructure d'une Microsporidie hyperparasite d'une grégarine d'Ephémère[END_REF], unicellular cnidarians [START_REF] Morris | Hyperparasitism has wide-ranging implications for studies on the invertebrate phase of myxosporean (Myxozoa) life cycles[END_REF] and rotifers [START_REF] Gorbunov | Parasites in rotifers from the Volga delta[END_REF][START_REF] Wolska | Parasites of zooplankton and periphyton assemblages in the littoral zone of lakes in Drawa National Park, Poland[END_REF]. Most of these observations are based on light microscopy and/or transmission electron microscopy therefore allowing only morphological descriptions. By now, only two microsporidian species parasitizing protists in brackish and river ecosystems are characterized by morphological and molecular approaches (i.e. 18S rDNA sequences): Euplotespora binucleata and Globosporidium paramecii parasitizing the ciliates Euplotes woodruffi and Paramecium aurelia, respectively [START_REF] Fokin | Euplotespora binucleata n. gen., n. sp. (Protozoa: Microsporidia), a parasite infecting the hypotrichous ciliate Euplotes woodruffi, with observations on microsporidian infections in ciliophora[END_REF][START_REF] Yakovleva | The first case of microsporidiosis in Paramecium[END_REF]. Thus, the global diversity of Microsporidia in aquatic ecosystems is largely unknown, especially in small sized hosts (i.e. <150μm).

A first metabarcoding analysis revealed an important diversity of Microsporidia, of which about 50% couldn't be affiliated to a genus and about 25% not to a family [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF]. We hypothesized that some of these Microsporidia may infect understudied hosts, such as protists or microzooplankton. To test this hypothesis, we studied in parallel the diversity of Microsporidia and other eukaryotes, as well as their variations over a year in a lake ecosystem, using a targeted metabarcoding approach, focusing on fractions containing small sized hosts. Potential new hostmicrosporidian interactions were searched through co-occurrence network analysis based on metabarcoding data, as well as through a TSA-FISH approach.

Results

Microsporidian diversity

A total of 1 472 OTUs were obtained from 2874995 18S rRNA gene sequencing reads (1080350 reads after normalization). Three samples were removed because of their low reads number (<12 400) and poor representation of the diversity: 20/02/19-0.8/10-RNA, 06/05/19-10/150-RNA and 29/07/19-10/150-DNA, resulting in a dataset of 85 samples.

On average, 33 823 reads were obtained per sample and the rarefaction curves showed that a plateau was reached for most of the samples (Supplementary Information 3).

Microsporidia α-diversity varied among samples, the highest diversity being recorded in the 0.8-10 µm size fraction (Supplementary Information 4). Despite a high number of OTUs, the microsporidian community was clearly dominated by a low number of dominant OTUs, since 14 OTUs represented more than 50% of the total reads.

OTUs obtained in this study belonged to the canonical Microsporidia, and were affiliated at least to a clade (which roughly correspond to the class rank, [START_REF] Vossbrinck | Phylogeny of the Microsporidia[END_REF] after the first taxonomic affiliation (i.e. implemented in PANAM2 pipeline). Among the 10 clades defined in [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF] for canonical Microsporidia (from clade I to clade IX, clade IV considered as a 'super-clade' and being divided in two clades), our OTUs were distributed in five clades: I, IV1, IV2, V, VIII. The microsporidian community was clearly dominated by clades IV2 and I, which represented respectively 80.6% (77.4% reads) and 16.6% (20.6% reads) of the OTUs. The remaining OTUs were classified into clades IV1: 2% (1.2% reads), V: 0.4% (0.1% reads) and VIII: 0.3% (0.7% reads) (Fig. 1, Supplementary Information 5).

Within the five clades, eight groups (which mostly correspond to families, [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF] were identified following the analysis on PANAM2. Two of them accounted for 74% of the OTUs (83% of the reads): Aquatic outgroup (clade I, 16.6% OTUs, 20.6% reads) and Arlequin group (clade IV2, 57.3% OTUs, 62.2% reads) (Fig. 1, Supplementary Information 5). Among OTUs assigned to a genus, Crispospora (50.0% OTUs, 53.8% reads) and Pseudoberwaldia (7.5% OTUs, 11.1% reads) were the most represented (Fig. 1, Supplementary Information 5).

However, 39% of the OTUs were not assigned to a genus and 22% not even to a group following the analysis on PANAM2 (Supplementary Information 5). We therefore performed phylogenetic analyses on these OTUs, focusing on the most abundant (>500 reads) (Supplementary Information 6). These analyses placed these OTUs into five new groups previously identified in [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF]. Three of these new groups (Van8, Van10 and BPAR5) (Fig. 1) represent a significant portion of the observed diversity. The Van8 group, for example, represented 9.5% of the reads, and up to 90% of the reads in one sample (29/07/19-0.8/10-RNA). We furthermore identified several subgroups (proxies for genera) within both the known and new groups (Supplementary Information 6).

Variations of the microsporidian diversity

No significant differences were observed in the composition of the DNA and RNA samples (Supplementary Information 7). 95% of OTUs were shared by both types of samples. Only 48 and 32 OTUs were specific to DNA and RNA samples, respectively (these OTUs representing at most 0.08% of total reads). The mean rRNA:rDNA ratio computed for each OTU was 7.3, which suggests that much of the diversity discovered was active (i.e. rRNA:rDNA ratio >1).

Variations in the relative abundances of some taxa were however recorded between size fractions as well as across time (Fig. 2). For the size fractions, no major differences in the community composition were recorded at the group level. However, for the same sampling dates, some genera/subgroups showed considerable differences between the two size fractions (Fig. 2). Within the Van8 group, for example, the subgroup1 was almost exclusively found in the large size fraction, sometimes with high abundance (e.g. 50% of the reads in the sample 01/07/19). Subgroups 2 and 3, on the other hand, were mostly found in the small size fraction (Fig. 2).

Regarding variations across time, the composition of microsporidian communities greatly changed between two close sampling dates, from the clade to the genus/subgroup level. In the small size fraction, for example, the sample 01/07/19 was greatly dominated by the genus Crispospora, while the sample 29/07/19 was composed at 80% of the 'Van8 group ; subgroup 3' (Fig. 2). Some genera/subgroups seemed abundant for a particular period (e.g. 'Van8 group ; subgroup 3' between late July and November) or even at a single sampling date ('Arlequin group ; Cr1 subgroup': 02/12/2019), while others showed a saw-toothed abundance over the year (e.g. 'Van8 group ; subgroup 1') (Fig. 2). At the OTUs level, considerable variations across time could also be observed.

Indeed, more than 50% of the reads were represented by two or three OTUs in several samples, with temporal variations in these dominant OTUs (Supplementary Information 8).

Eukaryotic diversity

In the eukaryotic dataset, four samples were removed because of their low reads number (<12 400) and poor representation of the diversity: 18/04/19-10/150-RNA, 14/06/19-10/150-DNA, 29/07/19-0.8/10-DNA and 29/07/19-10/150-DNA, resulting in a dataset of 84 samples. A total of 631 OTUs were obtained from 3158730 18S rRNA gene sequencing reads (1044624 reads after normalization).

In terms of OTUs number, the main taxonomic groups were the Stramenopiles (238 OTUs), Alveolata (141) and Viridiplantae (59). If we consider the read abundance, the dominant groups were the Alveolata (33.3% of the reads), Stramenopiles (31%) and Metazoa (10.5%). The eukaryotic community was clearly dominated by the ciliates (Alveolata) which represented almost 30% of the reads (Fig. 3). The Cryptophyta (Cryptophyceae), Rotifera (Metazoa) and Chlorophyta (Viridiplantae) were also abundant with 9.7%, 6.3% and 5.4% of the reads, respectively.

Considerable differences in the eukaryotic community were identified between the two different size fractions. For example, rotifers and arthropods were almost exclusively found in the large size fraction. Diatoms and chytrids were also more abundant in the 10-150 µm fraction. On the opposite, some phyla were mostly found in the small size fraction as Cryptophyta, Chlorophyta, Cercozoa and Chrysophyceae (Fig. 3). Variations in the eukaryotic communities were also identified across time. Ciliates for example occur in all samples but were particularly abundant during spring and summer, whereas the Cryptophyta were mostly found during winter and spring. Other phyla were identified with variations throughout the sampling period but without any pattern, such as the Chrysophyceae, the arthropods and the rotifers. The latter were, however, sometimes really abundant as in the sample 19/11/18 (large size fraction) representing 61.5% of the reads (Fig. 3).

Co-occurrence networks

In order to suggest potential host-parasite relationships, eukaryotic and microsporidian OTUs with a total abundance higher than 0.05% were used to build a co-occurrence network. 1 640 associations were identified of which 1 324 (80.7%) were positive, in a simple connected network (only one component). We focused our analysis on positive edges only, as Microsporidia are obligate intracellular parasites, this requiring the simultaneous presence of both partners (Supplementary Information 9). Therefore, only positive edges between Microsporidia and eukaryotes were further considered, resulting in 88 microsporidian and 98 eukaryotic OTUs with 206 positive associations represented in the Sankey diagram (Fig. 4).

Microsporidia co-occurred mainly with Ciliophora, Rotifera and Arthropoda, these three phyla being included in almost 50% of the associations. The two most abundant genera of Microsporidia, Crispospora and Pseudoberwaldia, were notably found to co-occur mainly with Ciliophora (46% and 42% of the associations, respectively). To a lesser extent, associations were also found with other eukaryotes including Chrytridiomycota, Dinoflagellata, Bacillariophyta (diatoms), Chlorophyta and Chrysophyceae (Fig. 4). Among the new groups defined in [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF], the Van8 group (clade IV2) showed 16 associations with 11 eukaryotic taxa (about one third of these associations being with Chrysophyceae) and the Van10 group (clade IV2) showed 15 associations with 8 eukaryotic taxa (mostly with Chlorophyta and Rotifera) (Fig. 4) (see Supplementary Information 10 for a detailed table of the number of associations).

TSA-FISH

TSA-FISH was used to confirm potential host-parasite associations between Microsporidia and planktonic eukaryotes (<150 µm) highlighted by the co-occurrence network. All samples (10-150 µm size fraction) were screened with the USP01 probe, targeting all the Microsporidia recovered in the current analysis. Positive signals were observed inside rotifers identified as belonging to the genus Kellicottia by morphological observations (Fig. 5). This association was observed at four different dates ( 27 However due to a high abundance of Asterionella at theses dates, the prevalence was always <0.1%.

Moreover, a single observation (17/06/19 in the large size fraction) shows positive signals inside a ciliate identified as belonging to the genus Stentor by morphological observations (Fig. 6d-f).

Finally, other positive signals were observed, but the potentially infected hosts could not be identified (Supplementary Information 11).

Discussion

So far, few studies have investigated the diversity of Microsporidia in environmental samples. A cloning-sequencing study [START_REF] Ardila-Garcia | Microsporidian diversity in soil, sand, and compost of the Pacific Northwest[END_REF] and a metabarcoding study [START_REF] Williams | Group-specific environmental sequencing reveals high levels of ecological heterogeneity across the microsporidian radiation[END_REF] presented a first description of this diversity, mostly in terrestrial and marine environments.

However, due to a low number of reads, a limited comparison of Microsporidia communities between different sites was made, and variation of the microsporidian diversity through time was not evaluated. In a more recent metabarcoding analysis, an important microsporidian diversity was revealed in lacustrine environments, with more than 1 500 OTUs distributed across seven of the ten clades defined for canonical Microsporidia [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF] and several new groups (and probable new families and/or genera) were discovered. The diversity of Microsporidia in lake Aydat studied here, over one year, was of the same order of magnitude with 1 472 microsporidian OTUs identified. Besides, except two clades weakly represented in [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF], the same clades (I, IV1, IV2, V, VIII) were recovered. Similarly to the previous study, most of the OTUs were affiliated to clade IV2 and clade I, and most of the groups identified were also found in [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF] with the exception of OTUs belonging to the Enterocytozoonidae, which appear almost only in February in our study. Among OTUs assigned to a genus, Crispospora and Pseudoberwaldia were the most represented. These two genera are described as parasites of aquatic hosts (daphnia for Pseudoberwaldia [START_REF] Vávra | Establishment of a new microsporidian genus and species, Pseudoberwaldia daphniae (Microsporidia, Opisthosporidia), a common parasite of the Daphnia longispina complex in Europe[END_REF] or hosts having at least one aquatic development stage (chironomids for Crispospora [START_REF] Tokarev | Life cycle, ultrastructure, and molecular phylogeny of Crispospora chironomi g.n. sp.n. (Microsporidia: Terresporidia), a parasite of Chironomus plumosus L. (Diptera: Chironomidae)[END_REF]). Chironomids eggs and larvae are highly abundant in lake environments, especially when they are eutrophic and anthropized [START_REF] Langdon | Assessing lake eutrophication using chironomids: understanding the nature of community response in different lake types[END_REF]) such as lake Aydat, which could explain the abundance of Microsporidia assigned to Crispospora.

The new groups previously identified in [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF] and also found in this study represented a non-negligible part of the microsporidian diversity as they correspond to 10% of the OTUs included in our phylogenetic analyses, suggesting that a significant proportion of microsporidian groups/families and subgroups/genera remain to be studied. This unknown part of the Microsporidia diversity could correspond to parasites of small hosts (protists, microzooplankton), which currently represent only a small proportion of the microsporidian hosts described.

Between the two size fractions (0.8-10 and 10-150 µm) studied, moderate differences were observed at the finest taxonomic levels (genus/subgroup). Indeed, 'Van8 group ; subgroup 2', 'Van8 group ; subgroup 3' and 'Orthosomella-like group ; O4 subgroup' were found almost exclusively in the small size fraction and 'Van8 group ; subgroup 1' in the large size fraction. Such differences might be explained by the spore sizes, or the type of host parasitized. Indeed, the former could be <10 µm in size and/or infect only small hosts (assuming that Microsporidia could parasitize host <10 µm), and the latter could be large (some spores having a size >10 μm, [START_REF] Vávra | Structure of Microsporidia[END_REF]) and/or infect hosts larger than 10 µm. But as no species has been morphologically described in these subgroups, we know nothing about the size of their spores. However, we know the size of some of the Microsporidia found in this study. Crispospora chironomi for example has spores of 2.5

x 1.5 μm size which can be released in 20-30 μm thick wall capsules [START_REF] Tokarev | Life cycle, ultrastructure, and molecular phylogeny of Crispospora chironomi g.n. sp.n. (Microsporidia: Terresporidia), a parasite of Chironomus plumosus L. (Diptera: Chironomidae)[END_REF]. This is thus not surprising to find this genus in the two size fractions. Pseudoberwaldia are released as freespores from infected daphnia (spore size 4.4 x 2.6 µm, [START_REF] Vávra | Establishment of a new microsporidian genus and species, Pseudoberwaldia daphniae (Microsporidia, Opisthosporidia), a common parasite of the Daphnia longispina complex in Europe[END_REF]). Presence of this genus in 10-150 µm fraction thus raises the hypothesis that these Microsporidia were associated with other eukaryotes (such as protists or microzooplankton), either being passively ingested, or infecting them. This could also support the hypothesis of a secondary host as suspected for Pseudoberwaldia species [START_REF] Vávra | Establishment of a new microsporidian genus and species, Pseudoberwaldia daphniae (Microsporidia, Opisthosporidia), a common parasite of the Daphnia longispina complex in Europe[END_REF]. More striking differences were observed across time (Fig. 2). These variations in diversity identified over time were, for some, similar to those identified by [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF]. For example, abundances of Conglomerata and Pseudoberwaldia increased in June in our study, as observed in the same lake between April to June 2016. Such variations might be directly linked to environmental variables which could influence infection cycles or transmission efficiency of the parasite, as already shown for various parasites found in aquatic ecosystems [START_REF] Cable | Global change, parasite transmission and disease control: lessons from ecology[END_REF]. These environmental variables could also affect indirectly the parasite by influencing their host populations density and distribution, as Microsporidia have generally a narrow host specificity [START_REF] Murareanu | Generation of a Microsporidia Species Attribute Database and Analysis of the Extensive Ecological and Phenotypic Diversity of Microsporidia[END_REF]. The relationship between the different environmental variables (temperature, oxygen and photosynthetic pigment concentration (green algae, Cyanobacteria, diatoms, Cryptophyta)) and the distribution of the different microsporidian subgroups/genera was tested, and only oxygen and diatoms abundance were found to be significant (Supplementary Information 12 and 2). In vitro analyses showed that various parameters, including local changes in pH, salt concentration, UV radiation and temperature, could promote or inhibit the devagination of the polar tube and spore discharge, and therefore impact infection rates (Weiss et al., 2014). However, it is not known whether such variables may influence directly Microsporidia in their environment, thus influencing their distribution. Regarding the potential hosts, such as zooplankton or protists, some can show considerable temporal variations in lake ecosystems [START_REF] Lair | Effects of invertebrate predation on the seasonal succession of a zooplankton community: a two year study in Lake Aydat, France[END_REF][START_REF] Lepère | Succession and Regulation Factors of Small Eukaryote Community Composition in a Lacustrine Ecosystem (Lake Pavin)[END_REF] which could more likely explain the microsporidian variations observed here. In this study, we also identified important temporal variations among eukaryotic phyla such as the ciliates, rotifers and various phytoplanktonic phyla (Fig. 3).

Identifying the hosts of these Microsporidia then seems to be an essential step in the understanding of the microsporidian community variations over time. One approach to unravel these host-Microsporidia potential interactions is to use co-occurrence network analyses, which arguably provides the most exhaustive method for attempting to holistically assess community interactions [START_REF] Fath | Ecological network analysis: network construction[END_REF]. Using statistical tools, co-occurrence networks allow the highlighting of potential associations between microorganisms [START_REF] Chaffron | A global network of coexisting microbes from environmental and whole-genome sequence data[END_REF][START_REF] Lima-Mendez | Determinants of community structure in the global plankton interactome[END_REF], although this type of analyses do not ensure discovery of biotic interactions [START_REF] Blanchet | Co-occurrence is not evidence of ecological interactions[END_REF]. Furthermore, potential associations predicted bioinformatically are informative and useful to reduce the number of hypotheses that might be tested but will always require an experimental validation of the interaction [START_REF] Carr | Use and abuse of correlation analyses in microbial ecology[END_REF]. SPIEC-EASI method builds microbial networks avoiding spurious associations resulting from the application of correlation measures and has been used to predict previously unknown microbial associations [START_REF] Kurtz | Sparse and Compositionally Robust Inference of Microbial Ecological Networks[END_REF].

Using this method, diverse and various potential associations between Microsporidia and eukaryotes have been revealed (Fig. 4, Supplementary Information 9). 50% of these potential associations involved eukaryotic phyla already identified as Microsporidia hosts: Ciliophora, Apicomplexa, Arthropoda and Rotifera [START_REF] Murareanu | Generation of a Microsporidia Species Attribute Database and Analysis of the Extensive Ecological and Phenotypic Diversity of Microsporidia[END_REF]. Among these, several cooccurrences identified genera already described as hosts for Microsporidia: 10 co-occurrences with the ciliate genera Vorticella, Frontania and Stentor [START_REF] Görtz | Infections of Stentor roeseli and S. polymorphus (Ciliophora, Heterotrichida) by microsporidia[END_REF][START_REF] Fokin | Euplotespora binucleata n. gen., n. sp. (Protozoa: Microsporidia), a parasite infecting the hypotrichous ciliate Euplotes woodruffi, with observations on microsporidian infections in ciliophora[END_REF], four associations with the rotifer genus Brachionus [START_REF] Gorbunov | Parasites in rotifers from the Volga delta[END_REF][START_REF] Wolska | Parasites of zooplankton and periphyton assemblages in the littoral zone of lakes in Drawa National Park, Poland[END_REF] and 10 co-occurrences with arthropods belonging to the order Cyclopoida [START_REF] Stentiford | Microsporidia: diverse, dynamic, and emergent pathogens in aquatic systems[END_REF]. Co-occurences were also observed with other phyla which have so far never been described as hosts of Microsporidia: Chlorophyta, Chrytridiomycota, Chrysophyceae, Dinoflagellata, Bacillariophyta, Cryptomycota, Perkinsozoa, Streptophyta, Craspedida, Synurophyceae, Bicosoecida and Cryptophyta. Surprisingly, some of these cooccurrences involve phytoplankton organisms, whereas, to date infection by Microsporidia in phytoplankton has never been recorded. However, all these potential associations identified through the co-occurrence network do not reflect a temporal information. Unfortunately, the sample size is indeed too small here to consider subnetworks for each sampling point.

Although the method we used attempts to minimise spurious associations, we must bear in mind that the associations identified may not be pure parasitism. Indeed, an association could be identified, for example, between a microsporidian species and the host of its parasitic host (hyperparasitism) or be due to a common host (Microsporidia being sometimes opportunistic of another infection [START_REF] Didier | Microsporidiosis: an emerging and opportunistic infection in humans and animals[END_REF]). An association could also reflect a simple feeding relationship (ingestion of a Microsporidia spore by another eukaryote). Also, actual interactions may not have been identified because of the weakness of the signal in the data [START_REF] Blanchet | Co-occurrence is not evidence of ecological interactions[END_REF].

Actual associations can be observed with the use of fluorescence microscopy. Indeed, TSA-FISH has shown its effectiveness in the detection of host-parasite associations in the environment [START_REF] Mangot | Community Structure and Dynamics of Small Eukaryotes Targeted by New Oligonucleotide Probes: New Insight into the Lacustrine Microbial Food Web[END_REF][START_REF] Chambouvet | Intracellular Infection of Diverse Diatoms by an Evolutionary Distinct Relative of the Fungi[END_REF]. But to date, this method has never been applied to detect new Microsporidia-host associations. Using a newly designed specific probe for Microsporidia, positive signals were repeatedly obtained in rotifers (genus Kellicottia), especially in May and June samples (Fig. 5). These signals may rather correspond to developing stages of Microsporidia rather than ingested spores, as spores are usually not stained by FISH methods due to their impermeability [START_REF] Panek | Impact of the microsporidian Nosema ceranae on the gut epithelium renewal of the honeybee, Apis mellifera[END_REF]. Microsporidia-rotifers associations highlighted by the cooccurrence network were thus confirmed by these observations. Indeed, seven of the 25 cooccurrences with rotifers were with the Brachionidae, a rotifer family which includes the genus Kellicottia. Kellicottia rotifers are abundant in freshwater ecosystems, including lake Aydat where they can reach more than 600 individuals per litre in summer [START_REF] Lair | Grazing and assimilation rates of natural populations of planktonic rotifersKeratella cochlearis, Keratella quadrata andKellicottia longispina in a eutrophic lake (Aydat, France)[END_REF]. Rotifers account for a large proportion of freshwater zooplankton and act as intermediaries between primary producers (algae, cyanobacteria) and higher trophic levels (zooplankton, shellfish, fish) [START_REF] Rico-Martínez | Rotifers as Models in Toxicity Screening of Chemicals and Environmental Samples[END_REF]. This newly identified host-parasite association is thus of great importance considering the high prevalence rate observed (38% on average).

Although observed only once, the association between Microsporidia and the Stentor ciliate (Fig. 6d-f) also confirms the four co-occurrences identified with this genus. A case of microsporidian parasitism has already been described in Stentor roeseli and S. polymorphus [START_REF] Görtz | Infections of Stentor roeseli and S. polymorphus (Ciliophora, Heterotrichida) by microsporidia[END_REF].

For the first time to our knowledge, Microsporidia have also been observed in association with phytoplankton (i.e. the diatom Asterionella) even if these observations remained observed at a low prevalence rate (Fig. 6a-c). This association was also highlighted by the co-occurrence network with seven associations between Microsporidia and Fragilariaceae, the family to which the genus Asterionella belong. These surprising observations, if confirmed through electron microscopy (to determine whether these Microsporidia perform a complete life cycle in these hosts), could further expand the already diverse host range of Microsporidia. Such observations of physical associations with diatoms have already been described by [START_REF] Jones | Discovery of novel intermediate forms redefines the fungal tree of life[END_REF] with parasites formerly called Cryptomycota, which could actually be Microsporidia [START_REF] Bass | Clarifying the Relationships between Microsporidia and Cryptomycota[END_REF][START_REF] Adl | Revisions to the Classification, Nomenclature, and Diversity of Eukaryotes[END_REF]. This type of associations leads to many hypotheses on the role of Microsporidia in lacustrine environments. Indeed, the structure and the succession of the different planktonic populations are regulated by the nutritive resources as well as by different biotic factors including parasitism. In order to better understand the functioning of food webs, it appears important to appreciate the diversity of parasites (including Microsporidia) and to quantify their impact on the dynamics and diversity of microzooplankton and phytoplankton, which play a major role at the basis of aquatic food webs. Microsporidia, which now seem to be very abundant and diverse in lacustrine ecosystems, could thus play an important role in the regulation of planktonic populations. It will therefore be essential to confirm, identify and characterize these host-parasite associations more precisely. The use of electron microscopy would allow a more detailed observation of the associations. Also, the isolation and sequencing of a host-parasite pair could lead to the precise identification of both organisms.

Conclusion

In conclusion, our metabarcoding approach with microsporidian universal primers coupled with a network co-occurrence analyse and fluorescence microscopy unveiled a huge microsporidian diversity in a lake ecosystem associated with important variations across a year, and provided a hint on new microsporidian hosts. We notably identified repeated infections in Kellicottia (rotifer), and potential infections of phytoplankton. It now seems very important to study the diversity of Microsporidia on a larger scale, screening more environments and over a longer period of time.

Also, it will be essential to investigate more precisely the hosts of these Microsporidia in order to understand their role in the ecosystems functioning.

Experimental procedures

Study site and sampling procedure

The study was conducted in lake Aydat (Massif Central, France, 45°39′ 50′′ N, 2° 59′ 04′′ E). It is a dimictic and eutrophic lake with a maximum depth of 15 m, situated at an altitude of 825 m and a recreative lake in summer. From 19/11/18 to 07/01/20 water samples were collected in the euphotic zone of the lake at a permanent station located at the deepest zone of the water column using an integrated sampler IWS III (HYDRO-BIOS, Altenholz, Germany) (see Supplementary Information 1 for a global view of the experimental design). Sampling was performed monthly, except from 27th May to 20th June 2019 when it was done twice a week. Temperature and dissolved oxygen were measured along the water column using an oxycal-SL 197 multiparameter probe (WWT, Limonest, France). Phytoplankton biomass was estimated using a submersible fluorescence photometer (Fluoroprobe; BBE-Moldaenke, Kiel, Germany) (see Supplementary Information 2 for temperature, oxygen and phytoplankton biomass data). 14 litres of water were filtered successively through 150 μm and 10 μm pore-size filters in order to concentrate Microsporidia spores (<10 μm fraction, microsporidian spores being mainly <10 µm in length [START_REF] Murareanu | Generation of a Microsporidia Species Attribute Database and Analysis of the Extensive Ecological and Phenotypic Diversity of Microsporidia[END_REF]) and planktonic species susceptible to host Microsporidia (10-150 μm fraction). Part of the microbial biomass of each size-fraction was collected on 0.8-μm-pore-size polycarbonate filters (Millipore, Burlington, MA) at a very low vacuum to prevent cell damage (<15 kPa) and stored at -80°C until nucleic acids extraction (dry-stored filters for DNA extraction, in RLT buffer (RNeasy Mini kit, Qiagen, Hilden, Germany) with 1% 2-mercaptoethanol for RNA extraction). Two filters were produced and subsequently processed for each sample (Supplementary Information 1). The other part of the microbial biomass obtained was fixed with paraformaldehyde (final concentration, 2%) and incubated at 4°C for 24 h. Fixed cells were then collected on 0.8-μmpore-size polycarbonate filters (Millipore; pressure <15 kPa). Filters were preserved by dehydration for 1 min at 95°C, 1 min 30 s at 62°C and 1 min 30 s at 72°C, and a final elongation of 7 min at 72°C. Amplification of the V4 region of total eukaryotes (excluding Microsporidia) was performed using the universal primer 515F (GTGYCAGCMGCCGCGGTA) [START_REF] Caporaso | Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample[END_REF] and the eukaryotic primer 951R (TTGGYRAATGCTTTCGC) [START_REF] Mangot | Short-term dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF]. Each PCR was performed in the mix described above. The amplification conditions consisted of initial denaturation at 95°C for 5 min followed by 33 cycles for 1 min at 95°C, 1 min at 55°C and 1 min 30 s at 72°C, and a final elongation of 10 min at 72°C.

The amplicons were purified and concentrated using the QIAquick Gel Extraction kit (Qiagen).

Samples were then sequenced on a MiSeq Illumina instrument (2x250 bp) (Biofidal, Vaulx-en-Velin, France).

Amplicons analysis and taxonomic affiliation

The PANAM2 (Phylogenetic Analysis of Next-generation AMplicons v2 -https://github.com/mebteam/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] was used to process the sequencing data. The clean-up procedures consisted of eliminating sequences with ambiguous 'N' bases, those below 200 bp and chimeras. The sequences were then assembled and clustered into Operational Taxonomic Units (OTUs) with a similarity threshold of 99% for Microsporidia [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF] and 95% for other eukaryotes [START_REF] Mangot | Short-term dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF][START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF]. Finally, representative sequences of each OTU were affiliated by similarity and phylogeny using a custom curated reference database (Freshwater-database: https://github.com/panammeb/Freshwater-database). This database contains eukaryote references sequences extracted from the SSU Ref SILVA database [START_REF] Pruesse | SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB[END_REF], typical freshwater lineages identified in previous studies (e.g. [START_REF] Debroas | Evidence for an active rare biosphere within freshwater protists community[END_REF] and 528 sequences of 265 microsporidian species phylogenetically classified into clades, groups and genera [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF].

For Microsporidia, affiliations to groups and genera were considered as valid when the identity scores were above 85% and 94.5%, respectively [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF]. As clustering threshold used (especially for Microsporidia, i.e. 99%) may generate OTUs resulting from sequencing errors, we only kept for the final analysis OTUs representing more than 0.005% [START_REF] Bokulich | Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing[END_REF] of the total reads and OTUs present in at least two samples. Data were normalized using the SRS (Scaling with Ranked Subsampling) method [START_REF] Beule | Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities[END_REF].

Phylogenetic analysis of OTUs non-affiliated beyond the clade

All microsporidian OTUs obtained in this analysis were assigned at least to a clade, 78% to a group and 61% to a genus. We therefore performed phylogenetic analyses on OTUs non-affiliated beyond the clade (including those with identity scores below 85%) or beyond the group (including those with identity scores below 94.5%) in order to determine whether new groups (which roughly correspond to families) or new subgroups (similar to genera) could be highlighted. We focused our analyses on abundant OTUs (>500 reads, 619 OTUs), which represent 91% of the total reads. We included in these analyses several sequences from undescribed microsporidian species (found through BLAST search and/or bibliographic review), in addition to species from the database previously used. After an alignment using the MAFFT algorithm [START_REF] Katoh | MAFFT version 5: improvement in accuracy of multiple sequence alignment[END_REF], ML trees were obtained using a General Time-Reversible (GTR) model of evolution with gamma-distributed rate heterogeneity (G) and a proportion of invariable sites (I) (GTR + G + I), with 200 bootstrap replications and partial deletion (85%), using MEGA 7 [START_REF] Kumar | MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets[END_REF].

Diversity analyses

The rRNA:rDNA ratio was calculated for each OTU by dividing the number of RNA reads by the number of DNA reads. Diversity analyses were performed using R (R Development Core Team, 2012). Community richness and diversity indices (Richness observed, Chao1, Shannon and Simpson) were used to infer the taxa richness of Microsporidia. These estimators were computed with the phyloseq package [START_REF] Mcmurdie | phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data[END_REF] as well as the non-metric multidimensional scaling (NMDS) ordination based on a Bray-Curtis dissimilarity matrix [START_REF] Bray | An Ordination of the Upland Forest Communities of Southern Wisconsin[END_REF][START_REF] Paliy | Application of multivariate statistical techniques in microbial ecology[END_REF]. To investigate the potential relationships between the microsporidian community composition and biotic and abiotic factors, canonical correspondence analysis (CCA) was used [START_REF] Ter Braak | Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis[END_REF] with the vegan package [START_REF] Oksanen | vegan: Community Ecology Package[END_REF].

Co-occurrence network

Using statistical tools, co-occurrence networks are used to detect in situ associations between micro-organisms [START_REF] Chaffron | A global network of coexisting microbes from environmental and whole-genome sequence data[END_REF][START_REF] Lima-Mendez | Determinants of community structure in the global plankton interactome[END_REF]. We therefore used this approach to depict potential host-parasite associations between Microsporidia and other eukaryotes.

We focused the analysis on the large size fraction (10-150 µm, 40 samples) which might contain Microsporidia associated to potential new hosts among zooplankton and protists. DNA and RNA samples were used in order to have a sufficient number of samples. OTUs with a total abundance higher than 0.05% (206 microsporidian and 148 eukaryotic OTUs) were grouped and analysed to build a co-occurrence network with SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference [START_REF] Kurtz | Sparse and Compositionally Robust Inference of Microbial Ecological Networks[END_REF]). Raw data were used (before normalization), as the SpiecEasi function incudes a standardisation step (centered log-ratio transformation) to alleviate compositionality bias [START_REF] Aitchison | A new approach to null correlations of proportions[END_REF]. The co-occurrence network was inferred using the neighborhood selection [START_REF] Meinshausen | High-dimensional graphs and variable selection with the Lasso[END_REF]. We used the SPIEC-EASI parameters determined as those generating the sparser graphs by [START_REF] Benoiston | Méta-omique et méta-données environnementales : vers une nouvelle compréhension de la pompe à carbone biologique[END_REF]. Thus, the scaling factor (lambda.min.ratio) was set at 0.001, the number of tested lambda (nlambda) was set as 20 and the number of StARS subsamples (rep.num) was set to 20. In this network, OTUs are represented by nodes and a positive association between OTUs are represented by edges between them (only positive associations were kept in the output graph). Positive edges identified were extracted to visualize only associations between microsporidian genera/subgroups and eukaryotic phyla with a Sankey diagram using the networkD3 package on R (R Development Core Team, 2012; [START_REF] Allaire | networkD3: D3 JavaScript Network Graphs from R[END_REF].

TSA-FISH (Tyramide Signal Amplification-Fluorescent In Situ Hybridization)

To visualize potential microsporidian-hosts associations, TSA-FISH was performed, using a probe designed to target all the microsporidian OTUs identified in this metabarcoding analysis (USP01 probe, 5' CCAGGTTGATTCTGCCTGAC 3'). Hybridization conditions were determined beforehand on two microsporidian species cultured in the laboratory: Anncaliia algerae and Encephalitozoon cuniculi. The former was considered as a non-target species, as it had one mismatch with the USP01 probe, while the later was a target species (no mismatches). At 45°C and 10% deionized formamide, in situ hybridization signals were obtained on the target species and not on the non-target species (not shown), and these hybridization conditions were thus retained.

TSA-FISH was performed on each sample (22 sampling dates) of the large size fraction, in duplicate, as described in [START_REF] Lepère | Diversity and Dynamics of Active Small Microbial Eukaryotes in the Anoxic Zone of a Freshwater Meromictic Lake (Pavin, France)[END_REF] with the following modifications: hybridization buffer (10% deionized formamide), hybridization at 45°C overnight, washing steps at 47°C with washing buffer (450 mM NaCl, 0.01% sodium dodecyl sulfate, 20 mM Tris-HCl [pH 7.5]). The filters were mounted in a mixture containing antifading reagent AF1 (Citifluor; Electron Microscopy Sciences, Hatfield, PA), Vectashield fluorescent mounting medium (Vector laboratories, Burlingame, CA) and 4′,6-diamidino-2-phenylindole (DAPI).

Hybridized cells were visualized under two fluorescence microscopes: Leica DM IRB (Leica Microsystems, Wetzlar, Germany) and Zeiss Axio Imager 2 (Carl Zeiss, Oberkochen, Germany).

Fig. 1 .

 1 Fig.1. Taxonomic affiliation of the microsporidian OTUs (DNA and RNA samples).

Fig. 2 .

 2 Fig.2. Relative abundances of the most abundant (>0.5% of the total reads) microsporidian

Fig. 3 .

 3 Fig.3. Relative abundances of the most abundant (>0.5% of the total reads) eukaryotic phyla across

  /05/19, 06/06/19, 14/06/19 and 17/06/19) and the prevalence of potentially parasitized Kellicottia reached 50% (27/05/19) with a mean value of 38%. TSA-FISH also allowed the observation of positive signals on cells attached to diatoms (genus

	Asterionella) (Fig.6a-c) at five different dates (18/04/19, 08/10/19, 04/11/19, 02/12/19, 07/01/20).

Acknowledgements

The authors are very grateful to Jonathan Colombet and Hermine Billard for their help with sampling. We also thank Agnès Vellet for her contribution to the molecular approaches used in this study as well as Gisèle Bronner who helped with bioinformatics analyses. The work of MC was supported by a PhD fellowship funded by the 'ministère de l'enseignement supérieur de la recherche et de l'innovation'.

in an ethanol series (50, 80, and 100% ethanol for three min each) and stored at -20°C until TSA-FISH staining.

Nucleic acids extraction, amplification and sequencing

Filters prepared for DNA extraction were processed as described in [START_REF] Dubuffet | A phylogenetic framework to investigate the microsporidian communities through metabarcoding and its application to lake ecosystems[END_REF], while filters prepared for RNA extraction were processed with the RNeasy Mini kit with some modifications. Samples underwent a first grinding of three cycles, 30 Hz 30 s (Bead Beater Retsch, Haan, Germany), the supernatant was kept. 400 μl of RLT buffer with 1% 2-mercaptoéthanol and 0.1 g of 0.1 mm glass beads (Sigma-Aldrich, Saint-Louis, MO) were added and a second grinding was carried out under the same conditions, the supernatant was pooled with the first one. Protocol was then carried out according to the manufacturer's recommendations, with a triple treatment with DNAse I. RNA was finally eluted in 30 μl of RNAse free water. Quality and quantity of RNA was analysed with the RNA screen Tape kit and the 2200 Tape station (Agilent Technologies, Santa Clara, CA) and absence of DNA was checked by PCR. cDNA was obtained by reverse transcription with SuperScript III and randoms primers (Invitrogen, Waltham, MA) according to the manufacturer's recommendations. Amplification of the V1-V3 region of the microsporidian gene coding for the 18S rRNA was performed using the universal microsporidian V1F (CACCAGGTTGATTCTGCCTGAC) and 530R (CCGCGGCTGCTGGCAC) primers set [START_REF] Baker | Phylogenetic relationships among Vairimorpha and Nosema species (Microspora) based on ribosomal RNA sequence data[END_REF][START_REF] Weiss | Utility of microsporidian rRNA in diagnosis and phylogeny: a review[END_REF]. Each PCR was performed in a total volume of 30 μL containing 6 μL of 5x Green GoTaq Flexi buffer, 2.4 μL of 25 mM MgCl2, 0.18 μL of GoTaqG2 Flexi DNA Polymerase (Promega, Madison, WI, 5 U/μL), 0.6 μL of 10 mM each dNTP, 0.3 μL of 50 mg/mL BSA and 0.6 μL of each 10 μM primer. The amplification conditions consisted of initial denaturation at 95°C for 5 min followed by 35 cycles

Competing Interests

The authors declare they have no competing interests. Label = sampling date (DD/MM/YY), from 19/11/2018 (left) to 07/01/2020 (right) with a monthly sampling and additional sampling every 3 to 5 days in May and June 2019 See Supplementary Information 14 for relative abundances of the less abundant (<0.5% of the total reads) eukaryotic phyla across samples.