Marie Renaudie

Christine Dumas

Stéphane Vuilleumier

Barbara Ernst
email: barbara.ernst@unistra.fr

Biohydrogen production in a continuous liquid/gas hollow fiber membrane bioreactor: Efficient retention of hydrogen producing bacteria via granule and biofilm formation

Keywords: Hydrogen, bioreactor configuration, submerged membrane, hydrogen producing bacteria retention, in situ gas extraction

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Dark fermentation is a process by which hydrogen is produced by bacteria from biomass under anaerobic conditions. It has been extensively studied at lab scale for the valorization of organic waste at mesophilic temperature without needing a light source [START_REF] Etchebehere | Microbial communities from 20 different hydrogen-producing reactors studied by 454 pyrosequencing[END_REF][START_REF] Guo | Hydrogen production from agricultural waste by dark fermentation: A review[END_REF]. In vivo, a hydrogen yield (HY) of 4 molH2/molhexose is expected through the acetate pathway (Eq. ( 1)). With mixed culture, multiple metabolic pathways are coexisting in the reactive medium reducing the HY (Rafieenia et al., 2020). Butyrate pathway, as acetate pathway, is co-producing hydrogen with a lower HY (2 molH2/molhexose) (Eq. ( 2)). Metabolic pathways for propionate production (Eq. ( 3)), acetate production by homoacetogenesis (Eq. ( 4)) or methane production (Eq. ( 5)) are hydrogenotrophic (consume the produced hydrogen).

Finally, other metabolic pathways are concurrent from the substrate point of view (consume the substrate without hydrogen coproduction) i.e. solvent production (mainly ethanol (Eq. ( 6))) or lactate (Eq. ( 7)). By consuming the substrate or the hydrogen, all these various pathways are decreasing the final HY obtained. Previous studies at pilot scale have focused on the use of co-cultures or mixed cultures, either by dark fermentation alone [START_REF] Kim | Continuous hydrogen production from tofu processing waste using anaerobic mixed microflora under thermophilic conditions[END_REF][START_REF] Ren | Biohydrogen production from molasses by anaerobic fermentation with a pilot-scale bioreactor system[END_REF], in combination with photo-fermentation to maximize the hydrogen yield (HY) [START_REF] Zhang | Bioresource Technology Sequential dark and photo fermentation hydrogen production from hydrolyzed corn stover : A pilot test using 11 m 3[END_REF], or together with anaerobic digestion to enhance methane production [START_REF] Lee | Bioproduction of hydrogen from food waste by pilotscale combined hydrogen/methane fermentation[END_REF].

The retention of hydrogen-producing bacteria (HPB) in continuous systems [START_REF] Li | Fermentative Hydrogen Production From Wastewater and Solid Wastes by Mixed Cultures[END_REF], and the prevention of changes in microbial composition [START_REF] Badiei | Effect of hydraulic retention time on biohydrogen production from palm oil mill effluent in anaerobic sequencing batch reactor[END_REF] represent major challenges for long-term operation of bioreactors for hydrogen production. An efficient gas extraction is essential to avoid the inhibition of hydrogen production due to its accumulation in the bioreactor [START_REF] Clion | Key factors for biohydrogen production by dark fermentation[END_REF][START_REF] Kim | Effect of gas sparging on continuous fermentative hydrogen production[END_REF].

Diverse membrane-based technologies have been used to address these bottlenecks. An extra membrane gas separation unit (gas-gas separation) in order to separate the hydrogen from the carbon dioxide produced can be put in series with a bioreactor without direct impact on bioproduction except that of gas extraction and the possible CO2 recycling for enhanced mass transfer via bioreactor gas sparging ( [START_REF] Shen | Membrane fouling in a fermentative hydrogen producing membrane bioreactor at different organic loading rates[END_REF][START_REF] Shen | Effect of organic loading rate on fermentative hydrogen production from continuous stirred tank and membrane bioreactors[END_REF], likely the result of adhesion of polymeric substances of microbial origin at the membrane surface [START_REF] Jung | Bioreactor design for continuous dark fermentative hydrogen production[END_REF], often leads to bioreactor operation failure. Dynamic membranes, consisting of a biofilm on a solid/liquid microfiltration membrane may limit membrane fouling [START_REF] Park | Biohydrogen production integrated with an external dynamic membrane: A novel approach[END_REF]. Liquid/gas membrane bioreactors (L/G MBR), with a hydrogen-permeable membrane located within the culture medium of the bioreactor were developed to mimic the action of H2 consuming methanogens [START_REF] Zheng | Experimental and theoretical investigation of diffusion processes in a membrane anaerobic reactor for bio-hydrogen production[END_REF]. In another study, the use of a hollow fiber membrane improved hydrogen yield by 9%; however, the biogas diffused in the lumen of hollow fibers had to be removed by a vacuum pump, with additional energy cost [START_REF] Liang | Behavioral study on hydrogen fermentation reactor installed with silicone rubber membrane[END_REF]. In order to meet both requirements of in situ hydrogen extraction and bacterial immobilization, a continuous liquid/gas membrane bioreactor (L/G MBR) composed of a hollow fiber module was recently developed in our laboratory (Ernst et al., 2015). The specificity of this reactor is to integrate in a single unit the bioproduction of hydrogen in the shell part of the module and the extraction of the latter through the membrane in the lumen of the fibers; this system then constituting a gas-liquid contactor.

In this study, we investigated biohydrogen production performance of the submerged hollow fiber L/G MBR, and the potential role of the membrane as a support for the retention of hydrogen-producing bacteria, by documenting bacterial composition over time, and in the different sections of the bioreactor.

2 Materials and methods

Bioreactor setup and operation

The L/G MBR (0.5 L working volume, Figure 1) consisted of a cylindrical-shaped hollow-fiber membrane module. The hollow fibers (pore diameter: 0.1 µm), usually used for ultrafiltration, were made of polytetrafluoroethylene and sealed at each extremity of the membrane module. The useful external surface of the fibers is evaluated at 0.195 m 2 .

The composition of the feed solution was as follows: 12 g/L glucose, 1.8 g/L KH2PO4, 35 g/L (NH4)2 SO4, 2.5 g/L FeSO4, 7H2O, 0.5 g/L MgSO4, H2O, 0.5 g/L NiCl2, 6 H2O.

The feed solution was filtered on a Büchner filter with pore size of 40µm and stored at 4°C. A stirred tank was used for storage of feed solution and kept cold (6°C). The feed solution was swept by N2. Injection of feed solution into the bioreactor circuit was achieved using a peristaltic pump. Time zero of the fermentation experiment corresponded to the end of the filling phase of the entire circuit. The bioreactor was operated by continuous feeding throughout the experiment, applying a hydraulic retention time (HRT) of 12.5 h and an organic loading rate (OLR) of 0.9 g-glucose/L/h.

During steady-state, NaOH (0.5 M) was added continuously to the liquid medium using a peristaltic pump, and its speed was adjusted manually as required to maintain the pH of the liquid outlet at 4.8 ± 0.1. The pH was measured using a multi-parameter analyzer.

Temperature in the reactor was maintained at 37°C by heated water circulating within a heating coil placed around the membrane module.

The bioreactor had originally been seeded with heat-treated activated sludge from the anoxic zone of the Strasbourg Eurometropole wastewater treatment plant. Fresh activated sludge was collected and introduced in a stirred tank reactor equipped with a double jacket connected to a thermostated bath (Bioblock Scientific Polystat 5A). The sludge was agitated at 220 rpm in order to maintain the suspension and went through a thermal treatment at 70°C during 1 h under static air. At the end of the heat treatment, the temperature was then regulated at 37°C, the reactor was wrapped with an aluminum foil to be in dark conditions and the reactive medium was flushed by nitrogen to be under anaerobiose. A model substrate (1.04x10 -2 mmol/L of glucose) was used to feed the bacterial consortium. After 5h of fermentation, the hydrogen production was at its maximum and the reactive medium, adapted to dark fermentation was transferred in the virgin MBR where the fermentation occurs then in continuous (with a model feeding solution) for the first time. The bioreactor had been operated without reseeding for a series of successive fermentation tests lasting 7 to 15 days and then left unused for eight months. The experimented reported here describes the restart of hydrogen production 551 days after the original seeding of the membrane module, with eight months of process interruption after the last seeding.

In order to assess the bacterial development on the outer surface of the membrane without sacrificing the membrane module, three hollow fibers were added to the membrane module (figure 1) before the experiment to be cut after the experiment and used for the subsequent microbiological analyses. As the supplementary fibers were connected to the gaseous outlet, with the same orientation, no perturbation to the usual operation of the L/G MBR was expected, in terms of gas extraction and hydrodynamic behavior. Also, the induced augmentation of the total surface of the membrane was negligible when compared to the original total surface of the membrane; therefore no augmentation of the hydrogen production was expected.

Chemical analyses

Gas analyses

Gases in the gaseous outlet circulating in the lumen of the hollow fibers were analyzed by thermal conductivity detection using a gas microchromatograph (T3000, SRA, France), equipped with a molecular sieve column of 0.5 nm (10 m x 0.32 mm, 30 mm) and a PoraPLOT U column (8 m x 0.32 mm, 10 µm) for the separation of N2, CH4, O2, H2 and CO2.Series of 3 analyses were performed every 10 min, with a sampling time of 10 s.

Organic acids and alcohols analyses

The liquid outlet was sampled at time zero, then during the fermentation (twice a day) and centrifuged (4500 rpm, 30 min). Supernatant and pellet were stored separately at -18°C.

Organic acids (citrate, pyruvate, succinate, lactate, formate, acetate, propionate, butyrate, valerate and isovalerate) and glucose were analyzed simultaneously by high pressure liquid chromatography using a variable wavelength detector and a refraction index detector. Supernatant samples were thawed, centrifuged for 15 min at 15000 rpm, and filtered at 0.2 µm. 20µL of the resulting samples were injected onto a chromatographic column HiPlex-H (7.7 m×300 mm, 8 μm) (Agilent Technologies) heated at 65°C, eluted isocratically with5 mM aqueous H2SO4 at 0.4 mL/min. Each sample was analyzed twice.

Alcohols (ethanol, butanol, propan-1-ol) were analyzed by gas chromatography using a flame ionization detector (7890A GC, Agilent Technologies). Helium (0.6 mL/min) was used as a vector gas. Supernatants were centrifuged during 15 min at 15000 rpm then filtered at 0.2 µm and diluted in an internal standard. The resulting mixture was injected (0.5 µL) in split mode onto a chromatographic column HP-INNOWax (30 m x 0.250 mm, 0.5 µm) (Agilent Technologies) heated at 45°C, eluted isothermally for 4 min and then by a 10°C/min ramp up to200°C. Each diluted sample was analyzed 4 times.

Microbiological analyses

Sampling

Upon completion of the experiment (92 h), 7 mL of liquid outlet were sampled, the membrane module was totally emptied by the bottom opening (Figure 1), and the granules in the liquid fraction were collected. The additional hollow fibers were cut at both potting extremities and removed from the module via the top opening of the membrane module; they were then cut in three 6 cm long segments.

Results and discussion

The membrane bioreactor was originally seeded with heat-pretreated sludge from the WWTP of the city of Strasbourg. Several trial fermentations using the same feed solution of glucose substrate and nutrients were then carried out without further addition of bacterial biomass. Surprisingly, hydrogen production was maintained despite extensive rinsing and washing of the membrane bioreactor between trials. This suggested to us that the microbial populations may be stabilized on the surface of membrane fibers of the bioreactor. To investigate this idea, a new experiment was designed involving the fitting of an additional removable fiber to the membrane bioreactor to allow sacrificial analysis of the fibers after the experiment. The granules observed in the membrane bioreactor at the end of each fermentation test were also analyzed.

Evolution of gas and soluble metabolites productions

Bioreactor operation was resumed without reseeding for 551 days, with a process interruption of a year. A lag time of approximately 12 h was observed before onset of gas production (Figure 2a). This was accompanied by a pH drop from its initial value of 8.0, and its stabilization at 4.8±0.1 (Figure 2a). The hydrogen production rate (HPR) reached a maximum of 4. Clostridium pasteurianum (highly dominant at the end of the experiment).

Bacterial composition in different sections of the L/G MBR

Operation of the L/G MBR was stopped at 92 h, while H2 production was still in steadystate. Upon opening of the bioreactor, granules, both suspended and sedimented, were visible by eye, and suggested microbial development in the bioreactor. In contrast, no biofilm was detected by eye on the surface of the hollow fibers, although its presence was suspected. Further investigation of the fibers by scanning electron microscopy indeed confirmed biofilm development on the hollow fibers, although it was very thin (monolayer) and heterogeneous in appearance (Figure 4). Biofilm density was low and uneven in the top section (close to the liquid inlet) of the membrane module (Figure 4a), and significantly higher at the bottom (close to the liquid outlet) (Figure 4b). The majority of cells were rod-shaped, some undergoing sporulation, and no extracellular polymer matrix was apparent. Microbial granules (Figure 4c) were porous, also diverse in size, and appeared similar to previously reported H2-producing microbial granules [START_REF] Zhang | Biohydrogen production in a granular activated carbon anaerobic fluidized bed reactor[END_REF]. Cell morphologies in the granules were also mainly rods, but highly diverse in size (Figure 4d). The cells were mixed with clusters of particulate matter which may include dead cell material.

DNA analysis showed that the relative composition of bacterial communities varied in the different locations of the membrane module analyzed at the end of the experiment (Figure 4), although globally the same highly dominant taxa were observed as in the liquid outlet through time (Figure 3). Especially, this similarity suggests that the biofilm that developed on the supplementary hollow fibers (Figure 1) was representative to the overall biofilm production. In the sample of the fibers located at the top and at the bottom of the membrane module, and in the granules, 211, 100 and 68 OTU were detected respectively. In addition, Simpson index indicated a closely similar overall bacterial diversity in the microbial granules (0.34) and in the liquid outlet (0.28), while the diversity was higher in the biofilm at the hollow fiber surface (0.12 at the bottom and 0.09 at the top). Clostridium pasteurianum, Enterobacter sp. and Clostridium beijerinckii were the dominant taxa in the granules and the liquid outlet (Figure 4), although their relative abundances differed in the two sections. Clostridium pasteurianum was much more abundant in the liquid outlet (78.6% versus 52.2% in the microbial granules). Conversely, Enterobacter sp. and Clostridium beijerinckii showed higher abundance in the granules than in the liquid outlet (27.6% and 12.7% versus 4.7% and 5.8%, respectively). Although less abundant, Ethanoligenens harbinense was also present in both compartments (1.1% and 3.3% respectively in the liquid outlet and in the granules). As mentioned above, the taxonomic diversity was superior in the biofilm of the hollow fiber membrane, but the bacterial composition differed at the two sampled locations of the biofilm. Clostridium pasteurianum predominated, but with different proportions at the bottom (73.7%, as at the liquid outlet) and on the top of the fiber (51.9%, as in the microbial granules). Other dominant taxa included Clostridium drakei (2.5% and 9.6% at the bottom and the top of the fibers, respectively),

Ethanoligenens harbinense (6.7% and 4.0%) and Clostridium beijerinckii (3.5% and 4.2%). Bacterial strains belonging to these taxa were reported to have additional roles supporting H2 production: Ethanoligenens harbinense by granule formation [START_REF] Ren | Composition of extracellular polymeric substances influences the autoaggregation capability of hydrogen-producing bacterium Ethanoligenens harbinense[END_REF] and Enterobacter sp. by O2 scavenging [START_REF] Zhu | Evaluation of alternative methods of preparing hydrogen producing seeds from digested wastewater sludge[END_REF]. According to Remarkably and as a further advantage of our L/G MBR, good hydrogen production performances were readily restored even though the membrane module had not been reseeded for 551 days and the process had undergone a complete stop of eight months before several starts and stops of operation before the start of the experiment presented in this paper. It seems likely that part of the biofilm developing on the hollow fibers remains strongly attached to the membrane and serves as the seed culture for subsequent experiments. Indeed, the present work, by including additional membrane fibers for microbial analysis, allowed demonstrating that the main taxa detected in the liquid outlet also colonized the fibers as a biofilm. The dominance of Clostridium in our bioreactor design makes sense from this point of view as well, as biofilm formation by

Clostridium strains is well-documented [START_REF] Pantaléon | Biofilms of Clostridium species[END_REF]. The flexibility and stability of bioreactor operation afforded by a "self-seeding" membrane system as in our bioreactor may represent a clear asset for hydrogen production using organic waste as a substrate in the future.

As a final advantage of our L/G MBR, no bacterial washout was observed after more than 6 renewals of the reaction medium, possibly again as a consequence of HPB biofilm and granule formation. Bacterial immobilization in the form of granules has been used extensively in anaerobic wastewater treatment, e.g. for methanogenesis, as it allows applying higher organic loading rates and reducing the volume and surface of bioreactors [START_REF] Kato | Post-treatment of UASB effluent in an expanded granular sludge bed reactor type using flocculent sludge[END_REF]. In contrast to multilayered methanogenic granules [START_REF] Fang | Characterization of a hydrogen-producing granular sludge[END_REF], H2-producing granules were reported to be less complex, with composition and microstructure depending on the operating conditions (e.g. temperature, pH, substrate, presence and concentration of calcium and iron) [START_REF] Zhang | Biohydrogen production in a granular activated carbon anaerobic fluidized bed reactor[END_REF]. It was previously highlighted that in a fluidized bed reactor, from 0 to 5 days (time scale of our test), there was a majority of biofilm and a minority of HPB granules, and the tendency was reversed progressively from 12 days of operation [START_REF] Zhang | Biohydrogen production with anaerobic fluidized bed reactors-A comparison of biofilm-based and granule-based systems[END_REF] 7.

3.

3.

4.

5.

6.

2.

2. 3 . 2

 32 DNA sequencing DNA (DesoxyriboNucleic Acid) was extracted from the centrifugation pellets, the granules and the fiber samples previously stored at -18°C, using the Fast DNA Spin Kit for Soil (MP) following the protocol recommended by the manufacturer, and stored at -18°C. The concentration of the extracted DNA was determined fluorometrically with the Qubit® dsDNA HS assay kit (Invitrogen, USA) following the instructions of the manufacturer.Variable regions V4-V5 of the 16S ribosomal RiboNucleic Acid (rRNA) gene were amplified by polymerase chain reaction and sequenced using a MiSeq sequencer (Illumina) by a commercial provider (INRA Narbonne). The identification of the bacterial taxa was performed online using NCBI Nucleotide Blast and the 16S ribosomal RNA sequences database. The sequences presenting more than 97% identity were considered as belonging to the same phylotype. The bacterial diversity was estimated using Simpson's index[START_REF] Mouillot | A comparison of species diversity estimators[END_REF].

  , which suggests that the long-term operation of the L/G MBR could similarly combine high bacterial density through H2-producing granule formation, together with and efficient continuous H2 extraction. Technological development of H2-producing granules was previously implemented in the Upflow Anaerobic Sludge Blanket (UASB) configuration, i.e. a column bioreactor with an ascending liquid flow towards a top liquid outlet (Ning et al., 2013). Its major drawback was heterogeneous liquid circulation (i.e. hydraulic shortcuts, dead volumes) and the time of formation of mature granules. The UASB was improved by the development of the EGSB (Expanded Granular Sludge Blanket) bioreactor configuration, which aims to improve the liquid medium homogeneity by applying higher liquid fluxes (Bárcenas-Ruiz et al., 2016). A perspective of the present work would be to improve the dark fermentation process flexibility and efficiency by intensifying the EGSB process with a L/G membrane, acting both as an interface for an efficient in situ hydrogen extraction and as a reservoir of HPB for the formation of hydrogen producing granules during the start-up phase of the process. 5 Conclusion During the steady-state operation of the L/G MBR, glucose was totally consumed; a HPR of 135±22 mL/L/h and a HY of 1.1±0.2 molH2/molglucose were reached, with acetate and butyrate as the main metabolites products, testifying of an efficient bacterial production with the in situ extraction of hydrogen. The composition of the bacterial community in the liquid medium evolved over time, but remained mainly composed of HPB Clostridium beijerinckii, Clostridium pasteurianum and Enterobacter sp..The L/G membrane, acting both as an interface for an efficient in situ hydrogen extraction and as a reservoir of HPB for the formation of hydrogen producing granules during the start-up phase of the process, could be used to intensify the EGSB configuration. extraction, and J.-L. Weickert and N. Messaddeq (IGBMC, Université de Strasbourg-CNRS-INSERM) for the SEM images.

Figure 1 :

 1 Figure 1: Schematic diagram of the L/G MBR.

Figure 2 :

 2 Figure 2: Time course of (a) hydrogen and CO2 flow rates and pH and (b) glucose and

Figure 3 :

 3 Figure 3: Evolution of relative abundance of dominant taxa (>5%) in the liquid outlet of

Figure 4 :

 4 Figure 4: Scanning electron micrograph images of microbial colonization of the

Figure 4 :

 4 Figure 4: Spatial distribution in the bioreactor at the end of the experiment (92 h) of

  3 mL/L/min between 12 h and 28 h. As evidenced by pH monitoring, conditions in the bioreactor then remained stable for over 70h, with Clostridium pasteurianum together with Clostridium beijerinckii, Enterobacter sp. and Ethanoligenens harbinense to a lesser degree) are well-known H2 producers[START_REF] Cabrol | Microbial ecology of fermentative hydrogen producing bioprocesses: Useful insights for driving the ecosystem function[END_REF][START_REF] Hung | Roles of microorganisms other than Clostridium and Enterobacter in anaerobic fermentative biohydrogen production systems -A review[END_REF]. The composition of the bacterial community in the liquid medium evolved over time, but remained mainly composed of HPB Clostridium beijerinckii (predominant at the start of H2 production with the highest flow rate) and

	(lactate, isovalerate, propionate, succinate, formate, ethanol) were produced in more
	variable and lower quantities (<0.3 mmol/L/h; Figure 2b, and data not shown). The
	slight increase in ethanol productivity observed at 77h correlated with a transient
	sustained although somewhat irregular hydrogen production. Overall, an average HPR decrease in production of acetate and butyrate. Thus, the major co-products butyrate and
	of 135±22 mL/L/h, representing a hydrogen flow rate per unit of membrane surface of acetate from the glucose feed confirm that the metabolism selected by this bioreactor
	346±56 mL/m 2 /h, was obtained during monitored steady-state (45-92 h), with a stable design is well-tuned for H2 production (Hawkes et al., 2007). The hydrogen production
	H2/CO2 ratio (1.0±0.1). Thus, efficient, stable and long-lasting H2 production was seems to occur mainly through the butyrate pathway which was already mentioned as an
	achieved with our L/G MBR design, with continuous hydrogen extraction through efficient pathway (Dauptain et al., 2020).
	3.2 Evolution of the bacterial community in the liquid outlet
	Respectively, 156, 49 and 62 OTU were detected at 20 h, 53 h and 92 h of fermentation.
	1% and 8.6%
	respectively), while conversely, Clostridium pasteurianum increased very significantly
	Glucose conversion started from the beginning of the experiment and remained almost (53.9%). This trend continued until the end of the experiment (92 h), with the
	complete from 29 h (Figure 2b). Butyrate and acetate were the major metabolites proportion of Clostridium pasteurianum further increased (78.6%), and that of
	detected. They were produced in equimolar proportions until 20h, after which the Clostridium beijerinckii (5.8%) and Enterobacter sp. (4.7%) further reduced. Notably,
	butyrate/acetate ratio increased to approximately 2.2, as evidenced by the respective the proportion of Ethanoligenens harbinense, another well-known hydrogen producer

hollow fibers, and without needing to seed a hydrogen-producing bacterial community each time the process is started. Our approach presents several advantages. In comparison with other technologies making use of a sweeping gas

[START_REF] Kim | Effect of gas sparging on continuous fermentative hydrogen production[END_REF][START_REF] Mizuno | Enhancement of hydrogen production from glucose by nitrogen gas sparging[END_REF]

, there is no dilution of the produced gases with the L/G membrane configuration, which facilitates the subsequent gas purification step. Both HPR and HY remained stable at least up to 92h of fermentation. With a HRT of 12.5 h and an OLR of 0.9 g/L/h, a hydrogen production rate of 3.2±0.5 L/L/d was reached in the same order of magnitude than Shen et al. (2006) (4.6 L/L/d with a HRT of 8h and and OLR of 0.9 g/L/g). By decreasing the HRT (at 0.8 and 3 h, respectively) and increasing the OLR (at 7.9 and 5 g/L/h, respectively),

Kim et

al. (2006) and Park et al. (2018) obtained higher hydrogen production rate (40 and 48.9±4.6 L/L/d, respectively).

flow rates. In steady state, acetate flow rate is stable between 0.9 and 1.3 mmol/L/h when butyrate is stable between 2.3 and 2.7 mmol/L/h. Other organic acids and alcohols Simpson's index tended to diminish over time (0.54, 0.38 and 0.28 respectively at 20 h, 53 h and 92 h of fermentation), indicating that bacterial diversity increased in the liquid medium. The cumulated related abundances of Clostridium, Enterobacter and Ethanoligenens, known to produce H2, in the bioreactor outlet were over dominant and very similar throughout the experiment (Figure

3

): 92.4% at 20 h, 96.8% at 53 h and 95.6% at 92 h. At 20 h, Clostridium beijerinckii was the most abundant taxon (70.9%) followed by Enterobacter sp. (19.9%). The relative abundances of Clostridium beijerinckii and Enterobacter sp. then both decreased at 53 h (30.

[START_REF] Ren | Composition of extracellular polymeric substances influences the autoaggregation capability of hydrogen-producing bacterium Ethanoligenens harbinense[END_REF]

, steadily increased throughout the experiment, from 0.1% (at 20 h) to 3.3% (at 92 h). Thus, the dominant taxa observed in the liquid outlet (mainly

  [START_REF] Banu | Recent advances on biogranules formation in dark hydrogen fermentation system: mechanism of formation and microbial characteristics[END_REF], Enterobacter sp. and Clostridium sp. play a role in the formation of granules. Also, other detected taxa with significant relative abundance were more numerous in the fiber's biofilm than in the granules or the liquid outlet, and included Lactobacillus satsumensis, Lysinibacillus sp. and Stenotrophomonas sp.. Clostridium pasteurianum, Enterobacter aerogenes and Lactobacillus casei were reported to be selected[START_REF] Bakonyi | A novel gas separation integrated membrane bioreactor to evaluate the impact of self-generated biogas recycling on continuous hydrogen fermentation[END_REF]. Notably, the relative abundance of Clostridium was higher in our study (72-87% against 30-40%), as a possible consequence of in situ H2 extraction via the liquid/gas membrane. The influence of the type of bioreactor configuration on the type and relative abundance of selected bacterial taxa in MBR for hydrogen production is further exemplified by another study of a solid/liquid MBR[START_REF] Park | Biohydrogen production integrated with an external dynamic membrane: A novel approach[END_REF], where Clostridium butyricum was over dominant (about 90% of the total community), with the presence of Enterobacter sp. and Enterococcus sp..

	3.4 Interest of the L/G MBR
	It appears that the operating conditions in the L/G MBR constrain the bacterial
	development to a few HPB in the liquid medium. Similar bacterial community profiles
	were also reported in previous studies using other types of MBR. In a gas/gas MBR
	seeded with sludge,

Acknowledgements

The authors thank O. Théobald (ADEME) for his support of this work, Christelle Gruffaz (GMGM, Université de Strasbourg-CNRS) for her assistance in DNA

Funding

This work was supported by a joint PhD grant to M.R. from the French Environment and Energy Management Agency (ADEME) and Université de Strasbourg.

Author contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

CRediT authorship contribution statement

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Christophe Desterke

Ali G Turhan

Annelise Bennaceur-Griscelli

Frank Griscelli
email: franck.grisceli@gustaveroussy.fr

PPARγ cistrome repression during activation of lung monocyte-macrophages in severe COVID-19

Keywords: severe COVID-19, macrophage, cistrome, PPARγ, lung, immune score, innate immune response

The molecular mechanisms of cytokine storm in patients with severe COVID-19 infections are poorly understood. To uncover these events, we performed transcriptome analyses of lung biopsies from COVID-19 patients, revealing a gene enrichment pattern similar to that of PPARγ-knockout macrophages. Single-cell gene expression analysis of bronchoalveolar lavage fluids revealed a characteristic trajectory of PPARγ-related disturbance in the CD14+/CD16+ cells. We identified a correlation with the disease severity and the reduced expression of several members of the PPARγ complex such as EP300, RXRA, RARA, SUMO1, NR3C1, CCDC88A. CHIP-seq analyses confirmed repression of the PPARγ-RXRA-NR3C1 cistrome in COVID-19 lung samples. Further analysis of protein-protein networks highlighted an interaction between the PPARγ-associated protein SUMO1 and a nucleoprotein of the SARS virus. Overall, these results demonstrate for the first time, the involvement of the PPARγ complex in severe COVID-19 lung disease and suggest strongly its role in the major monocyte / macrophage-mediated inflammatory storm.

Introduction

The pandemic caused by the novel SARS-CoV-2 coronavirus has rapidly become the chief public health challenge for many countries around the world. Respiratory complications have been well documented in patients with this disease . This coronavirus harbors a viral S-protein which, during infection, binds with the human protein receptor ACE2 (P. Zhou et al., 2020). ACE2 is abundant in lung, heart, kidney, and adipose tissue and, thus, the binding of the S-protein to ACE2 enables membrane fusion and the introduction of SARS-CoV-2 RNA into these cells [START_REF] Turner | ACE2: from vasopeptidase to SARS virus receptor[END_REF]. The mean incubation period of the disease is about 3 to 9 days [START_REF] Li | Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia[END_REF] and about 18% of cases remain asymptomatic [START_REF] Nishiura | Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19)[END_REF]. About 41.8% of patients develop acute respiratory distress syndrome (ARDS), for which diabetes mellitus can be a contributing factor. Other comorbidities associated with ARDS include hypertension, cardiovascular disease, and chronic kidney disease (C. Wu et al., 2020). The severity of the disease is also age-associated, with the risk of the mortality increasing in patients over 60 years old [START_REF] Wu | Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention[END_REF]. One of the potential consequences of SARS-CoV-2 infection is an uncontrolled immune response in the lungs, which can require treatment in an intensive care unit. This immune response, associated with a cytokine storm, is heterogenetic and variable among individuals, and is still not well understood [START_REF] Mcgonagle | The Role of Cytokines including Interleukin-6 in COVID-19 induced Pneumonia and Macrophage Activation Syndrome-Like Disease[END_REF]. It also remains unclear why the heterogenetic immune response to COVID-19 worsens in certain patients; as yet, we lack adequate knowledge to predict which cases will evolve from mild to severe infection.

In this work, we investigated the immune infiltration in biopsied lung tissues from COVID-19 patients using different bioinformatics approaches, including whole transcriptome experiments, single-cell transcriptome characterization, and investigations of the epigenetic landscape. Mechanistically, we observed major deregulation of the innate immune response in these lung samples. In particular, we identified notable dysregulation of the PPARγ-dependent genetic program in macrophages, which is associated with an increase in the inflammation-promoting program in monocyte-macrophage cells. PPARγ repression was found in different independent cohorts and was strongly correlated in multiple patients with disease severity at the single-cell level in the CD14-CD16 myeloid population. PPARγ is a member of peroxisome proliferator-activated receptor family with PPARα and PPARβ.They have common structure comprising an amino terminal activation function-1 domain, a DNA binding domain, a hinge domain and a conserved activation function-2 domain which allow ligand binding [START_REF] Nolte | Ligand binding and coactivator assembly of the peroxisome proliferator-activated receptor-gamma[END_REF]. PPARγ was firstly described as important regulator of adipocyte differentiation and could have some unsaturated fatty acids, eicosapentaenoic acids and oxidized lipids as natural ligands [START_REF] Bell-Parikh | Biosynthesis of 15-deoxy-delta12,14-PGJ2 and the ligation of PPARgamma[END_REF].

In immune system and lung, PPARγ is expressed in various cell types such as monocytes and macrophages [START_REF] Standiford | Peroxisome proliferator-activated receptor-{gamma} as a regulator of lung inflammation and repair[END_REF], dendritic cells [START_REF] Gosset | Peroxisome proliferator-activated receptor gamma activators affect the maturation of human monocyte-derived dendritic cells[END_REF], epithelial airway cells [START_REF] Hetzel | Inhibition of MMP-9 expression by PPARgamma activators in human bronchial epithelial cells[END_REF]. During lung inflammation, PPARγ could repressed transcription factors that regulated expression of pro-inflammatory molecules like NFKB1, STAT1 and AP1 [START_REF] Honda | Peroxisome proliferatoractivated receptor gamma is expressed in airways and inhibits features of airway remodeling in a mouse asthma model[END_REF][START_REF] Straus | Anti-inflammatory actions of PPAR ligands: new insights on cellular and molecular mechanisms[END_REF]. In our work, PPARγ repression in lung was associated with the induction of several inhibitory immune checkpoints, the lipopolysaccharide-sensitive TLR2 receptor, and HLA class I molecules, and these relationships were all associated with disease severity. In an epigenetic analysis of patients with severe COVID-19, we detected the repression of components of PPARγ-associated cistrome, including NR3C1 and RXRA, which had RGS12 as target in its second promoter. Finally, we also observed the repression of SUMO1, a partner of PPARγ, which is able to interact with nucleoprotein of human SARS-CoV virus and could potentially affect sumoylation.

Results

Extensive innate response and interferon gamma activation in COVID-19 lung biopsies

Transcriptome data of human lung biopsy samples from the dataset GSE147507 [START_REF] Blanco-Melo | Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19[END_REF] were subjected to gene-set enrichment analysis using the 'hallmarks' gene set from the MSigDB database. As compared to healthy donor samples, the majority of samples from COVID-19 lungs were significantly enriched in genes associated with innate immunity, such as the interferon alpha response (with a normalized enrichment score (NES) of +7.72, p-value<0.001; Supplemental Figure 1A), TNFA signaling via NFKB (NES of +4.88, p-value<0.001; Figure 1), complement cascade (NES of +3.51, p-value<0.001; Supplemental Figure 1A), and reactive oxygen species pathway (NES of + 2.78, p-value<0.001; Supplemental Figure 1A). Likewise, these tissues were also enriched in genes linked with IL-6/Jak signaling, which plays a role in both innate and adaptive immunity (NES= +2.49, p-value<0.001; Supplemental Figure 1B). Furthermore, we observed dramatic enrichment in the interferon gamma response, which is involved in the adaptive immune response (NES=+8.11, p-value<0.001; Supplemental Figure 1C). Taken together, changes in the expression patterns of these immune-related genes enabled us to clearly differentiate, via unsupervised classification (Euclidean distances), between lung samples from COVID-19 patients and those from healthy donors (Supplemental Figure 1D).

These results suggested that in COVID-19 lung samples there was a dramatic upregulation of the interferon gamma response, which was accompagnied by a large innate immune response. Indeed, a network analysis of this immune-activated program revealed centralized connections around the interferon gamma response (Figure 1A), with some targets shared with the interferon alpha response. A large part of the network was organized around aspects of the innate response such as interferon alpha, TNFA, the complement cascade, and reactive oxygen species (ROS). The majority of the immune network (Figure 1A) was composed of connections between enzymes, followed by signaling molecules, receptors, and transcription factors. We detected expression changes for a few ligands including chemokines and interleukins (Figure 1A), which were mainly induced by the IFNα and IFNγ response. To validate these results, we obtained single-cell transcriptome data from the bronchoalveolar lavage fluid of healthy donors (n=6, green) and patients with mild blue) or severe red) from the dataset GSE145926. These data were merged together for the purpose of UMAP dimensionality reduction (Figure 1B) [START_REF] Liao | Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19[END_REF]; after canonical correlation and filtration, the merged transcriptome analysis comprised 90696 cells, with a representative proportion of each subgroup (Figure 1C). In this analysis, some of the main subpopulations identified were: T lymphocytes that expressed CD3E and/or CD8A (Supplemental Figure 2, at the top of the UMAP in Figure 1B), with a reduction in the CD8A cluster size in severe COVID-19 samples (Supplemental Figure 2); Natural Killer cells that expressed NKG7, for which the cluster size increased in severe disease (Figure 1D, Supplemental Figure 2, at the top right of the UMAP in Figure 1B), some epithelial cells that expressed KRT8 (Supplemental Figure 3, on the right of the UMAP in Figure 1B), and some B lymphocytes expressing MS4A1 alias CD20 (Supplemental Figure 3, on the top of the UMAP Figure 1B). This analysis also confirmed a major increase in the monocyte-macrophage markers CD14 and CD16 (alias FCGR3A;Italiani and Bora schi, 2014) with increased disease severity (Figure 1D,1E,1F). These results suggest a major role for the innate immune response in the lungs of COVID-19 patients, which intensifies with disease severity.

COVID-19 lung transcriptome reveals defects in cell proliferation, stem cell signaling, and metabolism, with major myeloid infiltration

A differentially expressed gene (DEG) analysis was performed on lung biopsies from COVID-19 patients and healthy donors; this revealed widespread repression of many gene pathways in COVID-19 lungs (Supplemental Figures 4A-4B), which could affect major functionalities of the cells in this organ. These results were consistent with those of a gene-set enrichment analysis (Supplemental Figure 5). Specifically, the gene-set enrichment analysis (performed using the 'hallmarks' gene set of the MsigDB database) highlighted repression of the mitosis spindle and p53 pathway (cell cycle gatekeeper) in samples of COVID-19 lungs compared to those of healthy donors (NES = -3.45 and -2.77, respectively, with p-value<0.001, Supplemental Figure 5A). We also detected the repression of signaling pathways that are implicated in stem cell functionality, such as the NOTCH pathway (NES = -1.70, p-value <0.001, Supplemental Figure 5B) and the beta-catenin signaling pathway (NES = -2.17, p-value<0.001, Supplemental Figure 5B). Furthermore, heme metabolism was also repressed in these samples (NES=-1.70, p-value<0.001, Supplemental Figure 5C). These results suggest that in COVID-19 lung pathology there are major defects in important functionalities linked with tissue homeostasis, such as the regulation of cell proliferation, stem cell signaling, and metabolism. Of the 108 genes that were found to be upregulated in COVID-19 lung samples (Supplemental Table 1), the majority were implicated in immune functionalities. In particular, the DEG analysis highlighted IFI6 as the most upregulated molecule in COVID-19 samples, and it was expressed in individual cells at progressively higher levels in more severe cases (Supplemental Figure 4C). A landscape immune score analysis, performed on lung biopsies with the xcell algorithm and downstream limma analysis (Supplemental Table 2), revealed a significant increase in immune score in COVID-19 lung samples, representing increases in neutrophil, monocyte, M1 macrophage, and adaptive dendritic cell infiltrations (Supplemental Figure 4D). At single cell level, CD68 macrophage M1 marker was confirmed to be increase in bronchoalveolar lavage fluid of mild COVID-19 as compared to healthy donor samples but it was not case in severe COVID-19. In contrast expression CD163 macrophage M2 marker seem to be constant between different groups of samples (Supplemental figure 6). In order to characterize the immune infiltration of COVID-19 lungs, transcriptome data from lung biopsies were cross-integrated with transcriptome data from human hematopoietic and non-hematopoietic tissues (GSE76340) [START_REF] Pont | Microarray Gene Expression Analysis to Evaluate Cell Type Specific Expression of Targets Relevant for Immunotherapy of Hematological Malignancies[END_REF], which were taken to be representative of the micro-environment of the lung samples. The resulting cross-matrix comprised 170 whole transcriptome analyses and was submitted to the cross-batch normalization algorithm Combat to correct batch error (Supplemental Figure 7, Supplemental table 3). After mathematical correction, the normalized matrix was reduced to the 108 genes that were found to be upregulated in COVID-19 lung samples (Supplemental Table 1). An unsupervised prinicipal component analysis revealed major differentiation between healthy donor lungs and COVID-19 lungs along the first principal axis (p-value = 3.215239e-29, Supplemental Figure 4E). Mononuclear cells, monocytes, and macrophages were found in positions similar to the COVID-19 lung samples, suggesting major infiltrations in this tissue (Supplemental Figure 4E) and confirming the results of the 'xcell' immune score analysis (Supplemental Figure 4C). In this principal component analysis, lung samples from healthy donors clustered together with normal primary bronchial epithelial cells, as well as endothelial cells and fibroblasts, suggesting that these cell populations are not implicated in the immune infiltration of COVID-19 lungs. Hematopoietic stem cells, B-cells, T-cells, and dendritic cells were found in intermediary positions between healthy donor and COVID-19 lung samples, which suggested that these cells played only a minor role in the immune infiltration (Supplemental Figure 4E). We also confirmed that the microarray batches coming from different datasets used in this analysis did not influence the results, as they were found to be well-distributed across the first principal axis (Supplemental Figure 8).

Induction of inhibitory immune checkpoints and CD48 in COVID-19 lung samples

Immune checkpoints have been widely discussed as new therapeutic targets for the regulation of the immune system in cancer therapy [START_REF] Pardoll | The blockade of immune checkpoints in cancer immunotherapy[END_REF]. In the case of COVID-19, a patient's prognosis has been found to depend, at least in part, on the immune response [START_REF] Xu | Effective treatment of severe COVID-19 patients with tocilizumab[END_REF]. For this reason, we next investigated the expression of immune checkpoints in COVID-19 lung biopsies compared to healthy donor samples, and constructed an expression heatmap with unsupervised classification that enabled us to differentiate COVID19 samples (COVA and COVB, COVID-19 lung biopsy replicate A and B) from those of healthy donors (HD1 and HD2, lung biopsies from healthy donors 1 and 2) (Supplemental Figure 9A). The majority of inhibitory immune checkpoints, including CEACAM1, IDO1, LGALS9, CYBB, PDL1 (alias CD274), and CD47, were found to be overexpressed in COVID-19 lung samples (Supplemental Figure 9A). Some of these, such as CD47, LGALS9, HAVCR2, IDO1, and CD274, were confirmed to be induced during severe disease progression (Supplemental Figure 9B) at a single-cell level in the CD14-CD16 subpopulation (Supplemental Figures 9C, 9D and Supplemental Figure 10). These results suggest a potential modification of communication among immune cells in COVID-19 lungs. Instead, our investigation of stimulatory immune checkpoints did not differentiate among COVID-19 and healthy donor lung samples (Supplemental Figure 9E). In bronchoalveolar lavage fluid at the single-cell level, CD48 and CD40 were found to be expressed in COVID-19 lung tissue; CD48 was associated with increased disease severity (Supplemental Figure 9F) but CD40 was not (Supplemental Figure 11). In cases of severe COVID-19, CD48 was found to be expressed in the CD14-CD16 subpopulation (Supplemental Figure 9G). These results mainly suggest that immune cell communication, which is regulated by immune checkpoints, may be modified during infection by COVID-19, particularly by the upregulation of inhibitory checkpoints.

Activation of HLA Class I molecules and pathogen-associated molecular patterns of lipopolysaccharide-sensitive TLRs in COVID-19 lung samples

When we examined the transcriptomes of the COVID-19 lung biopsies, we observed that, compared to data from healthy donors, there was a notable increase in antigen-presenting molecules from the HLA class I subcategory, including HLA-A, HLA-B, and HLA-C. We also detected an increase, though lower in magnitude, in antigen-presenting molecules from the class II subcategory: HLA-DPA1, HLA-DQA1, and HLA-DRB5 (Figure 2A). The strong expression of HLA class I molecules was confirmed with the single-cell transcriptome data obtained from bronchalveolar lavage fluid, especially in the case of severe COVID-19 (Figure 2B). Specifically, HLA-B was highly expressed in all lymphoid and myeloid cell subpopulations in the lungs of the patient with severe COVID-19 (Figure 2C). When we examined the expression of toll-like receptors (TLRs), we found no evidence for the induction of the TLR7 sensor in COVID-19 lung samples, but did observe upregulation of TLR4, which is sensitive to lipopolysaccharides (Figure 2D). In bronchoalveolar lavage fluid at the single-cell level, expression of the lipopolysaccharide (LPS)-sensors TLR2 and TLR4 was found to be induced in COVID-19 lungs (Figure 2E), but expression of TLR7 was unchanged. TLR2 expression appeared to increase with disease severity, especially in the CD14-CD16 cell populations (Figure 2F), while TLR4 induction was less dramatic (Figure 2G). These results provide evidence for CD14-CD16 myeloid infiltration in COVID-19 lung samples, with a substantial induction of HLA class I-presenting molecules and the TLR2 LPS sensor. To further characterize the transcriptome of COVID-19 lung biopsies, specifically with respect to the lympho-myeloid lineages, we performed transcriptome immune cell deconvolution on this subset of the bulk transcriptome data (Figure 3A). We observed that COVID-19 samples were segregated from those of healthy donors by a notable differentiation in lymphoid and myeloid components along the first principal axis (P-value=2.003047e-17, Figure 3A). COVID-19 samples were distinguished by positive correlations with myeloid markers (blue barplot, Figure 3B) and a single negative correlation with one lymphoid IFITM1 (pink barplot, Figure 3B). These results confirmed the strong influence of the myeloid signature, particularly when compared to that of the lymphoid signature, in COVID-19 lung samples. We observed a similar pattern at the single-cell level in the bronchoalveolar lavage liquid of patients with severe COVID-19 (Figure 3C), especially with the markers CCL7, FCERG1, and S100A11. In patients with severe COVID-19, S100A11 was found to be mainly induced in the CD14-CD16 subpopulations (Figure 3D). FCERG1 (Fc Fragment Of IgE Receptor Ig) is known to be expressed in monoyctes and macrophages [START_REF] Bournazos | The Role and Function of Fcγ Receptors on Myeloid Cells[END_REF], and here its induction was also found to be more restricted to the CD14-CD16 cell subpopulations (Figure 3E). These results suggested that, along with HLA and TLRs, some myeloid markers are also induced in the CD14-CD16 cell subpopulation during severe COVID-19.

Monocyte-macrophage dysregulation characterized by PPARγ repression in COVID-19 lung samples

A gene-set enrichment analysis performed using the MSigDB immune gene subset revealed that, compared to samples from healthy donors, COVID-19 samples were enriched in genes that had previously been found to be upregulated in macrophages following knockout of PPARγ (NES = +3.20, p-value<0.001, Figure 4A). Consistent with this, we determined that PPARγ expression was in fact lower in COVID-19 lung samples compared to those from healthy donors (Figure 4B). By unsupervised classification, an expression heatmap confirmed that the genes involved in this PPARγ-dependent program in macrophages were also overexpressed in COVID-19 lung samples (Figure 4C). We specifically identified interferon-related genes such as IFI6, IFITM2, and IFI44, TNF-related molecules such as TNFAIP3, interleukins such as IL1B, chemokines such as CCL20, molecules associated with reactive oxygen species such as SOD2 (superoxide dismutase 2), and immune checkpoints like CD48 (Figure 4C). Single-cell transcriptome data from bronchoalveolar lavage fluid confirmed the drastic repression of PPARγ in patients with severe COVID-19 (Figure 4D) that was exacerbated in the CD14-CD16 cell subpopulations (Figure 4E). To investigate this further, we examined patterns of PPARγ-dependent gene expression in single-cell transcriptomes of the CD14+/CD16+ cell subset of bronchoalveolar lavage fluid from healthy donors (n=6) and patients with severe COVID-19 (n=3, as compared to six healthy donor lungs) (Figure 5A); this subset comprised 559 cells from healthy donors and 1134 cells from patients with severe COVID-19. In this cell compartment, PPARγ-negative cells were representative of severe COVID-19 patients and PPARγ-positive cells were representative of samples from healthy donors (Figure 5B). Likewise, unsupervised principal component analysis differentiated, along the first principal axis, between cells from patients with severe COVID-19 and those from healthy donors (Figure 5C), and identified a specific gene expression signature that characterized these myeloid cells (Figure 5D). For example, CD16+/CD14+/PPARγ-negative cells from patients with severe COVID-19 especially repressed some membrane molecules like the tetraspanin CD9, the transferrin receptor TFRC, the cell junction molecule JAML, and the free fatty acid receptor FFAR4 (Figure 5E). In this cell compartment, inflammatory transcription factors such as NFKBIA and MAFB, as well as superoxide dismutase (SOD2), were overexpressed in severe COVID-19 samples (Figure 5F). Upon closer examination of the CD14+/CD16+ cell compartment (Figure 6A), it was also possible to subdivide the cell population based on PPARγ expression: high (over 8 copies of PPARγ per cell), medium (between 3 and 8 copies of PPARγ per cell), and low (fewer than 3 copies of PPARγ per cell) (Figure 6B). We constructed a pseudotime expression trajectory based on PPARγ expression, and this replicated the distribution of cells from severe COVID-19 samples and from healthy donor samples (Figure 6C), as well as the distribution of cells in the PPARγ-expression categories (Figure 6D). An expression heatmap of the pseudotime PPARγ trajectory enabled the identification of 6 clusters of molecules; clusters 1 and 5, in particular, closely followed the same pattern of pseudotime expression as PPARγ (Figure 6E). Within these clusters, we were intrigued to find RETN alias resistin, which is known to be associated with diseases such as noninsulin-dependent diabetes mellitus and acquired generalized lipodystrophy (Figure 6F), FABP4, a fatty-acid binding molecule (Figure 6F), and MRC1, which is implicated in the recognition of complex carbohydrate structures on glycoproteins (Figure 6F). In the same population of cells, we also confirmed a PPARγ-dependent induction trajectory of the inhibitory immune checkpoint IDO1 (Figure 6G). These results suggest that deregulation of this PPARγdependent program plays a central role in the macrophage response during severe COVID-19 lung infection. In particular, the CD14+/CD16+ cell compartment of bronchoalveolar lavage fluids demonstrated a characteristic immune disturbance in samples from patients with severe COVID-19.

Deregulation of PPARγ interactome in COVID-19 bronchoalveolar lavage fluid

To further investigate the dysregulation of the PPARγ-dependent macrophage program in the lungs of COVID-19 patients, we constructed an interactome of the PPARγ network and examined the changes in expression among groups. To do this, PPARγ interactions were collected from the innateDB database of immune network interactions [START_REF] Breuer | InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation[END_REF] (Figure 7A). Of the gene partners associated with PPARγ, we found evidence in the singlecell transcriptomes (bronchoalveolar lavage fluid) for changes in expression associated with COVID-19 infection (Figure 7B), specifically in SUMO1, NR3C1, KLF6, RXRA, CEBPD, and PLAGL1. At the single-cell level, a strong correlation was found between PPARγ and SUMO1 (r=0.47, Figure 7C), PPARγ and NR3C1 (r=0.44, Figure 7C), and PPARγ and RXRA (r=0.35, Figure 7C). A weaker correlation was detected between PPARγ and KLF6 (r=0.13, Figure 7D) in COVID-19 patients, and indeed, we did observe a strong activation of KLF6 associated with the severe cases of the disease (Figure 7E) in all lympho-myeloid cell compartments. We constructed another PPARγ interactome using the IntAct protein database (Figure 7F) and, again, found evidence of relationships between PPARγ and some of its partners at the single-cell level in the bronchoalveolar lavage fluid of COVID-19 patients (Figure 7G). For example, PPARγ expression was strongly correlated with that of EP300 (r=0.29, Figure 7H), CCDC88A (r=0.60, Figure 7H) and RXRA (r=0.45, Figure 7H) in COVID-19 cases. The expression of OPTN did not demonstrate a strong correlation to PPARγ (r=0.15, Figure 7I); however, its expression did progressively increase in the CD14-CD16 myeloid compartment with increased disease severity (Figure 7J). These results suggest that, along with PPARγ, some of its associated proteins are also affected by COVID-19, particularly in the CD14-CD16 cell population in severe cases of the disease. The corepression of PPARγ-associated DNA binding partners such as NR3C1, RARA, RXRA, and EP300 could be evidence for coregulation of the entire PPARγ cistrome in the CD14-CD16 cell population during severe COVID-19.

Co-repression of PPARγ-NR3C1-RXRA cistrome during COVID-19 disease in the lungs

We retrieved data from a PPARγ CHIP-sequencing experiment performed on the THP-1 monocyte cell line (Gene Expression Omnibus (GEO) sample GSM624141) [START_REF] Pott | PPARG binding landscapes in macrophages suggest a genome-wide contribution of PU.1 to divergent PPARG binding in human and mouse[END_REF] from the Cistrome project database and processed the dataset using version HG38 of the human genome. Promoter mapping was performed with the BETA cistrome application +/-100 kb around transcription starting sites [START_REF] Wang | Target analysis by integration of transcriptome and ChIP-seq data with BETA[END_REF]. This analysis confirmed the presence of a well-conserved signal in the mammalian promoter database (Figure 8A). When we integrated the PPARγ CHIP-seq data with the PPARγ-repressed signature found in the lungs of COVID-19 patients (Supplemental Table 2), we were able to identify a genomic program with a landscape of both distal and proximal promoters (Figure 8B and Supplemental Table 4). To confirm the relationships between the PPARγ-NR3C1-RXRA cistrome and the PPARγ repression signature of the COVID-19 transcriptomes, CHIP-seq data obtained from the THP-1 monocyte cell line were retrieved for both NR3C1 (GEO sample GSM2661793) [START_REF] Rollins | Glucocorticoid-induced phosphorylation by CDK9 modulates the coactivator functions of transcriptional cofactor GRIP1 in macrophages[END_REF] and RXRA (GEO sample GSM624142) [START_REF] Pott | PPARG binding landscapes in macrophages suggest a genome-wide contribution of PU.1 to divergent PPARG binding in human and mouse[END_REF] from the Cistrome project database. We also collected THP-1 CHIP-seq data for POLR2A (GEO sample GSM1905827) [START_REF] Yu | RNA polymerase II-associated factor 1 regulates the release and phosphorylation of paused RNA polymerase II[END_REF] to highlight transcriptional activity in this program. Using the 'deeptools' pipeline [START_REF] Ramírez | deepTools: a flexible platform for exploring deep-sequencing data[END_REF], all of these CHIP-seq experiments were integrated with the PPARγ-repressed signature from COVID-*19 lungs in order to identify common promoter signals +/-5 kb around transcription starting sites. As shown in Figure 8C, the proximal PPARγ-repressed epigenetic program in monocytes shared its normal transcriptionally active promoters with RXRA and NR3C1. To characterize the active promoters in monocytes, we retrieved THP-1 CHIP-seq experiments for histone H3 lysine 27 acetylation (GEO sample GSM2544237) [START_REF] Phanstiel | Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development[END_REF] from the Cistrome project database. From the PPARγ-repressed program (Supplemental Table 4 and Figure 8B), RGS12 was confirmed to present a PPARγ signal in its 2 nd promoter; its activity was demonstrated with the H3K27Ac histone mark and binding of RNA polymerase II (Figure 8D), suggesting that this target belongs to the PPARγ-repressed program during COVID-19 in the lungs. At the single-cell level, RGS12 was confirmed to be repressed in the CD14-CD16 cell compartment in COVID-19 lungs, especially in the severe form of the disease (Figure 8E).

SUMO1, from the COVID-19 PPARγ-repressed complex, interacts with nucleoprotein in human SARS virus

In order to investigate the interactions of the PPARγ complex with the human SARS virus, we explored the VirusHostNet 2.0 database [START_REF] Guirimand | VirHostNet 2.0: surfing on the web of virus/host molecular interactions data[END_REF] using the deregulated partners of PPARγ that we identified (Supplemental Table 5). By integrating these interactions with the PPARγ network, we were able to to create a virus-host network (Figure 8F). Using this, we determined that within the PPARγ-repressed complex, only SUMO1 is capable of interacting with a SARS virus, through the nucleoprotein NCAP_CVHSA of the human coronavirus. This result suggests that, during lung infection, repression of the PPARγ complex may be mediated via the interaction of SUMO1 with the virus, and that sumoylation function may also be affected.

Discussion

The COVID-19 pandemic has already affected hundreds of thousands of people and has become the greatest health challenge worldwide. The range of clinical presentations varies from asymptomatic and mild clinical symptoms to acute respiratory-distress syndrome (ARDS) and death. Respiratory complications have been well described in this disease, which is caused by infection with the virus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). Diabetes mellitus has been identified as a factor that contributes to the development of ARDS, while other associated comorbidities include hypertension, cardiovascular disease, and chronic kidney disease (C. Wu et al., 2020). In addition, a link has been demonstrated between obesity and COVID-19 cases that require invasive mechanical ventilation [START_REF] Simonnet | High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation[END_REF].

In this work, by comparing the transcriptomes of COVID-19 lung biopsies with those of healthy donors, we were able to characterize the immune infiltration in this tissue using different bioinformatics approaches. The major finding of our work is the discovery of a link between the severity of COVID-19 and repression of the PPARγ complex. In general, we observed that the adaptive immune response to the disease appeared to be driven by the induction of interferon gamma, while the innate response was largely the result of the induction of interferon alpha, TNF alpha, the complement cascade, and reactive oxygen species. Immune scoring of COVID-19 lung biopsies revealed major myeloid infiltration, specifically by monocytes, M1 macrophages, and neutrophils. Instead, the lymphoid scores for this sample were not significant, which suggests that the role of the adaptive immune response is less important than that of the innate response. This finding was confirmed by immune deconvolution analysis of the transcriptome data from COVID-19 lung samples. The SARS-CoV-2 coronavirus is a single-, positive-stranded RNA virus enveloped in a lipid bilayer (F. Wu et al., 2020) [START_REF] Lu | Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding[END_REF]. The lipid bilayer fuses with the host cell membrane, releasing RNA into the cytoplasm and resulting in the translation of various viral proteins. The replicated RNA genome and synthesized viral proteins reassemble into new viruses, which burst out of the cell [START_REF] Qinfen | The life cycle of SARS coronavirus in Vero E6 cells[END_REF]. When we analyzed the induction of toll-like receptors (TLRs) in COVID-19 lung samples, only the lipopolysaccharide sensor TLR2 was found to be upregulated with respect to controls, which is consistent with detection of the coronavirus envelope. The TLR7 sensor did not seem to be affected by COVID-19 infection in our samples, which suggests a lack of presentation of genetic material from the virus in the lungs; this is consistent with our other data demonstrating inefficiency in the adaptive immune response to this disease.

Compared to the virus that caused the SARS outbreak in 2003, the virus that causes COVID-19 uses the same mechanism to enter host cells, but at a slower speed. However, SARS-CoV-2 accumulates to higher concentrations in the body compared to SARS-CoV. This explains why COVID-19 has a longer incubation period and is more contagious, while SARS instead presents with more symptoms and increased disease severity [START_REF] Guo | The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak -an update on the status[END_REF]. Immune scoring of COVID-19 lung biopsies revealed the infiltration of monocytes/macrophages in this tissue, and gene-set enrichment analysis confirmed a major role for this type of cells in the immune program described in COVID-19 lungs. Several types of cytokines and chemokines belong to this dysregulated macrophage program. As in macrophage activation syndrome (MAS), macrophages in the lungs of COVID-19 patients could play a central role in the non-adapted lung immune response through their contributions to cytokine storm. In COVID-19 lung samples, we detected an increase in interleukin 1 (IL1A and IL1B) and its receptor IL1R2. IL-1 is a pro-inflammatory cytokine produced primarily by monocytes and macrophages. It is present in the inactive form, pro-IL-1β, but, upon monocyte/macrophage activation it is cleaved by caspase-1 and becomes biologically active. Via signaling through its receptor, IL-1β causes lymphocyte and endothelial activation as well as the production of other inflammatory cytokines including IL-6 (V. Pascual et al., 2005). IL-6 signaling, through the JAK cascade, was found to be enriched in COVID-19 lung samples compared to healthy donor tissues and expression of IL-6ST was likewise upregulated. IL-6 is a pleotropic cytokine produced in the early stages of inflammation and is central in driving the acute-phase response. One study of patients with MAS demonstrated that IL-6 produced activated macrophages in tissue obtained from liver biopsies [START_REF] Billiau | Macrophage activation syndrome: characteristic findings on liver biopsy illustrating the key role of activated, IFN-gamma-producing lymphocytes and IL-6-and TNFalpha-producing macrophages[END_REF]. For this reason, treatment with tocilizumab monoclonal antibody, which blocks IL-6 receptors, has been proposed to neutralize the cytokine storm that can occur during severe cases of COVID-19 [START_REF] Fu | Why tocilizumab could be an effective treatment for severe COVID-19?[END_REF]. Within the IL-6 pathway, we found that the SCOC3 (suppressor of cytokine signaling 3) protein was upregulated in COVID-19 lung samples and, at the same time, we detected an induction of inhibitory immune checkpoints such as PDL1. IL-6 was reported to signal, via SCOS3, for the induction of its receptor PD1 during a cytokine storm, which was then found to neutralize the cellmediated antiviral response [START_REF] Velazquez-Salinas | The Role of Interleukin 6 During Viral Infections[END_REF]. Among the inhibitory immune checkpoints that were upregulated in COVID-19 lungs, there is an FDA-approved targeted therapy (consisting of an antagonistic monoclonal antibody) for only one, CD274. However, a potential therapeutic strategy of targeting PDL1/PD1 in order to manipulate the adaptive immune response could be dangerous in COVID-19, because a variety of fatal adverse events related to the respiratory system have been recorded, including but not limited to pneumonia and respiratory failure [START_REF] Wang | Fatal Adverse Events Associated With Programmed Cell Death Ligand 1 Inhibitors: A Systematic Review and Meta-Analysis[END_REF].

In the lungs of COVID-19 patients, we detected a strong induction of the TNF-alpha response using gene-set enrichment analysis. TNF-alpha is a pleomorphic cytokine that has been implicated in the pathogenesis of several inflammatory diseases. It is produced largely by monocytes and macrophages that are activated by toll-like receptor ligands such as endotoxins as well as cytokines such as IL-18, and stimulates local endothelial cells as well as lymphocytes [START_REF] Shenoi | Tumor necrosis factor inhibitors in the management of juvenile idiopathic arthritis: an evidence-based review[END_REF]. IL-18 was also found to be upregulated in COVID-19 lung biopsies compared to controls; this is a unique cytokine in the IL-1 family because it is constitutively present in keratinocytes, epithelial cells, and blood monocytes [START_REF] Puren | Gene expression, synthesis, and secretion of interleukin 18 and interleukin 1beta are differentially regulated in human blood mononuclear cells and mouse spleen cells[END_REF]. In addition to promoting secretion of TNFα and chemokines by macrophages, IL-18 also induces production of the pro-inflammatory cytokine IFNγ by NK cells and T cells [START_REF] Dinarello | Interleukin-18 and the pathogenesis of inflammatory diseases[END_REF]. Indeed, using gene-set enrichment analysis, we observed substantial activation of the IFN-gamma response in COVID-19 lung samples, the primary function of which is to strongly activate monocytes and macrophages [START_REF] Schroder | Interferon-gamma: an overview of signals, mechanisms and functions[END_REF]. Activated macrophages are divided into several general classes based upon their respective stimuli and their resulting polarization, with M1 macrophages driven by IFN-gamma into a classical proinflammatory phenotype that is characterized by increased microbicidal ability, heightened responses to TLR ligands, and upregulated antigen processing and presentation. Consistent with this, we found that the immune scores of COVID-19 lung biopsies highlighted significant infiltration of macrophages of the M1 type. These cells are potent producers of pro-inflammatory cytokines, including IL-6, IL-12 and IL-23, as well as the chemokines IP-10, MIG, and ITAC, which recruit polarized Th1 cells in addition to NK cells [START_REF] Mosser | The many faces of macrophage activation[END_REF] [START_REF] Mantovani | The chemokine system in diverse forms of macrophage activation and polarization[END_REF]. There is also evidence that IFNγ may be a critical driver of hemophagocytosis by these activated macrophages [START_REF] Zoller | Hemophagocytosis causes a consumptive anemia of inflammation[END_REF]. This could be connected to the reduction in heme metabolism-related activity we observed in the transcriptome of COVID-19 lung samples. This dysregulation in secreted effectors in the lungs of COVID-19 patients places monocytes/macrophages at the center of the cytokine storm that has been observed in severe cases of this disease. This was further corroborated by our finding that a gene-set enrichment analysis performed on COVID-19 lung samples highlighted upregulation in a transcriptional program that was characteristic of macrophage cells in which PPARγ has been knocked out. Specifically, this program involved the activation of interferon-related genes, including several cytokines and chemokines. We determined that the expression of PPARγ was indeed downregulated in COVID-19 lung samples, which confirmed the origin of this macrophage dysregulation. Previous research has shown that PPARγ in macrophages limits pulmonary inflammation and promotes host recovery following respiratory viral infection (Huang et al., 2019b). In a murine influenza infection model with genetic-induced obesity (db/db), it was shown that the transcription factor PPARγ was downregulated in the lung macrophages of db/db mice after influenza infection. The treatment of 15-deoxy-Δ12, 14-prostaglandin J2 (15d-PGJ2), a PPAR-γ agonist, largely rescued the survival of db/db mice after influenza infection (Huang et al., 2019a). As was demonstrated in H1N1 infection, there could be a link between PPARγ and macrophages that drives inflammation in COVID-19 patients [START_REF] Josset | Implication of inflammatory macrophages, nuclear receptors, and interferon regulatory factors in increased virulence of pandemic 2009 H1N1 influenza A virus after host adaptation[END_REF]. It is possible that restoring higher levels of PPARγ expression in macrophages could attenuate the severe immune response that has been linked with these innate cells. Several PPARγ agonists exist on the market (in Phases II/III) for this purpose and they have already been proposed as an alternative therapy to target the cytokine storm [START_REF] Ciavarella | Pharmacological (or Synthetic) and Nutritional Agonists of PPAR-γ as Candidates for Cytokine Storm Modulation in COVID-19 Disease[END_REF]. In an animal model, it has been shown that loss of PPARγ can delay monocyte differentiation into macrophages and increase a pro-inflammatory phenotype with long-term LPS stimulation, characterized by an elevated production of the pro-inflammatory cytokines TNF-α, IL1-β, IL-6, and IL-12, and reduced production of the anti-inflammatory cytokine IL-10 (Heming et al., 2018). PPARγ has also been implicated in the homeostasis of lipid metabolism in macrophages [START_REF] Li | Peroxisome proliferator-activated receptor gamma ligands inhibit development of atherosclerosis in LDL receptor-deficient mice[END_REF] and PPAR receptors have been characterized as a crossroad between lipid metabolism and inflammation [START_REF] Chinetti | Peroxisome proliferator-activated receptors (PPARs): nuclear receptors at the crossroads between lipid metabolism and inflammation[END_REF]. In mice, systemic PPARγ deletion provokes severe type-2 diabetes [START_REF] Gilardi | Systemic PPARγ deletion in mice provokes lipoatrophy, organomegaly, severe type 2 diabetes and metabolic inflexibility[END_REF] and PPARγ has been associated with type-2 diabetes in humans via a genome-wide association study (GWAS) of Finns (Scott et al., 2007, p. 2). Within the hospitalized COVID-19 population, individuals with diabetes are over-represented (F. Zhou et al., 2020) and this disorder has been defined as a major comorbidity of COVID-19 [START_REF] Zhu | Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes[END_REF]. It is possible that the link between COVID-19 and type-2 diabetes could be PPARγ. Furthermore, genetic polymorphism in PPARγ has also been linked to body mass index (BMI) [START_REF] Fornage | Inverse effects of the PPAR(gamma)2 Pro12Ala polymorphism on measures of adiposity over 15 years in African Americans and whites. The CARDIA study[END_REF] via GWAS analysis by the Framingham Heart Study 100K project [START_REF] Fox | Genome-wide association to body mass index and waist circumference: the Framingham Heart Study 100K project[END_REF]. PPARγ could also represent a link with the obesity comorbidity found with COVID-19 that could reach 44 percent [START_REF] Richardson | Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area[END_REF].

The epigenetically repressed cistrome program identified in COVID-19 lungs extended to proteins of the classical retinoic acid pathway, such as RXRA, but also to the glucocorticoid receptor NR3C1; this result could suggest changes in the sensitivity of macrophages to glucocorticoids via their anti-inflammatory action on this cell type [START_REF] Mylka | The autophagy receptor SQSTM1/p62 mediates anti-inflammatory actions of the selective NR3C1/glucocorticoid receptor modulator compound A (CpdA) in macrophages[END_REF]. Inside the PPARγ interactome, optineurin was found to be induced during severe COVID-19; this molecule has been characterized as an interleukin-1 receptor-associated kinase 1-binding protein and has been implicated in inflammatory signaling [START_REF] Tanishima | Identification of optineurin as an interleukin-1 receptor-associated kinase 1binding protein and its role in regulation of MyD88-dependent signaling[END_REF]. Finally, within the PPARγ-associated repressed protein network, we identified SUMO1 as capable of interacting with a nucleoprotein of the human SARS virus. PPARγ sumoylation is important for the LPS response and transregulation of proinflammatory cytokines, via NCOR occupancy of kappaB binding sites [START_REF] Jennewein | Sumoylation of peroxisome proliferator-activated receptor gamma by apoptotic cells prevents lipopolysaccharide-induced NCoR removal from kappaB binding sites mediating transrepression of proinflammatory cytokines[END_REF]. Sumoylation could act as a brake on PPARγ-associated repression of the transcriptional activation of inflammatory response genes in macrophages (G. Pascual et al., 2005).

In this work, the immune response to COVID-19 infection in the lungs was investigated; using different approaches, we identified dysfunction in M1 monocytes/macrophages in the innate response process. This dysfunction was characterized by repression of PPARγ, which may play a key role in the cytokine storm of inflammatory monocytes/macrophages in the SARS-CoV-2-infected lung.

Limitations of the study: Integrative analysis through different bioinformatics methods on lung transcriptomes of COVID19 diseased patients, enabled the characterization of the lung innate response as major in this pathological tissue. A fundamental deregulation of PPARγ disruption was found in the innate immune response through monocyte-macrophage cells. Repression of PPARγ was confirmed at single cell level in the severity of the disease. Network analysis on the PPARγ interacting complex revealed also the disruption of some of its partners. PPARγ is an essential molecule implicated in macrophage activation and glucose, fatty acid, cholesterol metabolism: these mechanisms could link to the severity of the disease highly correlated to diabetes and obesity. Interestingly, PPARγ is a druggable target. This bioinformatics study on disturbed immune markers and immune cell deconvolution was validated at single cell level in an independent cohort of samples. Epigenetic integration in the analysis allowed to confirm the genome landscape implication of PPARγ-NR3C1-RXRA cistrome repression in severe lung COVID19 diseases. SUMO1 that represses the PPARγ partner in COVID19 diseased lungs was also found to interact with the SARS virus. The discovery of this original mechanism could help to better understand the huge innate inflammatory processes in COVID19 diseased lungs as well as the associated risk factors found in this disease.

Resource Availability

Lead Contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Frank Griscelli (franck.grisceli@gustaveroussy.fr).

Materials Availability

This study did not generate specific reagents.

Data and Code Availability

We obtained transcriptomes from COVID-19 lung biopsies from dataset GSE147507 in the Gene Expression Omnibus database (https://www.biorxiv.org/content/10.1101/2020.03.24.004655v1) and now publish in Cell on 28th May 2020 (https://www.sciencedirect.com/science/article/pii/S009286742030489X?via%3Dihub) [START_REF] Blanco-Melo | Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19[END_REF]. 'Volcanoplot' software was developed to analyze the significance of immune scores; the corresponding R functions are available at the web address: https://github.com/cdesterke/volacanoimmune. Chip2heat.sh software was developed in SHELL BASH (Bourne-again SHELL) and implemented with the deeptools pipeline, specifically computeMatrix and plotHeatmap, with the promoter plot saved in different formats:

PNG, PDF, SVG. This software can be downloaded at: https://github.com/cdesterke/chip2heat. The R code for the single-cell analyses for marker validation is detailed in the Transparent Methods section of the Supplemental Information and also provided at the following address: https://github.com/cdesterke/covid19sc. Additional resources with interactive Web interface of the single cell analysis: to facilitate data exploration of disturb CD14+/CD16+ double population in bronchoalveolar lavage fluid of severe COVID19 patients, we developed an interactive web interface available at https://cdesterke.shinyapps.io/COVID19LUNG/. This website was built with flexdasboard and shiny application inclusion and with graphical interactivity display by R plotly. This data dashboard allows exploring expression of biomarkers found on PPARγ trajectory in lung monocyte-macrophage disrupted cell population in severe COVID19. User need to select gene ID on the left sidebar and application will display expression of this selected marker with interactivity on TSNE graph, number of positive cells for this marker will be display in value box at top right of the dashboard, also expression by group will be display on violinplot, finally statistical summary (mean and standard deviation: sd) will display by group of samples.

Mendeley Dataset associated to this manuscript: DOI: doi:10.17632/3xnypzvcf7.1 -Supplemental Data S1: R bioinformatics code for single cell transcriptome analysis, related to figures from 1 to 8: DOI: http://dx.doi.org/10.17632/3xnypzvcf7.1#file-14fb92f7-2a84-478c-a1fc-99d41668f1e4 -Supplemental Table 3

Figures legends Figure 1 .

 legends1 Figures legends Figure 1. Immune network and immune checkpoint regulation in COVID-19 lung samples: A. Functional network of immune genes that were upregulated in lung samples from COVID-19 patients compared to those from healthy donors; functions are represented as octagons, and their size represents the number of direct edge connections; B. UMAP dimensionality reduction performed on merged single-cell transcriptome data from bronchoalveolar lavage fluid of healthy donors (green), patients with mild COVID-19 (blue), and patients with severe COVID-19 (red); major markers of cell subpopulations are indicated in black; C. Pie chart representing the proportions of different cell types in the merged single-cell transcriptome analysis; D. Dotplot of single-cell expression patterns (bronchoalveolar lavage fluid) of the main markers of each cluster, by patient of origin (HD, mild COVID-19, and severe COVID-19) (percent: percent of cells expressing each marker, expression level: color intensity), E. Dimensionally reduced (UMAP) single-cell transcriptome (bronchoalveolar lavage fluid) expression patterns for the monocyte markers FCGR3A (CD16) and CD14, by patient of origin (HD, mild COVID-19, and severe COVID-19).

Figure 2 .

 2 Figure 2. Overexpression of HLA molecules and TLRs in COVID-19 lung samples: A. Transcriptome expression heatmap of HLA molecules in COVID-19 lung biopsies and samples from healthy donors; B. Single-cell expression (bronchoalveolar lavage fluid) dotplot of HLA molecules from HDs and patients with mild or severe COVID-19 (percent: percent of cells expressing each marker, expression level: color intensity); C. UMAP representation of single-cell transcriptome (bronchoalveolar lavage fluid) expression data for HLA-B from HDs or patients with mild or severe COVID-19; D. Transcriptome expression heatmap of TLRs in biopsies from COVID-19 and healthy lungs; E. Single-cell expression (bronchoalveolar lavage fluid) dotplot of TLRs from HDs and patients with mild or severe COVID-19 (percent: percent of cells expressing each marker, expression level: color intensity); F. UMAP representation of single-cell transcriptome (bronchoalveolar lavage

Figure 3 .

 3 Figure 3. Main monocyte-macrophage infiltration in COVID-19 lung samples. A. Transcriptome deconvolution of lymphoid-myeloid populations from biopsies of COVID-19 and healthy lungs (p-value: differentiation of groups by Pearson correlation along the first principal axis) abbreviations: HD_lung: lung biopsy of healthy donor, Lung_COVID-19: lung biopsy of COVID-19 patient, MO: monocyte, M1: M1 macrophage, M2: M2 macrophage, IDC: immature dentritic cell, MDC: mature dentritic cells, LYB: B-lymphocyte, LYT: Tlymphocyte, BEC: primary bronchial epithelial cells; B. Barplot of correlations between genes (Pearson coefficients) and the lymphoid (purple) and myeloid (blue) deconvolution of COVID-19 lung biopsies as compared to those of healthy donors; C. Single-cell expression (bronchoalveolar lavage fluid) dotplot of markers that were positively correlated to COVID-19, by patient of origin: HD, mild COVID-19, and severe COVID-19 (percent: percent of cells expressing each marker, expression level: color intensity); D. UMAP representation of singlecell transcriptome (bronchoalveolar lavage fluid) expression data from HDs or patients with mild or severe COVID-19 for S100A11 and E. FCER1G.

Figure 4 .

 4 Figure 4. Deregulation of PPARγ program in COVID-19 lung samples: A. Gene-set enrichment analysis revealed that COVID-19 lung samples are enriched in genes that are upregulated in macrophages following PPARγ knockout (NES: normalized enrichment score, FDR: false discovery rate); B. Boxplot of PPARγ expression in COVID-19 lung biopsies compared to samples from healthy donors, data from two independent datasets (p-value from two-sided test with Welch correction); C. Expression heatmap of PPARγ-knockoutdependent macrophage expression program in the transcriptome of COVID-19 lung biopsies compared that of healthy donors (Euclidean distances); D. Violinplot of PPARγ count expression in single-cell transcriptome of bronchoalveolar lavage fluid; E. UMAP representation of single-cell transcriptome (bronchoalveolar lavage fluid) expression of PPARγ in HDs and patients with mild or severe COVID-19.

Figure 5 .

 5 Figure 5. Single-cell expression signature of CD14+/CD16+ cell compartment, in which PPARγ was repressed in the lungs of patients with severe COVID-19: A. Dot plot of PPARγ expression in CD14+/CD16+ (FCGR3A) cells in bronchoalveolar lavage fluid from patients with severe COVID-19 as compared to samples from healthy donors; B. UMAP dimensionality reduction for CD14, FCGR3A, PPARγ and groups (sc: severe COVID-19, control: healthy donors); C. Principal component analysis performed on CD14+/CD16+ PPARγ+/-cell compartment (sc: severe COVID-19, control: healthy donors); D. Expression heatmap of the predictive signature for the first principal axis of the analysis in (C); E. UMAP representation of the expression of membrane molecules that are repressed in the CD14+/CD16+ cell compartment of bronchoalveolar lavage fluid from severe COVID-19 cases; F. UMAP representation of the expression of molecules that are upregulated in the CD14+/CD16+ cell compartment of bronchoalveolar lavage fluid from severe COVID-19 cases.

Figure 6 .

 6 Figure 6. PPARγ expression trajectory in the CD14+/CD16+ cell compartment in lungs: A. TSNE dimensional reduction of CD14+/CD16+ cells from bronchoalveolar lavage fluid from patients with severe COVID-19 (sc, in blue) as compared to healthy donors (control, in red), B. TSNE dimensional reduction of CD14+/CD16+ cells with a projection of three expression categories for PPARγ: cells with high expression of PPARγ in red, cells with medium

Figure 7 .

 7 Figure 7. Deregulation of the PPARγ interactome in COVID-19 lung samples: A. PPARγ network interactome retrieved from InnateDB database; B. Single-cell expression (bronchoalveolar lavage fluid) dotplot of PPARγ interactome markers from innateDB in HDs and patients with mild or severe COVID-19 (percent: percent of cells expressing each marker, expression level: color intensity); C-D. (C) Single-cell expression biplot of PPARγ correlation with SUMO1, NR3C1, RXRA and (D) KLF6 (healthy donor in green, mild COVID-19 in blue, severe COVID-19 in red); E. UMAP representation of single-cell transcriptome (bronchoalveolar lavage fluid) expression of KLF6 from HDs or patients with mild or severe COVID-19; F. PPARγ network interactome retrieved from IntAct database; G. Single-cell expression (bronchoalveolar lavage fluid) dotplot of PPARγ interactome markers from IntAct in HDs and patients with mild or severe COVID-19 (percent: percent of cells expressing each marker, expression level: color intensity); H. Single-cell expression biplot of PPARγ correlation with EP300, CCDC88A, RARA, and (I) OPTN (healthy donor in green, mild COVID-19 in blue, severe COVID-19 in red); J. UMAP representation of single-cell transcriptome (bronchoalveolar lavage fluid) expression of OPTN from HDs or patients with mild or severe COVID-19.

Figure 8 .

 8 Figure 8. PPARγ-RXRA-NR3C1 cistrome repression in COVID-19 lungs and SUMO1-SARS interaction: A. Phastconservation plot of PPARγ CHIP-seq data from the THP-1 cell line (HG38 human genome) mapped on a database of mammalian promoters; B. Signalpromoter plot of PPARγ CHIP-seq data from THP-1 integrated with the repression signature found in COVID-19 lung transcriptomes (size of the circle is proportional to the negative logRatio found in the COVID-19 lung transcriptome, mapping +/-100kb around transcription starting sites (TSS)); C. Promoter heatmap for PPARγ-RXRA-NR3C1 cistrome and POLR2A from CHIP-seq data from THP-1 adapted to the repression signature found in the COVID-19 lung transcriptome (mapping +/-5kb around transcription starting sites (TSS)); D. PPARγ CHIP-seq signal found in the promoter of RGS12, mapped with POLR2A and Histone H3 K27 acetylation in THP-1 cells; E. UMAP representation of single-cell transcriptome (bronchoalveolar lavage fluid) expression of RGS12 in HDs or patients with mild or severe COVID-19 ; F. Human-SARS protein-protein interactome network, with integration of the PPARγ complex affected in COVID-19 lung infection (edges represent connections found in VirusHostNet database).

Acknowledgments:

We thank the association "Vaincre le cancer NRB" (https://www.vaincrelecancer-nrb.org/) which made it possible to finance the computer equipment used to carry out these analyses.This work was performed with grants from the ANR "Programme d'Investissements d'Avenir" of the INGESTEM National Infrastructure (ANR-11-INBS-0009-INGESTEM) and INSERM, University Paris Sud.

Declaration of Interests:

The authors do not have any conflict of interest to declare.