M. C. Abt, L. C. Osborne, L. A. Monticelli, T. A. Doering, T. Alenghat et al., , 2012.

, Commensal bacteria calibrate the activation threshold of innate antiviral immunity, Immunity, vol.37, pp.158-170

K. H. Antunes, J. L. Fachi, R. De-paula, E. F. Da-silva, L. P. Pral et al., Microbiota-derived acetate protects against respiratory syncytial virus infection through a GPR43-type 1 interferon response, Nat. Commun, vol.10, p.3273, 2019.

N. Arpaia, C. Campbell, X. Fan, S. Dikiy, J. Van-der-veeken et al., Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation, Nature, vol.504, pp.451-455, 2013.

M. N. Ballinger and T. J. Standiford, Postinfluenza bacterial pneumonia: host defenses gone awry, J. Interferon Cytokine Res, vol.30, pp.643-652, 2010.

A. Barthelemy, S. Ivanov, M. Hassane, J. Fontaine, B. Heurtault et al., Exogenous activation of invariant natural killer T cells by a-galactosylceramide reduces pneumococcal outgrowth and dissemination postinfluenza, vol.7, pp.1440-1456, 2016.

A. Barthelemy, S. Ivanov, J. Fontaine, D. Soulard, H. Bouabe et al., Influenza A virus-induced release of, 2017.

J. M. Blander, R. S. Longman, I. D. Iliev, G. F. Sonnenberg, and D. Artis,

K. C. Bradley, K. Finsterbusch, D. Schnepf, S. Crotta, M. Llorian et al., , 2019.

R. L. Brown, R. P. Sequeira, C. , and T. B. , The microbiota protects against respiratory infection via GM-CSF signaling, Nat. Commun, vol.8, p.1512, 2017.

K. F. Budden, S. L. Gellatly, D. L. Wood, M. A. Cooper, M. Morrison et al., Emerging pathogenic links between microbiota and the gut-lung axis, Nat. Rev. Microbiol, vol.15, pp.55-63, 2017.

A. Cait, M. R. Hughes, F. Antignano, J. Cait, P. A. Dimitriu et al., Microbiome-driven allergic lung inflammation is ameliorated by short-chain fatty acids, Mucosal Immunol, vol.11, pp.785-795, 2018.

J. Cao, D. Wang, F. Xu, Y. Gong, H. Wang et al., Activation of IL-27 signalling promotes development of postinfluenza pneumococcal pneumonia, EMBO Mol. Med, vol.6, pp.120-140, 2014.

K. Chakraborty, M. Raundhal, B. B. Chen, C. Morse, Y. Y. Tyurina et al., The mito-DAMP cardiolipin blocks IL-10 production causing persistent inflammation during bacterial pneumonia, Nat. Commun, vol.8, p.13944, 2017.

T. B. Clarke, Microbial programming of systemic innate immunity and resistance to infection, PLoS Pathog, vol.10, 2014.

T. B. Clarke, K. M. Davis, E. S. Lysenko, A. Y. Zhou, Y. Yu et al., Recognition of peptidoglycan from the microbiota by Nod1 enhances systemic innate immunity, Nat. Med, vol.16, pp.228-231, 2010.

A. K. Coussens, R. J. Wilkinson, and A. R. Martineau, Phenylbutyrate is bacteriostatic against Mycobacterium tuberculosis and regulates the macrophage response to infection, 2015.

, PLoS Pathog, vol.11, p.1005007

L. A. David, C. F. Maurice, R. N. Carmody, D. B. Gootenberg, J. E. Button et al.,

R. De-weirdt, S. Possemiers, G. Vermeulen, T. C. Moerdijk-poortvliet, H. T. Boschker et al., Human faecal microbiota display variable patterns of glycerol metabolism, FEMS Microbiol. Ecol, vol.74, pp.601-611, 2010.

E. Deriu, G. M. Boxx, X. He, C. Pan, S. D. Benavidez et al., Influenza virus affects intestinal microbiota and secondary Salmonella infection in the gut through type I interferons, PLoS Pathog, vol.12, p.1005572, 2016.

M. S. Desai, A. M. Seekatz, N. M. Koropatkin, N. Kamada, C. A. Hickey et al., A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility, Cell, vol.167, pp.1339-1353, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01439105

D. Erny, A. L. Hrab-e-de-angelis, D. Jaitin, P. Wieghofer, O. Staszewski et al., Host microbiota constantly control maturation and function of microglia in the CNS, Nat. Neurosci, vol.18, pp.965-977, 2015.

I. Galvão, L. P. Tavares, R. O. Corrêa, J. L. Fachi, V. M. Rocha et al., The metabolic sensor GPR43 receptor plays a role in the control of Klebsiella pneumoniae infection in the lung, Front. Immunol, vol.9, p.142, 2018.

H. E. Ghoneim, P. G. Thomas, and J. A. Mccullers, Depletion of alveolar macrophages during influenza infection facilitates bacterial superinfections, J. Immunol, vol.191, pp.1250-1259, 2013.

H. T. Groves, L. Cuthbertson, P. James, M. F. Moffatt, M. J. Cox et al., Respiratory disease following viral lung infection alters the murine gut microbiota, Front. Immunol, vol.9, p.182, 2018.

A. H. Hansen, E. Sergeev, D. Bolognini, R. R. Sprenger, J. H. Ekberg et al., Discovery of a potent thiazolidine free fatty acid receptor 2 agonist with favorable pharmacokinetic properties, J. Med. Chem, vol.61, pp.9534-9550, 2018.

M. Hassane, D. Demon, D. Soulard, J. Fontaine, L. E. Keller et al., Neutrophilic NLRP3 inflammasome-dependent IL-1b secretion regulates the gdT17 cell response in respiratory bacterial infections, Mucosal Immunol, vol.10, pp.1056-1068, 2017.

J. C. Horvat, K. W. Beagley, M. A. Wade, J. A. Preston, N. G. Hansbro et al., , 2007.

, Neonatal chlamydial infection induces mixed T-cell responses that drive allergic airway disease, Am. J. Respir. Crit. Care Med, vol.176, pp.556-564

B. D. Hudson, E. Christiansen, H. Murdoch, L. Jenkins, A. H. Hansen et al., Complex pharmacology of novel allosteric free fatty acid 3 receptor ligands, Mol. Pharmacol, vol.86, pp.200-210, 2014.

T. Ichinohe, I. K. Pang, Y. Kumamoto, D. R. Peaper, J. H. Ho et al., Microbiota regulates immune defense against respiratory tract influenza A virus infection, Proc. Natl. Acad. Sci. USA, vol.108, pp.5354-5359, 2011.

M. Kjos, R. Aprianto, V. E. Fernandes, P. W. Andrew, J. A. Van-strijp et al., Bright fluorescent Streptococcus pneumoniae for live-cell imaging of host-pathogen interactions, J. Bacteriol, vol.197, pp.807-818, 2015.

A. Koh, F. De-vadder, P. Kovatcheva-datchary, and F. Bä-ckhed, From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites, Cell, vol.165, pp.1332-1345, 2016.

J. M. Lankelma, E. Birnie, T. A. Weehuizen, B. P. Scicluna, C. Belzer et al., The gut microbiota as a modulator of innate immunity during melioidosis, PLoS Negl. Trop. Dis, vol.11, p.5548, 2017.

M. Levy, A. A. Kolodziejczyk, C. A. Thaiss, and E. Elinav, Dysbiosis and the immune system, Nat. Rev. Immunol, vol.17, pp.219-232, 2017.

G. Li, C. Xie, S. Lu, R. G. Nichols, Y. Tian et al., Intermittent fasting promotes white adipose browning and decreases obesity by shaping the gut microbiota, Cell Metab, vol.26, pp.672-685, 2017.

L. Macia, J. Tan, A. T. Vieira, K. Leach, D. Stanley et al., Metabolite-sensing receptors GPR43 and GPR109A facilitate dietary fibre-induced gut homeostasis through regulation of the inflammasome, Nat. Commun, vol.6, p.6734, 2015.

P. Mancuso, G. B. Huffnagle, M. A. Olszewski, J. Phipps, and M. Peters-golden, Leptin corrects host defense defects after acute starvation in murine pneumococcal pneumonia, Am. J. Respir. Crit. Care Med, vol.173, pp.212-218, 2006.

E. Mariñ-o, J. L. Richards, K. H. Mcleod, D. Stanley, Y. A. Yap et al., Gut microbial metabolites limit the frequency of autoimmune T cells and protect against type 1 diabetes, Nat. Immunol, vol.18, pp.552-562, 2017.

K. M. Maslowski and C. R. Mackay, Diet, gut microbiota and immune responses, Nat. Immunol, vol.12, pp.5-9, 2011.

K. M. Maslowski, A. T. Vieira, A. Ng, J. Kranich, F. Sierro et al., Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43, Nature, vol.461, pp.1282-1286, 2009.

J. P. Mcaleer and J. K. Kolls, Contributions of the intestinal microbiome in lung immunity, Eur. J. Immunol, vol.48, pp.39-49, 2018.

J. A. Mccullers, The co-pathogenesis of influenza viruses with bacteria in the lung, Nat. Rev. Microbiol, vol.12, pp.252-262, 2014.

L. A. Mcnamee and A. G. Harmsen, Both influenza-induced neutrophil dysfunction and neutrophil-independent mechanisms contribute to increased susceptibility to a secondary Streptococcus pneumoniae infection. Infect. Immun, vol.74, pp.6707-6721, 2006.

G. Milligan, B. Shimpukade, T. Ulven, and B. D. Hudson, Complex pharmacology of free fatty acid receptors, Chem. Rev, vol.117, pp.67-110, 2017.

A. S. Monto, S. Gravenstein, M. Elliott, M. Colopy, and J. Schweinle, , 2000.

, Clinical signs and symptoms predicting influenza infection, Arch. Intern. Med, vol.160, pp.3243-3247

M. Moriyama and T. Ichinohe, High ambient temperature dampens adaptive immune responses to influenza A virus infection, Proc. Natl. Acad. Sci. USA, vol.116, pp.3118-3125, 2019.

M. Nakamatsu, N. Yamamoto, M. Hatta, C. Nakasone, T. Kinjo et al., , 2007.

C. Paget and F. Trottein, Mechanisms of bacterial superinfection post-influenza: a role for unconventional T cells, Front. Immunol, vol.10, p.336, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02398297

C. Paget, S. Ivanov, J. Fontaine, F. Blanc, M. Pichavant et al., , 2011.

P. J. Planet, D. Parker, T. S. Cohen, H. Smith, J. D. Leon et al., Lambda interferon restructures the nasal microbiome and increases susceptibility to Staphylococcus aureus superinfection, vol.7, p.15, 2016.

N. Qin, B. Zheng, J. Yao, L. Guo, J. Zuo et al., Influence of H7N9 virus infection and associated treatment on human gut microbiota, Sci. Rep, vol.5, p.14771, 2015.

A. Rynda-apple, K. M. Robinson, and J. F. Alcorn, Influenza and bacterial superinfection: illuminating the immunologic mechanisms of disease, Infect. Immun, vol.83, pp.3764-3770, 2015.

T. J. Schuijt, J. M. Lankelma, B. P. Scicluna, F. De-sousa-e-melo, J. J. Roelofs et al., The gut microbiota plays a protective role in the host defence against pneumococcal pneumonia, Gut, vol.65, pp.575-583, 2016.

H. Shapiro, C. A. Thaiss, M. Levy, and E. Elinav, The cross talk between microbiota and the immune system: metabolites take center stage, Curr. Opin. Immunol, vol.30, pp.54-62, 2014.

K. R. Short, E. J. Kroeze, R. A. Fouchier, and T. Kuiken, Pathogenesis of influenza-induced acute respiratory distress syndrome, Lancet Infect. Dis, vol.14, pp.57-69, 2014.

A. L. Steed, G. P. Christophi, G. E. Kaiko, L. Sun, V. M. Goodwin et al., The microbial metabolite desaminotyrosine protects from influenza through type I interferon, Science, vol.357, pp.498-502, 2017.

K. Sun and D. W. Metzger, Influenza infection suppresses NADPH oxidase-dependent phagocytic bacterial clearance and enhances susceptibility to secondary methicillin-resistant Staphylococcus aureus infection, J. Immunol, vol.192, pp.3301-3307, 2014.

J. Tan, C. Mckenzie, M. Potamitis, A. N. Thorburn, C. R. Mackay et al., The role of short-chain fatty acids in health and disease, Adv. Immunol, vol.121, pp.91-119, 2014.

C. A. Thaiss, N. Zmora, M. Levy, and E. Elinav, The microbiome and innate immunity, Nature, vol.535, pp.65-74, 2016.

A. Trompette, E. S. Gollwitzer, K. Yadava, A. K. Sichelstiel, N. Sprenger et al., Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis, Nat. Med, vol.20, pp.159-166, 2014.

A. Trompette, E. S. Gollwitzer, C. Pattaroni, I. C. Lopez-mejia, E. Riva et al., Dietary fiber confers protection against flu by shaping Ly6c -patrolling monocyte hematopoiesis and CD8 + T cell metabolism, Immunity, vol.48, pp.992-1005, 2018.

T. Ulven, Short-chain free fatty acid receptors FFA2/GPR43 and FFA3/ GPR41 as new potential therapeutic targets, Front. Endocrinol, vol.374, pp.1543-1556, 2009.

J. Wang, F. Li, H. Wei, Z. Lian, R. Sun et al., Respiratory influenza virus infection induces intestinal immune injury via microbiota-mediated Th17 cell-dependent inflammation, J. Exp. Med, vol.211, pp.2397-2410, 2014.

Y. Wang, A. Dai, S. Huang, S. Kuo, M. Shu et al., Propionic acid and its esterified derivative suppress the growth of methicillin-resistant Staphylococcus aureus USA300, Benef. Microbes, vol.5, pp.161-168, 2014.

S. Yildiz, B. Mazel-sanchez, M. Kandasamy, B. Manicassamy, and M. Schmolke, Influenza A virus infection impacts systemic microbiota dynamics and causes quantitative enteric dysbiosis, vol.6, p.9, 2018.

H. Zelaya, S. Alvarez, H. Kitazawa, and J. Villena, Respiratory antiviral immunity and immunobiotics: beneficial effects on inflammation-coagulation interaction during influenza virus infection, Front. Immunol, vol.7, p.633, 2016.

, Bacterial loads were quantified using qPCR assays. Standard curves were constructed to optimize the experiments and perform absolute quantification. The standard was a mix of 17 genomic DNA extracted from different bacterial strains with an even 16S rRNA gene copy number of each strain. Briefly 4.8 ml (1 ng DNA) were added into 10 ml of total volume mix (Taqman Universal MasterMix, Thermofischer) and optimized primer/probe concentrations to obtain a 100 ± 10% qPCR efficiency on the standard and samples. Cycling condition were those recommended by the manufacturer. Each sample was analyzed in triplicates. The Ct values were calculated using default parameters of software provided by the realtime PCR instrument manufacturers

, Positive (artificial bacteria Community comprising 17 different bacteria (ABCv2)) and negative (sterile water) control were also included. Briefly, PCR reactions were performed using 5ng of genomic DNA and in-house fusion barcoded primers (at 0.2 mM final concentrations), with an annealing temperature of 50 C for thirty cycles, The V3-V4 region of the 16S rRNA gene was amplified using an optimized and standardized amplicon-library preparation protocol (Metabioteâ

, For b-diversity measures, we computed the weighted UniFrac distances. The principal coordinates analysis (PCoA) method was used to visualize group overall microbial differences. Differences in relative abundance of individual taxa, between mice cecal group samples, were assessed for significance using the Mann-Whitney U test controlling for false-discovery rate (FDR), implemented within the software package QIIME. The Wilcoxon signed-rank test (paired t test) was used for 16S analysis of fecal samples. Measurement of food consumption and pair-feeding experiments Food consumption was calculated daily during influenza infection. Briefly, a known amount of food was placed in a cage of six mice. The amount of remaining food was measured every 12 h. The amount of consumed food was calculated by the difference divided by six and expressed as food intake per mouse per day. To provide the pair-fed group with only as much food daily as is consumed by IAV-infected mice, we restricted the food access during the last three days for 15% (day 4), 35% (day 5) and 85% (day 6), respectively (sacrifice at day 7). Mice were anesthetized at day 0. The pair-feeding time point was determined using data generated from IAV-infected mice with the goal of achieving a $15% loss of body mass (as at 7 dpi). Food was supplied twice a day to pair-fed animals and water was available at all times. The ad libitum (normally nourished) group mice were allowed unrestricted access to food and water, the full-length 16S rRNA sequences were analyzed and chimeric sequences were removed from the dataset (in-house method based on the use of USEARCH8.1 algorithm)

D. Weirdt, Concentrations of SCFAs in plasma were determined after extraction with acetonitrile. Results are expressed as mmol/g of cecal content or as mM (blood). To assess the effects of SCFAs on lung defense against bacterial infection, Measurement of SCFA concentrations and treatment with acetate or FFAR2/FFAR3 agonists Concentrations of SCFAs in the cecal content were determined after extraction with diethyl ether using GC-2014 gas chromatography with AOC-20i auto injector (Shimadzu, Hertogenbosch, the Netherlands) as described, vol.30, pp.2934-2947, 2010.

, recolonized with IAV microbiota and mice infected with IAV were treated with acetate (200 mM, drinking water) five days before the S. pneumoniae challenge (1x10 6 c.f.u. for conventional and recolonized mice and 1x10 3 c.f.u. for IAV-infected mice, respectively)

, The FFAR2 agonist TUG-1375 and the FFAR3 agonist AR420626 (stock solutions in DMSO at 20mM) were inoculated by the i.n. route (50 ml, 1 mM) 16 h before S. pneumoniae infection. Histopathological evaluation of the lung of double-infected mice was performed as described previously, IAV-infected mice were also treated (drinking water) with a combination of acetate, propionate and butyrate (200mM, 50mM and 5mM, respectively), 2007.

. Macherey-nagel, . Hoerdt, and ). Germany, Relative mRNA levels (2 -DDCt ) were determined by comparing (a) the PCR cycle thresholds (Ct) for the gene of interest and the house keeping gene Gadph (DCt) and (b) DCt values for treated and control groups (DDCt). Data are expressed as a fold-increase over the mean gene expression level in mock-treated mice. Quantification of viral RNA was performed as, Quantification of viral loads and assessment of gene expression by quantitative RT-PCR Total RNA from lung tissues were extracted with the NucleoSpinâ RNA kit, 2011.

, 08 g/L and vancomycin 0.5g/l) in drinking water supplemented with glucose (10 g/l) for three weeks. The cages were changed every two days. Depletion of bacteria in the feces were checked after culture in thioglycollate broth medium (Sigma) for 24 h at 37 C. ABXtreated mice were colonized twice (three days and five days after ABX cessation) by oral administration of 200 ml of cecal suspension containing 1x10 9 bacteria recovered from naive mice, mock-treated mice or IAV-infected mice (7 dpi), Microbiota transfer experiments Mice received broad-spectrum ABXs

, ) and assembled using ImageJ software. The frequency of macrophages having internalized S. pneumoniae and the average number of internalized bacteria per macrophage were determined (more than 20 visual fields analyzed/mouse). To determine the pneumococcal load in alveolar macrophages, cells (CD45 + Siglec F hi CD11b -) were sorted using a FACSAria cytometer (BD Biosciences) (> 99% purity). DNA was extracted and analyzed using quantitative PCR (QuantStudio 12K Flex, Applied Biosystems). Data were normalized against expression of the gapdh gene and were expressed as DCt. To determine the bactericidal activity of macrophages, re-colonized mice were infected with 1x 10 6 c.f.u. of S. pneumoniae (serotype 1). Four hours later, BAL fluid cells were collected and extensively washed with PBS in the presence of 15 mg/ml of gentamycin (Thermo Fisher Scientific). Cells were then washed twice in sterile PBS and lysed in sterile double deionized water. To assess bacterial killing, the number of ingested viable bacteria was determined by quantitative plating of serial dilutions of the lysates onto blood agar plates. The number of viable bacteria was expressed per 1x10 5 cells. In vitro killing assay For the in vitro killing assay, macrophages were pre-treated with acetate (10mM) for 1 h and then exposed with opsonized S. pneumoniae at MOI 10. Cells were incubated at 4 C for 1 h, followed by 3h of incubation at 37 C for bacterial internalization. Cells were washed in sterile PBS, incubated with penicillin and streptomycin (30U/ml) for 30min to kill extracelllular bacteria and then washed and, In vivo phagocytosis and killing assays and assessment of pneumococcal load in alveolar macrophages To visualize bacteria associated with phagocytes or internalized by phagocytes, recolonized mice were infected with eGFP-expressing S. pneumoniae (1x10 6 c.f.u., serotype 1) (a gift from Dr JW Veening, university of Groningen, the Netherlands). Four hours later, BAL fluid cells (> 95% alveolar macrophages) were washed and plated (u-Slide 8 Well ibiTreat, IBIDI