# Macrophages recycle phagocytosed bacteria to fuel immunometabolic responses

Juliette Lesbats, Aurélia Brillac, Julie Reisz, Parnika Mukherjee, Charlène Lhuissier, Mónica Fernández-Monreal, Jean-William Dupuy, Angèle Sequeira, Gaia Tioli, Celia de la Calle Arregui, et al.

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Juliette Lesbats, Aurélia Brillac, Julie Reisz, Parnika Mukherjee, Charlène Lhuissier, et al. Macrophages recycle phagocytosed bacteria to fuel immunometabolic responses. Nature, 2025, 10.1038/s41586-025-08629-4. hal-04995082

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1 **Macrophages recycle phagocytosed bacteria to fuel immunometabolic responses**

2

3 Juliette Lesbats¹, Aurélia Brillac¹, Julie A. Reisz²,§, Parnika Mukherjee³,§, Charlène Lhuissier⁴,

4 Mónica Fernández-Monreal⁵, Jean-William Dupuy⁶,⁷, Angèle Sequeira⁴, Gaia Tioli¹,⁸, Celia de

5 la Calle Arregui⁹, Benoît Pinson¹⁰, Daniel Wendisch³, Benoît Rousseau¹¹, Alejo Efeyan⁹, Leif.

6 E Sander³,¹², Angelo D'Alessandro², and Johan Garaude¹,⁴,*

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8 ¹University of Bordeaux, INSERM, MRGM, U1211, F-33000 Bordeaux, France.

9 ²Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz

10 Medical Campus, Aurora, Colorado, United States of America

11 ³Department of Infectious Diseases, Respiratory Medicine, and Critical Care, Charité –

12 Universitätsmedizin Berlin, Berlin, Germany, Corporate member of Freie Universität Berlin

13 and Humboldt-Universität zu Berlin Berlin, Germany

14 ⁴ImmunoConcEpT, CNRS UMR 5164, INSERM ERL 1303, University of Bordeaux,

15 Bordeaux, France

16 ⁵Université de Bordeaux, CNRS, INSERM, Bordeaux Imaging Center (BIC), US4, UAR 3420,

17 F-33000 Bordeaux, France

18 ⁶University of Bordeaux, CNRS, INSERM, TBM-Core, US5, UAR3421, OncoProt, F-33000

19 Bordeaux, France

20 ⁷University of Bordeaux, Bordeaux Protéome, Bordeaux, France

21 ⁸Biomedical and Neuromotor Sciences, Alma Mater University of Bologna, Bologna, Italy

22 ⁹Metabolism and Cell Signaling Laboratory, Spanish National Cancer Research Center (CNIO),

23 Madrid, Spain

24 ¹⁰Service Analyses Métabolomiques, TBMCore, CNRS UAR 3427, INSERM US005,

25 Université Bordeaux, F-33076, Bordeaux, France

26 ¹¹University of Bordeaux, Animal Facility A2, Service Commun des Animaleries, F-33000

27 Bordeaux, France

28 ¹²Berlin Institute of Health (BIH), Berlin, Germany

29 ⁵These authors contributed equally to this work

30 *Correspondence: johan.garaude@inserm.fr

31

32 **Keywords:** Macrophages, immunometabolism, phagocytosis, nutrients, mTOR, bacterial

33 viability, mitochondria

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1 Abstract

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Macrophages specialize in phagocytosis, a cellular process that eliminates extracellular matter, including microbes, through internalization and degradation ¹,². Despite the critical role of phagocytosis during bacterial infection, the fate of phagocytosed microbial cargo and its impact on host cell is poorly understood. Here, we reveal that ingested bacteria constitute an alternative nutrient source that skews immunometabolic host responses. Tracing stable isotope-labelled bacteria, we found that phagolysosomal degradation of bacteria provides carbon atoms and amino acids that are recycled into various metabolic pathways, including glutathione and itaconate biosynthesis, and satisfy macrophage bioenergetic needs. Metabolic recycling of microbially-derived nutrients is regulated by the nutrient sensing mechanistic target of rapamycin complex C1 (mTORC1) and intricately tied to microbial viability. Dead bacteria, as opposed to live ones, are enriched in cyclic adenosine monophosphate (AMP), sustain the cellular AMP pool and subsequently activate AMP protein kinase (AMPK) to inhibit mTORC1. Consequently, killed bacteria strongly fuel metabolic recycling and support macrophage survival, but elicit decreased reactive oxygen species (ROS) production and a reduced IL-1β secretion compared to viable bacteria. These results reveal a novel insight into the fate of engulfed microbes and highlights a microbial viability-associated metabolite that triggers host metabolic and immune responses. Our findings hold promise for shaping immunometabolic intervention in various immune-related pathologies.
1 Main

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Nutrients are organic compounds used in biochemical reactions that produce energy or serve as
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building blocks for anabolism of biomolecules³,⁴. In addition, nutrients can differentially shape
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the activation, function and differentiation of immune cells such as macrophages ⁵,⁶. Although
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mammalian cells preferentially metabolize low-molecular-weight nutrients, which are taken up
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from the microenvironment ⁴, some cells have the capacity to feed metabolic pathways through
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alternative routes ⁴,⁷,⁸. Owing to their high phagocytic capacity, macrophages are potentially
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well-equipped with such alternative nutrient-acquisition apparatus ¹. Engulfed microbes are
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rapidly sequestrated into phagosomes that subsequently fuse with lysosomes, where the
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combined action of lysosomal proteases, lipases, glycosidases and nucleases, together with
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vacuolar H⁺-ATPase (v-ATPase) and lysosomal NADPH oxidase-mediated ROS production,
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facilitates degradation of internalized cargos ². Concomitant to the ingestion of microbes via
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phagocytosis is the engagement of pattern recognition receptors (PRRs) by microbial structures
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called microbe-associated molecular patterns (MAMPs). This mobilizes specific immune
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signaling pathways that, in addition to initiating macrophage activation and differentiation,
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reorganize intracellular metabolic pathways, scaling immune response intensity and specificity
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to the nature of the threat encountered ⁹⁻¹². For example, sensing of MAMPs associated with
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viable bacteria elicits specific immune responses by triggering Toll-like receptor (TLR), Nod-
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like receptor (NLR) or stimulator of interferon genes (STING) pathways ¹³⁻¹⁵, and reorganizing
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mitochondrial respiratory chain ¹⁶ to induce type-I interferons and interleukin (IL)-1β.

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Here, we aimed to characterize the metabolic fate of digested microbial products and its
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functional relevance for macrophages. Our investigation reveals that phagocytosis of bacteria
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provides bioenergetic and biosynthetic precursors that are differentially utilized by
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macrophages depending on microbial viability. This highlights the capacity of phagocytic cells
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to adjust their cellular metabolism to the ‘fueling’ capacity of the ingested cargos and how,
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together with the sensing of microbial viability, it regulates immunometabolic adaptations.
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28 Phagocytosis of bacteria fuels mitochondria

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To assess whether phagocytosis of bacteria could provide nutrients to macrophages, we
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compared metabolic reprogramming of macrophages stimulated with killed avirulent Gram-
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negative *Escherichia coli* (KEC) to the activation of TLR4 by the bacterial cell-wall component
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lipopolysaccharide (LPS). We reasoned that phagolysosomal nutrients derived from the
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internalization of whole bacteria would specifically modulate nutrient handling, which could
not be recapitulated in macrophages treated with LPS. As expected, we measured similar production of inflammatory cytokines IL-6, TNFα and IL-1β (Extended Data Fig. 1a) by bone marrow-derived macrophages (BMDMs) in response to both stimuli and similar induction of extracellular-signal regulated kinase (ERK) 1 and 2 phosphorylation (Extended Data Fig. 1b,c), consistent with the notion that LPS stimulation mimics infection by Gram-negative bacteria with regards to pro-inflammatory macrophage responses. Yet, RNA-sequencing analysis of macrophages 4 hours post stimulation, revealed that ~1200 genes were differentially expressed, and that many highly expressed genes in KEC-treated macrophages as compared to LPS-stimulated cells were associated with metabolic processes (Fig. 1a) and membrane transporters of metabolites (Fig. 1b and Extended Data Fig. 1d), suggesting that uptake of whole bacteria triggers metabolic rewiring distinct of that induced by LPS sensing. More precisely, the differential expression of several genes encoding amino acid transporters of the solute carrier (SLC) family was conferring a specific expression profile to macrophages treated with KEC compared to LPS (Fig. 1c and Extended Data Fig. 1e). Together, this suggests that sensing and uptake of whole bacteria induces distinct reprogramming of metabolic and nutrient handling pathways compared to TLR4 engagement by LPS alone.

Because LPS is well-known to induce mitochondrial reprogramming<sup>9,11,17,18</sup>, we then assessed whether genes involved in mitochondrial function were also specifically regulated by the phagocytosis of whole KEC. Among 1,140 genes referenced in MitoCarta as mitochondrial protein-encoding genes<sup>19</sup>, seventy-four differentially expressed genes conferred a specific mitochondrial gene signature to KEC-stimulated macrophages, as compared to LPS or untreated cells (Extended Data Fig. 1f). We therefore hypothesized that engulfed KEC could serve as a nutrient supply for mitochondrial oxidative phosphorylation. To test this, we cultured macrophages in a glucose-containing ‘respiratory medium’ and stimulated the cells with KEC, microspheres (beads) loaded with LPS (LPS-beads) or beads only. As beads are devoid of nutritive potential, they could not serve as fuel source for mitochondrial respiration, contrary to microorganisms, in this case KEC. Both KEC and LPS-beads increased the extracellular acidification rate (ECAR) after 20 min, as compared to beads alone and unstimulated cells (Fig. 1d), and LPS and KEC similarly induced ATP levels in macrophages 1 hour post-stimulation (Extended Data Fig. 1g). Taken together, this likely reflects a similar induction of glycolytic flux following PRR engagement in macrophages to satisfy bioenergetic demand<sup>16</sup>. In contrast, KEC significantly increased the oxygen consumption rate (OCR) in macrophages, as compared to beads alone, LPS-beads, or unstimulated controls (Fig. 1d and Extended Data Fig. 1h,i). Impairing phagosomal maturation using the v-ATPase inhibitor bafilomycin A1 or the actin
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polymerization specific inhibitor cytochalasin D, prevented OCR induction in response to KEC (Fig. 1e and Extended Data Fig. 1j). Furthermore, despite similar extracellular or intracellular AA levels measured in macrophages treated with LPS and KEC (Extended Data Fig. 1k), the addition of a cocktail of non-essential amino acids (NEAA) combined with LPS increased OCR levels, similar to KEC (Fig. 1f). The individual addition of the non-essential amino acid serine, arginine, leucine or glutamine combined with LPS also sufficed to induce OCR (Extended Data Fig. 1l). Taken together, these findings supported the hypothesis that phagolysosomal degradation of KEC could fuel mitochondrial metabolism by supplying nutrients, including amino acids.

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## Phagocytosed bacteria provide metabolic intermediates

Next, we evaluated whether macrophages could take up amino acids from the degradation of engulfed bacteria and incorporate them into protein biosynthetic pathways. For this, EC were subjected to stable isotope labelling by amino acids in culture (SILAC) and macrophages were subsequently cultured with labelled KEC (SILAC-KEC) (Fig. 2a). Eight hours after phagocytosis of SILAC-KEC, liquid chromatography combined with mass spectrometry (LC-MS) analysis identified macrophage-specific peptides that contained ¹³C-labelled arginine or lysine when macrophages were in direct contact with SILAC-KEC but not in BMDMs that were separated from SILAC-KEC by a trans-well (Fig. 2b). Increasing the incubation time of BMDMs with SILAC-KEC did not significantly increase the numbers of ¹³C-labelled Arg- or Lys-containing peptides suggesting that the incorporation of bacteria-derived amino acids occurs rapidly after phagocytosis, but might be suppressed upon macrophage activation (Extended Data Fig. 2a). Interestingly, among all identified proteins containing bacteria-derived amino acids, 51 proteins were common to all time points (Extended Data Fig. 2b). Functional enrichment analysis revealed that identified proteins were essentially associated with innate immune responses or metabolic pathways such as glycolysis (Extended Data Fig. 2b,c). Most of these proteins showed an increased abundance over time (Extended Data Fig. 2d), consistent with the activation of these intracellular pathways upon innate immune cell activation ²⁰,²¹. To further confirm that phagolysosomal digestion of engulfed KEC was required for incorporation of KEC-derived amino acids into protein synthesis, we treated BMDMs with the v-ATPase-specific inhibitor concanamycin A to impair phagosomal maturation. Concanamycin A significantly decreased the intracellular concentration of various amino acids in macrophages that have phagocytosed KEC (Extended Data Fig. 2e) and modified the expression of various genes coding for SLC-family transporters including amino
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acid transporters, as evaluated by LC-MS and RNA-sequencing, respectively (Extended Data Fig. 2f,g). Interestingly, altering the function of v-ATPase with concanamycin A also significantly decreased the number of ¹³C-labelled Arg- or Lys-containing peptides detected without altering the abundance of the related proteins in macrophages 6 hours after phagocytosis of KEC as compared to vehicle treated cells (Fig. 2c and Extended Data Fig. 2h). Together, these results indicated that phagolysosomal degradation of engulfed KEC is necessary for amino acid recycling into protein neo-synthesis in macrophages.

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We then investigated whether phagocytosis of KEC would more generally provide metabolic intermediates that could be metabolized by macrophages. To test this, we grew *E. coli* in a culture medium in which the only carbon source was uniformly ¹³C-labelled glucose (U-[¹³C]-glucose). This ensured the labelling of virtually all bacterial carbons after 48h of culture (Extended Data Fig. 2i,j). We then pulsed macrophages with labelled U-[¹³C]-KEC (Fig. 2d), incubated cells for 6 or 18 hours, and performed an ultra-high pressure LC-MS (UHPLC-MS) analysis of 45 selected metabolites to evaluate ¹³C incorporation within macrophage metabolites. First, we excluded fully labelled metabolites from our analysis since these isotopologues may be residual or unprocessed bacterial metabolites. Stable isotope tracing showed a substantial incorporation of KEC-derived ¹³C into macrophage metabolites when cells were cultured in direct contact with the bacteria, but not when the macrophages were separated from the microbes by a trans-well (Fig. 2e). This indicated that macrophages could catabolize bacteria and bacterially derived metabolites into their own metabolic pathways after phagocytosis. While we found significant ¹³C incorporation in metabolites of the TCA cycle and non-essential amino acids, we did not detect ¹³C in essential amino acids consistent with the incapacity of mammalian cells to produce those metabolites, confirming the specificity of our analysis (Fig. 2f). Furthermore, we found ¹³C enrichment into itaconate (Fig. 2f), a macrophage-specific metabolite that has anti-oxidant and anti-inflammatory properties ²²⁻²⁵, and that is not detected in EC because it lacks *cis*-aconitate decarboxylase ²⁶. The incorporation of ¹³C into itaconate was strictly dependent on a direct contact between macrophage and bacteria (Extended Data Fig. 2k).

We then evaluated if the metabolic recycling of engulfed bacteria by macrophages can occur *in vivo*. For this, we injected 4',6-diamidino-2-phenylindole (DAPI)-labelled U-[¹³C]KEC into the peritoneal cavity of mice that had been previously injected with thioglycolate to increase the number of peritoneal macrophages (Fig. 2g). Two hours later, DAPI⁺ phagocytic cells and DAPI⁻ non-phagocytic cells were sorted by flow cytometry and tested for ¹³C incorporation by UHPLC-MS analysis. The incorporation of ¹³C atoms into metabolites was
evident in DAPI⁺ but not DAPI⁻ cells as exemplified by the presence of isotopologues of glutamate or glutathione (Fig. 2g). Alternatively, we injected DAPI⁺ ¹³C-labelled or unlabelled KEC into the peritoneal cavity of mice. One hour after injection, cells that have phagocytosed DAPI⁺ KEC were essentially F4/80⁺ macrophages and Ly6G⁺ neutrophiles, as expected (Extended Data Fig. 2l). ¹³C-containing metabolites were detected in cells that have phagocytosed U-[¹³C]KEC but not in cells that have phagocytosed unlabeled-KEC (Extended Data Fig. 2m). These findings indicate that phagocytic cells have the capacity to metabolically recycle engulfed killed bacteria *in vivo*.

We then evaluated if macrophage ability to metabolize phagocytosed bacteria was restricted to *E. coli*. We evaluated ¹³C incorporation in macrophages after phagocytosis of uniformly ¹³C-labelled killed Gram-negative *Citrobacter rodentium* and *Pseudomonas aeruginosa*, and Gram-positive *Staphylococcus aureus* and *Listeria innocua*. UHPLC-MS analysis identified bacterially-derived ¹³C atoms incorporation in various macrophage metabolites independently of the bacterial specie used (Fig. 2h). Taken together, these data demonstrated that phagocytosis of killed bacteria constitutes a source of nutrients by providing metabolites that are recycled into macrophage metabolism.

## RagA regulates recycling of ingested bacteria

Next, we sought to identify possible mechanisms governing the recycling of microbially-derived nutrients produced by the degradation of KEC in macrophages. To cope with fluctuating nutrient availability and demands, cells possess signaling pathways that sense nutrient- and energy inputs, including the serine/threonine kinase mTORC1 ²⁷,²⁸, which can sense lysosomal amino acids and regulates metabolites efflux from lysosomes ²⁹⁻³¹. In response to amino acids, Rag GTPases, which function as heterodimers of RagA or RagB bound to RagC or RagD, recruit mTORC1 to the lysosomal surface where its kinase activity is stimulated by the Rheb GTPases ²⁸,³². Recent studies have demonstrated mTORC1 suppresses the utilization of extracellular proteins taken up through macropinocytosis in cancer cells expressing activated Ras pathways ⁷,⁸. Ras-dependent signaling pathways did not significantly contribute to the metabolic recycling of phagocytosed KEC in macrophages as shown by a similar bacterially-derived ¹³C incorporation in macrophages expressing constitutively active mutants of Kras or Hras compared to wild-type (WT) macrophages (Extended Data Fig. 3a). However, confocal imaging revealed that both RagA and mTOR were localized to LAMP1⁺ phagolysosomes that contained fluorescent LPS-beads after 30 min (Fig. 3a,b and Extended Data Fig. 3b,c). By contrast, RagA and mTOR did not show a clear localization at the vesicles containing GFP-
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expressing KEC. Furthermore, immunoblotting analysis at 4 hours post stimulation showed that KEC was not as efficient as LPS to activate the mTORC1 pathway, indicated by the phosphorylation of mTORC1 targets 4EBP1 and ribosomal S6 proteins (Fig. 3c and Extended Data Fig. 3d).

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To further determine the relevance of mTORC1 in controlling the efflux and recycling of the content of KEC-containing phagolysosomes, we generated BMDMs from transgenic RagA<sup>GTP/Δ</sup> mice that express a constitutively GTP-bound version of RagA<sup>33</sup>. As expected, RagA<sup>GTP/Δ</sup> macrophages had a lower expression of RagA and a constitutively activated mTORC1 pathway compared to WT cells, as shown by immunoblotting (Extended Data Fig. 3e,f). Metabolomic analysis of macrophages stimulated with KEC revealed that RagA<sup>GTP/Δ</sup> macrophages contained lower levels of amino acids as compared to WT cells after phagocytosis of KEC (Extended Data Fig. 3g). We then stimulated WT and RagA<sup>GTP/Δ</sup> macrophages with SILAC-KEC for 6 hours and evaluated the incorporation of bacterially-derived amino acid into neo-synthetized and found that enhanced RagA-dependent mTORC1 activation resulted in decreased uptake and use of arginine and lysine originated from KEC (Fig. 3d). Similarly, UHPLC-MS analysis showed that RagA<sup>GTP/Δ</sup> macrophages stimulated with U-[<sup>13</sup>C]-KEC incorporated significantly less <sup>13</sup>C compared to WT macrophages, indicating a reduced recycling of bacterially-derived metabolites (Fig. 3e). We then evaluated if blocking mTORC1 signaling could promote the uptake of bacterially-derived nutrients. For that, we generated BMDMs deficient for RagA from tamoxifen-inducible conditional knock-out mice <sup>34</sup>. As expected, tamoxifen treatment efficiently decreased RagA levels in macrophages as evaluated by immunoblotting (Extended Data Fig. 3h). Strikingly, RagA deficiency increased <sup>13</sup>C-labelled metabolites in macrophages 6 hours after phagocytosis of U-[<sup>13</sup>C]-KEC compared control cells (Fig. 3f). Taken together, our data suggest that the RagA/mTORC1 pathway regulates the fate of bacterially-derived nutrients in KEC-containing phagolysosomes.

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We then tested whether mTORC1 could connect recycling of killed bacteria-derived metabolites to metabolic reprogramming of macrophages. We noticed that metabolites involved in the glutathione (GSH) biosynthesis pathways were differentially produced in LPS- and KEC-treated macrophages (Extended Data Fig. 3i) and decreased upon treatment of KEC-stimulated BMDMs with concanamycin A or bafilomycin A (Extended Data Fig. 3j). This suggested that production of GSH, a critical component of the antioxidant responses in cells, was indeed linked to phagolysosomal handling of engulfed bacteria. Yet, the expression of genes implicated in GSH synthesis was similar in BMDMs treated by LPS or KEC for 4h as evaluated by bulk
1 RNA-sequencing (Extended Data Fig. 3k), indicating an adaptation of metabolic pathways
2 rather than a modulation of gene expression. We also found that RagA<sup>GTP/Δ</sup> macrophages
3 contained significantly lower levels of metabolites involved in GSH biosynthesis (Fig. 3g,h),
4 and lower amounts of bacteria-derived ¹³C in GSH compared to WT macrophages (Fig. 3i).
5 Conversely, RagA-deficient macrophages showed higher levels of GSH when challenged with
6 KEC (Fig. 3j) and incorporated more bacteria-derived ¹³C in GSH (Fig. 3k) after 6 hours, as
7 compared to WT macrophages. In conclusion, these results indicate that RagA is a metabolic
8 rheostat that links phagosolysosomal nutrient sensing to GSH biosynthesis in response to
9 phagocytosis of dead bacteria.
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11 Microbial viability drives metabolic recycling

12 Professional phagocytes have the capacity to distinguish viable microorganisms from their
13 killed counterparts ³⁵, thus gauging the level of infectious threat posed by microbial encounters.
14 We have previously shown that detection of bacterial viability induces rapid adjustments of
15 mitochondrial respiratory chain architecture and succinate dehydrogenase activity in
16 macrophages ¹⁶. We thus investigated whether microbial viability broadly defines the specific
17 reprogramming of metabolic pathways and orchestrates the recycling of internalized microbes.
18 Macrophages similarly engulfed and degraded EC or KEC as indicated by the loss of
19 fluorescence of GFP-expressing bacteria when evaluated by flow cytometry (Extended Data
20 Fig. 4a,b). We noted that GFP signal was almost undetectable after 6 hours suggesting that
21 internalized bacteria were largely degraded at this time point. We thus stimulated BMDMs with
22 EC or KEC for 6 hours and performed a metabolomics analysis by UHPLC-MS to assess central
23 energy and redox metabolites in classes such as amino acids, nucleotides, fatty acids, sulfur,
24 polyamines, urea cycle, Krebs cycle, pentose phosphate pathway or GSH homeostasis
25 (Supplementary Table 1). Principal component analysis (PCA) revealed a marked difference
26 of macrophage metabolism following uptake of EC as compared to KEC (Fig. 4a). Quantitative
27 pathway enrichment analysis revealed GSH metabolism as the top enriched metabolic pathways
28 in KEC-treated macrophages compared to EC (Extended Data Fig. 4c). Several metabolites
29 associated with GSH metabolism were indeed more abundant in KEC-treated macrophages
30 compared to EC (Fig. 4b), including GSH and glutathione disulfide (GSSG) (Fig. 4c and
31 Extended Data Fig. 4d). Furthermore, phagocytosis of KEC induced higher production of
32 itaconate than the phagocytosis of EC (Fig. 4d). Consistent with this, we measured reduced
33 levels of reactive oxygen species (ROS) in macrophages stimulated for 4 hours with KEC
34 compared EC (Fig. 4e) ¹⁶. However, transcriptome analysis did not show significant differences
in the expression of genes involved in GSH biosynthesis in macrophages stimulated with KEC
or EC for 4 hours (Extended Data Fig. 4e). Instead, several genes coding for metabolites and
amino acid transporters or associated with amino acid metabolism were differentially expressed
depending on bacterial viability (Extended Data Fig. 4f-i). Consistently, various NEAA
including the GSH precursors cysteine and glutamate were significantly more abundant in
KEC-treated macrophages as compared to EC-stimulated cells (Extended Data Fig. 4j). The
higher levels of itaconate, GSH and GSSG (Extended Data Fig. 5a) and the induction of the
GSH metabolism (Extended Data Fig. 5b,c) in macrophages stimulated with KEC compared
to EC was also evident at 18 hours post-stimulation, indicating that this metabolic signature
persisted over time. These data suggested that the altered GSH metabolism associated with the
detection of bacterial viability likely arose from a rewiring of metabolic fluxes and nutrient
handling pathways, rather than from a transcriptional reprogramming of GSH biosynthesis
associated genes.

We then assessed whether the recognition of bacterial viability modulated the capacity
of macrophages to incorporate microbial carbons into its own metabolites. We incubated
macrophages with live or killed U-[¹³C]-*E. coli* and evaluated the presence of ¹³C in 45 selected
metabolites. After 6 hours of incubation, we detected fewer fully labelled U-[¹³C]-metabolites
(i.e. bacterial metabolites) in macrophages that had engulfed U-[¹³C]-KEC compared to U-
[¹³C]-EC (Fig. 4f and Extended Data Fig. 5d). This was particularly evident for fully labelled
amino acids (Extended Data Fig. 5e). This indicated that many metabolites remained fully
labelled after phagocytosis of viable and metabolically active EC, and suggested that engulfed
KEC were better metabolic intermediate providers for macrophage than EC. Consistent with
this notion, we found that the fraction of isotopologues in macrophages that have phagocytosed
U-[¹³C]-KEC was higher as compared to cells that have engulfed live U-[¹³C]-EC after 6 hours
(Fig. 4f) and 18 hours of incubation (Extended Data Fig. 5f). Interestingly, constitutive
activation of RagA in KEC-stimulated macrophages decreased bacterially-derived ¹³C atom
recycling down to levels found in EC-stimulated cells (Extended Data Fig. 5g), consistent with
the role of mTORC1 in controlling the recycling of phagolysosomal nutrients.

We then tested whether this could be relevant for macrophage responses elicited upon
sensing of bacterial viability. We reasoned that during microbial infection, highly replicating
bacteria will necessarily compete for nutrients with host cells including macrophages, thereby
creating a nutrient-restricting microenvironment ³⁶,³⁷, a phenomenon that resembles to cancer
microenvironments ³⁸. In line with this, we found that phagocytosis of EC but not KEC by
macrophages strongly decreased cell viability when macrophages were cultured in a medium
deprived of amino acids (Fig. 4g and Extended Data Fig. 5h). Strikingly, EC-induced macrophage death was dampened by the addition of extracellular amino acids to the culture media. This suggests that, as opposed to EC, the phagocytosis of KEC can provide an additional source of nutrients including amino acids that sustains macrophage viability in nutrient deprived conditions. Because sensing of KEC, but not EC, decreased ROS production and enhanced itaconate and GSH production (Fig. 4b-e), we also tested if KEC could be a better source of metabolic intermediates than EC for antioxidant responses. We noted that amino acid deprivation strongly enhanced ROS production in EC-stimulated macrophages (Extended Data Fig. 5i). In these conditions, the addition of a cocktail of NEAA and EAA to the medium decreased EC-induced ROS production by macrophages down to levels found in KEC-treated macrophages (Extended Data Fig. 5j). This indicated that phagocytosis of KEC dampened ROS production by providing metabolic intermediates necessary for antioxidant responses. To test this, we determined the isotopologue fraction in itaconate and found that bacteria-derived ¹³C incorporation was enhanced in response to KEC compared to EC (Extended Data Fig. 5k). Similarly, we measured a marked difference in ¹³C enrichment into amino acids involved in the GSH biosynthetic pathway including proline, a nonpolar aliphatic amino acid that can be metabolized into GSH precursor glutamate, with a consistently higher percentage of isotopologues after phagocytosis of U-[¹³C]-KEC (Fig. 4h). Furthermore, consistent with the importance of the mTORC1 pathway in regulating both the metabolic recycling of engulfed bacteria and antioxidant responses, the constitutive activation of RagA in KEC-treated macrophage decreased bacterial carbons incorporation into GSH and itaconate down to levels measured in EC-stimulated cells (Extended Data Fig. 5l). Finally, because ROS are potent inducers of IL-1β ³⁹, an inflammatory cytokine specifically secreted in response to EC but not KEC ¹⁴,¹⁶, we evaluated the impact of amino acid supplementation on IL-1β production by macrophage upon sensing of bacterial viability. We found that the addition of amino acids to the culture medium of EC-stimulated macrophages decreased IL-1β but not TNFα production down to levels measured in KEC-treated macrophages (Fig. 4i and Extended Data Fig. 5m). Taken together, our data shows that while both live and killed bacteria constituted a source of nutrients upon phagocytosis, killed bacteria-derived carbons were more efficiently incorporated to support macrophage viability in harsh environment and sustains metabolic pathways that are specifically induced in response to killed bacteria such as GSH biosynthesis.

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cAMP is a metabolic signal of microbial death

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We then sought to determine bacterial and host signals involved in the regulation of metabolic
recycling of engulfed bacteria and metabolic responses upon sensing of bacterial viability. We
reasoned that the nature of bacterial metabolites of engulfed live or dead bacteria may drive the
specific metabolic adaptations found in macrophages. Strikingly, metabolomic analysis of KEC
and EC revealed that 3',5'-cyclic AMP (cAMP) was one of the very few metabolites that
specifically accumulate in KEC (Fig. 5a). Among metabolites of the ATP biosynthesis pathway
tested, cAMP was the sole to accumulate in KEC as compared to EC (Extended Data Fig. 6a).
We thus evaluated the presence of intracellular bacterially-derived cAMP in macrophages upon
phagocytosis of live or killed bacteria. Surprisingly, we did not detect uniformly ¹³C-labelled
bacterial cAMP in macrophage 6 hours or 18 hours after phagocytosis of U-[¹³C]KEC or U-
[¹³C]EC (Fig. 5b). Instead, we found that intracellular levels of uniformly ¹³C-labelled AMP
(U-[¹³C]AMP), the main product of cAMP hydrolysis, were higher in macrophages that have
phagocytosed U-[¹³C]KEC as compared to cells that have engulfed live U-[¹³C]EC (Fig. 5b).
This was not the case for uniformly ¹³C-labelled adenosine and adenine. Furthermore, levels of
U-[¹³C]-AMP were strongly decreased in macrophages treated with v-ATPase inhibitors or in
RagA<sup>GTP/Δ</sup> macrophages as compared to WT cells (Extended Data Fig. 6b). Taken together,
our data suggest that cAMP is a metabolic hallmark of bacterial death that can be converted
into AMP upon phagocytosis by macrophages.

We then ranked the metabolites found in macrophages following phagocytosis of U-[¹³C]-KEC according to the proportion of isotopologues. We found that metabolites from the nucleoside synthesis pathways including adenosine monophosphate (AMP), adenosine and adenine, were among the metabolites with the highest incorporation of KEC-derived carbons (Fig. 5c and Extended Data Fig. 6c). Furthermore, the ¹³C enrichment of adenosine derivatives hypoxanthine and xanthine was lower compared to adenine, suggesting that AMP breakdown to adenine was favored in activated macrophage compared to the xanthine pathway. In fact, fully labelled bacterial U-[¹³C]AMP represented up to 15% of the total pool of AMP (Extended Data Fig. 6d) and half of this AMP pool contained at least one labelled carbon in KEC-stimulated macrophages (Fig. 5c and Extended Data Fig. 6c). Inhibiting the v-ATPase or constitutively activating RagA altered the incorporation of bacteria-derived carbons into AMP (Extended Data Fig. 6e) further supporting the idea that KEC is an important provider of AMP and AMP intermediates upon phagocytosis. Interestingly, stimulation with EC but not KEC specifically diminished the AMP pool in macrophages compared to control cells (Fig. 5d and Extended Data Fig. 6d,f). We found no difference in adenosine and adenine levels in KEC-and EC-stimulated macrophages. Also, EC-mediated decrease of intracellular AMP pool was
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not associated with variation of intracellular ATP pool, which was similarly induced in response to EC and KEC (Fig. 5e), suggesting that a reduced intracellular AMP level was a specific hallmark of metabolic adjustments in response to recognition of viable bacteria. This raised the possibility that phagocytosis of KEC, by maintaining the AMP pool, alters the intracellular ATP/AMP ratio and may trigger AMP-dependent signaling pathways in macrophages.

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Because mTORC1 was implicated in the regulation of KEC metabolic recycling, we focused on the AMP-activated protein kinase (AMPK), a heterotrimeric complex composed of a catalytic α subunit and regulatory β and γ subunits, which is an inhibitor of the mTOR pathway ⁴⁰. Immunoblotting revealed that KEC, but not EC, induced the phosphorylation of the AMPKα subunit at Thr172 in the activation loop as early as 30min after macrophage stimulation (Fig. 5f). Stimulation of macrophages with KEC also efficiently induced AMPKα phosphorylation when compared to LPS (Extended Data Fig. 6g). Consistent with the notion that AMPK activation restrains mTORC1 activity, phosphorylation of the mTORC1 targets S6 protein and 4EBP1 was rapidly reduced in macrophages treated with KEC, but not EC, as assessed by immunoblot over a 4-hour time course (Fig. 5f and Extended Data Fig. 6h). Yet, we found no difference in the phosphorylation of S6 protein and 4EBP1 in macrophages in response to KEC and EC at 12h post-stimulation (Extended Data Fig. 6i), in line with work by Moretti et al. ¹³. Taken together, the findings suggested that phagocytosis of dead bacteria provides external AMP and sustains the intracellular pool of AMP to activate AMPK and inhibit mTORC1.

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We then assessed the relevance of the AMPK-mTORC1 axis for the control of bacterial viability-specific responses by macrophages. We found that the EC-specific ROS production was lowered to levels measured in KEC-treated macrophages when cells were treated with 8-bromo-cAMP (Extended Data Fig. 6j), N-acetyl-cysteine (Extended Data Fig. 6k) and 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) (Extended Data Fig. 6l), respectively a cell-permeable derivative of cAMP, a precursor of glutathione, and an AMPK-activating analog of AMP. This was consistent with the higher antioxidant responses and stronger AMPK activation observed in macrophages that have phagocytosed KEC as compared to EC. Furthermore, activating AMPK with AICAR decreased IL-1β secretion by EC-stimulated macrophage down to levels of KEC-treated cells (Extended Data Fig. 6m,n). In line with this, blocking mTORC1 through the genetic ablation of RagA in macrophages increased itaconate production, lowered ROS production, and decreased IL-1β secretion (Fig. 5g) in EC-treated macrophage to levels measured in KEC-stimulated cells. The
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pharmacological inhibition of mTORC1 with the specific inhibitor Torin 1 also lowered ROS production (Extended Data Fig. 6o) and decreased IL-1β secretion (Extended Data Fig. 6p) in macrophages stimulated with EC as compared to KEC-stimulated cells. Taken together our results indicate that both AMPK and mTORC1 contributed to the control of bacterial viability-specific immunometabolic responses of macrophages (Extended Data Fig. 6q).

## Discussion

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Previous studies documented the capacity of phagocytes to extract metabolic precursors or signaling metabolites from apoptotic cell-containing phagolysosomes$^{41-45}$. By contrast, it is assumed that microbe-containing phagolysosomes are tightly sealed vesicles that avoid the dispersion of potentially harmful molecules $^{1,2,46}$. Our findings reveal that, in addition to microbial peptides or nucleic acids that respectively contributes to antigen cross-presentation$^{47}$ or results in the activation of cytosolic PRRs $^{14,48,49}$, part of the material generated from the phagolysosomal destruction of internalized bacteria serves as buildings blocks for protein synthesis, intermediates in various anabolic pathways, and fuel for mitochondrial oxidative phosphorylation. On the one hand, this may constitute a mechanism to cope with a nutrient overload situation that necessarily occurs when macrophages internalize multiple bacteria at the time. On the other hand, catabolizing ingested bacteria may provide a competitive advantage to phagocytes in infected tissues when access to extracellular nutrients is limited or challenged by fast replicating bacteria $^{36,38,50-52}$. In line with this, the metabolic recycling of engulfed dead bacteria, as opposed to live ones, supports macrophage viability, dampens ROS production and decreases IL-1β secretion in a harsh microenvironment depleted in amino acids. Although bacteria-derived metabolites were spread among various metabolic and biosynthetic pathways in an apparently random manner, bacterial carbons were particularly abundant in metabolites of the GSH biosynthetic pathways and bacterial lysine and arginine were found in proteins implicated in glycolysis and lysosomal function. Taken together, this indicates that bacterial metabolites may be preferentially utilized in anabolic pathways that are strongly solicitated in activated macrophages or necessary for cell survival.

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We found mTORC1 to restrict the nutrient uptake from phagolysosomal degradation of live bacteria. This may constitute a ‘protective’ mechanism, shut down upon phagocytosis of dead bacteria, by which phagocytic cells avoid the use of potentially harmful molecules derived from infectious agents. An alternative possibility is that mTORC1 may limit the pool of metabolic precursors required for the synthesis of key components of the anti-inflammatory
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responses including GSH or itaconate. Together with recent evidence indicating that mTORC1
mediates autophagy of stressed endoplasmic reticulum upon sensing of live Gram-positive
bacteria ¹³, our data suggest that mTORC1 holds a central position in coordinating cellular and
metabolic responses to the viability of phagocytosed bacteria.

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LPS-mediated TLR4 activation is widely used as a proxy for Gram-negative bacteria-mediated macrophage activation <sup>53</sup>. It notably induces metabolic reprogramming in macrophages by rerouting extracellular amino acids or glucose and by mobilizing TCA cycle intermediates <sup>9,18,21,54-56</sup>. Although TLR4 signaling emerging or not from phagosomes that contain nutrient-deprived cargos activates signaling pathways downstream of mTORC1 <sup>57</sup>, it does not fully recapitulate the metabolic reprogramming following the phagocytosis of live or dead bacteria as shown by its inability to support mitochondrial respiration when access to amino acids is limited, by a differential expression of metabolic genes or by a lower activation of the GSH biosynthetic pathway. We attributed this, at least in part, to the lack of ‘nutritive potential’ of LPS as compared to whole bacteria. In addition, whole bacteria do not solely activate TLR4, but they engage additional PRRs, which may further fine tune mTORC1 activation, cellular metabolism, and mitochondrial reprogramming.

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Several molecules, collectively called vita-PAMPs, were found to signal microbial life to innate immune cells thereby defining specific cellular and metabolic responses <sup>13,14,16,35</sup>. So far, no bacterial molecule was found to signify microbial death to immune cells. Our data show that, as opposed to live bacteria, dead bacteria specifically accumulate cAMP, which is likely rapidly converted into AMP upon phagocytosis by macrophages to maintain intracellular AMP pool. This activates the AMPK pathway and inhibits ROS production, which was shown to strengthen microbe degradation in the phagolysosome and stimulate NLRP3-mediated IL-1β secretion <sup>58-60</sup>. Therefore, bacterial cAMP may be perceived by phagocytes as a marker of bacterial death under specific conditions. Collectively, our findings describe a cell-autonomous response by which macrophages ensure metabolic assimilation of phagocytosed microbes and specifically adjust cellular metabolism to bacteria viability and to the availability of nutrients. This ability of phagocytic cells may condition macrophages at bacterial infection sites and may be exploited for the formulation of new anti-infective drugs and vaccines.

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Figure legends

<caption>**Fig. 1: Phagocytosis of killed-bacteria fuels mitochondrial respiratory chain in macrophages.**</caption>

a, Volcano plot of gene expression levels determined by bulk RNA sequencing. Dark grey plots, genes that are significantly increased (right) or decreased (left) in KEC-stimulated macrophages versus LPS-stimulated macrophages for 4h; Log₂FC>1 and Log₁₀(P-value)>1. Orange dots, genes associated to metabolic process. b, Genes from (a), associated with transport activity, uniquely increased after stimulation with KEC compared to LPS, and categorized by function (right panel) and protein family (left panel). c, Heat map showing the top differentially expressed solute carrier (Slc) genes between LPS- and KEC-treated. Transporter families and amino acid transported are shown. d-f, Extracellular acidification rate (ECAR)(d) and oxygen consumption rate (OCR) measured in BMDMs treated or not with cytochalasin D and stimulated for 30min with KEC, latex beads or LPS-covered latex beads (Beads/LPS) in a minimal medium supplemented with glucose. n=3 (d) and n=5 (e,f) biological replicates (with 6 technical replicates). Data are mean +/- s.e.m., n=4 biological replicates unless otherwise noted. Statistical significance was assessed using one-way analysis of variance (ANOVA) using Tukey's multiple comparisons test (d,e,f).

<caption>**Fig. 2: Phagocytosis of killed-bacteria provides metabolic intermediates to macrophages.**</caption>

a, Schematic illustrating the experimental design used in (b,c). SILAC, stable isotope labelling using amino acid in cell culture. BMDMs, bone marrow-derived macrophages; LC-MS, liquid chromatography-mass spectrometry analysis. b,c, Number of macrophage-specific total peptides (gray) and heavy peptides (blue) identified in BMDMs plated in trans-well chambers (b) or treated with concanamycin A (c) and cultured for 6h separated (insert) or in contact (well) with SILAC-KEC. d, Schematic illustrating the experimental design used in (e). BMDMs, bone marrow-derived macrophages; UHPLC-MS, ultra-high pressure liquid chromatography-mass spectrometry analysis. e, Fractional labelling of metabolites in BMDMs plated in trans-well chambers and treated for 6h or 18h separated (insert) or in contact (well) with fully-labelled killed U-[¹³C]EC. Fraction of isotopologues was determined over 45 metabolites. f, Fractional labelling of the indicated metabolites from BMDMs after phagocytosis of killed U-[¹³C]EC. ND, not detected. g, Fractional labelling of metabolites in peritoneal phagocytes sorted from thioglycolate-treated mice injected with fully-labelled DAPI+U-[¹³C]KEC as shown in the schematic illustration above. Fraction of isotopologues was determined over 15 metabolites by UHPLC-MS (left panel). g, Fractional labelling of the indicated metabolites from BMDMs after phagocytosis of indicated killed U-[¹³C]-labelled bacteria. ND, not detected. Data are mean +/- s.e.m., n=3 (b,c,e), n=4 (f,h), and n=10 (g) biological replicates. Statistical significance was assessed using one-way (f,h) and two-way (e) analysis of variance (ANOVA) using Tukey's multiple comparisons test or two-tailed unpaired t test (b,c,g).

<caption>**Fig. 3: RagA regulates the metabolic recycling of phagocytosed bacteria.**</caption>

a,b, Immunofluorescence images (left) LAMP1 (lysosomes), RagA (a) and mTOR (b) in BMDMs stimulated with LPS-covered FITC-latex beads or GFP-expressing KEC for 20min. Scale bars, 1µm. Line scans on the right show normalized fluorescence intensity levels for the orange line (n=4 cells). c, Western blot of total and phospho- (p) S6 and 4EBP1 in BMDMs stimulated with LPS or KEC for the
indicated time. Vinculin is used as control. MW, molecular weight. **d**, Number of macrophage-specific total peptides (left) and heavy peptides (right) determined by LC-MS of *RagA*<sup>WT</sup> and *RagA*<sup>GTP/Δ</sup> BMDMs treated with SILAC-KEC for 6h. **e,f**, Fractional labelling of metabolites in control, *RagA*<sup>GTP/Δ</sup> (**e**), or *Cre*<sup>+</sup>::*RagA*<sup>ff</sup> (**f**) BMDMs treated for 6h or 18h with fully-labelled U-[<sup>13</sup>C]killed EC. Fraction of isotopologues was determined over 45 metabolites by LC-MS. **g**, Heat map of fold changes (log<sub>2</sub>) of indicated metabolite abundances in control and *RagA*<sup>GTP/Δ</sup> BMDMs stimulated with KEC for 6h relative to control (left panel). **P** values are for comparisons between metabolites abundances shown (right panel). **h-k**, Intracellular levels (**h**, *j*) and fractional labelling (**i**, *j*) of glutathione (GSH) in control, *RagA*<sup>GTP/Δ</sup> (**h**, *i*), or *Cre*<sup>+</sup>::*RagA*<sup>ff</sup> (**j**, *k*) BMDMs treated for 6h or 18h with fully-labelled U-[<sup>13</sup>C]killed EC. Data are mean +/- s.e.m., n=3 (**e**), n=4 (**d**, *f-i*), and n=5 (**j**, *k*) biological replicates. Statistical significance was assessed using two-tailed unpaired *t*-tests (**d**) and two-way ANOVA using Tukey's multiple comparisons test (**e**, *f*, **h-k**). ns, not significant.

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Fig. 4: Bacterial viability sets the metabolic recycling of phagocytosed bacteria.

a, Principal component analysis (PCA) of the metabolomic analysis (172 metabolites) in BMDMs treated with live *Escherichia coli* (EC) or killed EC (KEC) for 6h and analyzed by liquid chromatography-mass spectrometry (LC-MS). **b**, Schematic representation (left panel) of the glutathione biosynthetic pathway and its connection with other metabolic pathways. Heat map of metabolites related to glutathione (GSH) biosynthesis in BMDMs treated with KEC or Live EC for 6h (right panel). GGT, γ-glutamyl transpeptidase. **c,d**, Intracellular levels of the indicated metabolites in BMDMs treated as in (a). Data are expressed as fold change compared to resting cells. **e**, Reactive oxygen species (ROS) production by BMDMs treated as in (a). RLU, relative luminescence units. **f**, Intracellular levels of *E. coli* fully labeled U-[¹³C]-metabolites, U-[¹³C]-amino acids, and fractional labelling (right panel) of metabolites in BMDMs, 6h after phagocytosis of U-[¹³C]EC or U-[¹³C]KEC. **g**, Viability of macrophages treated with EC or KEC and cultured for the indicated time in amino acid (AA)-depleted RPMI supplemented or not with a cocktail of NEAA and EAA. **h**, Fractional labelling of the indicated metabolites in BMDMs treated as in (f). **i**, IL-1β levels in supernatants of BMDMs cultured in AA-depleted RPMI medium and stimulated for 18h with live EC or KEC in the presence or not of a cocktail of NEAA and EAA. Means +/- s.d., two technical replicates, representative of 3 experiments. Data are mean +/- s.e.m. unless otherwise mentioned, n=3 (**g**), n=4 (**e**, *f*, *h*) and n=7 (**a**, *c*, *d*) biological replicates. Statistical significance was assessed using two-tailed unpaired *t*-tests (**c**, *d*, *f*), one-way (*e*, *f*, *h*) and two-way (*g*) ANOVA using Tukey's multiple comparisons test. ns, not significant.

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Fig. 5: Killed bacteria-derived cAMP sustains anti-oxidant responses in macrophages.

a, Volcano plot of metabolite levels in EC and KEC. Dark grey plots show metabolites that are significantly increased (left) or decreased (right) in KEC versus EC; Log<sub>2</sub>FC>0.5 and Log<sub>10</sub>(P-value)>1.

b, Intracellular abundance of the indicated bacterial metabolites in BMDMs treated with U-[¹³C]KEC or U-[¹³C]EC for 6h and 18h. c, Fractional labelling of the indicated metabolites of the AMP biosynthetic pathway in BMDMs treated with U-[¹³C]KEC or U-[¹³C]EC for 6h and 18h. **d,e** Intracellular AMP (**d**) and ATP (**e**) levels in BMDMs treated with live EC of KEC for 6h. n=7 (**d**) and n=8 (**e**) biological replicates.

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f, Western blot of total and phospho- (p) S6, 4EBP1 and AMPK in BMDMs stimulated with EC or KEC

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for the indicated time. Vinculin is used as control. MW, molecular weight. g, Itaconate abundance (left),

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ROS production (middle) and IL-1β levels (right) in *RagA*<sup>WT</sup> or *RagA*<sup>GTP/Δ</sup> BMDMs stimulated with live

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EC or KEC. RLU, relative luminescence units. Data are mean +/- s.e.m. (b,d,e,g), n=4 biological

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replicates unless otherwise mentioned. Statistical significance was assessed using one-way (b,d,e) and

6

two-way (c,g) ANOVA using Tukey's multiple comparisons test. ns, not significant.

7
# 1 Methods

# 2 Key reagents

The following antibodies were used: Mouse monoclonal anti-vinculin (Sigma-Aldrich, V9131-100UL), Rabbit monoclonal Phospho-AMPKα (Thr172) (40H9) (Cell Signaling Technology, # 2535), Mouse monoclonal AMPKα (F6) (Cell Signaling Technology, Cat# 2793), Rabbit monoclonal Phospho-4E-BP1 (Thr37/46) (236B4) (Cell Signaling Technology, Cat# 2855), Mouse monoclonal 4E-BP1 (53H11) (Cell Signaling Technology, Cat# 9644), Rabbit monoclonal Phospho-S6 Ribosomal Protein (Ser235/236) (2F9) (Cell Signaling Technology, Cat# 4856), Mouse monoclonal Ribosomal Protein S6 (C-8) (Santa Cruz, Cat# sc-74459), Rabbit monoclonal RagA (D8B5) (Cell Signaling Technology, Cat# 4357), Rabbit monoclonal Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (20G11) (Cell Signaling Technology, Cat# 4376), Mouse monoclonal p44/42 MAPK (Erk1/2) (L34F12) (Cell Signaling Technology, Cat# 4696), Rabbit monoclonal mTOR (7C10) (Cell Signaling Technology, Cat# 2983), Mouse LAMP-1/CD107a Lumenal Domain Antibody (R&D systems, Cat# AF4320), WesternSure® Goat anti-Mouse HRP Secondary Antibody (Licor, Cat# 926-80010), WesternSure® Goat anti-Rabbit HRP Secondary Antibody (Licor, Cat# 926-80011), Rat anti-Ly-6G-BV510 (BD Bioscience, Cat#563040), Rat anti-F4/80-PerCP-Cy5.5 (eBioscience, Cat#567202). For ELISA, we used: Mouse IL-1 beta/IL-1F2 DuoSet ELISA (R&D systems, Cat# DY401), TNF alpha Mouse Uncoated ELISA Kit (ThermoFisher, Cat# 88-7324-88), IL-6 Mouse Uncoated ELISA Kit (ThermoFisher, Cat# 88-7064-88). Dilution of the antibodies ranged between 1/5000 and 1/200. The majority of antibodies used in this study were selected from published literature or which use has been optimized accordingly in the lab or by collaborators. Where possible they were validated and optimized using BMDMs as well as titration to ensure the optimal final concentration. We used the following inhibitors from Sigma-Aldrich: Bafilomycin A1 from *Streptomyces griseus* (Cat# B1793), Concanamycin A (Cat# 27689), Rapamycin (Cat# R8781), mTOR Inhibitor XI, Torin1 (Cat# 475991), Antimycin A from *Streptomyces* sp. (Cat# A8674), AICAR (Cat# A9978), 8-bromo cyclic adenosine monophosphate (Cat# 203800), Rotenone (Cat# R8875), (Z)-4-Hydroxytamoxifen (Cat# H7904). Other reagents include: D-glucose (Cat# G7021), L-Thymidine (Cat# SML0293), Trimethoprim (Cat# T7883-5G), L-Proline (Cat# P0380), L-Glutamic acid monosodium salt hydrate (Cat# G1626), L-Leucine (Cat# L-8000), L-Arginine (Cat# A5006), all from Sigma-Aldrich. LPS-EK (LPS from E. coli K12) was from Invivogen (Cat# tlrl-eklps).
1 **Mice**

2 C57BL/6N mice were purchased from Charles River Laboratories and housed in a pathogen-
3 free animal facility of the university of Bordeaux. Mice were maintained under standard
4 housing conditions on a 12 h light/dark cycle with food and water provided, at a constant room
5 temperature and humidity (21-24 °C and 45-65%, respectively). *Tg.hUbiquitin(UbC)-cre-
6 ERT2<sup>+/T::Rraga<sup>fl/fl</sup></sub> mice were originally generated in D. Sabatini's laboratory (Whitehead
7 Institute, Cambridge, MA) by crossing *Rraga<sup>fl/fl</sup>* with *hUbC-CreERT2* mice (The Jackson
8 laboratory, Bar Harbor, ME) where CRE is fused to the estrogen receptor T2 <sup>34</sup> and provided
9 by A. Efeyan (CNIO, Madrid, Spain). *Raga<sup>GTP/Δ</sup>* mice were originally generated by A. Efeyan
10 <sup>33</sup>. *Tg.hUbC-cre-ERT2<sup>+/T::Kras<sup>+/G12V</sup></sub>* mice were originally generated in the laboratory of M.
11 Barbacid and provided by D. Santamaria <sup>61</sup>. *Hras<sup>G12S</sup>* mice were originally generated at the
12 Institut Clinique de la Souris (ICS) and provided by R. Rossignol <sup>62</sup>. All transgenic mice were
13 on a C57BL/6N background. We used 8- to 10-week-old male or female animals for all
14 experiments and did not observe any gender bias. No randomization or blinding was used.
15 Transgenic lines were screened by PCR analysis for the presence of the transgenes in genomic
16 DNA purified from tail biopsies. Anaesthesia by isoflurane inhalation was used to kill the mice
17 to minimize suffering. Cervical dislocation was performed following anaesthesia to ensure
18 death.

19 All experimental procedures were approved by institutional care and use committees
20 (Comité d’éthique et d’expérimentation animale de l’université de Bordeaux) and performed in
21 agreement with EU directive 86/609/EEC and recommendation 2007/526/EC regarding the
22 protection of laboratory animals. The animal facility institutional agreement number is
23 A33063916.
24

25 **Bone marrow-derived macrophages**

26 Murine bone marrow-derived macrophages (BMDMs) were generated as described previously,
27 in RPMI 1640 supplemented with M-CSF (30% mycoplasma-free L929 cell supernatant, NCBI
28 Biosample accession # SAMN00155972) and 10% FBS, plus 100 µg/ml penicillin, 100 µg/ml
29 streptomycin, 10 mM HEPES, 1 nM sodium pyruvate and 50 µM 2-mercaptoethanol (all from
30 Gibco). For *Tg.hUbC-cre-ERT2<sup>+/T::Rraga<sup>fl/fl</sup></sub> and *Tg.hUbC-cre-ERT2<sup>+/T::Kras<sup>+/G12V</sup></sub>* mice,
31 tamoxifen was added to the culture on day 2 or 3 to a final concentration of 1µg/ml in order to
32 induce *CRE* expression. BMDMs were used on day 5 to 7 after seeding. Period of differentiation
33 of the cells, concentration of cells when replating and time-lapse between replating and
1

stimulation with bacteria were important parameters to maintain metabolic backgrounds

2

homogenous between experiments.

3

4

Bacterial strains

5

Escherichia coli K12, strain DH5α (herein E. coli) where purchased from Thermo (#EC0112).

6

Citrobacter rodentium (DBS100) was from B. Finlay. Staphylococcus aureus (Cat#12600),

7

Listeria innocua (Cat#19115), and Pseudomonas aeruginosa (Cat#27853) were purchased at

8

ATCC. Escherichia coli BL21-GFPOT have been described previously ¹⁶. Naturally occurring

9

thymidine auxotrophs (thyA⁻) *E. coli* were selected as previously described¹⁶ and cultured in

10

Luria-Bertani (LB) supplemented with thymidine (500µg/ml) and trimethoprim (50µg/ml). *E.*

11

*coli*-GFP were generated in Dr. Magarian Blander laboratory (Mount Sinai Immunological

12

institute, NYC, USA) by transforming pET28 vector (Novagen) containing a GFP-OT construct

13

(a kind gift from Dr. Trombeta) ⁶³ into BL21-*E. coli* (Novagen) as described previously ¹⁶. They

14

were grown in LB supplemented with 50 µg/ml kanamycin and 50 µg/ml chloramphenicol and

15

GFP-OT expression was induced by incubating log-phase bacteria in LB supplemented with 1

16

mM IPTG for 3 hours.

17

18

Preparation of viable and killed Bacteria

19

*ThyA*⁻ *E. coli* were grown overnight with shaking in LB supplemented with thymidine

20

(500µg/ml) and trimethoprim (50µg/ml), diluted 1/40, and grown until log-phase [optical

21

density at 600 nm (OD<sub>600</sub>) of 0.8-1.2]. *E. coli*-GFP were grown in the presence of 50 µg/ml

22

kanamycin and 50 µg/ml chloramphenicol. To induce GFP expression, overnight-grown

23

bacteria were diluted to an OD<sub>600</sub> of 0.8 and incubated for 4h in the presence of 1 mM Isopropyl

24

β-D-1-thiogalactopyranoside (IPTG). Bacteria were washed with phosphate buffer saline (PBS)

25

to remove LB salts before addition to cells. For heat-killing, *E. coli* were grown to log phase,

26

washed, re-suspended in PBS and subsequently incubated at 60°C for 60-90 min. For antibiotic

27

killing, log-phase bacteria were incubated for 6h to 12h with Streptomycin or Gentamycin

28

(50µg/ml). Aliquots of killed bacteria (KEC) were stored at -80°C until use. Efficient killing

29

was confirmed by overnight plating on LB-agar plates.

30

31

Bacterial stimulation of macrophages

32

Depending on the readouts, different amounts of BMDMs were used. For metabolomics,

33

proteomics, immunoblotting, 2x10⁶ BMDMs were plated 12-16h prior stimulation in non-tissue
cultured treated 6-well plate (BD Falcons). For ATP quantification, ROS production measurement, cytokine measurement, and GSH quantification, 1x10⁵ BMDMs were plated in 96-well plates. In some cytokine measurement experiments, 3x10⁵ BMDMs were plated in 24-well plates. For stimulation with live or killed bacteria, bacteria were washed with PBS. All bacteria were typically added at a ratio of 20 to 1 cell (MOI 20). In some experiments (isotopologues tracing, SILAC tracing and oxygraphy) a MOI 50 was used. Where indicated, a dose response was used and the following ratio were used: MOI 5, 10 and 20. One hour after stimulation with bacteria, Gentamycin (20µg/ml) was added to the medium and kept until the end of the experiment. For TLR4 activation, BMDMs were stimulated with 1µg/ml K12 *E. coli* purified LPS (Sigma).

11

## 12 Chemical treatment of macrophages

To inhibit lysosomal degradation, cells were treated with 25, 50 and 100 nM Bafilomycin A1. To inhibit phagocytosis, cells were treated with 5 and 10 µM Cytochalasin D. To inhibit mTOR, 50, 100, and 150 nM Rapamycin and 200, 300, and 400 nM Torin1 were used. To inhibit v-ATPase, cells were treated with 150 nM concanamycin A. To inhibit ROS production, cells were treated with 10mM N-acetyl cysteine (NAC). To activate AMPK, cells were treated with 10 to 100 µM of cell permeable 8-Bromo-cyclic AMP. All the above inhibitors were added to the cultured medium at least 2h before stimulation of the BMDMs. To induce Cre expression and delete *Rraga*fl/fl or induce *K-Ras*G12V, 1µg/ml tamoxifen was added to the culture medium after 2 days of differentiation. All the inhibitors and genetic models used in this study did not have any significant effect on phagocytosis of live or killed *E. coli* as tested by flow cytometry using RFP- or GFP-expressing *E. coli*. We ensured that all inhibitors did not alter cell viability using the Fixable Viability Stain 510 assay (#564406, BD Horizon) as per manufacturer protocol.

26

## 27 Metabolic assays (oxygraphy, ATP quantification, GSH quantification, ROS production)

Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in BMDMs were determined with an XF-96 Extracellular Flux Analyzer (FluxPak Seahorse XFe96, Agilent). Macrophages were seeded with 100µl of a minimal 'seahorse' medium (DMEM supplemented with 5mM glucose and 5mM pyruvate, pH 7.4) at 1 x10⁵ cells/well at least 6h before the experiment. If needed, inhibitors were added at the indicated concentration 1h prior to the experiment. All inhibitors tested for cell viability prior use. Macrophages were then stimulated

28

29

30

31

32

33
with 100ml of seahorse medium containing the following stimuli: KEC were used at a multiplicity of infection (MOI) of 50, LPS (1µg/ml), latex beads or LPS-covered latex beads at a MOI of 3. Plate was centrifuged at 400g for 1min and loaded on a XF-96 Extracellular Flux Analyzer. Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured every 15 minutes for 2 hours. Before the last measure 1 µM rotenone and 1 µM antimycin were added to the wells to determine non-mitochondrial OCR. Data were analyzed using Wave software v2.6 (Agilent). Basal respiration rate (BRR) was defined as OCRbasal minus OCR(rotenone + antimycin). In some experiments, medium was replaced by minimal 'seahorse' medium supplemented with 200 nM of the following amino acids (all from Sigma): serine, arginine, leucine, glutamine.

For ATP levels determination, two hundred thousand BMDMs were seeded on white flat-bottom 96-well plates the day before stimulation. After stimulation for the indicated time point, ATP levels were determined using bioluminescence-based ATP Determination kit (Molecular Probes) following manufacturer's protocol.

For ROS production determination, two hundred thousand BMDMs were seeded in 100 µl of complete RPMI on white flat-bottom 96-well plates the day before stimulation. For some experiments, medium was changed 12h prior to amino acid (AA)-free RPMI (XXX) supplemented or not with 200nM non-essential AA (NEAA), 200nM essential AA (EAA). BMDMs were stimulated with RPMI containing live or killed E. coli at MOI 20 or LPS (1µg/ml) supplemented with 6µl of a 5mg/ml Luminol (L012, Merck). Plate were spin down 1min at 2000 rpm and luminescence was recorded every 5 min over 1h using a microplate luminometer (Varioscan, Scanlab, Thermo). In some experiments, medium was replaced with RPMI medium supplemented with inhibitors 2 hours prior stimulation.

24

## Immunoblotting

BMDMs were washed with cold PBS, then lysed with RIPA buffer (Sigma-Aldrich, Cat# R0278) supplemented with protease inhibitor and phosphatase inhibitor cocktails (Complete Protease and PhoStop, Roche, Cat# 11697498001 and Cat# 4906837001). Lysates were incubated 30 min on ice and cleared by centrifugation at 14,000 rpm for 10 min at 4°C. Protein concentrations were determined using the Pierce BCA Protein assay kit (Thermo Scientific, Cat# 23227). Whole-cell extracts were denatured in Laemmli buffer (Biorad, Cat# 1610747) supplemented with 1mM DTT (Sigma, Cat# D0632) and then heated at 95°C for 10 min. Routinely, 30µg of protein extracts was loaded on SDS-PAGE (Biorad, Cat# 4568085, Cat#
1 4568086) for resolution and transferred on PVDF membrane (Biorad, Cat# 1704273) using a
2 Trans blot system (Biorad). Membrane were blocked PBS 0.2% Tween (PBS-T) for 1h at room
3 temperature and subsequently incubated with primary antibodies overnight. Membranes were
4 washed with PBS-T, incubated with fluorescence-conjugated secondary antibodies for 1h-2h,
5 washed again and visualized using a Li-Cor scanner or Odyssey. Alternatively, membranes
6 were incubated with peroxidase-conjugated secondary antibodies. For western blotting of
7 mTOR pathway-associated phospho-protein (S6Kinase, 4EBP-1, S6), membranes were
8 blocked in PBS supplemented with 3% BSA, 3% poly-ethylene glycol (PEG) and 1%
9 polyvinylpyrrolidone (PVP). For western blotting of phospho-AMPK, membranes were
10 blocked with TBS-T supplemented with 5% BSA and 50mM NaF. In that case, HRP-
11 conjugated secondary antibodies were used and signals were visualized using ECL reagents
12 (ThermoFisher, Cat# 34095, Cat# 34577). All blots were probed for vinculin as a loading
13 control. All western blots represent at least 2 independent experiments. Densitometric
14 quantification of the indicated signals from independent experiments were measured using
15 ImageJ software. Signals values were reported to vinculin signal value and then normalized to
16 resting conditions allowing inter experiments comparison.
17

17

18 Cytokine enzyme-linked immunosorbent assay (ELISA)

19 Supernatants from cultured BMDMs were collected at 18h after stimulation and used directly
20 or frozen at -80°C until use. Then, supernatants were left untreated (for IL-1β) or diluted 1/10
21 (for IL-6, IL-10 and TNFα). Enzyme-linked immunosorbent assay (ELISA) were performed
22 using commercial kits from Invitrogen (IL-6, TNFα, IL-10) and R&D system (IL-1β) following
23 manufacturer's protocol with the exception that all indicated volumes were divided by 2.
24 Horseradish peroxidase (HRP) activity was visualized using TMB solution (Eurobio scientific,
25 Cat# 5120-0076). Color development was stopped using TMB-Stop Solution (Eurobio
26 scientific, Cat# 5150-0020). Absorbances at 450nm were measured using a microplate reader
27 (Monaco, Safas). Cytokine concentrations were calculated by extrapolating absorbance values
28 from standard curves using Excel software.
29

29

30 Stable isotope labeling and metabolomics

31 For labeling of bacteria, 10µl of an overnight cultured of *thyA* - *E. coli* was added to 20 ml of a
32 filtered M9 minimal medium salts (Life Technologies) supplemented with 1 mM thiamine, 1
33 mM MgSO4, 0.1 M CaCl2, 500 µg/ml thymidine, 50 µg/ml trimethoprim, and 0.5% U-[13C1-6]
1

glucose (Campro Scientific, Cat# CS01-183_417). Bacteria were grown for 72h, washed with
PBS and subjected to heat-killing by re-suspension in PBS and subsequently incubation at 60°C
for 60-90 min. For antibiotic killing, bacteria were incubated for 6h to 12h with Streptomycin
or Gentamycin (50µg/ml). Bacteria were kept at 4°C until use.

2

3

4

5

BMDMs were seeded at 2.5x10⁶ cell/well in a non-tissue treated 6-well plate (2 wells per conditions) the day before the experiment. Cells were then stimulated with live or killed labelled-*E. coli* at MOI 50, centrifuged at 2000 rpm for 5min. BMDMs were incubated for 5 min and washed with PBS to remove non-ingested bacteria and further incubated for 6h or 18h. BMDMs were then washed with cold PBS and harvested with 5mM EDTA in PBS, frozen as dry cell pellet, and stored at -80°C until processing.

6

7

8

9

10

11

For *in vivo* experiments, C57BL6/N mice were injected or not with 0.5 ml of a thioglycolate solution (ClearLine, Cat#693418). Four days later, mice were injected with 1x10⁹ KEC or fully-labelled U-[¹³C]KEC previously labelled with 5 µg/ml 4',6-diamidino-2-phenylindole (DAPI) for 5min at room temperature in PBS. One or two hours later, peritoneal cells were collected with 10ml PBS and subjected to FACS sorting using a FACS-Aria or an Aurora CS, gating on DAPI+ and DAPI- cells. Sorted cells were frozen as dry cell pellet, and stored at -80°C until processing.

12

13

14

15

16

17

18

Metabolomics analyses were performed as previously described ⁶⁴. Metabolites were extracted from frozen macrophage pellets by the addition of ice cold 5:3:2 MeOH:MeCN:water (v/v/v) at a concentration of 4 million cells per mL then vortexed for 30 min at 4 °C. Supernatants were clarified by centrifugation (10 min, 12,000 g, 4 °C). The resulting metabolite extracts were analyzed (7 uL per injection) on a Vanquish ultra-high-pressure liquid chromatograph (Thermo) coupled to a Q Exactive mass spectrometer (Thermo). Metabolites were resolved on a Phenomenex Kinetex C18 column (2.1 x 150 mm, 1.7 µm) at 45 °C using a 5-minute gradient method in positive and negative ion modes (separate runs) over the scan range 65-975 m/z exactly as previously described ⁶⁴. Following data acquisition, .raw files were converted to .mzXML using RawConverter then metabolites assigned and peaks integrated using Maven (Princeton University) in conjunction with the KEGG database and an in-house standard library. ¹³C₂ isotopologue peak areas were corrected for the natural abundance of ¹³C₂ in the parent compound. Quality control was assessed using technical replicates run at beginning, end, and middle of each sequence as previously described ⁶⁵,⁶⁶.

19

20

21

22

23

24

25

26

27

28

29

30

31

32

In some experiments, ATP, ADP and AMP levels in bacteria were determined as follow: live and killed *ThyA*⁻ *E. coli* (10 ml of culture) were harvested by filtration (Sartolon polyamid 0.45 µm; Sartorius) and metabolites were extracted by the EtOH boiling method described in⁶⁷.

33

34
1

Metabolite separation was performed on an Integron chromatography station (Thermo
Electron) coupled to a Vanquish diode array UV-Vis detector. Metabolites separation was
achieved at 0.38 ml/min and 30°C on an AS11-HC-4µm (250 x 2 mm, Thermo Electron) using
the potassium hydroxide discontinuous gradient described in ⁶⁸. Adenylic nucleotides were
detected by their UV absorbance at 260 nm, identified by UV spectrum signature, and their
intracellular content was determined using standard curves obtained with pure compounds. The
nucleotide levels were normalized to the total protein content of bacteria.

2

3

4

5

6

7

8

9

Stable isotope labeling in culture (SILAC) of bacteria and mass spectrometry proteomics

10

For SILAC of bacteria, 10 µl of an overnight culture of *thyA* - *E. coli* added to 20 ml of a filtered M9 minimal medium salts (ThermoFisher, Cat# 13744-01) supplemented with 1mM thiamine, 1mM MgSO4, 0,1M CaCl2, 500µg/ml Thymidine, 50 µg/ml trimethoprim, 0.5% glucose, 0.25mg/ml L-Lysine 2HCl 13C6,15N2 (Cambridge Isotope Laboratories, Cat# CNLM-291-H) and 0.25mg/ml L-Arginine, HCl U-13C6 (Cambridge Isotope Laboratories, Cat# CLM-2265-H). Bacteria were grown for 72h, washed with PBS and subjected to heat-killing. For that, E. coli were re-suspended in PBS and subsequently incubated at 60°C for 60-90 min. For antibiotic killing, bacteria were incubated for 6h to 12h with Streptomycin or Gentamycin (50µg/ml). Bacteria were kept at 4°C until use.

11

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19

BMDMs were seeded at 2.5x10⁶ cell/well in a non-tissue treated 6-well plate (2 wells per conditions) the day before the experiment. Cells were stimulated with killed SILAC-*E. coli* at MOI 50, centrifuged at 2000 rpm for 5min. BMDMs were incubated for 5 min and washed with PBS to remove non-ingested bacteria and further incubated for 8h, 12h or 24h. Then, BMDMs were washed with PBS, harvested with 5mM EDTA in PBS, and lysed with 100 µl RIPA buffer (Biorad) supplemented with protease inhibitor and phosphatase inhibitor cocktails (Complete Protease and PhoStop – Roche). Protein concentration was determined using a Pierce BCA protein assay kit and samples were frozen at -80°C until processing.

20

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27

All proteomic analysis experiments were performed on total protein extracts from independent biological replicates. 10 µg of proteins were loaded on a 10% acrylamide SDS-PAGE gel and proteins were visualized by Colloidal Blue staining. Migration was stopped when samples had just entered the resolving gel and the unresolved region of the gel was cut into only one segment.

The steps of sample preparation and protein digestion by the trypsin were performed as previously described ⁶⁹. NanoLC-MS/MS analysis were performed using an Ultimate 3000 RSLC Nano-UHPLC system (Thermo Scientific, USA) coupled to a nanospray Orbitrap

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33
Fusion™ Lumos™ Tribrid™ Mass Spectrometer (Thermo Fisher Scientific, California, USA).
10 µL of each peptide extracts were loaded on a 300 µm ID x 5 mm PepMap C₁₈ precolumn
(Thermo Scientific, USA) at a flow rate of 10 µL/min. After a 3 min desalting step, peptides
were separated on a 50 cm EasySpray column (75 µm ID, 2 µm C18 beads, 100 Å pore size,
ES903, Thermo Fisher Scientific) with a 4-40% linear gradient of solvent B (0.1% formic acid
in 80% ACN) in 115 min. The separation flow rate was set at 300 nL/min. The mass
spectrometer operated in positive ion mode at a 1.9 kV needle voltage. Data were acquired
using Xcalibur 4.4 software in a data-dependent mode. MS scans (m/z 375-1500) were recorded
at a resolution of R = 120000 (@ m/z 200), a standard AGC target and an injection time in
automatic mode, followed by a top speed duty cycle of up to 3 seconds for MS/MS acquisition.
Precursor ions (2 to 7 charge states) were isolated in the quadrupole with a mass window of 1.6
Th and fragmented with HCD@28% normalized collision energy. MS/MS data were acquired
in the Orbitrap cell with a resolution of R=30000 (@m/z 200), a standard AGC target and a
maximum injection time in automatic mode. Selected precursors were excluded for 60 seconds.
Protein identification and label-free quantification (LFQ) were done in Proteome Discoverer
3.1. The CHIMERYS node using the prediction model inferys_3.0.0 fragmentation was used
to identify proteins in batch mode by searching against a Uniprot Mus musculus and
Escherichia coli K12 strain databases (54786 entries and 4362 entries, release March, 2024).
Two missed enzyme cleavages were allowed for trypsin. Peptide lengths of 7-30 amino acids,
a maximum of 3 modifications, charges of 2-4, and 20 ppm for fragment mass tolerance were
set. Oxidation (M) and SILAC (K, R) were searched as dynamic modifications.
Carbamidomethyl (C) modifications were searched as static modifications. Peptide validation
was performed using the Percolator algorithm and only "high confidence" peptides were
retained corresponding to a 1% false discovery rate at the peptide level. Minora feature detector
node (LFQ) was used along with the feature mapper and precursor ions quantifier. The
normalization parameters were selected as follows: (1) Unique peptides, (2) Precursor
abundance based on intensity, (3) Normalization mode: total peptide amount, (4) Protein
abundance calculation: summed abundances, (5) Protein ratio calculation: pairwise ratio based
and (6) Missing values were replaced with random values sampled from the lower 5% of
detected values. Quantitative data were considered for master proteins and quantified by a
minimum of 2 unique peptides.⁷⁸ The Mass spectrometry proteomics data have been deposited
to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset
identifier PXD040108, PXD040140, PXD040107, and PXD040106. Functional enrichment
analysis was performed using DAVID⁷⁰.
1

## RNA-sequencing

One million resting or stimulated BMDMs were snap frozen as dry cell pellet and stocked at -80°C until processing. DNA-free total RNA was isolated with Direct-zol RNA Microprep Kits following the instructions of the manufacturer. Library preparation was performed by the BIH Genomics MDC facility using the TruSeq Stranded mRNA kit. RNA-sequencing was performed at the MDC/BIH Genomics facility using the NovaSeq 6000 sequencer. RNA-seq reads were mapped onto reference *Mus musculus* nucleotide sequence of the GRCm39 primary assembly (chromosomes and scaffolds) and gene annotation on the primary assembly sequence regions (GENCODE version M28). The reads were mapped using alignment tool STAR version 2.7.10a and gene expression was quantified using R package Rsubread (version 1.22.2) function featureCounts with default parameters. For differential gene expression analysis and GO enrichment analysis, differentially expressed genes and pathways were detected using the workflow of the edgeR package (version 3.38.4) in R using the glmTreat() function. R package clusterProfiler version 4.4.4 was used to find enriched Gene Ontology (GO) terms using its enrichGO() function. R packages ggplot2 (version 3.4.3) and ComplexHeatmap (version 2.12.1) were used for making plots and heatmaps of scaled gene expression (z-score) respectively.

19

## Phagocytosis assay and Flow cytometry

BMDMs (3 x 10⁵ cells) were seeded in complete RPMI in triplicate on a non-treated 24-well plate (BD Biosciences). Cells were challenged with 3 x 10⁶ *E. coli*-GFP and centrifuged for 5 min at 400 g and incubated for 15min. Cells were washed 3 times with PBS, fresh complete RPMI supplemented with 50mM gentamycin was added and cells were placed back to the incubator. At the indicated time point, cells were washed with PBS, harvested in PBS containing 5 mM EDTA, fixed with a solution of PBS/1%PFA and analysed by flow cytometry. Cells were stained with the appropriate antibody cocktails in ice-cold PBS supplemented with 2 mM EDTA, 1% fetal calf serum and 0.2% sodium azide for 15min. Samples were analyzed with a FACS canto-3L or an LSR-Fortessa analyzers (BD Biosciences).

30

## Macrophage viability assay

To assess cell viability during sensing of bacteria, we used the IncuCyte Zoom™ system (EssenBioscience, MI USA). BMDMs were seeded in a 96-well plate at 1x10⁵ cells/well and cultured overnight in complete RPMI medium. Prior the assay, medium was changed to RPMI
1

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## Immunostaining, confocal microscopy and image analysis

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Twenty four hours before stimulation, $1 \times 10^5$ BMDMs were seeded on 13mm coverslips (Marienfeld, #0117530) in 24-well plates. BMDMs were stimulated for the indicated time with GFP-expressing *E. coli* (BL21 *E.coli*-GFP) at MOI 5 or with fluorescent 2µm streptavidin-beads (Fluoresbrite, Polyscience) covered with biotin-LPS (Invivogen). Cells were washed 3 x with PBS and then fixed with PBS supplemented with 4% paraformaldehyde (PFA, Sigma) for 20 min at RT. After washing with PBS, PFA was quenched by incubating the samples with PBS supplemented with 0.5 mM glycine for 20 min. Coverslips were then blocked with PBS supplemented with 0.5% BSA and 0.3% fish gelatin for 30 min. Coverslips were then incubated overnight with PBS/5% BSA containing the following primary antibodies: Rabbit-anti mTOR (dilution 1/100, Cell signaling technology (CST) #2983), Rabbit anti-RagA (dilution 1/100, CST#4357) and Goat anti-LAMP1 (dilution 1/200, Biotechne #AF4320). Coverslips were washed 3 times with PBS and incubated 2h with PBS/5%BSA containing secondary antibodies: Goat anti Rabbit AF594 (dilution 1/2000, # A-11012) and Goat-anti mouse ATTO 647N (dilution 1/2000, Sigma 50185). After washing with PBS, coverslips were mounted on glass slides using Prolongold or Fluoromount mounting medium. Confocal imaging was performed with TCS SP8 (Leica). Stack of images of at least 5 cells per condition were acquired with a HC Plan Apo CS2 63x oil numerical aperture 1.40 objective. 2-3 LPS beads/KEC per cell were selected and analyzed with Fiji software version 1.53c and 1.53f51, (NIH, USA). Intensity profiles were made over 5 µm scan lines that were traced and aligned based on the LPS beads or KEC channel signals from a single stack and then normalized to their maximum intensity.

## Quantification and statistical analysis
Error bars displayed throughout the manuscript represent standard error of the means (s.e.m.) unless otherwise noted. They were calculated from at least three biological replicates. No randomization or blinding was used. No statistical method was used to predetermined sample size. As indicated in figure legends, either a representative experiment or pool are shown, and the number of repetitions of each experiment and the number of experimental repeats is indicated. All calculations of significance were determined using GraphPad Prism 9.5.0 software. For comparisons between two groups with normally distributed data and equal variances, we used a two-tailed unpaired Student's t-test. When variances were unequal, we used Welch's t-test or Wilcoxon. For non-normally distributed data comparing two groups, we applied the Mann-Whitney test. One-way ANOVA was used to compare more than two groups with normally distributed data, followed by appropriate post hoc tests to determine group-specific differences. In cases in which some groups of data were normally distributed and others were not, or when some groups showed unequal variances, we consistently applied the most conservative statistical test.

**Data availability:** All data and softwares used in the study are available to any researcher for purposes of reproducing or extending these analyses upon reasonable request. Softwares used in this study are listed in the reporting summary and in the Methods section were appropriated. No new software or code was generated and used for data collection and analysis. The metabolomics data described in this study is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Worbench, https://www.metabolomicsworkbench.org where it has been assigned consecutive Study IDs ST003603 through ST003611 (9 data sets). Data for mass spectrometry proteomics are available via ProteomeXchange with identifier PXD058142, PXD058145, PXD058147, and PXD058174. Data for RNA-sequencing are available on SRA/ENA under the following identifier ERP153320.

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64. Nemkov, T., Reisz, J. A., Gehrke, S., Hansen, K. C. & D'Alessandro, A. High-throughput metabolomics: Isocratic and gradient mass spectrometry-based methods. in *Methods in Molecular Biology* vol. 1978 (2019).
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67. Pinson, B., Moenner, M., Saint-Marc, C., Granger-Farbos, A. & Daignan-Fornier, B. On-demand utilization of phosphoribosyl pyrophosphate by downstream anabolic pathways. *Journal of Biological Chemistry* **299**, (2023).

68. Ceschin, J. et al. Identification of yeast and human 5-Aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAr) transporters. *Journal of Biological Chemistry* **289**, (2014).

69. Campion, O. et al. LRP-1 matricellular receptor involvement in triple negative breast cancer tumor angiogenesis. *Biomedicines* **9**, (2021).

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## Supplementary information

Extended Data Figures 1 to 6

Supplementary Table 1

## Acknowledgments

We are grateful to C. Leonardi Perri, G. Bénard, J. Zorn, and all members of the MRGM and ImmunoConcEpT for helpful discussions and support; R. Rossignol and D. Santamaria for mice; and L. Rouyer for assistance in macrophage survival experiments. We are also grateful to the staff of technical units of the university of Bordeaux: Atika Zouine and Jean-Michel Griffon for flow cytometry (FACSility, TBMCore), Xavier Gauthereau for quantitative PCR (OneCell, TBMCore), Julien Izotte at animal facilities (SCA, Université de Bordeaux), and all members at Bordeaux Imaging Center (BIC, CNRS) and metabolomic facility (SAM, TBMCore). We also thank the MDC/BIH Genomics Platform, Berlin for technical support. Some illustrations were made using Biorender, license #B27JNHSUDH (Extended Data Fig. 6q), # BM27ORWV05 (Fig. 2d), and #AZ27ORWV6Y (Fig. 2a and Fig. 4f). We support inclusive, diverse, and equitable conduct of research.

**Funding:** This work was supported by the IDEX program of the university of Bordeaux (ANR-10-IDEX-03-02), French National Research Agency (ANR-19-CE15-0018, ANR-23-CE14-
1 0044, and ANR-23-CE17-0063-01), Canceropole-GSO, The Bristol-Myers Squibb Foundation
2 to J.G. J.L. was supported by a Ph.D. grant from the French Ministry of Research and the
3 university of Bordeaux. G.T. was the recipient of a fellowship from the Vinci Ph.D. program
4 of the Université Franco-Italienne. Work in Efeyan laboratory was funded by Grants PID2019-
5 104012RB-I00 and PID2022-136413OB-I00 funded by MCIN/AEI /10.13039/501100011033,
6 by EU/FEDER, and by the EU-H2020 Program (ERC-2014-STG-638891). Bordeaux Imaging
7 Center is a service unit of CNRS-INSERM and Bordeaux University, and a member of the
8 national infrastructure France BioImaging supported by ANR. The integrion chromatography
9 station was purchased with the financial support of both SIRIC BRIO (COMUCAN) and the
10 Region Nouvelle-Aquitaine (AAPPF2021-2020-12000110).
11

12 **Author contributions:** Conceptualization: J.G. Investigation, methodology, analysis, and
13 visualization: J.L., A.B., G.T, J.A.R., P.M, J.-W.D., C.L., A.S., M.F.-M., C.D.L.C.A., D.W.,
14 B.R., B.P., and J.G. Resources: A.E., A.D., L.E.S., and J.G. Writing – Original Draft: J.G., J.L.
15 Writing – Review & Editing: all authors. Funding acquisition: A.E, L.E.S., and J.G.
16 Supervision: J.G.

17
18 **Correspondence and request for materials:** Further information and requests for resources
19 and reagents should be directed to and will be fulfilled upon reasonable request by Johan
20 Garaude (johan.garaude@inserm.fr)

21
22 **Declaration of interests:** All authors declare no conflict of interest.

23
1 Extended Data Figures

2

3 Extended Data Fig. 1 | Killed *E. coli* specifically reprograms mitochondria metabolism.

4 a, Cytokine levels in supernatants from BMDMs stimulated with KEC, LPS for 18h and subsequently treated or not with ATP for 30min. b, Western blot of total and phosphor (p)-ERK1/2 in BMDMs stimulated with KEC or LPS for the indicated time. Vinculin is used as control. MW, molecular weight. c, Normalized pERK/ERK signal ratio from (b). Data are from 2 biological replicates. d, Volcano plot of genes expression levels determined by bulk RNA sequencing. Dark grey plots show genes that are significantly increased (right) or decreased (left) in KEC-stimulated macrophages versus LPS-stimulated macrophages; Log~2~FC>1 and Log~10~(P-value)>1. Color dots show genes coding for indicated transporter family. e, Heat map showing the top differentially expressed solute carrier (Slc) genes and their functions in BMDMs stimulated 4h with KEC as compared to LPS. f, Heat maps showing the top differentially expressed mitochondria-associated genes in BMDMs left untreated (Ctrl) or stimulated 4h with KEC or LPS. g, ATP levels in BMDMs treated with LPS or KEC for 2h. h, oxygen consumption rate (OCR) on BMDMs treated with KEC, LPS covered-latex beads (Beads/LPS) or bovine serum albumin + LPS (LPS+BSA) in a minimal medium supplemented with glucose. Data are mean +/- SD, N= 6 technical replicates. Representative of at least 3 experiments. i,j,l, Extracellular acidification rate (ECAR) (i) and oxygen consumption rate (OCR) (i,j,l) of BMDMs treated or not with bafilomycin A and subsequently stimulated for 30min with KEC or LPS in a minimal medium supplemented with glucose supplemented with the indicated amino acid (l). k, Intracellular or supernatant concentration of the indicated amino acids in BMDMs stimulated with LPS or KEC for 2h. Data are mean +/- s.e.m. unless otherwise mentioned, n=3 (a,g), n=4 (d-f,j,k), n=5 (i) and n=6 (l) biological replicates. Statistical significance was assessed using one-way ANOVA using Tukey's multiple comparisons test (a,g,i-l).

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28 Extended Data Fig. 2 | Killed *E. coli* is a source of metabolites for macrophage upon phagocytosis.

29 a, Number of macrophage-specific total peptides (left) and heavy peptides (right) determined by LC-MS of BMDMs treated with SILAC-KEC for the indicated time point. b, Venn diagram of macrophage-specific heavy proteins identified in (a) and overlapping across 8h, 12h and 18h time points (left). The top 5 pathways identified by DAVID functional enrichment analysis from 51 commonly heavy proteins from all time points (right). Shared protein lists from the top 2 pathways are shown. c, Top pathways identified by DAVID functional enrichment analysis from heavy proteins identified by LC-MS of BMDMs that have phagocytosed SILAC-KEC for the indicated time points. d, Heat map showing relative abundance ratio (vs 8h time point) of common SILAC proteins identified in BMDMs stimulated with SILAC-KEC for 8h, 12h and 24h. e, Heat map amino acid abundances in BMDMs stimulated with KEC upon v-ATPase inhibition or vehicle treatment relative to control (lower panel). *P* values are for comparisons between amino acid abundances shown (upper panel). f, Volcano plot of genes expression levels determined by bulk RNA sequencing. Dark grey plots show genes that are significantly increased (right) or decreased (left) in KEC-stimulated macrophages treated with concanamycin A versus vehicle; Log~2~FC>1 and

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Log₁₀(*P*-value)>1. Color dots show metabolite transporters of the indicated family. **g**, Heat map showing the top differentially expressed solute carrier (Slc) genes in BMDMs stimulated 4h with KEC upon treatment with concanamycin A or vehicle (DMSO). **h**, Volcano plot showing abundances of ¹³C-Arg or ¹³C-Lys containing proteins identified in BMDMs treated with concanamycin A and stimulated with SILAC-KEC. Dark grey plots show proteins that are significantly increased (right) or decreased (left) in concanamycin A-treated versus non-treated BMDMs; Log₂FC>0.5 and Log₁₀(*P*-value)>1. **i-j**, Fractional labelling of metabolites in *E. coli* after a 3 day-culture in minimal M9 medium supplemented with U-[¹³C]glucose. Fraction of isotopologues was determined over 15 metabolites by UHPLC-MS. ‘n’ is the total number of carbons for an individual metabolite (**i**). **k**, Fractional labelling of itaconate in BMDMs cultured in direct contact (well) or separated (insert, trans-well) from fully-labelled U-[¹³C]KEC for 6h or 18h. **l**, Flow cytometry analysis of peritoneal cells isolated from thioglycolate-treated mice injected with fully-labelled DAPI⁺U-[¹³C]KEC. Percentage of DAPI⁺ and DAPI⁻ cells for each mouse is shown on the right. **e**, Fractional labelling of metabolites in peritoneal phagocytes sorted from mice injected with DAPI⁺U-[¹³C]KEC or unlabelled DAPI⁺KEC. Fraction of isotopologues was determined over 15 metabolites by UHPLC-MS. Fraction of isotopologues for glutamate and GSH is. Data are mean +/-. s.e.m. unless otherwise mentioned, n=3 (**a-d,h,k**), n=4 (**e-g,l,j,m**), n=5 (**l**) biological replicates. Statistical significance was assessed using one-way ANOVA using Tukey's multiple comparisons test (**a,i,j**), or two-tailed unpaired *t* test (**l,m**).

19
20

21 Extended Data Fig. 3 | RagA is required for KEC-mediated metabolic reprogramming of
22 macrophages.

23 a, Fractional labelling of metabolites in *Kras*G12V- (Upper panel) and *Hras*G12A- (lower panel) expressing
24 BMDMs treated for 6h with U-[¹³C]KEC. Fraction of isotopologues was determined over 45 metabolites
25 by LC-MS. **b,c**, Representative confocal microscopy images of BMDMs stained with anti-LAMP1
26 (lysosomes) and anti-RagA (**b**) or mTOR (**c**) antibodies, 20min after stimulation with LPS-covered FITC-
27 latex beads or GFP-expressing KEC. Scale bars, 1µm. **d**, Normalized signal intensity (normalized to
28 control) of the indicated phospho-specific 4EBP1 and S6 protein antibodies as determined by western
29 blotting analysis BMDMs treated with LPS or KEC for different time points. Vinculin is used as a loading
30 control. **e,f**, Representative immunoblots of *RagA*WT and *RagA*GTP/Δ BMDMs treated with KEC for the
31 indicated time, stained with the indicated antibodies (**f**). Vinculin is used as a loading control and used
32 to calculate normalized signal ratio. In (**e**), three different BMDM preparations were used. **g**, Heat map
33 of fold changes (log₂) of amino acid abundances in *RagA*WT and *RagA*GTP/Δ BMDMs stimulated with KEC
34 relative to control (left). *P* values are for comparisons between amino acid abundances shown (right).
35 **h**, Representative immunoblots of *Cre⁺::RagA⁺/+* and *Cre⁺::RagA⁺/f/f* BMDMs treated with tamoxifen.
36 Vinculin is used as a loading control and used to calculate normalized signal ratio. **i**, Volcano plot of
37 metabolite levels in BMDMs treated with LPS or KEC determined by LC-MS analysis. Dark grey plots
38 show metabolites that are significantly increased (left) or decreased (right) in KEC-treated versus LPS-
39 treated BMDMs; Log₂FC>0.5 and Log₁₀(*P*-value)>1. Orange dots show metabolites associated to GSH
40 metabolism significantly modulated. **j**, Heat map of fold changes (log₂) of abundances of metabolites in
1

BMDMs treated with bafilomycin A1 (Bafilo.) or concanamycin A (ConcA) stimulated with KEC, relative to control (left). *P* values are for comparisons between metabolite abundances shown (right). *k*, Heat maps showing differential expression of genes associated with glutathione metabolism in BMDMs stimulated 4h with KEC or LPS. Data are mean +/- s.e.m. unless otherwise mentioned, n=3 (**a,d-f,k**), n=4 (**g,h,j,k**), n=7 (*i*) biological replicates. Statistical significance was assessed using two-way ANOVA using Tukey's multiple comparisons test (**d,f**), or two-tailed unpaired *t* test (**a,e,h**).

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9

**Extended Data Fig. 4** | Sensing of live and dead *E. coli* differentially modulates macrophage metabolism.

10

*a,b*, Flow cytometry analysis BMDMs after phagocytosis of live or killed RFP-expressing (*a*) or GFP-expressing *E. coli* (*b*) for the indicated time. Quantification of percentages and mean fluorescence intensity of GFP signal are shown, mean +/- s.d. of three technical replicates (*b*). *c*, Metabolite set enrichment analysis (MSEA) of the most altered metabolic pathways in KEC-treated BMDMs versus EC-treated BMDMs at 6h post-challenge. *d*, GSH/GSSG ratio in BMDMs stimulated with EC or KEC for 6h. *e-g*, Heat maps showing differential expression of genes associated with GSH biosynthesis (*e*), Slc transporters (*f*) and amino acid metabolism (*g*) between live EC- and KEC-treated BMDMs for 4h. *h*, Volcano plot of genes expression levels determined by bulk RNA sequencing. Dark grey plots show genes that are significantly increased (right) or decreased (left) in KEC-stimulated macrophages versus live EC-treated macrophages; Log<sub>2</sub>FC>1 and Log<sub>10</sub>(*q*-value)>1. Color dots show metabolite transporters of the indicated family. *i*, Volcano plot of metabolite levels in BMDMs. Dark grey plots show metabolites significantly increased (right) or decreased (left) in KEC-treated versus live EC-treated BMDMs; Log<sub>2</sub>FC>0.5 and Log<sub>10</sub>(*P*-value)>1. Orange dots show amino acids. *j*, Intracellular levels of amino acids in BMDMs treated as in (*d*). Data are mean +/- s.e.m. unless otherwise mentioned, n=4 (**c-h**), n=7 (*i,j*) biological replicates. Statistical significance was assessed using two-way ANOVA using Tukey's multiple comparisons test (**d,f**), or two-tailed unpaired *t* test (**a,e,h**).

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**Extended Data Fig. 5** | Sensing of bacterial viability modulates nutrient recycling upon phagocytosis.

29

30

*a*, Intracellular levels of itaconate, GSH and GSSG detected by LC-MS analysis of BMDMs treated with EC or KEC for 18h. *b*, Metabolite set enrichment analysis (MSEA) of the most altered metabolic pathways in KEC-treated BMDMs versus Live EC-treated BMDMs at 18h post-challenge. *c*, Heat map showing relative abundance (normalized peak ion intensity) of the most altered metabolites in BMDMs treated with KEC or Live EC for 18h. **d-g** Fractional levels of a pool of 45 *E. coli* fully labeled-metabolites detected by UHPLC-MS in BMDMs, 6h (**d,e,g**) or 18h (*f*) after phagocytosis of U-[¹³C]EC or U-[¹³C]KEC by WT (**d,e,f**) or *RagA*<sup>GTP/Δ</sup> BMDMs (**g**). *h*, Percentage of live macrophages treated with EC or KEC and cultured for the indicated time in DMEM without amino acid (AA) supplemented or not with a cocktail of non-essential and essential AA. *i,j*, Reactive oxygen species (ROS) production by BMDMs cultured in complete RPMI or minimal RPMI depleted of AA and stimulated with EC or KEC in

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40
1

the presence or absence of a cocktail of non-essential and essential AA. RLU, relative luminescence

2

units. **k**, **l**, Fractional labelling of itaconate in BMDMs stimulated with U U-[¹³C]EC or U-[¹³C]KEC for 6h.

3

**m**, TNF-α levels in supernatants of BMDMs cultured in RPMI medium without AA and stimulated for

4

18h with EC or KEC in the presence or not of a cocktail of non-essential and essential AA. Data are

5

mean +/- s.d. Representative of 4 experiments. Data are mean +/- s.e.m., unless otherwise

6

mentioned, n=3 (**i**,**j**), n=4 (**b**,**d**-**g**,**k**), n=7 (**a**,**c**) biological replicates. Statistical significance was

7

assessed using one-way (**d**,**f**,**g**,**i**,**k**,**l**) or two-way (**e**) ANOVA using Tukey's multiple comparisons test,

8

or two-tailed unpaired *t* test (**a**,**d**). ns, not significant.

9

10

**Extended Data Fig. 6** | **Phagocytosis of killed but not live E. coli sustains intracellular AMP pool to adjust antioxidant responses and IL-1β production.**

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**a**, Metabolites abundance in EC or heat-KEC. **b**, Intracellular abundance of bacterial AMP in WT or

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*RagA*<sup>GTP/Δ</sup> BMDMs treated or not with concanamycin A (Con.) or bafilomycin A1 (Baf.) and stimulated

15

with U-[¹³C]killed EC for 6h. **c**, Heat map of isotopologue levels among various metabolites in BMDMs

16

treated with U-[¹³C]KEC for 6h and 18h. **d**, **c**, Intracellular AMP isotopologue levels in WT or *RagA*<sup>GTP/Δ</sup>

17

(**e**) BMDMs treated with concanamycin A (Con.) or bafilomycin A1 (Baf.) and stimulated with U-

18

[¹³C]killed EC for 6h. **f**, Intracellular AMP, adenosine and adenine levels in BMDMs treated with EC of

19

KEC for 6h or 18h. **g**, Western blot of total and phospho-AMPK in BMDMs stimulated with LPS or KEC

20

for the indicated time. Vinculin is used as control. MW, molecular weight. **h**, Normalized signal ratio to

21

vinculin from immunoblots of BMDMs stimulated with LPS or KEC for the indicated time. **i**, Western blot

22

and normalized signal ratio to vinculin (right) of total and phosphor (p)- S6 and 4EBP1 in BMDMs

23

stimulated with EC or KEC for 18h. **j**, **k**, **m**, **n**, **p**, IL-1β levels in supernatants of BMDMs treated or not with

24

8-bromo-cAMP (**j**), N-acetyl-cysteine (**k**), 5-aminoimidazole-4-carboxamide ribonucleotide (**m**), or

25

Torin1 (**p**) and stimulated for 18h with EC or KEC. **l**, **o**, ROS production in BMDMs treated or not with

26

AICAR (**l**) or Torin 1 (**o**; mean +/- s.d., representative of 4 experiments) and stimulated with EC and

27

KEC for 18h. RLU, relative luminescence units. Data are mean +/- s.e.m. unless otherwise mentioned,

28

n=3 (**a**,**b**,**i**-**k**,**n**), n=4 (**c**-**e**,**h**,**l**,**m**,**p**), n=7 (**f**) biological replicates. Statistical significance was assessed

29

using one-way (**b**,**d**,**e**,**h**-**p**) or two-way (**f**) ANOVA using Tukey's multiple comparisons test, or two-tailed

30

unpaired *t* test (**a**). ns, not significant.

31

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33

34
<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /></figure>
a

<figure><img src="image_12.png" /></figure>

b

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

c

<figure><img src="image_15.png" /></figure>

d

<figure><img src="image_16.png" /></figure>

e

<figure><img src="image_17.png" /></figure>

f

<figure><img src="image_18.png" /></figure>

f

<figure><img src="image_19.png" /></figure>

h

<figure><img src="image_20.png" /></figure>

g

<figure><img src="image_21.png" /></figure>

i

<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_29.png" /></figure>
<figure><img src="image_30.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_36.png" /></figure>
<figure><img src="image_37.png" /></figure>

**Extended Data Fig. 1 | Killed *E. coli* specifically reprograms mitochondria metabolism.** **a**, Cytokine levels in supernatants from BMDMs stimulated with KEC, LPS for 18h and subsequently treated or not with ATP for 30min. **b**, Western blot of total and phospho- (p) ERK1/2 in BMDMs stimulated with EC or KEC for the indicated time. Vinculin is used as control. MW, molecular weight. **c**, Normalized pERK/ERK signal ratio from (b). Data are from 2 biological replicates. **d**, Volcano plot of genes expression levels determined by bulk RNA sequencing. Dark grey plots show genes that are significantly increased (right) or decreased (left) in KEC-stimulated macrophages versus LPS-stimulated macrophages; Log₂FC>1 and Log₂P-value>1. Color dots show genes coding for indicated transporter family. **e**, Heat maps showing the top differentially expressed solute carrier (Slc) genes and their functions in BMDMs stimulated 4h with KEC as compared to LPS. **f**, Heat maps showing the top differentially expressed mitochondria-associated genes in BMDMs left untreated (Ctrl) or

stimulated 4h with KEC or LPS. **g**, ATP levels in BMDMs treated with LPS or KEC for 2h. **h**, oxygen consumption rate (OCR) on BMDMs treated with KEC, LPS covered-latex beads (Beads/LPS) or bovine serum albumin + LPS (LPS+BSA) in a minimal medium supplemented with glucose. Data are mean +/- SD, N= 6 technical replicates. Representative of at least 3 experiments. **i,j,l**, Extracellular acidification rate (ECAR) (i) and oxygen consumption rate (OCR) (i,j,l) of BMDMs treated or not with bafilomycin A and subsequently stimulated for 30min with KEC or LPS in a minimal medium supplemented with glucose supplemented with the indicated amino acid (I). **k**, Intracellular or supernatant concentration of the indicated amino acids in BMDMs stimulated with LPS or KEC for 2h. Data are mean +/- s.e.m. unless otherwise mentioned, n=3 (a,g), n=4 (d-f,j,k), n=5 (i) and n=6 (l) biological replicates. Statistical significance was assessed using one-way ANOVA using Tukey's multiple comparisons test (a,g,i-l).
<figure><img src="image_38.png" /><figcaption>a</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>b</figcaption></figure>

Number of heavy proteins

<figure><img src="image_40.png" /><figcaption>Enrichment analysis of 51 overlapping proteins</figcaption></figure>

<figure><img src="image_41.png" /></figure>

Innate immunity

ACOD1, ANXA1, IFIT1, IFIT2

MPEG1, RSAD2

Glycolysis

PKM, GM10358, PGAM1, GAPDHRT, ENO1, ALDOART1

<figure><img src="image_42.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>Enrichment analysis (DAVID, molecular function)</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>24h post-<sup>13</sup>K, <sup>13</sup>R-KEC challenge</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>i</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>j</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>k</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>l</figcaption></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /><figcaption>m</figcaption></figure>

<figure><img src="image_55.png" /></figure>
**Extended Data Fig. 2** | Killed *E. coli* is a source of metabolites for macrophage upon phagocytosis. a, Number of macrophage-specific total peptides (left) and heavy peptides (right) determined by LC-MS of BMDMs treated with SILAC-KEC for the indicated time point. b, Venn diagram of macrophage-specific heavy proteins identified in (a) and overlapping across 8h, 12h and 18h time points (left). The top 5 pathways identified by DAVID functional enrichment analysis from 57 commonly heavy proteins from all time points (right). Shared protein lists from the top 2 pathways are shown. c, Top pathways identified by DAVID functional enrichment analysis from heavy proteins identified by LC-MS of BMDMs that have phagocytosed SILAC-KEC for the indicated time points. d, Heat map showing relative abundance ratio (vs 8h time point) of common SILAC proteins identified in BMDMs stimulated with SILAC-KEC for 8h, 12h and 24h. e, Heat map amino acid abundances in BMDMs stimulated with KEC upon v-ATPase inhibition or vehicle treatment relative to control (lower panel). P values are for comparisons between amino acid abundances shown (upper panel). f, Volcano plot of genes expression levels determined by bulk RNA sequencing. Dark grey plots show genes that are significantly increased (right) or decreased (left) in KEC-stimulated macrophages treated with concanamycin A versus vehicle; Log~2~FC>1 and Log~10~(P-value)>1. Color dots show metabolite transporters of the indicated family. g, Heat map showing the top differentially expressed solute carrier (Slc) genes in BMDMs stimulated

4h with KEC upon treatment with concanamycin A or vehicle (DMSO). h, Volcano plot showing abundances of ¹³C-Arg or ¹³C-Lys containing proteins identified in BMDMs treated with concanamycin A and stimulated with SILAC-KEC. Dark grey plots show proteins that are significantly increased (right) or decreased (left) in concanamycin A-treated versus non-treated BMDMs; Log~2~FC>0.5 and Log~10~(P-value)>1. i-j, Fractional labelling of metabolites in *E. coli* after a 3 day-culture in minimal M9 medium supplemented with U-[¹³C]glucose. Fraction of isotopologues was determined over 15 metabolites by UHPLC-MS. ‘n’ is the total number of carbons for an individual metabolite (i). k, Fractional labelling of itaconate in BMDMs cultured in direct contact (well) or separated (insert, trans-well) from fully-labelled U-[¹³C]KEC for 6h or 18h. l, Flow cytometry analysis of peritoneal cells isolated from thioglycolate-treated mice injected with fully-labelled DAPI⁺U-[¹³C]KEC. Percentage of DAPI⁺ and DAPI⁻ cells for each mouse is shown on the right. e, Fractional labelling of metabolites in peritoneal phagocytes sorted from mice injected with DAPI⁺U-[¹³C]KEC or unlabelled DAPI⁺KEC. Fraction of isotopologues was determined over 15 metabolites by UHPLC-MS. Fraction of isotopologues for Glutamate and GSH is. Data are mean +/- s.e.m. unless otherwise mentioned, n=3 (a-d,h,k), n=4 (e-g,l,j,m), n=5 (l) biological replicates. Statistical significance was assessed using one-way ANOVA using Tukey's multiple comparisons test (a,i,j), or two-tailed unpaired t test (l,m).
<figure><img src="image_56.png" /></figure>

**Extended Data Fig. 3** | RagA is required for KEC-mediated metabolic reprogramming of macrophages. **a**, Fractional labelling of metabolites in *Kras*<sup>G12V</sup> (Upper panel) and *Hras*<sup>G12A</sup> (lower panel) in BMDMs treated for 6h with U-[¹³C]KEC. Fraction of isotopologues was determined over 45 metabolites by LC-MS. **b,c**, Representative confocal microscopy images of BMDMs stained with anti-LAMP1 (lysosomes) and anti-RagA (**b**) or mTOR (**c**) antibodies, 20min after stimulation with LPS-covered FITC-latex beads or GFP-expressing KEC. **d**, Normalized signal intensity (normalized to control) of the indicated phospho-specific 4EBP1 and S6 protein antibodies as determined by western blotting analysis BMDMs treated with LPS or KEC for different time points. Vinculin is used as a loading control. **e,f**, Representative immunoblots of *RagA*<sup>WT</sup> and *RagA*<sup>GTP/Δ</sup> BMDMs treated with KEC for the indicated time (**f**). Vinculin is used as a loading control and used to calculate normalized signal ratio. In (**e**), three different BMDM preparations were used. **g**, Heat map of fold changes (log₂) of amino acid abundances in *RagA*<sup>WT</sup> and *RagA*<sup>GTP/Δ</sup> BMDMs stimulated with KEC relative to control (left). **P** values are for comparisons between amino acid abundances shown (right). **h**, Representative

immunoblots of Cre⁺::RagA<sup>WT</sup> and Cre⁺::RagA<sup>Δ</sup> BMDMs treated with tamoxifen. Vinculin is used as a loading control and used to calculate normalized signal ratio. **i**, Volcano plot of metabolite levels in BMDMs treated with LPS or KEC determined by LC-MS analysis. Dark grey plots show metabolites that are significantly increased (left) or decreased (right) in KEC-treated versus LPS-treated BMDMs; Log<sub>2</sub>FC>0.5 and Log<sub>10</sub>(P-value)>1. Orange dots show metabolites associated to GSH metabolism significantly modulated. **j**, Heat map of fold changes (log₂) of abundances of metabolites in BMDMs treated with bafilomycin A1 (Bafilo.) or concanamycin A (ConcA) stimulated with KEC, relative to control (left). **P** values are for comparisons between metabolite abundances are shown (right). **k**, Heat maps showing differential expression of genes associated with glutathione metabolism in BMDMs stimulated 4h with KEC or LPS. Data are mean +/- s.e.m. unless otherwise mentioned, n=3 (**a,d-f,k**), n=4 (**g,h,j,k**), n=7 (i) biological replicates. Statistical significance was assessed using two-way ANOVA using Tukey's multiple comparisons test (**d,f**), or two-tailed unpaired *t* test (**a,e,h**).
<figure><img src="image_57.png" /></figure>

**Extended Data Fig. 4** | Sensing of live and dead *E. coli* differentially modulates macrophage metabolism. **a,b,** Flow cytometry analysis BMDMs after phagocytosis of live or killed RFP-expressing (a) or GFP-expressing *E. coli* (b) for the indicated time. Quantification of percentages and mean fluorescence intensity of GFP signal are shown, mean =/− s.d. of three technical replicates (b). **c,** Metabolite set enrichment analysis (MSEA) of the most altered metabolic pathways in KEC-treated BMDMs versus Live EC-treated BMDMs at 6h post-challenge. **d,** GSH/GSSG ratio in BMDMs stimulated with EC or KEC for 6h. **e-g,** Heat maps showing differential expression of genes associated with GSH biosynthesis (e), Slc transporters (f) and amino acid metabolism (e) between live EC- and KEC-treated BMDMs for 4h. **h,** Volcano plot of genes expression levels

determined by bulk RNA sequencing. Dark grey plots show genes that are significantly increased (right) or decreased (left) in KEC-stimulated macrophages versus live EC-treated macrophages; Log~2~FC>1 and Log~10~(q-value)>1. Color dots show metabolite transporters of the indicated family. **i,** Volcano plot of metabolite levels in BMDMs. Dark grey plots show metabolites significantly increased (right) or decreased (left) in KEC-treated versus live EC-treated BMDMs; Log~2~FC>0.5 and Log~10~(P-value)>1. Orange dots show amino acids. **j,** Intracellular levels of amino acids in BMDMs treated as in (d). Data are mean +/− s.e.m. unless otherwise mentioned, n=4 (**c**-**h**), n=7 (i,j) biological replicates. Statistical significance was assessed using two-way ANOVA using Tukey's multiple comparisons test (d,f), or two-tailed unpaired t test (a,e,h).
<figure><img src="image_58.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_60.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_62.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>i</figcaption></figure>

<figure><img src="image_64.png" /><figcaption>j</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>k</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>l</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>m</figcaption></figure>

<figure><img src="image_68.png" /></figure>

Extended Data Fig. 5 | Sensing of bacterial viability modulates nutrient recycling upon phagocytosis. a, Intracellular levels of itaconate, GSH and GSSG detected by LC-MS analysis of BMDMs treated with EC or KEC for 18h. b, Metabolite set enrichment analysis (MSEA) of the most altered metabolic pathways in KEC-treated BMDMs versus Live EC-treated BMDMs at 18h post-challenge. c, Heat map showing relative abundance (normalized peak ion intensity) of the most altered metabolites in BMDMs treated with KEC or Live EC for 18h. d,g Fractional levels of a pool of 45 E. coli fully labeled-metabolites detected by UHPLC-MS in BMDMs, 6h (d,e,g) or 18h (f) after phagocytosis of U-[¹³C]EC or U-[¹³C]KEC by WT (d,e,f) or RagA<sup>GTP/Δ</sup> BMDMs (g). h, Percentage of live macrophages treated with EC or KEC and cultured for the indicated time in DMEM without amino acid (AA) supplemented or not with a cocktail of

non-essential and essential AA. i,j, Reactive oxygen species (ROS) production by BMDMs cultured in complete RPMI or minimal RPMI depleted of AA and stimulated with EC or KEC in the presence or absence of a cocktail of non-essential and essential AA. RLU, relative luminescence units. k,l Fractional labelling of metabolites in WT or RagA<sup>GTP/Δ</sup> BMDMs stimulated with U-[¹³C]EC or U-[¹³C]KEC for 6h. m, TNF-α levels in supernatants of BMDMs cultured in RPMI medium without AA and stimulated for 18h with EC or KEC in the presence or not of a cocktail of non-essential and essential AA. Data are mean +/- s.d. Representative of 4 experiments. Data are mean +/- s.e.m. unless otherwise mentioned, n=3 (i,j), n=4 (b,d,g,k), n=7 (a,c) biological replicates. Statistical significance was assessed using one-way (d,f,g,i,k,l) or two-way (e) ANOVA using Tukey's multiple comparisons test, or two-tailed unpaired *t* test (a,d). ns, not significant.

<figure><img src="image_69.png" /><figcaption>c</figcaption></figure>
a

<figure><img src="image_70.png" /></figure>

b

<figure><img src="image_71.png" /></figure>

c

<figure><img src="image_72.png" /></figure>

d

<figure><img src="image_73.png" /></figure>

e

<figure><img src="image_74.png" /></figure>

f

<figure><img src="image_75.png" /></figure>

g

<figure><img src="image_76.png" /></figure>

h

<figure><img src="image_77.png" /></figure>

i

<figure><img src="image_78.png" /></figure>

j

<figure><img src="image_79.png" /></figure>

k

<figure><img src="image_80.png" /></figure>

m

<figure><img src="image_81.png" /></figure>

n

o

<figure><img src="image_82.png" /></figure>

p

q

<figure><img src="image_83.png" /></figure>
**Extended Data Fig. 6** | Phagocytosis of killed but not live *E. coli* sustains intracellular AMP pool to adjust antioxidant responses and IL-1β production. a, Metabolites abundance in EC or heat-KEC. b, Intracellular abundance of bacterial AMP in WT or *RagA*<sup>GTP/β</sup> BMDMs treated or not with concanamycin A (Con.) or bafilomycin A1 (Baf.) and stimulated with U-[<sup>13</sup>C]killed EC for 6h. c, Heat map of isotopologue levels among various metabolites in BMDMs treated with U-[<sup>13</sup>C]KEC for 6h and 18h. d, e, Intracellular AMP isotopologue levels in WT or *RagA*<sup>GTP/D</sup> (e) BMDMs treated with concanamycin A (Con.) or bafilomycin A1 (Baf.) and stimulated with U-[<sup>13</sup>C]killed EC for 6h. f, Intracellular AMP, adenosine and adenine levels in BMDMs treated with EC of KEC for 6h or 18h. g, Western blot of total and phospho-AMPK in BMDMs stimulated with LPS or KEC for the indicated time. Vinculin is used as control. MW, molecular weight. h, Normalized signal ratio to vinculin from immunoblots of BMDMs

stimulated with LPS or KEC for the indicated time. i, Western blot and
normalized signal ratio to vinculin (right) of total and phospho- S6 and
4EBP1 in BMDMs stimulated with EC or KEC for 18h. j,k,m,n,p, IL-1β levels
in supernatants of BMDMs treated or not with 8-bromo-cAMP (j),
N-acetyl-cysteine (k), 5-aminoimidazole-4-carboxamide ribonucleotide
(m), or Torin1 (p) and stimulated for 18h with EC or KEC. l,o, ROS
production in BMDMs treated or not with AICAR (l) or Torin 1 (o; mean +/-
s.d., representative of 4 experiments) and stimulated with EC and KEC for
18h. RLU, relative luminescence units. Data are mean +/- s.e.m. unless
otherwise mentioned, n=3 (a,b,i-k,n), n=4 (c-e,h,l,m,p), n=7 (f) biological
replicates. Statistical significance was assessed using one-way (b,d,e,h-p)
or two-way (f) ANOVA using Tukey's multiple comparisons test, or
two-tailed unpaired t test (a). ns, not significant.
**Supplementary Information**

**Macrophages recycle phagocytosed bacteria to fuel immunometabolic responses**

Juliette Lesbats¹, Aurélia Brillac¹, Julie A. Reisz²,§, Parnika Mukherjee³,§, Charlène Lhuissier⁴, Mónica Fernández-Monreal⁵, Jean-William Dupuy⁶,⁷, Angèle Sequeira⁴, Gaia Tioli¹,⁸, Celia De La Calle⁹, Benoît Pinson¹⁰, Daniel Wendisch³, Benoît Rousseau¹¹, Alejo Efeyan⁹, Leif. E Sander³,¹², Angelo D’Alessandro², and Johan Garaude¹,⁴,*

This file contains Supplementary Table 1, which lists metabolites analyzed by ultra-high pressure liquid chromatography coupled with mass spectrometry (UHPLC-MS) in this study.
**Supplementary Information**

**Macrophages recycle phagocytosed bacteria to fuel immunometabolic responses**

Juliette Lesbats¹, Aurélia Brillac¹, Julie A. Reisz²,§, Parnika Mukherjee³,§, Charlène Lhuissier⁴, Mónica Fernández-Monreal⁵, Jean-William Dupuy⁶,⁷, Angèle Sequeira⁴, Gaia Tioli¹,⁸, Celia De La Calle⁹, Benoît Pinson¹⁰, Daniel Wendisch³, Benoît Rousseau¹¹, Alejo Efeyan⁹, Leif. E Sander³,¹², Angelo D’Alessandro², and Johan Garaude¹,⁴,*

This file contains Supplementary Table 1, which lists metabolites analyzed by ultra-high pressure liquid chromatography coupled with mass spectrometry (UHPLC-MS) in this study.
<table><caption>Supplementary Table 1 | List of metabolites analyzed by UHPLC-MS and their associated metabolic pathways.</caption><thead><tr><td>Compound</td><td>CmpdID</td><td>Pathway</td></tr></thead><tbody><tr><td>Alanine</td><td>C00041</td><td>Amino acids</td></tr><tr><td>Arginine</td><td>C00062</td><td>Amino acids</td></tr><tr><td>Asparagine</td><td>C00152</td><td>Amino acids</td></tr><tr><td>Aspartate</td><td>C00049</td><td>Amino acids</td></tr><tr><td>Cysteine</td><td>C00097</td><td>Amino acids</td></tr><tr><td>Glutamate</td><td>C00025</td><td>Amino acids</td></tr><tr><td>Glutamine</td><td>C00064</td><td>Amino acids</td></tr><tr><td>Glycine</td><td>C00037</td><td>Amino acids</td></tr><tr><td>Histidine</td><td>C00135</td><td>Amino acids</td></tr><tr><td>Leucine</td><td>C00123</td><td>Amino acids</td></tr><tr><td>Lysine</td><td>C00047</td><td>Amino acids</td></tr><tr><td>Methionine</td><td>C00073</td><td>Amino acids</td></tr><tr><td>Phenylalanine</td><td>C00079</td><td>Amino acids</td></tr><tr><td>Proline</td><td>C00148</td><td>Amino acids</td></tr><tr><td>Serine</td><td>C00065</td><td>Amino acids</td></tr><tr><td>Threonine</td><td>C00188</td><td>Amino acids</td></tr><tr><td>Tryptophan</td><td>C00078</td><td>Amino acids</td></tr><tr><td>Tyrosine</td><td>C00082</td><td>Amino acids</td></tr><tr><td>Valine</td><td>C00183</td><td>Amino acids</td></tr><tr><td>AMP</td><td>C00020</td><td>Nucleotides</td></tr><tr><td>dAMP</td><td>C00360</td><td>Nucleotides</td></tr><tr><td>Adenosine</td><td>C00212</td><td>Nucleotides</td></tr><tr><td>Adenine</td><td>C00147</td><td>Nucleotides</td></tr><tr><td>GMP</td><td>C00144</td><td>Nucleotides</td></tr><tr><td>CMP</td><td>C00055</td><td>Nucleotides</td></tr><tr><td>Cytidine</td><td>C00475</td><td>Nucleotides</td></tr><tr><td>UMP</td><td>C00105</td><td>Nucleotides</td></tr><tr><td>IMP</td><td>C00130</td><td>Nucleotides</td></tr><tr><td>Inosine</td><td>C00294</td><td>Nucleotides</td></tr><tr><td>Hypoxanthine</td><td>C00262</td><td>Nucleotides</td></tr><tr><td>Xanthine</td><td>C00385</td><td>Nucleotides</td></tr><tr><td>Allantoate</td><td>C00499</td><td>Nucleotides</td></tr><tr><td>5-Hydroxyisourate</td><td>C11821</td><td>Nucleotides</td></tr><tr><td>5-6-Dihydrothymine</td><td>C00906</td><td>Nucleotides</td></tr><tr><td>4-Pyridoxate</td><td>C00847</td><td>Nucleotides</td></tr><tr><td>Nicotinamide</td><td>C00153</td><td>Nucleotides</td></tr><tr><td>UDP-glucose</td><td>C00029</td><td>Nucleotides</td></tr><tr><td>ADP-D-ribose</td><td>C01882</td><td>Nucleotides</td></tr><tr><td>NAD+</td><td>C00003</td><td>Nucleotides</td></tr><tr><td>Phosphate</td><td>C00009</td><td>Phosphates</td></tr><tr><td>Diphosphate</td><td>C00013</td><td>Phosphates</td></tr><tr><td>D-Glucose</td><td>C00031</td><td>Glycolysis</td></tr><tr><td>Hexose phosphate</td><td>C02965</td><td>Glycolysis</td></tr><tr><td>2/3-Phospho-D-glycerate</td><td>C00631</td><td>Glycolysis</td></tr><tr><td>Pyruvate</td><td>C00022</td><td>Glycolysis</td></tr><tr><td>Lactate</td><td>C01432</td><td>Glycolysis</td></tr><tr><td>Maltose</td><td>C00208</td><td>Other sugars</td></tr><tr><td>Mannitol</td><td>C00392</td><td>Other sugars</td></tr><tr><td>D-Arabinol</td><td>C01904</td><td>Other sugars</td></tr><tr><td>Citrate</td><td>C00158</td><td>TCA cycle</td></tr><tr><td>2-Oxoglutarate</td><td>C00026</td><td>TCA cycle</td></tr><tr><td>2-Oxoglutaramate</td><td>C00940</td><td>TCA cycle</td></tr><tr><td>Succinate</td><td>C00042</td><td>TCA cycle</td></tr><tr><td>Fumarate</td><td>C00122</td><td>TCA cycle</td></tr><tr><td>Malate</td><td>C00149</td><td>TCA cycle</td></tr><tr><td>2-Hydroxyglutarate/Citramalate</td><td>C02630</td><td>Alternative Carboxylic acids</td></tr><tr><td>Itaconate</td><td></td><td>Alternative Carboxylic acids</td></tr><tr><td>D-Glucono-1-5-lactone 6-phosphate</td><td>C01236</td><td>Pentose Phosphate Pathway</td></tr><tr><td>Sedoheptulose 1-phosphate</td><td>C06222</td><td>Pentose Phosphate Pathway</td></tr><tr><td>alpha-D-Ribose 1-phosphate</td><td>C00620</td><td>Pentose Phosphate Pathway</td></tr><tr><td>Glutathione</td><td>C00051</td><td>GSH homeostasis</td></tr></tbody></table>
<table><tr><td>Glutathione disulfide</td><td>C00127</td><td>GSH homeostasis</td></tr><tr><td>5-Oxoproline</td><td>C01879</td><td>GSH homeostasis</td></tr><tr><td>S-Glutathionyl-L-cysteine</td><td>C05526</td><td>GSH homeostasis</td></tr><tr><td>Cys-Gly</td><td>C01419</td><td>GSH homeostasis</td></tr><tr><td>Ascorbate</td><td>C00072</td><td>GSH homeostasis</td></tr><tr><td>Dehydroascorbate</td><td>C05422</td><td>GSH homeostasis</td></tr><tr><td>gamma-L-Glutamyl-L-cysteine</td><td>C00669</td><td>Gamma-glutamyls</td></tr><tr><td>gamma-Glutamyl-Se-methylselenocysteine</td><td>C05695</td><td>Gamma-glutamyls</td></tr><tr><td>gamma-L-Glutamyl-D-alanine</td><td>C03738</td><td>Gamma-glutamyls</td></tr><tr><td>gamma-Glutamyl-gamma-aminobutyrate</td><td>C15767</td><td>Gamma-glutamyls</td></tr><tr><td>gamma-L-Glutamylputrescine</td><td>C15699</td><td>Gamma-glutamyls</td></tr><tr><td>(5-L-Glutamyl)-L-glutamine</td><td>C05283</td><td>Gamma-glutamyls</td></tr><tr><td>L-Homocysteine</td><td>C00155</td><td>Serine biosynthesis and one-carbon metabolism</td></tr><tr><td>Dimethylglycine</td><td>C01026</td><td>Serine biosynthesis and one-carbon metabolism</td></tr><tr><td>3-Phosphonooxypyruvate</td><td>C03232</td><td>Serine biosynthesis and one-carbon metabolism</td></tr><tr><td>S-Adenosyl-L-methionine</td><td>C00019</td><td>Serine biosynthesis and one-carbon metabolism</td></tr><tr><td>Ornithine</td><td>C01602</td><td>Urea cycle</td></tr><tr><td>L-Citrulline</td><td>C00327</td><td>Urea cycle</td></tr><tr><td>Argininosuccinate</td><td>C03406</td><td>Urea cycle</td></tr><tr><td>Cadaverine</td><td>C01672</td><td>Polyamines</td></tr><tr><td>Putrescine</td><td>C00134</td><td>Polyamines</td></tr><tr><td>Spermidine</td><td>C00315</td><td>Polyamines</td></tr><tr><td>Spermine</td><td>C00750</td><td>Polyamines</td></tr><tr><td>N-Acetylneuraminate</td><td>C00270</td><td>Aminosugars</td></tr><tr><td>N-Glycoloyl-neuraminate</td><td>C03410</td><td>Aminosugars</td></tr><tr><td>alpha-D-Glucosamine 1-phosphate</td><td>C06156</td><td>Aminosugars</td></tr><tr><td>1-4-beta-D-Xylan</td><td>C02352</td><td>Aminosugars</td></tr><tr><td>UDP-N-acetyl-D-glucosamine</td><td>C00043</td><td>Aminosugars</td></tr><tr><td>Creatine</td><td>C00300</td><td>Arginine and proline metabolism</td></tr><tr><td>Creatinine</td><td>C00791</td><td>Arginine and proline metabolism</td></tr><tr><td>4-Acetamidobutanoate</td><td>C02946</td><td>Arginine and proline metabolism</td></tr><tr><td>N-Acetyl-L-citrulline</td><td>C15532</td><td>Arginine and proline metabolism</td></tr><tr><td>N-Acetyllornithine</td><td>C00437</td><td>Arginine and proline metabolism</td></tr><tr><td>Guanidinoacetate</td><td>C00581</td><td>Arginine and proline metabolism</td></tr><tr><td>trans-4-Hydroxy-L-proline</td><td>C01157</td><td>Arginine and proline metabolism</td></tr><tr><td>5-Aminopentanoate</td><td>C00431</td><td>Arginine and proline metabolism</td></tr><tr><td>Taurine</td><td>C00245</td><td>Sulfur metabolism</td></tr><tr><td>Hypotaurine</td><td>C00519</td><td>Sulfur metabolism</td></tr><tr><td>3-Sulfino-L-alanine</td><td>C00606</td><td>Sulfur metabolism</td></tr><tr><td>L-Cysteate</td><td>C00506</td><td>Sulfur metabolism</td></tr><tr><td>L-Methionine S-oxide</td><td>C02989</td><td>Sulfur metabolism</td></tr><tr><td>Indole-3-acetate</td><td>C00954</td><td>Indole and Tryptophan</td></tr><tr><td>Indolepyruvate</td><td>C00331</td><td>Indole and Tryptophan</td></tr><tr><td>kynurenine</td><td>C00328</td><td>Indole and Tryptophan</td></tr><tr><td>N-formyl kynurenine</td><td>C02700</td><td>Indole and Tryptophan</td></tr><tr><td>Anthranilate</td><td>C00108</td><td>Indole and Tryptophan</td></tr><tr><td>Picolinic acid</td><td>C10164</td><td>Indole and Tryptophan</td></tr><tr><td>g-Oxalo-crotonate</td><td>C03453</td><td>Indole and Tryptophan</td></tr></table>
<table><tr><td>Glycerol 3-phosphate</td><td>C00093</td><td>Glycerophospholipid biosynthesis</td></tr><tr><td>Ethanolamine phosphate</td><td>C00346</td><td>Glycerophospholipid biosynthesis</td></tr><tr><td>sn-glycero-3-Phosphoethanolamine</td><td>C01233</td><td>Glycerophospholipid biosynthesis</td></tr><tr><td>Acetylcholine</td><td>C01996</td><td>Glycerophospholipid biosynthesis</td></tr><tr><td>acetyl-carnitine</td><td>C02571</td><td>Carnitine and fatty acid metabolism</td></tr><tr><td>propionyl-carnitine</td><td>C03017</td><td>Carnitine and fatty acid metabolism</td></tr><tr><td>butanoyl-l-carnitine</td><td>C02862</td><td>Carnitine and fatty acid metabolism</td></tr><tr><td>O-dodecenoyl-carnitine</td><td>HMDB13326</td><td>Carnitine and fatty acid metabolism</td></tr><tr><td>Hexanoic acid (caproate)</td><td>C01585</td><td>Saturated Fatty acids</td></tr><tr><td>Heptanoic acid</td><td>C17714</td><td>Saturated Fatty acids</td></tr><tr><td>Octanoic acid (caprylate)</td><td>C06423</td><td>Saturated Fatty acids</td></tr><tr><td>Nonanoic acid (pelargonate)</td><td>C01601</td><td>Saturated Fatty acids</td></tr><tr><td>Dodecanoic acid</td><td>C02679</td><td>Saturated Fatty acids</td></tr><tr><td>Tetradecanoic acid</td><td>C06424</td><td>Saturated Fatty acids</td></tr><tr><td>Hexadecanoic acid</td><td>C00249</td><td>Saturated Fatty acids</td></tr><tr><td>Octadecanoic acid (stearic acid)</td><td>C01530</td><td>Saturated Fatty acids</td></tr><tr><td>Tetradecenoic acid</td><td>C08322</td><td>Monounsaturated Fatty Acids</td></tr><tr><td>Hexadecenoic acid</td><td>C08362</td><td>Monounsaturated Fatty Acids</td></tr><tr><td>Octadecenoic acid (oleic acid)</td><td>C00712</td><td>Monounsaturated Fatty Acids</td></tr><tr><td>Linoleate</td><td>C01595</td><td>Poly-unsaturated Fatty Acids</td></tr><tr><td>Octadecatrienoic acid (linolenic acid)</td><td>C06427</td><td>Poly-unsaturated Fatty Acids</td></tr><tr><td>Eicosatetraenoic acid</td><td>C00219</td><td>Poly-unsaturated Fatty Acids</td></tr><tr><td>Eicosapentaenoic acid</td><td>C06428</td><td>Poly-unsaturated Fatty Acids</td></tr><tr><td>Docosahexaenoic acid</td><td>C06429</td><td>Poly-unsaturated Fatty Acids</td></tr><tr><td>(8Z-11Z-14Z)-Icosatrienoic acid</td><td>C03242</td><td>Essential fatty acids</td></tr><tr><td>(5Z-8Z-11Z-14Z-17Z)-Icosapentaenoic acid</td><td>C06428</td><td>Essential fatty acids</td></tr><tr><td>(7Z-10Z-13Z-16Z-19Z)-Docosa-7-10-13-16-19-pentaenoic acid</td><td>C16513</td><td>Essential fatty acids</td></tr></table>