# Immunometabolic reprogramming of macrophages by gut microbiota-derived cadaverine controls colon inflammation

Rodrigo de Oliveira Formiga, Qing Li, Yining Zhao, Márcio Augusto Campos

Ribeiro, Perle Guarino-Vignon, Rand Fatouh, Leonard Dubois, Laura

Creusot, Virginie Puchois, Salomé Amouyal, et al.

► To cite this version:

Rodrigo de Oliveira Formiga, Qing Li, Yining Zhao, Márcio Augusto Campos Ribeiro, Perle Guarino-Vignon, et al. Immunometabolic reprogramming of macrophages by gut microbiota-derived cadaverine controls colon inflammation. Cell Host & Microbe, 2025, 33 (11), pp.1855-1872.e10. 10.1016/j.chom.2025.09.009. hal-05311416

HAL Id: hal-05311416

https://hal.inrae.fr/hal-05311416v1

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# Cell Host & Microbe

## Immunometabolic reprogramming of macrophages by gut microbiota-derived cadaverine controls colon inflammation

**Graphical abstract**

<figure><img src="image_2.png" /></figure>

**Authors**

Rodrigo de Oliveira Formiga, Qing Li, Yining Zhao, ..., Marie-Laure Michel, Timothy Wai, Harry Sokol

**Correspondence**

harry.sokol@gmail.com

**In brief**

de Oliveira Formiga et al. present that gut-microbiota-derived cadaverine reprograms macrophage energy metabolism in a concentration-dependent fashion, promoting either anti- or pro-inflammatory immunophenotypes. In IBD, elevated cadaverine is associated with increased flare risk and is regulated by *Enterobacteriaceae*, suggesting microbial and metabolic pathways as potential therapeutic targets.

**Highlights**

* Cadaverine rewires macrophage metabolism via Nrf2 activation

* Concentration dictates anti- or pro-inflammatory outcomes

* Gut *Enterobacteriaceae* control cadaverine levels in inflammation

* High cadaverine in IBD is associated with an increased flare risk
Article

# Immunometabolic reprogramming of macrophages by gut microbiota-derived cadaverine controls colon inflammation

Rodrigo de Oliveira Formiga,¹,² Qing Li,¹,² Yining Zhao,³ Márcio Augusto Campos Ribeiro,³ Perle Guarino-Vignon,¹,²

Rand Fatouh,¹,² Leonard Dubois,¹,² Laura Creusot,¹,² Virginie Puchois,¹,² Salomé Amouyal,¹,² Iria Alonso Salgueiro,¹,²

Marius Bredon,¹,² Loïc Chollet,²,⁴ Tatiana Ledent,¹ Cyril Scandola,⁵ Jean-Philippe Auger,⁶ Camille Danne,¹,²

Gerhard Krönke,⁶,⁷ Emma Tkacz,¹,² Patrick Emond,⁸ Guillaume Chevreux,⁹ Hang Phuong Pham,¹⁰

Clément Pontoizeau,¹¹,¹² Antonin Lamaziere,¹,² Rafael José Argüello,¹³ Nathalie Rolhion,¹,² Marie-Laure Michel,²,⁴

Timothy Wai,³ and Harry Sokol¹,²,⁴,¹⁴,¹⁵,*

¹Sorbonne Université, INSERM UMRS-938, Centre de Recherche Saint-Antoine, CRSA, AP-HP, Paris 75012, France

²Gut, Liver & Microbiome Research (GLIMMER) FHU, Paris, France

³Mitochondrial Biology Unit, Institut Pasteur, CNRS UMR3691, Université Paris Cité, Paris 75015, France

⁴Université Paris-Saclay, INARe, AgroParisTech, Micalis Institute, Jouy-en-Josas 78350, France

⁵Ultrastructural Bioimaging Unit, Institut Pasteur, Université Paris Cité, Paris 75015, France

⁶Department of Internal Medicine 3, University of Erlangen-Nuremberg, Universitätsklinikum Erlangen, Erlangen 91054, Germany

⁷Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, Deutsches Rheuma-Forschungszentrum Berlin, Berlin 10117, Germany

⁸Département Métabolomique, Université de Tours, INSERM UMRS-1253, Plateforme Scientifique et Technique Analyse des Systèmes Biologiques, Tours 37000, France

⁹Université Paris Cité, CNRS, UMRS-7592, ProteoSeine Core Facility, Institut Jacques Monod, Paris 75013, France

¹⁰Parean Biotechnologies, Saint-Malo 35400, France

¹¹Pediatrics Department, Necker Hospital, APHP, Biochemistry, Metabolomics Unit, Reference Center for Inborn Error of Metabolism, University Paris Cité, Paris 75015, France

¹²Inserm UMR_S1163, Institut Imagine, Paris 75015, France

¹³Aix Marseille Univ, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille 13288, France

¹⁴Gastroenterology Department, Saint-Antoine Hospital, APHP, Paris 75012, France

¹⁵Lead contact

*Correspondence: harry.sokol@gmail.com

https://doi.org/10.1016/j.chom.2025.09.009

## SUMMARY

Cadaverine is a polyamine produced by the gut microbiota with links to health and disease, notably inflammatory bowel disease (IBD). Here, we show that cadaverine shapes monocyte-macrophage immunometabolism in a context- and concentration-dependent fashion to impact macrophage functionality. At baseline, cadaverine is taken up via L-lysine transporters and activates the thioredoxin system, while during inflammation, cadaverine signals through aconitate decarboxylase 1 (Acod1)-itaconate. Both pathways induce activation of transcription factor, nuclear factor erythroid 2-related factor 2 (Nrf2), which supports mitochondrial respiration and promotes immunoregulatory macrophage polarization. Conversely, under higher concentrations, cadaverine acts via histamine 4 receptor, leading to glycolysis-driven inflammation and pro-inflammatory functions in macrophages. Likewise, cadaverine exhibits paradoxical effects in experimental colitis, either protective or detrimental, evoking opposite fates on macrophages depending on levels dictated by Enterobacteriaceae. In IBD patients, elevated cadaverine correlated with higher flare risk. Our findings implicate cadaverine as a microbiota-derived metabolite manipulating macrophage energy metabolism with consequences in intestinal inflammation and implications for IBD pathogenesis.

## INTRODUCTION

The gut microbiome regulates the host's cell energy metabolism, notably through its microbial byproducts and bioactive metabolites, such as short-chain fatty acids, secondary bile acids, tryptophan derivatives, and polyamines.¹⁻³

To meet the host cellular energetic demands, those mediators shape the catabolism of dietary components for nutrient utilization⁴⁻⁶ and fuel central metabolic pathways, notably glycolysis and mitochondrial respiration.² Bioenergetic perturbations, metabolic shifts, and gut
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Cell Host & Microbe
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dysbiosis have been associated with altered cellular responses
in inflammatory bowel disease (IBD).⁷⁻⁹ Indeed, an energy-defi-
cient state due to diminished butyrate oxidation as metabolic
fuel for epithelial cells was highlighted in ulcerative colitis.⁷ In
Crohn's disease, also deeply impacted by gut microbiota imbal-
ance, intestinal macrophages exhibit sustained metabolic re-
programming toward glycolysis.⁸ Therefore, identifying which
gut microbial actors shape immunometabolic alterations stands
out as an approach to deciphering the mechanisms involved in
IBD physiopathology.

Polyamines are polycationic molecules with pleiotropic
biochemical activities, involved in the regulation of gene
expression, cell proliferation, cell signaling, and also linked to
cytoprotection against different stressors, including reactive
oxygen species (ROS), heat, starvation, and ultraviolet stress.¹⁰
They are derived from the α-decarboxylation of dietary amino
acids in mammalian cells, even though, a significant portion
of their production is attributed to the gut microbiota.¹¹⁻¹³
The main polyamines putrescine, spermine, and spermidine
are products of L-ornithine catabolism, while only cadaverine
is synthesized from L-lysine. Different bioenergetic mecha-
nisms are attributed to polyamines, particularly their impact
on mitochondrial function.¹⁴ Cadaverine has been previously
reported to reduce breast cancer aggressiveness, at least in
part, through alterations in the energy metabolism of tumoral
cells.¹⁵ The production of such metabolites is tightly controlled,
with both low or high levels associated with pathological condi-
tions¹⁶; however, the biological relevance of its intestinal
synthesis by bacteria in health and disease still needs to be
addressed.

Here, we discovered that cadaverine rewires the energy meta-
bolism of monocytes and macrophages. By directing the bioen-
ergetic flux toward either glycolysis or oxidative phosphorylation
(OXPHOS) in a context- and concentration-dependent fashion,
cadaverine shapes macrophage pro- or anti-inflammatory polar-
ization in vitro as well as during intestinal inflammation. The
observed effects are tightly related to its gut concentrations
and controlled by the microbiome, notably Enterobacteriaceae.
In patients with IBD, cadaverine levels were associated with
the risk of disease recurrence. Such discoveries at the metabolic
level hold promise for identifying therapeutic strategies that

target cellular energy metabolism and the gut microbiota to
modulate the immune system in IBD.

RESULTS

Cadaverine reprograms energy metabolism of human monocytes toward glucose utilization and mitochondrial dependence

To identify microbial metabolites able to modulate host cell energy metabolism, we used a luciferase-based ATP assay to screen gut-associated metabolites.¹¹ Preliminary results identified cadaverine as capable of significantly increasing ATP levels in THP-1 monocytes. This was reduced by 2-deoxyglucose (2-DG), which blocks glycolysis, and oligomycin A (oligoA), a mitochondrial ATP synthase inhibitor (Figure S1).

We validated these findings in human peripheral blood mono-
nuclear cells (PBMCs) using the single-cell energetic metabolism
by profiling translation inhibition (SCENITH) method, which mea-
sures protein synthesis (PS) as a proxy for ATP and global meta-
bolic profiles by flow cytometry.¹⁷ FlowSOM clusters overlaid
with puromycin (PS/ATP) fold expression showed higher fluores-
cence in HLA-DR⁺ cells, slightly increased by 100 µM cadaverine
(Figures S2A–S2D). The strongest effect was in monocytes/den-
dritic cells SSC-A/HLA-DR⁺ up, displaying an increase in
glucose utilization to fuel the mitochondrial pathway. CD19⁺ B
cells (SSC-A/HLA-DR⁺ down) showed no response (Figures
S2A–S2F). Upon lipopolysaccharide (LPS) immunometabolic
stimulation,¹⁸ cadaverine further elevated ATP and glucose
dependence in HLA-DR⁺ cells but inhibited LPS-induced glycol-
ysis while maintaining mitochondrial activity (Figures S2G
and S2H). Blood monocytes subsets include intermediate
(CD14⁺⁺CD16⁺), classical (CD14⁺⁺CD16⁻), and non-classical
(CD14⁻CD16⁺⁺) populations (Figure S2I). Cadaverine signifi-
cantly increased ATP and glucose dependence in intermediate
and classical monocytes, especially under LPS, and maintained
mitochondrial dependence even under immunometabolic acti-
vation (Figures S2J–S2L). Surface marker expression and
viability were unaffected (Figures S3A–S3D), and no significant
PS/ATP changes occurred in T or B lymphocytes, though
CD3⁺CD4⁻CD8⁻ and CD3⁺CD8⁺ T cells showed moderate
metabolic shifts (Figures S3E–S3G). These findings indicate

Figure 1. Cadaverine supports OXPHOS in monocytes and macrophages

(A-I and L-N) CD14+ monocytes incubated ± cadaverine (100 µM) or LPS (100 ng/mL) for 2 or 6 hours.

(A) Differential expression profile of cadaverine versus control 2 h according to WikiPathways WP Ontology. NES, normalized enrichment score.

(B and C) GLUT-1 membrane abundance and 2-NBDG uptake (MFI) assessed by flow cytometry.

(D) Intracellular lactate concentration (pmol/mL).

(E) PDH activity (OD<sub>450</sub> slope).

(F) Volcano plot of differently expressed genes (cadaverine versus control at 2 h).

(G-I) Seahorse XF glucose/pyruvate oxidation stress test in response to UK5009 (2 µM, pyruvate carrier inhibitor) pre-incubation. Respective OCR values before UK5099/oligoA (basal respiration) and after FCCP injection (maximal respiration).

(J and K) BMDM stimulated or not with LPS (50 ng/mL) or cadaverine (100 µM) for 24 h. Representative confocal images of BMDM stained with 100 nM tet-
ramethylrhodamine ethyl ester perchlorate (TMRE) (orange) and NucBlue (blue nuclei). Scale bar, 50 µm. Fluorescence normalized by the total cell area.

(L and M) Flow cytometry analysis of CD14⁺ monocytes showing the percentages of MitoTracker Far Red (MitoFR) negative events.

(N) ATP/ADP ratio in CD14⁺ monocytes and expressed as relative luminescence units (RLUs).

(O) Transmission electron microscopy of mitochondria cristae in mouse monocytes. Scale bar, 500 nm. Mitochondria morphology and ultrastructure assessed using Fiji (ImageJ).

(B–E, H, I, L, and M–O) Mean ± SEM, one-way ANOVA with Tukey/Dunnett test (*p* < 0.05; **p** < 0.01; ***p** < 0.001; ****p** < 0.0001). For scatter dot plots, values from individual healthy donors or segmented mitochondria shown.

See also Figures S1–S5.
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that cadaverine enhances monocyte energy metabolism, partic-
ularly CD14⁺ subsets, mainly via mitochondrial respiration.

Cadaverine is transported by L-lysine carriers to
support the metabolic flux toward OXPHOS via
modulation of mitochondrial membrane potential and
ultrastructure

Since fluctuations in intracellular pH (pHi) can directly impact
cellular energy metabolism, we investigated whether cadaverine
might be linked to buffer capacity, even though polyamines have
not been referred to as buffering agents at physiological pH. ¹⁹
Flow cytometry analysis of phRodo sensors in human monocytes
incubated with cadaverine, spermine, or spermidine at 100 µM
showed no significant changes in baseline pHi. Yet, cadaverine
treatment effectively prevented the LPS-induced acidification
(Figure S3H), resulting from lactate and proton production during
aerobic glycolysis, indicating that, while not only acting as a
buffer per se, it may modulate monocytes’ metabolism through
other mechanisms.

To examine how cadaverine acts on mitochondrial respiration, following cadaverine stimulation of sorted human CD14⁺ monocytes, RNA sequencing (RNA-seq) analysis demonstrated that genes regulating OXPHOS were among the top enriched pathways (Figure 1A). Mechanistically, cadaverine, LPS, or their combination induced the translocation of the glucose transporter (GLUT)-1 to monocyte membrane (Figure 1B), and increased glucose uptake, as demonstrated using the fluorescent D-glucose analog, 2-(7-Nitro-2,1,3-benzoxadiazol-4-yl)-D-glucosamine (2-NBDG) (Figure 1C). However, while LPS amplified glycolysis-derived lactate production (Figure 1D), cadaverine directed the metabolic flux toward the mitochondria, increasing the activity of the pyruvate dehydrogenase (PDH) complex (Figure 1E), which converts pyruvate into acetyl-coenzyme A (CoA). Cadaverine also downregulated the pyruvate dehydrogenase kinase (PDK)-4 expression (Figure 1F), whose phosphorylation PDH inhibits the production of acetyl-CoA. Assessment of tricarboxylic acid (TCA) cycle intermediates using targeted metabolomics revealed the accumulation of citrate, isocitrate, α-ketoglutarate, succinate, fumarate, and malate, but not of cis-aconitate, as a feature of cadaverine treatment, which occurs only in the presence of LPS (Figure S4A), suggesting distinct metabolic responses to cadaverine under basal and inflammatory settings. Seahorse analysis confirmed that cadaverine further enhanced OXPHOS (basal and maximal respiration) in

both human monocytes and bone-marrow-derived mouse mac-
rophages (BMDMs) in a similar manner. Importantly, treatment
decreased the LPS-induced metabolic switch to glycolysis
(Figures 2G–2I, S4B, and S4C). This was closely linked to
glucose/pyruvate oxidation, as demonstrated by reduced basal
and maximal oxygen consumption when monocytes were
treated with the mitochondrial pyruvate carrier (MPC) inhibitor
UK5099 (Figures 1G–1I). On the other hand, inhibition of gluta-
mine and long-chain fatty acid oxidation by BPTES and eto-
moxir, respectively, had a lesser impact (Figures S4D and S4E).

Cadaverine also increased mitochondrial membrane potential (ΔΨm) while rescuing the LPS-induced reductions in ΔΨm and alterations in mitochondrial mass (Figures 1J–1M). Consequently, cadaverine-treated groups displayed higher ATP/ADP ratio under basal conditions and maintained cellular energy balance, even in the presence of inflammation (Figure 1N). Electron microscopy revealed morphological changes in mitochondrial ultrastructure, characterized by increased number of cristae per mitochondria and cristae density in BMDMs. Cadaverine was also able to revert LPS-induced general alterations in area, length, and width (Figure 1O), characteristics of mitochondrial fragmentation, pointing to a protective effect on mitochondrial health.

Next, we used mouse embryonic fibroblasts (MEFs) deficient for mitochondrial dynamin like GTPase (OPA1, OPA1<sup>KO</sup>), which is a key mitochondrial shaping protein that safeguards mitochondrial cristae structure and OXPHOS function.²⁰,²¹ Strikingly, higher basal and maximal respiration rates were reached in cadaverine-treated wild-type (WT) and OPA1-deficient cells, highlighting that cadaverine can enhance respiration even in the presence of cristae dysfunction. Similar results were achieved in translocase of outer mitochondrial membrane protein 70 (TOMM70)-deficient HeLa cells (TOMM70<sup>KO</sup>), which exhibit impaired respiration (Figures S4F and S4G), underscoring that cadaverine can overcome mitochondrial dysfunction. To test whether cadaverine acts directly on isolated mitochondria, we used high-resolution respirometry with or without different TCA substrates and OXPHOS inhibitors. Preincubation with cadaverine failed to enhance oxygen consumption (JO<sub>2</sub>) under the assessed conditions showing no differences to the untreated control (Figure S4H). Also, we also ruled out the possibility that cadaverine enhances OXPHOS as an uncoupler agent, since no differences were found under carbonyl cyanide m-chlorophenyl hydrazone (CCCP)-induced uncoupling in the presence

<caption>Figure 2. Cadaverine triggers immunomodulatory effects in monocyte subsets and M2-macrophage polarization dependent on mitochondrial respiration</caption>

(A–F) PBMC or CD14⁺ sorted monocytes incubated ± cadaverine (100 µM) or LPS (100 ng/mL) for 2–6 h, analyzed by SCENITH.
(A) Differential expression profile of cadaverine versus control 2 h according to WikiPathways WP Ontology. NES, normalized enrichment score.
(B and C) IL-10 and TNF-α intracellular abundance (MFI) in monocyte subsets by flow cytometry.
(D and E) ELISA determination of IL-10 and TNF-α (pg/mL) in the culture media supernatants.
(F) Flow cytometry analysis of TNF-α intracellular abundance (MFI) in intermediate and classical monocytes incubated ± 2-DG (100 mM) or oligoA (5 µM).
(G) LEGENDplex cytokines (MFI) in culture media of BMDM treated ± cadaverine (100 µM) or LPS (100 ng/mL) for 24 h.
(H–L) CD14⁺ sorted monocytes differentiated into M0 macrophages and polarized to the M1 phenotype (50 ng/mL LPS plus 20 ng/mL IFN-γ) or the M2 phenotype (20 ng/mL IL-4) for 24 h. CCR7⁺ and CD80⁺ cells (%) used as M1-markers and CD206⁺ cells (%) as M2-marker by flow cytometry. Mitochondrial involvement assessed by incubation with oligoA (1 µM) during polarization.
(I and L) ELISA determination of IL-1β and IL-10 (pg/mL) in the culture media supernatants.
(B and H–L) Mean ± SEM, one-way ANOVA with Tukey/Dunnett test (*p* < 0.05; **p* < 0.01; ***p* < 0.001; ****p* < 0.0001). For scatter dot plots, values from individual healthy donors shown.
See also Figure S2.
<figure><img src="image_32.png" /><figcaption>Figure 3. Cadaverine supports OXPHOS and immunoregulation in a context-dependent manner converging to Nrf2 activation</figcaption></figure>

(A–J) BMDM ± cadaverine (100 µM) ± LPS (100 ng/mL) for 6 h.

(A) Protein levels (arbitrary units) of ACOD1 and itaconate intra- and extracellular concentrations.

(B) Protein levels of TXNDC-12, -15, and -17 and TXN2.

(C–H) Seahorse XF Cell Mito Stress assay. Respective oxygen consumption rate (OCR) values before oligoA (basal respiration) and after FCCP injection (maximal respiration).

(C–I) WT or ACOD1-deficient BMDMs pre-incubated 30 min before treatments ± CitraAc (1 mM, Acod1 inhibitor), ML385 (10 µM, Nrf2 inhibitor) or PX-12 (10 µM, thioredoxin inhibitor).

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of the metabolite (Figure S4I). Our findings imply the existence of
intermediary factors, present in intact cells but absent in isolated
mitochondria *in vitro* systems, which in turn are involved in the
observed cadaverine-related metabolic potential.

Then, we examined the implication of membrane receptor
activation, particularly G-protein-coupled receptors (GPCRs),
in cadaverine-mediated effects. Previous studies have reported
that cadaverine selectively activates histamine receptor 4 (H4R)
at micromolar concentrations, unlike other polyamines.²² Phar-
macological inhibition of H4R or GPCRs did not reverse cadav-
erine's activity (Figures S5A and S5B), which is indicative of alter-
native pathways, possibly an intracellular target after membrane
transport. We thus investigated the implication of endocytosis or
solute carriers (SLCs) involved in the uptake of amino acids and
amino-acid-like molecules.²³ Inhibition of clathrin- and caveolin-
dependent endocytosis using dynasore and genistein, respec-
tively, had no significant impact on mitochondrial respiration
(Figures S5C and S5D). In contrast, competition using high con-
centrations of L-lysine, as well as pharmacological inhibition of
cationic and neutral amino acid transporters with BCH and ranit-
idine, significantly affected cadaverine's metabolic effects
(Figures S5E and S5F). These findings support an import-depen-
dent, rather than receptor-mediated, mechanism for cadaver-
ine's activity on mitochondrial function.

Cadaverine immunomodulatory effects are related to
mitochondrial respiration in monocytes and
macrophages

We also explored whether cadaverine-induced metabolic re-
programming is linked to immunomodulatory properties.
Pathway enrichment analysis from RNA-seq data of human
CD14⁺ cells incubated with cadaverine under LPS stimuli
pinpoint the downregulation of different inflammation-related
pathways (Figure 2A). Notably, stimulation of PBMC with cadav-
erine resulted in a significantly increased interleukin-10 (IL-10)
and decreased tumor necrosis factor alpha (TNF-α) production
by classical monocytes with or without LPS challenge, as as-
sessed by flow cytometry (Figures 2B and 2C). A similar reduc-
tion in TNF-α production was observed in intermediate mono-
cytes upon LPS co-incubation (Figure 2B). These results were
confirmed by quantification of cytokines in the culture superna-
tants (Figures 2D and 2E). OligoA completely abolished cadav-
erine-mediated effects on intracellular TNF-α production.
Conversely, in monocytes stimulated solely with LPS, oligoA
treatment led to an exacerbation of pro-inflammatory responses,
an event ablated under 2-DG (Figure 2F), underscoring the
reliance of pro-inflammatory monocytes on glycolytic
metabolism.¹⁸

Cadaverine's impact was later assessed on BMDM under both
basal conditions and LPS-induced inflammation. Using a

13-plex cytokine panel, cadaverine treatment consistently increased the release of anti-inflammatory mediators while reducing pro-inflammatory cytokine levels in both settings (Figure 2G). Later, in CD14⁺-derived M0 macrophages under LPS plus interferon (IFN)-γ displayed higher CD80 and CCR7 membrane expression, and also the release of IL-1β in culture media, characteristics of M1 phenotype, all of which were attenuated by cadaverine (Figures 2H, 2I, and 2K). Conversely, IL-4-driven M2 polarization in the presence of cadaverine increased CD206 expression and IL-10 production (Figures 2J and 2L). Such results were strongly dependent on OXPHOS, as indicated by their sensitivity to oligoA, highlighting cadaverine's role in promoting immunoregulatory macrophage polarization through metabolic reprogramming.

The mechanisms of cadaverine in promoting OXPHOS-
dependent immunometabolic responses are distinct
under basal and inflammatory conditions

To investigate the mechanisms involved in cadaverine-medi-
ated responses, proteomic profiling revealed that macro-
phages treated with cadaverine alone closely resembled un-
treated cells. LPS stimulation provoked marked changes,
while combining cadaverine with LPS elicited further proteo-
mic shifts (Figure S6A). Besides, cadaverine enhanced the
LPS-dependent upregulation of aconitate decarboxylase 1
(ACOD1) (Figures 3A, S6B, and S6C), a rate-limiting enzyme
of itaconate synthesis, previously established as a key immu-
nometabolic mediator in macrophage activation.²⁴,²⁵ Intra-
and extracellular itaconate levels also demonstrated a sub-
stantial increase under LPS incubation, which was further
potentiated by cadaverine (Figure 3A). Under inflammation,
cadaverine boosted the levels of TCA metabolites, leading
to the elevation of all assessed intermediates and a corre-
sponding reduction in *cis*-aconitate (Figure S4A), possibly
converted to itaconate by ACOD1. Furthermore, both genetic
ablation (Figures 3C, 3G, and 3H) and pharmacological
ACOD1 inhibition in BMDM by citraconic acid (CitraAc) abro-
gated cadaverine-induced OXPHOS, but only in groups under
LPS stimulation (Figures 3D, 3G, and 3H). Itaconate has been
previously associated to the activation of the nuclear factor
erythroid 2-related factor 2 (Nrf2) in macrophages.²⁶ Here,
pharmacological Nrf2 inhibition by ML385 significantly
affected cadaverine activity in both basal and LPS-associated
inflammatory settings (Figures 3E, 3G, and 3H), acting as a
common denominator for the two different yet interconnected
pathways.

To explain the findings on basal cadaverine stimulation, we
observed a group of thioredoxin domain-containing proteins,
including (TXNDC12) and mitochondrial thioredoxin (TXN2),
as top upregulated proteins (Figures 3B, S6B, and S6C).

(I) IL-10 and TNF-α intracellular abundance (MFI) by flow cytometry.

(J) Nrf2 activity assay in nuclear extracts of BMDM treated or not for 2 or 6 h ± cadaverine (100 µM), LPS (100 ng/mL), or ML334 (50 µM, Nrf2 pathway activator).

(K) Fold change to control of Nrf2-related genes by RT-qPCR in BMDM treated for 2 or 6 h ± cadaverine (100 µM), LPS (100 ng/mL), or ML334 (50 µM, activator of the Nrf2 pathway).

(L) Schematic mechanism and pharmacological tools.

(A, B, and G-J) Mean ± SEM, one-way ANOVA with Tukey/Dunnett or t test for coupled analysis (*p* < 0.05; **p* < 0.01; ***p* < 0.001; ****p* < 0.0001). For scatter dot plots, values from individual replicates from two independent experiments shown.

See also Figure S6.
<figure><img src="image_33.png" /><figcaption>**Figure 4. Cadaverine given orally is partially absorbed in mice gastrointestinal tract and supports colon macrophages energy metabolism under gut microbiota perturbation**</figcaption></figure>

(A and B) Schematic design of experiment with single oral cadaverine administration (10 mg/kg p.o.) to mice, plasma collection post-gavage (0–360 min) and cadaverine dosage by UPLC-MS/MS. Plasma concentrations (nmol/L) shown over time with the corresponding pharmacokinetic parameters.

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Interestingly, the link between translocation of thioredoxin components to the nucleus and Nrf2 activation has been previously reported.²⁷,²⁸ The destabilization of the thioredoxin system with PX-12 blocked basal cadaverine activity in both metabolic flux and cytokine assessments, but not in the LPS setting (Figures 3F-3I). Interfering with ACOD1 or Nrf2 also affected cadaverine-mediated immunomodulation (Figure 3). In the presence or absence of LPS, cadaverine led to Nrf2 translocation to the nucleus, a feature of its activation (Figure 3J). Using RT-qPCR, we confirmed an upregulation of the Nrf2-associated genes Nfe2le, Nq01, Gclc, Gpx1, Cat, and Sod2, with expression levels varying between early and late time points (Figure 3K). Accordingly, Nrf2 pathway activation by the interaction probe ML334 resulted in increased nuclear accumulation of Nrf2 and robust transcriptional activation of target genes at both assessed time points (Figures 3J and 3K). Overall, these results reveal that the mechanisms driving cadaverine's effects differ between basal and inflammatory conditions, yet both pathways ultimately converge on Nrf2 activation (Figure 3L).

## Cadaverine impacts the energy metabolism of colon-associated macrophages *in vivo*

To assess whether cadaverine is able to modulate OXPHOS *in vivo*, we investigated its pharmacokinetics in adult C57BL/6 mice after a single oral administration at the dose of 10 mg/kg. Plasma was collected at various time points and analyzed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) (Figure 4A). After treatment, cadaverine, although only partially absorbed, reached a peak concentration ($C_{max}$) of 695 nmol/L at 30 min ($T_{max}$), followed by a mono-exponential decline consistent with first-order elimination. The elimination rate constant (k) was $0.0151 \text{ min}^{-1}$, corresponding to a half-life ($T_{1/2}$) of ~45.8 min. Systemic exposure (area under the curve [AUC]$_{0-360}$) was 61.9 nmol·min/L, with a calculated clearance (CL) of 0.0316 L/min and an apparent volume of distribution (Vd) of 2.09 L (Figure 4B). These parameters suggest limited systemic accumulation due to rapid elimination and/or extensive tissue distribution. The high Vd also supports potential tissue uptake and distal gut availability of non-absorbed/non-metabolized cadaverine.

We next treated mice with cadaverine to assess its impact on intestinal macrophage energy metabolism *in vivo*. To eliminate endogenous cadaverine and other microbiota-derived metabolites, mice were given a cocktail of broad-spectrum, non-absorbable antibiotics (ABXs) for 7 days (Figure 4C). ABX-treated mice developed an enlarged cecum (Figure 4D), as observed in germ-

free mice,²⁹ and exhibited a marked reduction in fecal DNA content, confirming gut microbiota depletion (Figure 4E). SCENITH analysis of CD64⁺ macrophages from the colonic lamina propria revealed an energy-deficient state induced by ABX, with decreased PS/ATP ratios and a shift toward glycolytic metabolism. Oral cadaverine administration in ABX-treated animals largely prevented these metabolic disruptions, restoring PS/ATP levels and mitochondrial dependence (Figures 4F and 4G). Cadaverine mitigated other ABX-induced mitochondrial alterations, including reductions in mitochondrial mass and $ΔΨ_m$, the accumulation of dysfunctional mitochondria (MitoGreen⁺MitoFR⁻), and the overproduction of mitochondrial ROS (MitoROS) (Figures 4H-4K). Oral cadaverine had no influence in microbiota-intact mice (Figures 4F-4K). Also, no signs of acute toxicity or inflammation-associated features (e.g., changes in colon length) were detected in the cadaverine-only group. These results demonstrate that it supports macrophage energy metabolism *in vivo*, particularly under gut microbiota imbalance.

## Cadaverine implication on colon inflammation is regulated by the gut *Enterobacteriaceae*

Since cadaverine demonstrated immunoregulatory properties *in vitro* and is able to modulate the energy metabolism of gut macrophages *in vivo*, we next evaluated the consequences of its administration in the dextran sodium sulfate (DSS)-induced colitis mouse model (Figure 5A). Mice subjected to 2% DSS experienced pronounced weight loss (Figures 5B and 5C) and a significantly elevated disease activity index (DAI) (Figures 5D and 5E) as well as a reduction of colon length (Figure 5F) compared with untreated control mice. Unexpectedly, oral administration of cadaverine exacerbated colitis, as evidenced by the worsening of all these parameters (Figures 5A-5F). Historical analysis further confirmed that cadaverine aggravated DSS-induced tissue damage (Figures 5G and 5H). In contrast to what we observed *in vitro*, under DSS-induced colitis cadaverine increased the percentage of M1-macrophages (F4/80⁺CD11b⁺CD11c⁺), displaying metabolic alterations associated with glycolysis-driven pro-inflammatory polarization (Figures 5I-5K).

To understand the apparent discrepancy with our previous findings, we investigated the gut microbiota of these mice, particularly the *Enterobacteriaceae* family, known to contribute to polyamine synthesis.³⁰ Cadaverine-treated mice in the absence of inflammation had no significant changes in microbiota composition compared with sham controls. In contrast, DSS inflammation alone significantly impacted several bacterial taxa, including an increase in *Enterobacteriaceae* abundance

(C) Schematic design of experiment with mice treated with fresh water ± non-absorbable broad-spectrum ABXs and treated ± cadaverine (10 mg/kg p.o., q.d.) for 7 days.

(D and E) Representative caecum images and stool DNA (day 7 post-ABX).

(F-K) Analysis in macrophages CD64⁺ purified from colon tissue using SCENITH.

(F) PS/ATP levels (gMFI).

(G) Percentage (%) of glucose (red bars) or fatty acids (FAs)/amino acids (aa) (blue bars) dependence and % of mitochondrial dependence (yellow bars) or glycolytic capacity (green bars).

(H-K) Mitochondrial mass assessed using MitoTracker Green (MitoGreen, 100 nM), mitochondrial membrane potential assessed using MitoFR (200 nM). Percentage (%) of MitoGreen⁺MitoRed⁻ cells showing defective mitochondria and oxygen superoxide levels assessed using MitoSOX (5 µM) analyzed by flow cytometry.

(B and F-K) Mean ± SEM, one-way ANOVA with Tukey/Dunnett test (*p* < 0.05; **p* < 0.01; ***p* < 0.001; ****p* < 0.0001).

(B, F, and H-K) For scatter dot plots, values from individual mice shown.
<figure><img src="image_34.png" /></figure>

A

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

2% DSS
(Drinking water)

H₂O

Start
± Cadaverine p.o.

B

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

Time (days)

C

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

D

Disease Activity Index (DAI)

<figure><img src="image_38.png" /></figure>

Time (days)

E

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F

<figure><img src="image_40.png" /></figure>

G

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

H

Control

2% DSS

DSS + Cadaverine
(10 mg/kg)

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

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

I

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

J

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K

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L

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M

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N

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O

<figure><img src="image_51.png" /><figcaption>**Figure 5. Cadaverine gut overload induces deleterious effects in experimental DSS-induced colitis and in patients with IBD**</figcaption></figure>

(A) Schematic design of the DSS-induced colon inflammation model. Mice treated ± cadaverine (1–100 mg/kg, p.o., q.d.) throughout the experiment.

(B–E) Percentage (%) of body weight loss and DAI with the respective area under the curve (AUC).

(legend continued on next page)
(Figures S7A-S7E). DSS alone tended to increase cadaverine gut levels, consistent with previous observations in IBD patients.³¹,³² Oral treatment further enriched the cadaverine gut pool (Figure 5L). Interestingly, cadaverine absolute and relative levels were also found to be significantly elevated in the stool of IBD patients particularly during flare (Figures 5M and 5N). Since treatment aggravated inflammation in the conventional DSS model, the observed shifts in beta diversity and taxonomic composition (Figures S7A and S7C) are likely secondary to the inflammatory response, rather than a direct effect of cadaverine on the gut microbiome.

To explore the clinical relevance of these findings, we investigated shotgun metagenomics data of our French cohort of patients with IBD³³ and observed that gut lysine decarboxylase gene (LdCc/CadA) is overrepresented in patients as compared with healthy subjects. A stronger signal was displayed in Crohn's disease and flare, particularly for *Enterobacteriaceae* (*Escherichia* and *Klebsiella* genus) (Figures 6A-6C). In addition, in patients in remission, elevated stool cadaverine (Figure 5O) as well as reduced L-lysine levels (Figure 6D) were associated with higher risk of disease-related clinical events, such as surgery or flare. A positive correlation between cadaverine levels and IdcC/CadA gene abundance was also observed in IBD patients, particularly within the *Enterobacteriaceae* family (Figures 6E and 6F). Therefore, high levels of cadaverine-associated with gut microbiota imbalance may contribute to inflammation in IBD.

To examine whether cadaverine at high concentrations triggers pro-inflammatory responses in macrophages during colitis, we returned to *in vitro* assays and found that BMDM treated with this polyamine at concentrations exceeding 1 mM underwent metabolic reprogramming toward glycolysis, particularly in a pro-inflammatory microenvironment, characterized by elevated extracellular acidification rates (ECARs) (Figures S8A and S8B). This was consistent with the higher production of TNF-α in cells treated with high-dose cadaverine, which is inhibited by 2-DG and potentiated in the presence of oligoA (Figure S8C), a hallmark of glycolysis-mediated inflammation. Contrary to what was found for lower cadaverine concentrations, further analysis demonstrated that the effect of high-concentration cadaverine is dependent on H4R, with ECAR as well as basal glycolytic rate and compensatory glycolysis highly affected in the presence of a H4R antagonist (H4RA) (Figures S8D-S8K). Such findings support the notion that cadaverine may act as a double-edged

sword in metabolic regulation, with concentration-dependent activity modulated by the inflammatory milieu.

Given that cadaverine synthesis seems to predominantly originate from *Enterobacteriaceae*, we targeted this bacterial family with colistin before DSS inflammation (Figure 7A). Indeed, as previously demonstrated by our group,³⁴ colistin is a narrow-spectrum ABX, with a mild relevance on the gut microbiome besides inducing a significant reduction in gut *Enterobacteriaceae* and cadaverine amounts (Figures 7B-7D and S7F-S7J), but with no effect itself on colitis severity (Figures 7E-7I). Subsequent oral cadaverine administration in colistin-treated mice restored its gut concentration to the basal levels (Figure 7D) and provided protection against DSS-induced inflammation, reducing body weight loss and tissue damage (Figures 7E-7K). Besides, in colistin-treated mice, cadaverine promoted the polarization of colonic macrophages to the M2 anti-inflammatory phenotype (F4/80⁺CD11b⁺CD206⁺). Colistin treatment enriched a CD11c⁺/CD206⁺ subset, previously implicated in adipose tissue inflammation,³⁵ which was elevated during DSS inflammation but moderated with cadaverine (Figure 7L). Metabolic profiling revealed that cadaverine alleviated the energy-deficient state in M0- and M1-macrophages induced by inflammation, with minimal alterations on M2- and CD11c⁺CD206⁺ subsets (Figure 6M). It also supported mitochondrial dependence, counteracting DSS across macrophage subsets, and reversing dysfunction in mitochondrial mass, ΔΨm, and MitoROS levels (Figures 7N and 7O). As an alternative strategy to deplete the endogenous cadaverine production, we eliminated cadaverine precursor by pre-treating mice with a L-lysine-deficient diet prior to the dinitrobenzene sulfonic acid (DNBS)-induced colitis. As this diet altered water intake, it is therefore incompatible with the DSS protocol. In mice fed with L-lysine-deficient, but not with standard chow, cadaverine exhibited protective activity against intrarectal DNBS-induced colitis (Figures S9A-S9D), confirming the results obtained in the colistin setting. Therefore, overall data demonstrate that cadaverine regulates monocyte/macrophage function through concentration-dependent metabolic reprogramming, with high levels exacerbating inflammation, while the regulation of its production to an optimal range revealed protective effects.

Finally, to support our results on Nrf2 activation *in vivo*, we evaluated cadaverine in colistin-treated mice with or without ML385, a known Nrf2 inhibitor previously applied in colon

(F) Colon lengths (cm).

(G) and (H) Representative H&E-stained images of mid-colon cross-sections. Scale bar: 200 µm.

(I) Percentage (%) of lamina propria F4/80⁺CD11b⁺ tissue macrophages analyzed by flow cytometry according to the expression of CD11c (M1 macrophages) and CD206 (M2 macrophages).

(J-L) PS/ATP levels (gMFI) in lamina propria macrophages in the SCENITH assay. Percentage (%) of glucose (red bars) or FA/aa (blue bars) dependence and % of mitochondrial dependence (yellow bars) or glycolytic capacity (green bars).

(L and M) Absolute cadaverine concentration (nanomoles/gram of dry mass) in stools of mice (n = 5/group) or of IBD patients (n = 18) or healthy donors (n = 11). Only patients in flare were included. Mean ± SEM and Mann-Whitney test (*(p* < 0.01) indicated.

(N) Semi-quantitative dosage of cadaverine in stools from Crohn's disease (CD) and ulcerative colitis (UC) patients (CD remission, N = 264; CD flare, N = 199; UC remission, N = 123; UC flare, N = 80) or healthy donors (n = 83). (Wilcoxon test, Benjamini-Hochberg corrected, *(p* adj < 0.05, ***(p* adj < 0.01, ***(p* adj < 0.001; ***(p* adj < 0.0001) indicated.

(O) Risk probability according to cadaverine levels in stools (log-rank test). Only patients in remission were included in this analysis. Groups defined according to the median level of gut cadaverine.

(C, E-G, and J-L) Mean ± SEM, one-way ANOVA with Tukey/Dunnett test (*(p* < 0.05; ***(p* < 0.01; ***(p* < 0.001; ***(p* < 0.0001). For scatter dot plots, values from individual mice shown.

See also Figures S7 and S8.
<figure><img src="image_52.png" /><figcaption>**Figure 6. Cadaverine synthesis in the gut microbiota of healthy donors and IBD patients**</figcaption></figure>

(A) Stacked barplot of L-lysine decarboxylase reads per kilobase per million mapped reads (RPKM) in stools from CD and UC patients (CD remission, *N* = 264; CD flare, *N* = 199; UC remission, *N* = 123; UC flare, *N* = 80) and healthy donors (*N* = 83). Bacterial families color-coded. Total gene abundance per sample indicated by overlaid points and colored by disease group.

(B) Lysine decarboxylase RPKM by bacterial family, comparing *Enterobacteriaceae*, unclassified gene reads, and all other families grouped under "other families." Significant pairwise statistical comparisons (Wilcoxon test, Benjamini-Hochberg corrected, \*p adj < 0.05, \*\*p adj < 0.01, \*\*\*p adj < 0.001; \*\*\*\*p adj < 0.0001) indicated.

(legend continued on next page)
<figure><img src="image_53.png" /></figure>

inflammation mice models,³⁶ Pharmacological inhibition of Nrf2 potentiated colitis and abolished the beneficial potential of cadaverine. Notably, cadaverine oral treatment enhanced Nrf2 nuclear translocation in colonic tissue compared with both the sham and DSS groups, which was significantly affected by ML385 (Figures S9E-S9I). These data are in line with our *in vitro* observations and support the dependence on Nrf2 for cadaverine-associated anti-inflammatory property.

DISCUSSION

Bioenergetics shapes cellular responses in health and disease. While some cells use glycolysis even in oxygen-rich microenvironments as a faster way to produce ATP, mitochondrial respiration seems to be more energetic and proteomic efficient.³⁷ In macrophages, distinct metabolic status correlates to functional states and cell subsets displaying distinct behaviors. For example, polarized M1 pro-inflammatory macrophages present enhanced glycolytic metabolism and impaired mitochondrial respiration.³⁸ Such metabolic alterations can be linked to pathological outcomes, such as in IBD, in which abnormal glycolysis in intestinal macrophages occurs and may be linked to sustained TNF-mediated exosomal signaling.⁸

Here, we demonstrate that polyamine cadaverine may use L-lysine import mechanisms to boost monocyte-macrophage energy metabolism toward mitochondrial respiration and direct them to the M2 anti-inflammatory phenotype. Such effects seem to be independent of modifications in pHi or H4R activation. Our data support the emerging view of polyamines as immunometabolism mediators. Indeed, spermidine serves as a substrate for hypusination of a conserved lysine residue in eIF5A (eukaryotic translation initiation factor 5A), which upregulates mitochondrial proteins of the TCA cycle and OXPHOS, regulating macrophage activation.³⁹,⁴⁰ Additionally, putrescine can be absorbed by small intestine cells, particularly during fasting, where it is converted to succinate to fuel mitochondria.³ Still, we did not observe any direct relationship between cadaverine and eIF5A activation, while experiments with isolated mitochondria ruled out the possibility of direct cadaverine impact, underscoring that other intermediate mechanisms are at play.

Instead, we identified the thioredoxin system involved in basal cadaverine incubations, which regulates cellular redox status through disulfide reductase activity,⁴¹ and is linked to mitoROS scavenging,⁴²,⁴³ promotion of OXPHOS and mitochondrial biogenesis.⁴⁴ Conversely, under LPS-induced inflammation, cadaverine enhanced Acod1 and itaconate synthesis, previously addressed as components of the anti-inflammatory mechanism of glucocorticoids through OXPHOS-driven metabolic rewiring.²⁵

In both scenarios, cadaverine's effects depended on Nrf2 activation, a transcriptional factor widely associated with cytoprotection and regulation of over 600 target genes, including those involved in antioxidant response and immunoregulation.⁴⁵,⁴⁶ Furthermore, Nrf2 activation also favors bioenergetics since it maintains ΔΨm and substrate availability for mitoATP synthesis, whereas its deficiency leads to impaired mitochondrial function.⁴⁶ Our findings reveal that cadaverine acts in a context-dependent manner, both under basal conditions and during inflammation, to regulate cell immunometabolism, yet ultimately triggering a common pathway to drive its downstream activity.

Such outcomes were also considered concentration dependent. Under higher cadaverine concentrations, an opposite bioenergetic effect was observed in the same cells, characterized by a glycolytic profile, particularly if inserted in a pro-inflammatory microenvironment. This was mediated by H4R activation, as previously demonstrated,²² and associated with gut inflammation.⁴⁷ Importantly, these findings were supported by *in vivo* data showing the role of *Enterobacteriaceae* in contributing to elevated cadaverine levels upon gut inflammation. Indeed, an expansion of gut pathobionts from this family, mainly *E. coli* and *Klebsiella* strains, has been associated with IBD.⁴⁸,⁴⁹ Human data from our cohort supported these findings, showing the overrepresentation of L-lysine decarboxylase in the gut microbiome of IBD patients, consistent with the higher cadaverine amounts in patients at higher risk of relapse, reinforcing the implication of cadaverine in IBD. Also, they show how changes in gut microbiota composition may contribute to inflammation by overproducing bioactive mediators.

The concept of endogenous molecules exhibiting dual properties is not new. For instance, ouabain, a cardiotonic steroid, displays either pro- or anti-inflammatory effects depending on its concentrations and the targeted cell type.⁵⁰ Cadaverine, at serum concentrations reported in humans (100–800 nM), exerts anti-tumor activity on breast cancer cells, partly through reduction of mitochondrial oxidation.¹⁵ In our study, cadaverine levels in mouse plasma ranged from 3 to 11 nM, considerably lower than those associated with OXPHOS *in vitro*. In contrast, cadaverine in human stools, assessed in healthy donors and IBD patients during flare, ranged from 0.6 to 3,600 µM, depending on water content, approximately 75% in normal and up to 90% in diarrheic stools. In addition, a study reported that the duodenum and jejunum of rats contained 1–2 mM cadaverine, whereas in the ileum and colon was significantly less.¹³ Notably, we observed bioenergetic modulation only when cells were exposed to cadaverine above plasma range, especially for OXPHOS modulation, and even higher levels were needed to impact glycolysis. However, both ranges fall within those observed in the intestinal

<footnote>(C) Lysine decarboxylase RPKM for genera within *Enterobacteriaceae*, stratified by clinical groups. Significant pairwise statistical comparisons (Wilcoxon test, Benjamini-Hochberg corrected) indicated.</footnote>

<footnote>(D) Risk probability according to L-lysine levels in stools (log-rank test). Only patients in remission included. Groups defined according to the median level of gut L-lysine.</footnote>

<footnote>(E) Correlation between lysine decarboxylase abundance and cadaverine levels. Both axes are log-transformed. Linear regression line shown with 95% confidence interval shading. R2 and *p* value indicated.</footnote>

<footnote>(F) Correlation between cadaverine levels (log-transformed) and lysine decarboxylase gene expression by *Enterobacteriaceae* (log-transformed) across all samples. Linear regression line shown with 95% confidence interval shading. R2 and *p* value indicated.</footnote>

<footnote>(B, C, and E) For scatter dot plots, values from individual healthy donors or IBD patients shown. See also Figure S7.</footnote>
<figure><img src="image_54.png" /><figcaption>**Figure 7. Cadaverine protects from DSS-induced colitis in colistin-pre-treated mice**</figcaption></figure>

(A) Schematic design of experiment with mice pre-treated orally ± colistin (600 µg/gavage) for 7 days before DSS-induced colitis model. Cadaverine treatment (10 mg/kg p.o., q.d.) started on the 7th day and remained throughout the experiment.

(B and C) All bacteria and *Enterobacteriaceae* counts (counts/µL of DNA) in mice stools by qPCR using a standard *E. coli* curve.

(D) Absolute cadaverine concentration (nanomoles/gram of dry mass) in the stools of mice (n = 5/group).

(E-H) Percentage (%) of body weight loss and DAI and respective area under the curve (AUC) values.

(legend continued on next page)
environment. Given that gut cells are exposed to larger amounts of this metabolite than bloodstream cells, this suggests that physiological influence on metabolism occurs primarily in resident rather than circulatory cells. Nevertheless, during inflammation, increased gut permeability may allow luminal byproducts and metabolites, such as LPS or cadaverine, to enter the bloodstream, potentially reprogramming cells before they infiltrate the tissues.⁵¹ In this sense, we propose that cadaverine overload in IBD hinders its anti-inflammatory potential found only under narrow concentrations, regardless of location.

Therefore, exploring how gut factors modulate metabolism may help explain the alterations in cellular behavior that contribute to IBD pathogenesis, an aspect that remains largely unexplored. Our approach, in turn, provides potential insights and opens venues for a generation of microbiota-derived drugs and host-directed interventions targeting immune cells' energy metabolism for disease management.

## RESOURCE AVAILABILITY

### Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Harry Sokol (harry.sokol@gmail.com).

### Materials availability

This study did not generate new unique reagents.

### Data and code availability

* The mass spectrometry proteomics data are available on PRIDE repository with the dataset identifier PXD057652.

* RNA-seq data reported in this paper have been deposited in GEO (#24951153).

* 16S sequencing data reported in this paper have been deposited in ENA (PRJEB90432).

* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

## ACKNOWLEDGMENTS

We thank the CRSA Animal Facility for mouse care; Sandrine Truong and Na-thalie Bernard (Saint-Antoine Hospital, APHP, Paris) for assistance; and all members of the Sokol-Seksik team at CRSA-Sorbonne University and Sokol's Lab at INRAE-MICALIS-Paris-Saclay University for technical and scientific discussions. We are grateful for funding from the European Research Council (ERC, ENERGISED, ERC-2021-COG-101043802) and MSD-AVENIR. We also acknowledge equipment support from the French Government Programme Investissements d'Avenir France Biolmaging (FBI, ANR-10-INSB-04-01) and the Agence Nationale de la Recherche Investissement d'Avenir

programme, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" (ANR-10-LABX-62-IBEID).

## AUTHOR CONTRIBUTIONS

H.S. coordinated and conceptualized the study; R.d.O.F., T.W., and H.S. designed and/or performed analyses, interpreted the results, and drafted the manuscript. R.d.O.F., Q.L., Y.Z., M.A.C.R., L. Chollet, V.P., I.A.S., S.A., L. Creusot, C.D., E.T., and N.R. performed experiments; T.L. and C.S. provided technical help. H.P.P., P.G.-V., L.D., R.F., and M.B. performed bioinformatics analysis. J.-P.A., G.K., P.E., G.C., M.-L.M., A.L., C.P., R.J.A., C.D., N.R., and T. W. provided materials, performed dosages, and gave intellectual input. All authors reviewed and approved the manuscript.

## DECLARATION OF INTERESTS

H.S. discloses receiving lecture fees, serving on boards, or providing consultancy for Carenity, AbbVie, Astellas, Danone, Ferring, Mayoly Spindler, MSD, Novartis, Roche, Tillots, Enterome, BiomX, Takeda, and Biocodex. Additionally, H.S. holds stocks in Enterome and is a co-founder of Exelium Biosciences. The findings presented in this manuscript were not shared with these companies.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL AND STUDY PARTICIPANTS DETAILS

* Cell lines

* Human samples

* Mice samples

* METHOD DETAILS

* In vitro culture before assays

* ATP assessment

* ATP/ADP dosage

* Single-cell energetic metabolism by profiling translation inhibition (SCENITH)

* Flow cytometry

* Immunofluorescence

* Transmission Electron microscopy

* Metabolic flux analysis

* Fluorespirometry

* Targeted metabolomics

* RNA-seq analysis

* Pyruvate dehydrogenase (PDH) activity assay

* Nrf2 activation assay

* Proteomics

* ELISA

* LEGENDplex

* Gene Expression Analysis by Quantitative Reverse Transcription PCR (RT-qPCR)

(I) Colon length (cm) of mice.

(J and K) Representative H&E-stained images of mid-colon cross-sections. Scale bar: 200 µm.

(L) F4/80$^{+}$CD11b$^{+}$ lamina propria macrophages according to the expression (%) of CD11c (M1 macrophages) and CD206 (M2 macrophages). Double-negative (CD11c$^{-}$CD206$^{-}$, M0 macrophages) and double-positive (CD11c$^{+}$CD206$^{+}$) also shown.

(M and N) PS/ATP levels (gMFI) in lamina propria macrophages in the SCENITH assay. Percentage (%) of glucose (red bars) or FAs/aa (blue bars) dependence and % of mitochondrial dependence (yellow bars) or glycolytic capacity (green bars).

(O) Mitochondrial mass assessed using MitoGreen (100 nM), mitochondrial membrane potential assessed using MitoFR (200 nM), Percentage (%) of MitoGreen$^{+}$MitoRed$^{-}$ cells showing defective mitochondria and oxygen superoxide levels assessed using MitoSOX (5 µM) analyzed by flow cytometry.

(B-D, F, H-J, and M-O) Mean ± SEM, one-way ANOVA with Tukey/Dunnett or t test for coupled analysis (*p* < 0.05; **p* < 0.01; ***p* < 0.001; ****p* < 0.0001). For scatter dot plots, values from individual mice/samples shown.

See also Figures S7 and S9.
* Pharmacokinetic profiling of cadaverine following oral administration

* Antimicrobials (ABX)-induced gut microbiota depletion

* DSS-induced colon inflammation

* Histopathological analysis

* Cell purification from mice gut tissues

* Fecal DNA extraction and bacterial quantification by RT-qPCR

* 16S sequencing and analysis of gut microbiota composition

* DNBS-induced colon inflammation

* Lysine decarboxylase gene assessment

## QUANTIFICATION AND STATISTICAL ANALYSIS

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.chom.2025.09.009.

Received: December 11, 2024

Revised: July 21, 2025

Accepted: September 4, 2025

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## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td><strong>Antibodies</strong></td><td></td><td></td></tr><tr><td>Anti-human GLUT-1 (FITC)</td><td>R&amp;D Systems</td><td>Cat #FAB1418F; RRID:AB_2191041</td></tr><tr><td>Anti-human HLA-DR (Alexa Fluor 700)</td><td>BD Biosciences</td><td>Cat #560743; RRID:AB_1727526</td></tr><tr><td>Anti-human CD19 (PE-Cy7)</td><td>BD Biosciences</td><td>Cat #557835; RRID:AB_396893</td></tr><tr><td>Anti-human CD16 (Alexa fluor 647)</td><td>BD Biosciences</td><td>Cat #557710; RRID:AB_396819</td></tr><tr><td>Anti-human CCR7 (Brilliant violet 607)</td><td>BD Biosciences</td><td>Cat #566754; RRID:AB_2869850</td></tr><tr><td>Anti-human CD3 (PE)</td><td>BioLegend</td><td>Cat #300441; RRID:AB_2562047</td></tr><tr><td>Anti-human CD4 (Brilliant Violet 785)</td><td>BioLegend</td><td>Cat #317442; RRID:AB_2563242</td></tr><tr><td>Anti-human CD8 (Brilliant Violet BV421)</td><td>BioLegend</td><td>Cat # 344748; RRID:AB_2629584</td></tr><tr><td>Anti-human HLA-DR (APC)</td><td>BioLegend</td><td>Cat #327022; RRID:AB_2734311</td></tr><tr><td>Anti-human CD14 (Pacific blue)</td><td>BioLegend</td><td>Cat #301828; RRID:AB_2275670</td></tr><tr><td>Anti-human CD80 (PE-Cy7)</td><td>BioLegend</td><td>Cat # 375407; RRID:AB_2890819</td></tr><tr><td>Anti-human CD206 (Alexa fluor 700)</td><td>BioLegend</td><td>Cat #321132; RRID:AB_2616869</td></tr><tr><td>Anti-human Arg-1 (PE)</td><td>BioLegend</td><td>Cat # 369704; RRID:AB_2571900</td></tr><tr><td>Anti-human IL-10 (APC)</td><td>BioLegend</td><td>Cat #506807; RRID:AB_315457</td></tr><tr><td>Anti-human IL-10 (PE)</td><td>BioLegend</td><td>Cat #506804; RRID:AB_315454</td></tr><tr><td>Anti-human TNF-α (Alexa fluor 700)</td><td>BioLegend</td><td>Cat #502928; RRID:AB_2561315</td></tr><tr><td>Anti-human TNF-α (FITC)</td><td>BioLegend</td><td>Cat #502906; RRID:AB_315258</td></tr><tr><td>Anti-mouse CD45 (FITC)</td><td>BioLegend</td><td>Cat #157213; RRID:AB_2894427</td></tr><tr><td>Anti-mouse CD45 (BV785)</td><td>BioLegend</td><td>Cat #103149; RRID:AB_2564590</td></tr><tr><td>Anti-mouse F4/80 (BVAPC)</td><td>BioLegend</td><td>Cat #123116; RRID:AB_893481</td></tr><tr><td>Anti-mouse F4/80 (PE)</td><td>BioLegend</td><td>Cat #111704; RRID:AB_2936728</td></tr><tr><td>Anti-mouse CD11b (APC)</td><td>BioLegend</td><td>Cat #101212; RRID:AB_312795</td></tr><tr><td>Anti-mouse CD11b (BV421)</td><td>BioLegend</td><td>Cat #101235; RRID:AB_10897942</td></tr><tr><td>Anti-mouse CD11b (APC-Cy7)</td><td>BioLegend</td><td>Cat #101226; RRID:AB_830642</td></tr><tr><td>Anti-mouse CD11c (PE)</td><td>BioLegend</td><td>Cat #117308; RRID:AB_313777</td></tr><tr><td>Anti-mouse CD206 (Alexa fluor 700)</td><td>BioLegend</td><td>Cat #141734; RRID:AB_2629637</td></tr><tr><td>Anti-mouse CD64 (APC)</td><td>BioLegend</td><td>Cat #161005; RRID:AB_2910336</td></tr><tr><td>Anti-mouse IL-10 (Alexa fluor 488)</td><td>BioLegend</td><td>Cat #505013; RRID:AB_493513</td></tr><tr><td>Anti-mouse IL-10 (PE)</td><td>BioLegend</td><td>Cat # 505008; RRID:AB_315362</td></tr><tr><td>Anti-mouse TNF-α (BV421)</td><td>BioLegend</td><td>Cat #506327; RRID:AB_10900823</td></tr><tr><td>Anti-mouse TNF-α (FITC)</td><td>BioLegend</td><td>Cat #506304; RRID:AB_315425</td></tr><tr><td>Human TruStain FcX™</td><td>BioLegend</td><td>Cat #422302; RRID:AB_2818986</td></tr><tr><td>Mouse TruStain FcX™</td><td>BioLegend</td><td>Cat #101320; RRID:AB_1574975</td></tr><tr><td><strong>Bacterial and virus strains</strong></td><td></td><td></td></tr><tr><td><em>Escherichia coli</em> MG1655</td><td>ATCC</td><td>Cat #700926</td></tr><tr><td><strong>Biological samples</strong></td><td></td><td></td></tr><tr><td>Human whole blood</td><td>Saint-Antoine Hospital</td><td>IRB 00003835</td></tr><tr><td>Human stools</td><td>Saint-Antoine Hospital</td><td>IRB 00003835</td></tr><tr><td><strong>Chemicals, peptides, and recombinant proteins</strong></td><td></td><td></td></tr><tr><td>Cadaverine 95%</td><td>Sigma-Aldrich</td><td>Cat #D22606</td></tr><tr><td>Cadaverine ≥97.0% (GC)</td><td>Sigma-Aldrich</td><td>Cat #33211</td></tr><tr><td><em>E. coli</em> K12 strain LPS</td><td>Invivogen</td><td>Cat #tlrl-eklps</td></tr><tr><td>FVS Brilliant violet 510</td><td>BD Biosciences</td><td>Cat #564406</td></tr></tbody></table>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>FVS Brilliant violet 450</td><td>BD Biosciences</td><td>Cat #562247</td></tr><tr><td>Human recombinant IL-4</td><td>Miltenyi</td><td>Cat #130-093-917</td></tr><tr><td>Human recombinant IFN-γ</td><td>Miltenyi</td><td>Cat #130-096-873</td></tr><tr><td>Human recombinant M-CSF</td><td>Miltenyi</td><td>Cat #130-096-491</td></tr><tr><td>Mouse recombinant IL-4</td><td>Miltenyi</td><td>Cat #130-097-757</td></tr><tr><td>Mouse recombinant IFN-γ</td><td>Miltenyi</td><td>Cat #130-105-785</td></tr><tr><td>Mouse recombinant M-CSF</td><td>Miltenyi</td><td>Cat #130-101-706</td></tr><tr><td>Brefeldin A</td><td>Sigma</td><td>Cat# B7651</td></tr><tr><td>Citraconate acid</td><td>MedChemExpress</td><td>Cat #HY-113298</td></tr><tr><td>ML385</td><td>MedChemExpress</td><td>Cat #HY-100523</td></tr><tr><td>PX-12</td><td>MedChemExpress</td><td>Cat #HY-13734</td></tr><tr><td>ML334</td><td>MEMD Millipore</td><td>Cat #505987</td></tr><tr><td>L-lysine</td><td>Sigma-Aldrich</td><td>Cat # L5501</td></tr><tr><td>BCH</td><td>MedChemExpress</td><td>Cat #HY-108540</td></tr><tr><td>Genistein</td><td>MedChemExpress</td><td>Cat #HY-14596</td></tr><tr><td>Dynasore</td><td>MedChemExpress</td><td>Cat #HY-15304</td></tr><tr><td>Ranitidine</td><td>MedChemExpress</td><td>Cat #HY-B0693</td></tr><tr><td>A 943931 dihydrochloride</td><td>Abcam</td><td>Cat #ab142809</td></tr><tr><td>FR900359</td><td>MedChemExpress</td><td>Cat #HY-117037</td></tr><tr><td>NF449 octasodium</td><td>MedChemExpress</td><td>Cat #HY-112461A</td></tr><tr><td>Pertussis Toxin</td><td>Sigma Aldrich</td><td>Cat #P2980</td></tr><tr><td>Mitotracker green</td><td>Thermo Fischer</td><td>Cat #M46750</td></tr><tr><td>Mitotracker Deep Red</td><td>Thermo Fischer</td><td>Cat #M46753</td></tr><tr><td>MitoSOX</td><td>Thermo Fischer</td><td>Cat #M36008</td></tr><tr><td>pHrodo™ AM Variety Pack</td><td>Thermo Fischer</td><td>Cat #P35380</td></tr><tr><td>Tetramethylrhodamine, ethyl ester, TMRE</td><td>Abcam</td><td>Cat #ab113852</td></tr><tr><td>(2-[(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl) Amino)-2-Deoxyglucose), 2-NBDG</td><td>Thermo Fischer</td><td>Cat #N13195</td></tr><tr><td>Dextran sulfate sodium salt, DSS</td><td>MP Biomedicals</td><td>Cat #02160110-CF</td></tr><tr><td>Neomycin trisulfate salt hydrate</td><td>Sigma-Aldrich</td><td>Cat #N6386</td></tr><tr><td>Vancomycin hydrochloride</td><td>Mylan</td><td>Cat #D0474</td></tr><tr><td>Gentamycin sulphate salt</td><td>Sigma-Aldrich</td><td>Cat #G1264</td></tr><tr><td>Colistin methane sulfonate sodium</td><td>Sigma-Aldrich</td><td>Cat #C1511</td></tr><tr><td>Amphotericin B</td><td>Sigma-Aldrich</td><td>Cat #A9528</td></tr><tr><td>Collagenase type VIII</td><td>Sigma-Aldrich</td><td>Cat #C2139</td></tr><tr><td>DNase I</td><td>Roche</td><td>Cat #11284932001</td></tr><tr><td colspan="3">Critical commercial assays</td></tr><tr><td>CellTiter-Glo® 2.0 reagent</td><td>Promega</td><td>Kit G9242</td></tr><tr><td>SCENITH</td><td>GammaOmics</td><td>www.scenith.com</td></tr><tr><td>FOXP3 fixation and permeabilization buffer</td><td>Thermofisher eBioscience™</td><td>Kit 00-5523-00</td></tr><tr><td>PDH activity assay</td><td>Abcam</td><td>Kit ab109902</td></tr><tr><td>Nrf2 Transcription Factor Assay Kit</td><td>Abcam</td><td>Kit ab207223</td></tr><tr><td>NE-PER Nuclear and Cytoplasmic Extraction Reagents</td><td>Thermo Fischer</td><td>Kit 78833</td></tr><tr><td>Seahorse XF Real-Time ATP Rate Assay</td><td>Agilent</td><td>Kit 103592-100</td></tr><tr><td>Seahorse XF Cell Mito Stress Test</td><td>Agilent</td><td>Kit 103015-100</td></tr><tr><td>Seahorse Long Chain Fatty Acid Oxidation Stress Test</td><td>Agilent</td><td>Kit 103672-100</td></tr><tr><td>Seahorse Glucose/Pyruvate Oxidation Stress Test</td><td>Agilent</td><td>Kit 103673-100</td></tr><tr><td>Seahorse Glutamine Oxidation Stress Test</td><td>Agilent</td><td>Kit 103674-100</td></tr><tr><td>Protein assay kit</td><td>Interchim</td><td>Cat# FT-40840A</td></tr><tr><td>CytoTox 96® Non-Radioactive Cytotoxicity Assay</td><td>Promega</td><td>Cat# G1780</td></tr></tbody></table>

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<figure><img src="image_55.png" /></figure>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>Duoset human IL-10 ELISA Kit</td><td>R&amp;D Systems</td><td>Cat #DY217B</td></tr><tr><td>Duoset human TNF-α ELISA Kit</td><td>R&amp;D Systems</td><td>Cat #DY210-05</td></tr><tr><td>Duoset human IL-1β ELISA Kit</td><td>R&amp;D Systems</td><td>Cat #DY201-05</td></tr><tr><td>RNeasy Mini Kit</td><td>Qiagen</td><td>Cat #74104</td></tr><tr><td>LEGENDplex™</td><td>BioLegend</td><td>Cat #740846</td></tr><tr><td>ADP/ATP dosage kit</td><td>Sigma Aldrich</td><td>Cat #MAK135</td></tr></tbody></table>

Deposited data

<table><tr><td>Database: RNA sequencing of CD14+ cells</td><td>This study</td><td>Accession ID: #24951153</td></tr><tr><td>Dataset: Proteomics of BMDM from C57BL/6 mice</td><td>This study</td><td>Accession ID: #PXD057652</td></tr><tr><td>Dataset: 16s on stools from C57BL/6 mice</td><td>This study</td><td>Accession ID:</td></tr></table>

Experimental models: Cell lines

<table><tr><td>THP-1 monocytic cell line</td><td>ATCC</td><td>TIB-202</td></tr><tr><td>MEF (C57BL/6) fibroblast cell line</td><td>ATCC</td><td>SCRC-1008</td></tr><tr><td>HeLa epithelial cell line</td><td>ATCC</td><td>CRM-CCL-2</td></tr></table>

Experimental models: Organisms/strains

<table><tr><td>Mouse: C57BL/6</td><td>Charles River</td><td>Strain #:000664</td></tr><tr><td>Mouse: C57BL/6NJ-Acod1em1(IMPC)J/J</td><td>The Jackson Laboratory</td><td>Strain #029340</td></tr></table>

Oligonucleotides

<table><tr><td>Total bacteria Uni 16SR; 5'-ATTACCGCGGCTGCTGGC-3'</td><td>Eurogentec</td><td>PMID: 19805153</td></tr><tr><td>Total bacteria Uni 16SF; 5'-ACTCCTACGGGAGGCAGCAGT-3'</td><td>Eurogentec</td><td>PMID: 19805153</td></tr><tr><td>Enterobacteriaceae Eco1457F; 5'-CATTGACGTTACCCGCAGAAGAAGC-3'</td><td>Eurogentec</td><td>PMID: 15184159</td></tr><tr><td>Enterobacteriaceae Eco1652R; 5'-CTCTACGAGACTCAAGCTTGC-3'</td><td>Eurogentec</td><td>PMID: 15184159</td></tr><tr><td>Nrf2; Forward CTGAACTCCTGGACGGACTA; Reverse CGGTGGGTCTCCGTAAATGG</td><td>Eurogentec</td><td>PMID: 28482326</td></tr><tr><td>Gclc; Forward GGACAAACCCCAACCATCC; Reverse GTTGAACTCAGACATCGTTCCT</td><td>Eurogentec</td><td>PMID: 28482326</td></tr><tr><td>Nqo1; Forward AGGATGGGAGGTACTCGAATC; Reverse TGCTAGAGATGACTCGGAAGG</td><td>Eurogentec</td><td>PMID: 28482326</td></tr><tr><td>Cat; Forward GGAGGCGGGAACCCAATAG; Reverse GTGTGCCATCTCGTCAGTGAA</td><td>Eurogentec</td><td>PMID: 28482326</td></tr><tr><td>Gpx1; Forward CCACCGTGTATGCCTTCTCC; Reverse AGAGAGACGCGACATTCTCAAT</td><td>Eurogentec</td><td>PMID: 28482326</td></tr><tr><td>Sod2; Forward TGGACAAACCTGAGCCCTAAG; Reverse CCCAAAGTCACGCTTGATAGC</td><td>Eurogentec</td><td>PMID: 28482326</td></tr></table>

Software and algorithms

<table><tr><td>GraphPad Prism 9</td><td>Graphpad Software</td><td>www.graphpad.com</td></tr><tr><td>Qlucore Omics Explorer 3.9</td><td>Qlucore</td><td>www.qlucore.com/cloud-hub</td></tr><tr><td>Biorender</td><td>Biorender</td><td>https://www.biorender.com/</td></tr><tr><td>GraphPad Prism 10</td><td>GraphPad Software, Inc.</td><td>https://graphpad.com/scientificsoftware/prism/</td></tr><tr><td>CaseViewer</td><td>3DHISTECH</td><td>https://www.3dhistech.com/solutions/caseviewer/</td></tr><tr><td>RStudio</td><td>R Studio</td><td>https://rstudio.com</td></tr><tr><td>FlowJo_v10.8.1</td><td>Tree Star</td><td>https://www.flowjo.com/solutions/flowjo/downloads</td></tr><tr><td>ImageJ</td><td>NIH</td><td>https://imagej.nih.gov/ij/</td></tr></table>
# EXPERIMENTAL MODEL AND STUDY PARTICIPANTS DETAILS

## Cell lines

The human monocyte THP-1 cell line was cultured with RPMI-1640 GlutaMAX™ Supplement (Gibco, Thermo Fisher) media supplemented with 10% fetal bovine serum (FBS) (Thermo Fisher). Wild-type, OPA1-deficient mouse embryonic fibroblasts (MEF) and TOM70-deficient human epithelial HeLa cell lines were cultured in DMEM 1x High D-Glucose supplemented with 5 or 10% FBS. Cells were cultured at 37°C with 5% CO₂.

## Human samples

Blood was collected in endotoxin-free tubes with K3-EDTA. Stools were collected in tubes containing ethanol or RNA shield and sent for metabolomics or sequencing analysis. The study cohort comprised individuals from the French population, including healthy donors (males = 32, females = 62, not available = 4) and patients diagnosed with IBD (males = 346, females = 410), with an age range of 14 to 86 years. Data regarding ancestry, race, and ethnicity were not recorded. All participants gave their informed consent for blood collection, after declaring no symptoms of chronic or infectious diseases. The research protocol followed the World Medical Association Declaration of Helsinki. Approval for human studies was obtained from the local ethics committee (Comité de Protection des Personnes Ile-de-France IV, IRB 00003835 Suivitheque study; registration number 2012/05NICB). From the EDTA-anticoagulated blood, the PBMC were collected using the Histopaque-1077 (Sigma Aldrich) density gradient centrifugation method. Depending on the experiment CD14⁺ monocytes were purified right after PBMC collection using the CD14 MicroBeads (Miltenyi Biotec) according to the manufacturer's instructions. Cells were counted and resuspended in RPMI-1640 GlutaMAX™ Supplement medium containing 10% FBS for further experiments.

## Mice samples

Fifteen-week-old wild-type C57BL/6 mice were purchased from JAX™/Charles River Lab Services and housed in a pathogen-free animal facility from Saint-Antoine Research Center/Sorbonne University Plateforme d'hébergement et d'expérimentation animale (PHEA), Paris, France. Experimental mice were maintained at 21°C under a 12-hour light-dark cycle with water and food *ad libitum*. This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the French Ministry of Agriculture, the European Union and ARRIVE guidelines. All animal experiments were approved by the Institutional Ethics Committee and the French Ministry of Research (APAFIS #37837-202206300029683 v3 and #47879-2024030216059527 v3). All efforts were made to minimize animal suffering. Bone marrow cells from wild-type and Irg1/Acod1 C57BL/6 knockout mice were kindly provided by Dr. Jean-Philippe Auger and Dr. Gerhard Krönke from FAU, Erlangen, Nürnberg, Germany.

# METHOD DETAILS

## In vitro culture before assays

The THP-1 monocytic cell line was cultured with gut-derived metabolites (1-1000 µM) in media containing or not the metabolic inhibitors 2-DG (100 mM) or oligoA (1 µM) for 4 or 6h for ATP assessment. PBMC or purified CD14⁺ monocytes were cultured with or without cadaverine (1-1000 µM) in the presence or absence of standard lipopolysaccharide (LPS) from *E. coli* K12 strain (100 ng/mL). Details for the specific incubation time point or drug concentration in each experiment are found in the Figure legends. For the assessment of cytokine intracellular levels by flow cytometry brefeldin A (5 µg/mL) as incubated at least 4 hours before antibody staining. For studies with human CD14⁺ monocytes-derived macrophages or mice bone marrow derived-macrophages (BMDM), cells were cultured with 50 ng/mL human or mouse macrophage-colony stimulation factor (M-CSF) at 37°C with 5% CO₂ for 6 days before the experimental protocols. For experiments with polarized macrophages M0- were cultured only with media, M1- were obtained after incubation with 50 ng/mL LPS plus 20 ng/mL interferon-gamma (IFN-γ), and M2-macrophages with 20 ng/ mL interleukin (IL)-4 at 37°C with 5% CO₂ for 24 hours. For the mechanistic assessments containing inhibitors or activation small molecules, cells were pre-incubated at least 30 minutes before treatments with citraconate acid (CitraAc, 1 mM), PX-12 (10 µM), ML385 (10 µM), GPCRs blocking cocktail (1 µM NF449 + 100 nM FR900359 + 100 ng/mL pertussis toxin), dynasore (80 µM), genistein (150 µM), H4R antagonist (H4R<sub>b</sub>, 5 µM), high concentration L-lysine (L-lysine<sub>high</sub>, 5 mM), 2-aminobicyclo[2.2.1]heptane-2-carboxylic acid (BCH, 1 mM), ranitidine (200 µM) and ML334 (50 µM).

## ATP assessment

To an opaque-walled 96-well plate, 100 µL of media containing THP-1 cells (2 x 10⁴ cells/well) was added to the same volume of the CellTiter-Glo® 2.0 reagent, a luciferase-based assay for ATP assessment, composed of reconstituted buffer and substrate. The contents were mixed for 2 minutes on an orbital shaker to induce cell lysis following a 10-minute room temperature incubation to stabilize the luminescent signal. Luminescence was recorded and a standard curve with ATP (Sigma Aldrich) was performed using the same kit following manufacturer instructions. Results were normalized by the number of proteins assessed by Pierce BCA assay in each well and expressed as nM of ATP per mg de protein.
# Cell Host & Microbe

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### ATP/ADP dosage

Human CD14⁺ (1.0 x 10⁵ per well) were incubated as per the experimental conditions. Intracellular ATP and ADP levels were measured using the Sigma ATP/ADP Ratio Assay Kit according to the manufacturer's instructions. Luminescence was measured using a plate reader, and the results were expressed as the ATP/ADP ratio. Briefly, cells were lysed using the provided lysis buffer, and ADP in the lysate was enzymatically converted to ATP by the kit's ADP-converting enzyme. The resulting total ATP was quantified via a luciferase-based luminescence reaction, with luminescence intensity directly proportional to ATP concentration. Measurements were performed on a microplate luminometer, and the ATP/ADP ratio was calculated from the signals obtained, providing a readout of cellular energy status.

### Single-cell energetic metabolism by profiling translation inhibition (SCENITH)

As previously described by Argüello et al.¹⁷ with some modifications, cells either from PBMC (1.0 x 10⁵ per well) or mice gut tissue (1 x 10⁶ per well) were seeded in 96-well plates after treatment/activation specified in each Figure panel. Cells from each condition were split into 4 groups. One was left as an untreated control, and the last three others were treated for 15 minutes with either 2-DG (100 mM), oligoA (1µM), or a combination of the inhibitors. Protein translation was used as a readout for ATP synthesis assessment and targeted by adding to the cell culture the antibiotic puromycin (10 µg/mL) for 30 min at 37°C, 5% CO₂. After that, cells were washed in cold PBS 1X and stained with a combination of Fc receptors blockade, fluorescent cell viability marker, and primary conjugated antibodies against surface markers for 25 minutes at 4°C. After washing, cells were fixed and permeabilized using the FOXP3 fixation and permeabilization buffer, washed with permeabilization buffer and incubated with the intracellular blocking solution. Intracellular puromycinylation detection was achieved using an in-house produced Alexa Fluor 488 labeled anti-puromycin monoclonal antibody by incubating cells for 1 hour at 4°C diluted in permeabilization buffer. The dependence (%) on either glucose or fatty acids/ amino acids oxidation as source molecules as well as the glycolytic capacity (%) and mitochondrial dependence (%) for ATP production were calculated according to the previously described SCENITH method.

### Flow cytometry

Single-cell suspensions were prepared in staining buffer containing Fixable Viability Dye and Muman or mouse TruStain FcX Fc Receptor Blocking Solution. For staining of surface markers, the following antibodies were used: HLA-DR, GLUT-1, CD16, CD3, CD4, CD8, CD19, CD14, CD80, CCR7, CD206, CD64, F4/80, CD11b and/or CD11c; and incubated at 4°C for 20 min. After surface staining, depending on the experiment cells were fixed and permeabilized using the FOXP3 fixation and permeabilization buffer and incubated with the following antibodies for the intracellular staining IL-10 and TNF-α. For analysis of mitochondria parameters, the following cell-permeable dyes were incorporated in the mix of antibodies Mitotracker green (mitochondrial mass, 100 nM), Mitotracker Far Red (mitochondrial membrane potential, 50 nM), MitoSOX (mitochondrial reactive oxygen species, 5 µM). For the analysis of glucose uptake before the staining cells were incubated with media without glucose at 37°C, 5% CO₂ for 20 min. The fluorescent glucose analog (2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl) Amino) (2-NBDG, 400 µM, Thermo Fischer) was given to cells for 20 min at 37°C, 5% CO₂. The 2-NBDG uptake reaction was stopped by washing the cells twice with cold PBS 1X and maintained at 4°C for later flow cytometry analysis within 30 min. Flow cytometry data was acquired using CytoFLEX V5-B5-R3 Flow Cytometer (Beckman Coulter). Data was analyzed with FlowJo software (FlowJo LLC). Intracellular pH (pHi) was assessed using the pHrodo™ AM Intracellular pH Indicator Variety Pack according to the manufacturer's instructions. Briefly, after incubations, cells were washed with PBS, and resuspended in serum-free medium. Cells were incubated with the pHrodo™ Red or Green AM dye (10 µM) supplemented with PowerLoad™ concentrate to enhance dye retention and distribution. After a 30-minute incubation at 37 °C in the dark, cells were washed, resuspended in PBS, and immediately analyzed by flow cytometry. Changes in fluorescence intensity, which inversely correlate with intracellular pH, were quantified as MFI.

### Immunofluorescence

Differentiated BMDMs were seeded in 96-well CellCarrier Ultra imaging plates (PerkinElmer) before imaging. Nuclei were stained using NucBlue™ Live ReadyProbes™ Reagent (Thermo Fisher Scientific) and mitochondria were fluorescently labeled with 100 nM tetramethylrhodamine Ethyl Ester Perchlorate (TMRE) and incubated for 30 minutes at 37°C with 5% CO₂. Spinning disk confocal images were captured using the Operetta CLS or Opera Phenix High-Content Analysis systems (PerkinElmer) with 20× Water/1.0 NA, 40× Air/0.6 NA, 40× Water/1.1 NA, or 63× Water/1.15 NA objectives.

### Transmission Electron microscopy

Cells were chemically fixed with 2.5% glutaraldehyde in culture media for 1h at room temperature, washed in 1X PHEM buffer (60 mM PIPES, 25 mM HEPES, 10 mM EGTA, 2 mM MgCl₂, pH 7.3), and centrifugated at 9500 g. Cells were postfixed in a mix of 1% osmium tetroxide and 1.5% potassium ferrocyanide for 1h, washed in 1X PHEM, and incubated in 1% tannic acid for 30 min. After another wash in 1X PHEM, cells were incubated in 1% osmium tetroxide for 1h, washed in water, and dehydrated in ethanol increasing series from 25% to 100%. All samples were then incubated in propylene oxide and infiltrated in Epon resin/propylene oxide mix v/v. Samples were then embedded in pure Epon resin (EMS), centrifugated at 13680 g, followed by polymerization for 48 h at 60° C. Ultrathin sections (70 nm) were cut with a Leica Ultracut UC7 microtome, and stained with 4% uranyl acetate and then 3% lead citrate. Transmission electron microscopy (TEM) images were captured with Tecnai Spirit 120kV TEM equipped with a bottom-mounted Eagle 4kx4k camera (FEI, USA). Analysis of mitochondria ultrastructure was carried out using Fiji (ImageJ) according to Lam et al.⁵²
## Metabolic flux analysis

Oxygen Consumption Rate (OCR) and Extracellular Acidification Rate (ECAR) were measured with the Seahorse XFe96 Extracellular Flux Analyzer (Agilent Technologies). For that, 1x10⁵ human CD14⁺ monocytes suspension cells were immobilized on the bottom of poly-D-lysine (Gibco, Fischer Scientific) pre-coated Seahorse XFe96 plates. For experiments with mice BMDM, bone marrow cells (6.0 x 10⁴ cells per well) were seeded and differentiated directly on the plates. Measurements were carried out in quadruplicates in the XF RPMI medium or XF DMEM medium pH 7.4 supplemented with 10 mM glucose, 2 mM L-glutamine, and 1 mM sodium pyruvate. The metabolic flux was monitored under basal conditions or in response to 1.5 µM oligomycin, 2 µM FCCP (Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone), 0.5 µM Rotenone/Antimycin A, 4 µM Etomoxin, 2 µM UK5099 or 3 µM BPTES. Results were expressed as pmol per minute of 1000 cells counted after the assay using the BioTek Cytation 5 Cell Imaging Multimode Reader (Agilent Technologies).

## Fluorespirometry

For experiments using isolated mitochondria from the hearts of 8-week-old adult mice, ventricles were collected, diced into small pieces, and manually homogenized on ice in a 2-mL homogenizer with IB buffer (275 mM sucrose, 20 mM Tris, 1 mM EGTA-KOH, pH 7.2) containing 0.05% Trypsin-EDTA. Trypsin activity was quenched by adding fatty acid-free bovine serum albumin (BSA, 0.25 mg/mL) and a protease inhibitor cocktail (PIC, Roche). The cardiac homogenates were centrifuged at low speed (1,000 g, 10 min, 4°C) to remove nuclei and debris, followed by high-speed centrifugation (3,200 g, 15 min, 4°C) to isolate the crude mitochondrial fraction. The resulting mitochondrial pellet was resuspended in IB buffer, and protein concentration was measured using the Bradford assay. Mitochondrial oxygen consumption was evaluated using High-Resolution Respirometry (O2k-Fluorespirometer, Oroboros, AT). A total of 50 µg of cardiac mitochondria were resuspended in Mir05 buffer containing 3 mM MgCl2-6H2O, 60 mM Lactobionic Acid, 20 mM Taurine, 10 mM KH2PO4, 20 mM Hepes-KOH, 110 mM Sucrose, 0.5 mM EGTA-KOH, and BSA (1 g/L). From there two experimental protocols were employed. First, for assessing the direct effect on mitochondria either cadaverine (100 µM) or vehicle (PBS) was incubated for 4 hours with gentle agitation at 4°C before assessing oxygen consumption. Mitochondrial respiration capacity (OXPHOS) was evaluated by sequentially adding PGM substrates (10 mM pyruvate, 5 mM glutamate, 5 mM malate, state 2) in the presence of ADP (1 mM, state 3) to assess complex I-driven respiration. This was followed by the addition of rotenone (0.5 µM), ascorbate (2 mM), N,N,N',N'-tetramethyl-p-phenylenediamine (TMPD, 0.5 mM), and carbonyl cyanide m-chlorophenyl hydrazone (CCCP, 2 µM) to measure complex IV-driven respiration. The second experiment evaluated whether cadaverine acts as an uncoupling agent. OXPHOS was measured by sequentially adding PGM substrates [10 mM pyruvate, 5 mM glutamate, 5 mM malate, state 2] with ADP (1 mM, state 3) to assess complex I-driven respiration. Oligomycin (1 µM) was then added to inhibit ATP synthase (Complex V), followed by cadaverine (100 µM) or just PBS. CCCP (2 µM), a known uncoupling agent, served as a control. Subsequently, ascorbate (2 mM), TMPD (0.5 mM), and CCCP (2 µM) were added to measure complex IV-driven respiration.

## Targeted metabolomics

TCA cycle intermediates and itaconate were quantified in cellular lysates and culture supernatants by LC-MS/MS. TargetLynx® (Waters) software was used to create the calibration curve calculating the intensity ratio between the metabolite and the respective internal standards. Quantification is expressed as nanomolar (nM) or picomols per milliliter (pmol/mL) for metabolites dosed in supernatants or intracellularly, respectively. Absolute concentrations of cadaverine were measured with liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) using the AccQ TagTM Ultra reagent (Waters Corporation, Milford, MA, USA). For cadaverine absolute quantification plasma and frozen dry stool samples were derivatized according to manufacturer recommendations after protein precipitation. Cadaverine separation was performed with an Acquity™ UPLC system using a CORTECS™ UPLC C18 column (1.6 µm, 2.1 × 150 mm) coupled to microTQS™ tandem mass spectrometer operating with the software MassLinks (version 4.2, Waters Corporation, Milford, MA, USA). Detection was based on the transition m/z 222.2 > 171.1 corresponding to doubly derivatized cadaverine. Quantification was performed using fully labelled lysine on all carbons and nitrogen as an internal standard, along with an external calibration curve generated by spiking solution with a natural cadaverine standard.

## RNA-seq analysis

RNA was extracted from 100.000 human CD14⁺ monocytes using the Qiagen RNeasy kit. RNA sequencing, including Smarter stranded V3 library preparation, was carried out at the Genotyping/Sequencing platform of the ICM Paris Brain Institute (Hôpital de la Pitié-Salpêtrière CNRS UMR 7225 – Inserm U 1127 – Sorbonne Université UM75) on an Illumina Novaseq X sequencer. Illumina adapters and low-quality bases (Phred < 20) were trimmed using TrimGalore v0.6.7, and quality control of raw and trimmed paired end fastqs was performed with fastQC v0.12.0. Reads were quantified with Salmon v1.10.1 against a mapping-based index built from the GENCODE v45 (GRCh38) transcript set. The quantified transcripts were imported into R using the tximport package v1.30.0. Gene-level DESeqDataSet objects were constructed using DESeq2 v1.42.1 for differential expression analysis. Pre-filtering excluded genes with zero counts in more than 60% of samples within one condition and genes lacking an official symbol. Variance stabilizing transformation was applied to raw counts for visualization. Principal Component Analysis (PCA) was then conducted on the variance-stabilized and normalized dataset using prcomp to examine relationships among biological conditions. DESeq2's DESeq function was employed for differential expression analysis by estimating sample size factors, gene dispersion, fitting counts to a Negative Binomial Generalized Linear Model, and testing expression differences with contrasts and Wald's test. Overlap between significant gene lists was visualized with Venn diagrams generated by ggvenn v0.1.10. When three samples per group were available, EBSeq
# Cell Host & Microbe

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was used for differential analysis as recommended by Li et al. Functional enrichment analysis of significant genes was conducted with over-representation analysis (ORA) on the WP database via msigdb v7.5.1. ORA was separately performed for up-regulated genes (logFC > 0) and down-regulated genes (logFC < 0) in each comparison using fgsea's fora function. Additionally, pathway enrichment between conditions was tested through Gene Set Enrichment Analysis (GSEA) with the fgsea package. Visualization was achieved with GraphPad Prism 9, and the Benjamini-Hochberg method was applied to control the False Discovery Rate (FDR) at a significance level of alpha = 0.05.

### Pyruvate dehydrogenase (PDH) activity assay

PDH enzyme from cell lysates was immunocaptured onto microplates coated with an anti-PDH monoclonal antibody. Enzyme activity was determined by monitoring NAD⁺ reduction to NADH at an absorbance of 450 nm. Protein concentrations were measured using a BCA protein assay, and all samples were normalized to 100 µg of protein. The assay followed the manufacturer's instructions in kinetic mode over 15 minutes at room temperature, with results expressed as the rate of change in optical density (ΔmOD/min).

### Nrf2 activation assay

Nuclear extracts were prepared from cells using the NE-PER Nuclear and Cytoplasmic Extraction Reagents following the manufacturer's instructions. Briefly, cells were first lysed in cytoplasmic extraction buffer to release cytoplasmic contents, then nuclei were collected and lysed in nuclear extraction buffer. The protein concentration of nuclear lysates was determined using a standard protein assay. Nrf2 transcriptional activity was then measured using the Nrf2 Transcription Factor Assay Kit according to the kit protocol. In this ELISA-based assay, Nrf2 in the nuclear extracts binds to a specific immobilized DNA consensus sequence. Bound Nrf2 was detected using a primary antibody and a secondary HRP-conjugated antibody, followed by colorimetric detection at 450 nm using a microplate reader. Results were expressed as relative absorbance values (OD450 nm), reflecting the transcriptional activity of Nrf2 in the nuclear fraction.

### Proteomics

A sixfold volume of cold acetone (−20°C) was added to a sample containing 10 µg of protein extract. The samples were vortexed, incubated overnight at -20°C, and centrifuged at 11,000 rpm for 10 minutes at 4°C. The supernatant was removed, and the protein pellets were dissolved in an 8 M urea - 25 mM NH₄HCO₃ buffer. Samples were reduced with 10 mM TCEP-HCl and alkylated with 20 mM MMTS. After a 16-fold dilution in NH₄HCO₃, the samples were digested overnight at 37°C using a trypsin/Lys C enzyme mix at a 1:20 enzyme/substrate ratio. The digested peptides were loaded and desalted using Evotips Pure, following the manufacturer's protocol (Evosep One, Odense, Denmark). Analysis was conducted on a timsTOF Pro 2 mass spectrometer (Bruker Daltonics, Bremen, Germany) coupled with an Evosep One system operating the 30SPD method with a 44-minute gradient and a 48-minute total cycle time. A C18 analytical column (0.15 x 150 mm, 1.9 µm beads, ref EV-1106) was equilibrated at 40°C with a flow rate of 500 nL/min, using H₂O/0.1% FA as solvent A and ACN/0.1% FA as solvent B. The timsTOF Pro 2 was set to DIA-PASEF mode with 12 pydiAID frames and 3 mass windows per frame, resulting in a cycle time of 0.975 seconds as specified in Bruker application note LCMS 218. MS raw files were processed with Spectronaut 18 (Biognosys, Switzerland), using the SwissProt Mus Musculus database (11 2023, 17,191 entries). Specific tryptic cleavages were selected, allowing a maximum of 2 missed cleavages. Identification included variable modifications (Acetyl, Protein N-term; Oxidation, M) and fixed modifications (MMTS, C), with a maximum of 2 variable modifications. Identifications were filtered with a 1% precursor and protein Q-value cutoff. The protein LFQ method was set to automatic, with quantification at the MS2 level and cross-run normalization applied. Multivariate analysis was performed using Qlucore Omics Explorer 3.9 (Qlucore AB, Lund, Sweden), with a positive threshold of 1 for log2 transformation. This normalized data was used to evaluate differentially present proteins between groups. We quantified the signal-to-noise ratio across the different treatments and generated heatmaps illustrating the top 10 up- and down-regulated proteins within the MitoCarta3.0 dataset comprising an inventory of human and mouse mitochondrial proteins and pathways⁵³ using Morpheus (software.broadinstitute.org/morpheus). For that 1000 permutations were applied to the analysis. To identify distinct and overlapping up-regulated protein signatures Venn diagram (bioinformatics.psb.ugent.be) was used. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE⁵⁴ partner repository with the dataset identifier PXD057652.

### ELISA

Cell culture supernatants were collected from cells and stored at -80 °C until analysis. Concentrations of IL-10, TNF-α, and IL-1β were quantified using Duoset Human ELISA Kits according to the manufacturer's instructions. Briefly, 96-well plates were coated with the capture antibody overnight, blocked, and incubated with standards and samples. Detection was performed with biotinylated detection antibodies, followed by streptavidin-HRP and substrate development. Absorbance was measured at 450 nm using a microplate reader. Cytokine levels were calculated from standard curves and expressed as picograms per milliliter (pg/mL).

### LEGENDplex

BMDM were plated at 0.2 × 10⁶ cells per well in 150 µL culture medium and incubated for 24 hours. Supernatants were collected and cytokine profiling was performed using the LEGENDplex™ Macrophage/Microglia Panel according to the manufacturer's instructions. In brief, cytokine-specific capture beads were incubated with standards and samples, followed by detection antibody and
streptavidin-PE labeling. Data acquisition was performed on a flow cytometer, and results were analyzed using LEGENDplex™ Data Analysis Software (BioLegend). Cytokine concentrations are reported in picograms per milliliter (pg/mL).

## Gene Expression Analysis by Quantitative Reverse Transcription PCR (RT-qPCR)

Total RNA was extracted from BMDM or mouse colon tissue using the RNeasy Mini Kit (Qiagen, Hilden, Germany), following the manufacturer's instructions and including an on-column DNase I treatment to eliminate genomic DNA contamination. Complementary DNA (cDNA) synthesis was performed using SuperScript II Reverse Transcriptase (Life Technologies, Saint Aubin, France) with 1 µg of total RNA as input. Quantitative PCR was carried out using the Takyon SYBR Green PCR Kit (Eurogentec, Liège, Belgium) on a StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Gene-specific primers for mouse Rplp0 (reference gene) and Nfe2l2, Nqo1, Gclc, Gpx1, Cat, Sod2, H4R (target genes) were used (complete primers sequences found in STAR Methods). Relative gene expression levels were calculated using the 2⁻ΔΔCt method, with Rp/lp0 serving as the endogenous control and expression levels normalized to the wild-type group.

## Pharmacokinetic profiling of cadaverine following oral administration

Adult C57BL/6j mice (20 g, n = 6 per time point) received a single oral dose of cadaverine at 10 mg/kg (0.2 mg per mouse, ~1957 nmol, based on molecular weight of 102.18 g/mol). Blood was collected at 0, 30, 90, 180, and 360 minutes after administration, and plasma cadaverine concentrations were quantified using UPLC-MS/MS, as we previously described. Pharmacokinetic parameters were calculated using non-compartmental and exponential decay models. The maximum plasma concentration (Cmax) and time to reach this peak (Tmax) were determined directly from the observed concentration-time data. The terminal elimination rate constant (k) was calculated by nonlinear least-squares fitting of the log-transformed plasma concentrations from 30 to 360 minutes to a one-phase exponential decay model. The elimination half-life (T½) was then derived using the standard formula T½ = ln(2)/kT. Systemic exposure was evaluated by computing the area under the plasma concentration-time curve from 0 to 360 minutes (AUC₀⁻³六₀) using the trapezoidal rule. Total plasma clearance (CL) was estimated as the ratio of the administered dose (nmol) to AUC. The apparent volume of distribution (Vd) was calculated using the formula Vd = CL/k.

## Antimicrobials (ABX)-induced gut microbiota depletion

Mice were randomly assigned to three groups, one treated with conventional drinking water and the other two to which a cocktail of wide-spectrum ABX was added and given to mice for 7 days: neomycin (1 g/L), vancomycin (500 mg/L), gentamycin (35 mg/L), colistin (850000 U/L) and amphotericin B (10 mg/L) purchased from Sigma Aldrich. To the last ABX-treated group cadaverine (10 mg/kg) was also orally given by gavage once a day throughout the whole experiment. Amphotericin B was added to prevent fungal overgrowth or opportunistic infections. Mice were accompanied to assess possible signs of toxicity. After that, the small intestine and colon were collected to purify lamina propria immune cells as previously mentioned.

## DSS-induced colon inflammation

To induce colitis, mice were administered drinking water supplemented with 2% DSS salt (molecular weight, 36,000–50,000) for 7 days and then changed to just water for the next 1-2 (day 8/9 after DSS - inflammatory peak) or 5 days (day 12 after DSS - recovery phase). Cadaverine was given to mice by oral gavage once a day throughout the experiments before DSS administration and until euthanasia. For experiments targeting Enterobacteriaceae mice were given colistin 600 µg/gavage for 7 days prior to DSS. In all experiments, body weight, blood in the stool, and stool consistency were analyzed daily. The severity of colitis was assessed using the disease activity index (DAI), as previously described with some modifications.⁵⁵ Diarrhea was scored as follows: 0, normal; 2, loose stools; 4, watery diarrhea. Blood in stool was scored as follows: 0, normal; 2, slight bleeding; 4, gross bleeding. Weight loss was scored as follows: 0, none; 1, 1–5%; 2, 5%–10%; 3, 10%–15%; 4, <15%. DAI was the average of these scores. On the last day of the experiment mice colons were collected and measured (cm). After that tissue slices were also collected and stored for further determinations.

## Histopathological analysis

Colon fragments were collected, fixed in 4% paraformaldehyde (PFA) at room temperature for 24 hours, and subsequently embedded in paraffin. Sections of 5 µm thickness were prepared using a microtome and mounted on glass slides. Tissue sections were stained with hematoxylin and eosin (H&E) following standard protocols. Histological scoring was performed in a blinded manner using established criteria for DSS-induced colitis, including evaluation of epithelial damage, inflammatory cell infiltration, and tissue architecture, as previously described.⁵⁶

## Cell purification from mice gut tissues

For isolation of lamina propria immune cells, small intestine or colon tissues were collected and opened longitudinally and then washed with cold PBS 1X to remove fecal contents. Tissues were segmented into 0.5-1 cm long pieces and mixed with 10 mL pre-warmed buffer composed of 5 mM EDTA (Sigma Aldrich), 0.145 mg/mL dithiothreitol (DTT, Sigma Aldrich), 10 mM HEPES (Gibco, Thermo Fischer), 1% 10,000 U/mL Penicillin-Streptomycin (P/S, Gibco, Thermo Fischer), 5% FBS (Thermo Fisher), 1X HBSS (Gibco, Thermo Fischer) in ultra-purified water and incubated at 37°C for 20 min with constant 170 rpm stirring. After that supernatants were discarded, and tissue pellets were washed with cold PBS 1X and transferred to a new tube and digested with
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collagenase type VIII (280.5 U/mL) supplemented with DNase I (1 mg/mL) at 37°C for 20 min with constant 170 rpm stirring. All the contents were then passed through a 100 µm cell strainer. Lamina propria cells were obtained using the 40/80% Percoll (GE Health-care) centrifugation method and centrifuged at 600 g for 20 min. Cell rings were collected and resuspended in complete RPMI containing 10% FBS, 1% HEPES, 1% P/S, and 50 uM 2-Mercaptoethanol (Sigma-Aldrich).

Fecal DNA extraction and bacterial quantification by RT-qPCR

Fecal samples were weighed and resuspended at room temperature for 10 minutes in 250 µL of 4 M guanidine thiocyanate in 0.1 M Tris buffer (pH 7.5) and 40 µL of 10% N-lauroyl sarcosine (Sigma-Aldrich, USA). Next, 500 µL of 5% N-lauroyl sarcosine in 0.1 M phosphate buffer (pH 8.0) was added, and the tubes were incubated at 70 °C for 1 hour. To this, 750 µL of sterilized silica beads (0.1- and 0.6-mm diameter) was added, and the mixture was shaken in three 30-second intervals at 6.5 m/s using a FastPrep apparatus (MP Biomedicals, USA). Following the addition of 15 mg of polyvinylpolypyrrolidone, the tube was vortexed and centrifuged at 20,000g for 5 minutes. The supernatant was collected, and the remaining pellets were washed twice with 500 µL of TENP buffer (50 mM Tris, pH 8; 20 mM EDTA, pH 8; 100 mM NaCl; 1% polyvinylpolypyrrolidone), each wash followed by centrifugation at 20,000g for 5 minutes. The combined supernatant (~2 mL) was centrifuged briefly to remove debris and split into two tubes. Nucleic acids were precipitated by adding 1 volume of isopropanol, incubating for 10 minutes at room temperature, and centrifuging at 20,000g for 10 minutes. The resulting pellets were pooled, resuspended in 450 µL of 100 mM phosphate buffer (pH 8), and 50 µL of 5 M potassium acetate was added. After overnight incubation on ice, samples were centrifuged at 20,000g for 30 minutes. The supernatant was transferred to a new tube with 20 µL of RNase (1 mg/mL) and incubated at 37 °C for 30 minutes. Nucleic acids were precipitated by adding 50 µL of 3 M sodium acetate and 1 mL of absolute ethanol, incubating for 10 minutes at room temperature, followed by centrifugation at 20,000g for 15 minutes. The DNA pellet was washed with 70% ethanol, dried, and resuspended in 100 µL of Tris-EDTA buffer, and DNA suspensions were stored at -20 °C for subsequent analysis. Bacterial quantifications were performed by qPCR using SYBR™ Green PCR Master Mix (Applied Biosystems) in a StepOnePlus apparatus (Applied Biosystems). Each reaction was done in duplicate in a final volume of 20 µL with 0.2 µM of each primer and 5 µL of diluted DNA. Total bacteria were quantified using specific primers: Uni 16SR 5'-ATTACCGCGCTGCTGGC-3' and Uni 16SF 5'-ACTCCTACGGGAGGCAGCAGT-3', while Enterobacteriaceae were quantified using Eco1457F 5'-CATTGACGTTACCCGCAGAAGAAGC-3' and Eco1652R 5'-CTCTACGAGACTCAAGCTTGC-3'. The threshold cycle (CT) was determined for each gene in each sample and normalized to the CT value of the universal bacterial 16S rRNA gene. Data were analyzed using the 2-ΔΔCt method. A standard curve containing known amounts of *Escherichia coli* str. K-12 *substr.* MG1655 was used to assess the number of bacteria in each sample. Data was expressed as counts per microliter of DNA.

16S sequencing and analysis of gut microbiota composition

Microbial diversity was assessed by targeting the V3–V4 hypervariable regions of the bacterial 16S rRNA gene. Amplification was performed using primers specific for these regions: forward (5’-TACGGRAGGCAGCAG-3’) and reverse (5’-CTACCNGGGTATC-TAAT-3’), following the optimized and standardized protocol provided by the Metabiote® platform (GenoScreen, Lille, France). PCR reactions were conducted using 5 ng of genomic DNA as input, with a total of 192 unique barcoded primers (Metabiote MiSeq primers) used at a final concentration of 0.2 µM. The amplification involved 30 cycles with an annealing temperature of 50 °C. PCR products were purified using the Agencourt AMPure XP system (Beckman Coulter, Brea, CA, USA), quantified per the manufacturer’s instructions, and normalized to equal concentrations for multiplexing. Sequencing was carried out on the Illumina MiSeq platform using a 2 × 300 bp paired-end sequencing protocol. Raw reads underwent a multi-step bioinformatic preprocessing workflow. First, quality trimming was performed using PRINSEQ-lite, removing bases from the 3’ end with a Phred quality score below 30. Next, paired-end reads were assembled using FLASH, requiring a minimum 30 bp overlap and 97% sequence identity. Primer sequences were then removed using CutAdapt, with zero mismatches tolerated; reads lacking exact matches to both primers were discarded. Only high-quality, properly assembled sequences containing intact primer regions were retained for downstream analysis.

Raw sequencing data were demultiplexed and quality-filtered using the QIIME software package (version 1.8.0). Sequences were clustered into Operational Taxonomic Units (OTUs) at a 97% pairwise identity threshold using the UCLUST algorithm, and taxonomic classification was assigned with reference to the Greengenes database (version 13.5). To standardize sequencing depth across samples, rarefaction was performed at 20,000 and 250 sequences per sample, and the resulting data were used to assess the relative abundance of OTUs. Alpha diversity was calculated using the Shannon and Chao1 indices, while beta diversity was assessed using the Bray–Curtis dissimilarity metric. Principal Coordinates Analysis (PCoA) was applied to visualize differences in microbial community composition across experimental groups. To identify bacterial genera differentially associated with experimental conditions, MaAsLin (Multivariate Association with Linear Models) analysis was performed on relative abundance data. This enabled the detection of taxa with statistically significant variation across paired comparisons while accounting for potential covariates.

DNBS-induced colon inflammation

Adult female C57BL6j wild-type mice were fasted for 6 hours prior to the colitis induction. Anesthesia was induced using 3–5% isoflurane and maintained at 1–3% during the procedure. Mice received an intrarectal administration of 5 mg 2,4-dinitrobenzenesulfonic acid (DNBS, Sigma-Aldrich) dissolved in 100 µL of 50% ethanol (v/v) using a 1 mL syringe attached to a 50 mm polyethylene catheter. The catheter was lubricated and gently inserted approximately 3–4 cm into the rectum. Sham controls were handled identically but had the catheter inserted and withdrawn without any solution administered. Following injection, mice were held in an inverted
position for 90 seconds to prevent leakage of the solution from the rectum. Mice were then returned to their cages and monitored until recovery. If signs of dehydration were observed, mice were provided with 8% sucrose and 0.2% NaCl in drinking water to maintain hydration. The severity of colitis was assessed using mice weight and the disease activity index (DAI), as previously described. On the last day mice colons were measured (cm).

## Lysine decarboxylase gene assessment

Shotgun metagenomics data, from healthy individuals, Crohn's disease (CD) and ulcerative colitis (UC) patients, were filtered for low-quality (quality score Q<20) reads, fragmented reads (>75 bp) and sequencing adapters were removed using TrimGalore (version 0.6.10). Host-reads were removed using a sensitive-local alignment against the GRCh38 human genome. Functional analysis of the metagenomic samples (pathway profiles and gene-family abundances) was performed using HUMAnN3 (version v3.9.0, ChocoPhlAn database v31 and UniRef90 database release 201901b). Next, the abundance of genes per metagenomes encoding a lysine decarboxylase (UniRef90_P0A9H4, UniRef90_P52095, UniRef90_UPI0005CEFCCA, UniRef90_UPI00069BFB00, UniRef90_UPI000854D32A, UniRef90_UPI0009458150) calculated by HUMAnN3 (reads per kilobase, RPK) was extracted and then normalized by the number of total reads in the metagenome (RPKM). Genes with missing annotations or low-confidence protein predictions (containing "Putative" or "Predicted") were excluded. HUMAnN3 stratifies community functional profiles according to contributing species. We used taxonomic information provided by HUMAnN3 to extract the normalized abundance of lysine decarboxylase carried by Family of taxa, and by Genus inside the *Enterobacteriaceae* family. The RPKM values were further compared with targeted metabolomics data for cadaverine concentrations. Spearman and linear model-based (after log-transformation of the values) correlations were evaluated globally, by disease state, and across individual lysine decarboxylase homologs and taxonomic families. All analyses were performed in R (v4.3.0), visualization was performed using ggplot24, ggpubr, and patchwork, with comparisons of gene abundance across clinical states assessed using Wilcoxon rank-sum tests and adjusted for multiple comparisons using the Benjamini-Hochberg method.

## QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis was performed with GraphPad Prism software. Data were first tested for normality using the Shapiro-Wilk test. For parametric data when several groups were compared, one-way ANOVA followed by Dunnet or Tukey post-test was performed to assess the significance among conditions. When only two conditions were analyzed Student's t-test was used, according to the validity of the homoscedasticity hypothesis (* P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001). Statistical tests used for other types of analysis were specified in the respective sections in the figure legends. Non-significant (ns) was inserted in the figure panels only for specific coupled comparisons when inhibitors or other standards were applied otherwise is not shown.