Chemotherapy-driven intestinal dysbiosis and indole-3-propionic acid rewire myelopoiesis to promote a metastasis-refractory state

Domain: biology
Published: 2025-12-15
Editorial outcome: minor_revision

Abstract
The contribution of chemotherapy-induced tissue injury to individual susceptibility to metastasis remains largely unexplored. We report that chemotherapy indirectly prevents colorectal cancer (CRC) liver metastases by inducing a lasting systemic “chemomemory”. Chemotherapy-induced intestinal mucositis alters nutrient availability, promoting the expansion of tryptophan-metabolizing bacteria and production of the microbial metabolite indole-3-propionic acid (IPA). IPA reprograms bone marrow myelopoiesis by redirecting common myeloid progenitor fate toward the macrophage lineage, limiting generation of immunosuppressive Ly6ChighCCR2+ monocytes. This shift enhances CD4+ T cell antitumor function by promoting Th1 differentiation and spatially reorganizing CD8+ and CD4+ T cell interactions within the metastatic microenvironment. In a subset of CRC patients, circulating IPA levels increase after chemotherapy and inversely correlate with monocyte abundance, while high monocyte levels were associated with reduced survival. Our findings reveal that chemotherapy-induced intestinal injury normalizes pathological myelopoiesis through a microbiota-derived metabolite and identify IPA as a potential adjuvant to counteract monocyte-driven immunosuppression and metastasis.

Introduction
Colorectal cancer (CRC) is the world-wide second leading cause of cancer-related mortality1. Stage I and II CRC tumors can be surgically resected and are associated with an excellent prognosis. Recently, spectacular responses were reported for microsatellite instable CRCs treated with immune-checkpoint inhibitors such as anti-PD-12. However, this cancer subtype represents only 5-13% of all CRC cases, highlighting the need to improve therapy for the remaining 85-95% patients with microsatellite stable (MSS) CRCs. Fluoropyrimidine-based chemotherapy in combination with oxaliplatin after curative surgery remains a mainstay treatment for selected high-risk stage II and stage III CRCs. It minimizes tumor recurrence and prolongs survival3, however, a significant proportion of patients (30-50%) develop distant recurrence, mainly in the liver4.

The importance of microbiota for acute cytotoxic effect of chemotherapy has been demonstrated. Absence of MYD88 microbial product-sensing in the myeloid compartment of germ-free mice reduces oxaliplatin treatment effectiveness5. In addition, cyclophosphamide-induced bacteria translocation to secondary lymphoid organs stimulates an antitumor CD4+ T cell response, essential for optimal cyclophosphamide efficacy6. In the adjuvant settings, patients receive perioperative antibiotics. However, how the microbiota influence metastatic relapse in the adjuvant setting has not been addressed yet. A recent large retrospective clinical study EVADER-1 demonstrated that receiving antibiotics prior or during chemotherapy treatment significantly reduced disease-free survival in CRC patients7, suggesting a contribution of the microbiota or microbial-derived products. Yet, the mechanistic link between chemotherapy, microbiota alterations and cancer progression, including liver metastasis development, remains incompletely understood.

Here, we provide a part of the mechanistic explanation for this clinical observation. We demonstrate that prior exposure to standard of care 5-FU and oxaliplatin chemotherapy establishes a liver metastasis refractory state by durably rewiring gut microbiota to produce the tryptophan-derived metabolite, indole-3-propionic acid (IPA). We further identify myeloid cells as primary target of IPA, contrasting with previous observations8. IPA supplementation biased common myeloid progenitors fate toward the macrophage lineage, reducing immunosuppressive Ly6ChighCCR2+ monocyte output with minimal direct impact on lymphoid cells or T cell function. The reduction of immunosuppressive myeloid cells allowed expansion and activation of anti-tumor CD4+ T cells which limited CRC metastasis formation. The effects observed in mouse models are clinically relevant, as circulating IPA levels are negatively correlated with abundance of monocyte and are increased post-chemotherapy in a subset of chemo-naïve CRC patients. Our study underscores the necessity of characterizing the systemic effects of chemotherapy to anticipate the outcomes of cancer treatment and to discover new therapeutic strategies.

Results
5-FU and oxaliplatin exposure provide indirect lasting protection against CRC liver metastasis
Up to 50% of CRC patients develop intestinal mucositis after the first round of chemotherapy9, which may trigger the local and systemic release of cytokines, growth factors, and bacterial-derived products influencing subsequent metastatic relapse. To determine how chemotherapy induced intestinal mucositis influences metastatic relapse in the context of adjuvant CRC treatment, we first tested if a clinically relevant chemotherapeutic regimen (FO) consisting of one dose of 3.5 mg/kg oxaliplatin and one hour later 50 mg/kg 5-FU, followed by 50 mg/kg 5-FU on the next day directly inhibited CRC growth and induced intestinal mucositis (Fig. 1a)10. The FO-treated group displayed moderate, but significant, weight loss and a tendency towards reduced intestinal length 3 days after treatment initiation (Supplementary Fig. 1a, b). Analysis of the small intestine 3 days post-FO revealed reduced crypt cell proliferation, villi length and crypt depth (Fig. 1b, c), consistent with a cytotoxic effect of FO on transient amplifying cells. To assess both mucositis induction and tumor response, the FO regimen was initiated two weeks after implantation of Apcfl/fl; KrasLSLG12D; Tp53fl/fl; Vil-CreERT2 (AKP) tumor cells (Fig. 1d)11. Tumor weights were comparable between control and FO-treated mice five days post-treatment (Supplementary Fig. 1c). However, we observed decreased tumor cell proliferation and increased DNA double-strand breaks in FO-treated tumors, confirming the antitumor activity of FO (Supplementary Fig. 1d–g). We observed increased crypt depth and transit-amplifying cell proliferation in small intestine, indicative of ongoing intestinal regeneration five days post-FO (Fig. 1e, f). In contrast, the colon crypt proliferation and depth were similar between control and treated mice at the same time point (Supplementary Fig. 1h, i), aligning with human data showing oxaliplatin preferentially induces ileal cell death and spares healthy colon tissue in patients12. Accordingly, expression of proinflammatory cytokines Tnf (encoding TNFα) and Il1b was increased in the ileum at day 5 post-treatment (Supplementary Fig. 1j). Analysis of immune response revealed increased infiltration of myeloperoxidase (MPO+) neutrophils in the FO-treated ileal lamina propria at day 3 and 5 post-FO, while CD4+ and CD8+ T lymphocyte levels were unaffected (Supplementary Fig. 1k–r). These results confirm that the clinically relevant FO regimen directly inhibits CRC tumor growth and causes intestinal mucositis.

The liver is the most common site of distant spread for CRC, with liver metastases developing in up to 25% of patients undergoing surgery and adjuvant chemotherapy. FO has a relatively short half-life and is eliminated 4 days following acute administration13,14. To determine if FO chemotherapy-induced intestinal injury affects liver metastasis burden independently of its direct cytotoxic effect and to mimic the adjuvant treatment, we intrasplenically injected mice with either Apc-/-; KrasLSLG12D; Tp53-/-; Smad4-/- -mCherry (AKPS) organoids or MC38-GFP mouse colon adenocarcinoma cells five days after FO treatment (Fig. 1g). In this experimental setup, liver metastases form after the active drug has been cleared (Fig. 1g). AKPS and MC38 cell lines are models of microsatellite stable and instable CRC, respectively, and allowed testing if CRC subtype alters responses to FO pre-conditioning15,16. FO pre-conditioning markedly reduced liver metastases in both the AKPS and MC38 models (Fig. 1h–k), indicating a lasting anti-metastatic effect of chemotherapy, independent of its direct impact on tumor cells. We next investigated the duration of FO pre-conditioning-mediated tumor growth inhibition (Fig. 1l). Injection of MC38-GFP tumor cells ten days after FO pre-conditioning reduced metastasis formation, indicating that the metastasis refractory state is lasting (Fig. 1l–n; Supplementary Fig. 2a, b). Injecting MC38-GFP tumor cells twenty days after FO pre-conditioning, mimicking the end of a treatment cycle in patients, showed a tendency for persistence of metastasis refractory state (Supplementary Fig. 2c–e). In all, we demonstrate that FO-induced mucositis and its systemic effects maintain a lasting anti-tumor effect.

To determine if decreased liver metastatic burden was due to hepatotoxicity preventing metastatic cell implantation, we analyzed hepatic transaminase levels in the control and FO-treated mice. However, alanine aminotransferase (ALAT) and aspartate aminotransferase (ASAT) levels were similar between control and FO-treated mice indicating that direct hepatotoxicity is not inhibiting metastatic growth (Supplementary Fig. 2f, g). During metastasis, disseminated tumor cells extravasate to distant tissues within 24 h17. To test whether FO pre-conditioning inhibited survival of cancer cells in circulation or reduced their extravasation capacity, we analyzed the presence of MC38-GFP cells in the liver 24 h after their injection into the spleen by FACS (Supplementary Fig. 2h). The number of MC38-GFP cells was similar between FO pre-treatment and control mice, suggesting FO impacts metastatic outgrowth rather than earlier steps of metastatic dissemination (Supplementary Fig. 2i, j). In summary, these results indicate that pre-exposure to the standard-of-care CRC chemotherapy generates a durable metastasis-refractory state which we term “chemomemory” (Fig. 1o).

Anti-metastatic chemomemory is microbiota-dependent
Chemotherapeutic drugs not only impact cancer cells, but also the tumor microenvironment and gut microbiota composition and functions12,18,19. To assess if gut microbes are necessary for chemomemory, we depleted the microbiota using broad-spectrum antibiotics and assessed liver metastatic burden after FO pre-conditioning (Fig. 2a)20. Analysis of fecal 16S rRNA gene abundance confirmed efficient microbiota depletion in the antibiotic-treated group (Supplementary Fig. 3a). Liver metastases grew significantly more in antibiotic-treated mice compared to controls, despite FO pre-treatment (Fig. 2b–d). Fecal bacterial load negatively correlated with metastatic load (Fig. 2e), further suggesting an important role for the gut microbiota in promoting chemomemory.

FO-induced gut damage increases gut microbe diversity by altering nutrient availability
To determine how chemotherapy modulates the intestinal microbiota composition, we performed 16S rRNA sequencing of mouse feces 5 days post-FO treatment. Higher alpha and beta diversity have been associated with better responses to chemotherapy in breast cancer patients and there was greater alpha diversity in FO-treated intestines compared to PBS-treated controls (Supplementary Fig. 3b)21,22. There were also major changes in the bacterial community composition with clear separation between the two treatment groups (Supplementary Fig. 3c). FO promoted significant expansion of Firmicutes and Proteobacteria at the phylum level and the class Clostridia including Oscillospirales, Christensenellales, Lachnospirales at the order level (Fig. 2f, g). Selective expansion of these two bacterial phyla is consistent with reports that some Firmicutes are resistant to 5-FU cytotoxicity, while intestinal inflammation increases Proteobacteria abundance23,24.

To characterize how FO affects the small intestine metabolome, we performed metabolomic analysis of the ileum 5 days post-FO. Ileum from FO-treated mice displayed an increase in 53 metabolites and a decrease in 18 metabolites (Fig. 2h, Supplementary Data 1). Most notably, we observed increased abundance of compounds associated with the pro-inflammatory arachidonic acid metabolism pathway (Fig. 2h, Supplementary Data 1)25. In addition, FO-treated ileum contained less citrulline (Fig. 2h, Supplementary Data 1), a metabolite mostly produced by enterocytes and used as a marker of enterocyte mass26. Collectively, together with histological and immune infiltration analyses, these changes indicate that FO treatment transiently decreases enterocyte absorptive capacity and promotes intestinal inflammation.

FO-induced intestinal mucositis significantly decreased small intestinal villus size and hence the absorptive surface (Fig. 1b, c). Gut-resident microbes rely on specific nutrient sources27–29, therefore we hypothesized that altered colonic microbiota composition post-FO was due to differences in gut lumen nutrient availability. Consequently, we performed untargeted metabolomics on cecal contents from FO and control-treated mice. Over Representation Analysis (ORA) of metabolite subclass enrichment revealed that amino acids, peptides, and amino-acid metabolism by-products were the most significantly increased metabolites in FO-treated mice caeca (Fig. 2i)30. Amino acids and peptides are absorbed in the proximal small intestine31. Therefore, the observed higher cecal levels of single, and particularly dipeptide, amino acid content (Fig. 2i, j; Supplementary Data 2) is due to a reduction in the absorption capacity caused by small intestinal mucositis. Of interest, proteins are the preferred carbon and nitrogen source for Firmicutes in vivo, suggesting increased peptide concentration drives expansion of these taxa29. In all these data indicate that FO-induced intestinal mucositis modifies nutrient availability to the gut microbiota, which increases the abundance of Firmicutes and in particular the Clostridia class bacteria.

FO enhances circulating levels of microbial-derived indole-3-propionic acid
Since the anti-metastatic effects of FO pre-conditioning were gut microbiota-dependent and FO increased the abundance of Clostridia, we hypothesized that these bacterial populations produce a soluble factor that mediated chemomemory. Therefore, we performed untargeted polar metabolomics of portal blood 5 days post-FO (Fig. 3a) and found 95 and 68 metabolites were significantly increased or decreased by FO, respectively (Fig. 3b; Supplementary Data 1). Indole-3-propionic acid (IPA) was consistently increased in portal blood following FO treatment along with bile acid related metabolites and amino acids (Fig. 3c). Retention time comparison with IPA pure standard confirmed the IPA identity detected by untargeted flow injection analysis (Supplementary Fig. 4a).

Analysis of functional microbiome-encoded pathways using PICRUSt2 revealed an enrichment in tryptophan metabolism among taxa increased after FO treatment (Fig. 3d)32. Importantly, IPA is a by-product of tryptophan metabolism and produced exclusively by Clostridia and in particular Clostridiaceaes, a genus increased upon FO treatment (Supplementary Fig. 4b)33. The initial targeted metabolomics analysis showed an increase of IPA concentration in FO-treated serum over the upper detection limit (Supplementary Fig. 4c). Consequently, we performed the targeted LC-MS/MS in a second facility for the samples reaching detection limit and this second analysis further validated a >10-fold IPA increase in the majority of FO-treated mice (Fig. 3e).

To determine if the increase in IPA after FO was due to altered microbial metabolism, we analyzed metabolites in portal blood of mice co-treated with FO and antibiotics or corresponding controls. Antibiotic treatment significantly modified the portal metabolomes of control and FO-treated mice (Fig. 3f; Supplementary Fig. 4d; Supplementary Data 3). As expected, microbiota depletion led to accumulation of taurocholic acid, a primary bile acid, and a reduction in the abundance of microbial derived products such as secondary bile acid, 12-ketodeoxycholic acid, hippurate and p-cresol sulfate (Fig. 3f; Supplementary Data 3)34. The above metabolites were not modified upon FO treatment. In contrast, IPA levels were significantly increased after FO treatment in a microbiota-dependent manner (Fig. 3f, g). Furthermore, IPA abundance positively correlated with fecal bacterial load, as determined by 16S rRNA analysis (Supplementary Fig. 4e). In comparison, FO-dependent increases in proline, oxoadipate and cholate were not significantly modified by microbiota depletion (Supplementary Fig. 4f).

To assess if FO-induced increased levels of circulating IPA were durable, we measured polar metabolites from portal serum sampled 10 days after FO treatment. Among the metabolites increased at day 5, IPA and oxoadipate were still increased at day 10 (Supplementary Fig. 4g). Taken together, these data indicate that FO treatment increases the abundance of gut microbes which preferentially produce IPA, and this metabolite persists in portal blood long after FO is removed from the system (Fig. 3h).

Exogenous IPA recapitulates FO chemomemory by modulating the immune system
Since blood IPA concentration was increased after FO in a microbiota-dependent manner (Fig. 3c, g), we hypothesized that IPA was functionally important for FO-mediated chemomemory. Therefore, we first tested the ability of oral IPA supplementation to limit growth of AKPS and MC38 liver metastases (Fig. 4A). We chose an IPA dose of 20 mg/kg previously shown to promote physiologically relevant circulating IPA concentrations in mouse models35. IPA supplementation significantly reduced CRC liver metastatic load when given either before and/or after metastasis establishment (Fig. 4A–E; Supplementary Fig. 5a–c) and reduced cancer cell proliferation (Fig. 4F, G). We next tested if IPA could re-establish chemomemory in the absence of gut microbiota (Fig. 4H). Supplementing IPA to microbiota-depleted mice carrying intrasplenically injected AKPS cells reduced liver metastatic burden to levels similar to those observed in FO-treated, microbiota-competent mice (Figs. 4I, J and 1h, i). In all, these results show that dietary IPA supplementation is sufficient to recapitulate FO-induced anti-metastatic chemomemory.

To test if IPA directly inhibits cancer cell growth, we treated CRC tumoroids from patients and mouse AKPS organoids with IPA in vitro and analyzed cancer spheroid cell growth. The concentration of IPA in human blood ranges from 1 to 10 µM36–38, while in mice functionally relevant in vivo concentrations of IPA range from 3 to 15 µM35,39. Therefore, we tested the effects of high physiological (10 µM) and supraphysiological (50, 100 and 200 µM) IPA concentrations36,37. Only non-physiological concentration of 100 µM IPA inhibited growth of patient-derived MSS tumoroids (Supplementary Fig. 5d, e), while mouse organoids were resistant (Supplementary Fig. 5f, g). These results suggest that the anti-metastatic effect of IPA in vivo is primarily mediated by IPA’s indirect effects on the tumor or organ microenvironment. To test the contribution of immune cells, we analyzed hepatic AKPS metastasis growth after IPA administration in NOD-scid Il2rgnull (NSG) mice lacking B, T and functional NK cells (Fig. 4K). IPA was unable to suppress metastatic burden or tumor cell proliferation in NSG mice (Fig. 4L–O). These results indicated IPA anti-metastatic activity is at least in part mediated by its effect on immune cells.

A recent study reported that IPA enhanced antitumor immunity by directly inducing TCF1 expression in CD8+ T cells thereby promoting the expansion of CD8+TCF1+ stem-like cells8. To test if IPA is directly affecting T cell activation, we exposed CD3e and CD28 antibody-activated CD4+ or CD8+ T cells to physiological (10 µM) and supraphysiological (50, 500 µM) concentrations of IPA in vitro (Supplementary Fig. 5h, i). However, IPA treatment did not impact T cell proliferation nor activation in vitro (Supplementary Fig. 5j, k). CD4+ and CD8+ T cells treated with very high (500 µM) levels of IPA displayed reduced cytokine production (Supplementary Fig. 5j, k). Furthermore, IPA treatment did not change the abundance of CD8+TCF1+ stem-like cells in the metastatic liver of our model (Supplementary Fig. 5l, m), nor did it increase their abundance or TCF1 expression levels in vitro (Supplementary Fig. 5n–p).

IPA reorganizes inflammatory immune cell interaction to indirectly boost T cell mediated anti-metastasis immunity
Since IPA did not directly impact T cell activation, we further characterized changes in the tumor immune microenvironment driven by IPA combining flow cytometry and highly multiplexed imaging mass cytometry (CyTOF) (Supplementary Fig. 6a). Flow cytometry analysis showed that the abundance and proportion of NKT cells, NK cells, macrophages, B lymphocytes, CD8+ T cells, dendritic cells and neutrophils were similar between the IPA and control treatment groups (Supplementary Fig. 6b, c). However, Ly6Chigh monocytes were reduced in IPA-treated liver, while the proportion of myeloid cells expressing MHCII was increased (Fig. 5a; Supplementary Fig. 6d). IPA treatment resulted in the accumulation of effector CD4+ T cells with increased expression of IFNγ, TNFα and granzyme B (GZMB) (Fig. 5b–f; Supplementary Fig. 6e, f). We did not observe changes in the total liver CD8+ T cell numbers (Supplementary Fig. 6b, c). However, IPA significantly increased the proportion of CD8+ T cells expressing IFNγ, TNFα and GZMB (Supplementary Fig. 6g–i), indicating their enhanced antitumor activity.

Next, we assessed the spatial organization of immune cells in the metastatic liver microenvironment using highly multiplexed CyTOF imaging and identified the main resident and infiltrating immune populations in the liver along with hepatocytes, cancer cells, endothelial and biliary duct cells (Supplementary Fig. 6j, k, Supplementary Data 4)40. Using local spatial autocorrelation analysis, liver areas were divided into six distinct regions based on their cellular composition and organization (Fig. 5g; Supplementary Fig. 6l)41. Analysis of immune cell composition of metastasis regions revealed that IPA-treated mice displayed more total peri-metastatic CD4+ T cells, while metastases of control mice contained more CD8+ T cells, neutrophils (CD11b+Ly6g+) and monocytes (CD11b+Ly6g-F4/80-) (Fig. 5h). The immune cell composition of the non-tumor liver regions mirrored the composition of the tumor regions (Fig. 5h). Further characterization of CD4+ T cell subsets revealed that IPA promoted an accumulation of inflammatory CD4+Tbet+ (Th1) cells and reduced the abundance of immunosuppressive CD4+FOXP3+ Tregs (Fig. 5i, j), aligning with flow cytometry results. Cell-cell interaction analysis in the metastatic regions showed that CD8+ T cells were significantly and preferentially associated with CD4+ Tbet- cells and Tregs in control tumors (Fig. 5k; Supplementary Fig. 6m), a spatial organization associated with reduced response to immunotherapy42. In contrast, while perimetastatic CD8+ T cell numbers were decreased in IPA-treated liver metastasis, (Fig. 5h), they showed strong spatial colocalization with Th1 cells, an interaction that was absent in control livers (Fig. 5k; Supplementary Fig. 6m). This observation is functionally significant, as Th1 cells recruit cytotoxic CD8+ T cells and promote their activation43. CD8+ T cells did not interact with Tregs in IPA-treated metastases (Fig. 5k; Supplementary Fig. 6m).

Since IPA increased CD4+ T cell activation and abundance in the perimetastatic region, we next asked if these cells were necessary for IPA-driven chemomemory. Therefore, we depleted CD4+ T cells and assessed the ability of IPA to arrest liver metastatic growth (Fig. 5l). Analysis of blood and lymph nodes confirmed efficient CD4+ T cell depletion (Supplementary Fig. 6n). CD4+ T cell depletion abrogated the ability of IPA to suppress metastatic growth (Fig. 5m, n). Of note, the absence of CD4+ T cells in control non-IPA treated mice reduced the metastatic burden compared to IPA-treated mice (Fig. 5m, n), likely due to the depletion of regulatory CD4+ T cells in control mice. The infiltration of CD8+ T cells was similar between treatment groups (Supplementary Fig. 6o, p). These results show that IPA drives an indirect CD4+ T cell antitumor effect.

IPA reduces Ly6Chigh monocyte abundance promoting CD4+ T cell-driven antitumor immunity
Myeloid cells play a key role in CD4+ T cell priming and we observed an increased proportion of myeloid cells expressing MHCII in the livers of IPA-treated mice (Supplementary Fig. 6d), while perimetastatic monocytes and total liver Ly6Chigh monocytes were reduced (Fig. 5a, h)44,45. Ly6Chigh monocytes are immunosuppressive and reduce T cell activation while also promoting Treg differentiation46,47. Metastatic liver Ly6Chigh monocytes abundance negatively correlated with the presence of CD4+ T cells (Fig. 5o) but not with other major immune cell subtypes including CD8+ T or CD19+ B cells (Supplementary Fig. 6q, r). We therefore hypothesized that IPA-mediated loss of Ly6Chigh monocytes was driving CD4+ T cell antitumor effect in liver metastases. Consequently, we directly assessed the role of Ly6Chigh monocytes in the suppression of CD4+ T cell activation in vitro. We isolated Ly6Chigh monocytes from the metastatic liver and bone marrow of metastasis-bearing and control mice, as normal liver does not contain sufficient amount of these cells. We then co-cultured these cells with CD4+ T cells at a ratio of 1:4 to replicate the conditions present in the metastatic liver (Fig. 5p). Tumor-associated, but not bone marrow derived, Ly6Chigh monocytes significantly inhibited CD4+ T cell proliferation in vitro (Fig. 5q, r). These data showed that an IPA-driven reduction in Ly6Chigh monocytes abundance is sufficient to promote an indirect CD4+ T cell antitumor effect in the metastatic liver.

In all, these in vivo analyses indicate that IPA reorganizes the immune microenvironment of the metastatic liver by reducing the infiltration of immunosuppressive Ly6Chigh monocytes while increasing proinflammatory MHCII-expressing myeloid cell entry. This in turn promotes the expansion of proinflammatory Th1 cells in the metastatic niche, ultimately leading to their enhanced interaction with cytotoxic CD8+ T cells in the peritumoral region and promotion of a CD4+ T cells mediated antitumor effect (Fig. 5s).

IPA skews bone marrow myeloid progenitor differentiation to produce macrophages in lieu of Ly6Chigh monocytes
Given that the reduction of Ly6Chigh monocytes resulted in a CD4+ T cell-mediated antitumor effect, we investigated how IPA reduces the Ly6Chigh population. Monocytes are short-lived in the tumor microenvironment, and their production and maintenance are tightly regulated by bone marrow hematopoiesis48. Flow cytometry analysis of blood revealed a significant reduction of circulating Ly6Chigh monocytes in IPA-treated mice (Fig. 6a). However, consistent with observations in the metastatic liver (Supplementary Fig. 6b, c), IPA did not change circulating numbers of neutrophils, macrophages (CD11b+F4/80+), CD4+ and CD8+ T cells (Supplementary Fig. 7a, b). The decreased abundance of Ly6Chigh monocytes both in the blood and the livers suggests that IPA rewires the production of myeloid cells in the bone marrow. IPA did not alter the proportion of hematopoietic stem (HSC) cells, multipotent progenitor (MPP) cells, common lymphoid progenitor (CLP) cells, common myeloid progenitor (CMP) cells, megakaryocyte/erythroid progenitor (MEP) cells and granulocyte-monocyte progenitor (GMP) cells (Supplementary Fig. 7c). We tested if IPA treatment affected bone marrow capacity to produce myeloid cells. We isolated bone marrow cells from metastasis-bearing mice treated with control or IPA and tested the colony-forming ability of myeloid progenitors in a methylcellulose assay (Fig. 6b). We found that bone marrow cells from IPA-treated mice displayed significantly reduced myeloid colony forming units (CFU) compared to controls (Fig. 6c). To determine if IPA reduces the production of Ly6Chigh monocytes, we treated bone marrow cells with immunosuppressive myeloid cell-inducing media in the presence of vehicle or IPA (10 µM or 50 µM) for 5 days and analyzed resulting populations by FACS. At 10 µM, IPA did not affect overall cell viability or cellularity, whereas at 50 µM viability was slightly reduced (Fig. 6d). Most importantly, IPA significantly reduced the abundance of Ly6Chigh monocytes in a dose-dependent manner (Fig. 6e), while the abundance of neutrophils was unchanged by IPA treatment (Supplementary Fig. 7d, e). Thus, these results show that IPA impacts myeloid progenitor ability to produce myeloid cells and Ly6Chigh monocytes in vitro and in vivo.

To decipher how IPA blunts Ly6Chigh monocytes production, we performed RNAseq on bone marrow cells cultured in immunosuppressive myeloid cell-inducing media in the presence of vehicle or physiological and supraphysiological levels (10 µM and 50 µM, respectively) of IPA37. As 50 µM of IPA reduced cell viability, we focused our analysis on the 10 µM treatment. Differential expression analysis revealed 105 upregulated and 18 downregulated genes in IPA-treated bone marrow cells (Fig. 6f; Supplementary Data 5). Unbiased gene set enrichment analysis (GSEA) showed treatment with IPA led to concerted suppression of transcripts characteristic of immunosuppressive myeloid cell49, consistent with flow cytometry results (Fig. 6g). Furthermore, IPA increased pathways related to chemotaxis, macrophage activation, phagocytosis, antigen presentation and leukocyte differentiation (Fig. 6h). Notably, transcripts associated with macrophage differentiation (Cd68, Mafb, Flrt2, Atp6v0d2) and polarization (Arg1, Spp1, Ecm1, Epsti1, Dusp3) were upregulated by IPA (Fig. 6f)50–52. In contrast, expression of genes associated with immature myeloid cells, myeloid progenitor cells and differentiation regulators were reduced (Ms4a3, Chil3, Lcn2, Pak1, Klf5) (Fig. 6f)53–55. Although indole-derived metabolites are often linked to AHR signaling, the AHR transcriptional signature remained unchanged following IPA treatment, which is consistent with inability of IPA to activate AHR in biochemical assays (Supplementary Fig. 7f and Supplementary Data 5)38. Taken together, these data indicated that IPA accelerates myeloid cell maturation towards macrophages thus reducing the abundance and release of Ly6Chigh monocytes. In line with this hypothesis, pathways suppressed by IPA were linked with epigenetic changes such as methylation, histone modification but also reduction in DNA repair, aligning with a transition towards a more differentiated cell state (Fig. 6h)56,57.

To determine underlying gene regulatory networks responding to IPA treatment, we used the SCENIC algorithm adapted to bulk RNAseq analysis58,59. Rbpj, Mafb, Irf8, Klf4, Mitf and Elk3 regulons were significantly enriched in IPA-treated bone marrow cells, while Elf4, Maz and Fos regulons were more active in control cells (Supplementary Fig. 7g). Transcriptional activity of Rbpj, Mafb, Irf8 and Klf4 have been shown to promote myeloid cell maturation and macrophage differentiation50,51,60–64, while Mitf is essential for osteoclast differentiation downstream of M-CSF65. Conversely, Fos mutant mice accumulate macrophages in their bone marrow and Fos activity is reduced by IPA66. Furthermore, Elf4 and Maz regulons are downregulated by IPA and are important in hematopoietic progenitor maintenance and cell differentiation67–69.

IPA targets common myeloid progenitors fate
The bone marrow cell analysis of control and IPA-treated mice bearing metastases revealed that IPA treatment significantly increased frequency of macrophages while the total abundance of neutrophils and overall monocytes remained unchanged (Fig. 6i), supporting the increase macrophages maturation in the bone marrow.

To study the underlying mechanisms, we further analyzed cell cycle of CMPs and GMPs from control or IPA-treated mice. IPA significantly increased the proportion of quiescent CMPs compared to GMPs (Supplementary Fig. 7h), suggesting CMPs are the target of IPA.

To further investigate how IPA affects CMP differentiation, we performed a CMP adoptive transfer experiment. We induced metastases in CD45.1 mice and treated them with either control or IPA daily. We then FACS-sorted CMPs from bone marrow and transferred them into CD45.2 recipient mice (Fig. 6j), tracking CMP fate at early time points in vivo using established protocols70,71. The proportion of CD45.1-derived bone marrow cells and the frequency of monocytes from transferred CMPs were similar between groups (Fig. 6k). In contrast, CMPs from IPA-treated mice generated a higher proportion of macrophages in the bone marrow and fewer CCR2-expressing monocyte (Fig. 6k). Consistent with the key role of CCR2 in monocyte egress from bone marrow, we observed fewer CD45.1-derived monocytes in the blood of mice transferred with CMPs from IPA-treated donors (Fig. 6l)72.

Collectively, we propose that post-chemotherapy microbiota remodeling and increased IPA production play a central role in establishing a metastasis-refractory state. IPA primarily targets CMPs in the bone marrow, promoting their differentiation toward the macrophage lineage while limiting the release of Ly6ChighCCR2+ monocytes into circulation. This normalization of tumor-induced pathological myelopoiesis reduces circulating monocytes available for tumor infiltration, thereby enhancing anti-tumor T cell responses (Fig. 6m).

IPA potentiates chemotherapy response against CRC liver metastasis
To test the translational relevance of our observations we sought next to determine if IPA supplementation improves response to chemotherapy in mice harboring growing liver metastasis (Fig. 8a). We injected AKPS organoids intrasplenically and administered PBS or IPA orally from day three until the end of the experiments and treated mice with two cycles of FO with a one-week interval (Fig. 7a). Cancer cell proliferation was reduced in IPA-treated metastases as compared to controls as determined by staining for phosphorylated histone H3 (PH3) (Fig. 7b, c). Furthermore, combinatorial FO+IPA therapy significantly decreased metastatic burden and increased necrotic areas within liver metastatic lesions (Fig. 7d–g). Assessment of immune cell infiltration showed that IPA treatment increased CD4+ T cell infiltration into metastatic lesions both when used as monotherapy and in combination with FO, whereas FO alone had no significant effect (Fig. 7h, i). CD8+ T cell infiltration was not significantly modified in any treatment (Fig. 7j), further confirming that IPA mostly indirectly affects CD4+ T cells (Fig. 7i, j).

Chemotherapy increases IPA concentration and IPA negatively correlates with peripheral blood monocytes in CRC patients
To test the relevance of our observations from animal models to human disease, we recruited fourteen CRC patients with primary only or metastatic disease that were scheduled to receive adjuvant chemotherapy (Fig. 8a; Supplementary Table 1). Patients were chemotherapy naïve to reduce the interference of previous treatment exposure. Serum samples were collected before the initiation of the first cycle of chemotherapy (T1) and just before the initiation of the second cycle of the chemotherapy regimen (T2) (Fig. 8a). Of note, only two CRC patients were not treated with antibiotics within the last two months before blood collection (Supplementary Table 1). For control baseline values, we sampled serum from sex and partially age-matched healthy controls from the SwissChronoFood study, who did not receive antibiotics treatment within 2 months preceding blood collection (Supplementary Table 1)73. Targeted metabolomic analyses was carried out to quantify concentrations of tryptophan, a tryptophan-derived bacterial metabolite and precursor of IPA, 3-indolelactic acid (ILA) and IPA (Fig. 8b)33. When comparing the healthy cohorts to baseline values of CRC patients (T1), tryptophan concentration was similar between the two cohorts (Fig. 8c). A slight 20%, yet statistically significant, decrease in the concentration of ILA, was observed in the CRC cohort (Fig. 8c). Strikingly, IPA concentration in baseline (T1) CRC patient sera were nearly fivefold lower compared to control sera (Fig. 8c). IPA was undetectable in two patients and IPA levels were not correlated with age (Supplementary Fig. 8a). Although further studies are necessary, lower IPA levels in CRC patients may reflect depletion of IPA-producing bacteria by commonly used antibiotics.

When assessing the effect of chemotherapy, we found that circulating tryptophan and ILA levels were similar pre- and post-chemotherapy (T1 vs T2) (Fig. 8d). In contrast, IPA was significantly increased at T2 in 9 out of 14 patients as compared to IPA baseline concentration (T1), while it remained undetectable in two CRC patients (Fig. 8d). IPA concentration reached the healthy population concentration in 4 patients (Fig. 8d). Circulating IPA, but not tryptophan and ILA concentration, significantly and negatively correlated with monocyte abundance in the peripheral blood of CRC patients (Fig. 8e; Supplementary Fig. 8b–e). In contrast, IPA levels did not correlate with circulating neutrophils and lymphocytes (Fig. 8f, g). Most patients with substantial increase in IPA level at T2 showed a corresponding reduction of monocyte at T2 (Supplementary Fig. 8f, g) and IPA concentration positively correlated with the lymphocyte-to-monocyte ratio (Fig. 8h), a parameter associated with improved overall survival in CRC patients74. These results are consistent with our conclusions in animal models that IPA influences myeloid progenitors and limit tumor by reducing circulating levels of immunosuppressive monocytes. Importantly, survival analysis of 1024 CRC patients with localized CRC from the institutional database of the University Hospital of Geneva (HUG) showed that high monocyte concentration is associated with a worse overall survival (Fig. 8i), demonstrating the importance of reducing monocyte counts and the potential of using IPA to improve overall survival in patients with CRC. In all these data demonstrate that IPA-mediated chemomemory effect is observed in patients and IPA concentration is directly anti-correlated with monocyte abundance in CRC patients.

Methods
Humansamples
All experimental procedures conducted on clinical human tissue samples adhered to the ethical standards set by the institutional and/or national research committees. These procedures also complied with the principles outlined in the 1964 Helsinki Declaration. All participants in this study signed informed consent. The collection of blood samples during the clinical follow up of patients at the Department of Oncology in the University Hospital of Geneva (HUG) was authorized by the Commission Cantonale d’éthique de la recherche de Genève under the protocol 2023-01922. Blood samples were collected before treatment or at the end of treatment infusion and immediately processed for serum extraction. We used anonymized institutional database from the University Hospital of Geneva (HUG) for overall survival and monocytes count analysis. The patients were matched with controls from the SwissChronoFood study by sex and age (CER-VD 2017-00487, clinicaltrials.gov NCT03241121)73. For this analysis, we used data from the observational phase of this trial. The use of colorectal cancer cells was reviewed and approved by the ethics committee of the Canton of Vaud and the Centre Hospitalier Universitaire Vaudois (CHUV), under the protocol CHUV_DO_CTE_TRP_0001_2017.

Animalprocedures
All experiments were approved by the Animal Ethics Committee of the Canton of Vaud (VD3588 and VD3961). We used C57BL/6 female mice between 8-14 weeks (18g-24g) from Envigo. Experiments were performed in 8-14 weeks old females (18g-24g), aged matched and co-housed for at least 10 days. Experiments were initiated after at least 7 days acclimatation and cohousing. Chemotherapy cycle consisted of one intraperitoneal (i.p.) injection of oxaliplatin (3.5mg/kg, #2623, Tocris), followed 1h later by i.p. injection of 5-fluorouracil (5-FU, 50mg/kg, #F6627, Sigma-Aldrich) and a single dose of 5-FU (50mg/kg), the next day. The lesions were liver metastases. Size was not measured, but mouse well-being was monitored through body weight, activity, grooming, and posture, in accordance with the approved ethical protocol.

Mouseorganoidscultureandcellculture
Apc-/-;KrasLSLG12D;Tp53-/-;Smad4-/- -mCherry (AKPS) and Apc-/-;KrasLSLG12D;Tp53-/- (AKP) organoids were previously described90. Organoids were cultured in 24 well plates in 40 µl Matrigel dome (Corning; #BD356231 or Cultrex; #BME001) in 600 µl in advanced DMEM/F12 1x medium (Gibco #12634010) supplemented with 10 mM HEPES (Gibco #15630-056), 2 mM L-Glutamine (Life Technologies™), 100 mg/mL penicillin/streptomycin (1% PenStrep), B27 (Gibco; #17504-044), N2 (Gibco; #17502-048). For passaging, the organoids were mechanically dissociated and incubated at 4° for 20 min in cell recovery solution (Corning #354253). The organoids were incubated in TrypLE Select 1X (Gibco #12563-029) at 37° for 5 min. The single cell suspension was washed in PBS, resuspended in 40 µl Matrigel and cultured as describe. For organoid expansion, wells were split 1:5. For in vivo experiments, 1×105 cells per dome were plated 2 to 3 days before implantation. MC38-GFP cells were cultured in complete DMEM medium (Gibco, # 31966-021) supplemented with 1% P/S (Thermo Fisher Scientific, # 15140122) and 10% FBS at 37°C in 5% CO2.

HumanCRCtumoroidscultureandinvitroassays
Colorectal cancer cells were isolated from CRC biopsies obtained from the Centre Hospitalier Universitaire Vaudois (CHUV)91. Organoids were established by embedding CRC cells in growth factor-reduced Matrigel (~2×104 cells per 20 mL Matrigel dome) and cultured in Advanced DMEM/F-12 supplemented with 1× GlutaMAX (Thermo Fisher Scientific, Catalog No. 35050038), 10 mM HEPES (Thermo Fisher Scientific, Catalog No. 15630056), 100 µg ml−1 penicillin–streptomycin (Thermo Fisher Scientific, Catalog No. 15140122), 1× B-27 supplement (Thermo Fisher Scientific, Catalog No. 17504001), 1 mM N-acetylcysteine (Sigma-Aldrich, Catalog No. A9165), and 50 µg ml−1 Primocin (InvivoGen, Catalog No. ant-pm-2). A detailed protocol describing organoid culture can be found elsewhere. A luciferase-ZsGreen reporter was introduced in CRC cells as previously described92.
For monitoring viability, transduced CRC organoids were dissociated using TrypLE Express (Thermo Fisher Scientific, Catalog No. 12605028), filtered (40 µm), and seeded at 104 cells per well in black-wall round bottom ultra-low attachment spheroid microplates (Corning, Catalog No. 4515). IPA (Sigma #57400-5G) or vehicle (DMSO) sera were added at different concentrations. Live-cell luciferase readings were performed by adding 150 µg ml–1 luciferine (GOLDBIO, Catalog No. LUCK-500) and measuring luminescence in a Tecan Infinite F500 reader (Tecan).
For imaging experiments, transduced CRC organoids were dissociated as indicated above and seeded in microcavitiy plates (SUN Biosciences, Catalog No. Gri3D-96P-S-24-500) at 3×104 cells per well (~400 cells per microcavity). IPA and portal blood sera were used at the concentrations indicated above. Brightfield and fluorescent imaging was performed using a Nikon Eclipse Ti2 inverted microscope system equipped with a 4×/0.13 NA, 10×/0.30 NA, and 40×/0.3 NA air objectives and a DS-Qi2 camera (Nikon Corporation).

InvitroIPAorganoidsassay
To test the effect of IPA on AKPS organoids in vitro, 1×104 AKPS cells were plated in 40 µl Matrigel (Corning; #BD356231) dome in 24 wells plate and cultured in organoids cell culture medium containing 50, 100, 200 µM of IPA (sigma #57400-5G) or DMSO as vehicle for three days. At the end of the experiments, 5 pictures per wells were acquired using a stereomicroscope (Leica M205 FA). Organoids were collected in RLT buffer containing beta-mercaptoethanol and RNA was isolated using Qiagen mini kit (RNAsy Qiagen mini kit; #74134).

Livermetastasismodelintrasplenicinjection
The hepatic model of CRC metastasis in hemisplenectomized mice was generated as previously described93. In brief, AKPS organoids were mechanically dissociated for 20 min with gentle shaking at 4° using cell recovery solution (Corning). MC38-GFP cells were cultured as described above. Cancer cells were washed twice with PBS and resuspended in 40 µl Matrigel (AKPS) and PBS (MC38-GFP) for injection. Mice were anesthetized, the spleen was exteriorized via a small incision in the upper left flank, and 3-4×105 AKPS tumor organoids or 2.5×105 MC38-GFP cells were injected in the semi separate spleen parenchyma using insulin syringe. Peritoneal cavity and skin were sutured. Mice received buprenorphine as analgesia. Mice were analyzed according to the timeline indicated in the corresponding figures.

Microbiotadepletion,IPAgavageanddepletionofimmunecells
Microbiota depletion was carried as described previously20. Briefly, mice received 2.5 mg/ml enrofloxacin (Baytril 10%, Bayer) in drinking waters for two weeks followed by two weeks of 0.8 mg/ml amoxicillin and 0.114 mg/ml acid clavulanic (Co-Amoxi-Mepha, Mepha, Sandoz) in drinking water20. Antibiotics and cages were changed every other day. Mice were gavaged daily with IPA (Sigma #57400-5G) at 20 mg/kg diluted in sterile PBS, as described35. Control mice received PBS. To deplete CD4+ T cells, mice were i.p. injected with 10 mg/kg of anti-CD4 (Bioxcell; #BE0003-1) or 10 mg/kg isotype control IgG2b (BioXcell #BE0090) at day 3, 5 and 8 after hepatic metastases implantation.

Metabolomics
For polar metabolite extraction of tissue samples, 20 mg tissue was mixed with 200 µl acetonitrile:methanol:water (40:40:20) and homogenized with a TissueLyser for 2 min. Samples were centrifuged at 15,000g for 30 s at 4°C; the supernatants were transferred to a new tube and used for mass spectrometry analysis. For serum samples, polar metabolites were extracted with 10x volume of room temperature 80% methanol. Metabolite extracts were analysed using flow injection analysis–time-of-flight mass spectrometry on an Agilent 6550 Q-TOF instrument, as described by Fuhrer et al.94. Mass spectra were recorded from a mass/charge ratio of 50–1000 in high-resolution negative ionization mode. Ions were annotated by matching their measured mass with reference compounds from the Human Metabolome Database (HMDB 4.0), with a tolerance of 1 mD. Statistical significance was calculated using Student’s t-test and corrected for multiple hypothesis testing with the BH method. Statistical analyses were performed in MATLAB R2022b (The MathWorks) using an in-house developed pipeline and using Metaboanalyst to perform Metabolites subclass enrichment analysis30. Ion intensities data are provided in Supplementary Data 1–3.
The absolute serum concentrations of 3-indolelactic acid (m/z 204→142, RT=2.6), 3-indolepropionic acid (m/z 188→59, RT=3.5), and tryptophan (m/z 205→188, RT=0.93)) were quantitated at Metabolon facility by LC-MS/MS. Stock and internal standard solutions were prepared by dissolving an appropriate amount of material in ACN:water (1:1). These stock solutions were diluted to the Standard H level and further diluted to each of the other concentration levels. For sample preparation, serum was thawed on ice and vortexed for 10–15 s before aliquoting. For blank, blank-IS, and calibration standards, 0.0500 mL PBS was pipetted into the appropriate wells of a 2 mL 96-well plate. For matrix QC samples, 0.0500 mL QC serum was added, and for study samples, 0.0500 mL serum was dispensed into designated wells. Calibration standards were prepared by adding 0.0200 mL of the corresponding calibration spiking solution. Internal standard working solution (0.0200 mL) was added to calibration standards, blank-IS, QC samples, and study samples. Blank samples received 0.0400 mL ACN/water (1:1), while blank-IS, QC samples, and study samples received 0.0200 mL ACN/water (1:1). Methanol (0.200 mL) was then added to all wells. Plates were capped, vortexed for 1 min, and centrifuged for 10 min at 4000 rpm. A 0.100 mL aliquot of the resulting supernatant was transferred to a fresh 650 µL 96-well plate, capped, and submitted for LC-MS/MS analysis. SAMPLE ANALYSIS: A 2.0 µL aliquot of supernatant was injected onto an Agilent 1290/Sciex QTrap 5500 LC-MS/MS system equipped with a Waters Acquity BEH C18 column (1.7 µm, 2.1 × 100 mm). Chromatographic conditions were as follows: Column temperature: 50°C, Autosampler temperature: 4°C, Mobile Phase A1: 0.05% formic acid in water, Mobile Phase B1: 0.05% formic acid in acetonitrile, Flow rate: 0.550 mL/min, Injection volume: 2.0 µL (adjusted as needed for sensitivity), Needle wash: methanol/water (1:1). The MS/MS parameters were as follow Curtain Gas: 35, Collision Gas: Medium, Ion Spray Voltage: −4500 V, Temperature: 500°C, Gas 1: 70, Gas 2: 70, Scan Mode: Multiple Reaction Monitoring (MRM), negative mode. Further information can be found in Supplementary Data 6.
The peak area of the individual analyte productions was measured against the peak area of the productions of the corresponding internal standards. Quantitation was performed using a weighted linear least squares regression generated from fortified calibration standards prepared immediately prior to each run. The LC-MS/MS raw data is collected and processed using SCIEX software Analyst 1.6.3 and processed using SCIEX OS-MQ software.
Alternatively, targeted analysis of selected serum samples was performed at the metabolomic facility of the University of Lausanne. Serum (20 µL) was extracted with ice-cold methanol (180 µL) spiked with internal standards (IS). After centrifugation (4°C, 21,000g, 15 min), the supernatants were evaporated to dryness, reconstituted in water (50 µL), vortexed, and sonicated for 3 min. Each extract was then centrifuged again (4°C, 21,000g, 15 min) and the supernatant transferred to an LC–MS vial for LC–MS/MS analysis as described below.
Calibrators were generated following the same procedure, by the addition of methanol spiked with IS mixture to each calibrator (20 µL), vortexed and transferred to LC-MS vials for the injection.
Extracts were analyzed by Reversed Phase Liquid Chromatography coupled to tandem mass spectrometry (RPLC-MS/MS) on a 6495 triple quadrupole system (QqQ) interfaced with 1290 UHPLC system (Agilent Technologies), in the analytical conditions modified from van der Velpen et al.95. Data were acquired in Dynamic Multiple Reaction Monitoring (dMRM) mode with a total cycle time of 400 ms, applying optimized collision energies for each metabolite, using MassHunter (Agilent technologies, version B.07.00). Chromatographic separation was performed on a Acquity HSS T3 (1.8 µm 2.1 mm × 100 mm) column (Waters, Ireland), in positive ionization mode. The mobile phase consisted of A = 5 mM ammonium formate and 0.1% formic acid in H2O and B = 100% methanol. The gradient was as follows: 0 min 0% B, 2 min 0% B, 4 min 5% B, 5 min 10% B, 7 min 90% B, 8 min 0% B, 11 min 0% B. The flow rate was 300 µL/min, the column temperature 20°C, and the injection volume 2 µL. ESI source parameters were as follows: dry gas temperature 290°C, dry gas flow 14 L/min, nebulizer pressure 45 psi, sheath gas temperature 350°C, sheath gas flow 12 L/min, nozzle voltage 500 V, and capillary voltage +4000 V. Further information can be found in Supplementary Data 6.
Raw LC–MS/MS data were processed using MassHunter Quantitative Analysis 10.0. Absolute quantification of indole metabolites was performed based on calibration curves and the stable isotope-labeled internal standards (IS) which were used to determine the response factor for each metabolite. Linearity of the standard curves was evaluated for each metabolite using a ten-point range; in addition, peak area integration was manually curated and corrected where necessary.

DNAisolationstool
One fecal pellet per mouse was freshly collected into sterile 1.5 ml Biopure tube (Eppendorf). They were quickly placed on dry ice and stored at −80 until further use. We extracted total bacterial DNA with QiaAMP Fast DNA stool Mini kit (Qiagen # 51604) according to the manufacturer’s instructions. In the final step DNA was eluted in 80 ul AE elution buffer.

RT-qPCR
RNA was isolated as described by manufacturer (RNAsy Qiagen mini kit; #74134). RNA concentration and quality were determined using a 2100 Bioanalyzer (Agilent). First Strand cDNA Synthesis Kit (Roche, #04896866001) was used for cDNA production. qPCR analyzes were done using StepOnePlus (Applied Biosystems), SYBR Green PCR MasterMix (Kapa Biosystems/Bioline) and 1 uM primers pairs. Data were normalized to the housekeeping gene 18S, and gene expression was determined using the comparative C (ΔΔC) method. Results are shown as fold change over control samples. To validate microbiota depletion, bacterial load was quantified by qPCR on the 16s rRNA gene expression in stool as described elsewhere96. The average ct value of non-antibiotics treated mice was divided by the Ct value of each mouse and these values were logarithmically converted. Primer list can be found in Supplementary Table 2.

Assessmentofmicrobiomecompositionby16SrRNAsequencing
We extracted total bacterial DNA with QiaAMP Fast DNA stool Mini kit, as described above. V4 amplicons were generated using the KOD One PCR MasterMix (Sigma Aldrich) with universal bacterial primers – 515F (5’-AATGATACGGCGACCACCGAGATCTACAC-TATGGTAATT-GT-GTGCCAGCMGCCGCGGTAA-3’) and 806R (5’-CAAGCAGAAGACGGCATACGAGAT-NNNNNNNNNNNN-AGTCAGTCAG-CC-GGACTACHVGGGTWTCTA-3’), consisting of 5’ Illumina adapter/RC of Illumina 3’ adapter-(unique 12 bp long Golay barcode represented by ‘Ns’, exclusive to 3’ primer)-pad-linker-primer. Duplicates of each amplicon were mixed, quantified with Qubit dsDNA quantification assay (Invitrogen), and pooled at equimolar concentrations. The final 16S amplicon library was purified using GeneJET PCR purification kit (Thermo Scientific) and sequenced on the MiSeq Illumina platform (Illumina MiSeq V2 500 cycles, 250 PE).
Paired-end raw reads were demultiplexed and processed using a custom QIIME2 pipeline97. Briefly, sequences were trimmed with cutadapt algorithm, paired ends were merged with vsearch algorithm, quality-filtered using q-score method, and chimera-checked98,99. Greengenes2 2022.10 database was used to construct the reference sequence file, on which the Naïve Bayes classifier was trained and then used to perform taxonomic assignment to the species level using representative sequences from each OTU100. PICRUSt2 (v. 2.5.2) was used to predict functional abundances within microbiome communities32. The OTU sequences and abundances per sample obtained using the QIIME2 pipeline after chimera removal were used as input for KEGG orthology (KO) abundance estimation with PICRUSt2. The ggpicrust2 package (v. 1.7.3) for R was used to convert KO abundance data to KEGG pathway abundance data, for visualization and for differential abundance testing using the implemented ALDEx2 method101.

Flowcytometryofliverandblood
Livers were minced and resuspended in DMEM (Gibco, #31966-021) containing 2.5 mg/ml liberase (Roche, #05401020001) and 10 U DNAse (Sigma-Aldrich, #11284932001). Samples were then digested for 30 min at 37°C with gentle agitation, filtered through 100 µm strainer, washed three times in DMEM (Gibco, #31966-021) containing 2% FBS. For immune cell phenotyping, immune cells were separated from other cells by Percoll gradient. The single cells were incubated in 2 ml RBC lysis buffer (Invitrogen #00-433-57) for 5 min, washed three times in PBS and filtered through 70 µm strainer. Single cell suspensions were incubated with anti-CD16/CD32 antibody (#14-0161-82, eBioscience) and stained with surface antibody mix for 20 min at 4°C. For blood analysis, blood was collected by intracardiac puncture in EDTA-coated tubes. After centrifugation and a PBS wash step, cell pellets were incubated in RBC lysis buffer for 5 min. Subsequent blocking and staining steps followed the protocol described above.
The list of surface antibodies used is as followed: anti-CD45 (APC, Biolegend, #103111; PE-Cy7, Thermofisher #25-0451-82; BV605, Biolegend #103140), anti-CD4 (BUV737, Thermofisher #367-0041-82; FITC, Biolgends #100509), anti-CD62L (BUV395, BD #740218), Anti-I-Ab/IE (BV786, Biolegend #107645), anti-Ly6g (BV650, Biolegend #127641), anti-CD11c (SB605, Biolegend # 63-0114-82), anti-CD19 (Pacific blue, Biolegend #152415; PE, BD #557399), anti-CD44 (PerCP-Cy5.5, eBio #45-0441-82), anti-Ly6c (FITC, BD #553104; PerCP-Cy5.5, EBioscience #45-5932-81), anti-CD11b (PE-Cy5, Thermofisher Invitrogen #61-0112-82), anti-CD161 (PE, eBio #12-5941-82), anti-CD3e (APC-Cy7, Biolegend #100330), anti-CD8a (BV711, Biolegend #100748), anti-F4/80 (Alexa 647, Biolegend #123211), anti-PD-1 (Bv711, Biolegend #135231), anti-MHCII (BUV785, Thermofisher #417-5321-81). To excluded dead cells, cells were stained with Fixable Aqua Dead Cell Stain Kit (BV510, Thermofisher #L34957) or 7-AAD-Viability Staining (eBioscience #00-6993-50). For intracellular cytokine and transcription factor staining, cells were subjected to fixation and permeabilization using the Foxp3/Transcription Factor Staining Buffer Set (Thermofisher #00-5523-00) according to the manufacturer’s instructions. The list of intracellular antibodies used were as followed: anti-TNF-α (FITC, Biolegend #506304), Anti-IFN-γ (APC, Biolegend #505809), anti-granzyme B (Pacific blue, Biolegend #515407).
To detect MC38-GFP+ cells in the liver by FACS, the livers were processed as described above but without the gradient separation step. Total liver cells were stained with anti-CD45 (APC, Biolegend, #103111), anti-CD31 (PE-Cy7, Biolegend #102417) and fixable Fixable Aqua Dead Cell Stain Kit (Thermofisher #L34957). Gating strategy to isolate MC38-GFP cells: total cells (FSC-A vs SSC-a), single Cells (FSC-H vs FSC-A), single cells (SSC-A vs SSC-h), alive (CD45 vs BV510 aqua dead), GFP positive cells (SSC-A vs GFP). Samples were run on an LSR-II and Fortessa 1 and analyzed with FlowJo 10.

Analysisofhematopoieticstemandprogenitorcellsbyflowcytometryandcellsorting
After isolating from femur, tibia and pelvis of C57Bl6 mice, cell suspension of bone marrow was filtered through 70 µm cell strainer and erythroid cells were eliminated by incubation with red blood cells lysis buffer (BioLegend). For analysis of hematopoietic stem and progenitor cell (HSPC) compartments, cell suspensions were then stained with antibodies for progenitor and stem cell compartments as the following: HSCs: Lineage-cKit+Sca1+ (LKS) CD150+CD48-, Multipotent progenitors (MPPs): LKS CD150-, Common lymphoid progenitor (CLP): CD127+cKitlowSca1low, Common myeloid progenitors (CMPs): Lineage-cKit+Sca1– (KLS-) CD16/32lowCD34+, Granulocyte–macrophage progenitors (GMPs): KLS-CD16/32highCD34+ and Megakaryocyte-erythroid progenitors (MEPs): KLS-CD16/32lowCD34–. Cells were then washed with FACS buffer and analyzed by flow cytometry on BD LSRII.
For cell sorting, lineage positive cells in the cell suspensions were first removed with a magnetic lineage depletion kit (BD biosciences). Cell suspensions with depleted lineage positive cells were then stained with specific antibodies for LKS compartment and sorted on BD FACSAria III and Aria IIu. Isolation and staining were performed in ice-cold PBS supplemented with 1 mM EDTA.

CMPadoptivetransfer
For CMP adoptive transfer experiment, we followed existing protocols71. 20,000 or 40,000 CMP (Lineage-cKit+Sca1– (KLS-) CD16/32lowCD34+) were FACS sorted from the bone marrow of CD45.1 mouse bearing metastasis which received daily treatment with 20 mg/kg IPA or PBS. CMPs were injected in the tail vein of CD45.2 recipient mice. The bone marrow and blood were harvested 7 days later for CD45.1 cell fate tracking by flow cytometry.
Cell gating for sorting: Lymphocytes (FSC-A vs SSC-A), Single Cells (FSC-H vs FSC-A), Live cells (Zombie NIR- and SSC-A), Lin- (Lin- and SSC-A), Lineage-cKit+Sca1-, Common myeloid progenitors (CMPs): Lineage-cKit+Sca1– (KLS-) CD16/32lowCD34+.
Cell gating for analysis: Lymphocytes (FSC-A vs SSC-A), Single Cells (FSC-H vs FSC-A), alive (Aqua blue and FSC-A), CD45.1 (FSC-A and CD45.1), CD11b+ (CD11b+), Monocytes (Ly6c+Ly6g- gated in CD11b+), neutrophils (Ly6clow Ly6g+ gated in CD11b+), Macrophages (Ly6c−Ly6g−F4/80+ gated in CD11b+).

CFUassay
Colony-forming unit (CFU) assay with murine bone marrow cells was carried out using MethoCult GF M3434 (STEMCELL Technologies), formulated to support growth of multi-potential granulocyte, erythroid, macrophage, megakaryocyte progenitor cells, according to the manufacturer’s instructions. For in vivo experiment, 2×104 total bone marrow cells from each mouse treated with IPA or PBS were plated in duplicate. Colonies were counted 7 days after plating using Stem Vision software (STEMCELL Technologies).

Tlymphocyteassayandcocultureassay
CD4 and CD8 T cells were isolated from the spleen and lymph nodes of wild type mice following manufacturer instructions using CD4 and CD8 mouse isolation kits (MILTENYI BIOTEC INC #130-104-454 and #130-104-075,). T lymphocytes were labeled with carboxyfluorescein (CFSE, Thermofisher #C34570) as described by manufacturer. 1×105 CD4 or CD8 cells were stimulated with 2 µg/ml plate coated CD3 (Biolegend #100340) and 3 µg/ml soluble anti-CD28 (Biolegend #102116) resuspended in RPMI media containing 10% FBS, 1% PenStrep, and β-mercaptoethanol. In IPA exposure experiment, CD4 and CD8 cells were exposed to DMSO (control), 10 µM, 50 µM and 500 µM of IPA. In the experiment assessing TCF1 modulation by IPA, Il-2 (50 U/ml) was added to the culture. For coculture experiments, CD4 cells were prepared as above. Ly6Chigh monocytes were FACS sorted from the liver and bone marrow of mice and plated with CD4 cells at a ratio of 1:4. In all experiments, cells were stained with 7-AAD viability dye after 72 or 96 h culture and proliferation was measured as dilution in CFSE label on an LSR-II.

Immunosuppressivemyeloidcellinvitrogeneration
Bone marrow cells were isolated as described102. 1×106 bone marrow cells were plated in Immunosuppressive myeloid cell-generating media consisting of RPMI medium (Thermofisher # 11875093) supplemented with 10% FBS, 1% PenStrep, 1% glutamine, 20 ng/ml GM-CSF (Peprotech #315-03), 20 ng/ml IL-6 (Peprotech #216-16). 0.1% DMSO (control) or 10 µM or 50 µM IPA were added to the culture. The media was changed every two days and the number of Ly6Chigh monocytes and neutrophils was analysed after 5 days on an LSR-II.

RNAsequencing
RNA-seq libraries were prepared from 25 ng of total RNA with the Illumina Stranded mRNA Prep reagents (Illumina) using a unique dual indexing strategy, and following the official protocol automated on a Sciclone liquid handling robot (PerkinElmer). Libraries were quantified by a fluorometric method (QubIT, Life Technologies) and their quality assessed on a Fragment Analyzer (Agilent Technologies). Libraries were sequenced at the Lausanne Genomic Technologies Facility on an AVITI Sequencing Instrument (Element Biosciences). Fastq generation was done using Base2Fastq (v. 1.7, Element Biosciences). Sequencing reads were pre-processed and aligned to the reference genome using the RNA-seq pipeline of the bcbio-nextgen tool (v. 1.2.4). The pipeline involved the following steps. Sequencing quality was assessed with FastQC (v. 0.11.8). The reads were trimmed for quality and adapter using Atropos (v. 1.1.28)103. Trimmed reads were aligned to the Mus musculus genome (mm10) using hisat2 (v. 2.2.1). Numbers of reads were summarized at the gene level per sample using featureCounts (v. 2.0.1) and the Ensembl GRCm38.92 release of the mouse transcriptome annotation. Samtools (v. 1.9) was used for alignment file manipulation, and alignment quality was assessed with MultiQC (v. 1.9) and qualimap (v. 2.2.2d)104–107. Raw counts were imported into R (v. 4.3.0). Genes were filtered to only retain the ones expressed at more than 0.5 count per million (cpm) in at least 2 samples (n=14970 retained genes). Differential gene expression analysis between activated 10 and control samples was performed using the DESeq2 package (v. 1.14.1) and extracting the results using the results function with argument α=0.05108. Genes with a p-value<0.05 after Benjamini-Hochberg adjustment were considered as significantly differentially expressed between activated 10 vs control.
Gene set enrichment analysis (GSEA) was performed using the clusterProfiler package (v. 4.8.3) for R109. We performed GSEA against the Gene Ontology Biological Process collection with the gseGO function. With the GSEA function, we also tested for enrichment of a combined MDSC signature extracted from Table S5 of Alshetaiwiet al. 2020, and an AHR agonist signature extracted from Table S3 of Goudot et al. 201749,110. The parameters for both the gseGO and GSEA functions were: eps = 0, seed=T and a seed=1234. We inferred transcription factor activity in each sample using the SCENIC package (v. 1.3.1) for R (v. 4.1.2)111. The SCENIC package made use of functions from the RcisTarget (v. 1.14.0, GENIE3 (v. 1.16.0), and AUCell (v. 1.16.0) packages and the cisTarget databases (mm10__refseq-r80__10kb_up_and_down_tss.mc9nr.feather and mm10__refseq-r80__500bp_up_and_100bp_down_tss.mc9nr.feather). The AUC score obtained for each regulon in each sample was compared in activated 10 vs control using a T-test, followed by p-value adjustment with the Benjamini-Hochberg procedure. A heatmap of the scaled AUC score per sample of the significant regulons was generated using the ComplexHeatmap package (v. 2.16.0)112.

Imagingmasscytometry
Slides were stained following existing protocol (Supplementary Data 4)113. Data were analyzed following the “Analysis workflow for IMC data” at https://bodenmillergroup.github.io/IMCDataAnalysis/index.html, without batch effect and spillover correction steps. T cells were split into CD4+, CD8+, and Tregs (CD4+ Foxp3+), lymphocytes into macrophages (CD11b+ F4/80), myeloid cells (CD11b+F4/80- Ly6g-), and neutrophil-like (CD11b+Ly6G+), endothelial cells into CD31+ and CD31+ aSMA+ groups, and hepatocytes into periportal (CDH1+), pericentral (GS+), and intermediate (negative for both markers). Cells were additionally sorted into negative/positive groups based on marker levels for Tbet. Annotated cell quantities were then used for differential abundance analysis as described in chapter 6 of “Multi-Sample Single-Cell Analyses with Bioconductor” at https://bioconductor.org/books/3.13/OSCA.multisample/ using R package edgeR114 v. 3.42.2. In order to assess differential marker expression in cell types between conditions, differential state analysis was performed mimicking diffcyt115 diffcyt-DS-limma function, using R package limma116 v. 3.56.1. Spatial organization of identified cell types was assessed using R package lisaClust v. 1.8.2. Resulting regions contain mostly tumor cells (Tumor), inflammatory cells (Inflammatory), larger proportion of myeloid cells (Myeloid), or represent liver regions with most cells being hepatocytes (periportal–PP, intermediate–IM, pericentral–PC). Additionally, cells were tested for attraction/avoidance using imcRtools function testInteractions. The raw and processed data and the analysis scripts are available at https://doi.org/10.5281/zenodo.17574413.

Immunofluorescentstaining
Tissues were harvested, fixed in 4% PFA overnight, embedded into paraffin or OCT (Sakura). Paraffin sections were deparaffinized and subjected to heat-induced epitope retrieval using Citrate- (Vector Laboratories; H-3301-250) or Tris-based (Vector laboratories; H-3301-250) buffers. Cryosections were thawed for 20 min at room temperature, fixed in PFA 4% for 5 min, washed with PBS and permeabilized with 0.3% Triton-X-100 in PBS, followed by 30 min blocking, overnight incubation with primary antibody and secondary antibody staining for 1 h. Slides were mounted in Fluoromount-G mounting medium with DAPI (Invitrogen, #00-4959-52).

Imagesquantificationandanalysis
Sections of entire liver were scanned using a NanoZoomer S60 slide scanner (Hamamatsu) with a 20x objective. Images were analyzed with NDP.view2 (Hamamatsu), and open-source software Qupath117. For metastatic burden quantification, we isolated liver lobes, manually annotated each metastatic lesions defined as mCherry+, E-cadherin+ or CD44v6+ for AKPS or as GFP+ for MC38-GFP cells and calculated the total metastatic area in µm2. The number of lesions or their total area were normalized to the total liver area. For quantification of proliferating cells using PH3 staining, CD44v6 area (specific for AKPS metastasis) were isolated using pixel classifier in QuPath, converted into annotation and the number of PH3+ cells within the metastatic lesion were quantified using object classifier (nucleus mean measurement). The average of PH3+ cells per mouse was then calculated and used for statistical analysis. For quantification of necrosis within metastatic lesion, 4 to 5 metastatic lesions were isolated per mouse and necrotic area, defined as thinning of the epithelium, loss of cell architecture, nuclei and presence of cellular debris, was manually drawn. Necrotic area was then normalized to metastatic area and control mean.

Figurepreparation
Icons were created in BioRender under the license SABINE, A. (2025) https://BioRender.com/ pmy8rs7. Icons and graphs were assembled and modified in Affinity.

Statisticalanalysis
All data are shown as mean ± standard deviation. Data analyses were carried out with GraphPad Prism 10 (GraphPad) for MacOS. Two groups were analyzed using a two-tailed unpaired Student’s t-test, or in cases where experiments comprised more than two groups, a one-way ANOVA with Tukey post hoc test was employed. In vitro experiment, exposing T cells to IPA and Ly6Chigh monocytes generating experiments were analyzed using a multiple permutated one-way ANOVA. The numbers of biological replicates are stated in the figure legends. P-value < 0.05 were considered statistically significant. The statistical methods for each complex data analysis, such as RNA-seq, metabolomics, and spatial analysis, are detailed in their respective sections.

Dataavailability
The RNA-seq and 16 rRNA seq data generated in this study have been deposited in the GEO database under accession GSE282931 and GSE29690, respectively. The metabolomics data were deposited in MetaboLights (MTBLS13162). The raw and processed data and the analysis scripts for CyTOF are available at https://doi.org/10.5281/zenodo.17574413. The remaining data are available within the article and Supplementary Information. The source numbers for graphs are available in the Source Data file whenever possible. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript addresses the role of the gut microbiome in chemotherapy induced reduction in colorectal cancer metastasis to the liver. The authors posit that chemotherapy induced intestinal mucositis disrupts the gut epithelial cells resulting in reduced absorptive capacity, altering nutrient availability and causing dysbiosis. There are several studies in the literature that support this process. The novel aspects of this study is the demonstration of the expansion of tyrptophan-metabolizing bacteria that increases levels of IPA which drives myeloid cell maturation to macrophages resulting in reduced Ly6chigh monocyte production. The authors show that this further promotes CD4 T cell activity thus reducing tumor progression in the liver.\nThis is a well written manuscript with appropriate methodology. The studies are thorough and the conclusions follow from the data. I have minor suggestions and issues that can be addressed.\n1) It would be helpful to have a final schematic illustration of the mechanism by which “chemomemory” is thought to occur.\n2) It would be useful to get more details on the extent of the GI toxicity other than histological measurements. What is the inflammation state in the model among the various cohorts.\n3) Are there data on the changes in the immune response in the lamina propria?\n4) It is unclear why intestinal damage is determined after 3 days post FO in naïve mice but 5 days post FO in treated mice (Figs 1A and B)\n5) Fig 8: What are the monocyte counts for T2? While there is clear shift in IPA the monocyte counts are presented as a combination of the T1 and T2 in 8E..", "reviewer_2": "The manuscript demonstrates that standard CRC chemotherapy with 5-fluorouracil and oxaliplatin induces intestinal mucositis, which alters the gut microbiota composition, leading to increased levels of the bacterial metabolite indole-3-propionic acid (IPA). Elevated IPA reprograms bone marrow myelopoiesis, reducing immunosuppressive Ly6Chigh monocytes and promoting anti-tumor CD4+ T cell responses. This effect, termed \"chemomemory\" by the authors, reduces colorectal cancer liver metastasis in mice even after chemotherapy has cleared. IPA supplementation alone can mimic this protective effect, suggesting that it could be used to enhance the efficacy of chemotherapy. Analysis of 14 CRC patients revealed that chemotherapy increases serum IPA levels in some individuals, with IPA inversely correlating with circulating monocytes, supporting its clinical relevance.\nThe manuscript contains important data on a timely aspect of CRC and metastases. I would encourage the authors to clarify some key points and temper rather speculative interpretations before publication.\nMajor\nResults\nIn the section “Anti-metastatic chemomemory is microbiota-dependent”: Is mucositis itself microbiota-dependent in their model? Can the authors assess this from their metastasis experiments? While this may not be central to the paper’s claims, it could help clarify the proposed mechanism.\n“We tested the effects of high physiological (10 μM) and supraphysiological (50, 100 and 200 μM) IPA concentrations (43, 44). Only non-physiological concentration of 100 μM IPA inhibited growth of patient-derived MSS tumoroids” – How are “physiological” and “supraphysiological” concentrations defined? What evidence supports 10 μM as physiological in the tumor microenvironment?\n“IPA was unable to suppress metastatic growth or tumor cell proliferation in NSG mice (Fig. 4L-O). These results show that in vivo IPA anti-metastatic activity is primarily driven through the immune system.” – I recommend more cautious wording here, as NSG mice differ from immunocompetent models on multiple levels.\nRelated to Figure 7A-E: Did IPA treatment have any effect on overall tumor size? If not, can the authors speculate why, and could they show the data?\n“CRC patients present with lower IPA level as compared to the healthy population, suggesting a role for IPA in disease control and that IPA-producing bacteria are preferentially targeted by antibiotics used to treat these patients.” This interpretation appears highly speculative. Even if the observation holds in larger cohorts, reduced IPA levels could have multiple causes. I suggest a more careful phrasing.\nDiscussion\n5FU/Oxaliplatin is the standard adjuvant therapy for stage III CRC, yet many patients still develop metastases. Can the authors reflect on why the mechanisms they uncover might fail in a significant proportion of patients?\nMethods\nIt appears from the main Methods section that only GraphPad was used for statistical analyses. Is this sufficient for the more complex data, such as proteomics and spatial analyses?\nMinor\nIntroduction\nConsider rewording: stage I & II CRC are still “invasive”; consider removing “Non-invasive.”\n“antibiotics prior or during chemotherapy treatment significantly reduced disease-free survival in CRC patients (7), suggesting that gut microbiota confers protection against metastatic recurrence.” – Given the potential for many confounding factors, I suggest more cautious phrasing. For example:\n“…in CRC patients. However, how altered microbiota, for example due to antibiotics, and tumor progression are mechanistically linked remains incompletely understood.”\nResults\n“indicating that the metastasis refractory state is long-lasting (Fig. 1L-N; Fig. S1JK). In all, we demonstrate that FO induced mucositis and systemic effect maintain a lasting anti-tumor effect.” The authors use “long-lasting” for a period of ten days. Would “lasting” suffice?\n“…suppressed liver metastasis growth to levels similar to those observed in FO-treated, microbiota-competent mice (Fig. 4IJ; Fig. 1H-I).” – As the authors did not measure growth dynamically, it would be more accurate to use “liver metastasis size” or “burden.”\nMethods\nThe Supplementary Methods should be clearly referenced, or, as is more typical for the journal, all methods should be included in the main text.", "reviewer_3": "The manuscript by Bersier et al. proposes a mechanism how chemotherapy effect systemic immune memory by regulating intestinal microbiota and microbiota-dependent metabolite IPA, which in turn modulates myelopoiesis and subsequent T cell activation suppressing liver metastasis development. While the role of IPA in regulation of T cell immune responses in anti-cancer treatment was previously suggested, this work provides novel insights into the indirect, monocyte-dependent control of anti-tumor T cell responses in liver metastases. The manuscript provides strong experimental evidence supporting proposed mechanisms and use various loss- of- function and gain -of-function approaches and multiple metastasis models to support conclusions.\nOverall, this is an exciting study. However, some additional clarifications can further strengthen presented work.\n1. What would be the explanation on why antibiotic treatment does not suppress metastasis development (Fig 2)?\n2. Metastasis models are relatively short. Is this effect of chemotherapy on microbiota and subsequent immune system rewiring long lasting? It would be important to discuss it in the manuscript.\n3. Can authors clarified how would depletion of circulating monocytes affect chemotherapy /IPA mediated T cell immune responses in metastases?\n4. Fig 6 E. The gates defining neutrophils need to be corrected. Typically neutrophils are defined as Ly6Ghi, Ly6C+, unlike presented on this figure.", "reviewer_4": "Bersier et al. analyzed how chemotherapy-induced normal tissue injury can influence individual susceptibility to metastasis, specifically in colorectal cancer (CRC). The authors showed that chemotherapy indirectly prevents liver metastases by inducing a long-lasting systemic \"chemomemory.\" This effect was mediated by intestinal mucositis, which altered gut nutrient availability and promoted the expansion of tryptophan-metabolizing bacteria, leading to increased production of the microbial metabolite indole-3-propionic acid (IPA). They further showed that IPA reshaped bone marrow myelopoiesis, resulting in reduced numbers of immunosuppressive Ly6C-high monocytes in the circulation and liver. This reduction facilitated the accumulation of CD4+ Th1-like T cells and enhanced colocalization of CD8+ cytotoxic T cells, ultimately strengthening anti-tumor immunity.\nGeneral Remark 1: The authors have went to great lengths to describe the beneficial role of IPA in reinforcing the chemomemory in the context of 5-FU and Oxali treated CRC-bearing animals. The results are in this regard convincing, however they are not novel as this has been reported by other scientists recently (https://ascopubs.org/doi/10.1200/JCO.2024.42.16_suppl.e15194; PMID: 38490195).\nGeneral Remark 2: Despite very large animal experiment, the mechanism on how IPA actually works in the cell (eventual receptor or intracellular target) remain unknown.\nGeneral Remark 3: The metabolomics analysis has not been well detailed, targeted approach needs the specific information on which transitions have been used, retention times, etc. More detail is needed if these results are to be reproduced in the future.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the Reviewers for their positive feedback and thoughtful evaluation of our manuscript. Following the constructive review, we revised the manuscript as suggested and included new data to address reviewer’s concerns.\nComments are in blue, and our responses are written in black.\nPOINT BY POINT RESPONSES TO REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript addresses the role of the gut microbiome in chemotherapy induced reduction in colorectal cancer metastasis to the liver. The authors posit that chemotherapy induced intestinal mucositis disrupts the gut epithelial cells resulting in reduced absorptive capacity, altering nutrient availability and causing dysbiosis. There are several studies in the literature that support this process. The novel aspects of this study is the demonstration of the expansion of tyrptophan-metabolizing bacteria that increases levels of IPA which drives myeloid cell maturation to macrophages resulting in reduced Ly6chigh monocyte production. The authors show that this further promotes CD4 T cell activity thus reducing tumor progression in the liver.\nThis is a well written manuscript with appropriate methodology. The studies are thorough and the conclusions follow from the data. I have minor suggestions and issues that can be addressed.\nWe thank reviewer 1 for their comment, that helped clarify and strengthen our manuscript.\n1) It would be helpful to have a final schematic illustration of the mechanism by which “chemomemory” is thought to occur.\nThank you for useful suggestion. We have now added a final schematic figure to illustrate the mechanisms of chemomemory” (Figure 1 for reviewers and page 16, lines 433-438 and Figure 6M in the manuscript).\nChemomemory\nBone marrow Control IPA\nCommon myeloid Common myeloid\nIPA progenitor progenitor\nneutrophilsLy6chigh neutrophils Ly6chigh\nCCR2+ CCR2+ Myeloid cell\nmonocytes monocytes maturation\nMafbIrf8\nRbpjKlf4\nFO-induced\nintestinal mucositis\nTregs Th1 CD4\nTNF-α\nIFNγ\nIImmmmuunnoossuupppprreessssiioonn\nAntitumor immunity\nFigure 1 for reviewers. IPA role in the bone marrow myeloid compartment and liver metastatic microenvironment. In the absence of IPA, tumor induced Ly6chighCCR2+monocytes are released from bone marrow in the circulation and recruited to the tumor microenvironment, where they suppress antitumor immunity. IPA promotes maturation of these monocytes, decreasing their release in circulation. This in turn relieves immunosuppression and enhances antitumor immunity mediated by CD4+Th1 cells in metastatic niche, driving IPA-mediated chemomemory.\n1\n2) It would be useful to get more details on the extent of the GI toxicity other than histological measurements. What is the inflammation state in the model among the various cohorts.\nThe reviewer raises a valid point. In Figure 1, we demonstrated histological signs of mucositis, including reduced villus length and crypt depth. Additionally, our metabolomics analysis of the ileum at 5 days post FO revealed a decrease in citrulline levels, an established marker of enterocyte mass (Figure 2 for reviewers 2H, Fig. 2H) (3, 4). Please refer to page 7, lines 188-190 in the manuscript: “In addition, FO-treated ileum contained less citrulline (Fig. 2H, Table S1), a metabolite mostly produced by enterocytes and used as a marker of enterocyte mass(3).” Consistent with inflammation, we also observed increase in other arachidonic acid associated catabolites (5, 6)(page 7, lines 186-188 and Fig. 2H in the manuscript).\nFigure 2 for reviewers (from figure 2). FO increase metabolite associated with intestinal damage and inflammation. (H) Hierarchical clustering heatmap showing metabolite abundance changes in control and FO treated ileum (logFc2 >0.2; FDR<0.05). n= 5 per condition run in duplicate\nIn addition, we now analyzed expression levels of inflammatory cytokines in ileum of control or FO treated mice. This analysis demonstrates increase expression of Tnfa and Il1b, two additional markers of intestinal inflammation (Figure 3 for reviewers. 3A).\nWe have added these data in Supplementary Fig. 1J and modified the text accordingly on page 5, lines 122-123 in the revised manuscript.\n3) Are there data on the changes in the immune response in the lamina propria?\nTo further assess immune response in the lamina propria, we performed IHC on sections of mouse intestine at day 3 and day 5 post FO or vehicle treatment (control). Histological analysis showed that FO treatment significantly increased infiltration of MPO+ neutrophils, a known marker of mucositis, while no changes in CD4+ or CD8+ lymphocyte were found at both time 2 points (Figure 3 for reviewers. 3B-I). We have added these data in Supplementary Fig. 1K-R and modified the text accordingly on page 5, lines 123-127.\nThus, FO treatment, in addition to histological signs of intestinal damage, such as decreased villus and crypt length, also leads increased lamina propria inflammation and expression of inflammatory cytokines. All these new data are now included in Supplementary Figure 1.\nControl, day 5 Post-FO, day 5\nDAPIMPO\n40\n30\n20\n10\nFigure 3 for reviewers. FO induces intestinal inflammation. (A) qPCR analysis of Tnfa and Il1b in the ileum of mice 5 days after treatment with control (PBS) and FO. Data are normalized to housekeeping gene 18S. (B-C) MPO+ cells infiltrate the ileum of FO treated mice 3 days post-treatment. Images show MPO (green) and DAPI (blue). Scale bars: 100μm. Quantification MPO+ cells normalized to area. n=4 per groups. (D-E) MPO+ cells infiltrate the ileum of FO treated mice 5 days post-treatment. Images show MPO (green) and DAPI (blue). Scale bars: 100μm. Quantification MPO+ cells normalized to area. n=3 per groups. (F-G) CD4+ and CD8+ cell infiltration are similar 3 days post-treatment. Images show CD4 (green), CD8 (magenta) and DAPI (blue). Quantification CD4+ and CD8+ cells normalized to area and control mean. n=4 per groups. Scale bars: 100μm (H-I) CD4+ and CD8+ cell infiltration is similar 5 days post-treatment. Images show CD4 (green), CD8 (magenta) and DAPI (blue). Quantification CD4+ and CD8+ cells normalized to area and control mean. n=3 per groups. Scale bars: 100μm. Data are shown as mean ± SD and analyzed using two-tailed unpaired Student’s t-test (A, C, E, G, I).\n4) It is unclear why intestinal damage is determined after 3 days post FO in naïve mice but 5 days post FO in treated mice (Figs 1A and B)\nThis may be a misunderstanding: on Figure 1A and B, the data compare the intestine 3 days after control treatment (PBS, left panel; note that “control” has been added to the figure legend) and 5-FU+oxaliplatin (FO, right panel). To clarify the experimental setup, we have revised data 3 aera rep sllec +OPM Control, day 3 Post-FO, day 3 150 100 50 0 DAPIMPO Control FO 0.0513 aera rep sllec +OPM A 0.0323 1.5 1.0 0.5 0.0 Control FO aera rep +4DC 0.9843 1.5 1.0 0.5 0.0 Control FO aera rep +8DC Control, day 3 Control, day 5 2.0 1.5 1.0 0.5 0.0 Control FO Post-FO, day 3 Post-FO, day 5 0.7891 DAPICD4CD8 DAPICD4CD8 aera rep +4DC 0.1071 1.5 1.0 0.5 0.0 Control FO aera rep +8DC 15 10 5 0 Control FO 0.1364 ANRm evitaleR Tnfα 0.0461 25 20 15 10 5 0 Control FO ANRm evitaleR IL1ß B C 0.0362 D F G H I E presentation as shown below (Fig. 4 for reviewers; Fig. 1A-B in the manuscript). We hope these changes help improve the clarity of the information.\nControl, day 3 Post-FO, day 3\nKI67 DAPI\nFigure 4 for reviewers. FO induces intestinal damages. (A) Scheme of the experiment. Wild-type mice received 5-FU+oxaliplatin (FO) or PBS (control); intestines were harvested 3 days later. (B) FO reduced proliferation of intestinal crypt cells 3 days after treatment. KI67 (magenta) and DAPI (grey). Scale bars: 100μm.\n5) Fig 8: What are the monocyte counts for T2? While there is clear shift in IPA the monocyte counts are presented as a combination of the T1 and T2 in 8E.\nWe thank the reviewer for this question. Monocyte levels following chemotherapy vary among CRC patients. Notably, most patients who exhibited a substantial increase in IPA post-chemotherapy also showed a sustained reduction in monocyte counts at T2 (Figure 5 for reviewers. 5A-B). To emphasize this subgroup, we have highlighted these patients in red in both IPA and monocytes changes figures (Figure 5 for reviewers. 5A-B). Accordingly, we have revised the manuscript to include this information on page 18, lines 483-484: “Patients with substantial increase in IPA level at T2 showed a corresponding reduction of monocyte at T2 (Supplementary Figure. 8F-G)”.\nFigure 5 for reviewers. IPA increases post chemotherapy in subset of CRC patients. (A) IPA is significantly increased post- chemotherapy in CRC patients. (B) Patients with the strongest increase in IPA show monocyte decrease. (A) and (B) Highlighted in red are the patients with the IPA increase and monocytes reduction. Data are analyzed using Wilcoxon rank-sum test (A) and two-tailed paired T-test (B).\n4 )mμ( htgnel illiV 250 0.0005 200 150 100 50 40 Control FO )mμ( htgnel tpyrC A B C Day 3 Day0 1 2 3 Intestinal 100 0.0383 damage 90 analysis O xa 5-FU 80 70 FO blood concentration 60 50 Control FO 1000 800 600 400 200 0 T1 T2 lm/gn IPA 0.0342 1.0 0.8 0.6 0.4 0.2 0.0 T1 T2 stnuoc etyconom Figure 2 for reviewers A B Monocytes 0.5149\nReviewer #2 (Remarks to the Author):\nThe manuscript demonstrates that standard CRC chemotherapy with 5-fluorouracil and oxaliplatin induces intestinal mucositis, which alters the gut microbiota composition, leading to increased levels of the bacterial metabolite indole-3-propionic acid (IPA). Elevated IPA reprograms bone marrow myelopoiesis, reducing immunosuppressive Ly6Chigh monocytes and promoting anti-tumor CD4+ T cell responses. This effect, termed \"chemomemory\" by the authors, reduces colorectal cancer liver metastasis in mice even after chemotherapy has cleared. IPA supplementation alone can mimic this protective effect, suggesting that it could be used to enhance the efficacy of chemotherapy. Analysis of 14 CRC patients revealed that chemotherapy increases serum IPA levels in some individuals, with IPA inversely correlating with circulating monocytes, supporting its clinical relevance.\nThe manuscript contains important data on a timely aspect of CRC and metastases. I would encourage the authors to clarify some key points and temper rather speculative interpretations before publication.\nWe thank the reviewer for their positive comments that help improve the manuscript and its clarity.\nMajor\nResults\nIn the section “Anti-metastatic chemomemory is microbiota-dependent”: Is mucositis itself microbiota-dependent in their model? Can the authors assess this from their metastasis experiments? While this may not be central to the paper’s claims, it could help clarify the proposed mechanism.\nWe agree that, although not central to the conclusions of our study, this question is of both interest and importance. Chemotherapy-induced intestinal mucositis is partially microbiota-dependent. Highly proliferative intestinal epithelial cells in the crypts are the direct targets of FO chemotherapy, leading to initial epithelial damage. In addition, microbiota amplifies chemotherapy-induced mucositis (7–10). For example, co-administration of antibiotics with 5-FU or doxorubicin reduced intestinal inflammation and immune cell infiltration, leading to less severe mucositis, although microbiota depletion did not prevent direct crypt cell death (11). In addition, we show that FO treatment actively alters the microbiota composition, promoting both the expansion of tryptophan-metabolizing bacteria and Proteobacteria (please refer to Figure 2F in the manuscript), the known drivers mucosal inflammation (12).\nAs the central novel claim of our paper is the normalizing effect of IPA of pathological myelopoesis, we concentrated our effort in revision on understanding the IPA effects in bone marrow. Based on additional experiments we now show that:\n1. IPA treatment significantly increases the frequency of macrophages in the bone marrow, while the total abundance of neutrophils and overall monocytes remains unchanged (Figure 6 for reviewers. 6A).\n2. IPA significantly increases the proportion of quiescent common myeloid progenitors (CMPs) compared to other progenitor populations (Figure 6 for reviewers. 6B).\n3. CMPs isolated from IPA-treated mice with liver metastases generate more macrophages and fewer CCR2+ monocytes in the bone marrow, resulting in decreased circulating monocytes (Figure 6 for reviewers. 6D-E).\n“We tested the effects of high physiological (10 μM) and supraphysiological (50, 100 and 200 μM) IPA concentrations (43, 44). Only non-physiological concentration of 100 μM IPA inhibited growth of patient-derived MSS tumoroids” – How are “physiological” and “supraphysiological” concentrations defined? What evidence supports 10 μM as physiological in the tumor microenvironment?\nThank you for raising this important question. Physiological IPA levels have been assessed in humans and in animal models previously, and we found them consistent with our own results.\nTo clarify this point, we changed the text, included the references and explained further the choice of concentration used in the text (page 10, lines 258-261 in the manuscript).\n“IPA was unable to suppress metastatic growth or tumor cell proliferation in NSG mice (Fig. 4L-O). These results show that in vivo IPA anti-metastatic activity is primarily driven through the immune system.” – I recommend more cautious wording here, as NSG mice differ from immunocompetent models on multiple levels.\nWe agree that NSG mice differ from immune competent models in many aspects. Therefore, we took the advice the reviewer and changed the statement as follows: “These results indicated IPA anti-metastatic activity is in part mediated by its effect on immune cells”. Please refer to page 10, lines 268-269 in the manuscript.\nRelated to Figure 7A-E: Did IPA treatment have any effect on overall tumor size? If not, can the authors speculate why, and could they show the data?\nWe performed additional analysis on chemotherapy experiment as requested. The overall tumor size was significantly reduced by IPA, chemotherapy and combined treatment (Figure 7 for reviewers. 7A). The individual metastasis size was significantly reduced by IPA and chemotherapy, while the difference between chemotherapy and combined treatment was not statistically significant when comparing across the 4 conditions (Figure 7 for reviewers. 7B-C). However, pairwise comparison of FO to FO+IPA shows that addition of IPA leads to 50% reduction in the individual metastasis size (Figure 7 for reviewers. 7B-C).\nWe have incorporated the results in Fig. 7D-E and added text the following text:\n“Furthermore, combinatorial FO+IPA therapy significantly decreased metastatic burden and increased necrotic areas within a proportion of liver metastatic lesions (Fig. 7D-G)”. This can be found on page 17, lines 446-448 in the manuscript.\n8\n1.5 1.0 0.5 0.0 control I P A che m m o o + I P A che Figure 7 for reviewers. (A-B) FO+IPA reduce liver metastasis load and metastatic size. (C) FO+IPA decrease metastasis size by 50% compared to FO alone. Data are shown as mean ± SD and analyzed using one-way Anova with Tukey’s multiple comparisons test (A, B) two-tailed unpaired Student’s t-test (C).\n“CRC patients present with lower IPA level as compared to the healthy population, suggesting a role for IPA in disease control and that IPA-producing bacteria are preferentially targeted by antibiotics used to treat these patients.” This interpretation appears highly speculative. Even if the observation holds in larger cohorts, reduced IPA levels could have multiple causes. I suggest a more careful phrasing.\nWe agree with the reviewer that this claim is speculative, and many confounders could explain this variability. Therefore, we took the advice of the reviewer, and we rephrased this statement as follows: “Although further studies are necessary, we speculate that lower IPA levels in CRC patients may reflect depletion of IPA-producing bacteria by commonly used antibiotics.” Please refer to page 18, lines 472-474 in the manuscript.\nDiscussion\n5FU/Oxaliplatin is the standard adjuvant therapy for stage III CRC, yet many patients still develop metastases. Can the authors reflect on why the mechanisms they uncover might fail in a significant proportion of patients?\nThank you for this thoughtful suggestion. Our findings indicate that chemotherapy can induce a metastasis-refractory state through microbiota-dependent production of IPA, which alters myelopoiesis and enhances anti-tumor immunity. However, we observed considerable variability in post-chemotherapy IPA levels among patients, suggesting that this mechanism may be compromised in a subset of individuals. This could be due to inter-individual differences in microbiota composition and function, resulting in insufficient IPA production.\n9\nnaem lortnoc/aera sisatsatem <0.0001 0.0008 2.0 0.0003 1.5 0.0013 0.0089 1.0 0.5 0.0 c o ntr ol I P A c h e m o o + I P A m e h c ezis sisatsatem ezilamroN 0.0014 0.0203 0.8 0.0352 0.6 0.4359 0.4 0.2 0.0 o A m P c h e o + I m e h c ezis sisatsatem dezilamron A B C 0.0120\nAdditional extrinsic factors such as diet and medications may further hinder microbiota recovery or microbial metabolism (18, 19). For these reasons, it is important to explore novel strategies that leverage microbial products—such as postbiotics and other microbiota-based therapies containing IPA—to enhance and potentiate existing treatments.\nWe have now included the following statement in the manuscript (page 20, lines 528-532):\n“The high variability of IPA levels observed in patients suggests the presence of other patient-specific factors that may limit this protective effect. These include differences in microbiota composition and function, diet, and concomitant medications, all of which can result in insufficient IPA production. Understanding these variables could inform the development of more personalized adjuvant strategies based on microbial products.”\nMethods\nIt appears from the main Methods section that only GraphPad was used for statistical analyses. Is this sufficient for the more complex data, such as proteomics and spatial analyses?\nWe thank the reviewer for highlighting this point. The statistical methods for each complex data analysis, such as RNA-seq, metabolomics, and spatial analysis, were detailed in their respective sections. To improve clarity, we have now added additional information in the general statistics section of the Methods, referencing that the specific details can be found within each individual section. We have included the following information in the stastics part of methods: “The statistical methods for each complex data analysis, such as RNA-seq, metabolomics, and spatial analysis, are detailed in their respective sections.” This can be found on page 36, lines 966-968.\nMinor\nIntroduction\nConsider rewording: stage I & II CRC are still “invasive”; consider removing “Non-invasive.”\n“antibiotics prior or during chemotherapy treatment significantly reduced disease-free survival in CRC patients (7), suggesting that gut microbiota confers protection against metastatic recurrence.” – Given the potential for many confounding factors, I suggest more cautious phrasing. For example:\n“…in CRC patients. However, the mechanistic link between chemotherapy, microbiota alterations and cancer progression, including liver metastasis development, remains incompletely understood.”\n“indicating that the metastasis refractory state is long-lasting (Fig. 1L-N; Fig. S1JK). In all, we demonstrate that FO induced mucositis and systemic effect maintain a lasting anti-tumor effect.” The authors use “long-lasting” for a period of ten days. Would “lasting” suffice?\nWe agree with the reviewer. We replaced long lasting with lasting in the revised version of our manuscript.\n“…suppressed liver metastasis growth to levels similar to those observed in FO-treated, microbiota-competent mice (Fig. 4IJ; Fig. 1H-I).” – As the authors did not measure growth dynamically, it would be more accurate to use “liver metastasis size” or “burden.”\nWe have modified the text as proposed by the reviewer replacing “growth” with “burden”.\nThe Supplementary Methods should be clearly referenced, or, as is more typical for the journal, all methods should be included in the main text.\nWe have changed the method part accordingly.\nThe manuscript by Bersier et al. proposes a mechanism how chemotherapy effect systemic immune memory by regulating intestinal microbiota and microbiota-dependent metabolite IPA, which in turn modulates myelopoiesis and subsequent T cell activation suppressing liver metastasis development. While the role of IPA in regulation of T cell immune responses in anti-cancer treatment was previously suggested, this work provides novel insights into the indirect, monocyte-dependent control of anti-tumor T cell responses in liver metastases. The manuscript provides strong experimental evidence supporting proposed mechanisms and use various loss- of- function and gain -of-function approaches and multiple metastasis models to support conclusions.\nOverall, this is an exciting study. However, some additional clarifications can further strengthen presented work.\nWe thank the reviewer for positive and enthusiastic evaluation of our manuscript.\n1. What would be the explanation on why antibiotic treatment does not suppress metastasis development (Fig 2)?\nWe thank the reviewer for this comment. We do not expect antibiotics treatment to suppress metastasis in our model. Indeed, we found that chemotherapy increases circulating levels of IPA derived from gut microbiota, and IPA reduces accumulation of immunosuppressive myeloid cells, this is the reason why antibiotics reduced the chemomemory but are not directly affecting tumor burden of mice receiving control treatment. To clarify this point, we provide now a summary model of the main conclusion of our work on page 16, lines 433-438 and the summary model is in Figure 8 for reviewers and Fig. 6M in the revised manuscript.\nChemomemory\nBone marrow Control IPA\nCommon myeloid Common myeloid\nIPA progenitor progenitor\nneutrophilsLy6chigh neutrophils Ly6chigh\nCCR2+ CCR2+ Myeloid cell\nmonocytes monocytes maturation\nMafbIrf8\nRbpjKlf4\nFO-induced\nintestinal mucositis\nTregs Th1 CD4\nTNF-α\nIFNγ\nIImmmmuunnoossuupppprreessssiioonn\nAntitumor immunity\nFigure 8 for reviewers. In the absence of IPA, tumor induced Ly6chighCCR2+monocytes are released from bone marrow in the circulation and recruited to the tumor microenvironment, where they suppress antitumor immunity. IPA promotes maturation of these monocytes, decreasing their release in circulation. This in turn relieves immunosuppression and enhances antitumor immunity mediated by CD4+Th1 cells in metastatic niche, driving IPA-mediated chemomemory.\n12\n2. Metastasis models are relatively short. Is this effect of chemotherapy on microbiota and subsequent immune system rewiring long lasting? It would be important to discuss it in the manuscript.\nWe thank the reviewer for this suggestion. In the manuscript, we show the chemomemory effect 5 and 10 days after chemotherapy. We now analyzed 20 days post chemotherapy time point, where we find a tendency towards limitation of metastasis, suggesting a wearing of the effect of the one cycle of chemo between 10 and 20 days (Figure 9 for reviewers. 9A-B). Interestingly, a typical chemotherapy cycle lasts two weeks, and patients usually undergo multiple cycles. It will be important to determine in future studies whether repeated cycles lead to sustained high levels of IPA, long-lasting immune system rewiring, and potentially even stronger control of metastasis. We have included these new data (Supplementary Fig. 2C-E) and the following description of the result on page 6, lines 144-147: “Injecting MC-38-GFP tumor cells twenty days after FO pre-conditioning, mimicking the end of a treatment cycle, showed a tendency for persistence of metastasis refractory state (Supplementary Fig. 2C-E).” Additionally, the corresponding discussion can be found in the revised manuscript on page 19, lines 514-518 as follows: “A typical chemotherapy cycle lasts two weeks, and patients usually undergo 6 to 12 cycles of FOLFOX treatment. We found that the metastasis-refractory state lasts up to 10–20 days after a single FO cycle (Fig. 1L-N; Supplementary Fig. 2B-E). It will be important to determine in future studies whether repeated cycles lead to sustained high levels of IPA, prolonged immune system rewiring, and potentially stronger control of metastasis.”\n0.015\n0.010\n0.005\n0.000\ncontrol FO\nFigure 9 for reviewers. FO chemomemory is maintained in some mice 20 days post FO treatment. (A) Quantification of percentage and (B) count of MC38-GFP cancer cells in liver of mice in the indicated conditions. Control, n = 4. FO, n = 5. Data are shown as mean ± SD and analyzed using two-tailed unpaired Student’s t-test (A, B).\n3. Can authors clarified how would depletion of circulating monocytes affect chemotherapy /IPA mediated T cell immune responses in metastases?\nThank you for this comment. Our model is that chemotherapy expands tryptophan-metabolizing bacteria, leading to elevated IPA levels. In the bone marrow, IPA drives myeloid 13 sllec PFG % 0.0922 800 600 400 200 0 control FO marg rep stnuoc +PFG A B 0.1382 cell maturation toward macrophages and reduces the production and release of Ly6chigh monocytes, thereby limiting the infiltration of immunosuppressive myeloid cells into the tumor microenvironment. Additionally, this leads to increase proportion of myeloid cells expressing MHCII in the liver of IPA treated mice (please refer to Supplementary Fig. 6D). These changes enhance CD4+ T cell activity, ultimately limiting tumor progression in the liver. The corresponding model is now included on page 16, lines 433-438 of the revised manuscript (Fig. 6M).\nTo further study the reason for reduction in circulating Ly6chigh monocytes, we performed a CMP adoptive transfer experiment. We induced metastases in CD45.1 mice and treated them with either control or IPA daily treatment (Figure 10 for reviewers. 10C). We then FACS-sorted CMPs and adoptively transferred them into CD45.2 recipient mice, tracking CMP fate at early time points in vivo using established protocols (1, 2). The proportion of CD45.1-derived cells in the bone marrow was similar between groups, and we did not detect significant differences in the frequencies of monocytes or neutrophils derived from transferred CMPs. In contrast, CMPs from IPA-treated mice generated a higher proportion of macrophages in the bone marrow and fewer CCR2-expressing monocyte (Figure 10 for reviewers. 10D). Several studies have shown that blocking monocyte migration—either through CCR2 inhibition or using CCR2 knockout mouse models—enhances T cell–mediated immunity (20, 21). For example, in a model where myocardial infarction promoted the expansion of immunosuppressive Ly6chigh monocytes and tumor progression, depletion of these cells via a CCR2 inhibitor helped control tumor growth by enhancing CD8+ T cell–mediated immunity (22). Consistent with these data, we observed fewer monocytes in the blood of mice transferred with CMPs from IPA treated donors (Figure 10 for reviewers. 10E).\nAltogether, these results indicate that CMPs are the main target of IPA in bone marrow and that IPA promotes differentiation of CMPs towards macrophage lineage, while reducing release of monocytes in the periphery. These new data are included in the revised manuscript on page 16, lines 414-438 (in Fig. 6 and Supplementary Fig. 7), and we propose a model in which IPA promotes pre-mature CMP differentiation and retention in bone marrow. In turn, the reduction in circulating monocytes leads to decreased monocyte infiltration into the TME and improved anti-tumor immune response (Fig. 6M).\n4. Fig 6 E. The gates defining neutrophils need to be corrected. Typically neutrophils are defined as Ly6Ghi, Ly6C+, unlike presented on this figure.\nWe thank the reviewer for this comment and provide now this figure. These data are found now in Supplementary Fig. 7D-E in the revised manuscript and presented here in Figure 11 for reviewers.\n15\nneutrophils 0.5989 20 0.2756 Ly6chigh Ly6chigh Ly6chigh 0.0119 105 105 105 15 5 4 D C % 10 104 104 104 0 0 0 5 -104 Neutrophils -104 Neutrophils -104 Neutrophils 0 control 10uM 50uM BV -10 65 4 0 - L y6g 104 105 -104 0 104 105 -104 0 104 105 Figure 11 for reviewers. IPA does not affect neutrophils while Ly6chigh monocytes are reduced. (A) Percentage of neutrophils generated in the presence of vehicle (DMSO) or IPA in vitro. n= 8 per condition. Data show biological replicate pooled from two independent experiments (B) Gating startegy. Data are shown as mean ± SD and analyzed using repeated measure one-way Anova with Tukey’s multiple comparisons test (A).\n16\nc6yL - 5-5yC-PCreP A B Control 10µM 50µM\nReviewer #4 (Remarks to the Author):\nBersier et al. analyzed how chemotherapy-induced normal tissue injury can influence individual susceptibility to metastasis, specifically in colorectal cancer (CRC). The authors showed that chemotherapy indirectly prevents liver metastases by inducing a long-lasting systemic \"chemomemory.\" This effect was mediated by intestinal mucositis, which altered gut nutrient availability and promoted the expansion of tryptophan-metabolizing bacteria, leading to increased production of the microbial metabolite indole-3-propionic acid (IPA). They further showed that IPA reshaped bone marrow myelopoiesis, resulting in reduced numbers of immunosuppressive Ly6C-high monocytes in the circulation and liver. This reduction facilitated the accumulation of CD4+ Th1-like T cells and enhanced colocalization of CD8+ cytotoxic T cells, ultimately strengthening anti-tumor immunity\nWe thank the reviewer for careful revision of our work and valuable comments.\nGeneral Remark 1: The authors have went to great lengths to describe the beneficial role of IPA in reinforcing the chemomemory in the context of 5-FU and Oxali treated CRC-bearing animals. The results are in this regard convincing, however they are not novel as this has been reported by other scientists recently (https://ascopubs.org/doi/10.1200/JCO.2024.42.16_suppl.e15194; PMID: 38490195).\nWe thank the reviewer for this comment. Indeed, the ASCO abstract highlights the importance of IPA in enhancing 5-FU efficacy in colorectal cancer. However, since this study has not yet undergone peer review or been published, we are unable to fully evaluate their data. Nonetheless, we find the primary observation plausible and exciting, as it aligns with our study supporting a role for IPA in modulating chemotherapy response.\nThat said, based on the information provided in the abstract, we did not observe a direct effect of IPA on CD8⁺ T cells in our experiments (Figure 12 for reviewers. 12H-P). Specifically, treatment with IPA at physiological concentration did not influence CD8⁺ T cell proliferation or activation, as assessed by cytokine production.\nWe have discussed PMID: 38490195 in both the Results (on pages 10-11, lines 270-281 in the revised manuscript) and Discussion sections (on page 20, lines 533-540). Importantly, in our hands, IPA did not directly increase TCF1 expression in CD8⁺ T cells, either in vitro at physiological or supraphysiological concentrations (Figure 12 for reviewers. 12H-P) or in vivo within our metastatic model. Instead, we demonstrate that myeloid progenitor cells, rather than T cells, are the primary cellular targets of IPA in cancer. We performed additional experiments and now show that:\n4. IPA treatment significantly increases the frequency of macrophages in the bone marrow, while the total abundance of neutrophils and overall monocytes remains unchanged (Figure 13 for reviewers. 13A).\n18\n5. IPA significantly increases the proportion of quiescent common myeloid progenitors (CMPs) compared to other progenitor populations (Figure 13 for reviewers. 13B).\n6. CMPs isolated from IPA-treated mice with liver metastases generate more macrophages and fewer CCR2+ monocytes in the bone marrow, resulting in decreased circulating monocytes (Figure 13 for reviewers. 13D-E).\nFigure 13 for reviewers. IPA rewires CMP to mature into macrophages in the bone marrow and reduces monocyte release in the circulation. (A) IPA increases the abundance of macrophages in the bone marrow of metastasis bearing mice. n = 4 per groups. (B) CMP - + – - low + (Lineage cKit Sca1 (KLS )CD16/32 CD34 ) in IPA treated mice are more quiescent. - low + Control, n = 5. IPA, n = 5. GMP (KLS CD16/32 CD34 ). (C) Scheme of the experiment to analyze the fate of CMP treated with IPA in vivo. (D) CMP from IPA treated mice produce more macrophages in the bone marrow of recipient mice. Data show the percentage among CD45.1+ cells. Control, n = 5. IPA, n = 4. (E) CMP from IPA treated mice release less monocytes in the blood. Experiment 1: Control, n = 5. IPA, n = 4; Experiment 2: Control, n = 3. IPA, n = 4. Data are shown as mean ± SD and analyzed using two-tailed unpaired Student’s t-test (A, D, E) and two-way Anova with Bonferroni's multiple comparisons test (B).\nThese new results refine our initial model and indicate that CMPs are the main target of IPA in the bone marrow. IPA promotes CMP differentiation toward the macrophage lineage 19 while reducing the release of monocytes into the periphery. As CCR2 has been previously shown to be essential for monocyte egress from the bone marrow (20, 21), the observed reduction in CCR2+ monocytes likely contributes to this effect. These new data are included in the revised manuscript on pages 16, lines 414-438 (Fig. 6; Supplementary Fig. 7). We consider them both novel and important, as they identify a previously unrecognized cellular target of IPA that will help guiding future research.\nGeneral Remark 2: Despite very large animal experiment, the mechanism on how IPA actually works in the cell (eventual receptor or intracellular target) remain unknown.\nWe thank the reviewer for this valuable comment. We agree that elucidating the molecular mechanism underlying IPA molecular effectors in myeloid progenitors and myeloid cells is an important next step, especially for therapeutic applications. While many studies have linked IPA to AHR signaling, our experiments show that IPA does not alter AHR activity in bone marrow cells at 10 μM, as demonstrated by the lack of induction of AHR target gene signatures (Figure 14 for reviewers).\n0.2 0.0 −0.2 −0.4 0 2000 4000 6000 Position in the Ranked List of Genes Figure 14 for reviewers. AHR signature is not increased in bone marrow cells treated by IPA. These results are in line with previous biochemical analyses demonstrating that IPA is unable to activate AHR in AHR reporter assay in vitro or compete with a bona fide AHR ligand in direct binding assay (Figure 15 for reviewers. 15A-B) (15).\nFigure 15 for reviewers. Taken from(15). (A) IPA does not activate AHR in stably transfected luciferase reporter construct Hepa1.1 cell line. (B) In human CRC cells, IPA physiological concentration does not induce AHR gene transcription.\n20\nerocs tnemhcirnE A AHR agonist NES = -0.84 adj. p-val. = 0.687 A B\nWe have begun identifying potential IPA interactors using in silico modelling and chemoproteomic approaches. Fully characterizing the as-yet unknown intracellular targets and receptors of a small molecule such as IPA will likely require several years and falls beyond the scope of the current manuscript. Nevertheless, we include negative data demonstrating the absence of an AHR signature in IPA-treated cells and discuss IPA’s established inability to activate AHR (15) on page 15, lines 396-399 (Supplementary Fig. 7F in the revised manuscript). We believe these data are important, as they help narrow the search for relevant intracellular targets and avoid misattribution of IPA’s effects to the AHR pathway in bone marrow cells. We also include the reviewer’s comment in the limitation section on page 22, lines 594-597:\n« The molecular targets and effectors of IPA in myeloid progenitors, as well as its effects on other hematopoietic and metastatic niche components, remain to be identified. »\nGeneral Remark 3: The metabolomics analysis has not been well detailed, targeted approach needs the specific information on which transitions have been used, retention times, etc. More detail is needed if these results are to be reproduced in the future.\nThank you for the comments, we now included more detailed information for targeted approach as follows (on pages 27-28, lines 727-740 of the revised manuscript):\n“The absolute serum concentrations of 3-indolelactic acid( m/z 204→142, RT=2.6), 3-indolepropionic acid (m/z 188→59, RT=3.5), and tryptophan (m/z 205→188, RT=0.93)) were quantitated at Metabolon facility by LC-MS. Serum samples were spiked with stable labeled internal standards, homogenized, and subjected to protein precipitation with an organic solvent. The sample is centrifuged, and a portion of the supernatant was injected onto an Agilent 1290/Sciex QTrap 5500 LC-MS/MS system equipped with a C18 reversed phase ultrahigh performance liquid chromatography column. The mass spectrometer was operated in positive mode for tryptophan and negative mode for the remaining analytes using electrospray ionization (ESI). The peak area of the individual analyte product ions was measured against the peak area of the product ions of the corresponding internal standards. Quantitation was performed using a weighted linear least squares regression generated from fortified calibration standards prepared immediately prior to each run. The LC-MS/MS raw data is collected and processed using SCIEX software Analyst 1.6.3 and processed using SCIEX OS-MQ software.”\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank the reviewer for participating in the reviewing of our work and for their valuable comments.\n1. Cortez-Retamozo V, et al. Origins of tumor-associated macrophages and neutrophils. Proc Natl Acad Sci. 2012;109(7):2491–2496.\n2. Yáñez A, et al. Granulocyte-Monocyte Progenitors and Monocyte-Dendritic Cell Progenitors Independently Produce Functionally Distinct Monocytes. Immunity. 2017;47(5):890-902.e4.\n3. Bailly-Botuha C, et al. Plasma Citrulline Concentration Reflects Enterocyte Mass in Children With Short Bowel Syndrome. Pediatr Res. 2009;65(5):559–563.\n4. Crenn P, Messing B, Cynober L. Citrulline as a biomarker of intestinal failure due to enterocyte mass reduction. Clin Nutr. 2008;27(3):328–339.\n5. Wang B, et al. Metabolism pathways of arachidonic acids: mechanisms and potential therapeutic targets. Signal Transduct Target Ther. 2021;6(1):94.\n6. Kalinski P. Regulation of Immune Responses by Prostaglandin E2. J Immunol. 2012;188(1):21–28.\n7. Anderson CJ, et al. Metabolite-based inter-kingdom communication controls intestinal tissue recovery following chemotherapeutic injury. Cell Host Microbe. 2024;32(9):1469-1487.e9.\n8. Hamouda N, et al. Apoptosis, Dysbiosis and Expression of Inflammatory Cytokines are Sequential Events in the Development of 5‐Fluorouracil‐Induced Intestinal Mucositis in Mice. Basic Amp Clin Pharmacol Amp Toxicol. 2017;121(3):159–168.\n9. Li H-L, et al. Alteration of Gut Microbiota and Inflammatory Cytokine/Chemokine Profiles in 5-Fluorouracil Induced Intestinal Mucositis. Front Cell Infect Mi. 2017;7:455.\n10. Pedroso SHSP, et al. Evaluation of mucositis induced by irinotecan after microbial colonization in germ-free mice. Microbiology. 2015;161(10):1950–1960.\n11. Carr JS, King S, Dekaney CM. Depletion of enteric bacteria diminishes leukocyte infiltration following doxorubicin-induced small intestinal damage in mice. PLoS ONE. 2017;12(3):e0173429.\n12. Shin N-R, Whon TW, Bae J-W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 2015;33(9):496–503.\n13. Wang Y-C, et al. Indole-3-Propionic Acid Protects Against Heart Failure With Preserved Ejection Fraction. Circ Res. 2024;134(4):371–389.\n14. Tuomainen M, et al. Associations of serum indolepropionic acid, a gut microbiota metabolite, with type 2 diabetes and low-grade inflammation in high-risk individuals. Nutr Diabetes. 2018;8(1):35.\n15. Morgan EW, et al. Contribution of Circulating Host and Microbial Tryptophan Metabolites Toward Ah Receptor Activation. Int J Tryptophan Res. 2023;16:11786469231182510.\n16. Serger E, et al. The gut metabolite indole-3 propionate promotes nerve regeneration and repair. Nature. 2022;1–8.\n23\n17. Heumel S, et al. Shotgun metagenomics and systemic targeted metabolomics highlight indole-3-propionic acid as a protective gut microbial metabolite against influenza infection. Gut Microbes. 2024;16(1):2325067.\n18. Gacesa R, et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature. 2022;604(7907):732–739.\n19. Zhou X, et al. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe. 2024;32(4):506-526.e9.\n20. Sanford DE, et al. Inflammatory Monocyte Mobilization Decreases Patient Survival in Pancreatic Cancer: A Role for Targeting the CCL2/CCR2 Axis. Clin Cancer Res. 2013;19(13):3404–3415.\n21. Flores-Toro JA, et al. CCR2 inhibition reduces tumor myeloid cells and unmasks a checkpoint inhibitor effect to slow progression of resistant murine gliomas. Proc Natl Acad Sci. 2020;117(2):1129–1138.\n22. Koelwyn GJ, et al. Myocardial infarction accelerates breast cancer via innate immune reprogramming. Nat Med. 2020;26(9):1452–1458.\n24"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have responded appropriately to my comments. I have no further comments.", "reviewer_2": "Thank you for your thorough revision of the manuscript. All points raised in my previous review were addressed, and I do not have any further comments.", "reviewer_3": "Authors successfully addressed previously raised concerns and revised the manuscript accordingly. I have no further questions.", "reviewer_4": "Bersier et al. have submitted a revised manuscript that addresses some of the reviewers’ questions and provides additional clarity on others; however, several issues remain unresolved. The primary concern—the lack of mechanistic insight into the action of IPA in myeloid cells—remains pertinent. This is particularly important given that the present data appear to conflict with previous findings (PMID: 38490195), where IPA targets different cells via a distinct mechanism. While this does not invalidate the authors’ findings, it highlights the need for more definitive mechanistic understanding, especially in light of multiple reports demonstrating the beneficial role of IPA in cancer therapy.\nA second major concern pertains to the metabolomics analyses presented throughout the manuscript, including key figures (Figs. 2, 3, and 8), particularly those regarding IPA concentrations in patients. Based on the additional details provided, it is evident that the metabolomics analyses were conducted following the methodology of Fuhrer et al., 2011 (as per authors information), which no longer reflects current standards. Today, it is considered unacceptable to quantify or identify metabolites—as in Figure 3F (untargeted)—without the use of liquid chromatography (retention times are unavailable) and MS² validation. Direct injection and MS¹-based quantification may be acceptable in limited in vitro screening experiments with defined metabolite pools but not in complex biological matrices such as serum or cellular extracts. Similarly, the targeted quantification presented in Figure 3E is problematic. Using a single transition, as now described in the Materials and Methods section, is highly unconventional and does not meet current standards for reliable targeted metabolomics.\nFinally, it is unclear why the authors have only partially addressed reviewers’ requests for detailed MS methodology—a critical component of the study. The description provided (“...was injected onto an Agilent 1290/Sciex QTrap 5500 LC-MS/MS system equipped with a C18 reversed-phase ... column”) lacks essential details regarding the column type, dimensions, part number, gradient conditions, and solvent composition. Reporting retention times without providing these key parameters limits reproducibility and transparency.\nIn conclusion, the metabolomics analyses must be performed according to current state-of-the-art standards. Complete and detailed reporting in the Materials and Methods section is essential to ensure reproducibility and adherence to international guidelines.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_6": "So, in summary, in my view the authors have sufficiently addressed the points of critique from this reviewer, except for one point which relates to further updating the methods section on MS based metabolomics. In addition, to take away reviewer concerns of the validity of methodology used foruntargeted metabolomics, I would suggest that the authors provide raw data (MS peaks) to the reviewer to show how IPA (and perhaps some other relevant metabolites) was identified and quantified by untargeted metabolomics."}, "author_response": "We thank the Reviewers for their positive feedback and thoughtful evaluation of our manuscript. Following the constructive review, we revised the manuscript as suggested and included new data to address reviewer’s concerns.\nComments are in blue, and our responses are written in black.\nPOINT BY POINT RESPONSES TO REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have responded appropriately to my comments. I have no further comments\nWe thank Reviewer 1 for their initial comments and are pleased that we have satisfactorily addressed their concerns in the revised version of the manuscript.\nReviewer #2 (Remarks to the Author):\nThank you for your thorough revision of the manuscript. All points raised in my previous review were addressed, and I do not have any further comments.\nWe thank Reviewer 2 for their initial comments and are pleased that we have satisfactorily addressed their concerns in the revised version of the manuscript.\nReviewer #3 (Remarks to the Author):\nAuthors successfully addressed previously raised concerns and revised the manuscript accordingly. I have no further questions.\nWe thank Reviewer 3 for their initial comments and are pleased that we have satisfactorily addressed their concerns in the revised version of the manuscript.\nReviewer #4 (Remarks to the Author):\nBersier et al. have submitted a revised manuscript that addresses some of the reviewers’ questions and provides additional clarity on others; however, several issues remain unresolved. The primary concern—the lack of mechanistic insight into the action of IPA in myeloid cells—remains pertinent. This is particularly important given that the present data appear to conflict with previous findings (PMID: 38490195), where IPA targets different cells via a distinct mechanism. While this does not invalidate the authors’ findings, it highlights the need for more definitive mechanistic understanding, especially in light of multiple reports demonstrating the beneficial role of IPA in cancer therapy.\nWe thank reviewer 4 for their comments. As stated in our previous rebutal, we agree that elucidating the molecular mechanism underlying IPA molecular effectors in myeloid progenitors and myeloid cells is an important next step, especially for therapeutic applications. However, fully characterizing the as-yet unknown intracellular targets and receptors of a small molecule such as IPA will likely require several years and falls beyond the scope of the current manuscript.\nA second major concern pertains to the metabolomics analyses presented throughout the manuscript, including key figures (Figs. 2, 3, and 8), particularly those regarding IPA concentrations in patients. Based on the additional details provided, it is evident that the metabolomics analyses were conducted following the methodology of Fuhrer et al., 2011 (as per authors information), which no longer reflects current standards. Today, it is considered unacceptable to quantify or identify metabolites—as in Figure 3F (untargeted)—without the use of liquid chromatography (retention times are unavailable) and MS² validation. Direct injection and MS¹-based quantification may be acceptable in limited in vitro screening experiments with defined metabolite pools but not in complex biological matrices such as serum or cellular extracts.\nWe respectfully disagree with the Reviewer’s opinion that “Today, it is considered unacceptable to quantify or identify” metabolites in absence of chromatography. In untargeted metabolomics, quantification is based on MS1 scans – and FIA is by no mean inferior to (untargeted) LC-MS. The CVs are frequently better than with LC-MS (typically <10% across the dynamic range) because they don’t suffer from the errors associated with integrating 2D-chromatograms, that in metabolomics often feature asymmetric shapes and tails. Yes, FIA doesn’t allow resolving isomers, but this is a quest that can be addressed ad-hoc using the proper approach and the proper samples, which is what we did in the case for 3-IPA.\nSpecifically, we indeed analyzed serum samples using an mixed-mode LC (https://chemrxiv.org/engage/chemrxiv/article-details/6824924be561f77ed4c75578) using an Agilent 6546 QTOF. At the expected m/z of 188.0717, we observed only a single peak at low intensity in negative mode, and nothing in positive mode. Because of the low abundance, we struggled to collect an informative MS2 spectrum for spectral matching.\nTherefore, we moved on to confirm the identity by retention time.\nAccording to HMDB, there are 6 isomers with formula C11H11NO2: indole-3-propionic acid, 2-(1-methyl-1H-indol-3-yl)acetic acid, two indole variants with esterified tails, and two pyrroline derivatives with virtually no anionic group. Because of the preference in ionization in negative mode (over positive), we suspected that the peak could only be indole-3-propionic acid or 2-(1-methyl-1H-indol-3-yl)acetic acid, as all of the remaining candidates didn’t have an ionizable carboxylic group. Based on these notions, we opted to use the aforementioned mixed-mode LC system because the stationary phase acts a weak anion exchanger that retains acids, but not amines (see paper). When injecting pure standards of the two candidates, we observed that indole-3-propionic acid had exactly the same retention time observed in the serum samples (< 1 sec deviation), while the other candidate eluted later and was separated at baseline. Note that all other isomers without carboxylic group elute much earlier. As the only HMDB candidate that elutes at the observed time is indole-3-acetic acid, we could confirm its identity regardless of MS2 fragmentation.\nThe identification of IPA was further consolidated by the targeted analyses performed in the following by Metabolon, using a different method that also matched RT and MRM and, additionally, by the University of Lausanne metabolomics facility.\nMost importantly, the role and therefore the identity of IPA was validated in the functional tests that are included in the paper and demonstrate the effect IPA in potentiating the chemotherapeutic effect. As the functional validation is way more compelling than any spectral or analytical evidence, we decided to omit this long explanation from the manuscript, and included the data that initially led us to “discover” IPA, that is FIA-MS.\nWe have now included the following (page 8, lines 215-226):\n“Retention time comparison with IPA pure standard confirmed the IPA identity detected by untargeted FIA (Supplementary Fig. 4a).\nAnalysis of functional microbiome-encoded pathways using PICRUSt2 revealed an enrichment in tryptophan metabolism among taxa increased after FO treatment (Fig. 3d)32. Importantly, IPA is a by-product of tryptophan metabolism and produced exclusively by Clostridia and in particular Clostridiaceaes, a genus increased upon FO treatment (Supplementary Fig. 4b)33. The initial targeted metabolomics analysis showed an increase of IPA concentration in FO treated serum over the upper detection limit (Supplementary Fig. 4c). Consequently, we performed the targeted LC-MS/MS in a second facility for the samples reaching detection limit and this second analysis further validated a >10-fold IPA increase in the majority of FO treated mice (Fig. 3e).”\nSimilarly, the targeted quantification presented in Figure 3E is problematic. Using a single transition, as now described in the Materials and Methods section, is highly unconventional and does not meet current standards for reliable targeted metabolomics.\nWe are not aware of what Standards the Reviewer refers to. Outside of diagnostic and regulatory fields, it is common to have only one MRM per compound, see https://pmc.ncbi.nlm.nih.gov/articles/PMC3334318/, https://doi.org/10.1016/j.aca.2023.341791, https://www.nature.com/articles/nprot.2012.024, https://www.sciencedirect.com/science/article/pii/S0021967323005678, https://www.mdpi.com/2218-1989/14/11/622#app1-metabolites-14-00622 just to mention some work from various experts/labs in metabolomics. The use of a qualifier MRM is seen only when there is uncertainty in the RT, i.e. like in https://star-protocols.cell.com/protocols/1665.\nThe analysis was done by Metabolon, the leading CRO in the field, based on their internal library of known RT and MRM that are likely unique across their whole database (see more detailed information below). There is no reason to doubt the selectivity of their assays (for compounds with m/z 205, 204, and 188), in particular if we consider this measurement in the context of the whole study, including the earlier LC-MS analysis we did in our lab, validation by another metabolomic facility and the functional validation with IPA.\nFinally, it is unclear why the authors have only partially addressed reviewers’ requests for detailed MS methodology—a critical component of the study. The description provided (“...was injected onto an Agilent 1290/Sciex QTrap 5500 LC-MS/MS system equipped with a C18 reversed-phase ... column”) lacks essential details regarding the column type, dimensions, part number, gradient conditions, and solvent composition. Reporting retention times without providing these key parameters limits reproducibility and transparency.\nIn conclusion, the metabolomics analyses must be performed according to current state-of-the-art standards. Complete and detailed reporting in the Materials and Methods section is essential to ensure reproducibility and adherence to international guidelines.\nWe are happy to include in the revised manuscript the following additional information and in Supplementary Data 6 (pages 27-29, lines 711-774):\nThe absolute serum concentrations of 3-indolelactic acid( m/z 204→142, RT=2.6), 3-indolepropionic acid (m/z 188→59, RT=3.5), and tryptophan (m/z 205→188, RT=0.93)) were quantitated at Metabolon facility by LC-MS/MS. Stock and internal standard solutions were prepared by dissolving an appropriate amount of material in ACN: water (1:1). These stock solutions were diluted to the Standard H level and further diluted to each of the other concentration levels. For sample preparation, serum was thawed on ice and vortexed for 10–15 s before aliquoting. For blank, blank-IS, and calibration standards, 0.0500 mL PBS was pipetted into the appropriate wells of a 2 mL 96-well plate. For matrix QC samples, 0.0500 mL QC serum was added, and for study samples, 0.0500 mL serum was dispensed into designated wells. Calibration standards were prepared by adding 0.0200 mL of the corresponding calibration spiking solution. Internal standard working solution (0.0200 mL) was added to calibration standards, blank-IS, QC samples, and study samples. Blank samples received 0.0400 mL ACN/water (1:1), while blank-IS, QC samples, and study samples received 0.0200 mL ACN/water (1:1). Methanol (0.200 mL) was then added to all wells. Plates were capped, vortexed for 1 min, and centrifuged for 10 min at 4000 rpm. A 0.100 mL aliquot of the resulting supernatant was transferred to a fresh 650 µL 96-well plate, capped, and submitted for LC-MS/MS analysis. SAMPLE ANALYSIS: A 2.0 µL aliquot of supernatant was injected onto an Agilent 1290/Sciex QTrap 5500 LC-MS/MS system equipped with a Waters Acquity BEH C18 column (1.7 µm, 2.1 × 100 mm). Chromatographic conditions were as follows: Column temperature: 50 °C, Autosampler temperature: 4 °C, Mobile Phase A1: 0.05% formic acid in water, Mobile Phase B1: 0.05% formic acid in acetonitrile, Flow rate: 0.550 mL/min, Injection volume: 2.0 µL (adjusted as needed for sensitivity), Needle wash: methanol/water (1:1). The MS/MS parameters were as follow Curtain Gas: 35, Collision Gas: Medium, IonSpray Voltage: -4500 V, Temperature: 500 °C, Gas 1: 70, Gas 2: 70, Scan Mode: Multiple Reaction Monitoring (MRM), negative mode. Further information can be found in Supplementary Data 6.\nThe peak area of the individual analyte product ions was measured against the peak area of the product ions of the corresponding internal standards. Quantitation was performed using a weighted linear least squares regression generated from fortified calibration standards prepared immediately prior to each run. The LC-MS/MS raw data is collected and processed using SCIEX software Analyst 1.6.3 and processed using SCIEX OS-MQ software.\nAlternatively, targeted analysis of selected serum samples was performed at the metabolomic facility of the University of Lausanne. Serum (20 μL) was extracted with ice-cold methanol (180 μL) spiked with internal standards (IS). After centrifugation (4 °C, 21,000 g, 15 min), the supernatants were evaporated to dryness, reconstituted in water (50 μL), vortexed, and sonicated for 3 min. Each extract was then centrifuged again (4 °C, 21,000 g, 15 min) and the supernatant transferred to an LC–MS vial for LC–MS/MS analysis as described below. Calibrators were generated following the same procedure, by the addition of methanol spiked with IS mixture to each calibrator (20 μL), vortexed and transferred to LC-MS vials for the injection.\nExtracts were analyzed by Reversed Phase Liquid Chromatography coupled to tandem mass spectrometry (RPLC-MS/MS) on a 6495 triple quadrupole system (QqQ) interfaced with 1290 UHPLC system (Agilent Technologies), in the analytical conditions modified from van der Velpen et al.95. Data were acquired in Dynamic Multiple Reaction Monitoring (dMRM) mode with a total cycle time of 400 ms, applying optimized collision energies for each metabolite, using MassHunter (Agilent technologies, version B.07.00). Chromatographic separation was performed on a Acquity HSS T3 (1.8 μm 2.1 mm x 100 mm) column (Waters, Ireland), in positive ionization mode. The mobile phase consisted of A = 5 mM ammonium formate and 0.1% formic acid in H2O and B = 100% methanol. The gradient was as follows: 0 min 0 %B, 2 min 0 %B, 4 min 5 %B, 5 min 10 %B, 7 min 90 %B, 8 min 0 %B, 11 min 0 %B . The flow rate was 300 μL/min, the column temperature 20 °C, and the injection volume 2 μL. ESI source parameters were as follows: dry gas temperature 290 °C, dry gas flow 14 L/min, nebulizer pressure 45 psi, sheath gas temperature 350 °C, sheath gas flow 12 L/min, nozzle voltage 500 V, and capillary voltage +4000 V. Further information can be found in Supplementary Data 6.\nRaw LC–MS/MS data were processed using MassHunter Quantitative Analysis 10.0. Absolute quantification of indole metabolites was performed based on calibration curves and the stable isotope-labeled internal standards (IS) which were used to determine the response factor for each metabolite. Linearity of the standard curves was evaluated for each metabolite using a ten-point range; in addition, peak area integration was manually curated and corrected where necessary.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank Reviewer 5 for their initial comments and are pleased that we have satisfactorily addressed their concerns in the revised version of the manuscript.\nReviewer #6 - Arbitrating Reviewer\nSo, in summary, in my view the authors have sufficiently addressed the points of critique from this reviewer, except for one point which relates to further updating the methods section on MS based metabolomics. In addition, to take away reviewer concerns of the validity of methodology used foruntargeted metabolomics, I would suggest that the authors provide raw data (MS peaks) to the reviewer to show how IPA (and perhaps some other relevant metabolites) was identified and quantified by untargeted metabolomics.\nWe thank the arbitrating reviewer for assessing our work and their feedback. We have now completed the method part as follow:\nThe absolute serum concentrations of 3-indolelactic acid( m/z 204→142, RT=2.6), 3-indolepropionic acid (m/z 188→59, RT=3.5), and tryptophan (m/z 205→188, RT=0.93)) were quantitated at Metabolon facility by LC-MS/MS. Stock and internal standard solutions were prepared by dissolving an appropriate amount of material in ACN: water (1:1). These stock solutions were diluted to the Standard H level and further diluted to each of the other concentration levels. For sample preparation, serum was thawed on ice and vortexed for 10–15 s before aliquoting. For blank, blank-IS, and calibration standards, 0.0500 mL PBS was pipetted into the appropriate wells of a 2 mL 96-well plate. For matrix QC samples, 0.0500 mL QC serum was added, and for study samples, 0.0500 mL serum was dispensed into designated wells. Calibration standards were prepared by adding 0.0200 mL of the corresponding calibration spiking solution. Internal standard working solution (0.0200 mL) was added to calibration standards, blank-IS, QC samples, and study samples. Blank samples received 0.0400 mL ACN/water (1:1), while blank-IS, QC samples, and study samples received 0.0200 mL ACN/water (1:1). Methanol (0.200 mL) was then added to all wells. Plates were capped, vortexed for 1 min, and centrifuged for 10 min at 4000 rpm. A 0.100 mL aliquot of the resulting supernatant was transferred to a fresh 650 µL 96-well plate, capped, and submitted for LC-MS/MS analysis. SAMPLE ANALYSIS: A 2.0 µL aliquot of supernatant was injected onto an Agilent 1290/Sciex QTrap 5500 LC-MS/MS system equipped with a Waters Acquity BEH C18 column (1.7 µm, 2.1 × 100 mm). Chromatographic conditions were as follows: Column temperature: 50 °C, Autosampler temperature: 4 °C, Mobile Phase A1: 0.05% formic acid in water, Mobile Phase B1: 0.05% formic acid in acetonitrile, Flow rate: 0.550 mL/min, Injection volume: 2.0 µL (adjusted as needed for sensitivity), Needle wash: methanol/water (1:1). The MS/MS parameters were as follow Curtain Gas: 35, Collision Gas: Medium, IonSpray Voltage: -4500 V, Temperature: 500 °C, Gas 1: 70, Gas 2: 70, Scan Mode: Multiple Reaction Monitoring (MRM), negative mode. Further information can be found in Supplementary Data 6.\nThe peak area of the individual analyte product ions was measured against the peak area of the product ions of the corresponding internal standards. Quantitation was performed using a weighted linear least squares regression generated from fortified calibration standards prepared immediately prior to each run. The LC-MS/MS raw data is collected and processed using SCIEX software Analyst 1.6.3 and processed using SCIEX OS-MQ software.\nAlternatively, targeted analysis of selected serum samples was performed at the metabolomic facility of the University of Lausanne. Serum (20 μL) was extracted with ice-cold methanol (180 μL) spiked with internal standards (IS). After centrifugation (4 °C, 21,000 g, 15 min), the supernatants were evaporated to dryness, reconstituted in water (50 μL), vortexed, and sonicated for 3 min. Each extract was then centrifuged again (4 °C, 21,000 g, 15 min) and the supernatant transferred to an LC–MS vial for LC–MS/MS analysis as described below. Calibrators were generated following the same procedure, by the addition of methanol spiked with IS mixture to each calibrator (20 μL), vortexed and transferred to LC-MS vials for the injection.\nExtracts were analyzed by Reversed Phase Liquid Chromatography coupled to tandem mass spectrometry (RPLC-MS/MS) on a 6495 triple quadrupole system (QqQ) interfaced with 1290 UHPLC system (Agilent Technologies), in the analytical conditions modified from van der Velpen et al.95. Data were acquired in Dynamic Multiple Reaction Monitoring (dMRM) mode with a total cycle time of 400 ms, applying optimized collision energies for each metabolite, using MassHunter (Agilent technologies, version B.07.00). Chromatographic separation was performed on a Acquity HSS T3 (1.8 μm 2.1 mm x 100 mm) column (Waters, Ireland), in positive ionization mode. The mobile phase consisted of A = 5 mM ammonium formate and 0.1% formic acid in H2O and B = 100% methanol. The gradient was as follows: 0 min 0 %B, 2 min 0 %B, 4 min 5 %B, 5 min 10 %B, 7 min 90 %B, 8 min 0 %B, 11 min 0 %B . The flow rate was 300 μL/min, the column temperature 20 °C, and the injection volume 2 μL. ESI source parameters were as follows: dry gas temperature 290 °C, dry gas flow 14 L/min, nebulizer pressure 45 psi, sheath gas temperature 350 °C, sheath gas flow 12 L/min, nozzle voltage 500 V, and capillary voltage +4000 V. Further information can be found in Supplementary Data 6.\nRaw LC–MS/MS data were processed using MassHunter Quantitative Analysis 10.0. Absolute quantification of indole metabolites was performed based on calibration curves and the stable isotope-labeled internal standards (IS) which were used to determine the response factor for each metabolite. Linearity of the standard curves was evaluated for each metabolite using a ten-point range; in addition, peak area integration was manually curated and corrected where necessary.\nAdditionnally, we have further described how the identiy of IPA was validated as stated in rewiever 4 response:\nIn untargeted metabolomics, quantification is based on MS1 scans – and FIA is by no mean inferior to (untargeted) LC-MS. The CVs are frequently better than with LC-MS (typically <10% across the dynamic range) because they don’t suffer from the errors associated with integrating 2D-chromatograms, that in metabolomics often feature asymmetric shapes and tails. Yes, FIA doesn’t allow resolving isomers, but this is a quest that can be addressed ad-hoc using the proper approach and the proper samples, which is what we did in the case for 3-IPA.\nSpecifically, we indeed analyzed serum samples using an mixed-mode LC (https://chemrxiv.org/engage/chemrxiv/article-details/6824924be561f77ed4c75578) using an Agilent 6546 QTOF. At the expected m/z of 188.0717, we observed only a single peak at low intensity in negative mode, and nothing in positive mode. Because of the low abundance, we struggled to collect an informative MS2 spectrum for spectral matching.\nTherefore, we moved on to confirm the identity by retention time.\nAccording to HMDB, there are 6 isomers with formula C11H11NO2: indole-3-propionic acid, 2-(1-methyl-1H-indol-3-yl)acetic acid, two indole variants with esterified tails, and two pyrroline derivatives with virtually no anionic group. Because of the preference in ionization in negative mode (over positive), we suspected that the peak could only be indole-3-propionic acid or 2-(1-methyl-1H-indol-3-yl)acetic acid, as all of the remaining candidates didn’t have an ionizable carboxylic group. Based on these notions, we opted to use the aforementioned mixed-mode LC system because the stationary phase acts a weak anion exchanger that retains acids, but not amines (see paper). When injecting pure standards of the two candidates, we observed that indole-3-propionic acid had exactly the same retention time observed in the serum samples (< 1 sec deviation), while the other candidate eluted later and was separated at baseline. Note that all other isomers without carboxylic group elute much earlier. As the only HMDB candidate that elutes at the observed time is indole-3-acetic acid, we could confirm its identity regardless of MS2 fragmentation.\nThe identification of IPA was further consolidated by the targeted analyses performed in the following by Metabolon, using a different method that also matched RT and MRM and, additionally, by the University of Lausanne metabolomics facility.\nMost importantly, the role and therefore the identity of IPA was validated in the functional tests that are included in the paper and demonstrate the effect IPA in potentiating the chemotherapeutic effect. As the functional validation is way more compelling than any spectral or analytical evidence, we decided to omit this long explanation from the manuscript, and included the data that initially led us to “discover” IPA, that is FIA-MS."}]

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