Escherichia coli promotes colorectal cancer metastasis by maintaining enhancer-promoter loops through releasing neutrophil extracellular traps

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: major_revision

Abstract
The involvement of intestinal microbiota in the process of neutrophil-mediated colorectal cancer liver metastasis (CRCLM) is not yet fully understood. Here, we show that Escherichia coli (E. coli) is prevalent in CRC tissues with LM using 2bRAD-M-Seq and is linked to the release of neutrophil extracellular traps (NETs). Utilizing multi-omics and molecular techniques, we establish that E. coli recruits RIPK2, which promotes the binding of HNRNPK to the Atf3/Relb promoters in neutrophils, thereby enhancing their transcription. This process results in the upregulation of Ncf4, which triggers p-MLKL-mediated NET formation. NETs, in turn, increase the expression of TRPC1 and NFATC3 in CRC cells, promoting the calcium-dependent assembly of the STAT3/S100A8/9 heterotrimer. This trimer stabilizes STAT3-enhancer-promoter loops (EPLs), thereby reinforcing the Tns1 transcription and facilitating CRCLM. Our findings elucidate the mechanism by which E. coli-induced NETs promote CRCLM through epigenetic modifications, offering an insight into the role of EPLs in immune regulation and tumor progression.

Introduction
Intestinal flora plays a key role in colorectal cancer (CRC) metastasis by providing nutrients and inhibiting immune responses, fostering a tumor-supportive environment that allows CRC cells to evade detection1. This occurs mainly through the disruption of the intestinal barrier, facilitating CRC cell spread1. Neutrophils, the primary immune cells against bacterial infections, interact closely with intestinal flora and are pivotal in regulating tumor metastasis1. They express pattern recognition receptors (PRRs) like TLRs and Dectin-1 and are activated by opsonized particles, immune complexes, and chemokines2. While their role in neutrophil extracellular trap (NET) release remains unclear, PRR activation leads to degranulation, ROS production, and inflammation to combat pathogens3. Understanding neutrophil response mechanisms is essential for discovering therapeutic targets for cancers linked to abnormal neutrophil activation.

Calcium influx and S100A8/S100A9, a calcium-binding heterodimeric protein complex, are emerging as critical regulators in tumor progression. Physiological calcium signaling maintains cellular homeostasis, but dysregulated influx (via channels like TRPV, Orai) activates pro-oncogenic pathways (e.g., NF-κB, MAPK), upregulating S100A8/A9 expression4,5. S100A8/A9, released by myeloid cells or tumor cells, acts as a damage-associated molecular pattern, driving inflammation, immune evasion, angiogenesis, and metastasis by binding receptors (e.g., RAGE, TLR4) and amplifying calcium flux through TRP channel modulation4,5. However, key questions persist:(1) Spatiotemporal dynamics of calcium-S100A8/A9 crosstalk during tumor evolution and therapy resistance remain uncharacterized;(2) Differential roles of S100A8/A9 homodimers vs. heterodimers in calcium signaling are poorly defined;(3) Off-target effects limit therapeutic targeting of this axis (e.g., S100 inhibitors in clinical trials);(4) Whether S100A8/A9 contributes to calcium-driven tumorigenesis in non-inflammatory contexts is unclear6,7. Elucidating these gaps will advance precision strategies to disrupt calcium-S100A8/A9 interactions in cancer.

Enhancers and promoters form physical loops (enhancer-promoter loops, EPLs) via chromatin folding to precisely regulate gene expression, a process critically dysregulated in cancer. Aberrant EPLs drive oncogenesis by hyperactivating oncogenes (e.g., MYC, EGFR) through super-enhancer stabilization or silencing tumor suppressors (e.g., TP53) via enhancer hijacking, while mutations in chromatin remodelers (e.g., Cohesin, CTCF) further perturb loop architecture to rewire transcriptional programs8,9. Despite advances, key questions remain unresolved:(1) The spatiotemporal dynamics of EPLs during tumor evolution and therapy response are poorly characterized, limiting understanding of adaptive transcriptional reprogramming;(2) Tumor heterogeneity leads to context-dependent EPL configurations, yet pan-cancer principles are lacking;(3) Targeting EPLs via phase separation modulators (e.g., Cohesin inhibitors) faces challenges of off-target effects and incomplete efficacy10;(4) How cell interactions reshape EPLs in the tumor microenvironment remains unclear. Addressing these gaps is essential for translating EPL biology into precision oncology.

This study found that Escherichia coli (E. coli) from primary lesions in CRC liver metastasis (CRCLM) was crucial to the metastatic process. It recruited RIPK2, promoting NCF4-mediated NET release, which raised TRPC1 and NFATC3 expression and calcium influx in CRC cells. Calcium influx facilitated the S100A8/9 dimerization, activating STAT3 to form a STAT3/S100A8/9 heterotrimer, which enhanced STAT3-related enhanced-promoter loops for TNS1 transcription, thereby promoting CRCLM. This research highlighted E. coli’s role in CRC evasion and tumor immunity interaction, suggesting a potential therapeutic approach for CRC immune escape.

Results
E. coli triggered NET release to promote CRCLM
To identify bacteria associated with CRCLM, we analyzed 24 tissues with and without liver metastasis (LM) using type IIB restriction endonucleases site-associated DNA sequencing (2bRAD-M-Seq)11 (Supplementary Data 1). Principal coordinate analysis (PCA) revealed distinct microbiota between patients with and without LM (Fig. 1a), indicating that there were differences in the composition of the microbiota in CRC tissues with and without LM. We then examined the link between clinical factors, dietary habits, or genetic mutations and microbiome diversity. The smoking patients showed lower Chao1, Shannon, and Simpson indices than non-smoking people, indicating smoking disrupted the α-diversity of the microbiota (Supplementary Fig. 1, Supplementary Data 1). Other factors, including gender, age, tumor volume, alcohol consumption, diabetes, preoperative chemotherapy, genetic mutations, pathological grading, and T or N stage, didn’t influence the microbiome α-diversity (Supplementary Fig. 1, Supplementary Data 1). Subsequently, we investigated the differential abundance of bacterial species between the LM and non-LM groups, specifically focusing on their contribution to CRCLM. Notably, E. coli and Klebsiella pneumoniae (K. pneumoniae) exhibited increased prevalence in cases with LM (Figs. 1b, c, Supplementary Fig. 2a, Supplementary Table 1). To elucidate the effects of these two bacterial species, we developed various murine models to identify the most appropriate one for our study. We found that LM occurred only with MC38 cell injection into the posterior rectal wall (Supplementary Fig. 2b). Furthermore, E. coli and K. pneumoniae were found to promote LM, CRC growth, and Vimentin expression, with E. coli being the most impactful (Supplementary Fig. 2c–h). We then created an LM model in huHSC-C-NKG-ProF mice using human neutrophil infusions, showing that results from C57BL/6J mice were replicable in humanized immune system mice (Supplementary Fig. 2i–l). Moreover, E. coli was elevated in the portal vein and liver of patients with LM (Supplementary Fig. 2m, n), indicating that it migrated retrograde from the intestine through the portal vein into the liver. This suggested that E. coli still played a carcinogenic role in metastatic foci.

Next, we examined how E. coli contributes to CRCLM, finding it reduced E-Cadherin and increased N-Cadherin and Vimentin levels in mouse CRC tissues (Fig. 1d–f). Previous report has indicated that in vitro stimulation of CRC cells by E. coli didn’t alter Cadherin protein expression12; however, our findings suggested that the in vivo stimulation facilitated such alterations, suggesting E. coli relied on the tumor microenvironment (TME) to enhance CRC malignancy. To eliminate the confounding effects of other microorganisms, we compared metastasis progression following the removal or retention of the entire intestinal microbiota, followed by the transplantation of E. coli. We found that E. coli promoted LM, CRC growth, and Vimentin expression, irrespective of prior antibiotic use (Supplementary Fig. 3, 4). It is well-established that symbiotic bacteria play a synergistic role in immune regulation13. We defined the bacteria whose content in CRC tissues was highly correlated with E. coli (with R value > 0.8 and P value < 0.05) as symbiotic bacteria14. K. pneumoniae, Sphingomonas aerolata, and Lutimaribacter sp023703375 were identified as its symbionts (Supplementary Fig. 5). Only K. pneumoniae showed a cancer-promoting effect like E. coli, but weaker (Supplementary Fig. 6a–f). Similar results were found in huHSC-C-NKG-ProF mice (Supplementary Fig. 6g–i).

E. coli causes inflammation and attracts immune cells to tumors, affecting tumor immunity15,16. Therefore, we investigated whether E. coli promotes metastasis through specific immune cells by selectively removing macrophages, neutrophils, or T cells. E. coli was indicated to increase LM, CRC growth, and Vimentin expression, but this was reduced by removing macrophages or neutrophils, and the depletion of macrophages or neutrophils didn’t influence each other’s infiltration (Supplementary Fig. 7a–g). In contrast, removing T cells increased LM, CRC growth, and Vimentin expression (Supplementary Fig. 7a–c, 7e–g). Furthermore, neutrophil removal had a stronger inhibitory effect than macrophage removal, a finding consistent in huHSC-C-NKG-ProF mice (Supplementary Fig. 7a–c, 7e–j). Therefore, we focused on E. coli’s role in promoting metastasis via neutrophils, as bacterial infection triggers NET release17, aiding tumor evasion3. We found that disrupting NETs with DNase I or the PAD4 inhibitor GSK484 hydrochloride reduced LM, CRC growth, and Vimentin expression (Supplementary Fig. 8). Furthermore, E. coli boosted NET release in CRC (Fig. 1g), and E. coli abundance was positively correlated with the number of NETs (Supplementary Data 1–3, Fig. 1h), but its effects on Cadherins and Vimentin were weakened by neutrophil depletion (Fig. 1i–l). Therefore, we believed that E. coli induced CRCLM by promoting NET release.

Subsequently, we employed multicenter clinical samples to investigate the association between E. coli abundance and epithelial-mesenchymal transition (EMT) marker expression or NET levels in CRC tissues. In CRC tissues with LM obtained from our two centers, we observed an increase in E. coli and NET abundance, a decrease in E-Cadherin, and an elevation in N-Cadherin and Vimentin levels compared to non-LM tissues (Supplementary Fig. 9a–e, Supplementary Data 1, 2). E. coli abundance correlated positively with NETs levels, and N-Cadherin or Vimentin expression, but negatively with E-Cadherin levels (Supplementary Fig. 9f, Supplementary Data 1, 2). Moreover, higher E. coli or NET levels indicated poor prognosis (Supplementary Fig. 9g, Supplementary Data 1, 2). These results were consistent with findings from other centers (Supplementary Fig. 9h–n, Supplementary Data 3). We then examined the link between clinical factors, dietary habits, or genetic mutations and either the occurrence of CRCLM or the prevalence of E. coli to identify any unknown factors affecting both latter. Our findings showed that older age, mucous adenocarcinoma and diabetes increased metastasis risk, while the absence of lymph node metastasis or reduced E. coli abundance lowered the risk, with the latter having the most significant protective effect (Supplementary Data 4). Additionally, skipping preoperative chemotherapy reduced the risk of infection by E. coli (Supplementary Data 5), as some drugs like 5-fluorouracil suppress fast-growing bacteria18, potentially enabling drug-resistant E. coli to thrive and increase infection risk. Overall, E. coli contributed to CRCLM.

Neutrophils are known to express PRRs and are activated by bacterial infection2, so we studied how E. coli affects PRR expression and found it increased NOD1/2 and TLR2/4 levels in neutrophils (Fig. 1m). Using inhibitors on tumor-bearing mice, we discovered that blocking NOD1/2 (NOD1/2 antagonist-1) more effectively reduced NET formation in normal colorectal or CRC tissues than inhibiting TLR2/4 (Sparstolonin B) (Supplementary Fig. 10a). Moreover, inhibiting NOD1/2 weakened the NET release induced by E. coli, and this effect was stronger than that of inhibiting TLR2/4 (Supplementary Fig. 10a). Therefore, our focus was then on the role of RIPK2, a downstream molecule of NOD1/219, in E. coli-induced CRCLM. Since RIPK2 is crucial in pathogen defense19, we first evaluated its off-target effects. Our results showed that altering RIPK2 in neutrophils didn’t change systemic immune cell counts post-infection (Supplementary Fig. 10b, c), indicating it was safe. Functional assays revealed that Ripk2 conditional knockout (cKO) in neutrophils reduced E. coli’s effect on EMT marker expression and NET release in CRC tissues, while Ripk2 conditional knock-in (cKI) in neutrophils increased it (Fig. 1n, Supplementary Fig. 10d), highlighting RIPK2’s promoting role in E. coli-driven LM. Furthermore, in vivo assay indicated E. coli boosted NET release and LM, CRC growth, and Vimentin expression, and these effects were blocked by NOD1/2 antagonist-1 or Ripk2-cKO (Supplementary Fig. 11a–g). In huHSC-C-NKG-ProF mice, E. coli-induced NET release and metastasis were also inhibited by NOD1/2 antagonist-1 or RIPK2-KO neutrophils (Supplementary Fig. 11h–l), indicating E. coli promoted CRCLM by engaging RIPK2 through NOD1/2 in neutrophils.

RIPK2 recruited HNRNPK to activate Atf3/Relb expression, thus inducing NET release
To elucidate the role of RIPK2 in neutrophils in enhancing NET release, we conducted single-cell RNA sequencing (scRNA-Seq). Initially, we assessed the quality of the sequencing data, noting that a sequencing saturation of 80% and a median gene count of 2,700 per cell, indicating high-quality data (Supplementary Fig. 12a). Subsequently, we identified genes whose expression was modulated by Ripk2-cKO and investigated the functional role of RIPK2 through pathway enrichment analysis of these differentially expressed genes. Our findings revealed an increase in E. coli rRNA expression in Ripk2-cKO neutrophils and the upregulation of genes associated with the “Response to bacterium” pathway (Fig. 2a, b). qPCR further confirmed the upregulation of the expression of these 24 genes in neutrophils, including Hba-a2 (Supplementary Fig. 12b), suggesting that RIPK2 in neutrophils facilitated an effective immune response. To investigate which neutrophil subpopulations are regulated by RIPK2 and thus participate in LM, Uniform Manifold Approximation and Projection (UMAP)20 was performed. UMAP20 identified four neutrophil subpopulations: Atf3+ Relb+, Ifit3+ Ifit1+ Ifi27l2a+ Isg15+, Ifitm1+ Slpi+, and Cybb+ Cyba+ neutrophils (Fig. 2c, d). We then sorted and reinfused these subpopulations into mice to evaluate their functions. We indicated that Atf3+ Relb+ neutrophils, along with Ifit3+ Ifit1+ Ifi27l2a+ Isg15+, and Ifitm1+ Slpi+ neutrophils, promoted LM, CRC growth, and Vimentin expression, with Atf3+ Relb+ neutrophils having the greatest impact (Supplementary Fig. 12c–h). In contrast, Cybb+ Cyba+ neutrophils had the opposite effect (Supplementary Fig. 12c–h). Similar outcomes were observed in huHSC-C-NKG-ProF (Supplementary Fig. 12i–k). Given the pronounced metastasizing effect and predominance of Atf3+ Relb+ neutrophils, coupled with the observation that Ripk2-cKO most significantly reduced their infiltration compared to the other three subpopulations (Fig. 2e, f), our investigation centered on Atf3+ Relb+ neutrophils. Gene Set Enrichment Analysis (GSEA) indicated the downregulation of genes associated with “Transcriptional misregulation in cancer” in Atf3+ Relb+ neutrophils (Fig. 2g). Furthermore, qPCR analysis confirmed that Ripk2-cKO downregulated the expression of these 13 gene in Atf3+ Relb+ neutrophils, including Bcl2a1a (Supplementary Fig. 12l), thereby disrupting the transcriptional network in neutrophils. The findings indicated that RIPK2 served a regulatory function in neutrophil activity by sustaining Atf3+ Relb+ neutrophils. Subsequently, we conducted an exploration of the primary transcription factors within the Atf3+ Relb+ neutrophils utilizing Single-Cell Regulatory Network Inference and Clustering (SCENIC), and revealed ATF3 and Relb as the principal regulatory elements (Fig. 2h). Therefore, we explored RIPK2’s role in regulating the expression of ATF3/RelB and their effect on NET release. Ripk2-cKO was found to reduce ATF3/RelB expression in neutrophils (Fig. 2i). Rescue assays revealed that E. coli enhanced ATF3/RelB expression in neutrophils, whereas Ripk2-cKO mitigated these effects (Supplementary Fig. 12m). Furthermore, DNA pull-down-mass spectrometry (MS) revealed that HNRNPK bound to the promoters of Atf3/Relb in neutrophils (Fig. 2j, Supplementary Data 6, 7). Molecular docking and Chromatin Immunoprecipitation (ChIP)-qPCR confirmed HNRNPK’s binding in neutrophils, which was diminished by Ripk2-cKO (Fig. 2k–m). We further indicated that Ripk2-cKO reduced the levels of ATF3/RelB in neutrophils, while Hnrnpk-cKI alleviated this inhibitory effect (Fig. 2n, o), indicating RIPK2 enhanced their expression via recruiting HNRNPK. Rescue experiments indicated that E. coli induced the protein expression of ATF3/RelB, and this promoting effect was inhibited by Ripk2-cKO (Fig. 2p). Additionally, to understand how the RIPK2 signaling axis, activated by E. coli, influences NET release, we examined NET-related markers21 and observed that E. coli increased the levels of p-MLKL/MLKL in neutrophils (Supplementary Fig. 13a). What’s more, the absence of Nod1/2, Ripk2, Hnrnpk, Atf3, or Relb inhibited this phosphorylation (Supplementary Fig. 13b). Rescue assay further indicated that the phosphorylation of MLKL, which was induced by E. coli, was attenuated by the presence of NOD1/2 antagonist-1, Ripk2-cKO, Hnrnpk-cKO, and Atf3/Relb-cKO (Supplementary Fig. 13c).

We then explored whether E. coli uses RIPK2 to recruit HNRNPK, facilitating NET release. In C57BL/6J mice, E. coli and Ripk2-cKI increased NET release, LM, CRC growth, and Vimentin expression, while Ripk2-cKO or Hnrnpk-cKO reduced these effects, with Hnrnpk-cKO diminishing the tumor-promoting influence of E. coli and Ripk2-cKI (Fig. 2q, Supplementary Fig. 14a–g). In huHSC-C-NKG-ProF mice, neutrophils overexpressing RIPK2 (RIPK2-OE) and E. coli enhanced the release of NETs and facilitated LM (Supplementary Fig. 14h–k). Conversely, neutrophils deficient in RIPK2 (RIPK2-KO) or HNRNPK (HNRNPK-KO) exhibited a contrary effect, with HNRNPK-KO neutrophils attenuating the tumor-promoting effects associated with E. coli and RIPK2-OE (Supplementary Fig. 14h–k). We also investigated whether E. coli induced NET release by recruiting HNRNPK to increase ATF3/RelB expression, promoting LM. In C57BL/6J mice, E. coli and Hnrnpk-cKI increased NET release, LM, CRC growth, and Vimentin expression, while Atf3/Relb-cKO countered these effects (Supplementary Fig. 15a–g). Similarly, in huHSC-C-NKG-ProF mice, E. coli and HNRNPK-OE neutrophils promoted these cancer-promoting outcomes, whereas ATF3/RelB-KO neutrophils had the opposite impact (Supplementary Fig. 15h–k). This suggested HNRNPK, activated by E. coli, enhanced ATF3/RelB expression in neutrophils to promote NET-dependent LM.

ATF3/RelB collaborated to activate Ncf4 expression to induce NET release
To investigate the mechanism of ATF3/RelB in NET release, we analyzed their DNA-binding sites. We identified thousands of binding peaks for them, primarily at transcription start sites and in distal intergenic regions, introns, and promoters (Supplementary Fig. 16a–c). It is important to highlight that the binding sites for ATF3/RelB exhibited a high degree of proximity, and the distribution of signals was positively correlated (Fig. 3a, b), suggesting co-localization of ATF3/RelB facilitated NET release. Therefore, we aimed to ascertain whether ATF3/RelB enhances the release of NETs by facilitating the transcription of common genes associated with NETs. We noted that the overlap of peaks associated with ATF3 or RelB in infiltrating neutrophils within CRC was relatively minimal across their respective three replicates (Fig. 3c). We proposed that this observation might be attributed to the pronounced heterogeneity of neutrophils within the TME22, which led to variability in the peaks elicited by the same transcription factor across different tumor specimens. Among their common target genes, Ncf4 was associated with “NET formation”21,23,24 (Fig. 3c, d). NCF4 is part of the NADPH oxidase complex24, which facilitates MLKL translocation to the cell membrane, promoting chromatin DNA release through MLKL phosphorylation21. This observation aligned with the hypothesis that E. coli induced MLKL phosphorylation via the HNRNPK-ATF3/RelB signaling pathway (Supplementary Fig. 13). Subsequently, we examined the regulatory role of ATF3/RelB in the expression of NCF4. According to JASPAR25 predictions, ATF3/RelB were likely to bind to the TGAATCA and AGATTCCTCAGGGGGAAAGC sequences within the Ncf4 promoter, respectively (Fig. 3e, Supplementary Table 2). This binding was substantiated through molecular docking and ChIP-qPCR assays (Fig. 3e, f). Furthermore, the deletion of these sequences resulted in the loss of Ncf4 promoter binding to ATF3/RelB in neutrophils (Fig. 3g, Supplementary Fig. 16d), and while ATF3/RelB enhanced NCF4 expression, this effect was abolished following the sequence deletions (Fig. 3h, i).

Given the close proximity of the ATF3/RelB binding sites on the Ncf4 promoter in neutrophils (Fig. 3d), we hypothesized an interaction between these two transcription factors, a hypothesis that was subsequently validated through molecular docking and immunoprecipitation analyses (Fig. 3j, k). Additionally, we explored whether disrupting the ATF3/RelB complex would impede the binding of either component to the Ncf4 promoter. To investigate this, we engineered cells with site mutations at the ATF3/RelB complex interaction site. We indicated that mutations in ATF3 (K78A/K52A/K89A/H53A/H56A/C55A/K97A, collectively referred to as ATF3-7A) and RelB (R93A/L94A/D149A/G151A/P101A/H258A/P103A/R102A/E146A, collectively referred to as RelB-9A) inhibited their interaction and binding to the Ncf4 promoter, reducing NCF4 expression in neutrophils (Fig. 3l–o). Before proceeding to phenotypic studies, we also assessed the off-target effects associated with the cKO in neutrophils of four genes mentioned earlier: Hnrnpk, Atf3, RelB, and Ncf4. Similar to observations with Ripk2, mice harboring modifications in these four genes exhibited no alterations in systemic immune cell counts, suggesting the absence of off-target effects (Supplementary Fig. 16e, f).

Next, we analyzed whether E. coli facilitates NET-dependent CRCLM by activating NCF4 expression in neutrophils via the ATF3/RelB pathway. We discovered that E. coli and Atf3/Relb-cKI increased NET release, LM, CRC growth, and Vimentin expression, while Ncf4-cKO reduced these effects, counteracting E. coli and Atf3/Relb-cKI (Fig. 3p, Supplementary Fig. 17a–f). In huHSC-C-NKG-ProF mice, E. coli and ATF3/RelB-OE neutrophils enhanced these cancer-promoting processes, but NCF4-KO neutrophils inhibited them (Supplementary Fig. 17g–j). Hence, we proposed that ATF3/RelB interacted at the Ncf4 promoter, boosting Ncf4 transcription and NET release.

The MAPK and NF-κB pathways are known to facilitate NET release26, and RIPK2 is an activator of these pathways27; however, the relationship between these pathways and the RIPK2-ATF3/RelB-NCF4 axis has not been fully elucidated. Our study demonstrated that inhibitors targeting ERK1/2 (SCH772984), p38 (p38 MAPK-IN-1), JNK (JNK-IN-8), and NF-κB (JSH-23) didn’t influence the expression levels of RIPK2, ATF3, RelB, or NCF4 in neutrophils (Supplementary Fig. 18a). Furthermore, while Ripk2-cKO or cKI was involved in the phosphorylation of MAPK and NF-κB (Supplementary Fig. 18b), the Atf3/Relb/Ncf4-cKO or cKI didn’t impact the activation of these pathways (Supplementary Fig. 18c). Consequently, we hypothesized that RIPK2 facilitates NET-dependent CRCLM via activation of the MAPK and NF-κB pathways, independent of the ATF3/RelB-NCF4 axis. Our findings indicated that Ripk2-cKI enhanced NET formation, LM, CRC growth, and Vimentin expression, whereas the aforementioned inhibitors mitigated these phenotypes and diminished the effects of Ripk2-cKI (Supplementary Fig. 19a–g). These observations were corroborated in huHSC-C-NKG-ProF mice (Supplementary Fig. 19h–k). Therefore, RIPK2 appeared to promote NET-related CRCLM through two parallel pathways: the activation of MAPK/NF-κB and the ATF3/RelB-NCF4 axis.

Additionally, PAD4 releases NET independently of MLKL26, and its relationship with RIPK2-HNRNPK-ATF3/RelB-NCF4-MLKL axis was also unclear. We found that Ripk2, Hnrnpk, Atf3, Relb, Ncf4-cKO or inhibiting MLKL didn’t affect PAD4 expression in neutrophils, and GSK484 hydrochloride didn’t influence RIPK2, HNRNPK, ATF3, RelB, NCF4 protein expression or the MLKL phosphorylation level in neutrophils (Supplementary Fig. 20a, b). Thus, RIPK2 didn’t rely on PAD4 for NET formation, and PAD4 and MLKL operated as two parallel pathways in promoting NET formation.

NETs stimulated the TRPC1-S100A8/9-STAT3 pathway to facilitate CRCLM
To explore how NETs aid CRCLM, we used UMAP to identify 15 cell subpopulations in CRC tissues from our scRNA-Seq data (Supplementary Fig. 21a, b). Cellchat28 revealed that Ripk2-cKO neutrophils influenced CRC cells without receptor-ligand interactions (Fig. 4a). However, we observed ligand-receptor interactions between neutrophils and macrophages (Fig. 4a), prompting a detailed analysis of the interaction patterns and intensities between them. Our findings indicated that, although communication existed between neutrophils and macrophages, the signals were predominantly moderate or weak (Supplementary Fig. 21c). Furthermore, the removal of either neutrophils or macrophages didn’t impact the infiltration of the other cell type (Supplementary Fig. 7d), suggesting that the interaction between these two cell types was relatively weak. Consequently, we shifted our focus to investigate the impact of neutrophils on CRC cells, rather than that of macrophages. Since neutrophils affect tumor growth by modulating calcium signaling29, we examined the impact of Ripk2-cKO neutrophil depletion on calcium channel gene expression in CRC cells (Supplementary Fig. 21d). Our investigation revealed that Ripk2-cKO inhibited only Cacnaic and Trpc1 mRNA expression (Supplementary Fig. 21d). Given that CACNAIC predominantly operates in myocardium, smooth muscle, and the brain, rather than colorectal cells30, our research concentrated on examining TRPC1 in CRC cells. Ripk2-cKO neutrophil removal was shown to reduce TRPC1 expression in CRC cells, while PMA-activated NETs increased TRPC1 levels, and DNase I-inhibited neutrophils decreased them (Figs. 4b–4d, Supplementary Fig. 21d). Ripk2-cKO neutrophil removal also blocked calcium influx in CRC cells (Fig. 4e). We then studied how NETs up-regulate the expression of TRPC1 in CRC cells. It has been reported that TRPC1 enhances NFATC3’s nuclear translocation via the Ca²⁺-calcineurin pathway, while NFATC3 increases TRPC1 transcription, influencing vascular remodeling and hypertension31,32. We proposed a TRPC1-Ca²⁺-NFATC3-TRPC1 positive feedback loop. Ripk2-cKO neutrophil removal showed reduced NFATC3 expression in CRC cells; Nfatc3-KO in CRC cells decreased TRPC1 expression, whereas Nfatc3-OE increased it (Supplementary Fig. 21e–g). Furthermore, Ca2+ channel agonist 1 elevated NFATC3 and TRPC1 levels in CRC cells, counteracting the effects of Ripk2-cKO neutrophil loss (Supplementary Fig. 21h). Nfatc3-OE also mitigated the negative impact of Ripk2-cKO neutrophil loss on TRPC1 expression in CRC cells (Supplementary Fig. 21i), highlighting the feedback loop’s importance for maintaining TRPC1 expression in CRC cells. Therefore, NETs facilitated calcium ion influx via TRPC1, which subsequently upregulated NFATC3 expression. As a transcription factor, NFATC3 enhanced the expression of TRPC1.

To explore how TRPC1-mediated calcium influx promotes CRC escape, pathway enrichment analysis was performed and revealed that Ripk2-cKO neutrophil depletion inhibited the JAK-STAT pathway (Fig. 4f). Both Ripk2-cKO neutrophil removal and Trpc1-KO in CRC cells reduced STAT1/3 phosphorylation in CRC cells, while Trpc1-OE countered this effect (Fig. 4g–i, Supplementary Fig. 21j–l). Unlike neutrophil clearance, PMA-stimulated NETs increased STAT1/3 phosphorylation in CRC cells, which was diminished by Trpc1-KO (Fig. 4j, Supplementary Fig. 21m–p). This suggested NETs promoted CRCLM by boosting TRPC1 expression and activating STAT1/3 phosphorylation in CRC cells. Additionally, scratch and Transwell assays indicated that Trpc1-KO reduced CRC cell migration and invasion, but this inhibition was mitigated by STAT1 (2-NP) and STAT3 (Colivelin) agonists (Figs. 4k–m, Supplementary Fig. 22a–c).

Additionally, we conducted an analysis of the mechanism by which NETs facilitate CRCLM through the TRPC1-STAT1/3 pathway. S100A8 and S100A9, part of the calcium-binding S100 protein family, form heterodimers upon calcium binding33. We discovered that S100A8 interacted with S100A9 in CRC cells, but this interaction was disrupted in Trpc1-KO (Fig. 4n, o), indicating S100A8/9 were downstream of TRPC1. TRPC1 appeared to stabilize the S100A8/9 complex through calcium ions, as shown by rescue assays where Trpc1-OE increased S100A8’s interaction to S100A9 in CRC cells, an effect reduced by the calcium blocker Diltiazem hydrochloride (Supplementary Fig. 22d). However, the STAT1/3 agonist didn’t affect the S100A8/9 interaction (Supplementary Fig. 22e). Therefore, we next conducted an in-depth investigation into the regulatory effect of S100A8/9 interaction on the activation of STAT1/3 and its subsequent impact on CRCLM. Molecular docking and immunoprecipitation showed specific hydrogen bonds between S100A8 and S100A9, and mutations in these sites (S100A8-H27A/N25A/E6A/Q44A, S100A9-N99A/R101A/T48A, collectively referred to as S100A8/9-7A) disrupted their interaction in CRC cells (Figs. 4p, q). Trpc1-OE in CRC cells was found to enhance CRC cell migration and invasion, but S100A8/9-7A in CRC cells reduced this effect (Figs. 4r–t). Moreover, PMA-stimulated NETs increased STAT1/3 phosphorylation in CRC cells, but S100A8/9-7A inhibited p-STAT3/STAT3 without affecting p-STAT1/STAT1 (Fig. 4u, Supplementary Fig. 22f). Consequently, we concentrated on examining the role of STAT3, as opposed to STAT1, in facilitating CRCLM mediated by S100A8/9. Trpc1-OE was indicated to increase p-STAT3/STAT3, which S100A8/9-7A inhibited it (Fig. 4v). Lastly, S100a8/9-KO was shown to reduce CRC cell migration and invasion, but Colivelin countered these effects (Fig. 4w–y, Supplementary Fig. 22g). This suggested NETs promoted S100A8/9 dimer formation by increasing TRPC1 expression, enhancing STAT3 phosphorylation and CRCLM.

STAT3/S100A8/9 EPLs promoted CRCLM
S100A8/9 acts as the transcriptional coactivators and shares the motifs with STAT334, suggesting they might function as STAT3 cofactors. Immunofluorescence showed these proteins colocalized in CRC cell nuclei (Fig. 5a, Supplementary Fig. 23a), and molecular docking and immunoprecipitation confirmed their interaction (Figs. 5b-5d). Furthermore, Trpc1-KO disrupted this interaction in MC38 cells (Fig. 5e, f), indicating this interaction was driven by the “NETs-CRC cell” signal.

Subsequently, we investigated the impact of disrupting this heterotrimer on CRCLM. Site mutations in STAT3 (S372A/K488A/K244A/E455A/K318A/N315A/E311A, collectively termed STAT3-7A) or S100A8 (S62A/D59A/N64A/D63A/Q23A/K35A/N32A/K36A, collectively termed S100A8-8A) disrupted their interaction in CRC cells (Figs. 5g–i), as did mutations in STAT3 (E506A/R245A/I498A/Q503A/N489A, referred to as STAT3-5A) or S100A9 (R25A/T14A/R11A/T15A, referred to as S100A9-4A) (Fig. 5g, j, k). Functional assay showed that site mutations (STAT3-7A+5A/S100A8-8A/S100A9-4A, STAT3/S100A8/9-24A) reduced the migration and invasion of MC38 cells (Fig. 5l–n).

Given that S100A8/9 interacted with STAT3 and shared the motifs34, we hypothesized that the enhancer, promoter, and trimer of STAT3/S100A8/9 formed EPLs35, reducing the distance between the enhancers and promoters of specific genes, thereby facilitating CRCLM. Therefore, we disrupted the STAT3/S100A8/9 complex and compared STAT3-mediated chromatin changes before and after STAT3/S100A8/9 trimer disruption. Initially, we examined the influence of STAT3/S100A8/9-24A on the DNA-binding capability of STAT3. Cleavage Under Targets and Tagmentation (CUT&Tag) data indicated that, in wild-type (WT) CRC cells, STAT3 shared binding peaks with S100A8 (26.897%) and S100A9 (27.130%) (Supplementary Fig. 23b). S100A8 shared peaks with STAT3 (19.390%) and S100A9 (35.199%), and S100A9 shared peaks with STAT3 (16.449%) and S100A8 (29.624%) (Supplementary Fig. 23b). Moreover, in STAT3/S100A8/9-24A CRC cells, the shared binding peaks were elevated (Supplementary Fig. 23c), STAT3 exhibited a shared binding peak with S100A8 at 34.649% and with S100A9 at 36.349% (Supplementary Fig. 23c), suggesting that the disruption of the complex upregulated STAT3’s DNA binding activity. As expected, STAT3/S100A8/9-24A increased STAT3’s DNA-binding affinity (Supplementary Fig. 23d), suggesting that the complex obscured part of STAT3’s DNA-binding domain. Following this, we assessed the effects of STAT3/S100A8/9-24A on the formation of the STAT3/S100A8/9-EPLs. Our analysis revealed that the EPL structure on 12 chromosomes, including chromosome 1, exhibited damage (Supplementary Fig. 23e). In WT cells, a total of 4,459 EPLs were identified, whereas in STAT3/S100A8/9-24A cells, 652 EPLs were detected, indicating the mutation disrupted the integrity of the EPLs (Fig. 5o, p, Supplementary Fig. 23e). As anticipated, in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) data showed that STAT3’s DNA binding was elevated after mutations (Supplementary Fig. 23f), indicating that the mutations decreased EPL formation and exposed STAT3’s DNA-binding domain. Moreover, STAT3/S100A8/9-24A decreased EPL size from a median of 80 to 50 kb (Fig. 5q). It was notably that a gene’s cumulative EPL intensity wasn’t directly proportional to its number (Fig. 5r), implying that active enhancers didn’t solely interact with their promoters or enhancers. Further analysis pointed out that EPLs linked many enhancers to promoters, including oncogenes like Tns1, but these connections weakened after STAT3/S100A8/9-24A (Fig. 5s). Bioinformatics analysis demonstrated that, of the 5,111 EPLs, only 3,864 were defined as structures between enhancers and promoters of individual genes, rather than cross-structures among multiple genes. Consequently, our focus was on elucidating how the EPLs of these 3,864 genes was modulated by the STAT3/S100A8/9 complex. STAT3/S100A8/9-24A were indicated to downregulate 50 out of 3,864 EPLs (Fig. 5t, Supplementary Data 8). It was noteworthy that STAT3/S100A8/9-24A exclusively disrupted the loops of the link enhancer-promoter, whereas the loops of the link enhancer-enhancer or promoter-promoter remain unaffected by such mutations (Supplementary Data 8). We further analyzed STAT3’s binding to oncogene promoters or enhancers to determine if STAT3/S100A8/9-24A disrupt their expression or STAT3’s DNA binding on their cis-acting elements. We found that the STAT3/S100A8/9-24A didn’t impact STAT3’s binding ability at these gene locus (Supplementary Fig. 24a, b), but impede their transcription and protein expression, including Tns1 (Supplementary Fig. 24c, d), suggesting that interference with this complex inhibited EPLs, resulting in a downregulation of their expression, while preserving STAT3’s affinity for DNA. Finally, we employed pathway enrichment analysis to explore the functions of these differential EPLs. Enrichment analysis revealed that genes with EPLs at promoters and enhancers were related to migration and invasion (Fig. 5u). All in all, the STAT3/S100A8/9 complex played a critical role in the formation of EPLs. While it diminished the overall DNA-binding affinity of STAT3, its simultaneously reduced the spatial distance between enhancers and promoters on the cis-acting elements of various oncogenes, thereby facilitating their transcription. Notably, this process occurred without altering the affinity of STAT3 for the cis-acting elements of these oncogenes.

STAT3/S100A8/9-EPLs stabilized Tns1 transcription in CRC cells
Given the observed reduction in EPL binding at the Tns1 locus within the STAT3/S100A8/9-24A group, which was the most pronounced among the 50 differential EPLs (Fig. 5t), our study concentrated on TNS1. Visualization and ChIP-qPCR analyses confirmed STAT3 binding to the Tns1 promoters and enhancers, while STAT3/S100A8/9-24A were found to disrupt the EPLs at this gene locus (Figs. 6a–c). STAT3/S100A8/9-24A was shown to inhibit TNS1 mRNA and protein expression (Figs. 6d, e). Therefore, we examined whether STAT3/S100A8/9-EPLs facilitated CRC cell metastasis by stabilizing Tns1 transcription. Tns1-OE in CRC cells was found to reduce E-Cadherin and increased N-Cadherin and Vimentin expression, boosting cell migration and invasion, while Tns1-KO reversed these effects (Fig. 6f–i). Additionally, STAT3/S100A8/9-24A were indicated to upregulate E-cadherin expression, downregulate N-cadherin and Vimentin expression in MC38 cells, inhibit cell migration or invasion, and counteract the metastasis-promoting effects of TNS1 (Figs. 6j, k). Given that NETs facilitated metastasis through activation of the TRPC1-S100A8/9-STAT3-TNS1 axis in CRC cells (Figs. 4–6), it was imperative to determine the role of this axis in NET release. Our findings indicated that this axis didn’t regulate NET formation in CRC tissues (Supplementary Fig. 25a, b). This indicated that the signal axis by which NETs promoted CRCLM was unidirectional rather than positive feedback.

Next, we investigated whether the disruption of the STAT3/S100A8/9 heterotrimer inhibits CRCLM. Functional analysis in C57BL/6J mice showed that the STAT3/S100A8/9-24A reduced LM, CRC growth, and Vimentin expression (Supplementary Fig. 26a–f). Subsequently, we explored whether human STAT3 binds to S100A8/9 to form a complex and whether disruption of this complex inhibits CRCLM in patients. Molecular docking predicted hydrogen bond interactions between human STAT3 and S100A8 or S100A9 (Supplementary Fig. 26g). Immunoprecipitation assays showed that mutations in human STAT3 (S514A, Q511A, G254A, E407A, C251A, C328A, and R325A, referred to as STAT3-7A) or human S100A8 (K56A, R31A, Q69A, T62A, I60A, and E70A, referred to as S100A8-6A) disrupted STAT3’s interaction with S100A8 in CRC cells (Supplementary Fig. 26h, i), while mutations in human STAT3 (K409A, R325A, D334A, E397A, and S405A, referred to as STAT3-5A) or human S100A9 (E66A, R87A, and K59A, referred to as S100A9-3A) impaired STAT3 interaction to S100A9 (Supplementary Fig. 26j, k). The combined STAT3/S100A8/9-20A variant (comprising STAT3 mutations S514A, Q511A, G254A, E407A, C251A, C328A, K409A, R325A, D334A, E397A, S405A; S100A8 mutations K56A, R31A, Q69A, T62A, I60A, E70A; and S100A9 mutations E66A, R87A, K59A) was found to reduce LM, CRC growth, or Vimentin expression in huHSC-C-NKG-ProF mice (Supplementary Fig. 26l–n). Therefore, our results demonstrated that inhibiting the formation of the STAT3/S100A8/9 heterotrimer suppressed CRCLM.

RIPK2 promoted CRCLM by activating the TRPC1-S100A8/9-STAT3 signaling in CRC cells
Building upon the aforementioned findings, we hypothesized that RIPK2 in neutrophils facilitated LM by upregulating TRPC1 expression and activating STAT3 via the S100A8/9 pathway, then sustaining TNS1 expression through the STAT3/S100A8/9-EPL axis. To validate the robustness of this signaling mechanism, we conducted experiments using C57BL/6J mice with intact immune function and huHSC-C-NKG-ProF mice possessing humanized immune systems. In mice, RIPK2-deficient neutrophils were found to reduce LM, CRC growth, and Vimentin expression, but TRPC1-OE in CRC cells reversed this (Fig. 7a–c, Supplementary Fig. 27a–c, Supplementary Fig. 28a–d). Moreover, TRPC1-OE in CRC cells was shown to increase LM, CRC growth, and Vimentin expression, but STAT3/S100A8/9-24A mitigated this effect (Figs. 7d–f, Supplementary Fig. 27d–27f, Supplementary Fig. 28e–28g). Similarly, TNS1-OE in CRC cells was indicated to increase LM, CRC growth, and Vimentin expression, but STAT3/S100A8/9-24A reduced its impact (Figs. 7g–i, Supplementary Fig. 27g–i, Supplementary Fig. 28h–28k). Furthermore, S100A8/9 inhibitor Paquinimod and the STAT3 inhibitor Stattic decreased TNS1-driven LM, CRC growth, and Vimentin expression (Figs. 7j–l, Supplementary Fig. 27j–l, Supplementary Fig. 28l–n), suggesting the S100A8/9-STAT3-TNS1 axis as a potential drug target. Finally, we explored whether blocking RIPK2, STAT3, and S100A8/9 together more effectively inhibits E. coli-induced CRCLM. The combination of GSK583, Stattic, and Paquinimod proved more effective than individual inhibitors in reducing LM, CRC growth, and Vimentin expression (Supplementary Fig. 29a–f). These results were consistent in mice with humanized immune systems (Supplementary Fig. 29g–i).

Methods
Bacterial culture
E. coli (B98084, Mingzhoubio, China) was grown on Trypticase Soy Broth medium (Z699209, Merck, MA, USA), and cultured at 37°C for 24h. K. pneumoniae (B81566, Mingzhoubio) was grown on Nutrient Agar (CM0003B, Thermo Scientific, NY, USA) at 37°C for 24h. Lutimaribacter sp023703375 (TS459614, Testobio, China) was cultured in Medium 825 at 30°C for 24h. Sphingomonas aerolata (BMZ130204, Mingzhoubio) was cultured in DSM Medium 803 (M1395-01, ATCC, VA, USA) at 30°C for 24h. The bacteria were collected and washed three times with Normal Saline (S0817, Merck), filtered with a 70 mm mesh (352350, Corning, NY, USA), and stored at 4°C until use.

Patient samples
Study design. This study was structured as an open-label, multicenter, parallel-group investigation with dual objectives:(1) to discern the differences in bacterial composition between CRC tissues with and without LM, and (2) to examine the influence of distinct bacterial profiles on CRCLM. The dataset was segmented into four components, two of which originated from large hospitals in Fujian Province, including a specialized liver disease hospital. Furthermore, specimens from two external centers (Changsha, Shanghai) serve as validation sets. A total of 136 CRC specimens with or without LM were collected over a seven-year period, from January 2018 to January 2025, with comprehensive clinical information meticulously documented. The designated follow-up duration for all enrolled patients was 80 m from the time of enrollment. Nevertheless, early termination of follow-up might occur due to patient mortality (either from disease progression or unrelated causes) or loss to follow-up (inability to contact patients despite repeated attempts via telephone, mail, or clinical visits). In such instances, the date of the last known contact was documented as the conclusion of follow-up. The data were subjected to sensitivity analyses to assess the impact of missing data, incorporating multiple imputation techniques for censored observations. The consent has been obtained for reporting and sharing individual-level data.

This study involved human participants, and all procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants after they were fully informed of the study objectives, procedures, potential risks, benefits, and their right to withdraw at any time without penalty. Participants were compensated for their time and travel expenses with a $50 gift card or equivalent currency, which was provided upon completion of the study to acknowledge their contribution without inducing participation. Our study was designed with explicit attention to equity, ensuring no sex-based discrimination in participant recruitment, inclusion/exclusion criteria, or data analysis. The experimental conclusions were not dependent on a specific sex.

We included a total of 136 CRC patients, ensuring comprehensive data such as survival information, clinical indicators, lifestyle habits, and gene mutations. Specifically, 24 samples from our center were utilized for 2bRAD-M-Seq, 60 samples from our two centers were exclusively used for immunofluorescence detection, and 84 specimens from external centers were also employed for immunofluorescence detection. Of these, 105 were non-LM specimens, while 31 exhibited LM. Given the limited availability of CRC specimens with LM, we maintained a 1:3 ratio between the LM and non-LM groups. This approach ensured an adequate number of LM specimens while mitigating potential statistical biases due to an imbalanced ratio. CRCLM samples were derived from (1) CRC patients undergoing hepatectomy in Fujian Medical University Union Hospital and Mengchao Hepatobiliary Hospital of Fujian Medical University, or (2) tissue chips provided by ZK bioaitech (China) and Shanghai Xinchao (China). The inclusion criteria required participants to satisfy the following conditions at the time of enrollment:(1) be at least 20 years of age,(2) have a diagnosis of primary CRC,(3) have not undergone radiation, chemotherapy, or any other adjuvant therapy, and(4) have undergone their first CRC resection. Conversely, the exclusion criteria specified that participants would be disqualified if they met any of the following conditions:(1) a history of other cancers,(2) participation in other clinical trials or use of investigational drugs, including antibiotics, within the previous 30d,(3) development of distant metastases beyond the liver, or(4) pregnancy.

To account for potential confounding variables, we excluded individuals who had received antibiotics or chemo/radiotherapy within the past three months. All biological samples were immediately processed and stored at -80°C. For transport, samples were packed with dry ice to maintain -80°C. Standardized procedures for sample collection were implemented, followed by sequencing or MS with a unified bioinformatics analysis. By implementing these controls, we aimed to minimize false associations.

Mouse model
Experimental design was made to explicitly avoid sex-based bias, ensuring that the conclusions were not contingent upon any specific sex. To maintain experimental accuracy and consistency, particularly considering potential variations in hormonal fluctuations or physiological responses between sexes in the model system, female mice were exclusively used for all experiments. Each experimental group consisted of a minimum of six mice, meeting the required sample size for statistical robustness. Female C57BL/6J mice (GemPharmatech, China) or huHSC-C-NKG-ProF mice (Cyagen, China), aged four weeks, were housed in a specific pathogen-free barrier facility. The environmental conditions were stringently controlled, with a 12-hour light (07:00-19:00, approximately 300 lux at cage level) and 12-hour dark cycle, an ambient temperature of 22±2°C, and relative humidity maintained at 50±10%.

Animal ethics. The criteria for maximum tumor burden in mouse CRCLM are primarily focused on minimizing animal suffering and adhering to the 3R principle. These criteria are comprehensively assessed based on tumor volume, weight, and the overall condition of the animal. The specific guidelines are as follows: the maximum tumor diameter is generally restricted to 15 mm, and the tumor weight should not exceed 10% of the animal’s body weight. Regardless of whether the tumor diameter and weight reach these limits, euthanasia must be promptly administered if any of the following conditions are observed (1) Functional impairment, characterized by reduced activity, decreased appetite, or rapid weight loss (>10% of body weight within 24h).(2) Tissue damage, evidenced by the presence of ulcers (>1 mm), necrosis, or hemorrhage.(3) Systemic deterioration, manifested as cachexia, dehydration, or organ failure.

The specific model construction strategy was as follows:
(1). Construction of a patient-derived xenograft (PDX) model: It was initiated using fresh CRCLM tissue obtained from patients. The tissue was mechanically dissociated into small fragments, which were then cultured in Human Liver Organoid Culture Medium (abs9529, Absin, China) at 37°C for a period not exceeding 14d. Organoids, approximately 300 μm in diameter, were isolated using DPBS (10010001, Gibco, NY, USA) and resuspended to achieve a cell density of 2 × 10⁷ cells/mL. For engraftment, each huHSC-C-NKG-ProF mouse was administered a subcutaneous-like injection containing 1 × 10⁶ organoid cells in a total volume of 100 μL, targeting the posterior anal wall. The initial passage (P1) was sustained until the mice exhibited significant weight loss and decreased appetite, at which point humane euthanasia was performed. To propagate the model, P1 organoids were derived by processing the minced xenograft tissue in a manner analogous to the original patient sample, with this procedure being repeated for multiple subsequent passages. The PDX model was deemed successfully established by the third passage (P3), as the tumor characteristics had attained greater consistency and stability.
(2). Construction of cell- or patient organoid- derived CRCLM model: The mouse primary CRC model developed using AOM and DSS exhibits limitations in accurately representing both the early adenoma stage and the advanced metastatic progression of the disease58. Following a comparative analysis of various models, we selected the highly metastatic mouse MC38 cell line for our experimental study. MC38 cells (YC-A002, Ubigene, China) or patient organoids (5 × 106) were resuspended in 100 μL DPBS. After anesthesia, fix the mouse in a homemade fixator, fully expose the anus after disinfection, insert the needle into the back wall of the anus, and slowly inject the cell suspension58. The food intake and body weight of mice were monitored regularly. When the mice lost their weight and appetite, they were euthanized with CO2, and the CRC or LM tissues were removed and photographed. The formula for measuring the size of the tumor tissues was: Volume = Length × Width × Height. For comparison, CT26 cells (YC-B003, Ubigene) were injected into the back wall of the anus in the same manner.
(3). Infusion of neutrophils: After 3d of MC38 cell or patient organoid injection, resuspend 5 × 106 human or mouse neutrophils cells in 100 μL Normal Saline and inject into the tail vein of C57BL/6J or huHSC-C-NKG-ProF mice once every three days for a total of five times.
(4). Bacterial transplantation: To confirm the active involvement of E. coli or other bacteria in CRCLM and to prevent any confounding effects due to bacterial ectopia or colonization of other regions within the gastrointestinal tract, we opted to inject the bacteria into the posterior wall of the anus 3d following MC38 cell or patient organoid injection59. This approach was designed to replicate the extent of infection observed in CRC lesions in patients59. The specific details of this procedure were outlined as follows: After 3d of the injection of MC38 cells or patient organoids, the bacteria (5 × 107 CFU/mouse in 100 μL DPBS) was injected into the posterior wall of the anus once every day for a total of ten times.
(5). To eliminate the bacterial flora in mice, mice were orally fed with antibiotic premix (1 mL/kg/d, Metronidazole (1 mg/mL, M1547, Merck), Ampicillin (1 mg/mL, A5354, Merck), Neomycin (1 mg/mL, N1142, Merck), Gentamicin (1 mg/mL, G1914, Merck), and Vancomycin (0.5 mg/mL, V2002, Merck)) daily, and antibiotic premix (50 μL/mL) was added to the drinking water for 16d. We used Normal Saline as the control.
(6). Small molecule compound treatment: Before commencing the formal experiment, we gathered dosage data for the same drug or its structural analogs in mice of the same strain, emphasizing consistency in the administration route and the experimental model. Employing a stepwise escalation approach, we established three to five incremental dose groups. The dose that elicited the most significant effect at the target site was subsequently selected for the formal experiment. 3d after the injection of MC38 cells or patient organoids, Anti-CD3 Antibody (OKT-3) (3 mg/kg, HY-P990864, MedChemExpress, NJ, USA), Anumigilimab (2 mg/kg, HY-P99440, MedChemExpress), Ca2+ channel agonist 1 (4 mg/kg, HY-41076, MedChemExpress), Clodronate Liposomes (40337ES08, Yeasen, China), DMSO (HY-Y0320C, MedChemExpress), DNase I (10 mg/kg, 10104159001, Merck), GSK484 hydrochloride (4 mg/kg, HY-100514, MedChemExpress), GSK583 (4 mg/kg, HY-100339, MedChemExpress), InVivoMAb anti-mouse CD3ε (5 mg/kg, catalog number: BE0001-1, clone name: 145-2C11, Bio X Cell, PA, USA), InVivoMAb anti-mouse Ly6G (5 mg/kg, catalog number: BE0075, clone name: RB6-8C5, Bio X Cell), InVivoMAb rat IgG2a isotype control (5 mg/kg, catalog number: BE0251, clone name: RG7/1.30, Bio X Cell), JNK-IN-8 (3 mg/kg, HY-13319, MedChemExpress), JSH-23 (5 mg/kg, HY-13982, MedChemExpress), MLKL-IN-1 (5 mg/kg, HY-139878, MedChemExpress), NOD1/2 antagonist-1 (5 mg/kg, HY-146034, MedChemExpress), Paquinimod (5 mg/kg, HY-100442, MedChemExpress), PMA (5 mg/kg, P8139, Merck), p38 MAPK-IN-1 (5 mg/kg, HY-12839, MedChemExpress), SCH772984 (3 mg/kg, HY-50846, MedChemExpress), Sparstolonin B (5 mg/kg, HY-116213, MedChemExpress), or Stattic (5 mg/kg, HY-13818, MedChemExpress) were injected into the tail vein of mice once every three days for a total of five times.
(7). Chemical induction of CRC model: Mice received an intraperitoneal injection of AOM (10 mg/kg, HY-111375, MedChemExpress) and were given regular water for 14d, followed by 7d of 2.5% DSS (HY-116282C, MedChemExpress) in place of regular water, then regular water again for 14d, alternating between DSS and regular water.
(8). Strategy for constructing Atf3, Hnrnpk, Ncf4, Relb, or Ripk2-cKO mouse model: We selected Exon 2-Exon 3 of Atf3 transcript-201 and Exon 5-Exon 8 of Hnrnpk-202 as the flox region, respectively. Moreover, we selected Exon 2-Exon 7 of Ncf4-201 and Exon 3-Exon 4 of Relb transcript-202 as the flox region. According to the structure of Ripk2, Exon 2 of the Ripk2 transcript-201 was chosen as the flox region. Knock out these regions would result in disruption of protein function. Fertilized eggs were transplanted to female C57BL/6J mice for obtaining positive F0 mice (Atf3, T008416, GemPharmatech; Hnrnpk, T013592, GemPharmatech; Ncf4, T019846, GemPharmatech; Relb, T005772, GemPharmatech; and Ripk2, T051882, GemPharmatech). Primers for identification of Atf3 WT and KO individuals: Atf3-5’arm (F-GAGAGGTCGTCTGGGTGGAA, R-CCTAACACACTTTCGCTCACAG), Atf3-3’arm (F-AACCTGGCACCCTAGTTTGATA, R-GGAAAAGAAATCTGAGAGCCAA). Deletion of the floxed Hnrnpk allele and WT Hnrnpk exons was detected by PCR using the following primers: Hnrnpk-5’arm (F-CCAATGGTGAATTTGGTGAGAAG, R-TGCCTTTGTCTCCCTAATATCCCT), Hnrnpk-3’arm (F-CTGGCCATTCCTAGAGTAGTAGATAGGA, R-GTTAAGTGTCTAGCGTGATCAGGCTA). Primers for identification of Ncf4 WT and KO individuals: Ncf4-5’arm (F-AGTGAAGCAGAAGTGCATGGCAGA, R-AAGAACTTGCCAGGTCCTGGTGC), Ncf4-3’arm (F-TCTGAGGCGGAAAGAACCAG, R-GCTCAGGGGATAAAGAGGGTGCT). Primers for identification of Relb WT and KO individuals: Relb-5’arm (F-CCTCTAGTCCTGTCTGGGGAATG, R-GGTTCCATCCTGAAAGTCCACC), Relb-3’arm (F-CTGGAATCTTCAAATGTTTCCCAGG, R-TGTGTCACTGTCACCAAGCATGG). Deletion of the floxed Ripk2 allele and WT Ripk2 exons was detected by PCR using the following primers: Ripk2-5’arm (F-CATGACCATGTGATATGCCAGG, R-TACAGTTCCTCATGTTGTGGTGG), Ripk2-3’arm (F-AGCCCATTGGAAATTCTGTAGCC, R-AAATACCAATGTAGAGAATTGCCTCC). To obtain neutrophil-specific KO mice, the floxed Atf3, Hnrnpk, Ncf4, Relb, or Ripk2 mice were crossed with Ly6g-2A-DreERT2 mice (NM-KI-190036, Shanghai Model Organisms Center, China). For animal studies with Atf3 flox/flox Ly6g-2A-DreERT2 (Atf3-cKO), Hnrnpk flox/flox Ly6g-2A-DreERT2 (Hnrnpk-cKO), Ncf4 flox/flox Ly6g-2A-DreERT2 (Ncf4-cKO), Relb flox/flox Ly6g-2A-DreERT2 (Relb-cKO), or Ripk2 flox/flox Ly6g-2A-DreERT2 (Ripk2-cKO) mice, littermates with Atf3 flox/flox, Hnrnpk flox/flox, Ncf4 flox/flox, Relb flox/flox, or Ripk2 flox/flox as WT control were used.
(9). Construction of Atf3, Hnrnpk, Ncf4, Relb, or Ripk2-cKI mice: Utilizing CRISPR/Cas9 technology, a CAG-tdTomato-polyA expression cassette was incorporated into the Rosa26 locus via homologous recombination. A homologous recombination vector was engineered through In-Fusion cloning, comprising a 5’ homology arm, a tdTomato expression cassette, and a 3’ homology arm. Subsequently, Cas9 mRNA, guide RNA (gRNA), and the donor vector were microinjected into the fertilized eggs of female C57BL/6J mice to generate mice harboring loxP sites, which were then mated with Ly6g-2A-DreERT2 mice. Primers for cKI identification: WT primers (F-GGAGTGTTGCAATACCTTCTGGGAGTTC, R-TGTCCCTCCAATTTTACACCTGTTCAATTC), Transgene primers (F-CTGTAGGGCGCAGTAGTCCAG, R-GCTAGAACTAGTGGATCTCGAGCC), 5’ junction primers (F-CTGCCCGAGCGGAAACGCCACTGAC, R-CCTGGACTACTGCGCCCTACAGA), 3’ junction primers (F-GGCTCGAGATCCACTAGTTCTAGC, R-CCATTCTCAGTGGCTCAACAACAC), Cre primers (F-GCAACGTGCTGGTTATTGTG, R-GCCGCTCTAGAACTAGTGGATCC). The amplified fragments of Atf3, Hnrnpk, Ncf4, Relb, and Ripk2 were 728, 1543, 1168, 1859, and 4273 bp, respectively.
(10). Establishment of spontaneous multiple intestinal tumors: In Apcmin/+ mice (AnipheBio, China), intestinal adenomas appear at 2.5m, with their number staying constant but size increasing. After 4m, adenomas grow quickly, and by 7m, their large size cause anemia and death due to nutrient depletion.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Pan et al. show that Escherichia coli promotes colorectal cancer (CRC) liver metastasis by inducing\nneutrophil extracellular traps (NETs) via a RIPK2-dependent mechanism. These NETs, in turn, enhance calcium influx in\nCRC cells through upregulation of TRPC1, leading to the formation of a STAT3/S100A8/9 trimer that stabilizes enhancer-\npromoter loops (EPLs), particularly promoting Tns1 transcription and thus facilitating metastasis. This study provides\nimportant insights into how intestinal microbiota modulate tumor immunity and metastasis through molecular crosstalk\nbetween neutrophils and CRC cells.\nOverall, this is a scientifically compelling and technically well-executed study with important findings. The mechanistic data\nrelated to NET formation and downstream signaling within cancer cells are particularly strong. However, several aspects\nrequire clarification and additional experimental support to improve the coherence of the narrative, reinforce the mechanistic\nconclusions, and enhance the translational relevance.\nKey points include:\nMajor comments:\n1) The manuscript focuses on liver metastasis, but it remains unclear how the various targeting strategies (e.g., NETs,\nRIPK2, TRPC1, S100A8/9, STAT3) impact the primary tumor. Do these interventions affect tumor growth, local invasion? It\nwould be valuable to include in vivo analyses—such as histological assessment of invasion or tumor burden in the colon—\nto better understand the broader impact of these treatments.\n2) The study does not investigate whether E. coli is present within the metastatic lesions themselves. While the authors\nshow an enrichment of E. coli in the primary tumors of patients with liver metastases, it remains unclear whether E. coli\npersists in the metastatic sites. This is an important point because it would clarify whether E. coli acts solely at the primary\nsite to initiate metastasis or whether it continues to influence tumor behavior after dissemination. The authors should either\nevaluate E. coli presence in metastases or discuss its potential relevance to their proposed mechanism.\n3) The experimental design and interpretation of Figures S1b–d are difficult to follow. The legend refers to E. coli\ntransplantation, but this appears to have been performed only after broad-spectrum antibiotic treatment. Consequently, a\ncondition with E. coli alone is missing, making it hard to isolate the effect of E. coli itself. Additionally, the main text does not\nclearly state whether immune cell depletion was performed in this experiment (but so does the legend), although this would\nbe crucial for interpreting the mechanism. The sentence “E. coli has been witnessed to promote liver metastasis through\nCRC cells themselves (Fig. S1b–d)” suggests an immune-independent effect, yet the authors later shift focus to neutrophils\nas key mediators. This inconsistency needs to be addressed. Moreover, if CD45+ cell depletion was used, it is unclear\nwhether neutrophils would also be depleted, which complicates interpretation. Overall, both the rationale and the\nconclusions drawn from these experiments require clarification in the text. In the relevant figure panels, the authors report\n\"tumor volume,\" but since the study specifically investigates liver metastases, this should be referred to as \"metastasis\nvolume\" to avoid confusion. The terminology needs to be consistent with the biological context of the experiments.\n4) The authors primarily use mouse models involving E. coli inoculation. However, in patient samples, they report that E. coli\nenrichment is observed in the primary tumors of metastatic CRC patients, not necessarily through external inoculation. This\nraises concerns about the model's relevance: if the tumor cells are already highly metastatic (e.g., MC38 model), it is unclear\nwhy additional E. coli inoculation is needed. To better mirror the clinical situation, the authors could consider using a non-\nmetastatic or low-metastatic CRC model to assess whether E. coli can truly promote metastatic progression rather than\nmerely acting in an already aggressive setting.\n5) Neutrophils depletion must be properly validated (Fig. 1i). Using the same Ly6G antibody for both depletion and\nconfirmation of neutrophil removal is not methodologically sound and it seems like what was done here. The authors should\nemploy an independent marker—such as myeloperoxidase or neutrophil elastase staining—for validation. Additionally, the\nexact reference and clone of each depleting antibody should be clearly stated in the Materials and Methods section.\nMinor comments\n1) In Figure S1a, the legend states that E. coli rRNA levels were measured in primary lesions of CRC patients with liver\nmetastases using fluorescence in situ hybridization. However, the data shown appear to be from mouse samples, not patient\ntissues. This should be clarified, and if the figure indeed shows only mouse data, the legend must be corrected accordingly.\nIf patient data were intended, they should be shown explicitly.\n2) In Figures S2a–c, the data suggest possible crosstalk between macrophages and neutrophils, but this is not addressed in\nthe text. It might be important to clarify the effects of neutrophil depletion on macrophage populations, and vice versa\n(discussion only is fine if the authors do not have already the data generated).\n3) In Figures S2d–f, the authors examine the effects of DNase I and PAD4 inhibitor on NET formation. However, the impact\nof these treatments on NET levels is not directly validated. Experimental confirmation—such as quantification of Cit-\nH3+/MPO+ structures or other established NET markers—is necessary to support the interpretation.\n4) In Figure 1g, the NET staining is unclear. There appears to be substantial Cit-H3 signal that does not colocalize with\nMPO, raising questions about the identity of these structures. The authors should clarify what these Cit-H3+/MPO− signals\nrepresent and whether they are truly indicative of NETs. Additional staining or explanation is needed to support the\ninterpretation.\n5) In Figures 1i–l, the authors report that E. coli effects on cadherins and vimentin are weakened by neutrophil depletion.\nHowever, the figure panels do not clearly indicate which condition corresponds to which treatment, making interpretation\ndifficult. In addition, the neutrophil depletion group is only compared to untreated controls. To properly assess the specificity\nof the effect, the Ly6G antibody treatment should be compared to an appropriate isotype (IgG) control.\n6) In Figure S3a, it would have been informative to include a condition where the inhibitors were tested alone, without E. coli\nstimulation, to assess their baseline effects. Additionally, it remains unclear whether the same effects on NET formation\nwould be observed in non-tumor colon tissue. Including such controls would strengthen the conclusions.\n7) In Figure S3b, the authors state that neither Ripk2-cKI nor Ripk2-cKO altered immune cell counts following E. coli\ninfection. However, it is unclear from the figure whether these manipulations were systemic or restricted to CRC tissues.\nAlthough this is clarified in the Materials and Methods, it would improve readability to briefly state in the main text whether\nthe knock-in/knockout was tissue-specific or systemic.\n8) In Figure 1n, the authors assess EMT marker expression in Ripk2-cKO and cKI conditions, but only correlate NET levels\nwith EMT changes in the knock-in model (Fig. S3C). To fully support their conclusions, NET levels should also be evaluated\nin the Ripk2-cKO condition and correlated with EMT marker expression.\n9) The sentence “While Ripk2-cKI enhanced E. coli’s influence on EMT protein expression (Fig. 1n), indicating RIPK2 in\nneutrophils is crucial for E. coli-driven CRC liver metastasis” is grammatically incorrect. The use of \"while\" implies a\ncontrast, but none is presented. Consider revising the sentence for clarity\n10) In Figure 2, the proposed RIPK2/ATF3/RelB signaling cascade is central to the mechanism, but it is not directly shown to\nbe dependent on E. coli. A simple in vitro experiment—such as stimulating neutrophils with E. coli and assessing activation\nof this pathway—would strengthen the claim and help confirm that this signaling axis is specifically triggered by E. coli\nexposure.\n11) The manuscript presents an elaborate signaling cascade (RIPK2 → ATF3/RelB → NCF4 → MLKL) leading to NET\nformation. However, PAD4 inhibition is also shown to reduce NETs, yet the link between PAD4 activity and this signaling\npathway is not addressed. The authors should clarify how PAD4 fits into this model—does it act downstream, in parallel, or\nindependently of the RIPK2-driven pathway? This connection is important to fully understand the mechanism of NET\nregulation in this context.\n12) The labeling within the figures themselves (not just the legends) is often unclear or misleading. For instance, in Figure\n4d, it appears from the figure text that cells were treated directly with PMA, when in fact the treatment involved NETs\ngenerated by PMA-stimulated neutrophils. This type of mislabeling occurs in several figures and can easily lead to\nmisinterpretation. The authors should revise the figure annotations to accurately reflect the experimental setup, without\nrequiring readers to cross-reference the main text for clarification.\n13) Given that RIPK2 can signal through both NF-κB and MAPK pathways, it would be informative to test whether\nsimultaneous inhibition of these pathways alongside ATF3/RelB/NCF4 modulation has additive or synergistic effects on\nNET formation and metastasis. This could help clarify the relative contribution of each branch of the pathway and strengthen\nthe mechanistic understanding.\n14) Based on the current data, it remains unclear whether NET-induced pSTAT3 activation occurs in a TRPC1-dependent\nmanner.\n15) While the manuscript focuses on how neutrophil-derived NETs influence CRC cells via the\nTRPC1/S100A8/9/STAT3/TNS1 axis, it is not addressed whether targeting these pathways in CRC cells has any feedback\neffect on neutrophils or NET formation. Assessing whether interventions on TRPC1, S100A8/9, STAT3, or TNS1 in tumor\ncells alter neutrophil behavior or NET production could reveal important bidirectional interactions within the tumor\nmicroenvironment.\n16) In key experiments, it would be more informative to restrict pathway modulation to colorectal cancer (CRC) cells rather\nthan the whole organism, to better isolate tumor-intrinsic effects. While neutrophil-specific targeting would certainly\nstrengthen the mechanistic claims, it is not essential. However, tumor cell-specific interventions (e.g., via CRC cell-specific\nknockdown or overexpression) should be considered to support the proposed cell-autonomous roles of\nTRPC1/S100A8/9/STAT3/TNS1.\n17) The manuscript shows that NETs upregulate TRPC1 expression in CRC cells, but the mechanism underlying this effect\nis not explored. Do the authors have any hypothesis or supporting data on how NETs induce TRPC1 transcription or\nstabilization? Even a brief discussion of potential mechanisms—whether through direct signaling, DNA sensing, or\ninflammatory mediators—would strengthen the interpretation of this finding.", "reviewer_2": "The submitted work from Pan, et al, titled “Escherichia coli promotes colorectal cancer liver metastasis by maintaining\nSTAT3 enhancer-promoter loops through releasing RIPK2-related neutrophil extracellular traps”, shows promising\nobservations in combination with functional validations. However, I do have some concerns stated below. Note that many of\nmy concerns are widespread over multiple figures, however herein I have highlighted some of the figures, but I urge the\nauthors to evaluate all figures related to my comments.\nStatistical Methodology Transparency\nThe manuscript lacks sufficient detail regarding the choice of statistical tests. The authors must specify which normality\ntest(s) (e.g., Shapiro-Wilk, Kolmogorov-Smirnov) were used to determine whether parametric or non-parametric analyses\nwere appropriate. Several data distributions appear visually normal, while others do not—this inconsistency raises concerns\nabout whether the correct statistical approach was consistently applied.\nWestern Blot (WB) Controls and Integrity\nAcross multiple figures, including Figures 1m, 2i, 2m, 3k, 3O, 4g–i, 4n–p, 4t–u, and Supplementary Figures S2e, S3a, S5c–\ne, there are recurring concerns about WB presentation:\nCan the authors confirm that loading controls (e.g., GAPDH, Histone H3, Actin) were run on the same membranes as the\nrespective target proteins in all presented figures?\nIn Figure 1m, the TLR4 replicate appears mismatched with its GAPDH control (replicate 3 vs. replicate 1), and similar\ninconsistencies are noted for TLR2, NOD1, and NOD2. Please clarify the sample orders and confirm correct pairings.\nFigure 2i shows a GAPDH blot with ladders on both sides, whereas associated blots (e.g., ATF3, RelB) only show one\nladder—please verify that they were run on the same membrane.\nIn Figure 3O, the close molecular weights of GAPDH and NCF4 (39/36 kDa) necessitate confirmation that these were\nresolved properly and run on the same membrane.\nFigures 4g–i and 4t–u show only a single GAPDH control used for bands of very similar size (e.g., p-STAT3/p-STAT1 and\nSTAT3/STAT1). If these were separate runs, corresponding independent GAPDH controls should be provided.\nFor Figure 4N, the actin blot must be confirmed as originating from the same membrane as the target protein. The provided\nuncropped blots suggest overlapping lanes in the histone blot, which requires clarification.\nMore broadly, there are issues throughout the uncropped WB source data suggesting that not all loading controls are\npresented or properly aligned with target proteins. The authors should perform a comprehensive review of all WB\nexperiments, ensure correct control usage, and provide updated figures if necessary.\nSample Size Clarification and Data Consistency\nOn lines 67–68, the authors state that 12 CRC patients with liver metastasis were analyzed, referencing Table S1. However,\nthe table lists 19 patients with and 19 without metastasis. What criteria led to excluding some samples from analysis? This\nneeds to be clarified.\nIn Figure S2m, it is unclear how the total of 13 patients was distributed across experimental groups. Please specify sample\nallocation.\nIn Supplementary Figure S2e, the treatment dosage is not reported. The authors must clarify both the dose used and the\nrationale for dosage selection, including whether prior studies or titrations informed this.\nReplication and Quantification Issues\nSeveral figures, such as Figures 3e, 3g, and 6c, rely on only one or two replicates. To validate findings, at least triplicate\nexperiments are expected. Authors are strongly encouraged to repeat these experiments to ensure statistical and biological\nreliability.\nIn Figure 1n, it appears that the second E-Cadherin image may be zoomed out. All images should be presented on the same\nscale, and scale bars must be clearly indicated.\nIn Figure 3d, the lack of a scale bar makes interpretation difficult—please include it.\nFor Figure 1n (and potentially others), the authors should verify whether images presented are from representative wells,\nand clearly indicate which lanes correspond to the uncropped supplementary images.\nWB Quantification Presentation\nIn some cases, notably with p-MLKL and MLKL, quantification is shown in a single bar graph. It may be more appropriate to\npresent these in separate graphs, especially when they represent biologically distinct outcomes.\nMicrobiota Composition and Comparative Studies\nThe authors state in line 69 that “Principal coordinate analysis showed distinct microbiota compositions between the groups\n(Fig. 1a).” However, visual inspection of Figure 1a suggests considerable overlap between metastatic and non-metastatic\nsamples. The authors should justify their claim of separation and provide quantitative metrics (e.g., PERMANOVA p-value,\nR²) to support this.\nA recent publication from June 2024, titled “Tumor-resident microbiota contributes to colorectal cancer liver metastasis by\nlactylation and immune modulation”, presents findings related to microbiota and CRC metastasis that partially overlap with\nthe current study, particularly with regard to Figure 1b–c. The authors should acknowledge and compare their findings to this\nrecent work.\nMoreover, that paper reported no differences in E- or N-cadherin expression between E. coli-treated and control cells,\ncontrasting with the present study. The authors should discuss how their findings align or diverge from this prior evidence.\nRelevance of SD vs SEM in Supplementary Data\nIn Supplementary Figure 1, the authors use standard deviation (SD) instead of standard error of the mean (SEM). Please\nexplain why SD was preferred and why it is more appropriate in the context of this data.\nAssessment of Metastasis: Tumor Burden vs. Incidence\nThe use of tumor volume in the liver as a proxy for metastasis requires more nuanced interpretation. Supplementary Figure\nS2C shows metastasis occurring with or without E. coli presence, though tumor burden increases with E. coli, especially\nunder immune cell depletion. This suggests E. coli may promote accelerated metastatic infiltration rather than outright\nmetastasis incidence. The authors should refine their interpretation accordingly.\nFunctional Interpretation of Neutrophil Subtypes in Ripk2c-KO Mice\nThe reduction of AFT3⁺ and RelB⁺ neutrophils in Ripk2c-KO mice is interesting; however, these seem to be replaced by\nIfit1⁺, Ifit3⁺, Cyba⁺, and Cybb⁺ populations. The authors should discuss the functional implications of this neutrophil subtype\nshift in the context of E. coli-driven metastasis.", "reviewer_3": "The study uncovers a new mechanism by which E. coli promotes CRC liver metastasis through neutrophil extracellular traps\n(NETs) and enhancer-promoter loops (EPLs). This provides new insights into the role of intestinal flora in tumor metastasis\nand immune responses. Technically, the authors employed a combination of techniques, including 2bRAD-M analysis,\nsingle-cell RNA sequencing, and HiChIP assays, to investigate the complex interactions between E. coli, neutrophils, and\nCRC cells. This multidisciplinary approach adds depth and credibility to the study. In the end, this study identifies RIPK2 in\nneutrophils as a key mediator of E. coli-induced CRC liver metastasis. Targeting this pathway could potentially lead to new\ntherapeutic strategies for CRC patients. A few technical comments should be addressed for publication.\n1. Although the study uses patient samples, the findings need to be further validated in larger clinical cohorts. The current\nsample size may not be sufficient to draw definitive conclusions about the clinical significance of the results.\n2. While the study focuses on the role of neutrophils in E. coli-induced CRC liver metastasis, the exact mechanisms by\nwhich neutrophils interact with E. coli and promote metastasis are still not fully understood. Further studies are needed to\nclarify the complex interplay between neutrophils, E. coli, and CRC cells.\n3. The study does not fully address potential confounding factors that could influence the results. For example, the impact of\nother components of the intestinal flora, the patient's diet, or lifestyle factors on CRC liver metastasis was not investigated.\nCan authors provide more evidence on this aspect using standard statistical methods such as PERMANOVA?\n4. The study was conducted using mouse models and cell lines. It is unclear whether the findings can be generalized to\nhumans, as there may be differences in the immune response and tumor microenvironment between mice and humans. If no\nexisting studies, authors may insert some limitation statements in the Discussion.\n5. Regarding 2bRAD-M applications in analyzing microbial composition in tumor tissue, can the authors briefly clarify the\nadvantages of this technique over others (such as 16S amplicon sequencing) in this study? Is it possible to identify E. coli\nstrains in the clinical samples?\n6. Line 913. It is quite confusing to see \"OTUs\" here, as 2bRAD-M directly provides the taxonomic table to users. Bacterial\ntaxa at the species level were identified based on the species-unique 2b markers. OTUs should be revised as taxa.\n7. Line 918. Student’s t-test is NOT statistically valid to detect differentially abundant bacteria in the metagenomics data, as\nthe microbiome data is compositional and is NOT normally distributed. A few compositionally aware methods (ANCOM-BC,\nALDEx2, etc.) have been available in the field. Otherwise, you have to perform the Wilcoxon rank-sum test using CLR-\ntransformed data.\nAlso, LDA or Metastats is an outdated statistical tool for this purpose, which may significantly increase the false-positive rate\nof microbial marker identification. Please be careful and update the marker discovery results according to compositionally\naware statistical methods.\n8. Did the authors associate the tumor microbiome diversity with clinical phenotypes? Did the authors identify any bacteria\nthat co-occur with E. coli in the tumor tissues? Is it possible that a bacterial group, including E. coli rather than E. coli alone,\npromotes CRC liver metastasis through neutrophil extracellular traps? Can authors track the tumor-derived E. coli in the\nblood using 2bRAD-M?\n9. The extensive use of technical terms. While this is appropriate for a specialized readership, it could pose challenges to\nthose less familiar with the field. Some terms could benefit from more explicit definitions or explanations, especially when\nthey are first introduced. For example, when terms like \"2bRAD-M\", \"SCENIC\", and \"HiChIP\" are mentioned, a more\ndetailed description of what these techniques entail could help non-experts better understand the experimental methods and\nresults.", "reviewer_4": "The manuscript entitled “Escherichia coli promotes colorectal cancer liver metastasis by maintaining STAT3 enhancer3\npromoter loops through releasing RIPK2-related neutrophil extracellular traps” by Pan et al. revealed that E. coli promoted\nCRC liver metastasis via NETs. Mechanistically, the study demonstrates that HNRNPK, upregulated by E. coli-activated\nRIPK2, enhances Atf3/Relb transcription in neutrophils, driving MLKL-mediated NET release. NETs then enhance CRC\nliver metastasis by upregulating TRPC1 to form a STAT3/S100A8/9 trimer, stabilizing EPLs and the Tns1 framework. This\nwork offers new molecular insights into how E. coli contributes to NET-mediated CRC liver metastasis. While the manuscript\nis interesting, it needs more evidence on the molecular regulation mechanisms, particularly between the upstream\nmolecules of MLKL and MLKL. Therefore, it should be revised before publication in this journal. The following major issues\nneed to be addressed:\n1. The existing conclusions regarding the regulatory role between NOD1/2, RIPK2, HNRNPK, ATF3/RELB with MLKL in\nneutrophils lack supporting evidence. For instance, perform siRNA knockdown or overexpression experiments to detect the\nchanges in MLKL expression\n2. The regulation of E. coli and downstream HNRNPK-ATF3/RELB-NCF4 pathway in neutrophils should be supported by\nevidence to establish causality. The authors should figure out if this happens in vivo in their models of bacterial\ntransplantation.\n3. The regulation of NOD1/2, RIPK2, HNRNPK and downstream NCF4 in neutrophils lacks evidence.\n4. The regulation of NET, TRPC1 and downstream TNS1 in CRC cells should be supported by evidence to establish\ncausality.\nMinor issues:\n1. Figure 1A and 1B, In the legends and the Results section, the number of samples per group should be specified. “n=12”\ncould be misinterpreted as a total of 12 samples. Additionally, the sample number in the figure is inconsistent with that in\nTable S1.\n2. In Figure 2P, “citoh3” should be corrected to “citH3”. Furthermore, when citH3, a NET marker, is first introduced in the text,\nit should be appropriately described.\n3. The statement at line 194 does not have a corresponding figure.\n4. There are many types of calcium channel proteins. How did you rule out other candidates and select TRPC1?\n5. In Figure 5A, there is no quantification of these images, so it is unclear how often is this phenomenon observed. Is all\nSTAT3 staining positive for S100A8/9 and viceversa?\n6. The authors should show p-values instead of asterisks for statistics.\n7. The replication strategy for each experiment should be defined.", "reviewer_5": "In this work, Pan et al. studied on the role of E. coli in the progress of CRC. While some conclusions are of interest,\nthere are\nessential issues need to be considered before further processing.\n1. Although the article mentioned that the sample size was estimated based on preliminary experiments, the specific\ncalculation method and basis were not clear. When studying human diseases, the size of the sample directly affects the\nstatistical power of the results and the reliability of the conclusions. A small sample size may increase the chance of the\nresults and fail to accurately reflect the real situation.\n2. Lack of long-term follow-up data. The study mainly focused on the short-term effects of E. coli on CRC liver metastasis,\nbut did not provide follow-up data on the long-term prognosis and survival rate of patients. This is insufficient for evaluating the\nlong-term role of E. coli in CRC metastasis and its potential value as a therapeutic target.\n3. There are significant individual differences between CRC patients, including genetic background, lifestyle, and dietary\nhabits. These factors may affect the composition and function of the intestinal flora, and thus affect the role of E. coli in CRC\nmetastasis. However, the article does not mention whether these individual differences have been fully considered and\ncontrolled.\n4. This study mainly focused on E. coli, while the intestinal flora is a complex microbial community containing a variety of\nbacteria, fungi, viruses, etc. Studying E. coli alone may not fully reflect the overall role of intestinal flora in CRC liver\nmetastasis, and ignores the potential impact of other microorganisms.\n5. Although cell experiments and animal model experiments were conducted in this article, there may be differences\nbetween the results of cell experiments and in vivo experiments. Cell experiments cannot fully simulate the complex\nphysiological and pathological environment in the body, and animal models and humans also differ in physiological\nstructure and immune response. Therefore, the applicability of cell experimental results in humans needs to be further\nverified.\n4. Despite a variety of advanced technical means, such as single-cell RNA sequencing and HiChIP, are used, these\ntechnologies also have their limitations. For example, single-cell RNA sequencing may not be able to detect low-abundance\ntranscripts, and HiChIP technology may have false positive or false negative results when detecting chromatin loops. These\ntechnical limitations may affect the accuracy and reliability of the research results.\nData analysis\n5. Authors mentioned the use of statistical methods such as independent sample t-test and Wilcoxon rank sum test, but does\nnot elaborate on whether these methods are applicable to all data types and research questions. For example, when\ncomparing two groups of data, if the data do not meet the normal distribution or variance homogeneity, it may be\ninappropriate to use the independent sample t test. In addition, for the comparison of multiple groups of data, it is not\nmentioned whether multiple comparison correction was performed, which may lead to an increase in false positive results.\n6. When performing bioinformatics analysis on sequencing data, although some commonly used tools and databases were\nused, the optimization of the analysis process and the rationality of parameter settings were not detailed. For example, when\nperforming gene expression difference analysis, it was not mentioned whether batch effects, data normalization and other\nfactors were considered. For the results of pathway enrichment analysis, no detailed biological explanation and verification\nwere provided, and the specific mechanism of action of these pathways in CRC liver metastasis could not be determined. In\naddition, the details of 2bRAD-M profiling should be provided, e.g. database, sequence mapping tool and parameters. How\nwas the OTU (#915, a typo of OUT) table generated, and how a OTU was defined?\n7. Since the study found an association between E. coli and CRC liver metastasis and proposed a possible molecular\nmechanism, this association does not necessarily mean a causal relationship. There may be other undiscovered factors that\naffect the occurrence of E. coli and CRC liver metastasis at the same time, leading to the observed correlation. Therefore,\nfurther experiments are needed to verify whether E. coli directly causes CRC liver metastasis and its specific role in the\nmetastasis process.\n8. This study revealed the potential role of E. coli in CRC liver metastasis, its feasibility in clinical diagnosis and treatment\nhas not been fully explored. For example, whether the detection method based on E. coli has sufficient sensitivity and\nspecificity, and whether the treatment strategy against E. coli is safe and effective. These issues are the key to converting\nresearch results into clinical applications and need further research to solve.", "reviewer_6": "This is a wide-ranging study in which the authors explore the role of E. coli in promoting metastasis of colorectal cancer\ncells, via NET formation. The authors show a role for RIPK2 in neutrophils in response to infection, resulting in activation of\nSTAT3 in CRCs, in a S100A8/9 and Trpc1-dependent manner. They then show that the STAT3/S100A8/9 complex is\nassociated with enhancer-promoter loops and upregulates the expression of a number of genes including Tns1, which\npromotes metastasis.\nMy expertise is in chromatin biology, so I have mainly focused on these aspects of the manuscript. Overall, as a non-\nspecialist, I found the paper quite difficult to read, since there was very little explanation of the proteins or pathways involved\nand insufficient description of the work being presented. A lot of the information required to understand the experiments was\nonly included in the methods section, with minimal description of the experimental set-up included in the results or figure\nlegends. This made it very difficult to critically assess the conclusions drawn from these experiments.\nI have two main concerns about the data in the paper (detailed below). Firstly, based on the data presented, there seems to\nbe very little reproducibility between replicates in the ATF3 and RelB CUT&Tag experiments. Secondly, there appears to be\nan intrinsic flaw in the Hi-ChIP experiment, where STAT3 IP is used to look for looping events in the presence of a mutation\nthat likely disrupts STAT3 binding, potentially biasing the results.\nMajor points\n1. DNA pull-down (Figure 2j,l,m, Figure 3e,g,m, 6c) is an unusual technique to demonstrate protein binding to DNA.\nBinding of proteins to DNA would be more convincing (and much more quantitative) demonstrated by ChIP (either ChIP-qPCR\nor ChIP-seq) or CUT&Tag.\n2. I can see that Atf3 and RelB expression is dependent on RIPK2, but are the authors arguing that this activation is induced\nby E. coli? A western blot (or equivalent) showing whether Atf3 and RelB protein levels are increased in neutrophils\nfollowing E. coli infection would be helpful.\n3. ATF3/RelB CUT&Tag: what is the overlap between the two replicates for each experiment? The number of peaks is\nrelatively similar – but what proportion of them are the same in each replicate? Based on the venn diagram in Fig 3c there\nseems to be very little overlap in the genes bound in the two replicates, which does not give me confidence that they are\nreliably detecting the same binding – and therefore it’s difficult to draw conclusions from these data. Looking at the tracks in\nFig 3d the quality of the data looks relatively poor, which suggests that the peak calling may not have been entirely\nsuccessful. For me, I would like more reassurance of the reproducibility of these data to have any confidence in it.\nAs a minor point, lines 181-2 say that the peaks are mainly at the TSS, but the pie charts in Fig S4e show that nearly half of\npeaks are actually distal intergenic (and only about one quarter at the promoter).\n4. Immunoprecipitation (Figure 3k, l, Figure 4, 5, 6): According to the methods section, the IP was conducted in Tricine SDS\nsample buffer. What is the composition of this buffer? What is the salt concentration? Without knowing the stringency of the\nwash conditions it’s impossible to assess whether the interactions indicated by IP-WB are physiological or merely low-affinity\ninteractions that are the result of gentle IP conditions.\n5. Figure 5: The authors propose that S100A8/9 act as cofactors for STAT3 in CRC cells. They show co-\nimmunoprecipitation of soluble proteins, but no evidence that the proteins co-bind to DNA. The most straightforward way to\ndo this would be ChIP-seq/CUT&Tag for S100A8/9 and STAT3 and to compare the peak overlap between the two.\n6. A) Figure 5 Hi-ChIP: the authors perform Hi-ChIP for STAT3 in wild-type and STAT3/S100A8/9-24A cells to demonstrate\nthe loss of enhancer-promoter loops in the mutant, showing a large decrease in interactions. However, this experiment is\ninherently biased. The authors don’t show much data on whether STAT3 chromatin binding is disrupted in the mutant\ngenome-wide (although Fig 6h,i indicate a reduction at Tns1), but this seems likely. If STAT3 binding is lost in the mutant, it\nis obvious that STAT3-associated looping will decrease, because there is less STAT3 to pull-down in the Hi-ChIP. This\ndoesn’t mean that the loops are no longer present, merely that the technique can’t detect them in the mutant cells. If the\nauthors want to show that these enhancer-promoter loops are dependent on STAT3, they need to either show that STAT3\nbinding is not affected by the STAT3/S100A8/9-24A mutation (eg by reference-normalised CUT&Run), or they need to use a\nSTAT3-independent mechanism to measure looping (e.g. Hi-ChIP using H3K27ac [assuming that the mutation doesn’t\naffect H3K27ac at STAT3-bound sites] or promoter-capture HiC).\nB) I cannot see anything in the methods to explain how enhancers were identified in the assignment of enhancer-promoter\nloops. So far as I can see all interactions with TSSs were assumed to be enhancer-promoter loops – but this is clearly not\ncorrect. What about promoter-promoter interactions, or interactions with (non-enhancer/promoter) CTCF sites? Did the\nauthors account for this in their analysis?\nC) Fig S6a: what happens to the expression of Tns1 in the mutant?\nD) Lines 321-322: It is not possible to say that the loss of EPLs associated STAT3/S100A8/9-24A is what inhibits CRC liver\nmetastasis. Both things happen in the mutant, but a causal relationship has not been established by the authors. This point\nalso applies to lines 362-363: there is no evidence that STAT3 EPLs stabilise Tns1 transcription, and the experiments here\nonly show that Tns1 overexpression enhances cell migration and invasion. Whether this relates to EPLs is not tested.\nE) It is not clear to me how the authors go from the 4995+659 EPLs identified in Fig 5p to say that only 39 EPLs were\ndownregulated in the STAT3/S100A8/9-24A group. More explanation is needed to understand the analyses presented.\nMinor points\n1. Some of the language in the abstract and introduction could be rephrased – as written, it implies that E. coli directly\nrecruits RIPK2 to the ATF3 promoter, which doesn’t make sense (Neutrophile interaction with E. coli may result in the\nrecruitment of RIPK2, but it isn’t the direct recruiter).\n2. Fig 1b: typo in y-axis label (“Aundance” instead of “abundance”)\n3. Line 74: please make it clear that this experiment is done in mice (not patients). What model of CRC is being used here?\nBasic information to understand the experimental system should be included here, not solely confined to the methods\nsection.\n4. Lines 106-7: Please define cKO and cKI. In which cell types is the gene knocked out/knocked in? Where are the control\nexperiments showing successful editing?\n5. Lines 151-2: There is no mention of mass spectrometry in the methods section – only silver stain/western blotting after\nDNA pull-down. I also could not find any MS data within the figures/supplemental. Please amend.\n6. Fig 3g-i: Are these experiments conducted in neutrophils in which (both copies of) the Ncf4 promoter have been edited?\nPlease show evidence of successful editing (eg Sanger sequencing data). In these experiments, what does ATF3-/+ and\nRelB-/+ mean? Is this KO, OE or something else?\n7. Fig S5a-b/Lines 239-40: What dataset does this come from? It would be helpful if the main text mentioned that this was\nscRNA-seq.\n8. Line 300: “nuclear” should read “the nuclei”.\n9. Line 700: please include cross-linking conditions for Hi-C library preparation.", "reviewer_1_attached": "Major comments:\n1) The manuscript focuses on liver metastasis, but it remains unclear how the various targeting strategies\n(e.g., NETs, RIPK2, TRPC1, S100A8/9, STAT3) impact the primary tumor. Do these interventions affect\ntumor growth, local invasion? It would be valuable to include in vivo analyses—such as histological\nassessment of invasion or tumor burden in the colon—to better understand the broader impact of these\ntreatments.", "reviewer_1_round_2": "The authors have made a tremendous effort to address all reviewers’ comments, and the manuscript\nhas substantially improved. However, one important point remains unclear. The authors state that\nRIPK2-induced NET formation occurs independently of PAD4, based on the lack of cross-regulation\nbetween these pathways. Yet, inhibition of PAD4 also reduces NET formation and liver metastasis to a\nsimilar extent as RIPK2 blockade. This observation suggests that both pathways, while molecularly\nindependent, are functionally convergent. The authors should clarify whether these represent two parallel\nmechanisms leading to NET formation, or whether there is partial overlap downstream.\nFurthermore, the authors report numerous CitH3-positive cells that do not co-localize with MPO,\nindicating that many CitH3+ cells in their samples are non-neutrophils. Given that CitH3 generation\nrelies on PAD4-mediated histone citrullination, it is unclear how NETs can be reliably identified by CitH3\nstaining in a system that is claimed to be PAD4-independent. The authors should clarify how NETs were\ndefined and quantified in this context, and discuss the potential contribution of PAD4-\nindependent but CitH3-positive non-neutrophil cells to their analyses.\nFinally, I also wonder whether the high number of CitH3-positive cells observed in the tissue could be due to the\nantibody used. The authors mention using a Cell Signaling Technology antibody, whereas most studies in the field,\nparticularly those investigating liver metastasis, use the Abcam ab5103 antibody. In published data,\nsuch extensive CitH3 staining—where nearly all cells appear positive—has not been observed. The authors should\ncomment on this discrepancy and consider validating their findings with a widely accepted antibody.", "reviewer_2_round_2": "The authors have answered all my concerns. I therefore have nothing more to add.", "reviewer_3_round_2": "The authors have successfully addressed my comments. No more further comments.", "reviewer_4_round_2": "1. The results contradict established literature regarding the relationship between MLKL and PAD4, and\nthe mechanism by which MLKL promotes NET formation is unconvincing.\n2. The mechanism underlying the interaction between neutrophils/NETs and CRC cells via TRPC1\nremains unclear.\n3. Furthermore, the role of NETs in promoting EMT in CRC cells has been previously reported [PMID: 37684625, PMID:\n40433907, PMID: 34939675], which reduces the novelty of this manuscript.", "reviewer_5_round_2": "The authors have made substantial revisions and provided detailed responses that address most of the previous concerns.\nThe manuscript is now significantly improved in both clarity and experimental rigor. I appreciate the extensive additional\nanalyses and validation work. A few issues remain that should be clarified.\n1. The sample distribution across centers has been clarified; however, the statistical rationale for determining the final\nsample size remains unclear. Please briefly describe how the sample number was estimated (e.g., power analysis or prior\neffect size).\n2. The inclusion of long-term follow-up data strengthens the study. Please specify the median follow-up duration and clearly\ndefine the survival endpoints (e.g., overall survival or disease-free survival) used in the analysis.\n3. The new analyses of clinical and lifestyle factors (Table S5) are appreciated. To further strengthen this section, please\nclarify whether dietary habits and gene mutation data were uniformly available for all patients or limited to a subset.\nIf limited, indicate the number of cases included in these analyses.\n4. For multi-group or pathway enrichment analyses, please clarify whether multiple-comparison corrections (e.g., FDR or\nBonferroni) were applied.\n5. The analysis of K. pneumoniae and other symbiotic bacteria adds depth. Given the observed metastasis-promoting role of\nK. pneumoniae, a short discussion on possible synergistic interactions between E. coli and K. pneumoniae would be\nvaluable.\n6. The introduction of patient-derived organoids and humanized mice is an important addition that supports translational\nrelevance. Still, a concise comparison between these models and human CRC pathology would further emphasize their\nfidelity to clinical conditions.\n7. The discussion appropriately moves beyond antibiotic use to signaling pathway inhibition. It would further strengthen the\ntranslational value to mention whether RIPK2 or STAT3 inhibitors have shown preclinical or clinical activity in CRC or\nrelated cancers.\n8. The revised description of the 2bRAD-M analysis pipeline is comprehensive and clearly written. However, for\ntransparency, please state the software version or script availability (e.g., GitHub link or repository) if custom scripts were\nused.", "reviewer_6_round_2": "I thank the authors for taking the time to extensively revise their manuscript, with new experiments and additions to the text. I\nam satisfied that they have addressed my concerns.", "reviewer_1_round_3": "Most of the authors’ responses are clear, and I am satisfied with the majority of the revisions provided in this second round.\nHowever, I remain unconvinced by the explanation regarding CitH3 staining and, in particular, the choice of antibody.\nAlthough I understand the rationale they present, the field overwhelmingly relies on the Abcam antibody, and the very broad\nCitH3-positive signal shown in their tissue sections is unusual compared to published data.\nGiven that this is the second revision and that the rest of the manuscript has improved substantially, I leave it to the editor to\ndecide whether this remaining issue requires further experimental validation or whether a brief note in the text addressing\nthis limitation is sufficient.", "reviewer_5_round_3": "All my previous concerns have been addressed, I have no more question."}, "author_response": "Response: We appreciate your suggestion. Based on previous literature and our experimental findings,\nwe posit that MLKL and PAD4 represent two parallel pathways that facilitate the formation of NETs. As\ndetailed in the review by Leonardo Cristinziano et al. (Response figure 1, PMID: 34280576), an increase\nin intracellular reactive oxygen species can activate RIPK1/3 and MLKL, resulting in the translocation\nof MLKL from the cytoplasm to the plasma membrane (Response figure 1, PMID: 26531064, PMID: 29078325). This\nprocess is associated with the loss of cytoplasmic granules, nuclear membrane disruption, chromatin\ndecondensation, histone hypercitrullination, and NET compression (Response\nfigure 1, PMID: 26531064, PMID: 29078325). On the other hand, PAD4 contributes by converting\narginine residues on histones to citrulline, which also leads to chromatin decondensation (Response\nfigure 1, PMID: 20733033). Upon nuclear disintegration, citrullinated histones are released alongside\nnuclear DNA (Response figure 1, PMID: 20733033). Consequently, the mechanisms by which MLKL\nand PAD4 facilitate NET release are distinct yet complementary, rather than contradictory. Specifically\nas follows: “Thus, RIPK2 didn’t rely on PAD4 for NET formation, and PAD4 and MLKL operated as\ntwo parallel pathways in promoting NET formation.” (Lines 319~320, Page 9)\n\nResponse: We express our profound gratitude for your insightful suggestion, which facilitated an\nextensive review of the literature and significantly enhanced our comprehension of CitH3. Our findings\nindicated that CitH3 is not exclusive to neutrophils. CitH3 serves as a fundamental marker for the\nformation of NETs; however, its expression is not exclusive to these cells. CitH3 can also be identified\nin other cell types capable of undergoing “NETosis-like” cell death or possessing specialized\ncitrullination functions. For instance: 1) Mast cells: Studies have demonstrated that upon stimulation,\nmast cells can release extracellular traps (MCETs) composed of DNA and citrullinated histones.\nThis process closely resembles NETosis and constitutes a crucial antibacterial defense mechanism for mast\ncells (PMID: 22969769). 2) Monocytes/macrophages: Certain subpopulations of monocytes and\nmacrophages have been observed to citrullinate histones and form extracellular traps in response to\nspecific pathogens, such as HIV, or inflammatory stimuli (PMID: 23993650). 3) Eosinophils: Research\nindicates that eosinophils can also undergo ETosis, releasing extracellular traps characterized by\neosinophil peroxidase (PMID: 18506034).\n\nSignificantly, PAD4 is expressed in tumor cells and modifies the epigenetic landscape by citrullinating\nhistones, which in turn can activate or repress the expression of critical genes. For example, in liver\ncancer cells, PAD4 can interact with citrullinated histone H3 (H3R2/R8/R17) and coordinate with other\nepigenetic modifications, such as DNA methylation. This interaction results in the closure of the promoter\nregions of certain tumor suppressor genes, such as p53, thereby silencing their expression (PMID:\n36365233).\n\nTherefore, CitH3-positive cells may include neutrophils, mast cells, macrophages, eosinophils, and\nliver cancer cells. In conclusion, relying solely on PAD4 or CitH3 for the identification of NETs is\ninsufficient. It is essential to incorporate DNA markers such as DAPI, along with neutrophil-specific\nmarkers like NE or MPO. The true identification of NETs is achieved through the co-localization of these\nthree markers.\n\nResponse: Based on your suggestion, we compared these two antibodies, especially their application\nscenarios. Anti-CitH3 antibody (#97272, Cell Signaling Technology) employed in our study exhibits\nextensive positive signals, consistent with the representative figure available on the manufacturer’s\nofficial website (Response figure 2). This observation can be attributed to the antibody’s lack of\nspecificity for neutrophils. We further corroborate the suitability of Cell Signaling Technology #97272\nantibody as a reliable antibody for immunohistochemical applications (Response figure 3). Conversely,\nAbcam #ab5103 antibody, which you suggested for use in other studies, is not appropriate for\nimmunofluorescence or immunohistochemical experiments (Response figure 4). According to the\nmanufacturer’s website, the validated applications for this antibody are restricted to immunoprecipitation\n(IP), Western blotting (WB), and peptide array (PepArr) (Response figure 4). In summary, CitH3 is a\nubiquitously expressed protein, and the Cell Signaling Technology #97272 antibody is deemed\nappropriate for immunohistochemistry. Consequently, we regard the current findings as reliable.\n\nResponse: We thank you for your positive evaluation and for recommending our manuscript for\nacceptance. We are delighted that our revisions have fully addressed all previous concerns.\n\nResponse: We appreciate the time and effort you have dedicated to reviewing our work, as your insightful\ncomments have significantly improved the quality of our paper.\n\nResponse: We appreciate your suggestion. Combined with your suggestions and those of Reviewer #1,\nwe reply as follows. Based on previous literature and our experimental findings, we posit that MLKL\nand PAD4 represent two parallel pathways that facilitate the formation of NETs. As detailed in the review\nby Leonardo Cristinziano et al. (Response figure 1, PMID: 34280576), an increase in intracellular\nreactive oxygen species can activate RIPK1/3 and MLKL, resulting in the translocation of MLKL from\nthe cytoplasm to the plasma membrane (Response figure 1, PMID: 26531064, PMID: 29078325). This\nprocess is associated with the loss of cytoplasmic granules, nuclear membrane disruption, chromatin\ndecondensation, histone hypercitrullination, and NET compression (Response figure 1, PMID: 26531064,\nPMID: 29078325). On the other hand, PAD4 contributes by converting arginine residues on histones to\ncitrulline, which also leads to chromatin decondensation (Response figure 1, PMID: 20733033). Upon\nnuclear disintegration, citrullinated histones are released alongside nuclear DNA (Response figure 1,\nPMID: 20733033). Consequently, the mechanisms by which MLKL and PAD4 facilitate NET release are\ndistinct yet complementary, rather than contradictory. Specifically as follows: “Thus, RIPK2 didn’t rely\non PAD4 for NET formation, and PAD4 and MLKL operated as two parallel pathways in promoting NET\nformation.” (Lines 319~320, Page 9)\n\nResponse: We apologize for the lack of detail in our previous description. It is important to clarify that\nNETs do not directly regulate the expression of TRPC1 in CRC cells. Instead, they influence TRPC1\nexpression through an indirect mechanism involving NFATC3. Specifically as follows: “It has been\nreported that TRPC1 enhances NFATC3’s nuclear translocation via the Ca²⁺-calcineurin pathway, while\nNFATC3 increases TRPC1 transcription, influencing vascular remodeling and hypertension(24, 25). We\nproposed a TRPC1-Ca²⁺-NFATC3-TRPC1 positive feedback loop. Ripk2-cKO neutrophils removal\nshowed reduced NFATC3 expression in CRC cells; Nfatc3-KO in CRC cells decreased TRPC1\nexpression, whereas Nfatc3-OE increased it (Fig. S21e-S21g). Furthermore, Ca2+ channel agonist 1\nelevated NFATC3 and TRPC1 levels in CRC cells, counteracting the effects of Ripk2-cKO neutrophil\nloss (Fig. S21h). Nfatc3-OE also mitigated the negative impact of Ripk2-cKO neutrophil loss on TRPC1\nexpression in CRC cells (Fig. S21i), highlighting the feedback loop’s importance for maintaining TRPC1\nexpression in CRC cells. Therefore, NETs facilitated calcium ion influx via TRPC1, which subsequently\nupregulated NFATC3 expression. As a transcription factor, NFATC3 enhanced the expression of TRPC1.”\n(Lines 362~372, Page 11)\n\nResponse: We acknowledge the concerns regarding the limited novelty of the study suggesting that NETs\nfacilitate CRC liver metastasis. Given the editor’s indication that novelty is not a primary consideration\nat this stage, we intend to investigate, in future studies, the mechanisms through which neutrophils\ncontribute to CRC liver metastasis independently of NETs.\n\nResponse: Thank you for your suggestion. We have enhanced the “Materials and methods” section by\nproviding detailed information on the number and composition of samples in the “Patient samples”\nsection and briefly explaining the rationale for selecting such sample size. Specifically as follows: “In\nthis study, we included a total of 136 CRC patients, with or without liver metastasis, ensuring\ncomprehensive data such as survival information, clinical indicators, lifestyle habits, and gene mutations.\nSpecifically, 24 samples from our center were utilized for 2bRAD-M-seq, 60 samples from our center\nwere exclusively used for immunofluorescence detection, and 84 specimens from external centers were\nalso employed for immunofluorescence detection. Of these, 105 were non-metastatic specimens, while\n31 exhibited liver metastasis. Given the limited availability of CRC specimens with liver metastases, we\nmaintained a 1:3 ratio between the metastatic and non-metastatic groups. This approach ensured an\nadequate number of liver metastasis specimens while mitigating potential statistical biases due to an\nimbalanced ratio.” (Lines 706~714, Page 23)\n\nResponse: We appreciate your suggestion and have accordingly enhanced our definitions of follow-up\ntime and survival endpoints. Specifically as follows: “The follow-up period has been established at 80\nm, with a median follow-up duration of 54 m, and the survival endpoint has been defined as overall\nsurvival.” (Lines 724~726, Page 23)\n\nResponse: The data presented in Table S5 were obtained from a cohort of 136 patients, encompassing\nall participants in the study. This includes 84 samples procured from our institution and 52 samples\nsourced from external centers.\n\nResponse: In accordance with your recommendations, we have incorporated a correction method for\nmultiple comparisons in the sequencing-related experiments. Specifically as follows: “For multi-group\nor pathway enrichment analyses, we employ the FDR to adjust for multiple comparisons.” (Lines\n1082~1083, Page 31; Lines 1217~1218, Page 33; Lines 1249~1250, Page 34; Lines 1333~1334, Page\n36)\n\nResponse: In response to your recommendations, we have expanded the “Discussion” section to address\nthe synergistic promotion of CRC liver metastasis by E. coli and K. pneumoniae. Specifically as follows:\n“Our findings indicated that both E. coli and K. pneumoniae played a crucial, previously\nunderappreciated role in the tumor microenvironment as carcinogenic bacteria. They collaboratively\ndrove the progression to the most lethal stage of CRC-liver metastasis-via a synergistic mechanism that\nfacilitated NETs-dependent immune evasion. This insight provided a novel perspective on the complexity\nof cancer and held promise for the future development of precise microbiome-based anti-cancer\nstrategies.” (Lines 627~632, Page 21)\n\nResponse: In accordance with your recommendation, we have expanded the “Discussion” section to\nexamine the differences between patient CRC tissues and organoids or CRC tissues in mice with\nhumanized immune systems. Specifically as follows: “Furthermore, PDOX used in this study was\nsubject to several limitations. Firstly, the PDOX model, developed using mice with humanized immune\nsystems and organoids, might not accurately replicate the microenvironmental characteristics of human\nCRC tissues. The process of establishing and propagating PDOX models inevitably involves clonal\nselection, resulting in the enrichment of tumor cell subpopulations that are either adapted to in vitro\nculture conditions or possess growth advantages in mice, while other subpopulations might be lost(44).\nAdditionally, there exists a degree of spatial heterogeneity (variation in cell characteristics across\ndifferent regions of the tumor) and temporal heterogeneity (evolution of tumor clones before and after\ntreatment) between the PDOX model and patient-derived CRC tissues(44, 45). Consequently, the PDOX\nmodel might not fully capture the interactions between human tumors and other cells within the\nmicroenvironment(44, 45). Therefore, in future research endeavors, it is imperative to conduct Phase I\nclinical trials that assess the targeting of RIPK2 in neutrophils or STAT3/S100A8/9-EPLs in CRC cells\nas a strategic approach to enhance the understanding of the interaction between NETs and CRC.” (Lines\n671~683, Page 22)\n\nResponse: Based on your suggestions, we have supplemented the discussion on antibiotics, STAT3 or\nRIPK2 inhibitors, especially their applications in clinical trials or preclinical research. Specifically as\nfollows: “In comparison to the direct anti-tumor effects, the modulation of intestinal flora by antibiotics\nand their significant influence on cancer immunotherapy represent prominent and contentious areas of\ncurrent research. Substantial evidence suggests that the composition and diversity of the gut microbiome\nare critical determinants of the efficacy of immune checkpoint inhibitors, including anti-PD-1/PD-L1\nand anti-CTLA-4 antibodies(34). The administration of broad-spectrum antibiotics could markedly\ndisrupt the homeostasis of the gut microbiota, resulting in reduced microbial diversity and the depletion\nof beneficial microbiota. In comparison to the conventional use of antibiotics for the eradication of E.\ncoli, targeted therapeutic interventions focusing on STAT3 or RIPK2 present a more dependable\nalternative. Persistent activation of STAT3 is a hallmark of numerous cancers, playing a pivotal role in\nthe regulation of several core genes associated with tumor malignancy(35). Consequently, the inhibition\nof STAT3 could facilitate a synergistic effect across multiple pathways in tumor suppression. Current\nstrategies for targeting STAT3 encompass non-specific small molecules, such as Sta-21 and Stattic,\nhighly selective inhibitors aimed at the SH2 domain, such as BP-1-102, as well as simulated peptides\nand protein degradation-targeted chimeric (PROTACs) technology(36). In comparison to the targeted\ntherapy of STAT3, targeting RIPK2 presents the advantage of potentially offering a more favorable\ntherapeutic window. Genetic studies indicate that RIPK2 is not essential for normal physiological\nprocesses in adult organisms, suggesting that its inhibitors may exhibit a relatively high safety profile(37).\nRecent advancements have led to the development of highly selective RIPK2 small molecule inhibitors,\nsuch as compound IA, and degraders, such as PROTAC, which have demonstrated efficacy in inhibiting\ninflammation-driven tumor growth in preclinical models(38). Similar to STAT3 inhibitors, the\ncombination of RIPK2 inhibitors with immunotherapy is considered a highly promising approach, with\nthe objective of reversing immunosuppression by targeting tumor-associated chronic inflammation(35-\n37).” (Lines 634~657, Page 21~22)\n\nResponse: In this study, we utilized bacterial sequencing scripts primarily sourced from the work of Sun\nand colleagues, which were made available through the database script repository at\nhttps://github.com/shihuang047/2bRAD-M. It is important to note that no new scripts were developed in\nthis research.\n\nResponse: We thank you for your positive evaluation and for recommending our manuscript for\nacceptance. We are delighted that our revisions have fully addressed all previous concerns."}]

Decision letter