Acinetobacter baumannii promotes gastric cancer metastasis via NA-mediated NAD metabolism reprogramming and glycolytic activation

Yan Yang; Rui Yang; Yiran Chen; Chao He; Yingzi Zhang; Jing He; Jing Zhang; Haohao Wang; Jingdan Liang; Zixin Deng; Lisong Teng

doi:10.1136/gutjnl-2025-336161

. 2026 Feb 9;75(10):e336161. doi: 10.1136/gutjnl-2025-336161

Acinetobacter baumannii promotes gastric cancer metastasis via NA-mediated NAD metabolism reprogramming and glycolytic activation

Yan Yang ^1,^*,⁰, Rui Yang ^1,⁰, Yiran Chen ^1,², Chao He ¹, Yingzi Zhang ¹, Jing He ¹, Jing Zhang ¹, Haohao Wang ¹, Jingdan Liang ^3,^*, Zixin Deng ^3,^*, Lisong Teng ^1,^✉

PMCID: PMC13638492 PMID: 41663154

Abstract

Background

Gastric cancer (GC) is one of the most common malignancies worldwide and it is the third leading cause of cancer-related death in China. While Helicobacter pylori is a known GC pathogen, its abundance declines in tumours and the role of other bacteria in GC metastasis remains unclear.

Objective

We aim to investigate the mechanisms of other bacteria influencing GC progression and metastasis.

Design

Integrated intratumoural microbiome-metabolome analysis identified GC-associated microbes and metabolites. We then demonstrated the pro-metastatic role of Acinetobacter baumannii (A. baumannii, Ab) and its metabolite nicotinic acid (NA) using genetic, molecular and in vivo approaches.

Results

The abundance of A. baumannii was significantly increased in GC tissues, correlating with advanced tumour stage and intratumoural NA levels. Fluorescence in situ hybridisation confirmed its colonisation in GC tumours. In co-culture systems, A. baumannii increased NA levels, enhancing nicotinamide adenine dinucleotide (NAD) metabolism and increasing 1-Methylnicotinamide accumulation in tumour cells. Mutagenesis of the bacterial NA synthase gene pncA confirmed that A. baumannii excreted an NA-dependent pro-metastasis effect. Mechanically, A. baumannii promotes GC metastasis by reprogramming tumour cell glucose metabolism, reducing oxidative phosphorylation while enhancing glycolysis and activating the hypoxia-inducible factor-1 pathway in GC cells through metabolites both in vivo and in vitro.

Conclusions

This study elucidates the role of A. baumannii in enhancing NAD metabolism in GC cells through NA synthesis, consequently promoting GC metastasis. These findings establish a microbiota-metabolism axis as a mechanistic foundation for developing targeted therapeutic strategies against GC metastasis.

Keywords: GASTRIC CANCER, ENERGY METABOLISM, INTESTINAL MICROBIOLOGY

WHAT IS ALREADY KNOWN ON THIS TOPIC

Helicobacter pylori is a recognised causal factor for gastric cancer, but its abundance decreases in tumour tissues, suggesting other microbes contribute to progression—though their mechanisms remain unknown.

WHAT THIS STUDY ADDS

This work identifies Acinetobacter baumannii as a pro-metastatic driver in gastric cancer, demonstrating it synthesises nicotinic acid to perturb nicotinamide adenine dinucleotide metabolism, activate hypoxia-inducible factor-1 and enhance glycolysis to promote metastasis.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

It establishes a novel ‘microbiota-metabolism axis’ as a therapeutic target, potentially redirecting drug development toward bacterial metabolites and informing future microbiome-based cancer stratification strategies.

Introduction

Gastric cancer (GC) is the fifth most common cancer and a leading cause of cancer mortality worldwide. In China, GC is the third most prevalent malignancy in terms of both incidence and mortality.¹ GC is a multifactorial disease and the change of tumour microenvironment can promote the occurrence, development and metastasis of GC. As a part of the tumour microenvironment, gastric microbiota has received increasing attention. Stomach microbes can influence tumour growth and metastasis.^2
3 A number of previous studies have shown that there are significant differences in the microbes of GC tissues and normal tissues.^4–6 A recent study has found that during the progression of GC, the number of Helicobacter pylori (H. pylori, Hp) decreases or even disappears, suggesting that other microorganisms in the stomach may play a role in the spread of the disease.⁷

Our previous studies have shown that Acinetobacter abundance in GC tissues is significantly upregulated compared with normal tissues.⁸ Acinetobacter is a common Gram-negative bacterium. There are literature reports that various species of Acinetobacter, such as Acinetobacter lwoffii (A. lwoffii), Acinetobacter baumannii (A. baumannii, Ab) and Acinetobacter johnsonii (A. johnsonii), exist in healthy individuals.⁹ Ab is the most common Acinetobacter species, which is often associated with clinical infection. However, the role of A. baumannii in GC and its pathogenic molecular mechanisms is largely unclear.

In this study, we demonstrate that A. baumannii abundance correlates positively with GC clinical stage and accelerates metastatic dissemination. Mechanistically, we found that Ab can promote nicotinic acid (NA) production through nicotinamidase (PncA) enzymes. NA can promote nicotinamide adenine dinucleotide (NAD) metabolism and 1-Methylnicotinamide (1-MNA) production in GC cells. The metabolites can further reprogram cellular glucose metabolism, enhance glycolysis, activate the hypoxia-inducible factor-1 (HIF-1) pathway and promote GC metastasis.

Results

The abundance of A. baumannii significantly increases in GC tissues

Our previous findings and a meta-analysis of 10 gastric microbiota cohorts consistently demonstrate a significant increase in Acinetobacter abundance within GC tissues.^8
10 In this study, species-specific qPCR quantification further revealed that A. baumannii was predominantly enriched in GC tissues compared with other clinically prevalent species A. lwoffii and A. johnsonii, as assessed across GC stage-stratified cohorts^9
11 (online supplemental table S1). Significant enrichment of A. baumannii (p<0.001) and A. lwoffii (p<0.01) was observed in GC tissues compared with normal controls, while A. johnsonii showed no significant difference (figure 1A, online supplemental figure S1A,B). Notably, A. baumannii exhibited higher relative abundance and tissue positivity rates compared with both A. lwoffii and A. johnsonii. Specifically, A. baumannii showed a 60% positivity rate in tumour tissues vs 46.7% for A. johnsonii and 36.7% for A. lwoffii, with similar dominance in normal tissues (A. baumannii: 46.7% vs A. johnsonii/A. lwoffii: 43.3%) (online supplemental figure S1C).

Further analysis revealed markedly higher A. baumannii abundance in advanced GC tissues compared with early-stage ones (figure 1B). Consistently, A. baumannii levels showed positive correlation with TNM stage (ρ=0.417, p=0.017) (online supplemental figure S1F). In contrast, the abundances of A. johnsonii and A. lwoffii exhibited no significant differences between early and advanced GC stages (online supplemental figure S1D,E). We therefore focused on A. baumannii for subsequent mechanistic investigations.

The clinicopathological characteristics of the patient cohort were shown in figure 1C. Meanwhile, we also verified the colonisation of A. baumannii in human tissues by Fluorescence in Situ Hybridisation (FISH) (figure 1D). Probes EUB388 (universal) and Aci (Acinetobacter-specific) detected microbial colonisation in GC and normal tissues. Tumour tissues exhibited significantly elevated abundance of both total microbiota and Acinetobacter. Subsequently, transmission electron microscopy (TEM) was employed to examine GC MKN45, AGS, HGC27, normal epithelial cell line GES-1, A. baumannii, A. baumannii-MKN45, A. baumannii-AGS, A. baumannii-HGC27, A. baumannii-GES-1 co-cultured samples. The results revealed that A. baumannii aggregates were localised on the cell membrane surface, suggesting potential adhesive interactions between A. baumannii and host cells (figure 1E and online supplemental figure S1I). Using the fluorescent dye DMAO, we observed significantly higher adhesion of A. baumannii to GC cells compared with normal gastric mucosal cells. Additionally, A. baumannii showed significantly higher adhesion to GC cells than the control Escherichia coli DH5α (DH5α), indicating a strong adhesion capability of A. baumannii to GC cells (figure 1F,G and online supplemental figure S1G,H).

Given that A. baumannii adhesion is known to be mediated by host proteins like fibronectin (FN) and carcinoembryonic antigen-related cell adhesion molecule (CEACAM).^12
13 We investigated their expression after A. baumannii treatment. qPCR and Western blot analyses revealed that A. baumannii significantly increased FN mRNA levels and upregulated both FN and CEACAM in normal and tumour cells (online supplemental figure S1J,K). Notably, this upregulation effect was greater in tumour cells, suggesting that enhanced expression of these adhesion molecules may underlie the stronger binding affinity of A. baumannii for GC cells.

Furthermore, gavage mouse models confirmed sustained gastric colonisation by A. baumannii (online supplemental figure S2A). Viable A. baumannii isolates were successfully recovered and confirmed by full-length 16S rRNA sequencing from murine stomachs (online supplemental figure S2B,C). Consistently, in vitro simulations of gastric conditions showed that A. baumannii was capable of growth under acidic conditions, accompanied by an increase in the medium pH from 2.0 to 2.5 (online supplemental figure S2D,E).

To further characterise the colonisation profile and potential origin of A. baumannii, we examined its presence in diverse clinical and environmental samples. The bacterium was detected at comparable positivity rates and relative abundance in throat swabs from healthy individuals (n=20) and GC patients (n=30) (online supplemental figure S3A). Although A. baumannii was detectable in hospital environmental samples (n=10, from operating rooms and wards), its abundance remained markedly low (online supplemental figure S3D). It was also present in gastric biopsy tissues from non-hospitalised outpatients, with no significant difference between H. pylori-negative (n=51) and H. pylori-positive (n=58) gastritis tissues (online supplemental figure S3B). However, both the positivity rate and abundance were significantly reduced in patients with a history of H. pylori eradication therapy 1–2 years prior (online supplemental figure S3G), a trend supported by our animal colonisation model (online supplemental figure S3E,F). Moreover, A. baumannii abundance was significantly higher in GC tissues than in gastritis tissues (online supplemental figure S3C). Examination of other gastrointestinal cancers revealed significantly higher A. baumannii abundance in colorectal cancer (CRC) tissues (n=29) compared with adjacent normal tissues (online supplemental figure S3H), whereas no significant difference was found between tumour and normal tissues in esophageal squamous cell carcinoma (ESCC) (n=19) (online supplemental figure S3I). These data suggest that A. baumannii can be present as part of the commensal microbiota and is unlikely to be hospital-acquired. Its gastric colonisation appears independent of concurrent H. pylori infection but is sensitive to antibiotic exposure.

A. baumannii promotes GC metastasis in vitro and in vivo

Higher stage in GC means more lymph node metastasis and distant metastasis.¹⁴ Higher abundance of A. baumannii in advanced-stage GC suggests a potential association of A. baumannii with GC metastasis. Therefore, we further verified the effect of A. baumannii on GC metastasis through animal experiments.

We constructed liver and peritoneal metastasis models of GC in nude mice (figure 2A,B). DH5α was used as the microbial negative control, while Streptococcus anginosus (S. anginosus, SA), previously demonstrated to promote GC progression and metastasis, was chosen as the positive control.^15–17 4-week-old mice were segregated into control and A. baumannii co-cultured groups. Spleens were injected with 2×10⁶ MKN45 cells, either co-cultured with A. baumannii, DH5α, S. anginosus or non-co-cultured cells. A. baumannii and S. anginosus co-cultured cells significantly promote liver metastasis in mice (figure 2C and G and online supplemental figure S4C and H). FISH examination indicated the establishment of A. baumannii colonisation within the liver metastatic lesions in the A. baumannii co-cultured group (figure 2D and online supplemental figure S4D). In a nude mice peritoneal metastasis model, 2×10⁶ MKN45 co-cultured A. baumannii, DH5α, S. anginosus or non-co-cultured MKN45 cells were injected intraperitoneally. The A. baumannii co-cultured group displayed increased metastatic foci count compared with controls (figure 2E,H and online supplemental figure S4E, S4I,J). FISH analysis confirmed A. baumannii colonisation in the tumour sites of the A. baumannii co-cultured group (figure 2F and online supplemental figure S4F). HE staining for detecting liver metastases and peritoneal metastases in mice (figure 2D and F and online supplemental figure S4D,F). Note that, based on the clinical predominance of liver and peritoneal metastases in GC, this study focused on these sites, although preliminary data confirmed that A. baumannii also promotes lung metastasis^18–20 (online supplemental figure S4A,B, S4G).

To validate these findings in a host with a functional immune system, we employed immunocompetent 615 mice and the mouse GC cell line MFC. In a peritoneal metastasis model, A. baumannii co-culture similarly led to a significant increase in metastatic burden compared with the control group (online supplemental figure S5A–C). Single-cell RNA sequencing of metastatic nodules showed that A. baumannii treatment altered tumour cell clustering and led to a marked expansion of cancer stem cells. In contrast, the proportions of major immune cell types, including T cells, B cells and macrophages, were not significantly changed (online supplemental figure S5D–G). These results demonstrate that the pro-metastatic effect of A. baumannii is maintained in immunocompetent mice and its primary impact within the metastatic microenvironment appears to be on the tumour cells themselves. Based on this finding and to enable deeper mechanistic investigation, subsequent studies on the underlying molecular pathways were conducted using the established nude mouse models.

Bacteria can promote tumourigenesis and progression of tumours through virulence factors, metabolites and proteins.²¹ To investigate the mechanism by which A. baumannii promotes tumour metastasis, we conducted cellular experiments in vitro. GC cell lines HGC27, MKN45 and GES-1 were co-cultured with or without A. baumannii (MOI=100) for 4 hours. DH5α and phosphate-buffered saline (PBS) were used as bacterial and blank controls, respectively. Wound healing assay and Transwell migration assay were performed to evaluate the migratory capacity of tumour cells. A. baumannii co-culture increased the migration of both tumour cells and GES-1 cells, but the effect was stronger in the tumour cells. Inactivation of A. baumannii through heating may lead to the disappearance of its pro-metastatic effect. We filtered microbial cultures through a 0.22 µm membrane to obtain conditioned medium (CM), which significantly promoted GC cell migration. Proteinase K digestion of the CM maintained this effect, indicating that A. baumannii’s metabolites drive the migration (figure 2I–J). All experiments were performed with three biological replicates, and statistical significance among groups was evaluated using one-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparisons test against the NC group (figure 2K–P).

A. baumannii promotes GC metastasis through the metabolite NA

To further investigate the specific metabolite of A. baumannii that promotes GC metastasis, we conducted a re-analysis using the paired tissue 16S sequencing and metabolomic data from previous studies.²² Correlation analysis revealed a significant positive correlation between the relative abundance of Acinetobacter and the content of 1-MNA and NA (online supplemental figure S6G). Comparing the relative levels of metabolites in tumour tissues and normal control tissues, it was found that the levels of 1-MNA and NA were significantly elevated in GC tumour tissues (online supplemental figure S6A,B). Moreover, we discovered that the relative abundance of both NA and 1-MNA was significantly higher in advanced-stage (stage III/IV) GC tissues than those in early-stage (stage I/II) tissues (online supplemental figure S6C,D). However, the serum levels of these two metabolites showed no significant differences between patients with early-stage and advanced-stage GC (online supplemental figure S6E,F, online supplemental table S2).

A recent study has shown that gut microbiota can synthesise NA, which is synthesised into NAD⁺ through the Preiss-Handler pathway, promoting cell-related metabolism.²³ Tumour cells further metabolise NAD⁺ into nicotinamide (NAM), with 1-MNA being a related product of NAM metabolism²⁴ (figure 3A). Based on the correlation analysis mentioned above, we speculate that A. baumannii promotes GC metastasis by synthesising NA.

Accordingly, a targeted high-performance liquid chromatography-mass spectrometry (HPLC/MS) method was developed with authentic standards for metabolite profiling (figure 3B). Using HPLC/MS, we detected that treatment with A. baumannii increased the levels of NA, NAM, 1-MNA and NAD⁺ in cellular metabolites compared with the PBS control group. However, the NAD⁺/NADH (reduced NAD) ratio decreased (figure 3C–G). In the supernatant, NAD⁺ and NADH were not detected using HPLC/MS, but NA levels significantly increased after A. baumannii treatment, 1-MNA levels showed an upward trend and NAM did not show significant differences (online supplemental figure S7F–H). To validate these findings, we also used commercially available metabolic assay kits, which showed consistent results with the trends observed in cellular metabolites detected by HPLC/MS (online supplemental figure S7A–E). The results indicate that co-culture of A. baumannii and GC cells leads to a significant increase in the levels of NA and 1-MNA in the co-culture system.

The nicotinamidase (PncA) of the bacteria can convert nicotinamide (NAM) to NA. ²⁵Therefore, we constructed an A. baumannii mutant strain (AbΔpncA) with the pncA gene knocked out using homologous recombination methods (figure 4A–C). Based on the concentrations of NA and 1-MNA detected in the co-culture cells and supernatant using the kits and HPLC/MS, as well as previous relevant research, we treated GC cells with 20 µM of NA and 10 µM of 1-MNA.^26–28 The co-culture of A. baumannii and mutant strains was maintained for 4 hours before changing the supernatant and adding fresh medium supplemented with antibiotics (the bacterial strains used in the experiment were non-resistant strains). The wound healing assay, Transwell migration and Matrigel invasion experiment results suggest that NA, 1-MNA and A. baumannii treatment can all significantly enhance the migration of GC cells, whereas the mutant AbΔpncA does not exhibit this effect (figure 4D–L). The above experiments were performed with three biological replicates each and statistical significance among groups was assessed using one-way ANOVA followed by Dunnett’s test against the NC group.

To further validate the function of pncA, we constructed a complementation strain AbΔpncA::pET28a- P_BAD-oriA-pncA (online supplemental figure S8A,B). Functional assays confirmed that complementation with pncA rescued the ability to promote GC cell migration (online supplemental figure S8D–I). Moreover, treatment with the complemented strain restored the production of both NA and 1-MNA (online supplemental figure S8J–M). NA, as a weak acid, remains stable in the acidic gastric environment, which could support its local activity and absorption. Notably, our findings show that temperature, rather than pH, is the primary factor determining NA stability under these conditions (online supplemental figure S8C).

In mice models of liver metastasis and peritoneal metastasis, we found that treatment with NA, 1-MNA and A. baumannii promoted tumour metastasis, while the mutant strain AbΔpncA group did not exhibit this effect (figure 5A and D). Bar graphs demonstrated that metabolites and A. baumannii treatments significantly increased the number of liver metastatic foci and peritoneal metastatic nodules, as well as the weight of peritoneal tumours, in a murine model with five mice per group (figure 5C, F and G). The metastatic tumour foci were examined by H&E staining, while FISH was used to detect microbial colonisation and A. baumannii colonisation in the metastatic foci (figure 5B and E). NC, NA and 1-MNA groups showed microbial colonisation but no A. baumannii colonisation. The A. baumannii group and the mutant strain AbΔpncA group showed both microbial and A. baumannii colonisation.

Notably, tissues’ levels of 1-MNA and NA also showed positive correlations with the relative abundance of Sphingomonas and Alloprevotella. However, qPCR analysis of 30 paired GC and normal tissues revealed that only Sphingomonas was enriched in tumours, while Alloprevotella was decreased, a pattern consistent with our earlier report⁸ (online supplemental figure 6J, K). Both genera displayed lower tissue positivity and relative abundance compared with A. baumannii (figure 1A, online supplemental figures S1C S6J and K). Moreover, A. baumannii exhibited a markedly higher NA production capacity than either Sphingomonas spp. or Alloprevotella rava (online supplemental figure S6H). Phylogenetic analysis further showed that the PncA from A. baumannii forms a distinct lineage, suggesting its catalytic activity might be specific to this bacterium (online supplemental figure S6I and online supplemental table S4). Collectively, findings underscore the unique role and significance of A. baumannii in GC tissues.

Metabolites of A. baumannii driven NAD metabolic reprogramming

NAD⁺ is an essential metabolite and coenzyme that exists in two distinct forms in the cellular environment (oxidised NAD⁺ and reduced NADH). The maintenance of cellular NAD⁺ homeostasis involves three synthesis pathways: de novo pathway, Preiss-Handler pathway and salvage pathway.²⁹ Increased production of NA generated by co-cultured A. baumannii may promote the Preiss-Handler pathway and disrupt the NAD⁺ homeostasis in tumour cells.

We used kits for NAD⁺/NADH, 1-MNA and NA quantification in the medium and HPLC/MS for intracellular and medium metabolite analysis, normalised by total cellular protein. Treatment with NA and 1-MNA, similar to A. baumannii treatment, leads to an increase in intracellular levels of NAM, NA, 1-MNA and NAD⁺, while significantly reducing the NAD⁺/NADH ratio (figure 6A–F). The strength of this effect varies slightly in different GC cell lines such as HGC27 and MKN45, which may be related to the cells’ different metabolic profiles. Kit detection of metabolites in the cell culture medium shows trends similar to those observed intracellularly (figure 6J–N). However, using the same HPLC/MS method did not detect NAD⁺ and NADH in the supernatant, possibly due to the instability of these two metabolites (figure 6G–I). Both the HPLC/MS method and the Kit detection method indicate that the mutant strain AbΔpncA does not significantly alter the levels of NAD-related metabolites in tumour cells and in medium, suggesting AbΔpncA does not lead to a reprogramming of NAD metabolism in tumour cells (online supplemental figure S9A–N). The increase in NAD⁺ levels and the significant reduction in the NAD⁺/NADH ratio suggest a weakened cellular oxidative phosphorylation (OXPHOS) and enhanced glycolysis³⁰ (figure 6O).

A. baumannii and its metabolites enhance glycolysis in GC cells

To further substantiate the above speculation, we conducted additional tests to verify the cellular energy status by measuring the ADP/ATP ratio under different treatment conditions.³¹ Additionally, we measured lactate levels to assess cellular glycolytic activity.³² Furthermore, we evaluated mitochondrial respiration and glycolysis activity by measuring the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR).³³

The results from the ATP assay kit and ADP assay kit indicate that, except for the A. baumannii treatment which significantly decreases cellular ADP levels, other treatments do not significantly affect the ADP levels in GC cells (figure 7A and E). However, A. baumannii, NA and 1-MNA treatments significantly increase cellular ATP levels, while the mutant strain AbΔpncA does not exhibit this effect (figure 7B and F). Combining intracellular lactate levels, A. baumannii, NA and 1-MNA treatments leads to intracellular lactate accumulation (figure 7D and H, online supplemental figure S9O). The decrease in cellular ADP/ATP levels and the accumulation of intracellular lactate induced by A. baumannii and its metabolite treatments suggest that the cells are in a Warburg effect state³⁴ (figure 7C–D and G–H). The increase in ATP levels may be attributed to the rapid glycolysis process, where although each glucose produces less ATP, the speed of glycolysis may generate a greater amount of ATP in a short period of time.³⁵

The OCR and ECAR measurements show that the A. baumannii, NA and 1-MNA treatments result in a decrease in cellular OCR and an increase in ECAR (figure 7I–L). We also calculated the lactate production rate (LPR)/glucose consumption rate (GCR) ratio. Both A. baumannii and its metabolites significantly increased this ratio (figure 7M–N). The above results further confirm the enhancement of glycolysis.

Intriguingly, we observed that 1-MNA, a downstream metabolite of NAM, also perturbs NAD⁺ homeostasis and promotes glycolysis. Previous studies have reported that 1-MNA induces reactive oxygen species (ROS) production in macrophages, leading to HIF-1 pathway activation.^36
37 To examine whether 1-MNA exerts a similar effect in tumour cells, we measured intracellular ROS levels and observed a significant increase on A. baumannii, NA and 1-MNA treatment (figure 7O–P). Integrated with existing evidence, we proposed that 1-MNA perturbs NAD metabolism by inducing ROS-dependent HIF-1 activation, thereby enhancing glycolytic flux.³⁸

A. baumannii and its metabolites promote tumour metastasis by activating the HIF-1α pathway

The relationship between tumour cell glycolysis and the HIF-1 pathway has been well established through numerous studies and the activation of the HIF-1 pathway has also been linked to tumour metastasis in previous research.^39–42 Therefore, the enhancement of tumour cell glycolysis induced by A. baumannii and its metabolites treatment prompts us to further investigate whether it activates the HIF-1 pathway.

KEGG pathway enrichment analysis revealed that differentially expressed genes (DEGs) in A. baumannii-treated cells were significantly enriched in the HIF-1 signalling pathway (figure 8A). GSEA demonstrated upregulation of the HIF-1 pathway and concurrent downregulation of OXPHOS in A. baumannii-treated cells (figure 8B–C). Consistently, single-cell RNA sequencing of metastatic tumours from immunocompetent mice showed that all cells in the A. baumannii-treated group exhibited upregulated glycolysis (online supplemental figure S5H). Furthermore, the expanded cancer stem cell cluster displayed specific enrichment for both glycolysis and HIF-1 signalling pathways, directly mirroring this metabolic shift observed in vitro (online supplemental figure S5I).

Western blot and qPCR analyses of glucose transporter 1 (GLUT1), hexokinase 2 (HK2), pyruvate kinase M2 (PKM2), lactate dehydrogenase A (LDHA) and HIF-1α demonstrated that treatment with A. baumannii, NA and 1-MNA significantly upregulated both transcriptional levels and protein expression of these HIF-1 signalling pathway components in GC cells (figure 8D–M and T). Similarly, analysis of RNA and protein isolated from murine peritoneal metastatic tumours demonstrated that treatment with A. baumannii and its metabolites also activated the HIF-1 signalling pathway in the animal model (online supplemental figure S10A–E, I–N). Additionally, we measured the relative expression of epithelial–mesenchymal transition (EMT) markers (E-cadherin, N-cadherin and Vimentin) by qPCR and Western blotting in both cellular assays and animal models. We consistently observed that A. baumannii and its metabolites significantly upregulated N-cadherin and Vimentin while downregulating E-cadherin (figure 8N-S, U and online supplemental figure S10F–I, O–Q). These findings collectively support that A. baumannii and its metabolites may modulate tumour cell metabolic activity and metastatic potential through HIF-1 pathway activation and EMT pathway. Previous studies show that HIF-1 activation affects cancer cell stemness and the cytoskeleton.^43–45 We evaluated the impact of A. baumannii and its metabolites on these cellular processes. Results showed that A. baumannii and its metabolites significantly increased the expression of stemness-related genes and also enhanced filamentous actin (F-actin) production (online supplemental figure S11A–J).

To determine whether HIF-1α activation is a downstream consequence of metabolic reprogramming or a direct effect of microbial metabolites, we knocked down HIF-1α gene expression in tumour cells using siRNA. All three siRNAs (siRNA1, 2 and 3) significantly reduced HIF-1α expression in tumour cells (online supplemental figure S12A). siRNA1 and siRNA2 were selected for subsequent functional assays. Under normal oxygen conditions, knocking down HIF-1α (using either siRNA1 or siRNA2) did not significantly change the migration ability of GC cells compared with the siNC group. But after adding A. baumannii treatment, the migration ability was significantly reduced in both HIF-1α-knockdown groups (siRNA1-Ab and siRNA2-Ab) compared with the siNC-Ab group. Also, it is important to note that A. baumannii treatment still increased cell migration ability in the HIF-1α knockdown groups when compared with their own controls without A. baumannii treatment (online supplemental figure S12B–G). qPCR and Western blot analysis further revealed that HIF-1α knockdown marked suppressed A. baumannii -induced activation of the HIF-1 and EMT pathways (online supplemental figure S12H–Y). However, A. baumannii treatment still elicited a residual degree of pathway activation even after knockdown, suggesting that A. baumannii may additionally modulate HIF-1 and EMT through alternative mechanisms (online supplemental figure S12H–Y). In the murine peritoneal metastasis model, HIF-1α inhibitor PX-478 significantly suppressed the pro-metastatic effect of A. baumannii and inhibited the activation of the HIF and EMT pathways (online supplemental figure S13A–M).

Knockdown of HIF-1α in HGC27 and MKN45 cells also significantly reduced metabolite-induced metastasis, migration and pathway activation (figure 9A–I and online supplemental figure S14A–Q). However, a residual induction of HIF- and EMT-related gene expression was still observed following A. baumannii or metabolites treatment even after knockdown (figure 9 and online supplemental figure S14). These results suggest that while HIF-1α is a major mediator, A. baumannii and its metabolites may also engage additional, HIF-1α-independent mechanisms to modulate cancer cell migration and invasion.

Discussion

A. baumannii is a notorious multidrug-resistant pathogen associated with severe infections, particularly in clinical settings where it poses significant diagnostic and therapeutic challenges.⁴⁶ Its ability to evade host immune responses and antibiotic treatments has made it a formidable adversary in healthcare environments. Recent studies have shown that Acinetobacter is not only a pathogen in clinical settings but also a potential contributor to tumour progression. For instance, Acinetobacter increase is linked to a predisposition to ovarian cancer.⁴⁷ Acinetobacter seifertii can inhibit macrophage migration, which is associated with ovarian cancer prognosis.⁴⁸ The abundance of Acinetobacter species is elevated in GC tissues.⁸ It has also been reported that the abundance of Acinetobacter species in the urine of bladder cancer patients significantly increases.⁴⁹ We found that the abundance of A. baumannii in GC tissues was significantly higher than in normal control tissues and the abundance of A. baumannii was positively correlated with the staging of GC. The origin of A. baumannii in GC is still unknown. This bacterium is often linked to hospital-related infections. However, studies have also found it in healthy people.⁹ A recent meta-analysis detected it in both normal stomach tissue and GC samples.¹⁰ Our data from diverse samples indicate that A. baumannii can exist as a low-abundance commensal bacterium in the upper digestive tract, which is consistent with previous studies. Its detection in healthy individuals and non-hospitalised outpatients suggests community-based colonisation, not merely a hospital-acquired one. Notably, H. pylori infection did not significantly alter A. baumannii colonisation, while a history of antibiotic eradication therapy was associated with a marked and lasting reduction in A. baumannii levels. This suggests that therapeutic antibiotic regimens can durably reshape microbial colonisation, which may have downstream implications for cancer progression. Furthermore, A. baumannii was more abundant in gastric and CRCs but not in oesophageal cancer, indicating a tissue-specific association with certain gastrointestinal malignancies. Our findings may highlight the importance of enhanced surveillance and prevention of its colonisation in GC patients. In this study, we investigated the impact of A. baumannii on the metastatic mechanisms of GC.

In our study, we observed that A. baumannii significantly promoted peritoneal and liver metastasis in nude mice. Additionally, A. baumannii and its conditional medium enhanced the migration of GC cells and normal epithelial cells. This effect was stronger in the tumour cells. The differential effects may be attributed to intrinsic cellular differences and distinct signalling pathway activities between normal and tumour cells. This phenomenon is also consistent with reports of differential responses to agents like lycopene and the tumour-specific expression of pro-metastatic proteins such as RhoC.^50
51 These findings underscore the critical role of microbial factors in tumour metastasis. Previous research has highlighted the involvement of Hp in GC tumourigenesis and progression.⁵ Recent studies have also demonstrated that S. anginosus promotes gastric inflammation, atrophy and progression.^15–17 Additionally, faecal Streptococcus alteration is associated with GC liver metastasis.⁵² Currently, there is limited research on the impact of gastric tissue microbiota on GC metastasis, and there are still many microbes and mechanisms that remain to be explored.

Previous studies have highlighted the role of microbial metabolites and proteins in promoting tumour development and metastasis. For instance, certain bacteria have been shown to produce short-chain fatty acids that modulate immune responses and promote tumour growth.⁵³ Additionally, the FadA adhesin of Fusobacterium nucleatum upregulates the expression of Annexin A1 through E-cadherin, thereby promoting CRC progression.⁵⁴ The CagA protein of Hp can interact with multiple proteins to influence the development and progression of GC.⁵⁵ The TMPC membrane protein of S. anginosus can also interact with ANXA2 in cells to promote GC.

To identify specific metabolites of A. baumannii that drive tumour metastasis, we reanalysed paired 16S rRNA sequencing and untargeted metabolomics data from GC tissues.²² Correlation analysis revealed a significant positive association between A. baumannii relative abundance and tissue levels of NA and 1-MNA. Cell co-culture experiments confirmed that A. baumannii treatment elevated intracellular and extracellular NA and 1-MNA, aligning with emerging evidence of microbial ‘metabolic crosstalk’ in modulating host NAD homeostasis. NA acts as a precursor for the Preiss-Handler pathway, a major NAD biosynthesis route, while 1-MNA is a methylated downstream metabolite of NAM, a NAD degradation product.⁵⁶ Cellular NAD homeostasis involves three complementary pathways (Preiss-Handler, salvage and de novo synthesis), creating complex regulatory dynamics.²⁹ HPLC-MS and enzymatic assays demonstrated NAD metabolic disruption in A. baumannii-treated tumour cells, marked by aberrant NA, 1-MNA, NAM and NAD⁺/NADH ratios. These metabolic perturbations suggest that A. baumannii-induced NAD dysregulation may potentiate metastasis via Warburg effect activation. Previous studies have reported that gut microbiota can provide cells with NA.²³ Our study further confirmed that microbial metabolite NA can serve as a metabolic substrate for cellular metabolism, thereby altering the cellular metabolic state.

To validate the hypothesis that A. baumannii and its metabolite NA enhance tumour glycolysis, we systematically assessed cellular energy metabolism by measuring ADP/ATP levels, lactate production, OCR, ECAR and LPR/GCR ratio. These results demonstrated a marked reduction in OXPHOS activity (evidenced by decreased OCR and ATP/ADP ratio) alongside elevated glycolysis (increased ECAR and lactate accumulation).^57
58 RNA-seq analysis and Western blot analysis further revealed that A. baumannii treatment activated the HIF-1 signalling pathway while suppressing OXPHOS-related genes, with significant upregulation of glycolytic enzymes (eg, GLUT1, HK2, LDHA) at both transcriptional and protein levels. These findings strongly support that A. baumannii and NA drive metabolic rewiring toward glycolysis via HIF-1 activation, a hallmark of tumour metastasis. Intriguingly, the downstream NAM metabolite 1-MNA was also found to disrupt NAD⁺ homeostasis and enhance glycolysis. We further found that 1-MNA enhances glycolysis in tumour cells by promoting ROS production, which is consistent with previous findings in macrophages.^37
39

Our study reveals an A. baumannii-NA-cell NAD metabolism-HIF-1α axis that promotes GC metastasis. To understand how the HIF-1 pathway promotes metastasis, we examined its role in regulating EMT. Our results showed a clear decrease in E-cadherin, with increases in N-cadherin and Vimentin. These findings suggest that A. baumannii and its metabolites activate EMT, thereby enhancing metastasis. We also studied their impact on stemness and the cytoskeleton. The results indicated that A. baumannii and its metabolites increase stemness gene expression and promote F-actin formation. These findings align with existing studies. Previous research has shown that HIF-1 activation promotes metastasis through the EMT pathway.^59
60 Other reports indicate that HIF enhances metastasis by altering the cytoskeleton via RhoA/ROCK1 signalling.⁶¹ Additionally, HIF-1 activation has been shown to increase cell stemness, which further supports metastatic progression.^62
63 Furthermore, studies have indicated that activation of the EMT pathway can modulate cancer cell stemness and cytoskeletal organisation.^64–66 Therefore, our findings demonstrate that A. baumannii and its metabolites promote metastasis primarily by activating the HIF pathway and inducing EMT, with associated effects on stemness and cytoskeleton.

It is important to note that while HIF-1α knockdown substantially suppressed A. baumannii or metabolite-induced migration, a residual promigratory effect persisted, implying the involvement of additional HIF-1α-independent mechanisms. This is consistent with our finding that A. baumannii and its metabolites induce ROS accumulation, which is known to activate pro-EMT pathways such as FoxO and TNF, thereby revealing an additional layer of regulation independent of HIF-1α.⁶⁷

Our experiments confirmed that A. baumannii promotes peritoneal metastasis in immunocompetent mice. Single-cell analysis revealed that its primary prometastatic effects are exerted directly on tumour cells, through enhancement of glycolysis and activation of the HIF1signalling pathway. In this model, the overall composition of major immune cell types, such as T cells, B cells and macrophages, did not differ significantly between groups. However, pathway enrichment analysis suggests a potential modulation of T helper (Th) cell function. This aligns with prior studies showing that gut microbiota and their metabolites regulate Th cell responses. For instance, certain microbial metabolites can suppress Th cell differentiation, while segmented filamentous bacteria induce intestinal Th17 cells.^68
69 Therefore, while our data highlight a tumour cell-intrinsic mechanism, further research is also needed to explore whether and how A. baumannii may influence the tumour immune microenvironment, particularly through the modulation of T cell function.

Correlation analysis between the intratumoural microbiome and metabolome identified several other bacteria associated with elevated NA levels, including Alloprevotella and Sphingomonas. Given that the pncA gene, essential for bacterial NA synthesis, is present across diverse microbial taxa, it will be important to evaluate the oncogenic potential of this metabolic pathway in a broader bacterial context.^27
70 Interestingly, while phylogenetic analysis indicates that NA metabolising potential (via PncA) is distributed across several bacterial species, a combined assessment of in vivo tissue prevalence and in vitro NA production capacity distinguished A. baumannii as a distinctive contributor. In GC tissues, A. baumannii exhibited higher relative abundance and positivity rates than other NA-associated genera. Critically, under standardised culture conditions, A. baumannii demonstrated a substantially greater NA yield compared with Sphingomonas or Alloprevotella. Thus, within the gastric tumour microenvironment, A. baumannii functions as a particularly effective promoter of metastasis, primarily mediated through its enhanced production of NA. In our cohort, serum levels of NA and 1-MNA showed no significant variation across GC stages. Future studies could prioritise measuring these metabolites directly in tumour tissues or employ more frequent serial blood draws to capture potential short-term fluctuations that may link to prognosis.

Conclusions

In this study, we investigated the impact of A. baumannii on the metastatic mechanisms of GC. Our findings revealed that A. baumannii and its metabolites play a significant role in promoting tumour cell migration, affecting NAD⁺ homeostasis and glycolysis. Furthermore, our experiments indicated that the treatment with A. baumannii led to alterations in the cellular energy status and metabolic activity, resulting in enhanced glycolysis. Additionally, KEGG pathway enrichment analysis highlighted the significant enrichment of the HIF-1 pathway in A. baumannii-treated cells, suggesting a potential link between A. baumannii exposure and the activation of the HIF-1 pathway. These results shed light on the intricate interplay between A. baumannii, tumour cell metabolism and metastasis, providing valuable insights into the underlying mechanisms of A. baumannii-induced effects on GC progression.

Limitation

This study has limitations. While the use of an immunocompetent model established that metastasis promotion occurs in a functional immune system, the specific mechanisms of immune cell modulation by A. baumannii remain to be determined. The non-HIF-1α-dependent pathways through which A. baumannii and its metabolites promote metastasis remain unexplored. Nonetheless, our study highlights the translational potential of targeting A. baumannii and its metabolites as novel therapeutic strategies for GC.

Methods

Patients and samples

Tumour tissues (n=30) and matched normal control tissues (n=30) were collected from GC patients with informed consent. Additionally, serum samples from 108 GC patients across different clinical stages were collected for NA/1-MNA detection. Clinical characteristics of the GC patients are shown in online supplemental table S1 and online supplemental table S2. We expanded our clinical sample collection. This included 19 pairs of oesophageal cancer tissues and 29 pairs of CRC tissues, each with matched normal controls. We also collected throat swabs from 20 healthy individuals and 30 GC patients. Paraffin-embedded tissue sections were obtained from 51 H. pylori-negative gastritis patients and 58 H. pylori-positive patients (with matched post-treatment samples from 1 to 2 years later). Additionally, we gathered 10 environmental samples each from operating rooms and hospital wards. The investigation complied with pertinent ethical standards governing human participant research.

Bacteria and cancer cell lines

For cell lines, HGC27 was purchased from the Cell Bank of the Shanghai Institute of Cell Biology. MKN45(NICR, 1101HUM-PUMC000229) was purchased from China Infrastructure of Cell Line Resource. AGS (SCSP-5262) and GES-1 (GNHu65) were purchased from National Collection of Authenticated Cell Cultures. GCSR1 is a gastric signet ring cell cancer cell line that we established in our lab.⁷¹ The murine GC cell line MFC (BNCC100581) was obtained from the BeNa Culture Collection. They were grown in complete RPMI 1640 medium (supplemented with 10% fetal bovine serum (FBS)). All cells were maintained at 37℃ incubator with 5% CO2.

Ab was purchased from China Center for type Culture Collection (AB2018157). S. anginosus was purchased from American Type Culture Collection (ATCC 33397). A. baumannii and S. anginosus were grown at Trypticase Soya Agar (Sangon Biotech, B682002) plate or Trypticase Soy Broth (Sangon Biotech, B682007) medium in an anaerobic jar with a microaerophilic environment for 1 day at 37 ℃. Escherichia coli DH5α and Escherichia coli β2155 were purchased from Sangon Biotech. E. coli was cultured under aerobic conditions at 37°C. The bacterial strains Sphingomonas spp. (BNCC335865) and Alloprevotella rava (BNCC358370) were obtained from BeNa Culture Collection (BNCC). Sphingomonas spp. was cultured aerobically in TSA or TSB medium. Alloprevotella rava was grown under anaerobic conditions.

AbΔpncA was constructed via homologous recombination. The upstream and downstream regions of pncA were amplified using primers with chromosomal DNA of A. baumannii as template. The upstream and downstream fragments of the pncA gene were ligated using the fusion PCR method and the resulting fragment was cloned into the vector pCVD442 to obtain pCVD442-ΔpncA. The donor strain β2155/pCVD442Gm-ΔpncA was obtained via electroporation. A conjugation experiment was performed between the donor strain β2155/pCVD442Gm-ΔpncA and the Ab recipient strain. Gentamicin-resistant A. baumannii clones, in which the targeting plasmid had been integrated into the genome, were selected on gentamicin-containing plates and designated as Ab/pCVD442Gm-ΔpncA. Ab/pCVD442-ΔpncA lacking the resistance gene was selected via 10% sucrose screening. Primer sequences are listed in online supplemental table S3.

The pncA-complemented strain AbΔpncA:: pET28a-P_BAD-oriA-pncA was generated by electroporating the mutant AbΔpncA with a recombinant shuttle plasmid. This plasmid was constructed through insertion of the pncA gene into pET28a-P_BAD-oriA-pncA (KanR), which carries the A. baumannii oriA (origin of replication) and A. baumannii promoter P_BAD. The promoter and origin of replication sequences were synthesised based on literature reports and ligated into the vector plasmid.⁷² The pncA gene was amplified by PCR using primers pncA-1 and pncA-14 with the wild-type strain as the template. Positive clones were verified by PCR using validation primers pncA-com-F and pncA-com-R. Primer sequences were listed in online supplemental table S3.

Bacterial attachment assay

GC cells and control normal cells were cultured overnight. A. baumannii and DH5α were cultured to OD600=0.8. The bacteria were washed with PBS and stained with N, N-dimethylaniline N-oxide (DMAO, Beyotime Biotechnology, C2030S) dye for 30 min. After removing excess dye by PBS washing, the bacteria were resuspended in PBS. The resuspended A. baumannii or DH5α (MOI=100) were added to the GC cells and co-cultured for 30 min. Unattached bacteria were removed by PBS washing, fresh culture medium was added, and microbial adhesion to the cells was visualised using fluorescence microscopy.

Transmission electron microscopy

For TEM analysis of microbial and cellular ultrastructure, samples were dual-fixed (glutaraldehyde/OsO₄), dehydrated via graded ethanol-acetone series, embedded in Epon 812 resin and polymerised. Ultrathin sections (80 nm) were stained with uranyl acetate and lead citrate. Then imaged at 80 kV using a transmission electron microscope. Standard protocols ensured minimal osmotic disruption and high-resolution preservation of membranes.

Quantitative real-time PCR

Tissues RNA was extracted by Trizol reagent with prior beads-beating process. Total RNA from tissues and cells was extracted by Trizol. cDNA synthesis was performed with Evo M-MLV RT Reagent kit (Accurate Biology, AG11758), followed by RT-PCR (SYBR Green SupTaq HS, Accurate Biology, AG11762) analysis. For quantification of Ab, A. lwoffii, A. johnsonii, Alloprevotella and Alloprevotella tissue abundance, 16S rRNA was used as the endogenous reference gene, while β-actin served as the internal control for other cellular gene assays. DNA from environmental samples and throat swabs was extracted using the D2100 kit (Solarbio). DNA from gastritis paraffin-embedded tissue sections was isolated with the BTN70502 kit (BioLab). Primers are performed in online supplemental table S3.

Protein extraction and western blot analysis

Cultured cells or tissues were lysed in RIPA buffer (Sigma, V900854) enriched with phenylmethanesulphonyl fluoride (BOSTER, AR1192) and phosphatase inhibitor cocktail (FUDE BIO, FD1002). Lysis was performed on ice for 30 min. The lysate was then centrifuged at 14,000×g for 15 min at 4°C. Subsequently, a BCA Protein Assay Kit (Thermo Fisher Scientific, A55860) was used to determine the protein concentration. Protein expression level was normalised to β-actin.

Fluorescence in situ hybridisation

FISH was performed using Cy3-labelled species-specific probes: the Aci probe (5’-TTAGGCCAG ATGGCTGCC-3’) for A. baumannii, E. coli-probe (5’-GCAAAGGTATTAACTTTACTCCCTTCCTCC-3’) for Escherichia coli and S. anginosus-probe (5’-AGTTAAACAGTTTCCAAAGCCTAC-3’) for S. anginosus. Fluo-labelled EUB388 probe (sequence: 5’-GCT GCC TCC CGT AGG AGT-3’) was used to detect bacteria. After deparaffinisation and rehydration, specimens were treated with 0.2 N HCl and Proteinase K (Sangon Biotech, B900004-0100) for 10 min for each step. Prehybridisation was conducted with a buffer containing salmon sperm DNA (100 µg/mL) in super hybridisation solution (37°C, 1 hour). Sequential hybridisations with two directly labelled probes (Probe Aci and EUB388, 100 µM stock diluted 1:100 in hybridisation buffer) were performed overnight at 42°C, sealed with specialised cover slips and adhesive. Post-hybridisation washes included 2×SSC (Standard Saline Citrate) (40°C, 5 min), 1×SSC (40°C, 5 min), and PBS (40°C, 5 min). Nuclei were counterstained with DAPI (1:500 dilution, 5 min, dark), followed by PBS washes. Slides were mounted with antifade medium and stored protected from light prior to fluorescence microscopy.

HE staining

Four µm paraffin-embedded tissue sections were sequentially dehydrated in graded ethanol solutions (absolute ethanol, 95%, 85% and 75% ethanol; 5 min each), followed by a 1 min tap water rinse. Sections were stained with haematoxylin (Hubei Bios Biotech, BP0211) for 4 min, rinsed with tap water (2 min), and differentiated in 0.8% acid alcohol (2 s). After a tap water rinse, counterstaining was performed with eosin (Hubei Bios Biotech, BP0211) for 20 s, followed by rapid dehydration in 95% ethanol (5 s) and two changes of absolute ethanol (2 min each). Sections were mounted with a mounting medium (Tongsheng Technology) and imaged under a light microscope.

Immunofluorescence staining

GC cells were plated on glass coverslips in culture dishes. At 50% confluency, cells underwent two PBS washes followed by fixation with 4% paraformaldehyde for 15 min. Permeabilisation was performed using 0.5% Triton X-100 in PBS for 5 min. Cells were then incubated with FITC-conjugated phalloidin (YEASEN, 40 735ES) working solution under light-protected conditions at room temperature (20–30 min). After three 5 min PBS washes, nuclei were counterstained with DAPI (Beyotime Biotechnology, C1005). Imaging was conducted via confocal microscopy.

RNA isolation, RNA-sequencing and analysis

Total RNA was extracted from the tissues and cells using Trizol reagent. RNA-Seq of MKN45-NC and MKN45-Ab cells was performed by OE Biotech (Shanghai, China). All samples underwent simultaneous RNA extraction, library preparation and sequencing in a single batch to minimise technical variations. Given the absence of multi-batch processing, batch effect correction algorithms were deliberately omitted to prevent overcorrection artefacts. The clean reads were aligned to the human genome GRCh38 (Hg38) using HISAT2 (version 2.0.5). DESeq2 was used to analyse DEGs. DEGs were identified using a Benjamini-adjusted q-value threshold (q<0.05) combined with a |log₂FC|≥1 (fold change≥2 or ≤ 0.5). Bioinformatic analyses used OECloud tools (https://cloud.oebiotech.com).

These genes underwent KEGG pathway enrichment analysis via clusterProfiler version 4.0, applying Benjamini-Hochberg false discovery rate correction with significance set at FDR<0.05. Result visualisation was implemented using the R (V.3.2.0).

In vivo experiments

The metastasis models were established in 4-week-old female nude mice. After cell digestion and counting, the mice were injected with a mixture of matrix gel and splenic tissue to induce liver metastasis. The number of liver metastatic nodules was counted visually 4 weeks later after mouse dissection, and the number of liver metastases was calculated. All animal experiments were repeated five times.

For the peritoneal metastasis model, 2×10⁶ cells in 100 µL of PBS were injected into the peritoneal cavity of nude mice. After 4 weeks, mice were sacrificed, and the peritoneal cavities were examined. The number and weight of peritoneal metastases were calculated. All animal experiments were repeated five times.

Nude mice were subjected to tail vein injection of 2×10⁶ cells in 100 µL PBS. Lung metastases were assessed after 8 weeks. Five biological replicates comprised each experimental group.

A peritoneal metastasis model was established by intraperitoneal injection of 4-week-old mice with 3×10⁶ MKN45 cells alone or MKN45 cells pre-treated with A. baumannii. After 1 week, each group was further divided into two subgroups receiving either the HIF-1α inhibitor PX-478 (Selleck, S7612) (2 mg/mouse) or PBS control via intraperitoneal injection. All mice were euthanised 4 weeks post-cell injection for assessment of peritoneal metastatic burden through nodule counting and tumour weighing.

For the immunocompetent mouse metastasis model, 1.5×10⁶ MFC cells were injected intraperitoneally into 615 mice. After 2 weeks, the mice were euthanised and metastatic tumour nodules in the peritoneal cavity were collected, counted and weighed.

Although both female and male mice have been used in previous GC metastasis studies, female mice are more commonly employed in peritoneal metastasis-related studies.^73–78 To maintain consistency across all animal experiments and align with common practice, we standardised the use of female nude mice throughout our study.

Bacterial colonisation assay

Gastric tissues from MKN45-bearing mice gavaged with 10⁹ CFU A. baumannii or PBS (alternate days×7) were homogenised 14 days post-inoculation. Serial dilutions were plated on Leeds Acinetobacter Medium (LAM, HB8642, Hope Biol) and cultured micro-aerobically (37°C, 24–48 hours). Mucoid colonies underwent 16S rRNA sequencing and PCR for A. baumannii confirmation.

ELISA assay

The levels of NA and 1-MNA in treated cell culture supernatants serum samples from GC patients were quantified using sandwich ELISA kits according to the manufacturer’s instructions (YX-B41749; Yuanxin China; SP37661; Saipei, China).

To evaluate the acid stability of NA, PBS control group (pH 7) and experimental group (pH 1–2) were established. Both groups were supplemented with a defined amount of NA. The remaining concentration of NA was measured at 0, 4 and 8 hours to assess its stability under acidic conditions. The concentration of NA was measured using the ELISA method described above, with three biological replicates per experimental condition.

ROS detection assay

ROS detection was performed using the ROS Assay Kit (Beyotime Biotechnology, S0034S) according to manufacturer’s instructions. The probe was diluted 1:1000 in assay buffer to a final concentration of 10 µM. Harvested cells were resuspended in the probe solution at 1×10⁶ cells/mL and incubated at 37°C for 20 min with intermittent mixing. After triple washing with PBS buffer, cells were stimulated with ROS-inducing agents or test compounds. Fluorescence intensity (488 nm excitation/525 nm emission) was monitored in real-time or at designated intervals, with positive controls typically inducing detectable ROS elevation within 20–30 min.

LPR/GCR assay

Glucose consumption was measured via glucose oxidase-peroxidase methodology (Beyotime Biotechnology, S0202S), while lactate production was quantified through L-Lactate assay kit (Beyotime Biotechnology, S0208S). Rates were derived from linear-phase concentration changes normalised to 10⁶ viable cells. All metabolic assays were performed with three biological replicates.

NAD⁺/NADH assay

NAD⁺/NADH levels were measured using a WST-8-based assay kit (Beyotime Biotechnology, S0175). Briefly, 1×10⁶ HGC27/MKN45 cells were pelleted (1000×g, 10 min), medium removed and lysed in 200 µL ice-cold buffer. After centrifugation (12 000×g, 10 min, 4°C), 100 µL supernatant was heat-treated (60°C, 30 min) to degrade NAD⁺, then recentrifuged (10 000×g, 5 min, 4°C). For detection, 20 µL supernatant was added to 96-well plates, incubated (37°C, 10 min), mixed with 10 µL chromogenic solution and absorbance measured at 450 nm. NAD⁺/NADH ratios were calculated from standard curves.

Measurement of ATP, ADP and lactate content

The ATP assay kit (Beyotime Biotechnology, S0026B), ADP assay kit (Beyotime Biotechnology, S0307S) and L-Lactate assay kit (Beyotime Biotechnology, S0208S) were performed to measure the ATP, ADP and lactate level in cells and supernatant according to manufacturer’s instruction.

OCR and ECAR

ECAR was analysed using the pH-sensitive BBcellProbe P61 fluorescent probe supplied by the ECAR assay kit (BestBio, BB-48311). OCR was measured using the R01 fluorescent probe supplied by the OCR kit (BestBio, BB-48211). The plates were read on an EPOCH2 microplate reader (BioTek) in kinetic mode at 37°C for 2 hours. For ECAR measurement, readings were acquired at 2 min intervals using an excitation wavelength of 445 nm and an emission wavelength of 503 nm. OCR was similarly monitored at 2 min intervals with excitation at 450 nm and emission at 590 nm.

Wound healing, transwell migration and matrigel invasion assays

For Transwell migration assays, 5×10⁴ cells resuspended in 100 µL serum-free medium were seeded into the upper chambers of uncoated 8 µm pore-size polycarbonate membrane inserts (Corning, 24-well format). The lower chambers were filled with 700 µL medium supplemented with 10% FBS as a chemoattractant. After 24 hours incubation at 37°C under 5% CO₂, non-migrated cells on the upper membrane surface were gently removed using cotton swabs. Migrated cells adherent to the lower membrane surface were fixed with 100% methanol, stained with 0.25% crystal violet (20 min) and air-dried. Cell quantification was performed by counting 3–5 randomly selected fields per membrane under a light microscope 100× magnification.

For wound-healing assays, cells were cultured in RPMI-1640 with 10% FBS to full confluence in six-well plates. A uniform linear wound was introduced into the monolayer using a sterile 200 µL pipette tip. Immediately after wounding, the medium was replaced with serum-free RPMI-1640. Phase-contrast images of the wound area were acquired at baseline 0 hour and 24-hours postwounding using an inverted microscope (Olympus IX73) to quantify cell migration.

For Matrigel invasion assays, 5×10⁴ cells in 100 µL serum-free medium were seeded onto Matrigel-coated inserts (Corning, 3422). Lower chambers contained 700 µL 10% FBS medium as chemoattractant. After 24 hours incubation (37°C/5% CO₂), non-invading cells were removed. Invaded cells were methanol-fixed, crystal violet-stained (0.25%, 20 min), and quantified by counting 3–5 random fields/membrane at 100×magnification.

siRNA Transfection in GC Cells

GC cells were seeded in 6-well plates (2×10⁵ cells/well) and cultured to 50%–60% confluency prior to transfection. siRNA-lipid complexes were prepared by incubating 20 nM target-specific siRNA with INTERFERin (Polyplus, 101000028) in Opti-MEM (Thermo Fisher Scientific, 31985070) at room temperature for 20 min, followed by dropwise addition to cells. After 48–72-hour incubation, transfection efficiency was validated via qPCR and corresponding protein knockdown by western blotting (β-actin loading control).

High-performance liquid chromatography-tandem mass spectrometry

NAD-related metabolites were analysed using an Ultimate 3000 ultra-HPLC system coupled to a TSQ Altis triple quadrupole mass spectrometer (Thermo). Chromatographic separation was performed on an ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 µm; Waters, Cat. No. 186004800) with mobile phase A consisting of 5% acetonitrile in water containing 20 mM ammonium acetate and 20 mM ammonium hydroxide (pH 9.0), and mobile phase B as pure acetonitrile. The mass spectrometry detection was conducted in Multiple Reaction Monitoring mode with electrospray ionisation voltages set to 3500 V (positive ion mode) and 3000 V (negative ion mode). The ion source temperature was maintained at 350°C, with gas settings of 40 Arb (sheath gas), 8 Arb (auxiliary gas), and 2 Arb (sweep gas). Target metabolites were quantified using predefined precursor-to-product ion transitions (specific m/z values for each analyte).

Untargeted metabolomic analysis

Processed using Progenesis QI V2.3, the LC-MS data underwent peak alignment, normalisation and metabolite identification via accurate mass and MS/MS matching against HMDB, LipidMaps, METLIN and a custom database. After filtering missing values and low-score features, positive and negative ion data were merged. For the non-targeted metabolomics data, significantly altered metabolites were selected based on a variable importance in projection score >1.0 from the orthogonal partial least squares-discriminant analysis (OPLS-DA) analysis, combined with a p<0.05 from two-tailed Student’s t-tests. The relevant bioinformatic and statistical analyses were performed using the OECloud platform (https://cloud.oebiotech.com).

Single-cell RNA sequencing (scRNA-seq) and data analysis

Fresh peritoneal metastatic nodules were rinsed with cold RPMI 1640 (Corning, 10–040-CVR) containing 0.04% BSA (Miltenyi Biotec, 130-091-376). Tissues were minced and digested in RPMI 1640 with 0.04% BSA and 0.2% collagenase II (Gibco, 17101015) at 37°C for 30–60 min. The digestate was filtered through a 40 µm cell strainer (Falcon, 352340) and centrifuged. Red blood cells were lysed using RBC Lysis Buffer (Miltenyi Biotec, 130-094-183). Cells were washed and resuspended at 700–1200 cells/µL in appropriate buffer. Libraries were constructed with the 10×Genomics Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (PN-1000268) per the manufacturer’s instructions and sequenced on an Illumina Nova 6000 system (PE150). Single-cell RNA-seq data were aligned to the mouse reference genome (GRCm39) using Cell Ranger software (version 9.0.1) and analysed with the Scanpy package (version 4.10.4). After filtering low-quality cells and potential doublets using the DoubletDetection package (V.4.3.0), data were normalised and highly variable genes were selected. Principal Component Analysis was performed and batch effects were corrected on the resulting embeddings using harmonypy (version 0.0.10). Cell clustering was conducted with the Leiden algorithm and visualised in two dimensions using Uniform Manifold Approximation and Projection. DEGs were identified using the Wilcoxon rank-sum test (adjusted p value<0.05, |log₂ fold change|>1). KEGG pathway enrichment analyses were performed in R software (version 4.0.3). Cell clusters were assigned through known marker genes as follows, Malignant_Krt8/Tm4sf1, T cell_ Cd3d/Cd3e/Cd3g, B cell_Cd79a/Cd79b, Endothelial_Pecam1/Vwf, Dendritic cell (DC)_Flt3/Ccr7, Cancer stem cell_Prom1/Epcam, Fibroblast_Col1a2/Col3a1/Col1a1, Macrophage_C1qa/ C1qb/ C1qc, Neutrophil_S100a8/S100a9.^79–81 Sequencing and bioinformatics services were provided by OE Biotech (Shanghai).

Construction of the phylogenetic tree

PncA protein sequences were retrieved from the NCBI protein database and subjected to multiple sequence alignment with MUSCLE V.5.3. A phylogenetic tree was inferred using the neighbour-joining method with 1000 bootstrap replicates in MEGA 12.

Statistical analysis

The abundance of A. baumannii, A. lwoffii and A. johnsonii in human gastric tissues was compared by two-tailed Wilcoxon ranked sum test and P value was adjusted by FDR. Data are shown as the mean±SD. The relative abundance of microbiota in tissues of different stages, the number of metastatic foci and the count of adherent bacteria were analysed using the Mann-Whitney U test for comparisons between two groups. The statistical significance of differences between two groups in cellular experiments was analysed using Student’s t-test. Multiple group comparison was conducted by one-way ANOVA or Kruskal-Wallis test. The effects of HIF-1α knockdown on differently treated cell groups were evaluated by two-way ANOVA. Spearman’s rank correlation was employed to assess associations between tumour stage and tissue A. baumannii abundance, with correlation coefficients (ρ) and significance reported. Statistical analysis was performed using GraphPad Prism V.8.2 (GraphPad Software). P values less than 0.05 are considered significant. Details of statistical analysis were mentioned in each figure legend.

Patient and public involvement

No patients or members of the public were involved in the design, conduct, reporting or dissemination of this research.

Supplementary material

online supplemental file 1

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DOI: 10.1136/gutjnl-2025-336161

Acknowledgements

We extend our sincere gratitude to The Central Laboratory of Zhejiang University First Hospital for their expert guidance in establishing targeted metabolite quantification methods. We thank the Department of Pathology at The First Affiliated Hospital, Zhejiang University School of Medicine, for providing their assistance with specimen collection. We also acknowledge Shanghai OE Biotech for providing access to their integrated online platform for microbial-metabolomic correlation analysis, which advanced our dual-omics investigations. We are grateful to Professor Wei Zhuo, Professor Jinghao Sheng and Professor Quan Zhou from Zhejiang University for their valuable guidance throughout this research project. We thank Professor Jiang Cao from the Second Affiliated Hospital, Zhejiang University School of Medicine, for his valuable suggestions on manuscript revision. We also thank Professor Dexi Bi from the Shanghai Tenth People’s Hospital, Tongji University, for his guidance in constructing the phylogenetic tree. We appreciate the online drawing platform provided by the BioRender website. This Visual Abstract and the schematic diagrams were created with BioRender.com (https://BioRender.com) and are licensed under a CC BY 4.0 licence (XI28NP2PSM, KO28PQ50VD, WC28OF3HFV, AF295KCP20 and AI28OKQW5E).

Footnotes

Funding: This work was supported by the National Natural Science Foundation of China (No. 82302886), The Project of the Regional Diagnosis and Treatment Centre of the Health Planning Committee (No. JBZX-201903) and Department of Science and Technology of Zhejiang Province, 'Pioneer' and 'Leading Goose' R&D Program of Zhejiang, No. 2024C03146.

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Not applicable.

Ethics approval: The collection and use of patient-derived tumour specimens and serum for research was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang University, School of Medicine (Reference 2024-0670; Reference 2025-0916; Reference 2025-1246). The animal experiments conducted in this study were approved by the Animal Ethics Committee of The First Affiliated Hospital, Zhejiang University School of Medicine (Reference 2024-1216).

Data availability free text: Raw sequence data of 16S RNA microbiome have been uploaded to China National Microbiological Data Center (Project accession number NMDC10017675 and microbiome accession numbers NMDC40001084 to NMDC 40001117). The corresponding author has access to all data in the study. Single-cell RNA sequencing data of mouse tumours has been deposited in the Genome Sequence Archive of the National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics (GSA: CRA036641). The data are publicly accessible at https://ngdc.cncb.ac.cn/gsa. This paper does not report original code. Any additional information required to reanalyse the data reported in this paper is available from the lead contact on request. My abstract had been published online in the ESMO Asia 2024 Abstract Book, a supplement to the official ESMO journal, Annals of Oncology. YY, RY and LT (2024). Mechanism of Acinetobacter baumannii promoting gastric cancer metastasis by enhancing NAD metabolism, ANNALS OF ONCOLOGY, Meeting Abstract, 35, S1492-S1492Link: https://www.annalsofoncology.org/article/S0923-7534(24)04310-2/fulltext.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Data availability statement

Data are available on reasonable request.

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Associated Data

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Supplementary Materials

online supplemental file 1

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DOI: 10.1136/gutjnl-2025-336161

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gutjnl-75-10-s012.pdf^{(16.3MB, pdf)}

DOI: 10.1136/gutjnl-2025-336161

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gutjnl-75-10-s013.pdf^{(10.8MB, pdf)}

DOI: 10.1136/gutjnl-2025-336161

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gutjnl-75-10-s014.pdf^{(8.3MB, pdf)}

DOI: 10.1136/gutjnl-2025-336161

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gutjnl-75-10-s015.pdf^{(1MB, pdf)}

DOI: 10.1136/gutjnl-2025-336161

Uncited online supplemental file 1

gutjnl-75-10-s016.ppt^{(2.9MB, ppt)}

DOI: 10.1136/gutjnl-2025-336161

Data Availability Statement

Data are available on reasonable request.

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PERMALINK

Acinetobacter baumannii promotes gastric cancer metastasis via NA-mediated NAD metabolism reprogramming and glycolytic activation

Yan Yang

Rui Yang

Yiran Chen

Chao He

Yingzi Zhang

Jing He

Jing Zhang

Haohao Wang

Jingdan Liang

Zixin Deng

Lisong Teng

Abstract

Background

Objective

Design

Results

Conclusions

WHAT IS ALREADY KNOWN ON THIS TOPIC

WHAT THIS STUDY ADDS

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Introduction

Results

The abundance of A. baumannii significantly increases in GC tissues

A. baumannii promotes GC metastasis in vitro and in vivo

A. baumannii promotes GC metastasis through the metabolite NA

Metabolites of A. baumannii driven NAD metabolic reprogramming

A. baumannii and its metabolites enhance glycolysis in GC cells

A. baumannii and its metabolites promote tumour metastasis by activating the HIF-1α pathway

Discussion

Conclusions

Limitation

Methods

Patients and samples

Bacteria and cancer cell lines

Bacterial attachment assay

Transmission electron microscopy

Quantitative real-time PCR

Protein extraction and western blot analysis

Fluorescence in situ hybridisation

HE staining

Immunofluorescence staining

RNA isolation, RNA-sequencing and analysis

In vivo experiments

Bacterial colonisation assay

ELISA assay

ROS detection assay

LPR/GCR assay

NAD+/NADH assay

Measurement of ATP, ADP and lactate content

OCR and ECAR

Wound healing, transwell migration and matrigel invasion assays

siRNA Transfection in GC Cells

High-performance liquid chromatography-tandem mass spectrometry

Untargeted metabolomic analysis

Single-cell RNA sequencing (scRNA-seq) and data analysis

Construction of the phylogenetic tree

Statistical analysis

Patient and public involvement

Supplementary material

Acknowledgements

Footnotes

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

NAD⁺/NADH assay