Gut bacteria identified in colorectal cancer patients promote tumourigenesis via butyrate secretion

Okumura, Shintaro; Konishi, Yusuke; Narukawa, Megumi; Sugiura, Yuki; Yoshimoto, Shin; Arai, Yuriko; Sato, Shintaro; Yoshida, Yasuo; Tsuji, Shunya; Uemura, Ken; Wakita, Masahiro; Matsudaira, Tatsuyuki; Matsumoto, Tomonori; Kawamoto, Shimpei; Takahashi, Akiko; Itatani, Yoshiro; Miki, Hiroaki; Takamatsu, Manabu; Obama, Kazutaka; Takeuchi, Kengo; Suematsu, Makoto; Ohtani, Naoko; Fukunaga, Yosuke; Ueno, Masashi; Sakai, Yoshiharu; Nagayama, Satoshi; Hara, Eiji

doi:10.1038/s41467-021-25965-x

Download PDF

Article
Open access
Published: 28 September 2021

Gut bacteria identified in colorectal cancer patients promote tumourigenesis via butyrate secretion

Nature Communications volume 12, Article number: 5674 (2021) Cite this article

27k Accesses
92 Citations
79 Altmetric
Metrics details

Subjects

Abstract

Emerging evidence is revealing that alterations in gut microbiota are associated with colorectal cancer (CRC). However, very little is currently known about whether and how gut microbiota alterations are causally associated with CRC development. Here we show that 12 faecal bacterial taxa are enriched in CRC patients in two independent cohort studies. Among them, 2 Porphyromonas species are capable of inducing cellular senescence, an oncogenic stress response, through the secretion of the bacterial metabolite, butyrate. Notably, the invasion of these bacteria is observed in the CRC tissues, coinciding with the elevation of butyrate levels and signs of senescence-associated inflammatory phenotypes. Moreover, although the administration of these bacteria into Apc^Δ14/+ mice accelerate the onset of colorectal tumours, this is not the case when bacterial butyrate-synthesis genes are disrupted. These results suggest a causal relationship between Porphyromonas species overgrowth and colorectal tumourigenesis which may be due to butyrate-induced senescence.

Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk

Article Open access 20 February 2024

Sebastian Brandhorst, Morgan E. Levine, … Valter D. Longo

A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche

Article Open access 20 March 2024

Martha Zepeda-Rivera, Samuel S. Minot, … Christopher D. Johnston

A host–microbiota interactome reveals extensive transkingdom connectivity

Article 20 March 2024

Nicole D. Sonnert, Connor E. Rosen, … Noah W. Palm

Introduction

In concordance with dietary changes and increased longevity over the past decades, the morbidity and mortality rates of colorectal cancer (CRC) have been increasing dramatically and CRC has now become the second most common cancer worldwide¹. Thus, there is an urgent need for effective measures against CRC, and early detection and prevention of CRC are anticipated. The colon contains the densest metabolically active microbial community in healthy adults, and it is becoming clear that changes in the gut microbiota composition are associated with various diseases, including cancer^2,3,4,5. For example, the introduction of Fusobacterium nucleatum or Bacteroides fragilis into Apc^Min/+ mice accelerated the onset of intestinal tumours^6,7. Furthermore, it has been reported that pks+ E. coli, Peptostreptococcus anaerobius, Clostridium spp. and Bacteroides fragilis promote colonic tumourigenesis through colibactin production^8,9, activation of the PI3K-AKT pathways¹⁰, secondary bile acid production¹¹, and Bacteroides fragilis toxin production¹², respectively. Therefore, the use of gut microbiota for CRC screening and/or CRC prevention is attracting keen attention^13,14. However, since residential areas, medical treatments, and technical protocols strongly affect human gut microbiota variations^15,16,17, it is difficult to integrate and compare different sets of published data. More importantly, although many studies have reported that the abundance of certain gut bacteria is increased or decreased in patients with CRC^18,19,20,21, it is not clear which of these gut bacteria are actually involved in the development of CRC except for the aforementioned bacterial species^5,22. Thus, in order to better understand the mechanism of colorectal carcinogenesis and to develop more effective CRC screening methods, it is necessary to clarify which of the gut bacteria that are increased or decreased in colorectal cancer patients are actually involved in the development of CRC, and how they exert these effects. Toward this end, we need some reliable indicators that can detect potentially oncogenic stimuli.

In response to various oncogenic stimuli, such as the activation of certain oncogenes, the elevation of reactive oxygen species (ROS) levels, or exposure to a myriad of DNA damaging agents, normal cells undergo either apoptotic cell death or irreversible cell-cycle arrest, termed “cellular senescence”^{23,24,25,26,27,28}. Unlike apoptotic cells, however, senescent cells are viable for a long period of time and develop a secretory profile composed of pro-inflammatory and tumour-promoting factors, a typical signature termed the senescence-associated secretory phenotype (SASP)^29,30,31. Thus, although cellular senescence initially acts as a tumour suppression mechanism, the accumulation of senescent cells in vivo eventually promotes tumourigenesis through SASP, depending on the biological context^{2,32,33,34,35}.

Using the cellular senescence response as an indicator to identify gut bacteria with the ability to provoke potentially oncogenic stimuli, we show here that Porphyromonas gingivalis and Porphyromonas asaccharolytica, which correspond to two of the bacterial taxa enriched in CRC patients and are rarely detectable in healthy individuals (HI), are capable of inducing cellular senescence in cultured cells and intestinal epithelial organoids via secreting butyrate, a short-chain fatty acid (SCFA). Furthermore, Porphyromonas gingivalis and, to a lesser extent, Porphyromonas asaccharolytica also accelerate colorectal tumour development via butyrate secretion in mice. Our data reveal that the aberrant increase of these butyrate-producing bacteria in the colon is likely to be causally involved in CRC development, depending on the biological context.

Results

Identification of gut bacteria enriched in CRC patients

To examine whether and how alterations of the gut bacterial profile in CRC patients are causally involved in CRC development, we first collected stool samples from healthy individuals and patients with CRC, who underwent a colonoscopy to either diagnose CRC or confirm the absence of colonic lesions (Cohort-1). To avoid any unforeseen bias, the stool samples were collected before any treatments were performed, including colonoscopy, antibiotic medication, chemotherapy and radiotherapy (Supplementary Fig. 1a). Furthermore, individuals with digestive tract reconstruction or inflammatory bowel disease, which could influence the gut bacterial composition, were also excluded from this cohort study (Supplementary Fig. 1a). In total, the stool samples from 384 HI and 380 CRC patients, including 63 patients with early CRC (clinical characteristics of participants are shown in Supplementary Fig. 1b and c), were subjected to the meta-sequencing analysis of the variable regions 1 and 2 (V1 and V2) of the bacterial 16S rRNA gene³⁶. Amplicon sequence variants (ASVs) were identified from the demultiplexed raw 16S rRNA gene sequences by denoising via the DADA2 pipeline in QIIME2 (version 2019.10)³⁷, and were clustered into operational taxonomic units (OTUs) by using the de novo clustering algorithm of VSEARCH at a 97% similarity cutoff. The linear discriminant analysis effect size (LEfSe)³⁸ revealed that the abundances of 87 and 24 OTUs were increased and decreased in CRC patients, respectively.

In order to search for gut bacteria causally associated with CRC development, we next focused on groups of bacterial OTUs that are intensely increased or decreased in CRC patients. We noted that none of the bacterial OTUs that were abundant in HI were commonly absent in CRC patients, but 12 bacterial OTUs that were abundant in CRC patients were rarely present in HI (Fig. 1a). These 12 bacterial OTUs are most likely to correspond to bacterial species associated with periodontal disease³⁹, judging from a combination of SILVA database and BLAST database analyses using the V1-V2 region of the bacterial 16S rRNA gene sequence (Supplementary Fig. 1d). To confirm the reproducibility of these results and examine whether these bacterial species were also enriched in early CRC patients, an independent validation cohort (Cohort-2), consisting of 129 HI, 136 early CRC patients and 153 advanced CRC patients, was subjected to a similar analysis (Supplementary Fig. 2). Interestingly, comparable results were obtained using this independent validation cohort, and four of the 12 bacterial OTUs are also increased in a certain number of early CRC patients, although with lower frequencies and numbers than in advanced CRC patients (Fig. 1b). Intriguingly, these bacterial OTUs are no longer detected after surgical resection of CRC (Fig. 1c), further illustrating their strong association with CRC. However, these correlational studies alone cannot distinguish whether the increase of these bacteria, except for Fusobacterium nucleatum subsp.^40,41,42, in CRC patients is a cause or a consequence of CRC development. Bio-functional analyses are therefore needed to clarify this point.

**Fig. 1: Gut bacteria enriched in CRC patients.**

Porphyromonas species provoke cellular senescence

Accumulating evidence indicates that cellular senescence is induced by a variety of potentially oncogenic stimuli and suppresses or promotes tumourigenesis, depending on the biological context^25,26,32,33. Therefore, we decided to use the cellular senescence response as an indicator to determine whether these CRC-enriched bacteria are capable of provoking potentially oncogenic stimuli. Early passage normal human diploid fibroblasts (HDFs), the most commonly used cells in senescence studies, were cultured for 9 days in media containing culture supernatants of the 10 faecal bacterial species corresponding to the bacterial OTUs enriched in CRC patients, except Fusobacterium nucleatum subsp., followed by 3 days of culture in medium containing no bacterial culture supernatant (Fig. 2a and Supplementary Fig. 3). Interestingly, similar to doxorubicin (DXR), a DNA damaging agent known to induce cellular senescence⁴³, the bacterial culture supernatants of Porphyromonas asaccharolytica and Porphyromonas gingivalis provoked irreversible cell-cycle arrest in cultured HDFs (Fig. 2a). This was accompanied by typical signatures of cellular senescence, such as the increased expression of senescence-inducing genes (p16^INK4a and p21^Cip1/Waf1) and SASP factor genes (IL-1 and IL-6), the reductions of Lamin B1 expression and the phosphorylated form of pRB, an increase in the phosphorylated form of p53, and the elevation of intracellular ROS levels and DNA damage, without the elevation of an apoptosis marker (Fig. 2b–f). Notably, similar results were also observed in normal human colonic epithelial cells (Fig. 3) and in intestinal epithelial organoids derived from induced pluripotent stem (iPS) cells⁴⁴, except for p16^INK4a expression in intestinal epithelial organoids (Supplementary Fig. 4). Together, these results suggest that these two bacterial species have the ability to induce potentially oncogenic stimuli not only in fibroblasts but also in normal intestinal epithelial cells.

**Fig. 2: Induction of cellular senescence in fibroblasts by gut bacteria.**

**Fig. 3: Induction of cellular senescence in intestinal epithelial cells by gut bacteria.**

To explore this idea, we next sought to identify the potentially oncogenic stimulatory factor secreted from these two senescence-inducing bacteria. Lipopolysaccharide (LPS), a major component of the outer membrane of Gram-negative bacteria, to which both species belong, reportedly induces osteocyte senescence⁴⁵. However, the senescence-like cell-cycle arrest was induced similarly in primary mouse embryonic fibroblasts regardless of the presence or absence of MyD88 and TRIF, essential downstream mediators of LPS-TLR4 signalling⁴⁶ (Supplementary Fig. 5). Moreover, we could not find the bile acid-inducible (bai) genes that are required for bile acid transformation from primary to secondary bile acids⁴⁷ in these two senescence-inducing bacterial genomes (NC_015501.1, NC_010729.1). Thus, although LPS and secondary bile acids are reportedly associated with cellular senescence^2,45 and CRC^11,48 in certain settings, it is unlikely that they are potentially oncogenic stimulatory factors secreted from these two Porphyromonas species.

Porphyromonas species provoke cellular senescence via butyrate secretion

In seeking alternative factors, we next focused on short-chain fatty acids (SCFAs), major gut bacterial metabolites reported to have various effects on host homoeostasis, including tumourigenesis^22,49. Among the series of SCFAs tested, the butyrate levels were significantly higher in the bacterial culture supernatants of both Porphyromonas asaccharolytica and Porphyromonas gingivalis, as compared to those of other CRC-enriched bacteria that failed to induce senescence-like cell-cycle arrest (Fig. 4a). Butyrate reportedly possesses both tumour promotive and suppressive properties through energetic or epigenetic functions, depending on the cell type, duration and amount of exposure^22,49. Importantly, the treatment of HDFs with the same butyrate concentrations present in the bacterial culture supernatants of Porphyromonas gingivalis (1.53 mM) indeed provoked senescence-like phenotypes, but this was not the case with other SCFAs (Fig. 4b, c, Supplementary Fig. 6). Although the same concentration of butyrate present in the culture supernatant of Porphyromonas asaccharolytica (0.69 mM) alone weakly induced senescence-like cell-cycle arrest, a co-treatment with the other SCFAs present in this culture supernatant more robustly induced this phenomenon (Fig. 4b, c, Supplementary Fig. 6), implying that other SCFAs may also cooperate with butyrate in provoking senescence-like phenotypes, especially with low butyrate levels. Note that the treatment of normal human colonic epithelial cells with a mixture of SCFAs, at the same concentrations as in the culture supernatants of Porphyromonas asaccharolytica and Porphyromonas gingivalis, also caused senescence-like cell-cycle arrest (Supplementary Fig. 7). This is somewhat consistent with a previous observation that the CRC-associated microbiome showed an association with the pathways responsible for the conversion of different amino acids to acetate, butyrate, and propionate¹⁹. Furthermore, a total RNA sequencing analysis revealed that the alterations in the gene expression profiles caused by these bacterial culture supernatants, particularly the SASP factor genes and DNA damage response genes, are more similar to those induced by butyrate as compared to those induced by DXR in cultured HDFs (Fig. 4d, e), implying that butyrate is likely to be a key oncogenic stimulatory factor secreted from these two senescence-inducing bacteria.

**Fig. 4: Butyrate provokes cellular senescence in HDFs.**

Because butyrate induces the expression of the p16^INK4a and p21^Cip1/Waf1 genes (Fig. 4c), we next determined whether butyrate promotes cellular senescence through the induction of p16^INK4a and p21^Cip1/Waf1 expression. Notably, although the butyrate-treatment induced senescence-like cell-cycle arrest in primary skin fibroblasts prepared from wild-type mice, this was not the case in skin fibroblasts prepared from mice lacking both the p16^INK4a and p21^Cip1/Waf1 genes⁵⁰ (Supplementary Fig. 8). These results, together with previous observations that butyrate elicits the epigenetic induction of p16^INK4a, p21^Cip1/Waf1 and SASP in cultured human cells^51,52, again implied that butyrate is a major senescence-inducing factor secreted from these bacteria. To further verify this idea, we next tested if the blockade of butyrate production affects the senescence-inducing activity of butyrate-producing bacteria. Indeed, the reduction of butyrate production by deleting PGN_0725, PGN_1341 and PGN_1888, three CoA transferases involved in the last step of butyrate production⁵³, rendered the culture supernatant of Porphyromonas gingivalis incapable of inducing senescence-responses in HDFs (Fig. 4f, Supplementary Fig. 9). Taken together, these results strongly suggest that butyrate is one of the major potentially oncogenic stimulatory factors secreted from these senescence-inducing bacteria, although we cannot preclude the possibility that other factors may also be involved, depending on the biological context.

Bacterial invasion and signs of cellular senescence in CRC tissues

To further substantiate this idea, we next sought evidence that cellular senescence occurs in the CRC tissues enriched with these butyrate-producing bacteria. Although high levels (15–25 mM)⁵⁴ of butyrate are present in the colonic lumen, they are reportedly much lower in the intestinal epithelium due to the coverage with a dense layer of mucus, which prevents the translocation of gut bacteria into the underlying tissues⁴⁹. Interestingly, however, an in situ hybridization analysis using species-specific probes revealed that these butyrate-producing bacteria infiltrated the CRC tissues of the patients with faeces enriched with these bacteria (Fig. 5a, b, Supplementary Fig. 10a). Furthermore, to distinguish between Porphyromonas asaccharolytica and Porphyromonas uenonis, whose 16S rRNA sequences are more than 98% identical, the species-specific gene, rpoB, was isolated and sequenced from a patient (Supplementary Figs. 1d and 10b). Notably, the levels of butyrate in tumour regions are substantially higher than those in non-tumour regions, in the same resected specimens (Fig. 5c). These results, in conjunction with the observations that signs of cellular senescence, such as the induction of both p16^INK4a and SASP factor (IL-6) expression, were observed in the vicinity of the bacterial invasion within the tumour regions (Fig. 5b), strongly suggest that these two butyrate-producing bacteria are involved in the implementation of cellular senescence in human colorectal tissues. Note that a significant proportion of p16^INK4a expressing cells also expresses αSMA (Supplementary Fig. 10c), implying that a senescence-like phenotype may be induced in at least some of the fibroblasts in the tumour microenvironment.

**Fig. 5: Bacterial invasion and tumour promotion.**

Butyrate-producing bacteria accelerate the onset of colorectal tumours

Finally, we determined whether these two butyrate-producing bacteria can promote colorectal tumourigenesis in vivo. To this end, we used Apc^Δ14/+ mice, in which tumour development is enhanced in the distal colon and rectum⁵⁵, and Fusobacterium nucleatum subsp. as a positive control⁶. Notably, the numbers of colorectal tumours developed in Apc^Δ14/+ mice administered Porphyromonas gingivalis and, to a lesser extent, Porphyromonas asaccharolytica by gavage was increased, as compared to that in control (PBS) mice (Fig. 5d). This was accompanied by signs of cellular senescence and increased levels of butyrate in the colorectal tumours developed in Apc^Δ14/+ mice administered these bacteria (Supplementary Fig. 11a–d). These results are consistent with the observation that the levels of butyrate in the culture supernatant of Porphyromonas gingivalis were higher than those of Porphyromonas asaccharolytica (Fig. 4a–c), implying that high levels of bacterial butyrate may promote colorectal tumour development in vivo. To bolster this idea, we next examined whether the reduction of butyrate production by Porphyromonas gingivalis decreases the ability to enhance colorectal tumour formation in Apc^Δ14/+ mice. Indeed, the number of colorectal tumours developed in Apc^Δ14/+ mice administered a butyrate synthesis defective mutant of Porphyromonas gingivalis⁵³ by gavage was significantly lower than that in Apc^Δ14/+ mice administered wild-type Porphyromonas gingivalis (Fig. 5e). It is also worth mentioning that the number of colonic tumours produced in mice gavaged with Porphyromonas gingivalis tended to be reduced by a treatment with ABT-263, a well-established senolytic drug that preferentially eliminates senescent cells⁵⁶, although this reduction was not statistically significant (Fig. 5f). Notably, the size of the tumours in the group receiving ABT-263 was statistically significantly smaller than that in the group not receiving ABT-263 (Fig. 5f). Moreover, the proportion of p16^INK4a expressing fibroblasts (αSMA positive cells) in the tumour regions of mice treated with ABT-263 was lower than that in mice not treated with ABT-263 (Supplementary Fig. 11e). These results, together with previous observations that cellular senescence occurs in human colorectal tumours^30,57, strongly suggest that the aberrant increases of these butyrate-producing bacteria in the colon are likely to be causally involved in CRC development, possibly by provoking cellular senescence, at least in certain settings.

Discussion

One of the important questions remaining in the field of gut microbiota in CRC is the issue of whether and how gut microbiota alterations are causally associated with CRC development^5,22. In this study, we found that 12 faecal bacterial taxa were enriched in CRC patients and rarely detectable in HI, in two different cohort studies (Fig. 1a, b). Although some of the bacterial species corresponding to these bacterial taxa were reportedly increased in CRC patients in several different studies^18,19,20, it remained unclear whether these bacterial species, with the exception of Fusobacterium nucleatum, were oncogenic or not. Therefore, we explored this topic, using cellular senescence as an indicator. In this study, we used normal lung HDF as a tool to screen for gut bacteria that induce senescence-like phenotypes, because normal lung HDF is the most commonly studied cell in cellular senescence research (Fig. 2). However, it is important to emphasize that we have subsequently confirmed that two bacterial candidates identified using HDFs were able to induce the senescence-like phenotype in intestinal cells (Fig. 3 and Supplementary Fig. 5) and mice (Fig. 5d–f). Note that the SA-β-gal activity, a widely used senescence marker, is also induced when normal cells are rendered quiescent by contact inhibition or serum starvation^58,59,60, and is not required for the implementation of cellular senescence⁶¹. Therefore, in this study, we used other markers known to play key roles in establishing the senescent state, such as DNA damage foci, increased p21^Cip1/Waf1 and/or p16^INK4a expression, and decreased lamin B1 expression (Figs. 2, 3, 4, 5, Supplementary Figs. 4, 8, 9 and 11). In several experiments with cultured cells, we also confirmed the irreversibility of cell-cycle arrest, an essential phenotype of cellular senescence (Figs. 2a, 3a, c, Supplementary Figs. 5a and 7). Judging by these markers, we revealed that Porphyromonas gingivalis and, to a lesser extent, Porphyromonas asaccharolytica have the ability to elicit senescence-like phenotypes and colorectal tumour development via butyrate secretion (Figs. 4 and 5).

Several reports have shown that butyrate also possesses tumour suppression properties, likely through its action as a histone deacetylase (HDAC) inhibitor^22,49. On the other hand, numerous studies have demonstrated that butyrate promotes colorectal carcinogenesis in different CRC animal models and humans^22,49. This discrepancy in the effects of butyrate on tumourigenesis has been termed the butyrate paradox, and may be reconciled by the butyrate concentration⁶². For instance, although a low dose of butyrate promotes tumourigenesis, a high dose inhibits it⁴⁹. However, recent studies have shown that relatively high concentrations of butyrate can also enhance the development of CRC⁶³ and liver cancer⁶⁴, making it difficult to describe the butyrate paradox simply by differences in butyrate concentrations. Thus, considering the two faces of cellular senescence (tumour suppression and tumour promotion)^32,33 and our findings, it is tempting to speculate that the butyrate paradox can also be explained by the opposing effects of cellular senescence on tumourigenesis, at least in certain settings.

It is worth mentioning that the 12 faecal bacterial taxa enriched in the faeces of CRC patients and rarely detectable in those of HI apparently correspond to bacterial species associated with periodontal disease³⁹ (Supplementary Fig. 1d). These results are consistent with previous observations that many of the bacteria that are enriched in the faeces of CRC patients are associated with typical oral commensals^18,19,20,21. It is further interesting to note that a recent large cohort study revealed that women with a small number of teeth and moderate or severe periodontal inflammation had up to a 48% increased risk of developing CRC⁶⁵. Collectively, although we still know very little about the mechanism of ectopic colonisation of the gut by oral bacteria, it is clear that a bacterial link exists between periodontal disease and CRC.

Senescent cells can recruit various immune cells, such as macrophages, natural killer (NK) cells, neutrophils and T-lymphocytes via SASP, and the excessive accumulation of immune cells is known to cause chronic inflammation that may promote carcinogenesis⁶⁶. Furthermore, cellular senescence reportedly impairs the tight junction and barrier integrities of epithelial cells⁶⁷. Thus, it is also tempting to speculate that the aberrant increase of butyrate-producing bacteria in the colon may contribute to CRC development, possibly through promoting the infiltration of gut bacteria and immune cells into the gut epithelial barrier, at least in certain settings. However, we cannot exclude the possibility that some bacteria may promote CRC development without eliciting the senescence response^10,41. Moreover, the mechanism of induction of cellular senescence in vivo may be somewhat different from that in vitro. Thus, further studies are needed to understand the mechanisms of the gut bacterial link to CRC development. Nevertheless, while additional mechanisms may be at play here, our data raise the possibility that a subset of gut bacteria increased in CRC patients has the potential to give rise to cellular senescence and tumourigenesis through butyrate secretion. These findings expand our understanding of whether and how gut microbiota alterations are causally associated with CRC development and open up possibilities for its control.

Methods

Human faecal sample collection

Faeces were collected from colorectal cancer patients (CRC patients) and healthy individuals (HI) who visited the Cancer Institute Hospital of JFCR from December 2013 to March 2015 (Cohort-1) and January to September 2017 (Cohort-2) using a faecal sampling tool (TechnoSuruga Laboratory). Patients with a history of inflammatory bowel disease, those who had undergone gastrointestinal reconstructive surgery, those with severe hepatic dysfunction, those who received antibiotic treatment within 1 month, those who had a faecal sample within 3 days of colonoscopy, or those for whom complete personal information was not available were excluded from CRC patients and HI. Patients who received chemotherapy or radiation therapy before faecal sample collection, who had faecal samples collected after endoscopic tumour resection, or patients with CRC whose tumours were not adenocarcinomas (e.g., squamous cell carcinoma) were excluded. HI with a history of colorectal cancer or with malignancy at the time of stool collection were excluded. Informed consent was obtained from all participants for the use of anonymized samples and the publication of the patients’ clinical information in accordance with the protocol approved by the ethics committee of the JFCR. The tumour profiles of CRC patients were classified based on the Third English Edition of the Japanese Classification of Colorectal, Appendiceal and Anal Carcinoma. The statistical significance of clinical data was determined using a Wilcoxon rank-sum test (Cohort-1), a Kruskal–Wallis rank-sum test (Cohort-2) or a Fisher’s exact test (Cohort-1 and 2).

16S rRNA gene-sequencing analysis and microbiome analysis

Bacterial DNA extraction from faecal samples was performed using QIAamp Fast DNA Stool Mini Kit (QIAGEN) (samples of Cohort-1) or a Magtration System 12GC (Precision System Science, Japan) in TechnoSuruga Laboratory (samples of Cohort-2 and samples of Cohort-1 collected after tumour resection). Twenty-five cycles of amplification of the V1-V2 region of the bacterial 16S rRNA gene were performed using KOD FX Neo DNA polymerase (TOYOBO) with universal 16S rRNA primers followed by the secondary amplification adding the Illumina flow cell adapters and indices. The PCR primers used are shown in Supplementary Table 1. Meta-16S rRNA gene sequencings were carried out per 96 samples on the Illumina Miseq platform (Illumina) using Miseq Reagent Kit V2 (Illumina) (paired-end, 225 cycles × 2). Sequencing reads were processed according to the QIIME2 (version 2019.10) pipeline³⁷. Fastq files were de-noised with DADA2 plugin and amplicon sequence variants (ASVs) were counted. Subsequently, de novo clustering was performed on ASVs using the VSEARCH plugin to obtain operational taxonomic units (OTUs) with a similarity of more than 97%. These processes were performed separately for samples from Cohort-1 and Cohort-2. Open-reference clustering based on OTUs detected from Cohort-1 samples collected before tumour resection was performed on samples collected after tumour resection. Finally, the OTU counts were converted to relative abundance per sample. Phylogenetic classification of the detected OTUs was performed by a Naive Bayes classifier trained on the SILVA132 database (QIIME-compatible SILVA releases) in the QIIME2 pipeline. Identification of the specific bacterial species corresponding to each OTU was performed by using the 16S rRNA database provided by the National Center for Biotechnology Information (NCBI) (last modified at 2020/05/03) and a similarity search with BLAST+ (version 2.9.0). OTUs with significant differences in the relative abundance of Cohort-1 HI and CRC patients were detected by the linear discriminant analysis effect size (LEfSe) (version 1.0.8.post1)³⁸. OTUs detected in only one sample were removed prior to analysis. From the OTUs selected by LEfSe, OTUs that were only increased in CRC patients and rarely detected by HI were picked. The relative abundance of these OTUs was compared between HI, early CRC patients, and advanced CRC patients (Cohort-2). The relative abundances of OTUs in CRC patients of Cohort-1 were also compared before and after tumour resection.

Bacterial strain and culture conditions

Alloprevotella tannerae JCM 16135, Dialister pneumosintes JCM 10004, Fusobacterium nucleatum subsp. nucleatum JCM 8532, Gemella morbillorum JCM 12968, Parvimonas micra JCM 12970, Peptostreptococcus anaerobius JCM 1769, Peptostreptococcus stomatis JCM 15636, Porphyromonas asaccharolytica JCM 6323, Porphyromonas gingivalis JCM 8525, Prevotella intermedia JCM 11150 and Solobacterium moorei JCM 10645 were provided by the RIKEN BRC through the National Bio-Resource Project of the MEXT, Japan. Escherichia coli DH5α (DNA-903) was purchased from TOYOBO. A. tannerae was anaerobically cultured with modified Gifu Anaerobic Medium (mGAM). Other 11 bacteria were anaerobically cultured with Gifu Anaerobic Medium (GAM). For the preparation of bacterial conditioned medium, each bacterium was cultured for 2 to 3 days. P. gingivalis ATCC 33277 and its butyrate synthesis defective mutant strain PGAGU 118⁵³ which lacks three butyryl CoA:acetate CoA transferase, PGN_0725, PGN_1341 and PGN_1888 were anaerobically cultured with GAM to an OD600 value of 3.5. Cultured medium containing each bacterium was collected and centrifuged at 10,000 × g for 10 min. After filtration through a 0.22-μm mesh, the conditioned medium of each bacterium was collected and stocked at −80 °C until use. The SCFAs concentrations were measured immediately after the collection of the cell-free bacterial supernatants. For oral gavage to mice, F. nucleatum subsp. nucleatum, P. asaccharolytica, P. gingivalis JCM 8525, P. gingivalis ATCC 33277 and its butyrate synthesis defective mutant strain PGAGU 118 were anaerobically cultured in GAM and collected in a logarithmic growth phase. The colony-forming unit (CFU) was measured by serial dilution and plating on GAM agar.

Cell culture

Early passage (< 45 population doublings) human diploid fibroblasts (HDFs) TIG-3 cell, early passage mouse embryonic fibroblasts (MEFs) isolated from wild-type mice or mice lacking both Myd88 and Trif genes (Myd88^−/− Trif^−/− mouse) (Oriental BioService), and early passage skin fibroblasts isolated from wild-type mice or mice lacking both p16^INK4a and p21^Cip1/Waf1 (p16^−/−, p21^−/− mouse)⁵⁰ were cultured in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% foetal bovine serum (FBS). Normal human colonic epithelial cells (CCD 841 CoN) were cultured in Eagle’s minimum essential medium (EMEM) supplemented with 10% FBS. In some experiments, cells were cultured in a tissue culture medium containing 1/30 volume of filtered bacterial culture supernatant or SCFA at the indicated concentrations for 9 days, with the medium changed every 3 days. These cells were then cultured in plain DMEM supplemented with 10% FBS for an additional 3 or 7 days.

iPSC-derived intestinal organoid culture

Intestinal organoids were differentiated from the human-induced pluripotent stem cell (iPSC) line TkDN4-M supplied by the University of Tokyo^44,68. Conditioned medium containing mouse Wnt3a, human R-spondin 1, human noggin, and human hepatocyte growth factor (WRNH conditioned medium) was prepared by incubating WRNH-expressing L cells for 72 h⁴⁴. Approximately 300 cells were resuspended in 25 μl Matrigel with 20% organoid culture medium (advanced Dulbecco’s modified Eagle medium/F12 (Thermo Fisher Scientific) supplemented with 25% WRNH conditioned medium, 10 mM HEPES (pH 7.3) (Thermo Fisher Scientific), 2 mM GlutaMAX^TM (Thermo Fisher Scientific), 1× B-27 (Thermo Fisher Scientific), 50 ng/mL murine epidermal growth factor (mEGF) (Peprotech), 10 μM p38 Mitogen-activated protein kinase (MAPK) inhibitor (SB202190; Sigma-Aldrich), 500 nM transforming growth factor β receptor inhibitor (A83-01; Tocris) and 100 units/mL penicillin plus 100 μg/mL streptomycin) per well in 48-well culture plate at the passage and cultured with organoid culture medium plus 10 μM ROCK inhibitor Y-27632 (Wako)⁴⁴. Two days after passage, organoids were cultured with organoid culture medium containing bacterial conditioned medium of P. asaccharolytica, P. gingivalis JCM 8525 or E. coli, or plain bacterial culture medium (GAM) at the ratio of 1/30. Each medium was changed every 2 or 3 days.

Quantitative real-time PCR analysis

Total RNA was extracted using TRIzol (Thermo Fisher Scientific) or RNeasy mini kit (Qiagen) according to the manufacturer’s protocol. cDNA was synthesized using a PrimeScript RT reagent kit (Takara Bio Inc.). Quantitative real-time RT-PCR was performed on a StepOnePlus PCR System (Applied Biosystems) using SYBR Premix EX Taq (Takara Bio Inc.). The mRNA expression levels of each gene were calculated relative to β-actin expression levels. The PCR primer sequences used are shown in Supplementary Table 1.

Immunoblotting

Proteins were extracted using RIPA buffer with 1% Protease inhibitor cocktail (Nacalai Tesque). After determination of the protein concentration using Protein Quantification Assay (Takara Bio Inc.), all samples were denatured in Laemmli sample buffer for 5 min at 100 °C. The denatured samples were separated by SDS-polyacrylamide gel electrophoresis and transferred onto a polyvinylidene difluoride membrane (EMD Millipore). After blocking with 5% milk or 5% BSA, the membranes were incubated with the primary antibodies as follows: p16^INK4a (1:1000, IBL, 11104), p21^Cip1/Waf1 (1:1000, Cell Signaling Technology, 2947), Lamin B1 (1:5000, Abcam, ab16048), RB (1:500, Santa Cruz, sc-102), phospho-RB (Ser780) (1:1000, Cell Signaling Technology, 9307), p53 (1:1000, Santa Cruz, sc-6243), phospho-p53 (Ser15) (1:1000, Cell Signaling Technology, 9284) and α-Tubulin (1:2000, Sigma-Aldrich, T9026). The membranes were then incubated with the secondary antibodies as follows: anti-mouse IgG (1:2000, Cell Signaling Technology, 7076), anti-rabbit IgG (1:2000, Cell Signaling Technology, 7074) and visualized with Amersham ECL prime/select (GE Healthcare), followed by detection with chemiluminescence using LAS-3000mini imaging system (Fujifilm) and by analysis of data using Multi Guage V3.1 (Fujifilm). Uncropped and unprocessed scans of the blots are included in the Source data file.

ROS measurement

To assess the levels of intracellular ROS generation, cells were incubated with 20 μM DCF-DA (Calbiochem) at 37 °C for 15 min. The peak excitation wavelength for oxidized DCF (488 nm) and that for emission (525 nm) were measured by ARVO MX/Light 1420 Multilabel/Luminescence Counter (PerkinElmer).

Immunofluorescence analysis

Immunofluorescence analysis of TIG-3 cells and iPSC-derived intestinal organoids was performed using antibodies against γ-H2AX (Millipore, 05-636, 1:1000 dilution), phospho-(Ser/Thr) ATM/ATR substrate (pST/Q) (Cell Signaling Technology, 2851, 1:500 dilution) or Cleaved Caspase-3 (Asp175) (Cell Signaling Technology, 9661, 1:100 dilution). The secondary antibodies, donkey anti-mouse IgG Alexa Flour 488 (Thermo Fisher Scientific, A-21202), donkey anti-rabbit IgG Alexa Fluor 594 (Thermo Fisher Scientific, A-21207) or donkey anti-rabbit IgG Alexa Flour Plus 555 (Thermo Fisher Scientific, A-32794) was used with 1:1000 dilution for primary antibodies against γ-H2AX, pST/Q or Cleaved Caspase-3, respectively. DNA was stained with 4′,6-diamidino-2-phenylindole (DAPI) (Dojindo). Fluorescence images were obtained with a Fluorescence Microscope BZ-X710 (KEYENCE) or FLUOVIEW FV3000 confocal laser scanning microscope (OLYMPUS) and analysed using Image J 1.53e.

Apoptosis analysis

Apoptosis analysis of TIG-3 was performed using Annexin V-FITC (MBL Life Science, 4700-100) and Propidium iodide (PI) solution (Sigma-Aldrich, P4864) according to the manufacturer’s protocol. DNA was stained with DAPI (Dojindo).

EdU incorporation assay

Cell proliferation was evaluated using Click-iT™ Plus EdU Cell Proliferation Kit for Imaging Alexa Fluor™ 555 dye (Thermo Fisher Scientific, C10638) according to the manufacturer’s protocol.

RNA sequencing analysis

RNA sequencing library was prepared using TruSeq stranded mRNA Sample Prep Kit (Illumina) according to the manufacturer’s protocol. Sequencing was carried out on the Illumina HiSeq 2500 platform (Illumina) (a 75-base, single-end). The quality of the sequencing data was confirmed by FastQC (version 0.11.8). Sequencing reads were mapped to the GRCh38 human reference genome (Ensembl release 100) using STAR (version 2.7.4a). Counting of reads per gene was performed by HTSeq (version 0.12.4). Differential gene expression analysis was performed using edgeR (version 3.30.3). The gene symbols were based on org.Hs.eg.db (version 3.11.4). The statistical significance was determined with quasi-likelihood F-tests and the P values were adjusted by the Benjamini–Hochberg false-discovery rate (FDR) correction at 0.05.

Animal experiment

C57BL/6J Apc^Δ14/+ mice⁵⁵ which express a mutant adenomatous polyposis coli gene with deletion of exon 14 at the age of 6 weeks were subjected to oral gavage with 200 μl of PBS or PBS containing bacteria (F. nucleatum subsp. nucleatum, P. asaccharolytica, P. gingivalis JCM 8525, P. gingivalis ATCC 33277⁵³, butyrate synthesis defective mutant P. gingivalis PGAGU 118)⁵³ at 1 × 10⁸ CFU every other days for 8 weeks with or without ABT-263 (100 mg/kg or Vehicle p.o. five times a week for a total of 4 weeks with 2 non-administration periods for 1 week). At the end of the experiment, the mice were euthanized and the colon tissue was opened longitudinally. The number of tumours in the colon of each mouse was counted. The colonic tissue was then swiss-rolled and fixed with 4% paraformaldehyde, followed by a paraffin burial technique. Mice were maintained under temperature- and humidity-controlled conditions and were exposed to on a 12-h light–dark cycle. All mouse experiments were approved by the Animal Research Committee of Research Institute for Microbial Diseases, Osaka University.

Histology, immunohistochemistry and immunofluorescence

Samples of human colorectal cancer tissues and murine colon tissues were sectioned in 2-μm thick by a microtome, deparaffinized in xylene, rehydrated, and then stained with haematoxylin and eosin. For immunohistochemistry of murine colon tissues, deparaffinized and rehydrated sections were treated with microwave-oven heating in 10 mM citrate buffer (pH 6.0) for antigen retrieval and blocked using Streptavidin/Biotin Blocking Kit (Vector Laboratories). The primary antibodies used were as follows: p16 (1:200, Abcam, ab211542), p21 (1:200, Abcam, ab107099) and IL-6 (1:400, Abcam, ab6672). The secondary antibodies used were as follows: goat anti-rabbit IgG (1:1000, Vector Laboratories, BA-1000), goat anti-rat IgG (1:1000, Vector Laboratories, BA-9400). For immunohistochemistry of human colorectal cancer tissues, all procedures were carried out on the Leica Bond III Automated IHC and ISH system (Leica Microsystems) with the primary antibodies as follows: p16 (1:3000, Abgent, ALS16384) and IL-6 (1:100, Abcam, ab6672). Optical images were obtained with a BX53 Upright Microscope (OLYMPUS) and analysed using cellSens Standard 2.1 (OLYMPUS). Immunofluorescence of murine colon tissues was performed using the primary antibodies against ɑ-SMA (1:2000, Sigma-Aldrich, A5228) and p16 (1:200, Abcam, ab211542). Immunofluorescence of human colorectal cancer tissues was performed using the primary antibodies against ɑ-SMA (1:100, Abcam, ab125057) and p16 (1:1000, Abcam, ab81278). For immunofluorescence analysis of murine and human tissues, the secondary antibodies used were as follows: donkey anti-mouse IgG Alexa Flour 488 (1:1000, Thermo Fisher Scientific, A-21202), donkey anti-rabbit Alexa Flour Plus 555 (1:1000, Thermo Fisher Scientific, A-32794). The nuclei were stained with DAPI (Dojindo). Fluorescent images were obtained with a Fluorescence Microscope BZ-X710 (KEYENCE) and analysed using Image J 1.53e. The study using human clinical samples was approved by the Ethics Committee of the JFCR and Osaka University and all participants provided written informed consent.

In situ hybridization

Chromogenic RNA in situ hybridization targeting to specific bacterial species (P. asaccharolytica and P. gingivalis) in colorectal cancer tissues was performed using RNAscope® 2.5 HD Reagent Kit- RED (Advanced Cell Diagnostics (ACD), 322350) and Fast Red according to the manufacturer’s protocol⁴². The probes were as follows: RNAscope® Probe-B-P.asaccharolytica-23srRNA-O1 (ACD, 838331) targeting P. asaccharolytica (286-336 of NR_076875.1) and RNAscope® Probe-B-P.gingivalis-23srRNA (ACD, 843341) targeting P. gingivalis (122-162 of NR_076276.1). The study was approved by the Ethics Committee of the JFCR and Osaka University and all participants provided written informed consent.

Measurement of SCFAs

SCFA concentrations in bacterial culture supernatants were measured by ion-exclusion high-performance liquid chromatography⁶⁹ in the Institute of Nutrition and Pathology Inc. Concentrations of butyrate in CRC tissues and paired non-tumour tissues were measured by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) with chemical derivatization⁷⁰.

DNA cloning and sequencing

Amplification of rpoB gene in faecal samples was performed using KOD FX Neo DNA polymerase (TOYOBO) with primers shown in Supplementary Table 1. PCR products were cloned into pGEM-T vector (Promega, A3600). DNA was extracted using FavorPrep^TM Plasmid DNA Extraction Mini Kit (Favorgen, FAPDE001-1) and subjected to sequencing using Applied Biosystems 3500xL Genetic Analyzer (Thermo Fisher Scientific). Sequencing data of rpoB gene was compared with rpoB gene of P. asaccharolytica (CP002689.1) and P. uenonis (NZ_BAJM01000009) using A plasmid Editor (version 2).

Statistics

Statistical analysis was performed using R (version4.0.2) (Fig. 1a–c, Supplementary Figs. 1b, 2b) or Graph Pad Prism 9 (Figs. 2b, d–f, 3b, c, 4c, f, 5c–f, Supplementary Figs. 4b-e, 8b, 8c, 9b, 11c-e). Wilcoxon rank-sum test (Figs. 1a, 5e, f, Supplementary Fig. 11e), Wilcoxon signed-rank test (Figs. 1c, 5c, Supplementary Fig. 11d), or Student’s t-test (Fig. 4f, Supplementary Figs. 8b, c, 9b, 11c) was performed to compare the variables of the two sample groups. To compare the variables of the multiple sample groups, Kruskal–Wallis rank-sum test followed by pairwise Wilcoxon rank-sum test with the adjustment of P values by Benjamini–Hochberg FDR correction at 0.05 (Figs. 1b, 5d) or one-way analysis of variance followed by Tukey’s test (Figs. 2b, d, e, 3b, c, Supplementary Fig. 4b-d) or Dunnett’s test (Figs. 2f, 4c, Supplementary Fig. 4e) was performed. Data are expressed as the mean ± s.d. or mean ± s.e.m. as indicated in the figure legends. Statistical tests were two-tailed and P < 0.05 was considered significant.

Reporting summary

Further information on experimental design is available in the Nature Research Reporting Summary linked to this paper.

Data availability

Microbiome analysis (bacterial 16S rRNA gene meta-sequence) data and RNA sequencing (TIG-3 cells) data generated in this study have been deposited in the DNA Data Bank of Japan (DDBJ) with the accession codes DRA011735 or DRA011736, respectively (https://www.ddbj.nig.ac.jp). The deposited data are available in NCBI under accession numbers DRA011735 and DRA011736. The databases referred in microbiome analysis are as follows: SILVA (https://www.arb-silva.de/) and National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/). Bacterial gene data mentioned in the text are available in NCBI under accession numbers NC_015501.1, NC_00729.1, CP002689.1, NZ_BAJM01000009, PGN_0725, PGN_1341 and PGN_1888. The database referred in RNA sequencing analysis is available in Ensembl (https://www.ensembl.org/). Uncropped and unprocessed scans of the blots are included in the Source data file. Source data are provided with this paper.

References

Ferlay, J. et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int. J. Cancer 144, 1941–1953 (2019).
Article CAS PubMed Google Scholar
Yoshimoto, S. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499, 97–101 (2013).
Article ADS CAS PubMed Google Scholar
Schwabe, R. F. & Jobin, C. The microbiome and cancer. Nat. Rev. Cancer 13, 800–812 (2013).
Article CAS PubMed PubMed Central Google Scholar
Schroeder, B. O. & Backhed, F. Signals from the gut microbiota to distant organs in physiology and disease. Nat. Med. 22, 1079–1089 (2016).
Article CAS PubMed Google Scholar
Janney, A., Powrie, F. & Mann, E. H. Host-microbiota maladaptation in colorectal cancer. Nature 585, 509–517 (2020).
Article ADS CAS PubMed Google Scholar
Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207–215 (2013).
Article CAS PubMed PubMed Central Google Scholar
Wu, S. et al. A human colonic commensal promotes colon tumorigenesis via activation of T helper type 17 T cell responses. Nat. Med. 15, 1016–1022 (2009).
Article CAS PubMed PubMed Central Google Scholar
Nougayrède, J. P. et al. Escherichia coli induces DNA double-strand breaks in eukaryotic cells. Science 313, 848–851 (2006).
Article ADS PubMed CAS Google Scholar
Pleguezuelos-Manzano, C. et al. Mutational signature in colorectal cancer caused by genotoxic pks(+) E. coli. Nature 580, 269–273 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Long, X. et al. Peptostreptococcus anaerobius promotes colorectal carcinogenesis and modulates tumour immunity. Nat. Microbiol. 4, 2319–2330 (2019).
Article PubMed CAS Google Scholar
Fu, T. et al. FXR regulates intestinal cancer stem. Cell Prolif. Cell 176, 1098–1112 (2019).
CAS Google Scholar
Wu, S., Morin, P. J., Maouyo, D. & Sears, C. L. Bacteroides fragilis enterotoxin induces c-Myc expression and cellular proliferation. Gastroenterology 124, 392–400 (2003).
Article CAS PubMed Google Scholar
Sears, C. L. & Garrett, W. S. Microbes, microbiota, and colon cancer. Cell Host Microbe 15, 317–328 (2014).
Article CAS PubMed PubMed Central Google Scholar
Wong, S. H. & Yu, J. Gut microbiota in colorectal cancer: mechanisms of action and clinical applications. Nat. Rev. Gastroenterol. Hepatol. 16, 690–704 (2019).
Article CAS PubMed Google Scholar
He, Y. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat. Med. 24, 1532–1535 (2018).
Article CAS PubMed Google Scholar
Voigt, A. Y. et al. Temporal and technical variability of human gut metagenomes. Genome Biol. 16, 73 (2015).
Article PubMed PubMed Central CAS Google Scholar
Sinha, R. et al. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat. Biotechnol. 35, 1077–1086 (2017).
Article CAS PubMed PubMed Central Google Scholar
Wirbel, J. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019).
Article CAS PubMed PubMed Central Google Scholar
Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019).
Article CAS PubMed Google Scholar
Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).
Article PubMed PubMed Central CAS Google Scholar
Yachida, S. et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. 25, 968–976 (2019).
Article CAS PubMed Google Scholar
Irrazabal, T., Belcheva, A., Girardin, S. E., Martin, A. & Philpott, D. J. The multifaceted role of the intestinal microbiota in colon cancer. Mol. Cell 54, 309–320 (2014).
Article CAS PubMed Google Scholar
Collado, M. & Serrano, M. Senescence in tumours: evidence from mice and humans. Nat. Rev. Cancer 10, 51–57 (2010).
Article CAS PubMed PubMed Central Google Scholar
He, S. & Sharpless, N. E. Senescence in health and disease. Cell 169, 1000–1011 (2017).
Article CAS PubMed PubMed Central Google Scholar
Lee, S. & Schmitt, C. A. The dynamic nature of senescence in cancer. Nat. Cell Biol. 21, 94–101 (2019).
Article CAS PubMed Google Scholar
Gorgoulis, V. et al. Cellular senescence: defining a path forward. Cell 179, 813–827 (2019).
Article CAS PubMed Google Scholar
Campisi, J. & d’Adda di Fagagna, F. Cellular senescence: when bad things happen to good cells. Nat. Rev. Mol. Cell Biol. 8, 729–740 (2007).
Article CAS PubMed Google Scholar
Kruiswijk, F., Labuschagne, C. F. & Vousden, K. H. p53 in survival, death and metabolic health: a lifeguard with a licence to kill. Nat. Rev. Mol. Cell Biol. 16, 393–405 (2015).
Article CAS PubMed Google Scholar
Acosta, J. C. et al. Chemokine signaling via the CXCR2 receptor reinforces senescence. Cell 133, 1006–1018 (2008).
Article CAS PubMed Google Scholar
Kuilman, T. et al. Oncogene-induced senescence relayed by an interleukin-dependent inflammatory network. Cell 133, 1019–1031 (2008).
Article CAS PubMed Google Scholar
Coppe, J. P. et al. Senescence-associated secretory phenotypes reveal cell-nonautonomous functions of oncogenic RAS and the p53 tumor suppressor. PLoS Biol. 6, 2853–2868 (2008).
Article CAS PubMed Google Scholar
Rodier, F. & Campisi, J. Four faces of cellular senescence. J. Cell Biol. 192, 547–556 (2011).
Article CAS PubMed PubMed Central Google Scholar
Chan, A. S. L. & Narita, M. Short-term gain, long-term pain: the senescence life cycle and cancer. Genes Dev. 33, 127–143 (2019).
Article CAS PubMed PubMed Central Google Scholar
Childs, B. G. et al. Senescent cells: an emerging target for diseases of ageing. Nat. Rev. Drug Discov. 16, 718–735 (2017).
Article CAS PubMed PubMed Central Google Scholar
Tchkonia, T. & Kirkland, J. L. Aging, cell senescence, and chronic disease: emerging therapeutic strategies. JAMA 320, 1319–1320 (2018).
Article PubMed Google Scholar
Tanoue, T. et al. A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature 565, 600–605 (2019).
Article ADS CAS PubMed Google Scholar
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Article CAS PubMed PubMed Central Google Scholar
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Article PubMed PubMed Central Google Scholar
Flemer, B. et al. The oral microbiota in colorectal cancer is distinctive and predictive. Gut 67, 1454–1463 (2018).
Article CAS PubMed Google Scholar
Brennan, C. A. & Garrett, W. S. Fusobacterium nucleatum—symbiont, opportunist and oncobacterium. Nat. Rev. Microbiol. 17, 156–166 (2019).
Article CAS PubMed PubMed Central Google Scholar
Rubinstein, M. R. et al. Fusobacterium nucleatum promotes colorectal carcinogenesis by modulating E-cadherin/beta-catenin signaling via its FadA adhesin. Cell Host Microbe 14, 195–206 (2013).
Article CAS PubMed PubMed Central Google Scholar
Bullman, S. et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 358, 1443–1448 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Takahashi, A. et al. DNA damage signaling triggers degradation of histone methyltransferases through APC/C^Cdh1 in senescent cells. Mol. Cell 45, 123–131 (2012).
Article CAS PubMed Google Scholar
Takahashi, Y. et al. A refined culture system for human induced pluripotent stem cell-derived intestinal epithelial organoids. Stem Cell Rep. 10, 314–328 (2018).
Article CAS Google Scholar
Aquino-Martinez, R. et al. LPS-induced premature osteocyte senescence: implications in inflammatory alveolar bone loss and periodontal disease pathogenesis. Bone 132, 115220 (2020).
Article CAS PubMed PubMed Central Google Scholar
Kagan, J. C. et al. TRAM couples endocytosis of Toll-like receptor 4 to the induction of interferon-beta. Nat. Immunol. 9, 361–368 (2008).
Article CAS PubMed PubMed Central Google Scholar
Ridlon, J. M., Kang, D. J. & Hylemon, P. B. Bile salt biotransformations by human intestinal bacteria. J. Lipid Res. 47, 241–259 (2006).
Article CAS PubMed Google Scholar
Hsu, R. Y. et al. LPS-induced TLR4 signaling in human colorectal cancer cells increases beta1 integrin-mediated cell adhesion and liver metastasis. Cancer Res. 71, 1989–1998 (2011).
Article CAS PubMed Google Scholar
Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661–672 (2014).
Article CAS PubMed Google Scholar
Takeuchi, S. et al. Intrinsic cooperation between p16^INK4a and p21^Waf1/Cip1 in the onset of cellular senescence and tumor suppression in vivo. Cancer Res. 70, 9381–9390 (2010).
Article CAS PubMed Google Scholar
Munro, J., Barr, N. I., Ireland, H., Morrison, V. & Parkinson, E. K. Histone deacetylase inhibitors induce a senescence-like state in human cells by a p16-dependent mechanism that is independent of a mitotic clock. Exp. Cell Res. 295, 525–538 (2004).
Article CAS PubMed Google Scholar
Pazolli, E. et al. Chromatin remodeling underlies the senescence-associated secretory phenotype of tumor stromal fibroblasts that supports cancer progression. Cancer Res. 72, 2251–2261 (2012).
Article CAS PubMed PubMed Central Google Scholar
Sato, M. et al. Three CoA transferases involved in the production of short chain fatty acids in Porphyromonas gingivalis. Front. Microbiol. 7, 1146 (2016).
Article PubMed PubMed Central Google Scholar
Cummings, J. H., Pomare, E. W., Branch, W. J., Naylor, C. P. & Macfarlane, G. T. Short chain fatty acids in human large intestine, portal, hepatic and venous blood. Gut 28, 1221–1227 (1987).
Article CAS PubMed PubMed Central Google Scholar
Colnot, S. et al. Colorectal cancers in a new mouse model of familial adenomatous polyposis: influence of genetic and environmental modifiers. Lab. Invest. 84, 1619–1630 (2004).
Article CAS PubMed Google Scholar
Chang, J. et al. Clearance of senescent cells by ABT263 rejuvenates aged hematopoietic stem cells in mice. Nat. Med. 22, 78–83 (2016).
Article ADS CAS PubMed Google Scholar
Bartkova, J. et al. Oncogene-induced senescence is part of the tumorigenesis barrier imposed by DNA damage checkpoints. Nature 444, 633–637 (2006).
Article ADS CAS PubMed Google Scholar
Dimri, G. P. et al. A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc. Natl Acad. Sci. USA 92, 9363–9367 (1995).
Article ADS CAS PubMed PubMed Central Google Scholar
Cristofalo, V. J. SA beta Gal staining: biomarker or delusion. Exp. Gerontol. 40, 836–838 (2005).
Article CAS PubMed Google Scholar
Imai, Y. et al. Crosstalk between the Rb pathway and AKT signaling forms a quiescence-senescence switch. Cell Rep. 7, 194–207 (2014).
Article CAS PubMed Google Scholar
Lee, B. Y. et al. Senescence-associated beta-galactosidase is lysosomal beta-galactosidase. Aging Cell 5, 187–195 (2006).
Article CAS PubMed Google Scholar
Bultman, S. J. & Jobin, C. Microbial-derived butyrate: an oncometabolite or tumor-suppressive metabolite? Cell Host Microbe 16, 143–145 (2014).
Article CAS PubMed PubMed Central Google Scholar
Belcheva, A. et al. Gut microbial metabolism drives transformation of MSH2-deficient colon epithelial cells. Cell 158, 288–299 (2014).
Article CAS PubMed Google Scholar
Singh, V. et al. Dysregulated microbial fermentation of soluble fiber induces cholestatic liver cancer. Cell 175, 679–694 (2018).
Article CAS PubMed PubMed Central Google Scholar
Momen-Heravi, F. et al. Periodontal disease, tooth loss and colorectal cancer risk: results from the Nurses’ Health Study. Int. J. Cancer 140, 646–652 (2017).
Article CAS PubMed Google Scholar
Kale, A., Sharma, A., Stolzing, A., Desprez, P. Y. & Campisi, J. Role of immune cells in the removal of deleterious senescent cells. Immun. Ageing 17, 16 (2020).
Article PubMed PubMed Central Google Scholar
Krouwer, V. J., Hekking, L. H., Langelaar-Makkinje, M., Regan-Klapisz, E. & Post, J. A. Endothelial cell senescence is associated with disrupted cell-cell junctions and increased monolayer permeability. Vasc. Cell 4, 12 (2012).
Article CAS PubMed PubMed Central Google Scholar
Takayama, N. et al. Transient activation of c-MYC expression is critical for efficient platelet generation from human induced pluripotent stem cells. J. Exp. Med. 207, 2817–2830 (2010).
Article CAS PubMed PubMed Central Google Scholar
Tsukahara, T. et al. High-sensitivity detection of short-chain fatty acids in porcine ileal, cecal, portal and abdominal blood by gas chromatography-mass spectrometry. Anim. Sci. J. 85, 494–498 (2014).
Article CAS PubMed Google Scholar
Sugiura, Y. et al. Visualization of in vivo metabolic flows reveals accelerated utilization of glucose and lactate in penumbra of ischemic heart. Sci. Rep. 6, 32361 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We thank Dr. Kenya Honda (Keio University) for valuable advice for gut bacterial culture and Dr. Daisuke Okuzaki (Osaka University) for performing RNA-seq analysis. We also thank Ms. Satoko Otomi, Ms. Shizuka Murata, Mr. Masanori Tanaka, Dr. Tomohiro Tsuchida and Dr. Shoichi Saito (JFCR) for collecting human specimens, Ms. Yuko Wakabayashi, Ms. Tamae Kondo, Ms. Masae Suzuki (Osaka University), Ms. Satoko Baba and Dr. Ryoji Yao (JFCR) for technical supports throughout this study. We are grateful to Dr. Toshiki Mukai, Dr. Tomohiro Yamaguchi, Dr. Toshiya Nagasaki, Dr. Takashi Akiyoshi, Dr. Tsuyoshi Konishi (JFCR) and members of Hara’s laboratory for helpful discussion during the preparation of this manuscript. This work was supported in part by grants from the Japan Agency of Medical Research and Development (AMED) under grant numbers 19gm501000h0003 and 19cm0106401h0004, from the Japan Science and Technology Agency (JST) under grant number JPMJMS2022 and from the Japan Society for the Promotion of Science (JSPS) under grant numbers 19K16612, 18K15065, 16H06276 (AdAMS), 26250028 and 18K15188.

Author information

These authors contributed equally: Shintaro Okumura, Yusuke Konishi, Megumi Narukawa.

Authors and Affiliations

Research Institute for Microbial Diseases (RIMD), Osaka University, Suita, Japan
Shintaro Okumura, Yusuke Konishi, Megumi Narukawa, Shintaro Sato, Shunya Tsuji, Ken Uemura, Tatsuyuki Matsudaira, Tomonori Matsumoto, Shimpei Kawamoto, Hiroaki Miki & Eiji Hara
The Cancer Institute, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan
Shintaro Okumura, Shin Yoshimoto, Yuriko Arai, Akiko Takahashi, Kengo Takeuchi & Eiji Hara
Graduate School of Medicine, Kyoto University, Kyoto, Japan
Shintaro Okumura, Yoshiro Itatani, Kazutaka Obama & Yoshiharu Sakai
Keio University School of Medicine, Tokyo, Japan
Yuki Sugiura & Makoto Suematsu
LSI Medience Corporation, Tokyo, Japan
Shin Yoshimoto
School of Dentistry, Aichi Gakuin University, Nagoya, Japan
Yasuo Yoshida
Immunology Frontier Research Centre (IFReC), Osaka University, Suita, Japan
Masahiro Wakita & Eiji Hara
The Cancer Institute Hospital, JFCR, Tokyo, Japan
Manabu Takamatsu, Kengo Takeuchi, Yosuke Fukunaga, Masashi Ueno & Satoshi Nagayama
Osaka City University Graduate School of Medicine, Osaka, Japan
Naoko Ohtani

Authors

Shintaro Okumura
View author publications
You can also search for this author in PubMed Google Scholar
Yusuke Konishi
View author publications
You can also search for this author in PubMed Google Scholar
Megumi Narukawa
View author publications
You can also search for this author in PubMed Google Scholar
Yuki Sugiura
View author publications
You can also search for this author in PubMed Google Scholar
Shin Yoshimoto
View author publications
You can also search for this author in PubMed Google Scholar
Yuriko Arai
View author publications
You can also search for this author in PubMed Google Scholar
Shintaro Sato
View author publications
You can also search for this author in PubMed Google Scholar
Yasuo Yoshida
View author publications
You can also search for this author in PubMed Google Scholar
Shunya Tsuji
View author publications
You can also search for this author in PubMed Google Scholar
Ken Uemura
View author publications
You can also search for this author in PubMed Google Scholar
Masahiro Wakita
View author publications
You can also search for this author in PubMed Google Scholar
Tatsuyuki Matsudaira
View author publications
You can also search for this author in PubMed Google Scholar
Tomonori Matsumoto
View author publications
You can also search for this author in PubMed Google Scholar
Shimpei Kawamoto
View author publications
You can also search for this author in PubMed Google Scholar
Akiko Takahashi
View author publications
You can also search for this author in PubMed Google Scholar
Yoshiro Itatani
View author publications
You can also search for this author in PubMed Google Scholar
Hiroaki Miki
View author publications
You can also search for this author in PubMed Google Scholar
Manabu Takamatsu
View author publications
You can also search for this author in PubMed Google Scholar
Kazutaka Obama
View author publications
You can also search for this author in PubMed Google Scholar
Kengo Takeuchi
View author publications
You can also search for this author in PubMed Google Scholar
Makoto Suematsu
View author publications
You can also search for this author in PubMed Google Scholar
Naoko Ohtani
View author publications
You can also search for this author in PubMed Google Scholar
Yosuke Fukunaga
View author publications
You can also search for this author in PubMed Google Scholar
Masashi Ueno
View author publications
You can also search for this author in PubMed Google Scholar
Yoshiharu Sakai
View author publications
You can also search for this author in PubMed Google Scholar
Satoshi Nagayama
View author publications
You can also search for this author in PubMed Google Scholar
Eiji Hara
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

E.H. and S.N. designed the experiments. S.O., S.Y. and Y.A. performed meta 16S rRNA gene-sequencing analysis. Y.K. performed bioinformatics analysis. M.N., S.T. and K.U. performed bacterial analysis. Yuki S. measured butyrate concentration in clinical samples. S.S. provided intestinal epithelial organoids derived from human iPS cells. Y.Y. performed analysis using P. gingivalis mutant. H.M. helped the analysis using Apc^Δ14/+ mice. M.T. and K.T. helped in situ hybridization and immune-histochemistry using clinical samples. S.K., M.W., Tatsuyuki M., Tomonori M., A.T., K.O., Y.I., M.S., N.O., Y.F., M.U., Yoshiharu S., S.N. and E.H. analysed the data. E.H. wrote the manuscript and oversaw the projects. All the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Satoshi Nagayama or Eiji Hara.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Communications thanks Daniel Munoz-Espin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Reporting summary

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Okumura, S., Konishi, Y., Narukawa, M. et al. Gut bacteria identified in colorectal cancer patients promote tumourigenesis via butyrate secretion. Nat Commun 12, 5674 (2021). https://doi.org/10.1038/s41467-021-25965-x

Download citation

Received: 17 October 2020
Accepted: 06 September 2021
Published: 28 September 2021
DOI: https://doi.org/10.1038/s41467-021-25965-x

This article is cited by

The microbial landscape of colorectal cancer
- Maxwell T. White
- Cynthia L. Sears
Nature Reviews Microbiology (2024)
Multi-kingdom microbial signatures in excess body weight colorectal cancer based on global metagenomic analysis
- Xinyue Zhu
- Pingping Xu
- Na Jiao
Communications Biology (2024)
Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective
- Seyed Hamid Hoseini
- Parisa Enayati
- Nilufar Bagherzadeh Sohrabi
Journal of Gastrointestinal Cancer (2024)
Identification of colorectal cancer progression-associated intestinal microbiome and predictive signature construction
- Jungang Liu
- Xiaoliang Huang
- Weizhong Tang
Journal of Translational Medicine (2023)
Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models
- Tyler Kolisnik
- Arielle Kae Sulit
- Olin Silander
BMC Cancer (2023)

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Subjects

Abstract

Similar content being viewed by others

Introduction

Results

Identification of gut bacteria enriched in CRC patients

Porphyromonas species provoke cellular senescence

Porphyromonas species provoke cellular senescence via butyrate secretion

Bacterial invasion and signs of cellular senescence in CRC tissues

Butyrate-producing bacteria accelerate the onset of colorectal tumours

Discussion

Methods

Human faecal sample collection

16S rRNA gene-sequencing analysis and microbiome analysis

Bacterial strain and culture conditions

Cell culture

iPSC-derived intestinal organoid culture

Quantitative real-time PCR analysis

Immunoblotting

ROS measurement

Immunofluorescence analysis

Apoptosis analysis

EdU incorporation assay

RNA sequencing analysis

Animal experiment

Histology, immunohistochemistry and immunofluorescence

In situ hybridization

Measurement of SCFAs

DNA cloning and sequencing

Statistics

Reporting summary

Data availability

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Additional information

Supplementary information

Source data

Rights and permissions

About this article

Cite this article

Share this article

This article is cited by

Comments

Search

Quick links