Microbiome and spatially resolved metabolomics analysis reveal the anticancer role of gut Akkermansia muciniphila by crosstalk with intratumoral microbiota and reprogramming tumoral metabolism in mice

ABSTRACT

Although gut microbiota has been linked to cancer, little is known about the crosstalk between gut- and intratumoral-microbiomes. The goal of this study was to determine whether gut Akkermansia muciniphila (Akk) is involved in the regulation of intratumoral microbiome and metabolic contexture, leading to an anticancer effect on lung cancer. We evaluated the effects of gut endogenous or gavaged exogenous Akk on the tumorigenesis using the Lewis lung cancer mouse model. Feces, blood, and tumor tissue samples were collected for 16S rDNA sequencing. We then conducted spatially resolved metabolomics profiling to discover cancer metabolites in situ directly and to characterize the overall Akk-regulated metabolic features, followed by the correlation analysis of intratumoral bacteria with metabolic network. Our results showed that both endogenous and exogenous gavaged Akk significantly inhibited tumorigenesis. Moreover, we detected increased Akk abundance in blood circulation or tumor tissue by 16S rDNA sequencing in the Akk gavaged mice, compared with the control mice. Of great interest, gavaged Akk may migrate into tumor tissue and influence the composition of intratumoral microbiome. Spatially resolved metabolomics analysis revealed that the gut-derived Akk was able to regulate tumor metabolic pathways, from metabolites to enzymes. Finally, our study identified a significant correlation between the gut Akk-regulated intratumoral bacteria and metabolic network. Together, gut-derived Akk may migrate into blood circulation, and subsequently colonize into lung cancer tissue, which contributes to the suppression of tumorigenesis by influencing tumoral symbiotic microbiome and reprogramming tumoral metabolism, although more studies are needed.

KEYWORDS:

• Gut microbiota
• akkermansia muciniphila (akk)
• intratumoral microbiome
• spatially resolved metabolomics
• metabolism reprogramming
• crosstalk

Acknowledgments

We thank Shanghai Lu Ming Biotech Co., Ltd. (Shanghai, China), Lei Luo, Peipei Ji, Xueling Li and Tingting Yang for providing AFADESI Spatial-resolved metabolomics detection and bioinformatics analysis.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethical approval and consent to participate

All animal procedures with all protocols receiving ethical evaluation were approved by the Institutional Animal Care and Use Committee (IACUC) of Tongji Hospital of Tongji University. All studies were performed in accordance with the NIH Guide for the Care and Use of Laboratory Animals.

Data availability statement

The 16S rDNA sequencing data have been deposited to the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA846506. The SRA records can be accessible with the following link: https://www.ncbi.nlm.nih.gov/sra/PRJNA846506.). The dataset regarding the spatially resolved metabolomics profiling used and analyzed during the current study available from the corresponding author on reasonable request.

Authors’ contributions

All authors have read and approved the article. Z. Zhu, J. Cai, W. Hou, K. Xu, X. Wu conducted research. Y. Song and C. Bai edited the paper. Z. Zhang and Y. Mo conceived the study, designed experiments, and wrote the paper. Supervision: Z. Zhang.

Additional information

Funding

This work was funded by the National Natural Science Foundation of China (Grant No. 82272621, 81672939 and 81372513), and Shanghai Pujiang Program of China (Grant No. 15PJ1407500), and the projects of “Scientific and technological innovation action plan” of Shanghai Science and Technology Commission of China (Grant No. 20Y11901600, 20Z11900905, and 20Z11901104).