Brain tumor diagnostic model and dietary effect based on extracellular vesicle microbiome data in serum

• Published in: Experimental & molecular medicine Vol. 52; no. 9; pp. 1602 - 1613
• Main Authors: Yang, Jinho, Moon, Hyo Eun, Park, Hyung Woo, McDowell, Andrea, Shin, Tae-Seop, Jee, Young-Koo, Kym, Sungmin, Paek, Sun Ha, Kim, Yoon-Keun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.09.2020; Springer Nature B.V; Nature Publishing Group
• Subjects: 38/23; 631/114/1305; 692/53/2421; Biomarkers; Biomedical and Life Sciences; Biomedicine; Blood-brain barrier; Brain cancer; Brain tumors; Cancer; Dietary supplements; Discriminant analysis; Extracellular vesicles; Intestinal microflora; Learning algorithms; Machine learning; Medical Biochemistry; Metabolites; Metagenomics; Microbiomes; Microorganisms; Molecular Medicine; Regression analysis; Risk assessment; rRNA 16S; Sorghum; Stem Cells
• ISSN: 1226-3613; 2092-6413
• DOI: 10.1038/s12276-020-00501-x

The human microbiome has been recently associated with human health and disease. Brain tumors (BTs) are a particularly difficult condition to directly link to the microbiome, as microorganisms cannot generally cross the blood–brain barrier (BBB). However, some nanosized extracellular vesicles (EVs) released from microorganisms can cross the BBB and enter the brain. Therefore, we conducted metagenomic analysis of microbial EVs in both serum (152 BT patients and 198 healthy controls (HC)) and brain tissue (5 BT patients and 5 HC) samples based on the V3–V4 regions of 16S rDNA. We then developed diagnostic models through logistic regression and machine learning algorithms using serum EV metagenomic data to assess the ability of various dietary supplements to reduce BT risk in vivo. Models incorporating the stepwise method and the linear discriminant analysis effect size (LEfSe) method yielded 12 and 29 significant genera as potential biomarkers, respectively. Models using the selected biomarkers yielded areas under the curves (AUCs) >0.93, and the model using machine learning resulted in an AUC of 0.99. In addition, Dialister and [Eubacterium] rectale were significantly lower in both blood and tissue samples of BT patients than in those of HCs. In vivo tests showed that BT risk was decreased through the addition of sorghum, brown rice oil, and garlic but conversely increased by the addition of bellflower and pear. In conclusion, serum EV metagenomics shows promise as a rich data source for highly accurate detection of BT risk, and several foods have potential for mitigating BT risk. Brain cancer: Investigating the gut microbiome The gut microbiome affects brain health via tiny packets of microbial metabolites called extracellular vesicles (EVs) that are small enough to pass through the blood–brain barrier. The brain was thought to be sheltered from the microbiome’s effect on health by this barrier, which blocks microbes from entering the brain via the blood. Yoon-Keun Kim at MD Healthcare Inc. and Sun Ha Paek at Seoul National University, both in Seoul, South Korea, and co-workers compared the EVs in the blood of brain cancer patients and healthy individuals, and found the two groups harbored different types of microbes. Feeding mice brown rice oil, sorghum, and garlic markedly shifted their EV profiles towards those of the healthy microbiome. These results provide a foundation for investigating new methods for brain cancer risk assessment and dietary interventions to reduce that risk.