Metabolomics: A tool to measure metabolites in the Gut microbiome

Jerry John, D.Y Patil University

There are about 100 trillion micro-organisms in the human gastrointestinal tract. Most of these organisms include bacteria, and some are fungi, protozoa, and viruses. The collective genome of these micro-organisms is known as the microbiome.

About: 

From 23,000 genes of the human genome, 3 million genes encode for the microbiome. It produces thousands of metabolites that can influence human physiology. And it is responsible for numerous human health varying from host responses to cancer immunotherapy, metabolic disease, and obesity. Gut microbiomes have large communities of microbes, so it’s hard to retrieve accurate and quality data to understand the interaction between microbes and enzymes and specific metabolites production and uptake. There are some limited tools to detect the products of anaerobic biochemistry in the human gut. Han et.al. has proposed a pipeline that is microbiome-focused, integrated mass spectrometry to find out microbiota-dependent metabolites in given samples. 

Study by Han et.al:

The author introduced a technique of liquid chromatography-mass spectrometry to calculate the level of metabolites based on their mass, charge, and polarity. From collected samples of blood or feces, they have found 833 metabolites that were similar to microbial metabolism. They have also developed a pipeline for the analysis of compound and statistical work. Han et.al has grown 178 microbial strains cultures in multiple media types and various tissues from mice. The mice whose intestines have been colonized by the same strains, as alone or in a group of five or six species were observed for study. 

Results:

Based on the research, it was observed that phylogenetically distant species have similar metabolic profiles to the closely related ones. Atopobium parvulum and Catenibacterium mistsuokai were found to have similar metabolic profiles phylogenetically. They have also found some species-specific communities such as tyramine molecule was produced by Enterococcus faecalis. However, it’s not possible to distinguish between the member of different species with metabolic profiles. The machine learning  analyses had also given only 30% of correct data whereas, it should give 70% of accurate data. 

To get a more appropriate result they have compared the metabolomic analyses with the bacterial genome to identify the gene responsible for unknown metabolic capacities. It was founded that spe genes are responsible for the production of putrescine and agmatine molecules in different species. But spe genes were not present in 3 species of Fusobacterium. Here the authors observed the limitations of this analytical method.

As in conclusion, they have studied the similarities between in vitro and in vivo metabolic output. They have found strains with an important metabolic capacity like Citrobacter protucalensis. This organism produces agmatine from arginine and it has shown this capacity in culture and mice tissue also. Whereas in some cases these effects were reached beyond the gut level. However, no such high-level similarities were found between in vitro and in vivo data for overall metabolic output. There was a moderate relationship between the metabolic profile of strains from the intestine of mice colonized by it. But there was no proved relationship found in any other in vitro profiles of these strains and blood or urine samples of mice colonized by these strains.

Proposed Tool:

The retrieved results helped Han et.al to construct a new accurate model of complex metabolites communities.  They have performed the research by assessing various computational and mathematical methods. Based on these studies, he had proposed a helpful resource to understand the extensive metabolomics dataset. It is a python coded program that includes thousands of samples and analytical approaches. Also, it is consists of protocols, an analysis pipeline, and a metabolite reference library. The authors have suggested that this tool will apply with minimal calibration to different machines. 

Conclusion:

A metabolomics pipeline proposed by Han et.al could be used for similar experimental setups. It explains the properties of microbial metabolism. As per in the future, this work will lay a foundation to decode microbial metabolism by observing new therapeutics targets in the microbiome.

Also read: ‘DOOMSDAY’ GLACIER IS NOW LESS FEARED THAN EVER!

References:

1. Han, Shuo, et al. “A Metabolomics Pipeline for the Mechanistic Interrogation of the Gut Microbiome.” Nature, vol. 595, no. 7867, July 2021, pp. 415–20. DOI.org (Crossref), https://doi.org/10.1038/s41586-021-03707-9
2. Zhao, S., Lieberman, T. D., Poyet, M., Kauffman, K. M., Gibbons, S. M., Groussin, M., Xavier, R. J., & Alm, E. J. (2019). Adaptive evolution within gut microbiomes of healthy people. Cell Host & Microbe, 25(5), 656-667.e8. https://doi.org/10.1016/j.chom.2019.03.007
3. Kindschuh, W. F., & Korem, T. (2021). Deciphering metabolism, one microbe at a time. Nature, 595(7867), 355–357. https://doi.org/10.1038/d41586-021-01774-6

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About the Author:

I am an enthusiastic Bioinformatician who has a great interest in research and scientific writing. Currently, doing a master’s in bioinformatics from D.Y Patil University, Navi Mumbai. I believe that with every new sunrise, science brings up a new hope and life to this world. And I am trying to learn each one of them.

Publication:

1. How do the more advanced life form reproduce sexually https://bioxone.in/factcheck/does-evolution-affect-the-sexual-reproduction-of-living-begins/?fbclid=IwAR3UIkqqXS-pP9GbyTEmPk6EIeWxkD5_WM7c3a2cLiGibM2Fdd6-kADRja4
2. Mullerian mimicry in animals – An interesting concept https://bioxone.in/news/worldnews/mullerian-mimicry-in-animals-an-interesting-concept/?fbclid=IwAR1xI23mx_ekOlKfchW0HzQPvHvPpT3JZLPfTniUU4UyoZUG4WN9vQgfoKw
3. Is it a call from heaven – A premonition of death.