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Saturday, 30 July 2016

Human Microbiome and the genes on move

We humans share “culture” in much unique ways, like sharing customs, cuisine, music or language. Similarly for microbiome, they also share unique features by dwelling in and on us. In a recent research published in the journal Nature, Eric Alm and Ilana Brito from MIT and Harvard with associated colleagues took a deep insight into microbes as how they are in the developing world population influencing their make up.

Image from Pixabay

Horizontal gene transfer is a process by which certain genetic elements can move between organisms. These “mobile genes” inside microbes can help individuals to adapt to their environment.

In 2008, Human Microbiome Project (HMP) of National Institute of Health (NIH) began a survey of human microbiome on larger scale by gathering samples like saliva, skin swabs, and stool from healthy North Americans and primary those living in urban areas. The microbes from the samples were sequenced for the goal to understand how they influence health and disease.

In their current research they wanted to understand how the microbiome of a population from the developing world will compare to HMP dataset. Brito travelled quite far from urban North America to South Pacific islands of Fiji where native villagers live in remote and isolated communities.

“I wanted to track microorganisms that move from place to place, and I thought the best place for doing this was where all contacts are local contacts who use local water and food,” explains Brito, a postdoc in the lab of Eric Alm, an institute member at the Broad Institute, professor of biological engineering at MIT, and co-director of the MIT Center for Microbiome and Therapeutics.  “In contrast, in big cities, we come into contact with a lot of different people, eat food from around the world, and use lots of hygiene products and antibiotics which can prevent the transmission of even endogenous microbes.”

There were about 100-150 people living in each village and HMP had been limited to the amount of information collected from its participants. Brito conducted a thorough survey of the villagers she met and also collected samples. She not only captured the human microbiome but also reservoirs of microbial community. The name of this project was “Fiji Community Microbiome Project” or FijiCOMP.

Brito returned with more than 1000 samples and carried metagenomic sequencing of over 500 samples. It was a large shift from little data to larger data set. The information was large enough for the researchers that led them develop and assemble the information in new way.
Brito and Alm were looking into the mobile genes that have been shared among the species and their crucial functions.

“If you look at a microbial genome with 5,000 genes in it, which ones are particularly important?” Alm asks. “Probably not all 5,000 genes. Most of them are probably either housekeeping genes that every bacterium has or some random selfish gene. But if you go into an environment and see a particular gene being transferred to many different species, to every bug in this environment, which is maybe rich in tetracycline, [and if this is a] tetracycline resistance gene, then you’re like, aha! Then it’s likely that gene is one … of the 5,000 genes that’s super important.”

Brito and his colleagues looked at the gene transfers for not only at the Fijian samples but also from HMP to understand how local environment has influenced the microbiome. One of the major difference between FijiCOMP and HMP data was their diet.

“While 16S sequencing can identify which species are present and let us make associations between particular species and disease, what the mobile genes tell us is that even if we know the species, there seem to be culturally important genes that are crossing species boundaries that don’t show up in the 16S data,” says Alm. “So if we want to fully understand the public health impact of the microbiome overall, we need to not only track the species, but also the genes of interest. Combining single-cell and metagenomic analysis provides a powerful way to do it.”


Source: MIT News
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