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Study of Gut Microbe Metagenomes Unearths Gene Clusters for Predicting Diabetes Risk

NEW YORK (GenomeWeb News) – The groups of microbial genes present in an individual's gut can provide clues about that person's propensity for developing type 2 diabetes and related metabolic profiles, a new study suggests.

As they reported online today in Nature, researchers from Sweden and Denmark performed metagenomic sequencing on stool samples from almost 150 elderly women from Europe, including individuals with normal glucose control, impaired glucose control, or T2D itself.

From this data, the team defined sets of frequently co-occurring genes — dubbed metagenomic clusters, or MGCs — and came up with a statistical model that showed promise for finding individuals with T2D and/or related metabolic features based on their MGC profile.

Even so, comparisons with metagenomic data on gut samples from individuals in a Chinese population — first described by researchers at BGI-Shenzhen, the University of Copenhagen, and elsewhere in a Nature study last fall — indicated that the precise metagenomic markers linked to T2D can vary by population, consistent with past studies on the human gut microbiome in general.

Individuals' age is likely a factor, too, authors of the new study noted, arguing that "metagenomic predictive tools for T2D should be specific for the age and geographical location of the populations studied."

"Although it is likely that the same microbial-encoded functions contribute to disease in different populations, we observed that the most discriminatory MGCs differed between our European T2D subgroup and the Chinese T2D cohort," co-senior authors Frederik Bäckhed, a researcher affiliated with the University of Gothenburg and the University of Copenhagen, and Chalmers University of Technology's Jens Nielsen, and colleagues later wrote.

"This observation underscores the need to sample human populations and perform parallel studies in different continents," they added. "It also indicates that the development of T2D metagenomic predictive tools and diagnostic biomarkers should be specific to the populations studied."

For the analysis, Bäckhed, Nielsen, and colleagues used Illumina's HiSeq 2000 to sequence microbial DNA in stool samples from 145 European women enrolled at a hospital in Sweden.

All of the women were 70 years old when they were sampled, but not all had the same T2D status. Just over one third had been diagnosed with T2D, while another third showed diminished glucose tolerance. The remaining 43 women were T2D-free and had normal glucose tolerance patterns.

Using roughly three billion bases of sequence data per person, on average, the team tracked down sequences corresponding to almost 2,400 microbial reference genomes.

At the species level, the researchers saw more bugs from certain Lactobacillus species than usual in the gut microbiomes of those with T2D compared to the normal glucose control group, coupled with a dip in representation by several Clostridium species.

The presence or absence of the similar species showed ties to other T2D-related traits, too, they noted, such as blood glucose levels, triglyceride levels, or LDL/HDL cholesterol levels.

When they considered the metagenomic data as gene-focused, rather than species-focused, sets, the investigators learned even more about functional capabilities of gut communities of individuals with or without T2D.

Starting from 18.6 million predicted microbial genes in their original metagenomic sequence assembly, they narrowed in on 2.9 million microbial genes that were found in the guts of 10 or more study participants.

From those genes, the group put together collections of genes that tended to appear together, eventually coming up with more than 800 commonly co-occurring MGCs comprised of 104 genes or more apiece.

Some 26 such gene sets turned up at higher or lower frequency in the T2D cohort, study authors noted, including five MGCs that were over-represented in the T2D group and 21 others that tended to turn up less often.

At the functional level, the team saw that T2D-associated microbiomes tended to harbor genes from several pathways related to sugar and starch metabolism and the transport of sugars, amino acids, and ions, for instance.

The MGCs proved useful for predicting T2D status, too. A mathematical model based on the MGC genes sets could more accurately distinguish cases and controls than a model developed using microbial species level data, researchers reported.

Moreover, they found, "the T2D score obtained based on MGCs is similar to other published scores that combine several known risk factors for diabetes development."

It also identified nearly three dozen women from the impaired glucose function group who appear to have T2D-like metabolism.

While comparisons with existing gut metagenomic data from Chinese individuals suggested that an MGC-based classification scheme could predict T2D risk in Chinese individuals too, though, the researchers found population-level differences in the specific sets of genes that most closely coincided with disease.

"[T]he most discriminatory MGCs differed between the Chinese subjects and our cohort," the study's authors noted.

"However, it should be noted that, in contrast to our homogeneous cohort (70-year-old women), the T2D population in the previous study [of Chinese individuals] was older and included more men than the control population, which may affect the results," the researchers added.

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