Gut Check

Bridges Reveals Interaction between Diabetes and Digestive-Tract Microbes

Sept. 7, 2016

Why It's Important:

Many people appreciate how serious diabetes is: it can cause blindness, nerve pain and lack of circulation in the limbs that leads to amputation, to say nothing of heart attack or stroke. But patients with diabetes also have a high risk of acid reflux, abdominal pain, nausea, ulcers, Candida infections and diarrhea. "Diabetic gut" poses a mix of digestive problems that causes suffering, disability and great medical expense.

One very important aspect of diabetes is that it affects the population of microbes that live in healthy intestinesthe "microbiome." Both the species of microbes and their relative abundances can change, leading to a vicious cycle in which diabetes alters the microbiome, which in turn leads to more changes. Worse, doctors don't really know much about how the microbial population changes in diabetesor whether these changes can in turn affect the course of the disease elsewhere in the body. Many of the species of gut bacteria involvedhelpful and harmfulcan't be grown in the laboratory and so have never been identified.

"People have invested a lot of money and effort on the genetic factors that affect diabetes. There have not been so many studies on environmental factors such as the microbiome. We know that the microbial distribution in diabetes and the healthy gut are different. What we'd like to know is the causalitywhat changes are caused by the disease and how changes affect the course of the disease."

Wenxuan Zhong, University of Georgia

How PSC and XSEDE Helped:

Scientists Wenxuan Zhong and Ping Ma of the University of Georgia's Franklin College, with Zhong's graduate student Xin Xing, set out to identify and sequence the DNA of virtually all the microbes in the healthy and diabetes-affected human gut. Working with XSEDE Extended Collaborative Support Service expert Phil Blood of PSC, they used advanced DNA sequencing technology and PSC's Bridges supercomputer to assemble DNA from many microbial species in human fecal samples at once. In this method, called "metagenomic assembly," researchers use massive computation to sort the DNA-sequence fragments into their proper species at the same time that they assemble each species' total DNA sequenceits genome.

The Georgia team has now used Bridges to run their new computer algorithm, assembling all the DNA sequences extracted from samples from 145 people with and without diabetes. They have identified about 2,100 microbial species' genomes in these samples. Two of these species appear to be present in lower numbers in patients with type 2 diabetes than in people without the disease. One of these two microbes is closely related to Roseburia intestinalis, a helpful bacterium known to strengthen immune responses that are weak in people with diabetes. Future goals include using such differences to glean clues to treating and preventing gastrointestinal problems in diabetes.

"I think Bridges has a great capability to help us finish our metagenomic assemblies. Overall it's been a great experiencethe large memory nodes have been really, really helpful for usand the human support is very good. Dr. Blood has been very helpful in optimizing the code."

Xin Xing, University of Georgia

Deeper Dive: How Do You Untangle Thousands of Genomes?

The usual tool for assembling genetic sequences from small DNA fragments is to line up the fragments where their sequences overlap, stringing together the whole genome bit by bit. Like fitting puzzle pieces together, it's a memory-intensive process. When the computer looks at a new sequence, it must remember whether earlier fragments contained a matching sequence. The University of Georgia scientists used Bridges' 3-terabyte large memory nodes to carry this task out.

Because the metagenomic assembly task contains DNA fragments from numerous microbe species, though, they needed other clues as well to avoid assigning a given sequence to the wrong species. With Blood's help, they wrote a computational algorithm to run on Bridges that used clues such as the known sequences from related, previously studied microbes; the amount of DNA from each species versus its portion of the whole population; and other pieces of information, to sort the fragments and assemble each species' genome.

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