Jan. 19 — A new proof-of-concept study by researchers from the University of California San Diego has succeeded in training computers to “learn” what a healthy versus an unhealthy gut microbiome looks like based on its genetic makeup. Since this can be done by genetically sequencing fecal samples, the research suggests there is great promise for new diagnostic tools that are, unlike blood draws, non-invasive.
As recent advances in scientific understanding of Parkinson’s disease and cancer immunotherapy have shown, our gut microbiomes – the trillions of bacteria, viruses and other microbes that live within us – are emerging as one of the richest untapped sources of insight into human health.
The problem is these microbes live in a very dense ecology of up to one billion microbes per gram of stool. Imagine the challenge of trying to specify all the different animals and plants in a complex ecology like a rain forest or coral reef – and then imagine trying to do this in the gut microbiome, where each creature is microscopic and identified by its DNA sequence.
Determining the state of that ecology is a classic ‘Big Data’ problem, where the data is provided by a powerful combination of genetic sequencing techniques and supercomputing software tools. The challenge then becomes how to mine this data to obtain new insights into the causes of diseases, as well as novel therapies to treat them.
The new paper, titled “Using Machine Learning to Identify Major Shifts in Human Gut Microbiome Protein Family Abundance in Disease,” was presented last month at the IEEE International Conference on Big Data. It was written by a joint research team from UC San Diego and the J. Craig Venter Institute (JCVI). At UC San Diego, it included Mehrdad Yazdani, a machine learning and data scientist at the California Institute for Telecommunications and Information Technology’s (Calit2) Qualcomm Institute; Biomedical Sciences graduate student Bryn C. Taylor and Pediatrics Postdoctoral Scholar Justine Debelius; Rob Knight, a professor in the UC San Diego School of Medicine’s Pediatrics Department as well as the Computer Science and Engineering Department and director of the Center for Microbiome Innovation; and Larry Smarr, Director of Calit2 and a professor of Computer Science and Engineering. The UC San Diego team also collaborated with Weizhong Li, an associate professor at JCVI.
Metagenomics and Machine Learning
The software to carry out the study was developed by Li and run on the data-intensive Gordon supercomputer at the San Diego Supercomputer Center (SDSC), an Organized Research Unit of UC San Diego, using 180,000 core-hours. That’s equivalent to running a personal computer 24 hours a day for about 20 years.
The work began with a genetic sequencing technique known as “metagenomics,” which breaks up the DNA of the hundreds of species of microbes that live in the human large intestine (our “gut”). The technique was applied to 30 healthy people (using sequencing data from the National Institutes of Health’s Human Microbiome Program), together with 30 samples from people suffering from the autoimmune Inflammatory Bowel Disease (IBD), including those with ulcerative colitis and with ileal or colonic Crohn’s disease. This resulted in sequencing around 600 billion DNA bases, which were then fed into the Gordon supercomputer to reconstruct the relative abundance of these species; for instance, how many E. coli are present compared to other bacterial species.
Since each bacterium’s genome contains thousands of genes and each gene can express a protein, this technique made it possible to translate the reconstructed DNA of the microbial community into hundreds of thousands of proteins, which are then grouped into about 10,000 protein families.
To discover the patterns hidden in this huge pile of numbers, the researchers harnessed what they refer to as “fairly out-of-the-bag” machine-learning techniques originally developed for spam filters and other data mining applications. Their goal was to use these algorithms to classify major changes in the protein families found in the gut bacteria of both healthy subjects and those with IBD, based on the DNA found in their fecal samples.
The researchers first used standard biostatistics routines to identify the 100 most statistically significant protein families that differentiate health and disease states. These 100 protein families were then used as a “training set” to build a machine learning classifier that could classify the remaining 9,900 protein families in diseased versus healthy states. The goal was to find a “signature” for which protein families were elevated or suppressed in disease versus healthy states.
The entire article can be found here.
Source: Tiffany Fox, SDSC