Most of the new technology at the American Diabetes Association’s 71st Annual Scientific Sessions could be found on the exhibit hall floor, complete with demo models, video tutorials, and well-dressed company representatives. One of my favorite promising new technologies, though, was nowhere to be found in the exhibit hall; it was stashed away in a 15-minute presentation during the “Beta-Cell Survival in Diabetes” session.
Come again? Using differentially methy-what?
Okay, I admit the technology is not very consumer-friendly, but it’s really cool, so bear with me, and we’ll start from the beginning.
Beta-cell death is a problem in both type 1 and type 2 diabetes; in the former, cell death is induced as cells react to their increasingly toxic, auto-reactive environment, and in the latter, cell death is induced as cells become over-stressed. Currently, beta-cell death is very hard to measure. The best way is to look directly at tissue in the pancreas—but that’s not possible if you want to keep your human patient or mouse models alive. Without tissue samples, imaging of a living pancreas would be useful, but that proves difficult and inaccurate because of the location and nature of the pancreas. So, researchers will usually quantify beta-cell death using proxies like C-peptide (as an indicator of how much insulin is being secreted).
These proxies, though, are not reliable or accurate; ideally, if we want to understand exactly what causes beta-cell death and what we can do to manipulate the process, we should be able to measure the degree of beta-cell death while the subject is still living, and without too much indirection.
This sort of problem is not unique to diabetes; researchers face a similar issue with, for example, diagnosing cancers. Many cancers come with tangible symptoms or lumps, but how can you test for internal cancers in asymptomatic patients before it’s too late? Much research has been done to identify unique biological signatures, called biomarkers, for tumors. People started by looking for specific proteins or RNA sequences, but these have not proved reliable indicators of many tumors. More recently, though, a promising new biomarker is being tested—the methylation of DNA fragments circulated freely in the blood .
All right, so what’s that? Well, most DNA is locked away safely in the cell nucleus. However, there is also a small amount of DNA that circles in the blood stream, outside of any cells. This DNA enters the blood stream as a result of cell death and damage, and researchers can collect and measure the DNA that has entered the bloodstream.
The problem is, how do you know what to measure? In order to detect cancers, researchers tried first measuring DNA that was mutated; however, this proved ineffective, as many cancers have very low rates of mutation initially, and further, knowing that DNA has been mutated doesn’t tell you where the cancer might be.
But there is something else that we can measure on the free-floating DNA: methylation. DNA methylation is a type of epigenetic modification of DNA—that is, a modification of DNA that does not require mutating the actual DNA sequence at all. Instead, methylation requires that a methyl group, a type of hydrocarbon molecule, is attached to the nucleotides in a strand of DNA.
When DNA is methylated in a cell, it helps to alter the gene and other sequences encoded in the DNA transcription and turned into RNA and proteins. So, even though each cell has the same set of DNA sequences inside, cells will have different patterns of methylation, depending on the type of the cell and what DNA sequences it needs to have available for turning into RNA and proteins.
These differential patterns of methylation, then, become key indicators of cell type and function—for example, researchers have found that the blood of patients with ovarian cancer has much lower ratios of unmethylated-to-methylated DNA encoding the gene BRCA1 than the blood of control patients . It might be possible, therefore, to use a set of gene sequences like BRCA1 that are differentially methylated in ovarian cancer to develop a blood-based test for that particular cancer.
So, DNA methylation can be used as a biomarker for certain cell-type specific conditions. And that’s how we circle back around to our initial problem: measuring beta-cell death.
While a post-doctoral researcher in the laboratory of Kevan Herold at the Yale University School of Medicine, Dr. Akirav tried to determine whether this test for DNA methylation could be useful in quantifying beta-cell death, since the functional proxies were so inadequate. The hypothesis was that, since beta-cells are the only cells producing insulin in the body, DNA near the insulin gene will be less methylated in beta-cells than in all other cells, and, as a result, the proportion of DNA from the insulin gene that could be found in the blood stream that is unmethylated is a measure of the number of beta-cells that are dying and releasing DNA into the bloodstream.
Dr. Akirav and his colleagues then proceeded to test this hypothesis. First, they looked at islets themselves, and were able to confirm that the insulin gene DNA in beta-cells was less methylated than in other cells. Next, they gave normal mice streptozotocin, a chemical that kills beta-cells in mice, and measured the ratio of unmethylated insulin gene DNA in the peripheral blood of the mice. As soon as eight hours after administering streptozotocin, the amount of unmethylated insulin DNA is measurably detected; after twenty-four hours, even more so.
These findings in streptozotocin-induced diabetes confirmed that inducing beta-cell death directly resulted in an increase in circulating DNA from these cells; what about in autoimmunity-induced diabetes? To confirm their findings held, Akirav et al measured the ratio of unmethylated insulin DNA through the development of diabetes in the non-obese diabetic (NOD) mouse model. Here, as in the other mice, the hypothesis proved true, and Dr. Akirav observed in increase in the amount of unmethylated DNA. Interestingly, the ratio of hypo-to-hypermethylated insulin gene DNA peaked right before the full onset of diabetes at 14 weeks. Even so, the ratio was significantly elevated as compared to non-diabetic mice even after the mice were fully diabetic.
Given how important it is to gain a better scientific understanding of the processes underlying beta-cell death, an accurate assay for mice would be cool on its own (assuming Dr. Akirav’s initial processes could be optimized to require less blood, as the current procedure does require the death of the mice). However, a blood test that works in humans as well would be much more useful. So, Dr. Akirav and his colleagues are in the process of determining whether their results in mice hold true in human blood as well. Thus far, they have confirmed that non-diabetic humans do not have elevated levels of unmethylated insulin gene DNA; next up, they will be testing blood from type 1 diabetics to see whether the relative ratio of hypo-to-hypermethylation is increased. Additional tests in type 2 diabetics are also planned in Dr. Akirav’s new laboratory at Winthrop University Hospital, NY.
Needless to say, this particular technological advance lacks some of the flash and bang of those on the exhibit hall floor. Still, I was very impressed by the promise of this new technology—more effective, accurate, and feasible than the current best-in-class, which is either functional proxies or a prohibitively difficult tissue biopsy. And though the presenter this time around was no company representative, dollars to donuts says that as type 2 diabetes becomes a more prevalent and long-endured problem, measuring beta-cell death will become an increasingly important factor in determining treatment—and then it’s only a matter of time before the major diagnostics companies like Johnson & Johnson and Roche are offering clinic-ready blood tests for differential methylation of insulin gene DNA.
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2. Melnikov A, Scholtens D, Godwin A, Levenson V. Differential methylation profile of ovarian cancer in tissues and plasma. J Mol Diagn. 2009 Jan;11(1):60-5. Epub 2008 Dec 12. PubMed PMID: 19074590; PubMed Central PMCID: PMC2607567. http://www.ncbi.nlm.nih.gov/pubmed/19074590