Credit cards, their link between rare disorders and common diseases

By Julius Zieleniecki, Supervisor – Ingo Koeper

Drug research over the last decade has become increasingly directed toward designing molecules which target specific active sites of peptides or proteins in a membrane. Each drug undergoes rigorous phases of testing before reaching the shelf of your local drug store; at a phenomenal cost too. Each drug developed these days costs between one and 2.3 billion U.S. dollars to develop and bring to market. To date, one issue with drug development is that the understanding of how molecules interact with lipid bilayer membranes, the matrix containing proteins and peptides within cells, is unclear. This interaction is one suspected cause of potential side-effects as drugs, though specifically targeted, still indirectly interact with other molecules or moieties. My research over the last few years has been trying to elucidate how ethanol, a basic anesthetic, interacts with a membrane. This is all in a bid to slowly develop a clearer understanding of these non-specific interactions, potentially improving future therapeutics, however. Recently I came across some research released at the end of 2013 that complements drug innovations from a completely different angle. The Health Report on ABC Radio National aired a short interview with a researcher by the name of Andrey Rzhetsky from the University of Chicago. He discusses how databases containing millions of entries of de-identified insurance medical-patient billing information were used to elucidate whether there were links between statistically rare diseases and common diseases.

There are two types of diseases, those that only require one genetic defect (referred to as Mendelian diseases), such as cystic fibrosis and sickle-cell anemia, and those that are caused by multiple genetic defects and influenced by environmental factors, such as diabetes, heart diseases, and depression. To date, we know that ‘complex’ diseases have certain areas of the genome that “harbor pre-disposed genetic variations”, but we don’t know the exact genetic model (i.e. set of genome mutations) which express as certain phenotypes (complex diseases). Two simpler non-disease examples expressed phenotypes are eye and hair colour. Using the large body of data, researchers investigated whether there was a relationships between Mendelian and complex diseases. A key fact to remember at this point is that Mendelian diseases are typically caused by severe damage to a gene, but milder defects to that same gene can exist and yet not express as that phenotype, though potentially still ‘adding’ to complex diseases.

The analysis of the large body of information indicated that in certain cases all of the Mendelian diseases were needed to cause a complex disease (an additive phenotype), but in most cases only some Mendelian diseases were needed (a non-additive phenotype). In fact, it was found that in some cases, complex diseases have a unique set of Mendelian companions which can almost act as a barcode, and this was confirmed with genetic data from the Danish population. This research found many previously un-published links between rare and common diseases. For example, diabetes (which was found to be non-additive) has ‘dozens’ of statistically rare disorders which contribute to it.  Asthma and Autism were found to be combinations of diseases too (both non-additive phenotypes). A note by Andrey was that blood diseases which create a problem with delivering oxygen to the brain, such as Anemia, consequently worsen almost every phenotype. In conclusion, this research gives a deeper insight into how diseases operate; allows researchers to look into particular sites of patient DNA which they otherwise would not; and will hopefully lead to improved patient disease diagnoses and therapeutics.

References:

http://www.abc.net.au/radionational/programs/healthreport/genetic-causes-of-complex-diseases/5857196

Blair, D. et al., 2013. A Nondegenerate Code of Deleterious Variants in Mendelian Loci Contributes to Complex Disease Risk. Cell, 155(1).

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