Projeto Portugal 2030

Sumário

The RegAlt-T2D project addresses extremely important challenges at several levels. The outermost level is the problem of identifying the genetic background of Type 2 Diabetes (T2D) susceptibility. T2D is the most common form of diabetes, accounting for approximately 90% of all cases of diabetes. Retrospective studies have shown that the global incidence of diabetes in 2017 was 22.9 million, while its prevalence was 476.0 million and corresponding deaths where 1.37 million (8). These numbers are predicted to rise, where diabetes is estimated to affect 592 million worldwide by 2035 (from World Health Organization), being considered a pandemic of the 21st century. Apart from this dramatic scenario regarding human life, the repercussions in national health systems and economy cannot be ignored, where the cost of managing diabetes totals to EUR 150 billion in Europe alone (IDF (2019), Diabetes Atlas, 9th edition, International Diabetes Federation, Brussels). More accurate ways to predict the risk of T2D development could facilitate the targeted introduction of preventive measures in risk groups, this way managing better the staggering number of T2D patients. Aiming toward this goal, millions of Euros have been spent in Genome Wide Association Studies to identify risk allels for T2D, however the description of the genetic background for T2D susceptibility is yet vastly incomplete and the functional explanation for the vast majority of identified allels is not yet clear. This translates into a very unreliable way to predict T2D risk. We will approach to this problem by: a) focusing this study on regulatory sequences of the transcription (enhancers), since the large amount of putative regulatory sequences that overlap with T2D associated allels strongly suggest that these sequences are key players in T2D risk; b) Using a novel approach, we will decipher the impact of enhancer’s sequence alterations in function, which is currently vastly more challenging to predict than in other regions of the genome (e.g. coding sequences); c) Using advanced computational methods to analyze a finite cases of enhancer’s sequence alterations and its respective impact in genes’ function, we will build models that will predict the impact of any sequence variation in the selected studied enhancers. The RegAlt-T2D project will be a proof-of-principle to a next generation type of study to create more accurate models that will greatly improve the prediction of T2D risk. The RegAlt-T2D project also addresses a more internal level of challenges, applying novel concepts and going beyond the state-of-the-art. One of the main challenges is to obtain functional information from alteration of enhancer sequences without disturbing or separating them from their endogenous genomic landscape. This approach contrasts with most of the currently available state-of-the-art assays (e.g.MPRAs; 9, 10), that isolate enhancers and clone them in reporter constructs to infer the impact of nucleotide alterations in the enhancer’s function. These assays do not take into consideration locus specific compensatory or synergistic mechanisms within enhancers landscapes and use reporter genes as a proxy for the impact in the target gene expression, which is an unreliable approximation. RegAlt-T2D project goes beyond these limitations.