Myocardial infarction (MI) is caused by a sudden stop of blood flow that can lead to local ischemia in the heart and cause a pathologic remodeling, which ultimately give rise to heart failure (HF). Although it might present as a static event, this is a complex and dynamic process.
In this thesis, we aimed to assess HF considering the whole spectrum of the disease. From the acute phase, in which a patient suddenly falls victim to a drastic illness, to investigate the molecular transition towards its chronification and elucidate the mechanisms of action (MoA) of the most novel pharmaceutical therapies in chronic HF. Moreover, growing evidence supports the idea that specific biological processes are likely influenced by their biological context—for example, a specific tissue or a certain disease. This approach constantly generates vast amounts of data, such that putting together, analyzing, and interpreting this information constitutes an overwhelming task.
Consequently, we harnessed artificial intelligence techniques to combine molecular data with clinical responses observed in patients, thus generating a mathematical model capable of both reproducing existing knowledge and discern MoAs hidden under thousands of molecular interactions, otherwise inaccessible.
First, we analyzed the two drugs that are revolutionizing HF management: Entresto® (Sacubitril/Valsartan), which showed a reduction in the number of deaths and admissions by 22% in recent clinical trials, and Empagliflozin (a SGLT2 inhibitor indicated for type2 diabetes mellitus patients) that showed an unexpected 32% slash in development of new HF cases in the EMPAREG trial.
Our first study revealed that Sacubitril/Valsartan acts synergistically by blocking both cell death and the pathological makeover of the extracellular matrix of cardiac cells. Most importantly, we discovered a core of 8 proteins that emerge as key players in this process.
Secondly, the MoA of Empagliflozin was deciphered using deep learning analyses, which achieved 94.7% accuracy and showed an amelioration of cardiomyocyte cell death by restoring the activity of two genes suppressed during HF, XIAP and BIRC5. These results were confirmed in an in vivo rat model, and proved independent of the presence of diabetes, suggesting that Empagliflozin may emerge as a new standalone treatment in HF.
Although both drugs have very distinct indications and intrinsic MoAs, their benefits in slowing HF progression were remarkably similar, evidencing a key role for ventricular remodeling.
Thus, next we aimed to explore cardiac remodeling to delineate a structured and clear picture of the complete post-MI remodeling process towards HF. Here, we identified those altered proteins most related to cardiac remodeling in both MI and HF, and used them to look for processes with sustained enrichment throughout MI progression. Once we established which processes are affected at different stages and their evolution during MI, we finally sought to identify the key proteins driving these signaling cascades.
Chronic HF is the leading cause of inter-hospital mortality worldwide, which constitutes an authentic pandemic. However, many of these patients either develop HF derived from an acute event or experience a drastic worsening of the condition during the recurrent hospitalizations. Indeed, acute HF is the leading cause of intra-hospital mortality in more-developed countries, in which cardiogenic shock (CS) represents its most aggressive form. Yet, acute HF receives little attention compared to the chronic form of the disease By using transcriptomic and advanced proteomics techniques, we first investigated new potential biomarkers to aid CS management, which remains the leading intra-hospital cardiovascular cause of death worldwide. Assessing microRNA and proteins differentially expressed in afflicted patients, we describe the current status of biomarker research in CS, as well as a new molecular score, the CS4P, to reliably predict the prognostic outcomes of these patients.
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