Nuria Lozano-García’s research while affiliated with Complutense University of Madrid and other places

Background Longitudinal changes in gut microbiome and inflammation may be involved in the evolution of atherosclerosis after an acute coronary syndrome (ACS). We aimed to characterize repeated profiles of gut microbiota and peripheral CD4+ T lymphocytes during the first year after an ACS, and to address their relationship with atherosclerotic plaque changes. Methods Over one year we measured the microbiome, peripheral counts of CD4+ T populations and cytokines in 67 patients shortly after a first ACS. We compared baseline measurements to those of a matched population of 40 chronic patients. A subgroup of 20 ACS patients underwent repeated assessment of fibrous cap thickness (FCT) of a non-culprit lesion. Results At admission, ACS patients showed gut dysbiosis compared with the chronic group, which was rapidly reduced and remained low at 1-year. Also, their Th1 and Th2 CD4+ T counts were increased but decreased over time. The CD4+ T counts were related to ongoing changes in gut microbiome. Unsupervised clustering of repeated CD4+ Th0, Th1, Th2, Th17 and Treg counts in ACS patients identified two different cell trajectory patterns, related to cytokines. The group of patients following a high-CD4+ T cell trajectory showed a one-year reduction in their FCT [net effect = -24.2 µm; p = 0.016]. Conclusions Patients suffering an ACS show altered profiles of microbiome and systemic inflammation that tend to mimic values of chronic patients after 1-year. However, in one-third of patients, this inflammatory state remains particularly dysregulated. This persistent inflammation is likely related to plaque vulnerability as evident by fibrous cap thinning (Clinical Trial NCT03434483).

RNA design, also known as the RNA inverse folding problem, involves discovering a nucleotide sequence that folds into a target structure. This problem has been addressed from a wide number of approaches, improving the ability to solve it in a reasonable time over time. Despite all these efforts, today no method has completely solved the problem. We present GREED-RNA, a new RNA design algorithm, based on a simple greedy evolutionary strategy. The main feature is the use of several objective functions (Base-pair distance, Hamming distance, probability over ensemble, partition function, ensemble defect and GC-content) to select the best solution in each iteration, changing their weight according to the problem-solving conditions. The performance of GREED-RNA was tested using the Eterna100 benchmark, widely used in this area and never fully solved by any method. In addition, a comparative analysis against several published RNA design methods considering three metrics (solved structures, success rate and execution time), allowed us to verify that GREED-RNA performs better than previously developed methods, thus successfully improving the current ability to solve this problem. This tool also allows users to select a range within which the GC-content of the solution sequences must fall. Source code and results are available at https://github.com/iARN-unex/GREED-RNA.

At present, designing an RNA sequence that folds into a specific secondary structure is a problem that is not fully solved, due to its exponentially increasing complexity. To address this matter, many computational methods have been developed, but none of them has been able to completely and in an affordable time solve Eterna100, a widely recognized benchmark used to test the performance of RNA inverse folding algorithms. In previous publications we presented the m2dRNAs tool, a Multiobjective Evolutionary Algorithm, and its extension eM2dRNAs, which added a recursive decomposition of the target structure, thus simplifying the problem. At that time they successfully improved the ability to solve the RNA inverse folding problem, but were still unable to complete the Eterna100 benchmark. Here we introduce ES+eM2dRNAs, an improvement of eM2dRNAs that optimizes the decomposition process, as a drawback in its nature was identified.A comparative study of this new tool against its predecessors and other RNA design methods was performed using the two current versions of the Eterna100 benchmark. ES+eM2dRNAs was shown to be the best in all performance indicators considered (number of structures solved, success rate, and total run time). Moreover, it is able to solve two Eterna100 structures for which none of the compared methods had ever found a solution.