Diagnostic yield of chromosomal microarray in the largest Latino clinical cohort.

Copy number variants (CNVs) remain a major etiological cause of neurodevelopmental delay and congenital malformations. Chromosomal microarray analysis (CMA) represents the gold standard for CNVs molecular characterization. We applied CMA throughout the patient's clinical diagnostic workup, as the patient's medical provider requested. We collected CMA results of 3380 patients enrolled for 5 years (2016-2021). We found 830 CNVs in 719 patients with potential clinical significance, that is, (i) pathogenic, (ii) likely pathogenic, and (iii) variants of uncertain significance (VUS), from which 10.6% (predominantly involving chromosomes 15 and 22) were most likely the final cause underpinning the patients' clinical phenotype. For those associated with neurodevelopmental phenotypes, the rate of pathogenic or likely pathogenic findings among the patients with CNVs was 60.75%. When considering epileptic phenotypes, it was 59%. Interestingly, our protocol identified two gains harbored in 17q21.31 and 9q34.3, internationally classified initially as VUS. However, because of their high frequency, we propose that these two VUS be reclassified as likely benign in this widely heterogeneous phenotypic population. These results support the diagnostic yield efficiency of CMA in characterizing CNVs to define the final molecular cause of genetic diseases in this cohort of Colombian patients, the most significant sample of patients from a Latino population, and define new benign polymorphic CNVs.

Structural Protein Effects Underpinning Cognitive Developmental Delay of the PURA p.Phe233del Mutation Modelled by Artificial Intelligence and the Hybrid Quantum Mechanics-Molecular Mechanics Framework.

A whole-exome capture and next-generation sequencing was applied to an 11 y/o patient with a clinical history of congenital hypotonia, generalized motor and cognitive neurodevelopmental delay, and severe cognitive deficit, and without any identifiable Syndromic pattern, and to her parents, we disclosed a de novo heterozygous pathogenic mutation, c.697_699del p.Phe233del (rs786204835)(ACMG classification PS2, PM1, PM2, PP5), harbored in the PURA gene (MIM*600473) (5q31.3), associated with Autosomal Dominant Mental Retardation 31 (MIM # 616158). We used the significant improvement in the accuracy of protein structure prediction recently implemented in AlphaFold that incorporates novel neural network architectures and training procedures based on the evolutionary, physical, and geometric constraints of protein structures. The wild-type (WT) sequence and the mutated sequence, missing the Phe233, were reconstructed. The predicted local Distance Difference Test (lDDT) for the PURAwt and the PURA-Phe233del showed that the occurrence of the Phe233del affects between 220-320 amino acids. The distortion in the PURA structural conformation in the ~5 Å surrounding area after the p.Phe233del produces a conspicuous disruption of the repeat III, where the DNA and RNA helix unwinding capability occurs. PURA Protein-DNA docking corroborated these results in an in silico analysis that showed a loss of the contact of the PURA-Phe233del III repeat domain model with the DNA. Together, (i) the energetic and stereochemical, (ii) the hydropathic indexes and polarity surfaces, and (iii) the hybrid Quantum Mechanics-Molecular Mechanics (QM-MM) analyses of the PURA molecular models demarcate, at the atomic resolution, the specific surrounding region affected by these mutations and pave the way for future cell-based functional analysis. To the best of our knowledge, this is the first report of a de novo mutation underpinning a PURA syndrome in a Latin American patient and highlights the importance of predicting the molecular effects in protein structure using artificial intelligence algorithms and molecular and atomic resolution stereochemical analyses.

In Silico Design of a Peptide Receptor for Dopamine Recognition.

Dopamine (DA) is an important neurotransmitter with a fundamental role in regulatory functions related to the central, peripheral, renal, and hormonal nervous systems. Dopaminergic neurotransmission dysfunctions are commonly associated with several diseases; thus, in situ quantification of DA is a major challenge. To achieve this goal, enzyme-based biosensors have been employed for substrate recognition in the past. However, due to their sensitivity to changes in temperature and pH levels, new peptide bioreceptors have been developed. Therefore, in this study, four bioreceptors were designed in silico to exhibit a higher affinity for DA than the DA transporters (DATs). The design was based on the hot spots of the active sites of crystallized enzyme structures that are physiologically related to DA. The affinities between the chosen targets and designed bioreceptors were calculated using AutoDock Vina. Additionally, the binding free energy, ∆G, of the dopamine-4xp1 complex was calculated by molecular dynamics (MD). This value presented a direct relationship with the E_refine value obtained from molecular docking based on the ∆G functions obtained from MOE of the promising bioreceptors. The control variables in the design were amino acids, bond type, steric volume, stereochemistry, affinity, and interaction distances. As part of the results, three out of the four bioreceptor candidates presented promising values in terms of DA affinity and distance.