Hemoglobinopathy detection through an institutional neonatal screening program in Colombia

ABSTRACT Introduction: Hemoglobinopathies are among the most common genetic disorders of hemoglobin worldwide and a public health problem. In Colombia, even though geographical areas with high incidence of this disorder have been reported, the absence of a national screening program does not permit us to determine its prevalence. Objective: Establish the prevalence of hemoglobin variants in a population covered by the neonatal screening program of Clínica Colsanitas S.A., between June 2000 and December 2014, including eight capital cities in Colombia. Methods: A retrospective cross-sectional study was conducted. We collected data from reports of the neonatal hemoglobinopathy-screening program for full-term newborn babies between 5 and 15 days old. Qualitative hemoglobin analysis was performed using gel electrophoresis of blood samples taken from the babies' heels. Results: The overall prevalence of abnormal Hb was 1.3%. Within the groups of newborns affected with any hemoglobinopathy (n = 400), the most frequent abnormal structural hemoglobins found were HbS (43%), HbC (9%), fast Hb (8%). For quantitative hemoglobins, HbA2 was 3.7% and HbA kept slightly elevated in 14.7% of cases. Frequency of homozygosis for HbS was 0.01%. Barranquilla, Cartagena and Cali were the cities with the greatest frequency of hemoglobinopathies. No correlation between sex and abnormal hemoglobin was found. Discussion and conclusion: Taking in consideration data from the World Health Organization (WHO) on hemoglobinopathies, our prevalence of > 1% is considered high. Therefore, a more extended coverage and the need for a national screening program are priorities

A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML

Autoverification of the automated blood cell counter (CBC) in a reference laboratory in Bogota, Colombia

ABSTRACT Introduction: The clinical laboratory is part of the group of actors in health systems that are under increasing pressure by users and administrators to increase their productivity in order to respond efficiently to the increased volume of patients, optimizing costs and professional time. This pressure forced laboratories to perform a full review of their procedures and develop technical, logistical and computational tools to enable excellent response times. Objective: This study aimed to evaluate the implementation of the automated blood cell counter autoverification process and its impact on the safety of patients. Methods: Verification rules were designed in the connectivity software, based on manual validation criteria for laboratory professionals, according to the guidelines of the Clinical and Laboratory Standards Institute (CLSI) Guideline Auto10-A and the International Consensus Group for Hematology Review (ISLH). The autoverification percentage was established, and non-conforming product (NCP) percentages were estimated before and after the procedure. Pilot tests were also performed in different days so as to adjust the process. Results: 53.4% of automated blood cell counters autoverification were achieved, and, subsequently in the audit of 18 months, 60% was reached due to verification adjustments in the delta programmed filter. The NCPs rose from 0.065% to 0.0036% from the beginning to the end of the process. Conclusion: The autoverification process enabled to reduce the variability associated with human intervention, therefore the professional is able to focus on the pathological report analysis, reducing the risk of errors and advocating greater importance on patient safety

Targeting Neuroplasticity, Cardiovascular, and Cognitive-Associated Genomic Variants in Familial Alzheimer’s Disease

Background: The identification of novel genetic variants contributing to the widespread in the age of onset (AOO) of Alzheimer’s disease (AD) could aid in the prognosis and/or development of new therapeutic strategies focused on early interventions. Methods: We recruited 78 individuals with AD from the Paisa genetic isolate in Antioquia, Colombia. These individuals belong to the world largest multigenerational and extended pedigree segregating AD as a consequence of a dominant fully penetrant mutation in the PSEN1 gene and exhibit an AOO ranging from the early 30s to the late 70s. To shed light on the genetic underpinning that could explain the large spread of the age of onset (AOO) of AD, 64 single nucleotide polymorphisms (SNP) associated with neuroanatomical, cardiovascular and cognitive measures in AD were genotyped. Standard quality control and filtering procedures were applied, and single- and multi-locus linear mixed-effects models were used to identify AOO associated SNPs. A full two-locus interaction model was fitted to define how identified SNPs interact to modulate AOO. Results: We identified two key epistatic interactions between the APOE*E2 allele and SNPs ASTN2-rs7852878 and SNTG1-rs16914781 that delay AOO by up to ~8 years (95%CI: 3.2-12.7, P=1.83x10-3) and ~7.6 years (95%CI: 3.3-11.8, P = 8.69x10-4), respectively, and validated our previous finding indicating that APOE*E2 delays AOO of AD in PSEN1 E280 mutation carriers. Discussion: This new evidence involving APOE*E2 as an AOO delayer could be used for developing precision medicine approaches and predictive genomics models to potentially determine AOO in individuals genetically predisposed to AD

Generation of one iPSC line (IMEDEAi006-A) from an early-onset familial Alzheimer's Disease (fAD) patient carrying the E280A mutation in the PSEN1 gene

The mutation E280A in PSEN1 (presenilin-1) is the most common cause of early-onset familial Alzheimer's Disease (fAD). It presents autosomal dominant inheritance and frequently leads to the manifestation of the disease in relatively young individuals. Here we report the generation of one PSEN1 E280A iPSC line derived from an early-onset patient. OriP/EBNA1-based episomal plasmids containing OCT3/4, SOX2, KLF4, L-MYC, LIN28, BCL-xL and shp53 were used to reprogram oral mucosa fibroblasts. The iPSC line generated has normal karyotype, carry the E280A mutation, is free of plasmid integration, express high levels of pluripotency markers and can differentiate into all three germ layers.Funding was provided by the Basque Government grant: Elkartek 20017 (DRUG4AD)

Targeting neuroplasticity, cardiovascular, and cognitive-associated : Genomic variants in familial alzheimer’s disease

The identification of novel genetic variants contributing to the widespread in the age of onset (AOO) of Alzheimer’s disease (AD) could aid in the prognosis and/or development of new therapeutic strategies focused on early interventions. We recruited 78 individuals with AD from the Paisa genetic isolate in Antioquia, Colombia. These individuals belong to the world largest multigenerational and extended pedigree segregating AD as a consequence of a dominant fully penetrant mutation in the PSEN1 gene and exhibit an AOO ranging from the early 1930s to the late 1970s. To shed light on the genetic underpinning that could explain the large spread of the age of onset (AOO) of AD, 64 single nucleotide polymorphisms (SNP) associated with neuroanatomical, cardiovascular, and cognitive measures in AD were genotyped. Standard quality control and filtering procedures were applied, and single- and multi-locus linear mixed-effects models were used to identify AOO-associated SNPs. A full two-locus interaction model was fitted to define how identified SNPs interact to modulate AOO. We identified two key epistatic interactions between the APOE*E2 allele and SNPs ASTN2-rs7852878 and SNTG1-rs16914781 that delay AOO by up to ~ 8 years (95% CI 3.2–12.7, P = 1.83 × 10−3) and ~ 7.6 years (95% CI 3.3–11.8, P = 8.69 × 10−4), respectively, and validated our previous finding indicating that APOE*E2 delays AOO of AD in PSEN1 E280 mutation carriers. This new evidence involving APOE*E2 as an AOO delayer could be used for developing precision medicine approaches and predictive genomics models to potentially determine AOO in individuals genetically predisposed to AD. © 2018, The Author(s)

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

Targeting neuroplasticity, cardiovascular, and cognitive-associated : Genomic variants in familial alzheimer’s disease

The identification of novel genetic variants contributing to the widespread in the age of onset (AOO) of Alzheimer’s disease (AD) could aid in the prognosis and/or development of new therapeutic strategies focused on early interventions. We recruited 78 individuals with AD from the Paisa genetic isolate in Antioquia, Colombia. These individuals belong to the world largest multigenerational and extended pedigree segregating AD as a consequence of a dominant fully penetrant mutation in the PSEN1 gene and exhibit an AOO ranging from the early 1930s to the late 1970s. To shed light on the genetic underpinning that could explain the large spread of the age of onset (AOO) of AD, 64 single nucleotide polymorphisms (SNP) associated with neuroanatomical, cardiovascular, and cognitive measures in AD were genotyped. Standard quality control and filtering procedures were applied, and single- and multi-locus linear mixed-effects models were used to identify AOO-associated SNPs. A full two-locus interaction model was fitted to define how identified SNPs interact to modulate AOO. We identified two key epistatic interactions between the APOE*E2 allele and SNPs ASTN2-rs7852878 and SNTG1-rs16914781 that delay AOO by up to ~ 8 years (95% CI 3.2–12.7, P = 1.83 × 10−3) and ~ 7.6 years (95% CI 3.3–11.8, P = 8.69 × 10−4), respectively, and validated our previous finding indicating that APOE*E2 delays AOO of AD in PSEN1 E280 mutation carriers. This new evidence involving APOE*E2 as an AOO delayer could be used for developing precision medicine approaches and predictive genomics models to potentially determine AOO in individuals genetically predisposed to AD. © 2018, The Author(s)