Genetic modifiers of cognitive decline in PSEN1 E280A Alzheimer's disease.

INTRODUCTION: Rate of cognitive decline (RCD) in Alzheimer's disease (AD) determines the degree of impairment for patients and of burden for caretakers. We studied the association of RCD with genetic variants in AD. METHODS: RCD was evaluated in 62 familial AD (FAD) and 53 sporadic AD (SAD) cases, and analyzed by whole-exome sequencing for association with common exonic functional variants. Findings were validated in post mortem brain tissue. RESULTS: One hundred seventy-two gene variants in FAD, and 227 gene variants in SAD associated with RCD. In FAD, performance decline of the immediate recall of the Rey-Osterrieth figure test associated with 122 genetic variants. Olfactory receptor OR51B6 showed the highest number of associated variants. Its expression was detected in temporal cortex neurons. DISCUSSION: Impaired olfactory function has been associated with cognitive impairment in AD. Genetic variants in these or other genes could help to identify risk of faster memory decline in FAD and SAD patients.

Theory of Mind in Huntington's Disease: A Systematic Review of 20 Years of Research.

Background: People with Huntington's disease (HD) exhibit neurocognitive alterations throughout the disease, including deficits in social cognitive processes such as Theory of Mind (ToM). Objective: The aim is to identify methodologies and ToM instruments employed in HD, alongside relevant findings, within the scientific literature of the past two decades. Methods: We conducted a comprehensive search for relevant papers in the SCOPUS, PubMed, APA-PsyArticles, Web of Science, Redalyc, and SciELO databases. In the selection process, we specifically focused on studies that included individuals with a confirmed genetic status of HD and investigated ToM functioning in patients with and without motor symptoms. The systematic review followed the PRISMA protocol. Results: A total of 27 papers were selected for this systematic review, covering the period from 2003 to 2023. The findings consistently indicate that ToM is globally affected in patients with manifest motor symptoms. In individuals without motor symptoms, impairments are focused on the affective dimensions of ToM. Conclusions: Based on our analysis, affective ToM could be considered a potential biomarker for HD. Therefore, it is recommended that ToM assessment be included as part of neuropsychological evaluation protocols in clinical settings. Suchinclusion could aid in the identification of early stages of the disease and provide new opportunities for treatment, particularly with emerging drugs like antisense oligomers. The Prospero registration number for this review is CRD42020209769.

A distance-based kernel for classification via Support Vector Machines.

Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.

Uncovering the Genetic and Molecular Features of Huntington's Disease in Northern Colombia.

Huntington's disease (HD) is a genetic disorder caused by a CAG trinucleotide expansion in the huntingtin (HTT) gene. Juan de Acosta, Atlántico, a city located on the Caribbean coast of Colombia, is home to the world's second-largest HD pedigree. Here, we include 291 descendants of this pedigree with at least one family member with HD. Blood samples were collected, and genomic DNA was extracted. We quantified the HTT CAG expansion using an amplicon sequencing protocol. The genetic heterogeneity was measured as the ratio of the mosaicism allele's read peak and the slippage ratio of the allele's read peak from our sequence data. The statistical and bioinformatic analyses were performed with a significance threshold of p < 0.05. We found that the average HTT CAG repeat length in all participants was 21.91 (SD = 8.92). Of the 291 participants, 33 (11.3%, 18 females) had a positive molecular diagnosis for HD. Most affected individuals were adults, and the most common primary and secondary alleles were 17/7 (CAG/CCG) and 17/10 (CAG/CCG), respectively. The mosaicism increased with age in the participants with HD, while the slippage analyses revealed differences by the HD allele type only for the secondary allele. The slippage tended to increase with the HTT CAG repeat length in the participants with HD, but the increase was not statistically significant. This study analyzed the genetic and molecular features of 291 participants, including 33 with HD. We found that the mosaicism increased with age in the participants with HD, particularly for the secondary allele. The most common haplotype was 17/7_17/10. The slippage for the secondary allele varied by the HD allele type, but there was no significant difference in the slippage by sex. Our findings offer valuable insights into HD and could have implications for future research and clinical management.

Leading consumption patterns of psychoactive substances in Colombia: A deep neural network-based clustering-oriented embedding approach.

The number of health-related incidents caused using illegal and legal psychoactive substances (PAS) has dramatically increased over two decades worldwide. In Colombia, the use of illicit substances has increased up to 10.3%, while the consumption alcohol and tobacco has increased to 84% and 12%, respectively. It is well-known that identifying drug consumption patterns in the general population is essential in reducing overall drug consumption. However, existing approaches do not incorporate Machine Learning and/or Deep Data Mining methods in combination with spatial techniques. To enhance our understanding of mental health issues related to PAS and assist in the development of national policies, here we present a novel Deep Neural Network-based Clustering-oriented Embedding Algorithm that incorporates an autoencoder and spatial techniques. The primary goal of our model is to identify general and spatial patterns of drug consumption and abuse, while also extracting relevant features from the input data and identifying clusters during the learning process. As a test case, we used the largest publicly available database of legal and illegal PAS consumption comprising 49,600 Colombian households. We estimated and geographically represented the prevalence of consumption and/or abuse of both PAS and non-PAS, while achieving statistically significant goodness-of-fit values. Our results indicate that region, sex, housing type, socioeconomic status, age, and variables related to household finances contribute to explaining the patterns of consumption and/or abuse of PAS. Additionally, we identified three distinct patterns of PAS consumption and/or abuse. At the spatial level, these patterns indicate concentrations of drug consumption in specific regions of the country, which are closely related to specific geographic locations and the prevailing social and environmental contexts. These findings can provide valuable insights to facilitate decision-making and develop national policies targeting specific groups given their cultural, geographic, and social conditions.

Ancestry component as a major predictor of lithium response in the treatment of bipolar disorder.

BACKGROUND: Bipolar Disorder (BD) represents the seventh major cause of disability life-years-adjusted. Lithium remains as a first-line treatment, but clinical improvement occurs only in 30 % of treated patients. Studies suggest that genetics plays a major role in shaping the individual response of BD patients to lithium. METHODS: We used machine-learning techniques (Advance Recursive Partitioned Analysis, ARPA) to build a personalized prediction framework of BD lithium response using biological, clinical, and demographical data. Using the Alda scale, we classified 172 BD I-II patients as responders or non-responders to lithium treatment. ARPA methods were used to build individual prediction frameworks and to define variable importance. Two predictive models were evaluated: 1) demographic and clinical data, and 2) demographic, clinical and ancestry data. Model performance was assessed using Receiver Operating Characteristic (ROC) curves. RESULTS: The predictive model including ancestry yield the best performance (sensibility = 84.6 %, specificity = 93.8 % and AUC = 89.2 %) compared to the model without ancestry (sensibility = 50 %, Specificity = 94.5 %, and AUC = 72.2 %). This ancestry component best predicted lithium individual response. Clinical variables such as disease duration, the number of depressive episodes, the total number of affective episodes, and the number of manic episodes were also important predictors. CONCLUSION: Ancestry component is a major predictor and significantly improves the definition of individual Lithium response in BD patients. We provide classification trees with potential bench application in the clinical setting. While this prediction framework might be applied in specific populations, the used methodology might be of general use in precision and translational medicine.

ADGRL3 genomic variation implicated in neurogenesis and ADHD links functional effects to the incretin polypeptide GIP.

Attention deficit/hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder. Single nucleotide polymorphisms (SNPs) in the Adhesion G Protein-Coupled Receptor L3 (ADGRL3) gene are associated with increased susceptibility to developing ADHD worldwide. However, the effect of ADGRL3 non-synonymous SNPs (nsSNPs) on the ADGRL3 protein function is vastly unknown. Using several bioinformatics tools to evaluate the impact of mutations, we found that nsSNPs rs35106420, rs61747658, and rs734644, previously reported to be associated and in linkage with ADHD in disparate populations from the world over, are predicted as pathogenic variants. Docking analysis of rs35106420, harbored in the ADGLR3-hormone receptor domain (HRM, a common extracellular domain of the secretin-like GPCRs family), showed that HRM interacts with the Glucose-dependent insulinotropic polypeptide (GIP), part of the incretin hormones family. GIP has been linked to the pathogenesis of diabetes mellitus, and our analyses suggest a potential link to ADHD. Overall, the comprehensive application of bioinformatics tools showed that functional mutations in the ADGLR3 gene disrupt the standard and wild ADGRL3 structure, most likely affecting its metabolic regulation. Further in vitro experiments are granted to evaluate these in silico predictions of the ADGRL3-GIP interaction and dissect the complexity underlying the development of ADHD.

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.

Ataxia with Ocular Apraxia Type 1 (AOA1) (APTX, W279* Mutation): Neurological, Neuropsychological, and Molecular Outlining of a Heterogenous Phenotype in Four Colombian Siblings.

Hereditary ataxias are a group of devastating neurological disorders that affect coordination of gait and are often associated with poor coordination of hands, speech, and eye movements. Ataxia with ocular apraxia type 1 (AOA1) (OMIM: 606,350.0006) is characterized by slowly progressive symptoms of childhood-onset and pathogenic mutations in APTX; the only known cause underpinning AOA1. APTX encodes the protein aprataxin, composed of three domains sharing homology with proteins involved in DNA damage, signaling, and repair. We present four siblings from an endogamic family in a rural, isolated town of Colombia with ataxia and ocular apraxia of childhood-onset and confirmed molecular diagnosis of AOA1, homozygous for the W279* p.Trp279Ter mutation. We predicted the mutated APTX with AlphaFold to demonstrate the effects of this stop-gain mutation that deletes three beta helices encoded by amino acid 270 to 339 rescinding the C2H2-type zinc fingers (Znf) (C2H2 Znf) DNA-binding, the DNA-repair domain, and the whole 3D structure of APTX. All siblings exhibited different ages of onset (4, 6, 8, and 11 years old) and heterogeneous patterns of dysarthria (ranging from absence to mild-moderate dysarthria). Neuropsychological evaluation showed no neurocognitive impairment in three siblings, but one sibling showed temporospatial disorientation, semantic and phonologic fluency impairment, episodic memory affection, constructional apraxia, moderate anomia, low executive function, and symptoms of depression. To our knowledge, this report represents the most extensive series of siblings affected with AOA1 in Latin America, and the genetic analysis completed adds important knowledge to outline this family's disease and general complex phenotype of hereditary ataxias.

Executive function deficit in bipolar offspring: A neurocognitive endophenotype?

BACKGROUND: Recent studies in bipolar offspring (BO) showed that a low cognitive performance, especially executive function deficit, could be an early marker of bipolar disorder (BD). Nevertheless, these findings have not been replicated (specifically attentional control, flexibility, and working memory). In addition, most studies have focused on children and adolescents, but few studies analyze the executive function performance in BO adults. OBJECTIVE: Our goal was to compare the neurocognitive performance of BO with control parent-offspring (CO) in a sample that included various age groups. METHOD: We conducted a cohort study, including subjects between six to 30 years old. We evaluated 129 BO and 113 CO subjects using validated psychiatric diagnostic interviews and an extensive neuropsychological battery. RESULTS: Compared to the CO group, the BO group presented a lower performance in several executive functioning domains, mainly in tasks of attentional control, flexibility, and working memory. All age groups exhibited these findings. CONCLUSIONS: BO group presents executive function deficits, regardless of the age group: children, adolescents, and adults. This neurocognitive deficit should be accountable as a neurocognitive endophenotype candidate in BD.

Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3, DRD4, and SNAP25.

OBJECTIVE: To investigate whether single nucleotide polymorphisms (SNPs) in the ADGRL3, DRD4, and SNAP25 genes are associated with and predict ADHD severity in families from a Caribbean community. METHOD: ADHD severity was derived using latent class cluster analysis of DSM-IV symptomatology. Family-based association tests were conducted to detect associations between SNPs and ADHD severity latent phenotypes. Machine learning algorithms were used to build predictive models of ADHD severity based on demographic and genetic data. RESULTS: Individuals with ADHD exhibited two seemingly independent latent class severity configurations. SNPs harbored in DRD4, SNAP25, and ADGRL3 showed evidence of linkage and association to symptoms severity and a potential pleiotropic effect on distinct domains of ADHD severity. Predictive models discriminate severe from non-severe ADHD in specific symptom domains. CONCLUSION: This study supports the role of DRD4, SNAP25, and ADGRL3 genes in outlining ADHD severity, and a new prediction framework with potential clinical use.

Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD.

Temporal processing (TP) is associated with functions such as perception, verbal skills, temporal perspective, and future planning, and is intercorrelated with working memory, attention, and inhibitory control, which are highly impaired in individuals with attention deficit hyperactivity disorder (ADHD). Here we evaluate TP measures as potential endophenotypes in Caribbean families ascertained from probands affected by ADHD. A total of 232 individuals were recruited and clinically evaluated using an extensive battery of neuropsychological tasks and reaction time (RT)-based task paradigms. Further, the heritability (genetic variance underpinning phenotype) was estimated as a measure of the genetics apportionment. A predictive framework for ADHD diagnosis was derived using these tasks. We found that individuals with ADHD differed from controls in neuropsychological tasks assessing mental control, visual-verbal memory, verbal fluency, verbal, and semantic fluency. In addition, TP measures such as RT, errors, and variability were also affected in individuals with ADHD. Moreover, we determined that only omission and commission errors had significant heritability. In conclusion, we have disentangled omission and commission errors as possible TP endophenotypes in ADHD, which can be suitable to assess the neurobiological and genetic basis of ADHD. A predictive model using these endophenotypes led to remarkable sensitivity, specificity, precision and classification rate for ADHD diagnosis, and may be a useful tool for patients' diagnosis, follow-up, and longitudinal assessment in the clinical setting.

Utility of a Short Neuropsychological Protocol for Detecting HIV-Associated Neurocognitive Disorders in Patients with Asymptomatic HIV-1 Infection.

Human Immunodeficiency Virus type 1 (HIV-1) infection is a chronic disease that affects ~40 million people worldwide. HIV-associated neurocognitive disorders (HAND) are common in individuals with HIV-1 Infection, and represent a recent public health problem. Here we evaluate the performance of a recently proposed short protocol for detecting HAND by studying 60 individuals with HIV-1-Infection and 60 seronegative controls from a Caribbean community in Barranquilla, Colombia. The short evaluation protocol used significant neuropsychological tests from a previous study of asymptomatic HIV-1 infected patients and a group of seronegative controls. Brief screening instruments, i.e., the Mini-mental State Examination (MMSE) and the International HIV Dementia Scale (IHDS), were also applied. Using machine-learning techniques, we derived predictive models of HAND status, and evaluated their performance with the ROC curves. The proposed short protocol performs exceptionally well yielding sensitivity, specificity, and overall prediction values >90%, and better predictive capacity than that of the MMSE and IHDS. Community-specific cut-off values for HAND diagnosis, based on the MMSE and IHDS, make this protocol suitable for HAND screening in individuals from this Caribbean community. This study shows the effectivity of a recently proposed short protocol to detect HAND in individuals with asymptomatic HIV-1-Infection. The application of community-specific cut-off values for HAND diagnosis in the clinical setting may improve HAND screening accuracy and facilitate patients' treatment and follow-up. Further studies are needed to assess the performance of this protocol in other Latin American populations.

Exosomes: Potential Disease Biomarkers and New Therapeutic Targets.

Exosomes are extracellular vesicles released by cells, both constitutively and after cell activation, and are present in different types of biological fluid. Exosomes are involved in the pathogenesis of diseases, such as cancer, neurodegenerative diseases, pregnancy disorders and cardiovascular diseases, and have emerged as potential non-invasive biomarkers for the detection, prognosis and therapeutics of a myriad of diseases. In this review, we describe recent advances related to the regulatory mechanisms of exosome biogenesis, release and molecular composition, as well as their role in health and disease, and their potential use as disease biomarkers and therapeutic targets. In addition, the advantages and disadvantages of their main isolation methods, characterization and cargo analysis, as well as the experimental methods used for exosome-mediated drug delivery, are discussed. Finally, we present potential perspectives for the use of exosomes in future clinical practice.

ADGRL3, FGF1 and DRD4: Linkage and Association with Working Memory and Perceptual Organization Candidate Endophenotypes in ADHD.

Attention deficit hyperactivity disorder (ADHD) is a highly heritable neurobehavioral disorder that affects children worldwide, with detrimental long-term consequences in affected individuals. ADHD-affected patients display visual-motor and visuospatial abilities and skills that depart from those exhibited by non-affected individuals and struggle with perceptual organization, which might partially explain impulsive responses. Endophenotypes (quantifiable or dimensional constructs that are closely related to the root cause of the disease) might provide a more powerful and objective framework for dissecting the underlying neurobiology of ADHD than that of categories offered by the syndromic classification. In here, we explore the potential presence of the linkage and association of single-nucleotide polymorphisms (SNPs), harbored in genes implicated in the etiology of ADHD (ADGRL3, DRD4, and FGF1), with cognitive endophenotypes related to working memory and perceptual organization in 113 nuclear families. These families were ascertained from a geographical area of the Caribbean coast, in the north of Colombia, where the community is characterized by its ethnic diversity and differential gene pool. We found a significant association and linkage of markers ADGRL3-rs1565902, DRD4-rs916457 and FGF1-rs2282794 to neuropsychological tasks outlining working memory and perceptual organization such as performance in the digits forward and backward, arithmetic, similarities, the completion of figures and the assembly of objects. Our results provide strong support to understand ADHD as a combination of working memory and perceptual organization deficits and highlight the importance of the genetic background shaping the neurobiology, clinical complexity, and physiopathology of ADHD. Further, this study supplements new information regarding an ethnically diverse community with a vast African American contribution, where ADHD studies are scarce.

A new pipeline for structural characterization and classification of RNA-Seq microbiome data.

BACKGROUND: High-throughput sequencing enables the analysis of the composition of numerous biological systems, such as microbial communities. The identification of dependencies within these systems requires the analysis and assimilation of the underlying interaction patterns between all the variables that make up that system. However, this task poses a challenge when considering the compositional nature of the data coming from DNA-sequencing experiments because traditional interaction metrics (e.g., correlation) produce unreliable results when analyzing relative fractions instead of absolute abundances. The compositionality-associated challenges extend to the classification task, as it usually involves the characterization of the interactions between the principal descriptive variables of the datasets. The classification of new samples/patients into binary categories corresponding to dissimilar biological settings or phenotypes (e.g., control and cases) could help researchers in the development of treatments/drugs. RESULTS: Here, we develop and exemplify a new approach, applicable to compositional data, for the classification of new samples into two groups with different biological settings. We propose a new metric to characterize and quantify the overall correlation structure deviation between these groups and a technique for dimensionality reduction to facilitate graphical representation. We conduct simulation experiments with synthetic data to assess the proposed method's classification accuracy. Moreover, we illustrate the performance of the proposed approach using Operational Taxonomic Unit (OTU) count tables obtained through 16S rRNA gene sequencing data from two microbiota experiments. Also, compare our method's performance with that of two state-of-the-art methods. CONCLUSIONS: Simulation experiments show that our method achieves a classification accuracy equal to or greater than 98% when using synthetic data. Finally, our method outperforms the other classification methods with real datasets from gene sequencing experiments.

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.