Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population

Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.

Functional analysis of CTLA4 promoter variant and its possible implication in colorectal cancer immunotherapy

Background

Colorectal cancer (CRC) is a prevalent cancer, ranking as the third most common. Recent advances in our understanding of the molecular causes of this disease have highlighted the crucial role of tumor immune evasion in its initiation and progression. CTLA4, a receptor that acts as a negative regulator of T cell responses, plays a pivotal role in this process, and genetic variations in CTLA4 have been linked to CRC susceptibility, prognosis, and response to therapy.

Methods

We conducted a case-control study involving 98 CRC patients and 424 controls. We genotyped the CTLA4 c.-319C > T variant (rs5742909) and performed an association analysis by comparing allele frequencies between the patients and controls. To assess the potential functional impact of this variant, we first performed an In Silico analysis of transcription factor binding sites using Genomatix. Finally, to validate our findings, we conducted a luciferase reporter gene assay using different cell lines and an electrophoretic mobility shift assay (EMSA).

Results

The case-control association analysis revealed a significant association between CTLA4 c.-319C > T and CRC susceptibility (p = 0.023; OR 1.89; 95% CI = 1.11-3.23). Genomatix analysis identified LEF1 and TCF7 transcription factors as specific binders to CTLA4 c.-319C. The reporter gene assay demonstrated notable differences in luciferase activity between the c.-319 C and T alleles in COS-7, HCT116, and Jurkat cell lines. EMSA analysis showed differences in TCF7 interaction with the CTLA4 C and T alleles.

Conclusion

CTLA4 c.-319C > T is associated with CRC susceptibility. Based on our functional validation results, we proposed that CTLA4 c.-319C > T alters gene expression at the transcriptional level, triggering a stronger negative regulation of T-cells and immune tumoral evasion.

Pharmacogenomic profile of actionable molecular variants related to drugs commonly used in anesthesia: WES analysis reveals new mutations

Background: Genetic interindividual variability is associated with adverse drug reactions (ADRs) and affects the response to common drugs used in anesthesia. Despite their importance, these variants remain largely underexplored in Latin-American countries. This study describes rare and common variants found in genes related to metabolism of analgesic and anaesthetic drug in the Colombian population. Methods: We conducted a study that included 625 Colombian healthy individuals. We generated a subset of 14 genes implicated in metabolic pathways of common medications used in anesthesia and assessed them by whole-exome sequencing (WES). Variants were filtered using two pipelines: A) novel or rare (minor allele frequency-MAF <1%) variants including missense, loss-of-function (LoF, e.g., frameshift, nonsense), and splice site variants with potential deleterious effect and B) clinically validated variants described in the PharmGKB (categories 1, 2 and 3) and/or ClinVar databases. For rare and novel missense variants, we applied an optimized prediction framework (OPF) to assess the functional impact of pharmacogenetic variants. Allelic, genotypic frequencies and Hardy-Weinberg equilibrium were calculated. We compare our allelic frequencies with these from populations described in the gnomAD database. Results: Our study identified 148 molecular variants potentially related to variability in the therapeutic response to 14 drugs commonly used in anesthesiology. 83.1% of them correspond to rare and novel missense variants classified as pathogenic according to the pharmacogenetic optimized prediction framework, 5.4% were loss-of-function (LoF), 2.7% led to potential splicing alterations and 8.8% were assigned as actionable or informative pharmacogenetic variants. Novel variants were confirmed by Sanger sequencing. Allelic frequency comparison showed that the Colombian population has a unique pharmacogenomic profile for anesthesia drugs with some allele frequencies different from other populations. Conclusion: Our results demonstrated high allelic heterogeneity among the analyzed sampled, enriched by rare (91.2%) variants in pharmacogenes related to common drugs used in anesthesia. The clinical implications of these results highlight the importance of implementation of next-generation sequencing data into pharmacogenomic approaches and personalized medicine.

Characterization of ADME Gene Variation in Colombian Population by Exome Sequencing

In genes related to drug pharmacokinetics, molecular variations determine interindividual variability in the therapeutic efficacy and adverse drug reactions. The assessment of single-nucleotide variants (SNVs) is used with growing frequency in pharmacogenetic practice, and recently, high-throughput genomic analyses obtained through next-generation sequencing (NGS) have been recognized as powerful tools to identify common, rare and novel variants. These genetic profiles remain underexplored in Latin-American populations, including Colombia. In this study, we investigated the variability of 35 genes included in the ADME core panel (absorption, distribution, metabolism, and excretion) by whole-exome sequencing (WES) of 509 unrelated Colombian individuals with no previous reports of adverse drug reactions. Rare variants were filtered according to the minor allele frequencies (MAF) <1% and potential deleterious consequences. The functional impact of novel and rare missense variants was assessed using an optimized framework for pharmacogenetic variants. Bioinformatic analyses included the identification of clinically validated variants described in PharmGKB and ClinVar databases. Ancestry from WES data was inferred using the R package EthSEQ v2.1.4. Allelic frequencies were compared to other populations reported in the public gnomAD database. Our analysis revealed that rare missense pharmacogenetic variants were 2.1 times more frequent than common variants with 121 variants predicted as potentially deleterious. Rare loss of function (LoF) variants were identified in 65.7% of evaluated genes. Regarding variants with clinical pharmacogenetic effect, our study revealed 89 sequence variations in 28 genes represented by missense (62%), synonymous (22.5%), splice site (11.2%), and indels (3.4%). In this group, ABCB1, ABCC2, CY2B6, CYP2D6, DPYD, NAT2, SLC22A1, and UGTB2B7, are the most polymorphic genes. NAT2, CYP2B6 and DPYD metabolizer phenotypes demonstrated the highest variability. Ancestry analysis indicated admixture in 73% of the population. Allelic frequencies exhibit significant differences with other Latin-American populations, highlighting the importance of pharmacogenomic studies in populations of different ethnicities. Altogether, our data revealed that rare variants are an important source of variability in pharmacogenes involved in the pharmacokinetics of drugs and likely account for the unexplained interindividual variability in drug response. These findings provide evidence of the utility of WES for pharmacogenomic testing and into clinical practice.

Association Between the LZTFL1 rs11385942 Polymorphism and COVID-19 Severity in Colombian Population

Genetic and non-genetic factors are responsible for the high interindividual variability in the response to SARS-CoV-2. Although numerous genetic polymorphisms have been identified as risk factors for severe COVID-19, these remain understudied in Latin-American populations. This study evaluated the association of non-genetic factors and three polymorphisms: ACE rs4646994, ACE2 rs2285666, and LZTFL1 rs11385942, with COVID severity and long-term symptoms by using a case-control design. The control group was composed of asymptomatic/mild cases (n = 61) recruited from a private laboratory, while the case group was composed of severe/critical patients (n = 63) hospitalized in the Hospital Universitario Mayor-Méderi, both institutions located in Bogotá, Colombia. Clinical follow up and exhaustive revision of medical records allowed us to assess non-genetic factors. Genotypification of the polymorphism of interest was performed by amplicon size analysis and Sanger sequencing. In agreement with previous reports, we found a statistically significant association between age, male sex, and comorbidities, such as hypertension and type 2 diabetes mellitus (T2DM), and worst outcomes. We identified the polymorphism LZTFL1 rs11385942 as an important risk factor for hospitalization (p < 0.01; OR = 5.73; 95% CI = 1.2–26.5, under the allelic test). Furthermore, long-term symptoms were common among the studied population and associated with disease severity. No association between the polymorphisms examined and long-term symptoms was found. Comparison of allelic frequencies with other populations revealed significant differences for the three polymorphisms investigated. Finally, we used the statistically significant genetic and non-genetic variables to develop a predictive logistic regression model, which was implemented in a Shiny web application. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC = 0.86; 95% confidence interval 0.79–0.93). These results suggest that LZTFL1 rs11385942 may be a potential biomarker for COVID-19 severity in addition to conventional non-genetic risk factors. A better understanding of the impact of these genetic risk factors may be useful to prioritize high-risk individuals and decrease the morbimortality caused by SARS-CoV2 and future pandemics.

Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans

Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10⁻¹⁵). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms.

Genetic Profile of the Dystrophin Gene Reveals New Mutations in Colombian Patients Affected with Muscular Dystrophinopathy

Background

Duchenne and Becker muscular dystrophies (DMD/BMD) are the most common human dystrophinopathies with recessive X-linked inheritance. Dystrophin gene deletions and duplications are the most common mutations, followed by point mutations. The aim of this study is to characterize the mutational profile of the dystrophin gene in Colombian patients with DMD/BMD.

Material and Methods

Mutational profiling was determined in 69 affected patients using Sanger sequencing, next-generation sequencing (NGS) and/or multiplex ligation dependent-probes amplification (MLPA). Genetic variants were classified according to molecular consequence and new variants were determined through database and literature analysis.

Results

Mutational profile in affected patients revealed that large deletions/duplications analyzed by MLPA accounted for 72.5% of all genetic variations. By using Sanger sequencing or NGS, we identified point mutations in 15.9% and small deletions in 11.6% of the patients. New mutations were found, most of them were point mutations or small deletions (10.1%).

Conclusion

Our results described the genetic profile of the dystrophin gene in Colombian patients with DMD and contribute to efforts to identify molecular variants in Latin American populations. For our population, 18.8% of cases could be treated with FDA or MDA approved molecular therapies based on specific mutations. These data contribute to the establishment of appropriate genetic counseling and potential treatment.

Genetic and Molecular Aspects of Drug-Induced QT Interval Prolongation

Long QT syndromes can be either acquired or congenital. Drugs are one of the many etiologies that may induce acquired long QT syndrome. In fact, many drugs frequently used in the clinical setting are a known risk factor for a prolonged QT interval, thus increasing the chances of developing torsade de pointes. The molecular mechanisms involved in the prolongation of the QT interval are common to most medications. However, there is considerable inter-individual variability in drug response, thus making the application of personalized medicine a relevant aspect in long QT syndrome, in order to evaluate the risk of every individual from a pharmacogenetic standpoint.

A Novel Splice-Site Mutation in the ELN Gene Suggests an Alternative Mechanism for Vascular Elastinopathies

The ELN gene encodes elastin, a fundamental protein of the extracellular matrix that confers elasticity to different tissues including blood vessels. The formation of elastin fibers is a complex process involving monomer coacervation and subsequent crosslinking. Mutations in exons 1-29 of the ELN gene have been linked to supravalvular aortic stenosis (SVAS) whereas mutations in exons 30-33 are associated with autosomal dominant cutis laxa (ADCL). This striking segregation has led to the hypothesis that distinct molecular mechanisms underlie both diseases. SVAS is believed to arise through haploinsufficiency while ADCL is hypothesized to be caused by a dominant negative effect. Here, we describe a patient with SVAS harboring a novel splice-site mutation in the last exon of ELN. The location of this mutation is not consistent with current knowledge of SVAS, since all mutations reported in the C-terminus have been found in ADCL patients, and a thorough evaluation did not reveal significant skin involvement in this case. RT-PCR analysis of skin tissue showed that C-terminal mutations in the region can lead to the production of aberrant transcripts through intron retention and activation of cryptic splice sites and suggest that disruption of the very last exon can lead to functional haploinsufficiency potentially related to SVAS.

Possible Genetic Determinants of Response to Phenytoin in a Group of Colombian Patients With Epilepsy

Background

Epilepsy is a serious health problem worldwide. Despite the introduction of new antiepileptic drugs (AEDs) almost 30% of these patients have drug-resistant forms of the disease (DRE), with a significant increase in morbi-mortality.

Objective

Our objective was to assess the impact of some genetic factors and its possible association with treatment response and adverse drug reactions (ADRs) to phenytoin in 67 adult Colombian patients with epilepsy.

Methods

We conducted an analytical, observational, prospective cohort study to screen four polymorphisms in pharmacogenes: CYP2C9*2-c.430C>T (rs1799853), CYP2C9*3-c.1075A>C (rs1057910), ABCB1-c.3435T>C (rs1045642), and SCN1A-IVS5-91G>A (rs3812718), and their association with treatment response. Patients were followed for 1 year to confirm the existence of DRE (non-response) and ADRs using an active pharmacovigilance approach, followed by a consensus in order to classify ADRs according to causality, preventability, intensity and their relation with phenytoin dose, the duration of treatment, and susceptibility factors (DoTS methodology).

Results

A little more than half of evaluated subjects (52.2%) were non-responding to phenytoin. Regarding the genotype-phenotype correlation there was no association between polymorphisms of SCN1A and ABCB1 and DRE (non-response) (p = 0.34), and neither with CYP2C9 polymorphisms and the occurrence of ADRs (p = 0.42). We only found an association between polymorphic alleles of CYP2C9 and vestibular-cerebellar ADRs (dizziness, ataxia, diplopia, and dysarthria) (p = 0.001). Alleles CYP2C9*2-c.430C>T and CYP2C9*3-c.1075A>C were identified as susceptibility factors to ADRs in 24% of patients.

Conclusions

Decreased function alleles of CYP2C9 were highly predictive of vestibular-cerebellar ADRs to phenytoin in our study (p = 0.001). However, the genetic variants CYP2C9*2-c.430C>T, CYP2C9*3-c.1075A>C, ABCB1-c.3435T>C, and SCN1A-IVS5-91G>A, were not associated with treatment response in our study.