Frequency of rs1051338 and rs116928232 Variants in Individuals from Northwest Mexico.

BACKGROUND: LIPA, situated on chromosome 10q23.2-q23.3, encodes the enzyme lysosomal acid lipase (LAL) (EC 3.1.1.13). Genetic alterations in LIPA lead to lysosomal acid lipase deficiency (LALD), an inborn error causing lipid metabolism anomalies and impairing cholesterol and triacylglyceride degradation. Over 40 LIPA variants have been documented, yet this study focuses on just two. The rs1051338 variant (NM_000235:c.46A>C) affects the signal peptide in Exon 2, whereas rs116928232, located in Exon 8, alters the splice site (NM_000235:c.894G>A), impacting lysosomal acid lipase activity. Considering the diverse clinical manifestations of LALD and the rising hepatic steatosis prevalence in Mexican population, mainly due to diet, these variants were investigated within this demographic to uncover potential contributing factors. This study aimed to reveal the frequency of rs1051338 and rs116928232 among healthy mestizo individuals in Northwest Mexico, marking a significant genetic exploration in this demographic. METHODS: Three hundred ten healthy mestizo individuals underwent PCR-RFLP analysis for both variants, and Sanger sequencing was performed for variant rs116928232. Bioinformatic analysis was also performed to predict protein changes. RESULTS: Allele frequencies for rs1051338 (FA = 0.39, p value = 0.15) and rs116928232 (FA = 0.0016, p value = 0.49) aligned with reported data, while bioinformatic analysis allowed us to identify the protein alteration observed in both variants; finally, the variants showed no linkage between them (normalized D' = 1.03, p value = 0.56). CONCLUSIONS: Allelic frequencies closely matched reported data, and protein structure analysis confirmed variant impacts on LAL enzyme function. Notably, this study marks the first analysis of rs1051338 and rs116928232 in a healthy Mexican mestizo population.

Low Expression of E-Cadherin and Cdh1 Variants Associated with Diffuse Gastric Cancer

ABSTRACT Background Reduced or null expression of E-cadherin protein is a frequent cause of diffuse gastric cancer (DGC). More than 50% of patients with DGC present somatic variants in CDH1 gene. Objectives The objectives of this study were to study E-cadherin expression and identify variants in the CDH1 gene in gastric tumors of patients with DGC. Methods We studied 18 Mexican DGC patients who attended a hospital of the Mexican Social Security Institute; E-cadherin expression was determined by immunohistochemistry, and variants were identified by Sanger sequencing in promoter and coding regions. Predictive analysis was performed using PolyPhen-2 and HOPE software. Results We found that 56% of DGC patients showed reduced expression of E-cadherin. All patients carried CDH1 variants; overall, 12 different CDH1 variants were identified. Predictive analysis revealed that the rs114265540 variant was probably damaging, with a value of 0.985, indicating a functional impact on the E-cadherin protein. Variants rs34939176 and rs33964119 were identified as risk factors for DGC (odds' ratios [OR] = 31.3, 95% CI 6.3-154.0, p < 0.001; OR = 6.1, 95% CI 2.0-19.0, p < 0.001, respectively) given their elevated frequency and by comparing it with those reported for MXL population in the 1000 Genomes Project database. Conclusions In this Mexican population, the percentage of diffuse gastric tumors with reduced expression of E-cadherin was similar to that reported in other populations. All gastric tumors of DGC patients studied had somatic CDH1 gene variants; however, the rs114265540, rs34939176, and rs33964119 variants were importantly related to DGC.

Low expression of E-Cadherin and CDH1 variants associated with diffuse gastric cancer.

Background: Reduced or null expression of E-cadherin protein is a frequent cause of diffuse gastric cancer (DGC). More than 50% of patients with DGC present somatic variants in CDH1 gene. Objectives: The objectives of this study were to study E-cadherin expression and identify variants in the CDH1 gene in gastric tumors of patients with DGC. Methods: We studied 18 Mexican DGC patients who attended a hospital of the Mexican Social Security Institute; E-cadherin expression was determined by immunohistochemistry, and variants were identified by Sanger sequencing in promoter and coding regions. Predictive analysis was performed using PolyPhen-2 and HOPE software. Results: We found that 56% of DGC patients showed reduced expression of E-cadherin. All patients carried CDH1 variants; overall, 12 different CDH1 variants were identified. Predictive analysis revealed that the rs114265540 variant was probably damaging, with a value of 0.985, indicating a functional impact on the E-cadherin protein. Variants rs34939176 and rs33964119 were identified as risk factors for DGC (odds' ratios [OR] = 31.3, 95% CI 6.3-154.0, p < 0.001; OR = 6.1, 95% CI 2.0-19.0, p < 0.001, respectively) given their elevated frequency and by comparing it with those reported for MXL population in the 1000 Genomes Project database. Conclusions: In this Mexican population, the percentage of diffuse gastric tumors with reduced expression of E-cadherin was similar to that reported in other populations. All gastric tumors of DGC patients studied had somatic CDH1 gene variants; however, the rs114265540, rs34939176, and rs33964119 variants were importantly related to DGC.

Molecular genetic diagnosis by next-generation sequencing in a cohort of Mexican patients with haemophilia and report of novel variants.

INTRODUCTION: Molecular analysis in haemophilia is currently used in the diagnosis, treatment and prognosis of this disease. Hispanic populations in Latin America have been of interest to researchers due to the reportedly high prevalence of inhibitors in these patients. AIM: To perform next-generation sequencing (NGS) in a cohort of Mexican patients with HA and HB and correlate with clinical phenotypes. METHODS: Patients with Haemophilia A (HA) or haemophilia B (HB), were evaluated using NGS with an Ion AmpliSeq Custom Panel. Odds ratios (ORs) for associations between F8 variants and inhibitors were obtained. RESULTS: A total of 85 patients (60 with HA and 25 with HB) were included. Pathogenic variants in F8 were found in 93.3% of HA patients and in F9 in 96% of HB patients. Twelve novel potentially pathogenic variants were found. Inhibitors were observed in 20% of patients with severe HA. Four patients clinically diagnosed with HA were negative for F8 variants. CONCLUSION: Overall detection rate of pathogenic variants in F8 and F9 genes was 94.6%. We identified 12 non previously reported variants and pathogenic variants in other coagulation related genes. Molecular diagnosis of HA and HB permits better options for management, assessment and genetic counseling.

In silico analysis of missense mutations in exons 1-5 of the F9 gene that cause hemophilia B.

BACKGROUND: Missense mutations in the first five exons of F9, which encodes factor FIX, represent 40% of all mutations that cause hemophilia B. To address the ongoing debate regarding in silico identification of disease-causing mutations at these exons, we analyzed 215 missense mutations from www.factorix.org using six in silico prediction tools, which are the most common used programs for analysis prediction of impact of mutations on the protein structure and function, with further advantage of using similar approaches. We developed different algorithms to integrate multiple predictions from such tools. In order to approach a structural analysis on FIX we performed a modeling of five selected pathogenic mutations. RESULTS: SIFT, PolyPhen-2 HumDiv, SNAP2, and MutationAssessor were the most successful in identifying true non-causative and causative mutations. A proposed function integrating these algorithms (wgP4) was the most sensitive (90.1%), specific (22.6%), and accurate (87%) than similar functions, and identified 187 variants as deleterious. Clinical phenotype was significantly associated with predicted causative mutations at all five exons. However, PolyPhen-2 HumDiv was more successful in linking clinical severity to specific exons, while functions that integrate 4-6 predictions were more successful in linking phenotype to genotypes at the light chain (exons 3-5). The most important value of integrating multiple predictions is the inclusion of scores derived from different approaches. Modeling of protein structure showed the effects of pathogenic nsSNPs on structure and function of FIX. CONCLUSIONS: A simple function that integrates information from different in silico programs yields the best prediction of mutated phenotypes. However, the specificity, sensitivity, and accuracy of genotype-phenotype predictions depend on specific characteristics of the protein domain and the disease of interest as we validated by the structural analysis of selected pathogenic F9 mutations. The proposed function integrating algorithm (wgP4) might be useful for the analysis of nsSNPs impact on other genes.