SARS-CoV-2 Epitope Presentation by Class II HLA Genotypes Common in North American Populations: A Proposed Computational Approach for Vaccine Efficacy Evaluation

Background: Human Leukocyte Antigen (HLA) gene polymorphisms between ethnic groups have been shown to play a role in the heterogeneity of response to SARS-CoV-2, in terms of COVID-19 disease severity and susceptibility, in addition to socioeconomic factors. It was predicted that this finding may extend to vaccine responsiveness. Purpose: To the best of our knowledge, this study was the first that aimed to predict and evaluate the effectiveness of four COVID-19 vaccines across North American ethnic groups, in terms of their ability to trigger CD4+ T cell help, based on class II HLA allele frequencies. Methods: Various databases including the Immune Epitope Database (IEDB) were used in this computational approach. The number of peptide-HLA high-affinity pairs between the most common HLA II haplotypes and SARS-CoV-2 peptides in various vaccine types were retrieved and compared between ethnicities. From this, the efficiency of antigen presentation to CD4+ T cells was evaluated, a crucial component in the context of vaccination for cellular immunity and support in antibody generation. Results: Multiple discrepancies in vaccine effectiveness for ethnic minorities relative to the Caucasian group, overrepresented in vaccine clinical trials, were highlighted. Recommendations were issued in terms of which vaccine types could be most effective for particular ethnicities. Conclusion: There exists a genetic basis for differential responses to vaccines among ethnic groups in North America. However, given the multifactorial nature of vaccine responsiveness and limitations of computational methods, this study offers future research directions to undertake before the findings can be transferred to clinical and public health settings

Strand bias in complementary single-nucleotide polymorphisms of transcribed human sequences: evidence for functional effects of synonymous polymorphisms

BACKGROUND: Complementary single-nucleotide polymorphisms (SNPs) may not be distributed equally between two DNA strands if the strands are functionally distinct, such as in transcribed genes. In introns, an excess of A↔G over the complementary C↔T substitutions had previously been found and attributed to transcription-coupled repair (TCR), demonstrating the valuable functional clues that can be obtained by studying such asymmetry. Here we studied asymmetry of human synonymous SNPs (sSNPs) in the fourfold degenerate (FFD) sites as compared to intronic SNPs (iSNPs). RESULTS: The identities of the ancestral bases and the direction of mutations were inferred from human-chimpanzee genomic alignment. After correction for background nucleotide composition, excess of A→G over the complementary T→C polymorphisms, which was observed previously and can be explained by TCR, was confirmed in FFD SNPs and iSNPs. However, when SNPs were separately examined according to whether they mapped to a CpG dinucleotide or not, an excess of C→T over G→A polymorphisms was found in non-CpG site FFD SNPs but was absent from iSNPs and CpG site FFD SNPs. CONCLUSION: The genome-wide discrepancy of human FFD SNPs provides novel evidence for widespread selective pressure due to functional effects of sSNPs. The similar asymmetry pattern of FFD SNPs and iSNPs that map to a CpG can be explained by transcription-coupled mechanisms, including TCR and transcription-coupled mutation. Because of the hypermutability of CpG sites, more CpG site FFD SNPs are relatively younger and have confronted less selection effect than non-CpG FFD SNPs, which can explain the asymmetric discrepancy of CpG site FFD SNPs vs. non-CpG site FFD SNPs

Vitamin D levels and risk of type 1 diabetes:A Mendelian randomization study

[This corrects the article DOI: 10.1371/journal.pmed.1003536.]

Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease

Some individuals with a particular disease-causing mutation or genotype fail to express most if not all features of the disease in question, a phenomenon that is known as ‘reduced (or incomplete) penetrance’. Reduced penetrance is not uncommon; indeed, there are many known examples of ‘disease-causing mutations’ that fail to cause disease in at least a proportion of the individuals who carry them. Reduced penetrance may therefore explain not only why genetic diseases are occasionally transmitted through unaffected parents, but also why healthy individuals can harbour quite large numbers of potentially disadvantageous variants in their genomes without suffering any obvious ill effects. Reduced penetrance can be a function of the specific mutation(s) involved or of allele dosage. It may also result from differential allelic expression, copy number variation or the modulating influence of additional genetic variants in cis or in trans. The penetrance of some pathogenic genotypes is known to be age- and/or sex-dependent. Variable penetrance may also reflect the action of unlinked modifier genes, epigenetic changes or environmental factors. At least in some cases, complete penetrance appears to require the presence of one or more genetic variants at other loci. In this review, we summarize the evidence for reduced penetrance being a widespread phenomenon in human genetics and explore some of the molecular mechanisms that may help to explain this enigmatic characteristic of human inherited disease

Development of simple and effective PCR based assay to detect PCCA mutation (c.425G > A) among Saudi carriers and functional study of the homozygous PCCA mutations

The aim of this study is to develop a rapid and effective method to screen for Saudi carriers of one of the most common propionic acidemia mutations (c.425G > A) and to study the functional impact of this mutation. Using allele-specific primers, we have developed a qPCR assay that clearly distinguishes heterozygotes from mutated and wild type homozygotes that overcome the dependence on labor-intensive gene sequencing. We show here that (i) qPCR rapid test has strong accuracy in detecting (c.425G > A) mutation in heterozygotes and homozygotes individuals and that the Ct-value cut-offs were estimated to be and 23.37 ± 0.04 (CV-6 %, 95 %CI-7.25) for homozygote, 25.06 ± 0.02 (CV-3.5 %, 95 %CI-7.85) for heterozygote PCCA c.425G > A mutation and 29.55 ± 0.002 (CV-11 %, 95 %CI-1.41) for PCCA wild type; (ii) the incidence of PA heterozygotes/carriers in Saudi population is about 550/100,000; (iii) skin fibroblast assays show that homozygote c.425G > A mutation induced propionyl-CoA carboxylase activity abrogation, (iv) PA patients showed an increased level of propionyl carnitine C3 in blood and 3-hydroxy propionic acid and methyl citrate in urine. Conclusion: qPCR represent an effective strategy to assess for PCCA mutation carriers in the Saudi population and we believe that will help in preventing homozygosity in the population after been implemented in pre-marriage screening program.KFMC research grant.https://www.journals.elsevier.com/saudi-journal-of-biological-scienceshj2023Medical Microbiolog

The TCF7L2 locus and type 1 diabetes

<p>Abstract</p> <p>Background</p> <p><it>TCF7L2 </it>belongs to a subfamily of TCF7-like HMG box-containing transcription factors, and maps to human chromosome 10q25.3. A recent study identified genetic association of type 2 diabetes (T2D) with this gene, correlated with diminished insulin secretion. This study aimed to investigate the possibility of genetic association between <it>TCF7L2 </it>and type 1 diabetes (T1D).</p> <p>Methods</p> <p>The SNP most significantly associated with T2D, rs7903146, was genotyped in 886 T1D nuclear family trios with ethnic backgrounds of mixed European descent.</p> <p>Results</p> <p>This study found no T1D association with, and no age-of-onset effect from rs7903146.</p> <p>Conclusion</p> <p>This study suggests that a T2D mechanism mediated by <it>TCF7L2 </it>does not participate in the etiology of T1D.</p

Sequence Variation in Promoter of Ica1 Gene, Which Encodes Protein Implicated in Type 1 Diabetes, Causes Transcription Factor Autoimmune Regulator (AIRE) to Increase Its Binding and Down-regulate Expression

ICA69 (islet cell autoantigen 69 kDa) is a protein implicated in type 1 diabetes mellitus in both the non-obese diabetic (NOD) mouse model and humans. ICA69 is encoded by the Ica1 gene on mouse chromosome 6 A1-A2. We previously reported reduced ICA69 expression in the thymus of NOD mice compared with thymus of several non-diabetic mouse strains. We propose that reduced thymic ICA69 expression could result from variations in transcriptional regulation of the gene and that polymorphisms within the Ica1 core promoter may partially determine this transcriptional variability. We characterized the functional promoter of Ica1 in NOD mice and compared it with the corresponding portions of Ica1 in non-diabetic C57BL/6 mice. Luciferase reporter constructs demonstrated that the NOD Ica1 promoter region exhibited markedly reduced luciferase expression in transiently transfected medullary thymus epithelial (mTEC+) and B-cell (M12)-derived cell lines. However, in a non-diabetic strain, C57BL/6, the Ica1 promoter region was transcriptionally active when transiently transfected into the same cell lines. We concomitantly identified five single nucleotide polymorphisms within the NOD Ica1 promoter. One of these single nucleotide polymorphisms increases the binding affinity for the transcription factor AIRE (autoimmune regulator), which is highly expressed in thymic epithelial cells, where it is known to play a key role regulating self-antigen expression. We conclude that polymorphisms within the NOD Ica1 core promoter may determine AIRE-mediated down-regulation of ICA69 expression in medullary thymic epithelial cells, thus providing a novel mechanistic explanation for the loss of immunologic tolerance to this self-antigen in autoimmunity

Study of Transcriptional Effects in Cis at the IFIH1 Locus

Background: The Thr allele at the non-synonymous single-nucleotide polymorphism (nsSNP) Thr946Ala in the IFIH1 gene confers risk for Type 1 diabetes (T1D). The SNP is embedded in a 236 kb linkage disequilibrium (LD) block that includes four genes: IFIH1, GCA, FAP and KCNH7. The absence of common nsSNPs in the other genes makes the IFIH1 SNP the strongest functional candidate, but it could be merely a marker of association, due to LD with a variant regulating expression levels of IFIH1 or neighboring genes. Methodology/Principal Findings: We investigated the effect of the T1D-associated variation on mRNA transcript expression of these genes. Heterozygous mRNA from lymphoblastoid cell lines (LCLs), pancreas and thymus was examined by allelic expression imbalance, to detect effects in cis on mRNA expression. Using single-nucleotide primer extension, we found no difference between mRNA transcripts in 9 LCLs, 6 pancreas and 13 thymus samples, suggesting that GCA and FAP are not involved. On the other hand, KCNH7 was not expressed at a detectable level in all tissues examined. Moreover, the association of the Thr946Ala SNP with T1D is not due to modulation of IFIH1 expression in organs involved in the disease, pointing to the IFIH1 nsSNP as the causal variant. Conclusions/Significance: The mechanism of the association of the nsSNP with T1D remains to be determined, but does not involve mRNA modulation. It becomes necessary to study differential function of the IFIH1 protein alleles at Thr946Al

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays

A Genome-Wide Meta-Analysis of Six Type 1 Diabetes Cohorts Identifies Multiple Associated Loci

Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10−11) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10−9) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10−9) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D