Aims: The aim of this study is to elucidate the genetic landscape of microspherophakia (MSP) and describe the genotype-phenotype correlation of MSP. Additionally, the study seeks to enhance the understanding of the pathogenic mechanisms of MSP through the discovery of novel loci.
Methods: Patients diagnosed with MSP at the Eye and ENT Hospital of Fudan University, Shanghai, were included in the study and all underwent panel-based next-generation sequencing and bioinformatics analysis. Comprehensive ophthalmologic evaluations were conducted for each participant.
Results: Our analysis encompassed 118 eyes from 59 patients with MSP, revealing 13 gene variations linked to the condition. Notably, FBN1 mutations were identified in 31 patients (52.5%), highlighting its higher prevalence. Among the genetic variations discovered, 28 represented novel mutations. Statistical analysis unveiled significant associations between specific gene mutations and ocular biometric parameters: axial length (AL, p = 0.011), Z-score axial length (Z-AL, p < 0.001), white-to-white (WTW, p = 0.009), Z-score white-to-white (p = 0.012), mean keratometry (p < 0.001), astigmatism (AST, p = 0.021), anterior chamber depth (ACD, p = 0.003), lens thickness (LT, p = 0.012) and central endothelial cell count/mm2 (p = 0.005). Patients with FBN1 mutations had the longest AL, while those with CBS mutations showed significantly wilder WTW measurements. Patients with ADAMTS17 mutations presented with increased LT and decreased WTW, ADAMTSL4 mutations were linked to the greater Km and AST. Patients with LTBP mutations exhibited the largest WTW, and ASPH mutations was associated with the shortest AL but thick LT. Additionally, there was a relationship among gene mutations, diagnostic age and ocular biometric parameters.
Conclusion: The study demonstrates that MSP is associated with a diverse range of genetic mutations, with FBN1 being the most common. Novel mutations were identified, and significant correlations were found between specific genetic variations and ocular biometric parameters. These results provide new insights into the genetic underpinnings of MSP and its clinical characteristics, advancing our understanding of the condition's pathogenic mechanisms.
Background: Substantial data support a heritable basis for cardiac conduction disorders (CCDs), but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood, therefore, we sought to identify genetic loci associated with CCDs.
Methods: We performed meta-analyses of genome-wide association studies to identify genetic loci for atrioventricular block (AVB), left bundle branch block (LBBB), and right bundle branch block (RBBB) from public data from the UK Biobank and FinnGen consortium. We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and ECG-wide phenome-wide associations for each indexed SNP.
Results: Analysis comprised over 700,000 individuals for each trait. We identified 10, 4 and 0 significant loci for AVB (PLEKHA3, TTN, FNDC3B, SENP2, SCN10A, RRH, PPARGC1A, PKD2L2, NKX2-5 and TBX20), LBBB (PPARGC1A, HAND1, TBX5, and ADAMTS5) and RBBB, respectively. Transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of SCN10A and AVB. Phenome-wide associations identified traits with both cardiovascular and non- cardiovascular traits with indexed SNPs.
Conclusions: Our analysis highlight gene regions associated with channel function, cardiac development, sarcomere function and energy modulation as important potential effectors of CCDs susceptibility.
Purpose: Sengers-syndrome (S.S) is a genetic disorder characterized by congenital cataracts, hypertrophic cardiomyopathy, skeletal myopathy and lactic acidosis. All reported cases were genetically caused by biallelic mutations in the AGK gene. We herein report a pathogenic variant in TIMM29 gene, encoding Tim29 protein, as a novel cause of S.S. Notably, AGK and Tim29 proteins are components of the TIM22 complex, which is responsible for importing carrier proteins into the inner mitochondrial membrane.
Method: Clinical data of 17 consanguineous patients featuring S.S was obtained. Linkage analysis, and sequencing were used to map and identify the disease-causing gene. Tissues derived from the study participants and a Drosophila melanogaster model were used to evaluate the effects of TIMM29 variant on S.S.
Results: The patients presented with a severe phenotype of S.S, markedly elevated serum creatine-phosphokinase, combined mitochondrial-respiratory-chain-complexes deficiency, reduced pyruvate-dehydrogenase complex activity, and reduced adenine nucleotide translocator 1 protein. Histopathological studies showed accumulation of abnormal mitochondria. Homozygosity mapping and gene sequencing revealed a biallelic variant in TIMM29 NM_138358.4:c.514T > C NP_612367.1:p.(Trp172Arg). The knockdown of the Drosophila TIMM29 orthologous gene (CG14270) recapitulated the phenotype and pathology observed in the studied cohort. We expand the clinical phenotype of S.S and provide substantial evidence supporting TIMM29 as the second causal gene of a severe type of S.S, designated as S.S- TIMM29.
Conclusion: The present study uncovers several biochemical differences between the two S.S types, including the hyperCPKemia being almost unique for S.S-TIMM29 cohort, the different frequency of MMRCC and PDHc deficiencies among the two S.S types. We propose to designate the S.S associated with TIMM29 homozygous variant as S.S-TIMM29.
Background: Dysfunctions within the liver system are intricately linked to the progression of diabetic retinopathy (DR) and non-alcoholic fatty liver disease (NAFLD). This study leverages systematic analysis to elucidate the complex cross-talk and communication pathways among diverse cell populations implicated in the pathogenesis of DR and NAFLD.
Methods: Single-cell RNA sequencing data for proliferative diabetic retinopathy (PDR) and NAFLD were retrieved from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was conducted and followed by pseudo-time analysis to delineate dynamic changes in core cells and differentially expressed genes (DEGs). CellChat was employed to predict intercellular communication and signaling pathways. Additionally, gene set enrichment and variation analyses (GSEA and GSVA) were performed to uncover key functional enrichments.
Results: Our comparative analysis of the two datasets focused on T cells, macrophages and endothelial cells, revealing SYNE2 as a notable DEG. Notably, common genes including PYHIN1, SLC38A1, ETS1 (T cells), PPFIBP1, LIFR, HSPG2 (endothelial cells), and MSR1 (macrophages), emerged among the top 50 DEGs across these cell types. The CD45 signaling pathway was pivotal for T cells and macrophages, exerting profound effects on other cells in both PDR and NAFLD. Moreover, GSEA and GSVA underscored their involvement in cellular communication, immune modulation, energy metabolism, mitotic processes.
Conclusion: The comprehensive investigation of T cells, macrophages, endothelial cells, and the CD45 signaling pathway advances our understanding of the intricate biological processes underpinning DR and NAFLD. This research underscores the imperative of exploring immune-related cell interactions, shedding light on novel therapeutic avenues in these disease contexts.
Background: The protective effects of higher educational attainment (EA) and intelligence on COVID-19 outcomes are not yet understood with regard to their dependency on income. The objective of our study was to examine the overall as well as independent effects of the three psychosocial factors on the susceptibility to and severity of COVID-19. To accomplish this, we utilized genetic correlation, Mendelian randomization (MR), and multivariable MR (MVMR) analyses to evaluate genetic associations between EA, intelligence, household income, and three specific COVID-19 outcomes: SARS-CoV-2 infection, hospitalized COVID-19, and critical COVID-19.
Results: The genetic correlation analysis revealed that COVID-19 outcomes were negatively correlated with the three psychosocial factors (rg: -0.19‒-0.36). The MR analysis indicated that genetic liability to EA, intelligence, and income exerted overall protective effects against SARS-CoV-2 infection (OR: 0.86‒0.92), hospitalized COVID-19 (OR: 0.70‒0.80), and critical COVID-19 (OR: 0.65‒0.85). MVMR analysis revealed that elevated levels of EA conferred independent protective effects against SARS-CoV-2 infection (OR: 0.85), hospitalization due to COVID-19 (OR: 0.79), and critical COVID-19 (OR: 0.63). Furthermore, intelligence exhibited a negative association with the risk of SARS-CoV-2 infection (OR: 0.91), whereas a higher income was linked to an elevated risk of SARS-CoV-2 infection (OR: 1.13).
Conclusions: Our findings indicated that EA could significantly reduce the risk and severity of COVID-19, regardless of intelligence and income. However, the impact of intelligence or income on COVID-19 severity was not supported by our research.
Background: The etiology of prostate cancer remained elusive, whether plasma protein levels are associated with prostate cancer is still unknown.
Methods: We have performed Mendelian randomization analyses to calculate the causal effects of plasma proteins on the risk of prostate cancer in the PRACTICAL consortium dataset using cis-protein quantitative trait loci (cis-pQTL) variants as instrumental variables for plasma proteins, and cis-expression quantitative trait locus (cis-eQTL) for the circulating gene expression. We also replicated the findings in the FinnGen consortium.
Results: Genetically proxied levels of 4 plasma proteins (CREB3L4, HDGF, SERPINA3, GNPNAT1) were identified as positively correlated with an increased risk of prostate cancer, while an increase in genetically proxied levels of 5 plasma proteins (TNFRSF6B, GSK3A, EIF4B, CLIC1, SMAD2) were significantly associated with a decreased risk of prostate cancer in the PRACTICAL consortium. Among the identified proteins, the causal effects of six proteins including CREB3L4, HDGF, SERPINA3, TNFRSF6B, EIF4B, and SMAD2 remained significant in the replication analyses in the FinnGen consortium and when combined with meta-analyses (SMAD2: OR 0.710, 95% CI 0.578-0.873, p-value = 0.001; CREB3L4: OR 1.260, 95% CI 1.164-1.364, p-value < 0.0001; HDGF: OR 1.072, 95% CI 1.021-1.125, p-value = 0.005; SERPINA3: OR 1.138, 95% CI 1.091-1.187, p-value < 0.0001; TNFRSF6B: OR 0.656, 95% CI 0.496-0.869, p-value = 0.003; EIF4B: OR 0.701, 95% CI 0.618-0.796, p-value < 0.0001). SMAD2 and CREB3L4 gene expressions proxied with cis-expression quantitative trait loci are also significantly associated with the risk of prostate cancer in both consortiums and when combined with meta-analyses (SMAD2: OR 0.787, 95% CI 0.719-0.861, p-value = 1.00 × 10-4; CREB3L4: OR 1.219, 95% CI 1.033-1.438, p-value = 0.019).
Conclusions: Our consistent results highlighted the important roles of plasma SMAD2 and CREB3L4 in the risk of prostate cancer. Further investigations on these proteins may reveal their potential in the prevention and treatment of prostate cancer.
Aquaporin1 (AQP1) facilitates water transport. Its ability to be a biomarker at the pan-cancer level remains uninvestigated. We performed immunohistochemical staining on tissues from 370 individuals with kidney neoplasms to measure AQP1 expression. We utilized Kaplan-Meier survival analysis, Chi-square tests, and multivariate Cox regression analyses to assess the prognostic relevance of AQP1 expression. In the pan-cancer context, we explored AQP1's competing endogenous RNAs network, protein-protein interactions, genomic changes, gene set enrichment analysis (GSEA), the correlation of AQP1 expression with survival outcomes, drug sensitivity, drug molecular docking, tumor purity and immunity. AQP1 shRNA expressing 786-O cells were established. Cell proliferation was assessed by Cell Counting Kit-8 and colony formation. Transwell migration, invasion, and cell scratch assays were conducted. In our study, AQP1 expression was an independent protective factor for OS and PFS in renal cancer patients. AQP1 expression significantly correlated with survival outcomes in renal cancers, LGG, SARC, HNSC and UVM. PI-103 sensitivity was related to AQP1 expression and had potential binding cite with AQP1 protein. Knockdown of AQP1 reduced cell proliferation, migration and invasion. Our study uncovered AQP1 as a biomarker for favorable survival outcomes in renal cancers. Furthermore, the bioinformatic analysis promoted its implication in pan-cancer scope.
Objective: Thalassemia is among the most common inherited diseases worldwide. We aimed to analyze the genotype and frequency distribution of thalassemia in a general hospital in Beijing and provide a reference for genetic counseling and prenatal diagnosis.
Methods: A total of 3196 cases of thalassemia screened at Peking Union Medical College Hospital (PUMCH) between January 2018 and January 2022 were collected. Thalassemia genotypes were tested using gap polymerase chain reaction (gap-PCR), PCR, reverse dot blot (RDB), and Sanger sequencing analyses. The pathogenicity of the rare variants was analyzed using bioinformatics approaches.
Results: Total of 1936 positive routine α/β-thalassemia were detected from 3196 blood samples, including 733 α-thalassemia variants, 1170 β-thalassemia variants, and 33 cases with concurrent α- and β-thalassemia variants. Two novel variants, HBA2:c.300+82G>C and HBB:codon85(-T), were identified in HBA2 and HBB genes, respectively, and were not detected in the ExAC, gnomAD, HbVar, and HGMD databases.
Conclusions: The genotype distribution of thalassemia in a general hospital in Beijing is complex and heterogeneous. The novel variants in HBA2 and HBB are likely to underlie α/β-thalassemia in these patients.
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