Human Genetics最新文献

KITLG pathogenic variants have been associated to three distinct clinical presentations with different combinations of hearing loss and/or pigmentation abnormalities. However, its involvement in isolated hearing loss has not been confirmed since its initial description in two families. Besides, KITLG is so far the only gene prevailingly involved in unilateral isolated hearing loss. We therefore conducted a retrospective study of patients with KITLG alterations in the French national Reference Network for Genetic Hearing Loss and one case was added through the Genematcher exchange platform. We describe a series of monoallelic KITLG deletions and variations in a cohort of 14 symptomatic patients from eight unrelated families. All patients presented with unilateral, bilateral symmetric or asymmetric sensorineural hearing loss. When not profound, hearing loss was predominant on low frequencies. Most KITLG alterations are likely to result in loss-of-function and aggregate in the extracellular region, disrupting the KIT-binding domain or its structure. Penetrance is not complete, and unspecific pigmentation alterations were observed in only three patients. The present study confirms KITLG involvement in isolated unilateral, bilateral symmetric or asymmetric hearing loss. This confirmation indicates that genetic testing can be relevant in early-onset, non-sudden, isolated unilateral hearing loss.

Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are autosomal dominant neuromuscular disorders associated with expansions of microsatellites, respectively, in DMPK and CNBP. Their pathogenesis is linked to the global aberrant alternative splicing (AAS) of many genes and marks mostly muscular and neuronal tissues, while blood is the least affected. Recent data in DM1 skeletal muscles indicated that abnormalities in RNA metabolism also include global upregulation of circular RNAs (circRNAs). CircRNAs are a heterogeneous group considered splicing errors and by-products of canonical splicing. To elucidate whether circRNA dysregulation is an inherent feature of the myotonic environment, we perform their analysis in the frontal cortex and whole blood of DM1 and DM2 patients. We find a global elevation of circRNAs in both tissues, and its magnitude is neither correlated with the differences in their parental gene expression nor is associated with AAS published earlier. Aberrantly spliced cassette exons of linear transcripts affected in DM1 and DM2 are not among the circularized exons, which unique genomic features prerequisite back-splicing. However, the blueprint of the AAS of linear RNAs is found in a variety of circRNA isoforms. The heterogeneity of circRNAs also originates from the utilization of exonic and intronic cryptic donors/acceptors in back splice junctions, and intron-containing circRNAs are more characteristic of the blood. Overall, this study reveals circRNA dysregulation in various tissues from DM1 and DM2; however, their levels do not correlate with the AAS in linear RNAs, suggesting a potential independent regulatory mechanism underlying circRNA upregulation in myotonic dystrophy.

Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease, thus posing a challenge in the interpretation of genetic testing results. Here we report an improved method for predicting the VUS of Arylsulfatase A (ARSA) gene as part of the Critical Assessment of Genome Interpretation challenge (CAGI6). Our method uses a transfer learning approach that leverages a pre-trained protein language model to predict the impact of mutations on the activity of the ARSA enzyme, whose deficiency is known to cause a rare genetic disorder, metachromatic leukodystrophy. Our innovative framework combines zero-shot log odds scores and embeddings from the ESM, an evolutionary scale model as features for training a supervised model on gene variants functionally related to the ARSA gene. The zero-shot log odds score feature captures the generic properties of the proteins learned due to its pre-training on millions of sequences in the UniProt data, while the ESM embeddings for the proteins in the ARSA family capture features specific to the family. We also tested our approach on another enzyme, N-acetyl-glucosaminidase (NAGLU), that belongs to the same superfamily as ARSA. Our results demonstrate that the performance of our family models (augmented ESM models) is either comparable or better than the ESM models. The ARSA model compares favorably with the majority of state-of-the-art predictors on area under precision and recall curve (AUPRC) performance metric. However, the NAGLU model outperforms all pathogenicity predictors evaluated in this study on AUPRC metric. The improved AUPRC has relevance in a diagnostic setting where variant prioritization generally entails identifying a small number of pathogenic variants from a larger number of benign variants. Our results also indicate that genes that have sparse or no experimental variant impact data, the family variant data can serve as a proxy training data for making accurate predictions. Attention analysis of active sites and binding sites in ARSA and NAGLU proteins shed light on probable mechanisms of pathogenicity for positions that are highly attended.

The genetic relationship between migraine and stroke remains underexplored, particularly in the context of druggable targets. Previous studies have been limited by small sample sizes and a lack of focus on genetic-targeted therapies for these conditions. We analyzed the association and causality between migraine and stroke using multivariable logistic regression in the UK Biobank cohort and Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) data. Integrating expression quantitative trait loci (eQTLs) data from blood and brain regions, we explored the phenotypic and genetic links between migraine medications, drug target, and stroke. Additionally, we explored novel druggable genes for migraine and evaluated their effects on migraine signaling molecules and stroke risk. Migraine was significantly associated with stroke, particularly ischemic stroke (IS) and intracerebral hemorrhage (ICH), with MR analysis confirming a causal link to ICH. HTR1A emerged as a potential link between antidepressants (preventive medications for migraine) and stroke. We identified 17 migraine-related druggable genes, with 5 genes (HMGCR, TGFB1, TGFB3, KCNK5, IMPDH2) associated with nine existing drugs. Further MR analysis identified correlation of CELSR3 and IMPDH2 with cGMP pathway marker PRKG1, and identified KCNK5, PLXNB1, and MDK as novel migraine-associated druggable genes significantly linked to the stroke risks. These findings established the phenotypic and genetic link between migraine, its medication and stroke, identifying potential targets for single and dual-purpose therapies for migraine and stoke, and emphasized the need for further research to validate these associations.

The Genetics of Neurodevelopmental Disorders Lab in Padua provided a new intellectual disability (ID) Panel challenge for computational methods to predict patient phenotypes and their causal variants in the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6). Eight research teams submitted a total of 30 models to predict phenotypes based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. Here, we assess the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and their causal variants. We also evaluated predictions for possible genetic causes in patients without a clear genetic diagnosis. Like the previous ID Panel challenge in CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (Pathogenic/Likely Pathogenic, Variants of Uncertain Significance and Risk Factors) were provided. The phenotypic traits and variant data of 150 patients from the CAGI5 ID Panel Challenge were provided as training set for predictors. The CAGI6 challenge confirms CAGI5 results that predicting phenotypes from gene panel data is highly challenging, with AUC values close to random, and no method able to predict relevant variants with both high accuracy and precision. However, a significant improvement is noted for the best method, with recall increasing from 66% to 82%. Several groups also successfully predicted difficult-to-detect variants, emphasizing the importance of variants initially excluded by the Padua NDD Lab.

Developmental anomalies of the hearing organ, the cochlea, are diagnosed in approximately one-fourth of individuals with congenital. The majority of patients with cochlear malformations remain etiologically undiagnosed due to insufficient knowledge about underlying genes or the inability to make conclusive interpretations of identified genetic variants. We used exome sequencing for the genetic evaluation of hearing loss associated with cochlear malformations in three probands from unrelated families deafness. We subsequently generated monoclonal induced pluripotent stem cell (iPSC) lines, bearing patient-specific knockins and knockouts using CRISPR/Cas9 to assess pathogenicity of candidate variants. We detected FGF3 (p.Arg165Gly) and GREB1L (p.Cys186Arg), variants of uncertain significance in two recognized genes for deafness, and PBXIP1(p.Trp574*) in a candidate gene. Upon differentiation of iPSCs towards inner ear organoids, we observed developmental aberrations in knockout lines compared to their isogenic controls. Patient-specific single nucleotide variants (SNVs) showed similar abnormalities as the knockout lines, functionally supporting their causality in the observed phenotype. Therefore, we present human inner ear organoids as a potential tool to validate the pathogenicity of DNA variants associated with cochlear malformations.

Background: Genome-wide association studies have identified dozens of genomic loci for obesity. However, functional genes and their detailed genetic mechanisms underlying these loci are mainly unknown. In this study, we conducted an integrative study to prioritize plausibly functional genes by combining information from genome-, transcriptome- and proteome-wide association analyses.

Methods: We first conducted proteome-wide association analyses and transcriptome-wide association analyses for the six obesity-related traits. We then performed colocalization analysis on the identified loci shared between the proteome- and transcriptome-association analyses. Finally, we validated the identified genes with other plasma/blood reference panels. The highlighted genes were assessed for expression of other tissues, single-cell and tissue specificity, and druggability.

Results: We prioritized 4 high-confidence genes (FASN, ICAM1, PDCD6IP, and YWHAB) by proteome-wide association studies, transcriptome-wide association studies, and colocalization analyses, which consistently influenced the variation of obesity traits at both mRNA and protein levels. These 4 genes were successfully validated using other plasma/blood reference panels. These 4 genes shared regulatory structures in obesity-related tissues. Single-cell and tissue-specific analyses showed that FASN and ICAM1 were explicitly expressed in metabolism- and immunity-related tissues and cells. Furthermore, FASN and ICAM1 had been developed as drug targets.

Conclusion: Our study provided novel promising protein targets for further mechanistic and therapeutic studies of obesity.

Ischemic stroke (IS), characterized by complex etiological diversity, is a significant global health challenge. Recent advancements in genome-wide association studies (GWAS) and transcriptomic profiling offer promising avenues for enhanced risk prediction and understanding of disease mechanisms. GWAS summary statistics from the GIGASTROKE Consortium and genetic and phenotypic data from the UK Biobank (UKB) were used. Transcriptome-Wide Association Studies (TWAS) were conducted using FUSION to identify genes associated with IS and its subtypes across eight tissues. Colocalization analysis identified shared genetic variants influencing both gene expression and disease risk. Sum Transcriptome-Polygenic Risk Scores (STPRS) models were constructed by combining polygenic risk scores (PRS) and polygenic transcriptome risk scores (PTRS) using logistic regression. The predictive performance of STPRS was evaluated using the area under the curve (AUC). A Phenome-wide association study (PheWAS) explored associations between STPRS and various phenotypes. TWAS identified 34 susceptibility genes associated with IS and its subtypes. Colocalization analysis revealed 18 genes with a posterior probability (PP) H4 > 75% for joint expression quantitative trait loci (eQTL) and GWAS associations, highlighting their genetic relevance. The STPRS models demonstrated superior predictive accuracy compared to conventional PRS, showing significant associations with numerous UKB phenotypes, including atrial fibrillation and blood pressure. Integrating transcriptomic data with polygenic risk scores through STPRS enhances predictive accuracy for IS and its subtypes. This approach refines our understanding of the genetic and molecular landscape of stroke and paves the way for tailored preventive and therapeutic strategies.

There are hundreds of rare syndromic diseases involving hearing loss, many of which are not targeted for clinical genetic testing. We systematically explored the genetic causes of undiagnosed syndromic hearing loss using a combination of whole exome sequencing (WES) and a phenotype similarity search system called PubCaseFinder. Fifty-five families with syndromic hearing loss of unknown cause were analyzed using WES after prescreening of several deafness genes depending on patient clinical features. Causative genes were identified in 22 families, including both established genes associated with syndromic hearing loss (PTPN11, CHD7, KARS1, OPA1, DLX5, MITF, SOX10, MYO7A, and USH2A) and those associated with nonsyndromic hearing loss (STRC, EYA4, and KCNQ4). Association of a DLX5 variant with incomplete partition type I (IP-I) anomaly of the inner ear was identified in a patient with cleft lip and palate and acetabular dysplasia. The study identified COL1A1, CFAP52, and NSD1 as causative genes through phenotype similarity search or by analogy. ZBTB10 was proposed as a novel candidate gene for syndromic hearing loss with IP-I. A mouse model with homozygous Zbtb10 frameshift variant resulted in embryonic lethality, suggesting the importance of this gene for early embryonic development. Our data highlight a wide spectrum of rare causative genes in patients with syndromic hearing loss, and demonstrate that WES analysis combined with phenotype similarity search is a valuable approach for clinical genetic testing of undiagnosed disease.

The prevalence of overweight and obesity is increasing, leading to metabolic-associated fatty liver disease (MAFLD) characterized by excessive accumulation of liver fat and a risk of developing hepatocellular carcinoma (HCC). The driver gene mutations may play the roles of passengers that occur in single 'hotspots' and can promote tumorigenesis from benign to malignant lesions. We investigated the impact of high body weight and BMI on HCC survival using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. To explore the effects of obesity-related gene mutations on HCC, we collected driver mutation genes in 34 TCGA patients with BMI ≥ 27 and 23 TCGA patients with BMI < 27. The digital PCR performing the PBMC samples for the variant rate by clinical cohort of 96 NAFLD patients. Our analysis showed that obesity leads to significantly worse survival outcomes in HCC. Using cbioportal, we identified 414 driver mutation genes in patients with obesity and 127 driver mutation genes in non-obese patients. Functional analysis showed that obese-related genes significantly enriched the regulated lipid and insulin pathways in HCC. The insulin secretion pathway in patients with obesity HCC-specific survival identified ABCC8 and PRKCB as significant genes (p < 0.001). It revealed significant differences in gene mutation and gene expression profiles compared to non-obese patients. The digital PCR test ABCC8 variants were detected in PBMC samples and caused a 14.5% variant rate, significantly higher than that of non-obese NAFLD patients. The study findings showed that the gene ABCC8 was a patient with the obesity-related gene in NAFLD, which provides the probability that ABCC8 mutation contributes to the pre-cancer lesion biomarker for HCC.