BMC Medical Genomics最新文献

Genomic variants of uncertain significance (VUS) impede clinical decision-making. In this study, we employ a knock-in strategy in zebrafish to evaluate the COL1A2 c.2123G>A VUS, identified in a 78-year-old female with atypical femoral fractures. Using prime editing, we generated different col1a2 zebrafish lines respectively harboring the VUS, a known pathogenic variant, and a known benign variant. Comprehensive skeletal phenotyping revealed no significant abnormalities in zebrafish harboring the VUS. In contrast, zebrafish with the pathogenic variant showed an increased eye diameter, scoliosis, vertebral fusions, vertebral compressions, fractures, and increased mineralization of the notochord and intervertebral ligament compared to wild type controls. Our findings represent the first demonstration that COL1A2 variant modeling via prime editing in zebrafish not only aids in functional validation, but also holds promise for uncovering the underlying pathogenic mechanisms. This approach can be applied to investigate VUS in other genes as well.

Background: Oxford Nanopore adaptive sampling (NAS) is a method by which the long-read sequencing flowcell accepts or rejects DNA molecules that are actively being sequenced based on their initial ~ 500 bp sequences, selectively increasing target data output. NAS promises up to 5-10 × enrichment of target sequencing yield without additional sample preparation, but this optimal performance is dependent on ideal sample parameters which may be difficult to achieve under many real-world use-cases. We evaluated the use of NAS for profiling clinical sputum metagenomes.

Methods: We sequenced DNA extracted from clinical sputa and spike-in controls of a mock community of bacterial respiratory pathogens, using the current R10.4.1 MinION flowcell chemistry.

Results: We achieved at best 3.1 × enrichment of bacterial sequence output with NAS due to the shorter read lengths (~ 2.5 kb) from the PCR amplification necessary to compensate for low DNA extraction yields. More critically, we encountered rapid pore loss during our runs that reduced total sequencing yield by an estimated 80%. We were unable to mitigate the pore loss despite extensive attempts to reduce contaminant carry-over, and we could not determine its cause but ruled out NAS and pore underloading as contributing factors.

Conclusions: We conclude that the utility of NAS is often limited by the characteristics of the metagenomic sample studied, and that the factors contributing to pore loss need to be resolved before ONT sequencing can be reliably applied to long-read metagenomics.

Background: Acute eczema (AE) is a multifactorial inflammatory skin disease with complex immune dysregulation. The identification of key pathogenic genes may provide novel biomarkers and therapeutic targets.

Methods: An AE rat model was induced using DNCB. Transcriptome sequencing was performed on skin lesions, and differentially expressed genes (DEGs) were identified via DESeq2. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network construction, functional enrichment, and "Friends" analysis were applied to screen hub genes. Multiple machine learning algorithms, including LASSO, support vector machine (SVM), and random forest (RF), were integrated for candidate selection. Quantitative real-time PCR (qRT-PCR), Western blotting, and immunohistochemistry were used for validation.

Results: A total of 1717 DEGs (1190 upregulated, 527 downregulated) were identified. WGCNA and PPI analysis yielded 36 hub genes enriched in immune and inflammatory pathways, particularly Th1/Th2/Th17 differentiation, JAK-STAT, and PI3K-Akt signaling. Cross-validation by machine learning highlighted Icam1 as a top candidate. Experimental assays confirmed significant upregulation of ICAM-1 at mRNA and protein levels in AE lesions compared with controls.

Conclusion: Icam1 is a key gene potentially driving inflammatory infiltration in AE and may serve as a diagnostic target. This integrative bioinformatics-experimental approach provides a robust framework for discovering pathogenic genes in inflammatory skin diseases.

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. The phosphatidylinositol binding clathrin assembly protein (PICALM) gene has been implicated in neurodegenerative diseases. However, molecular genetic investigations into the role of the PICALM gene in PD within Chinese population are still lacking.

Aim: We aim to determine whether two single nucleotide polymorphisms rs642949 and rs510566, in the PICALM gene are associated with the risk and clinical features of PD.

Methods: A total of 400 individuals with PD and 477 healthy controls were recruited. Genotyping of the PICALM gene loci (rs510566 and rs642949) was performed using TaqMan-based polymerase chain reaction-restriction fragment length polymorphism using DNA samples.

Results: For rs642949, the GA genotype conferred a significantly reduced risk of PD under additive model (P_C = 0.030, OR = 0.687, 95% CI: 0.506-0.931). Additionally, in male patients, the AA genotype was associated with a lower risk of PD compared to females (P_{C genotype} = 0.008, OR = 0.355, 95% CI: 0.173-0.730; P_{C allele} = 0.044, OR = 0.721, 95% CI: 0.544-0.954). For rs510566, no significant differences were found in genotype frequencies, allele frequencies, or in any subgroup analysis (P > 0.05).

Conclusions: The rs642949 might be associated with susceptibility to PD. In contrast, no such association was found for the rs510566. Further fundamental research is warranted to investigate the PICALM gene in PD.

Background: Genetic factors are major contributors to autism spectrum disorders (ASD), with copy number variations (CNVs) playing a significant role. Our objective was to identify and assess CNVs and their associated gene-level impacts in Egyptian ASD patients.

Methods: Our cohort comprised 40 non-syndromic ASD children, ranging in age from one year and nine months to 12 years. We conducted karyotyping, multiple ligation-dependent probe amplification (MLPA), Methylation-Specific MLPA (MS-MLPA), and chromosomal microarray (CMA) analyses for all patients. Additionally, quantitative real-time PCR (qPCR) analyses were performed on selected patients and their parents.

Results: We identified pathogenic or likely pathogenic CNVs in seven patients (17.5%), variants of uncertain significance (VUS) in 10 patients (25%), and classified other variants as benign. Pathogenic or likely pathogenic CNVs affected chromosomes 16p11.2 (two patients), 15q11q13 (three patients), 22q11.2 (one patient), and 7q11.23 (one patient). Many of the affected genes are associated with neuronal functions. Patients with CNVs exhibited increases in three parameters on the Children's Autism Rating Scale (CARS): restrictive and repetitive behavior, adaptation to changes, and responses to taste, smell, and touch. According to the Autism Diagnostic Interview-Revised (ADI-R), patients with pathogenic or likely pathogenic CNVs had an increased third parameter for restricted, repetitive, and stereotyped patterns of behavior. Patients with pathogenic or likely pathogenic CNVs demonstrated some clinical manifestations involving microcephaly (two patients), macrocephaly (one patient), and abnormal electroencephalogram (EEG) with generalized epileptic focus (two patients). qRT-PCR was performed for some parents, and the CNVs were found to be de novo.

Conclusions: CNV detection via CMA proved invaluable and is considered a primary tool in ASD diagnosis. Distinguishing between inherited and de novo CNVs is crucial for accurate genetic counseling. Most genes involved in the detection of pathological or likely pathogenic CNVs are linked to neuronal functions, and these CNVs impact specific parameters in CARS and ADI-R assessments. Future efforts should prioritize a more comprehensive understanding of the genetic underpinnings of ASD, enabling the adoption of personalized treatment strategies.