BMC Medical Genomics最新文献

Background: DYT-TOR1A is an early-onset generalised movement disorder characterised by involuntary muscle contractions, leading to abnormal postures and repetitive movements. The trinucleotide GAG in-frame deletion (ΔGAG) in the TOR1A gene is the most common Mendelian form of dystonia. The TOR1A gene encodes for TorsinA protein, involved in several molecular functions, including chaperone activity, protein quality control, synaptic vesicle recycling, vesicular trafficking, protein folding, homeostasis of the nuclear envelope integrity, and lipid metabolism. There is increasing evidence that DYT-TOR1A dystonia is a neurodevelopmental disorder, beginning in the period where structural and functional abnormalities manifest early in physiological maturation. Recent publications have highlighted the importance of the extracellular matrix and lipid metabolism in dystonia, processes important to cellular neurodevelopment. In this study we aimed to elucidate the gene expression profiles of mutant DYT-TOR1A patient-derived iPSCs to identify key pathways and differentially expressed proteins involved in the early stages of neurodevelopment.

Results: Of the 39,065 genes analysed, 1,322 were found to be significantly dysregulated (p < 0.05). For the purposes of this study, we focus on the 36 most significantly dysregulated genes (q < 0.05) and discuss their biological relevance. We identified 28 upregulated and 8 downregulated genes in human DYT-TOR1A patient-derived induced pluripotent stem cells (iPSCs). Most of the encoded proteins constitute fundamental components of extracellular matrix and lipid metabolism, suggesting impairments related to the microenvironment for cell growth, and cell differentiation, membrane fluidity, receptor trafficking, as well as neurodevelopment. In addition, we identified changes in the expression of genes localised in the nucleus, suggesting dysregulation in transcription factors involved in development, particularly in the forebrain and hippocampus. Interestingly, a high proportion of dysregulated genes are localised on Chromosome 22q11.23, a locus already identified as key in dystonia.

Conclusions: Our study in iPSC-derived from DYT-TOR1A patients shows a transcriptomic profile, which validates previous candidate genes highlighted in other animal models of dystonia. We believe that our results will help elucidate early mechanisms in neurodevelopment of DYT-TOR1A dystonia.

Background: Coffin-Siris Syndrome 8 (CSS8; MIM# 618362) is a rare neurodevelopmental disorder caused by heterozygous variants in the SMARCC2 gene. Patients with CSS8 present with variable phenotypic presentations, with speech abnormalities, behavioral issues, hypotonia, and dysmorphic features being the most common. Here, we report a novel de novo inversion involving SMARCC2, identified through long-read whole-genome sequencing, which highlights its added diagnostic value in uncovering complex structural variants.

Case presentation: The patient is a 12-year-old male of Arab descent, born to healthy, non-consanguineous parents. He presented with speech difficulties and behavioral issues, including autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention-deficit/hyperactivity disorder (ADHD). Long-read sequencing identified a 233 kb inversion on chromosome 12, with breakpoint 1 disrupting SMARCC2 between exons 16 and 17. This inversion was confirmed by Sanger sequencing. Disruption of SMARCC2 is predicted to cause loss of function, resulting in haploinsufficiency.

Conclusions: This is the first report of a structural variant in a patient with CSS8. This finding expands our genetic understanding of CSS8 and demonstrates the diagnostic utility of long-read sequencing in uncovering clinically relevant structural variants that may be missed by conventional methods.

Purpose: To identify disease-causing gene variants in 114 probands with congenital cataract (CC), either isolated or accompanied by additional ocular abnormalities and multisystem disorders.

Methods: A total of 114 probands with CC who accept cataract surgery between 2021 and 2022 were enrolled. All probands and available family members underwent a comprehensive ophthalmologic examinations and received clinical diagnoses. Medical history and examination data were collected. Whole-exome sequencing (WES) was performed for all probands, and candidate variants were further validated by co-segregation analysis.

Results: Among 114 probands, 49 presented with isolated CC, and 11 exhibited CC combined with systemic abnormalities such as congenital heart disease (CHD), laryngeal cartilage hypoplasia and developmental delay, the remaining were associated with congenital ocular anomalies including iris hypoplasia, posterior capsular defect, microcornea, and microphthalmia. In total, 49 variants were identified across 19 genes, 23 of which were novel. The detection rate of variants by WES was substantially higher in inherited families (68.75%, 11/16) than in sporadic cases (38.78%, 38/98), with an overall detection rate of 42.98% (49/114).

Conclusions: This study provided a comprehensive characterization of the diverse clinical phenotypes of CC in Chinese cohort and updates the mutational landscape of CC-associated genes. By employing WES, we expand the genetic spectrum and highlight additional genotype-phenotype correlations in CCs.

Objective: Integrin alpha-3 (ITGA3) has been implicated in tumor metastasis in various cancers, but its role in epithelial ovarian cancer (EOC)-associated liver metastasis (LM) remains unclear. This study aimed to investigate its role in LM in primary EOC patients.

Methods: It was a retrospective study with a sample size of n = 235 receiving surgical resection at Puren Hospital Affiliated to Wuhan University of Science and Technology between January 2020 and December 2021, including 98 LM (LM group) and 137 non-LM cases (N-LM group). ITGA3 expression was assessed by immunohistochemistry. ROC curves were used for predictive performance analysis, Kaplan-Meier curves for survival analysis, and Cox regression analysis for identification of risk factors.

Results: Markedly elevated ITGA3 expression in tumor tissues was found in the LM group (P < 0.001), which demonstrated strong predictive value for LM in EOC patients (area under the curve (AUC) = 0.881, sensitivity = 70.41%, specificity = 87.59%, P < 0.001), and strongly correlated with tumor size and postoperative residual lesions (both P < 0.05). Compared with the L-ITGA3 group, the H-ITGA3 group had a higher incidence of postoperative LM (P < 0.001) and showed a left-shifted curve in Kaplan-Meier analysis (P < 0.001). ITGA3 expression in tumor tissues (HR = 5.977), tumor grade (HR = 1.441), and postoperative residual lesions (HR = 1.697) were identified as independent risk factors for postoperative LM.

Conclusions: ITGA3 expression in tumor tissue significantly aids in predicting LM in EOC patients and is independently and closely related to adverse clinicopathological outcomes.

Background: Low-pass genome sequencing (LP GS) has been widely used for the detection of copy number variations (CNVs). As a key algorithmic parameter of LP GS, window selection may influence the performance of LP GS. However, limited studies have investigated this parameter for the detection of small CNVs.

Methods: To evaluate of the impact of sliding window on true positive rate, additional interpretation workload and resolution, 40 simulated samples with 19 pre-defined CNVs of various read amounts were simulated. Fifty-seven clinical cases with previously ascertained CMA results (27 positive cases and 30 negative cases) were used to further evaluate the influence of sliding window for detection sensitivity and specificity.

Results: In general, the true positive rate increased with the increase of sequencing depth for simulated samples. The algorithm by sliding a 10-Kb window in 1-Kb increments showed higher true positive rate, especially for CNVs < = 30 Kb. For deletions of 30 Kb, the algorithm by sliding a 10-Kb window in 1-Kb increments showed a true positive rate of 100% for all read amounts, while the algorithm by sliding a 50-Kb window in 5-Kb increments had a detection sensitivity of 80.0% even with 100 M read amount. The results of overlap analysis showed that the algorithm by sliding a 10-Kb window in 1-Kb increments showed less variability for both deletions and duplications (especially for CNVs < = 30 Kb), indicating higher detection resolution. Further combining the potential introduction of the additional interpretation workload by 10-Kb window in 1-Kb increments, 50 M reads is recommended for detecting most small CNVs. For the 57 clinical cases, the algorithm by sliding a 50-Kb window in 5-Kb increments and the algorithm by sliding a 10-Kb window in 1-Kb increments showed detection sensitivity of 85.19% (23/27) and 96.30% (26/27), respectively. The algorithm by sliding a 10-Kb window in 1-Kb increments detected all the CNVs missed by sliding a 50-Kb window in 5-Kb increments except for one 25.8 Kb deletion. The specificity for both algorithms was calculated as 96.67% (29/30).

Conclusion: Window selection, together with sequencing depth, could influence CNV detection sensitivity and resolution of LP GS for small CNVs. This study provided a set of evaluation methods and pathways based on simulated samples and clinical cases. For CNVs < = 30 kb, 10-Kb window in 1-Kb increments and >= 50 M reads were recommended for LP GS. It would be advisable for clinical labs conducting LP GS to determine the range of sensitivity and resolution for different sliding windows and sequencing depth for CNV detection.

Background: Determining the underlying cause of developmental delay or intellectual disability (DD/ID) is challenging yet crucial. Establishing a genetic basis for cases of unexplained DD/ID is integral to informing clinical decisions and anticipating patient outcomes. In this report, we share our institutional insights derived from employing whole-genome sequencing (WGS) to investigate unexplained DD/ID in pediatric populations.

Methods: A retrospective study was conducted on 115 children aged 1 month to 16 years with unexplained DD/ID who underwent WGS. We analyzed demographic profiles and catalogued genetic variants identified, in conjunction with examining clinical variables potentially associated with diagnostic yield.

Results: WGS data from 115 pediatric patients identified a total of 33 pathogenic or likely pathogenic single nucleotide variants and small insertions/deletions, of which 22 were classified as diagnostic cases and 11 as carriers. In addition, 11 pathogenic or likely pathogenic copy number variations were detected. Clinical attributes such as gender, age at diagnosis, gestational maturity, birth weight, perinatal complications (anoxia, jaundice), comorbid symptoms, hereditary background, neuromuscular function (muscle tone and strength), presence of epilepsy, neuroimaging, and electroencephalography patterns did not show a significant association with WGS findings. Nonetheless, a noteworthy association emerged between earlier age at diagnosis and increased diagnostic yield via WGS.

Conclusions: WGS serves as a powerful tool for uncovering genetic etiologies in children with unexplained DD/ID, with meaningful implications for clinical care, genetic counseling for families, and long-term management planning.

Background: Newborn screening (NBS) enables early detection of metabolic disorders, but current tandem mass spectrometry (MS/MS) methods often lead to false positives and require confirmatory testing, causing diagnostic delays. We evaluated whether integrating genome sequencing, expanded metabolite profiling, and artificial intelligence/machine learning (AI/ML) could improve the accuracy of NBS.

Methods: We analyzed dried blood spots (DBS) from 119 screen-positive cases identified by the California NBS program across four disorders: GA-I, PA/MMA, OTCD, and VLCADD. Genome sequencing was performed to identify variants in condition-related genes using ACMG guidelines, and an AI/ML classifier trained on previously generated metabolomic data was applied to differentiate true and false positives.

Results: Genome sequencing confirmed 89% (31/35) of true positives based on the presence of two reportable variants. Among 84 false positives, 74% (62) had no variant, while 26% (22) carried a pathogenic/likely pathogenic variant or rare VUS in a condition-related gene. For VLCADD, half of false positives (15/29) were ACADVL variant carriers (P = 4.66 × 10⁻⁷). VLCADD biomarker levels were highest in patients, intermediate in carriers, and lowest in non-carriers, indicating that ACADVL variants elevate biomarker levels and increase false-positive rates. Metabolomics with AI/ML detected all true positives (100% sensitivity), while genome sequencing reduced false positives by 98.8%.

Conclusion: Targeted metabolomics with AI/ML showed high sensitivity for identifying true positives, though its ability to reduce false positives varied by condition. Genome sequencing effectively reduced false positives but lacked sufficient sensitivity as a standalone test. The elevated false-positive rate among pathogenic variant carriers underscores the potential value of parental or prenatal carrier screening to improve NBS accuracy. Integrating genomic and metabolomic data may enhance NBS precision and enable earlier diagnosis and intervention for rare diseases.

Background: Next-Generation short-read sequencing has limited diagnostic utility in phasing distantly separated variants and analysing genomic regions with high homology. Determining the phase of variants from parental chromosomes is critical for accurate identification of compound heterozygosity. Long-read sequencing technology is able to overcome these limitations through the analysis of long haplotypes of separated variants. This study has developed and validated a robust, end-to-end workflow for phasing and localising variants using long-range PCR (LR-PCR) and targeted Nanopore sequencing for clinical implementation.

Methods: NA24385 (HG002) reference DNA was used for all tests. Four PCR kits were tested to optimise LR-PCR for targets between 1 and 20 kb. Amplicons were barcoded and sequenced on Flongle flow cells, with up to eight amplicons on each flow cell. An in-house bioinformatic pipeline was developed to analyse the amplicons. This pipeline is capable of detecting chimeric reads (a known PCR artefact), and incorporating Clair3 for variant calling, and WhatsHap and HapCUT2 for phasing.

Results: The UltraRun LongRange PCR Kit performed with a 90% success rate for DNA amplification up to 22 kb. All 15 tested heterozygous Single Nucleotide Variant (SNV) pairs, and 10 small InDels, with inter-variant distances from 5.8 to 21.4 kb, were phased with 100% concordance to known phase. Furthermore, SNV calling within six low-mappability genes demonstrated precision and sensitivity of 1 against benchmark data. The median proportion of chimeric reads was maintained at 2.80% (range 1.79-16.12%) under optimised conditions.

Conclusions: This study establishes a reliable and affordable clinical diagnostic workflow for accurate phasing of variants separated by up to ~ 20 kb and for variant localisation in genomic regions not able to be sequenced by short-read sequencing. This integrated approach enables implementation in diagnostic settings to resolve complex genetic findings and improve variant interpretation. The bioinformatic pipeline and documentation are available at https://github.com/j-jamshidi/ONT_amp_phase .