Rare variant association analyses reveal the significant contribution of carbohydrate metabolic disturbance in severe adolescent idiopathic scoliosis.

BACKGROUND: Adolescent idiopathic scoliosis (AIS), the predominant genetic-influenced scoliosis, results in spinal deformities without vertebral malformations. However, the molecular aetiology of AIS remains unclear. METHODS: Using genome/exome sequencing, we studied 368 patients with severe AIS (Cobb angle >40°) and 3794 controls from a Han Chinese cohort. We performed gene-based and pathway-based weighted rare variant association tests to assess the mutational burden of genes and established biological pathways. Differential expression analysis of muscle tissues from 14 patients with AIS and 15 controls was served for validation. RESULTS: SLC16A8, a lactate transporter linked to retinal glucose metabolism, was identified as a novel severe AIS-associated gene (p=3.08E-06, false discovery rate=0.009). Most AIS cases with deleterious SLC16A8 variants demonstrated early onset high myopia preceding scoliosis. Pathway-based burden test also revealed a significant enrichment in multiple carbohydrate metabolism pathways, especially galactose metabolism. Patients with deleterious variants in these genes demonstrated a significantly larger spinal curve. Genes related to catabolic processes and nutrient response showed divergent expression between AIS cases and controls, reinforcing our genomic findings. CONCLUSION: This study uncovers the pivotal role of genetic variants in carbohydrate metabolism in the development of AIS, unveiling new insights into its aetiology and potential treatment.

Core planar cell polarity genes VANGL1 and VANGL2 in predisposition to congenital vertebral malformations.

Congenital scoliosis (CS), affecting approximately 0.5 to 1 in 1,000 live births, is commonly caused by congenital vertebral malformations (CVMs) arising from aberrant somitogenesis or somite differentiation. While Wnt/ß-catenin signaling has been implicated in somite development, the function of Wnt/planar cell polarity (Wnt/PCP) signaling in this process remains unclear. Here, we investigated the role of Vangl1 and Vangl2 in vertebral development and found that their deletion causes vertebral anomalies resembling human CVMs. Analysis of exome sequencing data from multiethnic CS patients revealed a number of rare and deleterious variants in VANGL1 and VANGL2, many of which exhibited loss-of-function and dominant-negative effects. Zebrafish models confirmed the pathogenicity of these variants. Furthermore, we found that Vangl1 knock-in (p.R258H) mice exhibited vertebral malformations in a Vangl gene dose- and environment-dependent manner. Our findings highlight critical roles for PCP signaling in vertebral development and predisposition to CVMs in CS patients, providing insights into the molecular mechanisms underlying this disorder.

Unraveling the genetic architecture of congenital vertebral malformation with reference to the developing spine.

Congenital vertebral malformation, affecting 0.13-0.50 per 1000 live births, has an immense locus heterogeneity and complex genetic architecture. In this study, we analyze exome/genome sequencing data from 873 probands with congenital vertebral malformation and 3794 control individuals. Clinical interpretation identifies Mendelian etiologies in 12.0% of the probands and reveals a muscle-related disease mechanism. Gene-based burden test of ultra-rare variants identifies risk genes with large effect sizes (ITPR2, TBX6, TPO, H6PD, and SEC24B). To further investigate the biological relevance of the genetic association signals, we perform single-nucleus RNAseq on human embryonic spines. The burden test signals are enriched in the notochord at early developmental stages and myoblast/myocytes at late stages, highlighting their critical roles in the developing spine. Our work provides insights into the developmental biology of the human spine and the pathogenesis of spine malformation.

SIGMA leverages protein structural information to predict the pathogenicity of missense variants.

Leveraging protein structural information to evaluate pathogenicity has been hindered by the scarcity of experimentally determined 3D protein. With the aid of AlphaFold2 predictions, we developed the structure-informed genetic missense mutation assessor (SIGMA) to predict missense variant pathogenicity. In comparison with existing predictors across labeled variant datasets and experimental datasets, SIGMA demonstrates superior performance in predicting missense variant pathogenicity (AUC = 0.933). We found that the relative solvent accessibility of the mutated residue contributed greatly to the predictive ability of SIGMA. We further explored combining SIGMA with other top-tier predictors to create SIGMA+, proving highly effective for variant pathogenicity prediction (AUC = 0.966). To facilitate the application of SIGMA, we pre-computed SIGMA scores for over 48 million possible missense variants across 3,454 disease-associated genes and developed an interactive online platform (https://www.sigma-pred.org/). Overall, by leveraging protein structure information, SIGMA offers an accurate structure-based approach to evaluating the pathogenicity of missense variants.

DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

BACKGROUND: Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and risk prediction models for prioritizing genetic testing among breast cancer patients do not meet the demands of clinical practice due to insufficient accuracy. METHODS: The study population comprised 3041 breast cancer patients enrolled from seven hospitals between October 2017 and 11 August 2019, who underwent germline genetic testing of 50 cancer predisposition genes (CPGs). Associations among GPVs in different CPGs and endophenotypes were evaluated using a case-control analysis. A phenotype-based GPV risk prediction model named DNA-repair Associated Breast Cancer (DrABC) was developed based on hierarchical neural network architecture and validated in an independent multicenter cohort. The predictive performance of DrABC was compared with currently used models including BRCAPRO, BOADICEA, Myriad, PENN II, and the NCCN criteria. RESULTS: In total, 332 (11.3%) patients harbored GPVs in CPGs, including 134 (4.6%) in BRCA2, 131 (4.5%) in BRCA1, 33 (1.1%) in PALB2, and 37 (1.3%) in other CPGs. GPVs in CPGs were associated with distinct endophenotypes including the age at diagnosis, cancer history, family cancer history, and pathological characteristics. We developed a DrABC model to predict the risk of GPV carrier status in BRCA1/2 and other important CPGs. In predicting GPVs in BRCA1/2, the performance of DrABC (AUC = 0.79 [95% CI, 0.74-0.85], sensitivity = 82.1%, specificity = 63.1% in the independent validation cohort) was better than that of previous models (AUC range = 0.57-0.70). In predicting GPVs in any CPG, DrABC (AUC = 0.74 [95% CI, 0.69-0.79], sensitivity = 83.8%, specificity = 51.3% in the independent validation cohort) was also superior to previous models in their current versions (AUC range = 0.55-0.65). After training these previous models with the Chinese-specific dataset, DrABC still outperformed all other methods except for BOADICEA, which was the only previous model with the inclusion of pathological features. The DrABC model also showed higher sensitivity and specificity than the NCCN criteria in the multi-center validation cohort (83.8% and 51.3% vs. 78.8% and 31.2%, respectively, in predicting GPVs in any CPG). The DrABC model implementation is available online at http://gifts.bio-data.cn/ . CONCLUSIONS: By considering the distinct endophenotypes associated with different CPGs in breast cancer patients, a phenotype-driven prediction model based on hierarchical neural network architecture was created for identification of hereditary breast cancer. The model achieved superior performance in identifying GPV carriers among Chinese breast cancer patients.

PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapid identification of causative genes. Since the interpretations of ES data require comprehensive clinical analyses, taking clinical expertise into consideration can speed the molecular diagnoses of Mendelian disorders. To leverage clinical expertise to prioritize candidate genes, we developed PhenoApt, a phenotype-driven gene prioritization tool that allows users to assign a customized weight to each phenotype, via a machine-learning algorithm. Using the ability to rank causative genes in top-10 lists as an evaluation metric, baseline analysis demonstrated that PhenoApt outperformed previous phenotype-driven gene prioritization tools by a relative increase of 22.7%-140.0% in three independent, real-world, multi-center cohorts (cohort 1, n = 185; cohort 2, n = 784; and cohort 3, n = 208). Additional trials showed that, by adding weights to clinical indications, which should be explained by the causative gene, PhenoApt performance was improved by a relative increase of 37.3% in cohort 2 (n = 471) and 21.4% in cohort 3 (n = 208). Moreover, PhenoApt could assign an intrinsic weight to each phenotype based on the likelihood of its being a Mendelian trait using term frequency-inverse document frequency techniques. When clinical indications were assigned with intrinsic weights, PhenoApt performance was improved by a relative increase of 23.7% in cohort 2 and 15.5% in cohort 3. For the integration of PhenoApt into clinical practice, we developed a user-friendly website and a command-line tool.

Disruptive NADSYN1 Variants Implicated in Congenital Vertebral Malformations.

Genetic perturbations in nicotinamide adenine dinucleotide de novo (NAD) synthesis pathway predispose individuals to congenital birth defects. The NADSYN1 encodes the final enzyme in the de novo NAD synthesis pathway and, therefore, plays an important role in NAD metabolism and organ embryogenesis. Biallelic mutations in the NADSYN1 gene have been reported to be causative of congenital organ defects known as VCRL syndrome (Vertebral-Cardiac-Renal-Limb syndrome). Here, we analyzed the genetic variants in NADSYN1 in an exome-sequenced cohort consisting of patients with congenital vertebral malformations (CVMs). A total number of eight variants in NADSYN1, including two truncating variants and six missense variants, were identified in nine unrelated patients. All enrolled patients presented multiple organ defects, with the involvement of either the heart, kidney, limbs, or liver, as well as intraspinal deformities. An in vitro assay using COS-7 cells demonstrated either significantly reduced protein levels or disrupted enzymatic activity of the identified variants. Our findings demonstrated that functional variants in NADSYN1 were involved in the complex genetic etiology of CVMs and provided further evidence for the causative NADSYN1 variants in congenital NAD Deficiency Disorder.

Whole Exome Sequencing Uncovered the Genetic Architecture of Growth Hormone Deficiency Patients.

Purpose: Congenital growth hormone deficiency (GHD) is a rare and etiologically heterogeneous disease. We aim to screen disease-causing mutations of GHD in a relatively sizable cohort and discover underlying mechanisms via a candidate gene-based mutational burden analysis. Methods: We retrospectively analyzed 109 short stature patients associated with hormone deficiency. All patients were classified into two groups: Group I (n=45) with definitive GHD and Group II (n=64) with possible GHD. We analyzed correlation consistency between clinical criteria and molecular findings by whole exome sequencing (WES) in two groups. The patients without a molecular diagnosis (n=90) were compared with 942 in-house controls for the mutational burden of rare mutations in 259 genes biologically related with the GH axis. Results: In 19 patients with molecular diagnosis, we found 5 possible GHD patients received known molecular diagnosis associated with GHD (NF1 [c.2329T>A, c.7131C>G], GHRHR [c.731G>A], STAT5B [c.1102delC], HRAS [c.187_207dup]). By mutational burden analysis of predicted deleterious variants in 90 patients without molecular diagnosis, we found that POLR3A (p = 0.005), SUFU (p = 0.006), LHX3 (p = 0.021) and CREB3L4 (p = 0.040) represented top genes enriched in GHD patients. Conclusion: Our study revealed the discrepancies between the laboratory testing and molecular diagnosis of GHD. These differences should be considered when for an accurate diagnosis of GHD. We also identified four candidate genes that might be associated with GHD.

Heterozygous Recurrent Mutations Inducing Dysfunction of ROR2 Gene in Patients With Short Stature.

PURPOSE: ROR2, a member of the ROR family, is essential for skeletal development as a receptor of Wnt5a. The present study aims to investigate the mutational spectrum of ROR2 in children with short stature and to identify the underlying molecular mechanisms. METHODS: We retrospectively analyzed clinical phenotype and whole-exome sequencing (WES) data of 426 patients with short stature through mutation screening of ROR2. We subsequently examined the changes in protein expression and subcellular location in ROR2 caused by the mutations. The mRNA expression of downstream signaling molecules of the Wnt5a-ROR2 pathway was also examined. RESULTS: We identified 12 mutations in ROR2 in 21 patients, including 10 missense, one nonsense, and one frameshift. Among all missense variants, four recurrent missense variants [c.1675G > A(p.Gly559Ser), c.2212C > T(p.Arg738Cys), c.1930G > A(p.Asp644Asn), c.2117G > A(p.Arg706Gln)] were analyzed by experiments in vitro. The c.1675G > A mutation significantly altered the expression and the cellular localization of the ROR2 protein. The c.1675G > A mutation also caused a significantly decreased expression of c-Jun. In contrast, other missense variants did not confer any disruptive effect on the biological functions of ROR2. CONCLUSION: We expanded the mutational spectrum of ROR2 in patients with short stature. Functional experiments potentially revealed a novel molecular mechanism that the c.1675G > A mutation in ROR2 might affect the expression of downstream Wnt5a-ROR2 pathway gene by disturbing the subcellular localization and expression of the protein.

Factors and predictive model associated with perioperative complications after long fusion in the treatment of adult non-degenerative scoliosis.

INTRODUCTION: Adult non-degenerative scoliosis accounts for 90% of spinal deformities in young adults. However, perioperative complications and related risk factors of long posterior instrumentation and fusion for the treatment of adult non-degenerative scoliosis have not been adequately studied. METHODS: We evaluated clinical and radiographical results from 146 patients with adult non-degenerative scoliosis who underwent long posterior instrumentation and fusion. Preoperative clinical data, intraoperative variables, and perioperative radiographic parameters were collected to analyze the risk factors for perioperative complications. Potential and independent risk factors for perioperative complications were evaluated by univariate analysis and logistic regression analysis. RESULTS: One hundred forty-six adult non-degenerative scoliosis patients were included in our study. There were 23 perioperative complications for 21 (14.4%) patients, eight of which were cardiopulmonary complications, two of which were infection, six of which were neurological complications, three of which were gastrointestinal complications, and four of which were incision-related complication. The independent risk factors for development of total perioperative complications included change in Cobb angle (odds ratio [OR] = 1.085, 95% CI = 1.035 ~ 1.137, P = 0.001) and spinal osteotomy (OR = 3.565, 95% CI = 1.039 ~ 12.236, P = 0.043). The independent risk factor for minor perioperative complications is change in Cobb angle (OR = 1.092, 95% CI = 1.023 ~ 1.165, P = 0.008). The independent risk factors for major perioperative complications are spinal osteotomy (OR = 4.475, 95% CI = 1.960 ~ 20.861, P = 0.036) and change in Cobb angle (OR = 1.106, 95% CI = 1.035 ~ 1.182, P = 0.003). CONCLUSIONS: Our study indicate that change in Cobb angle and spinal osteotomy are independent risk factors for total perioperative complications after long-segment posterior instrumentation and fusion in adult non-degenerative scoliosis patients. Change in Cobb angle is an independent risk factor for minor perioperative complications. Change in Cobb angle and spinal osteotomy are independent risk factors for major perioperative complications.