Klee, Eric W

ABSTRACT The cohesin complex, composed of SMC1, SMC3, RAD21, and STAG1/STAG2, is essential for chromosome cohesion, DNA repair, and transcriptional regulation. Pathogenic variants in cohesin components cause cohesinopathies. The classical characteristics of cohesinopathies include developmental delay (DD), intellectual disability (ID), feeding difficulties, hypotonia, short stature, hearing loss, and dysmorphic features. Here, we present a 5‐year‐old boy with classical cohesinopathy features, including DD/ID and feeding difficulties, along with non‐classical features such as hypoglycemia, recurrent fever, and immunodeficiency. Trio exome sequencing identified a novel de novo missense variant of uncertain significance (NM_005862.3:c.643G>A(p.Val215Ile)) in the STAG1 gene. The variant localizes to the RAD21 interaction interface, and molecular dynamics (MD) simulations revealed conformational changes comparable to other STAG1 variants reported as likely pathogenic in patients, supporting a deleterious effect which may disrupt the STAG1‐RAD21 interaction interface. This case expands the phenotypic and molecular spectrum of STAG1‐related cohesinopathy and advances our understanding of the disease mechanism.

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

BACKGROUND: Familial hypercholesterolemia (FH) is an autosomal dominant genetic disorder that increases risk for premature coronary artery disease and has accessible and effective interventions. The Dutch lipid clinic network is currently the most used diagnostic criterion; however, genetic sequencing provides a definitive diagnosis of FH. The goals of this study were to determine whether germline genetic screening using exome sequencing could be used to efficiently identify individuals who were genotype positive for FH. METHODS: Participants were recruited from 3 geographically and racially diverse sites in the United States (Rochester, MN; Phoenix, AZ; and Jacksonville, FL). Participants underwent Exome+ sequencing (dba Helix, San Mateo, CA) and return of results for specific genetic findings in APOB , LDLR , or PCSK9 . A chart review was performed to collect demographics, personal, and family cardiovascular history. RESULTS: At the time of the study, 84 413 participants were enrolled in the Tapestry study. Annotation and interpretation of all variants in genes for FH resulted in the identification of 419 likely pathogenic and pathogenic variants (prevalence, 0.50%), which included 116 APOB , 298 LDLR , and 5 PCSK9 . Sixty-six percent were female, the mean body mass index was 27.3, with 12.3% reporting a history of diabetes. Hypertriglyceridemia (≥150 mg/dL) was present in 39.5% and reduced HDL (<50 mg/dL) was present in 56.7% of patients. 27.5% of patients were not on cholesterol-lowering medications, and only 10% of FH carriers were at goal low-density lipoprotein cholesterol levels. A history of coronary artery disease was reported in 22.4% of the cohort. Nearly 90% of these participants were newly diagnosed carriers of FH. Only 30.8% of confirmed genetic diagnoses of FH satisfied clinical (Dutch lipid clinic network) criteria for a diagnosis. CONCLUSIONS: Our results emphasize the need for wider utilization of germline genetic sequencing for enhanced screening and detection of individuals who have familial hypercholesterolemia. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05212428.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had &lt; 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

DNA methylation is an essential epigenetic modification that plays a crucial role in regulating gene expression and maintaining genomic stability. With the advancement in sequencing technology, methylation studies have provided valuable insights into the diagnosis of rare diseases through the various identification of episignatures, epivariation, epioutliers, and allele-specific methylation. However, current methylation studies are not without limitations. This mini-review explores the current understanding of DNA methylation in rare diseases, highlighting the key mechanisms and diagnostic potential, and emphasizing the need for advanced methodologies and integrative approaches to enhance the understanding of disease progression and design more personable treatment for patients, given the nature of rare diseases.

Abstract The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores—0.98 ± 0.02 for lung cancer subtype differentiation and 0.83 ± 0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

Bi-allelic disruptive variants (nonsense, frameshift, and splicing variants) in KDM5B have been identified as causative for autosomal recessive intellectual developmental disorder type 65. In contrast, dominant variants, usually disruptive as well, have been more difficult to implicate in a specific phenotype, since some of them have been found in unaffected controls or relatives. Here, we describe individuals with likely pathogenic variants in KDM5B, including eight individuals with dominant missense variants. This study is a retrospective case series of 21 individuals with variants in KDM5B. We performed deep phenotyping and collected the clinical information and molecular data of these individuals’ family members. We compared the phenotypes according to variant type and to those previously described in the literature. The most common features were developmental delay, impaired intellectual development, behavioral problems, autistic behaviors, sleep disorders, facial dysmorphism, and overgrowth. DD, ASD behaviors, and sleep disorders were more common in individuals with dominant disruptive KDM5B variants, while individuals with dominant missense variants presented more frequently with renal and skin anomalies. This study extends our understanding of the KDM5B-related neurodevelopmental disorder and suggests the pathogenicity of certain dominant KDM5B missense variants.

PURPOSE The autosomal dominant cancer predisposition disorders hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) are genetic conditions for which early identification and intervention have a positive effect on the individual and public health. The goals of this study were to determine whether germline genetic screening using exome sequencing could be used to efficiently identify carriers of HBOC and LS. METHODS Participants were recruited from three geographically and racially diverse sites in the United States (Rochester, MN; Phoenix, AZ; Jacksonville, FL). Participants underwent Exome+ sequencing (Helix Inc, San Mateo, CA) and return of results for specific genetic findings: HBOC ( BRCA1 and BRCA1) and LS ( MLH1, MSH2, MSH6, PMS2, and EPCAM). Chart review was performed to collect demographics and personal and family cancer history. RESULTS To date, 44,306 participants have enrolled in Tapestry. Annotation and interpretation of all variants in genes for HBOC and LS resulted in the identification of 550 carriers (prevalence, 1.24%), which included 387 with HBOC (27.2% BRCA1, 42.8% BRCA2) and 163 with LS (12.3% MSH6, 8.8% PMS2, 4.5% MLH1, 3.8% MSH2, and 0.2% EPCAM). More than half of these participants (52.1%) were newly diagnosed carriers with HBOC and LS. In all, 39.2% of HBOC/LS carriers did not satisfy National Comprehensive Cancer Network (NCCN) criteria for genetic evaluation. NCCN criteria were less commonly met in underrepresented minority populations versus self-reported White race (51.5% v 37.5%, P = .028). CONCLUSION Our results emphasize the need for wider utilization of germline genetic sequencing for enhanced screening and detection of individuals who have LS and HBOC cancer predisposition syndromes.

Abstract Alpha-tubulin 4A encoding gene (TUBA4A) has been associated with familial amyotrophic lateral sclerosis and frontotemporal dementia, based on identification of likely pathogenic variants in patients from distinct amyotrophic lateral sclerosis and frontotemporal dementia cohorts. By screening a multicentric French cohort of 448 unrelated probands presenting with cerebellar ataxia, we identified ultra-rare TUBA4A missense variants, all being absent from public databases and predicted pathogenic by multiple in silico tools. In addition, gene burden analyses in the 100 000 Genomes project (100KGP) showed enrichment of TUBA4A rare variants in the inherited ataxia group compared to controls [odds ratio: 57.0847 (10.2−576.7); P = 4.02 ×10−7]. Taken together, we report 12 patients presenting with spasticity and/or cerebellar ataxia and harbouring a predicted pathogenic TUBA4A missense mutation, including five confirmed de novo cases and a mutation previously reported in a large family presenting with spastic ataxia. Cultured fibroblasts from three patients harbouring distinct TUBA4A missense showed significant alterations in microtubule organization and dynamics, providing insight of TUBA4A variants pathogenicity. Our data confirm the identification of a hereditary spastic ataxia disease gene with variable age of onset, expanding the clinical spectrum of TUBA4A associated phenotypes.

Abstract Intracellular trafficking involves an intricate machinery of motor complexes, including the dynein complex, to shuttle cargo for autophagolysosomal degradation. Deficiency in dynein axonemal chains, as well as cytoplasmic light and intermediate chains, have been linked with ciliary dyskinesia and skeletal dysplasia. The cytoplasmic dynein 1 heavy chain protein (DYNC1H1) serves as a core complex for retrograde trafficking in neuronal axons. Dominant pathogenic variants in DYNC1H1 have been previously implicated in peripheral neuromuscular disorders (NMD) and neurodevelopmental disorders (NDD). As heavy-chain dynein is ubiquitously expressed, the apparent selectivity of heavy chain dyneinopathy for motor neuronal phenotypes remains currently unaccounted for. Here, we aimed to evaluate the full DYNC1H1-related clinical, molecular and imaging spectrum, including multisystem features and novel phenotypes presenting throughout life. We identified 47 cases from 43 families with pathogenic heterozygous variants in DYNC1H1 (aged 0–59 years) and collected phenotypic data via a comprehensive standardized survey and clinical follow-up appointments. Most patients presented with divergent and previously unrecognized neurological and multisystem features, leading to significant delays in genetic testing and establishing the correct diagnosis. Neurological phenotypes include novel autonomic features, previously rarely described behavioral disorders, movement disorders and periventricular lesions. Sensory neuropathy was identified in nine patients (median age of onset 10.6 years), of which five were only diagnosed after the second decade of life, and three had a progressive age-dependent sensory neuropathy. Novel multisystem features included primary immunodeficiency, bilateral sensorineural hearing loss, organ anomalies and skeletal manifestations, resembling the phenotypic spectrum of other dyneinopathies. We also identified an age-dependent biphasic disease course with developmental regression in the first decade and, following a period of stability, neurodegenerative progression after the second decade of life. Of note, we observed several cases in whom neurodegeneration appeared to be prompted by intercurrent systemic infections with double-stranded DNA viruses (Herpesviridae) or single-stranded RNA viruses (Ross River fever, SARS-CoV-2). Moreover, the disease course appeared to be exacerbated by viral infections regardless of age and/or severity of neurodevelopmental disorder manifestations, indicating a role of dynein in anti-viral immunity and neuronal health. In summary, our findings expand the clinical, imaging and molecular spectrum of pathogenic DYNC1H1 variants beyond motor neuropathy disorders and suggest a life-long continuum and age-related progression due to deficient intracellular trafficking. This study will facilitate early diagnosis and improve counselling and health surveillance of affected patients.