👤 John Rosenberger

Thoracic aortic aneurysms frequently go undetected until serious complications like acute dissections or ruptures arise. Therefore, this study aims to identify potential blood circulating biomarkers enabling an easy and early diagnosis of thoracic aortic disease. Potential biomarker candidates were identified through two different techniques, untargeted and targeted proteomic as well as extracellular vesicle (EV) analyses. The biomarker levels were compared between two patient groups with thoracic aortic aneurysms and two control groups without thoracic aortic disease. In total, 80 patients (TAA group (n = 40) vs. control group (n = 40)) were matched for untargeted and targeted proteome analysis, and 85 for EV analysis (TAA group (n = 42) vs. control group (n = 43)), based on the availability of blood samples and excised aortic tissue. Levels of biomarker candidates were correlated with aortic diameter, patient age, and histological alterations in aortic tissue using linear and logistic regression models. The untargeted proteomic and EV analysis identified 1,037 and 1,077 proteins, respectively, of which 11 and 28 proteins showed significant differences in concentration between the study groups. Of these, 9 proteins correlated with the aortic diameter: ACTN1 (Regression coefficient B = 1.633, p < 0.001), CRP (B = 0.001, p = 0.004), TGM3 (B=-0.293, p = 0.010), KRT84 (B=-0.477, p = 0.010), IGHG3 (-0.267, p = 0.018), DPYSL2 (B = 0.644, p = 0.020), TSPAN8 (B-0.838, p = 0.042), IGKV3D-11 (B=-0.242, p = 0.046), and VDAC1 (B=-0.491, p = 0.047). Moreover, IGKV3D-11 (B=-3.257, p = 0.029), IGHG3 (B=-0.003, p = 0.034), and APOC3 (B=-2.104, p = 0.037) showed significant correlations with the grade of aortic medial layer degeneration. None of the proteins correlated with patient age. The study identified 9 biomarker candidates correlating with the aortic diameter. To enable the clinical use for diagnosis and prognostic assessment, these biomarkers need to be validated in larger external cohorts. Show less

CNS diseases are a prevailing cause of morbidity and mortality worldwide, and are influenced by environmental and biological factors, including genetic risk. Here, we generated genome-wide genetic data on a large cohort of brain tissue donors with in-depth clinical and neuropathological phenotyping, allowing for broad investigations into the risk and mechanisms of these neurological, neurodevelopmental, and psychiatric conditions. This resource consists of 9,663 donors with array-based genotyping and 9,543 donors with whole-genome sequencing completed. The clinical diagnoses of these donors include 148 central nervous system diseases clustered into 15 broad categories by International Classification of Diseases-10 (ICD-10) coding. These donors were collected by six repositories comprising the National Institutes of Health NeuroBioBank, with an average participant age of 60 years. While primarily older individuals of European descent, the cohort also contains younger donors and individuals from non-European backgrounds. Variants were detected in whole-genome sequencing (WGS), normalized and annotated to describe their functional impact, resulting in 171,121,209 unique variants and 1,078,774 non-silent variants. These raw and normalized data have been made available as a neurogenomics resource in the National Institute of Mental Health Data Archive (NIMH NDA) (nda.nih.gov), combined with donor-matched deep demographic and phenotypic data from the NeuroBioBank Portal (neurobiobank.nih.gov). To illustrate applications, we replicated the strong association observed in previous studies between pathogenic CAG nucleotide repeat expansions in the HTT gene with the clinical diagnosis of Huntington's disease, as well as associations of the APOE gene with Alzheimer's disease, and examined the association of polygenic risk scores with the three most common disease diagnoses in the cohort. Show less