Citations

ABSTRACT Background and Aims Cellular senescence is a hallmark of several liver diseases, including primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC). Senescent cholangiocytes exhibit a senescence‐associated secretory phenotype (SASP), characterized by profibroinflammatory mediator release. Current cost‐effective biomarkers predicting disease progression, particularly for PSC, are limited and often lack mechanistic relevance. We sought to define a plasma biomarker signature for PSC and PBC. Methods Plasma from early‐ and late‐stage PSC and PBC, alcoholic liver disease (ALD), inflammatory bowel disease (IBD) patients, and healthy controls was analyzed. Seventy‐one analytes were quantified using Luminex Multiplex Immunoassay or enzyme‐linked immunosorbent assay (ELISA). Principal component analysis (PCA) identified key patterns. Findings from the PSC cohort were then applied to additional cohorts. Results Second principal component (PC2) (17 analytes, 17.1% variability) best separated PSC from controls. ANOVA showed significant differences in PC2 between early PSC vs. controls ( p = 0.0001), late PSC vs. controls ( p < 0.0001), and early vs. late PSC ( p < 0.0001). PC2 analytes also distinguished early PBC vs. controls ( p < 0.0332), late PBC ( p < 0.0001), and ALD ( p < 0.0001), and early vs. late PBC ( p < 0.0001), but not IBD vs. controls ( p = 0.119). Logistic regression using PC2 demonstrated strong discrimination of early‐ and late‐stage PSC (AUC = 0.86) and control vs. early‐stage PSC (AUC = 0.83). Conclusion This is the first study to define a plasma SASP biomarker signature associated with cholestatic liver disease. These analytes track disease stage and represent both mechanistic indicators and potential clinical trial endpoints.

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. In this study, we introduce a novel, exomic foundational model that leverages a combination of the human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short-range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in predicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest sequence transformer for range adaptive nucleotide decoding variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuracy of 0.880 [an 8.2% improvement over the original nucleotide transformer (NT) and a 7% improvement over NT-v2]. Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomics-based diagnosis and personalized medicine, including tailored therapeutic development.

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 Background & Aims Steatotic liver disease (SLD) is characterised by liver fat accumulation exceeding 5%. Lean body weight (BMI ≤ 25 kg/m 2 ) with non‐alcoholic SLD is a rare phenotype, and the balance of risks related to metabolic conditions or genetic predisposition, or the risk of progressive liver disease, is not known. This study evaluates clinical and genetic predictors of non‐alcoholic SLD and advanced liver disease in lean individuals. Methods We used International Classification of Disease codes, radiology and pathology review to identify 177 lean non‐alcoholic SLD cases (47 cryptogenic, 130 MASLD), 677 matched lean controls, and 3090 overweight/obese SLD cases in the Mayo Clinic Biobank and Tapestry databases. We performed case–control and cross‐sectional comparisons of clinical and genetic factors between these groups, using univariable and multivariable analyses. Results Lean individuals with non‐alcoholic SLD exhibited metabolic and genetic profiles that were intermediate between those of lean controls without SLD and overweight/obese SLD individuals, including intermediate rates of diabetes, hypertension, hyperlipidaemia and of homozygosity for the risk allele of PNPLA3 . Multivariable analysis indicated that diabetes was an independent predictor of SLD among lean individuals. Among lean individuals with non‐alcoholic SLD, those with metabolic‐associated SLD (MASLD), as compared to those with cryptogenic SLD (without metabolic risk factor), were more likely to be homozygous for risk alleles in GCKR . The Fib‐4 score, a tool for screening advanced liver disease in SLD, accurately predicted advanced fibrosis among lean individuals with non‐alcoholic SLD. Conclusion Diabetes serves as a primary predictor of non‐alcoholic SLD in lean individuals. These results support the recommendation to screen for SLD in patients with diabetes, regardless of BMI. The GCKR risk allele is associated with MASLD in lean individuals, and the risk factors for cryptogenic SLD remain unclear.

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.

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. Here, we introduce a novel, exomic foundational model that leverages a combination of human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short- range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in pre- dicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest STRAND variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuarcy of 0.880 (8.2% improvement over the original NT and 7% improvement over NT-v2). Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomic based diagnosis and personalized medicine including tailored therapeutic development.

Background and Aims: Among those with primary sclerosing cholangitis (PSC), perihilar cholangiocarcinoma (pCCA) is often diagnosed at a late stage and is a leading source of mortality. Detection of pCCA in PSC when curative action can be taken is challenging. Our aim was to create a deep learning model that analyzed MRI to detect early-stage pCCA and compare its diagnostic performance with expert radiologists. Approach and Results: We conducted a multicenter, international, retrospective cohort study involving adults with large duct PSC who underwent contrast-enhanced MRI. Senior abdominal radiologists reviewed the images. All patients with pCCA had early-stage cancer and were registered for liver transplantation. We trained a 3D DenseNet-121 model, a form of deep learning, using MRI images and assessed its performance in a separate test cohort. The study included 398 patients (training cohort n=150; test cohort n=248). pCCA was present in 230 individuals (training cohort n=64; test cohort n=166). In the test cohort, the respective performances of the model compared to the radiologists were: sensitivity 87.9% versus 50.0%, p <0.001; specificity 84.1% versus 100.0%, p <0.001; area under receiving operating curve 86.0% versus 75.0%, p <0.001. Even when a mass was absent, the model had a higher sensitivity for pCCA than radiologists (91.6% vs. 50.6%, p <0.001) and maintained good specificity (84.1%). Conclusions: The 3D DenseNet-121 MRI model effectively detects early-stage pCCA in PSC patients. Compared to expert radiologists, the model missed fewer cases of cancer.

Introduction: The lack of understanding about the genetic and molecular basis of young stroke (<55 years of age) has hindered development of diagnostic, management, and therapeutic approaches. Whole exome sequencing (WES) can be leveraged as a tool for studying etiological processes underlying stroke. At the time of study inception, this is the single largest WES study in this unique young stroke population to uncover elusive stroke etiologies. Methods: The Mayo Clinic TAPESTRY database is a research program conducting whole exome sequencing of 100,000 participants. Of these, 406 participants met the criteria for young stroke (18-54 years of age) based on ICD-9 and ICD-10 diagnostic codes. Controls were TAPESTRY participants without a stroke-related ICD diagnosis. Cases were matched for age, gender, and race for a 1:4 case-control ratio. Ischemic stroke etiology was classified using TOAST classification. To uncover genetic mechanisms, a gene centric approach was used to identify variants in our cohort. A list of 66 genes known to be associated with young stroke was curated based on existing genetic databases. Various filtration strategies were used to identify potentially damaging variants (graphic 1). Results: There were 406 cases (363 ischemic and 43 hemorrhagic; 291 female; age at stroke onset 39.8±10.4; 368 were White) and 3,857 controls. TOAST classification yielded 20 patients with large artery atherosclerosis, 58 cardioembolic, 10 small vessel disease, 148 other determined (58 patent foramen ovale, 50 dissection, 48 hypercoagulable state, 11 Moyamoya, 11 vasculitis, 10 fibromuscular dysplasia), and 165 undetermined etiologies. Several individuals had multiple competing etiologies. Of cerebral hemorrhages, there were 6 aneurysmal, 6 arteriovenous malformation, 5 hypertensive, 2 substance abuse, 3 trauma, and 21 other etiologies. Across the entire cohort, 35,054 variants were found within the 66 genes identified from the literature. Filtration identified 48 potentially damaging variants across 30 genes in 44 patients (20 cryptogenic and 21 other: 12 patent foramen ovale, 5 vasculitis, 4 dissection, 4 cardioembolic) that were absent in controls. Conclusion: WES is a powerful tool that can be used to uncover potential genetic mechanisms in the young stroke population. An improved understanding of etiologies would improve diagnostics and pave the way for new preventive and therapeutic approaches, thus improving the overall care of young stroke patients.