medRxiv : the preprint server for health sciences最新文献

{"title":"Online Database of Clinical Algorithms with Race and Ethnicity.","authors":"Shyam Visweswaran, Eugene M Sadhu, Michele M Morris, Anushka R Vis, Malarkodi Jebathilagam Samayamuthu","doi":"10.1101/2023.07.04.23292231","DOIUrl":"10.1101/2023.07.04.23292231","url":null,"abstract":"<p><p>Some clinical algorithms incorporate an individual's race, ethnicity, or both as an input variable or predictor in determining diagnoses, prognoses, treatment plans, or risk assessments. Inappropriate use of race and ethnicity in clinical algorithms at the point of care may exacerbate health disparities and promote harmful practices of race-based medicine. We identified 42 risk calculators that use race as a predictor, five laboratory test results with different reference ranges recommended for different races, one therapy recommendation based on race, 15 medications with guidelines for initiation and monitoring based on race, and four medical devices with differential racial performance. Information on these clinical algorithms are freely available at http://www.clinical-algorithms-with-race-and-ethnicity.org. This resource aims to raise awareness about the use of race in clinical algorithms and to track the progress made toward eliminating its inappropriate use. The database will be actively updated to include clinical algorithms based on race that were missed, along with additional characteristics of these algorithms.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/79/da/nihpp-2023.07.04.23292231v1.PMC10350134.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10250670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Linear Mixed Model with Measurement Error Correction (LMM-MEC): A Method for Summary-data-based Multivariable Mendelian Randomization.","authors":"Ming Ding, Fei Zou","doi":"10.1101/2023.04.25.23289099","DOIUrl":"10.1101/2023.04.25.23289099","url":null,"abstract":"<p><p>Summary-data-based multivariable Mendelian randomization (MVMR) methods, such as MVMR-Egger, MVMR-IVW, MVMR median-based, and MVMR-PRESSO, assess the causal effects of multiple risk factors on disease. However, accounting for variances in summary statistics related to risk factors remains a challenge. We propose a linear mixed model with measurement error correction (LMM-MEC) that accounts for the variance of summary statistics for both disease outcomes and risk factors. In step I, a linear mixed model is applied to account for the variance in disease summary statistics. Specifically, if heterogeneity is present in disease summary statistics, we treat it as a random effect and adopt an iteratively re-weighted least squares algorithm to estimate causal effects. In step II, we treat the variance in the summary statistics of risk factors as multiple measurement errors and apply a regression calibration method for simultaneous multiple measurement error correction. In a simulation study, when using independent genetic variants as instrumental variables (IV), our method showed comparable performance to existing MVMR methods under conditions of no pleiotropy or balanced pleiotropy with the outcome, and it exhibited higher coverage rates and power under directional pleiotropy. Similar findings were observed when using genetic variants with low to moderate linkage disequilibrium (LD) (0 < <i>ρ</i> ² ≤ 0.3) as IVs, although coverage rates reduced for all methods compared to using independent genetic variants as IVs. In the application study, we examined causal associations between correlated cholesterol biomarkers and longevity. By including 739 genetic variants selected based on P values <5×10 ^-5 from GWAS and allowing for low LD ( <i>ρ</i> ² ≤ 0.1), our method identified that large LDL-c were causally associated with lower likelihood of achieving longevity.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9789776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"After the Infection: A Survey of Pathogens and Non-communicable Human Disease.","authors":"Michael Lape, Daniel Schnell, Sreeja Parameswaran, Kevin Ernst, Shannon O'Connor, Nathan Salomonis, Lisa J Martin, Brett M Harnett, Leah C Kottyan, Matthew T Weirauch","doi":"10.1101/2023.09.14.23295428","DOIUrl":"10.1101/2023.09.14.23295428","url":null,"abstract":"<p><p>There are many well-established relationships between pathogens and human disease, but far fewer when focusing on non-communicable diseases (NCDs). We leverage data from The UK Biobank and TriNetX to perform a systematic survey across 20 pathogens and 426 diseases, primarily NCDs. To this end, we assess the association between disease status and infection history proxies. We identify 206 pathogen-disease pairs that replicate in both cohorts. We replicate many established relationships, including <i>Helicobacter pylori</i> with several gastroenterological diseases and connections between Epstein-Barr virus with multiple sclerosis and lupus. Overall, our approach identified evidence of association for 15 pathogens and 96 distinct diseases, including a currently controversial link between human cytomegalovirus (CMV) and ulcerative colitis (UC). We validate this connection through two orthogonal analyses, revealing increased CMV gene expression in UC patients and enrichment for UC genetic risk signal near human genes that have altered expression upon CMV infection. Collectively, these results form a foundation for future investigations into mechanistic roles played by pathogens in NCDs. All results are easily accessible on our website, https://tf.cchmc.org/pathogen-disease.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3d/2c/nihpp-2023.09.14.23295428v1.PMC10516055.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41104621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes.","authors":"G L Barlow, C M Schürch, S S Bhate, D Phillips, A Young, S Dong, H A Martinez, G Kaber, N Nagy, S Ramachandran, J Meng, E Korpos, J A Bluestone, G P Nolan, P L Bollyky","doi":"10.1101/2023.03.15.23287145","DOIUrl":"10.1101/2023.03.15.23287145","url":null,"abstract":"<p><p>In autoimmune Type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans - islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 ⁺ T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA ⁺ T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9197159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Keyphrase Identification Using Minimal Labeled Data with Hierarchical Contexts and Transfer Learning.","authors":"Rohan Goli, Keerthana Komatineni, Shailesh Alluri, Nina Hubig, Hua Min, Yang Gong, Dean F Sittig, Lior Rennert, David Robinson, Paul Biondich, Adam Wright, Christian Nøhr, Timothy Law, Arild Faxvaag, Aneesa Weaver, Ronald Gimbel, Xia Jing","doi":"10.1101/2023.01.26.23285060","DOIUrl":"10.1101/2023.01.26.23285060","url":null,"abstract":"<p><strong>Background: </strong>Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. Ontology construction is traditionally a manual effort by human domain experts, and the newly advanced natural language processing techniques, such as KP identification, can be a critical complementary automatic part of building ontology. However, KP identification requires human expertise, consensus, and contextual understanding for data labeling.</p><p><strong>Methods: </strong>This paper presents a semi-supervised KP identification framework (long short-term memory-based encoders and the conditional random fields -based decoder models, BiLSTM-CRF) using minimal human labeled data based on hierarchical attention (i.e., at word, sentence, and abstract levels) over the documents and domain adaptation. We created synthetic labels for initial training and human-labeled data for fine-tuning. We also tested different options during NLP preprocessing and ML training to optimize the ML pipeline.</p><p><strong>Results: </strong>Our method outperforms the prior neural architectures by learning through synthetic labels for initial training, document-level contextual learning, language modeling, and fine-tuning with limited gold standard label data. After comparison, we found that the BIO encoding schema performed slightly better than Blue, and domain adaptation techniques can improve the quality of synthetic labels. In addition, document-level context, pre-trained LM, and pre-trained WE all contributed to better model performance in our tasks. Add 2 to 4 human-labeled documents for every 100 synthetic labeled documents improves the model performance without exhausting human-labeled documents too quickly.</p><p><strong>Conclusions: </strong>To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify KPs, which is trained on limited human labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging, and light-weighted deep learning models play an important role in real-time KP identification as a complementary approach to human experts' effort.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b9/97/nihpp-2023.01.26.23285060v2.PMC10246160.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10009443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Efficacy Prediction for EHR-based Emulated Trials in Repurposing Heart Failure Therapies.","authors":"Nansu Zong, Shaika Chowdhury, Shibo Zhou, Sivaraman Rajaganapathy, Yue Yu, Liewei Wang, Qiying Dai, Pengyang Li, Xiaoke Liu, Suzette J Bielinski, Jun Chen, Yongbin Chen, James R Cerhan","doi":"10.1101/2023.05.25.23290531","DOIUrl":"10.1101/2023.05.25.23290531","url":null,"abstract":"<p><strong>Introduction: </strong>The High mortality rates associated with heart failure (HF) have propelled the strategy of drug repurposing, which seeks new therapeutic uses for existing, approved drugs to enhance the management of HF symptoms effectively. An emerging trend focuses on utilizing real-world data, like EHR, to mimic randomized controlled trials (RCTs) for evaluating treatment outcomes through what are known as emulated trials (ET). Nonetheless, the intricacies inherent in EHR data-comprising detailed patient histories in databases, the omission of certain biomarkers or specific diagnostic tests, and partial records of symptoms-introduce notable discrepancies between EHR data and the stringent standards of RCTs. This gap poses a substantial challenge in conducting an ET to accurately predict treatment efficacy.</p><p><strong>Objective: </strong>The objective of this research is to predict the efficacy of drugs repurposed for HF in randomized trials by leveraging EHR in ET.</p><p><strong>Methods: </strong>We proposed an ET framework to predict drug efficacy, integrating target prediction based on biomedical databases with statistical analysis using EHR data. Specifically, we developed a novel target prediction model that learns low-dimensional representations of drug molecules, protein sequences, and diverse biomedical associations from a knowledge graph. Additionally, we crafted strategies to improve the prediction by considering the interactions between HF drugs and biological factors in the context of HF prognostic markers.</p><p><strong>Results: </strong>Our validation of the drug-target prediction model against the BETA benchmark demonstrated superior performance, with an average AUCROC of 97.7%, PRAUC of 97.4%, F1 score of 93.1%, and a General Score of 96.1%, surpassing existing baseline algorithms. Further analysis of our ET framework on identifying 17 repurposed drugs-derived from 266 phase 3 HF RCTs-using data from 59,000 patients at the Mayo Clinic highlighted the framework's remarkable predictive accuracy. This analysis took into account various factors such as biological variables (e.g., gender, age, ethnicity), HF medications (e.g., ACE inhibitors, Beta-blockers, ARBs, Loop Diuretics), types of HF (HFpEF and HFrEF), confounders, and prognostic markers (e.g., NT-proBNP, bUn, creatinine, and hemoglobin). The ET framework significantly improved the accuracy compared to the baseline efficacy analysis that utilized EHR data. Notably, the best results were improved in AUC-ROC from 75.71% to 93.57% and in PRAUC from 78.66% to 90.34%, compared to the baseline models.</p><p><strong>Conclusion: </strong>Our study presents an ET framework that significantly enhances drug efficacy emulation by integrating EHR-based analysis with target prediction. We demonstrated substantial success in predicting the efficacy of 17 HF drugs repurposed for phase 3 RCTs, showcasing the framework's potential in advancing HF treatment strategies.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b0/45/nihpp-2023.05.25.23290531v1.PMC10312819.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9754104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel autoantibody targets identified in patients with autoimmune hepatitis (AIH) by PhIP-Seq reveals pathogenic insights.","authors":"Arielle Klepper, James Asaki, Andrew F Kung, Sara E Vazquez, Aaron Bodansky, Anthea Mitchell, Sabrina A Mann, Kelsey Zorn, Isaac Avila-Vargas, Swathi Kari, Melawit Tekeste, Javier Castro, Briton Lee, Maria Duarte, Mandana Khalili, Monica Yang, Paul Wolters, Jennifer Price, Emily Perito, Sandy Feng, Jacquelyn J Maher, Jennifer C Lai, Christina Weiler-Normann, Ansgar W Lohse, Joseph DeRisi, Michele Tana","doi":"10.1101/2023.06.12.23291297","DOIUrl":"10.1101/2023.06.12.23291297","url":null,"abstract":"<p><strong>Background and aims: </strong>Autoimmune hepatitis (AIH) is a severe disease characterized by elevated immunoglobin levels. However, the role of autoantibodies in the pathophysiology of AIH remains uncertain.</p><p><strong>Methods: </strong>Phage Immunoprecipitation-Sequencing (PhIP-seq) was employed to identify autoantibodies in the serum of patients with AIH (<i>n</i> = 115), compared to patients with other liver diseases (metabolic associated steatotic liver disease (MASH) <i>n</i> = 178, primary biliary cholangitis (PBC), <i>n</i> = 26, or healthy controls, <i>n</i> = 94).</p><p><strong>Results: </strong>Logistic regression using PhIP-seq enriched peptides as inputs yielded a classification AUC of 0.81, indicating the presence of a predictive humoral immune signature for AIH. Embedded within this signature were disease relevant targets, including SLA/LP, the target of a well-recognized autoantibody in AIH, disco interacting protein 2 homolog A (DIP2A), and the relaxin family peptide receptor 1 (RXFP1). The autoreactive fragment of DIP2A was a 9-amino acid stretch nearly identical to the U27 protein of human herpes virus 6 (HHV-6). Fine mapping of this epitope suggests the HHV-6 U27 sequence is preferentially enriched relative to the corresponding DIP2A sequence. Antibodies against RXFP1, a receptor involved in anti-fibrotic signaling, were also highly specific to AIH. The enriched peptides are within a motif adjacent to the receptor binding domain, required for signaling and serum from AIH patients positive for anti-RFXP1 antibody was able to significantly inhibit relaxin-2 singling. Depletion of IgG from anti-RXFP1 positive serum abrogated this effect.</p><p><strong>Conclusions: </strong>These data provide evidence for a novel serological profile in AIH, including a possible functional role for anti-RXFP1, and antibodies that cross react with HHV6 U27 protein.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3f/66/nihpp-2023.06.12.23291297v2.PMC10312872.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9754091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic insights into the association between inflammatory bowel disease and Alzheimer's disease.","authors":"Lu Zeng, Charles C White, David A Bennett, Hans-Ulrich Klein, Philip L De Jager","doi":"10.1101/2023.04.17.23286845","DOIUrl":"10.1101/2023.04.17.23286845","url":null,"abstract":"<p><p>Myeloid cells, including monocytes, macrophages, microglia, dendritic cells and neutrophils are a part of innate immune system, playing a major role in orchestrating innate and adaptive immune responses. Both Alzheimer's disease (AD) and inflammatory bowel disease (IBD) susceptibility loci are enriched for genes expressed in myeloid cells, but it is not clear whether these myeloid risk factors are shared between the two diseases. Leveraging results of genome-wide association studies, we investigated the causal effect of IBD (including ulcerative colitis (UC) and Crohn's disease (CD)) variants on AD and its endophenotypes. Microglia and monocyte expression Quantitative Trait Locus (eQTLs) were used to examine the functional consequences of IBD and AD variants. Our results revealed distinct sets of genes and pathways of AD and IBD susceptibility loci. Specifically, AD loci are enriched for microglial eQTLs, while IBD loci are enriched for monocyte eQTLs. However, we also found that genetically determined IBD is associated with a protective effect against AD (p<0.03). Yet, a genetic propensity for the CD subtype is associated with increased amyloid accumulation (beta=7.14, p-value=0.02) and susceptibility to AD. Susceptibility to UC was associated with increased deposition of TDP-43 (beta=7.58, p-value=6.11×10^-4). The relation of these gastrointestinal inflammatory disease to AD is therefore complex; while the different subsets of susceptibility variants preferentially affect different myeloid cell subtypes, there do appear to be certain shared pathways and the possible protective effect of IBD susceptibility on the risk of AD which may provide therapeutic insights.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/39/e6/nihpp-2023.04.17.23286845v1.PMC10153331.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9459545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum.","authors":"David S M Lee, Kathleen M Cardone, David Y Zhang, Noah L Tsao, Sarah Abramowitz, Pranav Sharma, John S DePaolo, Mitchell Conery, Krishna G Aragam, Kiran Biddinger, Ozan Dilitikas, Lily Hoffman-Andrews, Renae L Judy, Atlas Khan, Iftikhar Kulo, Megan J Puckelwartz, Nosheen Reza, Benjamin A Satterfield, Pankhuri Singhal, Zoltan P Arany, Thomas P Cappola, Eric Carruth, Sharlene M Day, Ron Do, Christopher M Haggarty, Jacob Joseph, Elizabeth M McNally, Girish Nadkarni, Anjali T Owens, Daniel J Rader, Marylyn D Ritchie, Yan V Sun, Benjamin F Voight, Michael G Levin, Scott M Damrauer","doi":"10.1101/2023.07.16.23292724","DOIUrl":"10.1101/2023.07.16.23292724","url":null,"abstract":"<p><p>Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, which affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (P-value < 5×10^-8). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes (<i>MYBPC3</i>, <i>BAG3</i>) and in regulators of lipoprotein (<i>LPL</i>) and glucose metabolism (<i>GIPR</i>, <i>GLP1R</i>). These signals are enriched in myocyte and adipocyte cell types and can be clustered into 5 broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity, and arrhythmias. Gene burden studies across three biobanks (PMBB, UKB, AOU), including 27,208 individuals with HF and 349,126 without, uncover exome-wide significant (P-value < 1.57×10^-6) associations for HF and rare predicted loss-of-function (pLoF) variants in <i>TTN</i>, <i>MYBPC3</i>, <i>FLNC, and BAG3.</i> Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, while common variant heritability (4.3%, 95% CI 3.9-4.7%) is more diffusely spread throughout the genome. Finally, we show that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. Together, these findings provide a genetic link between dysregulated metabolism and HF, and suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ec/53/nihpp-2023.07.16.23292724v3.PMC10371173.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9945525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TGIRT-seq of Inflammatory Breast Cancer Tumor and Blood Samples Reveals Widespread Enhanced Transcription Impacting RNA Splicing and Intronic RNAs in Plasma.","authors":"Dennis Wylie, Xiaoping Wang, Jun Yao, Hengyi Xu, Elizabeth A Ferrick-Kiddie, Toshiaki Iwase, Savitri Krishnamurthy, Naoto T Ueno, Alan M Lambowitz","doi":"10.1101/2023.05.26.23290469","DOIUrl":"10.1101/2023.05.26.23290469","url":null,"abstract":"<p><p>Inflammatory breast cancer (IBC) is the most aggressive and lethal breast cancer subtype but lacks unequivocal genomic differences or robust biomarkers that differentiate it from non-IBC. Here, Thermostable Group II intron Reverse Transcriptase RNA-sequencing (TGIRT-seq) revealed myriad differences in tumor samples, Peripheral Blood Mononuclear Cells (PBMCs), and plasma that distinguished IBC from non-IBC patients and healthy donors across all tested receptor-based subtypes. These included numerous differentially expressed protein-coding gene and non-coding RNAs in all three sample types, a granulocytic immune response in IBC PBMCs, and over-expression of antisense RNAs, suggesting wide-spread enhanced transcription in both IBC tumors and PBMCs. By using TGIRT-seq to quantitate Intron-exon Depth Ratios (IDRs) and mapping reads to both genome and transcriptome reference sequences, we developed methods for parallel analysis of transcriptional and post-transcriptional gene regulation. This analysis identified numerous differentially and non-differentially expressed protein-coding genes in IBC tumors and PBMCs with high IDRs, the latter reflecting rate-limiting RNA splicing that negatively impacts mRNA production. Mirroring gene expression differences in tumors and PBMCs, over-represented protein-coding gene RNAs in IBC patient plasma were largely intronic RNAs, while those in non-IBC patients and healthy donor plasma were largely mRNA fragments. Potential IBC biomarkers in plasma included T-cell receptor pre-mRNAs and intronic, LINE-1, and antisense RNAs. Our findings provide new insights into IBC and set the stage for monitoring disease progression and response to treatment by liquid biopsy. The methods developed for parallel transcriptional and post-transcriptional gene regulation analysis have potentially broad RNA-seq and clinical applications.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5f/b3/nihpp-2023.05.26.23290469v1.PMC10312853.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10122265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}