bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Scalable Framework for Identifying Allelic Series from Summary Statistics
Authors: McCaw Zachary R , Gao Jianhui , Dey Rounak , Tucker Simon , Zhang Yiyan ,   insitro Research Team, Gronsbell Jessica , Li Xihao , Fox Emily , O’Dushlaine Colm , Soare Thomas W 
Published: November 2024

AbstractGenes for which a dose-response relationship exists between mutational severity and phenotypic impact make for logical therapeutic targets, as the effects of pharmacological modulation can be anticipated from the natural variation present in a population. We refer to genes where such a dose-response relationship exists as harboring allelic series, and have introduced the rare coding-variant allelic series test (COAST) for their identification. The original COAST required access to individual-level data. However, such data are often unavailable due to privacy concerns or logistical challenges. Meanwhile, single-variant summary statistics of the type produced by genome-wide association studies are plentiful. Here we introduce COAST-SS, an extension of COAST that accepts summary statistics as input, namely the per-variant effect sizes and standard errors, along with estimates of the minor allele frequency and local linkage disequilibrium (LD). As a running example, we consider identifying allelic series for circulating lipid traits, drawing on data from the UK Biobank, Million Veterans Program, and Trans-Omics of Precision Medicine Program. Through extensive analyses of real and simulated data, we demonstrate that COAST-SS provides p-values effectively equivalent to those from the original COAST. Interestingly, we find that when LD is low, as is expected among rare variants, COAST-SS is robust to misspecification of the LD matrix, providing valid inference even when the LD matrix is set to the identity matrix. We explore several strategies for annotating the pathogenicity of variants supplied to COAST-SS, finding that they often yield similar power for detecting candidate allelic series. Lastly, we employ COAST-SS to screen for lipid trait allelic series in a meta-analyzed cohort of up to 840K subjects. COAST-SS has been incorporated into the publicly availableAllelicSeries Rpackage.
Navigating the Fitness Landscapes of Plasmodium falciparum Dihydrofolate Reductase: Evolutionary Insights into Antifolate Resistance
Authors: Muzata Danny , Sanyal Dwipanjan , Shivram A , Pandey Deeptanshu , Chakraborti Soumyananda , Chivukula Aneesh S. , Jasuja Ravi , Uversky Vladimir N. , Zielinski Daniel , Upadhyay Prajna D. , Albers Mark , Chowdhury Sourav 
Published: November 2024

AbstractThe rapid emergence of drug resistance in malaria parasites poses a significant challenge to the efficacy of antifolate treatments. Traditional drug development approaches, which often rely on empirical screening with limited mechanistic insights, tend to overlook the complex evolutionary mechanisms that enablePlasmodium falciparumto evade drug inhibition while preserving enzyme functionality. In this study, we employed computational techniques to investigate the mutational landscape of dihydrofolate reductase (DHFR), focusing on regions essential for enzyme stability and resistance. Our analysis uncovered conserved residues essential for stability, mutation hotspots that enhance adaptability under drug pressure and co-evolving clusters revealing critical functional interdependencies. Through integrated approaches including mutational scanning, epistatic interaction modeling, and fitness trajectory mapping, we elucidated distinct evolutionary pathways that drive resistance. We were able to track the adaptive paths taken by wild-type residues upon mutation, revealing the steps required to reach high-fitness peaks within the rugged fitness landscape. These findings provide valuable insights into the molecular mechanisms of antifolate resistance. We suggest that future drug design should target co-evolving networks and conserved regions to support the development of next-generation therapies to overcome resistance.
Hybrid Transformer and Neural Network Configuration for Protein Classification Using Amino Acids
Authors: Labiosa Nathan , Kohli Aryan , Chung Christian , Korban Christopher 
Published: November 2024

AbstractThis study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic physical properties of proteins. Trained on a comprehensive dataset from the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the model demonstrates a classification accuracy of 95%, outperforming existing methods by at least 15%.The high accuracy achieved demonstrates the potential of this approach to innovate protein classification, facilitating advancements in drug discovery and the development of personalized medicine. By enabling precise protein function prediction, the hybrid model allows for specialized strategies in therapeutic targeting and the exploration of protein dynamics in biological systems.Future work will focus on enhancing the model’s generalizability across diverse datasets and exploring the integration of more machine learning techniques to refine predictive capabilities further. The implications of this research offer potential breakthroughs in biomedical research and the broader field of protein engineering.
DGRNA: a long-context RNA foundation model with bidirectional attention Mamba2
Authors: Yuan Ye , Chen Qushuo , Pan Xiaoyong 
Published: November 2024

AbstractRibonucleic acid (RNA) is an important biomolecule with diverse functions i.e. genetic information transfer, regulation of gene expression and cellular functions. In recent years, the rapid development of sequencing technology has significantly enhanced our understanding of RNA biology and advanced RNA-based therapies, resulting in a huge volume of RNA data. Data-driven methods, particularly unsupervised large language models, have been used to automatically hidden semantic information from these RNA data. Current RNA large language models are primarily based on Transformer architecture, which cannot efficiently process long RNA sequences, while the Mamba architecture can effectively alleviate the quadratic complexity associated with Transformers. In this study, we propose a large foundational model DGRNA based on the bidirectional Mamba trained on 100 million RNA sequences, which has demonstrated exceptional performance across six RNA downstream tasks compared to existing RNA language models.
Strain softening and hysteresis arising from 3D multicellular dynamics during long-term large deformation
Authors: Tsubota Ken-ichi , Horikoshi Shota , Hiraiwa Tetsuya , Okuda Satoru 
Published: November 2024

AbstractLiving tissues exhibit complex mechanical properties, including viscoelastic and elastoplastic responses, that are crucial for regulating cell behaviors and tissue deformations. Despite their significance, the intricate properties of three-dimensional (3D) multicellular tissues are not well understood and are inadequately implemented in biomaterial engineering. To address this gap, we developed a numerical method to analyze the dynamic properties of multicellular tissues using a 3D vertex model framework. By focusing on 3D tissues composed of confluent homogeneous cells, we characterized their properties in response to various deformation magnitudes and time scales. Stress relaxation tests revealed that large deformations initially induced relaxation in the shapes of individual cells. This process is amplified by subsequent transient cell rearrangements, homogenizing cell shapes and leading to tissue fluidization. Additionally, dynamic viscoelastic analyses showed that tissues exhibited strain softening and hysteresis during large deformations. Interestingly, this strain softening originates from multicellular structures independent of cell rearrangement, while hysteresis arises from cell rearrangement. Moreover, tissues exhibit elastoplastic responses over the long term, which are well represented by the Ramberg–Osgood model. These findings highlight the characteristic properties of multicellular tissues emerging from their structures and rearrangements, especially during long-term large deformations. The developed method offers a new approach to uncover the dynamic nature of 3D tissue mechanics and could serve as a technical foundation for exploring tissue mechanics and advancing biomaterial engineering.
Social inequalities in vaccine coverage and their effects on epidemic spreading
Authors: Manna Adriana , Karsai Márton , Perra Nicola 
Published: November 2024

AbstractVaccinations are fundamental public health interventions. Yet, inequalities in vaccines uptake across socioeconomic groups can significantly undermine their impact. Moreover, heterogeneities in vaccination coverage across socioeconomic strata are typically neglected by epidemic models and considered, if at all, only at posteriori. This limitation reduces their ability to predict and assess the effectiveness of vaccination campaigns. Here, we study the impact of socioeconomic inequalities in vaccination uptake on epidemic burden. We consider a modeling framework based on generalized contact matrices that extend traditional age-stratified approaches to incorporate socioeconomic status (SES) variables. We simulate epidemic dynamics under two scenarios. In the first vaccination campaigns are concurrent with epidemics. In the second instead, vaccinations are completed before the onset of infection waves. By using both synthetic and empirical generalized contact matrices, we find that inequalities in vaccine uptake can lead to non-linear effects on disease outcomes and exacerbate disease burden in disadvantaged groups of the population. We demonstrate that simple models ignoring SES heterogeneity produce incomplete or biased predictions of epidemic burden. Additionally, we show how inequalities in vaccine coverage interact with non-pharmaceutical interventions (NPIs) compounding differences across subgroups. Overall, our findings highlight the importance of integrating SES dimensions, alongside age, into epidemic models to inform more equitable and effective public health interventions and vaccination strategies.
Klotho plasma levels are an independent predictor of mortality in women with acute coronary syndrome
Authors: Cortés García Marcelino , Kallmeyer Mayor Andrea , Tarín Nieves , Cristobal Carmen , Pello Lázaro Ana María , Aceña Alvaro , Gutierrez-Landaluce Carlos , Huelmos Ana , Alonso Martín Joaquín , López Bescós Lorenzo , Mahillo-Fernandez Ignacio , Lorenzo Oscar , Gonzalez-Casaus Maria , Egido Jesus , Tuñon Jose 
Published: November 2024

ABSTRACTINTRODUCTIONAlterations in plasma levels of the components of the mineral metabolism (MM) system (calcidiol, fibroblast growth factor-23 [FGF23], phosphate, parathormone [PTH] and klotho) are related to cardiovascular diseases. However, gender differences of the whole MM system in patients with acute coronary syndrome (ACS) have not been reported so far.METHODSWe studied 1,230 patients with ACS. At baseline clinical data were collected and plasma levels of MM components were assessed. The primary outcome was a composite of acute ischaemic events, heart failure and all-cause mortality. Secondary outcomes included each component separately.RESULTS282 patients (22.9%) were female. At baseline, FGF23, PTH, phosphate, and klotho plasma levels were higher in women than in men. After 5.44 (3.03-7.46) years of follow-up, the primary outcome occurred in 28.0% women and 23.5% men, and death in 10.6% and 9.4% respectively. At multivariate Cox regression analysis, FGF23 levels were associated with incidence of the primary outcome in both, women (Hazard ratio [HR] 1.02 [95%CI:1.01-1.04];p=0.001) and men (HR 1.04 [1.00-1.03];p=0.016), in whom calcidiol levels were also an independent predictor of this outcome. Klotho (HR 0.80 [95%CI:0.67-0.96];p=0.019) and phosphate (HR=2.24 [95%CI: 1.11-4.50];p=0.025) were independent predictors of death in women, while calcidiol (HR=0.84 [95%CI0.72-0.98];p=0.024) and FGF23 levels (HR=1.02 [1.00-1.03];p=0.048) were predictors in men.CONCLUSIONSKlotho levels are inversely and independently related to all-cause mortality after an ACS in women, but not in men. Furthermore, the MM profile in ACS patients differs in both genders. Future research should explore the underlying mechanisms of these associations.
State-level structural racism and incident coronary heart disease
Authors: Safford Monika M. , Brown Tyson , Bryan Joanna , Brown Todd M. , Pinheiro Laura 
Published: November 2024

AbstractIntroductionBlack Americans have greater coronary heart disease (CHD) burden than White Americans, disparities that are largely socially determined. Discriminatory societal practices that systematically disadvantage Black Americans are forms of structural racism but few studies have examined structural racism and incident CHD. We sought to determine associations between three validated measures of structural racism and incident CHD, hypothesizing that greater state-level structural racism is associated with incident CHD for Black but not White individuals.MethodsWe used data from the national REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort, which enrolled 30,239 Black and White community dwelling adults between 2003-7 who were contacted every 6 months with retrieval of medical records and expert adjudication of myocardial infarction and cause of death. Incident CHD was defined as myocardial infarction or death due to CHD. Structural racism variables included Black:White % living below the Federal poverty line, Black:White % uninsured, and the Dissimilarity Index (DI), a measure of residential racial segregation. Structural racism variables were dichotomized at the median. Separate race-stratified Cox proportional hazards models examined associations between each measure of structural racism and incident CHD.ResultsThe 24,099 participants free of CHD at baseline included 10,286 Black and 13,813 White participants. Mean age at baseline was 64 years, 58% were women, and 47% had annual household income <$35,000. Greater structural racism was significantly associated with incident CHD for Black but not White participants. For high Black:White poverty, Black HR=1.17 (95% CI 1.01-1.35), White HR=0.93 (0.83-1.06); for high Black:White uninsurance, Black=HR 1.34 (1.06-1.70), White HR=1.20 (0.98-1.47); for high DI, Black HR=1.17 (1.01-1.35), White HR=0.99 (0.88-1.12). Findings suggest that structural racism variables indirectly influence CHD via individual-level income and education. Results were similar for men and women and for older and younger individuals. Significant associations were observed for fatal but not nonfatal CHD events.ConclusionsStructural racism was associated with higher incidence of CHD for Black but not White individuals. If these associations are causal, changing state level laws to combat poverty in Black communities, expand Medicaid, and reduce segregation could potentially lessen Black:White disparities in CHD.
Impact of brain frailty on complications and outcomes in acute ischemic stroke
Authors: Cheng XiaoQing , Cai Wu , Li ChuYan , Shen Xi , Li JiaNan , Huang LiJun , Tang JinJing , Pang HuiMin , Luo BaiYan , Liu Ya , QianChen  , Xiao Lulu , Zhu WuSheng , Cao ZeHong , Liu XiaoYu , Zhang LongJiang , Yin XinDao , Zhang ZhiQiang , Shi Feng , Xing Wei , Lu GuangMing 
Published: November 2024

ABSTRACTBACKGROUNDThis study aimed to determine the association of brain frailty with acute complications and prognosis in patients with acute ischemic stroke (AIS) due to occlusion of large vessels in the anterior circulation, and to further assess its predictive value.METHODS AND RESULTSThis multicenter, retrospective study included patients with AIS due to large vessel occlusion in the anterior circulation. All patients underwent MRI within seven days of stroke onset, measuring subcortical and cortical atrophy and leukoaraiosis as indicators of brain frailty.The study included 1,090 patients with a median age of 64 (interquartile range, 55–73) years and a median National Institutes of Health Stroke Scale (NIHSS) score of 9 (interquartile range, 4.5–15). Multivariable logistic regression analysis showed that independent risk factors for the unfavorable clinical outcome included: NIHSS score (OR, 1.17; 95% CI, 1.13–1.22), blood glucose (OR, 1.15; 95% CI, 1.04–1.26), infarct volume (OR, 1.32; 95% CI, 1.15–1.52), subcortical atrophy (OR, 1.27; 95% CI, 1.18–1.37), severe cortical atrophy (OR, 5.46; 95% CI, 1.71–17.45), and severe leukoaraiosis (OR, 4.68; 95% CI, 1.93–11.31). However, brain frailty was not significantly associated with AIS complications (malignant cerebral edema, parenchymal hemorrhage). Including brain atrophy indicators in the model significantly improved its unfavorable clinical outcome predictive power (AUC increased from 0.762 to 0.822;p< 0.001). The results remained stable in subgroup analyses across treatment modalities.CONCLUSIONSBrain frailty was significantly associated with the unfavorable clinical outcome but not with acute complications. Brain frailty indicators contributed to the predictive efficacy, regardless of treatment modality.
Metabolic Polygenic Risk Scores for Prediction of Obesity, Type 2 Diabetes, and Related Morbidities
Authors: Kim Min Seo , Chen Qiuli , Sui Yang , Yang Xiong , Wang Shaoqi , Weng Lu-Chen , Cho So Mi Jemma , Koyama Satoshi , Zhu Xinyu , Yu Kang , Chen Xingyu , Zhang Rufan , Yin Wanqing , Liao Shuangqiao , Liu Zhaoqi , Alkuraya Fowzan S , Natarajan Pradeep , Ellinor Patrick T. , Fahed Akl C. , Wang Minxian 
Published: November 2024

AbstractObesity and type 2 diabetes (T2D) are metabolic diseases with shared pathophysiology. Traditional polygenic risk scores (PRS) have focused on these conditions individually, yet the single disease approach falls short in capturing the full dimension of metabolic dysfunction. We derived biologically enriched metabolic PRS (MetPRS), a composite score that uses multi-ancestry genome-wide association studies of 22 metabolic traits from over 10 million people. MetPRS, optimized to predict obesity (O-MetPRS) and T2D (D-MetPRS), was validated in the UK Biobank (UKB, n=15,000), and tested in UKB hold-out set (n=49,377), then externally tested in 3 cohorts – All of Us (n=245,394), Mass General Brigham (MGB) Biobank (n=53,306), and a King Faisal Specialist Hospital and Research Center cohort (n=6,416). O-MetPRS and D-MetPRS outperformed existing PRSs in predicting obesity and T2D across 6 ancestries (European, African, East Asian, South Asian, Latino/admixed American, and Middle Eastern). O-MetPRS and D-MetPRS also predicted morbidities and downstream complications of obesity and T2D, as well as the use of GLP-1 receptor agonists in contemporary practice. Among 37,329 MGB participants free of T2D and obesity at baseline, those in the top decile of O-MetPRS had a 103% relatively higher chance, and those in the top decile of D-MetPRS had an 80% relatively higher chance of receiving a GLP-1 receptor agonist prescription compared to individuals at the population median of MetPRS. The biologically enriched MetPRS is poised to have an impact across all layers of clinical utility, from predicting morbidities to informing management decisions.
JG2: an updated version of the Japanese population-specific reference genome
Authors: Sriwichaiin Sirawit , Makino Satoshi , Funayama Takamitsu , Otsuki Akihito , Kawashima Junko , Okamura Yasunobu , Tadaka Shu ,   the Tohoku Medical Megabank Project Study Group, Katsuoka Fumiki , Kumada Kazuki , Tsutsumi Shuichi , Kinoshita Kengo , Yamamoto Masayuki , Tamiya Gen , Takayama Jun 
Published: November 2024

AbstractThis study presents the construction of JG2, an updated population-specific reference genome for the Japanese population. Utilizing data from three individuals previously employed in the construction of JG1, several methodologies were employed to enhance genomic coverage and assembly quality. Hi-C sequencing technology facilitated phase-aware assembly, generating two haploid assemblies per individual and enabling improved representation of genetic variation. A meta-assembly strategy and a majority decision approach further refined assembly quality by combining the best sequences from multiple assemblies and minimizing the inclusion of rare variants. The resulting JG2 genome comprises chromosome-level sequences, mitochondrial chromosomes, and unplaced scaffolds, offering more comprehensive coverage of the Japanese genome. Comparative analyses with other reference genomes demonstrated the accuracy and representativeness of JG2, highlighting its utility for genetic research involving the Japanese population. Overall, by adopting the phased assembly technique, JG2 represents a significant advancement over the collapsed assembly-based JG1, providing researchers with a more precise and comprehensive resource for understanding the genetic landscape of the Japanese population. The sequences and annotations are available on the jMorp website (https://jmorp.megabank.tohoku.ac.jp/).
The nitrite reductase activity of xanthine oxidoreductase sustains cardiovascular health
Authors: Dyson Nicki , Khambata Rayomand S. , Parakaw Tipparat , Massimo Gianmichele , Khuat Ngan HH. , Noor Annika A. , Gee Lorna C. , Lim Ivy , Siddique Umme , Sullivan Andrew J. , Ho Jonathan W. , Rathod Krishnaraj , Barnes Michael R. , Cabrera Claudia P. , Ahluwalia Amrita 
Published: November 2024

AbstractBackgroundXanthine oxidoreductase (XOR) is a multi-functional enzyme that metabolises purines generating uric acid and is a generator of reactive oxygen species. Both functions have been implicated in the pathogenesis of cardiovascular disease. More recently, a third function of XOR as a nitrite reductase has been identified and been shown to play a key role in the benefits of targeting the non-canonical pathway for nitric oxide (NO) generation in the cardiovascular disease setting. This effect has been specifically attributed to XOR dependent recovery of NO levels. However, whether XOR derived NO plays any role in maintaining cardiovascular homeostasis in health is unknown. To explore this, we used global and hepatocyte-specificXdh-deleted mice to assess cardiovascular homeostasis.MethodsXdh+/+andXdh+/-,Xdhfl/fland Xdhfl/flAlbCre+/-(HXOR KO) mice littermates, matched for sex and age, were used for in vivo cardiovascular phenotyping; blood pressure, cardiac function, endothelial reactivity, and leukocyte trafficking. Tissues from these mice were used for biochemical measurements of nitrate, nitrite, and markers of NO downstream signalling.ResultsXdh+/-and HXOR KO mice expressed significantly attenuated liver and plasma nitrite reductase activity and platelet cGMP levels versus littermate controls. These effects were associated with increased systolic blood pressure, left ventricular remodelling, and increased leukocyte activation. These effects were associated with and likely driven by endothelial dysfunction evident in both mouse models. This dysfunction was reflected by increased endothelial adhesion molecule expression (P-selectin), increased ischaemia-induced vasoconstriction, during vessel occlusion, and an impaired flow-mediated dilation response of the iliac artery in vivo.ConclusionsIn summary, XOR derived NO is critical for maintaining vascular homeostasis under physiological conditions and is key in mediating the benefits of dietary nitrate regimes in cardiovascular pathology