bluuebunny/crossref_metadata_2025_split huggingface dataset:
Opposite-sex associations are linked with annual fitness, but sociality is stable over lifetime
Authors: Dunning Jamie , Burke Terry , Chan Alex Hoi Hang , Chik Heung Ying Janet , Evans Tim , Schroeder Julia 
Published: January 2022

AbstractAnimal sociality, an individual’s propensity to associate with others, has fitness consequences through mate choice, for example, directly, by increasing the pool of prospective partners, and indirectly through increased survival, and individuals benefit from both. Annually, fitness consequences are realised through increased mating success and subsequent fecundity. However, it remains unknown whether these consequences translate to life-time fitness. Here, we quantified social associations and their link to fitness annually and over lifetime, using a multi-generational, genetic pedigree. We used social network analysis to calculate variables representing different aspects of an individual’s sociality. Sociality showed high within-individual repeatability. We found that birds with more opposite-sex associates had higher annual fitness than those with fewer, but this did not translate to lifetime fitness. Instead, for lifetime fitness, we found evidence for stabilizing selection on opposite-sex sociality, and sociality in general, suggesting that reported benefits are only short-lived in a wild population, and that selection favours an average sociality.
A unique class of Zn2+-binding PBPs underlies cephalosporin resistance and sporogenesis of Clostridioides difficile
Authors: Sacco Michael D. , Wang Shaohui , Adapa Swamy R. , Zhang Xiujun , Gongora Maura V. , Gatdula Jean R. , Lewandowski Eric M. , Hammond Lauren R. , Townsend Julia A. , Marty Michael T. , Wang Jun , Eswara Prahathees J. , Jiang Rays H.Y. , Sun Xingmin , Chen Yu 
Published: January 2022

Abstractβ-Lactam antibiotics, particularly cephalosporins, are major risk factors for C. difficile infection (CDI), the most common hospital acquired infection. These broad-spectrum antibiotics irreversibly inhibit penicillin-binding proteins (PBPs), essential enzymes that assemble the bacterial cell wall. Little is known about the C. difficile PBPs, yet they play central roles in the growth, infection, and transmission of this pathogen. In this study we discover that PBP2, essential for vegetative growth, is the primary bactericidal target for β-lactams in C. difficile. We further demonstrate PBP2 is insensitive to cephalosporin inhibition, revealing a key cause of the well-documented, but poorly understood, cephalosporin resistance in C. difficile. For the first time, we determine the crystal structures of C. difficile PBP2, which bears several highly unique features, including significant ligand-induced conformational changes and an active site Zn2+-binding motif that influences β-lactam binding and protein stability. Remarkably, this motif is shared in two other C. difficile PBPs essential for sporulation, PBP3 and SpoVD. While these PBPs are present in a wide range of bacterial taxa, including species in extreme environments and the human gut, they are mostly found in anaerobes, typically Firmicutes. The widespread presence of this convergently evolved thiol-containing motif and its cognate Zn2+ suggests it may function as a redox-sensor to regulate cell wall synthesis for survival in adverse environments. Collectively, our findings address important etiological questions surrounding C. difficile, characterize new elements of PBP structure and function, and lay the groundwork for antibiotic development targeting both C. difficile growth and sporulation.
Mapping functional regions of essential bacterial proteins with dominant-negative protein fragments
Authors: Savinov Andrew , Fernandez Andres , Fields Stanley 
Published: January 2022

AbstractMassively-parallel measurements of dominant negative inhibition by protein fragments have been used to map protein interaction sites and discover peptide inhibitors. However, the underlying principles governing fragment-based inhibition have thus far remained unclear. Here, we adapt a high-throughput inhibitory fragment assay for use inEscherichia coli, applying it to a set of ten essential proteins. This approach yielded single amino acid-resolution maps of inhibitory activity, with peaks localized to functionally important interaction sites, including oligomerization interfaces and folding contacts. Leveraging these data, we perform a systematic analysis to uncover principles of fragment-based inhibition. We determine a robust negative correlation between susceptibility to inhibition and cellular protein concentration, demonstrating that inhibitory fragments likely act primarily by titrating native protein interactions. We also characterize a series of trade-offs related to fragment length, showing that shorter peptides allow higher-resolution mapping but suffer from lower inhibitory activity. We employ an unsupervised statistical analysis to show that the inhibitory activities of protein fragments are largely driven not by generic properties such as charge, hydrophobicity, and secondary structure, but by the more specific characteristics of their bespoke macromolecular interactions. AlphaFold computational modeling of peptide complexes with one protein shows that the inhibitory activity of peptides is associated with their predicted ability to form native-like interactions. Overall, this work demonstrates fundamental characteristics of inhibitory protein fragment function and provides a foundation for understanding and controlling protein interactionsin vivo.Significance StatementPeptide fragments derived from protein sequences can inhibit interactions of their parental proteins, providing a promising avenue for drug development. Here we employ a massively-parallel assay to measurein vivoinhibition by fragments that tile the full sequences of ten essential bacterial proteins. We leverage these data to decipher principles of fragment-based inhibition, showing how parental protein concentration drives activity and how protein fragment length interplays with activity and specificity. We employ statistical analysis to parse the roles of biophysical properties in fragment-to-fragment variation, and AlphaFold modeling to determine the relationship between measured inhibitory activity and predicted native-like binding. These results provide a path towards rational design of peptide inhibitors and broader principles of protein-protein interactions in living cells.
Isolated Grauer’s gorilla populations differ in diet and gut microbiome
Authors: Michel Alice , Minocher Riana , Niehoff Peter-Philip , Li Yuhong , Nota Kevin , Gadhvi Maya A. , Su Jiancheng , Iyer Neetha , Porter Amy , Ngobobo-As-Ibungu Urbain , Binyinyi Escobar , Pekeyake Radar Nishuli , Parducci Laura , Caillaud Damien , Guschanski Katerina 
Published: January 2022

AbstractThe animal gut microbiome has been implicated in a number of key biological processes, ranging from digestion to behavior, and has also been suggested to facilitate local adaptation. However, studies in wild animals rarely compare multiple populations that differ ecologically, which is the level at which local adaptation may occur. Further, few studies simultaneously characterize diet and the gut microbiome from the same sample, despite the likely presence of co-dependencies. Here, we investigate the interplay between diet and gut microbiome in three geographically isolated populations of the critically endangered Grauer’s gorilla, which we show to be genetically differentiated. We find population- and social group-specific dietary and gut microbial profiles and co-variation between diet and gut microbiome, despite the presence of core microbial taxa. There was no detectable effect of age, sex, or genetic relatedness on the microbiome. Diet differed considerably across populations, with the high-altitude population consuming a lower diversity of plants compared to low-altitude populations, consistent with food plant availability constraining diet. The observed pattern of covariation between diet and gut microbiome is likely a result of long-term social and ecological factors. Our study suggests that the gut microbiome is sufficiently plastic to support flexible food selection and hence contribute to local adaptation.
Metabolic rescue ameliorates mitochondrial encephalo-cardiomyopathy in murine and human iPSC models of Leigh syndrome
Authors: Yoon Jin-Young , Daneshgar Nastaran , Chu Yi , Chen Biyi , Hefti Marco , Irani Kaikobad , Song Long-Sheng , Brenner Charles , Abel E. Dale , London Barry , Dai Dao-Fu 
Published: January 2022

ABSTRACTMice with deletion of complex I subunit Ndufs4 develop mitochondrial encephalomyopathy resembling Leigh syndrome (LS). We report that LS mice also develop severe cardiac bradyarrhythmia and diastolic dysfunction. Human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs) with Ndufs4 deletion recapitulate LS cardiomyopathy. Mechanistically, we demonstrate a direct link between complex I deficiency, decreased intracellular NAD+/ NADH and bradyarrhythmia, mediated by hyperacetylation of the cardiac sodium channel NaV1.5, particularly at K1479 site. Neuronal apoptosis in the cerebellar and midbrain regions in LS mice was associated with hyperacetylation of p53 and activation of microglia. Targeted metabolomics revealed increases in several amino acids and citric acid cycle intermediates, likely due to impairment of NAD+-dependent dehydrogenases, and a substantial decrease in reduced Glutathione (GSH). Metabolic rescue by nicotinamide riboside (NR) supplementation increased intracellular NAD+/ NADH, restored metabolic derangement, reversed protein hyperacetylation through NAD+-dependent Sirtuin deacetylase, and ameliorated cardiomyopathic phenotypes, concomitant with improvement of NaV1.5 current and SERCA2a function measured by Ca2+- transients. NR also attenuated neuronal apoptosis and microglial activation in the LS brain and human iPS-derived neurons with Ndufs4 deletion. Our study reveals direct mechanistic explanations of the observed cardiac bradyarrhythmia, diastolic dysfunction and neuronal apoptosis in mouse and human iPSC models of LS.
Towards Predicting 30-Day Readmission among Oncology Patients: Identifying Timely and Actionable Risk Factors
Authors: Hwang Sy , Urbanowicz Ryan , Lynch Selah , Vernon Tawnya , Bresz Kellie , Giraldo Carolina , Kennedy Erin , Leabhart Max , Bleacher Troy , Ripchinski Michael R. , Mowery Danielle L. , Oyer Randall A. 
Published: January 2022

1AbstractPurposePredicting 30-day readmission risk is paramount to improving the quality of patient care. Previous studies have examined clinical risk factors associated with hospital readmissions. In this study, we compare sets of patient, provider, and community-level variables that are available at two different points of a patient’s inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models in order to identify and target appropriate actionable interventions that can potentially reduce avoidable readmissions.MethodsUsing EHR data from a retrospective cohort of 2460 oncology patients, two sets of binary classification models predicting 30-day readmission were developed; one trained on variables that are available within the first 48 hours of admission and another trained on data from the entire hospital encounter. A comprehensive machine learning analysis pipeline was leveraged including preprocessing and feature transformation, feature importance and selection, machine learning modeling, and post-analysis.ResultsLeveraging all features, the LGB (Light Gradient Boosting Machine) model produced higher, but comparable performance: (AUROC: 0.711 and APS: 0.225) compared to Epic (AUROC: 0.697 and APS: 0.221). Given features in the first 48-hours, the RF (Random Forest) model produces higher AUROC (0.684), but lower AUPRC (0.18) and APS (0.184) than the Epic model (AUROC: 0.676). In terms of the characteristics of patients flagged by these models, both the full (LGB) and 48-hour (RF) feature models were highly sensitive in flagging more patients than the Epic models. Both models flagged patients with a similar distribution of race and sex; however, our LGB and random forest models more inclusive flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, cancer type), provider (winter discharge, hospital admission type), community (zip income, marital status of partner).ConclusionWe demonstrated that we could develop and validate models comparable to existing Epic 30-day readmission models, but provide several actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.
Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance prioritisation
Authors: Lyon Matthew S. , Millard Louise A. C. , Smith George Davey , Gaunt Tom R. , Tilling Kate 
Published: January 2022

AbstractBlood biomarkers include disease intervention targets that may interact with genetic and environmental factors resulting in subgroups of individuals who respond differently to treatment. Such interactions may be observed in genetic effects on trait variance. Variance prioritisation is an approach to identify genetic loci with interaction effects by estimating their association with trait variance, even where the modifier is unknown or unmeasured. Here, we develop and evaluate a regression-based Brown-Forsythe test and variance effect estimate to detect such interactions. We provide scalable open-source software (varGWAS) for genome-wide association analysis of SNP-variance effects (https://github.com/MRCIEU/varGWAS) and apply our software to 30 blood biomarkers in UK Biobank. We find 468 variance quantitative trait loci across 24 biomarkers and follow up findings to detect 82 gene-environment and six gene-gene interactions independent of strong scale or phantom effects. Our results replicate existing findings and identify novel epistatic effects ofTREHrs12225548 xFUT2rs281379 andTREHrs12225548 xABOrs635634 on alkaline phosphatase andZNF827rs4835265 xNEDD4Lrs4503880 on gamma glutamyltransferase. These data could be used to discover possible subgroup effects for a given biomarker during preclinical drug development.
Phylogeography of hepatitis B: The role of Portugal in the early dissemination of HBV worldwide
Authors: Marcelino Rute , Ezeonwumelu Ifeanyi Jude , Janeiro André , Mimoso Paula , Matos Sónia , Briz Veronica , Pimentel Victor , Pingarilho Marta , Marinho Rui Tato , Marcelino José Maria , Taveira Nuno , Abecasis Ana 
Published: January 2022

AbstractIn 2015, 257 million people lived with chronic hepatitis B infection. In Portugal 1.4% of the population is chronically infected with hepatitis B virus. Limited information is available regarding the genetic diversity, origin of HBV in Portugal and its role in the dissemination of HBV worldwide. In this study, we aimed to investigate the epidemic history and transmission dynamics of HBV genotypes that are endemic in Portugal. HBV pol gene was sequenced from 130 patients followed in Lisbon. HBV genotype A (HBV/A) was the most prevalent genotype (n=54, 41.5%), followed by D [HBV/D; (n=44, 33.8%)], and E [HBV/E; (n=32, 24.6%)]. Spatio-temporal evolutionary dynamics were reconstructed using a Bayesian Markov Chain Monte Carlo method, as implemented in BEAST v1.10.4, with a GTR nucleotide substitution model, an uncorrelated lognormal relaxed molecular clock model under a Bayesian skyline plot and a continuous diffusion model. Our results indicate that HBV/D4 was the first subgenotype to be introduced in Portugal by the end of the XIX century, around 1857 (HPD 95% 1699 - 1931) followed by subgenotypes D3 and A2 a few decades later. Genotype E and subgenotype A1 were introduced in Portugal later, almost simultaneously. Our results also indicate a very important role of Portugal in the exportation of HBV subgenotypes D4 and A2 to Brazil and Cape Verde, respectively, in the beginning of the XX century. This work clarifies the epidemiological history of HBV in Portugal and show that Portugal had an important role in the global spread of this virus.
Comparison of Mammography Artificial Intelligence Algorithms for 5-year Breast Cancer Risk Prediction
Authors: Arasu Vignesh A. , Habel Laurel A. , Achacoso Ninah S. , Buist Diana S.M. , Cord Jason B. , Esserman Laura J. , Hylton Nola. M. , Glymour M. Maria , Kornak John , Kushi Lawrence H. , Lewis Don A. , Liu Vincent X. , Miglioretti Diana L. , Navarro Daniel A. , Sieh Weiva , Shen Li , Sofrygin Oleg , Yoon Hyo-Chun , Lee Catherine 
Published: January 2022

AbstractPURPOSETo examine the ability of 5 artificial intelligence (AI)-based computer vision algorithms, most trained to detect visible breast cancer on mammograms, to predict future risk relative to the Breast Cancer Surveillance Consortium clinical risk prediction model (BCSC v2).PATIENTS AND METHODSIn this case-cohort study, women who had a screening mammogram in 2016 at Kaiser Permanente Northern California with no evidence of cancer on final imaging assessment were followed through September 2021. Women with prior breast cancer or a known highly penetrant gene mutation were excluded. From the 329,814 total eligible women, a random subcohort of 13,881 women (4.2%) were selected, of whom 197 had incident cancer. All 4,475 additional incident cancers were also included. Continuous AI-predicted scores were generated from the index 2016 mammogram. Risk estimates were generated with the Kaplan-Meier method and time-varying area under the curve [AUC(t)].RESULTSFor incident cancers at 0-1 year (interval cancer risk), BCSC demonstrated an AUC(t) of 0.62 (95% CI, 0.58-0.66), and the AI algorithms had AUC(t)s ranging from 0.66-0.71, all significantly higher than BCSC (P< .05). For incident cancers at 1 to 5 years (5-year future cancer risk), BCSC demonstrated an AUC(t) of 0.61 (95% CI, 0.60-0.62), and the AI algorithms had AUC(t)s ranging from 0.63 to 0.67, all significantly higher than BCSC. Combined BCSC and AI models demonstrated AUC(t)s for interval cancer risk of 0.67-0.73 and for 5-year future cancer risk of 0.66-0.68.CONCLUSIONThe AI mammography algorithms we evaluated had significantly higher discrimination than the BCSC clinical risk model for interval and 5-year future cancer risk. Combined AI and BCSC models had slightly higher discrimination than AI alone.
Severe and common mental disorders and risk of hospital admissions for Ambulatory Care Sensitive Conditions (ACSCs): prospective cohort study using UK Biobank
Authors: Niedzwiedz Claire L. , Aragón María José , Breedvelt Josefien J. F. , Smith Daniel J , Prady Stephanie L. , Jacobs Rowena 
Published: January 2022

AbstractBackgroundPeople with mental disorders have an excess chronic disease burden. One mechanism to potentially reduce the public health and economic costs of mental disorders is to reduce preventable hospital admissions. Ambulatory care sensitive conditions (ACSCs) are a defined set of chronic and acute illnesses not considered to require hospital treatment if patients receive adequate primary healthcare. We examined the relationship between both severe and common mental disorders and risk of emergency hospital admissions for ACSCs and factors associated with increased risk.MethodsBaseline data from England (N=445,814) were taken from UK Biobank, which recruited participants aged 37-73 years during 2006 to 2010, and were linked to hospital admission records up to 31st December 2019. Participants were grouped into those who had a history of either schizophrenia, bipolar disorder, depression or anxiety, or no record of mental disorder. Cox proportional hazard models (for the first admission) and Prentice, Williams and Peterson Total Time models (PWP-TT, which account for all admissions) were used to assess the risk (using hazard ratios (HR)) of hospitalisation for ACSCs among those with mental disorders compared to those without, adjusting for factors in different domains, including sociodemographic (e.g. age, sex, ethnicity), socioeconomic (e.g. deprivation, education level), health and biomarkers (e.g. multimorbidity, inflammatory markers), health-related behaviours (e.g. smoking, alcohol consumption), social isolation (e.g. social participation, social contact) and psychological (e.g. depressive symptoms, loneliness).ResultsPeople with schizophrenia had the highest risk of hospital admission for ACSCs compared to those with no mental disorder (HR=4.40, 95% CI: 4.04 - 4.80). People with bipolar disorder (HR=2.48, 95% CI: 2.28 – 2.69) and depression or anxiety (HR=1.76, 95% CI: 1.73 – 1.80) also had higher risk. Associations were more conservative when accounting for all admissions. Although adjusting for a range of factors attenuated the observed associations, they still persisted, with socioeconomic and health-related variables contributing most.ConclusionsPeople with severe mental disorders had highest risk of preventable hospital admissions, with the risk also elevated amongst those with depression and anxiety. Ensuring people with mental disorders receive adequate ambulatory care is essential to reduce the large health inequalities experienced by these groups.
Vaccinating children against COVID-19: commentary and mathematical modelling
Authors: Hawkes Michael T. , Good Michael F. 
Published: January 2022

AbstractWith the recent licensure of mRNA vaccines against COVID-19 in the 5-11 year old age group, the public health impact of a childhood immunization campaign is of interest. Using a mathematical epidemiological model, we project that childhood vaccination carries minimal risk and yields modest public health benefits. These include large relative reductions in child morbidity and mortality, although the absolute reduction is small because these events are rare. Furthermore, the model predicts “altruistic” absolute reductions in adult cases, hospitalizations, and mortality. However, vaccinating children to benefit adults should be considered from an ethical as well as a public health perspective. From a global health perspective, an additional ethical consideration is the justice of giving priority to children in high-income settings at low risk of severe disease while vaccines have not been made available to vulnerable adults in low-income settings.
Genomic insights into the phylogeny and biomass-degrading enzymes of rumen ciliates
Authors: Li Zongjun , Wang Xiangnan , Zhang Yu , Yu Zhongtang , Zhang Tingting , Dai Xuelei , Pan Xiangyu , Jing Ruoxi , Yan Yueyang , Liu Yangfan , Gao Shan , Li Fei , Huang Youqin , Tian Jian , Yao Junhu , Xing XvPeng , Shi Tao , Ning Jifeng , Yao Bin , Huang Huoqing , Jiang Yu 
Published: January 2022

AbstractUnderstanding the biodiversity and genetics of the gut microbiome has important implications for host physiology. One underexplored and elusive group is ciliated protozoa, which play crucial roles in regulating gut microbial interactions. Integrating single-cell sequencing and an assembly-and-identification pipeline, we acquired 52 high-quality ciliate genomes of 22 rumen morphospecies for all major abundant clades. With these genomes, we firstly resolved the taxonomic and phylogenetic framework that reclassified them into 19 species spanning 13 genera and reassigned the genus Dasytricha from Isotrichidae to a new family Dasytrichidae. Via extensive horizontal gene transfer and gene family expansion, rumen ciliates possess a broad array of enzymes to synergistically degrade plant and microbial carbohydrates. In particular, ∼80% of the degrading enzymes in Diplodiniinae and Ophryoscolecinae act on plant cell wall, and the high activities of their cellulase, xylanase and lysozyme reflect the potential of ciliate enzymes for biomass-conversion. Additionally, the new ciliate dataset greatly facilitated the rumen metagenomic analyses by allowing ∼12% of reads to be classified.