bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Functional Map of the Human Intrinsically Disordered Proteome
Authors: Pritišanac Iva , Alderson T. Reid , Kolarić Đesika , Zarin Taraneh , Xie Shuting , Lu Alex , Alam Aqsa , Maqsood Abdullah , Youn Ji-Young , Forman-Kay Julie D. , Moses Alan M. 
Published: March 2024

AbstractIntrinsically disordered regions (IDRs) represent at least one-third of the human proteome and defy the established structure-function paradigm. Because IDRs often have limited positional sequence conservation, the functional classification of IDRs using standard bioinformatics is generally not possible. Here, we show that evolutionarily conserved molecular features of the intrinsically disordered human proteome (IDR-ome), termed evolutionary signatures, enable classification and prediction of IDR functions. Hierarchical clustering of the human IDR-ome based on evolutionary signatures reveals strong enrichments for frequently studied functions of IDRs in transcription and RNA processing, as well as diverse, rarely studied functions, ranging from sub-cellular localization and biomolecular condensates to cellular signaling, transmembrane transport, and the constitution of the cytoskeleton. We exploit the information that is encoded within evolutionary conservation of molecular features to propose functional annotations for every IDR in the human proteome, inspect the conserved molecular features that correlate with different functions, and discover frequently co-occurring IDR functions on the proteome scale. Further, we identify patterns of evolutionary conserved molecular features of IDRs within proteins of unknown function and disease-risk genes for conditions such as cancer and developmental disorders. Our map of the human IDR-ome should be a valuable resource that aids in the discovery of new IDR biology.
End resection and telomere healing of DNA double-strand breaks during nematode programmed DNA elimination
Authors: Estrem Brandon , Davis Richard E. , Wang Jianbin 
Published: March 2024

ABSTRACTMost DNA double-strand breaks (DSBs) are harmful to genome integrity. However, some forms of DSBs are essential to biological processes, such as meiotic recombination and V(D)J recombination. DSBs are also required for programmed DNA elimination (PDE) in ciliates and nematodes. In nematodes, the DSBs are healed with telomere addition. While telomere addition sites have been well-characterized, little is known regarding the DSBs that fragment nematode chromosomes. Here, we used embryos from the nematodeAscaristo study the timing of PDE breaks and examine the DSBs and their end processing. Using END-seq, we characterize the DSB ends and demonstrate that DNA breaks are introduced before mitosis, followed by extensive end resection. The resection profile is unique for each break site, and the resection generates 3’ overhangs before the addition of telomeres. Interestingly, telomere healing occurs much more frequently on retained DSB ends than on eliminated ends. This biased repair of the DSB ends inAscarismay be due to the sequestration of the eliminated DNA into micronuclei, preventing their ends from telomere healing. Additional DNA breaks occur within the eliminated DNA in bothAscarisandParascaris, ensuring chromosomal breakage and providing a fail-safe mechanism for nematode PDE.
Interpretable and predictive models based on high-dimensional data in ecology and evolution
Authors: Jahner Joshua P. , Buerkle C. Alex , Gannon Dustin G. , Grames Eliza M. , McFarlane S. Eryn , Siefert Andrew , Bell Katherine L. , DeLeo Victoria L. , Forister Matthew L. , Harrison Joshua G. , Laughlin Daniel C. , Patterson Amy C. , Powers Breanna F. , Werner Chhaya M. , Oleksy Isabella A. 
Published: March 2024

AbstractThe proliferation of high-dimensional data in ecology and evolutionary biology raise the promise of statistical and machine learning models that are highly predictive and interpretable. However, high-dimensional data are commonly burdened with an inherent trade-off: in-sample prediction of outcomes will improve as additional predictors are included in the model, but this may come at the cost of poor predictive accuracy and limited generalizability for future or unsampled observations (out-of-sample prediction). To confront this problem of overfitting, sparse models can focus on key predictors by correctly placing low weight on unimportant variables. We competed nine methods to quantify their performance in variable selection and prediction using simulated data with different sample sizes, numbers of predictors, and strengths of effects. Overfitting was typical for many methods and simulation scenarios. Despite this, in-sample and out-of-sample prediction converged on the true predictive target for simulations with more observations, larger causal effects, and fewer predictors. Accurate variable selection to support process-based understanding will be unattainable for many realistic sampling schemes in ecology and evolution. We use our analyses to characterize data attributes for which statistical learning is possible, and illustrate how some sparse methods can achieve predictive accuracy while mitigating and learning the extent of overfitting.
Single-animal, single-tube RNA extraction for comparison of relative transcript levels via qRT-PCR in the tardigradeHypsibius exemplaris
Authors: Kirk Molly J. , Xu Chaoming , Paules Jonathan , Rothman Joel H. 
Published: March 2024

ABSTRACTThe tardigradeHypsibius exemplarisis an emerging model organism renowned for its ability to survive environmental extremes. To explore the molecular mechanisms and genetic basis of such extremotolerance, many studies rely on RNA-sequencing (RNA-seq), which can be performed on populations ranging from large cohorts to individual animals. Reverse Transcription Polymerase Chain Reaction (RT-PCR) and RNA interference (RNAi) are subsequently used to confirm RNA-seq findings and assess the genetic requirements for candidate genes, respectively. Such studies require an efficient, accurate, and affordable method for RNA extraction and measurement of relative transcript levels by quantitative RT-PCR (qRT-PCR). This work presents an efficient single-tardigrade, single-tube RNA extraction method (STST) that not only reliably isolates RNA from individual tardigrades but also reduces the required time and cost for each extraction. This RNA extraction method yields quantities of cDNA that can be used to amplify and detect multiple transcripts by quantitative PCR (qRT-PCR). The method is validated by analyzing dynamic changes in the expression of genes encoding two heat-shock-regulated proteins, Heat-Shock Protein 70 β2 (HSP70 β2) and Heat-Shock Protein 90α (HSP90α), making it possible to assess their relative expression levels in heat-exposed individuals using qRT-PCR. STST effectively complements existing bulk and single tardigrade RNA extraction methods, permitting rapid and affordable examination of individual tardigrade transcriptional levels by qRT-PCR.SUMMARYThis work presents a rapid RNA extraction and transcript level comparison method for analyzing gene expression in the tardigradeHypsibius exemplaris.Using physical lysis, this high-throughput method requires a single tardigrade as the starting material and results in robust production of cDNA for quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR).
A transcription factor (TF) inference method that broadly measures TF activity and identifies mechanistically distinct TF networks
Authors: Jones Taylor , Sigauke Rutendo F. , Sanford Lynn , Taatjes Dylan J. , Allen Mary A. , Dowell Robin D. 
Published: March 2024

AbstractTF profiler is a method of inferring transcription factor regulatory activity, i.e. when a TF is present and actively regulating transcription, directly directly from nascent sequencing assays such as PRO-seq and GRO-seq. Transcription factors orchestrate transcription and play a critical role in cellular maintenance, identity and response to external stimuli. While ChIP assays have measured DNA localization, they fall short of identifying when and where transcription factors are actively regulating transcription. Our method, on the other hand, uses RNA polymerase activity to infer TF activity across hundreds of data sets and transcription factors. Based on these classifications we identify three distinct classes of transcription factors: ubiquitous factors that play roles in cellular homeostasis, driving basal gene programs across tissues and cell types, tissue specific factors that act almost exclusively at enhancers and are themselves regulated at transcription, and stimulus responsive TFs which are regulated post-transcriptionally but act predominantly at enhancers. TF profiler is broadly applicable, providing regulatory insights on any PRO-seq sample for any transcription factor with a known binding motif.
Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data
Authors: Hall Michael B. , Wick Ryan R. , Judd Louise M. , Nguyen An N. T. , Steinig Eike J. , Xie Ouli , Davies Mark R. , Seemann Torsten , Stinear Timothy P. , Coin Lachlan J. M. 
Published: March 2024

AbstractVariant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance prediction. This study presents a comprehensive benchmarking of SNP and indel variant calling accuracy across 14 diverse bacterial species using Oxford Nanopore Technologies (ONT) and Illumina sequencing. We generate gold standard reference genomes and project variations from closely-related strains onto them, creating biologically realistic distributions of SNPs and indels.Our results demonstrate that ONT variant calls from deep learning-based tools delivered higher SNP and indel accuracy than traditional methods and Illumina, with Clair3 providing the most accurate results overall. We investigate the causes of missed and false calls, highlighting the limitations inherent in short reads and discover that ONT’s traditional limitations with homopolymer-induced indel errors are absent with high-accuracy basecalling models and deep learning-based variant calls. Furthermore, our findings on the impact of read depth on variant calling offer valuable insights for sequencing projects with limited resources, showing that 10x depth is sufficient to achieve variant calls that match or exceed Illumina.In conclusion, our research highlights the superior accuracy of deep learning tools in SNP and indel detection with ONT sequencing, challenging the primacy of short-read sequencing. The reduction of systematic errors and the ability to attain high accuracy at lower read depths enhance the viability of ONT for widespread use in clinical and public health bacterial genomics.
Eicosapentaenoic acid induces an anti-inflammatory transcriptomic landscape in T cells implicating a pathway independent of triglyceride lowering in cardiovascular risk reduction
Authors: Reilly Nathalie A. , Dekkers Koen F. , Molenaar Jeroen , Arumugam Sinthuja , Kuipers Thomas B. , Ariyurek Yavuz , Hoeksema Marten A. , Jukema J. Wouter , Heijmans Bastiaan T. 
Published: March 2024

AbstractA twice-daily dose of highly purified eicosapentaenoic acid (EPA) reduces the risk of atherosclerotic cardiovascular disease among patients with high triglycerides and either known cardiovascular disease or those at high risk for developing it. However, the process by which EPA exerts its beneficial effects remains poorly understood. Here, we show that EPA can induce an anti-inflammatory transcriptional profile in non-activated CD4+T cells. We find that EPA-exposed CD4+T cells downregulate immune response related genes, such asHLA-DRA, CD69, andIL2RA, while upregulating genes involved in oxidative stress prevention, such asNQO1. Furthermore, transcription footprint analysis based on ATAC-sequencing reveals downregulation of GATA3 and PU.1, key transcription factors in TH2 and TH9 differentiation, and upregulation of REV-ERB, an antagonist of TH17 differentiation. By in parallel examining T cell responses to oleic acid, a monounsaturated fatty acid, and palmitic acid, a saturated fatty acid, we find that both the intensity of the transcriptomic response and the involvement of anti-inflammatory pathways is highly specific for EPA. Thus, EPA can induce an anti-inflammatory transcriptomic landscape in CD4+T cells, a process that may contribute to the unexpectedly strong beneficial effects of EPA on the risk of atherosclerotic cardiovascular disease in clinical trials.
Variability and correlations among vital rates and their influence on population growth in mule and black-tailed deer
Authors: Ruprecht Joel , Forrester Tavis D. , Clark Darren A. , Wisdom Michael J. , Smith Joshua B. , Levi Taal 
Published: March 2024

ABSTRACTTo reverse observed range-wide population declines, managers of mule and black- tailed deer (Odocoileus hemionus) require information on the vital rates and life stages that are most influential to population growth for which to target management actions. We conducted a range-wide literature review and used hierarchical models to provide biological descriptions of mule and black-tailed deer vital rates, their variability, and how they correlate with one another. We then used matrix models and life-stage simulation analysis to determine the individual vital rates that contributed most to lambda, i.e., annual population growth rate. Adult female survival was the vital rate with the greatest ability to predict lambda and explained 62% of the variation in population growth. While annual juvenile survival explained 44% of the variation in lambda, summer and winter juvenile survival by themselves were far less explanatory than adult female survival. Winter fawn:doe ratios, a metric often collected by management agencies, only explained 10% of the variation in lambda. When adult female survival was 0.84, our simulations estimated an equal probability that a population would increase versus decrease, and correspondingly, the estimate of lambda was 1.0 with a 95% credible interval of 0.88–1.14. Simulations suggested that populations with adult survival rates less than 70% would always decline, but as survival increased beyond this value lambda increased linearly and never plateaued. In contrast, the probability of observing a stable lambda plateaued when annual juvenile survival reached approximately 0.5. The mean lambda calculated from all simulated values within the observed range of vital rate values across the species’ geographical distribution was 0.975, and in 61% of the simulations lambda was < 1. After 20 years, we estimated that this distribution of lambda values would cause populations to decrease in 92% of instances with a mean decrease of 44%. Our results align with the observed declines in mule deer populations throughout their range over recent decades and suggest that these trends will continue until management can improve survival of adult females.
Impact of Chemical Dynamics of Commercial PURE Systems on Malachite Green Aptamer Fluorescence
Authors: Jurado Zoila , Murray Richard M. 
Published: March 2024

AbstractThe malachite green aptamer (MGapt) is known for its utility in RNA measurementin vivoand lysate-based cell-free protein systems. However, MGapt fluorescence dynamics do not accurately reflect mRNA concentration. Our study finds that MGapt fluorescence is unstable in commercial PURE systems. We discovered that the chemical composition of the cell-free reaction strongly influences MGapt fluorescence, which leads to inaccurate RNA calculations. Specific to the commercial system, we posit that MGapt fluorescence is significantly affected by the system’s chemical properties, governed notably by the presence of dithiothreitol (DTT). We propose a model that, on average, accurately predicts MGapt measurement within a 10% margin, leveraging DTT concentration as a critical factor. This model sheds light on the complex dynamics of MGapt in cell-free systems and underscores the importance of considering environmental factors in RNA measurements using aptamers.
N-acetyltransferase 10-mediated mRNA N4-acetylation is Essential for the Translational Regulation During Oocyte Meiotic Maturation in Mice
Authors: Chen Lu , Liu Shao-Yuan , Su Rui-Bao , Wu Yu-Ke , Wang Wen-Jing , Wu Xuan , Zhang Song-Ying , Qiao Jie , Sha Qian-Qian , Fan Heng-Yu 
Published: March 2024

AbstractMammalian oocyte maturation is driven by the strict translational regulation of maternal mRNAs stored in the cytoplasm. However, the function and mechanism of post-transcriptional chemical modifications, especially the newly identified N4-acetylcytidine (ac4C) modification catalyzed by N-acetyltransferase 10 (NAT10), are unknown. In this study, we developed a low-input ac4C sequencing technology, ac4C LACE-seq, and mapped 8241 ac4C peaks at the whole-transcriptome level using 50 mouse oocytes at the germinal vesicle stage. We profiled the mRNA landscapes of NAT10-interactions and ac4C modifications. The NAT10-interacted and ac4C-modified transcripts are associated with high translation efficiency in oocytes. Oocyte-specificNat10knockout wiped out ac4C signals in oocytes and caused severe defects in meiotic maturation and female infertility. ac4C LACE-seq results indicated thatNat10deletion led to a failure of ac4C deposition on mRNAs encoding key maternal factors, such as MSY2, ZAR1, BTG4, and cyclin B1, which regulate transcriptome stability and maternal-to-zygotic transition.Nat10-deleted oocytes showed decreased mRNA translation efficiency during meiotic maturation, partially due to the direct inhibition of ac4C sites on specific transcripts. In summary, we developed a low-input, high-sensitivity mRNA ac4C profiling approach and highlighted the important physiological function of ac4C in the precise regulation of oocyte meiotic maturation by enhancing translation efficiency.
Genome-wide CRISPRa screens nominate modulators of CAR T cell survival within distinct tumor cytokine milieus
Authors: Curtis Benjamin C , Spurrell Cailyn H , Flint Lindsay , Lande Aalton M , Leonardi Marissa , Rosser James M , Valdez Ardizon , Murren Nat , Chai Tiffanie , Fitzgerald Michael , Martinez-Reyes Jasmin , Saxby Christopher P , Oda Shannon K , Jensen Michael CV 
Published: March 2024

ABSTRACTChimeric Antigen Receptor (CAR) T cell therapy has revolutionized the treatment of B cell malignancies and translating this success to other cancers remains an ongoing clinical objective. Next-generation T cell products in development aim to genetically modulate many facets of cell behavior, for which gene-nominating platforms provide a useful framework for prioritization. Among competing screening approaches, CRISPR activation (CRISPRa) technology permits gain-of-function (GoF) gene surveys at genome-wide scale, but routine implementation in primary T cells has been stymied by high cell requirements (∼107- 108) and abbreviated activity. Here, we describe a novel cell manufacturing schema using an all-in-one transposon-based gene delivery system coupled with CAR-restricted cell expansion to generate yields (109) of primary T cells bearing CAR and CRISPRa transgenes that are well above the threshold needed for genome-scale screening. CRISPRa activity is sustained via the inclusion of divergent, duplicate Elongation Factor 1α core/human T-cell leukemia virus (EF1α-HTLV) hybrid promoters; while guide RNA representation is preserved through late lentiviral transduction, thus preventing bottlenecking and premature candidate pruning. CRISPRa-CAR T cells manufactured via this pipeline retain potent on-target gene-overexpression (>85% target+) across varied cell subsets (e.g. Tim-3+Lag3+or serial-challenge) and timescales (>14 days). When deployed to survival-based genome-wide selection landscapes, CRISPRa-CAR pools nominate known and novel endogenous genes capable of enhancing CD8+CAR T survival in cytokine-rich (e.g.MYC, FUT6, IRF4, GSE1) and cytokine-depleted (e.g.CSF2RB,STAT6,IRF4,GSE1) settings of tumor challenge. This system will have broad utility for therapy-enhancing gene discovery.
Cell culture differentiation and proliferation conditions influence thein vitroregeneration of the human airway epithelium
Authors: Redman Elisa , Fierville Morgane , Cavard Amélie , Plaisant Magali , Arguel Marie-Jeanne , Ruiz Garcia Sandra , McAndrew Eamon M , Girard-Riboulleau Cédric , Lebrigand Kevin , Magnone Virginie , Ponzio Gilles , Gras Delphine , Chanez Pascal , Abelanet Sophie , Barbry Pascal , Marcet Brice , Zaragosi Laure-Emmanuelle 
Published: March 2024

AbstractThe human airway mucociliary epithelium can be recapitulatedin vitrousing primary cells cultured in an Air-Liquid Interface (ALI), a reliable surrogate to perform pathophysiological studies. As tremendous variations exist between media used for ALI-cultured human airway epithelial cells, our study aimed to evaluate the impact of several media (BEGMTM, PneumaCultTM, “Half&Half” and “Clancy”) on cell type distribution using single-cell RNA sequencing and imaging. Our work revealed the impact of these media on cell composition, gene expression profile, cell signaling and epithelial morphology. We found higher proportions of multiciliated cells in PneumaCultTM-ALI and Half&Half, stronger EGF signaling from basal cells in BEGMTM-ALI, differential expression of the SARS-CoV-2 entry factorACE2, and distinct secretome transcripts depending on media used. We also established that proliferation in PneumaCultTM-Ex Plus favored secretory cell fate, showing the key influence of proliferation media on late differentiation epithelial characteristics. Altogether, our data offer a comprehensive repertoire for evaluating the effects of culture conditions on airway epithelial differentiation and will help to choose the most relevant medium according to the processes to be investigated such as cilia, mucus biology or viral infection. We detail useful parameters that should be explored to document airway epithelial cell fate and morphology.