bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanotransduction and inflammation: an updated comprehensive representation
Authors: Suryiagandhi Vennila , Ma Ying , Paparozzi Veronica , Guarnieri Tiziana , di Pietro Biagio , Dimitri Giovanna Maria , Tieri Paolo , Sala Claudia , Lai Darong , Nardini Christine 
Published: June 2024

AbstractMechanotransduction is the process that enables the conversion of mechanical cues into biochemical signaling. While all our cells are well known to be sensitive to such stimuli, the details of the systemic interaction between mechanical input and inflammation are not always well integrated. Often, they are considered and studied in relatively compartmentalized areas, and we therefore argue here that to understand the relationship of mechanical stimuli with inflammation – with high translational potential - it is crucial to offer and analyze a unified view of mechanotransduction. We therefore offer here a pathway representation, recollected with the standard systems biology markup language (SBML) and explored with network biology approaches. We present RAC1 as an exemplar and emerging molecule with potential for medical translation.
Survey of Chemical Unfolding Complexity as a Unique Stability Assessment Assay for Monoclonal Antibodies
Authors: Floyd J. Alaina , Shaver Jeremy M. 
Published: June 2024

AbstractSeventy-two intentionally sequence-diverse antibody variable regions were selected, expressed as IgG1 antibodies, and evaluated by chemical unfolding to survey the complexities of denaturant induced unfolding behavior. A two-transition fit well described the curves and uncovered a wide range of sensitivities to denaturant. Four general types of unfolding curves were observed: balanced traces (each transition responsible for half of the total unfolding transition), low-unfolding traces (first transition is a majority of the unfolding curve), high-unfolding traces (the second transition is the majority of the unfolding curve), and coincident traces (the two transitions are found close to each other, approximating a single transition).The complexity of the data from this survey indicates that focusing on the first inflection point or fitting a single transition model is likely an over-simplistic method for measuring stability by the chemical unfolding assay. Additionally, other conformational assays, such as thermal and low pH unfolding, showed no correlation with the chemical unfolding results, indicating that each of these assays provide alternate information on the different pathways of antibody conformational stability. These results provide a basis for beginning to better connect unfolding behavior to other physical phenotypic behaviors and production process behaviors.
Supporting early detection of biological invasions through short-term spatial forecasts of detectability
Authors: Capinha César , Monteiro António T. , Ceia-Hasse Ana 
Published: June 2024

AbstractEarly detection of invasive species is crucial to prevent biological invasions. To increase the success of detection efforts, it is often essential to know the phenological stages in which the invasive species are found. This includes knowing, for example, if invasive insect species are in their adult phase, invasive plants are flowering, or invasive mammals have finished their hibernation. Unfortunately, this kind of information is often unavailable or is provided at very coarse temporal and spatial resolutions. On the other hand, opportunistic records of the location and timing of observations of these stages are increasingly available from biodiversity data repositories. Here, we demonstrate how to apply these data for predicting the timing of phenological stages of invasive species. The predictions are made across Europe, at a daily temporal resolution, including in near real time and for multiple days ahead. We apply this to detectability-relevant phenological stages of four well-known invasive species: the freshwater jellyfish, the geranium bronze butterfly, the floating primrose-willow, and the garden lupine. Our approach uses machine learning and statistical-based algorithms to identify the set of temporal environmental conditions (e.g., temperature values and trends, precipitation, snow depth, and wind speed) associated with the observation of each phenological stage, while accounting for spatial and temporal biases in recording effort. Correlation between predictions from models and the actual timing of observations often exceeded values of 0.9. However, some inter-taxa variation occurred, with models trained on several thousands of observation records performing consistently better than those based on a few hundred records. The analysis of daily predictions also allowed mapping EU-wide regions with similar phenological dynamics (i.e., ‘phenoregions’). Our results underscore the significant potential of opportunistic biodiversity observation data in developing models capable of predicting and forecasting species phenological stages across broad spatial extents. This information has the potential to significantly improve decision-making in invasion surveillance and monitoring activities.
Damsel: Analysis and visualisation of DamID sequencing in R
Authors: Page Caitlin G , Londsdale Andrew , Mitchell Katrina A , Schröder Jan , Harvey Kieran F. , Oshlack Alicia 
Published: June 2024

ABSTRACTSummaryDamID sequencing is a technique to map the genome-wide interaction of a protein with DNA. Damsel is the first Bioconductor package to provide an end to end analysis for DamID sequencing data within R. Damsel performs quantification and testing of significant binding sites along with exploratory and visual analysis. Damsel produces results consistent with previous analysis approaches.Availability and implementationThe R package Damsel is available for install through the Bioconductor projecthttps://bioconductor.org/packages/release/bioc/html/Damsel.htmland the code is available on GitHubhttps://github.com/Oshlack/Damsel/Contactcaitlin.page@petermac.orgSupplementary informationAvailable through the journal.
High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis
Authors: Singh Divya , Robin Tal , Urbakh Michael , Reuveni Shlomi 
Published: June 2024

ABSTRACTSingle-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the first moment of the enzymatic turnover time depends linearly on the reciprocal of the substrate concentration. This, in turn, provides robust and convenient means to determine the maximal turnover rate and the Michaelis-Menten constant. Yet, the information provided by these parameters is incomplete and does not allow full characterization of enzyme kinetics at the single molecule level. Here we show that the missing kinetic information can be accessed via a set of high-order Michaelis-Menten equations that we derive. These equations capture universal linear relations between the reciprocal of the substrate concentration and distinguished combinations of turnover time moments, essentially generalizing the Michaelis-Menten equation to moments of any order. We demonstrate how key observables such as the lifetime of the enzyme-substrate complex, the rate of substrate-enzyme binding, and the probability of successful product formation, can all be inferred using these high-order Michaelis-Menten equations.
Optimizing drug synergy prediction through categorical embeddings in Deep Neural Networks
Authors: Lastre Manuel González , de Prado Salas Pablo González , Guantes Raúl 
Published: June 2024

AbstractCancer treatments often lose effectiveness as tumors develop resistance to single–agent therapies. Com-bination treatments can overcome this limitation, but the overwhelming combinatorial space of drug-dose interactions makes exhaustive experimental testing impractical. Data-driven methods, such as Machine and Deep Learning, have emerged as promising tools to predict synergistic drug combinations. In this work, we benchmark the use of categorical embeddings within Deep Neural Networks to enhance drug synergy predictions. These learned and transferable encodings capture similarities between the elements of each category, demonstrating particular utility in scarce data scenarios.
Addressing the dynamic nature of reference data: a new nt database for robust metagenomic classification
Authors: Martí Jose Manuel , Kok Car Reen , Thissen James B. , Mulakken Nisha J. , Avila-Herrera Aram , Jaing Crystal J. , Allen Jonathan E. , Be Nicholas A. 
Published: June 2024

AbstractBackgroundAccurate metagenomic classification relies on comprehensive, up-to-date, and validated reference databases. While the NCBI BLAST Nucleotide (nt) database, encompassing a vast collection of sequences from all domains of life, represents an invaluable resource, its massive size —currently exceeding 1012 nucleotides— and exponential growth pose significant challenges for researchers seeking to maintain current nt-based indices for metagenomic classification. Recognizing that no current nt-based indices exist for the widely used Centrifuge classifier, and the last public version was released in 2018, we addressed this critical gap by leveraging advanced high-performance computing resources.ResultsWe present new Centrifuge-compatible nt databases, meticulously constructed using a novel pipeline incorporating different quality control measures, including reference decontamination and filtering. These measures demonstrably reduce spurious classifications, and through temporal comparisons, we reveal how this approach minimizes inconsistencies in taxonomic assignments stemming from asynchronous updates between public sequence and taxonomy databases. These discrepancies are particularly evident in taxa such asListeria monocytogenesandNaegleria fowleri, where classification accuracy varied significantly across database versions.ConclusionsThese new databases, made available as pre-built Centrifuge indexes, respond to the need for an open, robust, nt-based pipeline for taxonomic classification in metagenomics. Applications such as environmental metagenomics, forensics, and clinical metagenomics, which require comprehensive taxonomic coverage, will benefit from this resource. Our new nt-based index highlights the importance of treating reference databases as dynamic entities, subject to ongoing quality control and validation akin to software development best practices. This dynamic update approach is crucial for ensuring the accuracy and reliability of metagenomic analysis, especially as databases continue to expand in size and complexity.
On the potential for GWAS with phenotypic population means and allele-frequency data (popGWAS)
Authors: Pfenninger Markus 
Published: June 2024

ABSTRACTIt is vital to understand the genomic basis of differences in ecologically important traits if we are to understand the impact of global change on biodiversity and enhance our ability for targeted intervention. This study explores the potential of a novel genome-wide association study (GWAS) approach for identifying loci underlying quantitative polygenic traits in natural populations, based on phenotypic population means and genome-wide allele frequency data as obtained e.g. by PoolSeq approaches. Extensive population genetic forward simulations demonstrate that the approach is generally effective for oligogenic and moderately polygenic traits and relatively insensitive to low heritability. However, applicability is limited for highly polygenic architectures and pronounced population structure. The required sample size is moderate with very good results being obtained already for a few dozen populations scored. When combined with machine learning for feature selection, the method performs very well in predicting population means. The data efficiency of the method, particularly when using pooled sequencing and bulk phenotyping, makes GWAS studies more accessible for research in biodiversity genomics. Moreover, in a direct comparison to individual based GWAS, the proposed method performed constistently better with regard to the number of true positive loci identified and prediction accuracy. Overall, this study highlights the promise of popGWAS for dissecting the genetic basis of complex traits in natural populations.
Enhancing the biological insight from limited proteolysis-coupled mass spectrometry
Authors: Nagel L. , Grossbach J. , Cappelletti V. , Doerig C. , Picotti P. , Beyer A. 
Published: June 2024

AbstractLimited proteolysis combined with mass spectrometry (LiP-MS) facilitates probing structural changes on a proteome-wide scalein situ. Distinguishing the different signal contributions, such as changes in protein abundance, from protein abundance changes remains challenging. We propose a two-step approach, first removing unwanted variations from the LiP signal that are not caused by protein structural effects and subsequently inferring the effects of variables of interest on the remaining signal. Using LiP-MS data from three species we demonstrate that our framework provides a uniquely powerful approach for deconvolving LiP-MS signals and inferring protein structural changes.
Intra-genomic genes-to-genes correlation enables genome representation
Authors: Chen Jingjie , Ma Xuchuan , He Junwei , Wang Yingxia , Ren Yuji , Qi Li , Song Liuyang , Ruan Lin , Fan Cun , Jiang Jun , Bao Yongjia , Huang Tao , Cheng Jingbo , Liu Xing , Chen Fang , Huang Yichen , Zhao Na , Guo Chunming 
Published: June 2024

AbstractThe pan-genome consists of core genes shared by all members of a taxonomy and accessory genes found in only a subset, holding the keys to advancing our understanding of evolution and tackling medical challenges. Here, we discovered a strong intra-genomic correlation among bacterial genes within each ofEscherichia coli,Listeria monocytogenes,Staphylococcus aureus, andCampylobacter jejuni. With a convolutional neural network assisted core genome knock-out simulation, we found that different combinations of fewer than 20 highly variable core genes could recover the sub-species type classified by complete core genome with accuracy >95%. This observation led us to test the genes-to-genes predictability: with more than 52,000 assemblies from each species, combinations of highly variable core genes could predict the sequence variants of other core genes (average accuracy >94%) within the same genome and could also predict sequence variants (average accuracy >91%) as well as the presence (average AUROC >0.91) of some accessory genes. Furthermore, combinations of highly variable core genes could also predict multiple antibiotic resistances (AUROC >0.80) in large published datasets ofE. coli,S. aureus, andMycobacterium tuberculosis. Collectively, we propose that genes within the same genome can strongly correlate with each other. Therefore, the strain phylogeny and the stauts of other genes could be uniformly represented by combinations of highly variable core genes, which could further represent certain phenotypes includingin vitroresistance.
Interferon signaling is enhanced by ATR inhibition in glioblastoma cells irradiated with X-rays, protons or carbon ions
Authors: Rødland Gro Elise , Temelie Mihaela , Serban Ana Maria , Eek Mariampillai Adrian , Edin Nina Frederike Jeppesen , Malinen Eirik , Gilbert Antoine , Chevalier François , Savu Diana I. , Syljuåsen Randi G. 
Published: June 2024

AbstractBackground and purposeInterferon signaling plays an important role in antitumor immune responses. Inhibitors of the DNA damage response, such as ATR inhibitors, can increase interferon signaling upon conventional radiotherapy with X-rays. However, whether such inhibitors also increase interferon (IFN) signaling after high linear energy transfer (LET) particle irradiation is not known.Materials and methodsHuman glioblastoma U-251 and T98G cells were treated with X-rays, protons (linear energy transfer (LET): 7 and 38 keV/μm) and carbon ions (LET: 28 and 73 keV/μm), with and without ATR inhibitor (VE822) or ATM inhibitor (AZD1390). DNA damage signaling and cell cycle distribution were assayed by immunoblotting and flow cytometry, and radiosensitivity by clonogenic survival. IFN-β secretion was measured by ELISA and STAT1 activation by immunoblotting.ResultsHigh-LET protons and carbon ions caused stronger activation of the DNA damage response compared to low-LET protons andX-rays at similar radiation dose. G2 checkpoint arrest was abrogated by the ATR inhibitor and prolonged by the ATM inhibitor after all radiation types. The inhibitors increased radiosensitivity, as measured after X- and carbon-ion-irradiation. ATR inhibition increased IFN signaling after both low-LET and high-LET irradiation in both cell lines. In T98G, IFN signaling was also enhanced by ATM inhibition. Notably, T98G cells secreted markedly more IFN-β when the inhibitors were combined with high-LET compared to low-LET irradiation.ConclusionOur results show that ATR inhibition can increase IFN signaling after both X-, proton- and carbon-ion-irradiation. Additionally, IFN induction is strongly dependent on LET in one of the tested cell lines.
The human pathogenMycobacterium tuberculosisand the fish pathogenMycobacterium marinumtrigger the same core set of late innate immune response genes in zebrafish larvae
Authors: Dirks Ron P. , Ordas Anita , Jong-Raadsen Susanne , Brittijn Sebastiaan A. , Haks Mariëlle C. , Henkel Christiaan V. , Oravcova Katarina , Racz Peter I. , Tuinhof-Koelma Nynke , Korzeniowska nee Wiweger Malgorzata I. , Gillespie Stephen H. , Meijer Annemarie H. , Ottenhoff Tom H.M. , Jansen Hans J. , Spaink Herman P. 
Published: June 2024

AbstractZebrafish is a natural host of variousMycobacteriumspecies and a surrogate model organism for tuberculosis research.Mycobacterium marinumis evolutionarily one of the closest non-tuberculous species related toM. tuberculosisand shares the majority of virulence genes. Although zebrafish is not a natural host of the human pathogen, we have previously demonstrated successful robotic infection of zebrafish embryos withM. tuberculosisand performed drug treatment of the infected larvae. In the present study we examined for how longM. tuberculosiscan be propagated in zebrafish larvae and tested a time series of infected larvae to study the transcriptional response via Illumina RNA deep sequencing (RNAseq). Granuloma-like structures carrying fluorescently labeledM. tuberculosiscould be detected up to 9 days post infection. The continued presence of viableM. tuberculosisin the zebrafish larvae was further confirmed using the molecular bacterial load (MBL) assay. The infected larvae showed a clear and specific transcriptional immune response with a high similarity to the response of zebrafish larvae infected with the surrogate speciesM. marinum. We conclude thatM. tuberculosiscan be propagated in zebrafish larvae for at least one week after infection and provide further evidence thatM. marinumis a good surrogate model forM. tuberculosis.
T cell responsiveness to IL-10 defines the immunomodulatory effect of costimulation blockade via anti-CD154 and impacts transplant survival
Authors: Iglesias Marcos , Bibicheff Darrel , Komin Alexander , Chicco Maria , Guinn Samantha , Foley Brendan , Raimondi Giorgio 
Published: June 2024

AbstractCostimulation blockade (CoB)-based immunotherapy is a promising alternative to immunosuppression for transplant recipients; however, the current limited understanding of the factors that impact its efficacy restrains its clinical applicability. In this context, pro- and anti-inflammatory cytokines are being recognized as having an impact on T cell activation beyond effector differentiation. This study aims at elucidating the impact of direct IL-10 signaling in T cells on CoB outcomes. We used a full-mismatch skin transplantation model where recipients had a T cell-restricted expression of a dominant negative IL-10 receptor (10R-DN), alongside anti-CD154 as CoB therapy. Unlike wild-type recipients, 10R-DN mice failed to benefit from CoB. This accelerated graft rejection correlated with increased accumulation of T cells producing TNF-α, IFN-γ, and IL-17. In vitro experiments indicated that while lack of IL-10 signaling did not change the ability of anti-CD154 to modulate alloreactive T cell proliferation, the absence of this pathway heightened TH1 effector cell differentiation. Furthermore, deficiency of IL-10 signaling in T cells impaired Treg induction, a hallmark of anti-CD154 therapy. Overall, these findings unveil an important and novel role of IL-10 signaling in T cells that defines the success of CoB therapies and identifies a target pathway for obtaining robust immunoregulation.
Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics
Authors: Velez-Arce Alejandro , Lin Xiang , Li Michelle M. , Huang Kexin , Gao Wenhao , Fu Tianfan , Pentelute Bradley L. , Kellis Manolis , Zitnik Marinka 
Published: June 2024

AbstractDrug discovery AI datasets and benchmarks have not traditionally included single-cell analysis biomarkers. While benchmarking efforts in single-cell analysis have recently released collections of single-cell tasks, they have yet to comprehensively release datasets, models, and benchmarks that integrate a broad range of therapeutic discovery tasks with cell-type-specific biomarkers. Therapeutics Commons (TDC-2) presents datasets, tools, models, and benchmarks integrating cell-type-specific contextual features with ML tasks across therapeutics. We present four tasks for contextual learning at single-cell resolution: drug-target nomination, genetic perturbation response prediction, chemical perturbation response prediction, and protein-peptide interaction prediction. We introduce datasets, models, and benchmarks for these four tasks. Finally, we detail the advancements and challenges in machine learning and biology that drove the implementation of TDC-2 and how they are reflected in its architecture, datasets and benchmarks, and foundation model tooling.
In VivoProximity Labeling Identifies a New Function for the Lifespan and Autophagy-regulating Kinase Pef1, an Ortholog of Human Cdk5
Authors: Zhang Haitao , Zhang Dongmei , Li Ling , Willard Belinda , Runge Kurt W. 
Published: June 2024

ABSTRACTCdk5 is a highly-conserved, noncanonical cell division kinase important to the terminal differentiation of mammalian cells in multiple organ systems. We previously identified Pef1, theSchizosaccharomyces pombeortholog of cdk5, as regulator of chronological lifespan. To reveal the processes impacted by Pef1, we developed APEX2-biotin phenol-mediated proximity labeling inS. pombe.Efficient labeling required a short period of cell wall digestion and eliminating glucose and nitrogen sources from the medium. We identified 255 high-confidence Pef1 neighbors in growing cells and a novel Pef1-interacting partner, the DNA damage response protein Rad24. The Pef1-Rad24 interaction was validated by reciprocal proximity labeling and co-immunoprecipitation. Eliminating Pef1 partially rescued the DNA damage sensitivity of cells lacking Rad24. To monitor how Pef1 neighbors change under different conditions, cells induced for autophagy were labeled and 177 high-confidence Pef1 neighbors were identified. Gene ontology (GO) analysis of the Pef1 neighbors identified proteins participating in processes required for autophagosome expansion including regulation of actin dynamics and vesicle-mediated transport. Some of these proteins were identified in both exponentially growing and autophagic cells. Pef1-APEX2 proximity labeling therefore identified a new Pef1 function in modulating the DNA damage response and candidate processes that Pef1 and other cdk5 orthologs may regulate.