bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ergosterol mediates aggregation of natamycin in the yeast plasma membrane
Authors: Szomek Maria , Akkerman Vibeke , Lauritsen Line , Walther Hanna-Loisa , Juhl Alice Dupont , Thaysen Katja , Egebjerg Jacob Marcus , Covey Douglas F. , Lehmann Max , Wessig Pablo , Foster Alexander J. , Poolman Bert , Werner Stephan , Schneider Gerd , Müller Peter , Wüstner Daniel 
Published: December 2023

AbstractPolyene macrolides are antifungal substances, which interact with cells in a sterol-dependent manner. While being widely used, their mode of action is poorly understood. Here, we employ ultraviolet-sensitive (UV) microscopy to show that the antifungal polyene natamycin binds to the yeast plasma membrane (PM) and causes permeation of propidium iodide into cells. Right before membrane permeability becomes compromised, we observed clustering of natamycin in the PM that was independent of PM protein domains. Aggregation of natamycin was paralleled by cell deformation and membrane blebbing as revealed by soft X-ray microscopy. Substituting ergosterol for cholesterol decreased natamycin binding and resulted in reduced clustering of natamycin in the PM. Blocking of ergosterol synthesis necessitates sterol import via the ABC transporters Aus1/Pdr11 to ensure natamycin binding. Quantitative imaging of dehydroergosterol (DHE) and cholestatrienol (CTL), two analogs of ergosterol and cholesterol, respectively, revealed a largely homogeneous lateral sterol distribution in the PM, ruling out that natamycin binds to pre-assembled sterol domains. Depletion of sphingolipids using myriocin increased natamycin binding to yeast cells, likely by increasing the ergosterol fraction in the outer PM leaflet. We conclude that ergosterol-specific aggregation of natamycin in the yeast PM underlies its antifungal activity, which can be synergistically enhanced by inhibitors of sphingolipid synthesis.SignificanceErgosterol is the major sterol in the membranes of fungi and a major target for antifungal treatments. Polyene macrolides, such as natamycin, are known to target ergosterol but the underlying mechanisms for their preference for this yeast sterol compared to mammalian cholesterol is not understood. This study shows that natamycin forms aggregates when associated with yeast S. cerevisiae in an ergosterol-dependent manner. Cholesterol can only partially substitute for ergosterol with respect to natamycin binding and aggregation. Membrane-associated aggregation of natamycin is not the result of pre-formed sterol domains in the cell membrane, as we show by direct visualization of minimally modified ergosterol and cholesterol analogs. Inhibiting sphingolipid synthesis increased membrane association and antifungal activity of natamycin, suggesting that targeting sphingolipids in combination with polyene macrolides could lead to novel drug treatment approaches against fungal infections.
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Authors: Steach Holly , Viswanath Siddharth , He Yixuan , Zhang Xitong , Ivanova Natalia , Hirn Matthew , Perlmutter Michael , Krishnaswamy Smita 
Published: December 2023

AbstractThe ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.
Joint Learning of Node Semantics and Graph Topology using a Transformer in the sparse network regime
Authors: Ubingazhibov Aidyn , Gomez-Cabrero David , Kiani Narsis A. , Tegner Jesper 
Published: December 2023

AbstractThe human interactome is a valuable tool for unraveling disease mechanisms, advancing precision medicine, facilitating drug discovery, and identifying biomarkers. Yet, current interactomes are incomplete, in part due to limited experimental coverage. Therefore, augmenting the human interactome by predicting missing links in the Protein-Protein interaction network (PPI), is a core challenge for precision medicine. This study proposes an end-to-end trainable transformer-based neural network for enhanced aggregation of Gene Ontology (GO) terms features. We augment the model’s predictive capabilities by incorporating semantic anc2vec features, complementing the structural node2vec embeddings specifically designed for sparse PPIs. As a result, by integrating semantic and graph features, we demonstrate superior performance in link prediction.
FITM2 deficiency results in ER lipid accumulation, ER stress, reduced apolipoprotein B lipidation, and VLDL triglyceride secretionin vitroand in mouse liver
Authors: Wang Haizhen , Nikain Cyrus , Amengual Jaime , La Forest Maxwell , Yu Yong , Wang Meng C. , Watts Russell , Lehner Richard , Qiu Yunping , Cai Min , Kurland Irwin J. , Goldberg Ira J. , Rajan Sujith , Hussain M. Mahmood , Brodsky Jeffrey L. , Fisher Edward A. 
Published: December 2023

Structured AbstractObjectivesTriglyceride (TG) association with apolipoprotein B100 (apoB100) serves to form very low density lipoproteins (VLDL) in the liver. The repertoire of factors that facilitate this association is incompletely defined. FITM2, an integral endoplasmic reticulum (ER) protein, was originally discovered as a factor participating in cytoplasmic lipid droplets (LDs) in tissues that do not form VLDL. We hypothesized that in the liver, in addition to promoting cytosolic LD formation, FITM2 would also transfer TG from its site of synthesis in the ER membrane to nascent VLDL particles within the ER lumen.MethodsExperiments were conducted using a rat hepatic cell line (McArdle-RH7777, or McA cells), an established model of mammalian lipoprotein metabolism, and mice. FITM2 expression was reduced using siRNA in cells and by liver specific cre-recombinase mediated deletion of theFitm2gene in mice. Effects of FITM2 deficiency on VLDL assembly and secretionin vitroandin vivowere measured by multiple methods, including density gradient ultracentrifugation, chromatography, mass spectrometry, simulated Raman spectroscopy (SRS) microscopy, sub-cellular fractionation, immunoprecipitation, immunofluorescence, and electron microscopy.Main findings1) FITM2-deficient hepatic cellsin vitroandin vivosecrete TG-depleted VLDL particles, but the number of particles is unchanged compared to controls; 2) FITM2 deficiency in mice on a high fat diet (HFD) results in decreased plasma TG levels. The number of apoB100-containing lipoproteins remains similar, but shift from VLDL to LDL density; 3) Bothin vitroandin vivo, when TG synthesis is stimulated and FITM2 is deficient, TG accumulates in the ER, and despite its availability this pool is unable to fully lipidate apoB100 particles; 4) FITM2 deficiency disrupts ER morphology and results in ER stress.Principal conclusionsThe results suggest that FITM2 contributes to VLDL lipidation, especially when newly synthesized hepatic TG is in abundance. In addition to its fundamental importance in VLDL assembly, the results also suggest that under dysmetabolic conditions, FITM2 may be a limiting factor that ultimately contributes to non-alcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH).
Phenotypic consequences of logarithmic signaling in MAPK stress response
Authors: Jashnsaz Hossein , Neuert Gregor 
Published: December 2023

SummaryHow cells respond to dynamic environmental changes is crucial for understanding fundamental biological processes and cell physiology. In this study, we developed an experimental and quantitative analytical framework to explore how dynamic stress gradients that change over time regulate cellular volume, signaling activation, and growth phenotypes. Our findings reveal that gradual stress conditions substantially enhance cell growth compared to conventional acute stress. This growth advantage correlates with a minimal reduction in cell volume dependent on the dynamic of stress. We explain the growth phenotype with our finding of a logarithmic signal transduction mechanism in the yeast Mitogen-Activated Protein Kinase (MAPK) osmotic stress response pathway. These insights into the interplay between gradual environments, cell volume change, dynamic cell signaling, and growth, advance our understanding of fundamental cellular processes in gradual stress environments.
Beyond Active Learning: Using 3-Dimensional Learning to Create Scientifically Authentic, Student-Centered Classrooms
Authors: Cooper Melanie M , Caballero Marcos D. , Carmel Justin H. , Duffy Erin M. , Ebert-May Diane , Fata-Hartley Cori L. , Herrington Deborah G. , Laverty James T. , Nelson Paul C. , Posey Lynmarie A. , Stoltzfus Jon R. , Stowe Ryan L. , Sweeder Ryan D. , Tessmer Stuart , Underwood Sonia M. 
Published: December 2023

AbstractIn recent years, much of the emphasis for transformation of introductory STEM courses has focused on “active learning”, and while this approach has been shown to produce more equitable outcomes for students, the construct of “active learning” is somewhat ill-defined, and can encompass a wide range of pedagogical techniques. Here we present an alternative approach for how to think about the transformation of STEM courses that focuses instead on what students should know and what they can do with that knowledge. This approach, known as three-dimensional learning (3DL), emerged from the National Academy’s “A Framework for K-12 Science Education”, which describes a vision for science education that centers the role of constructing productive causal accounts for phenomena. Over the past 10 years, we have collected data from introductory biology, chemistry, and physics courses to assess the impact of such a transformation on higher education courses. Here we report on an analysis of video data of class sessions that allows us to characterize these sessions asactive, 3D, neither, orboth 3D and active. We find that 3D classes are likely to also involve student engagement (i.e. be active), but the reverse is not necessarily true. That is, focusing on transformations involving 3DL also tends to increase student engagement, whereas focusing solely on student engagement might result in courses where students are engaged in activities that do not involve meaningful engagement with core ideas of the discipline.
Identification roles of NFE2L3 in Digestive System Cancers
Authors: Li Fan , Wen Zhili 
Published: December 2023

Abstract(1) BackgroundThe morbidity and mortality rate of Digestive System Cancers (DSC) continue to threaten human lives and health. Nuclear factor erythroid 2-like protein 3 (NFE2L3) is associated with the development, growth, and progression of multiple cancers. Nevertheless, the clinical value and underlying mechanisms of NFE2L3 in DSC remains unknown. This study aimed to clarify the possibilities of NFE2L3 as a novel biomarker in DSC.(2) MethodsWe obtained the data of NFE2L3 in cancers from database to evaluate the expression level and clinical value of NFE2L3 in DSC, and to explore underlying mechanism and biological functions of NFE2L3 in DSC.(3) ResultsNFE2L3 expression is up-regulated in DSC and have both prognostic and diagnostic value. NFE2L3 contributes to oncogenesis through a variety of mechanisms. In vitro experiment showed that NFE2L3 stimulated the proliferation and migration ability of gastric cancer cells.(4) ConclusionOur study confirms the clinical applicability of NFE2L3 as a promising biomarker for DSC.
Predictive Power of Machine Learning Models for Relapse Outcomes in Acute Myeloid Leukemia: Unveiling Key Genes and Pathways for Improved Patient Management
Authors: Radak Mehran , Fallahi Hossein , Karami Keyvan 
Published: December 2023

AbstractAcute Myeloid Leukemia (AML) is a challenging form of blood cancer requiring accurate relapse prediction for effective therapy and patient management. In this study, we applied multiple machine learning techniques to a dataset of AML patients in order to develop a reliable model for predicting relapse and guiding treatment decisions. We utilized various feature selection methods to identify the most relevant features associated with relapse. Additionally, we investigated gene ontology using the Gene Ontology (GO) database to gain insights into the biological processes and KEGG pathways related to the selected features. Our findings revealed key genes and pathways implicated in AML relapse. Among the machine learning models, Decision Tree (DT) showed the highest accuracy in predicting relapse outcomes. Furthermore, we compared the performance of DT models across different feature selections, highlighting the significance of specific factors such as MCL1, WBC, HGB, and BAD.p112 in relapse prediction. The results of our study have important implications for tailoring treatment plans and improving patient outcomes in AML. By accurately identifying patients at high risk of relapse, our model can aid in early interventions and personalized therapies. Ultimately, our research contributes to advancing the field of machine learning in AML and lays the foundation for developing effective strategies to combat relapse in this disease.
Mathematical model of RNA-directed DNA methylation predicts tuning of negative feedbacks required for stable maintenance
Authors: Dale Renee , Mosher Rebecca A. 
Published: December 2023

AbstractRNA-directed DNA Methylation (RdDM) is a plant-specificde novomethylation pathway that is responsible for maintenance of asymmetric methylation (CHH, where H=A, T, or G) in euchromatin. Loci with CHH methylation are transcriptionally silent and produce 24-nucleotide (nt) short interfering (si) RNAs. These siRNAs direct additional CHH methylation to the locus, thereby maintaining methylation states through DNA replication. To understand the necessary conditions to produce stable CHH methylation, we developed a stochastic mathematical model of RdDM. The model describes DNA target search of DNA or RNA by siRNAs derived from CHH-methylated loci. When the siRNA (bound by an Argonaute protein) finds the matching locus, the model uses the dwell time of the matched complex to determine the degree of CHH reinforcing methylation. Reinforcing methylation occurs either throughout the cell cycle (steady reinforcement), or immediately following replication (bursty reinforcement). Each simulation occurs over 10 cell cycles, and for 7 simulation replicates. We use nonparametric statistics to compare initial and final CHH methylation distributions to determine whether the simulation conditions produce stable maintenance. We apply this method to the low CHH methylation case, wherein the median is only 8%, and many loci have less than 8% methylation. The resulting model predicts that siRNA production must be linearly proportional to CHH methylation levels at each locus, that bursty reinforcement produces more stable systems, and that slightly higher levels of siRNA production are required for DNA target search, compared to RNA target search. Unlike CG methylation, which typically exhibits bi-modality, with loci having either 100% or 0% methylation, CHH methylation putatively exists at a range of methylation fractions. Our model predicts that careful tuning of the negative feedbacks in the system are required to balance the positive feedback loop of increasing CHH methylation and increasing siRNA production, enabling stable maintenance of a range of CHH methylation across replication events.
Effect of Insomnia on Repetitive Transcranial Magnetic Stimulation Treatment Outcomes for Depression
Authors: Kweon Jamie , Fukuda Andrew , Gobin Polly , Haq Lamaan , Brown Joshua C. , Carpenter Linda L. 
Published: December 2023

AbstractWhile repetitive transcranial magnetic stimulation (rTMS) is safe and effective for 50-60% of those treatment-resistant depression, it is critical to identify factors to optimize therapy to help those who do not respond. Baseline sleep characteristics have been investigated as a potential predictor of TMS efficacy but results from various studies have been conflicting. We aimed to explore whether baseline sleep quality, specifically insomnia related symptoms, is associated with TMS outcomes in a naturalistic sample of 975 patients receiving a standard course of rTMS from two sites. One site recorded information on concurrent medication use. Among these 353 patients, we also examined whether pharmacological treatment of insomnia affected TMS treatment response. Depression was measured using the 30-item Inventory of Depressive Symptomology Self Report (IDS-SR) in site one and an abbreviated 16-item Quick Inventory of Depressive Symptomology (QIDS) in site two. Sleep disturbances were measured using three sleep-related questions overlapping between the two questionnaires. We found that sleep quality improves after TMS and correlates with improvement in depression. Upon dichotomous categorization of the sample by insomnia and hypnotics use, we found that among those who had significant insomnia at baseline, those not using sleep medications had significantly worse post-treatment IDS-SR scores compared to those receiving pharmacological treatments for sleep (p=.021). Together, our results suggest that while baseline insomnia is not associated with response to TMS treatment, treating insomnia may affect the trajectory of TMS therapy. Future prospective studies are necessary to examine the effect of insomnia treatment alongside TMS for depression.
Assessing the validity of a self-reported clinical diagnosis of schizophrenia
Authors: Woolway Grace E , Legge Sophie E , Lynham Amy , Smart Sophie E , Hubbard Leon , Daniel Ellie R , Pardiñas Antonio F , Escott-Price Valentina , O’Donovan Michael C , Owen Michael J , Jones Ian R , Walters James TR 
Published: December 2023

AbstractBackgroundDiagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia.MethodsThe study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC).ResultsIn the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts.ConclusionsSelf-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended.
Investigating the Determinants of COVID-19 Vaccine Uptake and Attitudes in Iraq: A Study Unveiling the Negative Impact of Misinformation and Vaccine Conspiracy
Authors: Sallam Malik , Kareem Nariman , Alkurtas Mohammed 
Published: December 2023

AbstractVaccine hesitancy is a major barrier challenging the control of infectious diseases. Previous studies demonstrated high rates of COVID-19 vaccine hesitancy in the Middle East. The current study aimed to investigate the attitudes towards COVID-19 vaccination and COVID-19 vaccine uptake among the adult population in Iraq. This cross-sectional self-administered survey-based study was conducted in August–September 2022. Recruitment of possible participants was done using chain-referral sampling. The survey instrument assessed participants’ demographics, attitudes toward COVID-19 vaccination, beliefs in COVID-19 misinformation, vaccine conspiracy beliefs, and sources of information regarding the vaccine. The study sample comprised a total of 2544 individuals, with the majority reporting the uptake of at least one dose of COVID-19 vaccination (n=2226, 87.5%). Positive attitudes towards COVID-19 vaccination were expressed by the majority of participants (n=1966, 77.3%), while neutral attitudes were expressed by 345 participants (13.6%), and negative attitudes were expressed by 233 participants (9.2%). Strong, moderate, slight, and absence of COVID-19 misinformation were expressed by 12.4%, 22.6%, 36.2%, and 28.7% participants, respectively. The majority of participants showed a neutral attitude towards COVID-19 vaccine conspiracies (n=1464, 57.5%), while 607 participants embraced these conspiracies (23.9%), and 473 disagreed with such beliefs (18.6%). In the multivariate analysis, factors associated with positive attitudes towards COVID-19 vaccination included disbelief in COVID-19 misinformation and disagreement with vaccine conspiracies. Higher COVID-19 vaccine uptake was significantly associated with history of COVID-19 infection, higher income, residence outside the capital, disbelief in COVID-19 misinformation, disagreement with vaccine conspiracies, and reliance on reputable information sources. COVID-19 vaccine coverage prevailed among the participants, with a majority having positive attitudes towards COVID-19 vaccination. Disbelief in COVID- 19 misinformation and disagreement with vaccine conspiracies were correlated with positive vaccine attitudes and higher vaccine uptake. These insights can inform targeted interventions to enhance vaccination campaigns.