bluuebunny/crossref_metadata_2025_split huggingface dataset:
MU-BRAIN: MUltiethnic Brain Rna-seq for Alzheimer INitiative
Authors: Yang Zikun , Cieza Basilio , Reyes-Dumeyer Dolly , Lee Annie , Ma Yiyi , Yilmaz Elanur , Lantigua Rafael , Miller Gary W , Brown Lewis M , Honig Lawrence , Ciener Benjamin , Leskinen Sandra , Sivakumar Sharanya , Vardarajan Badri , Dugger Brittany N , Jin Lee-Way , Murray Melissa E , Dickson Dennis W , Rissman Robert A , Hiniker Annie , Pericak-Vance Margaret , Vance Jeff , Foroud Tatiana M , Kizil Caghan , Teich Andrew F , Mayeux Richard , Tosto Giuseppe 
Published: February 2024

AbstractAlzheimer’s Disease (AD) exhibits a complex molecular and phenotypic profile. Investigating gene expression plays a crucial role in unraveling the disease’s etiology and progression. Transcriptome data across ethnic groups lack, negatively impacting equity in intervention and research.We employed 565 brains across six U.S. brain banks (n=399 non-Hispanic Whites,n=113 Hispanics,n=12 African Americans) to generated bulk RNA sequencing from prefrontal cortex. We sought to identify cross-ancestry and ancestry-specific differentially expressed genes (DEG) across Braak stages, adjusting for sex, age at death, and RNA quality metrics. We further validated our findings using the Religious Orders Study/Memory Aging Project brains (ROS/MAP;n=1,095) and performed metanalysis (n=1,660). We conducted Gene Set and Variation and Enrichment analysis (GSVA; GSEA). We employed a machine-learning approach for phenotype prediction and gene prioritization to construct a polytranscriptomics risk score (PTRS) splitting our sample into training and testing sub-samples, either randomly or by ethnicity (“ancestry-agnostic” and “ancestry-aware”, respectively). Lastly, we validated top DEG using single-nucleus RNA sequencing (snRNAseq) data.We identified several DEG associated with Braak staging: AD-known genesVGF(Padj=3.78E- 07) andADAMTS2(Padj=1.21E-04) were consistently differentially expressed across statistical models, ethnicities, and replicated in ROS/MAP. Genes from the heat shock protein (HSP) family, e.g.HSPB7(Padj=3.78E-07), were the top differentially expressed genes and replicated in ROS/MAP. Ethnic-stratified analyses prioritizedTNFSF14andSPOCD1as top Hispanics DEG. GSEA highlighted “Alzheimer disease” (Padj=4.24E-06) and “TYROBP causal network in microglia” (Padj=1.68E-08) pathways. Up- and down-regulated genes were enriched in several pathways (e.g. “Immune response activation signal pathways”, “Vesicle-mediated transport in synapse”, “cognition”). Ancestry-agnostic and ancestry-aware PTRS effectively classified brains (AUC=0.77 and 0.73 respectively) and replicated in ROS/MAP. snRNAseq validated prioritized genes, includingVGF (downregulated in neurons;Padj=1.1 E-07).This is the largest diverse AD transcriptome in post-mortem brain tissue, to our knowledge. We identified perturbated genes, pathways and network expressions in AD brains resulting in cross- ethnic and ethnic-specific findings, ultimately highlighting the diversity within AD pathogenesis. The latter underscores the need for an integrative and personalized approach in AD studies.
nERdy: network analysis of endoplasmic reticulum dynamics
Authors: Samudre Ashwin , Gao Guang , Cardoen Ben , Nabi Ivan Robert , Hamarneh Ghassan 
Published: February 2024

AbstractThe endoplasmic reticulum (ER) comprises smooth tubules, ribosome-studded sheets, and peripheral sheets that can present as tubular matrices. ER shaping proteins determine ER morphology, however, their role in tubular matrix formation requires reconstructing the dynamic, convoluted ER network. Existing reconstruction methods are sensitive to parameters or require extensive annotation and training for deep learning. We introduce nERdy, an image processing based approach, and nERdy+, a D4-equivariant neural network, for accurate extraction and representation of ER networks and junction dynamics, outperforming previous methods. Comparison of stable and dynamic representations of the extracted ER structure reports on tripartite junction movement and distinguishes tubular matrices from peripheral ER networks. Analysis of live cell confocal and STED time series data shows that Atlastin and Reticulon 4 promote dynamic tubular matrix formation and enhance junction dynamics, identifying novel roles for these ER shaping proteins in regulating ER structure and dynamics.
Neuronal-class specific molecular cues drive differential myelination in the neocortex
Authors: Jokhi Vahbiz , Domínguez-Iturza Nuria , Kim Kwanho , Shetty Ashwin S. , Yuan Wen , Di Bella Daniela , Abbate Catherine , Oyler-Castrillo Paul , Jin Xin , Simmons Sean , Levin Joshua Z. , Brown Juliana R. , Arlotta Paola 
Published: February 2024

ABSTRACTIn the neocortex, oligodendrocytes produce distinct amounts of myelin in each cortical layer and along the axons of individual neuron types. Here we present a comprehensive single-cell molecular map of mouse cortical oligodendrocytes across different cortical layers and stages of myelination, spanning the initiation of cortical myelination into adulthood. We apply this dataset to show that neuron-class specific signals drive oligodendrocyte maturation and differential myelination across cortical layers. We find that each layer contains a similar compendium of oligodendrocyte classes, indicating that oligodendrocyte heterogeneity cannot explain layer-specific myelination. To evaluate whether neuronal diversity drives differential myelination across cortical layers, we generated a predicted ligand-receptor interactome between projection neuron types and oligodendrocyte states, across cortical layers and time.In vivofunctional testing identifiedFgf18,Ncam1, andRspo3as novel, neuron-derived pro-myelinating signals. Our results highlight neuron-class-dependent control of myelin distribution in the neocortex.
Tree sequences as a general-purpose tool for population genetic inference
Authors: Whitehouse Logan S. , Ray Dylan , Schrider Daniel R. 
Published: February 2024

ABSTRACTAs population genetics data increases in size new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient, but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks (CNNs) applied to population genetic alignments. To better utilize these new data structures we propose and implement a graph convolutional network (GCN) to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard CNN approaches on a set of previously defined benchmarking tasks including recombination rate estimation, positive selection detection, introgression detection, and demographic model parameter inference. We show that tree sequences can be directly learned from using a GCN approach and can be used to perform well on these common population genetics inference tasks with accuracies roughly matching or even exceeding that of a CNN-based method. As tree sequences become more widely used in population genetics research we foresee developments and optimizations of this work to provide a foundation for population genetics inference moving forward.
EasyOmics-A graphical interface for population-scale omics data association, integration and visualization
Authors: Han Yu , Du Qiao , Dai Yifei , Gu Shaobo , Liu Wei , Zhu Mingjia , Feng Landi , Liu Jianquan , Zan Yanjun 
Published: February 2024

AbstractThe rapid growth of population-scale whole genome resequencing, RNA sequencing, bisulfate sequencing, metabolomics and proteomic profiling has led quantitative genetics into a big omics data era. Performing omics data association analysis, such as genome, transcriptome, proteome and methylome wide association analysis, and integrative analysis on multiple omics dataset requires various bioinformatics tools rely on advanced programming skills and command-line environments, which are challenging for most wet-lab biologists. Here, we present EasyOmics a stand-alone R Shiny application with a user-friendly interface for wet-lab biologists to perform population-scale omics data association, integration and visualization. The toolkit incorporates multiple functions designed to meet the increasing demand for big omics data analyses, ranging from data quality control, heritability estimation, genome wide association analysis, conditional association analysis, omics quantitative trait locus mapping, omics wide association analysis, omics data integration and data visualization etc. A wide variety of publication quality graphs can be prepared in EasyOmics using R plot engine with point-and-click. EasyOmics is a platform-independent software that can be run under all operating systems with a docker container for easy installation. It is freely available to non-commercial users at docker hubhttps://hub.docker.com/repository/docker/yuhan2000/gwas/general.
Prior cocaine use diminishes encoding of latent information by orbitofrontal, but not medial, prefrontal ensembles
Authors: Mueller Lauren E. , Konya Caitlin , Sharpe Melissa J. , Wikenheiser Andrew M. , Schoenbaum Geoffrey 
Published: February 2024

AbstractMaladaptive decision-making is a hallmark of substance use disorders, though how drugs of abuse alter neural representations supporting adaptive behavior remains poorly understood. Past studies show the orbitofrontal (OFC) and prelimbic (PL) cortices are important for decision making, tracking both task-relevant and latent information. However, previous studies have focused on how drugs of abuse impact the firing rates of individual units. More work at the ensemble level is necessary to accurately characterize potential drug-induced changes. Using single-unit recordings in rats during a multidimensional decision-making task and then applying population and ensemble level analyses, we show that prior use of cocaine altered the strength and structure of task-relevant and latent representations in the OFC, changes relatable to suboptimal decision making in this and perhaps other settings. These data expand our understanding of the neuropathological underpinnings of maladaptive decision-making in SUDs, potentially enabling enhanced future treatment strategies.
Fetal brain response to maternal inflammation requires microglia
Authors: Elaine LaMonica Ostrem Bridget , Dominguez Iturza Nuria , Stogsdill Jeffrey , Faits Tyler , Kim Kwanho , Levin Joshua Z. , Arlotta Paola 
Published: February 2024

ABSTRACTIn uteroinfection and maternal inflammation can adversely impact fetal brain development. Maternal systemic illness, even in the absence of direct fetal central nervous system infection, is associated with an increased risk of autism and schizophrenia in affected offspring. The cell types mediating the response of the fetal brain to maternal inflammation are largely unknown, hindering the development of therapies to prevent and treat adverse neuropsychiatric outcomes. Here, we show that microglia, the resident phagocytes of the brain, are enriched for expression of receptors for relevant pathogens and cytokines, throughout embryonic development. Using a rodent maternal immune activation (MIA) model in which polyinosinic:polycytidylic acid is injected into pregnant dams, we demonstrate long-lasting transcriptional changes in fetal microglia that persist into postnatal life. We find that MIA induces widespread gene expression changes in neuronal and non-neuronal cells; importantly, these responses are abolished by selective genetic deletion of microglia, indicating that microglia are required for the transcriptional response of other cortical cell types to MIA. These findings demonstrate that microglia play a critical, durable role in fetal response to maternal inflammation, pointing at microglia as a potential therapeutic cell target.
Olive Flowering dependence on winter temperatures - linking empirical results to a dynamic model
Authors: Smoly Ilan , Elbaz Haim , Engelen Chaim , Wechsler Tahel , Elbaz Gal , Ben-Ari Giora , Samach Alon , Friedlander Tamar 
Published: February 2024

AbstractIncreasing winter temperatures jeopardize the yield of fruit trees requiring a prolonged and sufficiently cold winter to flower. Assessing the exact risk to different crop varieties is the first step in mitigating the harmful effect of climate change. Since empirically testing the impacts of many temperature scenarios is very time-consuming, quantitative predictive models could be extremely helpful in reducing the number of experiments needed. Here, we focus on olive (Olea europaea) – a traditional crop in the Mediterranean basin, a region expected to be severely affected by climatic change. Olive flowering and consequently yield depend on the sufficiency of cold periods and the lack of warm ones during the preceding winter. Yet, a satisfactory quantitative model forecasting its expected flowering under natural temperature conditions is still lacking. Previous models simply summed the number of ‘cold hours’ during winter, as a proxy for flowering, but exhibited only mediocre agreement with empirical flowering values, possibly because they overlooked the order of occurrence of different temperatures.We empirically tested the effect of different temperature regimes on olive flowering intensity and flowering-gene expression. To predict flowering based on winter temperatures, we constructed a dynamic model, describing the response of a putative flowering factor to the temperature signal. The crucial ingredient in the model is an unstable intermediate, produced and degraded at temperature-dependent rates. Our model accounts not only for the number of cold and warm hours but also for their order. We used sets of empirical flowering and temperature data to fit the model parameters, applying numerical constrained optimization techniques, and successfully validated the model outcomes. Our model more accurately predicts flowering under winters with warm periods yielding low-to-moderate flowering and is more robust compared to previous models.This model is the first step toward a practical predictive tool, applicable under various temperature conditions.
Systematic Identification of UBE2C As a Prognostic Biomarker and Correlated with Immunosuppression and Invasiveness in Glioma
Authors: Feng Hao , Fu Anhui , Yang Rong , Qiao Fei 
Published: February 2024

AbstractGlioma is one of the common tumors of the central nervous system, which presents difficulties in clinical diagnosis and treatment due to its characteristics of immunosuppression and cell invasion phenotypes. If the condition and prognosis of glioma can be predicted during the process of diagnosis and treatment, it will be more conducive to timely intervention or evaluation of glioma. Therefore, we still need to search for more valuable tumor markers. The differential/risk genes and enrichment analysis based on glioma samples (The Cancer Genome Atlas, TCGA). Target gene UBE2C were obtained by the expression correlation and differential expression analysis for the enrichment results. UBE2C were evaluated by clinical grading, survival prognosis and cell experiments. The correlation of UBE2C with immune invasion, immune checkpoint, network analysis and cell invasiveness of gliomas was analyzed by TCGA-glioma data and STRING, respectively. The results suggests that the high expression and risk of UBE2C in gliomas may be a factor that promotes malignant phenotype of tumor cells. The immune phenotype shows that IL6 and IL10 may be the key nodes affecting the immunosuppressive phenotype of glioma. Further, the tumor cells aggressive genes from the MMP family can be correlated with immunosuppressive phenotypes via UBE2C-IL6/IL10 axis, especially displayed by MMP2/MMP9. The UBE2C may systemic effects the malignant phenotype, immunosuppression and cell invasiveness of tumors systematically, which reflects UBE2C as a potential biomarker of glioma and therapeutic target for this tumor.
Age-related dysregulation of the retinal transcriptome in African turquoise killifish
Authors: Bergmans Steven , Noel Nicole C. L. , Masin Luca , Harding Ellen G. , Krzywańska Aleksandra M. , Schutter Julie D. De , Ayana Rajagopal , Hu Chi-Kuo , Arckens Lut , Ruzycki Philip A. , MacDonald Ryan B. , Clark Brian S. , Moons Lieve 
Published: February 2024

AbstractAge-related vision loss caused by retinal neurodegenerative pathologies is becoming more prevalent in our ageing society. To understand the physiological and molecular impact of ageing on retinal homeostasis, we used the short-lived African turquoise killifish, a model known to naturally develop central nervous system (CNS) ageing hallmarks and vision loss. Bulk and single-cell RNA-sequencing (scRNA-seq) of three age groups (6-, 12-, and 18-week-old) identified transcriptional ageing fingerprints in the killifish retina, unveiling pathways also identified in the aged brain, including oxidative stress, gliosis, and inflammageing. These findings were comparable to observations in ageing mouse retina. Additionally, transcriptional changes in genes related to retinal diseases, such as glaucoma and age-related macular degeneration, were observed. The cellular heterogeneity in the killifish retina was characterised, confirming the presence of all typical vertebrate retinal cell types. Data integration from age-matched samples between the bulk and scRNA-seq experiments revealed a loss of cellular specificity in gene expression upon ageing, suggesting potential disruption in transcriptional homeostasis. Differential expression analysis within the identified cell types highlighted the role of glial/immune cells as important stress regulators during ageing. Our work emphasises the value of the fast-ageing killifish in elucidating molecular signatures in age-associated retinal disease and vision decline. This study contributes to the understanding of how age-related changes in molecular pathways may impact CNS health, providing insights that may inform future therapeutic strategies for age-related pathologies.HighlightsThe aged killifish retina displays several ageing hallmarks, such as oxidative stress, gliosis and inflammageing, at the transcriptome level.Risk genes for neurodegenerative disorders show dysregulation in the old killifish retina.All vertebrate retinal cell types are present in the killifish retina.Transcriptional dysregulation in the aged killifish retina is observed across cell types.
Structural basis of TMPRSS2 zymogen activation and recognition by the HKU1 seasonal coronavirus
Authors: Fernández Ignacio , Saunders Nell , Duquerroy Stéphane , Bolland William H. , Arbabian Atousa , Salazar Eduard Baquero , Blanc Catherine , Lafaye Pierre , Haouz Ahmed , Buchrieser Julian , Schwartz Olivier , Rey Félix A. 
Published: February 2024

ABSTRACTThe human seasonal coronavirus HKU1-CoV, which causes common colds worldwide, relies on the sequential binding to a cell-surface glycan and to TMPRSS2 for entry into target cells. TMPRSS2 is a cell surface protease synthesized as a zymogen that undergoes autolytic activation to process its substrates. Several respiratory viruses - in particular coronaviruses - use TMPRSS2 for proteolytic priming of their surface spike protein to drive membrane fusion upon receptor binding. We describe the crystal structure of the HKU1-CoV receptor binding domain in complex with TMPRSS2, showing that it recognizes residues lining the catalytic groove. Combined mutagenesis of interface residues and comparison across species highlight positions 417 and 469 as determinants of HKU1-CoV host tropism. The structure of a receptor- blocking nanobody in complex with zymogen or activated TMPRSS2 further provides the structural basis of the TMPRSS2 activating conformational change, altering loops recognized by HKU1-CoV and dramatically increasing its binding affinity.
Genetic Stability ofMycobacterium smegmatisunder the Stress of First-Line Antitubercular Agents: Assessing Mutagenic Potential
Authors: Molnár Dániel , Surányi Éva Viola , Trombitás Tamás , Füzesi Dóra , Hirmondó Rita , Tóth Judit 
Published: February 2024

AbstractThe sustained success ofMycobacterium tuberculosisas a pathogen arises from its ability to persist within macrophages for extended periods and its limited responsiveness to antibiotics. Furthermore, the high incidence of resistance to the few available antituberculosis drugs is a significant concern, especially since the driving forces of the emergence of drug resistance are not clear. Drug-resistant strains ofMycobacterium tuberculosiscan emerge throughde novomutations, however, mycobacterial mutation rates are low. To unravel the effects of antibiotic pressure on genome stability, we determined the genetic variability, phenotypic tolerance, DNA repair system activation, and dNTP pool upon treatment with current antibiotics usingMycobacterium smegmatis.Whole-genome sequencing revealed no significant increase in mutation rates after prolonged exposure to first-line antibiotics. However, the phenotypic fluctuation assay indicated rapid adaptation to antibiotics mediated by non-genetic factors. The upregulation of DNA repair genes, measured using qPCR, suggests that genomic integrity may be maintained through the activation of specific DNA repair pathways. Our results, indicating that antibiotic exposure does not result inde novoadaptive mutagenesis under laboratory conditions, do not lend support to the model suggesting antibiotic resistance development through drug pressure-induced microevolution.
Spatial context non-uniformly modulates inter-laminar communication in the primary visual cortex
Authors: Xu Xize , Morton Mitchell P. , Hudson Nyomi V. , Nandy Anirvan S. , Jadi Monika P. 
Published: February 2024

ABSTRACTOur visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. Yet how the spatial organization of objects influences neural activity in this hierarchy remains poorly understood. We investigate how the inter-laminar interactions in the primary visual cortex (V1) are affected by visual stimuli in isolation or with flanking stimuli at various spatial configurations that are known to cause non-uniform degradation of perception. By employing dimensionality reduction approaches to simultaneous layer-specific population recordings, we establish that cortical layers interact through a structurally stable communication subspace. Spatial configuration of contextual stimuli differentially modulates inter-laminar communication efficacy, the balance between feedforward and feedback signaling, and contextual signaling in the superficial layers. Remarkably, these modulations mirror the spatially non-uniform aspects of perceptual degradation. Our results suggest a model of retinotopically non-uniform cortical connectivity in the output layers of V1 that influences communication in the sensory hierarchy.