bluuebunny/crossref_metadata_2025_split huggingface dataset:
Role of Paravertebral muscle myostatin upregulation in the development of idiopathic scoliosis
Authors: Li Jiong , Xiang Gang , He Sihan , Yang Guanteng , Guo Chaofeng , Tang Mingxing , Zhang Hongqi 
Published: May 2022

AbstractParavertebral muscle (PVM) abnormalities play important roles in the pathogenesis of idiopathic scoliosis (IS), and elevated oxidative stress could result in PVM injury in IS patients, but the underlying mechanism of oxidative stress generation is still unclear. Increased apoptosis, impaired myogenesis and elevated oxidative stress were found in primary skeletal muscle mesenchymal progenitor cells (hSM-MPCs), which are essential for the myogenesis process of vertebrate skeletal muscles, of IS patients. Through RNA-sequencing and further analysis, we identified significantly upregulated myostatin (MSTN) in IS hSM-MPCs. Overexpression of MSTN in hSM-MPCs from control patients increased the expression of NADPH oxidase 4, promoted reactive oxygen species production and apoptosis, and suppressed myogenesis. However, MSTN knockdown decreased the expression of NADPH oxidase 4, inhibited reactive oxygen species production and apoptosis, and enhanced myogenesis in IS hSM-MPCs. In addition, overexpression of MSTN in the PVMs of mice induced elevated oxidative stress and scoliosis without abnormal vertebral structure. Altogether, our study suggested that abnormal PVM changes and accumulated oxidative stress in IS patients may result from upregulation of MSTN, which could contribute to the development of IS.
Machine learning models aimed at identifying risk factors for reducing morbidity and mortality still need to consider confounding related to calendar time variations
Authors: Rieckmann Andreas , Nguyen Tri-Long , Dworzynski Piotr , Fisker Ane Bærent , Rod Naja Hulvej , Ekstrøm Claus Thorn 
Published: May 2022

AbstractMachine learning models applied to health data may help health professionals to prioritize resources by identifying risk factors that may reduce morbidity and mortality. However, many novel machine learning papers on this topic neither account for nor discuss biases due to calendar time variations. Often, efforts to account for calendar time (among other confounders) are necessary since patterns in health data – especially in low- and middle-income countries – may be influenced by calendar time variations such as temporal changes in risk factors and changes in the disease and mortality distributions over time (epidemiological transitions), seasonal changes in risk factors and disease and mortality distributions, as well as co-occurring artefacts in data due to changes in surveillance and diagnostics. Based on simulations, real-life data from Guinea-Bissau, and examples drawn from recent studies, we discuss how including calendar time variations in machine learning models is beneficial for generating more relevant and actionable results. In this brief report, we stress that explicitly handling temporal structures in machine learning models still remains to be considered (like in general epidemiological studies) to prevent resources from being misdirected to ineffective interventions.
Depressive and anxiety symptoms during the COVID-19 pandemic: A two-year follow-up
Authors: Bu Feifei , Steptoe Andrew , Fancourt Daisy 
Published: May 2022

AbstractBackgroundThere has been much research into the mental health impact of the COVID-19 pandemic and how it is related to time-invariant individual characteristics (e.g. age and gender). However, there is still a lack of research showing long-term trajectories of mental health across different stages of the pandemic. And little is known regarding the longitudinal association of time-varying contextual and individual factors (e.g. COVID-19 policy response and pandemic intensity) with mental health outcomes. This study aimed to provide a longitudinal profile of how depressive and anxiety symptoms changed by month between March 2020 and April 2022, and to examine their longitudinal associations with time-varying contextual and individual level factors.Methods and findingsDrawing data from a large panel study of over 58,000 adults living in England, we showed that mental health changes were largely in line with changes in COVID-19 policy response and pandemic intensity. Further, data were analysed using fixed-effects, with models fitted separately across three stages of the COVID-19 pandemic. We found that more stringent policy response was associated with increased mental health symptoms, in particular during lockdown periods. Higher COVID-19 deaths were also associated with poorer mental health, but this association weakened over time. Finally, there was also evidence for the longitudinal association of mental health with individual level factors, including confidence in government/healthcare/essentials, COVID-19 knowledge, COVID-19 stress, COVID-19 infection and social support.ConclusionsOur results provided empirical evidence on how changes in contextual and individual level factors were related to depressive and anxiety symptoms. While some factors clearly acted as consistent predictors of mental health during a pandemic, other factors were dependent on the specific situations occurring within society. This could provide important implications for policy making and for a better understanding of mental health of the general public during a national or global health crisis.
Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition
Authors: Liwicki Foteini Simistira , Gupta Vibha , Saini Rajkumar , De Kanjar , Abid Nosheen , Rakesh Sumit , Wellington Scott , Wilson Holly , Liwicki Marcus , Eriksson Johan 
Published: May 2022

ABSTRACTThe recognition of inner speech, which could give a ‘voice’ to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.
Sperm collection and computer-assisted sperm analysis in the teleost model Japanese medaka (Oryzias latipes)
Authors: Closs Lauren , Sayyari Amin , Fontaine Romain 
Published: May 2022

ABSTRACTJapanese medaka (Oryzias latipes) are a teleost fish and an emerging vertebrate model for ecotoxicology, developmental, genetics, and physiology research. Medaka are also used extensively to investigate vertebrate reproduction, which is an essential biological function as it allows a species to perpetuate. Sperm quality is an important indicator of male fertility and thus reproduction success. Techniques for extracting sperm and sperm analysis are well documented for many species, including for teleost fish. Collecting semen is relatively simple in larger fish but can be more complicated in small model fish as they produce less sperm and are more delicate. This article therefore describes two methods of sperm collection in the small model fish Japanese medaka: testes dissection and abdominal massage. We demonstrate that both approaches are viable for medaka and show that abdominal massage can be performed a repeated number of times as the fish quickly recover from the procedure. We also describe a protocol for computer-assisted sperm analysis in medaka to objectively assess several important indicators of medaka sperm quality (motility, progressivity, duration of motility, relative concentration). The use of these procedures combined with the other advantages of using this small teleost model will greatly improve the understanding of the environmental, physiological, and genetic factors influencing fertility in vertebrate males.SUMMARYThis article describes two quick and efficient methods to collect semen from the small model fish medaka (Oryzias latipes), as well as a protocol to reliably assess sperm quality using computer-assisted sperm analysis (CASA).
Host-directed therapy with 2-Deoxy-D-glucose inhibits human rhinoviruses, endemic coronaviruses, and SARS-CoV-2
Authors: Wali Laxmikant , Karbiener Michael , Chou Scharon , Kovtunyk Vitalii , Adonyi Adam , Gösler Irene , Contreras Ximena , Stoeva Delyana , Blaas Dieter , Stöckl Johannes , Kreil Thomas R. , Gualdoni Guido A. , Gorki Anna-Dorothea 
Published: May 2022

ABSTRACTRhinoviruses (RVs) and coronaviruses (CoVs) upregulate host cell metabolic pathways such as glycolysis to meet their bioenergetic demands for rapid multiplication. Using the glycolysis inhibitor 2-deoxy-D-glucose (2-DG), we assessed the dose-dependent inhibition of viral replication of minor- and major-receptor group RVs in epithelial cells. 2-DG disrupted RV infection cycle by inhibiting template negative-strand as well as genomic positive-strand RNA synthesis, resulting in less progeny virus and RV-mediated cell death. Assessment of 2-DG’s intracellular kinetics revealed that after a short-exposure to 2-DG, the active intermediate, 2-DG6P, is stored intracellularly for several hours. Finally, we confirmed the antiviral effect of 2-DG on pandemic SARS-CoV-2 and showed for the first time that 2-DG also reduces replication of endemic human coronaviruses (HCoVs). These results provide further evidence that 2-DG could be utilized as a broad-spectrum antiviral.HIGHLIGHTS2-DG inhibits replication of minor- and major-group rhinoviruses in epithelial cells including human nasal epithelial cell.2-DG disrupts rhinovirus infection cycle and reduces rhinovirus-mediated cell death in vitro.2-DG treatment attenuates viral load of endemic coronaviruses in vitro.
Meta-Analyzed Atopic Dermatitis Transcriptome (MAADT) is strongly correlated with disease activity, and consistent with therapeutic effects
Authors: Li Xingpeng , He Wen , Zhang Ying , Page Karen , Hyde Craig , Maciejewski Mateusz 
Published: May 2022

AbstractBackgroundAtopic Dermatitis (AD) is a persistent inflammatory disease of the skin to which a few novel treatment options have recently become available. Multiple published datasets, from RNA sequencing (RNA-seq) and microarray experiments performed on lesional (LS) and non-lesional (NL) skin biopsies collected from AD patients, provide a useful resource to better define an AD gene signature and evaluate therapeutic effects.MethodsWe evaluated 22 datasets using defined selection criteria and leave-one-out analysis and then carried out a meta-analysis (M-A) to combine 4 RNA-seq datasets and 5 microarray datasets to define a disease gene signature for AD skin tissue. We used this gene signature to evaluate its correlation to disease activity in published AD datasets, as well as the treatment effect of some of the existing and experimental therapies.ResultsWe report the AD gene signatures developed separately from the RNA-seq or the microarray datasets, as well as a gene signature from datasets combined across these two technologies; all 3 gene signatures showed a strong correlation to the disease activity score (SCORAD) – microarray: Pearson’s ρ = 0.651, p-value < 0.01, RNA-seq: ρ = 0.640, p < 0.01, combined: ρ = 0.649, p < 0.01. The gene signature improvement (GSI) of two existing effective therapies, Dupilumab and Cyclosporine, as well as that of other experimental treatments, is consistent with their reported cohort level efficacy from the associated clinical trials.ConclusionsThe M-A derived AD gene signature provides an evolution of an important resource to correlate gene expression to disease activity and will be helpful for evaluating potential treatment effects for novel therapies.
PROTON MOTIVE FORCE INHIBITORS ARE DETRIMENTAL TO METHICILLIN-RESISTANTSTAPHYLOCOCCUS AUREUSPERSISTER CELLS
Authors: Mohiuddin Sayed Golam , Ghosh Sreyashi , Kavousi Pouria , Orman Mehmet A. 
Published: May 2022

ABSTRACTMethicillin-resistantStaphylococcus aureus(MRSA) strains are resistant to conventional antibiotics. These pathogens can form persister cells, which are transiently tolerant to bactericidal antibiotics, making them extremely dangerous. Previous studies have shown the effectiveness of proton motive force (PMF) inhibitors at killing bacterial cells; however, whether these agents can launch a new treatment strategy to eliminate persister cells mandates further investigation. Here, using known PMF inhibitors and two different MRSA isolates, we showed that antipersister potency of PMF inhibitors seemed to correlate with their ability to disrupt PMF and permeabilize cell membranes. By screening a small chemical library to verify this correlation, we identified a subset of chemicals (including nordihydroguaiaretic acid, gossypol, trifluoperazine, and amitriptyline) that strongly disrupted PMF in MRSA cells by dissipating either the transmembrane electric potential (ΔΨ) or the proton gradient (ΔpH). These drugs robustly permeabilized cell membranes and reduced persister levels below the limit of detection. Overall, our study further highlights the importance of cellular PMF as a target for designing new antipersister therapeutics.
Dendritic growth and synaptic organization from activity-independent cues and local activity-dependent plasticity
Authors: Kirchner Jan H. , Euler Lucas , Fritz Ingo , Castro André Ferreira , Gjorgjieva Julijana 
Published: May 2022

AbstractDendritic branching and synaptic organization shape single-neuron and network computations. How they emerge simultaneously during brain development as neurons become integrated into functional networks is still not mechanistically understood. Here, we propose a mechanistic model in which dendrite growth and the organization of synapses arise from the interaction of activity-independent cues from potential synaptic partners and local activity-dependent synaptic plasticity. Consistent with experiments, three phases of dendritic growth – overshoot, pruning, and stabilization – emerge naturally in the model. The model generates stellate-like dendritic morphologies that capture several morphological features of biological neurons under normal and perturbed learning rules, reflecting biological variability. Model-generated dendrites have approximately optimal wiring length consistent with experimental measurements. In addition to establishing dendritic morphologies, activity-dependent plasticity rules organize synapses into spatial clusters according to the correlated activity they experience. We demonstrate that a trade-off between activity-dependent and -independent factors influences dendritic growth and synaptic location throughout development, suggesting that early developmental variability can affect mature morphology and synaptic function. Therefore, a single mechanistic model can capture dendritic growth and account for the synaptic organization of correlated inputs during development. Our work suggests concrete mechanistic components underlying the emergence of dendritic morphologies and synaptic formation and removal in function and dysfunction, and provides experimentally testable predictions for the role of individual components.
Targeted long-read sequencing facilitates phased diploid assembly and genotyping of the human T cell receptor alpha, delta and beta loci
Authors: Rodriguez Oscar L. , Silver Catherine A. , Shields Kaitlyn , Smith Melissa L. , Watson Corey T. 
Published: May 2022

AbstractT cell receptors (TCRs) recognize peptide fragments presented by the major histocompatibility complex (MHC) and are critical to T cell mediated immunity. Early studies demonstrated an enrichment of polymorphisms within TCR-encoding (TR) gene loci. However, more recent data indicate that variation in these loci are underexplored, limiting understanding of the impact of TR polymorphism on TCR function in disease, even though: (i) TCR repertoire signatures are heritable and (ii) associate with disease phenotypes. TR variant discovery and curation has been difficult using standard high-throughput methods. To address this, we expanded our published targeted long-read sequencing approach to generate highly accurate haplotype resolved assemblies of the human TR beta (TRB) and alpha/delta (TRA/D) loci, facilitating the detection and genotyping of single nucleotide polymorphisms (SNPs), insertion-deletions (indels), structural variants (SVs) and TR genes. We validate our approach using two mother-father-child trios and 5 unrelated donors representing multiple populations. Comparisons of long-read derived variants to short-read datasets revealed improved genotyping accuracy, and TR gene annotation led to the discovery of 79 previously undocumented V, D, and J alleles. This demonstrates the utility of this framework to resolve the TR loci, and ultimately our understanding of TCR function in disease.
User-friendly electron microscopy protocols for the visualization of biological macromolecular complexes in three dimensions: Visualization of planta clathrin-coated vesicles at ultrastructural resolution
Authors: Johnson Alexander , Kaufmann Walter A , Sommer Christoph , Costanzo Tommaso , Dahhan Dana A , Bednarek Sebastian Y , Friml Jiří 
Published: May 2022

AbstractBiological systems are the sum of their dynamic 3-dimensional (3D) parts. Therefore, it is critical to study biological structures in 3D and at high resolutions to gain insights into their physiological functions. Electron microscopy of metal replicas of unroofed cells and isolated organelles has been a key technique to visualize intracellular structures at nanometer resolution. However, many of these protocols require specialized equipment and personnel to complete them. Here we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs) - an essential trafficking organelle lacking detailed structural characterization due to their low preservation in classical electron microscopy techniques. First, we establish a protocol to visualize CCVs in unroofed cells using scanning-transmission electron microscopy (STEM) tomography, providing sufficient resolution to define the clathrin coat arrangements. Critically, the samples are prepared directly on electron microscopy grids, removing the requirement to use extremely corrosive acids, thereby enabling the use of this protocol in any electron microscopy lab. Secondly, we demonstrate this standardized sample preparation allows the direct comparison of isolated CCV samples with those visualized in cells. Finally, to facilitate the high-throughput and robust screening of metal replicated samples, we provide a deep learning analysis workflow to screen the ‘pseudo 3D’ morphology of CCVs imaged with 2D modalities. Overall, we present accessible ways to examine the 3D structure of biological samples and provide novel insights into the structure of plant CCVs.
PKC-alpha promotes phosphorylation of KRAS suppressing its oncogenic properties
Authors: Joglekar Tejashree , Ku Albert , Schollaert Ethan , Gong Yinan , Stewart-Ornstein Jacob , Urisman Anatoly , Wang Man-Tzu 
Published: May 2022

AbstractOncogenic KRAS-driven cancers have long been considered as “undruggable” due to limited therapeutic options. While the recent success of KRAS-G12C inhibitors argues against the “undruggability” of KRAS, this treatment only benefits a small proportion of patients with KRAS mutant cancers, leaving an urgent need for modalities to target other KRAS mutants. KRAS-calmodulin (CaM) signaling axis reportedly regulates the oncogenic properties of KRAS through its C-terminal hypervariable region. Phosphorylation of KRAS by activated protein kinase C (PKC) uncouples KRAS-CaM, resulting in growth inhibition effective against the entire spectrum of KRAS hotspot mutations. However, broadly activating PKC could mediate tumor promoting signaling nodes and cause systemic toxicity, undermining its applicability as an anti-KRAS therapy. Here, we found that prostratin induces KRAS phosphorylation, resulting in an elevated level of active CaM in the cytosol of KRAS mutant cells, and consequentially suppresses their malignancies. A whole-genome wide CRISPR/Cas9 knockout screening, further confirmed by biochemical analysis, revealed that prostratin acts through activating PKCα. Functional studies confirmed PKCα as the sole kinase to phosphorylate KRAS and, therefore, a KRAS suppressor. Activation of PKCα induces senescence in KRAS mutant tumor cells through PTPN14, accompanied by a secretory phenotype contributing to the growth inhibition, and parallelly mediates a nuclear translocation of a CaM-dependent transcription activator, CAMTA-1, which can be a biomarker to indicate the activity of PKCα-KRAS-CaM axis. Our findings reveal a previously understudied regulation of KRAS-CaM axis by PKCα, which can be an actionable target for developing anti-KRAS therapeutics.One Sentence SummaryThis study deciphers a PKCα-led tumor suppressive effect specific to the “undruggable” KRAS-mutant tumor cells through the phosphorylation of KRAS and a consequently altered KRAS-CaM signaling axis.
Small Molecule Activators of B56-PP2A Restore 4E-BP Expression and Function to Suppress Cap-dependent Translation in Cancer Cells
Authors: Lum Michelle A. , Jonas-Breckenridge Kayla A. , Black Adrian R. , Woods Nicholas T. , O’Connor Caitlin , Avelar Rita A. , DiFeo Analisa , Narla Goutham , Black Jennifer D. 
Published: May 2022

ABSTRACTDysregulation of cap-dependent translation is a hallmark of cancer, with key roles in supporting the transformed phenotype. The eIF4E binding proteins (4E-BP1, 2, 3) are major negative regulators of cap-dependent translation that are inactivated in tumors through inhibitory phosphorylation by oncogenic kinases (e.g., mTOR) or by downregulation. Previous studies from our group and others have linked tumor suppressive PP2A family serine/threonine phosphatases to activation of 4E-BP1. Here, we leveraged novel small molecule activators of PP2A (SMAPs) (e.g., DT-061, DT-1154) that are being developed as antitumor agents to (a) explore the role of a subset of B56-PP2As in regulation of 4E-BP activity, and (b) to evaluate the potential of B56-PP2A reactivation for restoring translation control in tumor cells. We show that SMAPs promote PP2A-dependent hypophosphorylation of 4E-BP1/4EBP2 in the presence of active upstream inhibitory kinases (mTOR, ERK, AKT), supporting a role for B56-PP2As as 4E-BP phosphatases. Unexpectedly, DT-061 also led to robust PP2A-dependent upregulation of 4E-BP1, but not 4E-BP2 or 4E-BP3. Cap-binding assays and eIF4E immunoprecipitation showed that SMAP/B56-PP2A blocks the formation of the eIF4F translation initiation complex. Bicistronic reporter assays that directly measure cap-dependent translation activity confirmed the translational consequences of these effects. siRNA knockdown pointed to B56α-PP2A as a mediator of SMAP effects on 4E-BPs, although B56β- and/or B56ε-PP2A may also play a role. 4E-BP1 upregulation involved ATF4-dependent transcription of the 4E-BP1 gene (EIF4EBP1) and the effect was partially dependent on TFE3/TFEB transcription factors. Thus, B56-PP2A orchestrates a translation repressive program involving transcriptional induction and hypophosphorylation of 4E-BP1, highlighting the potential of PP2A-based therapeutic strategies for restoration of translation control in cancer cells.