bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tomato cystatin SlCYS8 as a trigger of drought tolerance and tuber yield in potato
Authors: Dorval Maude , Langlois Andréane , Tremblay Jonathan , Veillet Gabrielle , Chiasson Marc-Antoine , Goulet Marie-Claire , Gumiere Thiago , Pepin Steeve , Goulet Charles , Michaud Dominique 
Published: September 2024

AbstractCurrent climate change scenarios predict an increased incidence of drought episodes likely to affect potato crops worldwide. Potato exhibits a low-density, shallow root system that makes it particularly vulnerable to water shortage and any successful attempt to implement drought tolerance in cultivated potato varieties is potentially relevant from an agronomic standpoint. In this study, we assessed the potential of tomato cystatin SlCYS8 to promote drought tolerance in SlCYS8-expressing potato lines by induction of stress-related pleiotropy. Up to now, protease inhibitors of the cystatin protein superfamily have been mostly considered as biotechnological tools to engineer pest or pathogen resistance in crops, but several recent studies have also revealed a possible link between abiotic stress tolerance and these regulatory proteins in leaf tissue. SlCYS8-expressing plantlets grown on culture medium containing the drought mimic polyethylene glycol (PEG) exhibited an elevated root-to-shoot ratio, an indicator of drought tolerance in potato. A similar conclusion could be drawn with greenhouse-grown acclimated plants, confirming a relative root growth-promoting effect for the recombinant inhibitor upon water deficit. SlCYS8-potato lines also showed a high tuber yield compared to the control line under both limiting and non-limiting water regimes, suggesting an improved efficiency of the primary metabolism and the avoidance of a growth– stress response tradeoff in the modified lines. Accordingly, SlCYS8 expression was associated with a stress response-oriented proteome in leaves likely explained by pleiotropic effects of the recombinant cystatin driving the constitutive expression of stress-related proteins and the upregulation of primary metabolism-associated proteins. Overall, these data suggest the potential of cystatins as molecular triggers of tuber biomass production and drought resilience in potato. Complementary studies will be warranted to assess tuber yield of the SlCYS8-lines under different water regimes in field conditions.
Deep Learning Predicts Non-Normal Peptide FAIMS Mobility Distributions Directly from Sequence
Authors: McKetney Justin , Miller Ian J. , Hutton Alexandre , Sinitcyn Pavel , Coon Joshua J. , Meyer Jesse G. 
Published: September 2024

ABSTRACTPeptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions’ FAIMS mobility using a multi-label multi-output classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced higher F2 score than either model alone. Finally, we explore cases where the models make mistakes and demonstrate predictive performance of F2=0.66 (AUROC=0.928) on a new test dataset of nearly 40,000 differentE. colipeptide ions. The deep learning model is easily accessible viahttps://faims.xods.org.
Impact of anatomic variability and other vascular factors on lamina cribrosa hypoxia
Authors: Lu Yuankai , Hua Yi , Lee Po-Yi , Theophanous Andrew , Tahir Shaharoz , Tian Qi , Sigal Ian A. 
Published: September 2024

AbstractInsufficient oxygenation in the lamina cribrosa (LC) may contribute to axonal damage and glaucomatous vision loss. To understand the range of susceptibilities to glaucoma, we aimed to identify key factors influencing LC oxygenation and examine if these factors vary with anatomical differences between eyes. We reconstructed 3D, eye-specific LC vessel networks from histological sections of four healthy monkey eyes. For each network, we generated 125 models varying vessel radius, oxygen consumption rate, and arteriole perfusion pressure. Using hemodynamic and oxygen supply modeling, we predicted blood flow distribution and tissue oxygenation in the LC. ANOVA assessed the significance of each parameter. Our results showed that vessel radius had the greatest influence on LC oxygenation, followed by anatomical variations. Arteriole perfusion pressure and oxygen consumption rate were the third and fourth most influential factors, respectively. The LC regions are well perfused under baseline conditions. These findings highlight the importance of vessel radius and anatomical variation in LC oxygenation, providing insights into LC physiology and pathology. Pathologies affecting vessel radius may increase the risk of LC hypoxia, and anatomical variations could influence susceptibility. Conversely, increased oxygen consumption rates had minimal effects, suggesting that higher metabolic demands, such as those needed to maintain intracellular transport despite elevated intraocular pressure, have limited impact on LC oxygenation.
An improved nanobody-based approach to capture and visualize specific interaction networks of binary protein complexes in living cells
Authors: Sleiman Nawal Hajj , Carnesecchi Julie , Jia Yunlong , Delolme Fréderic , Gilquin Laurent , Gouet Patrice , Merabet Samir 
Published: September 2024

AbstractProtein interaction networks (or interactomes) are formed progressively, each interaction influencing the next one. Accordingly, a same protein will establish different interactomes depending on its first associated cofactor, thereby diversifying its function in the cell. In contrast to their central role, few methods exist to capture interactomes of dimeric protein complexes. Here, we tackle this issue by introducing an innovative method based on bimolecular fluorescence complementation and the specific binding of a nanobody fused to a proximity-dependent biotinylating enzyme. This method was applied to visualize and capture specific interactions of the cytoplasmic TAZ/14-3-3e and nuclear TAZ/TEAD2 complexes, which are major downstream effectors of the Hippo signaling pathway. Among other interactions, we identified SERPINB4 as a novel regulator of TAZ and 14-3-3e proliferative activity in mesenchymal stromal cells. Molecular dissections in living cells revealed the central role of a unique residue of TAZ for recruiting SERPINB4 specifically in the presence of 14-3-3e. Overall, our work demonstrates the importance of considering binary protein complexes for deciphering interactomes and establishes a novel sensitive method in this perspective.
Correlated and Anticorrelated Binocular Disparity Modulate GABA+ and Glutamate/glutamine Concentrations in the Human Visual Cortex
Authors: Matuszewski Jacek , Alvarez Ivan , Clarke William T. , Parker Andrew J. , Bridge Holly , Ip I. Betina 
Published: September 2024

AbstractBinocular disparity is used for perception and action in three dimensions. Neurons in the primary visual cortex respond to binocular disparity in random dot patterns, even when the contrast is inverted between eyes (false depth cue). In contrast, neurons in the ventral stream largely cease to respond to false depth cues. This study evaluated whether GABAergic inhibition is involved in suppressing false depth cues in the human ventral visual cortex.We compared GABAergic inhibition (GABA+) and glutamatergic excitation (Glx) during the viewing of correlated and anticorrelated binocular disparity in 18 participants using single voxel proton magnetic-resonance spectroscopy (MRS). Measurements were taken from the early visual cortex (EVC) and the lateral occipital cortex (LO). Three visual conditions were presented per voxel location: correlated binocular disparity; anticorrelated binocular disparity; or a blank grey screen with a fixation cross. To identify differences in neurochemistry, GABA+ or Glx levels were compared across viewing conditions.In EVC, correlated disparity increased Glx over anticorrelated and rest conditions, also mirrored in the Glx/GABA+ ratio. In LO, anticorrelated disparity decreased GABA+ and increased Glx. Joint effects on GABA+ and Glx were summarised by the Glx/GABA+ ratio, which showed increased excitatory over inhibitory drive to anticorrelated disparity in LO. Glx during viewing of anticorrelation in LO was predictive of its object-selective BOLD-activity.We provide evidence that early and ventral visual cortices change GABA+ and Glx concentrations during presentation of correlated and anticorrelated disparity, suggesting a contribution of cortical excitation and inhibition in disparity selectivity.Significance StatementThe visual system must correctly match elements from the left and right eye for proper reconstruction of binocular depth. At the earliest part of binocular processing, false matches can activate depth detectors, however, the activation to false matches is absent in the ventral visual stream. We tested whether GABAergic inhibition contributes to the suppression of false matches in the ventral stream by measuring GABAergic inhibition and glutamatergic excitation in the human visual cortex during the presentation of correct and false matches. Correct matches increased excitation in response in the early visual cortex, and false matches increased excitation and decreased in the ventral visual cortex. These results suggest a role for excitation and inhibition in distinguishing depth cues for stereoscopic vision.
Identification of Growth Differentiation Factor-15 as An Early Predictive Biomarker for Metabolic Dysfunction-Associated Steatohepatitis: A Nested Case-control Study of UK Biobank Proteomic Data
Authors: Wang Hao , Xu Xiaoqian , Sun Yameng , You Hong , Jia Jidong , He You-Wen , Kong Yuanyuan 
Published: September 2024

AbstractBackground & AimsThe lack of non-invasive biomarkers for the early prediction of patients with metabolic dysfunction-associated steatohepatitis (MASH) is a major challenge for timely intervention. This study aims to determine the predictive capability for MASH long before its diagnosis by using six previously identified diagnostic biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD) with proteomic data from the UK Biobank.MethodsA nested case-control study comprising of a MASH group and three age- and sex-matched controls groups (metabolic dysfunction-associated steatosis, viral hepatitis, and normal liver controls) were conducted. Olink proteomics, anthropometric and biochemical data at baseline levels were obtained from the UK Biobank. The baseline levels of CDCP1, FABP4, FGF21, GDF15, IL-6 and THBS2 were analyzed prospectively to determine their predictive accuracy for subsequent diagnosis with a mean lag time of over 10 years.ResultsAt baseline, GDF15 demonstrated the best performance for predicting MASH occurrence at 5 and 10 years later, with an AUC of 0.90 at 5 years and 0.86 at 10 years. A predictive model based on four biomarkers (GDF15, FGF21, IL-6, and THBS2) showed AUCs of 0.88 at both 5 and 10 years. Furthermore, a protein-clinical model that included these four circulating protein biomarkers along with three clinical factors (BMI, ALT and TC) yielded AUCs of 0.92 at 5 years and 0.89 at 10 years.ConclusionGDF15 at baseline levels outperformed other individual circulating protein biomarkers for the early prediction of MASH. Our data suggest that GDF15 and the GDF15-based model may be used as easy-to-implement tools to identify patients with high risk of developing MASH at a mean lag time of over 10 years.
A Comprehensive Proteogenomic and Spatial Analysis of Innate and Acquired Resistance of Metastatic Melanoma to Immune Checkpoint Blockade Therapies
Authors: Wei Shiyou , Du Kuang , Lan Hongbin , Yang Zhenyu , Deng Yulan , Wei Zhi , Frederick Dennie T. , Lee Jinho , Labrie Marilyne , Tian Tian , Moll Tabea , Chen Yeqing , Sullivan Ryan J. , Mills Gordon , Boland Genevieve M. , Flaherty Keith T. , Liu Lunxu , Herlyn Meenhard , Zhang Gao 
Published: September 2024

AbstractWhile a subset of patients with metastatic melanoma achieves durable responses to immune checkpoint blockade (ICB) therapies, the majority ultimately exhibit either innate or acquired resistance to these treatments. However, the molecular mechanisms underlying resistance to ICB therapies remain elusive and are warranted to elucidate. Here, we comprehensively investigated the tumor and tumor immune microenvironment (TIME) of paired pre- and post-treatment tumor specimens from metastatic melanoma patients who were primary or secondary resistance to anti-CTLA-4 and/or anti-PD-1/PD-L1 therapies. Differentially expressed gene (DEG) analysis and single-sample gene set enrichment analysis (ssGSEA) with transcriptomic data identified cell cycle and c-MYC signaling as pathway-based resistance signatures. And weighted gene co-expression network analysis (WGCNA) revealed the activation of a cross-resistance meta-program involving key signaling pathways related to tumor progression in ICB resistant melanoma. Moreover, spatially-resolved, image-based immune monitoring analysis by using NanoString’s digital spatial profiling (DSP) and Cyclic Immunofluorescence (CyCIF) showed infiltration of suppressive immune cells in the tumor microenvironment of melanoma with resistance to ICB therapies. Our study reveals the molecular mechanisms underlying resistance to ICB therapies in patients with metastatic melanoma by conducting such integrated analyses of multi-dimensional data, and provides rationale for salvage therapies that will potentially overcome resistance to ICB therapies.Statement of translational relevanceThis study paves the way for the creation of innovative therapeutic strategies, aimed at subverting resistance to immune checkpoint blockade (ICB) therapies in metastatic melanoma patients. By unraveling the specific molecular mechanisms underlying resistance, scientists can design effective alternative treatments that target pathways such as pathways associated with cell cycle dysregulation and c-MYC signaling. Furthermore, through the application of advanced immune monitoring techniques such as NanoString Digital Spatial Profiling (DSP) and Cyclic Immunofluorescence (CyCIF), this study has significantly enriched our understanding of the tumor microenvironment. This enhanced characterization facilitates the discovery of potential biomarkers that may forecast a patient’s response to ICB treatment. Ultimately, these advancements could potentially refine patient outcomes and foster the development of more personalized cancer treatments in the future.
Multi-center improvement in screening for dystonia in young people with cerebral palsy
Authors: Aravamuthan Bhooma R. , Lott Emma , Pehlivan Esra , Chintalapati Keerthana , Grenard Deborah , Roge Desiree , Gelineau-Morel Rose , Kyle Dante , Becu Christie , Kruer Michael , Katus Linn , Gross Paul , Bailes Amy ,   Cerebral Palsy Research Network
Published: September 2024

AbstractBackground and ObjectivesDystonia is a common, debilitating, and often treatment refractory motor symptom of cerebral palsy (CP), affecting 70-80% of this population based on research assessments. However, routine clinical evaluation for dystonia in CP has failed to match these expected numbers. Addressing this diagnostic gap is a medical imperative because the presence of dystonia rules in or out certain treatments for motor symptoms in CP. Therefore, our objective was to optimize rates of clinical dystonia screening to improve rates of clinical dystonia diagnosis.MethodsUsing the quality improvement (QI) infrastructure of the Cerebral Palsy Research Network (CPRN), we developed and implemented interventions to increase the documentation percentage of five features of dystonia in young people with CP, aged 3-21 years old. This QI initiative was implemented by seven physiatry and pediatric movement disorders physicians at four tertiary-care pediatric hospitals between 10/10/21 and 7/1/23. We collected visit data cross-sectionally across all participating sites every 2 weeks and tracked our progress using control charts.ResultsWe assessed 847 unique visits, mostly for established patients (719/847, 85%) who were 9.2 years old on average (95% CI 8.8-9.5). By 4/10/22, the mean percentage of dystonia screening elements documented across all sites rose from 39% to 90% and the mean percentage of visits explicitly documenting the presence or absence of dystonia rose from 65% to 94%. By 10/23/22, the percentage of visits diagnosing dystonia rose from 57% to 74%. These increases were all sustained through the end of the study period in 7/1/23.DiscussionUsing a rigorous QI-driven process across four member sites of a North American learning health network (CPRN), we demonstrated that we could increase screening for dystonia and that this was associated with increased clinical dystonia diagnosis, matching expected research-based rates. We propose that similar screening should take place across all sites caring for people with CP.
A Systematic Review of Air Pollution Exposure and Brain Structure and Function during Development
Authors: Morrel Jessica , Dong Michelle , Rosario Michael A. , Cotter Devyn L. , Bottenhorn Katherine L. , Herting Megan M. 
Published: September 2024

AbstractObjectivesAir pollutants are known neurotoxicants. In this updated systematic review, we evaluate new evidence since our 2019 systematic review on the effect of outdoor air pollution exposure on childhood and adolescent brain structure and function as measured by magnetic resonance imaging (MRI).MethodsUsing PubMed and Web of Science, we conducted an updated literature search and systematic review of articles published through March 2024, using key terms for air pollution and functional and/or structural MRI. Two raters independently screened all articles using Covidence and implemented the risk of bias instrument for systematic reviews informing the World Health Organization Global Air Quality Guidelines.ResultsWe identified 222 relevant papers, and 14 new studies met our inclusion criteria. Including six studies from our 2019 review, the 20 publications to date include study populations from the United States, Netherlands, Spain, and United Kingdom. Studies investigated exposure periods spanning pregnancy through early adolescence, and estimated air pollutant exposure levels via personal monitoring, geospatial residential estimates, or school courtyard monitors. Brain MRI occurred when children were on average 6-14.7 years old; however, one study assessed newborns. Several MRI modalities were leveraged, including structural morphology, diffusion tensor imaging, restriction spectrum imaging, arterial spin labeling, magnetic resonance spectroscopy, as well as resting-state and task-based functional MRI. Air pollutants were associated with widespread brain differences, although the magnitude and direction of findings are largely inconsistent, making it difficult to draw strong conclusions.ConclusionPrenatal and childhood exposure to outdoor air pollution is associated with structural and functional brain variations. Compared to our initial 2019 review, publications doubled—an increase that testifies to the importance of this public health issue. Further research is needed to clarify the effects of developmental timing, along with the downstream implications of outdoor air pollution exposure on children’s cognitive and mental health.HighlightsAir pollutants have emerged as ubiquitous neurotoxicantsThis field of study has grown substantially in the last 5 yearsExclusion of highly exposed populations poses a barrier to result generalizabilityLongitudinal investigations are needed to understand developmental impacts of APAdditional research should aim to clarify which children may be most vulnerableAbstract FigureGraphical Abstract
Predicting cancer patient mortality within 30 days of radiotherapy consultation to inform palliative radiotherapy fractionation decisions
Authors: Kiser Kendall J. , Vaidyanathan Ashish , Schuelke Matthew J. , Denzer Joshua , Landreth Trudy , Abraham Christopher D. , Wilcox Adam B. 
Published: September 2024

AbstractBackgroundRadiation and medical oncologists evaluate patients’ risk of imminent mortality with scales like Karnofsky Performance Status (KPS) and predicate treatment decisions on these evaluations. However, we hypothesized that statistical models derived from structured electronic health record (EHR) data could predict patient deaths within 30 days of radiotherapy consultation better than models developed only with patient age and physician-reported KPS.MethodsClinical data from patients who consulted in a radiotherapy department from June 2018 – February 2024 were abstracted from EHR databases, including patient demographics, laboratory results, medications, comorbidities, KPS, cancer stages, oncologic treatment histories, oncologist notes, radiologist reports, and pathologist narratives. A subset of structured features known or believed to be associated with mortality were curated and used to train and test logistic regression, random forest, and gradient-boosted decision classifiers.ResultsOf 38,262 patients, 951 (2.5%) died within 30 days of radiotherapy consultation. From 34.5 gigabytes of tabular data, 2,977 clinical features were chosen or derived by a radiation oncologist, then reduced to 1,000 features using ANOVA F values. Using an event probability classification threshold of 0.2, optimized logistic regression, random forest, and gradient-boosted decision classifiers tested with high accuracy (0.97, 0.98, and 0.98, respectively) and F1 scores (0.50, 0.54, and 0.52). The areas under receiver operating and precision-recall curves for the random forest model were respectively 0.94 and 0.55, which outperformed a model trained only with patient age and KPS (0.61 and 0.06). Models prominently weighed features that were rationally associated with mortality.ConclusionStatistical models developed from a physician-curated feature space of structured EHR data predicted patient deaths within 30 days of radiotherapy consultation better than a model developed only with a patient’s age and physician-assessed KPS. With clinically explicable feature weights, these models could influence treatment decisions such as the length of palliative radiotherapy courses.