bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nociceptor Neurons Control Pollution-Mediated Neutrophilic Asthma
Authors: Wang Jo-Chiao , Crosson Theo , Nikpoor Amin Reza , Gupta Surbhi , Rafei Moutih , Talbot Sebastien 
Published: August 2024

ABSTRACTThe immune and sensory nervous systems, having evolved together, use a shared language of receptors and transmitters to maintain homeostasis by responding to external and internal disruptions. Although beneficial in many cases, neurons can exacerbate inflammation during allergic reactions, such as asthma. Our research modeled asthma aggravated by pollution, exposing mice to ambient PM2.5particles and ovalbumin. This exposure significantly increased bronchoalveolar lavage fluid neutrophils and γδ T cells compared to exposure to ovalbumin alone. We normalized airway inflammation and lung neutrophil levels by silencing nociceptor neurons at inflammation’s peak using intranasal QX-314 or ablating TRPV1-expressing neurons. Additionally, we observed heightened sensitivity in chemical-sensing TRPA1 channels in neurons from pollution-exacerbated asthmatic mice. Elevated levels of artemin were detected in the bronchoalveolar lavage fluid from pollution-exposed mice, with artemin levels normalizing in mice with ablated nociceptor neurons. Upon exposure PM2.5particles, alveolar macrophages expressing pollution-sensing aryl hydrocarbon receptors, were identified as the source of artemin. This molecule enhanced TRPA1 responsiveness and increased neutrophil influx, providing a novel mechanism by which lung-innervating neurons respond to air pollution and suggesting a potential therapeutic target for controlling neutrophilic airway inflammation in asthma, a clinically intractable condition.
Remodelling of the glycome of B-cell precursor acute lymphoblastic leukemia cells developing drug-tolerance
Authors: Oliveira Tiago , Zhang Mingfeng , Chen Chun-Wei , Packer Nicolle H. , von Itzstein Mark , Heisterkamp Nora , Kolarich Daniel 
Published: August 2024

AbstractReduced responsiveness of precursor B-acute lymphoblastic leukemia (BCP-ALL) to chemotherapy can be first detected in the form of minimal residual disease leukemia cells that persist after 28 days of initial treatment. The ability of these cells to resist chemotherapy is partly due to the microenvironment of the bone marrow, which promotes leukemia cell growth and provides protection, particularly under these conditions of stress. It is unknown if and how the glycocalyx of such cells is remodelled during the development of tolerance to drug treatment, even though glycosylation is the most abundant cell surface post-translational modification present on the plasma membrane. To investigate this, we performedomicsanalysis of BCP-ALL cells that survived a 30-day vincristine chemotherapy treatment while in co-culture with bone marrow stromal cells. Proteomics showed decreased levels of some metabolic enzymes. Overall glycocalyx changes included a shift from Core-2 to less complex Core-1 O-glycans, and reduced overall sialylation, with a shift from α2-6 to α2-3 linked Neu5Ac. Interestingly, there was a clear increase in bisecting complex N-glycans with a concomitant increased mRNA expression ofMGAT3, the only enzyme known to form bisecting N-glycans. These small but reproducible quantitative differences suggest that individual glycoproteins become differentially glycosylated. Glycoproteomics confirmed glycosite-specific modulation of cell surface and lysosomal proteins in drug-tolerant BCP-ALL cells, including HLA-DRA, CD38, LAMP1 and PPT1. We conclude that drug-tolerant persister leukemia cells that grow under continuous chemotherapy stress have characteristic glycotraits that correlate with and perhaps contribute to their ability to survive and could be tested as neoantigens in drug-resistant leukemia.
On the same wavelength: The relationship between neural synchrony and cognitive ability during movie watching in late childhood and early adolescence
Authors: Lyons KM , Ho W. , Stevenson RA , Owen AM , Stojanoski B 
Published: August 2024

AbstractDevelopment during late childhood and early adolescence is associated with vast improvements to thinking and reasoning abilities. Coinciding with developing cognitive abilities, the environments children navigate become more complex, with an expanding social circle giving rise to richer and more elaborate experiences. If cognitive development is associated with more complex experiences, do individual differences in cognitive abilities influence how children and adolescents experience their world? To address this question, we investigated the relationships between intersubject correlation (ISC) during movie watching and cognitive scores in children and adolescents (aged 7 -15, N = 309). Data for the current study was obtained from the Healthy Brain Biobank. As part of the Healthy Brain Biobank protocol, participants watched a 10-minute clip ofDespicable Mewhile in the fMRI scanner. They also completed the Weschler Intelligent Scale for Children (WISC). We compared the degree to which participants brain activity synchronized to other participants brain activity during the movie, as measured by ISC, and investigated if scores on the WISC predicted this synchrony. We found adolescents (ages 11-15) with higher cognitive scores showed greater ISC during movie watching in brain networks associated with social processing and executive functions compared to those with below average cognitive scores. These networks included the frontoparietal and default mode networks. This pattern was not evident in children (ages 7-11) who differed in their cognitive scores. These results suggest that adolescents with more mature cognitive abilities may have more similar experiences of naturalistic stimuli. Our results also suggest that children may be less reliant on the frontoparietal network when processing movies, compared to previous findings in adults.
Anxiety and risk-taking behavior maps onto opioid and alcohol polysubstance consumption patterns in male and female mice
Authors: Patarino Makenzie , Wang Ziheng Christina , Wong Katrina , Lee Suhjung Janet , Skillen Emma , Nag Richa , Baskin Britahny , Schindler Abigail G. 
Published: August 2024

AbstractPolysubstance use is prevalent in the population but remains understudied in preclinical models. Alcohol and opioid polysubstance use is associated with negative outcomes, worse treatment prognosis, and higher overdose risk; but underlying mechanisms are still being uncovered. Examining factors that motivate use of one substance over another in different contexts in preclinical models will better our understanding of polysubstance use and improve translational value. Here we assessed baseline anxiety-like and locomotive behavior and then measured voluntary consumption of multiple doses of alcohol and fentanyl in group housed male and female mice using our novel Socially Integrated Polysubstance (SIP) system. Fifty-six male (n=32) and female (n=24) adult mice were housed in groups of 4 for one week with continuous access to food, water, two doses of ethanol (5% and 10%) and two doses of fentanyl (5 ug/ml and 20 ug/ml). Our analyses revealed sex differences across multiple domains – female mice consumed more liquid in the dark cycle, had higher activity, a higher preference for both ethanol and fentanyl over water, and their fentanyl preference increased over the seven days. We then used machine-learning techniques to reveal underlying relationships between baseline behavioral phenotypes and subsequent polysubstance consumption patterns, where anxiety- and risk-taking-like behavioral phenotypes mapped onto discrete patterns of polysubstance use, preference, and escalation. By simulating more translationally relevant substance use and improving our understanding of the motivations for different patterns of consumption, this study contributes to the developing preclinical literature on polysubstance use with the goal of facilitating better treatment outcomes and novel therapeutic strategies.
Climate, population size, and dispersal influences mutational load across the landscape inVitis arizonica
Authors: Fiscus Christopher J. , Aguirre-Liguori Jonás A. , Gaut Garren R. J. , Gaut Brandon S. 
Published: August 2024

ABSTRACTThe interplay between genetics and the environment determines a population’s ability to survive. Plants, being immobile, are particularly vulnerable to environmental shifts and must adapt to their local environment or face extinction. While considerable efforts have been devoted to identifying adaptive genetic variation and to model its relationship with climate, the role of deleterious variation has been largely overlooked. To address this gap, we studied the landscape genomics ofVitis arizonica, a grape species endemic to the American Southwest and a crop wild relative of the domesticated grapevine. We estimated mutational load, a component of genetic load, in 162 individuals sampled across the present species range and built temporal species distribution models (SDMs) to project the historical, present, and future distributions ofV. arizonicato infer species’ range dynamics. Mutational load was highest for individuals at an inferred leading edge of range expansion. Using random forest regression (RF) models, we examined the relationship between mutational load and climatic variation. The RF models, which we transformed by a weighting method to account for correlated predictors, identified climatic variables, historical dispersion distances, and heterozygosity as high-ranking features. Our findings show that mutational load can be predicted and identifies features that contribute to load. These results provide a foundation for integrating mutational load into broader efforts to understand species adaptation and maladaptation in the face of climate change.SIGNIFICANCE STATEMENTThe accumulation of deleterious genetic variation (i.e., genetic load) in a genome can reduce organismal fitness and hinder adaptation to local conditions. While the genetic mechanisms contributing to genetic load have been well-studied, its interaction with environmental variation is less understood. Here we explored the relationship between climatic variation, species range, and genetic load inVitis arizonica, a wild grape species native to the American Southwest. We identified associations between climatic variation and genetic load and built a machine learning model to predict genetic load under future climate scenarios. Our findings suggest that the species range will expand and that genetic load will slightly increase at the population level by the end of the century. This work enhances our understanding of the environmental factors influencing genetic load.
A multi-omics approach to identify deleterious mutations in plants
Authors: Baruch Omer , Levy Avraham A. , Mafessoni Fabrizio 
Published: August 2024

AbstractCrops lose genetic variation due to strong founder effects during domestication, accumulating and potentially exposing recessive deleterious alleles. Therefore, identifying those deleterious variants in domesticated varieties and their functional orthologs in wild relatives is key for plant breeding, food security and in rescuing the biodiversity of cultivated crops. We explored a machine learning strategy to estimate the impact of new and existing mutations in plant genomes, leveraging multi-omics data, encompassing genomic, epigenomic and transcriptomic information. Specifically, we applied a support-vector-machine framework, previously applied to animal datasets, to published omics data of two important crops of the genus Solanum - tomato and potato - and for the model plantArabidopsis thaliana. We show that our approach provides biologically plausible inferences on the role of mutations occurring in different genomic regions and predictions that correlate with natural genetic variation for the three species, supporting the validity of our estimates. Finally, we show that our estimates outperform existing methods relying exclusively on phylogenetic conservation and not leveraging the availability of omics data for crop species. This approach provides a simple score for researchers to prioritize variants for gene editing and breeding purposes.
Pancreatic endocrine cells are transduced by adeno-associated virus serotypes 2 and 9 but not 6
Authors: Ahuja Vishal , Jeyabalan Sanjeev , Tzanakakis Emmanuel S. 
Published: August 2024

ABSTRACTAdeno-associated viruses (AAVs) have emerged as powerful tools for delivery of genes to a variety of cell types including pancreatic endocrine cells. Currently, AAV serotype 8 (AAV8) is the main AAV vector employed for infecting pancreatic cells for transgene transfer. We aimed to address whether alternative serotypes (AAV2, AAV6, and AAV9) commonly used for gene transfer can be effective in transducing pancreatic cells efficiently. We also screened the additives heparin and neuraminidase to further understand the interaction between the individual AAV types included in this work and the cells for optimal infection. Murine pancreatic β-cells and α-cells as well as fibroblasts were infected with AAV serotypes 2, 6, and 9 carrying the transgene for enhanced green fluorescent protein (eGFP). AAV2 outperformed AAV9 in transducing pancreatic cells, while AAV6 induced cytotoxicity. Both AAV2 and AAV9 displayed slightly higher tropism for α-cells than for β-cells. Compared to the pancreatic cells, the fraction of GFP-expressing cells at various multiplicities of infection was consistently lower for fibroblasts. Incubation of AAV2 with heparin prior to transduction failed to induce any GFP expression in β-cells, indicating that the primary site used for initial interaction with pancreatic cells are heparan sulfate proteoglycans. Treatment of β-cells with neuraminidase prior to AAV9 infection appeared to improve the number of GFP-positive cells, but the increase was not statistically significant. These findings expand the repertoire of available serotypes for AAV-mediated delivery of transgenes to pancreatic endocrine cells and may contribute to gene therapy strategies for pancreas pathologies.
Predicting small-molecule inhibition of protein complexes
Authors: Yaseen Adiba , Roy Soumyadip , Akhter Naeem , Ben-Hur Asa , Minhas Fayyaz 
Published: August 2024

AbstractMotivationProtein-Protein Interactions (PPIs) are crucial in biological processes and disease mechanisms, underscoring the importance of discovering PPI inhibitors in drug development. Machine learning can expedite this discovery process. Although machine learning techniques for predicting general compound inhibition are available, we are not aware of any that accurately forecast the inhibitory effect of a compound on aspecificprotein complex, utilizing inputs from both the compound and the protein complex.MethodsWe present the firsttargetedmachine learning based predictor of small molecule based inhibition of protein complexes. Our proposed graph neural network integrates the structure of a protein complex, its protein-protein binding site or interface features and a compound’s SMILES representation to predict the potential of the given compound to inhibit the interaction between proteins in the given complex in a targeted manner.ResultsValidated on the 2p2i-DB-v2 database, encompassing 714 inhibitors across 23 complexes with over 12,000 instances, our model achieves superior predictive accuracy (cross-validation AUC-ROC of 0.86), outperforming established kernel methods and pre-trained neural networks. We further tested the predictive performance of our model on two independent external datasets – one collected from recent publications and another consisting of putative inhibitors of the SARS-CoV-2-Spike and Human-ACE2 protein complex with AUC-ROCs of 0.82 and 0.78, respectively. Our targeted predictor introduces a novel approach for PPI inhibitor discovery, laying foundational work for future advancements in addressing this complex and previously unexplored prediction challenge.AvailabilityCode/supplementary material available:https://github.com/adibayaseen/PPI-Inhibitors
Rapid Changes in Risk Preferences Originate from Bayesian Inference on Parietal Magnitude Representations
Authors: de Hollander Gilles , Grueschow Marcus , Hennel Franciszek , Ruff Christian C. 
Published: August 2024

AbstractRisk preferences – the willingness to accept greater uncertainty to achieve larger potential rewards – determine many aspects of our lives and are often interpreted as an individual trait that reflects a general ’taste’ for risk. However, this perspective cannot explain why risk preferences can change considerably across contexts and even across repetitions of the identical decisions. Here we provide modelling and neural evidence that contextual shifts and moment-to-moment fluctuations in risk preferences can emerge mechanistically from Bayesian inference on noisy magnitude representations in parietal cortex. Our participants underwent fMRI while choosing between safe and risky options that were either held in working memory or present on the screen. Risky options that were held in working memory were less likely to be chosen (risk aversion) when they had large payoffs but more likely to be chosen (risk-seeking) when they had small payoffs. These counterintuitive effects are mechanistically explained by a computational model of the Bayesian inference underlying the perception of the payoff magnitudes: Options kept in working memory are noisier and therefore more prone to central tendency biases, leading small (or large) payoffs to be overestimated (or underestimated) more. Congruent with the behavioural modelling, fMRI population-receptive field modelling showed that on trials where intraparietal payoff representations were noisier, choices were also less consistent and less risk-neutral, in line with participants resorting more to their prior belief about potential payoffs. Our results highlight that individual risk preferences and their puzzling changes across contexts and choice repetitions are mechanistically rooted in perceptual inference on noisy parietal magnitude representations, with profound implications for economic, psychological, and neuroscience theories of risky behaviour.
The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth
Authors: Sumiya Motofumi , Katahira Kentaro , Akechi Hironori , Senju Atsushi 
Published: August 2024

AbstractRisk preference changes nonlinearly across development. Although extensive developmental research on the neurotypical population has shown that risk preference is highest during adolescence, developmental changes in risk preference in autistic people, who tend to prefer predictable behaviors, have not been investigated. Here, we aimed to investigate these changes and underlying computational mechanisms. Using a game-like risk-sensitive reinforcement learning task, we found a significant difference in nonlinear developmental changes in risk preference between the autistic and neurotypical groups (N = 75; age range, 6–30 years). The computational modeling approach with reinforcement learning models revealed that individual preferences for surprise modulated such preferences. These findings indicate that for neurotypical people, adolescence is a developmental period involving risk preference, possibly due to lower surprise aversion. Conversely, for autistic people, who show opposite developmental trajectories of risk preference, adolescence could be a developmental period involving risk avoidance because of low surprise preference.
Regulation of plant metabolism under elevated CO2
Authors: Shokouhi Danial , Hernandez Jakob Sebastian , Walther Dirk , Kepp Gabriele , Schwenkert Serena , Leister Dario , Gremmels Jürgen , Zuther Ellen , Alpers Jessica , Nägele Thomas , Heyer Arnd G. 
Published: August 2024

AbstractPlant responses to changing environments afford complex regulation at transcriptome and proteome level to maintain metabolic homeostasis. Homeostasis itself constitutes a complex and dynamic equilibrium of metabolic reactions and transport processes among cellular compartments. In the present study, we aimed at the highest possible resolution of this network by combining analysis of transcriptome, proteome and subcellular resolved metabolome of plants exposed to rising carbon dioxide concentrations over a time course of one week. To prove suitability of our approach, we included mutants affected in photorespiratory metabolism and, thus, should deviate from the wildtype in their response to elevated CO2. Our multi-omics analysis revealed that thehpr1-1mutant, defective in peroxisomal hydroxypyruvate reduction, is also affected in cytosolic pyruvate metabolism, reaching out to cysteine synthesis, while the hexokinase mutanthxk1displays a disturbed redox balance upon changing CO2levels. For the third mutant, defective in the mitochondrial protein BOU, we found compelling evidence that the function of this transporter is related to lipoic acid metabolism, thus challenging current interpretations. This demonstrates that the combined omics approach introduced here opens new insights into complex metabolic interaction of pathways shared among different cellular compartments.
Sustainability in human resource management practices used by small and medium-sized enterprises: a systematic review
Authors: Purgał-Popiela Joanna 
Published: August 2024

PurposeThis study aims to determine the current state of research on sustainability in the context of human resource management (HRM) practices adopted by small and medium-sized enterprises (SMEs), identify the most important gaps and propose an agenda for future studies.Design/methodology/approachThrough a systematic literature review (SLR), this study comprehensively examines the relevant literature on sustainability-related aspects of HRM in SMEs. It includes descriptive and thematic analyses of 29 research articles published in high-ranked academic journals selected from 506 records retrieved from four major databases (Scopus, Web of Science, ScienceDirect and EBSCOhost).FindingsThe findings suggest a growing interest in sustainability-related HRM in SMEs after 2020. Notably, most studies focused on green and social sustainability concerns, with limited attention to the triple bottom line (TBL) perspective. A detailed examination revealed variations among the three streams of research in terms of methodology, location of studies, nature of sustainability-HRM linkage and placement of HRM practices in research models. Despite these differences, positive effects of HRM practices on sustainability integration seem evident, including impacts on employee competences, behaviors and company-level outcomes.Originality/valueThis article is the first to systematically review the literature on the sustainability-HRM nexus specifically for SMEs. It contributes by identifying thematic clusters, methodological trends, contextual aspects and outcomes of sustainability-HRM integration, thereby laying the groundwork for future research.