bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linker histone regulates the myeloid versus lymphoid bifurcation of multipotent hematopoietic stem and progenitors
Authors: Karatepe Kutay , Mafra de Faria Bruna , Zhang Jian , Chen Xinyue , Pinto Hugo , Fyodorov Dmitry , Sefik Esen , Willcockson Michael , Flavell Richard , Skoultchi Arthur , Guo Shangqin 
Published: September 2024

AbstractMyeloid-biased differentiation of multipotent hematopoietic stem and progenitor cells (HSPCs) occurs with aging or exhaustion. The molecular mechanism(s) responsible for this fate bias remain unclear. Here we report that linker histone regulates HSPC fate choice at the lymphoid versus myeloid bifurcation. HSPCs expressing H1.0 from a doxycycline (dox) inducible transgene favor the lymphoid fate, display strengthened nucleosome organization and reduced chromatin accessibility at genomic regions hosting key myeloid fate drivers. The transcription factorHlfis located in one of such regions, where chromatin accessibility and gene expression is reduced in H1.0highHSPCs. Furthermore, H1.0 protein in HSPCs decreases in an aspartyl protease dependent manner, a process enhanced in response to interferon alpha (IFNα) signaling. Aspartyl protease inhibitors preserve endogenous H1.0 levels and promote the lymphoid fate of wild type HSPCs. Thus, our work uncovers a point of intervention to mitigate myeloid skewed hematopoiesis.
Individual differences in decision-making shape how mesolimbic dopamine regulates choice confidence and change-of-mind
Authors: Kocharian Adrina , Redish A. David , Rothwell Patrick E. 
Published: September 2024

ABSTRACTNucleus accumbens dopamine signaling is an important neural substrate for decision-making. Dominant theories generally discretize and homogenize decision-making, when it is in fact a continuous process, with evaluation and re-evaluation components that extend beyond simple outcome prediction into consideration of past and future value. Extensive work has examined mesolimbic dopamine in the context of reward prediction error, but major gaps persist in our understanding of how dopamine regulates volitional and self-guided decision-making. Moreover, there is little consideration of individual differences in value processing that may shape how dopamine regulates decision-making. Here, using an economic foraging task in mice, we found that dopamine dynamics in the nucleus accumbens core reflected decision confidence during evaluation of decisions, as well as both past and future value during re-evaluation and change-of-mind. Optogenetic manipulations of mesolimbic dopamine release selectively altered evaluation and re-evaluation of decisions in mice whose dopamine dynamics and behavior reflected future value.
Vocalisations indicate behavioural type inGlossophaginebats
Authors: Schabacker Theresa , Castiglione Raffaella , Snijders Lysanne , Knoernschild Mirjam 
Published: September 2024

AbstractVocalisations play a crucial role in the social systems of many animals, and may inadvertently reveal behavioural characteristics of the sender. Bats, the second largest mammalian order, rely extensively on vocalisations due to their nocturnal lifestyle and complex social systems, making them ideal for studying links between vocalisations and consistent behavioural traits. In this study, we developed a new testing regime to investigate if consistent individual vocalisation differences in nectarivorous bats are associated with specific behavioural types. We exposed 60 wild, maleGlossophaga soricina handleyibats to novel and risky stressors, and assessed their behavioural and vocal responses. Proactive, exploratory, and bold bats were more likely to produce social calls, and among the vocalising bats, more agitated bats produced higher numbers of social calls. We thus show that bat vocalisation behaviour can be indicative of a certain behavioural type, potentially allowing conspecifics to assess personalities from a distance, which in turn could impact subsequent social interactions, group dynamics, and reproductive success. Our results, in combination with previous findings in birds, suggest that advertent or inadvertent long-distance broadcasting of personality may be widespread, thus opening up new exciting questions about the links between vocalisations and sociality.
Updated science-wide author databases of standardized citation indicators including retraction data
Authors: Ioannidis John P. A. , Pezzullo Angelo Maria , Cristiano Antonio , Boccia Stefania , Baas Jeroen 
Published: September 2024

ABSTRACTCitation metrics are widely used in research appraisal, but they provide incomplete views of scientists’ impact and research track record. Other indicators of research practices should be linked to citation data. We have updated a Scopus-based database of highly-cited scientists (top-2% in each scientific subfield according to a composite citation indicator) to incorporate retraction data. Using data from the Retraction Watch database (RWDB), retraction records were linked to Scopus citation data. Of 55,237 items in RWDB as of August 15, 2024, we excluded non-retractions, retractions clearly not due to any author error, retractions where the paper had been republished, and items not linkable to Scopus records. Eventually 39,468 eligible retractions were linked to Scopus. Among 217,097 top-cited scientists in career-long impact and 223,152 in single recent year (2023) impact, 7,083 (3.3%) and 8,747 (4.0%), respectively, had at least one retraction. Scientists with retracted publications had younger publication age, higher self-citation rates, and larger publication volume than those without any retracted publications. Retractions were more common in the life sciences and rare or nonexistent in several other disciplines. In several developing countries, very high proportions of top-cited scientists had retractions (highest in Senegal (66.7%), Ecuador (28.6%) and Pakistan (27.8%) in career-long citation impact lists). Variability in retraction rates across fields and countries suggests differences in research practices, scrutiny, and ease of retraction. Addition of retraction data enhances the granularity of top-cited scientists’ profiles, aiding in responsible research evaluation. However, caution is needed when interpreting retractions, as they do not always signify misconduct; further analysis on a case-by-case basis is essential. The database should hopefully provide a resource for meta-research and deeper insights into scientific practices.
Application of novel 3D culture device for human omental adipocytes and macrophages derived from surgery
Authors: Shimoji Miyuki , Akabori Hiroya 
Published: September 2024

AbstractVisceral adipose tissue plays a key role in the inflammation, inducing metabolic dysfunction. The culturing system of major components, adipocytes andadiposetissuemacrophages (ATM) have been improved up to date, for example, the ceiling culture system, three-dimensional collagen gels and membrane mature adipocyte aggregate cultures (MAAC). Here we applied for a novel 3D culture device, cellbed of human omental adipocytes with ATM derived from surgery and presented the first morphological report. The pilot study has the limitation of resolution due to the thickness, however, the simple method would be a convenient assay to detect their morphological alteration after surgery. In addition, the combination of cell morphological observation on the cellbed and adipocytokine secretion capability would give an insight into the mechanism of adipose tissue-derived inflammation after surgery. The cellbed composed of high-grade silica glass fiver has been already provided the morphological study with the critical signaling transduction in human oral cancer research. The novel simple 3D device will prevail to spread the practical diversity in the functional clinical research.
Capturing spatiotemporal variation in salt marsh belowground biomass, a key resilience metric, through geoinformatics
Authors: Runion Kyle D , Mishra Deepak R , Alber Merryl , Lever Mark A , O’Connell Jessica L 
Published: September 2024

AbstractThe Belowground Ecosystem Resiliency Model (BERM) is a geoinformatics tool that was developed to predict belowground biomass (BGB) ofSpartina alterniflorain salt marshes based on remote sensing of aboveground characteristics and other readily available hydrologic, climatic, and physical data. We sought to characterize variation inS. alternifloraBGB over both temporal and spatial gradients through extensive marsh field observations in coastal Georgia, USA, to quantify their relationship with a suite of predictor variables, and to use these results to improve performance and expand the parameter space of BERM. We conducted pairwise comparisons ofS. alternifloragrowth metrics measured at nine sites over three to eight years and found that BGB grouped by site differed in 69% of comparisons, while only in 21% when grouped by year. This suggests that BGB varies more spatially than temporally. We used the BERM machine learning algorithms to evaluate how variables relating to biological, climatic, hydrologic, and physical attributes covaried with these BGB observations. Flooding frequency and intensity were most influential in predicting BGB, with predictor variables related to hydrology composing 61% of the total feature importance in the BERM framework. When we used this expanded calibration dataset and associated predictors to advance BERM, model error was reduced from a normalized root mean square error of 13.0% to 9.4% in comparison to the original BERM formulation. This reflects both an improvement in predictive performance and an expansion in conditions for potential model application. Finally, we used regression commonality analysis to show that model estimates reflected the spatiotemporal structure of BGB variation observed in field measurements. These results can help guide future data collection efforts to describe landscape-scale BGB trends. The advanced BERM is a robust tool that can characterizeS. alternifloraproductivity and resilience over broad spatial and temporal scales.
Glycosite Mapping andin situMass Spectrometry Imaging of MUC2 Glycopeptides via On-slide Digestion with Mucinase StcE
Authors: Lowery Sarah C. , Tran Isabella P. , Grimsley Grace , Stubler Rachel , Mahoney Keira E. , Lucas Taryn M. , Charkoftaki Georgia , Santos-Neto Alvaro , Varki Nissi , Vasiliou Vasilis , Drake Richard R. , Malaker Stacy A. 
Published: September 2024

AbstractMany cancers are characterized by altered mucin expression and glycosylation, although the mechanistic relationship between tumor glycosylation and disease progression is not well-defined. Herein, our goal was to map specific mucin glycoforms in diseased tissue, enabling correlation of the tumor glycan profile with malignant features. To this end, we developed a workflow implementing on-tissue digestion with mucinase StcE, followed by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and liquid chromatography coupled to mass spectrometry (LC-MS). To optimize our workflow, we analyzed four different mucinous carcinomas derived from colon, esophageal, and salivary gland tissue.Using this technique, we deduced the spatial distribution of StcE-generated O-glycopeptides within mucinous tumors using MALDI-IMS. Subsequent LC-MS analyses revealed the identity of different species detected in imaging experiments, in addition to comprehensively characterizing the mucinome and proteome of each tissue. Our coupled MS approach unveiled a striking mucin 2 (MUC2) expression pattern in two colorectal mucinous adenocarcinomas, in which different glycoforms clearly stratified regions within the tumor. Notably, our LC-MS experiments obtained near-complete sequence coverage over the mucin domains of MUC2, enabling glycoproteomic mapping of this canonical mucin in unprecedented depth. MUC2 glycosylation was dominated by the T and Tn antigens, with surprisingly little sialylation detected. However, O-glycans containing mono- and di-O-acetylated sialic acid were detected in low abundance. Finally, we obtained spectral evidence for an endogenous O-acetylated GalNAc, an O-glycan structure not previously reported in the literature. Overall, this proof-of-concept work underscores the potential of this technique to generate new research avenues in oncology and beyond.Significance StatementAberrant mucin expression and glycosylation are hallmarks of cancer, but how these changes promote malignant processes are not well understood. Solid tumors are highly heterogeneous in their cellular and molecular composition, and many advanced spatial techniques have emerged in recent years to study the tumor microenvironment (TME) for better understanding disease progression. Spatially resolved glycoprotein analyses typically detect either the protein or glycan components, but not both. We developed a workflow using a dual mass spectrometry approach to map the location of intact glycopeptides in mucinous tumors, enabled by on-tissue digestion with the mucin-specific protease StcE. Future applications of this method on larger patient cohorts will enhance our understanding of glycans in malignancy, identify disease biomarkers, and define therapeutic targets.
Microbiome and climate: Skin microbial diversity and community functions ofPolypedates megacephalus(Anura: Rhacophoridae) associated with bioclimate
Authors: Sun Dan , Liu Yewei , Zhou Shipeng , Meegaskumbura Madhava 
Published: September 2024

ABSTRACTThe microbiome inhabiting animal skin plays a crucial role in host fitness by influencing both the composition and function of microbial communities. Environmental factors, including climate, significantly impact microbial diversity and the functional attributes of these communities. However, it remains unclear how specific climatic factors affect amphibian skin microbial composition, community function, and the relationship between these two aspects. Given that amphibians are poikilotherms, and thus more susceptible to temperature fluctuations, understanding these effects is particularly important. Here, we investigated the skin microbiome of the rhacophorid tree frogPolypedates megacephalusacross different climatic regimes using 16S rRNA gene sequencing. Skin swab samples were collected from nine populations ofP. megacephalusadults in the Guangxi region, China. The majority of the core microbiota were found to belong to the genusPseudomonas. Our findings indicate that microbial community diversity, composition, and function are associated with changes in climatic conditions. Specifically, the taxonomic and functional diversity of the skin microbiome increased in response to greater climate variability, particularly in temperature fluctuations. Additionally, the functional attributes of microbial communities changed in parallel with shifts in community diversity and composition, suggesting that environmental filtering driven by climate changes negatively impacts microbial community functional redundancy. These results highlight the critical influence of climatic factors on amphibian skin microbiomes and offer new insights into how microbial composition and function contribute to host adaptation in varying environmental conditions.IMPORTANCEThis study is important in understanding the association between climate variability, microbial diversity, and host adaptation in amphibians, particularly vulnerable to environmental changes due to their poikilothermic nature. Amphibians rely on their skin microbiome for key functions like disease resistance, yet little is known about how climate fluctuations impact these microbial communities. By analyzing the microbiome ofPolypedates megacephalusacross different climatic regimes, our analysis reveals that while climate variability enhances microbial diversity, it reduces functional redundancy. These findings highlight the potential ecological consequences of climate change and emphasize the need to integrate microbiome health into amphibian conservation strategies.
Mapping the combinatorial coding between olfactory receptors and perception with deep learning
Authors: Chithrananda Seyone , Amores Judith , Yang Kevin K. 
Published: September 2024

AbstractThe sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules activate a subset of our olfactory receptors and combinations of unique receptor activations code for unique odors. Understanding this relationship is crucial for unraveling the mysteries of human olfaction and its potential therapeutic applications. Despite this, predicting molecule-OR interactions remains incredibly difficult. Here, we develop a novel, biologically-inspired approach that first maps odorant molecules to their respective OR activation profiles and subsequently predicts their odor percepts. Despite a lack of overlap between molecules with OR activation data and percept annotations, our joint model improves percept prediction by leveraging the OR activation profile of each odorant as auxiliary features in predicting its percepts. We extend this cross receptor-percept approach, showing that sets of molecules with very different structures but similar percepts, a common challenge for chemosensory prediction, have similar predicted OR activation profiles. Lastly, we further probe the odorant-OR model’s predictive ability, showing it can distinguish binding patterns across unique OR families, as well as between protein-coding genes or frequently occuring pseudogenes in the human olfactory subgenome. This work may aid in the potential discovery of novel odorant ligands targeting functions of orphan ORs, and in further characterizing the relationship between chemical structures and percepts. In doing so, we hope to advance our understanding of olfactory perception and the design of new odorants with desired perceptual qualities.
Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear baselines
Authors: Ahlmann-Eltze Constantin , Huber Wolfgang , Anders Simon 
Published: September 2024

AbstractAdvanced deep-learning methods, such as foundation models, promise to learn representations of biology that can be employed to predictin silicothe outcome of unseen experiments, such as the effect of genetic perturbations on the transcriptomes of human cells. To see whether current models already reach this goal, we benchmarked five foundation models and two other deep learning models against deliberately simplistic linear baselines. For combinatorial perturbations of two genes for which only the individual single perturbations had been seen, we find that the deep learning-based approaches did not perform better than a simple additive model. For perturbations of genes that had not yet been seen, the deep learning-based approaches did not outper-form the baseline of predicting the mean across the training perturbations. We hypothesize that the poor performance is partially because the pre-training data is observational; we show that a simple linear model reliably outperforms all other models when pre-trained on another perturbation dataset. While the promise of deep neural networks for the representation of biological systems and prediction of experimental outcomes is plausible, our work highlights the need for clear setting of objectives and for critical benchmarking to direct research efforts.Contactconstantin.ahlmann@embl.de
Scheduled feeding improves behavioral outcomes and reduces inflammation in a mouse model of Fragile X syndrome
Authors: Wang Huei Bin , Smale Natalie E. , Brown Sarah H. , Villanueva Sophia Anne Marie , Zhou David , Mulji Aly , Nguyen-Ngo Kyle , Harvey John R. , Ghiani Cristina A. , Colwell Christopher S. 
Published: September 2024

AbstractFragile X syndrome (FXS) is a neurodevelopmental disorder caused by the abnormal expansion of CGG repeats in the fragile X mental retardation 1 (FMR1) gene. Many FXS patients experience sleep disruptions, and we sought to explore these symptoms along with the possible benefits of a scheduled feeding intervention using theFmr1knockout (KO) mouse model. These mutants displayed clear evidence for sleep and circadian disturbances including delay in the onset of sleep and fragmented activity rhythms with increases in cycle-to-cycle variability. Importantly, theFmr1KO mice exhibited deficits in their circadian behavioral response to light with reduced masking, longer time to resetting to shifts in the Light-Dark cycle, altered synchronization to a skeleton photoperiod and lower magnitude light-induced phase shifts of activity rhythms. Investigation of the retinal input to the surprachiasmatic nucleus (SCN) with the neurotracer cholera toxin (β subunit) and quantification of the light-evoked cFos expression in the SCN revealed an abnormal retinal innervation of the SCN in theFmr1KO, providing a possible mechanistic explanation for the observed behavioral deficits. Interestingly, disruptions in social and repetitive behaviors correlated with sleep duration and fragmentation. Understanding the nature of the deficits, we decided to apply a scheduled feeding regimen (6-hr/18-hr feed/fast cycle) as a circadian-based strategy to boast circadian rhythms independently of light. This intervention significantly improved the activity rhythms and sleep in the mutants. Strikingly, the scheduled feeding ameliorated social interactions and reduced repetitive behaviors as well as the levels of Interferon-gamma and Interleukin-12 in theFmr1KO mutants, suggesting that timed eating may be an effective way to lessen inflammation. Collectively, this work adds support to efforts to develop circadian based interventions to help with symptoms of neurodevelopmental disorders.
SaccharomycesGenome Database: Advances in Genome Annotation, Expanded Biochemical Pathways, and Other Key Enhancements
Authors: Engel Stacia R. , Aleksander Suzi , Nash Robert S. , Wong Edith D. , Weng Shuai , Miyasato Stuart R. , Sherlock Gavin , Cherry J. Michael 
Published: September 2024

ABSTRACTBudding yeast (Saccharomyces cerevisiae) is the most extensively characterized eukaryotic model organism and has long been used to gain insight into the fundamentals of genetics, cellular biology, and the functions of specific genes and proteins. TheSaccharomycesGenome Database (SGD) is a scientific resource that provides information about the genome and biology ofS. cerevisiae. For more than 30 years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation for budding yeast along with search and analysis tools to explore these data. Here we describe recent updates at SGD, including the two most recent reference genome annotation updates, expanded biochemical pathways representation, changes to SGD search and data files, and other enhancements to the SGD website and user interface. These activities are part of our continuing effort to promote insights gained from yeast to enable the discovery of functional relationships between sequence and gene products in fungi and higher eukaryotes.