bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolic control by the Bithorax Complex-Wnt signaling crosstalk inDrosophila
Authors: Hemba-Waduge Rajitha-Udakara-Sampath , Liu Mengmeng , Li Xiao , Sun Jasmine L. , Budslick Elisabeth A. , Bondos Sarah E. , Ji Jun-Yuan 
Published: June 2024

AbstractAdipocytes distributed throughout the body play crucial roles in lipid metabolism and energy homeostasis. Regional differences among adipocytes influence normal function and disease susceptibility, but the mechanisms driving this regional heterogeneity remain poorly understood. Here, we report a genetic crosstalk between theBithorax Complex(BX-C) genes and Wnt/Wingless signaling that orchestrates regional differences among adipocytes inDrosophilalarvae. Abdominal adipocytes, characterized by the exclusive expression ofabdominal A(abd-A) andAbdominal B(Abd-B), exhibit distinct features compared to thoracic adipocytes, with Wnt signaling further amplifying these disparities. Depletion ofBX-Cgenes in adipocytes reduces fat accumulation, delays larval-pupal transition, and eventually leads to pupal lethality. Depleting Abd-A or Abd-B reduces Wnt target gene expression, thereby attenuating Wnt signaling-induced lipid mobilization. Conversely, Wnt signaling stimulatedabd-Atranscription, suggesting a feedforward loop that amplifies the interplay between Wnt signaling andBX-Cin adipocytes. These findings elucidate how the crosstalk between cell-autonomousBX-Cgene expression and Wnt signaling define unique metabolic behaviors in adipocytes in different anatomical regions of fat body, delineating larval adipose tissue domains.
Perturbation-driven transcriptional heterogeneity impacts cell fitness
Authors: Nadal-Ribelles Mariona , Solé Carme , Diez-Villanueva Anna , Stephan-Otto Attolini Camille , Matas Yaima , Steinmetz Lars , de Nadal Eulalia , Posas Francesc 
Published: June 2024

AbstractHeterogeneity is inherent to living organisms and it determines cell fate and phenotypic variability1–3. Indeed, even isogenic cell populations vary in quantifiable phenotypes. Here we generated a high-resolution single-cell yeast transcriptome atlas using genotype and clone RNA barcoded deletions to profile 3500 mutants under control and stress conditions in a genome-scale genetic and environmental perturbation screen. We uncovered a myriad of cell states within a population with specific transcriptional architectures that are both intrinsically and extrinsically regulated, thereby suggesting a continuum of cell states. Cell state occupancy and transition can be genetically modulated with specific mutants that act as state attractors, resulting in differential fitness. By exploiting the power of intra-genetic variability, we identified regulators of transcriptional heterogeneity that are functionally diverse and influenced by the environment.One-Sentence SummaryThe yeast single cell-transcriptome atlas based on RNA-traceable genetic perturbations served to map cellular states and define their underlying genetic basis.
Insights into Immune Gene Prediction and Function Through the Evolutionary History ofADFGene Family
Authors: Chen Huan , Day Brad 
Published: June 2024

SummaryACTIN DEPOLYMERIZING FACTORS (ADFs) are key regulators of actin cytoskeletal dynamics and plant immunity.We predicted the potential immune-associated function of 38 genes fromArabidopsisusing gene expression values from 24,123 RNA-Seq datasets and 34 single-cell datasets through machine learning algorithms.The evolutionary relationships ofADFfamily members from 38 eukaryotic species were evaluated, including an assessment of the sub-function(s) of these members.Our results show that theADFclade in plant and other kingdoms are separated, withADF3, 5, 7, 9,and10possessing collinear relationships within species, andADF 2,3,4,6,7, and 10possessing evolved, new, sub-functions related to response to Fe, copper-deficiency, and ABA signaling inArabidopsis. Expanded, multiple, roles forADF1,4,and6were also identified.This study not only provides an analysis of the expanded role for the ADF family of genes/proteins, but also provides insight into, and a framework for, the identification and study of the evolutionary history of genes having putative roles in immune signaling.
Cell-scale porosity in microporous annealed particle (MAP) scaffolds modulates immune response and promotes formation of innervated muscle fibers in volumetric muscle loss injuries
Authors: Ayala Areli Rodriguez , Christ George , Griffin Donald 
Published: June 2024

AbstractVolumetric muscle loss (VML) is caused by severe traumatic injuries to skeletal muscle and is characterized by the irreversible loss of contractile tissue and permanent functional deficits. VML injuries cannot be healed by endogenous mechanisms and are exceptionally difficult to treat in the clinic due to the excessive upregulation of the inflammatory response, which leads to fibrosis, denervation of muscle fibers, and impaired regeneration. These injuries lead to long-term disability. Using a rodent model of VML in the tibialis anterior, this study presents microporous annealed particle (MAP) hydrogel scaffolds as a biomaterial platform for improved muscle regeneration in VML injuries, specifically highlighting the benefits of cell-scale porosity. In contrast to bulk (i.e., nanoporous) hydrogel scaffolds, MAP scaffolds promote integration by avoiding the foreign body response, decreasing the rate of implant degradation, and shifting macrophage polarization to favor regeneration. In addition, cell migration and angiogenesis throughout the implant precede the degradation of MAP scaffolds, including the formation of muscle fibers and neuromuscular junctions within MAP scaffolds prior to degradation. These fibers and junctions continue to develop as the implant degrades, indicating that MAP hydrogel scaffolds are a promising therapeutic approach for VML injuries.
Root System Architecture Reorganization Under Decreasing Soil Phosphorus Lowers Root System Conductance ofZea mays
Authors: Bauer Felix Maximilian , Baker Dirk Norbert , Giraud Mona , Baca Cabrera Juan Carlos , Vanderborght Jan , Lobet Guillaume , Schnepf Andrea 
Published: June 2024

AbstractThe global supply of phosphorus is decreasing. At the same time, climate change reduces the water availability in most regions of the world. Insights on how decreasing phosphorus availability influences plant architecture is crucial to understand its influence on plant functional properties, such as the root system’s water uptake capacity. In this study we investigated the structural and functional responses ofZea maysto varying phosphorus fertilization levels focusing especially on the root system’s conductance. A rhizotron experiment with soils ranging from severe phosphorus deficiency to sufficiency was conducted. We measured architectural parameters of the whole plant and combined them with root hydraulic properties to simulate time-dependent root system conductance of growing plants under different phosphorus levels. We observed changes of the root system architecture, characterized by decreasing crown root elongation and reduced axial root radii with declining phosphorus availability. Modeling revealed that only plants with optimal phosphorus availability sustained a high root system conductance, while all other phosphorus levels led to a significantly lower root system conductance, both under light and severe phosphorus deficiency. We postulate that phosphorus deficiency initially enhances root system function for drought mitigation but eventually reduce biomass and impairs root development and water uptake in prolonged or severe cases of drought. Our results also highlight the fact that root system organization, rather than its total size, is critical to estimate important root functions.
Understanding Complex Chromatin Dynamics of Primary Human Neutrophils During PMA Induced NETosis
Authors: Atteberry Brandi , Berman Benjamin P. , Kelly Theresa K , Cayford Justin 
Published: June 2024

AbstractBackgroundPrimary human neutrophils play a pivotal role in innate immunity, mainly through the formation of neutrophil extracellular traps (NETs) in a process known as NETosis. This cell-death pathway is crucial for combating infections but is also implicated in many inflammatory diseases such as sepsis, systemic lupus erythematosus, rheumatoid arthritis, and others.MethodsThe study presented here investigates chromatin dynamics during NETosis by stimulating primary human neutrophils with phorbol 12-myristate 13-acetate (PMA). We adapt the ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) method to isolated neutrophils and characterize a time-dependent chromatin response.ResultsWe find that chromatin accessibility patterns are consistent across individual donors and most chromatin changes occur within 30 minutes, with many continuing across the 90 minutes assessed in this study. Regulatory regions gaining accessibility are associated with activity of pathways that have been implicated in NOX-dependent NET formation.ConclusionsOur findings enhance the understanding of the chromatin changes underlying NETosis and also identify potential early-acting targets for modulating this process in inflammatory diseases.
ARTEM: a method for RNA and DNA tertiary motif identification with backbone permutations, and its example application to kink-turn-like motifs
Authors: Baulin Eugene F. , Bohdan Davyd R. , Kowalski Dawid , Serwatka Milena , Świerczyńska Julia , Żyra Zuzanna , Bujnicki Janusz M. 
Published: June 2024

ABSTRACTThe functions of non-coding RNAs are largely defined by their three-dimensional structures. RNA 3D structure is organized hierarchically and consists of recurrent building blocks called tertiary motifs. The computational problem of RNA tertiary motif search remains largely unsolved, as standard approaches are restrained by sequence, interaction network, or backbone topology. We developed the ARTEM superposition algorithm, which is free from these limitations. Here, we present a version of ARTEM that allows automated searches of RNA and DNA structure databases to identify 3D structure motifs. We exemplify it by a search of motifs isosteric to the kink-turn motif. This widespread motif plays a role in many aspects of RNA function, and its mutations are known to cause several human syndromes. With ARTEM, we discovered two new kink-turn topologies, multiple no-kink variants of the motif, and showed that a ribosomal junction in bacteria forms either a kink or a no-kink variant depending on the species. Additionally, we identified kink-turns in the catalytic core of group II introns, whose structures have not previously been characterized as containing kink-turns. ARTEM opens a fundamentally new way to study RNA and DNA 3D folds and motifs and analyze their correlations and variations.
Robust virtual staining of landmark organelles
Authors: Liu Ziwen , Hirata-Miyasaki Eduardo , Pradeep Soorya , Rahm Johanna , Foley Christian , Chandler Talon , Ivanov Ivan , Woosley Hunter , Lao Tiger , Balasubramanian Akilandeswari , Marreiros Rita , Liu Chad , Leonetti Manu , Aviner Ranen , Arias Carolina , Jacobo Adrian , Mehta Shalin B. 
Published: June 2024

AbstractCorrelative dynamic imaging of cellular landmarks, such as nuclei and nucleoli, cell membranes, nuclear envelope and lipid droplets is critical for systems cell biology and drug discovery, but challenging to achieve with molecular labels. Virtual staining of label-free images with deep neural networks is an emerging solution for correlative dynamic imaging. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining leaves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Current approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. We report training protocols for virtual staining of nuclei and membranes robust to variations in imaging parameters, cell states, and cell types. We describe a flexible and scalable convolutional architecture, UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including human cell lines, neuromasts of zebrafish and stem cell (iPSC)-derived neurons, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and cell membranes. The models rescue missing labels, non-uniform expression of labels, and photobleaching. We share three pre-trained models (VSCyto3D, VSNeuromast, and VSCyto2D) and a PyTorch-based pipeline (VisCy) for training, inference, and deployment that leverages current community standards for image data and metadata.
Peptide sequencing via reverse translation of peptides into DNA
Authors: Zheng Liwei , Sun Yujia , Eisenstein Michael , Soh Hyongsok Tom 
Published: June 2024

AbstractScalable methods that can accurately sequence peptides at single-amino acid resolution could significantly advance proteomic studies. We present a protein sequencing method based on the “reverse translation” of peptide sequence information into DNA barcodes that document the identity, position, and the originating peptide of each amino acid. We employ a modified Edman degradation process that converts peptides into DNA-barcoded amino acids, which are subsequently detected by proximity extension assay, yielding multi-barcoded DNA outputs that can be PCR amplified and sequenced. Using our method, we sequenced multiple consecutive amino acids within a model peptide. This method also enables the differentiation of single amino acid substitutions, and the identification of post-translational modifications and their positions within multiple peptides simultaneously. With further development, we anticipate that this method will enable highly parallelde novoprotein sequencing with single-molecule sensitivity.
Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling
Authors: Pourmirzaei Mahdi , Esmaili Farzaneh , Pourmirzaei Mohammadreza , Wang Duolin , Xu Dong 
Published: June 2024

AbstractThis paper proposes a versatile tokenization method and introduces Prot2Token, a model that combines autoregressive language modeling with protein language models (PLMs) to tackle various protein prediction tasks using protein sequences. Leveraging our tokenization method, Prot2Token adapts existing PLMs for multiple tasks such as protein-level prediction, residue-level prediction, and protein-protein interaction prediction through next-token prediction of tokenized target label sequences. By incorporating prompt tokens into the decoder, Prot2Token enables multi-task training in a single end-to-end session. Our results demonstrate that Prot2Token not only matches the performance of specialized models across various tasks but also paves the way for integrating protein tasks with large language models (LLMs), representing an important step towards creating general-purpose PLMs for advanced protein language processing (PLP). Additionally, we use Prot2Token to develop S-ESM, a structure-aware version of the ESM model, which achieves competitive performance with state-of-the-art methods in 3D structure-related tasks using only protein sequences. Code is available at:https://github.com/mahdip72/prot2token.
A Neuropathology Case Report of a Woman with Down Syndrome who Remained Cognitively Stable
Authors: Liou Jr-Jiun , Lou Jerry , Nakagiri Jamie , Yong William , Hom Christy L. , Doran Eric W. , Totoiu Minodora , Lott Ira , Mapstone Mark , Keator David B. , Brickman Adam M. , Wright Sierra , Nelson Brittany , Lai Florence , Xicota Laura , Dang Lam-Ha T. , Li Jinghang , Santini Tales , Mettenburg Joseph M. , Ikonomovic Milos D. , Kofler Julia , Ibrahim Tamer , Head Elizabeth ,   the Alzheimer Biomarker Consortium - Down Syndrome
Published: June 2024

AbstractIn this neuropathology case report, we present findings from an individual with Down syndrome (DS) who remained cognitively stable despite Alzheimer’s disease (AD) neuropathology. Clinical assessments, fluid biomarkers, neuroimaging, and neuropathological examinations were conducted to characterize her condition. Notably, her ApoE genotype was E2/3, which is associated with a decreased risk of dementia. Neuroimaging revealed stable yet elevated amyloid profiles and moderately elevated tau levels, while neuropathology indicated intermediate AD neuropathologic change with Lewy body pathology and cerebrovascular pathology. Despite the presence of AD pathology, the participant demonstrated intact cognitive functioning, potentially attributed to factors such as genetic variations, cognitive resilience, and environmental enrichment. The findings suggest a dissociation between clinical symptoms and neuropathological changes, emphasizing the complexity of AD progression in DS. Further investigation into factors influencing cognitive resilience in individuals with DS, including comorbidities and social functioning, is warranted. Understanding the mechanisms underlying cognitive stability in DS could offer insights into resilience to AD neuropathology in people with DS and in the general population and inform future interventions.
DALSO: domain ALS ontology
Authors: Podsiadły-Marczykowska Teresa , Andersen Peter , Gromicho Marta , Grosskreutz Julian , Kuźma-Kozakiewicz Magdalena , Petri Susanne , Szacka Katarzyna , Uysal Hilmi , de Carvalho Mamede , Piotrkiewicz Maria 
Published: June 2024

AbstractAmyotrophic lateral sclerosis (ALS) is an incurable, rapidly progressive neurodegenerative disease. During the course of ALS, virtually all skeletal muscles are gradually affected, including the respiratory muscles, and the disease is usually fatal within 2–5 years of symptom onset. Unequivocal and conclusive tests for ALS do not exist, its disease etiology is still unknown, and therapeutic options are limited. This paper presents the ALS domain ontology (DALSO), model containing formalized, semantic descriptions of a wide range of modeled disease related notions such as patient demographics, clinical findings and history, disease clinical features and diagnostic classifications, risk factors, genetics and pathophysiological mechanisms of motor neuron degeneration. The DALSO’s aim and information scope, design assumption, structure and implementation details are also described. DALSO covers the broad range of significant biomedical concepts ranging from clinical to molecular feature of the modeled disease, it represents a comprehensive, structured knowledge source for the ALS disease domain. To the best of authors’ knowledge, the DALSO is the first attempt to develop a formal, computational model representing knowledge of this fatal motor neuron disease. It provides the means for integrating and annotating clinical and research data, not only at the generic domain knowledge level, but also at the level of individual patient case studies. The DALSO is expressed in OWL2 language, contains 910 classes, is consistent and free of logical errors. Its syntactic correctness was validated by the Fact++ reasoner.
The Predictors of COVID-19 Case Fatalities in Nigerian Health Systems: A Secondary Data Analysis
Authors: Akinjeji Adewale , Oladigbolu Remi , Adedokun Adetunji , Onuorah Ogonna , Emerenini Franklin 
Published: June 2024

AbstractBackgroundCOVID-19, caused by the novel SARS-CoV-2 is the worst catastrophe in this century that affected more than 800 million people and caused more than 7 million deaths. During the pandemic, the burden of COVID-19 increased significantly, posing a threat to public health infrastructure, testing protocols, national healthcare capacity, and disease control measures. To assess the impacts of the Nigerian Health Systems on COVID-19 fatalities, the researchers evaluated the association between healthcare system capability and mortality rate of COVID-19 patients through adjustments for healthcare spending as a proportion of the GDP, population density, and the proportion of the population that are 65 years and above across the 36 States and Abuja, FCT.MethodsThe study utilized secondary data abstracted from the World Bank records, Worldometer, and Post-Pandemic Health Financing by the States in Nigeria (2020 to 2022). It used data from the 36 States of the country and the FCT, Abuja. The dependent variable was COVID-19 case fatality (Case Fatality Rate across the study areas), the predictor variable was Healthcare Capacity Index (aggregate of number of doctors/nurses/midwives/hospital bed space per 1,000 population categorized into low, middle, and high Healthcare Capacity index), and the covariates were population density, health expenditure as a percentage of GDP, and the proportion of the population that are 65 years and above. A negative binomial regression model was used to assess the predictors of case fatality after adjusting for other covariates at an alpha of <0.05 and 95% confidence interval.ResultsAlmost half of the States in Nigeria were in the middle Healthcare Capacity Index 16 (43.2%) with only 7 (18.9%) in the high Healthcare Capacity Index (HCI). The regression analysis shows that HCI was a predictor of COVID-19 case fatality as the States with high HCI compared with low HCI were 9.4 times more likely to have lower COVID-19 case fatalities (AOR=0.106, p=0.063, 95% CI[0.010-1.131]), and those with middle HCI compared with low were 6.4 times more likely to have lower COVID-19 case fatality (aOR=0.156, p=0.006, 95% CI [0.041-0.593]). Although States with a higher proportion of the population that were 65 years and above were about 2 times more likely to have higher COVID-19 case fatality (aOR 1.99, p=0.154, 95% CI [0.771-5.172]), this was not statistically significant due to the small sample size (37 States)ConclusionThe research further buttressed the pivotal role that effective multidimensional healthcare capacity is a pertinent strategy to mitigate future case fatalities from Public Health Events of International Concerns (PHEICs).
Bayesian marker-based principal component ridge regression – a flexible multipurpose framework for quantitative genetics in wild study systems
Authors: Aspheim Janne C. H. , Aase Kenneth , Bolstad Geir H. , Jensen Henrik , Muff Stefanie 
Published: June 2024

AbstractAs larger genomic data sets become available for wild study populations, the need for flexible and efficient methods to estimate and predict quantitative genetic parameters, such as the adaptive potential and measures for genetic change, increases. Animal breeders have produced a wealth of methods, but wild study systems often face challenges due to larger effective population sizes, environmental heterogeneity and higher spatio-temporal variation. Here we adapt methods previously used for genomic prediction in animal breeding to the needs of wild study systems. The core idea is to approximate the breeding values as a linear combination of principal components (PCs), where the PC effects are shrunk with Bayesian ridge regression. Thanks to efficient implementation in a Bayesian framework using integrated nested Laplace approximations (INLA), it is possible to handle models that include several fixed and random effects in addition to the breeding values. Applications to a Norwegian house sparrow meta-population, as well as simulations, show that this method efficiently estimates the additive genetic variance and accurately predicts the breeding values. A major benefit of this modeling framework is computational efficiency at large sample sizes. The method therefore suits both current and future needs to analyze genomic data from wild study systems.