bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Guanidine Hydrochloride on the shapes of Prothymosin-αandα-Synuclein is dramatically different
Authors: Liu Zhenxing , Thirumalai D. 
Published: October 2024

AbstractThe effects of Guanidine Hydrochloride (GdmCl) on two Intrinsically Disordered Proteins (IDPs) are investigated using simulations of the Self-Organized Polymer-IDP (SOP-IDP) model. The impact of GdmCl is taken into account using the Molecular Transfer Model(MTM). We show that, due to dramatic reduction in the stiffness of the highly charged Prothymosin-α(ProTα) with increasing concentration of GdmCl ([GdmCl]), the radius of gyration (Rg) decreases sharply till about 1.0M. Above 1.0M, ProTαexpands, caused by the swelling effect of GdmCl. In contrast,Rgofα-Synuclein (αSyn) swells as continuously as [GdmCl] increases, with most of the expansion occurring at concentrations less than 0.2M. Strikingly, the amplitude of the Small Angle X-ray Scattering (SAXS) profiles for ProTαincreases till [GdmCl]≈ 1.0M and decreases beyond 1.0M. The [GdmCl]-dependent SAXS profiles forαSyn, which has a pronounced bump at small wave vector (q∼ 0.5nm−1) at low [GdmCl] (≤ 0.2M), monotonically decrease at all values of [GdmCl]. The contrasting behavior predicted by the combination of MTM and SOP-IDP simulations may be qualitatively understood by modeling ProTαas a strongly charged polyelectrolyte with nearly uniform density of charges along the chain contour andαSyn as a nearly neutral polymer, except near the C-terminus where the uncompensated negatively charged residues are located. The precise predictions for the SAXS profiles as a function of [GdmCl] can be readily tested.
Genomic analysis of laboratory-evolved, heat-adaptedEscherichia colistrains
Authors: McGuire Bailey E. , Nano Francis E. 
Published: October 2024

ABSTRACTAdaptive laboratory evolution to high incubation temperatures represents a complex evolutionary problem, and each study to date performed inEscherichia colihas resulted in a different set of mutations. We performed adaptive laboratory evolution ofE. colito heat by passaging a culture at elevated temperatures for 150 days. Throughout the adaptive evolution we expressed a set of genes that induce hyper-mutagenesis. These growth conditions yielded a strain with a maximum growth temperature approximately 2 °C above that of the parental strain. We preserved evolved isolates weekly and obtained and analyzed whole-genome sequencing data for three isolates from different time points. We identified hundreds of mutations, including mutations in components of the RNA polymerase (RpoB, RpoC and RpoD), Rho, and the heat shock proteins GroES, GroEL, DnaK, ClpB, IbpA and HslU. We compared the proteomes of the starting strain and final strain grown at 37 °C and 42.5 °C and identified changes in abundance between samples for GroESL, HslVU, DnaK, ClpB and other important proteins. This study details a distinct evolutionary route towards enhanced thermotolerance, contributes to our understanding of adaptation to heat inEscherichia coliand may provide insights into heat adaptation in other organisms.
Plastic mechanisms for unraveling a universal trade-off between water loss and respiration
Authors: Assis Braulio A. , Ghalambor Cameron K. , Riddell Eric 
Published: October 2024

AbstractThe expression of phenotypes is often constrained by functional conflicts between traits, and the resulting trade-offs can impose limits on phenotypic and taxonomic diversity. However, organisms can circumvent trade-offs in specific environmental contexts through phenotypic plasticity, though the mechanisms that drive trade-offs or allow organisms to resolve trade-offs are often cryptic. The trade-off between water loss and gas exchange poses a fundamental challenge for terrestrial life because the capacity to absorb oxygen is limited by the risk of desiccation from water loss across respiratory surfaces. Here, we investigated the capacity to mitigate this trade-off in a lungless salamander that breathes exclusively across its skin. We measured water loss and oxygen uptake using respirometry in field- and laboratory-based studies to identify plastic responses to environmental variation, coupled with gene expression analyses to investigate biological pathways that regulate the trade-off. Though the trade-off between oxygen uptake and water loss was generally strong, we observed that its strength changed over time in the field and laboratory. At the molecular level, we found support for antagonistic pleiotropy in multiple pathways that simultaneously affect both physiological traits (e.g., via vasoconstriction and downregulation of aerobic respiration), and for mechanisms that mitigate the trade-off by affecting only one trait (e.g., oxygen binding affinity, melanin synthesis). As organisms disentangle such conflicts, however, alternative trade-offs are likely to arise. Our study provides evidence that alternative pathways allow organisms to mitigate pleiotropic conflicts, which ultimately may allow greater phenotypic diversity and persistence in novel environments.
Neural population variance explains adaptation differences during learning
Authors: Stealey Hannah M. , Zhao Yi , Lu Hung-Yun , Contreras-Hernandez Enrique , Chang Yin-Jui , Tobler Philippe , Lewis-Peacock Jarrod A. , Santacruz Samantha R. 
Published: October 2024

AbstractVariability, a ubiquitous feature of neural activity, plays an integral role in behavior. However, establishing a causal relationship between neural signals and behavior is difficult. By defining a mathematical mapping between neural spiking activity and behavior, we investigate the role of spiking variability in adaptation during a brain-computer interface (BCI) behavior in male rhesus macaques (Macaca mulatta,n=2). Recent BCI evidence demonstrates that creating novel neural patterns is harder than repurposing existing patterns to respond to changes in external input. However, what limits the ability to repurpose, or adapt, patterns under different magnitudes of change is less well-characterized. Here, we present evidence that variance in neural spiking activity reveals differences in learnability between easy and hard adaptation conditions and across sessions. Furthermore, our study illuminates the limitations in neural changes underlying behavior within a neurofeedback paradigm.Significance StatementVariability in neural activity is a major driver of behavioral variability, though it is unclear how variability is balanced with stable neural activity as new behaviors become more practiced. By using a brain-computer interface methodology, we define a mathematical mapping between neural spiking activity and a behavioral control signal. Through thoughtful manipulation of this mapping, we incite the subjects (rhesus macaques) to learn and adapt neural activity to regain behavioral proficiency. We find that metrics of neural population variability are differentially modulated depending on difficulty of the imposed manipulation. Our exciting results provide important implications for brain-computer interface applications as well as our understanding of learning and adaptation more broadly. Our work represents an important step forwards towards understanding population neural dynamics in this critical component of behavior.
Temporal High-Resolution Atlas of Human Blood Leukocyte Composition in Response to Respiratory Virus Inoculation
Authors: Zhang Yu , Liu Tianyi , Liu Chuwen , Sun Xuejun , Liu Pei , Hagood James S. , Pickles Raymond J. , Zou Fei , Zheng Xiaojing 
Published: October 2024

AbstractBackgroundViral respiratory infections are a significant global health burden. Human immune responses to infections caused by respiratory viruses are complex and dependent on the length of time the host has been infected. Human viral challenge studies provide a useful opportunity to understand the timing of human immune responses as samples can be obtained pre-inoculation and post-inoculation by defined virus inocula. Transcriptomic data is available from a large amount of publicly available viral challenge studies and genome-wide transcriptomic data is primarily derived from bulk samples containing mixed cell subpopulations. Traditional bulk transcriptomic analysis methods often utilize deconvolution techniques to study changes in cell proportions of major immune cell types.Methods and Principal FindingsIn this study, we digitally sorted the cellular diversity of 31 blood immune cell types over time in human challenge studies after inoculation by H3N2 influenza virus H1N1 influenza virus, and RSV using xCell, a digital cell separation method, on human bulk transcriptional repository data. We identified common inoculation-induced changes in cell subpopulations across studies of the three viruses with time kinetics. We observed a consistent elevation of myeloid cells and their subtypes post-inoculation across various viral inoculations. Lymphocytes and their subpopulations were reduced in symptomatic and infected (Sx/Inf) participants compared to asymptomatic and uninfected (Asx/Uninf) participants. H3N2 evoked the earliest transcriptional immune responses, followed by H1N1, RSV infection elicited a delayed transcriptional response compared to both influenza viruses likely due to slower replication kinetics. Our analysis also revealed unique immune cell composition changes caused by specific viruses. We found that pre-existing baseline memory T cells were associated with resistance to influenza viral infection and that the key cell type is effector memory (Tem) CD8 T cells.ConclusionThese findings identify specific target cell subpopulations which respond to respiratory virus inoculation over time, shedding light on mechanisms for eliciting a robust baseline level of immune memory against multiple viral strains. These studies highlight potential immune cell responses that can be utilized to enhance vaccine design.
TheCandidaGenome Database: Annotation and Visualization Updates
Authors: Lew-Smith Jodi , Binkley Jonathan , Sherlock Gavin 
Published: October 2024

AbstractTheCandidaGenome Database (CGD;www.candidagenome.org) is unique in being both a model organism database and a fungal pathogen database. As a fungal pathogen database, CGD hosts locus pages for five species of the best-studied pathogenic fungi in theCandidagroup. As a model organism database, the speciesCandida albicansserves as a model both for otherCandidaspp. and for non-Candidafungi that form biofilms and undergo routine morphogenic switching from the planktonic form to the filamentous form, which is not done by other model yeasts. As pathogenicCandidaspecies have become increasingly drug resistant, the high lethality of invasive candidiasis in immunocompromised people is increasingly alarming. There is a pressing need for additional research into basicCandidabiology, epidemiology and phylogeny, and potential new antifungals. CGD serves the needs of this diverse research community by curating the entire gene-basedCandidaexperimental literature as it is published, extracting, organizing and standardizing gene annotations. Most recently, we have begun linking clinical data on disease to relevant Literature Topics to improve searchability for clinical researchers. Because CGD curates for multiple species and most research focuses on aspects related to pathogenicity, we focus our curation efforts on assigning Literature Topic tags, collecting detailed mutant phenotype data, and assigning controlled Gene Ontology terms with accompanying evidence codes. Our Summary pages for each feature include the primary name and all aliases for that locus, a description of the gene and/or gene product, detailed ortholog information with links, a JBrowse window with a visual view of the gene on its chromosome, summarized phenotype, Gene Ontology, and sequence information, references cited on the summary page itself, and any locus notes. The database serves as a community hub, where we link to various types of reference material of relevance toCandidaresearchers, including colleague information, news, and notice of upcoming meetings. We routinely survey the community to learn how the field is evolving and how needs may have changed. A key future challenge is management of the flood of high-throughput expression data to make it as useful as possible to as many researchers as possible. The central challenge for any community database is to turn data into knowledge, which the community can access, use, and build upon.
Targeting FEN1 to enhance efficacy of PARP inhibition in triple-negative breast cancer
Authors: Frederick Mallory I. , Fyle Elicia , Clouvel Anna , Abdesselam Djihane , Hassan Saima 
Published: October 2024

AbstractPatients with triple-negative breast cancer (TNBC) have limited targeted therapeutic options. PARP inhibitors (PARPi) have demonstrated an important role forBRCA-mutant patients with early TNBC. Combination approaches with PARPi can broaden the use of PARPi to a larger cohort of TNBC patients. We selected six genes from our previously identified 63-gene signature that was associated with PARPi response. siFEN1 increased cells in G2/M arrest, DNA damage and particularly apoptosis. Targeting FEN1 with a chemical inhibitor enhanced the efficacy of PARPi in 7/10 cell lines, and synergy was demonstrated mainly in PARPi-resistant TNBC cell lines. ABRCA2-mutant cell line with acquired resistance to olaparib (HCC1395-OlaR) was strongly synergistic, with a combination index value of 0.20. The combination of PARPi and FEN1 inhibition also showed synergy in a PARPi-resistant xenograft-derived organoid model. Two mechanisms which explain the underlying efficacy are rapid progression in DNA replication fork speed and enhancement of DNA damage. The combination induced the highest fork speed (47% difference in comparison to control, P<0.0001) when FEN1 inhibition and PARPi equally increased fork speed individually in a cell line with a pre-existing increase in replication stress. The combination also increased DNA damage at lower drug concentrations, driving response in most of the synergistic cell lines. Gene expression analysis suggested that the sensitizing role of FEN1 inhibition in PARPi-resistant cell lines may be due to downregulation of pathways including mismatch repair. Therefore, targeting FEN1 shows great therapeutic potential as a targeted combination approach, particularly in the context of PARPi-resistant TNBC.
scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Authors: Wang Gefei , Zhao Jia , Lin Yingxin , Liu Tianyu , Zhao Yize , Zhao Hongyu 
Published: October 2024

AbstractRecent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODAL’s effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.
Designing Experimental Protocols to Elicit Vigilance Variations in Controlled Laboratory Settings
Authors: Zahran Saeed 
Published: October 2024

AbstractVigilance plays a vital role in numerous high-risk professions, where real-time monitoring of vigilance levels is highly beneficial. Brain-Computer Interfaces (BCIs) utilizing EEG signals, along with machine learning and deep learning algorithms, present a promising solution for the classification and monitoring of vigilance levels. This study aimed to create a dataset for training such models by evaluating four experimental paradigms: the Hitchcock Air Traffic Controller Task (ATC), the simultaneous line task, the successive line task, and the Oddball task. Subjective reports and behavioral performance were analyzed to determine the effectiveness of these tasks in inducing vigilance decrements over time. The findings reveal that both the ATC and simultaneous line tasks effectively induced significant declines in subjective vigilance ratings and behavioral performance. In contrast, the Oddball task was less successful in generating a noticeable vigilance decrement. This research demonstrates the potential of the ATC and simultaneous line tasks to induce vigilance variations, providing valuable datasets for training vigilance detection algorithms. Additionally, it highlights the importance of considering non-linear fluctuations in vigilance and the need for more advanced techniques to accurately classify different vigilance states. Such improvements in vigilance monitoring could substantially enhance safety and well-being in critical work environments.
Fourth generation CAR Tregs withPDCD1-driven IL-10 have enhanced suppressive function
Authors: Boardman Dominic A , Mangat Sonya , Gillies Jana K , Fung Vivian CW , Haque Manjurul , Mojibian Majid , Tuomela Karoliina , Wardell Christine M , Brown Andrew , Lam Avery J , Levings Megan K 
Published: October 2024

ABSTRACTThe potency of regulatory T cell (Treg) therapy has been transformed through use of chimeric antigen receptors (CAR). However, to date, CAR Treg therapy has not achieved long-lasting tolerance in mouse models, suggesting that additional engineering is required to unlock the full potential of these cells. We previously found that human Tregs produce minimal amounts of IL-10 and have a limited capacity to control innate immunity in comparison to type I regulatory (Tr1) cells. Seeking to create “hybrid” CAR Tregs that were engineered with Tr1-like properties, we examined whether thePDCD1locus could be exploited to endow Tregs with the ability to secrete high levels of IL-10 in a CAR-regulated manner. CRISPR-mediated PD1-deletion increased the activation potential of CAR Tregs without compromisingin vivostability. Knock-in ofIL10under control of the PD1 promoter facilitated CAR-mediated secretion of IL-10 in large quantities, and improved CAR Treg function, as determined by significant inhibition of dendritic cell antigen presentation and enhanced suppression of alloantigen- and islet autoantigen-specific T cells. Overall, CRISPR-mediated engineering to simultaneously remove an inhibitory signal and enhance suppressive mechanisms is a new approach to enhance the therapeutic potency of CAR Tregs.
Glioblastoma invasion relies on actomyosin contractility and metalloproteinase activity
Authors: Sumajit Hyeje , Böhringer David , Stefanovic Branko , Mark Christoph , Irianto Jerome 
Published: October 2024

AbstractGlioblastoma multiforme (GBM) is one of the most aggressive and invasive types of brain tumors with a 5-year survival rate of less than 20%. Due to its highly invasive phenotype, GBM cells tend to invade the surrounding normal brain tissue, making it challenging to fully resect the tumor and resulting in a relapse of the disease even after surgery. Hence, it is crucial to improve our mechanistic understanding of GBM invasion phenotype, in the hope of inhibiting the disease progression. Here, we used patient-derived GBM neurospheres as a model to study GBM invasion. When these neurospheres were cultured in Matrigel, the GBM cells spontaneously invaded the surrounding microenvironment. We first characterized the invasion rate through live imaging and the forces exerted by cells as they invade the Matrigel using traction force microscopy (TFM) analysis. We observed a significant increase in cellular contractility as the cells invaded the surrounding Matrigel, prompting us to investigate the role of myosin in the cell invasion. To test this, the GBM neurospheres were treated with a myosin inhibitor, Blebbistatin. Myosin inhibition led to reduced GBM invasion and contractility. Furthermore, we investigated the role of metalloproteinases (MMPs) and showed that GBM invasion relies on MMP activity in the absence of myosin. Together, this data shows the interplay between myosin and MMPs in GBM invasion.
Metazoan Diversity in Chilean Hypersaline Lakes Unveiled by Environmental DNA
Authors: Saccò Mattia , Campbell Matthew A. , Aguilar Pablo , Salazar Gonzalo , Berry Tina E. , Heydenrych Matthew J. , Lawrie Angus , White Nicole E. , Harrod Chris , Allentoft Morten E. 
Published: October 2024

AbstractSaline and hypersaline wetlands are biodiversity hotspots for metazoans such as aquatic invertebrates and wading birds. However, the survival of these habitats and their biota is increasingly threatened by a combination of pressures from climate change and extractive processes, jeopardizing the long-term ecological functioning of these ecosystems. With the goal of improving conservation efforts through state-of-the-art survey techniques in hypersaline ecosystems, this study tests the use of environmental DNA (eDNA) methods for metazoan biomonitoring. We employed a multi-assay approach utilizing three genetic markers—12S rRNA, 18S rRNA, and COI —to analyze biodiversity in two types of environmental substrates, sediment and water. These samples were collected from three hypersaline lakes situated at high altitude in Northern Chile: Salar de Atacama (Laguna Puilar), Salar de Pujsa, and Salar de Tara. In addition, we compared the eDNA outputs with results generated from aquatic macroinvertebrate assessments using kick-nets to evaluate the potential for complementary sampling approaches. Our eDNA analyses revealed a total of 21 and 22 taxa across the three hypersaline lakes in sediment and water, respectively. Within both substrates, the highest diversity was found in Salar de Tara (15 taxa within sediment and 13 taxa from water). Our multi-assay design was able to detect a range of resident hypersaline taxa with different conservation status, spanning from rotifers (Encentrum) to endangered snails (Heleobia atacamensis), to amphipods (Hyalella) and flamingos (Pheonicopterus). Macroinvertebrate presence/absence data derived from conventional kick-net surveys further validated Salar de Tara as the most biodiverse system. Compared to net-based assessments, eDNA analysis allowed more refined taxonomic assignments for copepods and ostracods, while certain taxa such as Ephydridae or Hirudinea were not detected through molecular tests. Overall, this study provides evidence that eDNA is an effective tool to elucidate fine scale taxa assemblages and can refine conservation efforts in hypersaline lakes. Given the fast pace of research developments in the field of molecular ecology, eDNA hosts great potential to become a central actor in hypersaline bioassessments in the near future.