bluuebunny/crossref_metadata_2025_split huggingface dataset:
HELP: A computational framework for labelling and predicting human common and context-specific essential genes
Authors: Granata Ilaria , Maddalena Lucia , Manzo Mario , Guarracino Mario Rosario , Giordano Maurizio 
Published: April 2024

AbstractMachine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human GeneEssentialityLabelling &Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the reference sets of essential gene annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of essential genes while confirming the existence of essentiality nuances.Author summaryEssential genes (EGs) are commonly defined as those required for an organism or cell’s growth and survival. The essentiality is strictly dependent on both environmental and genetic conditions, determining a difference between those considered common EGs (cEGs), essential in most of the contexts considered, and those essential specifically to one or few contexts (context-specific EGs, csEGs). In this paper, we present a library of tools and methodologies to address the identification and prediction of cEGs and csEGs. Furthermore, we attempt to experimentally explore the statement that essentiality is not a binary property by identifying, predicting and analysing an intermediate class between the Essential (E) and Not Essential (NE) genes. Among the multi-source data used to predict the EGs, we found the best attributes combination to capture the essentiality. We demonstrated that the additional class of genes we defined as “almost Essential” shows differences in these attributes from the E and NE genes. We believe that investigating the context-specificity and the dynamism of essentiality is particularly relevant to unravelling crucial insights into biological mechanisms and suggesting new candidates for precision medicine.
A Computational Algorithm for Optimal Design of Bioartificial Organ Scaffold Architectures
Authors: Bukač Martina , Čanić Sunčica , Muha Boris , Wang Yifan 
Published: April 2024

AbstractWe develop a computational algorithm based on a diffuse interface approach to study the design of bioartificial organ scaffold architectures. These scaffolds, composed of poroelastic hydrogels housing transplanted cells, are linked to the patient’s blood circulation via an anastomosis graft. Before entering the scaffold, the blood flow passes through a filter, and the resulting filtered blood plasma transports oxygen and nutrients to sustain the viability of transplanted cells over the long term. A key issue in maintaining cell viability is the design of ultrafiltrate channels within the hydrogel scaffold to facilitate advection-enhanced oxygen supply ensuring oxygen levels remain above a critical threshold to prevent hypoxia. In this manuscript, we develop a computational algorithm to analyze the plasma flow and oxygen concentration within hydrogels featuring various channel geometries. Our objective is to identify the optimal hydrogel channel architecture that sustains oxygen concentration throughout the scaffold above the critical hypoxic threshold.The computational algorithm we introduce here employs a diffuse interface approach to solve a multi-physics problem. The corresponding model couples the time-dependent Stokes equations, governing blood plasma flow through the channel network, with the time-dependent Biot equations, characterizing Darcy velocity, pressure, and displacement within the poroelastic hydrogel containing the transplanted cells. Subsequently, the calculated plasma velocity is utilized to determine oxygen concentration within the scaffold using a diffuse interface advection-reaction-diffusion model. Our investigation yields a scaffold architecture featuring a hexagonal channel network geometry that meets the desired oxygen concentration criteria. Unlike classical sharp interface approaches, the diffuse interface approach we employ is particularly adept at addressing problems with intricate interface geometries, such as those encountered in bioartificial organ scaffold design. This study is significant because recent developments in hydrogel fabrication make it now possible to control hydrogel rheology [20, 14], and utilize computational results to generate optimized scaffold architectures.MSC codes76S05; 76-04; 76D05; 92-10; 92-04
Warming-induced excess deaths of infected animals depend on pathogen kingdom and evolutionary history
Authors: Li Jingdi , Guttmann Nele , Drew Georgia , Hector Tobias , Wolinska Justyna , King Kayla 
Published: April 2024

AbstractClimate change is causing extreme heating events. Simultaneously, climate change and human activities are leading to more prolonged and intense infectious disease outbreaks. The extent to which warming and infection may together impact host species persistence is, however, unclear. Using a meta-analysis of >190 effect sizes representing 101 ectothermic animal host-pathogen systems, we provide broad evidence that experimentally increased temperatures drove higher pathogen virulence, specifically pathogen-induced host mortality. Such pattern was mainly driven by excess host death caused by bacterial infections combined with warming, particularly if the pathogenic bacteria were naturally established within the host species, though novel infections without known host-pathogen evolutionary history were more lethal at lower temperatures. Importantly, larger temperature increases were associated with more host deaths hinting at the escalating threat for animal species as the world continues to warm. We found that the virulence of fungal pathogens increased only when temperatures were shifted upwards towards their thermal optimum. The magnitude of these effects was not impacted by host life-stage, immune complexity, or variable experimental protocols. Overall, our findings reveal distinct patterns of pathogen virulence change under warmer temperatures, suggesting that the impact of global warming on infectious disease outcomes would depend on pathogen traits (taxonomic kingdom, thermal tolerance) and host-pathogen evolutionary history.Author SummaryHuman-induced climate warming is one of the biggest challenges in our times. Simultaneously, climate change is associated with more intense infectious disease outbreaks, suggesting that temperature rises also influence disease dynamics. Growing numbers of studies have investigated the effect of warming on disease severity (or pathogen virulence) in different animal host-pathogen systems. However, individual studies did not always agree with each other, and how increased temperature and pathogen infection together impact animal survival remains unclear. Here, we resolved this uncertainty by conducting a meta-analysis of >190 effect sizes representing 101 animal host-pathogen systems. We provided broad evidence that, higher temperatures caused more deaths of infected animals, particularly for animals with bacterial infections under warmer conditions. We found that larger temperature rises were associated with more animal deaths, suggesting the increased threat for host species as the world continues to warm. We also found that pathogenic fungi were more sensitive to heat than bacterial pathogens, and temperature changes the virulence of fungal pathogens in relation to their thermal optimum.
Tandem-repeat proteins introduce tuneable properties to engineered biomolecular condensates
Authors: Chris Ng Tin Long , Hoare Mateo P. , Maristany M. Julia , Wilde Ellis J. , Sneideris Tomas , Huertas Jan , Agbetiameh Belinda K. , Furukawa Mona , Joseph Jerelle A. , Knowles Tuomas P.J. , Collepardo-Guevara Rosana , Itzhaki Laura S. , Kumita Janet R. 
Published: April 2024

AbstractThe cell’s ability to rapidly partition biomolecules into biomolecular condensates is linked to a diverse range of cellular functions. Understanding how the structural attributes of biomolecular condensates are linked with their biological roles can be facilitated by the development synthetic condensate systems that can be manipulated in a controllable and predictable way. Here, we design and characterise a tuneable synthetic biomolecular condensate platform fusing modular consensus-designed tetratricopeptide repeat (CTPR) proteins to intrinsically-disordered domains. Trends between the CTPR structural attributes and condensate propensity were recapitulated across different experimental conditions and byin silicomodelling, demonstrating that the CTPR domain can systematically affect the condensates in a predictable manner. Moreover, we show that incorporating short binding motifs into the CTPR domain results in specific target-protein recruitment into the condensates. Our model system can be rationally designed in a versatile manner to both tune condensate propensity and endow the condensates with new functions.
Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling
Authors: Lee Suemin , Wang Dedi , Seeliger Markus A. , Tiwary Pratyush 
Published: April 2024

AbstractUnderstanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomiclevel understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long timescales. Recent advances in rare event sampling have allowed us to reach these timescales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitudes of timescales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an approximate reaction coordinate (RC) and use it to guide the enhanced sampling method metadynamics. We demonstrate the performance of our algorithm by applying it to six different protein-ligand complexes with available benchmark residence times, including the dissociation of the widely studied anti-cancer drug Imatinib (Gleevec) from both wild-type Abl kinase and drug-resistant mutants. We show how our protocol can recover quantitatively accurate residence times, potentially opening avenues for deeper insights into drug development possibilities and ligand recognition mechanisms.TOC Graphic
Toxic Effects of Biogenic and Synthesized Silver Nanoparticles on Sea UrchinEchinometra lucunterEmbryos
Authors: Bruni Mariana , Ottoni Cristiane , Abessa Denis 
Published: April 2024

AbstractDue to their broad-spectrum antimicrobial action and ease of synthesis, silver nanoparticles (AgNPs) are one of the most widely used nanomaterials in different industrial and ecological areas. AgNPs are released into marine ecosystems; however, their ecotoxicological effects have been overlooked. In this study, we evaluated the toxic effects of biogenic and synthesized AgNPs on sea urchinEchinometra lucunterembryos and compared them to those of AgNO3. Fertilized eggs were exposed to five concentrations of the test compounds and a negative control for 48 h under controlled conditions. The EC50-48h of biogenic and synthetic AgNPs and AgNO3were 0.31, 4.095, and 0.01 μg L-1, evidencing that AgNPs are less toxic than AgNO3, and that synthetic AgNP is less toxic. Toxicity toE. lucunterembryos could be explained by the fact that Ag affects DNA replication and induces the formation of pores in the cellular wall, leading to apoptosis.
Regional Glymphatic Dysfunction is linked to Spinocerebellar Ataxia Type 3 pathophysiology
Authors: Hua Lin , Xu Manxi , Zhang Linwei , Gao Fei , Zeng Xinglin , Yang Aocai , Luan Jixin , Shmuel Amir , Ma Guolin , Yuan Zhen 
Published: April 2024

AbstractSpinocerebellar ataxia type 3 (SCA3) involves neuroinflammation and imbalance between production and clearance of proteins which affects the glymphatic system, the lymphatic-like, fluid-transport system in the brain. However, it is unclear whether SCA3 is related to impairments in glymphatic function. Using multimodal imaging data, 34 SCA3 patients and 36 age-, sex- and educational matched healthy controls (HCs) were compared using multiple glymphatic measurements, including choroid plexus (CP) and cerebrospinal fluid (CSF) volume, diffusion tensor imaging along the perivascular (DTI-ALPS) index, and coupling relationship between blood-oxygen-level-dependent signals and CSF flow (BOLD-CSF coupling). Then, we evaluated regional glymphatic function by dividing DTI-ALPS and BOLD-CSF coupling into anterior, middle, posterior, and cerebellum regions, thereby identifying the spatial variation of glymphatic function in the two groups. We demonstrated that compared with HCs, larger CP and CSF volumes were found in SCA3 patients. More importantly, for DTI-ALPS index and BOLD-CSF coupling, these surrogate markers for glymphatic clearance were weaker in SCA3 patients. Furthermore, altered regional glymphatic functions were most prominent in midbrain, cerebellum and middle regions. Crucially, the altered midbrain, cerebellum, middle and global glymphatic functions were accompanied by the severity of ataxia and other SCA3 symptoms. Similar to other neurodegenerative disorders, the association between multiple glymphatic indexes and SCA3 symptoms suggested that waste clearance is disrupted in SCA3 patients, which shed light on the pathogenesis of this disease from a glymphatic lens. Our findings highlighted the dysregulated glymphatic function as a novel diagnostic marker for SCA3.
Bessel Beam Optical Coherence Microscopy Enables Multiscale Assessment of Cerebrovascular Network Morphology and Function
Authors: Glandorf Lukas , Wittmann Bastian , Droux Jeanne , Glück Chaim , Weber Bruno , Wegener Susanne , El Amki Mohamad , Leitgeb Rainer , Menze Bjoern , Razansky Daniel 
Published: April 2024

AbstractUnderstanding the morphology and function of large-scale cerebrovascular networks is crucial for studying brain health and disease. However, reconciling the demands for imaging on a broad scale with the precision of high-resolution volumetric microscopy has been a persistent challenge. In this study, we introduce Bessel beam optical coherence microscopy with an extended focus to capture the full cortical vascular hierarchy in mice over 1000 × 1000 × 360 μm3field-of-view at capillary level resolution. The post-processing pipeline leverages a supervised deep learning approach for precise 3D segmentation of high-resolution angiograms, hence permitting reliable examination of microvascular structures at multiple spatial scales. Coupled with high-sensitivity Doppler optical coherence tomography, our method enables the computation of both axial and transverse blood velocity components as well as vessel-specific blood flow direction, facilitating a detailed assessment of morpho-functional characteristics across all vessel dimensions. Through graph-based analysis, we deliver insights into vascular connectivity, all the way from individual capillaries to broader network interactions, a task traditionally challenging forin vivostudies. The new imaging and analysis framework extends the frontiers of research into cerebrovascular function and neurovascular pathologies.
The genetic diversity ofStrongyloides papillosusin Pakistani goats revealed by whole genome sequencing
Authors: Afshan Kiran , Liu Yuchen , Viney Mark 
Published: April 2024

AbstractStrongyloidesnematodes are parasites of livestock, andS. papillosusinfects ruminant livestock that can cause disease. Recent genomic analysis of severalStrongyloidesspecies is now facilitating the population genomic analyses of naturalStrongyloidesinfections, for example finding thatS. rattiin wild UK rats exists as an assemblage of long-lived, asexual lineages. Here we have initiated an investigation into the population genomics ofS. papillosusin goats in Pakistan. We find thatS. papillosusis common, with a prevalence of 28%; that the population is genetically diverse and that individual goats commonly have mixed-genotype infections; and that there is evidence of only limited admixture. These results now provoke further questions about the host range of differentS. papillosusgenotypes that can in the future be investigated by further population genomic analyses.
Correlated host movements can reshape spatio-temporal disease dynamics: modeling the contributions of space use to transmission risk using movement data
Authors: Vargas Soto Juan S. , Kosiewska Justin , Muller Lisa I. , Grove Dan , Metts Dailee , Wilber Mark Q. 
Published: April 2024

AbstractDespite decades of epidemiological theory making relatively simple assumptions about host movements, it is increasingly clear that non-random movements drastically affect disease transmission. To better predict transmission risk, theory needs to simultaneously account for how the environment affects host space use and how social dynamics affect correlation in space use. We develop new theory that decomposes the relative contributions of fine-scale space use and correlated movements to spatio-temporal transmission risk. Using analytical results, simulations, and empirical movement data, we show that even weak correlations can increase transmission risk by orders of magnitude compared to independent movement. Accounting for correlation is especially critical for pathogens with direct transmission or short environmental persistence. Our theory provides clear expectations for what has been observed empirically but largely ignored in disease models—movement correlation can reshape epidemiological landscapes, creating transmission hotspots whose magnitude and location are not necessarily predictable from spatial overlap alone.
A newOpn4crerecombinase mouse line to target intrinsically photosensitive retinal ganglion cells (ipRGCs)
Authors: Dyer Brannen , Yu Sue O. , Brown R. Lane , Lang Richard A. , D’Souza Shane P. 
Published: April 2024

ABSTRACTIntrinsically photosensitive retinal ganglion cells (ipRGCs) play a crucial role in several physiological light responses. In this study we generate a newOpn4creknock-in allele (Opn4cre(DSO)), in which cre is placed immediately downstream of theOpn4start codon. This approach aims to faithfully reproduce endogenousOpn4expression and improve compatibility with widely used reporters. We evaluated the efficacy and sensitivity ofOpn4cre(DSO)for labeling in retina and brain, and provide an in-depth comparison with the extensively utilizedOpn4cre(Saha)line. Through this characterization,Opn4cre(DSO)demonstrated higher specificity in labeling ipRGCs, with minimal recombination escape. Leveraging a combination of electrophysiological, molecular, and morphological analyses, we confirmed its sensitivity in detecting all ipRGC types (M1-M6). Using this new tool, we describe the topographical distributions of ipRGC types across the retinal landscape, uncovering distinct ventronasal biases for M5 and M6 types, setting them apart from their M1-M4 counterparts. In the brain, we find vastly different labeling patterns between lines, withOpn4cre(DSO)only labeling ipRGC axonal projections to their targets. The combination of off-target effects ofOpn4cre(Saha)across the retina and brain, coupled with diminished efficiencies of bothCrelines when coupled to less sensitive reporters, underscores the need for careful consideration in experimental design and validation with anyOpn4credriver. Overall, theOpn4cre(DSO)mouse line represents an improved tool for studying ipRGC function and distribution, offering a means to selectively target these cells to study light-regulated behaviors and physiology.
Combining pupillometry and drift-diffusion models reveals auditory category learning dynamics
Authors: McHaney Jacie R. , Roark Casey L. , McGinley Matthew J. , Chandrasekaran Bharath 
Published: April 2024

AbstractPupillometry and drift-diffusion models (DDM) have emerged as powerful tools to offer insights into learning processes and decisional dynamics. Specifically, pupillary dilation can serve as a metric of arousal and cognitive processing, and DDMs examine processes underlying perceptual decision-making using behavioral accuracies and response times. Methodological constraints have complicated the combination of the two methods in the study of learning. DDMs require precise response times, yet pupillary responses are slow and are impacted by motor movements. Here, we developed a learning task that separately optimized measurement of behavioral response times and pupil dilation during learning. We modified a standard learning paradigm to include trials with and without timing delays between stimulus presentation and response. Delayed trials optimized measurement of the pupil, while immediate trials optimized behavioral response times. We tested whether decision-making processes estimated with DDMs could be recovered from delayed trials where pupil response measurement was optimized. Our results indicated that pupil responses on delayed trials showed distinct markers of learning that were not present on immediate trials. DDM parameters from delayed trials also showed expected trends for learning. Finally, we demonstrated that DDM parameters elicit differential pupillary responses. Together, these results indicate that learning dynamics and decisional processes can be decoded from pupillary responses.
DNA glycosylases Ogg1 and Mutyh mediate gene expression of PRC2 target genes important for neuronal processes underlying memory formation
Authors: Abentung Andreas , Sakshaug Teri , Dumaru Rabina , Liabakk Nina-Beate , Yang Mingyi , Wang Junbai , Bjørås Magnar , Scheffler Katja 
Published: April 2024

AbstractBase excision repair (BER) initiated by DNA glycosylases is known to preserve genomic integrity by removing damaged bases. Recently, several DNA glycosylases were identified as potential readers of epigenetic modifications and proteins involved in BER have been associated with active DNA demethylation. DNA glycosylases Ogg1 and Mutyh were shown to alter the hippocampal transcriptome associated with cognitive function and independent of global DNA damage accumulation. However, the mechanism of DNA glycosylases in regulating cognition and their role in epigenetic remodeling in the brain remains elusive. Here we report that the combined deficiency of Ogg1 and Mutyh impairs spatial but not associative long-term memory. We demonstrate that Ogg1 or Mutyh modulate DNA methylation at gene regulatory regions of polycomb repressive complex 2 (PRC2) target genes in the adult hippocampus. Moreover, we find that the distribution of the PRC2 complex and histone modifications associated with PRC2 activity changes in both hippocampal neurons and glia depend on Ogg1 and Mutyh. Epigenetic alterations correlated with cell-type specific gene expression changes which were associated with pathways important for neuronal function and cognition. Our results provide a novel role for Ogg1 and Mutyh beyond DNA repair in modulating the epigenome to control transcriptional responses in the brain important for memory formation.