bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring Differences in Functional Connectivity in Australian Rules Football Players : A Resting-State fMRI Study on the Default Mode Network
Authors: Tran Mi , Shrikumar Avanti , Hellewell Sarah C , Welton Thomas , Kirkby Max , Maller Jerome J , Smith Paul , Pearce Alan , Grieve Stuart M 
Published: September 2024

AbstractThe effects of non-concussive impacts in contact-sports such as in Australian rules football (ARF) are still largely unexplored. These impacts are often but not always lower in intensity, but occur more frequently than actual concussions. Since non-concussive impacts are often asymptomatic, their significance may be underestimated. Acute or subacute measurement of non-concussive injury is challenging as the pathological response and injury is poorly described. There is therefore a need for a greater understanding of the pathological consequences of exposure. Growing evidence indicates that resting-state functional connectivity (rs-fMRI) changes in the Default Mode Network (DMN) may be an important biomarker that is sensitive to characterize these impacts. In this work, we examined functional connectivity changes within the DMN of ARF players to evaluate its potential as an early biomarker for non-concussive impacts. Based on rs-fMRI, we compare the DMN of 47 sub-elite ARF players (mean age 21.5±2.7 years [SD], males 57%) and 42 age-matched healthy controls (mean age 23.2±2.3 years [SD], males 48%) using Independent Component Analysis (ICA) and Dual Regression. This approach permits an unbiased decomposition of brain activity into networks with principled handling of statistical error. An 83% increase in DMN connectivity (as measured by the Strictly Standardized Mean Difference on values derived from Dual Regression) was observed in ARF players in the left retrosplenial cingulate cortex compared to healthy controls (FDR-corrected p-value from dual regression = 0.03, 95% CI computed via bootstrapping was 58% to 116%). The AUC for distinguishing ARF players from controls was 0.80 (95% CI; [0.71, 0.89]), equating to a PPV of 78% and a NPV of 74%. These results are preliminary; future work could investigate robustness to different random initializations of ICA and validate the findings on an independent testing set, as well as investigate longitudinal changes in ARF players over the course of a playing season.
Filamentation activates bacterial NLR-like antiviral protein
Authors: Wang Yiqun , Tian Yuqing , Yang Xu , Yu Feng , Zheng Jianting 
Published: September 2024

AbstractBacterial antiviral STANDs (Avs) are evolutionarily related to the nucleotide-binding leucine-rich repeat containing receptors (NLRs) widely distributed in immune systems across animals and plants.EfAvs5, an Avs type 5 protein fromEscherichia fergusonii, contains an N-terminal SIR2 effector domain, a nucleotide-binding oligomerization domain (NOD) and a C-terminal sensor domain, conferring protection against diverse phage invasions. Despite the established roles of SIR2 and STAND in prokaryotic and eukaryotic immunity, the mechanism underlying their collaboration remains unclear. Here we present cryo-EM structures ofEfAvs5 filaments, elucidating the mechanisms of dimerization, filamentation, filament clustering, ATP binding and NAD+hydrolysis, all of which are crucial for anti-phage defense. The SIR2 domains and NODs engage in the intra- and inter-dimer interaction to form an individual filament, while the outward C-terminal domains contribute to bundle formation. Filamentation potentially stabilizes the dimeric SIR2 configuration, thereby activating the NADase activity ofEfAvs5.EfAvs5 is deficient in the ATPase activity, but elevated ATP concentrations can impede its NADase activity. Together, we uncover the filament assembly of Avs5 as a unique mechanism to switch enzyme activities and perform anti-phage defenses, emphasizing the conserved role of filamentation in immune signaling across diverse life forms.KeypointsEfAvs5 depletes NAD+for anti-phage defense.EfAvs5 assembles as bundled filaments that can hydrolyze NAD+.The SIR2 domain and NOD collaborate to form an individual filament.The building block of the filament isEfAvs5 dimer.The activity of theEfAvs5 complex is regulated by ATP.
Genetic-Dependent Brain Signatures of Resilience: Interactions among Childhood Abuse, Genetic Risks and Brain Function
Authors: Lu Han , Rolls Edmund T. , Liu Hanjia , Stein Dan J. , Sahakian Barbara J. , Elliott Rebecca , Jia Tianye , Xie Chao , Xiang Shitong , Wang Nan , Banaschewski Tobias , Bokde Arun L.W. , Desrivières Sylvane , Flor Herta , Grigis Antoine , Garavan Hugh , Heinz Andreas , Brühl Rüdiger , Martinot Jean-Luc , Martinot Marie-Laure Paillère , Artiges Eric , Nees Frauke , Orfanos Dimitri Papadopoulos , Lemaitre Herve , Poustka Luise , Hohmann Sarah , Holz Nathalie , Fröhner Juliane H. , Smolka Michael N. , Vaidya Nilakshi , Walter Henrik , Whelan Robert , Schumann Gunter , Feng Jianfeng , Luo Qiang ,   the IMAGEN Consortium
Published: September 2024

SummaryResilience to emotional disorders is critical for adolescent mental health, especially following childhood abuse. Yet, brain signatures of resilience remain undetermined due to the differential susceptibility of the brain’s emotion processing system to environmental stresses. Analyzing brain’s responses to angry faces in a longitudinally large-scale adolescent cohort (IMAGEN), we identified two functional networks related to the orbitofrontal and occipital regions as candidate brain signatures of resilience. In girls, but not boys, higher activation in the orbitofrontal-related network was associated with fewer emotional symptoms following childhood abuse, but only when the polygenic burden for depression was high. This finding defined a genetic-dependent brain (GDB) signature of resilience. Notably, this GDB signature predicted subsequent emotional disorders in late adolescence, extending into early adulthood and generalizable to another independent prospective cohort (ABCD). Our findings underscore the genetic modulation of resilience-brain connections, laying the foundation for enhancing adolescent mental health through resilience promotion.
Probing the role of synaptic adhesion molecule RTN4RL2 in setting up cochlear connectivity
Authors: Karagulyan Nare , Überegger Maja , Qi Yumeng , Babai Norbert , Hofer Florian , Johnson Chacko Lejo , Wang Fangfang , Luque Maria , Glueckert Rudolf , Schrott-Fischer Anneliese , Hua Yunfeng , Moser Tobias , Bandtlow Christine 
Published: September 2024

AbstractSound encoding depends on the precise and reliable neurotransmission at the afferent synapses between the sensory inner hair cells (IHCs) and spiral ganglion neurons (SGNs). The molecular mechanisms contributing to the formation, as well as interplay between the pre- and postsynaptic components remain largely unclear. Here, we tested the role of the synaptic adhesion molecule and Nogo/RTN4 receptor homolog RTN4RL2 (also referred to as NgR2) in the development and function of afferent IHC-SGN synapses. Upon deletion of RTN4RL2 in mice (RTN4RL2-/-), presynaptic IHC active zones showed enlarged synaptic ribbons and a depolarized shift in the activation of CaV1.3 Ca2+channels. The postsynaptic densities (PSDs) of SGNs were smaller and deficient of GluA2/3 despite maintained Gria2 mRNA expression in SGNs. Next to synaptically engaged PSDs we observed “orphan” PSDs located away from IHCs. They likely belong to a subset of SGN peripheral neurites that do not contact the IHCs in RTN4RL2-/-cochleae as found by volume electron microscopy reconstruction of SGN neurites. Auditory brainstem responses of RTN4RL2-/-mice showed increased sound thresholds indicating impaired hearing. Together, these findings suggest that RTN4RL2 contributes to the proper formation and function of auditory afferent synapses and is critical for normal hearing.
Disclosing temperature sensitivity of West Nile virus transmission: novel computational approaches to mosquito-pathogen trait responses
Authors: Heidecke Julian , Wallin Jonas , Fransson Peter , Singh Pratik , Sjödin Henrik , Stiles Pascale , Treskova Marina , Rocklöv Joacim 
Published: September 2024

AbstractTemperature influences the transmission of mosquito-borne pathogens with significant implications for disease risk in the context of climate change. Mathematical models of mosquito-borne infections rely on functions that capture mosquito-pathogen interactions in response to temperature to better estimate transmission dynamics. To derive these functions, experimental studies provide valuable data on the temperature sensitivity of mosquito life-history traits and pathogen transmission. However, the scarcity of experimental data and inconsistencies in methodologies for analysing and comparing temperature responses across mosquito species and pathogens present challenges to accurately modelling mosquito-borne infections. In this study, we investigated the thermal biology of West Nile virus (WNV), a major mosquito-borne infection. We critically reviewed existing experimental studies, obtaining temperature responses for eight mosquito-pathogen traits across 15 mosquito species. Using this data, we employed Bayesian hierarchical models to estimate temperature response functions for each trait and estimate their variation between species and experiments. We incorporate the resulting functions into mathematical models to estimate the temperature sensitivity of WNV transmission, focusing on six competent mosquito species of the genusCulex. Our study finds similarities in the temperature response amongCulexspecies, with a general optimal transmission temperature around 24°C. We demonstrate that differing mechanistic assumptions in published models can result in a temperature optimum variation exceeding 3°C, underscoring the need for model scrutiny. Additionally, we identify substantial variability between trait temperature responses across experiments on the same mosquito species, indicating significant intra-species variation in trait performance. We provide recommendations for future experimental studies, emphasizing the critical need for additional data on all mosquito-pathogen traits, except for mosquito larva-to-adult development rate and survival, which are relatively well-documented. Incorporating additional data into our multi-species approach enhances the accuracy of temperature trait response estimates. These improvements are critical for forecasting future shifts in mosquito-borne disease risk due to climate change.
Revisiting the taxonomy ofEnterococcus casseliflavusand related species
Authors: Soares de Medeiros Lima Matheus Miguel , Prichula Janira , Sakamoto Tetsu 
Published: September 2024

AbstractEnterococcus casseliflavus, a commonly mobile and yellow-colored bacterium, is a commensal member of the gastrointestinal tract. It is occasionally found in cases of bacteremia and other human infections. A concern is that all strains of this species have thevanCgene group on their chromosome, which confers resistance to vancomycin. The classification ofE. casseliflavusis challenging, as it presents 99% identity in 16S analysis withE. gallinarumand, mainly, withE. flavescens, often being classified as a single species. This study aimed to revisit the taxonomy ofE. casseliflavusand other related species by carrying out a comprehensive analysis of the genomic data available for these species in public databases.analyzing the genomic data. For this, 155 genomes ofE. casseliflavusrelated species (E. casseliflavus, E. flavescens, E. entomosocium, andE. innesii) were retrieved and submitted to Average Nucleotide Identity (ANI) and phylogenomic analysis. Both approaches showed three well-delineated clusters which correspond to threeEnterococcusspecies (E. casseliflavus, E. flavescensandE. innesii). Here we suggest (1) the removal of synonym status betweenE. flavescensandE. cassliflavus, and (2) addition of synonym status betweenE. entomosociumandE. casseliflavus.
Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD
Authors: Taubenheim Jan , Kadibalban A. Samer , Zimmermann Johannes , Taubenheim Claudia , Tran Florian , Rosenstiel Philip , Aden Konrad , Kaleta Christoph 
Published: September 2024

AbstractInflammatory bowel diseases (IBDs) are chronic disorders involving dysregulated immune responses. Despite the role of disrupted host-microbial interaction in the pathophysiology of IBD, the underlying metabolic principles are not fully understood. We densely profiled microbiome, transcriptome and metabolome signatures from longitudinal IBD cohorts before and after advanced drug therapy initiation and reconstructed metabolic models of the gut microbiome and the host intestine to study host-microbiome metabolic cross-talk in the context of inflammation. We identified concomitant changes in metabolic activity across data layers involving NAD, amino acid, one-carbon and phospholipid metabolism. In particular on the host level, elevated tryptophan catabolism depleted circulating tryptophan, thereby impairing NAD biosynthesis. Reduced host transamination reactions disrupted nitrogen homeostasis and polyamine/glutathione metabolism. The suppressed one-carbon cycle in patient tissues altered phospholipid profiles due to limited choline availability. Simultaneously, microbiome metabolic shifts in NAD, amino acid and polyamine metabolism exacerbated these host metabolic imbalances. Leveraging host and microbe metabolic models, we predicted dietary interventions remodeling the microbiome to restore metabolic homeostasis, suggesting novel therapeutic strategies for IBD.
Overview of theSaccharomyces cerevisiaepopulation structure through the lens of 3,034 genomes
Authors: Loegler Victor , Friedrich Anne , Schacherer Joseph 
Published: September 2024

AbstractWith the rise of high-throughput sequencing technologies, a holistic view of genetic variation within populations – through population genomics studies – appears feasible, although it remains an ongoing effort. Genetic variation arises from a diverse range of evolutionary forces, with mutation and recombination being key drivers in shaping genomes. Studying genetic variation within a population represents a crucial first step in understanding the relationship between genotype and phenotype and the evolutionary history of species. In this context, the budding yeastSaccharomyces cerevisiaehas been at the forefront of population genomic studies. In addition, it has a complex history that involves adaptation to a wide range of wild and human-related ecological niches. Although to date more than three thousand diverse isolates have been sequenced, there is currently a lack of a resource bringing together sequencing data and associated metadata for all sequenced isolates. To perform a comprehensive analysis of the population structure ofS. cerevisiae, we collected genome sequencing data from 3,034 natural isolates and processed the data uniformly. We determined ploidy levels, identified single nucleotide polymorphisms (SNPs), small insertion-deletions (InDels), copy number variations (CNVs), and aneuploidies across the population, creating a publicly accessible resource for the yeast research community. Interestingly, we showed that this population captures ∼93% of the species diversity. Using neighbor-joining and Bayesian methods, we redefined the populations, revealing clustering patterns primarily based on ecological origin. This work represents a valuable resource for the community and efforts have been made to make it evolvable and integrable to future yeast population studies.
Benchmarking coarse-grained simulation methods for investigation of transport tunnels in enzymes
Authors: Mandal Nishita , Stevens Jan A. , Poma Adolfo B. , Surpeta Bartlomiej , Sequeiros-Borja Carlos , Thirunavukarasu Aravind Selvaram , Marrink Siewert J. , Brezovsky Jan 
Published: September 2024

AbstractEnzymes are pivotal to numerous biological processes, often featuring buried active sites linked to the surrounding solvent through intricate and dynamic tunnels. These tunnels are vital for facilitating substrate access, enabling product release, and regulating solvent exchange, which collectively influence enzymatic function and efficiency. Consequently, knowledge of tunnels is key for a holistic understanding of the effect of mutations as well as predicting drug residence times. Unfortunately, most transport tunnels are transient, i.e., equipped by molecular gates, rendering their opening a rare event that is often notoriously hard to study with conventional molecular dynamics simulations. To overcome the sampling limitation of such simulations, this study investigated the efficacy of three different coarse-grained (CG) molecular dynamics simulation methods for inferring enzyme tunnel structure and dynamics. Here, we covered the Martini and SIRAH models with different restraint protocols providing stability to CG proteins while to some extent biasing the sampling towards a reference structure. By contrasting CG results with all-atom simulations, we benchmarked the ability of CG methods to replicate ensemble characteristics of complex tunnel networks in haloalkane dehalogenase LinB and two of its mutants with engineered tunnel networks. The assessed tunnel parameters are essential for prioritizing functionally relevant tunnels and delineating the effect of mutations on transport tunnels. Our findings reveal that while CG methods significantly enhance the efficiency of tunnel analyses, some of them, like Martini with Elastic network restraints, were limited in recapitulating all-atom tunnel dynamics due to the structural bias applied. In contrast, the Martini Gō model even captured the intricate details of mutation perturbing tunnel dynamics. All studied CG methods performed well in capturing the geometry of tunnel ensembles in line with all-atom simulations. Additionally, the wider applicability of CG methods was verified by analyzing tunnel networks of nine enzymes from different combinations of structural and functional classes, demonstrating their potential to uncover new tunnel phenomena and validate their utility in broader biological and functional contexts. This comprehensive evaluation underscores the strengths and constraints of CG simulations in capturing enzyme tunnels and benefiting from their computational speed for studying huge datasets of enzymes. These insights are valuable for enzyme engineering, drug design, and understanding enzyme function while benefitting from the efficiency of coarse-grained models.
The Plastisphere – Marinefungicommunities in theplastics age
Authors: Kirstein Inga V. , Reich Marlis , Yang Yanyan , Timmermann Maike , Wichels Antje , Gerdts Gunnar 
Published: September 2024

AbstractFungi play important roles in biofilms, are very versatile in their ecological role, and are considered as plastic degraders. Here we aim to increase the resolution of the fungal members of the Plastisphere, to understand fungal substrate specificities and related potential ecological impacts. Fifteen-month-old fungal Plastisphere communities were investigated on 9 different plastic types and glass in seawater from the North Sea. By integrating scanning electron microscopy (SEM) imaging, ITS-based fingerprinting, and re-evaluated 18S rRNA gene sequence data through a fungal-specific phylogeny-based pipeline, we observed fungal Plastispheres and identified specific characteristics based on morphotypes, phylogeny, and biodiversity across different substrate types. Plastic types selected for specific fungal communities with polyolefine communities indicating significantly higher diversity compared to all other plastic types. Furthermore, specific plastic types may select for specific fungal taxa and their potential hosts, highlighting the complexity of marine biofilm food webs, and related ecological implications.
Deep Video Analysis for Bacteria Genotype Prediction
Authors: Dabouei Ali , Mishra Ishan , Kapur Kuwar , Cao Chengzhi , Bridges Andrew A. , Xu Min 
Published: September 2024

AbstractGenetic modification of microbes is central to many biotechnology fields, such as industrial microbiology, bioproduction, and drug discovery. Understanding how specific genetic modifications influence observable bacterial behaviors is crucial for advancing these fields. In this study, we propose a supervised model to classify bacteria harboring single gene modifications to draw connections between phenotype and genotype. In particular, we demonstrate that the spatiotemporal patterns ofVibrio choleraegrowth, recorded in terms of low-resolution bright-field microscopy videos, are highly predictive of the genotype class. Additionally, we introduce a weakly supervised approach to identify key moments in culture growth that significantly contribute to prediction accuracy. By focusing on the temporal expressions of bacterial behavior, our findings offer valuable insights into the underlying mechanisms and developmental stages by which specific genes control observable phenotypes. This research opens new avenues for automating the analysis of phenotypes, with potential applications for drug discovery, disease management, etc. Furthermore, this work highlights the potential of using machine learning techniques to explore the functional roles of specific genes using a low-resolution light microscope.
A Bayesian framework to infer and cluster mutational signatures leveraging prior biological knowledge
Authors: Buscaroli Elena , Haghighi Azad Sadr , Bergamin Riccardo , Milite Salvatore , Garcia Edith Natalia Villegas , Tasciotti Arianna , Ansuini Alessio , Ramazzotti Daniele , Calonaci Nicola , Caravagna Giulio 
Published: September 2024

AbstractThe concept of mutational signatures, with its promising translational potential, provides key insights into the mutational processes underlying cancer. Current efforts are increasingly directed toward developing comprehensive catalogues of signatures linked to various tumour types and therapeutic responses. However, the existence of multiple catalogues, generated by different groups using distinct methodologies, underscores the need for standardisation across the field, and a cohesive framework that integrates established signatures remains to be fully realised. Here, we introduce a set of Bayesian algorithms that merge predefined signature catalogues with newly identified signatures, offering a systematic approach to expanding existing collections. By leveraging a diverse array of mutational signatures, our method also groups patients based on shared mutational patterns, further enhancing the translational relevance of these catalogues. We demonstrate that this approach enables the identification of both known and novel molecular subtypes across nearly 7,000 samples spanning three major cancers: breast, colon, and lung. Building on prior research, we propose a robust strategy to deepen our understanding of mutational processes in cancer.