bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-Resolution Multimodal Profiling of Human Epileptic Brain Activity via Explanted Depth Electrodes
Authors: Dwiwedi Anuj Kumar , Mahesh Arun , Sanfeliu Albert , Larkin Julian , Siwicki Rebecca , Sweeney Kieron J. , O’Brien Donncha F. , Widdess-Walsh Peter , Picelli Simone , Henshall David C. , Tiwari Vijay K. 
Published: July 2024

ABSTRACTUnderstanding neurological disorders necessitates systems-level approaches that integrate multimodal data, but progress has been hindered by limited sample availability, and the absence of combined electrophysiological and molecular data from live patients. Here, we demonstrate that intracranial stereoelectroencephalography (SEEG) electrodes used for identifying the seizure focus in epilepsy patients enable the integration of RNA sequencing, genomic variants and epigenome maps with in vivo recordings and brain imaging. Specifically, we report a method, MoPEDE (Multimodal Profiling of Epileptic Brain Activity via Explanted Depth Electrodes) that recovers extensive protein-coding transcripts, DNA methylation and mutation profiles from explanted SEEG electrodes matched with electrophysiological and radiological data allowing for high-resolution reconstructions of brain structure and function in human patients. Our study shows that epilepsies of different aetiologies have distinct molecular landscapes and identify transcripts correlating with neurophysiological signals, including immediate early genes, inflammation markers, and axon guidance molecules. Additionally, we identify DNA methylation profiles indicative of transcriptionally permissive or restrictive chromatin states. While gene expression gradients corresponded with the assigned epileptogenicity index, we found outlier molecular fingerprints in some electrodes, potentially indicating seizure generation or propagation zones not detected during electroclinical assessments. These findings validate that RNA profiles, genetic variation and genome-wide epigenetic data from explanted SEEG electrodes offer high-resolution surrogate molecular landscapes of brain activity. Our transformative MoPEDE approach has the potential to enhance diagnostic decisions and deepen our understanding of epileptogenic network processes in the human brain.
Quantifying compositional variability in microbial communities with FAVA
Authors: Morrison Maike L. , Xue Katherine S. , Rosenberg Noah A. 
Published: July 2024

AbstractMicrobial communities vary across space, time, and individual hosts, presenting new challenges for the development of statistics measuring the variability of community composition. To understand differences across microbiome samples from different host individuals, sampling times, spatial locations, or experimental replicates, we present FAVA, a new normalized measure for characterizing compositional variability across multiple microbiome samples. FAVA quantifies variability across many samples of taxonomic or functional relative abundances in a single index ranging between 0 and 1, equaling 0 when all samples are identical and equaling 1 when each sample is entirely comprised of a single taxon. Its definition relies on the population-genetic statisticFST, with samples playing the role of “populations” and taxa playing the role of “alleles.” Its convenient mathematical properties allow users to compare disparate data sets. For example, FAVA values are commensurable across different numbers of taxonomic categories and different numbers of samples considered. We introduce extensions that incorporate phylogenetic similarity among taxa and spatial or temporal distances between samples. We illustrate how FAVA can be used to describe across-individual taxonomic variability in ruminant microbiomes at different regions along the gastrointestinal tract. In a second example, a longitudinal analysis of gut microbiomes of healthy human adults taking an antibiotic, we use FAVA to quantify the increase in temporal variability of microbiomes following the antibiotic course and to measure the duration of the antibiotic’s influence on microbial variability. We have implemented this tool in an R package,FAVA, which can fit easily into existing pipelines for the analysis of microbial relative abundances.Significance statementStudies of microbial community composition across time, space, or biological replicates often rely on summary statistics that analyze just one or two samples at a time. Although these statistics effectively summarize the diversity of one sample or the compositional dissimilarity between two samples, they are ill-suited for measuring variability across many samples at once. Measuring compositional variability among many samples is key to understanding the temporal stability of a community across multiple time points, or the heterogeneity of microbiome composition across multiple experimental replicates or host individuals. Our proposed measure, FAVA, meets the need for a statistic summarizing compositional variability across many microbiome samples all at once.
Recombination landscape and karyotypic variations revealed by linkage mapping in the grapevine downy mildew pathogenPlasmopara viticola
Authors: Dvorak Etienne , Mazet Isabelle D. , Couture Carole , Delmotte François , Foulongne-Oriol Marie 
Published: July 2024

AbstractPlasmopara viticola, the causal agent of grapevine downy mildew, is a biotrophic oomycete engaged in atight coevolutionary relationship with its host. Rapid adaptation of the pathogen is favored by annual sexual reproduction which generates genotypic diversity. With the aim of studying the recombination landscape across theP. viticolagenome, we generated two half-siblings F1 progenies (N=189 and 162). Offspring were genotyped by targeted sequencing of a set of 5,000 informative SNPs. This enabled the construction of unprecedented high-density linkage maps for this species. The reference genome could be assembled into 17 pseudo-chromosomes, anchoring 88% of its physical length. We observed a strong collinearity between parental genomes and extensive synteny with the downy mildewPeronospora effusa. In the consensus map, the median recombination rate was 13.8 cM/Mb. The local recombination rate was highly variable along chromosomes and recombination was suppressed in putative centromeric regions. Recombination rate was found negatively correlated with repeats coverage and positively correlated with gene coverage. However, genes encoding secreted proteins and putative effectors were under-represented in highly recombining regions. In both progenies, about 5% of the individuals presented karyotypic anomalies. Aneuploidies and triploidies almost exclusively originated from the male-transmitted chromosomes. Triploids resulted from fertilization by diploid gametes, but also from dispermy. Obligatory sexual reproduction each year may explain the lower level of karyotypic variation inP. viticolacompared to other oomycetes. The linkage maps will be useful to guide futurede novochromosome-scale assemblies ofP. viticolagenomes and to perform forward genetics.SummarySexual reproduction contributes to the rapid adaptation of plant pathogens thanks to the recombination of genetic variants. By crossing strains of grapevine downy mildew, we built a set of linkage maps and describe the recombination landscape along the 17 chromosomes of the genome. The recombination rate varied greatly and was higher in gene-rich regions, but not in effector-rich regions. We observed an abnormal number of chromosomes in 5% of the offspring, which was mostly due to the male parent.
A lasting impact of serotonergic psychedelics on visual processing and behavior
Authors: West Chloe L. , Bastos Georgia , Duran Annabel , Nadeem Samen , Ricci David , Rader Groves Anna M. , Wargo Joseph A. , Peterka Darcy S. , Van Leeuwen Neil , Hamm Jordan P. 
Published: July 2024

AbstractSerotonergic psychedelics (e.g., psilocybin) have shown potential for treating psychiatric disorders, with therapeutic effects lasting weeks after a single dose. Predictive processing theories posit that psychedelics work by loosening priors or high-level beliefs, including ingrained biases that have become pathological, leading to shifts in bottom-up vs top-down information processing that reconfigure perception, cognition, and mood. Because 5-HT2A receptors, the primary target of psychedelics, are enriched in visual cortices, we investigated whether psychedelics alter visual processing in a manner consistent with predictive processing theories. People who recently (<3 weeks) used 5-HT2A-agonist psychedelics (psilocybin, LSD) exhibited slowed response latencies and increased cortical involvement in generating saccades to targets in predictable locations, along with a generalization of sensory prediction errors (i.e., deviance detection) during passive visual processing. Individuals who recently used a 5-HT1A- selective psychedelic (5-MeO-DMT) displayed similar changes in saccade production, but unaltered deviance detection, suggesting circuit-specific effects. Mice administered DOI (5- HT2A-agonist) exhibited altered deviance detection within primary visual cortex (V1), along with weakened top-down feedback to V1 from higher cortical area ACa. These results concord with the hypothesis that psychedelics shift the balance from top-down to bottom-up in sensory cortical circuits – an effect that persists beyond the acute exposure period.
The Role of Glucocorticoid and Nicotinic Acetylcholine Receptors in the Reward-Enhancing Effects of Nicotine in the ICSS Procedure in Male and Female Rats
Authors: Chellian Ranjithkumar , Behnood-Rod Azin , Bruijnzeel Adriaan W. 
Published: July 2024

AbstractTobacco use disorder is a chronic disorder that affects more than one billion people worldwide and causes the death of millions each year. The rewarding properties of nicotine are critical for the initiation of smoking. Previous research has shown that the activation of glucocorticoid receptors (GRs) plays a role in nicotine self-administration in rats. However, the role of GRs in the acute rewarding effects of nicotine are unknown. In this study, we investigated the effects of the GR antagonist mifepristone and the nicotinic acetylcholine receptor (nAChR) antagonist mecamylamine on the reward-enhancing effects of nicotine using the intracranial self-stimulation (ICSS) procedure in adult male and female rats. The rats were prepared with ICSS electrodes in the medial forebrain bundle and then trained on the ICSS procedure. Nicotine lowered the brain reward thresholds and decreased response latencies similarly in male and female rats. These findings suggest that nicotine enhances the rewarding effects of ICSS and has stimulant properties. Treatment with the GR antagonist mifepristone did not affect the reward-enhancing effects of nicotine but increased response latencies, suggesting a sedative effect. Mecamylamine did not affect the brain reward thresholds or response latencies of the control rats, but prevented the nicotine-induced decrease in brain reward thresholds and reward latencies. These findings indicate that the rewarding effects of nicotine are mediated via the activation of nAChRs, and that the activation of GRs does not contribute to the acute rewarding effects of nicotine. These studies enhance our understanding of the neurobiological mechanisms underlying tobacco use disorder.
Dissecting cell-free DNA fragmentation variation in tumors using cell line-derived xenograft mouse
Authors: Fu Ruiqing , Su He Amy , Tian Yafei , Chen Hongyan , Lu Daru 
Published: July 2024

AbstractCell-free DNA (cfDNA) is increasingly studied for its diverse applications in non-invasive detection. Non-randomly cleaved by nucleases and released into the bloodstream, cfDNA exhibits a variety of intrinsic fragmentation patterns indicative of cell status. Particularly, these fragmentation patterns have recently been demonstrated to be effective in predicting cancer and its tissue-of-origin, owing to increased variation of fragmentation features observed in tumor patients. However, there remains a lack of detailed exploration of altered cfDNA fragmentation profiles in tumors, which consist of a mixture of both non-tumor cfDNA and circulating tumor DNA (ctDNA). Hence, we leveraged the human tumor cell line-derived xenograft (CDX) mouse model, where different tumor cell lines were implanted into different anatomical sites, to isolate pure ctDNA and separately investigate the fragment properties of CDX-induced cfDNA and ctDNA. We found an enrichment of short cfDNA fragments in both CDX-induced cfDNA and ctDNA compared to normal plasma cfDNA, with more elevated short fragments in ctDNA. Moreover, the CDX-induced cfDNA fragmentation features distinguished between CDX models of different tumor cell lines, while the ctDNA fragmentation features conversely discriminate between CDX models of different anatomical sites. The results suggested that both non-tumor cfDNA and ctDNA contribute to the increased variation observed in tumors, and that cfDNA fragmentation may be highly variable and susceptible to regulations by both original cells and cells within the local niche.
Social clues in risky pools: Perceived conspecific rivals strongly modify individual oviposition decisions in response to larval predation and competition
Authors: Ramesh Ashwini , Sharma Manvi , Isvaran Kavita 
Published: July 2024

ABSTRACTReproductive choices are imperative in shaping organismal fitness across diverse taxa. Such choices are particularly critical in organisms with biphasic lifecycles, as females must maximize offspring survival pre-oviposition, with no parental care extended afterward. Consequently, females face strong site selection pressures to effectively respond to offspring competition and predation risks. Ovipositing females encounter yet another challenge during site selection: assessing future competition for their offspring from potential conspecific rivals. Our current knowledge, based on average social versus solitary behaviours, fails to clarify how social signals influence individual behavior within groups. To address these challenges, we leveraged the unique oviposition biology of the mosquitoAedes aegyptiwhere only blood-fed females can lay eggs. By tracking individual behaviour in a social setting, we ask: how does social information from perceived conspecific rivals influence an individual’s oviposition site selection? In our lab-based experiment, we examined oviposition strategies at two spatial scales under varying larval competition and predation risk. Our findings reveal that social information exerts a stronger influence on egg-laying behavior at larger spatial scales, i.e., at the scale of pool networks, than between neighboring pools. Social cues facilitated oviposition with increasing larval predation, as social females transitioned from rejecting to accepting pool networks. Conversely, under larval competition, social cues led to inhibition, with females withholding their eggs likely in anticipation of future competition. At finer spatial scales, social information only weakly modified oviposition behavior despite potential negative fitness consequences for the offspring. Thus, perceived conspecific risk strongly modifies oviposition—facilitation, inhibition, or no effect—and is scale-dependent.
H3.3 deposition counteracts the replication-dependent enrichment of H3.1 at chromocenters in embryonic stem cells
Authors: Arfè S. , Karagyozova T. , Forest A. , Hmidan H. , Meshorer E. , Quivy J.-P. , Almouzni G. 
Published: July 2024

AbstractChromocenters in mouse cells are membrane-less nuclear compartments that represent typical heterochromatin stably maintained during the cell cycle. Here, we explore how histone H3 variants, replicative H3.1/H3.2 or replacement H3.3, mark these domains during the cell cycle. In mouse embryonic stem cells (ESCs), neuronal precursor cells (NPCs) as well as immortalized 3T3 cells, we find a strong and distinct H3.1 enrichment at chromocenters, with some variation in ESCs. Mechanistically, this H3.1 selective enrichment depends on the DNA Synthesis Coupled (DSC) deposition pathway operating in S phase. Yet, this selective enrichment is challenged when we target H3.3 deposition through the DNA Synthesis Independent (DSI) deposition pathway mediated by HIRA. Altering the H3.1/H3.3 equilibrium at chromocenters in ESCs affects its heterochromatin properties leading to mitotic defects. We thus reveal opposing mechanisms for H3.1 and H3.3 deposition with different enforcement according to cell cycle and potency which determine their ratio at chromocenters and are critical for genome stability and cell survival.
Food texture preference reveals multisensory contributions of gustatory organs in behaviour and physiology
Authors: Komarov Nikita , Fritsch Cornelia , Maier G. Larisa , Bues Johannes , Biočanin Marjan , Avalos Clarisse Brunet , Dodero Andrea , Kwon Jae Young , Deplancke Bart , Sprecher Simon G. 
Published: July 2024

SummaryFood presents a multisensory experience, with visual, taste, and olfactory cues being important in allowing an animal to determine the safety and nutritional value of a given substance1. Texture, however, remains a surprisingly unexplored aspect, despite providing key information about the state of the food through properties such as hardness, liquidity, and granularity. Food perception is achieved by specialised sensory neurons, which themselves are defined by the receptor genes they express. While it was assumed that sensory neurons respond to one or few closely-related stimuli, more recent findings challenge this notion and support evidence that certain sensory neurons are more broadly tuned. In theDrosophilataste system, gustatory neurons respond to cues of opposing hedonic valence or to olfactory cues. Here, we identified that larvae ingest and navigate towards specific food substrate hardnesses, and probed the role of gustatory organs in this behaviour. By developing a genetic tool targeting specifically gustatory organs, we show that these organs are major contributors for evaluation of food texture and ingestion decision-making. We find that ablation of gustatory organs not only results in loss of chemosensation, but also navigation and ingestion preference to varied substrate textures. Furthermore, we show that certain neurons in the primary taste organ exhibit varied and concurrent physiological responses to mechanical and multimodal stimulation. We show that individual neurons house independent mechanisms for multiple sensory modalities, challenging assumptions about capabilities of sensory neurons. We propose that further investigations, across the animal kingdom, may reveal higher sensory complexity than currently anticipated.
Gene regulatory network structure informs the distribution of perturbation effects
Authors: Aguirre Matthew , Spence Jeffrey P. , Sella Guy , Pritchard Jonathan K. 
Published: July 2024

AbstractGene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.
Stochastic Modeling of Biophysical Responses to Perturbation
Authors: Chari Tara , Gorin Gennady , Pachter Lior 
Published: July 2024

AbstractRecent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the ‘how’ behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.
Generative deep learning models for cognitive performance trajectories in real-world scenarios
Authors: Expósito Denis , Maltseva Elina , Sastre-Barrios Carolina , de Piérola Iñigo Fernández , Cortes Jesus M. 
Published: July 2024

AbstractThe increasing prevalence of cognitive disorders, such as Alzheimer’s disease, imposes significant challenges on healthcare systems and society. The ability to predict the future cognitive performance (CP) is crucial for professionals in neuropsychology, and real-world data emerges as an important source of complete and reliable information. However, its inherent complexities requires the use of advanced models to make predictions. To do so, we have implemented and compared three deep learning predictive strategies from CP trajectories: multilayer perceptron (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM). The three models showed robustness on their predictions in different patient datasets. The CNN was the most suitable architecture due to its local pattern recognition capabilities and its robustness to overfitting. Therefore, professionals can have a complementary support for targeting treatment approaches to patients needs and anticipate undesired outcomes (e.g. cognitive impairment). Nonetheless, further studies are needed to validate whether neuropsychological interventions based on score predictions lead to improved intervention efficacy compared to traditional approaches for controlled patient groups.