Biology subjects

Taylor, M.

Publications and source records attributed to Taylor, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Liposomal DAGLβ Inhibitor Shows Substantial Antinociceptive Potency Mediated by AMPK in a Neuropathic Pain Mouse Models

Diacylglycerol lipase-beta (DAGL{beta}), a biosynthetic enzyme for the endocannabinoid 2-arachidonoylglycerol (2-AG), is highly expressed in macrophages and serves as a potential contributor to inflammatory and hypernociceptive responses. Moreover, the DAGL{beta} inhibitor KT109 produces antinociceptive effects in various rodent pain models, and its liposome formulation dramatically increases its antinociceptive potency in a lipopolysaccharide mouse model of inflammatory pain compared to free KT109. Here, we further optimized the liposomal KT109 formulation through adjustment of lipid composition and improved processing, achieving 100-fold higher drug encapsulation efficiency than the previous formulation, uniform size distribution, and excellent storage stability. Further, the second generation liposomal KT109 showed remarkable potency (ED50 [95% CL] = 0.226 [0.113-0.454] mg/kg) in a mouse model of paclitaxel-induced neuropathic pain, compared to free KT109 (ED50 [95% CL] = 31 [21-46] mg/kg). Liposomal KT109 (0.4 mg/kg) fully reversed paclitaxel-induced mechanical hypersensitivity for up to 24 hours, and its antinociceptive effects were sustained following a week of daily injections, with no detectable tolerance. In addition, the adenosine monophosphate-activated protein kinase (AMPK) inhibitor, dorsomorphin, prevented the antinociceptive effects of KT109, which is consistent with previous work demonstrating AMPK activation mediates the antinociceptive phenotype of DAGL{beta} knockout mice.

pharmacology and toxicology↗

bioRxiv2026

Effects of the nemertean egg predator Carcinonemertes carcinophila (Carcinonemertidae: Monostilifera) on the reproductive output and fecundity of the Florida Atlantic blue crab Callinectes sapidus.

The blue crab, Callinectes sapidus, is an important crab fishery in Florida, valued at approximately $14.5 M in 2023. However, female blue crabs are host to the nemertean worm Carcinonemertes carcinophila, which preys upon its eggs. Other nemerteans from this genus have been linked to the collapse of fisheries, such as the Dungeness crab fishery in the 1960s and 1970s and the red king crab fishery in the 1980s. In 2022, the newly-discovered nemertean Carcinonemertes conanobrieni was found to parasitize the egg masses of female Caribbean spiny lobsters, Panulirus argus, and has had drastic impacts on the reproductive output and fecundity of this species. We hypothesized that nemertean egg parasites might have similar impacts on blue crabs. To investigate this, we collected 52 ovigerous female blue crabs from Jacksonville and Ormond Beach, 61.5% of which were infested with C. carcinophila. Infestation status did not significantly affect fecundity or reproductive output; instead, body mass and embryo stage were the strongest predictors. Infestation was more prevalent in crabs with late-stage embryos, consistent with nemertean life cycle timing. While most hosts carried few worms, a subset showed high infestation intensity, and both infestation status and nemertean density were significant predictors of embryo loss. This is a positive sign for the Florida blue crab fishery, as it suggests that the blue crab nemertean might not be as impactful to that fishery as others.

ecology↗

bioRxiv2026

Consequences of sustained modulation of VIP-expressing neurons in mouse corticolimbic regions on anxiety-related avoidance behaviors, social functioning, and associative fear learning

GABAergic inhibitory neurons play a central role in shaping network dynamics, and accordingly, their dysfunction is implicated in the pathophysiology of various psychiatric, neurodevelopmental, and neurological disorders. In the past decade, studies on vasoactive intestinal polypeptide-expressing inhibitory neurons (VIP+ neurons) have positioned them as critical components of this circuitry. In particular, VIP+ neurons have been proposed to function within a canonical disinhibitory circuit motif, preferentially inhibiting other interneuron subtypes, to ultimately modulate the gain and activity of local pyramidal neurons. However, their contribution to psychiatrically relevant behavioral endophenotypes is still not fully understood, limiting our ability to target these neurons for therapeutic purposes. In the present study, we systematically assessed the functional contribution of VIP+ neurons to affective and social behaviors. To this end, we used a chemogenetic approach to manipulate the activity of VIP+ neurons in core corticolimbic regions, including the medial prefrontal cortex (mPFC), basolateral amygdala (BLA), and ventral hippocampus (vHPC). The consequences of either activating or inhibiting VIP+ neurons in these regions were observed while mice underwent a battery of behavioral tasks assessing anxiety-related avoidance behaviors, social interest and recognition, and associative fear learning. While sustained modulation of VIP+ neurons had no significant effect on anxiety-related or social behaviors in any of the brain regions assessed, it produced a robust reduction in associative fear learning specifically in the mPFC. These findings demonstrate that the consequences of sustained VIP+ neuron modulation depend on both anatomical location and behavioural context. Moreover, they identify VIP+ neurons in the mPFC as potentially clinically relevant targets for interventions aimed at modulating fear-related circuits.

neuroscience↗

bioRxiv2026

Synanthropic flies harbor a distinct, pathogen-enriched microbial compartment in livestock landscapes

Zoonotic and other livestock-associated pathogens move readily within animal production systems, yet their dispersal into surrounding environments is poorly characterized. We evaluated whether synanthropic filth flies act as spatially integrating sentinels of livestock-associated pathobiomes. From 30 roadside sites in six Texas counties stratified by livestock production intensity, we collected 3,373 Calliphoridae spp. and 12 Musca domestica, together with co-located soil, water, plant, and fecal samples, and characterized 195 metagenomes (122 insect, 73 environmental). Using diversity, differential-abundance, machine-learning, indicator-species, and network analyses, we found three robust patterns. First, fly metagenomes form a microbial compartment compositionally distinct from co-located environmental matrices (PERMANOVA R{superscript 2} = 0.094, F = 19.81, P = 0.001) and carry markedly higher relative abundances of pathogen-associated taxa, so insect sampling is complementary to, not redundant with, environmental sampling. Second, the dominant landscape signal is categorical rather than graded: cattle presence, not a Low-Medium-High density gradient, most strongly correlated with community structure, with the ruminant-associated anaerobe Peptostreptococcus russellii as the most reproducible biomarker. Third, intensification produced taxon-level enrichments (notably the feedlot pathogen Trueperella pyogenes) and progressive co-occurrence-network fragmentation, without a monotonic dose-response. A curated 12-pathogen panel failed to classify landscape context, whereas whole-community analysis succeeded. Filth flies thus provide a pragmatic, spatially integrated readout that distinguishes low-impact from livestock-impacted landscapes, offering a defensible tool for One Health pathogen surveillance. IMPORTANCEZoonotic and other livestock-associated pathogens emerging from animal production are among the most pressing threats to global health but tracking how they spill into the wider environment is difficult and expensive. We show that common filth flies, abundant, easy to trap, and free ranging across farms, fields, and human spaces, act as living samplers that concentrate and report the microbial signature of nearby livestock. Sampling flies captured information that soil and water sampling missed, and a single binary signal, the presence or absence of cattle, was associated with the microbial community more strongly than any measure of animal density. Narrow tests targeting a short curated list of pathogens were not successful in differentiating sites, while analyzing the whole microbial community provided more discriminating power. These findings support a practical, low-cost strategy in which insects serve as biological sentinels for the detection of livestock-associated pathogens across agricultural landscapes.

microbiology↗

bioRxiv2026

Unveiling the Hidden Rules: Enhancing NMD Prediction for Protein-Truncating Variants

Nonsense-mediated decay (NMD) is a conserved RNA quality-control pathway that degrades transcripts containing premature termination codons. Because roughly a third of pathogenic variants in ClinVar can lead to truncated protein synthesis, predicting whether such transcripts undergo NMD is central to interpreting variant effects, yet the canonical 50-55 nucleotide rule explains only about half of observed outcome variability. Using paired whole-genome and RNA-sequencing from 10,306 individual samples in the Trans-Omics for Precision Medicine (TOPMed) program, we quantified NMD efficiency for 5,749 germline truncating variants via allele-specific expression and trained a gradient-boosting classifier, TrunCat, that distinguished NMD-sensitive from NMD-escape transcripts with [~]78% ROC-AUC (Receiver Operating Characteristic - Area Under the Curve). A reduced model using the ten features with the highest mean SHAP (SHapley Additive exPlanations) value as a measure of each features average contribution to predictions nearly matched this performance. Applied across large variant databases and a rare-disease cohort, the model produced NMD outcome predictions, with variants of uncertain significance showing higher predicted escape than pathogenic ones. This framework confirms the canonical rule, identifies non-canonical determinants, and offers a scalable resource for interpreting protein-truncating variants.

genomics↗

bioRxiv2026

Transient MYC Mimicking the Exercise Response Orchestrates Multifaceted Skeletal Muscle Adaptations

Despite decades of study, MYCs physiological role in adult tissues remains obscured by the models used to investigate it. Most work relies almost exclusively on chronic or constitutive MYC induction that recapitulates the sustained activity found in tumorigenesis and expectedly produces pathological outcomes. What has gone largely untested is how MYC operates when induced in a controlled and physiologically relevant manner, as it is during adaptive processes such as exercise. Using a recombination-independent strategy in adult skeletal muscle, transient MYC bursts drive coordinated hypertrophic-metabolic reprogramming followed by a shift in myosin fiber type, recapitulating adaptations characteristic of concurrent endurance and resistance training. A single pleiotropic transcription factor governing several aspects of muscle health reframes perspectives on a gene understood almost entirely in the context of pathology. We uncover a previously unrecognized muscle-specific role for MYC in controlling muscle cell composition and present a multi-timepoint multi-omic resource for interrogating MYC: data.myoanalytics.com/study/myc_transient/gene.

Cell Biology↗

bioRxiv2026

Brain dynamics supporting high cognitive performance reorganize after midlife

Quantifying functional brain aging trajectories at scale remains a fundamental challenge due to the scanner-bound limitations of traditional neuroimaging. Here, we deploy whole-head Time-Domain functional Near-Infrared Spectroscopy (TD-fNIRS) to map task-evoked cortical dynamics during a 30-minute cognitive battery across the adult lifespan (N = 302, age 18-87, 45% racial or ethnic minority). We developed a robust General Cognitive Factor (GCF) tracking age-related performance decline (r = -0.57, p < 0.0001). Analysis of brain activity patterns revealed systemic, age-dependent neural dedifferentiation, highlighting a distinct neurocognitive inflection point around age 55. Prior to this threshold, high performers exhibit more variable neural activation across tasks; post-age 55, high performance is sustained through reduced spatial differentiation, signaling a compensatory strategy. Furthermore, subjective anxiety and depression disrupt these compensatory mechanisms, and subjective cognitive complaints register as distinct neural signatures before behavioral GCF decline manifests. Together, these findings establish a scalable framework for mapping functional brain health across the human lifespan, uncovering the neural mechanics of cognitive resilience and vulnerability before behavioral decline manifests.

neuroscience↗

bioRxiv2026

Benchmarking normalisation methods for differential binding analysis in CUT&RUN

CUT&RUN (Cleavage Under Targets and Release Using Nuclease) is an increasingly popular method for profiling protein interactions (transcription factors, histone modifications, etc) with DNA across the whole genome. When performing differential binding analysis of CUT&RUN data to identify genomic regions where interaction profiles vary between conditions, data normalisation is essential for accurate biological interpretations. Despite this, there are no clear guidelines on the optimal normalisation method for CUT&RUN datasets. Here, we examine five normalisation approaches (spike-in, library size, background, reads-in-peak and greenlist) and highlight that different methods can result in widely discrepant interpretations of the data. We test these normalisation methods by simulating a variety of plausible differential binding scenarios as well as an in-house generated dataset. We determined that normalisation by either (i) library size or (ii) background to be the most robust. Importantly, we find spike-in normalisation to be the least reliable method. Our findings inform the use of normalisation methods for CUT&RUN data and should thus facilitate reproducible and robust analysis.

bioinformatics↗

bioRxiv2026

hTERT Expression, Regulation, and Prognostic Significance in Pediatric Medulloblastoma

BackgroundTelomerase reactivation, a hallmark of many cancers, is associated with expression of its catalytic subunit, hTERT. However, the prognostic significance of telomere maintenance mechanisms in pediatric medulloblastoma remains poorly defined. MethodsIn this multi-institutional retrospective study of telomerase expression and hTERT regulation in newly diagnosed children with medulloblastoma, hTERT and MYC expression were assessed by qRT-PCR, normalized to non-neoplastic brain control samples. hTERT promoter methylation was analyzed using quantitative pyrosequencing and Illumina 450k methylation array. Cox proportional-hazard regression analyses evaluated the association of hTERT expression with progression-free survival (PFS) or overall survival (OS). Spearman correlation and Kruskal-Wallis tests correlated hTERT promoter methylation and expression and assessed variations among medulloblastoma subgroups, respectively. ResultsAmong 74 patients with available hTERT expression and outcome data, higher expression was associated with worse OS (HR=1.22, 95% CI: 1.01-1.47, p=0.036) and PFS (HR=1.17, 95% CI: 1.00-1.37, p=0.051) after adjusting for subgroup. Similar results were obtained when adjusting for metastatic status. Group 3 patients had the highest hTERT expression (p=0.001). Pyrosequencing data were available for 61 patients and 450k methylation array data for 292 patients. hTERT promoter was differentially methylated across subgroups with WNT followed by group 3 demonstrating the highest methylation on 450k (p<0.0001), findings that were confirmed by pyrosequencing. hTERT promoter methylation positively correlated with hTERT expression (Spearman correlation=0.42, p=0.02 by 450k and 0.34, p=0.007 by pyrosequencing). No significant correlation was observed between hTERT and MYC expression. ConclusionElevated hTERT expression is associated with worse PFS and OS in medulloblastoma across subgroups, supporting telomerase inhibition as a potential therapeutic strategy.

cancer biology↗

bioRxiv2026

Charting developmental trajectories of dynamic brain networks during emotional face processing

Emotional face processing is a critical component in the development of social cognition through childhood. The neural mechanisms supporting this development can be understood by tracking age-related changes in brain activity. Prior work in this area with MEG relies on static, bandlimited, or region-specific measures, which do not capture the dynamic, distributed nature of brain activity. Here we used Dynamic Network Modes (DyNeMo) to analyze MEG data from a large cohort of typically developing individuals (N=224, ages 5-40) during an emotional faces vigilance task. DyNeMo is a data-driven, generative modelling approach which captures functional networks as a set of whole-brain spatiospectral "modes" whose relative mixture (i.e., activation levels) can change dynamically in response to stimuli. We inferred six modes from the MEG data and characterized developmental trajectories in task-related activation and mode connectivity. Across modes we observed distinct developmental trajectories in both measures. With respect to mode activation, a visual mode and a frontotemporal mode (whose labels reflect their respective spatial profiles) increased nonlinearly with age; meanwhile, activation in temporal and sensorimotor modes decreased linearly with age. Meanwhile, connectivity broadly increased with age in all modes, but with different degrees of nonlinearity. These results suggest developmental dissociations between different modes (e.g., visual versus sensorimotor), as well as within individual modes (task-related mode activation versus connectivity). These results provide a comprehensive and complex picture of functional network development underlying emotional face processing. Significance StatementBrain networks supporting social cognition undergo profound changes from childhood through adolescence to adulthood. However, current understanding of how these networks develop has been limited by conventional analyses of brain imaging data, which typically provide a static picture of brain activity. Here we leveraged a cutting-edge, data-driven modeling approach (DyNeMo) to characterize age-related changes in distributed and dynamic networks supporting emotional face processing, in a large cohort of children and young adults (N=224). We inferred six functional networks whose activation levels changed rapidly in response to emotional faces. Network activation and connectivity exhibited profound and distinct non-linear changes with age, indicating that emotional face processing is supported by complex interactions among multiple dynamic networks, each with different maturational trajectories.

neuroscience↗

bioRxiv2026

Aviti Sequencing and Marker Gene Data Analysis

Accurate identification of microbial species in complex populations and communities relies on the isolation of representative marker 16S, ITS, and 18S sequences through the use of DNA extraction, PCR, and sequencing. Aviti sequencing has brought an improvement in the read quality and depth of marker gene sequencing technology. Quality scores exceeding Q40 representing highly accurate sequencing allows researchers to ask more questions of their marker gene data. However, this improvement in quality and throughput also brings with it a surprising increase in diversity of amplicon sequencing variants (ASVs) making further analysis and comparisons to previous studies on Illumina platforms challenging. This increased diversity causes downstream processing issues, including an over-reporting of chimeric ASVs. Here we identify this problem and put forward straightforward solutions to retain counts and reduce technically introduced diversity, as well as tying chimeric read identification to minimum parent distance. Through the use of synthetic mock samples, we discovered that erroneous ASVs are systematically substitution errors introduced by the upstream PCR methods. This error can be reduced significantly bioinformatically through clustering of ASVs within 99% similarity. Further we highlight technically introduced variation as a result of variable region length, sample misassignment, and sample biomass. Collectively, these results improve the similarity of Aviti and Illumina datasets for better comparisons of microbial studies from different platforms.

bioinformatics↗

bioRxiv2026

Leveraging TNFR2 for antitumour immunity: T reg depletion and myeloid reprogramming versus T cell costimulation

Although checkpoint inhibitors have revolutionized cancer treatment, responses are largely restricted to targeting CTLA-4 and PD-(L)1. TNFR2 has been identified as a target of interest, but a lack of understanding of whether agonists or blockers should be used, and whether Fc{gamma}R interactions promote effcacy, has hampered therapeutic antibody development. Here, we engineered two distinct -TNFR2 types: ligand-blocking Fc{gamma}R-engaging versus non-ligand-blocking agonist antibodies. Both showed potent anti-tumor effcacy across multiple syngeneic mouse tumor models and combined effectively with -PD-1. The ligand-blocking antibody depleted intratumoral T regs and reprogrammed the myeloid compartment, directing monocytes towards a pro-inflammatory macrophage and dendritic cell trajectory. Surprisingly, this effect depended on both inhibitory and activating Fc{gamma}Rs. In contrast, the agonist directly co-stimulated T and NK cells, through partially Fc{gamma}R-independent mechanisms. Both antibodies converged on activating tumor-specific CD8+ T cells mediating tumor rejection. Clinical assessment of both ligand-blocking (BI-1808) and agonist (BI-1910) -TNFR2 antibodies is currently ongoing. Graphical abstractO_LILigand-blocking anti-TNFR2 mAbs (BI-1808 and 3F10) deplete CCR8+ T regs and activate myeloid cells, remodeling the TME and leading to CD8+ T cell killing of cancer cells. T reg depletion and myeloid activation are mediated through interaction with activating Fc{gamma}Rs and the inhibitory Fc{gamma}RIIB, respectively. C_LIO_LIAgonist anti-TNFR2 mAbs (BI-1910 and 5A05) intrinsically agonize TNFR2 on T cells and NK cells, which is enhanced by Fc{gamma}RIIB crosslinking. The resulting broad lymphocyte activation mediates cancer cell killing. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/697572v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@3ecc17org.highwire.dtl.DTLVardef@c936a7org.highwire.dtl.DTLVardef@23189aorg.highwire.dtl.DTLVardef@243a05_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Harnessing artificial intelligence to automate environmental predictions

Predicting heterogeneous and non-linear processes remains a fundamental challenge in Earth sciences. Here, we present an artificial intelligence (AI)-guided framework that iteratively combines predictive modeling with targeted field sampling to rapidly improve environmental predictions. We demonstrate our workflow by predicting oxygen consumption, a key process of stream metabolism, across the contiguous United States (CONUS). Our approach consisted of 18 iterative loops of measurements and models, combining distributed participatory field sampling, lab analysis, automated machine learning (ML) predictions, and error and distinctiveness analyses to autonomously guide the next sampling at optimal site locations. Through our approach, we increased the predictive power of sediment oxygen consumption across CONUS by over fifteenfold between the first and last iteration. Relative to our last sampling iteration, our first sampling missed sites with high rates and underestimated median oxygen consumption rates by 68%. In addition to identifying areas of high oxygen consumption rates, iterations enabled refinement of laboratory and data handling methods, and engagement with a broad community of field researchers. We conclude that AI-guided iterative loops between targeted sampling and predictive modeling are a powerful and efficient approach for improving predictions of heterogeneous environmental processes.

ecology↗

bioRxiv2025

Tree microbiomes and methane emissions in upland forests

RationaleUpland forest trees emit CH , but whether emissions derive from internal microbial production or soil-derived transport remains debated. Methanogens have been detected in heartwood of several species, yet the prevalence of wood-associated methanogenesis, its metabolic basis, and its relationship to co-occurring methanotrophy are poorly understood. MethodsWe measured 1,148 stem fluxes and 276 soil fluxes, sampled internal stem gases including {delta}{superscript 1}3CH , quantified methanogens and methanotrophs via ddPCR in 564 samples, characterized communities via 16S rRNA sequencing, and upscaled fluxes. Key resultsMethanogens were detected in 97% of heartwood samples (up to 10 copies g {superscript 1}) at concentrations exceeding soil by [~]2 orders of magnitude; methane consumers were likewise near-ubiquitous across forest compartments. Wood harbored distinct microbial communities dominated by hydrogenotrophic Methanobacteriaceae, corroborated by depleted {delta}{superscript 1}3CH . Vertical flux profiles indicated soil transport only in wet microsites, with uniform emissions across height consistent with internal production across most upland species. Species-level methanogen:methanotroph ratios predicted emissions (R{superscript 2} = 0.51), indicating net flux reflects the balance between production and oxidation. Main conclusionMethane-cycling microbes are widespread in upland trees, and net methane flux reflects the species-level balance between production and consumption. Internal methanogenesis contributes widely to upland tree emissions; resolving ecosystem-scale magnitude requires improved quantification of woody surface area and vertical flux variability.

ecology↗

bioRxiv2025

A CRISPR-CAS9 high throughput machine-learning platform for modulation of genes involved in Parkinson's disease-associated PINK1-mitophagy in iPSC-derived dopaminergic neurons

Parkinsons disease (PD) is a progressive neurodegenerative disorder characterised by the loss of dopaminergic neurons, driven by complex molecular mechanisms that are not fully understood. To address this issue, we have developed a novel high-content phenotypic screening platform using human induced pluripotent stem cell-derived dopaminergic neurons to investigate the PINK1-PARKIN mitophagy pathway, a critical process in PD pathogenesis. Utilising high throughput, 384 well arrayed CRISPR-CAS9 genetic manipulation and high-content immunofluorescence imaging complemented with machine learning analysis, we examined ubiquitin (Ub) pSer65 levels. Ub pSer65, a potential PD clinical biomarker, is a key marker of mitophagy initiation in dopaminergic neurons upon mitophagy initiation using exogenous stimuli to mimic the disease relevant environment. The CRISPR-CAS9 knockout (KO) screen revealed two distinct phenotypic classes: essential genes causing cell death upon deletion, and genes modulating Ub pSer65 levels. Notably, KO of PINK1, PARKIN, and TOM7 genes decreased Ub pSer65 upregulation during mitophagy activation, confirming their established roles in the pathway and validating the suitability of the platform for target identification. This innovative platform provides a precise tool to further interrogate PD-associated genes, offering insights into mitophagy-related pathogenic mechanisms and identification of potential therapeutic targets. By bridging functional genomics with disease-specific neuronal models, this approach presents a promising strategy for advancing PD research and developing targeted interventions. To our knowledge, this is the first reported use of a human, translationally relevant cell model to study genetic perturbation within a disease relevant phenotype.

neuroscience↗

bioRxiv2025

A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the datasets utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

neuroscience↗

bioRxiv2025

EZHIP boosts neuronal-like synaptic gene programs and depresses polyamine metabolism

It is currently understood that the characteristic loss of the repressive histone mark H3K27me3 in PFA ependymoma and diffuse midline glioma (DMG) are caused by complementary mechanisms mediated by EZHIP and the oncohistone H3K27M, respectively. To support the complementarity of these mechanisms, rare H3K27M-negative DMGs express EZHIP. Interestingly, EZHIP is one of the few genes recurrently mutated in PFA. The significance of EZHIP mutations in PFA, and whether EZHIP has wider functions in addition to repression of H3K27me3 deposition, are not known. Here, we investigated the mutational landscape of EZHIP in pediatric brain tumors. We found that EZHIP mutations occur not only in PFA, but also in rare medulloblastoma and pediatric high-grade glioma (HGG), including in H3K27-positive DMG. Contrary to current expectations, we show that mutant EZHIP is expressed in H3K27M-positive DMG. All the EZHIP-mutated HGG cases also have EGFR mutations. Further, we pursued better understanding of the function of EZHIP by expressing it in human-derived neural models. Our transcriptomic analyses indicate that EZHIP expression potentiates neuronal-like gene programs associated with synaptic function. Metabolomics data indicate that EZHIP leads to repression of methionine and polyamine metabolism, suggesting links between metabolic and epigenetic changes that are observed in PFA. Collectively, our results expand the repertoire of tumor types known to harbor EZHIP mutations and shed light on EZHIP-dependent metabolic and transcriptional programs in relevant neural models.

cancer biology↗

bioRxiv2025

Blaming Luck, Claiming Skill: Self-Attribution Bias in Error Assignment

Mistakes are valuable learning opportunities, yet in uncertain environments, whether a lack of reward is due to poor performance or bad luck can be hard to tell. To investigate how humans address this issue, we developed a visuomotor task where rewards depended on either skill or chance. Participants consistently displayed a self-attribution bias, crediting successes to their own ability while blaming failures on randomness, an effect that influenced their subsequent decisions. Computational modelling revealed two underlying mechanisms--a distorted perception of ability and a positivity bias in the skill condition. Notably, while distorted self-perception shaped behaviour, it did not affect confidence; instead, self-attribution bias led to overconfidence in external blame. These findings suggest a more complex picture in which self-attribution biases arise from both perceptual distortions and post-decision evaluations, highlighting the need for an interplay between experimental design and computational modelling to understand behavioural biases.

animal behavior and cognition↗