Biology subjects

Vidal, M.

Publications and source records attributed to Vidal, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Universally conserved isoasparylation sustains ribosome biogenesis and function

All living beings use ribosomes to synthesize proteins from -amino acids. Inherited from our common ancestor, a few universally conserved ribosome biogenesis steps, introducing essential ribosomal RNA modifications, have survived to our days. However, it was unknown whether similar primordial core mechanisms could also target ribosomal proteins. Here we show that the assembly and the function of ribosomes depend on the installation of an unusual {beta}-amino acid, isoaspartate, within the ribosomal protein uS11. In bacteria, mitochondria, and plastids, this modification is catalyzed by the metzincin-like hydrolase YbeY, whereas in archaea and eukaryotes, it is introduced by the atypical kinase Fap7. The formation of this strategically positioned isoaspartate enables correct maturation of the small ribosomal subunit to support protein synthesis and normal cell physiology.

biochemistry↗

bioRxiv2026

Dynamic engagement of dual-role regulators by the Sin3 complex

Gene expression is governed by dynamic switches between repressive and activating transcriptional states1,2. Among the molecules mediating these transitions, chromatin readers and transcription factors play pivotal roles3,4. However, how they assemble with regulatory machineries to enable crosstalk between gene repression and activation remains unknown. Here, we use an integrative structural dynamics approach - combining cryo-EM, crosslinking mass spectrometry, fragment-resolved protein interactome mapping and crystallography - to show how the dual-role chromatin reader Cti6 and transcription factors Ash1 and Ume6 engage the Sin3 deacetylase complex, a major regulatory hub in eukaryotes5. We find that Cti6 competes with Ash1 to drive its dynamic recruitment to a shared peripheral module, while Ume6 engages the Sin3 scaffold through a defined, minimal interface. Using high-throughput mutational scanning, we reveal deleterious and gain-of-function mutations in Sin3, identifying evolutionarily conserved residues essential for anchoring transcription factors. Together, these results provide structural and functional insights into how dual-role regulators engage the central Sin3 complex, revealing subtle assembly principles that may facilitate crosstalk between gene repression and activation. They also establish an integrative multidisciplinary framework to dissect the dynamics of macromolecular assemblies across biological systems.

molecular biology↗

bioRxiv2026

Connexins are essential for the contribution of latent progenitors to self-repair after spinal cord injury

Gap junctions are important regulators of the biology of neural stem cells. Both in vertebrates with regenerative abilities and neonatal rodents, ependymal cells communicate via connexin (Cx) 43 and Cx26. Gap junction coupling and Cx26 are down-regulated as the ependyma becomes quiescent in adulthood, but injury overrules this developmental down-regulation suggesting a role for Cx signalling in the reactivation of ependymal cells. Here, we aim to explore the role of Cx26 and Cx43 in the response of ependymal cells to injury. We find that Cx26 is critical for the proliferative response to injury and thereby scar formation. Cx43 plays a key role in the communication between ependymal cells of Ca2+ signals induced by activation of P2X7 receptors that trigger downstream events. Our data show that Cxs are relevant targets to manipulate the ependymal stem cell niche to achieve a better self-repair after spinal cord injury.

neuroscience↗

bioRxiv2025

Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI

Disruption of protein-protein interactions (PPIs) is a major mechanism of a variants deleterious effect. Computational tools are needed to assess such variants at scale, yet existing predictors rarely consider loss of specific interactions, particularly when variants perturb binding interfaces without significantly affecting protein stability. To address this problem, we present MutPred-PPI, a graph attention network that predicts interaction-specific (edgetic) effects of missense variants by operating on AlphaFold 3-based protein complex contact graphs with protein language model embeddings imposed upon nodes. We systematically evaluated our model with stringent group cross-validation as well as benchmark data recently collected within the IGVF Consortium. MutPred-PPI outperformed all baseline methods across all evaluation criteria, achieving an AUC of 0.85 on seen proteins and 0.72 on previously unseen proteins in cross-validation, demonstrating strong generalizability despite scarce training data. To demonstrate biomedical relevance, we applied MutPred-PPI to variants from ClinVar, HGMD, COSMIC, gnomAD, and two de novo neurodevelopmental disorder-linked datasets. Disease-associated variants from Clin-Var and HGMD showed strong enrichment for both quasi-null and edgetic effects, whereas population variants from gnomAD increasingly preserved interactions with higher allele frequencies. Notably, we observed a strong edgetic disruption signature in highly recurrent cancer variants from both the full COSMIC dataset and a subset of variants from oncogenes. Recurrent tumor suppressor gene variants and autism spectrum disorder-associated variants exhibited moderate quasi-null enrichment, whilst neurodevelopmental disorder-linked variants showed a weak edgetic disruption signature. These results indicate distinct PPI perturbation mechanisms across disease types and show that MutPred-PPI captures functionally relevant molecular effects of pathogenic variants.

bioinformatics↗

bioRxiv2025

Exploring Single-Cell Gene Regulatory Dynamics in Rett Syndrome

Rett syndrome is a monogenic disorder with an incidence of 95% in women, characterized by the complexity of studying the associated phenotype due to the heterogeneity in patient tissues from the stochastic silencing of the affected X chromosome. Furthermore, we are largely unaware of the cascade of alterations that occur in neurons due to transcriptional changes induced by the affected MECP2 gene. To address these challenges, an in-depth network analysis was implemented using organoid single-cell transcriptomic data derived from human patients. We performed a Weighted correlation network analysis and trajectory analysis to understand the differences in the developmental processes between samples, we followed by the generation of gene regulatory networks for each relevant cell developmental pathways to assess the master regulator that are involved in this process, with the differential expressed genes with potential therapeutic implications being identified by integration with SFARI and Genes4Epi. The results were adapted into dynamic Boolean models fitted with the transcriptomic data for validation in which we evaluated the attractor field from each reachable state. These approaches allowed us to explore differences in regulatory behavior in the developmental pathways. Our study provides an insight that pinpoints the cellular stages on which the regulation and compensatory mechanism activate and regulate Rett syndrome. We identified 19 Master regulators for the Dopaminergic developmental trajectory, as well as 34 Master regulator genes for the Gabaergic developmental trajectory. Dynamic Boolean modeling of these systems showcased a comprehensive understanding of the disrupted developmental pathways of Rett syndrome, highlighting the transitional states of potential within maturation trajectories as the key point of divergence in regulation for Rett syndrome. After complementing with enrichment and clinical relevant variant analysis, we identify the key actors in this system as NR2F1 and TCF4, with TCF4 suggesting a symmetrical compensatory relationship with MeCP2, and NR2F1 as possible link with wider developmental conditions, this concluded with highlighting the possibility of regulation in this condition being affected by the MAPK-ERK pathway of transcriptional regulation, offering a novel angle for targeted research.

neuroscience↗

bioRxiv2025

Cortical Origins of the Flash-Lag Effect distortions: The Influence of Retinotopic Map Architecture

The flash-lag effect (FLE) is an illusion whereby the position of a moving object is perceived as being offset in the direction of movement when compared to a flashed static stimulus. This perceptual misalignment has been posited as a key phenomenon in explaining our ability to accurately predict the future position of moving objects, despite the delays in neuronal processing. Our working hypothesis is that the FLE is resulting from the anticipation generated by propagation of neural activity within visual cortical retinotopical maps. According to this hypothesis, the FLE should be affected by discontinuities and anisotropies of the retinotopic map architecture. Using psychophysics we show that the FLE is strongly affected by crossing and the direction of motion in respect to retinotopic features, such as vertical and horizontal meridians and the fovea. The specificity of how early visual cortical retinotopic maps are splitted and magnified around these features led us to suggest that the FLE distortions emerge from propagation in retinotopically organized networks, particularly V1. This work bridges the gap between human psychophysics and the known constraints of retinotopic maps layout, offering a testable framework for future studies of motion position encoding in the visual hierarchy.

neuroscience↗

bioRxiv2025

Minimal impact of ivermectin on immune response and transcriptional profiles in naïve adults with mild COVID-19

Ivermectin (IVM), an antiparasitic drug, was repurposed to treat COVID-19 based on its in vitro antiviral effects. However, it was abandoned after multiple clinical trials reported a lack of efficacy. Immunomodulatory effects have been proposed but remain unclear, yet they may be relevant given IVM use for other infections. We assessed the IVM immunomodulatory effect in 24 participants from a clinical trial evaluating its potential to reduce COVID-19 transmission in mild cases within 48 hours of symptoms onset. The IVM-treated patients showed non-significant lower viral loads, and a significantly shorter duration of hyposmia/anosmia. We measured IgG, IgA, and IgM against five SARS-CoV-2 antigens, and 30 cytokines by Luminex, alongside whole blood RNA sequencing, pan-leukocyte immunophenotyping, and SARS-CoV-2-specific T cell analysis by flow cytometry from day 1 to day 28 post-treatment. All antibody responses increased from day 4, while 13 cytokines significantly decreased over time (adjusted p<0.05). IVM-treated patients had only significantly higher anti-nucleocapsid IgG levels at day 4 (adjusted p=0.041) and 7 (adjusted p=0.045) compared to placebo. SARS-CoV-2-specific CD4+ and CD8+ T cells increased over time, with significantly higher effector memory CD4+ T cells at day 7 compared to day 1 (p=0.027) and the only difference between groups was lower frequencies of spike-specific naive CD4+ T cells at day 7 in IVM-treated participants (0.006% vs 0.036% p=0.02). Transcriptomic data showed downregulation of innate and antiviral blood transcriptional modules (BTMs) over time, with an increase in adaptive immune related BTMs. While no differential gene expression was detected, the IVM-treated had upregulated innate and downregulated T cell and cell cycle BTMs compared to placebo. Overall, our comprehensive longitudinal analysis of early immune responses in mild COVID-19 revealed no robust immunological effects of IVM, consistent with clinical trials results and suggesting a lack of efficacy of IVM in COVID-19 treatment. AUTHOR SUMMARYIvermectin (IVM), an antiparasitic drug, was tested in clinical trials as a potential treatment for COVID-19 due to its in vitro antiviral properties and hypothetical immunomodulatory effects. In this study, we explored the immunomodulatory effects of IVM in a clinical trial involving 24 mild COVID-19 patients who received IVM within 48 hours of symptoms onset. The IVM group showed a trend towards lower viral loads post-treatment, and a significantly shorter duration of hyposmia/anosmia. We comprehensively analyzed immune responses by measuring antibody levels, cytokine profiles, immune cell subsets and whole blood gene expression over 28 days. The IVM group only had increased levels of IgG against the SARS-CoV-2 nucleocapsid compared to the control group. IVM did not affect the kinetics of SARS-CoV-2 T cells, despite a slight decrease in naive CD4+ T cells. Additionally, gene expression analysis showed a decrease in innate and antiviral responses and an increase of adaptive responses over time that were slightly stronger in the placebo compared to the IVM group. In conclusion, despite some differences in IVM treated participants, our detailed analyses do not support significant immunomodulatory effects that could benefit disease progression.

immunology↗

bioRxiv2025

Eucalyptus cover as the primary driver of native forest bird reductions: evidence from a stand-scale analysis in NW Iberia

The rapid expansion of exotic eucalyptus plantations across the Iberian Peninsula, particularly in northwest Spain, where they now cover 30% of the regions forested area, has profoundly transformed rural landscapes, raising serious concerns about its impact on native biodiversity. This study investigates the influence of structural and floristic attributes of eucalyptus plantations and native forests on forest bird communities, focusing on species abundance and occurrence at the stand level. We conducted point count surveys and vegetation assessments across 240 plots, applying Generalized Linear Models (GLMs) and multimodel inference (MMI) to identify key drivers of avian diversity. Vegetation structure and composition differed substantially between native forests and eucalyptus plantations. Bird species richness and abundance were significantly lower in eucalyptus plantations. The proportion of eucalyptus emerged as the strongest predictor of these reductions, likely due to the limited availability of key resources such as natural cavities and arthropods. Mature native trees were pivotal in supporting forest bird species, particularly those associated with mature forest ecosystems. In contrast, mature eucalyptus trees failed to serve as adequate surrogates for mature native trees, benefiting only a small subset of forest specialists. Similarly, the well-developed shrub layer in eucalyptus plantations provided limited support for generalist bird species. To mitigate biodiversity loss, we recommend integrating unmanaged retention strips within eucalyptus plantations to enhance habitat heterogeneity and structural diversity, ensuring critical resources for birds and other forest wildlife while balancing forestry productivity with conservation goals.

ecology↗

bioRxiv2024

Comparative effects of proton and photon irradiation on the molecular and cellular profiles of triple-negative breast cancer: the crucial impact of VEGFC on tumor microenvironment remodeling

Metastatic triple-negative breast cancers (TNBC) are among the most aggressive types of breast cancer and are often treated with adjuvant radiotherapy and chemotherapy. Despite initial efficacy, relapses are common, leading to poor prognosis. Understanding the response of tumor microenvironment to radiotherapy is crucial, particularly comparing photon (X) and proton (P) radiotherapy due to proton radiations reduced side effects. MethodsWe investigated the effects of single and multiple X and P irradiations on various cell types within the tumor microenvironment, including vascular and lymphatic endothelial cells, fibroblasts, and TNBC tumor cells. VEGFC, a key factor in lymphatic vessel formation and metastasis, was a primary focus. We used protein arrays to evaluate the effects of irradiation and examined the impact of VEGFC inactivation on the sensitivity to X and P radiation. Additionally, we tested tumor-forming capabilities of irradiated cells and assessed the impact of genetic or therapeutic VEGFC inhibition on TNBC growth. Transcriptomic and proteomic analyses further characterized the differences between X and P tumors, providing deeper insights into their distinct molecular profiles. ResultsBoth X and P irradiations caused a transient increase in VEGFC levels, along with other pro-angiogenic, pro-lymphangiogenic, and pro-fibrotic factors, such as angiopoietin 2, artemin, endostatin, IGFBP2, serpinE1, PDGFA, and DPPIV. Endothelial cells exposed to multiple rounds of radiation showed enhanced proliferation but lost the ability to form pseudo vessels, indicating an endothelial-mesenchymal transition. Tumor cells lacking VEGFC were more sensitive to radiation, and anti-VEGFC antibodies significantly suppressed TNBC cells proliferation, both naive and multi-irradiated. Tumor xenografts formed by multi-irradiated cells grew larger in nude mice, particularly following proton irradiation, while X-irradiated tumors exhibited a more pro-lymphangiogenic phenotype compared to P-irradiated tumors. ConclusionsOur findings show that while P multi-irradiated TNBC cells form larger tumors, X multi-irradiated tumors are more aggressive, with elevated expression of genes linked to angiogenesis, lymphangiogenesis, and endothelial-mesenchymal transition. Targeting VEGFC during photon or proton radiotherapy could reduce metastasis and improve TNBC prognosis.

cancer biology↗

bioRxiv2024

CellSeg3D: self-supervised 3D cell segmentation for microscopy

Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inherent complexity of generating 3D ground truth data and quantifying nuclei in 3D volumes. We developed a Python package called CellSeg3D that provides access to these models in Jupyter Notebooks and in a napari GUI plugin. Recognizing the scarcity of high-quality 3D ground truth data, we created a fully human-annotated mesoSPIM dataset to advance evaluation and benchmarking in the field. To assess model performance, we benchmarked our approach across four diverse datasets: the newly developed mesoSPIM dataset, a 3D platynereis-ISH-Nuclei confocal dataset, a separate 3D Platynereis-Nuclei light-sheet dataset, and a challenging and densely packed Mouse-Skull-Nuclei confocal dataset. We demonstrate that our self-supervised model, WNet3D - trained without any ground truth labels - achieves performance on par with state-of-the-art supervised methods, paving the way for broader applications in label-scarce biological contexts.

cell biology↗

bioRxiv2024

A comprehensive two-hybrid analysis to explore the L. pneumophila effector-effector interactome

Legionella pneumophila uses over 300 translocated effector proteins to rewire host cells during infection and create a replicative niche for intracellular growth. To date, several studies have identified L. pneumophila effectors that indirectly and directly regulate the activity of other effectors, providing an additional layer of regulatory complexity. Amongst these are "metaeffectors" - a special class of effectors that regulate the activity of other effectors once inside the host. A defining feature of metaeffectors is direct, physical interaction with a target effector. Metaeffector identification to date has depended on phenotypes in heterologous systems and experimental serendipity. Using a multiplexed, recombinant-barcode-based yeast two-hybrid technology we screened for protein-protein interactions amongst all L. pneumophila effectors and several components of the Dot/Icm type IV secretion system (>167,000 protein combinations). Of the 52 protein interactions identified by this approach, 44 are novel protein interactions, including ten novel effector-effector interactions (doubling the number of known effector-effector interactions).

microbiology↗

bioRxiv2024

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

systems biology↗

bioRxiv2023

Quantifying massively parallel microbial growth with spatially mediated interactions

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterized at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to an interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments, i.e. interpopulation interactions are mediated via the shared environment. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses. Author summaryImage-based platforms allow obtaining population size estimates for massively parallel growth experiments on substrate plates at relatively low cost. However, such population size data has been shown to display a high degree of spatial variability, which occurs even with isogenic populations. Here we first quantified the importance of spatial location on growth variation using a machine learning approach, and then developed spatially aware population growth models to explain the spatial structure of the growth data. Ultimately, we showed that a spatial consumer-resource model with local microhabitats connected via diffusion can fully explain the observed spatial variation in growth while allowing the inference of intrinsic growth parameters of specific populations. This result provides a method for systematic extraction of spatial growth models and paves the way for massively parallel eco-evolutionary experimentation.

ecology↗

bioRxiv2023

Pervasive mislocalization of pathogenic coding variants underlying human disorders

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease-causing. This creates a new bottleneck: determining the functional impact of each variant - largely a painstaking, customized process undertaken one or a few genes or variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,547 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of unknown significance. Our publicly available resource will likely accelerate the understanding of coding variation in human diseases.

cell biology↗

bioRxiv2023

Dysregulated actin dynamics and cofilin correlate with TDP-43 pathology in sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal, rapidly progressive neurodegenerative disorder affecting motor neurons, that overlaps significantly with frontotemporal dementia (FTD). Most cases are sporadic (90%) with undefined aetiology, but pathological forms of TAR-binding protein 43 (TDP-43), involving its misfolding, aggregation and mislocalisation from the nucleus to the cytoplasm, are present in motor neurons in almost all cases (97%) and [~]45% FTD cases. Actin is the most abundant protein in eukaryotic cells, with structural roles in the cytoskeleton and diverse signalling functions. This includes neuronal-specific roles in dendritic spines, synapses, axonal growth cones, and plasticity. Actin is in constant dynamic equilibrium between two forms: free monomeric, globular actin (G-actin) and polymeric, filamentous actin (F-actin). Actin dynamics is regulated by several key actin-binding proteins, including tropomyosin 4.2 (Tpm4.2) and cofilin, which depolymerises actin filaments. Cofilin is activated by phosphorylation at Ser3 via LIM domain kinase1/2 (LIMK1/2), which is also regulated by phosphorylation via Rac1/cdc42. Here we demonstrate that actin dynamics is closely associated with pathological TDP-43 in ALS. More F-actin relative to G-actin was detected in lumbar spinal cords from both sporadic ALS patients and a mouse model displaying TDP-43 pathology (rNLS), and in neuronal cells expressing cytoplasmic TDP-43. Hence actin dynamics is dysregulated in sporadic ALS, resulting in more actin polymerization. We also detected increased levels of Tpm 4.2, Rac1/cdc42, and increased phosphorylation of both LIMK1/2 and cofilin, in sporadic ALS patients. TDP-43 also physically interacted with actin in vitro and in cell lysates, providing additional insights into actin dysregulation in ALS. rNLS mice display motor neuron loss and key ALS/MND behavioural phenotypes, and increased cofilin phosphorylation was also detected in these animals at symptom onset, implying that actin dynamics actively contributes to neurodegeneration. Moreover, pharmacological induction of actin polymerization produced features typical of pathological TDP-43 (cytoplasmic mis-localisation and formation of inclusions and stress granules) implying that actin dysregulation contributes to TDP-43 pathology in ALS. Importantly, we also detected more cofilin phosphorylation in spinal motor neurons from sporadic patients compared to healthy controls, revealing that our observations are clinically relevant and present in the relevant cell type. This study therefore identifies dysregulated actin dynamics as a novel disease mechanism associated with TDP-43 pathology and hence most ALS cases. It also implies that regulating cofilin or LIMK1/2 phosphorylation may be a novel therapeutic strategy in ALS, FTD and other diseases involving TDP-43 pathology.

neuroscience↗

bioRxiv2023

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

systems biology↗

bioRxiv2023

Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors

Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA sequence-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage, and identify cooperativity and antagonism between human TFs and viral proteins. pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.

systems biology↗

bioRxiv2023

The role of confidence in decision-making and its pupillary correlate during the interaction with a partner

Perceptual confidence reflects the ability to evaluate the evidence that supports perceptual decisions. It is thought to play a critical role in guiding decision-making, but only a few empirical studies have actually investigated the function of confidence. To address this issue, we designed a perceptual task in which participants provided a confidence judgment on the accuracy of their perceptual decision. Then, they viewed the response of a machine or human partner, and they were instructed to decide whether to keep or change their initial response. We observed that confidence predicted participants decision to keep or change their initial responses more than task difficulty and perceptual accuracy. This suggests that confidence, as a subjective evaluation of uncertainty, enables us to weigh our decisions, driving the interaction with a partner. Furthermore, confidence judgments could be predicted by pre-response pupil dynamics, suggesting that arousal changes are linked to confidence computations. This study contributes to our understanding of the function of confidence in decision-making and highlights the possibility of using pupil dynamics as a proxy of confidence.

neuroscience↗