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Zoia, M.

Publications and source records attributed to Zoia, M..

2 recordsLinked to original sources

Functional architecture of cardiac TF regulatory landscapes in control of mammalian heart development

Congenital heart disease (CHD), the most common human birth defect, often results from disruptions in gene regulatory networks (GRNs) that control cardiac lineage specification and cell type identity during heart development. A conserved core set of cardiac transcription factors (TFs) orchestrates these processes through combinatorial interactions that are cell type-specific and tightly regulated across space and time. However, the genomic enhancer architecture that integrates upstream effectors to establish precise cardiac TF dosage and downstream transcriptional output remains largely unresolved. Here, we assessed the functional necessity of five developmental heart enhancer modules previously linked to the regulation of Gata4 and Hand2, core cardiac TFs exhibiting overlapping roles in myocardial and endocardial development. While individual enhancer deletions in mouse embryonic hearts revealed a surprising degree of transcriptional resilience, a subset of Gata4 enhancers proved indispensable for embryonic progression in a genetically compromised background. To achieve higher precision in cardiac cell type-specific enhancer prediction, we applied single-nucleus multiome profiling, enabling the delineation of cardiac cistromes underlying heart morphogenesis. By integrating this resource with deep learning applications, site-directed transgenesis, and chromatin conformation modeling, we mapped the cardiac enhancer repertoire and regulatory signatures that orchestrate Hand2 dynamics across distinct cardiac compartments and lineages. Genome editing further revealed an essential role for the Hand2-upstream regulatory interval (H2-URI) in transcriptional control of endocardial lineage effectors and, consequently, trabecular network formation and cardiac cushion patterning. Together our findings highlight substantial resilience in the cis-regulatory architectures governing cardiac TF dynamics and demonstrate that combinatorial integration of upstream lineage identities across modular enhancer landscapes establishes the cardiac cell type-specific programs driving heart morphogenesis. These results advance the reconstruction of cardiac GRNs and enhance the functional interpretation of CHD-associated variants.

developmental biology↗

The neural code behind real-world recognition abilities

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities--super-recognisers--and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared computations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain computations of super-recognisers and mid-level computations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and computations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. SignificanceThe ability to robustly recognise faces is crucial to our success as social beings. Yet, we still know little about the brain mechanisms allowing some individuals to excel at face recognition. This study builds on a sizeable neural dataset measuring the brain activity of individuals with extraordinary face recognition abilities--super-recognisers--to tackle this challenge. Using state-of-the-art computational methods, we show robust prediction of face recognition abilities in single individuals from a mere second of brain activity, and revealed specific brain computations supporting individual differences in face recognition ability. Doing so, we provide direct empirical evidence for an association between semantic computations and face recognition abilities in the human brain--a key component of prominent face recognition models.

neuroscience↗