Biology subjects

Noble, D.

Publications and source records attributed to Noble, D..

7 recordsLinked to original sources

bioRxiv2026

SCN1A haploinsufficiency disrupts neural progenitor networks and accelerates neurogenesis in human Dravet syndrome forebrain assembloids

Dravet syndrome is a severe childhood neurological disorder caused mostly due to mutations in the sodium channel gene SCN1A. Although the disorder is widely attributed to dysfunction of inhibitory neurons, growing evidence suggests that disease mechanisms may begin much earlier during brain development. To investigate the developmental consequences of SCN1A haploinsufficiency, we generated patient-derived dorsal-ventral forebrain assembloids that model key features of human corticogenesis. We found that SCN1A was expressed in ventricular zone neural progenitors and that its reduced expression was associated with impaired growth, disrupted ventricular zone organization, and progressive depletion of progenitor populations. We further demonstrated reduced progenitor proliferation and self-renewal, accelerated cell-cycle exit, and premature neuronal differentiation, leading to increased production of excitatory neurons and enhanced network excitability. Single cell transcriptomic analyses revealed reduced cycling radial glial cells, accelerated progenitor maturation, and dysregulation of gap junction and calcium signaling pathways. Following these findings, we examined progenitor calcium activity and found that SCN1A haploinsufficiency disrupted coordinated functional network organization, resulting in reduced recurrent connectivity and fragmentation of functional cellular assemblies. These abnormalities occurred despite preserved intrinsic electrophysiological properties of individual progenitors, indicating a defect in population-level coordination rather than single cell electrophysiology. Together, our findings identify disrupted progenitor communication as an early developmental consequence of SCN1A haploinsufficiency and suggest that abnormalities within neural progenitor populations contribute to altered corticogenesis, excitatory neurogenesis, and circuit hyperexcitability in Dravet syndrome. This work expands current disease models beyond interneuron dysfunction and highlights the developing ventricular zone as a potential therapeutic target.

neuroscience↗

bioRxiv2026

The Synthetic Epitope Atlas: High-Throughput Design and Validation of De Novo Antibody-Antigen Complexes

AO_SCPLOWBSTRACTC_SCPLOWDe novo antibody design models lack sufficient training data to reliably generalize. We demonstrate scalable generation of structural training data for machine learning-driven antibody design by linking in silico designs of antibody-antigen complexes to high-throughput experimental binding validation. Using AlphaSeq, a yeast-based platform for measuring protein binding affinities, we measure the affinity and specificity of thousands of de novo "synthetic epitope proteins" (SEPs) designed to bind to VHHs. The resulting Synthetic Epitope Atlas (SEPIA) pairs over 26 million on- and off-target affinity measurements with computationally designed VHH-SEP "pseudo-structures." We validate strong, specific binding for 1,161 pseudo-structures and >75,000 VHH and SEP mutational variants. We show that these pseudo-structures complement existing structural databases and enable ML models to outperform confidence metrics commonly used to rank de novo antibody designs. Taken together, SEPIA establishes a scalable framework for improving de novo antibody design by augmenting sparse structural data with large-scale experimental binding data.

synthetic biology↗

bioRxiv2024

AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity

Antibodies are versatile therapeutic molecules that utilize combinatorial sequence diversity to cover a vast fitness landscape. However, designing optimal antibody sequences remains a major challenge. Recent advances in deep learning provide opportunities to address this challenge by learning sequence-function relationships to accurately predict fitness landscapes. These models enable efficient in silico prescreening and optimization of antibody candidates. By focusing experimental efforts on the most promising candidates guided by deep learning predictions, antibodies with optimal properties can be designed more quickly and effectively. Here we present AlphaBind, a domain-specific model that utilizes protein language model embeddings and pre-training on millions of quantitative laboratory measurements of antibody-antigen binding strength to achieve state-of-the-art performance for guided affinity optimization of parental antibodies. We demonstrate that an AlphaBind-powered antibody optimization pipeline can deliver candidates with substantially improved binding affinity across four parental antibodies (some of which were already affinity-matured) and using two different types of training data. Resulting candidates, ranging up to 11 mutations from parental sequence, yield a sequence diversity that allows for optimization of other biophysical characteristics, all while using only a single round of data generation for each parental antibody. AlphaBind weights and code are publicly available at: https://github.com/A-Alpha-Bio/alphabind.

synthetic biology↗

bioRxiv2023

Short- and long-term high-fat diet exposure differentially alters phasic and tonic GABAergic signalling onto lateral orbitofrontal pyramidal neurons

The chronic consumption of caloric dense high-fat foods is a major contributor to increased body weight, obesity, and other chronic health conditions. The orbitofrontal cortex (OFC) is critical in guiding decisions about food intake and is altered with diet-induced obesity. Obese rodents have altered morphological and synaptic electrophysiological properties in the lateral orbitofrontal cortex (lOFC). Yet the time course by which exposure to a high-fat diet (HFD) induces these changes is poorly understood. Here male mice are exposed to either short- (7 day) or long-term (90 day) HFD. Long-term HFD exposure increases body weight, and glucose signaling compared to short-term HFD or a standard chow control diet (SCD). Both short and long-term HFD exposure increased the excitability of lOFC pyramidal neurons. However, phasic and tonic GABAergic signalling was differentially altered depending on HFD exposure length, such that tonic GABAergic signaling was decreased with early exposure to the HFD and phasic signaling was changed with long-term diet exposure. Furthermore, alterations in the short-term diet exposure were transient, as removal of the diet restored electrophysiological characteristics similar to mice fed SCD whereas long-term HFD electrophysiological changes were persistent and remained after HFD removal. Finally, we demonstrate that changes in reward devaluation occur early with diet exposure. Together, these results suggest that the duration of HFD exposure differentially alters lOFC function and provides mechanistic insights into the susceptibility of the OFC to impairments in outcome devaluation. Significant statementThis study provides mechanistic insight on the impact of short- and long-term high fat diet (HFD) exposure on GABAergic function in the lateral orbitofrontal cortex (lOFC), a region known to guide decision making. We find short-term HFD exposure induces transient changes in firing and tonic GABA action on lOFC pyramidal neurons, whereas long-term HFD induces obesity and has lasting changes on firing, tonic GABA and inhibitory synaptic transmission onto lOFC neurons. Given that GABAergic signaling in the lOFC can influence decision making around food, these results have important implications in present society as palatable energy dense foods are abundantly available.

neuroscience↗

bioRxiv2021

Surveying the global landscape of post-transcriptional regulators

Numerous proteins regulate gene expression by modulating mRNA translation and decay. In order to uncover the full scope of these post-transcriptional regulators, we conducted an unbiased survey that quantifies regulatory activity across the budding yeast proteome and delineates the protein domains responsible for these effects. Our approach couples a tethered function assay with quantitative single-cell fluorescence measurements to analyze [~]50,000 protein fragments and determine their effects on a tethered mRNA. We characterize hundreds of strong regulators, which are enriched for canonical and unconventional mRNA-binding proteins. Regulatory activity typically maps outside the RNA-binding domains themselves, highlighting a modular architecture that separates mRNA targeting from post-transcriptional regulation. Activity often aligns with intrinsically disordered regions that can interact with other proteins, even in core mRNA translation and degradation factors. Our results thus reveal networks of interacting proteins that control mRNA fate and illuminate the molecular basis for post-transcriptional gene regulation.

molecular biology↗

bioRxiv2020

Flexible multiplane structured illumination microscope with a four-camera detector

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a simple multi-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This simple setup allows for the rapid acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics.

biophysics↗

bioRxiv2020

Sex and Power: sexual dimorphism in trait variability and its eco-evolutionary and statistical implications

Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioral traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the estrus-mediated variability hypothesis); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the greater male variability hypothesis. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex-bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance.

evolutionary biology↗