Conrad, P. | Search bioRxiv

bioRxiv2026

A screen for stress-induced sleep genes in C. elegans reveals a role for glutamate signaling

Sleep is an essential behavioral state that evolved early in animals, possibly with the advent of the nervous system. The complexity of sleep neural networks varies significantly across phylogeny, yet common signaling molecules exist. Stress-induced sleep (SIS) of Caenorhabditis elegans is controlled by two sleep interneurons (ALA and RIS), within a 302-celled nervous system. Even in this simple framework, a complex array of signaling molecules is expressed. Here, we surveyed some of these genes for roles in SIS. These included neuropeptides, g-protein coupled receptors, a two-pored potassium channel, and glutamate signaling components. We found that multiple genes are required for sleep maintenance (i.e., amounts), and/or the precise timing of sleep initiation. In particular, we identified an important role for glutamate signaling. The conserved ionotropic glutamate receptor glr-5, regulates sleep maintenance and timing, and is required in a 3-celled circuit of interneurons connected by gap junctions and chemical synapses with RIS. This work suggests that numerous redundant and/or parallel mechanisms have evolved to modulate a simple sleep-regulating circuit in C. elegans, and we speculate that conserved pathways may play similar roles in animals with more complex systems.

genetics↗

bioRxiv2024

Deep Learning Analysis on Images of iPSC-derived Motor Neurons Carrying fALS-genetics Reveals Disease-Relevant Phenotypes

Amyotrophic lateral sclerosis (ALS) is a devastating condition with very limited treatment options. It is a heterogeneous disease with complex genetics and unclear etiology, making the discovery of disease-modifying interventions very challenging. To discover novel mechanisms underlying ALS, we leverage a unique platform that combines isogenic, induced pluripotent stem cell (iPSC)-derived models of disease-causing mutations with rich phenotyping via high-content imaging and deep learning models. We introduced eight mutations that cause familial ALS (fALS) into multiple donor iPSC lines, and differentiated them into motor neurons to create multiple isogenic pairs of healthy (wild-type) and sick (mutant) motor neurons. We collected extensive high-content imaging data and used machine learning (ML) to process the images, segment the cells, and learn phenotypes. Self-supervised ML was used to create a concise embedding that captured significant, ALS-relevant biological information in these images. We demonstrate that ML models trained on core cell morphology alone can accurately predict TDP-43 mislocalization, a known phenotypic feature related to ALS. In addition, we were able to impute RNA expression from these image embeddings, in a way that elucidates molecular differences between mutants and wild-type cells. Finally, predictors leveraging these embeddings are able to distinguish between mutant and wild-type both within and across donors, defining cellular, ML-derived disease models for diverse fALS mutations. These disease models are the foundation for a novel screening approach to discover disease-modifying targets for familial ALS.

neuroscience↗