Javaherian, A. | Search bioRxiv

bioRxiv2021

Opposing roles of p38α phosphorylation and arginine methylation in driving TDP-43 proteinopathy.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder typically characterized by insoluble inclusions of hyperphosphorylated TDP-43. The mechanisms underlying toxic TDP-43 accumulation are not understood. Persistent activation of p38 mitogen-activated protein kinase (MAPK) is implicated in ALS. However, it is unclear how p38 MAPK affects TDP-43 proteinopathy. Here, we demonstrate that inhibition of p38 MAPK reduces pathological TDP-43 phosphorylation, aggregation, cytoplasmic mislocalization, and neurotoxicity. We establish that p38 MAPK phosphorylates TDP-43 at pathological serine 409/410 (S409/S410) and serine 292 (S292), which reduces TDP-43 liquid-liquid phase separation (LLPS) but allows pathological TDP-43 aggregation. Moreover, we show that protein arginine methyltransferase 1 methylates TDP-43 at R293. Importantly, S292 phosphorylation reduces R293 methylation, and R293 methylation reduces S409/S410 phosphorylation. R293 methylation permits TDP-43 LLPS and reduces pathological TDP-43 aggregation. Thus, strategies to reduce p38-mediated TDP-43 phosphorylation and promote R293 methylation could have therapeutic utility for ALS and related TDP-43 proteinopathies.

molecular biology↗

bioRxiv2020

Super-human cell death detection with biomarker-optimized neural networks

Cell death is an essential process in biology that must be accounted for in live microscopy experiments. Nevertheless, cell death is difficult to detect without perturbing experiments with stains, dyes or biosensors that can bias experimental outcomes, lead to inconsistent results, and reduce the number of processes that can be simultaneously labelled. These additional steps also make live microscopy difficult to scale for high-throughput screening because of the cost, labor, and analysis they entail. We address this fundamental limitation of live microscopy with biomarker-optimized convolutional neural networks (BO-CNN): computer vision models trained with a ground truth biosensor that detect live cells with superhuman, 96% accuracy more than 100 times faster than previous methods. Our models learn to identify important morphological characteristics associated with cell vitality without human input or additional perturbations, and to generalize to other imaging modalities and cell types for which they have no specialized training. We demonstrate that we can interpret decisions from BO-CNN models to gain biological insight into the patterns they use to achieve superhuman accuracy. The BO-CNN approach is broadly useful for live microscopy, and affords a powerful new paradigm for advancing the state of high-throughput imaging in a variety of contexts.

cell biology↗