Search bioRxiv⌕ Search

bioRxiv · 10.1101/567735

FUS ALS-causative mutations impact FUS autoregulation and the processing of RNA-binding proteins through intron retention

Abstract

Mutations in the RNA-binding protein FUS cause amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disease in which the loss of motor neurons induces progressive weakness and death from respiratory failure, typically only 3-5 years after onset. FUS plays a role in numerous aspects of RNA metabolism, including mRNA splicing. However, the impact of ALS-causative mutations on splicing has not been fully characterised, as most disease models have been based on FUS overexpression, which in itself alters its RNA processing functions. To overcome this, we and others have recently created knock-in models, and have generated high depth RNA-sequencing data on FUS mutants in parallel to FUS knockout. We combined three independent datasets with a joint modelling approach, allowing us to compare the mutation-induced changes to genuine loss of function. We find that FUS ALS-mutations induce a widespread loss of function on expression and splicing, with a preferential effect on RNA binding proteins. Mutant FUS induces intron retention changes through RNA binding, and we identify an intron retention event in FUS itself that is associated with its autoregulation. Altered FUS regulation has been linked to disease, and intriguingly, we find FUS autoregulation to be altered not only by FUS mutations, but also in other genetic forms of ALS, including those caused by TDP-43, VCP and SOD1 mutations, supporting the concept that multiple ALS genes interact in a regulatory network.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Humphrey, J., Birsa, N., Milioto, C., Robaldo, D., Bentham, M., Jarvis, S., Bodo, C., Garone, M. G., Devoy, A., Rosa, A., Bozzoni, I., Fisher, E., Ruepp, M.-D., Schiavo, G., Isaacs, A., Plagnol, V., Fratta, P.. 2019-03-04. FUS ALS-causative mutations impact FUS autoregulation and the processing of RNA-binding proteins through intron retention. https://doi.org/10.1101/567735

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Next generation Human Neocortical Neurosolver model with biophysically realistic somatic, dendritic and synaptic dynamics

The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling tool for multiscale interpretation of human electroencephalography (EEG) and magnetoencephalography (MEG) signals. HNN's foundation is a pre-tuned canonical neocortical circuit model consisting of excitatory pyramidal neurons and inhibitory interneurons in supra- (L2/3) and infragranular (L5) layers. This model was originally developed based on studies in rodent and non-human primate cortices when dynamics of human neurons were poorly characterized. Yet recent advances in recordings from human tissue now provide unprecedented detail to update the model. Here, we present a significantly updated model for use in the HNN software, with electrophysiological properties and synaptic connections tuned to human tissue. Beyond reproducing biologically realistic somatic dynamics, the pyramidal cells simulate dendritic calcium (Ca2+) spikes in the L5 extratelencephalic-projecting (ET) neurons, and shorter dendritic spikes in the L2/3 pyramidal neurons. We demonstrate how the updated circuitry and new features in the HNN software can be used to disentangle somatic, synaptic, and dendritic contributions to human E-/MEG signatures. The model is distributed with workflows to simulate event-related responses and oscillations, and tutorials demonstrating how membrane potentials, Ca2+ dynamics, and synaptic currents can be recorded and visualized. These enhancements allow extensive model validations based on in vitro and in vivo electrophysiology, voltage and Ca2+ imaging, and provide users with novel insights on the mechanisms underlying E-/MEG signals.

neuroscience↗

A biological light sheet links cellular voltage to mesoscopic brain dynamics

Membrane voltage is the fundamental currency of neural computation, yet cellular-resolution voltage imaging of large neuronal populations in vivo remains limited by trade-offs among imaging speed, field of view, and optical sectioning. We overcome this challenge by encoding a biological light sheet in the brain: Selective Plane Expression (SPress) restricts fluorescent indicator expression to a user-defined, 20-m-thick plane, suppressing background at its source. Following one-time two-photon photoactivation, SPress enables standard widefield microscopes to capture high-contrast kilohertz voltage dynamics from hundreds of densely labeled neurons across millimeter-scale cortical sheets and columns in vivo. These recordings reveal mesoscopic subthreshold waves that flexibly reorganize population-wide spike timing, together with cellularscale wavefront microstructure that preserves stereotyped local recruitment biases. By shifting optical sectioning from instrumentation to gene expression, SPress makes kilohertz cellular resolution voltage imaging accessible across mesoscopic scales.

neuroscience↗

Persistent hippocampal traces of lifelong spatial memory

Memory presents a biological paradox: the brain changes continuously, yet a brief experience can influence behavior for a lifetime. How such memories are stored in brain circuits remains unclear. Here we show that two days in a binary-tree maze leave mice with spatial memories that survive repeated 90-day absences over as much as a year. Across return visits, mice explored the maze more efficiently than when naive and resumed direct runs to a reward location sooner, both by a factor of 3. Chronic calcium imaging of the same hippocampal CA1 neurons showed that although most neurons had changed their preferred locations, the population activity at a given maze location still resembled the pattern from 90 days earlier. Furthermore, even when a neuron's place fields changed location, they tended to remain at the same branching level of the binary tree, reflecting the local geometry of the maze. In return sessions where the neural tuning for branching level was preserved more accurately, the mouse explored the environment more efficiently. Population patterns were best preserved near the reward location, and more reliable neural activity there accompanied earlier direct runs to the reward. With repeated experience, spatial representations changed less, both within a visit and across the long absences. A brief experience thus left selective hippocampal traces that survived months of absence despite drift, grew more durable with each return, and were correlated with memory in navigation behavior.

neuroscience↗