Biology subjects

Gastaldi, C.

Publications and source records attributed to Gastaldi, C..

3 recordsLinked to original sources

bioRxiv2025

Improving angiogenesis ameliorates the efficacy of ASO-based exon-skipping for the treatment of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a severe X-linked disease caused by mutations in the DMD gene, resulting in the absence of functional dystrophin. Antisense oligonucleotide (ASO)-based therapies aim to restore the open reading frame and produce a truncated but functional dystrophin protein. Although several ASOs are FDA-approved, dystrophin restoration in patient biopsies remains low, underlining the need to improve ASO efficacy. One major limitation is poor ASO biodistribution to skeletal muscle, influenced by both ASO chemistry and pathological features of dystrophic tissue. In DMD patients and mdx mice, microvascular abnormalities and impaired angiogenesis likely restrict ASO delivery. Here, we hypothesized that enhancing muscle vascularization could improve ASO biodistribution and therapeutic outcomes. Mdx mice were treated with a pro-angiogenic treatment prior to ASO administration targeting exon 23 of dystrophin pre-mRNA. Angiogenic stimulation increased capillary density and improved ASO delivery, exon skipping, and dystrophin expression compared to ASO alone. These molecular improvements were associated with increased myofiber size, larger mean cross-sectional area, and decreased serum myomesin levels, without signs of toxicity. This study provides proof-of-concept that promoting angiogenesis can enhance the efficacy of ASO-based treatments, offering a complementary strategy to improve therapeutic outcomes in DMD.

molecular biology↗

bioRxiv2023

A dynamic attractor network model of memory formation, reinforcement and forgetting

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic hypothesis of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.

neuroscience↗

bioRxiv2021

When shared concept cells support associations: theory of overlapping memory engrams

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction cmin of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction cmax of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds. Authors contributionsAll authors contributed to conception of the study and writing of the manuscript. CG and TS developed the theory. CG wrote the code for all figures. EDF and RQQ provided the experimental data. EDF and CG analyzed the data. WG and CG developed algorithms to fit the experimental data.

neuroscience↗