Biology subjects

Biagi, M. C.

Publications and source records attributed to Biagi, M. C..

6 recordsLinked to original sources

bioRxiv2025

Mutant FUS perturbs m6A regulation by repressing ALKBH5, while restoring m6A levels alleviates ALS pathology in vivo

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder marked by the progressive loss of motor neurons, where aberrant stress granule (SG) dynamics has emerged as a hallmark, especially in cases involving mutations in RNA-binding proteins such as FUS and TDP-43. We previously showed that m6A RNA modification is elevated in FUS-associated ALS and that restoring its physiological levels through METTL3 inhibition is sufficient to rescue the aberrant stress granule phenotypes in different human cellular models. Importantly, here we show that reducing m6A levels ameliorates locomotor defects and neuromuscular junction (NMJ) abnormalities in Drosophila melanogaster models of ALS expressing the human FUSP525L mutant. Moreover, we demonstrate that the increase in m6A is attributable to reduced expression of the m6A eraser ALKBH5, resulting from impaired translation caused by direct FUS binding to its transcript. We further show that ALKBH5 downregulation contributes to RNA and protein alterations observed in FUS-associated ALS, driving the abnormal SG dynamics characteristic of the disease. This study supports the hypothesis that restoring correct m6A levels could serve as a potential therapeutic strategy for ALS.

molecular biology↗

bioRxiv2025

A Short 63-Nucleotide Element Promotes Efficient circRNA Translation

Circular RNAs (circRNAs) are a class of RNA with multiple functions, including the ability to be translated. Several intrinsic features of circRNAs, such as high stability, confer them advantages over linear RNAs; therefore, circRNA-based drugs have recently received increasing attention. However, the inefficiency of their cap-independent translation and the difficulties in the large-scale production of long circRNAs negatively impact on their use in therapy. Some efforts have been done to solve these issues related to circRNA adoption, but, to date, circRNA translation still relies on long IRESs (600-800) and chemical group addition. In this study, identified a 63-nt element able to drive circRNA translation comparably to the most commonly used IRESs. This element includes a a 13-nt sequence previously reported to enhance linear RNA translation and a segment of the UTR of the endogenously translated circRNA circZNF609. Notably, this element combines a comparable IRES-like efficiency to a considerably shorter length, expanding the landscape of ORFs potentially suitable for being translated from circRNAs and enhancing their potential as therapeutic agents in therapy.

molecular biology↗

bioRxiv2023

M6A reduction relieves FUS-associated ALS granules

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease due to gradual motorneurons (MN) degeneration1. Among the processes associated to ALS pathogenesis, there is the formation of cytoplasmic inclusions produced by mutant protein aggregation, among which the RNA binding protein FUS2. In this work we show that such inclusions are significantly reduced in number and dissolve faster when the RNA m6A content is diminished as a consequence of the m6A writer METTL3 knock-down. These effects were observed both in neuronal cell lines and in iPSC-derived human motor neurons expressing mutant FUS. Importantly, stress granules formed in ALS condition showed a distinctive transcriptome with respect to control cells; interestingly, after METTL3 downregulation, it reverted to similar to control. Finally, we show that FUS inclusions are reduced also in patient-derived fibroblasts treated with STM-2457, a well characterized inhibitor of METTL3 activity, paving the way for its possible use for counteracting aggregate formation in ALS.

molecular biology↗

bioRxiv2022

Towards the identification and optimization of the "dose-response" relationship of transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic tool in several neuropsychiatric disorders, but its mechanisms of action remain incompletely understood and its effectiveness is highly variable from subject to subject. Some of this inter-subject variance is thought to stem from individual differences in head anatomy, which result in different electric field distributions across individuals for the same stimulation settings. In a recent block-randomized, sham-controlled pilot study, 18 older adults with mild-to-moderate motor and cognitive impairments underwent a 2-week, 10-session intervention of 2 mA bipolar tDCS with the anode over the left dorsolateral prefrontal cortex (DLPFC) and cathode over the right supraorbital region (anodal F3 vs. FP2 using sponge electrodes in the 10-20 EEG system). tDCS, compared to sham stimulation, improved average cognitive-motor performance over a two-week follow-up. For a subset of 12 participants from this cohort who underwent structural brain MRIs at baseline, we derived tDCS-induced electric field properties using MRI-derived realistic, individualized finite element modeling. We hypothesized that the electric field component orthogonal (En) to the cortex over the left DLPFC would correlate with observed cognitive-motor improvements. Across the 12 subjects the average tDCS-induced electric field over the target area was highly variable with a standard deviation of the surface average En over the left DLPFC of 21% of the mean. For those subjects who were randomized to tDCS intervention (n=6), those who exhibited greater improvement in cognitive-motor performance were exposed to greater normal component of the tDCS electric field averaged over the left DLPFC (r95 % [isin] [-0.99,-0.59]). In contrast, averaged over the right DLPFC, En was a poor predictor of outcome (r95 % [isin] [-0.08,0.97]), as was the amplitude of the current injected (a widely adopted dosing parameter). In light of these results, we conducted a retrospective modeling experiment using personalized montage optimization algorithms to achieve a desired En amplitude over the left DLPFC. The optimized montages induced an average En on the left DLPFC comparable to the bipolar montages, but with half the variability (standard deviation was 11% compared to 21% with bipolar montages). In addition, these results suggest that optimized multichannel montages afford more focal targeting, with lower "spill-over" of electric field onto other cortical areas. These proof-of-concept results, though based upon a small sample size, indicate that assessment of the E-field distribution is critical when evaluating the effects of tDCS intervention. They also reinforce the importance of controlled cortical electric field dosing in tDCS and the potential advantages of optimized multichannel stimulation. Main findings: O_LIThe lDPFC averaged normal component of the electric field correlated with cognitive-motor improvements following tDCS intervention. C_LIO_LIIn older adults, head and brain anatomical differences resulted in high inter-subject variability of tDCS-induced electric fields. C_LIO_LItDCS montages optimized from modeling and delivered via multichannel electrode arrays can control variance and maximize electric field on target, while minimizing effects elsewhere. C_LI

neuroscience↗

bioRxiv2021

Modeling implanted metals in electrical stimulation applications

BackgroundMetal implants impact the dosimetry assessment in electrical stimulation techniques. Therefore, they need to be included in numerical models. While currents in the body are ionic, metals only allow electron transport. In fact, charge transfer between tissues and metals requires electric fields to drive the electrochemical reactions at the interface. Thus, metal implants may act as insulators or as conductors depending on the scenario. Objective/HypothesisThe aim of this paper is to provide a theoretical argument that guides the choice of the correct representation of metal implants using purely electrical models but considering the electrochemical nature of the problem in the technology of interest. MethodsWe built a simple model of a metal implant exposed to a homogeneous electric field of various magnitudes to represent both weak (e.g., tDCS), medium (TMS) or strong field stimulation. The same geometry was solved using two different models: a purely electric one (with different conductivities for the implant), and an electrochemical one. As an example of application, we also modeled a transcranial electrical stimulation (tES) treatment in a realistic head model with a skull plate using a high and low conductivity value for the plate. ResultsMetal implants generally act as electric insulators when exposed to electric fields up to around 100 V/m (tES and TMS range) and they only resemble a perfect conductor for fields in the order of 1000 V/m and above. The results are independent of the implants metal, but they depend on its geometry. Conclusion(s)Metal implants can be accurately represented by a simple electrical model of constant conductivity, but an incorrect model choice can lead to large errors in the dosimetry assessment. In particular, tES modeling with implants incorrectly treated as conductors can lead to errors of 50% in induced fields or more. Our results can be used as a guide to select the correct model in each scenario.

bioengineering↗

bioRxiv2020

Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain

The segmentation of structural MRI data is an essential step for deriving geometrical information about brain tissues. One important application is in transcranial electrical stimulation (e.g., tDCS), a non-invasive neuromodulatory technique where head modeling is required to determine the electric field (E-field) generated in the cortex to predict and optimize its effects. Here we propose a deep learning-based model (StarNEt) to automatize white matter (WM) and gray matter (GM) segmentation and compare its performance with FreeSurfer, an established tool. Since good definition of sulci and gyri in the cortical surface is an important requirement for E-field calculation, StarNEt is specifically designed to output masks at a higher resolution than that of the original input T1w-MRI. StarNEt uses a residual network as the encoder (ResNet) and a fully convolutional neural network with U-net skip connections as the decoder to segment an MRI slice by slice. Slice vertical location is provided as an extra input. The model was trained on scans from 425 patients in the open-access ADNI+IXI datasets, and using FreeSurfer segmentation as ground truth. Model performance was evaluated using the Dice Coefficient (DC) in a separate subset (N=105) of ADNI+IXI and in two extra testing sets not involved in training. In addition, FreeSurfer and StarNEt were compared to manual segmentations of the MRBrainS18 dataset, also unseen by the model. To study performance in real use cases, first, we created electrical head models derived from the FreeSurfer and StarNEt segmentations and used them for montage optimization with a common target region using a standard algorithm (Stimweaver) and second, we used StarNEt to successfully segment the brains of minimally conscious state (MCS) patients having suffered from brain trauma, a scenario where FreeSurfer typically fails. Our results indicate that StarNEt matches FreeSurfer performance on the trained tasks while reducing computation time from several hours to a few seconds, and with the potential to evolve into an effective technique even when patients present large brain abnormalities.

bioinformatics↗