Biology subjects

Pellegrini, F.

Publications and source records attributed to Pellegrini, F..

8 recordsLinked to original sources

bioRxiv2026

Monomeric TDP-43 binds GU-rich RNA via its NTD but only its oligomers block cryptic exon inclusion

TDP-43 aggregation is a pathological feature of several neurodegenerative diseases, resulting in nuclear loss of function and RNA dysregulation, including cryptic exon derepression. TDP-43 comprises two RNA recognition motifs required for canonical RNA binding, a low-complexity domain implicated in phase separation, and an N-terminal domain (NTD) that mediates oligomerization. While NTD-driven oligomerization is critical for TDP-43 splicing function, some studies suggest a direct interaction of NTD with RNA. Here, we investigated RNA binding by the NTD and its functional interplay with oligomerization. We show that the NTD preferentially binds UG-rich RNA via residues located at the oligomerization interface. The NTD engaged RNA only on monomeric TDP-43, as RNA binding and oligomerization compete for the same interface and rely on similar interaction modes. By disentangling these competing activities, we demonstrated that oligomerization, but not NTD-RNA interaction, is essential for TDP-43-dependent alternative splicing and repression of cryptic exons. Our findings uncover a previously unappreciated RNA-binding activity within the TDP-43 NTD that directly competes with oligomerization and establish oligomerization as a key determinant of TDP-43 cryptic exon repression.

molecular biology↗

bioRxiv2026

TDP-43 dysfunction induces cryptic circular RNAs in ALS/FTD

Nuclear depletion of TDP-43 is a defining pathological feature of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), leading to widespread RNA misprocessing, including the formation of cryptic exons. Here, we identified TDP-43 as a regulator of circular RNA (circRNA) biogenesis in multiple human neuronal cell models, and showed that its dysfunction induces the de novo formation of cryptic circular RNAs (c-circRNAs). Analysis of post-mortem brain transcriptomic data identified a subset of c-circRNAs which are specific for ALS and FTD cases with TDP-43 pathology. Further, we developed highly sensitive rolling-circle amplification-based circRNA detection assays that allow to distinguish TDP-43 pathology in human CNS tissues with a 0.99 AUC. We found that c-circRNAs can co-occur with cryptic linear splicing events, uncovering complex RNA misprocessing hotspots that induce loss of disease-relevant proteins, including RPTOR and EHMT1. Notably, one of these c-circRNAs originates from UNC13A, a gene whose cryptic exon has previously been linked to one of the major GWAS hits in ALS/FTD and that is being pursued as a therapeutic target through splice-switching ASOs. We showed that c-circUNC13A is co-regulated with the linear cryptic transcript and suppression of UNC13A cryptic exon results in c-circUNC13A reduction in cultured neurons and in vivo, highlighting its potential as a target engagement biomarker for emerging UNC13A-directed therapies. Overall, this work identifies a novel molecular mechanism for TDP-43 dysfunction, opening novel avenues for understanding disease pathogenesis and developing much needed pathology biomarkers.

neuroscience↗

bioRxiv2025

Sensitivity to TDP-43 loss and degradation resistance determine cryptic exon biomarker potential

Cryptic splicing caused by TDP-43 proteinopathy is a hallmark of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). However, which cryptic splicing events (CEs) are the most sensitive to TDP-43 depletion, where CEs localise within cells, and how specific CEs are in human tissues is poorly defined. Analyses of in vitro TDP-43 knockdowns and postmortem RNA-seq datasets revealed that a small subset out of thousands of CEs are specific markers for TDP-43 proteinopathy in vivo. Nonsense-mediated decay (NMD) masked a portion of CEs, influencing their subcellular localization and detectability in tissue. Dose-dependent TDP-43 depletion identified "early-responsive" CEs, which possess stronger splice sites and denser, more canonical TDP-43 binding motifs. Finally, we developed a composite cryptic burden score that effectively captured TDP-43 pathology across heterogeneous tissues and correlated with regional vulnerability and genetic background. Our work identifies robust biomarkers and offers new insights into TDP-43-mediated splicing dysregulation in neurodegeneration.

molecular biology↗

bioRxiv2025

Decoding RNA-RNA Interactions: The Role of Low-Complexity Repeats and a Deep Learning Framework for Sequence-Based Prediction

RNA-RNA interactions (RRIs) are fundamental to gene regulation and RNA processing, yet their molecular determinants remain unclear. In this work, we analyze several large-scale RRI datasets and identify low-complexity repeats (LCRs), including simple tandem repeats, as key drivers of RRIs. Our findings reveal that LCRs enable thermodynamically stable interactions with multiple partners, positioning them as key hubs in RNA-RNA interaction networks. These RRIs appear to be important for several aspects of RNA metabolism. Sequencing-based analysis of the lncRNA Lhx1os interactors validates the importance of LCRs in shaping contacts potentially involved in neuronal development. Recognizing the pivotal role of sequence determinants, we develop RIME, a deep learning model that predicts RRIs by leveraging embeddings from a nucleic acid language model. RIME outperforms traditional thermodynamics-based tools, successfully captures the role of LCRs and prioritizes high-confidence interactions, including those established by lncRNAs. RIME is freely available at https://tools.tartaglialab.com/rna_rna.

biochemistry↗

bioRxiv2024

Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of Parkinson's disease

Deep brain stimulation is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinsons disease, subthalamic deep brain stimulation clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex - basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully invasive neural multisite recordings in patients undergoing deep brain stimulation surgery with normative MRI-based whole-brain connectomics. Our findings demonstrate that dopamine and stimulation exert distinct mesoscale effects through modulation of local neural population activity. In contrast, at the macroscale, stimulation mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex - basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and deep brain stimulation, laying the foundation for advanced closed-loop neurostimulation therapies.

neuroscience↗

bioRxiv2023

Distinguishing between- from within-site phase-amplitude coupling using antisymmetrized bispectra

Phase-amplitude coupling (PAC) is a form of cross-frequency coupling in which the amplitude of a fast oscillation is locked to the phase of a slow oscillation. PAC has been proposed as a mechanism for integrating slow large-scale networks with fast-oscillating local processes in the brain. On a signal level, PAC can be observed in single time series, reflecting local dynamics, or between two time series, potentially reflecting a functional interaction between distinct brain sites. To investigate the role of PAC as a mechanism of brain signalling, it is important to distinguish these two cases. However, when mixtures of underlying signals are observed, between-site PAC can spuriously emerge even if the true interaction is only local (within-site). This problem arises in electrophysiological recordings where mixing occurs due to volume conduction or the presence of a shared electrical reference. To address this problem, we propose to estimate PAC using the anti-symmetrized bispectrum (ASB-PAC). It has previously been shown that the cross-bispectrum can be used to measure PAC while efficiently sup-pressing Gaussian noise, and that the anti-symmetrized bispectra vanish for mixtures of independent sources. However, ASB-PAC has so far not been used to assess the presence of genuine between-site PAC. Using simulations, we here investigate the performance of different algorithms to detect PAC in a mixed signal setting as well as the performance of the same methods to distinguish genuine between-site PAC from within-site PAC. This is done in a minimal two-channel setup as well as in a more complex setting that assesses PAC on reconstructions of simulated EEG sources. We observe that bispectral PAC methods are considerably better at detecting simulated PAC in the volume conduction setting than three conventional PAC estimators. ASB-PAC achieves the highest performance in detecting genuine between-site PAC interactions while detecting the fewest spurious interactions. Using the ASB-PAC could therefore greatly facilitate the interpretation of future PAC studies when discriminating local from remote effects. We demonstrate the application of ASB-PAC on EEG data from a motor imagery experiment. Additionally, we present an upgraded version of the free ROIconnect plugin for the EEGLAB toolbox, which includes PAC and ASB-PAC metriscs based on bispectra.

neuroscience↗

bioRxiv2022

Identifying best practices for detecting inter-regional functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipeline that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis EEG data recorded during motor imagery.

neuroscience↗

bioRxiv2019

Motion Coherence and Luminance Contrast Interact in Driving Visual Gamma-Band Activity

Synchronized neuronal population activity in the gamma-frequency range (> 30 Hz) correlates with the bottom-up drive of various visual features. It has been hypothesized that gamma-band synchronization enhances the gain of neuronal representations, yet evidence remains sparse. We tested a critical prediction of the gain hypothesis, which is that features that drive synchronized gamma-band activity interact super-linearly. To test this prediction, we employed whole-head magnetencephalography (MEG) in human subjects and investigated if the strength of visual motion (motion coherence) and luminance contrast interact in driving gamma-band activity in visual cortex. We found that gamma-band activity (64 to 128 Hz) monotonically increased with coherence and contrast while lower frequency activity (8 to 32 Hz) decreased with both features. Furthermore, as predicted for a gain mechanism, we found a multiplicative interaction between motion coherence and contrast in their joint drive of gamma-band activity. The lower frequency activity did not show such an interaction. Our findings provide evidence, that gamma-band activity acts as a cortical gain mechanism that nonlinearly combines the bottom-up drive of different visual features in support of visually guided behavior.

neuroscience↗