Biology subjects

Ponzoni, L.

Publications and source records attributed to Ponzoni, L..

7 recordsLinked to original sources

bioRxiv2026

Hippocampal Ring Finger Protein 10-dependent signaling supports cognitive flexibility

The ability to flexibly adapt behavior to changing environmental contingencies is a core component of brain function and relies on experience-dependent remodeling of neural circuits. While cognitive flexibility has been primarily attributed to prefrontal-striatal networks, the contribution of hippocampus and their underlying molecular substrates remains less understood. Here, we show that the dorsal hippocampus has a key role in cognitive flexibility. In particular, Ring Finger Protein 10 (RNF10)-mediated signaling, linking activation of synaptic NMDARs to specific transcriptional programs in the dorsal CA1, is necessary for cognitive flexibility. In fact, in vivo downregulation, through gene deletion and silencing of RNF10, resulting in impaired long-term synaptic plasticity, suppressed cognitive flexibility. This was reflected in the impaired ability to disengage from previously acquired contextual, visual, and spatial information and to adapt behavior to changed context. Overall, our results identified RNF10 as a key in vivo player necessary for the balance between cognitive stability and flexibility.

animal behavior and cognition↗

bioRxiv2022

Prioritizing virtual screening with interpretable interaction fingerprints

Machine learning-based drug discovery success depends on molecular representation. Yet traditional molecular fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit that calculates and encodes protein-ligand interactions into new hashed fingerprints inspired by Extended Connectivity Finger-Print (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP). LUNA also provides visual strategies to make the fingerprints interpretable. We performed three major experiments exploring the fingerprints use. First, we trained machine learning models to reproduce DOCK3.7 scores using 1 million docked Dopamine D4 complexes. We found that EIFP-4,096 performed (R2 = 0.61) superior to related molecular and interaction fingerprints. Secondly, we used LUNA to support interpretable machine learning models. Finally, we demonstrate that interaction fingerprints can accurately identify similarities across molecular complexes that other fingerprints over-look. Hence, we envision LUNA and its interface fingerprints as promising methods for machine learning-based virtual screening campaigns. LUNA is freely available at https://github.com/keiserlab/LUNA.

bioinformatics↗

bioRxiv2021

Rescuing epileptic and behavioral alterations in a Dravet syndrome mouse model by inhibiting eEF2K

Dravet Syndrome is a severe childhood pharmacoresistant epileptic disorder caused mainly by mutations in the SCN1A gene, which encodes for the 1 subunit of the type I voltage-gated sodium channel (NaV1.1), that cause imbalance between excitation and inhibition in the brain. We recently found that eEF2K knock out mice displayed enhanced GABAergic transmission and tonic inhibition and were less susceptible to epileptic seizures. In Scn1a+/- mice, a mouse model of the Dravet syndrome, we found that the activity of eEF2K/eEF2 pathway was enhanced. Then, we demonstrated that both genetic deletion and pharmacological inhibition of eEF2K were able to reduce the epileptic phenotype of Scn1a+/- mice. Interestingly we also found that motor coordination defect, memory impairments, and stereotyped behavior of the Scn1a+/- mice were reverted by eEF2K deletion. The analysis of spontaneous inhibitory postsynaptic currents (sIPSCs) suggested that the rescue of the pathological phenotype was driven by the potentiation of GABAergic synapses. Our data indicate that pharmacological inhibition of eEF2K could represent a novel therapeutic intervention for treating epilepsy and related comorbidities in the Dravet syndrome.

neuroscience↗

bioRxiv2020

Deletion of calcineurin from astrocytes reproduces proteome signature of Alzheimer's disease and epilepsy and predisposes to seizures

In astrocytes, calcineurin (CaN) is involved in neuroinflammation and gliosis, while its role in healthy CNS or in early neuro-pathogenesis is poorly understood. Here we report that in astroglial CaN KO (ACN-KO) mice, at one month of age, proteome is deranged in hippocampus and cerebellum. Bioinformatic analysis reveals association with Alzheimers disease (AD) and epilepsy. We found significant overlap with the proteome of an AD mouse model and of human subjects with drug-resistant epilepsy. In Barnes maze ACN-KO mice learned the task but adopted serial search strategy. Strikingly, from five months of age ACN-KO mice develop spontaneous seizures with an inflammatory signature of epileptic brains. These results suggest that astroglial CaN KO impairs hippocampal connectivity, produces proteome features of neurological disorders and predisposes mice to seizures. We suggest that astroglial CaN may serve as a novel Ca2+-sensitive switch which regulates protein expression and homeostasis in the CNS.

neuroscience↗

bioRxiv2019

Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants. Here we present a significant extension that integrates coevolutionary data from Pfam database and we also introduce a new interface (Rhapsody) that enables fully automated assessment of pathogenicity. Benchmarked against a dataset of about 20,000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of our approach by in silico saturation mutagenesis study of human H-Ras. The tool is made available both as a webtool (rhapsody.csb.pitt.edu) and an open source Python package (pip install prody-rhapsody).

bioinformatics↗

bioRxiv2019

LRRK2 G2019S kinase activity triggers neurotoxic NSF aggregation

Parkinsons disease (PD) is characterized by the progressive degeneration of dopaminergic neurons within the substantia nigra pars compacta and the presence of protein aggregates in surviving neurons. LRRK2 G2019S mutation is one the major determinant of familial PD cases and leads to late-onset PD with pleomorphic pathology, including alpha-synuclein accumulation and deposition of protein inclusions. LRRK2 binds and phosphorylates N-ethylmaleimide sensitive factor (NSF). We observed aggregates containing NSF in basal ganglia specimens from G2019S carrier PD patients and in cellular and animal models expressing the LRRK2 G2019S variant. We found that LRRK2 G2019S kinase activity induces the accumulation of NSF in toxic aggregates. Noteworthy, the induction of autophagy cleared NSF aggregation and rescued motor and cognitive impairment observed in aged hG2019S BAC mice. We suggest that LRRK2 G2019S pathological phosphorylation hampers substrate catabolism, thus causing the formation of cytotoxic protein inclusions. HighlightsO_LILRRK2 phosphorylates NSF in vivo C_LIO_LINSF aggregates in complementary LRRK2 G2019S models C_LIO_LILRRK2 G2019S kinase activity induces NSF accumulation in toxic aggregates C_LIO_LIAutophagy induction rescues hG2019S BAC mice motor and cognitive impairment C_LI

neuroscience↗

bioRxiv2017

From Sequence to Function: Coevolving Amino Acids Encode Structural and Functional Domains

Amino acids interactions within protein families are so optimized that the sole analysis of evolutionary co-mutations can identify pairs of contacting residues. It is also known that evolution conserves functional dynamics, i.e., the concerted motion or displacement of large protein regions or domains. Is it, therefore, possible to use a pure sequence-based analysis to identify these dynamical domains? To address this question, we introduce here a general co-evolutionary coupling analysis strategy and apply it to a curated sequence database of hundreds of protein families. For most families, the sequence-based method partitions amino acids into few clusters. When viewed in the context of the native structure, these clusters have the signature characteristics of viable protein domains: they are spatially separated but individually compact. They have a direct functional bearings too, as shown for various reference cases. We conclude that even large-scale structural and functionally-related properties can be recovered from inference methods applied to evolutionary-related sequences. The method introduced here is available as a software package and web server (http://spectrus.sissa.it/spectrus-evo_webserver).

bioinformatics↗