Author

Leonardo Caporali

Istituto delle Scienze Neurologiche di BolognaORCID 0000-0002-0666-4380Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Assay:DNA methylation· 2 Organism:human· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

A. UrbaniiD· 1 shared Agnese GiovannettiiD· 1 shared Alberto DaneseiD· 1 shared Alberto Pietro PastiiD· 1 shared Alessandra MarescaiD· 1 shared Alessandro NapoliiD· 1 shared Alessandro Rapone· 1 shared Andrea Villani· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBioinformatics

A. UrbaniORCID

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Author profile →

2 papers

Recent·Most cited

Cell-free mtDNA drives pyroptosis and inflammation in m.3243A>G mitochondrial disease models

Monica Moresco, Alessandro Rapone, Concetta Valentina Tropeano, Mariantonietta Capristo, Giada Capirossi, Danara Ormanbekova, Claudio Fiorini, Alberto Pietro Pasti, Francesco Valle, Alberto Danese, Simone Patergnani, Leonardo Caporali, Chiara La Morgia, Anu Suomalainen, Paolo Pinton, , Alessandra Maresca

This study shows that the m.3243A>G mutation increases cell-free mtDNA release, triggering pyroptosis and inflammation in MELAS models.

bioRxiv · Jul 1, 2026 · PreprintCell Biology

nAPOGEE: A machine-learning platform for clinically actionable pathogenicity assessment of all mitochondrial noncoding variants

Salvatore Daniele Bianco, Agnese Giovannetti, Marta Adinolfi, Francesco Petrizzelli, Andrea Villani, Manuel Mangoni, Tommaso Biagini, Niccolò Liorni, Alessandro Napoli, Vincent Procaccio, Marie T. Lott, Shiping Zhang, Leonardo Caporali, Daniele Ghezzi, A. Urbani, , Tommaso Mazza

nAPOGEE assesses pathogenicity of mitochondrial noncoding variants using machine learning, revealing consistent spatial correlation of predicted pathogenicity.

bioRxiv · Dec 23, 2025 · PreprintBioinformatics