@article{SCOPUS_ID:86000084600,

title = {Genetic variants and phenotypic data curated for the CAGI6 intellectual disability panel challenge},

author = {Maria Cristina Aspromonte and Alessio Del Conte and Roberta Polli and Demetrio Baldo and Francesco Benedicenti and Elisa Bettella and Stefania Bigoni and Stefania Boni and Claudia Ciaccio and Stefano D’Arrigo and Ilaria Donati and Elisa Granocchio and Isabella Mammi and Donatella Milani and Susanna Negrin and Margherita Nosadini and Fiorenza Soli and Franco Stanzial and Licia Turolla and Damiano Piovesan and Silvio C. E. Tosatto and Alessandra Murgia and Emanuela Leonardi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-86000084600&origin=inward},

doi = {10.1007/s00439-025-02733-1},

year  = {2025},

date = {2025-01-01},

journal = {Human Genetics},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {© The Author(s) 2025.Neurodevelopmental disorders (NDDs) are common conditions including clinically diverse and genetically heterogeneous diseases, such as intellectual disability, autism spectrum disorders, and epilepsy. The intricate genetic underpinnings of NDDs pose a formidable challenge, given their multifaceted genetic architecture and heterogeneous clinical presentations. This work delves into the intricate interplay between genetic variants and phenotypic manifestations in neurodevelopmental disorders, presenting a dataset curated for the Critical Assessment of Genome Interpretation (CAGI6) ID Panel Challenge. The CAGI6 competition serves as a platform for evaluating the efficacy of computational methods in predicting phenotypic outcomes from genetic data. In this study, a targeted gene panel sequencing has been used to investigate the genetic causes of NDDs in a cohort of 415 paediatric patients. We identified 60 pathogenic and 49 likely pathogenic variants in 102 individuals that accounted for 25% of NDD cases in the cohort. The most mutated genes were ANKRD11, MECP2, ARID1B, ASH1L, CHD8, KDM5C, MED12 and PTCHD1 The majority of pathogenic variants were de novo, with some inherited from mildly affected parents. Loss-of-function variants were the most common type of pathogenic variant. In silico analysis tools were used to assess the potential impact of variants on splicing and structural/functional effects of missense variants. The study highlights the challenges in variant interpretation especially in cases with atypical phenotypic manifestations. Overall, this study provides valuable insights into the genetic causes of NDDs and emphasises the importance of understanding the underlying genetic factors for accurate diagnosis, and intervention development in neurodevelopmental conditions.},

note = {Cited by: 0; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85213063415,

title = {MOBIDB in 2025: integrating ensemble properties and function annotations for intrinsically disordered proteins},

author = {Damiano Piovesan and Alessio Del Conte and Mahta Mehdiabadi and Maria Cristina Aspromonte and Matthias Blum and Giulio Tesei and Sören Bülow and Kresten Lindorff-Larsen and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85213063415&origin=inward},

doi = {10.1093/nar/gkae969},

year  = {2025},

date = {2025-01-01},

journal = {Nucleic Acids Research},

volume = {53},

number = {D1},

pages = {D495-D503},

publisher = {Oxford University Press},

abstract = {© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.The MobiDB database (URL: https://mobidb.org/) aims to provide structural and functional information about intrinsic protein disorder, aggregating annotations from the literature, experimental data, and predictions for all known protein sequences. Here, we describe the improvements made to our resource to capture more information, simplify access to the aggregated data, and increase documentation of all MobiDB features. Compared to the previous release, all underlying pipeline modules were updated. The prediction module is ten times faster and can detect if a predicted disordered region is structurally extended or compact. The PDB component is now able to process large cryo-EM structures extending the number of processed entries. The entry page has been restyled to highlight functional aspects of disorder and all graphical modules have been completely reimplemented for better flexibility and faster rendering. The server has been improved to optimise bulk downloads. Annotation provenance has been standardised by adopting ECO terms. Finally, we propagated disorder function (IDPO and GO terms) from the DisProt database exploiting sequence similarity and protein embeddings. These improvements, along with the addition of comprehensive training material, offer a more intuitive interface and novel functional knowledge about intrinsic disorder.},

note = {Cited by: 2; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85217180047,

title = {CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs)},

author = {Maria Cristina Aspromonte and Alessio Del Conte and Shaowen Zhu and Wuwei Tan and Yang Shen and Yexian Zhang and Qi Li and Maggie Haitian Wang and Giulia Babbi and Samuele Bovo and Pier Luigi Martelli and Rita Casadio and Azza Althagafi and Sumyyah Toonsi and Maxat Kulmanov and Robert Hoehndorf and Panagiotis Katsonis and Amanda Williams and Olivier Lichtarge and Su Xian and Wesley Surento and Vikas Pejaver and Sean D. Mooney and Uma Sunderam and Rajgopal Srinivasan and Alessandra Murgia and Damiano Piovesan and Silvio C. E. Tosatto and Emanuela Leonardi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85217180047&origin=inward},

doi = {10.1007/s00439-024-02722-w},

year  = {2025},

date = {2025-01-01},

journal = {Human Genetics},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {© The Author(s) 2025.The Genetics of Neurodevelopmental Disorders Lab in Padua provided a new intellectual disability (ID) Panel challenge for computational methods to predict patient phenotypes and their causal variants in the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6). Eight research teams submitted a total of 30 models to predict phenotypes based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. Here, we assess the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and their causal variants. We also evaluated predictions for possible genetic causes in patients without a clear genetic diagnosis. Like the previous ID Panel challenge in CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (Pathogenic/Likely Pathogenic, Variants of Uncertain Significance and Risk Factors) were provided. The phenotypic traits and variant data of 150 patients from the CAGI5 ID Panel Challenge were provided as training set for predictors. The CAGI6 challenge confirms CAGI5 results that predicting phenotypes from gene panel data is highly challenging, with AUC values close to random, and no method able to predict relevant variants with both high accuracy and precision. However, a significant improvement is noted for the best method, with recall increasing from 66% to 82%. Several groups also successfully predicted difficult-to-detect variants, emphasizing the importance of variants initially excluded by the Padua NDD Lab.},

note = {Cited by: 1; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85181761325,

title = {PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins},

author = {Hamidreza Ghafouri and Tamas Lazar and Alessio Del Conte and Luiggi G. Tenorio Ku and Peter Tompa and Silvio C. E. Tosatto and Alexander Miguel Monzon and Maria C. Aspromonte and Pau Bernadó and Belén Chaves-Arquero and Lucia Beatriz Chemes and Damiano Clementel and Tiago N. Cordeiro and Carlos A. Elena-Real and Michael Feig and Isabella C. Felli and Carlo Ferrari and Julie D. Forman-Kay and Tiago Gomes and Frank Gondelaud and Claudiu C. Gradinaru and Tâp Ha-Duong and Teresa Head-Gordon and Pétur O. Heidarsson and Giacomo Janson and Gunnar Jeschke and Emanuela Leonardi and Zi Hao Liu and Sonia Longhi and Xamuel L. Lund and Maria J. Macias and Pau Martin-Malpartida and Davide Mercadante and Assia Mouhand and Gabor Nagy and María Victoria Nugnes and José Manuel Pérez-Cañadillas and Giulia Pesce and Roberta Pierattelli and Damiano Piovesan and Federica Quaglia and Sylvie Ricard-Blum and Paul Robustelli and Amin Sagar and Edoardo Salladini and Lucile Sénicourt and Nathalie Sibille and João M. C. Teixeira and Thomas E. Tsangaris and Mihaly Varadi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85181761325&origin=inward},

doi = {10.1093/nar/gkad947},

year  = {2024},

date = {2024-01-01},

journal = {Nucleic Acids Research},

volume = {52},

number = {D1},

pages = {D536-D544},

publisher = {Oxford University Press},

abstract = {© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network—all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.},

note = {Cited by: 16; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85188297172,

title = {Best practices for the manual curation of intrinsically disordered proteins in DisProt},

author = {Federica Quaglia and Anastasia Chasapi and Maria Victoria Nugnes and Maria Cristina Aspromonte and Emanuela Leonardi and Damiano Piovesan and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85188297172&origin=inward},

doi = {10.1093/database/baae009},

year  = {2024},

date = {2024-01-01},

journal = {Database},

volume = {2024},

publisher = {Oxford University Press},

abstract = {© The Author(s) 2024. Published by Oxford University Press.The DisProt database is a resource containing manually curated data on experimentally validated intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) from the literature. Developed in 2005, its primary goal was to collect structural and functional information into proteins that lack a fixed three-dimensional structure.Today, DisProt has evolved into a major repository that not only collects experimental data but also contributes to our understanding of the IDPs/IDRs roles in various biological processes, such as autophagy or the life cycle mechanisms in viruses or their involvement in diseases (such as cancer and neurodevelopmental disorders). DisProt offers detailed information on the structural states of IDPs/IDRs, including state transitions, interactions and their functions, all provided as curated annotations. One of the central activities of DisProt is the meticulous curation of experimental data from the literature. For this reason, to ensure that every expert and volunteer curator possesses the requisite knowledge for data evaluation, collection and integration, training courses and curation materials are available. However, biocuration guidelines concur on the importance of developing robust guidelines that not only provide critical information about data consistency but also ensure data acquisition.This guideline aims to provide both biocurators and external users with best practices for manually curating IDPs and IDRs in DisProt. It describes every step of the literature curation process and provides use cases of IDP curation within DisProt.},

note = {Cited by: 1; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85213041340,

title = {Searching and Using MobiDB Resource 6 to Explore Predictions and Annotations for Intrinsically Disordered Proteins},

author = {Maria Cristina Aspromonte and Federica Quaglia and Alexander Miguel Monzon and Damiano Clementel and Alessio Del Conte and Damiano Piovesan and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85213041340&origin=inward},

doi = {10.1002/cpz1.70077},

year  = {2024},

date = {2024-01-01},

journal = {Current Protocols},

volume = {4},

number = {12},

publisher = {John Wiley and Sons Inc},

abstract = {Current Protocols© 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.Intrinsically disordered proteins (IDPs) make up around 30% of eukaryotic proteomes and play a crucial role in cellular processes and in pathological conditions such as neurodegenerative disorders and cancers. However, IDPs exhibit dynamic conformational ensembles and are often involved in the formation of biomolecular condensates. Understanding the function of IDPs is critical to research in many areas of science. MobiDB is a unique resource that serves as a comprehensive knowledgebase of IDPs and intrinsically disordered regions (IDRs), combining disorder annotations from experimental evidence and predictions for a broad range of protein sequences. Over the past decade, MobiDB has evolved with a focus on expanding annotation coverage, standardizing annotation provenance, and enhancing database accessibility. The latest MobiDB, version 6, released in July 2024, includes significant improvements, such as the integration of AlphaFoldDB predictions and a new homology transfer pipeline that has substantially increased the number of entries with high-quality annotations. The user interface has also been updated, highlighting annotation features, clarifying the entry page, and providing an immediate overview of disorder, binding, and disorder functions information in the protein sequence. This protocol guides the user through applications of the MobiDB, including disorder prediction, curated data analysis, and exploration of interaction data. This guide covers how to perform a search in MobiDB annotations using the web interface and the MobiDB REST API for programmatic access. The protocols use a step-by-step walkthrough using the human growth hormone receptor to demonstrate MobiDB's functions for visualization and interpretation of protein disorder data. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching MobiDB query formats. Basic Protocol 2: Searching MobiDB selected datasets and selected proteomes. Basic Protocol 3: Performing a search on the Statistics page in MobiDB. Support Protocol: Programmatic access with MobiDB REST API. Basic Protocol 4: Visualizing and interpreting a MobiDB Entry: The GHR use case.},

note = {Cited by: 0; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85176208048,

title = {DisProt in 2024: improving function annotation of intrinsically disordered proteins},

author = {Maria Cristina Aspromonte and Maria Victoria Nugnes and Federica Quaglia and Adel Bouharoua and Silvio C. E. Tosatto and Damiano Piovesan and Vasileios Sagris and Vasilis J. Promponas and Anastasia Chasapi and Erzsébet Fichó and Galo E. Balatti and Gustavo Parisi and Martín González Buitrón and Gabor Erdos and Matyas Pajkos and Zsuzsanna Dosztányi and Laszlo Dobson and Alessio Del Conte and Damiano Clementel and Edoardo Salladini and Emanuela Leonardi and Fatemeh Kordevani and Hamidreza Ghafouri and Luiggi G. Tenorio Ku and Alexander Miguel Monzon and Carlo Ferrari and Zsófia Kálmán and Juliet F. Nilsson and Jaime Santos and Carlos Pintado-Grima and Salvador Ventura and Veronika Ács and Rita Pancsa and Mariane Goncalves Kulik and Miguel A. Andrade-Navarro and Pedro José Barbosa Pereira and Sonia Longhi and Philippe Le Mercier and Julian Bergier and Peter Tompa and Tamas Lazar},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85176208048&origin=inward},

doi = {10.1093/nar/gkad928},

year  = {2024},

date = {2024-01-01},

journal = {Nucleic Acids Research},

volume = {52},

number = {D1},

pages = {D434-D441},

publisher = {Oxford University Press},

abstract = {© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of DisProt brings significant advancements, including a broader representation of functions and an enhanced curation process. These improvements aim to increase both the quality of annotations and their coverage at the sequence level. Higher coverage has been achieved by adopting additional evidence codes. Quality of annotations has been improved by systematically applying Minimum Information About Disorder Experiments (MIADE) principles and reporting all the details of the experimental setup that could potentially influence the structural state of a protein. The DisProt database now includes new thematic datasets and has expanded the adoption of Gene Ontology terms, resulting in an extensive functional repertoire which is automatically propagated to UniProtKB. Finally, we show that DisProt’s curated annotations strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights the utility of DisProt in explaining apparent uncertainty of certain well-defined predicted structures, which often correspond to folding-upon-binding fragments. Overall, DisProt serves as a comprehensive resource, combining experimental evidence of disorder information to enhance our understanding of intrinsically disordered proteins and their functional implications.},

note = {Cited by: 30; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85145966264,

title = {MobiDB: 10 years of intrinsically disordered proteins},

author = {Damiano Piovesan and Alessio Del Conte and Damiano Clementel and Alexander Miguel Monzon and Martina Bevilacqua and Maria Cristina Aspromonte and Javier A Iserte and Fernando E Orti and Cristina Marino-Buslje and Silvio C. E Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85145966264&origin=inward},

doi = {10.1093/nar/gkac1065},

year  = {2023},

date = {2023-01-01},

journal = {Nucleic Acids Research},

volume = {51},

number = {1 D},

pages = {D438-D444},

publisher = {Oxford University Press},

abstract = {© 2023 The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.},

note = {Cited by: 76; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85164785564,

title = {The Gene Ontology knowledgebase in 2023},

author = {Suzi A. Aleksander and James Balhoff and Seth Carbon and J. Michael Cherry and Harold J. Drabkin and Dustin Ebert and Marc Feuermann and Pascale Gaudet and Nomi L. Harris and David P. Hill and Raymond Lee and Huaiyu Mi and Sierra Moxon and Christopher J. Mungall and Anushya Muruganugan and Tremayne Mushayahama and Paul W. Sternberg and Paul D. Thomas and Kimberly Van Auken and Jolene Ramsey and Deborah A. Siegele and Rex L. Chisholm and Petra Fey and Maria Cristina Aspromonte and Maria Victoria Nugnes and Federica Quaglia and Silvio Tosatto and Michelle Giglio and Suvarna Nadendla and Giulia Antonazzo and Helen Attrill and Gil Dos Santos and Steven Marygold and Victor Strelets and Christopher J. Tabone and Jim Thurmond and Pinglei Zhou and Saadullah H. Ahmed and Praoparn Asanitthong and Diana Luna Buitrago and Meltem N. Erdol and Matthew C. Gage and Mohamed Ali Kadhum and Kan Yan Chloe Li and Miao Long and Aleksandra Michalak and Angeline Pesala and Armalya Pritazahra and Shirin C. C. Saverimuttu and Renzhi Su and Kate E. Thurlow and Ruth C Lovering and Colin Logie and Snezhana Oliferenko and Judith Blake and Karen Christie and Lori Corbani and Mary E. Dolan and Li Ni and Dmitry Sitnikov and Cynthia Smith and Alayne Cuzick and James Seager and Laurel Cooper and Justin Elser and Pankaj Jaiswal and Parul Gupta and Sushma Naithani and Manuel Lera-Ramirez and Kim Rutherford and Valerie Wood and Jeffrey L. De Pons and Melinda R. Dwinell and G. Thomas Hayman and Mary L. Kaldunski and Anne E. Kwitek and Stanley J. F. Laulederkind and Marek A. Tutaj and Mahima Vedi and Shur-Jen Wang and Peter D'Eustachio and Lucila Aimo and Kristian Axelsen and Alan Bridge and Nevila Hyka-Nouspikel and Anne Morgat and Stacia R. Engel and Kalpana Karra and Stuart R. Miyasato and Robert S. Nash and Marek S. Skrzypek and Shuai Weng and Edith D. Wong and Erika Bakker and Tanya Z. Berardini and Leonore Reiser and Andrea Auchincloss and Ghislaine Argoud-Puy and Marie-Claude Blatter and Emmanuel Boutet and ...},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85164785564&origin=inward},

doi = {10.1093/genetics/iyad031},

year  = {2023},

date = {2023-01-01},

journal = {Genetics},

volume = {224},

number = {1},

publisher = {Oxford University Press},

abstract = {© 2023 The Author(s). Published by Oxford University Press on behalf of The Genetics Society of America.The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO - a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations - evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs) - mechanistic models of molecular "pathways"(GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.},

note = {Cited by: 843; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85142646250,

title = {Expanding the genetics and phenotypic spectrum of Lysine-specific demethylase 5C (KDM5C): a report of 13 novel variants},

author = {Emanuela Leonardi and Maria Cristina Aspromonte and Denise Drongitis and Elisa Bettella and Lucia Verrillo and Roberta Polli and Meriel McEntagart and Laura Licchetta and Robertino Dilena and Stefano D’Arrigo and Claudia Ciaccio and Silvia Esposito and Vincenzo Leuzzi and Annalaura Torella and Demetrio Baldo and Fortunato Lonardo and Giulia Bonato and Serena Pellegrin and Franco Stanzial and Renata Posmyk and Ewa Kaczorowska and Miryam Carecchio and Monika Gos and Sylwia Rzońca-Niewczas and Maria Giuseppina Miano and Alessandra Murgia},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85142646250&origin=inward},

doi = {10.1038/s41431-022-01233-4},

year  = {2023},

date = {2023-01-01},

journal = {European Journal of Human Genetics},

volume = {31},

number = {2},

pages = {202-215},

publisher = {Springer Nature},

abstract = {© 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.Lysine-specific demethylase 5C (KDM5C) has been identified as an important chromatin remodeling gene, contributing to X-linked neurodevelopmental disorders (NDDs). The KDM5C gene, located in the Xp22 chromosomal region, encodes the H3K4me3-me2 eraser involved in neuronal plasticity and dendritic growth. Here we report 30 individuals carrying 13 novel and one previously identified KDM5C variants. Our cohort includes the first reported case of somatic mosaicism in a male carrying a KDM5C nucleotide substitution, and a dual molecular finding in a female carrying a homozygous truncating FUCA1 alteration together with a de novo KDM5C variant. With the use of next generation sequencing strategies, we detected 1 frameshift, 1 stop codon, 2 splice-site and 10 missense variants, which pathogenic role was carefully investigated by a thorough bioinformatic analysis. The pattern of X-chromosome inactivation was found to have an impact on KDM5C phenotypic expression in females of our cohort. The affected individuals of our case series manifested a neurodevelopmental condition characterized by psychomotor delay, intellectual disability with speech disorders, and behavioral features with particular disturbed sleep pattern; other observed clinical manifestations were short stature, obesity and hypertrichosis. Collectively, these findings expand the current knowledge about the pathogenic mechanisms leading to dysfunction of this important chromatin remodeling gene and contribute to a refinement of the KDM5C phenotypic spectrum.},

note = {Cited by: 11; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}