Andrea Cavalli | Search PubMed

PubMed2025

Polygenic variants in DNA repair genes are associated with neurodevelopmental disorders, regression and increased burdens of somatic variants and short tandem repeat expansions.

PURPOSE: Developmental regression, characterized by the loss of acquired milestones, occurs in some individuals with neurodevelopmental disorders (NDDs); yet, its molecular basis remains unclear. Studies suggest that DNA damage repair (DDR) genes, such as FAN1, may protect against neurological dysfunction by modulating the somatic stability of short tandem repeats (STRs). This study explores the contribution of DDR gene variants in NDD cases presenting with regression. METHODS: We analyzed 1087 NDD patients, focusing on those carrying variants in DDR genes and presenting regression. We assessed the sensitivity to DNA damage using mitomycin C on lymphoblastoid cells. Somatic variants and STR expansions were evaluated through high-depth short-read genome sequencing. To further investigate the pathogenetic role of STR expansions, we performed long-read genome sequencing on the most severely affected proband. RESULTS: Probands with regression carried multiple DDR gene variants, several within the Fanconi anemia pathway. Their lymphoblastoid cells showed increased sensitivity to mitomycin C-induced cytotoxicity compared with parental and control samples. Probands with severe phenotypes and regression exhibited an accumulation of somatic variants and STR instability, enriched in neurodevelopmental genes. CONCLUSION: Our findings suggest that polygenic DDR gene variants may contribute to developmental regression in NDDs by promoting the accumulation of somatic variants and STR expansions.

Humans↗

PubMed2025

IsoBayes: a Bayesian approach for single-isoform proteomics inference.

MOTIVATION: Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. RESULTS: Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: (i) infer the presence/absence of each protein isoform (via a posterior probability), (ii) estimate its abundance (and credible interval), and (iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. AVAILABILITY AND IMPLEMENTATION: IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

Proteomics↗