Biology subjects

Tatoni, D.

Publications and source records attributed to Tatoni, D..

2 recordsLinked to original sources

bioRxiv2024

MAPK15 Protects Against The Development Of Metabolic Dysfunction-Associated Steatotic Liver Disease

Accumulation of lipids in the liver characterizes metabolic dysfunction-associated steatotic liver disease (MASLD), the most prevalent chronic liver disease worldwide. As liver injury progresses to metabolic dysfunction-associated steatohepatitis (MASH), MASLD can predispose individuals to cirrhosis and hepatocellular carcinoma. Here, we characterized the first knockout mouse model for mitogen-activated protein kinase 15 (MAPK15) and revealed its critical role in controlling lipid homeostasis in the liver. Indeed, Mapk15-/- mice exhibited a MASLD-like phenotype, and hepatocellular models allowed us to demonstrate that dysregulated accumulation of lipids was due to increased expression and membrane localization of the CD36 fatty acid translocase. Consistently, Mapk15-/- mice exhibited elevated hepatic levels of CD36 and feeding them with a western-type diet significantly accelerated their progression to a MASH-like phenotype. Ultimately, transcriptomic analysis of human cohorts revealed increased liver expression of MAPK15 in MASLD patients, compared to unaffected individuals, ultimately supporting a protective role for MAPK15 against this disease. Overall, our data highlight a critical role for MAPK15 in liver physiopathology, by contributing to maintain physiological intracellular levels of lipids in this tissue.

cell biology↗

bioRxiv2024

Efficient and effective identification of cancer neoantigens from tumor only RNA-seq

The growing accessibility of sequencing experiments has significantly accelerated the development of personalized immunotherapies based on the identification of cancer neoantigens. Still, the prediction of neoantigens involves lengthy and inefficient protocols, requiring simultaneous analysis of sequencing data from paired tumor/normal exomes and tumor transcriptome, often resulting in a low success rate. To date, the feasibility of adopting a more efficient strategy has not been fully evaluated. To this end, we developed ENEO, a computational approach to detect cancer neoantigens using solely the tumor RNA-seq data while addressing the lack of matched control through a Bayesian probabilistic model. ENEO was assessed on TESLA benchmark dataset, reporting efficient identification of DNA-alterations derived neoantigens and compelling results against state-of-art exome-based methods. We further validated the method on two independent cohorts, encompassing different tumor types and experimental procedures. Our work demonstrates that a tumor-only RNA-based approach, such as the one implemented in ENEO, maintains accuracy in identifying mutated peptides resulting from expressed genomic alterations, while also broadening the pool of potential pMHCs with RNAspecific mutations in a faster and cost-effective way. ENEO is freely available at https://github.com/ctglab/ENEO

bioinformatics↗