Gherardini, L. | bioRxiv Science

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↗

bioRxiv2022

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning

The pervasive impact of Alzheimers disease on aging society represents one of the main challenges at this time. Current investigations highlight two specific misfolded proteins in its development: Amyloid-{beta} and{tau} . Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on a machine learning approach. The proposed method applies an autoregressive model, constrained by structural connectivity, to predict concentrations of Amyloid-{beta} two years after the provided baseline. In experiments, the autoregressive model generally outperformed the state-of-art models yielding the lowest average prediction error (mean-squared-error 0.0062). Moreover, we assess its effectiveness and suitability for real case scenarios, for which we provide a web service for physicians and researchers. Despite predicting amyloid pathology alone is not sufficient to clinical outcome, its prediction can be helpful to further plan therapies and other cures.

neuroscience↗