Robinson, T. P. | bioRxiv Science

bioRxiv2025

Hepatic Yap1 activates systemic catabolism and muscle loss during organ repair: evidence for a liver-derived common mechanism with cancer cachexia.

Recovery from critical injury concomitant with restoration of functional organ mass invokes a systemic catabolic response leading to muscle and fat loss, known as cachexia. We interrogated this process using mouse models of organ repair, including liver regeneration after hepatectomy, Yap1-mediated hepatomegaly, and pneumonectomy. Both hepatectomy and Yap1 induced systemic catabolism. Muscle and adipose wasting scaled with degree of liver growth, with 10-25% reduction of muscle mass and 50-80% of fat mass. In contrast, non-regenerative lung injury did not induce tissue wasting. Liver growth elevated resting energy expenditure. Tracer studies demonstrated redistribution of muscle-derived cholesterol and amino acids to regenerating liver. Gene expression changes in livers and muscles showed high concordance between the liver growth models and cancer cachexia models, including pancreatic adenocarcinoma cachexia. We propose that cachexia is a normal and essential reparative process in organ repair and regeneration and further, that cancer cachexia is a pathological exacerbation of an adaptive process mediated by activation of Yap1 in the liver.

physiology↗

bioRxiv2019

Downscaling livestock census data using multivariate predictive models: sensitivity to modifiable areal unit problem

The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori scales and shapes choices could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearsons r correlation statistics and RMSE were carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneus distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearsons r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products.

ecology↗