Biology subjects

Aderhold, A.

Publications and source records attributed to Aderhold, A..

2 recordsLinked to original sources

bioRxiv2024

AZIN2-dependent polyamine metabolism determines adipocyte progenitor fate and protects against obesity and dysmetabolism

Adipose tissue homeostasis plays a critical role in metabolic disease but the metabolic circuitry regulating adipose tissue dynamics remains unclear. In this study, polyamine metabolism emerges as an important regulator of adipose tissue pathophysiology. We identify AZIN2 (Antizyme inhibitor 2), a protein promoting polyamine synthesis and acetylation, as a major regulator of total acetyl-CoA in adipocyte progenitors (APs). AZIN2 deficient APs demonstrate increased H3K27 acetylation marks in genes related to lipid metabolism, cell cycle arrest and cellular senescence, and enhanced adipogenesis compared to wild-type counterparts. Upon high-fat diet (HFD)-induced obesity, global AZIN2 deficiency in mice provokes adipose tissue hypertrophy, AP senescence, lipid storage perturbations, inflammation and insulin resistance. IL4 promotes Azin2 expression in APs but not mature adipocytes due to diminished IL4 receptor expression in the latter. In human visceral and subcutaneous adipose tissue, AZIN2 expression positively correlates with expression of early progenitor markers and genes associated with protection against insulin resistance, while it negatively correlates with markers of lipogenesis. In sum, AZIN2-driven polyamine metabolism preserves adipose tissue health, a finding that could be therapeutically harnessed for the management of obesity-associated metabolic disease.

cell biology↗

bioRxiv2023

Weak interactions cause poor performance of common network inference models

O_LINetwork inference models have been widely applied in ecological, genetic and social studies to infer unknown interactions. However, little is known about how well the models perform and whether they produce reliable results when confronted with networks where weak interactions predominate and for different amounts of data. This is an important consideration as empirical interaction strengths are commonly skewed towards weaker interactions, which is especially relevant in ecological networks, and a number of studies suggest the importance of weak interactions for ensuring the dynamic stability of a system. C_LIO_LIHere we investigate four commonly used network methods (Bayesian Networks, Graphical Gaussian Models, L1-regularised regression with the least absolute shrinkage and selection operator, and Sparse Bayesian Regression) and employ network simulations with different interaction strengths to assess their accuracy and reliability. C_LIO_LIThe results show poor performance, in terms of the ability to discriminate between existing relationships and no relationships, in the presence of weak interactions, for all the selected network inference methods. C_LIO_LIOur findings suggest that though these models have some promise for network inference with networks that consist of medium or strong interactions and larger amounts of data, data with weak interactions does not provide enough information for the models to reliably identify interactions. Therefore, networks inferred from data of that type should be interpreted with caution. C_LI

ecology↗