Gonzalez-Soto, M. | Search bioRxiv

bioRxiv2026

Alpha-linolenic acid and dietary protein minimally but differentially regulate white adipose tissue lipolysis in female mice fed moderate-fat diets

Omega-3 polyunsaturated fatty acids (n-3 PUFA) influence white adipose tissue (WAT) lipid buffering capacity; however, sex-specific regulation remains understudied. We recently reported that male mice fed a diet containing high alpha-linolenic acid (ALA) had increased WAT mass, reduced serum triglycerides and elevated lipolysis compared to mice fed a diet containing recommended levels of ALA, independent of background dietary protein. The current study examined whether subcutaneous and visceral WAT (scWAT, vWAT) lipolytic activity was altered in female C57BL/6N mice (n=16/group) fed low-ALA (1% energy) or high-ALA (3% energy) diets containing skim milk protein (SMP) or soy protein isolate (SPI) for 8 weeks. Body weight, WAT depot weights and serum triglycerides were unchanged in response to ALA content. Lipolytic markers were mostly unchanged by ALA content except for an increase in adipose triglyceride lipase (ATGL) in vWAT, while diets containing SPI modestly reduced serum cholesterol levels and increased total hormone-sensitive lipase (HSL) content in vWAT. Collectively, WAT lipolytic markers in female mice showed minimal response to diets containing high ALA content, unlike that previously reported in male mice. These results highlight the importance of considering both sexes to ensure generalizability of findings when investigating diet regulation of WAT lipid metabolism.

physiology↗

bioRxiv2023

BunDLe-Net: Neuronal Manifold Learning Meets Behaviour

Neuronal manifold learning techniques represent high-dimensional neuronal dynamics in low-dimensional embeddings to reveal the intrinsic structure of neuronal manifolds. A common goal of these techniques is to learn embeddings that allow a good reconstruction of the original data. We introduce a novel neuronal manifold learning technique, BunDLe-Net, that learns a low-dimensional Markovian embedding of the neuronal dynamics which pre-serves only those aspects of the neuronal dynamics that are relevant for a given behavioural context. In this way, BunDLe-Net eliminates neuronal dynamics that are irrelevant for decoding behaviour, effectively de-noising the data to reveal better the intricate relationships between neuronal dynamics and behaviour. We show that BunDLe-Net learns highly consistent manifolds across animals that reveal the building blocks of their neuronal manifolds on a variety of data sets, ranging from calcium imaging data recorded in the nematode C. elegans to spiking data from the rat hippocampus and primate somatosensory cortex.

animal behavior and cognition↗