Medley, K. | Search bioRxiv

bioRxiv2021

Enhanced lipogenesis through Pparγ helps cavefish adapt to food scarcity.

Nutrient availability varies seasonally and spatially in the wild. The resulting nutrient limitation or restricted access to nutrients pose a major challenge for every organism. While many animals, such as hibernating animals, evolved strategies to overcome periods of nutrient scarcity, the cellular mechanisms of these strategies are poorly understood. Cave environments represent an extreme example of nutrient deprived environments since the lack of sunlight and therefore primary energy production drastically diminishes the nutrient availability. Here, we used Astyanax mexicanus, which includes river-dwelling surface fish and cave adapted cavefish populations to study the genetic adaptation to nutrient limitations. We show that cavefish populations store large amounts of fat in different body regions when fed ad libitum in the lab. We found higher expression of lipogenesis genes in cavefish livers when fed the same amount of food as surface fish, suggesting an improved ability of cavefish to use lipogenesis to convert available energy into triglycerides for storage into adipose tissue. Moreover, the lipid metabolism regulator, Peroxisome proliferator-activated receptor {gamma} (Ppar{gamma}), is upregulated at both transcript and protein levels in cavefish livers. Chromatin Immunoprecipitation sequencing (ChIP seq) showed that Ppar{gamma} binds cavefish promoter regions of genes to a higher extent than surface fish. Finally, we identified two possible regulatory mechanisms of Ppar{gamma} in cavefish: higher amounts of ligands of the nuclear receptor, and nonsense mutations in per2, a known repressor of Ppar{gamma}. Taken together, our study reveals that upregulated Ppar{gamma} promotes higher levels of lipogenesis in the liver and contributes to higher body fat accumulation in cavefish populations, an important adaptation to nutrient limited environments.

evolutionary biology↗

bioRxiv2017

Tellurium Notebooks - An Environment for Dynamical Model Development, Reproducibility, and Reuse

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system that facilitates building reproducible dynamical models and reusing models by 1) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 2) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.\n\nAuthor summaryThere is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human-readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human-readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard-encoded models and simulations, regardless of the users level of technical knowledge of said standards.

systems biology↗