Enhanced lipogenesis through Pparγ helps cavefish adapt to food scarcity.

Nutrient availability varies seasonally and spatially in the wild. While many animals, such as hibernating animals or migrating birds, evolved strategies to overcome periods of nutrient scarcity,1,2 the cellular mechanisms of these strategies are poorly understood. Cave environments represent an example of nutrient-deprived environments, since the lack of sunlight and therefore primary energy production drastically diminishes the nutrient availability.3 Here, we used Astyanax mexicanus, which includes river-dwelling surface fish and cave-adapted cavefish populations, to study the genetic adaptation to nutrient limitations.4-9 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.10-12 Moreover, the lipid metabolism regulator, peroxisome proliferator-activated receptor γ (Pparγ), is upregulated at both transcript and protein levels in cavefish livers. Chromatin immunoprecipitation sequencing (ChIP-seq) showed that Pparγ binds cavefish promoter regions of genes to a higher extent than surface fish and inhibiting Pparγ in vivo decreases fat accumulation in A. mexicanus. Finally, we identified nonsense mutations in per2, a known repressor of Pparγ, providing a possible regulatory mechanism of Pparγ in cavefish. Taken together, our study reveals that upregulated Pparγ 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.

A portable structural analysis library for reaction networks.

The topology of a reaction network can have a significant influence on the network's dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper, we describe a portable software library, libStructural, that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with extensive on-line documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it simple to install on any standard Python distribution. The bulk of the source code is licensed under the open source BSD license with other parts using as either the MIT license or more simply public domain. All source is available on GitHub (https://github.com/sys-bio/Libstructural).

Fluorescence alteration of MPA capped CdSe quantum dots by spontaneous biomarker protein adsorption.

Quantum dots (QDs) have significant potentials in biomedical applications of bioimaging and biosensing. Spontaneous adsorption of proteins on QDs surface is a common phenomenon, which occurred to serum proteins in biological samples, and has been observed to enhance QDs fluorescence. In this study, fluorescence alteration of 3-mercaptopropionic acid (MPA) capped CdSe quantum dots by four individual biomarker proteins was investigated. By monitoring the fluorescence emission of QDs, the biomarker protein adsorbed spontaneously on QDs surface was recognized and quantified. When alpha fetoprotein (AFP) or heat shock protein 90 alpha (HSP90α) were present, the QDs became brighter. The presence of cytochrome C (CytoC) or lysozyme (Lyz) made the QDs dimmer first, and then brighter. Within five minutes response time all four biomarker proteins were detected individually with the estimated detection limit in the range of 1-10 ng/mL and good linear dynamic ranges. The results suggested that the fluorescence of QDs was responsive to not only serum proteins but also biomarker proteins. The fluorescence response was able to correlate quantitatively with the amount of biomarker proteins in relatively low concentrations. These results provide more information to understand QDs and support their applications in biomedical fields.

Editorial Reproducibility of Computational Models.

Here we describe Tellurium, a Python based platform for supporting the development of reproducible models in systems biology. The tool exploits a number of available standards, including SBML, SED-ML and COMBINE archives to achieve its goal.