Effects of thermophily-relevant temperature variation and sex on digestive performance in pythons.

Different physiological performances are often optimized at slightly varying temperatures, which can lead to ectotherms selecting higher body temperatures during certain physiological efforts (e.g., digestion, reproduction). Such thermophilic responses can lead to temperature-based tradeoffs between two physiological activities with differing optimal temperatures or between optimizing a physiological activity and water balance, as water loss is elevated at higher temperatures. For example, ectotherms will often select a higher body temperature after consuming a meal, but the extent to which body temperature is elevated after eating is affected by its hydric state. Despite this known hydration state-based suppression of thermophily associated with digestion, the impact of this reduced body temperature on digestion performance is unknown. Accordingly, we determined whether small, thermophily-relevant changes in body temperature impact digestive efficiency or passage time and whether sex influenced the extent of the effect. Eighteen (9 female and 9 male) Children's pythons (Antaresia childreni) each consumed a meal at three temperatures (29 °C, 30 °C, and 31 °C), and gut passage time and digestive efficiency were determined. We found that neither metric was affected by temperature over the range tested. However, digestive efficiency was significantly impacted by the interaction between sex and temperature with males having significantly lower digestive efficiency than females at 31 °C, but not 29 °C or 30 °C. Our results provide insight into the effects of temperature on digestive physiology across narrow temperature ranges as well as demonstrate a sex-based difference in digestive physiology.

Comparative proteomic analysis of tail regeneration in the green anole lizard, Anolis carolinensis.

As amniote vertebrates, lizards are the most closely related organisms to humans capable of appendage regeneration. Lizards can autotomize, or release their tails as a means of predator evasion, and subsequently regenerate a functional replacement. Green anoles (Anolis carolinensis) can regenerate their tails through a process that involves differential expression of hundreds of genes, which has previously been analyzed by transcriptomic and microRNA analysis. To investigate protein expression in regenerating tissue, we performed whole proteomic analysis of regenerating tail tip and base. This is the first proteomic data set available for any anole lizard. We identified a total of 2,646 proteins - 976 proteins only in the regenerating tail base, 796 only in the tail tip, and 874 in both tip and base. For over 90% of these proteins in these tissues, we were able to assign a clear orthology to gene models in either the Ensembl or NCBI databases. For 13 proteins in the tail base, 9 proteins in the tail tip, and 10 proteins in both regions, the gene model in Ensembl and NCBI matched an uncharacterized protein, confirming that these predictions are present in the proteome. Ontology and pathways analysis of proteins expressed in the regenerating tail base identified categories including actin filament-based process, ncRNA metabolism, regulation of phosphatase activity, small GTPase mediated signal transduction, and cellular component organization or biogenesis. Analysis of proteins expressed in the tail tip identified categories including regulation of organelle organization, regulation of protein localization, ubiquitin-dependent protein catabolism, small GTPase mediated signal transduction, morphogenesis of epithelium, and regulation of biological quality. These proteomic findings confirm pathways and gene families activated in tail regeneration in the green anole as well as identify uncharacterized proteins whose role in regrowth remains to be revealed. This study demonstrates the insights that are possible from the integration of proteomic and transcriptomic data in tail regrowth in the green anole, with potentially broader application to studies in other regenerative models.

Recombinant inbred line panels inform the genetic architecture and interactions of adaptive traits in Drosophila melanogaster.

The distribution of allelic effects on traits, along with their gene-by-gene and gene-by-environment interactions, contributes to the phenotypes available for selection and the trajectories of adaptive variants. Nonetheless, uncertainty persists regarding the effect sizes underlying adaptations and the importance of genetic interactions. Herein, we aimed to investigate the genetic architecture and the epistatic and environmental interactions involving loci that contribute to multiple adaptive traits using two new panels of Drosophila melanogaster recombinant inbred lines (RILs). To better fit our data, we re-implemented functions from R/qtl (Broman et al. 2003) using additive genetic models. We found 14 quantitative trait loci (QTL) underlying melanism, wing size, song pattern, and ethanol resistance. By combining our mapping results with population genetic statistics, we identified potential new genes related to these traits. None of the detected QTLs showed clear evidence of epistasis, and our power analysis indicated that we should have seen at least one significant interaction if sign epistasis or strong positive epistasis played a pervasive role in trait evolution. In contrast, we did find roles for gene-by-environment interactions involving pigmentation traits. Overall, our data suggest that the genetic architecture of adaptive traits often involves alleles of detectable effect, that strong epistasis does not always play a role in adaptation, and that environmental interactions can modulate the effect size of adaptive alleles.

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud.

BACKGROUND: Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. RESULTS: We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. CONCLUSIONS: This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.