How well do you know your mutation? Complex effects of genetic background on expressivity, complementation, and ordering of allelic effects.

For a given gene, different mutations influence organismal phenotypes to varying degrees. However, the expressivity of these variants not only depends on the DNA lesion associated with the mutation, but also on factors including the genetic background and rearing environment. The degree to which these factors influence related alleles, genes, or pathways similarly, and whether similar developmental mechanisms underlie variation in the expressivity of a single allele across conditions and among alleles is poorly understood. Besides their fundamental biological significance, these questions have important implications for the interpretation of functional genetic analyses, for example, if these factors alter the ordering of allelic series or patterns of complementation. We examined the impact of genetic background and rearing environment for a series of mutations spanning the range of phenotypic effects for both the scalloped and vestigial genes, which influence wing development in Drosophila melanogaster. Genetic background and rearing environment influenced the phenotypic outcome of mutations, including intra-genic interactions, particularly for mutations of moderate expressivity. We examined whether cellular correlates (such as cell proliferation during development) of these phenotypic effects matched the observed phenotypic outcome. While cell proliferation decreased with mutations of increasingly severe effects, surprisingly it did not co-vary strongly with the degree of background dependence. We discuss these findings and propose a phenomenological model to aid in understanding the biology of genes, and how this influences our interpretation of allelic effects in genetic analysis.

Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator.

Insulin signaling plays key roles in development, growth and metabolism through dynamic control of glucose uptake, global protein translation and transcriptional regulation. Altered levels of insulin signaling are known to play key roles in development and disease, yet the molecular basis of such differential signaling remains obscure. Expression of the insulin receptor (InR) gene itself appears to play an important role, but the nature of the molecular wiring controlling InR transcription has not been elucidated. We characterized the regulatory elements driving Drosophila InR expression and found that the generally broad expression of this gene is belied by complex individual switch elements, the dynamic regulation of which reflects direct and indirect contributions of FOXO, EcR, Rbf and additional transcription factors through redundant elements dispersed throughout ∼40 kb of non-coding regions. The control of InR transcription in response to nutritional and tissue-specific inputs represents an integration of multiple cis-regulatory elements, the structure and function of which may have been sculpted by evolutionary selection to provide a highly tailored set of signaling responses on developmental and tissue-specific levels.

An image database of Drosophila melanogaster wings for phenomic and biometric analysis.

BACKGROUND: Extracting important descriptors and features from images of biological specimens is an ongoing challenge. Features are often defined using landmarks and semi-landmarks that are determined a priori based on criteria such as homology or some other measure of biological significance. An alternative, widely used strategy uses computational pattern recognition, in which features are acquired from the image de novo. Subsets of these features are then selected based on objective criteria. Computational pattern recognition has been extensively developed primarily for the classification of samples into groups, whereas landmark methods have been broadly applied to biological inference. RESULTS: To compare these approaches and to provide a general community resource, we have constructed an image database of Drosophila melanogaster wings - individually identifiable and organized by sex, genotype and replicate imaging system - for the development and testing of measurement and classification tools for biological images. We have used this database to evaluate the relative performance of current classification strategies. Several supervised parametric and nonparametric machine learning algorithms were used on principal components extracted from geometric morphometric shape data (landmarks and semi-landmarks). For comparison, we also classified phenotypes based on de novo features extracted from wing images using several computer vision and pattern recognition methods as implemented in the Bioimage Classification and Annotation Tool (BioCAT). CONCLUSIONS: Because we were able to thoroughly evaluate these strategies using the publicly available Drosophila wing database, we believe that this resource will facilitate the development and testing of new tools for the measurement and classification of complex biological phenotypes.