Histone concentration regulates the cell cycle and transcription in early development.

The early embryos of many animals, including flies, fish and frogs, have unusually rapid cell cycles and delayed onset of transcription. These divisions are dependent on maternally supplied RNAs and proteins including histones. Previous work suggests that the pool size of maternally provided histones can alter the timing of zygotic genome activation (ZGA) in frogs and fish. Here, we examine the effects of under- and overexpression of maternal histones in Drosophila embryogenesis. Decreasing histone concentration advances zygotic transcription, cell cycle elongation, Chk1 activation and gastrulation. Conversely, increasing histone concentration delays transcription and results in an additional nuclear cycle before gastrulation. Numerous zygotic transcripts are sensitive to histone concentration, and the promoters of histone-sensitive genes are associated with specific chromatin features linked to increased histone turnover. These include enrichment of the pioneer transcription factor Zelda, and lack of SIN3A and associated histone deacetylases. Our findings uncover a crucial regulatory role for histone concentrations in ZGA of Drosophila.

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.

Adaptation to larval crowding in Drosophila ananassae and Drosophila nasuta nasuta: increased larval competitive ability without increased larval feeding rate.

The standard view of adaptation to larval crowding in fruitflies, built on results from 25 years of multiple experimental evolution studies on Drosophila melanogaster, was that enhanced competitive ability evolves primarily through increased larval feeding and foraging rate, and increased larval tolerance to nitrogenous wastes, at the cost of efficiency of food conversion to biomass. These results were at odds from the predictions of classical K-selection theory, notably the expectation that selection at high density should result in the increase of efficiency of conversion of food to biomass, and were better interpreted through the lens of α-selection. We show here that populations of D. ananassae and D. n. nasuta subjected to extreme larval crowding evolve greater competitive ability and pre-adult survivorship at high density, primarily through a combination of reduced larval duration, faster attainment of minimum critical size for pupation, greater time efficiency of food conversion to biomass and increased pupation height, with a relatively small role of increased urea/ammonia tolerance, if at all. This is a very different suite of traits than that seen to evolve under similar selection in D. melanogaster, and seems to be closer to the expectations from the canonical theory of K-selection. We also discuss possible reasons for these differences in results across the three species. Overall, the results reinforce the view that our understanding of the evolution of competitive ability in fruitflies needs to be more nuanced than before, with an appreciation that there may be multiple evolutionary routes through which higher competitive ability can be attained.

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.

Causes and consequences of genetic background effects illuminated by integrative genomic analysis.

The phenotypic consequences of individual mutations are modulated by the wild-type genetic background in which they occur. Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist or about the mechanisms underlying it. We also lack knowledge on how mutations interact with genetic background to influence gene expression and how this in turn mediates mutant phenotypes. Furthermore, how genetic background influences patterns of epistasis remains unclear. To investigate the genetic basis and genomic consequences of genetic background dependence of the scalloped(E3) allele on the Drosophila melanogaster wing, we generated multiple novel genome-level datasets from a mapping-by-introgression experiment and a tagged RNA gene expression dataset. In addition we used whole genome resequencing of the parental lines-two commonly used laboratory strains-to predict polymorphic transcription factor binding sites for SD. We integrated these data with previously published genomic datasets from expression microarrays and a modifier mutation screen. By searching for genes showing a congruent signal across multiple datasets, we were able to identify a robust set of candidate loci contributing to the background-dependent effects of mutations in sd. We also show that the majority of background-dependent modifiers previously reported are caused by higher-order epistasis, not quantitative noncomplementation. These findings provide a useful foundation for more detailed investigations of genetic background dependence in this system, and this approach is likely to prove useful in exploring the genetic basis of other traits as well.

The conditional nature of genetic interactions: the consequences of wild-type backgrounds on mutational interactions in a genome-wide modifier screen.

The phenotypic outcome of a mutation cannot be simply mapped onto the underlying DNA variant. Instead, the phenotype is a function of the allele, the genetic background in which it occurs and the environment where the mutational effects are expressed. While the influence of genetic background on the expressivity of individual mutations is recognized, its consequences on the interactions between genes, or the genetic network they form, is largely unknown. The description of genetic networks is essential for much of biology; yet if, and how, the topologies of such networks are influenced by background is unknown. Furthermore, a comprehensive examination of the background dependent nature of genetic interactions may lead to identification of novel modifiers of biological processes. Previous work in Drosophila melanogaster demonstrated that wild-type genetic background influences the effects of an allele of scalloped (sd), with respect to both its principal consequence on wing development and its interactions with a mutation in optomotor blind. In this study we address whether the background dependence of mutational interactions is a general property of genetic systems by performing a genome wide dominant modifier screen of the sd(E3) allele in two wild-type genetic backgrounds using molecularly defined deletions. We demonstrate that ~74% of all modifiers of the sd(E3) phenotype are background-dependent due in part to differential sensitivity to genetic perturbation. These background dependent interactions include some with qualitative differences in the phenotypic outcome, as well as instances of sign epistasis. This suggests that genetic interactions are often contingent on genetic background, with flexibility in genetic networks due to segregating variation in populations. Such background dependent effects can substantially alter conclusions about how genes influence biological processes, the potential for genetic screens in alternative wild-type backgrounds identifying new loci that contribute to trait expression, and the inferences of the topology of genetic networks.

Does your gene need a background check? How genetic background impacts the analysis of mutations, genes, and evolution.

The premise of genetic analysis is that a causal link exists between phenotypic and allelic variation. However, it has long been documented that mutant phenotypes are not a simple result of a single DNA lesion, but are instead due to interactions of the focal allele with other genes and the environment. Although an experimentally rigorous approach focused on individual mutations and isogenic control strains has facilitated amazing progress within genetics and related fields, a glimpse back suggests that a vast complexity has been omitted from our current understanding of allelic effects. Armed with traditional genetic analyses and the foundational knowledge they have provided, we argue that the time and tools are ripe to return to the underexplored aspects of gene function and embrace the context-dependent nature of genetic effects. We assert that a broad understanding of genetic effects and the evolutionary dynamics of alleles requires identifying how mutational outcomes depend upon the 'wild type' genetic background. Furthermore, we discuss how best to exploit genetic background effects to broaden genetic research programs.