Image-Level and Group-Level Models for Drosophila Gene Expression Pattern Annotation

Background: Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison. Results: We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach. Conclusion: In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation

A Mesh Generation and Machine Learning Framework for Drosophila Gene Expression Pattern Image Analysis

Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions. Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/. Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods

New resources for the Drosophila 4th chromosome : FRT101F enabled mitotic clones and Bloom syndrome helicase enabled meiotic recombination

Genes on the long arm of the Drosophila melanogaster 4th chromosome are difficult to study because the chromosome lacks mitotic and meiotic recombination. Without recombination numerous standard methods of genetic analysis are impossible. Here, we report new resources for the 4th. For mitotic recombination, we generated a chromosome with an FRT very near the centromere in 101F and a derivative that carries FRT101F with a distal ubiquitously expressed GAL80 transgene. This pair of chromosomes enables both unmarked and MARCM clones. For meiotic recombination, we demonstrate that a Bloom syndrome helicase and recombination defective double mutant genotype can create recombinant 4th chromosomes via female meiosis. All strains will be available to the community via the Bloomington Drosophila Stock Center. Additional resources for studies of the 4th are in preparation and will also be made available. The goal of the 4th Chromosome Resource Project is to accelerate the genetic analysis of protein-coding genes on the 4th, including the 44 genes with no demonstrated function. Studies of these previously inaccessible but largely conserved genes will close longstanding gaps in our knowledge of metazoan development and physiology.Peer reviewe

A Mesh Generation and Machine Learning Framework for Drosophila Gene Expression Pattern Image Analysis

Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions. Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/. Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods

Distinct Molecular Evolutionary Mechanisms Underlie the Functional Diversification of the Wnt and TGFβ Signaling Pathways

The canonical Wnt pathway is one of the oldest and most functionally diverse of animal intercellular signaling pathways. Though much is known about loss-of-function phenotypes for Wnt pathway components in several model organisms, the question of how this pathway achieved its current repertoire of functions has not been addressed. Our phylogenetic analyses of 11 multigene families from five species belonging to distinct phyla, as well as additional analyses employing the 12 Drosophila genomes, suggest frequent gene duplications affecting ligands and receptors as well as co-evolution of new ligand–receptor pairs likely facilitated the expansion of this pathway’s capabilities. Further, several examples of recent gene loss are visible in Drosophila when compared to family members in other phyla. By comparison the TGFβ signaling pathway is characterized by ancient gene duplications of ligands, receptors, and signal transducers with recent duplication events restricted to the vertebrate lineage. Overall, the data suggest that two distinct molecular evolutionary mechanisms can create a functionally diverse developmental signaling pathway. These are the recent dynamic generation of new genes and ligand–receptor interactions as seen in the Wnt pathway and the conservative adaptation of ancient pre-existing genes to new roles as seen in the TGFβ pathway. From a practical perspective, the former mechanism limits the investigator’s ability to transfer knowledge of specific pathway functions across species while the latter facilitates knowledge transfer

The Sno Oncogene Antagonizes Wingless Signaling during Wing Development in Drosophila

The Sno oncogene (Snoo or dSno in Drosophila) is a highly conserved protein and a well-established antagonist of Transforming Growth Factor-β signaling in overexpression assays. However, analyses of Sno mutants in flies and mice have proven enigmatic in revealing developmental roles for Sno proteins. Thus, to identify developmental roles for dSno we first reconciled conflicting data on the lethality of dSno mutations. Then we conducted analyses of wing development in dSno loss of function genotypes. These studies revealed ectopic margin bristles and ectopic campaniform sensilla in the anterior compartment of the wing blade suggesting that dSno functions to antagonize Wingless (Wg) signaling. A subsequent series of gain of function analyses yielded the opposite phenotype (loss of bristles and sensilla) and further suggested that dSno antagonizes Wg signal transduction in target cells. To date Sno family proteins have not been reported to influence the Wg pathway during development in any species. Overall our data suggest that dSno functions as a tissue-specific component of the Wg signaling pathway with modest antagonistic activity under normal conditions but capable of blocking significant levels of extraneous Wg, a role that may be conserved in vertebrates

The TGF-β Family: Signaling Pathways, Developmental Roles, and Tumor Suppressor Activities

Intercellular communication is a critical process for all multicellular organisms, and communication among cells is required for proper embryonic development and adult physiology. Members of the Transforming Growth Factor-β (TGF-β) family of secreted proteins communicate information between cells via a complex signaling pathway, and family members are capable of inducing a wide range of cellular responses. The purpose of this review is to provide the reader with a broad introduction to our current understanding of three aspects of the TGF-β family. These are the molecular mechanisms utilized by TGF-β signaling pathways, the developmental roles played by TGF-β family members in a variety of species, and the growing list of cancers in which various TGF-β signaling pathways display tumor suppressor activity

Informatics approaches to understanding TGFβ pathway regulation

In recent years, informatics studies have predicted several new ways in which the transforming growth factor β (TGFβ) signaling pathway can be post-translationally regulated. Subsequently, many of these predictions were experimentally validated. These approaches include phylogenetic predictions for the phosphorylation, sumoylation and ubiquitylation of pathway components, as well as kinetic models of endocytosis, phosphorylation and nucleo-cytoplasmic shuttling. We review these studies and provide a brief `how to' guide for phylogenetics. Our hope is to stimulate experimental tests of informatics-based predictions for TGFβ signaling, as well as for other signaling pathways, and to expand the number of developmental pathways that are being analyzed computationally