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

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

Hippo Pathway Phylogenetics Predicts Monoubiquitylation of Salvador and Merlin/Nf2

<div><p>Recently we employed phylogenetics to predict that the cellular interpretation of TGF-β signals is modulated by monoubiquitylation cycles affecting the Smad4 signal transducer/tumor suppressor. This prediction was subsequently validated by experiments in flies, frogs and mammalian cells. Here we apply a phylogenetic approach to the Hippo pathway and predict that two of its signal transducers, Salvador and Merlin/Nf2 (also a tumor suppressor) are regulated by monoubiquitylation. This regulatory mechanism does not lead to protein degradation but instead serves as a highly efficient “off/on” switch when the protein is subsequently deubiquitylated. Overall, our study shows that the creative application of phylogenetics can predict new roles for pathway components and new mechanisms for regulating intercellular signaling pathways.</p> </div

Overall amino acid substitution rates for families in the Wnt, Hippo and TGF-ß pathways.

a<p>Substitution rate per residue per billion years shown in ascending order by pathway. For each pathway an example of two physically interacting proteins with concordant rates is <u>underlined</u> (e.g., Shaggy/Gsk3 and Axin) and an example of two physically interacting proteins with discordant rates is <b>bold</b> (e.g., Fz and Arrow/Lrp).</p

Yorkie/Yap/Taz and Scalloped/Tead Maximum Likelihood trees.

<p>A) Yorkie/Yap/Taz (Taz is formally known as Wwtr1) vertebrate topology matches the species tree. Among invertebrates there is strong pairing of the echinoderm and hemichordate but weak connections of this pair to cephalochordates, urochordates and vertebrates in relationships that deviate from the species tree. The Bayesian tree (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051599#pone.0051599.s001" target="_blank">Figure S1E</a>) shows one difference - the urochordate has switched places with the cephalochordate exaggerating the deviation from the species tree. B) Scalloped/Tead vertebrate topology matches the species tree. Among invertebrates, the tree clusters the hemichordate and echinoderm with vertebrate Tead1 without the urochordate and clusters the urochordate with vertebrate Tead3, both are deviations from the species tree. The Bayesian tree (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051599#pone.0051599.s001" target="_blank">Figure S1F</a>) shows one difference - the echinoderm and hemichordate move away from vertebrate Tead1 as outliers, an arrangement that is a slightly better match to the species tree.</p

Schematic of the Hippo kinase pathway in flies and humans.

<p>A) Left side. In the protostome <i>D. melanogaster</i>, the transmembrane atypical cadherin ligand Dachsous (Ds) activates the transmembrane atypical cadherin receptor Fat (Ft). Fat then inhibits Dachs (D) and activates Expanded (Ex). Inhibition of Dachs prevents it from destabilizing the Warts (Wts) serine-threonine kinase and activation of Expanded leads to increased kinase activity of Warts. Expanded accomplishes this via a complex containing Merlin and Kibra that facilitates phosphorylation of the Hippo (Hpo) serine-threonine kinase, the Warts kinase and their respective co-factors Salvador (Sav) and Mats. Warts then phosphorylates the transcription co-activator Yorkie (Yki). Phosphorylated Yorkie is bound by 14-3-3 proteins and sequestered in the cytoplasm. When not phosphorylated, Yorkie translocates to the nucleus, binds transcription factors such as Scalloped (Sd), and influences target gene expression leading to increased cell proliferation and decreased apoptosis. A subset of known target genes is shown. Right side. In the deuterostome <i>H. sapiens,</i> proteins are shown in the same color and subcellular location as their corresponding <i>D. melanogaster</i> proteins: Hippo is Mst1/2, Warts is Lats1/2, Mats is Mob1a/b, Yorkie is Yap/Taz, Scalloped is Tead1/2/3/4, Expanded is Frmd1/6, and Merlin is Nf2. The roles of mammalian homologs of Dachsous, Fat, Expanded and Dachs have not yet been confirmed and they are shown with dashed lines and no color. Mammalian target genes are not homologs of the fly target genes. B) Formal names of the nine coelomate species in this study and their phylum/subphylum classifications.</p

Warts/Lats and Mats/Mob Maximum Likelihood trees.

<p>A) Warts/Lats vertebrate topology matches the species tree. Among invertebrates, these proteins should be sequentially joined to the vertebrate cluster and thus this part of the Warts/Lats tree deviates from the species tree. The Bayesian tree (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051599#pone.0051599.s001" target="_blank">Figure S1C</a>) shows one difference - rather than the hemichordate and echinoderm clustering together they are sequentially clustered with the large urochordate, cephalochordate and vertebrate group, an arrangement that better matches the species tree. B) Mats/Mob vertebrate topology matches the species tree except for difficulty resolving human, mouse and chicken Mob1a. Among invertebrates a cluster of the echinoderm and urochordate sequences is attached to the vertebrate cluster with the hemichordate and cephalochordates as outliers. The association of urochordates with vertebrates matches the species tree but the inclusion of echinoderms without hemichordates and cephalochordates does not. The Bayesian tree (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051599#pone.0051599.s001" target="_blank">Figure S1D</a>) shows one difference - the urochordate switches places with the hemichordate leading to an arrangement that better matches the species tree but with no confidence.</p