Expression patterns of <i>dcc</i> and <i>netrin</i>.

<p>(<b>A</b>) <i>dcc</i> is expressed in the dorsal telencephalic region at 24 hpf and 36 hpf. (<b>B</b>) <i>dcc</i> and <i>lhx5</i> are co-expressed in the dorsal telencephalon at 20 hpf. ADt neurons are migrating from their medial positions in the neural tube to lateral positions at 20 hpf. Scale bar: 100 µm for lateral view; 60 µm for frontal view.</p

Dcc is required for correct asymmetric outgrowth of ADt neuronal axons.

<p>(<b>A</b>) Dcc expression was reduced after injection of <i>dcc</i> translation-blocking morpholinos into zebrafish embryos. Endogenous Dcc protein was detected as a band of approximately 170 kb. Tubulin served as a loading control. M: size marker. (<b>B</b>) ADt neurons project axons dorsally when Dcc function is inhibited by morpholino injection. Images of live animals were acquired as in Fig. 1C. The pixel intensity value of aberrant axon is shown in the bottom left corner of each panel. Scale bar = 50 µm. (<b>C</b>) Quantitation of ADt neuronal axon defects. Horizontal axis shows the treatment group labels and vertical axis shows the percentage of embryos in each phenotypic category (Grade 0–3) for each treatment group. Numbers inside parentheses denote numbers of animals analyzed for each treatment group. Asterisks and brackets represent <i>p</i><0.05 by Mann-Whitney U test.</p

Effects of inhibition of Netrin1 or Neogenin1 function on the ADt axons.

<p>(<b>A</b>) ADt neurons project axons dorsally when Netrin1 function is inhibited (<i>ntn</i>-MO). Knockdown of Neogenin1 function doesn’t cause ADt neuron to project axon dorsally (<i>neo</i>-MO). Images were processed as in Fig. 3B. The pixel intensity value of aberrant axon is shown in the bottom left corner of each panel. Scale bar = 50 µm. (<b>B</b>) Quantitation of ADt neuronal axon defects. Horizontal axis shows the treatment group labels and vertical axis shows the percentage of embryos in each phenotypic category (Grade 0–3) for each treatment group. Numbers inside parentheses denote numbers of animals analyzed for each treatment group. (<b>C</b>) Synergistic effects between sub-threshold Dcc-Netrin1 and Dcc-Neogenin morpholino knockdowns. <i>dcc</i>-sub_MO and <i>ntn</i>-sub_MO: sub-threshold concentration morpholino. At least three independent injections were performed for each treatment group. Numbers inside parentheses denote numbers of animals analyzed. Asterisks represent <i>p</i><0.001 by ANOVA test.</p

Inhibition of Dcc function causes ADt axons to project dorsally or to form multiple processes.

<p>(<b>A</b>) Labeling of individual ADt neurons by mosaic expression of fluorescent protein tdTomato. Image of a live 36 hpf <i>Tg(lhx5BAC:Kaede)</i> animal that was injected with <i>emx3:Gal4FF</i> and <i>UAS:tdTomato</i> plasmids is shown. The tdTomato labeled neuron projected an axon into the AC. Merge panel shows the position of the tdTomato labeled soma (marked by an arrowhead). Scale bar = 50 µm. (<b>B</b>) Injection of <i>dcc</i> morpholino causes ADt neurons to project axons dorsally or to form multiple processes. Labeled ADt neurons are marked by arrowheads in the merged panels. Left panels show an ADt neuron with a normal ventrally projecting axon in a control animal. Middle panels show an ADt neuron with an aberrant dorsally projecting axon in a <i>dcc</i>-MO injected animal. Right panels show an ADt neuron with both ventrally and dorsally projecting processes. Black arrow in the single slice images indicates the origin of the axon on the surface of the cell body. Red bar indicates the middle of the dorsal and the ventral side of the labeled neuron cell body. Scale bar equals to 20 µm in the projected images or 10 µm in the single slice images. (<b>C</b>) Additional examples of ADt neurons with multiple aberrant axons in <i>dcc</i>-MO injected animals. Scale bar = 15 µm.</p

An example of lexigraphically dissimilar phenotype descriptions from two publications [32], [33] that are semantically similar in that they pertain to the same anatomical structure.

<p>The ‘dorsal arrector’ and the ‘posterior pectoral-spine serrae’ are both parts of the pectoral fin, which is immediately apparent to both humans and computers from the structure of the anatomy ontology. Some of the data relationships shown, such as <i>PHENOSCAPE:exhibits</i> and those from CDAO (Comparative Data Analysis Ontology, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010500#pone.0010500-Prosdocimi1" target="_blank">[30]</a>), are not explicit in Phenex. Instead, these are generated by the interpretation of NeXML documents within the Phenoscape Knowledgebase data loading software.</p

Machine reasoning about phenotypes: enhancing expert knowledge about the genetics of a fossil transition

<div>The Devonian era transition from aquatic fins to terrestrial limbs in tetrapodamorph vertebrates is well-studied in the fossil record, and the genes responsible for the complex suite of anatomical changes have been the topic of much speculation. A recent review by Mastick and Mabee (MM) found evidence for 162 different fin-limb candidate genes in the evo-devo literature. As a test case for the usefulness of machine reasoning about phenotypes, we asked to what extent would an expert system recover the same set of candidate genes using only knowledge about (i) the fin-limb phenotypes from the relevant fossil taxa and (ii) the phenotypes from perturbing individual genes, as catalogued by the relevant model organism (zebrafish, mouse, Xenopus) and human databases. We used the Phenoscape Knowledgebase (kb.phenoscape.org) to compute an information theoretic measure of semantic similarity between ontologically curated phenotypes as an indication of the strength of a candidate gene association. The distribution of phenotypic semantic similarity scores between fossil and gene phenotypes is significantly displaced upwards in the MM candidates relative to the non-candidates. To understand the reasons for genes that performed counter to expectation, we examine the clustering of candidates and non-candidates within protein interaction networks. Our results demonstrate the potential of machine reasoning to accurately rank the strength of evidence for candidate genes when presented with a large volume of descriptive phenotype information. This approach could in principle be used to replace, evaluate and/or enhance candidate gene hypotheses culled from the literature. <br></div

Correspondence between Entity-Quality statements and evolutionary characters.

<p>A. Comparison of the structure of phenotypic descriptions using character-character state vs. Entity-Quality ( =  ‘Phenotype’) syntaxes. B. The defined relationship between an attribute quality type (<i>shape</i>) and a value quality type (<i>triangular</i>) within the Phenotype and Trait Ontology (PATO).</p

Phenex screenshot of window configured with panels for editing of taxon lists, voucher specimens, and publication information.

<p>Phenex screenshot of window configured with panels for editing of taxon lists, voucher specimens, and publication information.</p

NeXML fragments demonstrating embedded Phenex annotations.

<p>A. A taxon. B. A character and character state.</p

Phenex screenshot of window configured with panels for editing of character and character states data, phenotypes (i.e. EQ statements), and character-by-taxon matrix.

<p>Phenex screenshot of window configured with panels for editing of character and character states data, phenotypes (i.e. EQ statements), and character-by-taxon matrix.</p