The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos

R-Smad Competition Controls Activin Receptor Output in Drosophila

Animals use TGF-β superfamily signal transduction pathways during development and tissue maintenance. The superfamily has traditionally been divided into TGF-β/Activin and BMP branches based on relationships between ligands, receptors, and R-Smads. Several previous reports have shown that, in cell culture systems, “BMP-specific” Smads can be phosphorylated in response to TGF-β/Activin pathway activation. Using Drosophila cell culture as well as in vivo assays, we find that Baboon, the Drosophila TGF-β/Activin-specific Type I receptor, can phosphorylate Mad, the BMP-specific R-Smad, in addition to its normal substrate, dSmad2. The Baboon-Mad activation appears direct because it occurs in the absence of canonical BMP Type I receptors. Wing phenotypes generated by Baboon gain-of-function require Mad, and are partially suppressed by over-expression of dSmad2. In the larval wing disc, activated Baboon cell-autonomously causes C-terminal Mad phosphorylation, but only when endogenous dSmad2 protein is depleted. The Baboon-Mad relationship is thus controlled by dSmad2 levels. Elevated P-Mad is seen in several tissues of dSmad2 protein-null mutant larvae, and these levels are normalized in dSmad2; baboon double mutants, indicating that the cross-talk reaction and Smad competition occur with endogenous levels of signaling components in vivo. In addition, we find that high levels of Activin signaling cause substantial turnover in dSmad2 protein, providing a potential cross-pathway signal-switching mechanism. We propose that the dual activity of TGF-β/Activin receptors is an ancient feature, and we discuss several ways this activity can modulate TGF-β signaling output

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

Excessive Baboon signaling perturbs wing development in a Mad-dependent manner.

<p><i>vestigial-</i>GAL4 (<i>vg</i>) was used to express combinations of Baboon, dSmad2, and RNAi for <i>mad</i> and <i>dSmad2.</i> Normal wing development (A; one copy of <i>vg</i>-GAL4) was disrupted by Babo* expression (B, C; Babo*mod and Babo*strong are UAS insertions with varying activity as characterized in other assays). The Babo* phenotype was abrogated by simultaneous <i>mad</i> RNAi (H) and resembled <i>mad</i> RNAi alone (E). In contrast, the crumpling defect of Babo* wings was enhanced in conjunction with <i>dSmad2</i> RNAi (I) and was more severe than <i>dSmad2</i> RNAi alone (F). Overexpression of FLAG-dSmad2 did not affect wing formation (D), but partially rescued the Babo* overexpression phenotype, producing normal sized flat wings with residual peripheral vein defects (G). For each genotype, a representative wing is shown out of 4–7 wings photographed. Within a genotype there was only slight variation in appearance, except that 3/6 dSmad2 RNAi wings had vein defects and a blister (shown) and 3/6 had vein defects without a blister (not shown).</p

P-Mad elevation in <i>dSmad2</i> null mutant tissues depends on <i>baboon</i>.

<p>The schematic depicts the location of the l(X)G0348 <i>P</i> element insertion in relation to the <i>dSmad2</i> locus (A). The F4 excision product removed the entire coding region of <i>dSmad2</i> and portions of the <i>P</i>-element. The genomic breakpoints are indicated above the <i>dSmad2</i> mRNA; they were determined by sequencing PCR products, indicated by dotted lines. (B–D) P-Mad was detected by IHC of fixed larval tissues from several genotypes. For each image, a merged DAPI (blue) and P-Mad (red) panel is displayed above the isolated P-Mad channel. (B) Single confocal sections of P-Mad staining in the fat body. Under the staining conditions employed, endogenous nuclear P-Mad in a control fat body was barely detected (B1), but was increased in a <i>dSmad2</i> null mutant animal (B2). <i>Baboon</i> single mutants and <i>dSmad2</i>; <i>baboon</i> double mutants had normal P-Mad staining (B3,4). (C, D) P-Mad staining at two representative positions along the digestive tract. Images are Maximal Intensity Projections of 3 micron interval confocal sections through the entire sample. The P-Mad primary antibody was omitted from “No Ab Control” samples to convey any background staining and auto-fluorescence in the red channel. (C) In the gastric caeca near the proventriculus, <i>dSmad2</i> mutants showed elevated P-Mad (C3) compared to wildtype control males (C2). <i>baboon</i> single mutants and <i>dSmad2</i>; <i>baboon</i> double mutants showed wildtype levels (C4 and C5). Distal Malpighian tubule staining (lumpy tubes marked with asterisks) showed the same pattern, with the <i>dSmad2</i> mutant displaying the strongest P-Mad staining (D3 compared to D2, D4 and D5).</p

Baboon phosphorylation of Mad is inhibited by dSmad2.

<p>(A) Phospho-Smad accumulation upon exposure to Daw ligand. S2 cells transfected with Flag-Mad were treated with dsRNA for <i>dSmad2</i> or GFP, and transfected with Flag-dSmad2 as indicated. Phospho-Smad and Flag signals were assayed on samples before Daw exposure and after 1 and 3 hours of incubation. Note that there is a gel artifact affecting the appearance of the Flag bands in several lanes. Quantified P-Mad band intensities are plotted to illustrate that accumulation of P-Mad depends on the level of dSmad2. The experiment was repeated with several batches of reporter cells, and the relative signals for the 3 hour time point were always observed in the same order. The 1 hour time points were near background detection and are less reliable due to noise. (B) Mutated dSmad2 proteins with varying phosphorylation efficiency modulate the rate of P-Mad accumulation. Babo* stimulation revealed the steady-state levels of P-dSmad2 and P-Mad. Daw exposure for 1 or 4 hours showed the difference in response to short-term signaling between the WT and HD forms of dSmad2. In both conditions, P-Mad levels were inversely correlated to P-dSmad2 levels. (C–K) Wing imaginal discs from third instar larvae were stained to detect P-Mad and imaged by confocal microscopy. For each condition at least three discs were imaged, and a representative Maximal Intensity Projection encompassing the wing blade is shown (6 sections @ 3 micron interval for C,D,F–H; all sections @ 3 micron interval for E; 5 sections @ 2 micron interval for I–K). Anterior is to the left and the scale bar shown in panel C applies to C–K. The normal P-Mad staining pattern is shown for <i>vg</i>-GAL4 alone (C; scale bar = 100 microns). Expression of a UAS-<i>dSmad2</i> RNAi construct did not alter the P-Mad pattern or the shape of the wing disc (D). Wing discs from <i>babo^fd4/fd4</i> homozygotes showed nearly normal pMad gradient (E). Expression of Babo* altered P-Mad staining, obliterating the normal gradient in the pouch (F). Note the normal P-Mad staining outside of the pouch where <i>vg</i>-Gal4 is not expressed. Babo* and <i>dSmad2</i> RNAi together generated ectopic P-Mad in the entire wing pouch (G). Providing Babo* with additional Punt also produced ectopic P-Mad (H). <i>tkv</i> RNAi prevented P-Mad accumulation in the middle of the wing pouch (I), and addition of Babo* did not counteract this P-Mad pattern (J). Additional knockdown of <i>dSmad2</i> led to ectopic P-Mad (K), which paralleled the results without <i>tkv</i> RNAi. Data in panels C, D, F, and G were from the same experiment and were stained in parallel. Panel H is from a different experiment, but the pouch signals can be compared to the others because the endogenous P-Mad along the posterior margin has similar staining. Samples in panels I–K were stained and processed in parallel.</p

Stimulation of the Baboon receptor leads to phosphorylation of both R-Smads independently of BMP Type I receptors.

<p>S2 cells were transiently transfected with FLAG-tagged Smad expression constructs and analyzed by Western blot for C-terminal phosphorylation (P-dSmad2 and P-Mad). The FLAG-Mad band is shown as a loading control (FLAG-dSmad2 is not a useful loading control because of signaling-induced degradation as described later in the text). For all Western blot figures, a thin horizontal line indicates different infrared channels from the same blot. Co-expression of a constitutively active form of Baboon (Babo*) led to phosphorylation of dSmad2 and Mad (A, left blot). Exposure of cells expressing endogenous Baboon to the Dawdle ligand (Daw) had the same effect (A, right blot). RNAi treatment was used to determine receptor requirement for ligand activity (B). Controls confirmed that Dpp ligand treatment caused Mad phosphorylation independently of Baboon (B, left half). Dpp activity required the Punt Type II receptor and the Tkv and Sax BMP Type I receptors (B, right half). In contrast, Daw signaling to Mad required Baboon and Punt, but not Tkv and Sax.</p

All isoforms of Baboon and mammalian Activin receptors can initiate cross-talk.

<p>(A) Overexpression of wildtype Babo_a or Babo_b in S2 cells led to phosphorylation of dSmad2 and Mad. In this experiment there was a detectable background level of P-dSmad2 well below the Babo-induced levels. Arrowhead indicates that the displayed image contains two portions of one blot. (B) Constitutively active versions of mammalian Activin receptors Alk4 and Alk7 (Alk4* and Alk7*), like Babo*, induce phosphorylation of <i>Drosophila</i> Mad in S2 cells.</p