F-domain valency determines outcome of signaling through the angiopoietin pathway

Abstract

Angiopoietin 1 and 2 (Ang1 and Ang2) modulate angiogenesis and vascular homeostasis through engagement of their very similar F-domain modules with the Tie2 receptor tyrosine kinase on endothelial cells. Despite this similarity in the underlying receptor binding interaction, the two angiopoietins have opposite effects: Ang1 induces phosphorylation of protein kinase B (AKT), strengthens cell-cell junctions and enhances endothelial cell survival while Ang2 antagonizes these effects1–4. To investigate the molecular basis for the opposing effects, we examined the protein kinase activation and morphological phenotypes produced by a series of computationally designed protein scaffolds presenting the Ang1 F-domain in a wide range of valencies and geometries. We find two broad phenotypic classes distinguished by the number of presented F-domains: scaffolds presenting 4 F-domains have Ang2 like activity, upregulating pFAK and pERK but not pAKT, and failing to induce cell migration and tube formation, while scaffolds presenting 6 or more F-domains have Ang1 like activity, upregulating pAKT and inducing migration and tube formation. The scaffolds with 8 or more F-domains display superagonist activity, producing stronger phenotypes at lower concentrations than Ang1. When examined in vivo, superagonist icosahedral self-assembling nanoparticles caused significant revascularization in hemorrhagic brains after a controlled cortical impact injury.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

“…We demonstrate the use of our tool on both near-complete (most or all of the organism’s neurons are included in the graph) connectomes as well as on partial connectomes. Here, we compare connectomes of the invertebrate nematode C. elegans 24 , 27 , the partial connectome of the invertebrate fruit fly from the Howard Hughes Medical Institute Janelia Hemibrain project 2 , and the partial vertebrate mouse visual cortex connectome from the Intelligence Advanced Research Project Activity (IARPA) Machine Intelligence from Cortical Networks (MICrONS) project 10 , 28 , 29 . Further dataset details are available in Table 1 .…”

“…As a simple first step towards tool validation, we begin by demonstrating the utility of DotMotif in performing declarative queries on a large connectome graph. In order to illustrate compatibility with data stored in the neuPrint data format 6 , we ran a DotMotif search query on the partial Drosophila melanogaster connectome (dubbed “Hemibrain” ) 2 . We then validated these results with the neuPrint API at neuprint.janelia.org 6 .…”

“…This process enables researchers to convert terabytes or even petabytes of imagery into megabytes or gigabytes of graph data. Conversion to a network format reduces the cost and complexity of interrogating the data, at the expense of losing information about cellular morphology 2 , 3 . Though this graph representation uses substantially less storage-space on disk, answering even seemingly simple network questions (e.g., identifying local graph structure around a particular neuron, or comparing the downstream targets of a certain cell type) may still exceed the computational power, timelines, and budgets available to many research teams, due to the exponential nature of many graph algorithms 4 , 5 .…”

“…Many graphs generated by the connectomics community in recent years have spanned multiple gigabytes of hard drive space 2 , 10 , rendering conventional graph toolkits, such as the common NetworkX library 19 (or its counterparts in other programming languages) under-powered to address the needs of the scientific community. These tools, which often require all graph data to be stored in RAM, would require impractically expensive compute hardware in order to run fully in-memory, and would require impractically long timelines in order to run while swapping data from memory to disk.…”

Antimalarial drugs—are they beneficial in rheumatic and viral diseases?—considerations in COVID-19 pandemic

“…This virus is similar in structure to the SARS-CoV-1, and it contains 80% of the same nucleotides. The disease caused by SARS-CoV-2 is called COVID-19 (coronavirus disease 2019) [ 105 ]. Since the epidemic started, the entire medical world began to look for new and effective drugs, which will help to control the spread of the disease.…”