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  1. Abstract Motivation

    Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into consideration higher-order interactions. Disease genes in these networks have been shown to exhibit rich higher-order structure in their vicinity, and considering these higher-order interaction patterns in network clustering have the potential to reveal new disease-associated modules.


    We propose a higher-order community detection method which identifies community structure in networks with respect to specific higher-order connectivity patterns beyond edges. Higher-order community detection on four different protein–protein interaction networks identifies biologically significant modules and disease modules that conventional edge-based clustering methods fail to discover. Higher-order clusters also identify disease modules from genome-wide association study data, including new modules that were not discovered by top-performing approaches in a Disease Module DREAM Challenge. Our approach provides a more comprehensive view of community structure that enables us to predict new disease–gene associations.

    Availability and implementation

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  2. Abstract

    The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constriction of apical domains of epithelial cells, is key to the development of complex cellular patterns. Apical constriction occurs in almost all multicellular organisms, but one of the most well-characterized systems is the Folded-gastrulation (Fog)-induced apical constriction that occurs inDrosophila. The binding of Fog to its cognizant receptors Mist/Smog results in a signaling cascade that leads to the activation of NMII-generated contractility. Despite our knowledge of key molecular players involved in Fog signaling, we sought to explore whether other proteins have an undiscovered role in its regulation. We developed a computational method to predict unidentified candidate NMII regulators using a network of pairwise protein–protein interactions called an interactome. We first constructed aDrosophilainteractome of over 500,000 protein–protein interactions from several databases that curate high-throughput experiments. Next, we implemented several graph-based algorithms that predicted 14 proteins potentially involved in Fog signaling. To test these candidates, we used RNAi depletion in combination with a cellular contractility assay inDrosophilaS2R + cells, which respond to Fog by contracting in a stereotypical manner. Of the candidates we screened using this assay, two proteins, the serine/threonine phosphatase Flapwing and the putative guanylate kinase CG11811 were demonstrated to inhibit cellular contractility when depleted, suggestive of their roles as novel regulators of the Fog pathway.

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  3. Schwartz, Russell (Ed.)
  4. null (Ed.)
    Abstract Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at 
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  5. Fehon, Richard (Ed.)
    To identify novel regulators of nonmuscle myosin II (NMII) we performed an image-based RNA interference screen using stable Drosophila melanogaster S2 cells expressing the enhanced green fluorescent protein (EGFP)-tagged regulatory light chain (RLC) of NMII and mCherry-Actin. We identified the Rab-specific GTPase-activating protein (GAP) RN-tre as necessary for the assembly of NMII RLC into contractile actin networks. Depletion of RN-tre led to a punctate NMII phenotype, similar to what is observed following depletion of proteins in the Rho1 pathway. Depletion of RN-tre also led to a decrease in active Rho1 and a decrease in phosphomyosin-positive cells by immunostaining, while expression of constitutively active Rho or Rho-kinase (Rok) rescues the punctate phenotype. Functionally, RN-tre depletion led to an increase in actin retrograde flow rate and cellular contractility in S2 and S2R+ cells, respectively. Regulation of NMII by RN-tre is only partially dependent on its GAP activity as overexpression of constitutively active Rabs inactivated by RN-tre failed to alter NMII RLC localization, while a GAP-dead version of RN-tre partially restored phosphomyosin staining. Collectively, our results suggest that RN-tre plays an important regulatory role in NMII RLC distribution, phosphorylation, and function, likely through Rho1 signaling and putatively serving as a link between the secretion machinery and actomyosin contractility. 
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  6. null (Ed.)