🍩 Database of Original & Non-Theoretical Uses of Topology

(found 2 matches in 0.001011s)

  1. Delineation of a Conserved Arrestin-Biased Signaling Repertoire in Vivo (2015)

    Stuart Maudsley, Bronwen Martin, Diane Gesty-Palmer, Huey Cheung, Calvin Johnson, Shamit Patel, Kevin G. Becker, William H. Wood, Yongqing Zhang, Elin Lehrmann, Louis M. Luttrell
    Abstract Biased G protein–coupled receptor agonists engender a restricted repertoire of downstream events from their cognate receptors, permitting them to produce mixed agonist-antagonist effects in vivo. While this opens the possibility of novel therapeutics, it complicates rational drug design, since the in vivo response to a biased agonist cannot be reliably predicted from its in cellula efficacy. We have employed novel informatic approaches to characterize the in vivo transcriptomic signature of the arrestin pathway-selective parathyroid hormone analog [d-Trp12, Tyr34]bovine PTH(7-34) in six different murine tissues after chronic drug exposure. We find that [d-Trp12, Tyr34]bovine PTH(7-34) elicits a distinctive arrestin-signaling focused transcriptomic response that is more coherently regulated across tissues than that of the pluripotent agonist, human PTH(1-34). This arrestin-focused network is closely associated with transcriptional control of cell growth and development. Our demonstration of a conserved arrestin-dependent transcriptomic signature suggests a framework within which the in vivo outcomes of arrestin-biased signaling may be generalized.

  2. Persistent Homology Analysis of Brain Transcriptome Data in Autism (2019)

    Daniel Shnier, Mircea A. Voineagu, Irina Voineagu
    Abstract Persistent homology methods have found applications in the analysis of multiple types of biological data, particularly imaging data or data with a spatial and/or temporal component. However, few studies have assessed the use of persistent homology for the analysis of gene expression data. Here we apply persistent homology methods to investigate the global properties of gene expression in post-mortem brain tissue (cerebral cortex) of individuals with autism spectrum disorders (ASD) and matched controls. We observe a significant difference in the geometry of inter-sample relationships between autism and healthy controls as measured by the sum of the death times of zero-dimensional components and the Euler characteristic. This observation is replicated across two distinct datasets, and we interpret it as evidence for an increased heterogeneity of gene expression in autism. We also assessed the topology of gene-level point clouds and did not observe significant differences between ASD and control transcriptomes, suggesting that the overall transcriptome organization is similar in ASD and healthy cerebral cortex. Overall, our study provides a novel framework for persistent homology analyses of gene expression data for genetically complex disorders.