Systems Biology of the Vasopressin V2 Receptor: New Tools for Discovery of Molecular Actions of a GPCR

2021 ◽  
Vol 62 (1) ◽  
Author(s):  
Lihe Chen ◽  
Hyun Jun Jung ◽  
Arnab Datta ◽  
Euijung Park ◽  
Brian G. Poll ◽  
...  
Keyword(s):  
Systems Biology ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Water Channel ◽  
Peptide Hormone ◽  
Annual Review ◽  
Publication Date ◽  
Large Scale Data ◽  
V2 Receptor ◽  
Scale Data

Systems biology can be defined as the study of a biological process in which all of the relevant components are investigated together in parallel to discover the mechanism. Although the approach is not new, it has come to the forefront as a result of genome sequencing projects completed in the first few years of the current century. It has elements of large-scale data acquisition (chiefly next-generation sequencing–based methods and protein mass spectrometry) and large-scale data analysis (big data integration and Bayesian modeling). Here we discuss these methodologies and show how they can be applied to understand the downstream effects of GPCR signaling, specifically looking at how the neurohypophyseal peptide hormone vasopressin, working through the V2 receptor and PKA activation, regulates the water channel aquaporin-2. The emerging picture provides a detailed framework for understanding the molecular mechanisms involved in water balance disorders, pointing the way to improved treatment of both polyuric disorders and water-retention disorders causing dilutional hyponatremia. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Serine/threonine phosphatases and aquaporin-2 regulation in renal collecting duct

2017 ◽  
Vol 312 (1) ◽  
pp. F84-F95 ◽  
Author(s):  
Sophia M. LeMaire ◽  
Viswanathan Raghuram ◽  
Cameron R. Grady ◽  
Christina M. Pickering ◽  
Chung-Lin Chou ◽  
...  
Keyword(s):  
Large Scale ◽  
Collecting Duct ◽  
Water Channel ◽  
Aquaporin 2 ◽  
Proteomic Data ◽  
Large Scale Data ◽  
Duct Cells ◽  
Collecting Duct Cells ◽  
Renal Collecting Duct ◽  
Scale Data

Phosphorylation of the aquaporin-2 (AQP2) water channel at four COOH-terminal serines plays a central role in the regulation of water permeability of the renal collecting duct. The level of phosphorylation at these sites is determined by a balance between phosphorylation by protein kinases and dephosphorylation by phosphatases. The phosphatases that dephosphorylate AQP2 have not been identified. Here, we use large-scale data integration techniques to identify serine-threonine phosphatases likely to interact with AQP2 in renal collecting duct principal cells. As a first step, we have created a comprehensive list of 38 S/T phosphatase catalytic subunits present in the mammalian genome. Then we used Bayes’ theorem to integrate available information from large-scale data sets from proteomic and transcriptomic studies to rank the known S/T phosphatases with regard to the likelihood that they interact with AQP2 in renal collecting duct cells. To broaden the analysis, we have generated new proteomic data (LC-MS/MS) identifying 4538 distinct proteins including 22 S/T phosphatases in cytoplasmic fractions from native inner medullary collecting duct cells from rats. The official gene symbols corresponding to the top-ranked phosphatases (common names in parentheses) were: Ppp1cb (PP1-β), Ppm1g (PP2C), Ppp1ca (PP1-α), Ppp3ca (PP2-B or calcineurin), Ppp2ca (PP2A-α), Ppp1cc (PP1-γ), Ppp2cb (PP2A-β), Ppp6c (PP6C), and Ppp5c (PP5). This ranking correlates well with results of prior reductionist studies of ion and water channels in renal collecting duct cells.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

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