petal: Co-expression network modelling in R.

BMC Syst Biol. 2016;10(Suppl 2):51

Authors: Petereit J, Smith S, Harris FC, Schlauch KA

Abstract
BACKGROUND: Networks provide effective models to study complex biological systems, such as gene and protein interaction networks. With the advent of new sequencing technologies, many life scientists are grasping for user-friendly methods and tools to examine biological components at the whole-systems level. Gene co-expression network analysis approaches are frequently used to successfully associate genes with biological processes and demonstrate great potential to gain further insights into the functionality of genes, thus becoming a standard approach in Systems Biology. Here the objective is to construct biologically meaningful and statistically strong co-expression networks, the identification of research dependent subnetworks, and the presentation of self-contained results.
RESULTS: We introduce petal, a novel approach to generate gene co-expression network models based on experimental gene expression measures. petal focuses on statistical, mathematical, and biological characteristics of both, input data and output network models. Often over-looked issues of current co-expression analysis tools include the assumption of data normality, which is seldom the case for hight-throughput expression data obtained from RNA-seq technologies. petal does not assume data normality, making it a statistically appropriate method for RNA-seq data. Also, network models are rarely tested for their known typical architecture: scale-free and small-world. petal explicitly constructs networks based on both these characteristics, thereby generating biologically meaningful models. Furthermore, many network analysis tools require a number of user-defined input variables, these often require tuning and/or an understanding of the underlying algorithm; petal requires no user input other than experimental data. This allows for reproducible results, and simplifies the use of petal. Lastly, this approach is specifically designed for very large high-throughput datasets; this way, petal's network models represent as much of the entire system as possible to provide a whole-system approach.
CONCLUSION: petal is a novel tool for generating co-expression network models of whole-genomics experiments. It is implemented in R and available as a library. Its application to several whole-genome experiments has generated novel meaningful results and has lead the way to new testing hypothesizes for further biological investigation.

PMID: 27490697 [PubMed - as supplied by publisher]

Functional Translational Readthrough: A Systems Biology Perspective.

PLoS Genet. 2016 Aug;12(8):e1006196

Authors: Schueren F, Thoms S

Abstract
Translational readthrough (TR) has come into renewed focus because systems biology approaches have identified the first human genes undergoing functional translational readthrough (FTR). FTR creates functional extensions to proteins by continuing translation of the mRNA downstream of the stop codon. Here we review recent developments in TR research with a focus on the identification of FTR in humans and the systems biology methods that have spurred these discoveries.

PMID: 27490485 [PubMed - as supplied by publisher]

The selectivity of the Na(+)/K(+)-pump is controlled by binding site protonation and self-correcting occlusion.

Elife. 2016 Aug 4;5

Authors: Rui H, Artigas P, Roux B

Abstract
The Na(+)/K(+)-pump maintains the physiological K(+) and Na(+) electrochemical gradients across the cell membrane. It operates via an 'alternating-access' mechanism, making iterative transitions between inward-facing (E1) and outward-facing (E2) conformations. Although the general features of the transport cycle are known, the detailed physicochemical factors governing the binding site selectivity remain mysterious. Free energy molecular dynamics simulations show that the ion binding sites switch their binding specificity in E1 and E2. This is accompanied by small structural arrangements and changes in protonation states of the coordinating residues. Additional computations on structural models of the intermediate states along the conformational transition pathway reveal that the free energy barrier toward the occlusion step is considerably increased when the wrong type of ion is loaded into the binding pocket, prohibiting the pump cycle from proceeding forward. This self-correcting mechanism strengthens the overall transport selectivity and protects the stoichiometry of the pump cycle.

PMID: 27490484 [PubMed - as supplied by publisher]

Mango: combining and analyzing heterogeneous biological networks.

BioData Min. 2016;9:25

Authors: Chang J, Cho H, Chou HH

Abstract
BACKGROUND: Heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. Existing software for network analysis has limited scalability to large data sets or is only accessible to software developers as libraries. In addition, the polymorphic nature of the data sets requires a more standardized method for integration and exploration.
RESULTS: Mango facilitates large network analyses with its Graph Exploration Language, automatic graph attribute handling, and real-time 3-dimensional visualization. On a personal computer Mango can load, merge, and analyze networks with millions of links and can connect to online databases to fetch and merge biological pathways.
CONCLUSIONS: Mango is written in C++ and runs on Mac OS, Windows, and Linux. The stand-alone distributions, including the Graph Exploration Language integrated development environment, are freely available for download from http://www.complex.iastate.edu/download/Mango. The Mango User Guide listing all features can be found at http://www.gitbook.com/book/j23414/mango-user-guide.

PMID: 27489569 [PubMed]

Cell-free determination of binary complexes that comprise extended protein-protein interaction networks of Yersinia pestis.

Mol Cell Proteomics. 2016 Aug 3;

Authors: Keasey SL, Natesan M, Pugh C, Kamata T, Wuchty S, Ulrich RG

Abstract
Binary protein interactions form the basic building blocks of molecular networks and dynamic assemblies that control all cellular functions of bacteria. While these protein interactions are a potential source of targets for the development of new antibiotics, few high-confidence data sets are available for the large proteomes of most pathogenic bacteria. We used a library of recombinant proteins from the plague bacterium Yersinia pestis to probe planar microarrays of immobilized proteins that represented approximately 85% (3,552 proteins) of the bacterial proteome, resulting in > 77,000 experimentally determined binary interactions. Moderate (KD ~ μM) to high-affinity (KD ~ nM) interactions were characterized for >1,600 binary complexes by surface plasmon resonance imaging of microarrayed proteins. Core binary interactions that were in common with other gram-negative bacteria were identified from the results of both microarray methods. Clustering of proteins within the interaction network by function revealed statistically enriched complexes and pathways involved in replication, biosynthesis, virulence, metabolism, and other diverse biological processes. The interaction pathways included many proteins with no previously known function. Further, a large assembly of proteins linked to transcription and translation were contained within highly interconnected sub-regions of the network. The two-tiered microarray approach used here is an innovative method for detecting binary interactions, and the resulting data will serve as a critical resource for the analysis of protein interaction networks that function within an important human pathogen.

PMID: 27489291 [PubMed - as supplied by publisher]

Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data.

Cancer Inform. 2016;15:151-61

Authors: Young MR, Craft DL

Abstract
We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful for clustering cancer cohorts into subtypes. Variants of the method allow it to be used on datasets that do and do not contain noncancerous samples. Activity levels of all types of pathways, broadly grouped into metabolic, cellular processes and signaling, and immune system, are useful for separating the pan-cancer cohort. In the clustering of specific cancer types, certain pathway types become more valuable depending on the site being studied. For lung cancer, signaling pathways dominate; for pancreatic cancer, signaling and metabolic pathways dominate; and for melanoma, immune system pathways are the most useful. This work suggests the utility of pathway-level genomic analysis and points in the direction of using pathway classification for predicting the efficacy and side effects of drugs and radiation.

PMID: 27486299 [PubMed]

Properties of Life: Toward a Coherent Understanding of the Organism.

Acta Biotheor. 2016 Aug 2;

Authors: Rosslenbroich B

Abstract
The question of specific properties of life compared to nonliving things accompanied biology throughout its history. At times this question generated major controversies with largely diverging opinions. Basically, mechanistic thinkers, who tried to understand organismic functions in terms of nonliving machines, were opposed by those who tried to describe specific properties or even special forces being active within living entities. As this question included the human body, these controversies always have been of special relevance to our self-image and also touched practical issues of medicine. During the second half of the twentieth century, it seemed to be resolved that organisms are explainable basically as physicochemical machines. Especially from the perspective of molecular biology, it seemed to be clear that organisms need to be explained solely by the chemical functions of their component parts, although some resistance to this view never ceased. This research program has been working quite successfully, so that science today knows a lot about the physiological and chemical processes within organisms. However, again new doubts arise questioning whether the mere continuation of this analytical approach will finally generate a fundamental understanding of living entities. At the beginning of the twenty-first century the quest for a new synthesis actually comes from analytical empiricists themselves. The hypothesis of the present paper is that empirical research has been developed far enough today, that it reveals by itself the materials and the prerequisites to understand more of the specific properties of life. Without recourse to mysterious forces, it is possible to generate answers to this age-old question, just using recent, empirically generated knowledge. This view does not contradict the results of reductionistic research, but rather grants them meaning within the context of organismic systems and also may increase their practical usefulness. Although several of these properties have been discussed before, different authors usually concentrated on a single one or some of them. The paper describes ten specific properties of living entities as they can be deduced from contemporary science. The aim is to demonstrate that the results of empirical research show both the necessity as well as the possibility of the development of a new conception of life to build a coherent understanding of organismic functions.

PMID: 27485949 [PubMed - as supplied by publisher]

Predicting G Protein-Coupled Receptor Downstream Signaling by Tissue Expression.

Bioinformatics. 2016 Aug 2;

Authors: Hao Y, Tatonetti NP

Abstract
MOTIVATION: G protein-coupled receptors (GPCRs) are central to how cells respond to their environment and a major class of pharmacological targets. However, comprehensive knowledge of which pathways are activated and deactivated by these essential sensors is largely unknown. To better understand the mechanism of GPCR signaling system, we integrated five independent genome-wide expression datasets, representing 275 human tissues and cell lines, with protein-protein interactions and functional pathway data.
RESULTS: We found that tissue-specificity plays a crucial part in the function of GPCR signaling system. Only a few GPCRs are expressed in each tissue, which are coupled by different combinations of G-proteins or β-arrestins to trigger specific downstream pathways. Based on this finding, we predicted the downstream pathways of GPCR in human tissues and validated our results with L1000 knockdown data. In total, we identified 154,988 connections between 294 GPCRs and 690 pathways in 240 tissues and cell types.
AVAILABILITY AND IMPLEMENTATION: The source code and results supporting the conclusions of this article are available at http://tatonettilab.org/resources/GOTE/source_code/ CONTACT: nick.tatonetti@columbia.edu SUPPLEMENTARY INFORMATION: Supplemental information including 24 figures and 11 tables can be found with this article online.

PMID: 27485444 [PubMed - as supplied by publisher]

Multiple sclerosis: clinical profiling and data collection as prerequisite for personalized medicine approach.

BMC Neurol. 2016;16:124

Authors: Ziemssen T, Kern R, Thomas K

Abstract
Multiple sclerosis (MS) is a highly heterogeneous disease as it can present inter-individually as well as intra-individually, with different disease phenotypes emerging during different stages in the long-term disease course. In addition to advanced immunological, genetic and magnetic resonance imaging (MRI) profiling of the patient, the clinical profiling of MS patients needs to be widely implemented in clinical practice and improved by including a greater range of relevant parameters as patient-reported outcomes. It is crucial to implement a high standard of clinical characterization of individual patients as this is key to effective long-term observation and evaluation.To generate reliable real-world data, individual clinical data should be collected in specific MS registries and/or using intelligent software instruments as the Multiple Sclerosis Documentation System 3D. Computational analysis of biological processes will play a key role in the transition to personalized MS treatment. Major breakthroughs in the areas of bioinformatics and computational systems biology will be required to process this complex information to enable improved personalization of treatment for MS patients.

PMID: 27484848 [PubMed - in process]

An integrated approach of network-based systems biology, molecular docking, and molecular dynamics approach to unravel the role of existing antiviral molecules against AIDS-associated cancer.

J Biomol Struct Dyn. 2016 Aug 2;:1-12

Authors: Omer A, Singh P

Abstract
A serious challenge in cancer treatment is to reposition the activity of various already known drug candidates against cancer. There is a need to rewrite and systematically analyze the detailed mechanistic aspect of cellular networks to gain insight into the novel role played by various molecules. Most Human Immunodeficiency Virus infection-associated cancers are caused by oncogenic viruses like Human Papilloma Viruses and Epstein-Bar Virus. As the onset of AIDS-associated cancers marks the severity of AIDS, there might be possible interconnections between the targets and mechanism of both the diseases. We have explored the possibility of certain antiviral compounds to act against major AIDS-associated cancers: Kaposi's Sarcoma, Non-Hodgkin Lymphoma, and Cervical Cancer with the help of systems pharmacology approach that includes screening for targets and molecules through the construction of a series of drug-target and drug-target-diseases network. Two molecules (Calanolide A and Chaetochromin B) and the target "HRAS" were finally screened with the help of molecular docking and molecular dynamics simulation. The results provide novel antiviral molecules against HRAS target to treat AIDS defining cancers and an insight for understanding the pharmacological, therapeutic aspects of similar unexplored molecules against various cancers.

PMID: 27484103 [PubMed - as supplied by publisher]

Different Epidermal Growth Factor Receptor (EGFR) Agonists Produce Unique Signatures for the Recruitment of Downstream Signaling Proteins.

J Biol Chem. 2016 Mar 11;291(11):5528-40

Authors: Ronan T, Macdonald-Obermann JL, Huelsmann L, Bessman NJ, Naegle KM, Pike LJ

Abstract
The EGF receptor can bind seven different agonist ligands. Although each agonist appears to stimulate the same suite of downstream signaling proteins, different agonists are capable of inducing distinct responses in the same cell. To determine the basis for these differences, we used luciferase fragment complementation imaging to monitor the recruitment of Cbl, CrkL, Gab1, Grb2, PI3K, p52 Shc, p66 Shc, and Shp2 to the EGF receptor when stimulated by the seven EGF receptor ligands. Recruitment of all eight proteins was rapid, dose-dependent, and inhibited by erlotinib and lapatinib, although to differing extents. Comparison of the time course of recruitment of the eight proteins in response to a fixed concentration of each growth factor revealed differences among the growth factors that could contribute to their differing biological effects. Principal component analysis of the resulting data set confirmed that the recruitment of these proteins differed between agonists and also between different doses of the same agonist. Ensemble clustering of the overall response to the different growth factors suggests that these EGF receptor ligands fall into two major groups as follows: (i) EGF, amphiregulin, and EPR; and (ii) betacellulin, TGFα, and epigen. Heparin-binding EGF is distantly related to both clusters. Our data identify differences in network utilization by different EGF receptor agonists and highlight the need to characterize network interactions under conditions other than high dose EGF.

PMID: 26786109 [PubMed - indexed for MEDLINE]

(Per)chlorate in Biology on Earth and Beyond.

Annu Rev Microbiol. 2016 Jul 25;

Authors: Youngblut MD, Wang O, Barnum TP, Coates JD

Abstract
Respiration of perchlorate and chlorate [collectively, (per)chlorate] was only recognized in the last 20 years, yet substantial advances have been made in our understanding of the underlying metabolisms. Although it was once considered solely anthropogenic, pervasive natural sources, both terrestrial and extraterrestrial, indicate an ancient (per)chlorate presence across our solar system. These discoveries stimulated interest in (per)chlorate microbiology and the application of advanced approaches highlight exciting new facets. Forward and reverse genetics revealed new information regarding underlying molecular biology and associated regulatory mechanisms. Structural and functional analysis characterized core enzymes and identified novel reaction sequences. Comparative genomics elucidated evolutionary aspects, and stress analysis identified novel response mechanisms to reactive chlorine species. Finally, systems biology identified unique metabolic versatility and novel mechanisms of (per)chlorate respiration, including symbiosis and a hybrid enzymatic-abiotic metabolism. While many published studies focus on (per)chlorate and their basic metabolism, this review highlights seminal advances made over the last decade and identifies new directions and potential novel applications. Expected final online publication date for the Annual Review of Microbiology Volume 70 is September 08, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

PMID: 27482739 [PubMed - as supplied by publisher]

SYSTEMS BIOLOGY. Protein isoforms: more than meets the eye.

Nat Methods. 2016 Apr;13(4):291

Authors: Larochelle S

PMID: 27482571 [PubMed - in process]

Lineage tracing of human B cells reveals the in vivo landscape of human antibody class switching.

Elife. 2016;5

Authors: Horns F, Vollmers C, Croote D, Mackey SF, Swan GE, Dekker CL, Davis MM, Quake SR

Abstract
Antibody class switching is a feature of the adaptive immune system which enables diversification of the effector properties of antibodies. Even though class switching is essential for mounting a protective response to pathogens, the in vivo patterns and lineage characteristics of antibody class switching have remained uncharacterized in living humans. Here we comprehensively measured the landscape of antibody class switching in human adult twins using antibody repertoire sequencing. The map identifies how antibodies of every class are created and delineates a two-tiered hierarchy of class switch pathways. Using somatic hypermutations as a molecular clock, we discovered that closely related B cells often switch to the same class, but lose coherence as somatic mutations accumulate. Such correlations between closely related cells exist when purified B cells class switch in vitro, suggesting that class switch recombination is directed toward specific isotypes by a cell-autonomous imprinted state.

PMID: 27481325 [PubMed - in process]

Place recognition using batlike sonar.

Elife. 2016;5

Authors: Vanderelst D, Steckel J, Boen A, Peremans H, Holderied MW

Abstract
Echolocating bats have excellent spatial memory and are able to navigate to salient locations using bio-sonar. Navigating and route-following require animals to recognize places. Currently, it is mostly unknown how bats recognize places using echolocation. In this paper, we propose template based place recognition might underlie sonar-based navigation in bats. Under this hypothesis, bats recognize places by remembering their echo signature - rather than their 3D layout. Using a large body of ensonification data collected in three different habitats, we test the viability of this hypothesis assessing two critical properties of the proposed echo signatures: (1) they can be uniquely classified and (2) they vary continuously across space. Based on the results presented, we conclude that the proposed echo signatures satisfy both criteria. We discuss how these two properties of the echo signatures can support navigation and building a cognitive map.

PMID: 27481189 [PubMed - in process]

Reconstructing Causal Biological Networks through Active Learning.

PLoS One. 2016;11(3):e0150611

Authors: Cho H, Berger B, Peng J

Abstract
Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

PMID: 26930205 [PubMed - indexed for MEDLINE]

An Analysis of the Truncated Bid- and ROS-dependent Spatial Propagation of Mitochondrial Permeabilization Waves during Apoptosis.

J Biol Chem. 2016 Feb 26;291(9):4603-13

Authors: Jacob SF, Würstle ML, Delgado ME, Rehm M

Abstract
Apoptosis is a form of programmed cell death that is essential for the efficient elimination of surplus, damaged, and transformed cells during metazoan embryonic development and adult tissue homeostasis. Situated at the interface of apoptosis initiation and execution, mitochondrial outer membrane permeabilization (MOMP) represents one of the most fundamental processes during apoptosis signal transduction. It was shown that MOMP can spatiotemporally propagate through cells, in particular in response to extrinsic apoptosis induction. Based on apparently contradictory experimental evidence, two distinct molecular mechanisms have been proposed to underlie the propagation of MOMP signals, namely a reaction-diffusion mechanism governed by anisotropies in the production of the MOMP-inducer truncated Bid (tBid), or a process that drives the spatial propagation of MOMP by sequential bursts of reactive oxygen species. We therefore generated mathematical models for both scenarios and performed in silico simulations of spatiotemporal MOMP signaling to identify which one of the two mechanisms is capable of qualitatively and quantitatively reproducing the existing data. We found that the explanatory power of each model was limited in that only a subset of experimental findings could be replicated. However, the integration of both models into a combined mathematical description of spatiotemporal tBid and reactive oxygen species signaling accurately reproduced all available experimental data and furthermore, provided robustness to spatial MOMP propagation when mitochondria are spatially separated. Our study therefore provides a theoretical framework that is sufficient to describe and mechanistically explain the spatiotemporal propagation of one of the most fundamental processes during apoptotic cell death.

PMID: 26699404 [PubMed - indexed for MEDLINE]

Mechanotransduction in primary human osteoarthritic chondrocytes is mediated by metabolism of energy, lipids, and amino acids.

J Biomech. 2015 Dec 16;48(16):4253-61

Authors: Zignego DL, Hilmer JK, June RK

Abstract
Chondrocytes are the sole cell type found in articular cartilage and are repeatedly subjected to mechanical loading in vivo. We hypothesized that physiological dynamic compression results in changes in energy metabolism to produce proteins for maintenance of the pericellular and extracellular matrices. The objective of this study was to develop an in-depth understanding for the short term (<30min) chondrocyte response to sub-injurious, physiological compression by analyzing metabolomic profiles for human chondrocytes harvested from femoral heads of osteoarthritic donors. Cell-seeded agarose constructs were randomly assigned to experimental groups, and dynamic compression was applied for 0, 15, or 30min. Following dynamic compression, metabolites were extracted and detected by HPLC-MS. Untargeted analyzes examined changes in global metabolomics profiles and targeted analysis examined the expression of specific metabolites related to central energy metabolism. We identified hundreds of metabolites that were regulated by applied compression, and we report the detection of 16 molecules not found in existing metabolite databases. We observed patient-specific mechanotransduction with aging dependence. Targeted studies found a transient increase in the ratio of NADP+ to NADPH and an initial decrease in the ratio of GDP to GTP, suggesting a flux of energy into the TCA cycle. By characterizing metabolomics profiles of primary chondrocytes in response to applied dynamic compression, this study provides insight into how OA chondrocytes respond to mechanical load. These results are consistent with increases in glycolytic energy utilization by mechanically induced signaling, and add substantial new data to a complex picture of how chondrocytes transduce mechanical loads.

PMID: 26573901 [PubMed - indexed for MEDLINE]

Altered cytoskeletal organization characterized lethal but not surviving Brtl+/- mice: insight on phenotypic variability in osteogenesis imperfecta.

Hum Mol Genet. 2015 Nov 1;24(21):6118-33

Authors: Bianchi L, Gagliardi A, Maruelli S, Besio R, Landi C, Gioia R, Kozloff KM, Khoury BM, Coucke PJ, Symoens S, Marini JC, Rossi A, Bini L, Forlino A

Abstract
Osteogenesis imperfecta (OI) is a heritable bone disease with dominant and recessive transmission. It is characterized by a wide spectrum of clinical outcomes ranging from very mild to lethal in the perinatal period. The intra- and inter-familiar OI phenotypic variability in the presence of an identical molecular defect is still puzzling to the research field. We used the OI murine model Brtl(+/-) to investigate the molecular basis of OI phenotypic variability. Brtl(+/-) resembles classical dominant OI and shows either a moderately severe or a lethal outcome associated with the same Gly349Cys substitution in the α1 chain of type I collagen. A systems biology approach was used. We took advantage of proteomic pathway analysis to functionally link proteins differentially expressed in bone and skin of Brtl(+/-) mice with different outcomes to define possible phenotype modulators. The skin/bone and bone/skin hybrid networks highlighted three focal proteins: vimentin, stathmin and cofilin-1, belonging to or involved in cytoskeletal organization. Abnormal cytoskeleton was indeed demonstrated by immunohistochemistry to occur only in tissues from Brtl(+/-) lethal mice. The aberrant cytoskeleton affected osteoblast proliferation, collagen deposition, integrin and TGF-β signaling with impairment of bone structural properties. Finally, aberrant cytoskeletal assembly was detected in fibroblasts obtained from lethal, but not from non-lethal, OI patients carrying an identical glycine substitution. Our data demonstrated that compromised cytoskeletal assembly impaired both cell signaling and cellular trafficking in mutant lethal mice, altering bone properties. These results point to the cytoskeleton as a phenotypic modulator and potential novel target for OI treatment.

PMID: 26264579 [PubMed - indexed for MEDLINE]

A Systems Level Analysis of Vasopressin-mediated Signaling Networks in Kidney Distal Convoluted Tubule Cells.

Sci Rep. 2015;5:12829

Authors: Cheng L, Wu Q, Kortenoeven ML, Pisitkun T, Fenton RA

Abstract
The kidney distal convoluted tubule (DCT) plays an essential role in maintaining body sodium balance and blood pressure. The major sodium reabsorption pathway in the DCT is the thiazide-sensitive NaCl cotransporter (NCC), whose functions can be modulated by the hormone vasopressin (VP) acting via uncharacterized signaling cascades. Here we use a systems biology approach centered on stable isotope labeling by amino acids in cell culture (SILAC) based quantitative phosphoproteomics of cultured mouse DCT cells to map global changes in protein phosphorylation upon acute treatment with a VP type II receptor agonist 1-desamino-8-D-arginine vasopressin (dDAVP). 6330 unique proteins, containing 12333 different phosphorylation sites were identified. 185 sites were altered in abundance following dDAVP. Basophilic motifs were preferential targets for upregulated sites upon dDAVP stimulation, whereas proline-directed motifs were prominent for downregulated sites. Kinase prediction indicated that dDAVP increased AGC and CAMK kinase families' activities and decreased activity of CDK and MAPK families. Network analysis implicated phosphatidylinositol-4,5-bisphosphate 3-kinase or CAMKK dependent pathways in VP-mediated signaling; pharmacological inhibition of which significantly reduced dDAVP induced increases in phosphorylated NCC at an activating site. In conclusion, this study identifies unique VP signaling cascades in DCT cells that may be important for regulating blood pressure.

PMID: 26239621 [PubMed - indexed for MEDLINE]