Dynamic co-expression network analysis of lncRNAs and mRNAs associated with venous congestion

Li,Jinshun; Xu,Yuqin; Xu,Jia; Wang,Jinhua; Wu,Liying

doi:10.3892/mmr.2016.5480

Dynamic co-expression network analysis of lncRNAs and mRNAs associated with venous congestion

Authors:
- Jinshun Li
- Yuqin Xu
- Jia Xu
- Jinhua Wang
- Liying Wu
View Affiliations

Affiliations: Department of Cardiology, Heilongjiang Provincial Hospital, Harbin, Heilongjiang 150001, P.R. China, Infectious Disease Department, Harbin Binghua Hospital, Harbin, Heilongjiang 150086, P.R. China, Department of Nephrology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China, Department of Pharmacy Intravenous Admixture Services, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
Published online on: July 7, 2016 https://doi.org/10.3892/mmr.2016.5480
Pages: 2045-2051
Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Venous congestion and volume overload are important in cardiorenal syndromes, in which multiple regulated factors are involved, including long non‑coding RNAs (lncRNAs). To investigate the underlying role of lncRNAs in regulating the development of venous congestion, an Affymetrix microarray associated with peripheral venous congestion was annotated, then a bipartite dynamic lncRNA-mRNA co-expression network was constructed in which nodes indicated lncRNAs or mRNAs. The nodes were connected when the lncRNAs or mRNAs were dynamically co‑expressed. Following functional analysis of this network, several dynamic alternative pathways were identified, including the calcium signaling pathway during venous congestion development. Additionally, certain lncRNAs (LINC00523, LINC01210 and RP11-435O5.5) were identified that may potentially dynamically regulate certain proteins, including plasma membrane calcium ATPase (PMCA) and G protein‑coupled receptor (GPCR), in the calcium signaling pathway. Particularly, the dynamically regulated switch of LINC00523 from co‑expression with PMCA to GPCR may be involved in damage to steady state intracellular calcium. In brief, the current study demonstrated a potential novel mechanism of lncRNA function during venous congestion.

View Figures

View References

1	Gheorghiade M and Braunwald E: Reconsidering the role for digoxin in the management of acute heart failure syndromes. JAMA. 302:2146–2147. 2009. View Article : Google Scholar : PubMed/NCBI
2	Gheorghiade M and Pang PS: Acute heart failure syndromes. J Am Coll Cardiol. 53:557–573. 2009. View Article : Google Scholar : PubMed/NCBI
3	Inohara T, Kohsaka S, Sato N, Kajimoto K, Keida T, Mizuno M and Takano T; ATTEND Investigators: Prognostic impact of renal dysfunction does not differ according to the clinical profiles of patients: Insight from the acute decompensated heart failure syndromes (ATTEND) registry. PloS One. 9:e1055962014. View Article : Google Scholar : PubMed/NCBI
4	Colombo PC, Ganda A, Lin J, Onat D, Harxhi A, Iyasere JE, Uriel N and Cotter G: Inflammatory activation: Cardiac, renal and cardio-renal interactions in patients with the cardiorenal syndrome. Heart Fail Rev. 17:177–190. 2012. View Article : Google Scholar
5	Colombo PC, Onat D, Harxhi A, Demmer RT, Hayashi Y, Jelic S, LeJemtel TH, Bucciarelli L, Kebschull M, Papapanou P, et al: Peripheral venous congestion causes inflammation, neurohormonal and endothelial cell activation. Eur Heart J. 35:448–454. 2014. View Article : Google Scholar
6	Wang BW, Chang H, Lin S, Kuan P and Shyu KG: Induction of matrix metalloproteinases-14 and -2 by cyclical mechanical stretch is mediated by tumor necrosis factor-alpha in cultured human umbilical vein endothelial cells. Cardiovasc Res. 59:460–469. 2003. View Article : Google Scholar : PubMed/NCBI
7	Onat D, Jelic S, Schmidt AM, Pile-Spellman J, Homma S, Padeletti M, Jin Z, Le Jemtel TH, Colombo PC and Feng L: Vascular endothelial sampling and analysis of gene transcripts: A new quantitative approach to monitor vascular inflammation. J Appl Physiol (1985). 103:1873–1878. 2007. View Article : Google Scholar
8	Colombo PC, Banchs JE, Celaj S, Talreja A, Lachmann J, Malla S, DuBois NB, Ashton AW, Latif F, Jorde UP, et al: Endothelial cell activation in patients with decompensated heart failure. Circulation. 111:58–62. 2005. View Article : Google Scholar
9	Mercer TR, Dinger ME and Mattick JS: Long non-coding RNAs: Insights into functions. Nat Rev Genet. 10:155–159. 2009. View Article : Google Scholar : PubMed/NCBI
10	Wang KC and Chang HY: Molecular mechanisms of long noncoding RNAs. Mol Cell. 43:904–914. 2011. View Article : Google Scholar : PubMed/NCBI
11	Spizzo R, Almeida MI, Colombatti A and Calin GA: Long non-coding RNAs and cancer: A new frontier of translational research? Oncogene. 31:4577–4587. 2012. View Article : Google Scholar : PubMed/NCBI
12	Schonrock N, Harvey RP and Mattick JS: Long noncoding RNAs in cardiac development and pathophysiology. Circ Res. 111:1349–1362. 2012. View Article : Google Scholar : PubMed/NCBI
13	Ishii N, Ozaki K, Sato H, Mizuno H, Saito S, Takahashi A, Miyamoto Y, Ikegawa S, Kamatani N, Hori M, et al: Identification of a novel non-coding RNA, MIAT, that confers risk of myocardial infarction. J Hum Genet. 51:1087–1099. 2006. View Article : Google Scholar : PubMed/NCBI
14	Lee JH, Gao C, Peng G, Greer C, Ren S, Wang Y and Xiao X: Analysis of transcriptome complexity through RNA sequencing in normal and failing murine hearts. Circ Res. 109:1332–1341. 2011. View Article : Google Scholar : PubMed/NCBI
15	Liao Q, Xiao H, Bu D, Xie C, Miao R, Luo H, Zhao G, Yu K, Zhao H, Skogerbø G, et al: ncFANs: A web server for functional annotation of long non-coding RNAs. Nucleic Acids Res. 39(Web Server issue): W118–W124. 2011. View Article : Google Scholar : PubMed/NCBI
16	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
17	Liao Q, Liu C, Yuan X, Kang S, Miao R, Xiao H, Zhao G, Luo H, Bu D, Zhao H, et al: Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network. Nucleic Acids Res. 39:3864–3878. 2011. View Article : Google Scholar : PubMed/NCBI
18	Eisen MB, Spellman PT, Brown PO and Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 95:14863–14868. 1998. View Article : Google Scholar : PubMed/NCBI
19	Witthoft A, Filosa JA and Karniadakis GE: Potassium buffering in the neurovascular unit: Models and sensitivity analysis. Biophysical J. 105:2046–2054. 2013. View Article : Google Scholar
20	Liu T, Jiang CY, Fujita T, Luo SW and Kumamoto E: Enhancement by interleukin-1b of AMPA and NMDA receptor-mediated currents in adult rat spinal superficial dorsal horn neurons. Mol Pain. 9:162013. View Article : Google Scholar
21	Young R and Worthley LI: Current concepts in the management of heart failure. Crit Care Resusc. 6:31–53. 2004.
22	Liu S, Premont RT and Rockey DC: G-protein-coupled receptor kinase interactor-1 (GIT1) is a new endothelial nitric-oxide synthase (eNOS) interactor with functional effects on vascular homeostasis. J Biol Chem. 287:12309–12320. 2012. View Article : Google Scholar : PubMed/NCBI
23	Cicek FA, Ozgur EO, Ozgur E and Ugur M: The interplay between plasma membrane and endoplasmic reticulum Ca(2+) ATPases in agonist-induced temporal Ca(2+) dynamics. J Bioenerg Biomembr. 46:503–510. 2014. View Article : Google Scholar : PubMed/NCBI
24	Roy N, Chakraborty S, Paul Chowdhury B, Banerjee S, Halder K, Majumder S, Majumdar S and Sen PC: Regulation of PKC mediated signaling by calcium during visceral leishmaniasis. PloS One. 9:e1108432014. View Article : Google Scholar : PubMed/NCBI