Identification of key genes associated with the effect of osmotic stimuli on intervertebral discs using microarray analysis

  • Authors:
    • Guangxiao Ni
    • Guobin Liu
    • Kunlun Yu
  • View Affiliations

  • Published online on: July 24, 2017     https://doi.org/10.3892/ol.2017.6657
  • Pages: 4249-4255
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

The present study aimed to explore the effect of osmotic stimuli on intervertebral discs (IVDs) using microarray analysis. Gene expression dataset GSE1648 was downloaded from the Gene Expression Omnibus database. There were 11 IVD cell samples in this dataset, which included 4 hyperosmotic stimuli samples, 3 hypoosmotic stimuli samples and 4 isosmotic stimuli samples. The differentially expressed genes (DEGs) in hyperosmotic or hypoosmotic IVD cells (designated DEGs‑hyper or DEGs‑hypo) were identified, compared with isosmotic cells, using the limma package of R software. The Database for Annotation, Visualization and Integrated Discovery was used to perform a Gene Ontology (GO) term enrichment analysis for the DEG sets. Protein‑protein interaction (PPI) network and microRNA (miRNA) gene‑regulatory network data for the DEG sets were obtained using the Human Protein Reference Database (HPRD) and the TargetScan database, respectively, and these networks were constructed and visualized using Cytoscape software. There was a total of 43 DEGs in DEGs‑hyper and 9 in DEGs‑hypo. Analysis of DEGs‑hyper revealed that 41 GO terms were significantly enriched. In total, 376 pairs and 382 nodes were involved in the PPI network, and 1,314 miRNA‑gene pairs and 422 nodes were contained in the miRNA‑gene‑regulated network. The results of the present study indicated that potential target genes (including NCOA3, SOS1, XPO1, ZBTB18, EFNB2 and SOBP) may be involved in the effect of osmotic stimuli on IVD, and the biological processes of apoptosis and cell death may be associated with the effect of high osmolality on IVD disease. The potential targets identified in the present study are more reliable than those identified by previous studies.

Introduction

Intervertebral disc (IVD) disease is a common surgical disease, and IVD degeneration is hypothesized to be the first step in degenerative spinal changes (1). IVD degeneration is typically associated with disc herniation and back pain, which has a marked effect on the sufferer's life and causes severe chronic pain (2). Furthermore, lower back pain may limit the activity of individuals <45 years, which has a marked socio-economic impact (3). The etiology of lower back pain is unclear, but in 40% of cases it is associated with IVD degeneration (4). It is reported that ~40% of individuals <30 years of age, and >90% of individuals >55 years, exhibit moderate-to-severe levels of IVD degeneration (5,6). It is estimated that the costs associated with lumbar disc and lower back disorders exceed $100 billion/year in the USA (7). Traditionally, inflammation has mostly been observed as detrimental and is associated with disease progression. Although the etiology remains unknown, the osmolality has been identified to be associated with IVD disease (810). Katz (7) reported that the osmotic environment exerted an appreciable effect on gene expression and also affected responses to mechanical stimuli. Mavrogonatou and Kletsas (11) indicated that high osmolality was able to activate the G1 and G2 cell cycle checkpoints and affect the DNA integrity of nucleus pulposus IVD cells, triggering an enhanced DNA repair response. Although changes in the extracellular osmolality markedly influenced the behavior of IVD cells, their response to this condition has not yet been fully elucidated. In the present study, the associated biological processes and potential biomarkers in the response of IVD to osmotic stimuli were investigated using gene expression analysis.

Materials and methods

Gene expression profile

The gene expression dataset GSE1648 (8) was downloaded from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) database. There was a total of 11 IVD cell samples in GSE1648, including 4 hyperosmotic stimuli samples, 3 hypoosmotic stimuli samples and 4 isosmotic stimuli samples.

Data pre-processing and identification of differentially expressed genes (DEGs)

For the expression profile, the original data were converted into a recognizable format using R software version 3.1.1 (from bioconductor.org/packages/release/bioc/html/biomaRt.html), and the Affy package (12) version 1.48.0 (from bioconductor.org/packages/release/bioc/html/affy.html) was used for the background correction and normalization. The DEGs in hyperosmotic (designated DEGs-hyper) or hypoosmotic (DEGs-hypo) IVD cells, compared with isosmotic cells, were identified using the limma package (13) version 3.18.13 (from bioconductor.org/packages/release/bioc/html/limma.html) based on gene expression differences of P<0.05 and |log2(fold-change)|>0.05.

Gene Ontology (GO) term enrichment analysis

Functional annotation of DEGs is a necessary and critical step in the analysis of microarray data. The Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/) (14) is a tool providing a comprehensive set of functional annotation. Enriched GO terms from DEGs-hyper were identified using DAVID with a threshold of P<0.05.

Construction of the protein-protein interaction (PPI) network

The Human Protein Reference Database (HPRD) was used as a centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome (15). PPI network data for DEGs-hyper was obtained from HPRD and visualized using Cytoscape (16) software.

Establishment and analysis of the microRNA (miRNA)-regulated-gene network

The software TargetScan (17) was used to predict biological targets of miRNAs by searching for the presence of conserved 8-mer and 7-mer sites that matched the seed region of each miRNA. TargetScan screened for miRNAs associated with the regulation of DEGs-hyper, and miRNA-gene connections were obtained. An miRNA-regulated-gene network was established and visualized using Cytoscape software, and every node was analyzed according to the number of connections with other nodes (degree). miRNAs for which the degree was ≥1 were removed from the analysis.

Results

DEGs

A total of 43 DEGs (34 up- and 9 downregulated) were identified in DEGs-hyper, and the cluster graph is presented in Fig. 1. However, only 9 DEGs were obtained in DEGs-hypo, which were not studied further.

GO terms of DEGs-hyper

In total, 41 GO terms were enriched in DEGs-hyper, and the 10 most significantly enriched GO terms (e.g., regulation of apoptosis, regulation of programmed cell death and regulation of cell death) are presented in Table I.

Table I.

Top 10 most significantly enriched GO terms of differentially expressed genes in intervertebral disc cells of hyperosmotic stimuli samples compared with the isosmotic stimuli.

Table I.

Top 10 most significantly enriched GO terms of differentially expressed genes in intervertebral disc cells of hyperosmotic stimuli samples compared with the isosmotic stimuli.

CategoryGO IDGO nameNumber of genesP-value
BPGO:0042981Regulation of apoptosis9 3.48×10−4
BPGO:0043067Regulation of programmed cell death9 3.73×10−4
BPGO:0010941Regulation of cell death9 3.82×10−4
BPGO:0006913Nucleocytoplasmic transport5 4.20×10−4
BPGO:0051169Nuclear transport5 4.41×10−4
BPGO:0012501Programmed cell death70.00232
BPGO:0008219Cell death70.00519
BPGO:0030183B cell differentiation30.00537
BPGO:0016265Death70.00537
BPGO:0051168Nuclear export30.00828

[i] GO, Gene Ontology; ID, identifier; BP, biological process.

PPI network of DEGs-hyper

The PPI network was constructed and is presented in Fig. 2, and included 376 pairs and 382 nodes. The top 20 nodes (e.g., NCOA3, SOS1 and XPO1), as defined by highest degree, are presented in Table II.

Table II.

Top 20 nodes with a higher degree in the protein-protein interaction network.

Table II.

Top 20 nodes with a higher degree in the protein-protein interaction network.

GeneDegree
NCOA349
SOS145
XPO145
RRAS227
TP53BP223
BCL622
NXF122
EFNB221
SGK116
ARNTL13
LSM610
PTPRE10
VCAM110
BDNF  9
GPSM2  7
FNBP4  6
GLUL  5
MALT1  4
MAPK1  4
PPM1D  4

[i] Degree indicates the number of connections of each node with other nodes in the network.

miRNA-regulated-gene network of DEGs-hyper

In total, 1,314 miRNA-regulated-gene connections were identified with TargetScan. A miRNA-gene-regulated network was established, including 1,314 connections and 422 nodes (Fig. 3). The top 20 nodes (e.g., ZBTB18, EFNB2 and SOBP) in the regulated network, as determined by degree, are presented in Table III.

Table III.

Top 20 nodes with higher degree in the microRNA-gene-regulated network.

Table III.

Top 20 nodes with higher degree in the microRNA-gene-regulated network.

GeneDegree
ZBTB1881
EFNB273
SOBP72
NXF168
BDNF66
PTBP364
PPM1D59
BAMBI58
NCOA355
KLHL2447
SGK145
GAREM42
NUAK142
SOS142
BCL638
XPO137
FAM110B34
TNFAIP834
KLHL929

[i] Degree indicates the number of connections of each node with other nodes in the network.

Discussion

The normal upper limit of water in the human IVD is ~500 mOsm/kg, which is higher than that routinely encountered in the majority of other parts of the body (~300 mOsm/kg) (18). It was previously reported that ~18% of the fluid of IVD was lost and re-imbibed during a diurnal cycle, with consequent changes in osmolality (19). Changes in osmolality are an important component of the physicochemical environment of IVD, as variations in disc loading leads to alterations of disc hydration. A study of IVD cells in a three-dimensional alginate culture system confirmed that the biological response to altered osmolarity is mediated, in part, by changes at the transcriptional level (20). In the present study, 43 DEGs were identified in hyperosmotic cells, whereas 9 DEGs were identified in hypoosmotic cells, compared with isosmotic cells. These results suggest that IVD is more sensitive to hyperosmotic stimuli than to hypoosmotic stimuli, and that the effects of hyperosmotic stimuli may be far greater at the transcriptional level. Hyperosmotic stimuli were previously demonstrated to elicit calcium transience in IVD cells that were modulated by the stability of the actin cytoskeleton (21,22). In the present study, DEGs-hyper were the basis of further research, whereas DEGs-hypo were not further studied.

The enriched GO terms for DEGs-hyper were predominantly associated with the biological processes of apoptosis and cell death, and the regulation of these processes. Three subfamilies were identified: Extracellular-signal-regulated kinase (ERK1/2), p38 mitogen-activated protein kinase (p38 MAPK, p38) and c-Jun N-terminal kinase (JNK1/2). Nucleus pulposus cell apoptosis is one of the changes associated with IVD degeneration (23). p38 and JNK1/2 are serine/threonine protein kinases activated by various stress stimuli, including osmotic shock, toxic compounds and pro-inflammatory cytokines (24). ERK1/2 was previously demonstrated to be activated by high osmolality in nucleus pulposus cells in vitro (25). Dong et al (26) reported that high osmolality activated p38 MAPK, JNK1/2 and ERK1/2 in rabbit nucleus pulposus cells. The activated p38 MAPK and JNK1/2 induced cell apoptosis; by contrast, the activation of ERK1/2 promoted cell survival. A recent study (27) indicated that the effects of osmolality on nucleus pulposus cell apoptosis depended on the osmolality level (hypo-, iso- or hyper-) and osmolality mode (constant or cyclic). Furthermore, inhibition of the ERK1/2 pathway promoted nucleus pulposus cell apoptosis in this process (27). Therefore, it was suspected that the biological processes of apoptosis and cell death may be associated with the effect of hyperosmolality on IVD diseases.

The PPI network was analyzed, and nuclear receptor coactivator 3 (NCOA3), SOS Ras/Rac guanine nucleotide exchange factor 1 (SOS1) and exportin 1 (XPO1) were the top three nodes as defined by degree. NCOA3 was identified to be associated with the pathophysiology of osteoarthritis (OA) (28,29). A recent study concluded that NCOA3 was subject to a cis-acting expression quantitative trait locus in articular cartilage, which was associated with the OA association signal and associated with the OA-associated allele of the functional single-nucleotide polymorphism rs116855380 (30). Although there is no direct evidence that NCOA3 is associated with IVD degeneration, IVD degeneration and OA may exhibit similarities in their occurrence and development, as they are joint degeneration diseases. SOS1 primarily encoded membrane-bound guanine nucleotide-binding proteins in humans, which function in the transduction of signals that control cell growth and differentiation (31).

It was previously reported that SOS1 participates in the process of apoptosis (32,33). It was identified that the biological processes of apoptosis and cell death may be associated with the effect of high osmolality on IVD diseases. The mutation of SOS1 serves an important role in the salt-tolerance and osmotic stimuli of Arabidopsis and tobacco (3437). XPO1 is a specific receptor for leucine-rich nuclear export sequences (38) and was identified to mediate the nuclear export of proteins in a range of species (39,40). Ferrigno et al (41) indicated that XPO1 mediated the export of high osmolality glycerol response MAPK to the cytoplasm of cells adapted to hyper-osmotic stimuli. Thus, NCOA3, SOS1 and XPO1 may be genes directly affected by osmotic stimuli in IVD cells, although more trials and clinical validation are required. Similarly, ZBTB18, EFNB2 and SOBP were the top 3 nodes in the miRNA-gene-regulated network, and they may exhibit an intimate association with the effects of osmotic stimuli on IVD.

The dataset GSE1648 was created by Boyd et al (8); however, the present study adopted different methods and obtained novel results compared with the Boyd et al study (8). First, differential expression analysis was performed for cells in hyperosmotic and hypoosmotic conditions in the present study, whereas Boyd et al (8) only conducted differential expression analysis of cells in hyperosmotic conditions. Secondly, the PPI network and miRNA-gene-regulated network were constructed in the present study; however, this was not carried out in the study by Boyd et al (8). Finally, potential target genes (NCOA3, SOS1, XPO1, ZBTB18, EFNB2 and SOBP) were identified from the PPI network and the miRNA-gene-regulated network, whereas they were not identified in the study by Boyd et al (8). These genes exhibited more interactions compared with those identified by Boyd et al, and therefore they were considered more reliable.

References

1 

Boos N, Weissbach S, Rohrbach H, Weiler C, Spratt KF and Nerlich AG: Classification of age-related changes in lumbar intervertebral discs: 2002 Volvo Award in basic science. Spine (Phila Pa 1976). 27:2631–2644. 2002. View Article : Google Scholar : PubMed/NCBI

2 

Stewart WF, Ricci JA, Chee E, Morganstein D and Lipton R: Lost productive time and cost due to common pain conditions in the US workforce. JAMA. 290:2443–2454. 2003. View Article : Google Scholar : PubMed/NCBI

3 

Andersson GB: Epidemiological features of chronic low-back pain. Lancet. 354:581–585. 1999. View Article : Google Scholar : PubMed/NCBI

4 

Schwarzer AC, Aprill CN, Derby R, Fortin J, Kine G and Bogduk N: The prevalence and clinical features of internal disc disruption in patients with chronic low back pain. Spine (Phila Pa 1976). 20:1878–1883. 1995. View Article : Google Scholar : PubMed/NCBI

5 

Stolworthy DK, Bowden AE, Roeder BL, Robinson TF, Holland JG, Christensen SL, Beatty AM, Bridgewater LC, Eggett DL, Wendel JD, et al: MRI evaluation of spontaneous intervertebral disc degeneration in the alpaca cervical spine. J Orthop Res. 33:1776–1783. 2015. View Article : Google Scholar : PubMed/NCBI

6 

Cheung KM, Karppinen J, Chan D, Ho DW, Song YQ, Sham P, Cheah KS, Leong JC and Luk KD: Prevalence and pattern of lumbar magnetic resonance imaging changes in a population study of one thousand forty-three individuals. Spine (Phila Pa 1976). 34:934–940. 2009. View Article : Google Scholar : PubMed/NCBI

7 

Katz JN: Lumbar disc disorders and low-back pain: Socioeconomic factors and consequences. J Bone Joint Surg Am. 88:(Suppl 2). S21–S24. 2006. View Article : Google Scholar

8 

Boyd LM, Richardson WJ, Chen J, Kraus VB, Tewari A and Setton LA: Osmolarity regulates gene expression in intervertebral disc cells determined by gene array and real-time quantitative RT-PCR. Ann Biomed Eng. 33:1071–1077. 2005. View Article : Google Scholar : PubMed/NCBI

9 

Setton LA and Chen J: Mechanobiology of the intervertebral disc and relevance to disc degeneration. J Bone Joint Surg Am. 88:(Suppl 2). S52–S57. 2006. View Article : Google Scholar

10 

Ma H, Xie YZ, Zhao J and Ye B: Small molecule-enrichment analysis in response to osmotic stimuli in the intervertebral disc. Genet Mol Res. 11:3668–3675. 2012. View Article : Google Scholar : PubMed/NCBI

11 

Mavrogonatou E and Kletsas D: High osmolality activates the G1 and G2 cell cycle checkpoints and affects the DNA integrity of nucleus pulposus intervertebral disc cells triggering an enhanced DNA repair response. DNA Repair (Amst). 8:930–943. 2009. View Article : Google Scholar : PubMed/NCBI

12 

Gautier L, Cope L, Bolstad BM and Irizarry RA: affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI

13 

Diboun I, Wernisch L, Orengo CA and Koltzenburg M: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. 7:2522006. View Article : Google Scholar : PubMed/NCBI

14 

Sherman BT, da Huang W, Tan Q, Guo Y, Bour S, Liu D, Stephens R, Baseler MW, Lane HC and Lempicki RA: DAVID Knowledgebase: A gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics. 8:4262007. View Article : Google Scholar : PubMed/NCBI

15 

Prasad Keshava TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, et al: Human protein reference database-2009 update. Nucleic Acids Res. 37:(Database issue). D767–D772. 2009. View Article : Google Scholar : PubMed/NCBI

16 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

17 

Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP and Burge CB: Prediction of mammalian microRNA targets. Cell. 115:787–798. 2003. View Article : Google Scholar : PubMed/NCBI

18 

Urban JP: The role of the physicochemical environment in determining disc cell behaviour. Biochem Soc Trans. 30:858–864. 2002. View Article : Google Scholar : PubMed/NCBI

19 

McMillan DW, Garbutt G and Adams MA: Effect of sustained loading on the water content of intervertebral discs: Implications for disc metabolism. Ann Rheum Dis. 55:880–887. 1996. View Article : Google Scholar : PubMed/NCBI

20 

Chen J, Baer AE, Paik PY, Yan W and Setton LA: Matrix protein gene expression in intervertebral disc cells subjected to altered osmolarity. Biochem Biophys Res Commun. 293:932–938. 2002. View Article : Google Scholar : PubMed/NCBI

21 

Pritchard S, Erickson GR and Guilak F: Hyperosmotically induced volume change and calcium signaling in intervertebral disk cells: The role of the actin cytoskeleton. Biophys J. 83:2502–2510. 2002. View Article : Google Scholar : PubMed/NCBI

22 

Pritchard S and Guilak F: The role of F-actin in hypo-osmotically induced cell volume change and calcium signaling in anulus fibrosus cells. Ann Biomed Eng. 32:103–111. 2004. View Article : Google Scholar : PubMed/NCBI

23 

Freemont AJ: The cellular pathobiology of the degenerate intervertebral disc and discogenic back pain. Rheumatology (Oxford). 48:5–10. 2009. View Article : Google Scholar : PubMed/NCBI

24 

Johnson NL, Gardner AM, Diener KM, Lange-Carter CA, Gleavy J, Jarpe MB, Minden A, Karin M, Zon LI and Johnson GL: Signal transduction pathways regulated by mitogen-activated/extracellular response kinase kinase kinase induce cell death. J Biol Chem. 271:3229–3237. 1996. View Article : Google Scholar : PubMed/NCBI

25 

Hiyama A, Gogate SS, Gajghate S, Mochida J, Shapiro IM and Risbud MV: BMP-2 and TGF-beta stimulate expression of beta1,3-glucuronosyl transferase 1 (GlcAT-1) in nucleus pulposus cells through AP1, TonEBP and Sp1: Role of MAPKs. J Bone Miner Res. 25:1179–1190. 2010.PubMed/NCBI

26 

Dong ZH, Wang DC, Liu TT, Li FH, Liu RL, Wei JW and Zhou CL: The roles of MAPKs in rabbit nucleus pulposus cell apoptosis induced by high osmolality. Eur Rev Med Pharmacol Sci. 18:2835–2845. 2014.PubMed/NCBI

27 

Li P, Gan Y, Wang H, Xu Y, Li S, Song L, Zhang C, Ou Y, Wang L and Zhou Q: Role of the ERK1/2 pathway in osmolarity effects on nucleus pulposus cell apoptosis in a disc perfusion culture. J Orthop Res. 35:86–92. 2017. View Article : Google Scholar : PubMed/NCBI

28 

Evangelou E, Kerkhof HJ, Styrkarsdottir U, Ntzani EE, Bos SD, Esko T, Evans DS, Metrustry S, Panoutsopoulou K, Ramos YF, et al: A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip. Ann Rheum Dis. 73:2130–2136. 2014. View Article : Google Scholar : PubMed/NCBI

29 

Tsezou A: Osteoarthritis year in review 2014: Genetics and genomics. Osteoarthritis Cartilage. 22:2017–2024. 2014. View Article : Google Scholar : PubMed/NCBI

30 

Gee F, Rushton MD, Loughlin J and Reynard LN: Correlation of the osteoarthritis susceptibility variants that map to chromosome 20q13 with an expression quantitative trait locus operating on NCOA3 and with functional variation at the polymorphism rs116855380. Arthritis Rheumatol. 67:2923–2932. 2015. View Article : Google Scholar : PubMed/NCBI

31 

Pierre S, Bats AS and Coumoul X: Understanding SOS (Son of Sevenless). Biochem Pharmacol. 82:1049–1056. 2011. View Article : Google Scholar : PubMed/NCBI

32 

Xiao Z, Li L, Li Y, Zhou W, Cheng J, Liu F, Zheng P, Zhang Y and Che Y: Rasfonin, a novel 2-pyrone derivative, induces ras-mutated Panc-1 pancreatic tumor cell death in nude mice. Cell Death Dis. 5:e12412014. View Article : Google Scholar : PubMed/NCBI

33 

Chang YL, Ho BC, Sher S, Yu SL and Yang PC: miR-146a and miR-370 coordinate enterovirus 71-induced cell apoptosis through targeting SOS1 and GADD45beta. Cell Microbiol. 17:802–818. 2015. View Article : Google Scholar : PubMed/NCBI

34 

Yue Y, Zhang M, Zhang J, Duan L and Li Z: SOS1 gene overexpression increased salt tolerance in transgenic tobacco by maintaining a higher K(+)/Na(+) ratio. J Plant Physiol. 169:255–261. 2012. View Article : Google Scholar : PubMed/NCBI

35 

Ariga H, Katori T, Yoshihara R, Hase Y, Nozawa S, Narumi I, Iuchi S, Kobayashi M, Tezuka K, Sakata Y, et al: Arabidopsis sos1 mutant in a salt-tolerant accession revealed an importance of salt acclimation ability in plant salt tolerance. Plant Signal Behav. 8:e247792013. View Article : Google Scholar : PubMed/NCBI

36 

Kim JH, Nguyen NH, Jeong CY, Nguyen NT, Hong SW and Lee H: Loss of the R2R3 MYB, AtMyb73, causes hyper-induction of the SOS1 and SOS3 genes in response to high salinity in Arabidopsis. J Plant Physiol. 170:1461–1465. 2013. View Article : Google Scholar : PubMed/NCBI

37 

Katschnig D, Bliek T, Rozema J and Schat H: Constitutive high-level SOS1 expression and absence of HKT1;1 expression in the salt-accumulating halophyte Salicornia dolichostachya. Plant Sci. 234:144–154. 2015. View Article : Google Scholar : PubMed/NCBI

38 

Stade K, Ford CS, Guthrie C and Weis K: Exportin 1 (Crm1p) is an essential nuclear export factor. Cell. 90:1041–1050. 1997. View Article : Google Scholar : PubMed/NCBI

39 

Engel K, Kotlyarov A and Gaestel M: Leptomycin B-sensitive nuclear export of MAPKAP kinase 2 is regulated by phosphorylation. EMBO J. 17:3363–3371. 1998. View Article : Google Scholar : PubMed/NCBI

40 

Toone WM, Kuge S, Samuels M, Morgan BA, Toda T and Jones N: Regulation of the fission yeast transcription factor Pap1 by oxidative stress: Requirement for the nuclear export factor Crm1 (Exportin) and the stress-activated MAP kinase Sty1/Spc1. Genes Dev. 12:1453–1463. 1998. View Article : Google Scholar : PubMed/NCBI

41 

Ferrigno P, Posas F, Koepp D, Saito H and Silver PA: Regulated nucleo/cytoplasmic exchange of HOG1 MAPK requires the importin beta homologs NMD5 and XPO1. EMBO J. 17:5606–5614. 1998. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

October-2017
Volume 14 Issue 4

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Ni G, Liu G and Yu K: Identification of key genes associated with the effect of osmotic stimuli on intervertebral discs using microarray analysis. Oncol Lett 14: 4249-4255, 2017
APA
Ni, G., Liu, G., & Yu, K. (2017). Identification of key genes associated with the effect of osmotic stimuli on intervertebral discs using microarray analysis. Oncology Letters, 14, 4249-4255. https://doi.org/10.3892/ol.2017.6657
MLA
Ni, G., Liu, G., Yu, K."Identification of key genes associated with the effect of osmotic stimuli on intervertebral discs using microarray analysis". Oncology Letters 14.4 (2017): 4249-4255.
Chicago
Ni, G., Liu, G., Yu, K."Identification of key genes associated with the effect of osmotic stimuli on intervertebral discs using microarray analysis". Oncology Letters 14, no. 4 (2017): 4249-4255. https://doi.org/10.3892/ol.2017.6657