Impact of microRNA-130a on the neutrophil proteome

BMC Immunology

Jack Bernard Cowland³  Niels Borregaard³  Niels Henrik Helweg Heegaard²  Lars Juhl Jensen⁴  Ole Østergaard¹  Jan Christian Refsgaard⁴  Corinna Cavan Pedersen³ 

[1] Department of Autoimmunology & Biomarkers, Statens Serum Institut, Artillerivej 5, Copenhagen S, DK-2300, Denmark;Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, University of Southern Denmark, J.B. Winsløws Vej 19, Odense C, DK-5000, Denmark;The Granulocyte Research Laboratory, Department of Hematology, National University Hospital, University of Copenhagen, 9322, Blegdamsvej 9, Copenhagen Ø, DK-2100, Denmark;Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, Copenhagen N, DK-2200, Denmark

关键词: miRNA target network;    RAIN;    Quantitative proteomics;    pSILAC;    Neutrophils;    miR-130a;   

Others  :  1234113
DOI  :  10.1186/s12865-015-0134-8

 received in 2015-09-09, accepted in 2015-11-11,  发布年份 2015

【 摘 要 】

Background

MicroRNAs (miRNAs) are important for the development and function of neutrophils. miR-130a is highly expressed during early neutrophil development and regulates target proteins important for this process. miRNA targets are often identified by validating putative targets found by in silico prediction algorithms one at a time. However, one miRNA can have many different targets, which may vary depending on the context. Here, we investigated the effect of miR-130a on the proteome of a murine and a human myeloid cell line.

Results

Using pulsed stable isotope labelling of amino acids in cell culture and mass spectrometry for protein identification and quantitation, we found 44 and 34 proteins that were significantly regulated following inhibition of miR-130a in a miR-130a-overexpressing 32Dcl3 clone and Kasumi-1 cells, respectively. The level of miR-130a inhibition correlated with the impact on protein levels. We used RAIN, a novel database for miRNA–protein and protein–protein interactions, to identify putative miR-130a targets. In the 32Dcl3 clone, putative targets were more up-regulated than the remaining quantified proteins following miR-130a inhibition, and three significantly derepressed proteins (NFYC, ISOC1, and CAT) are putative miR-130a targets with good RAIN scores. We also created a network including inferred, putative neutrophil miR-130a targets and identified the transcription factors Myb and CBF-β as putative miR-130a targets, which may regulate the primary granule proteins MPO and PRTN3 and other proteins differentially expressed following miR-130a inhibition in the 32Dcl3 clone.

Conclusion

We have experimentally identified miR-130a-regulated proteins within the neutrophil proteome. Linking these to putative miR-130a targets, we provide an association network of potential direct and indirect miR-130a targets that expands our knowledge on the role of miR-130a in neutrophil development and is a valuable platform for further experimental studies.

【 授权许可】

   
2015 Pedersen et al.

【 图 表 】

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

【 参考文献 】

• [1]Borregaard N. Neutrophils, from marrow to microbes. Immunity. 2010; 33(5):657-70.
• [2]Rosenbauer F, Tenen DG. Transcription factors in myeloid development: balancing differentiation with transformation. Nat Rev Immunol. 2007; 7(2):105-17.
• [3]Tenen DG, Hromas R, Licht JD, Zhang DE. Transcription factors, normal myeloid development, and leukemia. Blood. 1997; 90(2):489-519.
• [4]Nakajima H, Watanabe N, Shibata F, Kitamura T, Ikeda Y, Handa M. N-terminal region of CCAAT/enhancer-binding protein epsilon is critical for cell cycle arrest, apoptosis, and functional maturation during myeloid differentiation. J Biol Chem. 2006; 281(20):14494-502.
• [5]Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB. Common features of microRNA target prediction tools. Front Genet. 2014; 5:23.
• [6]Thomson DW, Bracken CP, Goodall GJ. Experimental strategies for microRNA target identification. Nucleic Acids Res. 2011; 39(16):6845-53.
• [7]Tsitsiou E, Lindsay MA. microRNAs and the immune response. Curr Opin Pharmacol. 2009; 9(4):514-20.
• [8]Di LG, Garofalo M, Croce CM. MicroRNAs in cancer. Annu Rev Pathol. 2014; 9:287-314.
• [9]Hager M, Pedersen CC, Larsen MT, Andersen MK, Hother C, Gronbaek K et al.. MicroRNA-130a-mediated down-regulation of Smad4 contributes to reduced sensitivity to TGF-beta1 stimulation in granulocytic precursors. Blood. 2011; 118(25):6649-59.
• [10]Larsen MT, Hother C, Hager M, Pedersen CC, Theilgaard-Monch K, Borregaard N et al.. MicroRNA profiling in human neutrophils during bone marrow granulopoiesis and in vivo exudation. PLoS One. 2013; 8(3):e58454.
• [11]Larsen MT, Hager M, Glenthoj A, Asmar F, Clemmensen SN, Mora-Jensen H et al.. miRNA-130a regulates C/EBP-epsilon expression during granulopoiesis. Blood. 2014; 123(7):1079-89.
• [12]John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. Human MicroRNA targets. PLoS Biol. 2004; 2(11):e363.
• [13]Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005; 120(1):15-20.
• [14]Witkos TM, Koscianska E, Krzyzosiak WJ. Practical Aspects of microRNA Target Prediction. Curr Mol Med. 2011; 11(2):93-109.
• [15]Inomata M, Tagawa H, Guo YM, Kameoka Y, Takahashi N, Sawada K. MicroRNA-17-92 down-regulates expression of distinct targets in different B-cell lymphoma subtypes. Blood. 2009; 113(2):396-402.
• [16]Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell. 2011; 146(3):353-8.
• [17]Easow G, Teleman AA, Cohen SM. Isolation of microRNA targets by miRNP immunopurification. RNA. 2007; 13(8):1198-204.
• [18]Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. Widespread changes in protein synthesis induced by microRNAs. Nature. 2008; 455(7209):58-63.
• [19]Landthaler M, Gaidatzis D, Rothballer A, Chen PY, Soll SJ, Dinic L et al.. Molecular characterization of human Argonaute-containing ribonucleoprotein complexes and their bound target mRNAs. RNA. 2008; 14(12):2580-96.
• [20]Sood P, Krek A, Zavolan M, Macino G, Rajewsky N. Cell-type-specific signatures of microRNAs on target mRNA expression. Proc Natl Acad Sci U S A. 2006; 103(8):2746-51.
• [21]Sun R, Fu X, Li Y, Xie Y, Mao Y. Global gene expression analysis reveals reduced abundance of putative microRNA targets in human prostate tumours. BMC Genomics. 2009; 10:93. BioMed Central Full Text
• [22]Vinther J, Hedegaard MM, Gardner PP, Andersen JS, Arctander P. Identification of miRNA targets with stable isotope labeling by amino acids in cell culture. Nucleic Acids Res. 2006; 34(16):e107.
• [23]Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAs on protein output. Nature. 2008; 455(7209):64-71.
• [24]Lossner C, Warnken U, Pscherer A, Schnolzer M. Preventing arginine-to-proline conversion in a cell-line-independent manner during cell cultivation under stable isotope labeling by amino acids in cell culture (SILAC) conditions. Anal Biochem. 2011; 412(1):123-5.
• [25]Rappsilber J, Ishihama Y, Mann M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem. 2003; 75(3):663-70.
• [26]Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008; 26(12):1367-72.
• [27]Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011; 10(4):1794-805.
• [28]Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc. 1995; 57(1):289-300.
• [29]Kinsella RJ, Kahari A, Haider S, Zamora J, Proctor G, Spudich G et al.. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database (Oxford). 2011; 2011:bar030.
• [30]Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A, Chou CH et al.. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2014; 42(Database issue):D78-85.
• [31]Yuan J, Wu W, Xie C, Zhao G, Zhao Y, Chen R. NPInter v2.0: an updated database of ncRNA interactions. Nucleic Acids Res. 2014; 42(Database issue):D104-8.
• [32]Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014; 42(Database issue):D92-7.
• [33]Croft L, Szklarczyk D, Jensen LJ, Gorodkin J. Multiple independent analyses reveal only transcription factors as an enriched functional class associated with microRNAs. BMC Syst Biol. 2012; 6:90. BioMed Central Full Text
• [34]Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol. 2011; 18(10):1139-46.
• [35]Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ et al.. Combinatorial microRNA target predictions. Nat Genet. 2005; 37(5):495-500.
• [36]Rennie W, Liu C, Carmack CS, Wolenc A, Kanoria S, Lu J et al.. STarMir: a web server for prediction of microRNA binding sites. Nucleic Acids Res. 2014; 42(Web Server issue):W114-8.
• [37]Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J et al.. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015; 43(Database issue):D447-52.
• [38]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al.. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11):2498-504.
• [39]Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37(1):1-13.
• [40]Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4(1):44-57.
• [41]Ebner OA, Selbach M. Whole cell proteome regulation by microRNAs captured in a pulsed SILAC mass spectrometry approach. Methods Mol Biol. 2011; 725:315-31.
• [42]Kaller M, Oeljeklaus S, Warscheid B, Hermeking H. Identification of microRNA targets by pulsed SILAC. Methods Mol Biol. 2014; 1188:327-49.
• [43]Brodersen P, Voinnet O. Revisiting the principles of microRNA target recognition and mode of action. Nat Rev Mol Cell Biol. 2009; 10(2):141-8.
• [44]Vlachos IS, Hatzigeorgiou AG. Online resources for miRNA analysis. Clin Biochem. 2013; 46(10–11):879-900.
• [45]Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem. 2007; 389(4):1017-31.
• [46]Bantscheff M, Lemeer S, Savitski MM, Kuster B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem. 2012; 404(4):939-65.
• [47]Tacheny A, Dieu M, Arnould T, Renard P. Mass spectrometry-based identification of proteins interacting with nucleic acids. J Proteomics. 2013; 94:89-109.
• [48]Bjerregaard MD, Jurlander J, Klausen P, Borregaard N, Cowland JB. The in vivo profile of transcription factors during neutrophil differentiation in human bone marrow. Blood. 2003; 101(11):4322-32.
• [49]Yao C, Qin Z, Works KN, Austin GE, Young AN. C/EBP and C-Myb sites are important for the functional activity of the human myeloperoxidase upstream enhancer. Biochem Biophys Res Commun. 2008; 371(2):309-14.
• [50]Sturrock A, Franklin KF, Wu S, Hoidal JR. Characterization and localization of the genes for mouse proteinase-3 (Prtn3) and neutrophil elastase (Ela2). Cytogenet Cell Genet. 1998; 83(1–2):104-8.
• [51]Klausen P, Bjerregaard MD, Borregaard N, Cowland JB. End-stage differentiation of neutrophil granulocytes in vivo is accompanied by up-regulation of p27kip1 and down-regulation of CDK2, CDK4, and CDK6. J Leukoc Biol. 2004; 75(3):569-78.
• [52]Benatti P, Dolfini D, Vigano A, Ravo M, Weisz A, Imbriano C. Specific inhibition of NF-Y subunits triggers different cell proliferation defects. Nucleic Acids Res. 2011; 39(13):5356-68.
• [53]Gronemeyer T, Wiese S, Ofman R, Bunse C, Pawlas M, Hayen H et al.. The proteome of human liver peroxisomes: identification of five new peroxisomal constituents by a label-free quantitative proteomics survey. PLoS One. 2013; 8(2):e57395.
• [54]Yamaga R, Ikeda K, Boele J, Horie-Inoue K, Takayama K, Urano T et al.. Systemic identification of estrogen-regulated genes in breast cancer cells through cap analysis of gene expression mapping. Biochem Biophys Res Commun. 2014; 447(3):531-6.
• [55]Chelikani P, Fita I, Loewen PC. Diversity of structures and properties among catalases. Cell Mol Life Sci. 2004; 61(2):192-208.
• [56]Santen GW, Kriek M, van Attikum H. SWI/SNF complex in disorder: SWItching from malignancies to intellectual disability. Epigenetics. 2012; 7(11):1219-24.