Identification of miRNA biomarkers of pneumonia using RNA‑sequencing and bioinformatics analysis

Huang,Sai; Feng,Cong; Zhai,Yong‑Zhi; Zhou,Xuan; Li,Bei; Wang,Li‑Li; Chen,Wei; Lv,Fa‑Qin; Li,Tan‑Shi

doi:10.3892/etm.2017.4151

Identification of miRNA biomarkers of pneumonia using RNA‑sequencing and bioinformatics analysis

Authors:
- Sai Huang
- Cong Feng
- Yong‑Zhi Zhai
- Xuan Zhou
- Bei Li
- Li‑Li Wang
- Wei Chen
- Fa‑Qin Lv
- Tan‑Shi Li
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Affiliations: Department of Hematology, People's Liberation Army General Hospital, Beijing 100853, P.R. China, Department of Emergency, People's Liberation Army General Hospital, Beijing 100853, P.R. China, Department of Ultrasound, People's Liberation Army General Hospital, Beijing 100853, P.R. China
Published online on: February 21, 2017 https://doi.org/10.3892/etm.2017.4151
Pages: 1235-1244

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Abstract

Pneumonia is a lower respiratory tract infection that causes dramatic mortality worldwide. The present study aimed to investigate the pathogenesis of pneumonia and identify microRNA (miRNA) biomarkers as candidates for targeted therapy. RNA from the peripheral blood plasma of participants with pneumonia (severe, n=9; non‑severe, n=9) and controls (n=9) was isolated and paired‑end sequencing was performed on an Illumina HiSeq4000 system. Following the processing of raw reads, the sequences were aligned against the Genome Reference Consortium human genome assembly 38 reference genome using Bowtie2 software. Reads per kilobase of transcript per million mapped read values were obtained and the limma software package was used to identify differentially expressed miRNAs (DE‑miRs). Then, DE‑miR targets were predicted and subjected to enrichment analysis. In addition, a protein‑protein interaction (PPI) network of the predicted targets was constructed. This analysis identified 11 key DE‑miRs in pneumonia samples, including 6 upregulated miRNAs (including hsa‑miR‑34a and hsa‑miR‑455) and 5 downregulated miRNAs (including hsa‑let‑7f‑1). All DE‑miRs kept their upregulation/downregulation pattern in the control, non‑severe pneumonia and severe pneumonia samples. Predicted target genes of DE‑miRs in the subjects with non‑severe pneumonia vs. the control and the subjects with severe pneumonia vs. the non‑severe pneumonia group were markedly enriched in the adherens junction and Wnt signaling pathways. KALRN, Ras homolog family member A (RHOA), β‑catenin (CTNNB1), RNA polymerase II subunit K (POLR2K) and amyloid precursor protein (APP) were determined to encode crucial proteins in the PPI network constructed. KALRN was predicted to be a target of hsa‑mir‑200b, while RHOA, CTNNB1, POLR2K and APP were predicted targets of hsa‑let‑7f‑1. The results of the present study demonstrated that hsa‑let‑7f‑1 may serve a role in the development of cancer and the Notch signaling pathway. Conversely, hsa‑miR‑455 may be an inhibitor of pneumonia pathogenesis. Furthermore, hsa‑miR‑200b might promote pneumonia via targeting KALRN.

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1	Organization WH: The top 10 causes of death. July. 2013, simplewhoint/mediacentre/factsheets/fs310/en2014
2	Wunderink RG and Waterer GW: Clinical practice. Community-acquired pneumonia. N Engl J Med. 370:543–551. 2014. View Article : Google Scholar : PubMed/NCBI
3	Said MA, Johnson HL, Nonyane BA, Deloria-Knoll M, O'Brien KL; AGEDD Adult Pneumococcal Burden Study Team, ; Andreo F, Beovic B, Blanco S, Boersma WG, et al: Estimating the burden of pneumococcal pneumonia among adults: A systematic review and meta-analysis of diagnostic techniques. PLoS One. 8:e602732013. View Article : Google Scholar : PubMed/NCBI
4	Rakha MA, Abdelmoneim AN, Farhoud S, Pièche S, Cousens S, Daelmans B and Bahl R: Does implementation of the IMCI strategy have an impact on child mortality? A retrospective analysis of routine data from Egypt. BMJ Open. 3:pii.e0018522013. View Article : Google Scholar
5	Floyd J, Wu L, Hay Burgess D, Izadnegahdar R, Mukanga D and Ghani AC: Evaluating the impact of pulse oximetry on childhood pneumonia mortality in resource-poor settings. Nature. 528:S53–S59. 2015. View Article : Google Scholar : PubMed/NCBI
6	Choi SH, Hong SB, Ko GB, Lee Y, Park HJ, Park SY, Moon SM, Cho OH, Park KH, Chong YP, et al: Viral infection in patients with severe pneumonia requiring intensive care unit admission. Am J Respir Crit Care Med. 186:325–332. 2012. View Article : Google Scholar : PubMed/NCBI
7	Berkley JA, Munywoki P, Ngama M, Kazungu S, Abwao J, Bett A, Lassauniére R, Kresfelder T, Cane PA, Venter M, et al: Viral etiology of severe pneumonia among Kenyan infants and children. JAMA. 303:2051–2057. 2010. View Article : Google Scholar : PubMed/NCBI
8	Sapru A, Hansen H, Ajayi T, Brown R, Garcia O, Zhuo H, Wiemels J, Matthay MA and Wiener-Kronish J: 4G/5G polymorphism of plasminogen activator inhibitor-1 gene is associated with mortality in intensive care unit patients with severe pneumonia. Anesthesiology. 110:1086–1091. 2009. View Article : Google Scholar : PubMed/NCBI
9	España PP, Capelastegui A, Bilbao A, Diez R, Izquierdo F, de Goicoetxea MJ Lopez, Gamazo J, Medel F, Salgado J, Gorostiaga I, et al: Utility of two biomarkers for directing care among patients with non-severe community-acquired pneumonia. Eur J Clin Microbiol Infect Dis. 31:3397–3405. 2012. View Article : Google Scholar : PubMed/NCBI
10	Wong KY, Huang X and Chim CS: DNA methylation of microRNA genes in multiple myeloma. Carcinogenesis. 33:1629–1638. 2012. View Article : Google Scholar : PubMed/NCBI
11	Abd-El-Fattah AA, Sadik NA, Shaker OG and Aboulftouh ML: Differential microRNAs expression in serum of patients with lung cancer, pulmonary tuberculosis, and pneumonia. Cell Biochem Biophys. 67:875–884. 2013. View Article : Google Scholar : PubMed/NCBI
12	Ramirez G, Uribe-Boll J, Cruz A, Jimenez L, Banales J, Romero S, Hidalgo A, Bautista E, Merino E and Moran J: Circulating microRNA profiles in patients with severe pneumonia associated to the A/H1N1 virus. Am J Respir Crit Care Med. 189:A26942014.
13	Ren S, Peng Z, Mao JH, Yu Y, Yin C, Gao X, Cui Z, Zhang J, Yi K, Xu W, et al: RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell Res. 22:806–821. 2012. View Article : Google Scholar : PubMed/NCBI
14	Wang Z, Gerstein M and Snyder M: RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev Genet. 10:57–63. 2009. View Article : Google Scholar : PubMed/NCBI
15	Weiwu D: Guidelines for the diagnosis and treatment of community acquired pneumonia. Chinese J Tuberculosis Respiratory Dis. 29:651–655. 2001.
16	Griffiths-Jones S, Saini HK, van Dongen S and Enright AJ: miRBase: Tools for microRNA genomics. Nucleic Acids Res. 36:D154–D158. 2008. View Article : Google Scholar : PubMed/NCBI
17	Smyth GK: Limma: Linear models for microarray dataBioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer; New York, NY: pp. 397–420. 2005, View Article : Google Scholar
18	Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Royal Statistical Society Series B (Methodological). 57:289–300. 1995.
19	John B, Enright AJ, Aravin A, Tuschl T, Sander C and Marks DS: Human microRNA targets. PLoS Biol. 2:e3632004. View Article : Google Scholar : PubMed/NCBI
20	Palmieri D, Capponi S, Geroldi A, Mura M, Mandich P and Palombo D: TNFα induces the expression of genes associated with endothelial dysfunction through p38MAPK-mediated down-regulation of miR-149. Biochem Biophys Res Commun. 443:246–251. 2014. View Article : Google Scholar : PubMed/NCBI
21	Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, et al: The gene ontology (GO) database and informatics resource. Nucleic Acids Res. 32(Database Issue): D258–D261. 2004.PubMed/NCBI
22	Kanehisa M, Goto S, Sato Y, Furumichi M and Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Rese. 40(Database Issue): D109–D114. 2012. View Article : Google Scholar
23	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI
24	Yu G, Wang LG, Han Y and He QY: ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS. 16:284–287. 2012. View Article : Google Scholar : PubMed/NCBI
25	Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43:D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI
26	Kohl M, Wiese S and Warscheid B: Cytoscape: Software for visualization and analysis of biological networks. Methods Mol Biol. 696:291–303. 2011. View Article : Google Scholar : PubMed/NCBI
27	Tan KS, Choi H, Jiang X, Yin L, Seet JE, Patzel V, Engelward BP and Chow VT: Micro-RNAs in regenerating lungs: An integrative systems biology analysis of murine influenza pneumonia. BMC Genomics. 15:5872014. View Article : Google Scholar : PubMed/NCBI
28	Zhao B, Han H, Chen J, Zhang Z, Li S, Fang F, Zheng Q, Ma Y, Zhang J, Wu N and Yang Y: MicroRNA let-7c inhibits migration and invasion of human non-small cell lung cancer by targeting ITGB3 and MAP4K3. Cancer Lett. 342:43–51. 2014. View Article : Google Scholar : PubMed/NCBI
29	Wang X, Cao L, Wang Y, Wang X, Liu N and You Y: Regulation of let-7 and its target oncogenes (Review). Oncol Lett. 3:955–960. 2012.PubMed/NCBI
30	Liu XS, Chopp M, Zhang RL, Tao T, Wang XL, Kassis H, Hozeska-Solgot A, Zhang L, Chen C and Zhang ZG: MicroRNA profiling in subventricular zone after stroke: MiR-124a regulates proliferation of neural progenitor cells through Notch signaling pathway. PLoS One. 6:e234612011. View Article : Google Scholar : PubMed/NCBI
31	Pillé JY, Denoyelle C, Varet J, Bertrand JR, Soria J, Opolon P, Lu H, Pritchard LL, Vannier JP, Malvy C, et al: Anti-RhoA and Anti-RhoC siRNAs inhibit the proliferation and invasiveness of MDA-MB-231 breast cancer cells in vitroin vivo. Mol Ther. 11:267–274. 2005. View Article : Google Scholar : PubMed/NCBI
32	Chan CH, Lee SW, Li CF, Wang J, Yang WL, Wu CY, Wu J, Nakayama KI, Kang HY, Huang HY, et al: Deciphering the transcriptional complex critical for RhoA gene expression and cancer metastasis. Nat Cell Biol. 12:457–467. 2010. View Article : Google Scholar : PubMed/NCBI
33	DeLeo FR and Musser JM: Axis of coinfection evil. J Infect Dis. 201:488–490. 2010. View Article : Google Scholar : PubMed/NCBI
34	Soong G, Martin FJ, Chun J, Cohen TS, Ahn DS and Prince A: Staphylococcus aureus protein A mediates invasion across airway epithelial cells through activation of RhoA GTPase signaling and proteolytic activity. J Biol Chem. 286:35891–35898. 2011. View Article : Google Scholar : PubMed/NCBI
35	Ganter MT, Roux J, Su G, Lynch SV, Deutschman CS, Weiss YG, Christiaans SC, Myazawa B, Kipnis E and Wiener-Kronish JP: Role of small GTPases and alphavbeta5 integrin in Pseudomonas aeruginosa-induced increase in lung endothelial permeability. Am J Respir Cell Mol Biol. 40:108–118. 2009. View Article : Google Scholar : PubMed/NCBI
36	Lin Y, Li Z, Ozsolak F, Kim SW, Arango-Argoty G, Liu TT, Tenenbaum SA, Bailey T, Monaghan AP, Milos PM and John B: An in-depth map of polyadenylation sites in cancer. Nucleic Acids Res. 40:8460–8471. 2012. View Article : Google Scholar : PubMed/NCBI
37	Misquitta-Ali CM, Cheng E, O'Hanlon D, Liu N, McGlade CJ, Tsao MS and Blencowe BJ: Global profiling and molecular characterization of alternative splicing events misregulated in lung cancer. Mol Cell Biol. 31:138–150. 2011. View Article : Google Scholar : PubMed/NCBI
38	Krause K, Karger S, Sheu SY, Aigner T, Kursawe R, Gimm O, Schmid KW, Dralle H and Fuhrer D: Evidence for a role of the amyloid precursor protein in thyroid carcinogenesis. J Endocrinol. 198:291–299. 2008. View Article : Google Scholar : PubMed/NCBI
39	Takayama K, Tsutsumi S, Suzuki T, Horie-Inoue K, Ikeda K, Kaneshiro K, Fujimura T, Kumagai J, Urano T, Sakaki Y, et al: Amyloid precursor protein is a primary androgen target gene that promotes prostate cancer growth. Cancer Res. 69:137–142. 2009. View Article : Google Scholar : PubMed/NCBI
40	Polakis P: Casein kinase 1 A Wnt'er of disconnect. Curr Biol. 12:R499–R501. 2002. View Article : Google Scholar : PubMed/NCBI
41	Woenckhaus M, Merk J, Stoehr R, Schaeper F, Gaumann A, Wiebe K, Hartmann A, Hofstaedter F and Dietmaier W: Prognostic value of FHIT, CTNNB1, and MUC1 expression in non-small cell lung cancer. Hum Pathol. 39:126–136. 2008. View Article : Google Scholar : PubMed/NCBI
42	Shen J, Liao J, Guarnera MA, Fang H, Cai L, Stass SA and Jiang F: Analysis of MicroRNAs in sputum to improve computed tomography for lung cancer diagnosis. J Thorac. 9:33–40. 2014. View Article : Google Scholar
43	Tan KS, Choi H, Jiang X, Yin L, Ju ES, Patzel V, Engelward BP and Chow VT: Micro-RNAs in regenerating lungs: An integrative systems biology analysis of murine influenza pneumonia. BMC Genomics. 15:1–13. 2014. View Article : Google Scholar : PubMed/NCBI
44	Dewan AT, Egan KB, Hellenbrand K, Sorrentino K, Pizzoferrato N, Walsh KM and Bracken MB: Whole-exome sequencing of a pedigree segregating asthma. BMC Medical Genetics. 13:1–9. 2012. View Article : Google Scholar : PubMed/NCBI
45	Li YJ, Ping C, Tang J and Zhang W: MicroRNA-455 suppresses non-small cell lung cancer through targeting ZEB1. Cell Biol Int. 40:621–628. 2016. View Article : Google Scholar : PubMed/NCBI