Collagen Cross-Linking Therapy on Important Functional Genes Involved in Keratoconus Patients Using Protein-Protein Interactions (PPI) Network and Differential Expression Analysis
Journal of Ophthalmic and Optometric Sciences,
Vol. 3 No. 3 (2019),
10 July 2019
,
Page 1-15
https://doi.org/10.22037/joos.v3i3.36709
Abstract
Keratoconus (KC) is a progressive eye condition marked by corneal protrusion, thinning, and scarring that adversely affects the ability to see and quality of life. A defective corneal extracellular matrix (ECM) assembly ultimately results in myopia, irregular astigmatism, and severe visual impairment. Most commonly occurring in adolescence, this condition is one of the most commonly indicated clinical indications for corneal transplantation. In contrast to corneal transplantation, corneal collagen crosslinking (CXL) increases corneal stiffness without inflicting any invasive surgery. This study used RNA-Seq data to investigate which functional genes are regulated by CXL in keratoconus. The RNA-Seq expression matrix related to individuals with Keratoconus (KC) and patients treated with collagen crosslinking (CXL) were obtained from the Gene Expression Omnibus (GEO)[1] database 1at the National Center for Biotechnology Information (NCBI) were retrieved. Differential expression analysis and functional enrichment analysis were conducted. The hub genes were then identified with the protein-protein interactions (PPI) network. Finally, transcription factor (TF) genes were identified that regulate these networks. We identified 126 genes in KC and CXL-treated patients that affect TF-mediated network adjustments that improve treatment. In addition, they may play a role in the pathogenesis of keratoconus. Visualization of the PPI networks enabled us to identify 63 highly connected (Hub) genes which served an essential biological function in regulatory networks.
- Keratoconus
- PPI
- Differential expression analysis
- transcription factor
- Systems biology
How to Cite
References
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: Archive for functional genomics data sets - Update. Nucleic Acids Res. 2013;41(D1):991–5.
Sturbaum C.W. · Peiffer JRL. Pathology of Corneal Endothelium in Keratoconus. 1993;
Romero-Jiménez M, Santodomingo-Rubido J, Wolffsohn JS. Keratoconus: A review. Contact Lens Anterior Eye. 2010;33(4):157–66.
Kennedy RH, Bourne WM, Dyer JA. A 48-year clinical and epidemiologic study of keratoconus. Am J Ophthalmol [Internet]. 1986;101(3):267–73. Available from: http://dx.doi.org/10.1016/0002-9394(86)90817-2
Davidson AE, Hayes S, Hardcastle AJ, Tuft SJ. The pathogenesis of keratoconus. Eye [Internet]. 2014;28(2):189–95. Available from: http://dx.doi.org/10.1038/eye.2013.278
Mastropasqua L. Collagen cross-linking: when and how? A review of the state of the art of the technique and new perspectives. Eye Vis [Internet]. 2015;2(1):1–10. Available from: http://dx.doi.org/10.1186/s40662-015-0030-6
Hassan Z, Nemeth G, Modis L, Szalai E, Berta A. Collagen cross-linking in the treatment of pellucid marginal degeneration. Indian J Ophthalmol. 2014;62(3):367–70.
Krumeich JH, Daniel J, Knalle A. Live-epikeratophakia for keratoconus. J Cataract Refract Surg. 1998;24(4):456–63.
Daxer A. Biomechanics of corneal ring implants. Cornea. 2015;34(11):1493–8.
Ellis W. Radial keratotomy in a patient with keratoconus. J Cataract Refract Surg [Internet]. 1992;18(4):406–9. Available from: http://dx.doi.org/10.1016/S0886-3350(13)80081-8
Wollensak G, Spörl E, Seiler T. Behandlung von keratokonus durch kollagenvernetzung. Ophthalmologe. 2003;100(1):44–9.
Kelly TL, Williams KA, Coster DJ. Corneal transplantation for keratoconus: A registry study. Arch Ophthalmol. 2011;129(6):691–7.
Hafezi F, Kanellopoulos J, Wiltfang R, Seiler T. Corneal collagen crosslinking with riboflavin and ultraviolet A to treat induced keratectasia after laser in situ keratomileusis. J Cataract Refract Surg. 2007;33(12):2035–40.
Hayes S, Boote C, Kamma-Lorger CS, Rajan MS, Harris J, Dooley E, et al. Riboflavin/UVA collagen cross-linking-induced changes in normal and keratoconus corneal stroma. PLoS One. 2011;6(8):4–9.
Hayes, Sally Morgan, Sia n R. O’Brart, David P. O’Brart, Naomi Meek and KM. A study of stromal riboflavin absorption in ex vivo porcine corneas using new and existing delivery protocols for corneal cross-linking. 2016;
Kling S, Hammer A, Conti A, Hafezi F. Corneal cross-linking with riboflavin and UV-A in the mouse cornea in vivo: Morphological, biochemical, and physiological analysis. Transl Vis Sci Technol. 2017;6(1):19–22.
You J, Corley SM, Wen L, Hodge C, Höllhumer R, Madigan MC, et al. RNA-Seq analysis and comparison of corneal epithelium in keratoconus and myopia patients. Sci Rep. 2018;8(1):1–13.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):1–21.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26(1):139–40.
McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40(10):4288–97.
Edward Y Chen, Christopher M Tan, Yan Kou, Qiaonan Duan, Zichen Wang GVM, Ma’ayan NRC and A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. 2013;
Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011;39(SUPPL. 1):561–8.
Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, et al. A travel guide to Cytoscape plugins. Nat Methods. 2012;9(11):1069–76.
Gregory WM, Davison CR, Buckley R. Prognostic Factors for the Progression of Keratoconus. Ophthalmology [Internet]. 101(3):439–47. Available from: http://dx.doi.org/10.1016/S0161-6420(94)31313-3
Coskunseven E, Kymionis GD, Tsiklis NS, Atun S, Arslan E, Jankov MR, et al. One-Year Results of Intrastromal Corneal Ring Segment Implantation (KeraRing) using Femtosecond Laser in Patients with Keratoconus. Am J Ophthalmol. 2008;145(5):775–80.
Shetty R, Kurian M, Anand D, Mhaske P, Narayana KM, Shetty BK. Intacs in advanced keratoconus. Cornea. 2008;27(9):1022–9.
Alió JL, Claramonte PJ, Cáliz A, Ramzy MI. Corneal modeling of keratoconus by conductive keratoplasty. J Cataract Refract Surg. 2005;31(1):190–7.
Joseph R, Srivastava OP, Pfister RR. Modeling keratoconus using induced pluripotent stemcells. Investig Ophthalmol Vis Sci. 2016;57(8):3685–97.
Kabza M, Karolak JA, Rydzanicz M, Szcześniak MW, Nowak DM, Ginter-Matuszewska B, et al. Collagen synthesis disruption and downregulation of core elements of TGF-β, Hippo, and Wnt pathways in keratoconus corneas. Eur J Hum Genet. 2017;25(5):582–90.
Blalock TD, Duncan MR, Varela JC, Goldstein MH, Tuli SS, Grotendorst GR, et al. Connective tissue growth factor expression and action in human corneal fibroblast cultures and rat corneas after photorefractive keratectomy. Investig Ophthalmol Vis Sci. 2003;44(5):1879–87.
Runager K, Enghild JJ, Klintworth GK. Focus on molecules: Transforming growth factor beta induced protein (TGFBIp). Exp Eye Res. 2008;87(4):298–9.
Chao-Shern C, DeDionisio LA, Jang JH, Chan CC, Thompson V, Christie K, et al. Evaluation of TGFBI corneal dystrophy and molecular diagnostic testing. Eye [Internet]. 2019;33(6):874–81. Available from: http://dx.doi.org/10.1038/s41433-019-0346-x
Bykhovskaya Y, Gromova A, Makarenkova HP, Rabinowitz YS. Abnormal Regulation of Extracellular Matrix and Adhesion Molecules in Corneas of Patients with Keratoconus. Int J Keratoconus Ectatic Corneal Dis. 2016;5(2):63–70.
Burkitt Wright EMM, Spencer HL, Daly SB, Manson FDC, Zeef LAH, Urquhart J, et al. Mutations in PRDM5 in brittle cornea syndrome identify a pathway regulating extracellular matrix development and maintenance. Am J Hum Genet [Internet]. 2011;88(6):767–77. Available from: http://dx.doi.org/10.1016/j.ajhg.2011.05.007
Park M, Li Q, Shcheynikov N, Zeng W, Muallem S. NaBC1 is a ubiquitous electrogenic Na+-coupled borate transporter essential for cellular boron homeostasis and cell growth and proliferation. Mol Cell. 2004;16(3):331–41.
Romero MF, Chen AP, Parker MD, Boron WF. The SLC4 family of bicarbonate (HCO3-) transporters. Mol Aspects Med [Internet]. 2013;34(2–3):159–82. Available from: http://dx.doi.org/10.1016/j.mam.2012.10.008
Beuerman AGRSLZSDR. Identification of biomarkers for disease progession of Keratoconus. 2013;
H L, S N, S H, M T-A, F K, M M-J, et al. High-throughput analysis of the interactions between viral proteins and host cell RNAs. Computers in biology and medicine. 2021;135:104611-. doi: 10.1016/J.COMPBIOMED.2021.104611.
Mousavian Z, Díaz J, Masoudi-Nejad A. Information theory in systems biology. Part II: protein-protein interaction and signaling networks. Seminars in cell & developmental biology. 2016;51:14-23. doi: 10.1016/J.SEMCDB.2015.12.006.
Mousavian Z, Kavousi K, Masoudi-Nejad A. Information theory in systems biology. Part I: Gene regulatory and metabolic networks. Seminars in Cell and Developmental Biology. 2016;51:3-13. doi: 10.1016/j.semcdb.2015.12.007.
Masoudi-Nejad A, Goto S, Endo TR, Kanehisa M. KEGG bioinformatics resource for plant genomics research. Methods in molecular biology (Clifton, NJ). 2007;406:437-58. doi: 10.1007/978-1-59745-535-0_21.
Torkamanian-Afshar M, Lanjanian H, Nematzadeh S, Tabarzad M, Najafi A, Kiani F, et al. RPINBASE: An online toolbox to extract features for predicting RNA-protein interactions. Genomics. 2020;112(3). doi: 10.1016/j.ygeno.2020.02.013.
Mortezaei Z, Lanjanian H, Masoudi-Nejad A. Candidate novel long noncoding RNAs, MicroRNAs and putative drugs for Parkinson's disease using a robust and efficient genome-wide association study. Genomics. 2017;109(3-4). doi: 10.1016/j.ygeno.2017.02.004.
MotieGhader H, Masoudi-Sobhanzadeh Y, Ashtiani SH, Masoudi-Nejad A. mRNA and microRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms. (1089-8646 (Electronic)).
Ghasemi M, Seidkhani H, Tamimi F, Rahgozar M, Masoudi-Nejad A. Centrality Measures in Biological Networks. Current Bioinformatics. 2014;9(4):426-41. doi: 10.2174/15748936113086660013.
Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. Trader as a new optimization algorithm predicts drug-target interactions efficiently. Scientific Reports 2019 9:1. 2019;9(1):1-14. doi: 10.1038/s41598-019-45814-8.
Kouhsar M, Azimzadeh Jamalkandi S, Moeini A, Masoudi-Nejad A. Detection of novel biomarkers for early detection of Non-Muscle-Invasive Bladder Cancer using Competing Endogenous RNA network analysis. Scientific Reports 2019 9:1. 2019;9(1):1-15. doi: 10.1038/s41598-019-44944-3.
Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. DrugR+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy. Computers in Biology and Medicine. 2019;109:254-62. doi: 10.1016/J.COMPBIOMED.2019.05.006.
- Abstract Viewed: 154 times
- pdf Downloaded: 93 times
