A Brief Reconnoitre about Effects of MMP9 on Aortic Dissection

Abstract

Objective: Matrix metalloproteinases (MMPs) are the extracellular ma-trix regulators that frequently investigate cardiovascular diseases and cancer metastasis. Our study aimed to examine specific polymorphisms in the MMP9 gene in our patients with aortic dissection and compare the effect of MMP9 on aortic dissection with expression datasets.

Materials and Methods: Q279R and P574R polymorphisms were analyzed in 44 aortic dissection patients and 40 healthy donors via polymerase chain reaction-restriction fragment length polymorphism. (PCR-RFLP) methods. Q279R and P574R prevalence was statistically compared with the medical data of the patients. Additionally, we col-lected datasets of aortic dissection from NCBI GEO to reanalyze GEO2R and RStudio to see metalloproteinase activity on samples. Later, enrich-ment analysis was processed on widely used databases.

Results: Genotypic distribution of alleles was similar in the two study groups. In addition to this, female CG carriers had a higher risk of de-veloping aortic dissection than those of males. As the results of the protein-protein interaction analysis of MMP9 and patients’ clinical data, hypertension was found to be the significant outcome of P574R varia-tion in the patients. In array analysis, MMP9 expression did not change critically, but TIMPs had been downregulated in many samples. Also, MMP9 targeted miRNA expression levels were detected as low in aortic tissue and blood.

Conclusion: Q279R and P574R are two polymorphisms that do not di-rectly affect MMP9 protein structure. Consequently, studied polymor-phisms and performed meta-analysis show that MMP9 does not spark off the phenotype but sets the stage for aortic dissection development as seen in the statistical results. Furthermore, enrichment analysis on datasets shows MMP9 was not a primary reason for vascular remodeling.

Keywords

• Aortic Dissection
• MMP9
• Q279R
• P574R
• Polymorphism
• Gene Expression Data

MMP9 Geninin Aort Diseksiyonundaki Olası Etkilerinin Araştırılması

Abstract

Amaç: Kardiyovasküler hastalıklar ve kanser metastazında sıklıkla araştı-rılan matriks metalloproteinazlar (MMPs) ekstraselüler matriks düzenle-yicileridir. Çalışmamız, aort diseksiyonu olan hastalarda MMP9 genindeki spesifik polimorfizmleri incelemeyi ve MMP9'un aort diseksiyonu üzerin-deki etkisini ekspresyon veri setleri ile karşılaştırmayı amaçlamaktadır.

Gereç ve Yöntem: Q279R ve P574R polimorfizmleri 44 aort diseksiyon tanısı almış ve 40 sağlıklı bireyde polimeraz zincir reaksiyonu - restrik-siyon parça uzunluğu polimorfizmi (PCR-RFLP) yöntemiyle çalışıldı. Q279R ve P574R prevalansı istatistiksel olarak hastaların tıbbi verileriyle karşılaştırıldı. Buna ek olarak, NCBI GEO veri tabanından aort diseksiyon veri setleri toplandı ve MMP9 ifadesindeki farklılıkları görmek amacıyla bu veri setleri GEO2R ve RStudio ile yeniden analiz edildi. Elde edilen sonuçların informatik analizi için çevrimiçi veri tabanları kullanıldı.

Bulgular: CG alleli taşıyıcısı kadınların aort diseksiyonu geliştirme riski erkeklerden daha yüksek bulunmasına rağmen her iki çalışma grubun-da da allellerin genotipik dağılımı benzer bulunmuştur. MMP9'un pro-tein-protein etkileşim analizinin ve hastaların tıbbi verilerinin incelen-mesinin sonucu olarak, P574R hipertansiyonu olan hastalarda önemli bir bulgu olarak değerlendirilmiştir. Array verisi analizinde ise MMP9 ifa-desinde kritik bir değişim gözlemlenmemiş olup, birçok örnekte TIMPifade seviyelerinde azalma tespit edilmiştir. Ayrıca MMP9’u hedefleyen miRNA ekspresyon seviyelerinin aort dokusu ve kanda düşük olduğu saptanmıştır.

Sonuç: Q279R ve P574R, MMP9 protein yapısını doğrudan etkilemeyen iki polimorfizmdir. İncelenen polimorfizmler ve gerçekleştirilen meta-a-nalizler, MMP9'un fenotipi doğrudan etkilemediği, ancak istatistiksel sonuçlarda görüldüğü gibi aort diseksiyonu gelişimi için zemin hazır-ladığını göstermektedir.

Keywords

• Aort diseksiyon
• MMP9
• Q279R
• P574R
• gen ifade verisi

References

1. 1. Mukherjee D, Eagle KA. Aortic dissection--an update. Curr Probl Cardiol 2005; 30: 287-325. [CrossRef] google scholar
2. 2. Criado FJ. Aortic dissection: a 250-year perspective. Tex Heart Inst J 2011; 38: 694-700. google scholar
3. 3. Gawinecka J, Schönrath F, von Eckardstein A. Acute aortic dissec-tion: pathogenesis, risk factors and diagnosis. Swiss Med Wkly 2017; 147: w14489. [CrossRef] google scholar
4. 4. Ekmekçi A, Uluganyan M, Güngör B, Abacı N, Ozcan KS, Ertaş G, et al. Association between endothelial nitric oxide synthase intron 4a/b polymorphism and aortic dissection. Turk Kardiyol Dern Ars 2014; 42: 55-60. [CrossRef] google scholar
5. 5. Salhab K, Gioia W, Rabenstein AP, Gubernikoff G, Schubach S. Medical Management of Three Patients with an Acute Type A Aor-tic Dissection: Case Series and a Review of the Literature. Aorta 2018; 6: 98-101. [CrossRef] google scholar
6. 6. Sherifova S, Holzapfel GA. Biomechanics of aortic wall failure with a focus on dissection and aneurysm: A review. Acta Biomater 2019; 99: 1-17. [CrossRef] google scholar
7. 7. Zitka O, Kukacka J, Krizkova S, Huska D, Adam V, Masarik M, et al. Matrix metalloproteinases. Curr Med Chem 2010; 17: 3751-68. [CrossRef] google scholar
8. 8. Yabluchanskiy A, Ma Y, Iyer RP, Hall ME, Lindsey ML. Matrix metal-loproteinase-9: Many shades of function in cardiovascular dis-ease. Physiology 2013; 28: 391-403. [CrossRef] google scholar

9. 9. O'Farrell TJ, Pourmotabbed T. The fibronectin-like domain is re-quired for the type V and XI collagenolytic activity of gelatinase B. Arch Biochem Biophys 1998; 354: 24-30. [CrossRef] google scholar
10. 10. Papazafiropoulou A, Tentolouris N. Matrix metalloproteinases and cardiovascular diseases. Hippokratia 2009; 13: 76-82. google scholar
11. 11. Brown DL, Hibbs MS, Kearney M, Loushin C, Isner JM. Identifica-tion of 92-kD gelatinase in human coronary atherosclerotic le-sions. Association of active enzyme synthesis with unstable angi-na. Circulation 1995; 9: 2125-31. [CrossRef] google scholar
12. 12. Pan S, Lai H, Shen Y, Breeze C, Beck S, Hong T, et al. DNA meth-ylome analysis reveals distinct epigenetic patterns of ascending aortic dissection and bicuspid aortic valve. Cardiovasc Res 2017; 113: 692-704. [CrossRef] google scholar
13. 13. Pan S, Wu D, Teschendorff AE, Hong T, Wang L, Qian M, et al. JAK2-centered interactome hotspot identified by an integrative network algorithm in acute Stanford type A aortic dissection. PLoS One 2014; 9: e89406. [CrossRef] google scholar
14. 14. Kimura N, Futamura K, Arakawa M, Okada N, Emrich F, Okamura H, et al. Gene expression profiling of acute type A aortic dissection combined with in vitro assessment. Eur J Cardiothorac Surg 2017; 52: 810-7. [CrossRef] google scholar
15. 15. Dong J, Bao J, Feng R, Zhao Z, Lu Q, Wang G, et al. Circulating mi-croRNAs: a novel potential biomarker for diagnosing acute aortic dissection. Sci Rep 2017; 7: 12784. [CrossRef] google scholar
16. 16. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--up-date. Nucleic Acids Res 2013; 41: D991-5. [CrossRef] google scholar
17. 17. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Ce-pas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in ge-nome-wide experimental datasets. Nucleic Acids Res 2019; 47: D607-D613. [CrossRef] google scholar
18. 18. UniProt Consortium. UniProt: a worldwide hub of protein knowl-edge. Nucleic Acids Res 2019; 47: D506-D515. [CrossRef] google scholar
19. 19. Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improve-ments in enrichment analysis tools. Nucleic Acids Res 2019; 47: D419-D426. [CrossRef] google scholar
20. 20. 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: 15-20. [CrossRef] google scholar
21. 21. Sticht C, De La Torre C, Parveen A, Gretz N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One 2018; 13: e0206239. [CrossRef] google scholar
22. 22. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging mis-sense mutations. Nat Methods 2010; 7: 248-9. [CrossRef] google scholar
23. 23. Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Di Costanzo L, et al. RCSB Protein Data Bank: biological macromolecular struc-tures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res 2019; 47: D464-D474. [CrossRef] google scholar
24. 24. Elkins PA, Ho YS, Smith WW, Janson CA, D'Alessio KJ, McQueney MS, et al. Structure of the C-terminally truncated human ProM-MP9, a gelatin-binding matrix metalloproteinase. Acta Crystallogr D Biol Crystallogr 2002; 58: 1182-92. [CrossRef] google scholar
25. 25. Cha H, Kopetzki E, Huber R, Lanzendörfer M, Brandstetter H. Struc-tural basis of the adaptive molecular recognition by MMP9. J Mol Biol 2002; 320: 1065-79. [CrossRef] google scholar
26. 26. SIttisoponpisan S, Islam SA, Khanna T, Alhuzimi E, David A, Ster-nberg MJE. Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated? J Mol Biol 2019; 431: 2197-212. [CrossRef] google scholar
27. 27 Kuhlman B, Bradley P Advances in protein structure prediction and design Nat Rev Mol Cell Biol 2019; 20: 681-97 [CrossRef] google scholar
28. 28 Galis ZS, Khatri JJ Matrix metalloproteinases in vascular remodel-ing and atherogenesis: the good, the bad, and the ugly Circ Res 2002; 90: 251-62 [CrossRef] google scholar
29. 29 Wen D, Zhou XL, Li JJ, Hui RT Biomarkers in aortic dissection Clin Chim Acta 2011; 412: 688-95 [CrossRef] google scholar
30. 30 Roth GJ, Majerus PW The mechanism of the effect of aspirin on human platelets I Acetylation of a particulate fraction protein J Clin Invest 1975; 56: 624-32 [CrossRef] google scholar
31. 31 Catterall WA, Perez-Reyes E, Snutch TP, Striessnig J International Union of Pharmacology XLVIII Nomenclature and structure-func-tion relationships of voltage-gated calcium channels Pharmacol Rev 2005; 57: 411-25 [CrossRef] google scholar
32. 32 Sobey CG Potassium channel function in vascular disease Arte-rioscler Thromb Vasc Biol 2001; 21: 28-38 [CrossRef] google scholar
33. 33 Gong Y, Hart E, Shchurin A, Hoover-Plow J Inflammatory macro-phage migration requires MMP-9 activation by plasminogen in mice J Clin Invest 2008; 118: 3012-24 [CrossRef] google scholar
34. 34 Smigiel KS, Parks WC Matrix Metalloproteinases and Leukocyte Activation Prog Mol Biol Transl Sci 2017; 147: 167-95 [CrossRef] google scholar
35. 35 Snitker S, Xie K, Ryan KA, Yu D, Shuldiner AR, Mitchell BD, et al Correlation of circulating MMP-9 with white blood cell count in humans: effect of smoking PLoS One 2013; 8: e66277 [CrossRef] google scholar