Data-independent acquisition mass spectrometry analysis for identification of cerebrospinal fluid biomarker of reversible cerebral vasoconstriction syndrome

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張展華(CHan-Hua Chang) 查詢紙本館藏

畢業系所

化學學系

論文名稱

(Data-independent acquisition mass spectrometry analysis for identification of cerebrospinal fluid biomarker of reversible cerebral vasoconstriction syndrome)

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摘要(中)

可逆性腦血管收縮症候群 (Reversible cerebral vasoconstriction syndromes, RCVS)是一種中樞神經系統疾病，有著可逆性及多發性腦動脈收縮、雷擊式頭痛和較少見的多發性神經缺陷等症狀，但目前對此疾病的診斷以及蛋白質分子機制還未全盤了解，所以我們計畫分析腦脊髓液中蛋白質的組成和表現量，並找到可以用於診斷和治療的新型蛋白質生物標記，在之前的研究中，我們使用了數據依賴型採集模式質譜 (data dependent acquisition mass spectrometry, DDA-MS) 分析了40個腦脊髓液樣品，最後發現了18個生物標記候選蛋白。
我們使用了最新的且再現性更高的方法非數據依賴型採集模式質譜 (data independent acquisition mass spectrometry, DIA-MS)來分析腦脊髓液樣品，首先我們要先建立DIA-MS方法，我們混合了16個腦脊髓液物樣品並使用層析技術，構建了含有958個蛋白質的圖普庫，其中含有前驅離子荷質比、碎片離子荷質比、碎片離子相對強度和相對滯留時間（iRT）等信息，未使用層析技術所建立的圖譜庫只有475個蛋白質相比之下使用層析技術所見的圖普庫提供了更高的蛋白質鑑定覆蓋率，再和擁有1647蛋白質個的公開腦脊髓液圖譜庫相比，我們的圖譜庫在測試的樣品中可以提供744個蛋白質鑑定結果，而來自公開腦脊髓液圖譜庫則鑑定到612個蛋白質，是因為我們自己建立的脊髓液圖譜庫對於自己的腦脊髓液樣品更相近，我們還使用20個合成肽，針對18個候選蛋白進一步增加圖譜庫的資訊來幫助我們更完整地鑑定到18個候選蛋白，在測試的樣品中更進一步針對18個候選蛋白增加了5個肽和1個蛋白的鑑定。
我們應用了新開發的DIA-MS方法定量比較12個控制和13個病人的腦脊髓液蛋白質組，根據DIA-MS數據我們利用直接建立圖譜庫的策略將48種蛋白質增加到RCVS圖譜庫，再用這個圖譜庫和DIA-MS數據進行比對鑑定，並通過積分碎片離子圖譜組合峰面積進行定量，最後在25個腦脊髓液樣品中鑑定到762個蛋白質，和之前利用數據依賴性採集質譜（DDA-MS）方法分析40份腦脊髓液樣品的結果相比，多鑑定到了333個蛋白質。比對人類蛋白質組圖譜，DIA-MS方法提供更多鑑定數以及更多腦源性蛋白質。進一步定量顯示在雙尾t檢驗後，病人和控制個體之間117個蛋白質差異表達（p <0.05）。使用IPA和DAVID對117個差異表達蛋白進行的生物富集分析富集了與炎症相關的功能，此外鑑定了18個候選蛋白中的13個，鑑定到的這13個候選蛋白中有3個包括PRNP，THBS4和CA1和之前的結果也是一樣的也是差異表達蛋白。 PRNP在先前和目前的結果中具有增加的趨勢，並且與朊病毒疾病，對氧化應激的反應和腦源性蛋白質相關。這三種蛋白質可以作為進一步研究的相容性候選部分。

摘要(英)

Reversible cerebral vasoconstriction syndromes (RCVS) are a group of central nerves system disorders that show reversible multifocal cerebral arteries narrowing, thunderclap headache and less commonly focal neurologic deficits. Due to limited knowledge about RCVS, the molecule mechanism are still unknown and the diagnosis tools remain to be developed. Revealing the cerebrospinal fluid (CSF) protein compositions and expression may provide an opportunity to advance our knowledge about the role of proteins in neurological disorders and to identify novel protein biomarkers for diagnosis and treatment purposes. In our previous study, we have analyzed 40 CSF sample with data dependent acquisition mass spectrometry (DDA-MS) method and revealed 18 candidate proteins.
In this thesis, we devolved a data independent acquisition mass spectrometry (DIA-MS) method for analyzing the CSF proteome. In the first part, we have generated a RCVS proteome mass spectral library by reversed phase stagetip fractionation strategies using DDA method together with LC-MS/MS analysis. Using CSF mixture sample (N=16), a library with precursor m/z, fragment m/z, fragment relative intensity, and relative retention time (iRT) information from 958 proteins were constructed, which provide higher proteome coverage compare to 475 proteins identified without fractionation method. Compared to the public CSF spectral library which had 1647 protein, our library can provide 744 proteins identification result which is more than 612 proteins from public CSF library, because of the own library is more specific to own CSF samples. We further optimized the DIA approach for quantitative proteomics by synthetic peptides and ALD4, and 5 peptides and 1 proteins were quantified additionally. The developed DIA-MS method was applied to quantitatively compare the CSF proteome of 12 non-RCVS controls and 13 RCVS patients. All the DIA-MS data were processed by generated DIA based spectral library, spectral library based identification and quantified by integrating the peak area of fragment alignment. Totally, we increased 48 proteins into the RCVS library and identified 762 proteins in 25 CSF samples, which revealed more RCVS proteome composition of 333 proteins from previous results of 40 CSF samples by data dependent acquisition mass spectrometry (DDA-MS) method. The DIA-MS method provides more identification and also more brain originated proteins based on human proteome map.
Further quantitation show that 117 proteins are differentially expressed between the RCVS and control individuals after two tailed t test (p<0.05). The biological enrichment analysis using IPA and DAVID of 117 differentially expressed proteins (DEPs) enrich functions related to inflammation, immune response, prothrombin, leukocyte and superoxide radicals degradation. 13 of 18 candidate proteins which are selected from previous results of 40 CSF samples are identified, and 3 of 13 identified candidate proteins including PRNP, THBS4 and CA1 are also DEPs. PRNP has the increase trend in previous and currently results and is associated with prion disease, response to oxidative stress and brain originated protein. These three proteins can be the confidence candidate portions for further investigation.

關鍵字(中)

★ 可逆性腦血管收縮症候群
★ 腦脊髓液
★ 非數據依賴型採集模式質譜

關鍵字(英)

★ RCVS
★ CSF
★ DIA-MS

論文目次

Table of contents
中文摘要 i
Abstract iii
Acknowledgment v
Table of contents vi
List of figures viii
List of tables ix
1. Introduction 1
1.1 Reversible cerebral vasoconstriction syndrome (RCVS) 1
1.2 Characterization and clinical significance of cerebrospinal fluid (CSF) 3
1.3 Current application of CSF proteomic analysis in central nerve system disorders 4
1.4 Data-independent acquisition mass spectrometry (DIA-MS) for comprehensive CSF proteome 6
1.5 Thesis objective 7
2. Material and method 9
2.1 Material and chemical reagents 9
2.2 Sample collection 10
2.3 Sample preparation 10
2.4 Experiment regarding to determination of internal standard and the spike ratio 11
2.5 MALDI-TOF analysis of synthetic peptides 11
2.6 Experiment regarding to the generation of RCVS library 11
2.7 DIA-MS analysis of CSF samples 13
2.8 Data process of data-dependent acquisition 14
2.8 Data process regarding to the generation of RCVS library 15
2.9 Data process of DIA 15
2.10 Biological information 16
3. Result and Discussion 17
3.1 Analytical workflow of DIA-MS 17
3.2 Determination of internal standard and the spike ratio 18
3.2 Construction of RCVS library for Data-independent acquisition analysis 18
3.3 Optimization of the DIA-MS data analysis workflow 21
3.4 Biological analysis of differentially expressed protein 24
3.5 Brain originated protein 25
3.6 Comparison of DIA data and the previous DDA data 26
3.7 The 18 candidate proteins in DIA results 27
3.8 Quantitation result of 117 differentially expressed proteins 28
4. Conclusion 30
Reference 32
Figure 38
Table 57
Supplementary Figure 65
Supplementary Table 89

參考文獻

1. Bogousslavsky, J.; Despland, P. A.; Regli, F.; Dubuis, P. Y., Postpartum cerebral angiopathy: reversible vasoconstriction assessed by transcranial Doppler ultrasounds. Eur Neurol 1989, 29 (2), 102-5.
2. Singhal, A. B.; Caviness, V.; Begleiter, A.; Mark, E.; Rordorf, G.; Koroshetz, W., Cerebral vasoconstriction and stroke after use of serotonergic drugs. Neurology 2002, 58 (1), 130-133.
3. Raroque Jr, H. G.; Tesfa, G.; Purdy, P. J. S., Postpartum cerebral angiopathy. Is there a role for sympathomimetic drugs? Stroke 1993, 24 (12), 2108-2110.
4. Dodick, D. W.; Brown, R. D., Jr.; Britton, J. W.; Huston, J., 3rd, Nonaneurysmal thunderclap headache with diffuse, multifocal, segmental, and reversible vasospasm. Cephalalgia 1999, 19 (2), 118-23.
5. Calabrese, L. H.; Gragg, L. A.; Furlan, A. J., Benign angiopathy: a distinct subset of angiographically defined primary angiitis of the central nervous system. J Rheumatol 1993, 20 (12), 2046-50.
6. Calabrese, L. H.; Dodick, D. W.; Schwedt, T. J.; Singhal, A. B., Narrative review: reversible cerebral vasoconstriction syndromes. Ann Intern Med 2007, 146 (1), 34-44.
7. Sanchez-Montanez, A.; Morana, G.; Mancardi, M. M.; Janis, S.; Severino, M.; Verrina, E.; Rossi, A., Reversible cerebral vasoconstriction mimicking posterior reversible encephalopathy syndrome in an infant with end-stage renal disease. Cephalalgia 2015, 35 (11), 1031-3.
8. Chen, S. P.; Fuh, J. L.; Wang, S. J.; Tsai, S. J.; Hong, C. J.; Yang, A. C., Brain-derived neurotrophic factor gene Val66Met polymorphism modulates reversible cerebral vasoconstriction syndromes. PLoS One 2011, 6 (3), e18024.
9. Ducros, A.; Boukobza, M.; Porcher, R.; Sarov, M.; Valade, D.; Bousser, M. G., The clinical and radiological spectrum of reversible cerebral vasoconstriction syndrome. A prospective series of 67 patients. Brain 2007, 130 (Pt 12), 3091-101.
10. Coffino, S. W.; Fryer, R. H., Reversible Cerebral Vasoconstriction Syndrome in Pediatrics: A Case Series and Review. J Child Neurol 2017, 32 (7), 614-623.
11. Chen, S. P.; Yang, A. C.; Fuh, J. L.; Wang, S. J., Autonomic dysfunction in reversible cerebral vasoconstriction syndromes. J Headache Pain 2013, 14 (1), 94.
12. Chen, S. P.; Chung, Y. T.; Liu, T. Y.; Wang, Y. F.; Fuh, J. L.; Wang, S. J., Oxidative stress and increased formation of vasoconstricting F2-isoprostanes in patients with reversible cerebral vasoconstriction syndrome. Free Radic Biol Med 2013, 61, 243-8.
13. Skeik, N.; Porten, B. R.; Kadkhodayan, Y.; McDonald, W.; Lahham, F., Postpartum reversible cerebral vasoconstriction syndrome: review and analysis of the current data. Vasc Med 2015, 20 (3), 256-65.
14. Singhal, A. B.; Topcuoglu, M. A.; Fok, J. W.; Kursun, O.; Nogueira, R. G.; Frosch, M. P.; Caviness, V. S., Jr., Reversible cerebral vasoconstriction syndromes and primary angiitis of the central nervous system: clinical, imaging, and angiographic comparison. Ann Neurol 2016, 79 (6), 882-94.
15. Ducros, A., Reversible cerebral vasoconstriction syndrome. Lancet Neurol 2012, 11 (10), 906-17.
16. Topcuoglu, M. A.; Kursun, O.; Singhal, A. B., Coexisting vascular lesions in reversible cerebral vasoconstriction syndrome. Cephalalgia 2017, 37 (1), 29-35.
17. Singhal, A. B.; Hajj-Ali, R. A.; Topcuoglu, M. A.; Fok, J.; Bena, J.; Yang, D.; Calabrese, L. H., Reversible cerebral vasoconstriction syndromes: analysis of 139 cases. Arch Neurol 2011, 68 (8), 1005-12.
18. Bernard, K. R.; Rivera, M., Reversible Cerebral Vasoconstriction Syndrome. J Emerg Med 2015, 49 (1), 26-31.
19. Chen, S. P.; Fuh, J. L.; Lirng, J. F.; Wang, Y. F.; Wang, S. J., Recurrence of reversible cerebral vasoconstriction syndrome: a long-term follow-up study. Neurology 2015, 84 (15), 1552-8.
20. Nowak, D. A.; Rodiek, S. O.; Henneken, S.; Zinner, J.; Schreiner, R.; Fuchs, H. H.; Topka, H., Reversible segmental cerebral vasoconstriction (Call-Fleming syndrome): are calcium channel inhibitors a potential treatment option? Cephalalgia 2003, 23 (3), 218-22.
21. Hajj-Ali, R. A.; Furlan, A.; Abou-Chebel, A.; Calabrese, L. H., Benign angiopathy of the central nervous system: cohort of 16 patients with clinical course and long-term followup. Arthritis Rheum 2002, 47 (6), 662-9.
22. Singhal, A. B., Postpartum angiopathy with reversible posterior leukoencephalopathy. Arch Neurol 2004, 61 (3), 411-6.
23. Muehlschlegel, S.; Rordorf, G.; Bodock, M.; Sims, J. R., Dantrolene mediates vasorelaxation in cerebral vasoconstriction: a case series. Neurocrit Care 2009, 10 (1), 116-21.
24. Topcuoglu, M. A.; Chan, S. T.; Silva, G. S.; Smith, E. E.; Kwong, K. K.; Singhal, A. B., Cerebral vasomotor reactivity in reversible cerebral vasoconstriction syndrome. Cephalalgia 2017, 37 (6), 541-547.
25. Hajj-Ali, R. A.; Singhal, A. B.; Benseler, S.; Molloy, E.; Calabrese, L. H., Primary angiitis of the CNS. Lancet Neurol 2011, 10 (6), 561-72.
26. Hühmer, A. F.; Biringer, R. G.; Amato, H.; Fonteh, A. N.; Harrington, M. G. J. D. m., Protein analysis in human cerebrospinal fluid: Physiological aspects, current progress and future challenges. 2006, 22 (1-2), 3-26.
27. Kroksveen, A. C.; Opsahl, J. A.; Aye, T. T.; Ulvik, R. J.; Berven, F. S., Proteomics of human cerebrospinal fluid: discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics. J Proteomics 2011, 74 (4), 371-88.
28. Roche, S.; Gabelle, A.; Lehmann, S., Clinical proteomics of the cerebrospinal fluid: Towards the discovery of new biomarkers. Proteomics Clin Appl 2008, 2 (3), 428-36.
29. Zigdon, H.; Savidor, A.; Levin, Y.; Meshcheriakova, A.; Schiffmann, R.; Futerman, A. H., Identification of a biomarker in cerebrospinal fluid for neuronopathic forms of Gaucher disease. PLoS One 2015, 10 (3), e0120194.
30. Collins, M. A.; An, J.; Hood, B. L.; Conrads, T. P.; Bowser, R. P. J. J. o. p. r., Label-Free LC–MS/MS Proteomic Analysis of Cerebrospinal Fluid Identifies Protein/Pathway Alterations and Candidate Biomarkers for Amyotrophic Lateral Sclerosis. J. Proteome Res 2015, 14 (11), 4486-4501.
31. Waldera-Lupa, D. M.; Etemad-Parishanzadeh, O.; Brocksieper, M.; Kirchgaessler, N.; Seidel, S.; Kowalski, T.; Montesinos-Rongen, M.; Deckert, M.; Schlegel, U.; Stuhler, K., Proteomic changes in cerebrospinal fluid from primary central nervous system lymphoma patients are associated with protein ectodomain shedding. Oncotarget 2017, 8 (66), 110118-110132.
32. Dayon, L.; Nunez Galindo, A.; Wojcik, J.; Cominetti, O.; Corthesy, J.; Oikonomidi, A.; Henry, H.; Kussmann, M.; Migliavacca, E.; Severin, I.; Bowman, G. L.; Popp, J., Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res Ther 2018, 10 (1), 66.
33. Gillet, L. C.; Navarro, P.; Tate, S.; Rost, H.; Selevsek, N.; Reiter, L.; Bonner, R.; Aebersold, R., Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 2012, 11 (6), O111 016717.
34. Ting, Y. S.; Egertson, J. D.; Payne, S. H.; Kim, S.; MacLean, B.; Kall, L.; Aebersold, R.; Smith, R. D.; Noble, W. S.; MacCoss, M. J., Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data. Mol Cell Proteomics 2015, 14 (9), 2301-7.
35. Kelstrup, C. D.; Bekker-Jensen, D. B.; Arrey, T. N.; Hogrebe, A.; Harder, A.; Olsen, J. V., Performance Evaluation of the Q Exactive HF-X for Shotgun Proteomics. J Proteome Res 2018, 17 (1), 727-738.
36. Krämer, A.; Green, J.; Pollard Jr, J.; Tugendreich, S., Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 2013, 30 (4), 523-530.
37. Huang, D. W.; Sherman, B. T.; Lempicki, R. A., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols 2009, 4 (1), 44.
38. Barkovits, K.; Linden, A.; Galozzi, S.; Schilde, L.; Pacharra, S.; Mollenhauer, B.; Stoepel, N.; Steinbach, S.; May, C.; Uszkoreit, J.; Eisenacher, M.; Marcus, K., Characterization of Cerebrospinal Fluid via Data-Independent Acquisition Mass Spectrometry. J Proteome Res 2018, 17 (10), 3418-3430.
39. Macron, C.; Lane, L.; Nunez Galindo, A.; Dayon, L., Deep Dive on the Proteome of Human Cerebrospinal Fluid: A Valuable Data Resource for Biomarker Discovery and Missing Protein Identification. J Proteome Res 2018, 17 (12), 4113-4126.
40. Escher, C.; Reiter, L.; MacLean, B.; Ossola, R.; Herzog, F.; Chilton, J.; MacCoss, M. J.; Rinner, O. J. P., Using i RT, a normalized retention time for more targeted measurement of peptides. 2012, 12 (8), 1111-1121.
41. Duarte, T. T.; Spencer, C. T., Personalized Proteomics: The Future of Precision Medicine. Proteomes 2016, 4 (4), 29.
42. Piehowski, P. D.; Petyuk, V. A.; Orton, D. J.; Xie, F.; Moore, R. J.; Ramirez-Restrepo, M.; Engel, A.; Lieberman, A. P.; Albin, R. L.; Camp, D. G. J. J. o. p. r., Sources of technical variability in quantitative LC–MS proteomics: human brain tissue sample analysis. 2013, 12 (5), 2128-2137.
43. Callister, S. J.; Barry, R. C.; Adkins, J. N.; Johnson, E. T.; Qian, W. J.; Webb-Robertson, B. J.; Smith, R. D.; Lipton, M. S., Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J Proteome Res 2006, 5 (2), 277-86.
44. Kim, M. S.; Pinto, S. M.; Getnet, D.; Nirujogi, R. S.; Manda, S. S.; Chaerkady, R.; Madugundu, A. K.; Kelkar, D. S.; Isserlin, R.; Jain, S.; Thomas, J. K.; Muthusamy, B.; Leal-Rojas, P.; Kumar, P.; Sahasrabuddhe, N. A.; Balakrishnan, L.; Advani, J.; George, B.; Renuse, S.; Selvan, L. D.; Patil, A. H.; Nanjappa, V.; Radhakrishnan, A.; Prasad, S.; Subbannayya, T.; Raju, R.; Kumar, M.; Sreenivasamurthy, S. K.; Marimuthu, A.; Sathe, G. J.; Chavan, S.; Datta, K. K.; Subbannayya, Y.; Sahu, A.; Yelamanchi, S. D.; Jayaram, S.; Rajagopalan, P.; Sharma, J.; Murthy, K. R.; Syed, N.; Goel, R.; Khan, A. A.; Ahmad, S.; Dey, G.; Mudgal, K.; Chatterjee, A.; Huang, T. C.; Zhong, J.; Wu, X.; Shaw, P. G.; Freed, D.; Zahari, M. S.; Mukherjee, K. K.; Shankar, S.; Mahadevan, A.; Lam, H.; Mitchell, C. J.; Shankar, S. K.; Satishchandra, P.; Schroeder, J. T.; Sirdeshmukh, R.; Maitra, A.; Leach, S. D.; Drake, C. G.; Halushka, M. K.; Prasad, T. S.; Hruban, R. H.; Kerr, C. L.; Bader, G. D.; Iacobuzio-Donahue, C. A.; Gowda, H.; Pandey, A., A draft map of the human proteome. Nature 2014, 509 (7502), 575-81.
45. Wang, M.; Weiss, M.; Simonovic, M.; Haertinger, G.; Schrimpf, S. P.; Hengartner, M. O.; von Mering, C., PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life. Mol Cell Proteomics 2012, 11 (8), 492-500.
46. Westergard, L.; Christensen, H. M.; Harris, D. A., The cellular prion protein (PrPC): Its physiological function and role in disease. Bba-Mol Basis Dis 2007, 1772 (6), 629-644.
47. Kim, D. S.; Li, K. W.; Boroujerdi, A.; Peter Yu, Y.; Zhou, C. Y.; Deng, P.; Park, J.; Zhang, X.; Lee, J.; Corpe, M.; Sharp, K.; Steward, O.; Eroglu, C.; Barres, B.; Zaucke, F.; Xu, Z. C.; Luo, Z. D., Thrombospondin-4 contributes to spinal sensitization and neuropathic pain states. J Neurosci 2012, 32 (26), 8977-87.
48. Kuo, W. H.; Yang, S. F.; Hsieh, Y. S.; Tsai, C. S.; Hwang, W. L.; Chu, S. C., Differential expression of carbonic anhydrase isoenzymes in various types of anemia. Clin Chim Acta 2005, 351 (1-2), 79-86.
49. Kawabori, M.; Kacimi, R.; Kauppinen, T.; Calosing, C.; Kim, J. Y.; Hsieh, C. L.; Nakamura, M. C.; Yenari, M. A., Triggering receptor expressed on myeloid cells 2 (TREM2) deficiency attenuates phagocytic activities of microglia and exacerbates ischemic damage in experimental stroke. J Neurosci 2015, 35 (8), 3384-96.
50. Liu, G.; Liu, Y.; Jiang, Q.; Jiang, Y.; Feng, R.; Zhang, L.; Chen, Z.; Li, K.; Liu, J., Convergent genetic and expression datasets highlight TREM2 in Parkinson’s disease susceptibility. Molecular neurobiology 2016, 53 (7), 4931-4938.
51. Zhu, C.; Herrmann, U. S.; Li, B.; Abakumova, I.; Moos, R.; Schwarz, P.; Rushing, E. J.; Colonna, M.; Aguzzi, A., Triggering receptor expressed on myeloid cells-2 is involved in prion-induced microglial activation but does not contribute to prion pathogenesis in mouse brains. Neurobiology of aging 2015, 36 (5), 1994-2003.
52. Berendsen, A. D.; Pinnow, E. L.; Maeda, A.; Brown, A. C.; McCartney-Francis, N.; Kram, V.; Owens, R. T.; Robey, P. G.; Holmbeck, K.; de Castro, L. F.; Kilts, T. M.; Young, M. F., Biglycan modulates angiogenesis and bone formation during fracture healing. Matrix Biol 2014, 35, 223-31.
53. Liu, N.-N.; Dong, Z.-L.; Han, L.-L., MicroRNA-410 inhibition of the TIMP2-dependent MAPK pathway confers neuroprotection against oxidative stress-induced apoptosis after ischemic stroke in mice. Brain research bulletin 2018, 143, 45-57.
54. El-Latif, M. A.; Azzam, H.; Othman, M.; Warda, O.; El-Sharawy, S.; Ghoneim, H., Assessment of annexin A5 and annexin A2 levels as biomarkers for pre-eclampsia: A pilot study. Pregnancy Hypertens 2017, 8, 65-69.
55. Cohen, J. S.; Srivastava, S.; Farwell, K. D.; Lu, H. M.; Zeng, W. Q.; Lu, H.; Chao, E. C.; Fatemi, A., ELP2 is a novel gene implicated in neurodevelopmental disabilities. Am J Med Genet A 2015, 167 (6), 1391-1395.
56. Bosshard, M.; Aprigliano, R.; Gattiker, C.; Palibrk, V.; Markkanen, E.; Backe, P. H.; Pellegrino, S.; Raymond, F. L.; Froyen, G.; Altmeyer, M.; Bjoras, M.; Dianov, G. L.; van Loon, B., Impaired oxidative stress response characterizes HUWE1-promoted X-linked intellectual disability. Sci Rep 2017, 7 (1), 15050.
57. Gan, Y.; Ji, X.; Hu, X.; Luo, Y.; Zhang, L.; Li, P.; Liu, X.; Yan, F.; Vosler, P.; Gao, Y.; Stetler, R. A.; Chen, J., Transgenic overexpression of peroxiredoxin-2 attenuates ischemic neuronal injury via suppression of a redox-sensitive pro-death signaling pathway. Antioxid Redox Signal 2012, 17 (5), 719-32.
58. Wendel, U.; Persson, N.; Risinger, C.; Bengtsson, E.; Nodin, B.; Danielsson, L.; Welinder, C.; Nordin Fredrikson, G.; Jansson, B.; Blixt, O., A novel monoclonal antibody targeting carboxymethyllysine, an advanced glycation end product in atherosclerosis and pancreatic cancer. PLoS One 2018, 13 (2), e0191872.
59. Fang, F.; Lue, L.-F.; Yan, S.; Xu, H.; Luddy, J. S.; Chen, D.; Walker, D. G.; Stern, D. M.; Yan, S.; Schmidt, A. M., RAGE-dependent signaling in microglia contributes to neuroinflammation, Aβ accumulation, and impaired learning/memory in a mouse model of Alzheimer’s disease. The FASEB Journal 2010, 24 (4), 1043-1055.
60. Khatun, R.; Wu, Y.; Kanenishi, K.; Ueno, M.; Tanaka, S.; Hata, T.; Sakamoto, H. J. P., Immunohistochemical study of transferrin receptor expression in the placenta of pre-eclamptic pregnancy. 2003, 24 (8-9), 870-876.
61. Rifkind, J. M.; Mohanty, J. G.; Nagababu, E., The pathophysiology of extracellular hemoglobin associated with enhanced oxidative reactions. Front Physiol 2014, 5, 500.

指導教授

陳玉如侯敦仁(Yu-Ju Chen Duen-Ren Hou)

審核日期

2019-8-21

推文