C型肝炎病毒所引起肝纖維化的檢驗標記之研究

Abstract

蛋白質體學是近年來發展極為快速，可以廣泛應用於各種領域的技術，尤其是在醫學領域中，可應用於疾病的檢測與生物標記的發展上。由於體內的蛋白質種類相當的多，科學家們利用各項檢體前處理來減低所分析蛋白體的複雜度，以提昇蛋白質的檢測效果。其中，醣蛋白是體內非常重要的成分，存在於血液數千種的蛋白質中有50%以上是醣蛋白，且醣化是非常重要的蛋白轉譯後修飾，在許多疾病上，部分的蛋白醣化修飾的改變可做為偵測疾病的重要生物標記，因此，利用蛋白質體學研究醣蛋白的變化情形可以作為發展新的生物標記的方式。 肝纖維化發生的主因之一為C型肝炎病毒的感染而導致慢性發炎，若反覆發生可能導致肝硬化或是肝癌，目前所用來診斷肝纖維化的主要方式為活體肝組織切片，有高侵犯性及採樣誤差等缺點，而其他血液中的生物標記檢驗往往對肝纖維化不具有專一性，且敏感度不高，因此，發展新的檢驗標記是刻不容緩的。 在本研究中，我們利用交叉比對病人與正常人血清檢體的N-醣蛋白體來篩選肝纖維化的標誌。首先，將血清中的醣蛋白氧化之後，利用hydrazide bead來抓取醣蛋白，再用胰蛋白酵素切除蛋白質不含醣的胜肽，最後利用醣解酵素切下原先含有醣修飾的胜肽，送入質譜儀中進行胜肽序列分析，再以分析軟體進行資料庫比對鑑定出蛋白質以得到醣蛋白質體。 本論文分兩部分來呈現實驗結果。第一部份，參照文獻發表的實驗條件進行肝纖維化病人檢體的醣蛋白質體分析，發現了七個蛋白質在病人檢體中有增加的情形，這些候選生物標記包含三個已知的肝纖維化標記，包括ICAM-1, TIMP-1與fibronectin，及四個未被發表過與肝纖維化有關的醣蛋白。利用ELISA加以確認後，發現這個方法的確可以應用來篩選疾病的生物標記。第二部份，我們以相同的化學抓取醣蛋白的方法，利用醣解酵素處理條件的不同，發現在加強醣解酵素處理條件下，可以增加含量低的醣蛋白的檢出率，且得到九個新發現的肝纖維化的標誌，這些新發現的生物標記作為未來發展早期偵測肝纖維化的標記的可行性正在進行評估。

Proteomic analysis is a highly-sensitive and high-throughput technique useful to screen biomarkers for disease management or drug discovery. N-glycoproteins are important components in blood, and glycosylation is a common post-translational modification crucial for function of many proteins. Therefore N-glycoproteome is a good target for biomarkers discovery. Hepatitis C virus infection is widespread in the world leading to complicated diseases which includes liver fibrosis and its end stage, cirrhosis, as well as the progression to hepatocellular carcinoma. Currently, the diagnosis of liver fibrosis is dependent on biopsy, which is highly invasive and may miss fibrosis due to patchy distribution of the lesion. The discovery of less-invasive and higher sensitive biomarkers to diagnose liver progression is urgent. In this study, N-glycoproteome profiling of sera from liver fibrosis patients and normal controls was used for biomarkers discovery. The technique is based on capturing of glycoproteins via covalent linkages of carbohydrate moiety with hydrazide-beads; removing non-glycosylated peptides by trypsin digestion, and then using peptide-N-glycosidase (PNGase F) to release peptides that linked to the beads via NXS/T motif in which X represents any amino acid residue except proline. These formerly N-linked peptides were identified by nanoLC-MS/MS and the results were analyzed by computational proteomics analysis system (CPAS) software. Results of this study will be divided into two parts. In the first part, liver fibrosis was used as a disease model to evaluate the method. We found seven candidate fibrosis markers. These candidate biomarkers consist of three known fibrosis biomarkers, ICAM-1, TIMP-1 and fibronectin, and four new biomarkers. In the second part, we used a modified condition and found that the new protocol facilitated finding of low abundant proteins as candidate biomarkers. The modified capturing protocol used a more extensive PNGase F treatment condition and nine candidate markers were obtained. The results indicate that N-glycoproteome profiling approach is promising for the discovery of serum biomarkers. Its advantages including simple protocol, high sensitivity and fewer samples promise the application on biomarkers screening. The value of these newly identified biomarkers as serum markers for liver fibrosis is under investigation.