3 數據分析
代謝組學得到的是大量的、多維的信息,為了充分抽提數據中的潛在信息,對數據的分析需要應用一係列的化學計量學方法。在代謝組學的研究中,數據分析過程中應用的技術集中在模式識別技術上,模式識別技術主要有主成分分析(principalcomponents analysis,PCA)、非線形映射(nonlinearmapp ing,NLM)、簇類分析(hierarchical cluster analysis,HCA)等非監督(unsupervised)研究方法和SMICA(soft independentmodeling of class analogy)、PLS-DA(PLS-discrmi inant analysis),ANN(artificial neural newtork)等監督(supervised)研究方法。PCA將分散於一組變量上的信息集中於幾個綜合指標上,如糖代謝、脂質代謝、氨基酸代謝等,利用主成分(PC)描述機體代謝的變化情況,能夠簡化數據並易化分析[19]。
4 代謝組學技術在微生物領域的應用
4.1 對微生物進行分類及篩選突變體
隨著分子生物學的迅速發展,聯合基因型與表型對微生物進行分類已成為一種趨勢。代謝組學技術的發展使代謝譜分析方法(metabolic profiling)逐漸成為一種快速、高通量、全麵的表型分類方法,通過對代謝組的分析對細菌進行分類。Bu ndy等[20]通過代謝譜分析成功地區分開臨床病理來源以及實驗室來源的不同杆菌(bacillus cereus)。在未發生明顯表型變化的突變體內,突變基因有可能導致某些代謝途徑發生變化,通過代謝組分析可以發現該突變體並研究相應基因的功能[21]。
4.2 在代謝工程領域的應用
U.Nasution[22]利用代謝組學手段通過動態檢測恒化培養的Penicillium chrysogenum的糖酵解、三羧酸循環過程,認為通過修飾其新陳代謝能得到高產量的藥物,這些高產量的藥物與細胞內初級代謝有關。Dalluge等[23]利用LC-MS-MS方法監控發酵過程中的氨基酸譜紋,實現對整個發酵係統的牙體牙髓牙周病學雜誌(Chin J Conserv Dent)2010,20(1)53高通量快速監控。Bernd Kammerera等[24]用反相高效液相色譜、電噴射質譜、三聯四極質譜在Strep tomyces species中尋找未知的媒介物、次級代謝產物,同時用MS-MS檢測極低濃度的氨基香豆素抗生素,檢測到一種新的化合物香豆黴素A1和10種不同的媒介物(有3種是新發現的),從而探索Streptomyces spespecies生物合成和發現新的潛在的抗生素。
5 代謝組學在口腔領域中的應用
隨著代謝組學技術的發展,該方法也逐漸應用於口腔領域。李淼等[25]用代謝組學技術鑒定區分了兩種口腔常見致齲菌變形鏈球菌和黏性放線菌,認為代謝組學技術有望應用於口腔細菌的快速鑒定。Yan等[26]用代謝組學的方法對口腔鱗狀細胞癌、口腔扁平苔蘚和口腔白斑進行了診斷,提示其作為一種有效的方法有望用於對口腔癌變及癌前病變進行篩選。
代謝組學作為功能基因組學的重要組成部分,直接反應了生物體的生理狀態,雖然其尚處於研究應用的初級階段,但通過代謝組學的研究極有可能去發現新的生化途徑並解釋正常的或病理的功能,如在口腔領域通過研究口腔致齲菌及其突變株的糖酵解途徑去分析不同的致病機理。代謝組學的成功發展不僅需要能快速檢測、鑒別和定量複雜代謝產物的技術,還需要微生物學家、分析化學家、統計學家、信息學家的共同合作。相信隨著代謝組學的發展一定能快速推進生物醫學領域的飛速發展。
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