基因組代謝網(wǎng)絡(luò)模型方法模擬重組大腸桿菌生產(chǎn)羥基-L-脯氨酸和葫蘆巴堿的研究
發(fā)布時(shí)間：2018-08-28 12:24
【摘要】：目的將基因組規(guī)模代謝網(wǎng)絡(luò)模型與代謝工程(重組大腸桿菌生產(chǎn)羥基-L-脯氨酸和葫蘆巴堿)結(jié)合,對(duì)大腸桿菌模型進(jìn)行相關(guān)途徑修改后,對(duì)比不同分析方法的模擬效果,以及預(yù)測(cè)可行的基因敲除策略,并優(yōu)化M9培養(yǎng)基培養(yǎng)大腸桿菌的條件和嘗試重組葫蘆巴堿合成酶的表達(dá)生產(chǎn)。方法1.下載大腸桿菌BL21(DE3)的代謝網(wǎng)絡(luò)模型:iB21_1397,并添加合成羥基-L-脯氨酸和葫蘆巴堿的合成途徑,形成兩個(gè)新的模型。2.對(duì)未修改的iB21_1397模型進(jìn)行基本的FBA(flux balance analysis)分析,以及必需基因預(yù)測(cè),指導(dǎo)M9培養(yǎng)基的優(yōu)化。3.將添加代謝途徑后的產(chǎn)羥基-L-脯氨酸和葫蘆巴堿模型,進(jìn)行細(xì)胞生長(zhǎng)表型的模擬,并且結(jié)合使用FVA、OptKnock、GDLS、IdealKnock等不同方法進(jìn)行基因敲除策略的預(yù)測(cè),比較各模擬結(jié)果的差異。4.根據(jù)模擬結(jié)果設(shè)計(jì)五組改良后的M9培養(yǎng)基:葡萄糖組、甘油組、葡萄糖鐵組、葡萄糖甘油組和倍量葡萄糖組。制備懸菌液后各接種0.1 mL至新鮮的各種改良M9培養(yǎng)基中培養(yǎng)。研究菌落數(shù)生長(zhǎng)差異時(shí),在培養(yǎng)12 h后,接種至LB瓊脂培養(yǎng)基中,18 h后計(jì)數(shù)。研究對(duì)數(shù)生長(zhǎng)期菌落數(shù)差異時(shí),則分別在培養(yǎng)12 h、18 h、24 h后,接種至各種相應(yīng)的M9瓊脂培養(yǎng)基中,72 h后計(jì)數(shù)。5.通過(guò)基因庫(kù)里的葫蘆巴堿合成酶CTgS2(BAC43759.1)的信息,合成基因片段,利用限制性內(nèi)切酶NdeⅠ、XhoⅠ在pET24a(+)的酶切位點(diǎn),將基因片段與載體pET24a(+)連接。將連接產(chǎn)物轉(zhuǎn)化進(jìn)大腸桿菌DH5α,挑取轉(zhuǎn)化子進(jìn)行擴(kuò)增培養(yǎng),并提取質(zhì)粒進(jìn)行酶切和測(cè)序驗(yàn)證。將驗(yàn)證正確的重組質(zhì)粒進(jìn)行復(fù)制擴(kuò)增后轉(zhuǎn)化進(jìn)大腸桿菌BL21(DE3)表達(dá),對(duì)轉(zhuǎn)化成功的細(xì)菌在相同的培養(yǎng)條件下,以終濃度為0.3 mM、0.6 mM、1 mM的IPTG以及終濃度為0.8 g/L的TNDA-1蛋白促進(jìn)劑為誘導(dǎo)劑,分別誘導(dǎo)6h、8h、10h。誘導(dǎo)結(jié)束后將細(xì)菌體破碎并進(jìn)行SDS-PAGE電泳,觀察重組蛋白的表達(dá)情況。成果1.成功實(shí)踐了代謝網(wǎng)絡(luò)模型的改造和修改,FBA各分析方法基本都能執(zhí)行成功,并與文獻(xiàn)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行比對(duì)。2.用代謝網(wǎng)絡(luò)模型預(yù)測(cè)了促進(jìn)羥基-L-脯氨酸產(chǎn)量的基因敲除策略:可通過(guò)敲除酮戊二酸脫氫酶、果糖6-磷酸醛縮酶、異檸檬酸裂合酶、磷酸甘油酸酯脫氫酶實(shí)現(xiàn)合成酶的過(guò)表達(dá),該策略結(jié)合比對(duì)其他分析結(jié)果后發(fā)現(xiàn)其具有一定可信度。3.用代謝網(wǎng)絡(luò)模型預(yù)測(cè)了促進(jìn)葫蘆巴堿產(chǎn)量的基因敲除策略:可通過(guò)敲除乙醛脫氫酶、蘋(píng)果酸酶、丙酮酸激酶、轉(zhuǎn)氫酶實(shí)現(xiàn)合成酶的過(guò)表達(dá)。4.M9培養(yǎng)基優(yōu)化實(shí)驗(yàn)方面,就碳源的選擇而言,葡萄糖的增菌效果是較甘油明顯的。培養(yǎng)基中添加Fe2+或增加同類碳源濃度,都能促進(jìn)細(xì)菌生長(zhǎng),且增菌效果沒(méi)有明顯的差異,只有在培養(yǎng)24 h后出現(xiàn)差異,基本符合模擬結(jié)果。5.重組葫蘆巴堿合成酶研究方面,重組質(zhì)粒經(jīng)過(guò)酶切和測(cè)序驗(yàn)證后轉(zhuǎn)化進(jìn)大腸桿菌BL21(DE3),進(jìn)行不同濃度誘導(dǎo)劑和不同誘導(dǎo)時(shí)間的蛋白誘導(dǎo),經(jīng)SDS-PAGE電泳,發(fā)現(xiàn)各條件下都沒(méi)有順利誘導(dǎo)出目標(biāo)蛋白。但是通過(guò)本實(shí)驗(yàn),對(duì)重組蛋白技術(shù)有了一定的認(rèn)識(shí),并在連接載體的選擇、基因重組驗(yàn)證等技術(shù)方面積累了相關(guān)的經(jīng)驗(yàn)。結(jié)論1.代謝工程可以便利地結(jié)合基因組規(guī)模代謝網(wǎng)絡(luò)模型進(jìn)行模擬和分析,而FBA分析則在預(yù)測(cè)最大產(chǎn)量和預(yù)估可提升空間的方面有很大的指導(dǎo)作用。2.用IdealKnock方法篩選的備選敲除反應(yīng)比用FVA方法篩選的要更有效,更適合用于OptKnock進(jìn)行基因敲除預(yù)測(cè)。3.OptKnock方法雖然在預(yù)測(cè)基因敲除方面耗時(shí)較長(zhǎng),但是只要結(jié)合適合的模型反應(yīng)預(yù)處理方法,計(jì)算成功率比GDLS方法高。4.經(jīng)過(guò)模擬預(yù)測(cè),對(duì)于重組大腸桿菌生產(chǎn)羥基-L-脯氨酸的代謝網(wǎng)絡(luò)模型,敲除酮戊二酸脫氫酶(AKGDH)、果糖6-磷酸醛縮酶(F6PA)、異檸檬酸裂合酶(ICL)、磷酸甘油酸酯脫氫酶(PGCD),可以有利于脯氨酸4-羥化酶的過(guò)表達(dá)。5.對(duì)于重組大腸桿菌生產(chǎn)葫蘆巴堿的代謝網(wǎng)絡(luò)模型,敲除乙醛脫氫酶(ALDD2y)、蘋(píng)果酸酶(ME2)、丙酮酸激酶(PYK)、NAD(P)轉(zhuǎn)氫酶(THD2pp),可以有利于葫蘆巴堿合成酶的過(guò)表達(dá)。6.代謝網(wǎng)絡(luò)FBA分析的模擬結(jié)果,對(duì)M9培養(yǎng)基的優(yōu)化具有很強(qiáng)指導(dǎo)意義,對(duì)于細(xì)胞生長(zhǎng),加入適當(dāng)量的Fe2+,可以有效地促進(jìn)生長(zhǎng),無(wú)需靠提高葡萄糖的濃度,碳源方面,葡萄糖在促進(jìn)細(xì)胞生長(zhǎng)方面比甘油稍優(yōu),但是如果生產(chǎn)中需要使用甘油,需要適當(dāng)?shù)靥岣吒视蜐舛?或者搭配葡萄糖,才能達(dá)到單純葡萄糖做碳源時(shí)的生長(zhǎng)效果。7.本研究利用基因庫(kù)里的葫蘆巴堿合成酶CTgS2(BAC43759.1)的信息,合成基因片段,利用限制性內(nèi)切酶NdeⅠ、XhoⅠ,將基因片段與載體pET24a(+)連接。重組質(zhì)粒pET24a-CTgS2轉(zhuǎn)化進(jìn)大腸桿菌DH5α中擴(kuò)增,轉(zhuǎn)化大腸桿菌BL21(DE3)中表達(dá),沒(méi)有成功誘導(dǎo)出目的蛋白,考慮誘導(dǎo)失敗有可能與載體的選擇,和基因重組過(guò)程中驗(yàn)證體系欠缺完善有關(guān)。
[Abstract]:Objective To combine genome-scale metabolic network model with metabolic engineering (recombinant E.coli producing hydroxy-L-proline and cucurbitacin) to modify the relevant pathways of E.coli model, compare the simulation results of different analytical methods, and predict the feasible gene knockout strategy, and optimize the conditions of E.coli culture in M9 medium. Methods 1. Download the metabolic network model of Escherichia coli BL21 (DE3): iB21_1397, and add the synthetic routes of hydroxy-L-proline and cucurbitacin to form two new models. Gene prediction is needed to guide the optimization of M9 medium. 3. Hydroxyl-L-proline and cucurbitacin production models with metabolic pathways were used to simulate cell growth phenotype. FVA, OptKnock, GDLS, IdealKnock were used to predict gene knockout strategies, and the differences of simulation results were compared. Five groups of improved M9 media were prepared and inoculated in 0.1 mL to fresh M9 medium respectively. The difference of colony number was studied when cultured in LB agar medium 12 hours later and counted after 18 hours. When the long-term colonies were different, they were inoculated into various M9 agar media 12, 18 and 24 hours after culture, and counted after 72 hours. 5. Gene fragments were synthesized by the cucurbitacin synthase CTgS2 (BAC43759.1) in the gene library, and the gene fragments and vectors were digested by restriction endonucleases Nde I and Xho I at the site of pET24a (+). PET24a (+) ligation. The conjugated product was transformed into E. coli DH5a, and the transformant was selected for amplification and culture, and the plasmid was extracted for enzyme digestion and sequencing verification. 6 mM, 1 mM IPTG and 0.8 g/L TNDA-1 protein promoter were induced for 6, 8 and 10 hours respectively. After induction, the bacterial bodies were broken and SDS-PAGE electrophoresis was performed to observe the expression of recombinant proteins. Comparing with the experimental data in the literature. 2. Using metabolic network model, we predicted the gene knockout strategy to promote the production of hydroxy-L-proline: synthase overexpression could be achieved by knocking out ketoglutarate dehydrogenase, fructose-6-phosphate aldolase, isocitrate lyase, and phosphoglyceride dehydrogenase, which combined with other analysis results. 3. Gene knockout strategies to promote cucurbitacin production were predicted by metabolic network model: synthase overexpression could be achieved by knocking out acetaldehyde dehydrogenase, malic acid enzyme, pyruvate kinase, and transhydrogenase. 4. M9 medium optimization experiment showed that glucose was more effective than glycerol in the selection of carbon source. Adding Fe2+ to the medium or increasing the concentration of the same carbon source can promote the growth of bacteria, and there is no significant difference in the growth of bacteria. Only after 24 hours of culture, there is a difference, basically in line with the simulation results. 5. In the study of recombinant cucurbitacin synthase, the recombinant plasmid was transformed into E. coli BL21 (DE3) after digestion and sequencing verification. Through SDS-PAGE electrophoresis, it was found that the target protein was not successfully induced under all conditions. However, through this experiment, we have a certain understanding of the recombinant protein technology, and accumulated relevant experience in connection vector selection, gene recombination verification and other technologies. Conclusion 1. Xie can easily simulate and analyze the genome-scale metabolic network model, while FBA analysis has a great guiding role in predicting the maximum yield and predicting the upgradable space. 2. The alternative knockout response screened by Ideal Knock method is more effective than that screened by FVA method and is more suitable for OptKnock gene analysis. Knock-out prediction. 3. Although the OptKnock method is time-consuming in predicting gene knockout, the calculation success rate is higher than that of GDLS method as long as the appropriate model reaction pretreatment method is combined. 4. After simulation prediction, for the metabolic network model of recombinant E. coli producing hydroxy-L-proline, the knockout ketoglutarate dehydrogenase (AKGDH), fructose 6-phosphorus are predicted. Acetaldehyde dehydrogenase (F6PA), isocitrate lyase (ICL), phosphoglyceride dehydrogenase (PGCD) can facilitate the over-expression of proline 4-hydroxylase. 5. It is advantageous to knock out acetaldehyde dehydrogenase (ALDD2y), malate enzyme (ME2), pyruvate kinase (PYK), NAD (P) transhydrogenase (THD2pp) in the metabolic network model of cucurbitacin production by recombinant E. coli. The simulated results of FBA analysis of metabolic network are of great guiding significance to the optimization of M9 medium. Fe2+ can effectively promote cell growth without increasing glucose concentration. In terms of carbon source, glucose is slightly superior to glycerol in promoting cell growth, but it can improve cell growth. If glycerol is needed in production, it is necessary to increase the glycerol concentration properly, or to mix with glucose to achieve the growth effect of pure glucose as carbon source. 7. In this study, the cucurbitacin synthase CTgS2 (BAC43759.1) in the gene library was used to synthesize gene fragments, and the restriction endonuclease Nde I, Xho I was used to synthesize gene fragments. The recombinant plasmid pET24a-CTgS2 was transformed into E. coli DH5a and expressed in E. coli BL21 (DE3). The target protein was not successfully induced. The failure of induction might be related to the choice of vector and the lack of validation system in the process of gene recombination.
【學(xué)位授予單位】：廣州中醫(yī)藥大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R91;Q78
[Abstract]:Objective To combine genome-scale metabolic network model with metabolic engineering (recombinant E.coli producing hydroxy-L-proline and cucurbitacin) to modify the relevant pathways of E.coli model, compare the simulation results of different analytical methods, and predict the feasible gene knockout strategy, and optimize the conditions of E.coli culture in M9 medium. Methods 1. Download the metabolic network model of Escherichia coli BL21 (DE3): iB21_1397, and add the synthetic routes of hydroxy-L-proline and cucurbitacin to form two new models. Gene prediction is needed to guide the optimization of M9 medium. 3. Hydroxyl-L-proline and cucurbitacin production models with metabolic pathways were used to simulate cell growth phenotype. FVA, OptKnock, GDLS, IdealKnock were used to predict gene knockout strategies, and the differences of simulation results were compared. Five groups of improved M9 media were prepared and inoculated in 0.1 mL to fresh M9 medium respectively. The difference of colony number was studied when cultured in LB agar medium 12 hours later and counted after 18 hours. When the long-term colonies were different, they were inoculated into various M9 agar media 12, 18 and 24 hours after culture, and counted after 72 hours. 5. Gene fragments were synthesized by the cucurbitacin synthase CTgS2 (BAC43759.1) in the gene library, and the gene fragments and vectors were digested by restriction endonucleases Nde I and Xho I at the site of pET24a (+). PET24a (+) ligation. The conjugated product was transformed into E. coli DH5a, and the transformant was selected for amplification and culture, and the plasmid was extracted for enzyme digestion and sequencing verification. 6 mM, 1 mM IPTG and 0.8 g/L TNDA-1 protein promoter were induced for 6, 8 and 10 hours respectively. After induction, the bacterial bodies were broken and SDS-PAGE electrophoresis was performed to observe the expression of recombinant proteins. Comparing with the experimental data in the literature. 2. Using metabolic network model, we predicted the gene knockout strategy to promote the production of hydroxy-L-proline: synthase overexpression could be achieved by knocking out ketoglutarate dehydrogenase, fructose-6-phosphate aldolase, isocitrate lyase, and phosphoglyceride dehydrogenase, which combined with other analysis results. 3. Gene knockout strategies to promote cucurbitacin production were predicted by metabolic network model: synthase overexpression could be achieved by knocking out acetaldehyde dehydrogenase, malic acid enzyme, pyruvate kinase, and transhydrogenase. 4. M9 medium optimization experiment showed that glucose was more effective than glycerol in the selection of carbon source. Adding Fe2+ to the medium or increasing the concentration of the same carbon source can promote the growth of bacteria, and there is no significant difference in the growth of bacteria. Only after 24 hours of culture, there is a difference, basically in line with the simulation results. 5. In the study of recombinant cucurbitacin synthase, the recombinant plasmid was transformed into E. coli BL21 (DE3) after digestion and sequencing verification. Through SDS-PAGE electrophoresis, it was found that the target protein was not successfully induced under all conditions. However, through this experiment, we have a certain understanding of the recombinant protein technology, and accumulated relevant experience in connection vector selection, gene recombination verification and other technologies. Conclusion 1. Xie can easily simulate and analyze the genome-scale metabolic network model, while FBA analysis has a great guiding role in predicting the maximum yield and predicting the upgradable space. 2. The alternative knockout response screened by Ideal Knock method is more effective than that screened by FVA method and is more suitable for OptKnock gene analysis. Knock-out prediction. 3. Although the OptKnock method is time-consuming in predicting gene knockout, the calculation success rate is higher than that of GDLS method as long as the appropriate model reaction pretreatment method is combined. 4. After simulation prediction, for the metabolic network model of recombinant E. coli producing hydroxy-L-proline, the knockout ketoglutarate dehydrogenase (AKGDH), fructose 6-phosphorus are predicted. Acetaldehyde dehydrogenase (F6PA), isocitrate lyase (ICL), phosphoglyceride dehydrogenase (PGCD) can facilitate the over-expression of proline 4-hydroxylase. 5. It is advantageous to knock out acetaldehyde dehydrogenase (ALDD2y), malate enzyme (ME2), pyruvate kinase (PYK), NAD (P) transhydrogenase (THD2pp) in the metabolic network model of cucurbitacin production by recombinant E. coli. The simulated results of FBA analysis of metabolic network are of great guiding significance to the optimization of M9 medium. Fe2+ can effectively promote cell growth without increasing glucose concentration. In terms of carbon source, glucose is slightly superior to glycerol in promoting cell growth, but it can improve cell growth. If glycerol is needed in production, it is necessary to increase the glycerol concentration properly, or to mix with glucose to achieve the growth effect of pure glucose as carbon source. 7. In this study, the cucurbitacin synthase CTgS2 (BAC43759.1) in the gene library was used to synthesize gene fragments, and the restriction endonuclease Nde I, Xho I was used to synthesize gene fragments. The recombinant plasmid pET24a-CTgS2 was transformed into E. coli DH5a and expressed in E. coli BL21 (DE3). The target protein was not successfully induced. The failure of induction might be related to the choice of vector and the lack of validation system in the process of gene recombination.
【學(xué)位授予單位】：廣州中醫(yī)藥大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R91;Q78
【相似文獻(xiàn)】
相關(guān)期刊論文 前10條
1 趙靜;俞鴻;駱建華;曹志偉;李亦學(xué);;應(yīng)用復(fù)雜網(wǎng)絡(luò)理論研究代謝網(wǎng)絡(luò)的進(jìn)展[J];科學(xué)通報(bào);2006年11期
2 趙靜;陶林;俞鴻;駱建華;曹志偉;李亦學(xué);;代謝網(wǎng)絡(luò)的蝴蝶結(jié)結(jié)構(gòu)特征及其功能意義[J];科學(xué)通報(bào);2007年01期
3 王卓;陳琦;劉雷;;代謝網(wǎng)絡(luò)進(jìn)化過(guò)程中拓?fù)浣Y(jié)構(gòu)與功能之間的關(guān)聯(lián)[J];科學(xué)通報(bào);2009年02期
4 陳琦;王卓;魏冬青;;代謝網(wǎng)絡(luò)流分析進(jìn)展及應(yīng)用[J];科學(xué)通報(bào);2010年14期
5 丁德武;丁彥蕊;陸克中;須文波;黃海生;;重建代謝網(wǎng)絡(luò)及其結(jié)構(gòu)與功能的分析[J];計(jì)算機(jī)與應(yīng)用化學(xué);2010年05期
6 丁德武;黃海生;陸克中;何小青;計(jì)博婧;;代謝網(wǎng)絡(luò)結(jié)構(gòu)與功能的復(fù)雜網(wǎng)絡(luò)分析[J];計(jì)算機(jī)與應(yīng)用化學(xué);2011年03期
7 陳t，
本文編號(hào)：2209390
本文鏈接：http://sikaile.net/yixuelunwen/yiyaoxuelunwen/2209390.html
最近更新
教材專著
