T7启动子在哺乳类动物细胞中启劝外源基因表达的研究

人低密度脂蛋白（ＬＤＬ）受体基因ｃＤＮＡ和氯霉素乙酰转移酶基因（ＣＡＴ）及ｐｏｌｙＡ信号序列被克隆进行ＰＧＥＭ４载体的Ｔ７噬菌避动子下游,构建成质粒ＰＴ７ＬＤＬＲ和ＰＴ７ＣＡＴ,两个重组质粒转化ＣＨＯ细胞,ＰＣＲ和ＣＡＴ酶实验显示：两个基因被Ｔ７噬菌体启动子所启动,结果证实真核生物ＲＮＡ聚合酶能够识别Ｔ７启动子,转录外源基因,常用的含有Ｔ７启动子的质粒可同时的核生物和真核生物的表达载体。     

T7启动子具有真核生物聚合酶Ⅱ顺式作用因子的功能

根据ａ－鹅膏蕈碱（ａ－ａｍａｎｉｔｉｎ）对真核生物ＲＮＡ聚合酶的选择性抑制,以氯霉素乙酰转 移酶基因（ＣＡＴ）作为报道基因进行体内表达实验,证明Ｔ７噬菌体启动子可为真核生物ＲＮＡ 聚合酶Ⅱ所启动。应用建立的竞争性ＤＮＡ－蛋白质凝胶泳动技术,分别以ＴＡＴＡ框、ＣＡＡＴ 框、ＧＣ框和八核苷酸序列（ｏｃｔａｍｅｒ）为竞争性寡核苷酸分子,发现人工合成的Ｔ７启动子可能 与 ＴＦⅡＤ起始转录因子结合,形成 ＤＮＡ－核蛋白质结合物。将 ＴＡＴＡ框和八核苷酸序列分别 接入Ｔ７启动子上游,ＣＡＴ实验显示八核苷酸序列可以增强ＲＮＡ聚合酶Ⅱ对Ｔ７启动子的转录 起始作用。实验结果表明Ｔ７启动子可作为ＲＮＡ聚合酶Ⅱ的顺式作用因子。     

7启动子具有真核生物聚合酶Ⅱ顺式作用因子的功能

根据α－鹅膏蕈碱（α－ａｍａｉｎｔｉｎ）对真核生物ＲＮＡ聚合酶的选择性抑制,以氯霉素乙酰转移酶基因（ＣＡＴ）作为报道进行体内表达实验,证明Ｔ７噬菌体启动子可为真核生物ＲＮＡ聚合酶Ⅱ所启动。应用建立的竞争性ＤＮＡ－蛋白质凝胶泳动技术,分别以ＴＡＴＡ框、ＣＡＡＴ框、ＧＣ框和八核苷权序列（ｏｃｔａｍｅｒ）为竞争性寡核苷酸分子,发现人工合成的Ｔ７启动子可能与ＴＦⅡＤ起始转录因子结合,形成ＤＮＡ－核蛋白质结     

苜蓿根瘤菌nodC蛋白在大肠杆菌中的过量生成和分离纯化

苜蓿根瘤菌ｎｏｄＣ蛋白是结瘤基因ｎｏｄＣ编码的４３ｋＤ多肽。应用噬菌体Ｔ７ＲＮＡ聚合酶／启动子表达系统,ｐＴ７－５作为载体质粒,构建了带有ｎｏｄＣ基因的ｐＢＦ６克隆,经量生成ＮｏｄＣ,占杆菌ＪＡＫＥ中获得表达,过量生成ＮｏｄＣ,占细胞总蛋白量的５％。经细胞膜蛋白组份的分离,Ｂｉｏ－ｇｅｌ柱层析,ＳＤＳ－ＰＡＧＥ电泳等获得了比较纯化的ＮｏｄＣ。     

苜蓿根瘤菌nodC蛋白在大肠杆菌中的过量生成和分离纯化

苜蓿根瘤菌（Ｒｈｉｚｏｂｉｕｍｍｅｌｉｌｏｔｉ）ｎｏｄＣ蛋白是结瘤基因ｎｏｄＣ编码的４３ｋＤ多肽（ＮｏｄＣ）。应用噬菌体Ｔ７ＲＮＡ聚合酶／启动子表达系统．ｐＴ７－５作为载体质粒．构建了带有ｎｏｄＣ基因的ＰＢＦ６克隆．经诱导在大肠杆菌ＪＡＫＥ中获得表达,过量生成ＮｏｄＣ,占细胞总蛋白量的５％。经细胞膜蛋白组份的分离,Ｂｉｏ－ｇｅｌ柱层析,ＳＤＳ－ＰＡＧＥ电泳等获得了比较纯化的ＮｏｄＣ。     

原核表达系统的真核化

Ｔ7噬菌体ＲＮＡ聚合酶显著的特点是只识别其自身ＤＮＡ序列中特定的启动子[1] ;而Ｔ7噬菌体启动子属于组成型的启动子 ,又只能被相应的Ｔ7噬菌体的ＲＮＡ聚合酶 (约 10 0ｋＤ的单链蛋白质 )所识别 ,并且被Ｔ7ＲＮＡ聚合酶所启始的转录非常活跃[2 ,3] ,宿主细胞的ＲＮＡ聚合酶所进行的转录根本无法同其竞争 ,这样宿主细胞中几乎所有的转录都被Ｔ7ＲＮＡ聚合酶所操纵。另外 ,Ｔ7ＲＮＡ聚合酶几乎能完整地转录在Ｔ7启动子下游的所有ＤＮＡ序列。基于这些优点 ,1986年Ｓｔｕｄｉｅｒ和Ｍｏｆｆａｔ建立了一套新的原核表达系统———Ｔ7ＲＮＡ聚…     

利用脊髓灰质炎病毒5‘NCR和RNA聚合酶基因可提高外源基因表达

将含脊髓灰质炎病毒（ＰＶ）ＲＮＡ聚合酶的不同长度基因片段克隆到载体ｐＳＧ５质粒上,分别构建了４个表达ＲＮＡ聚合酶的质粒。体外转录实验证明,ｐＳＧ５－ＰＯＬ１．９９和ｐＳＧ５－ＰＯＬ２．０３质粒转染细胞的提取物促进了特异的ＲＮＡ转录,表明两质闰可表达ＲＮＡ聚合酶。将ＰＶ的５’ＮＣＲ序列插在载体ｐＧＲＥＥＮ ＬＡＮＴＥＲＮ－１的ＣＭＶ启动子下游,构建了ｐＧＲＥＥＮ ＬＡＮＴＥＲＮ－１－５’ＮＣＲ质粒;     

胰岛素受体的配体结合结构域的克隆与表达

用ＰＣＲ方法从人胎盘ｃＤＮＡ 中获得编码胰岛素受体α亚基中结合胰岛素的相对独立的结构域Ｌ１、Ｌ２以及人工设计的Ｌ１－（Ａｌａ）１０－Ｌ２的基因,克隆入含Ｔ７噬菌体ＲＮＡ聚合酶启动子的表达质粒ｐＥＴ－３ａ中,转化大肠杆菌ＢＬ２１（ＤＥ３）,用ＩＰＴＧ诱导表达成功。ＤＮＡ测序、氨基酸组成分析以及蛋白质Ｎ端测序证明所表达的蛋白质正确。经过包涵体的分离、洗涤、溶解和纯化,得到了纯的变性状态受体的胰岛素高亲     

一种新型丙型肝炎病毒培养方法的建立

以丙肝病毒 (ＨＣＶ)为切入点 ,建立一套通过基因操作技术在体外细胞培养系统中生产高滴度反转录病毒颗粒的大规模病毒培养技术平台 ,并将该技术应用于其它难以体外大规模培养的反转录病毒的生产。该体系包括一株插入Ｔ7ＲＮＡ聚合酶基因的重组痘苗病毒ｖＴＦ 3和 2个重组质粒 ,质粒ＰＴ7ＨＣＶ在上游Ｔ7启动子和Ｔ7终止子之间插入ＨＣＶ基因组ｃＤＮＡ ,可通过Ｔ7ＲＮＡ聚合酶指导转录产生 9 5ｋｂ的ＨＣＶＲＮＡ ;另一质粒Ｐ...     

在酵母体内用RNA聚合酶Ⅱ表达大肠杆菌tRNA~（Sec）基因

我们将大肠杆菌的硒代半胱氨酸ｔＲＮＡ基因（ＳｅｌＣ基因）连接到分泌型表达质粒ＰＶＴ１０２Ｕ－αＭＦＬ中，并调整好阅读框架，使蛋白翻译的终止密码子位于ＳｅｌＣ基因的下游．将该质粒转化酵母，通过ＳＤＳ－ＰＡＧＥ分析，在ＳＤ液体培养基中检测出７～８ｋｄ的蛋白条带，这与理论值是相符合的。同时，我们抽提出酵母的总ＲＮＡ用互补与该ｔＲＮＡＴＣ茎环区的２１－ｍｅｒ寡核苷酸，经５′标记后作为探讨进行Ｎｏｒｔｈｅｒｎｂｌｏｔ。结果有两条较强的条带和一条较弱的条带，较强条带中一条相当于７９０ｎｔｓ左右，另一条相当于３７０ｎｔｓ左古，根据组建的表达型质粒结构资料推测７９０ｎｔｓ左右的条带是由ＲＮＡ聚合酶ＩＩ转录的未被加工的前体；３７０ｎｔｓ左右的条带是５′端被加工而３′端未被加工的分子，较弱的条带则是相当于９０ｎｔｓ左右的成熟ｔＲＮＡ分子。因此，我们认为ＲＮＡ聚合酶ＩＩ可以转录ｔＲＮＡ基因。由于实验用酵母的３′内切核酸酶含量较低，致使带长尾巴的ｔＲＮＡ前体３′端加工速度缓慢。     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

High-level expression of full-length antibodies using trans-complementing expression vectors

Recombinant antibodies are increasingly used as therapeutics for a wide variety of diseases. Generation of cell lines expressing high levels of recombinant antibody typically requires labor-intensive cloning and screening steps. We describe a mammalian expression system for the high-level production of full-length antibody molecules. It has been shown that the dihydrofolate reductase (DHFR) selectable marker can be divided into two fragments that, with the aid of a leucine zipper, can re-associate to form an active molecule. Using bicistronic vectors, we linked the expression of each antibody chain to the expression of a DHFR fragment. Survival in selective media requires expression of both DHFR fragments that, by virtue of these vectors, also selects for the expression of both antibody chains. Initial pools produced 5 microg of Ab/10(6) cells/d (qP = microg/10(6) cells/d). Expression of each antibody chain in conjunction with a portion of DHFR also leads to concurrent amplification of both antibody chains in the presence of methotrexate, a DHFR inhibitor, and results in a two- to fivefold increase in antibody production with basal qPs ranging from 10-25 ug/10(6) cells/d. Shake-flask cultures of amplified pools produced up to 600 mg/L of antibody in 7 days. This system allows for rapid generation of antibodies without cloning and greatly simplifies selection of cell lines for the production of potential antibody therapeutics.     

Mining gene expression profiles: expression signatures as cancer phenotypes

Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.