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收稿日期：２００７－１１－２４录用日期：２００７－１２－３０基金项目：国家自然科学基金项目（Ｎｏ．５０７７８１７０）；国家高技术研究发展计划（８６３）项目（Ｎｏ．２００７ＡＡ０６Ｚ４１４）作者简介：李剑（１９８０—），女，博士研究生，Ｅ－ｍａｉｌ：ｊｉａｎ＿ｌｉ＠ｍａｉｌｓ．ｇｕｃａｓ．ａｃ．ｃｎ；＊通讯作者（Ｃｏｒｒｅｓｐｏｎｄｉｎｇａｕｔｈｏｒ）：Ｅ－ｍａｉｌ：ｍａｍｅｉ＠ｒｃｅｅｓ．ａｃ．ｃｎ酵母双杂交技术构建重组人雌激素受体基因酵母李剑，马梅＊，饶凯锋，王子健中国科学院生态环境研究中心环境水质学国家重点实验室，北京１０００８５摘要：应用酵母双杂交技术构建重组人雌激素受体基因酵母，用以检测类／抗雌激素化合物和环境样品的类／抗雌激素活性．采用多聚酶链反应（ＰＣＲ）法扩增人雌激素受体（ｈＥＲ）基因，构建诱饵质粒ｐＧＢＫＴ７－ＥＲＬＢＤ；分别提取并纯化两种含有ＥＲ共激活因子基因的靶质粒ｐＧＡＤ４２４－ＧＲＩＰ１和ｐＧＡＤ４２４－ＳＲＣ１；诱饵质粒和靶质粒同时转染酵母细胞Ｙ１８７，于营养缺陷型培养基（ＳＤ／－Ｔｒｐ／－Ｌｅｕ）上筛选阳性菌落，分别构建两种ＥＲ双杂交酵母ＥＲ＋ＧＲＩＰ１和ＥＲ＋ＳＲＣ１．考察双杂交酵母与不同激素：１７β－雌二醇（Ｅ２）、二氢睾酮（ＤＨＴ）、孕酮（ＰＧ）以及三碘甲状腺原氨酸（Ｔ３）的结合情况，并考察了雌激素受体拮抗剂４－羟基他莫昔芬（４－ＯＨＴ）与酵母的相互作用．结果表明：ＥＲ＋ＧＲＩＰ１和ＥＲ＋ＳＲＣ１酵母均能够专一性的和Ｅ２结合，并存在显著的剂量－效应关系，Ｅ２对ＥＲ＋ＧＲＩＰ１和ＥＲ＋ＳＲＣ１酵母β－半乳糖苷酶活性诱导的ＥＣ５０值分别为７．３×１０－１１ｍｏｌ・Ｌ－１和１．５×１０－１０ｍｏｌ・Ｌ－１，其中ＥＲ＋ＧＲＩＰ１酵母细胞诱导产生的酶活性值明显高于ＥＲ＋ＳＲＣ１酵母细胞．４－ＯＨＴ能够抑制Ｅ２诱导ＥＲ＋ＧＲＩＰ１酵母细胞产生的酶活性，并存在显著的剂量－效应关系，ＩＣ５０值为１．０×１０－７ｍｏｌ・Ｌ－１．表明重组人雌激素受体基因酵母可以用于检测化合物和环境样品的类／抗雌激素活性．关键词：酵母双杂交；人雌激素受体；重组基因酵母；类／抗雌激素活性文章编号：１６７３－５８９７（２００８）１－０２１－０６中图分类号：Ｘ５２４文献标识码：ＡＣｏｎｓｔｒｕｃｔｉｏｎｔｈｅＲｅｃｏｍｂｉｎａｎｔＨｕｍａｎＥｓｔｒｏｇｅｎＲｅｃｅｐｔｏｒ（ｈＥＲ）ＧｅｎｅＹｅａｓｔＵｓｉｎｇＴｗｏ－ＨｙｂｒｉｄＹｅａｓｔＴｅｃｈｎｉｑｕｅＬＩＪｉａｎ，ＭＡＭｅｉ＊，ＲＡＯＫａｉ－ｆｅｎｇ，ＷＡＮＧＺｉ－ｊｉａｎＳｔａｔｅＫｅｙＬａｂｏｒａｔｏｒｙｏｆＥｎｖｉｒｏｎｍｅｎｔａｌＡｑｕａｔｉｃＣｈｅｍｉｓｔｒｙ，ＲｅｓｅａｒｃｈＣｅｎｔｅｒｆｏｒＥｃｏ－ＥｎｖｉｒｏｎｍｅｎｔａｌＳｃｉｅｎｃｅｓ，ＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ，Ｂｅｉｊｉｎｇ１０００８５Ｒｅｃｅｉｖｅｄ２４Ｎｏｖｅｍｂｅｒ２００７ａｃｃｅｐｔｅｄ３０Ｄｅｃｅｍｂｅｒ２００７Ａｂｓｔｒａｃｔ：Ｔｈｅｒｅｃｏｍｂｉｎａｎｔｈｕｍａｎｅｓｔｒｏｇｅｎｒｅｃｅｐｔｏｒ（ｈＥＲ）ｇｅｎｅｙｅａｓｔｗａｓｃｏｎｓｔｒｕｃｔｅｄｕｓｉｎｇｔｗｏ－ｈｙｂｒｉｄｙｅａｓｔｔｅｃｈｎｉｑｕｅ．ＩｔｃａｎｂｅｕｓｅｄｔｏｓｃｒｅｅｎｔｈｅｅｎｖｉｒｏｎｍｅｎｔａｌｃｏｍｐｏｕｎｄｓｗｉｔｈｈＥＲａｎｔ／ａｇｏｎｉｓｔｉｃａｃｔｉｖｉｔｙ．Ｔｈｅｙｅａｓｔｅｘｐｒｅｓｓｉｏｎｐｌａｓｍｉｄ，ｐＧＢＫＴ７－ＥＲＬＢＤ，ｗａｓｃｏｎｓｔｒｕｃｔｅｄｂｙＰＣＲａｍｐｌｉｆｙｉｎｇ，ｃｌｏｎｉｎｇｔｈｅＥＲｇｅｎｅ．Ｏｔｈｅｒｔｗｏｙｅａｓｔｅｘｐｒｅｓｓｉｏｎｐｌａｓｍｉｄｓｗｅｒｅｅｘｔｒａｃｔｅｄａｎｄｐｕｒｉｆｉｃａｔｅｄ，ｉｎｃｌｕｄｉｎｇｐＧＡＤ４２４－ＧＲＩＰ１ｃｏｄｉｎｇｆｏｒＥＲｃｏａｃｔｉｖａｔｏｒ，ＧＲＩＰ１，ａｎｄｐＧＡＤ４２４ＳＲＣ１ｃｏｄｉｎｇｆｏｒＳＲＣ１，ａｎｏｔｈｅｒＥＲｃｏａｃｔｉｖａｔｏｒ．ＴｈｅｙｅａｓｔｃｅｌｌｓＹ１８７ｗｅｒｅｃｏｔｒａｎｓｆｏｒｍｅｄｗｉｔｈｔｈｅｐＧＢＫＴ７－ＥＲＬＢＤａｎｄｐＧＡＤ４２４ＧＲＩＰ１ｏｒｐＧＡＤ４２４ＳＲＣ１，ａｎｄｓｅｌｅｃｔｅｄｂｙｇｒｏｗｔｈｏｎｓｙｎｔｈｅｔｉｃｄｅｘｔｒｏｓｅ（ＳＤ）ｍｅｄｉｕｍ（ｌａｃｋｉｎｇｔｒｙｐｔｏｐｈａｎａｎｄｌｅｕｃｉｎｅ）ａｄｄｅｄａｇａｒ．Ｔｈｅｎ，ｔｈｅｔｗｏ－ｈｙｂｒｉｄＥＲ＋ＧＲＩＰ１ｙｅａｓｔａｎｄＥＲ＋ＳＲＣ１ｙｅａｓｔｗｅｒｅｃｏｎｓｔｒｕｃｔｅｄ，ｒｅｓｐｅｃｔｉｖｅｌｙ．Ｗｅｔｅｓｔｅｄｔｈｅｂｉｎｄｉｎｇｏｆｔｗｏ－ｈｙｂｒｉｄｙｅａｓｔｔｏｄｉｆｆｅｒｅｎｔｈｏｒｍｏｎｅｓｉｎｃｌｕｄｉｎｇ１７β－ｅｓｔｒｏｇｅｎ（Ｅ２），ｄｉｈｙｄｒｏｔｅｓｔｏｓｔｅｒｏｎｅ（ＤＨＴ），ｐｒｏｇｅｓｔｅｒｏｎｅ（ＰＧ），ａｎｄ３，３′，５－ｔｒｉｉｏｄｏ－Ｌ－ｔｈｙｒｏｎｉｎｅ（Ｔ３）．Ｆｕｒｔｈｅｒｍｏｒｅ，ｔｈｅｉｎｔｅｒａｃｔｉｏｎｏｆｔｗｏ－ｈｙｂｒｉｄｙｅａｓｔｗｉｔｈｋｎｏｗｎＥＲａｎｔａｇｏｎｉｓｔ，４－ｈｙｄｒｏｘｙｔａｍｏｘｉｆｅｎ（４－ＯＨＴ），ｗａｓａｌｓｏｉｎｖｅｓｔｉｇａｔｅｄ．ＴｈｅｒｅｓｕｌｔｓｓｈｏｗｅｄｔｈａｔＥＲ＋ＧＲＩＰ１ｙｅａｓｔａｎｄＥＲ＋ＳＲＣ１ｙｅａｓｔｈａｄｓｐｅｃｉｆｉｃｂｉｎｄｉｎｇｗｉｔｈＥ２ａｎｄｔｈｅａｃｔｉｖｉｔｙｏｆｒｅｃｏｍｂｉｎａｎｔｇｅｎｅｙｅａｓｔｉｎｄｕｃｅｄｂｙＥ２ｗａｓｄｅｍｏｎｓｔｒａｔｅｄｉｎａｃｏｎｃｅｎｔｒａｔｉｏｎ－ｄｅｐｅｎｄｅｄｍａｎｎｅｒ．ＴｈｅＥＣ５０ｖａｌｕｅｓｏｆＥ２ｆｏｒＥＲ＋ＧＲＩＰ１ｙｅａｓｔａｎｄＥＲ＋ＳＲＣ１ｙｅａｓｔｗｅｒｅ７．３×１０－１１ｍｏｌ・Ｌ－１ａｎｄ１．５×１０－１０ｍｏｌ・Ｌ－１，ｒｅｓｐｅｃｔｉｖｅｌｙ．Ｉｎａｄｄｉｔｉｏｎ，ｔｈｅβ－ｇａｌａｃｔｏｓｉｄａｓｅａｃｔｉｖｉｔｙｉｎｄｕｃｅｄｂｙＥＲ＋ＧＲＩＰ１ｗａｓｈｉｇｈｅｒｔｈａｎｔｈｅａｃｔｉｖｉｔｙｉｎｄｕｃｅｄｂｙＥＲ＋ＳＲＣ１．Ｗｅａｌｓｏｏｂｓｅｒｖｅｄｔｈａｔ４－ＯＨＴｉｎｈｉｂｉｔｅｄｔｈｅβ－ｇａｌａｃｔｏｓｉｄａｓｅａｃｔｉｖｉｔｙｉｎｄｕｃｅｄｂｙＥ２ｉｎａｃｏｎｃｅｎｔｒａｔｉｏｎｄｅｐｅｎｄｅｎｔｍａｎｎｅｒ，ｔｅｓｔｅｄｂｙＥＲ＋ＧＲＩＰ１ｙｅａｓｔａｓｓａｙ．ＡｎｄｔｈｅＩＣ５０ｖａｌｕｅｗａｓ１．０×１０－７ｍｏｌ・Ｌ－１．ＡｌｌｏｆｔｈｅｒｅｓｕｌｔｓｓｕｐｐｏｒｔｔｈａｔｔｈｅｒｅｃｏｍｂｉｎａｎｔｈＥＲｇｅｎｅｙｅａｓｔｃａｎｂｅｕｓｅｄｆｏｒｓｃｒｅｅｎｃｈｅｍｉｃａｌｓａｎｄｅｎｖｉｒｏｎｍｅｎｔａｌｓａｍｐｌｅｓｗｉｔｈａｎｔ／ａｇｏｎｉｓｔｉｃａｃｔｉｖｉｔｙ．Ｋｅｙｗｏｒｄｓ：ｔｗｏ－ｈｙｂｒｉｄｙｅａｓｔ；ｈｕｍａｎｅｓｔｒｏｇｅｎｒｅｃｅｐｔｏｒ；ｒｅｃｏｍｂｉｎａｎｔｇｅｎｅｙｅａｓｔ；ＥＲａｎｔ／ａｇｏｎｉｓｔｉｃａｃｔｉｖｉｔｙ第３卷第１期２００８年２月生态毒理学报ＡｓｉａｎＪｏｕｒｎａｌｏｆＥｃｏｔｏｘｉｃｏｌｏｇｙＶｏｌ．３，Ｎｏ．１Ｆｅｂ．２００８生态毒理学报第３卷１引言（Ｉｎｔｒｏｄｕｃｔｉｏｎ）经典的激素－受体理论认为：天然雌激素主要通过与雌激素受体中的配体结合域（ＬＢＤ）结合形成激素－受体复合物，再通过受体中的ＤＮＡ结合域（ＤＢＤ）识别并结合雌激素效应元件（ＥＲＥ），从而转录激活靶基因，发挥雌激素效应（楼丽广等，１９９７）．根据此分子作用机制已建立了环境雌激素酵母测评体系（何世华等，２００２）和重组受体基因报道基因酵母等方法（Ｒｏｕｔｌｅｄｇｅｅｔａｌ．，１９９６），用于检测环境内分泌干扰物通过雌激素受体介导而产生的类雌激素作用．２０世纪９０年代通过对天然雌激素受体作用机理的研究又发现了一系列雌激素受体的共激活因子（Ｃｏａｃｔｏｒ）（Ｈｏｎｇｅｔａｌ．，１９９６；Ｍａｎｇｅｌｓｄｏｒｆｅｔａｌ．，１９９５），它们不直接与ＤＮＡ结合，而是通过直接或间接与受体相互作用，促进基因转录．在此基础上形成的新的受体作用理论认为，天然雌激素与受体的ＬＢＤ结合后形成复合物并发生构象的改变，进一步结合协同激活因子，促进转录．为考察人雌激素受体ＬＢＤ表达蛋白和受体协同激活因子蛋白的相互作用，本研究基于该机制发展酵母双杂交技术，构建双杂交重组雌激素受体基因酵母．酵母双杂交技术的基本原理是利用ＧＡＬ４蛋白的ＤＮＡ结合域序列（ＧＡＬ４ＤＮＡ－ＢＤ）和ＥＲ－ＬＢＤ基因结合，构建出ＢＤ－ＥＲ－ＬＢＤ融合表达载体；同时将ＧＡＬ４蛋白转录激活结构域（ＧＡＬ４ＤＮＡ－ＡＤ）和受体协同激活因子蛋白结合，构建ＡＤ－受体协同激活因子融合表达载体．将这两个表达载体共转化至酵母细胞内表达．当没有雌激素或环境雌激素存在时，ＥＲ－ＬＢＤ和受体协同激活因子蛋白无相互作用，导致ＧＡＬ４ＤＮＡ－ＢＤ与ＧＡＬ４ＤＮＡ－ＡＤ蛋白在空间上不能接近，报告基因不表达；当雌激素或环境雌激素存在时，该类化合物首先与雌激素受体ＬＢＤ蛋白结合形成复合物，该复合物进而结合受体协同激活因子蛋白，使得ＧＡＬ４ＤＮＡ－ＢＤ与ＧＡＬ４ＤＮＡ－ＡＤ在空间上接近，启动报告基因表达，通过测定报道基因ＬａｃＺ表达产物β－半乳糖苷酶活性，表征化合物的类雌激素活性．因此，基于新的受体作用理论建立的双杂交酵母系统更加接近哺乳动物内分泌系统的真实作用情况．核受体协同激活因子ＧＲＩＰ１和ＳＲＣ１是目前研究得比较多的协同激活因子，研究发现人的雌激素受体与ＧＲＩＰ１和ＳＲＣ１可相互作用，因此，本工作同时构建了ＥＲ－ＧＲＩＰ１和ＥＲ－ＳＲＣ１两株双杂交酵母，筛选与雌激素存在较强作用的菌株，建立环境雌激素双杂交酵母筛选体系．环境内分泌干扰物在接近或低于无可见不良效应浓度水平（ＮＯＡＥＬ）时仍可诱发生物效应，是当前风险评价中的一个重大问题（卫立等，２００７），因此，建立环境内分泌干扰物快速筛选方法体系的工作，已经刻不容缓．２材料与方法（Ｍａｔｅｒｉａｌｓａｎｄｍｅｔｈｏｄｓ）２．１主要实验材料与试剂含有人ＥＲαＬＢＤ（ａａ２８２－５９５）的ｐＡＳＶ３质粒，由英国Ｎｏｔｔｉｎｇｈａｍ大学Ｈｅｅｒｙ教授惠赠；ｐＧＡＤ４２４－ＧＲＩＰ１（ａａ５－１４６２）质粒和ｐＧＡＤ４２４－ＳＲＣ１（ａａ３１３－９７７）质粒由美国加州大学Ｓｔａｌｌｃｕｐ教授惠赠．感受态大肠杆菌ＤＨ５α、质粒小提试剂盒、凝胶回收质粒试剂盒、ｐＧＥＭ－Ｔｖｅｃｔｏｒ载体、Ｘ－ｇａｌ溶液、１ｋｂＤＮＡｍａｒｋｅｒ、ＤＮＡｍａｒｋｅｒⅣ、氨苄青霉素、卡那霉素均购自Ｔｉａｎｇｅｎ生物试剂公司．ｄＮＴＰｍｉｘｔｕｒｅ、Ｔａｑ酶、ＢａｍＨΙ酶和ＥｃｏＲΙ酶购自Ｔａｋａｒａ公司．Ｙ１８７酵母菌株（ＭＡＴα，ｕｒａ３－５２，ｈｉｓ３－２００，ａｄｅ２－１０１，ｔｒｐ１－９０１，ｌｅｕ２－３１１２，ｇａｌ４△，ｍｅｔ－，ｇａｌ８０△，ＵＲＡ３：：ＧＡＬ１ＵＡＳ－ＧＡＬ１ＴＡＴＡ－ｌａｃＺ）、ｐＧＢＫＴ７－ＢＤ克隆载体及测序引物，ｐＣＬ１质粒、ｐＧＢＫＴ７－５３质粒、ｐＧＡＤＴ７－Ｔ质粒、ｐＧＡＤＴ７－Ｌａｍ质粒、酵母ＹＰＤＡ培养基、ＳＤ／－Ｔｒｐ／－Ｌｅｕ培养基、ＴＥ溶液、１×ＴＥ／１×ＬｉＡｃ溶液、ＰＥＧ／ＬｉＡｃ溶液均购自Ｃｌｏｎｔｅｃｈ公司．ＤＭＳＯ、半硫酸腺苷购于Ｓｉｇｍａ公司．ＤＮＡ序列测定由北京诺赛基因组研究中心完成．２．２ＰＣＲ扩增ＥＲＬＢＤ与序列分析在上下游引物的５’－端分别引入ＥｃｏＲΙ和ＢａｍＨΙ单一酶切位点，设计新基因序列特异性的引物，上游引物Ｐ１：５’－ＴＣＧＣＣＧＧＡＡＴＴＣＧＣＴＧＧＡＧＡＣＡＴＧＡＧＡ－３’；下游引物Ｐ２：５’－ＴＡＡＣＧＣＧＧＡＴＣＣＴＣＡＧＡＣＴＧＴＧＧＣＡＧＧ－３’以含有ＥＲＬＢＤ（ａａ２８２－５９５）的ｐＡＳＶ３质粒作为模板进行ＰＣＲ扩增．ＰＣＲ反应条件为：９４℃变性５ｍｉｎ，然后开始ＰＣＲ循环，９４℃３０ｓ，６１℃３０ｓ，７２℃１ｍｉｎ，共３０个循环，然后７２℃变性１０ｍｉｎ，反应体积为５０μＬ，扩增产物通过０．８％琼脂糖凝胶电泳分析．"""""""""" """２２李剑等：酵母双杂交技术构建重组人雌激素受体基因酵母第１期２．３诱饵质粒的构建将ＥＲＬＢＤ的ＰＣＲ产物用Ｔ４连接酶连接于Ｔ－载体，反应体系为：ＰＣＲ产物０．６ｐｍｏｌ，Ｔ－载体ＤＮＡ０．０６ｐｍｏｌ，１０×连接缓冲液５μＬ，Ｔ４ＤＮＡ连接酶２μＬ（７００Ｕ），反应体积为５０μＬ，１６℃反应过夜．将１０μＬ连接产物转化感受态大肠杆菌ＤＨ５α，氨苄青霉素抗性筛选阳性克隆，提取Ｔ－ＥＲＬＢＤ质粒并纯化．采用ＥｃｏＲΙ和ＢａｍＨΙ双酶切Ｔ－ＥＲＬＢＤ质粒验证目的ＥＲＬＢＤ基因片段，并对目的基因片段进行切胶回收，连接到经相同双酶切处理切胶回收的ｐＧＢＫＴ７载体上构成诱饵质粒ｐＧＢＫＴ７－ＥＲＬＢＤ．反应体系与Ｔ－载体的连接类似．将１０μＬ连接产物转化感受态大肠杆菌ＤＨ５α，卡那霉素抗性筛选阳性克隆，提取并纯化质粒ｐＧＢＫＴ７－ＥＲＬＢＤ．诱饵质粒产物采用ＥｃｏＲΙ和ＢａｍＨΙ双酶切，０．８％琼脂糖凝胶电泳鉴定，同时进行序列测定，测序引物为：５’－ＴＴＴＴＣＧＴＴＴＴＡＡＡＡＣＣＴＡＡＧＡＧＴＣ－３’．２．４靶质粒的提取纯化与鉴定１０μＬ靶质粒ｐＧＡＤ４２４－ＧＲＩＰ１或ｐＧＡＤ４２４－ＳＲＣ１转化感受态大肠杆菌ＤＨ５α，氨苄青霉素抗性筛选阳性克隆，同时添加蓝白斑筛选，挑取阳性克隆，提取质粒并纯化．０．８％琼脂糖凝胶电泳鉴定，同时进行序列测定，测序引物为ｐＧＡＤ４２４通用引物．２．５酵母双杂交实验挑取新鲜培养的酵母Ｙ１８７菌株单菌落，在５００ｍＬＹＰＤＡ培养基中３０℃振荡培养到对数生长期后（６００ｎｍ处的吸光度值为０．４￣０．６），１０００×ｇ离心５ｍｉｎ沉淀细胞，悬浮于无菌的ＴＥ溶液中，重复离心操作，将酵母细胞沉淀溶于２．５ｍＬ新鲜配制的无菌的１×ＴＥ／１×ＬｉＡｃ溶液中．将ｐＧＢＫＴ７－ＥＲＬＢＤ质粒和ｐＧＡＤ４２４－ＧＲＩＰ１质粒同时转染至酵母细胞Ｙ１８７，构建ＥＲ－ＧＲＩＰ１双杂交酵母，反应体系为：０．１ｍｇ载体ＤＮＡ，０．１μｇｐＧＢＫＴ７－ＥＲＬＢＤ质粒，０．１μｇｐＧＡＤ４２４－ＧＲＩＰ１质粒，０．１ｍＬ的酵母细胞悬液，０．６ｍＬ无菌的ＰＥＧ／ＬｉＡｃ溶液．２００ｒｐｍ、３０℃下孵育３０ｍｉｎ，加入７０μＬＤＭＳＯ，４２℃水浴热击１５ｍｉｎ，冰浴静置１￣２ｍｉｎ，１４，０００ｒｐｍ室温下细胞离心５ｓ，０．５ｍＬ无菌ＴＥ缓冲液重悬细胞．涂布在ＳＤ／－Ｔｒｐ／－Ｌｅｕ营养缺陷型的选择性固体培养基上３０℃培养２￣４ｄ．相同的方法将ｐＧＢＫＴ７－ＥＲＬＢＤ质粒和ｐＧＡＤ４２４－ＳＲＣ１质粒同时转染至酵母细胞Ｙ１８７构建ＥＲ－ＳＲＣ１双杂交酵母．２．６菌落转移滤膜分析能够在选择性培养基上生长的克隆，采用菌落转移滤膜分析的方法进一步剔除假阳性结果．具体操作如下：灭菌的干燥滤膜１＃（Ｗｈａｔｍａｎ，英国）覆盖生长有双杂交酵母菌落（直径大于２ｍｍ）的平板上，将菌落转移到滤膜，液氮反复冻融；另取滤膜２＃浸没到含Ｘ－ｇａｌ和雌二醇的缓冲溶液中润湿，将含有菌落的滤膜１＃覆盖到润湿的滤膜２＃上，３０℃孵育直到克隆出现明显的蓝色，并对显色的克隆计数，计算转化率．选择具有蓝色的阳性克隆到ＳＤ／－Ｔｒｐ／－Ｌｅｕ选择性培养基上继续培养至菌落直径大于２ｍｍ，挑取菌落接种于ＳＤ／－Ｔｒｐ／－Ｌｅｕ液体培养基中，３０℃、２００ｒｐｍ振荡培养过夜．第２天部分细胞添加１５％的无菌甘油后－８０℃冷冻保存，另一部分与配体结合后检测β－半乳糖苷酶的活性．转化率（ｃｆｕ・μｇ－１）＝克隆数（ｃｆｕ）×总重悬体积（μＬ）涂布体积（μＬ）×稀释因子×质粒ＤＮＡ使用量（μｇ）（１）２．７构建双杂交酵母质量控制为了对双质粒同时转染酵母细胞的流程进行控制，并对合成的质粒进行鉴定，考察质粒转染酵母细胞的效率，添加平行的质控实验．阳性对照实验设置两组：第一组，采用ｐＣＬ１标准质粒（编码野生型的ＧＡＬ４蛋白，能够诱导β－半乳糖苷酶活性）转染酵母细胞，并计算转化效率．第二组采用同时转染ｐＧＢＫＴ７－５３标准质粒（编码ＧＡＬ４ＤＮＡ－ＢＤ蛋白）和ｐＧＡＤＴ７－Ｔ标准质粒（编码ＧＡＬ４ＤＮＡ－ＡＤ蛋白），计算转化效率；阴性对照采用ｐＧＡＤＴ７－Ｌａｍ标准质粒（编码ＧＡＬ４ＤＮＡ－ＢＤ和人体ｌａｍｉｎＣ杂合蛋白）和ｐＧＡＤ４２４－ＧＲＩＰ１质粒或ｐＧＡＤ４２４－ＳＲＣ１质粒同时转染酵母细胞，计算转化率．２．８β－半乳糖苷酶活性的检测β－半乳糖苷酶活性的检测方法参照本实验室已经报道的方法（李剑等，２００６）．β－半乳糖苷酶相对酶活性Ｕ的计算公式如下：Ｕ＝（ＡＳ－ＡＢ）／ｔ・Ｖ・Ｄ・ＯＤＳ（２）式中，Ｕ为β－半乳糖苷酶活性；ｔ为反应时间（ｍｉｎ）；２３生态毒理学报第３卷Ｖ为测试体积（ｍＬ）；Ｄ为稀释因子；ＯＤＳ为测试样品５９５ｎｍ处的吸光度值；ＡＳ为测试样品在４２０ｎｍ处的吸光度值ＯＤ４２０；ＡＢ为空白对照在４２０ｎｍ处的吸光度．３结果与分析（Ｒｅｓｕｌｔｓａｎｄａｎａｌｙｓｉｓ）３．１ＥＲＬＢＤ基因片段的ＰＣＲ产物电泳分析取ＰＣＲ产物１０μＬ于０．８％琼脂糖凝胶电泳，结果显示，在分子量１０００ｂｐ左右出现一条带，与ＥＲＬＢＤ（ａａ２８３－５９５）基因（片段长９６３ｂｐ）大小一致，如图１．３．２ｐＧＢＫＴ７－ＥＲＬＢＤ诱饵质粒的构建与鉴定ＰＣＲ产物通过Ｔ４ＤＮＡ连接酶连接到Ｔ载体上，构建Ｔ－ＥＲＬＢＤ质粒，经ＥｃｏＲΙ和ＢａｍＨΙ双酶切鉴定（图２ａ），为约３０００ｂｐ的载体片段以及约１０００ｂｐ的插入片段，表明质粒构建正确．将ｐＧＢＫＴ７和Ｔ－ＥＲＬＢＤ质粒用ＥｃｏＲΙ和ＢａｍＨΙ双酶切，酶切产物电泳后切胶回收，ｐＧＢＫＴ７和插入片段的回收产物采用Ｔ４ＤＮＡ连接酶连接，构建ｐＧＢＫＴ７－ＥＲＬＢＤ诱饵质粒．ｐＧＢＫＴ７－ＥＲＬＢＤ质粒经酶切鉴定正确（图２ｂ），可见约７０００ｂｐ的载体片段以及约１０００ｂｐ的插入片段，同时对质粒的测序结果进一步证实上述质粒正确克隆了ＥＲＬＢＤ基因片段，插入方向及阅读框正确．３．３靶质粒的提取纯化与鉴定１０μＬ靶质粒ｐＧＡＤ４２４－ＧＲＩＰ１或ｐＧＡＤ４２４－ＳＲＣ１的ＤＮＡ纯化产物，于０．８％琼脂糖凝胶电泳．结果显示，在分子量约１０ｋｂ处各有一条带，与ｐＧＡＤ４２４－ＧＲＩＰ１和ｐＧＡＤ４２４－ＳＲＣ１报道的ＤＮＡ大小一致（Ｄｉｎｇｅｔａｌ．，１９９８），如图３．同时对质粒的测序结果进一步证实上述质粒基因片段，插入方向及阅读框正确．３．４酵母双杂交实验ｐＧＢＫＴ７－ＥＲＬＢＤ质粒分别和ｐＧＡＤ４２４－ＧＲＩＰ１质粒以及ｐＧＡＤ４２４－ＳＲＣ１质粒同时转化感受态酵母细胞Ｙ１８７，重悬细胞，将转化后的酵母细胞分别涂布到５块含有ＳＤ／－Ｔｒｐ／－Ｌｅｕ固体培养基的平板上，计算质粒的平均转化率，如表１所示．结果表明：双杂交雌激素受体酵母以及阳性对照样品的转化率较高，均大于３．５×１０４ｃｆｕ・μｇ－１；阴性对照样品转化率为０，即选择性培养基上无克隆生长；阳性对照和阴性对照样品间存在显著性差异（ｐ＜０．０１），表明整个转化过程得到较好的控制．２４李剑等：酵母双杂交技术构建重组人雌激素受体基因酵母第１期表１不同质粒同时转化酵母细胞Ｙ１８７的转化率Ｔａｂｌｅ１Ｔｈｅｔｒａｎｓｆｏｒｍａｔｉｏｎｅｆｆｉｃｉｅｎｃｙｏｆｄｉｆｆｅｒｅｎｔｐｌａｓｍｉｄｆｏｒｔｈｅｙｅａｓｔｃｅｌｌｓ３．５双杂交雌激素受体基因酵母剂量－效应关系的建立将ＥＲ＋ＧＲＩＰ１和ＥＲ＋ＳＲＣ１双杂交酵母细胞暴露到不同浓度的１７β－雌二醇（Ｅ２，Ｓｉｇｍａ，美国）溶液中，检测诱导产生的酶活性，建立了Ｅ２对酶活性诱导的剂量－效应关系曲线，如图４所示．结果表明，Ｅ２对ＥＲ＋ＧＲＩＰ１和ＥＲ＋ＳＲＣ１双杂交酵母酶活性诱导均存在明显的剂量－效应关系，最大效应浓度分别为：２×１０－１０ｍｏｌ・Ｌ－１和５×１０－１０ｍｏｌ・Ｌ－１；ＥＣ５０值分别为７．３×１０－１１ｍｏｌ・Ｌ－１和１．５×１０－１０ｍｏｌ・Ｌ－１；与国际上报道的Ｅ２在重组酵母系统中检测的ＥＣ５０结果类似（Ｒｅｈｍａｎｎｅｔａｌ．，１９９９；Ｇａｉｄｏｅｔａｌ．，１９９７），表明本实验建立的的双杂交酵母方法与报道的其它方法相比对Ｅ２具有较高的敏感度，初步表明双杂交雌激素受体基因酵母测评体系是成功的．比较两株酵母发现，ＥＲ＋ＧＲＩＰ１酵母株具有较大的酶活性诱导值以及较小的ＥＣ５０值，表明Ｅ２依赖的ＥＲ与受体共激活因子ＧＲＩＰ１的结合能力较强，因此ＥＲ＋ＧＲＩＰ１酵母株对Ｅ２具有更高的灵敏度．３．６双杂交雌激素受体酵母专一性的检测将双杂交雌激素受体基因酵母，暴露到其他３种人体激素：二氢睾酮（ＤＨＴ，Ｓｉｇｍａ，美国）、孕酮（ＰＧ，ＭＰｂｉｏｍｅｄｉｃａｌｓ，德国）和三碘甲状腺原氨酸（Ｔ３，Ｓｉｇｍａ，美国）中，通过检测其β－半乳糖苷酶活性，考察不同激素与受体的结合情况．结果表示为ＤＨＴ、ＰＧ和Ｔ３诱导酶活性与Ｅ２（２×１０－１０ｍｏｌ・Ｌ－１）诱导酶活性的相对百分比，如图５所示．在１０－１１￣１０－６ｍｏｌ・Ｌ－１浓度范围内，ＤＨＴ、ＰＧ和Ｔ３对两株雌激素受体酵母的β－半乳糖苷酶活性几乎没有诱导；而Ｅ２对酶活性存在明显诱导，与ＤＨＴ、ＰＧ和Ｔ３相比存在显著性差异（ｐ＜０．０１）．因此，Ｅ２对ＥＲ＋ＧＲＩＰ１和ＥＲ＋ＳＲＣ１双杂交酵母存在专一性诱导．３．７４－羟基他莫昔芬抑制１７β－雌二醇诱导双杂交雌激素受体基因酵母酶活性的剂量－效应关系他莫昔芬或４－羟基他莫昔芬（４－ＯＨＴ，Ｓｉｇｍａ，美国）是常用的雌激素受体拮抗剂，在研究化合物或环境样品的抗雌激素效应时，常被选为标准拮抗剂．因此，本实验选取４－ＯＨＴ，考察其对Ｅ２诱导雌激素受体酵母阳性对照阴性对照双杂交酵母ＥＲ＋ＧＲＩＰ１ＥＲ＋ＳＲＣ１ｐＣＬ１ｐＧＢＫＴ７－５３＋ｐＧＡＤＴ７－ＴｐＧＡＤＴ７－Ｌａｍ＋ｐＧＡＤ４２４－ＧＲＩＰ１ｐＧＡＤＴ７－Ｌａｍ＋ｐＧＡＤ４２４－ＳＲＣ１转化率（１０４ｃｆｕ・μｇ－１）３．５±０．３５±０．５５±０．７４．５±０．３０．０±０．００．０±０．０２５生态毒理学报第３卷双杂交雌激素受体基因酵母酶活性的抑制效应．选取具有较高的酶活性诱导效应的ＥＲ＋ＧＲＩＰ１双杂交酵母细胞，将不同浓度的４－ＯＨＴ分别与Ｅ２（２×１０－１０ｍｏｌ・Ｌ－１）共同孵育，检测诱导酶活性．结果表示为与Ｅ２（２×１０－１０ｍｏｌ・Ｌ－１）单独诱导酶活性的相对百分比，如图６所示．结果表明，４－ＯＨＴ对Ｅ２诱导的酶活性存在明显的抑制效应；根据抑制的剂量－效应关系，４－ＯＨＴ的半数抑制效应浓度（ＩＣ５０）值为１．０×１０－７ｍｏｌ・Ｌ－１．Ｚｈｏｕ等（１９９８）报道４－ＯＨＴ对Ｅ２抑制的ＩＣ５０为１．４４×１０－７ｍｏｌ・Ｌ－１，与本试验的结果类似．以上研究结果表明，所构建的ＥＲ＋ＧＲＩＰ１和ＥＲ＋ＳＲＣ１双杂交酵母细胞均能选择性地检测雌激素物质，酶活性诱导均存在明显的剂量－效应关系，ＥＲ＋ＧＲＩＰ１酵母株对Ｅ２具有更高的灵敏度，初步结果显示上述方法能够同时应用于类／抗雌激素效应物质的筛选．通讯作者简介：马梅（１９６７—），女，博士，中科院生态环境研究中心环境水质学国家重点实验室副研究员，硕士生导师，主要研究方向为水生态毒理学，已发表文章７０余篇．ＲｅｆｅｒｅｎｃｅｓＤｉｎｇＸＦ，ＡｎｄｅｒｓｏｎＣＭ，ＭａＨ，ＨｏｎｇＨ，ＵｈｔＲＭ，ＫｕｓｈｎｅｒＰＪ，ＳｔａｌｌｃｕｐＭＲ．１９９８．Ｎｕｃｌｅａｒｒｅｃｅｐｔｏｒ－ｂｉｎｄｉｎｇｓｉｔｅｓｏｆｃｏａｃｔｉｖａｔｏｒｓｇｌｕｃｏｃｏｒｔｉｃｏｉｄｒｅｃｅｐｔｏｒｉｎｔｅｒａｃｔｉｎｇｐｒｏｔｅｉｎ１（ＧＲＩＰ１）ａｎｄｓｔｅｒｏｉｄｒｅｃｅｐｔｏｒｃｏａｃｔｉｖａｔｏｒ１（ＳＲＣ－１）：Ｍｕｌｔｉｐｌｅｍｏｔｉｆｓｗｉｔｈｄｉｆｆｅｒｅｎｔｂｉｎｄｉｎｇｓｐｅｃｉｆｉｃｉｔｉｅｓ［Ｊ］．ＭｏｌｅｃｕｌａｒＥｎｄｏｃｒｉｎｏｌｏｇｙ，１２（２）：３０２－３１３ＧａｉｄｏＫＷ，ＬｅｏｎａｒｄＬＳ，ＬｏｖｅｌｌＳ，ＧｏｕｌｄＪＣ，Ｂａｂａ!Ｄ，ＰｏｒｔｉｅｒＣＪ，ＭｃＤｏｎｎｅｌｌＤＰ．１９９７．Ｅｖａｌｕａｔｉｏｎｏｆｃｈｅｍｉｃａｌｓｗｉｔｈｅｎｄｏｃｒｉｎｅｍｏｄｕｌａｔｉｎｇａｃｔｉｖｉｔｙｉｎａｙｅａｓｔ－ｂａｓｅｄｓｔｅｒｏｉｄｈｏｒｍｏｎｅｒｅｃｅｐｔｏｒｇｅｎｅｔｒａｎｓｃｒｉｐｔｉｏｎａｓｓａｙ［Ｊ］．ＴｏｘｉｃｏｌＡｐｐｌＰｈａｒｍａｃｏｌ，１４３：２０５－２１２ＨｅＳＨ，ＬｉａｎｇＺＨ，ＺｈａｎＷ，ＪｉａＬＺ，ＣｈａｏＦＨ．２００２．Ｅｓｔａｂｌｉｓｈｍｅｎｔｏｆｔｈｅｒｅｃｏｍｂｉｎａｎｔｙｅａｓｔａｓｓａｙｓｙｓｔｅｍｆｏｒｅｎｖｉｒｏｎｍｅｎｔａｌｅｓｔｒｏｇｅｎｓ［Ｊ］．ＪｏｕｒｎａｌｏｆＥｎｖｉｒｏｎｍｅｎｔａｎｄＨｅａｌｔｈ，１９（１）：５７－５９（ｉｎＣｈｉｎｅｓｅ）ＨｏｎｇＨ，ＫｏｈｌｉＫ，ＴｒｉｖｅｄｉＡ，ＪｏｈｎｓｏｎＤＬ，ＳｔａｌｌｃｕｐＭＲ．１９９６．ＧＲＩＰ１，ａｎｏｖｅｌｍｏｕｓｅｐｒｏｔｅｉｎｔｈａｔｓｅｒｖｅｓａｓａｔｒａｎｓｃｒｉｐｔｉｏｎａｌｃｏ－ａｃｔｉｖａｔｏｒｉｎｙｅａｓｔｆｏｒｔｈｅｈｏｒｍｏｎｅｂｉｎｄｉｎｇｄｏｍａｉｎｓｏｆｓｔｅｒｏｉｄｒｅｃｅｐｔｏｒｓ［Ｊ］．ＰｒｏｃＮａｔｌＡｃａｄＳｃｉＵＳＡ，９３：４９４８－４９５２ＬｉＪ，ＣｕｉＱ，ＭａＭ，ＲａｏＫＦ，ＷａｎｇＺＪ．２００６．ＡｍｅｔａｂｏｌｉｃａｃｔｉｖａｔｉｏｎｍｅｔｈｏｄｆｏｒｓｃｒｅｅｎｉｎｇｉｎｄｉｒｅｃｔｅｓｔｒｏｇｅｎｐｏｌｌｕｔａｎｔｓｂａｓｅｄｏｎＨ４ＩＩＥｃｅｌｌｌｉｎｅ［Ｊ］．ＡｃｔａＳｃｉｅｎｔｉａｅＣｉｒｃｕｍｓｔａｎｔｉａｅ，２６（８）：１３２０－１３２５（ｉｎＣｈｉｎｅｓｅ）ＭａｎｇｅｌｓｄｏｒｆＤＪ，ＴｈｕｍｍｅｌＣ，ＢｅａｔｏＭ，ＨｅｒｒｌｉｃｈＰ，ＳｃｈüｔｚＧ，ＵｍｅｓｏｎｏＫ，ＢｌｕｍｂｅｒｇＢ，ＫａｓｔｎｅｒＰ，ＭａｒｋＭ，ＣｈａｍｂｏｎＰ，ＥｖａｎｓＲＭ．１９９５．Ｔｈｅｎｕｃｌｅａｒｒｅｃｅｐｔｏｒｓｕｐｅｒｆａｍｉｌｙ：ｔｈｅｓｅｃｏｎｄｄｅｃａｄｅ［Ｊ］．Ｃｅｌｌ，８３：８３５－８３９ＲｅｈｍａｎｎＫ，ＳｃｈｒａｍｍＫＷ，ＫｅｔｔｒｕｐＡＡ．１９９９．Ａｐｐｌｉｃａｂｉｌｉｔｙｏｆａｙｅａｓｔｏｅｓｔｒｏｇｅｎｓｃｒｅｅｎｆｏｒｔｈｅｄｅｔｅｃｔｉｏｎｏｆｏｅｓｔｒｏｇｅｎ－ｌｉｋｅａｃｔｉｖｉｔｉｅｓｉｎｅｎｖｉｒｏｎｍｅｎｔａｌｓａｍｐｌｅｓ［Ｊ］．Ｃｈｅｍｏｓｐｈｅｒｅ，３８：３３０３－３３１２ＲｏｕｔｌｅｄｇｅＥＪ，ＳｕｍｐｔｅｒＪＰ．１９９６．Ｅｓｔｒｏｇｅｎｉｃａｃｔｉｖｉｔｙｏｆｓｕｒｆａｃｔａｎｔｓａｎｄｓｏｍｅｏｆｔｈｅｉｒｄｅｇｒａｄａｔｉｏｎｐｒｏｄｕｃｔｓａｓｓｅｓｓｅｄｕｓｉｎｇａｒｅｃｏｍｂｉｎａｎｔｙｅａｓｔｓｃｒｅｅｎ［Ｊ］．ＥｎｖｉｒｏｎＴｏｘｉｃｏｌＣｈｅｍ，１５：２４１－２４８ＷｅｉＬ，ＺｈａｎｇＨＣ，ＺｈａｎｇＡＱ，ＹｉｎＤＱ．２００７．Ｒｅｓｅａｒｃｈｐｒｏｇｒｅｓｓｉｎｔｈｅｌｏｗ－ｄｏｓｅｅｆｆｅｃｔｓｏｆｅｎｖｉｒｏｎｍｅｎｔａｌｅｎｄｏｃｒｉｎｅｄｉｓｒｕｐｔｉｎｇｃｈｅｍｉｃａｌｓ［Ｊ］．ＡｓｉａｎＪｏｕｒｎａｌｏｆＥｃｏｔｏｘｉｃｏｌｏｇｙ，２（１）：２５－３１（ｉｎＣｈｉｎｅｓｅ）ＺｈｏｕＧＣ，ＣｕｍｍｉｎｇｓＲ，ＬｉＹ，ＭｉｔｒａＳ，ＷｉｌｋｉｎｓｏｎＨＡ，ＥｌｂｒｅｃｈｔＡ，ＨｅｒｍｅｓＪＤ，ＳｃｈａｅｆｆｅｒＪＭ，ＳｍｉｔｈＲＧ，ＭｏｌｌｅｒＤＥ．１９９８．Ｎｕｃｌｅａｒｒｅｃｅｐｔｏｒｓｈａｖｅｄｉｓｔｉｎｃｔａｆｆｉｎｉｔｉｅｓｆｏｒｃｏａｃｔｉｖａｔｏｒｓ：ｃｈａｒａｃｔｅｒｉｚａｔｉｏｎｂｙｆｌｕｏｒｅｓｃｅｎｃｅｒｅｓｏｎａｎｃｅｅｎｅｒｇｙｔｒａｎｓｆｅｒ［Ｊ］．ＭｏｌｅｃｕｌａｒＥｎｄｏｃｒｉｎｏｌｏｇｙ，１２（１０）：１５９４－１６０４中文参考文献何世华，梁增辉，战威，贾凌志，晁福寰．２００２．环境雌激素重组酵母测评系统的建立［Ｊ］．环境与健康杂志，１９（１）：５７－５９李剑，崔青，马梅，饶凯锋，王子健．基于Ｈ４ＩＩＥ细胞株测试间接雌激素效应物质的代谢活化方法［Ｊ］．环境科学学报，２００６，２０（８）：１３２０－１３２５楼丽广，胥彬．１９９７．甾体激素作用的分子机制［Ａ］．／／金正均，王永铭，苏定冯．药理学进展［Ｃ］．北京：人民卫生出版社．１１４－１２７卫立，张洪昌，张爱茜，尹大强．２００７．环境内分泌干扰物低剂量－效应研究进展［Ｊ］．生态毒理学报，２（１）：２５－３１◆２６。

ORIGINAL PAPERAgrobacterium -mediated transformation of the winter jujube (Zizyphus jujuba Mill.)X.F.Gu ÆH.Meng ÆG.Qi ÆJ.R.ZhangReceived:16October 2007/Accepted:9April 2008/Published online:14May 2008ÓSpringer Science+Business Media B.V.2008Abstract Winter jujube,a species that originated in China,is the most prominent elite variety of jujube (Zizyphus jujuba Mill.).Due to its economic value and its recalcitrance to improvements through traditional plant breeding approaches,genetic transformation techniques may have a great potential in providing the means to transfer one or more selected desirable traits into the plant genome.We reported here an improved protocol for the Agrobacterium-mediated transforma-tion of shoot tips of winter jujube.We have identified a set of optimum transformation conditions that take into account Agrobacterium inoculum density,Agrobacterium incubation period,co-cultivation con-ditions,and vacuum (use of a vacuum pump to create a negative-pressure environment).The highest trans-formation frequency (5.2%)was obtained when the shoot-tip explants were infected for 10min and co-cultured for 4days with Agrobacterium at OD 6000.8under a negative pressure of 0.59105Pa.PCR and southern blot analyses confirmed the presence of transgenic plants and the stable integration of the target gene into the genome of regenerated plants.A histochemical staining analysis for GUS activity in the transgenic shoot tips also validated the efficiency of the transformation system.Keywords Winter jujube (Zizyphus jujuba Mill.)ÁGenetic transformation ÁShoot tip ÁNegative pressureAbbreviations BA 6-BenzylaminopurineCTAB Cetyltrimethyl ammonium bromide GA 3Gibberellic acid GUS Glucuronidase IAA Indoleacetic acid IBA Indolebutyric acidIntroductionWinter jujube,a species that originated in China,is the most prominent elite variety of jujube (Zizyphus jujuba Mill.)(Xu et al.2003).The fresh fruit of this species is highly valued among Asian consumers for its unique,delicious flavor,tenderness,juiciness,and high nutritional value.These traits have made it an economically valuable crop in Asia,especially in China (Li et al.1999).However,a key limitation to the commercial development of this crop is its relatively short-shelf-life:it is difficult maintain fresh storage quality at room temperature.(Wang et al.1999).The jujube tree and fruit are also susceptible to many disease and pests (Gao 2006).To date,winter jujube has proven relcalcitrant to improvement byX.F.Gu ÁH.Meng ÁG.Qi ÁJ.R.Zhang (&)School of Life Science,Shandong University,27Shanda Nanlu,Jinan 250100,People’s Republic of China e-mail:jrzhang@Plant Cell Tiss Organ Cult (2008)94:23–32DOI 10.1007/s11240-008-9383-zbreeding due toflower abscission,embryo abortion, and a long juvenile period(Liu et al.2004;Li et al. 2007).Genetic transformation tools,however,are not bound by the usual limitations of traditional plant breeding and,as such,provide the means to transfer one or more selected traits to winter jujube,thereby improving the commercial exploitation of this fruit. Recent studies have implicated abscissic acid(ABA) as a major player in regulating fruit ripening and senescence in plant(Cutler and Krochko1999;Sala and Lafuente2002).This suggests that the genetic modification of the endogenesis ABA biosynthetic pathway in winter jujube could result in delayed fruit ripening and increased storage and shelf-life.In addition,the introduction of genes encoding virus-resistant proteins,such as the b-1.3-glucanase gene and chitinase gene,into winter jujube would improve the plant’s resistance against fungal diseases(Li et al. 2003).Efficient and reliable in vitro regeneration and gene transformation systems are the two main requirements for the successful genetic transformation of plants.In the past,many woody species have appeared to be recalcitrant to genetic manipulation because of the difficulty in obtaining alternative explants in vitro as targets for transformation (Li et al.2007).Winter jujube has also resisted the establishment of an efficient in vitro system and, consequently,genetic modification of this plant is still a relatively inefficient procedure and has met with no large-scale success.He et al.(2003,2004)reported the transformation of in vitro internode and embryo stem explants of three jujube genotypes,but they did not provide sufficient details and the transformation efficiency was low.In an earlier publication,we reported the establishment of an efficient in vitro culture system of winter jujube for regenerating shoot tips(Gu and Zhang2005).Shoot tips were capable of regenerating cells of the shoot apical meristem that could serve as targets for genetic transformation(Dutt et al.2007).The currently preferred method for plant transformation is an Agrobacterium-mediated transformation system due to single-or low-copy integration and a high conversion efficiency–with up to85%transgenic plants being obtained in some systems(Dai et al.2001;Shou et al.2004).Here,we report on the establishment of a repro-ducible Agrobacterium-mediated transformation procedure using shoot tips of winter jujube.Various parameters of the system,including Agrobacterium inoculum density,Agrobacterium incubation period, co-cultivation conditions,and vacuum,were evalu-ated.The PCR and southern blot methods were used to confirm the development of transgenic plants. Materials and methodsPlant materials and establishment of shoot cultureMicropropagation cultures of Zhanhua winter jujube (Zizyphus jujuba Mill.)were established from dor-mant buds(Gu and Zhang2005).The dormant buds were obtained from50to55-year-old elite plants growing at the Zhanhua winter jujube institute (Zhanhua,Bingzhou,China).Regenerated shoots were cultured on shoot growth medium comprising MS medium(Murashige and Skoog1962)supple-mented with 5.77l M gibberellic acid(GA3)and 0.89l M benzylaminopurine(BA).The shoots were cultivated with30day sub-culture intervals at 25±1°C under a14/10h(light/dark)photoperiod with light supplied by cool-whitefluorescent lamps at an intensity of60l mol m-2s-1.The in vitro-grown shoot tips,approximately5–6mm in length,were then pre-cultured on shoot growth medium for7days before transformation.Sensitivity of shoot tips of winter jujubeto herbicidePrior to genetic transformation,the amount of the herbicide‘chlorsulfuron’(25%by weight in Lu¨hu-anglong;Shenyang Agricultural Chemical Company, China)required to inhibit shoot tip growth was tested. The non-transformed shoot tips were placed on shoot growth medium supplemented with0,0.125,0.25, 0.375,0.5,0.625,or0.75mg/l chlorsulfuron and subcultured three times at7day intervals.The survival rates of the explants were evaluated.The concentration of chlorsulfuron that killed most of the plants was used in subsequent transformation experiments.Agrobacterium strain and plasmidAgrobacterium strain LBA4404,which harbors plas-mid pCAMBIA1300with the betA and als genescontrolled by the cauliflower mosaic virus(CaMV) 35S promoter and terminator sequences,was used as the vector system for transformation.The als gene was cloned from Arabidopsis thaliana and confers resis-tance to the herbicide chlorsulfuron;this gene is the 197N mutation in the ALS gene encoding the enzyme acetolactate synthase,which is targeted by herbicides. The betA gene from Escherichia coli encodes choline dehydrogenase,a key enzyme in the biosynthesis of glycine betaine from choline(Fig.1).Agrobacterium cells were cultured overnight at28°C with shaking (180rpm)in YEP medium(yeast extract10g l-1, peptone10g l-1,sodium chloride5g l-1,pH7.0) supplemented with50mg l-1rifamycin and 50mg l-1kanamycin.The cells were pelleted at 1,500g for5min and resuspended in liquid MS medium with100l M acetosyringone(AS). Transformation of shoot tips with Agrobacterium Following the removal of two or three young leaves from the pre-cultured shoot tips to expose the meristem along with a few microscopic leaf primor-dia,a thin needle was used to nick the explants in the meristem tissue near the apical region.The wounded explants were then immersed in the Agrobacterium suspension for10min.The OD600was adjusted from 0.2to1.2before inoculation.To evaluate the effect of different inoculation durations,we varied the time in which the wounded explants were exposed to the Agrobacterium suspension(range:5–20min)and created negative pressure(0.59105Pa)using a vacuum pump for different infection durations(5,10, 15,and20min)at an OD6000.8Agrobacterium solution.Following inoculation,shoot tips explants were blotted dry on sterilefiler paper to remove excess Agrobacterium and then transferred to fresh shoot growth medium for2–6days co-cultivation at 25°C in the dark.After co-cultivation,the explants werefirst transferred to fresh shoot growth medium containing200mg l-1cefotaxime for7days to inhibit the growth of the Agrobacterium and then transferred onto the selection medium(shoot growth medium supplemented with0.5mg l-1chlorsulfuron)for21days at7day subculture intervals to stimulate the production of transgenic shoots.All the above processes were carried out in darkness.Surviving chlorsulfuron-resistant shoot tips were transferred onto shoot growth medium and allowed to proliferate for4weeks.The surviving shoots were rooted and transferred to soil as described by Gu and Zhang (2005).Histochemical staining for b-glucuronidaseactivityThe pCAMBIA1301plasmid contained the histo-chemical reporter gene b-glucuronidase(GUS)in which an intron sequence were inserted into the coding sequence,and the als gene driven by the CaMV35S promoter and terminator sequences were used to confirm the efficiency of the transformation system.Shoots regenerated from wild-type and infected explants were cut into segments and incu-bated in a GUS-staining solution at37°C overnight as described by Jefferson et al.(1987)and Niu et al. (2000).The plant tissues were gradually destained in 70%ethanol to remove the chlorophylls and other pigments prior to visual analysis.The results of GUS expression were documented by digital photography using an Olympus light microscope(BX51)equipped with an Olympus C500camera.PCR and southern blot analysisA CTAB protocol was used to isolate genomic DNA from young leaves removed from surviving plants that had developed roots and been grown in the greenhouse for30day(Permingeat et al.1998).The PCR amplification of the betA and als genes were carried out using als gene primer als1(50GAG GAC ACG CTG AAA TCA CC30)and als2(50GCA TCA GGG TTA GCA ACA G30),and betA gene primer bet1(CGC TAC AGG GTA AAC GCT ACA AC) and bet2(CCT CAC GGC TGC GAA TAA ATC C), respectively.The primers non-t1(50AAG CCA CTT ACT TTG CCA TCT30)and non-t2(50TTT GCT CGG AAG AGT ATG AAG30)flanking the non-T-DNA sequence of the T-DNA right border sequence of pCAMBIA1300-bet A-als were designed to determine whether there was any contamination of Agrobacterium in the transgenic plants,as verified by PCR.The PCR amplification was carried out ina Fig.1T-DNA structure of pCAMBIA1300-betA-als25l l reaction volume containing12.5ng of plasmid (used as positive control DNA)or125ng of plant DNA with12.5l M of each of the primer,5l M dNTP’s,and0.67U of Taq polymerase in19PCR buffer with25mM of MgCl2.The amplification cycling conditions consisted of one cycle at95°C for 5min,followed by35cycles(amplification at95°C for60s,56°C for60s,and72°C for60s),with a final7min extension at72°C.Southern blotting was performed to confirm the stable integration of the foreign gene in the transgenic plants.A20l g aliquot of genomic DNA was digested with EcoR I and separated by electrophoresis on a TBE-buffered0.8%(w/v)agarose gel;the excised fragments were then transferred to a Hy-bond–N+nylon membrane(Roche,Mannheim, Germany).The PCR-amplified betA gene was labeled with DIG-dUTP using a DIG-High Prime DNA Labeling and Detection kit(Roche,Mannheim, Germany).Hybridization was carried out at65°C, and immunological detection steps were performed using the DIG Nucleic Acid Detection kit according to the manufacturer’s instructions(Roche). Experimental design and data analysisEach experiment was repeated three times with at least100shoot tips for each herbicide treatment and two times for the transformation system experiment. The transformation frequency was calculated as the total number of transgenic plantlets produced relative to the total number of explants infected by Agrobac-terium.The data were analyzed using SAS version 6.12(SAS Institute,Cary,NC).Analysis of variance (ANOVA)was used to test the statistical significance, and the significance of differences among means was carried out using Duncan’s(1955)multiple range test at a significance of P=0.05.Results and discussionSensitivity of shoot-tip explants to different concentrations of the herbicide chlorsulfuronThe effects of various concentrations of the herbicide chlorsulfuron were evaluated on shoot-tip explants to determine the appropriate selection dose.Our analysis of the data revealed that fewer than20%the shoot tips were able to survive in the presence of0.5mg l-1 chlorsulfuron and that no shoots survived and no transgenic plants were obtained in the presence of higher concentrations of chlorsulfuron(data not shown).Therefore,0.5mg l-1chlorsulfuron was used in subsequent transformation experiments.Influence of Agrobacterium cell density, inoculation period and co-cultivation period,and vacuum condition on transgenic frequencyA critical factor in shoot-tip transformation systems is the density of the Agrobacterium inoculum in the inoculation medium(Li et al.2007).We obtained the best transformation frequency(3.2%)using Agro-bacterium inoculum at OD6000.8(Table1).A reduction in the mean transformation frequency was observed following inoculation with higher densities of Agrobacterium cells,possibly due to increased damage and increased production of poisons to the receptor cells.Bacterium densities of0.08–1.2have often been used in plant genetic transformation systems.Humara et al.(1999)co-cultured cotyledon explants of Pinus with Agrobacterium cells at a density of1(OD600),while Yang et al.(2005)and Miguel and Oliveira(1999)reported that the use of Table1Effect of Agrobacterium concentration(OD600value) on the transformation frequency of shoot-tip explants of winter jujube aOD600valueNumber of shoottips evaluated bNumber oftransgenicplantsTransformationfrequency(%)c0.228320.7d0.43104 1.3d0.62556 2.4ab0.82217 3.2a1.02394 1.7bc1.22643 1.1cda Shoot-tip explants were inoculated with LBA4404during an infection exposure time of10min and then co-cultured for 4daysb Each treatment consisted of two repeat experiments with at least100replicates in each experimentc Means within a column followed by the same letter are not significantly different,as indicated by Duncan’s multiple range test(P=0.05).Transformation frequency was defined as: (number of transgenic plants/total number of explants evaluated)9100%OD6000.3and0.5Agrobacterium density produced the optimal transformation rate in sugar beet and almond,respectively.As a general rule,Yu et al. (2002)found that the transformation frequency increased as the inoculum OD value(0.08–0.6) decreased in their system using hypocotyls of sweet orange and citrange as explants.It has been deter-mined that the density of Agrobacterium inoculum resulting in the highest transformation frequency is genotype-dependent.The duration of the exposure interval to Agrobac-terium cells also influences the transformation frequency of explants.Winter jujube shoot tip explants incubated for10min with Agrobacterium cells at density OD600=0.8showed a significantly increased frequency of transformation than those transformed for5min,while exposure to Agrobac-terium for more than15min resulted in a decline in transformation frequency(Table2).The exposure of the explants to Agrobacterium from1to30min as this range has been previously shown to influence transformation frequency in wood plant transforma-tion(Costa et al.2002;Yu et al.2002;Chen et al. 2006).Costa et al.(2002)selected long infection exposure times of as much as20min for the transformation of grape fruit epicotyl explants, whereas the exposure times of longer than10min decreased the transformation efficiency in Washing-ton navel orange(Bond and Roose1998).Our results are similar to those obtained in the Washington navel orange transformation system–explants of winter jujube did not show increased transformation fre-quency with an extended exposure time to Agrobacterium.Using the optimal conditions described above,the effect of varying the length of the co-cultivation period was investigated.Table3shows that the transformation frequency increased from 1.8%at 2days to3.2%at4days;however,extending the co-cultivation period to longer than4days resulted in an abundant proliferation of Agrobacterium and tissue necrosis and subsequent cell death.An increased transformation frequency has been found to be positively correlated with increases in the length of the co-cultivation up to a period of3–5days in some species(Niu et al.2000;Costa et al.2002;Kim et al. 2004).Yang et al.(2005)reported that the application of a vacuum during the transformation of sugar beet enhanced the transformation frequency.In our sys-tem,a negative pressure of0.59105Pa,created by the vacuum pump in the vacuum desiccator,resulted in a1.63-fold increase(5.2%)in the transformation frequency in comparison to that obtained at atmo-spheric pressure(Table4).It has been suggested that a vacuum pump creates a negative-pressure environ-ment that results in an increase in effective Agrobacterium volatilization,a condition conducive to the transfer of a foreign gene into plant cells.Table2Effect of the length of the exposure time to Agro-bacterium inoculum on the transformation frequency of shoot-tip explants of winter jujube aInoculation time(min)Number ofshoot tipsevaluated bNumber oftransgenicplantsTransformationfrequency(%)c53254 1.2b102217 3.2a152315 2.2ab202594 1.5ba Shoot-tip explants were inoculated with LBA4404at OD0.8 and co-cultured for4daysb Each treatment consisted of two repeat experiments with at least100replicates in each experimentc Means within a column followed by the same letter are not significantly different,as indicated by Duncan’s multiple range test(P=0.05).Transformation frequency was defined as: (number of transgenic plants/total number of explants evaluated)9100%Table3Effect of the length of the co-cultivation period on the transformation frequency of shoot-tip explants of winter jujube aDuration ofco-cultivationperiod(days)Number ofshoot tipsevaluated bNumber oftransgenicplantsTransformationfrequency(%)c22715 1.8a42217 3.2a62305 2.2aa Shoot-tip explants were inoculated with LBA4404at a density of OD0.8during an infection exposure time of10minb Each treatment consisted of two repeat experiments with at least100replicates in each experimentc Means within a column followed by the same letter are not significantly different,as indicated by Duncan’s multiple range test(P=0.05).Transformation frequency was defined as: (number of transgenic plants/total number of explants evaluated)9100%Histochemical staining of GUS activityIn order to confirm the efficiency of our transforma-tion system,we used the pCAMBIA1301plasmid containing the GUS reporter gene to transform winter jujube in our optimal Agrobacterium-mediated trans-formation system.The results of the GUS histochemical assays indicated that the GUS gene was expressed in the apical meristems of shoot tips (Fig.2a).Histochemical staining of GUS activity revealed that at least50%of the infected shoot tips were GUS positive after co-cultivation.A detailed histochemical staining for GUS activity was con-ducted to observation in vascular tissues(Fig.2b,c). Shoot tips from a transgenic plant showed strong GUS activity,while that from a wild-type winter jujube had no detectable GUS activity(Fig.2d). Regeneration of stably transformed plantsof winter jujube following Agrobacterium-mediated transformationUsing our optimal transformation procedure,we immersed the shoot tips in the Agrobacterium suspension(OD6000.8)for10min under the vacuum treatment,then co-cultured the shoot tips for4days on shoot growth medium(Fig.3a,b).The shoot tips that survived on selection medium were transferred onto Nitsch basal medium supplemented with 1.14l M indole-3-acetic acid(IAA)and 2.46l M indole-butyric acid(IBA)to induce root formation (Fig.3c,d).The rooted plants were then transplanted into plastic pots containing autoclaved vermiculite and soil(1/1,v/v)(Fig.3e)and the pots covered with plasticfilm and placed in a greenhouse maintained at 25/20°C(day/night,12/12h).The plants were irri-gated with a solution of1/10-strength MS inorganic salts at2–3day intervals and subsequently potted in soil for further growth(Fig.3f).PCR analyses were performed using als and betA gene-specific primer and genomic DNA isolated from the herbicide chlorsulfuron-resistant plants and wild-type plants.The als and betA gene fragments were amplified from chlorsulfuron-resistant plants (Figs.4,5).However,due to the possible presence of the Ti-plasmid DNA in the plant genomic DNA extracts,a technique involving only gene primers cannot be used with100%certainty to verify the presence of transgenic plants.The primers non-t1and non-t2located outside of the T-DNA region of pCAMBIA1300were therefore used to analyze the transgenic plants that had been identified by the gene-specific primer.The positive control DNA produced an amplified328-bp plasmid fragment,and some genomic DNA samples of transgenic plant did not amplify the specific target fragment(Fig.6:lanes 1–5,8).These results demonstrate that the PCR method used to analyze the transgenic winter jujube plants appears to be effective.Southern hybridization confirmed stable integration of the target gene intoTable4Effect of negative pressure on the transformation frequency of shoot-tip explants of winter jujube aInoculation time Number of shoottips evaluated b Number oftransgenic plantsTransformationfrequency(%)cNormal pressure(1.09105Pa)53254 1.2b102217 3.2a152315 2.2ab202594 1.5b Negative pressure(0.59105Pa)52395 2.1z1024913 5.2x1526011 4.2y202454 1.6za Shoot tips explants were inoculated with LBA4404at a density of OD0.8and co-cultured for4daysb Each treatment consisted of two repeat experiments with at least100replicates in each experimentc Means within a column in the normal pressure treatment or negative pressure treatment followed by the same letter are not significantly different,as indicated by Duncan’s multiple range test(P=0.05).Transformation frequency was defined as:(number of transgenic plants/total number of explants evaluated)9100%.Values in italics are significantly different in the same transformation procedure under atmospheric or negative pressure,as indicated by Duncan’s multiple range test(P=0.05)the winter jujube genome using the DIG-labeled betA gene probe.Bands of the betA gene were observed in the transgenic plants,whereas no hybridization fragments were visualized in the wild-type plants (Fig.7),indicating the stable insertion of target gene into the winter jujube genome.Woody species have often been found to be recalcitrant to the establishment of an efficient system for regenerating plantlets.Thus,the development of efficient and reliable plant transformation systems for woody plants requires the appropriate explants–for example:embryogenic calluses of Ponkan manda-rin (Li et al.2002),epicotyl segments of citrus (Ballester et al.2007),internodal stems of citrus(Gutie´rrez-E et al.1997),and shoots of cherry (Gutie`rrez-Pesce et al.1998).Winter jujubeis Fig.2Histochemicalstaining of b -glucuronidase (GUS)activity in a transgenic winter jujube plant.(a )Histochemical staining of GUS activity of the whole shoot tips of a non-transgenic (left )and transgenic plant (right )following a 4dayco-culture in MS medium containing 5.77l M gibberellic acid (GA 3)and 0.89l Mbenzylaminopurine (BA).Bar :0.4cm.(b )Histochemical staining of GUS activity of shoot tips of transgenic winter jujube plants following a 4day co-culture in MS medium containing 5.77l M GA 3and 0.89l M BA.Bar :0.3mm.(c )Histochemical staining of GUS activity of the central infected-cells of shoot tips of transgenic winter jujube plants following selection for 7days in MS medium containing 5.77l M GA 3,0.89l M BA,and 2mg l -1herbicide.Bar :0.3mm.(d )Histochemical staining of GUS activity of the shoot-tip section of a non-transgenic (left )and transgenic plant (right ).Bar :0.1cmparticularly difficult to culture in vitro.Chen et al.(2002)encountered major difficulties in regenerating plantlets from callus of winter jujube,and only a few plantlets differentiated from the callus.This lack of a system by which to produce appropriate explantscurrently limits the transformation of winter jujube.Shoot apical meristems obtained through in vitro micropropagation may be a represent alternative as the target for transformation (Dutt et al.2007).Similar beneficial results have been reported in sugar beet (Yang et al.2005),maize (Zhang et al.2005),and cotton (Lv et al.2004).The system of transfor-mation in winter jujube using shoot tips that is presented here opens the door to the genetic regula-tion of ABA biosynthesis and disease resistance in winter jujube.In conclusion,we have improved the transforma-tion protocol for shoot tips of winter jujube using an Agrobacterium -mediated method.The highest trans-formation frequency (5.2%)was obtained when the shoot tips were infected for 10min at an Agrobac-terium inoculum density of OD 6000.8underaFig.3The procedure used to obtain transformed winter jujube plants.(a )Pre-cultured shoot tips of winter jujube.Bar :1cm.(b )Shoot tips infected with Agrobacterium .Bar :1cm.(c )Selected putative transformed plantlets.Bar:1cm.(d )Rootedputative transformed plants.Bar :1.6cm.(e )Putative trans-formed plants at the soiled pot.Bar :2.4cm.(f )Transgenic plants at the soiled pot.Bar :12cmFig.4Identification of transgenic winter jujube plants based on PCR detection of the als nes 1–7:transgenic winter ne 8:wild-type winter jujube as negative ne 9:pCAMBIA1300-betA -als as positive ne M:molecular weight marks ofDL2000Fig.5Identification of transgenic winter jujube plants based on PCR detection of the betA gen nes:1–7transgenic winter jujube .Lane 8:wild-type winter jujube as negative ne 9:pCAMBIA1300-betA -als as positive ne M:molecular weight marks ofDL2000Fig.6PCR amplifications of a non-TDNA fragment to determine whether there was contamination of Agrobacterium or nes 1–7:putative transgenic winter ne 8:wild-type winter jujube as negative ne 9:pCAM-BIA1300-betA -als plasmid as positive ne M:molecular weight marks of DL2000negative pressure of 0.59105Pa,followed by co-cultivation for 4days.The PCR and southern blot analyses confirmed the insertion of the foreign gene in the winter jujube genome.Acknowledgments This research was supported by the National Natural Science Foundation of China (30300242)and the project of Department of Science and Technology of Shandong.ReferencesBallester A,Cervera M,Pena L (2007)Efficient production oftransgenic citrus plants using isopentenyl transferase positive selection and removal of the marker gene by site-specific recombination.Plant Cell Rep 26:39–45Bond JE,Roose ML (1998)Agrobacterium -mediated trans-formation of the commercially important citrus cultivar Washington navel orange.Plant Cell Rep 18:229–234Chen YQ,Lu LT,Deng W,Yang XY,McAvoy R,Zhao DG,Pei Y,Luo KM,Duan H,Smith W,Thammina C,Zheng XL,Ellis D,Li Y (2006)In vitro regeneration and Agrobacterium -mediated genetic transformation of Euonymus alatus .Plant Cell Rep 25:1043–1051Costa MGC,Otoni WC,Moore GA (2002)An evaluationof factors affecting the efficiency of Agrobacterium -mediated transformation of Citrus paradis e 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(1987)GUS fusions:b -glucuronidase as a sensitive and versatile gene fusion marker in higher plants.EMBO J 6:3901–3907Kim KH,Lee YH,Kim D,Park YH,Lee JY,Hwang YS,KimYH (2004)Agrobacterium -mediated genetic transforma-tion of Perilla frutescens .Plant Cell Rep 23:386–390Li DD,Shi W,Deng XX (2002)Agrobacterium -mediatedtransformation of embryogenic calluses of Ponkan man-darin and the regeneration of plants containing the chimeric ribonuclease gene.Plant Cell Rep 21:153–156Li HW,Feng SQ,Zhao YM (1999)Primary study on thestorage of Chinese jujube dongzao.J Shanxi Agric Sci 27:65–67Li YZ,Zheng XH,Tang HL,Zhu JW,Yang JM (2003)Increase of b -1.3-glucanase and chitinase activities in cotton callus cells treated by dalicylic acid and toxin of verticillium dahliae .Acta Botanica Scinica 45:802–808Li ZN,Fang F,Liu GF,Bao MZ (2007)Stable Agrobacterium -mediated genetic transformation of London plane tree (Platanus acerifolia Willd.)Plant Cell Rep26:641–650Fig.7Southern blot analysis of transgenic winter jujube plants.The plants genomic DNA was digested with Eco ne M:molecular weight markers of k DNA\Hin dIII ne 1:pCAMBIA1300-betA -als plasmid as positive ne 2:wild-type winter jujube as negative ne 3:transgenic plant。

Abstract Soil microorganisms mediate many processes such as nitrification,denitrification,and methanogenesis that regulate ecosystem func-tioning and also feed back to influence atmo-spheric chemistry.These processes are of particular interest in freshwater wetland ecosys-tems where nutrient cycling is highly responsive to fluctuating hydrology and nutrients and soil gas releases may be sensitive to climate warming.In this review we briefly summarize research from process and taxonomic approaches to the study of wetland biogeochemistry and microbial ecology,and highlight areas where further research is needed to increase our mechanistic understanding of wetland system functioning.Research in wetland biogeochemistry has most often been focused on processes (e.g.,methanogenesis),and less often on microbial communities or on popu-lations of specific microorganisms of interest.Research on process has focused on controls over,and rates of,denitrification,methanogenesis,and methanotrophy.There has been some work on sulfate and iron transformations and wetland en-zyme activities.Work to date indicates an important process level role for hydrology and soil nutrient status.The impact of plant species composition on processes is potentially critical,but is as yet poorly understood.Research on microbial communities in wetland soils has pri-marily focused on bacteria responsible for meth-anogenesis,denitrification,and sulfate reduction.There has been less work on taxonomic groups such as those responsible for nitrogen fixation,or aerobic processes such as nitrification.Work on general community composition and on wetland mycorrhizal fungi is particularly sparse.The general goal of microbial research has been to understand how microbial groups respond to the environment.There has been relatively little work done on the interactions among environ-mental controls over process rates,environmental constraints on microbial activities and community composition,and changes in processes at theJ.L.M.Gutknecht ÆT.C.Balser (&)Department of Soil Science,University of Wisconsin-Madison,1525Observatory Dr.,Madison,WI 53706,USAe-mail:tcbalser@R.M.Goodman ÆT.C.BalserGaylord Nelson Institute for Environmental Studies,University of Wisconsin-Madison,550N.Park St.,Madison,WI 53706,USA R.M.GoodmanDepartment of Plant Pathology,University of Wisconsin-Madison,1630Linden Dr.,Madison,WI 53706,USA Present Address:R.M.GoodmanRutgers Cook College,88Lipman Dr.,Suite 104,New Brunswick,NJ 08901,USAPlant Soil (2006)289:17–34DOI 10.1007/s11104-006-9105-4Linking soil process and microbial ecology in freshwater wetland ecosystemsJessica L.M.Gutknecht ÆRobert M.Goodman ÆTeri C.BalserReceived:1February 2006/Accepted:21August 2006/Published online:10October 2006ÓSpringer Science+Business Media B.V.2006ecosystem level.Finding ways to link process-based and biochemical or gene-based assays is becoming increasingly important as we seek a mechanistic understanding of the response of wetland ecosystems to current and future anthropogenic perturbations.We discuss the potential of new approaches,and highlight areas for further research.Keywords Microbial ecologyÆWetlandsÆMicrobial functionÆWetland ecologyÆNitrificationÆDenitrificationÆMethanogenesisÆMicrobial communitiesÆMycorrhizal fungi IntroductionDuring the past two decades there has been increasing interest in understanding factors con-trolling ecosystem processes such as decomposi-tion of organic matter,nitrification,nitrogen fixation,denitrification,methanotrophy,and methanogenesis.Research to date has been pri-marily focused in two areas:biogeochemical studies of process,and microbial ecological stud-ies of populations and community structure. While significant progress has been made in each area,such as description of processes like meth-anogenesis(Le Mer and Roger2001),or the occurrence and population dynamics of the organisms responsible for a given process like methanogenic bacteria(Utsumi et al.2003),we still lack a mechanistic understanding of the connection between measured processes and the biology of the organisms responsible for those processes.Such a connection is now not only possible,but is also critical in increasing our ability to protect,restore and manage ecosystems.This linkage is particularly important in fresh-water wetland ecosystems.These wetlands, because of their complex hydrology and nutrient cycling and presence in both urban and unman-aged areas,are uniquely positioned to influence biogeochemical cycling in many regions and at many scales.Wetland ecosystems are character-ized by hydric soils and hydrophilic plant com-munities(Mausbach and Parker2001)and have fluctuating hydrology that gives rise to interplay between aerobic and anaerobic processes (Davidsson et al.1997;Stepanauskas et al.1996) (Fig.1).Human activities such as alteration of water,sediment,and nutrient loads to wetlands may significantly alter wetland plant communities (Kercher and Zedler2004),and wetland microbial communities(Mentzer et al.2006).In high lati-tude ecosystems,hydric soils currently under per-mafrost may be critical as sources of radiatively active gases(Freeman et al.2001).A mechanistic understanding of carbon and nutrient cycling in wetlands is thus important for global scale climate modeling efforts,as well as for regional scale res-toration and protection of wetland systems.In general,wetland studies have tended to focus either on measurement of microbial-mediated process(e.g.,measurements of nitrate or methane evolved from a system)or they focus on characterization of microbial(usually bacte-rial)populations or communities(ing ge-netic probes to research a specific microorganism or characterizing community lipid or DNA com-position).The type of work conducted in man-aged versus unmanaged wetlands also seems to be split;process-based studies of nutrient and carbon fluxes have more often been focused on unman-aged systems,while wastewater treatment facili-ties,polluted areas,and constructed wetlands have been the focus of the majority of microbi-ally-based research(Gilliam1994).These differences in methodological approach, often dictated by epistemological differences be-tween microbiologists and ecosystem ecologists, have resulted in a limit to our conceptual under-standing of the link between wetland microbial community composition,biogeochemical pro-cesses rates,and points of control.The lack of interdisciplinary combination may thus be a bar-rier to a more synthetic understanding of fresh-water wetland ecosystem function and instead results in a more qualitative,compartmentalized understanding.Work to date has laid the foundation for future guiding questions:How do we scale up from microorganisms to regional or global ecosystem function?How can we tie microbial physiology/ metabolism to larger scale nutrient cycling and ecosystem function?Is it important to tie the biology of the organisms to their functional pro-cesses?The answers to these questions will almostcertainly require overcoming methodological barriers between microbial taxonomic or phono-logic assays (genomics,microscopy,molecular techniques)and process measurements,as well as epistemological barriers (i.e.,differences in ‘ways of knowing’science)between researchers trying to link these fields (Balser et al.this issue).In this review we summarize current research from both process and structural/taxonomicviewpoints,discuss work to date on combining structural and functional techniques,and finally suggest research areas that will benefit most from the combined approaches.The overall goal is to explore ways to we can increase our mechanistic understanding of wetland system functioning in the context of current anthropogenic changes.Microbially mediated processes and controls The dominant processes,studied in a variety of ways (Table 1),that have been the focus of wetland microbial research include denitrification and nitrification,methanogenesis and methano-trophy,sulfate and iron oxidation/reduction,and enzyme activities (Tables 2and 3).While there are certainly other microbially mediated processes important to wetlands (e.g.,nitrogen fixation and the mobilization of secondary nutri-ents such as copper,manganese and magnesium),these have been less studied and fall outside the scope of this review.Table 1Methods used to study wetland microbial processes MethodExample references in situ measurements Chang and Yang (2003)Isotopic labelingStepanauskas et al.(1996)Laboratory incubation Lowrance et al.(1995)Potential assay (lab incubationoptimizing conditionsfor the process of interest)Groffman and Crawford (2003)Enzyme activity assaysGroffman et al.(1996)(denitrifying enzymeactivity)Fig.1Wetland structure.Water table height,depth from surface,and distance from plant roots create oxic to anoxic gradients.The result is a complex interplay between anaerobic and aerobic conditions that allows for a wide range in processes to occur in wetland soilsDenitrification,the anaerobic transformation of nitrate to nitrous oxide and dinitrogen gas (Myrold2005)has been studied widely due to its potential importance in removal of nitrate (Gilliam1994)(Table2).Concern over methane as a greenhouse gas has prompted much of the current research on methanogenesis(the anaer-obic production of methane from organic matter breakdown performed by methanogenic archaea), another widely studied wetland process(Table3; Wolf and Wagner2005).Aerobic processes such as nitrification and methanotrophy have been studied less in freshwater wetlands and may occur only in surface waters or other aerobic niches in wetland soils(such as near roots,Colmer2003; Fig.1).Rates are lower than those for anaerobic processes in wetlands(Tables2and3),but may still be significant for wetland nutrient cycling (Duncan and Groffman1994;Le Mer and Roger 2001).Iron and sulfate reduction have been studied primarily because of their importance in acid mine drainage(Sparks2003),but other than at acid mine sites,these processes have been studied less and are less well understood.Sulfate reduction appears to have similar variability and rates as methanogenesis(Table3).Finally,enzy-matic degradation of large polymers such as cellulose or chitin is particularly of interest in wetland and riparian soils as an indicator of biogeochemical cycling,and as a potential source of soil feedback to climate change(Dick and Tabatabai1992;Freeman et al.1997,2001). Measurements of enzyme activity in wet soils are relatively few,and they vary widely acrossTable2Wetland denitrification ratesEcosystem type Denitrification rate(a kgN ha–1yr–1orb mgN g soil–1d–1)Method used ReferenceMaple swamp(SPD)1 5.7a Laboratory incubation Hanson et al.(1994)Maple swamp(PD)1 6.3a Laboratory incubation Hanson et al.(1994)Maple swamp(VPD)116.3a Laboratory incubation Hanson et al.(1994)Riparian forest68a Laboratory incubation Lowrence et al.(1995)Riparian forest(SPD)1,2 4.9a Laboratory incubation Groffman and Hanson(1997) Riparian forest(PD)1,28.3a Laboratory incubation Groffman and Hanson(1997) Riparian forest(VPD)1,239.3a Laboratory incubation Groffman and Hanson(1997) Wet meadow 2.68a in situ Goodroad and Keeney(1984) Wet meadow735a Incubation/isotopic labeling Stepanauskas et al.(1996)Wet meadow546a in situ Stepanauskas et al.(1996)Wet meadow sand430a Laboratory incubation Davidsson and Leonardson(1997) Wet meadow peat220a Laboratory incubation Davidsson and Leonardson(1997) Wet meadow peat562a in situ Davidsson and Stahl(2000)Wet meadow sandy loam102a in situ Davidsson and Stahl(2000)Wet meadow silt loam255a in situ Davidsson and Stahl(2000) Coastal wetland3205a Laboratory incubation Tomaszek et al.(1997)Riparian forest 5.6b Potential assay Pavel et al.(1996)Riparian forest86b Denitrifing enzyme activity Groffman and Crawford(2003) Riverine wetlands-silty 1.6b Laboratory incubation Johnston et al.(2001)Riverine wetlands-clayey 2.7b Laboratory incubation Johnston et al.(2001)12wetlands0.6–108b Denitrifing enzyme activity Groffman et al.(1996)10US wetlands8.2–130b Laboratory incubation D’Angelo and Reddy(1999) Maple swamp(PD)1 4.2b Denitrifing enzyme activity Duncan and Groffman(1994) Maple swamp(VPD)110.2b Denitrifing enzyme activity Duncan and Groffman(1994) Riparian forest4 1.9b in situ Clement et al.(2002)Denitrification rates from a variety of wetlands have been assessed.Rates have been converted to a common unit(1)PD=poorly drained soil,VPD=very poorly drained soil,SPD=somewhat poorly drained soil(2)Values are averages over two sample years from soils over a toposequence of parent materials(3)Values are an average of laboratory incubation rates(4)Rates from Clement et al.(2002)are an average over toposequence zonesTable3Wetland process ratesMethod used ReferenceProcess Ecosystem type Process rate(a kg ha–1yr–1orb mg g soil–1d–1)Nitrification Swamp forest15a Potential assay Zak and Grigal(1991)Nitrification Maple swamp PD10.3b Potential assay Duncan and Groffman(1994) Nitrification Maple swamp VPD1b Potential assay Duncan and Groffman(1994) Nitrification Riparian forest–0.07b Potential assay Groffman and Crawford(2003) Nitrification Riparian forest0.12b Potential assay Groffman and Crawford(2003) Nitrification Riparian forest0.1b in situ Clement et al.(2002)Nitrification Riparian wet meadow0.1b in situ Clement et al.(2002)Nitrification12wetlands–0.25–1.0b Potential assay Groffman et al.(1996) Methanogenesis N.Taiwan wetland159a in situ Chang and Yang(2003) Methanogenesis N.Taiwan wetland12.3a in situ Chang and Yang(2003) Methanogenesis N.Taiwan wetland20.2a in situ Chang and Yang(2003) Methanogenesis Boreal peatlands–10.58–2,883a Laboratory incubation Huttunen et al.(2003) Methanogenesis Review of many0–28,470a Laboratory and in situ Le Mer and Roger(2001) Methanotrophy Review of many0–620a Laboratory and in situ Le Mer and Roger(2001) Sulfate reduction10US wetlands10–110b Laboratory incubation D’Angelo and Reddy(1999) Iron reduction Riparian Forest6379a Laboratory incubation Roden and Wetzel(1996) Several process rates from a variety of wetlands have been assessed.Rates have been converted to a common unit (1)PD=poorly drained soil,VPD=very poorly drained soil.Values are an average of3sitesFig.2Relationshipsamong controls overwetland ecosystemmicrobial communitiesand element cycling.Arrows indicaterelationships,and widthof arrows indicatesrelative importance ofrelationship for ecosystemfunctioning.Dashedarrows representinteractions that arepoorly understood,eventhough they may beimportantdifferent wetland ecosystems(Kang and Freeman 1999;Burns and Ryder2001;Mentzer et al.2006). All of these processes vary greatly between wet-land types(Tables2and3),and the explanation likely lies in greater understanding of process controls.Factors such as temperature,moisture,and seasonality of temperature and moisture act to control wetland microbial activities,resulting in changes in key biogeochemical cycles(Fig.2).A review of the factors controlling wetland pro-cesses may offer insight into the large variation in processes and rates among wetland types(such as riparian forests,wet meadows,and fens)and provide an overall framework for understanding potentially important factors in wetland ecosys-tem function.Hydrology has consistently proved an impor-tant controlling variable.Studies with experi-mentally varied water level have yielded relatively straightforward and predictable results. In general,increased water level increases the rate of anaerobic processes(denitrification, methanogenesis,and sulfate reduction),and decreases rates of aerobic processes(nitrification) presumably by decreasing available oxygen and thereby increasing anaerobic soil microsites (Table4).Drying/wetting cycles may also be important in increasing enzyme activities(Burns and Ryder2001;Corstanje and Reddy2004) and stimulating denitrification in wet cycles and increasing nitrification in dry cycles(Qiu and McComb1996;Tanner et al.1999;Eaton2001; Venterink et al.2002).Wet-up cycles after sea-sonal wetland dry-down may be important for nitrogen cycling and loss from the system(Smith and Tiedje1979).The study not only of water quantity,but also of dynamic processes such as drying/wetting cycles is important in understand-ing wetland functioning,asfluctuating hydrology is a dominant feature of wetland nutrient cycling. Indeed,researchers are realizing more and more that temporalfluctuations in soil environments are critical in understanding ecosystem processes (Bardgett and Shine1999;Mentzer et al.2006).Soil fertility and/or substrate availability also influences wetland process rates.For the most part, microbial processes in wetlands have higher rates under conditions of higher soil fertility,or when the substrate of the process in question is added or is abundant(Table4).Exceptions have been re-ported by King(1996)for methanotrophy,and by Feng and Hsieh(1998)for sulfate reduction (Table4).However,the King(1996)study was performed in a peat marsh(distinct from other wetland types),and controls on methanotrophy there may be distinct from non-peat accumulating wetlands.Alternatively,factors other than meth-ane availability control methanotrophy in peat wetlands.The exception in sulfate reduction may simply be the low number of studies on sulfate cycling compared with other freshwater wetland processes(Tables3and4).Feng and Hsieh found that sulfate loading increased sulfate reduction in only one of two swamp soils.They attributed the difference to soil properties.pH is another important but poorly studied control over wetland process soil pH may regulate methanogenesis(Yavitt et al.2005), methanotrophy(Dedysh and Panikov1997b) oxidative enzyme activities(Williams et al.2000), and nitrogen transformations(Davidsson and Stahl2000).The role of pH in affecting process rates and in structuring microbial communities has received increased attention,and is an area where there is need for more future research.Perhaps the least resolved level of control over wetland functioning is the effect of plant species presence and relative abundance(Table4).While it is relatively well established that the presence of plants usually increases microbial process rates in wetlands(Table4),the importance of plant species composition or plant community structure remains unclear(Kao et al.2003;Kao-Kniffin and Balser in press).There is wide variation in results to date,likely due to the extremely limited number of studies in this area.The few that have been done have yielded inconsistent results. While plants can influence microbial activities directly through provision of carbon,and indi-rectly through rhizosphere ventilation,the mechanistic link between above and below ground community structure has yet to be estab-lished(Bardgett and Shine1999;Wolters et al. 2000).However,examination of plant species effects based on their nutrient content might prove helpful as a framework for this under-standing.Work by Hume et al.(2002)indicatesthat denitrification rates can be related to plant carbon quality.Another useful direction for future research might be to focus not only on empirical measurements of the end-result(pro-cess rates),but also on the plant/microbe inter-actions associated with a given process.Plant species-specific interactions with bacterial or fungal populations can influence process rate and occurrence.For example,nitrogen-fixing bacteria require close association with plant roots and depend on very specific host–microorganism interactions(Graham2005;Wolf and Wagner 2005).A small body of research has demonstrated that rhizoplane(root surface)dwelling nitrogen-fixing bacteria have been shown to vary between plant species(Chelius and Lepo1999;Bergholz et al.2001;Prieme et al.2002).In direct contrast, rhizoplane dwelling methanotrophs do not appear to vary between plant species(Calhoun and King 1998).Therefore plant species and plantTable4Controls over wetland process Process Control factorsWater Temperature Seasonality Soilfertility Substrate PlantpresencePlant speciesDenitrification+1+2/–3Spring and,orFall4/no effect5+6+7+8+9/–10Nitrification–11nd Summer12/no effect5+13+14+15–16 Methanogenesis+17+18/–19Summer20+21+22+23+24 Methanotrophy nd nd Summer25nd+26/–27+28ndIron reduction nd nd nd+29+29+30,31ndSulfate reduction+32nd nd+33/–33+33/–33,34nd nd Hydrolytic enzymeactivity–35+36/–36Summer36/no effect36nd+37/–38nd nd Oxidative enzymeactivityno effect39nd nd nd nd nd ndResearch is summarized to determine whether a process increases(+)or decreases(–)in response to each control factor. Superscript numbers indicate number(below)for references.‘Water’and‘temperature’indicates a process rate changes when water level or temperature are increased.‘Seasonality’is the most active season for each process.‘Soil fertility’indicates a process rate change in sites of differing fertility or added fertility(including available organic carbon).‘‘Sub-strate’’indicates response of the given process to substrate additions;for instance,the response of denitrification to nitrate.‘‘Plant presence’’indicates process rate change when plants/roots are present.‘‘Plant Species’’indicates whether process rates change under different plant species.References:(1)Smith and Tiedje(1979);Ambus and Christensen(1993);Hanson et al.(1994);Groffman and Hanson(1997);Davidsson and Leonardson(1997);Jordan et al.(1998);Flite et al.(2001); Hunter and Faulkner(2001);Groffman and Crawford(2003)(2)Willems et al.(1997)(3)Kuschk et al.(2003)(4)Zak and Grigal(1991);Ambus and Christensen(1993);Hanson et al.(1994);Lowrance et al.(1995);Davidsson and Leonardson (1997);Tobias et al.(2001)(5)Clement et al.(2002)(6)Ambus and Christensen(1993);Verhoeven et al.(1996);Groffman and Hanson(1997);Jordan et al.(1998);Bachand and Horne(2000);Davidsson and Stahl(2000);Van Hoewyk et al.(2000); Casey and Klaine(2001);Brusse and Gunkel(2002);Groffman and Crawford(2003)(7)Ambus and Christensen(1993); Kirkham and Wilkins(1993);Schipper et al.(1993);Hanson et al.(1994);Seitzinger(1994);Davidsson and Leonardson (1997);Delaune et al.(1998);Jordan et al.(1998);White and Reddy(1999);Casey and Klaine(2001);Davidsson et al. (2002)(8)Smith and Delaune(1984);Kristensen et al.(1998);Tanner et al.(1999)(9)Lowrence et al.(1995);Eriksson and Andersson(1999);Bachand and Horne(2000)(10)Otto et al.1999;Johnston et al.(2001);Clement et al.(2002)(11)Qiu and McComb(1996)(12)Zak and Grigal(1991)(13)Zhu and Ehrenfeld(1999)(14)Matheson et al.(2003)(15)Engelaar et al.(1995)(16)Otto et al.(1999)(17)Coles and Yavitt(2002);Freeman et al.(2002);Rask et al.(2002);Bellisario et al. (1999);Wickland et al.(1999);Van den Pol-Van Dasselaar et al.(1999);Macdonald et al.(1998);Hargreaves and Fowler (1998)(18)Westermann(1993);Granberg et al.(2001);Updegraff et al.(1998);Yavitt et al.(2000)(19)Updegraff et al. (1998);Yavitt et al.(2000)(20)Wieder and Yavitt(1991);Huang et al.(2005)(21)Basiliko and Yavitt(2001);Yavitt and Lang(1990);Weider and Yavitt(1991)(22)Segers(1998);Brauer et al.(2004)(23)Segers(1998);Kim et al.(1998);Coles and Yavitt(2004)(24)Strom et al.(2003);Rask et al.(2002)(25)Segers(1998)(26)Vandernat et al.(1997);Dedysh and Panikov(1997a);Megonigal and Schlesinger(2002)(27)King(1996)(28)Vandernat et al.(1997)(29)Roden and Wetzel (2002)(30)Weiss et al.(2004)(31)Roden and Wetzel(1996)(32)Devito and Hill(1999)(33)Feng and Hsieh(1998)(34) Vile et al.(2003a)(35)Freeman et al.(1996,1998);Kang et al.(1998);Kang and Freeman(1999);Yavitt et al.(2004)(36) Kang and Freeman(1999)(37)Shackle et al.(2000);Gusewell and Freeman(2003)(38)Wright and Reddy(2001)(39) Freeman et al.(1996);Williams et al.(2000)community composition may influence nitrogen fixation rates but not methane consumption rates; thus showing the importance of understanding interactions between specific microorganisms, plant species,and the related process rates.Interactions among biogeochemical cycles have not been well studied,but may be another important control over wetland processes.For example,methane production may be limited by microbial iron oxide reduction(based on a decrease in methanogenesis in rhizosphere sam-ples with high rates of iron reduction)(Roden and Wetzel1996,2003).Electronflow can also be diverted from methanogenesis toward iron oxide reduction when microorganisms are in the pres-ence of crystalline iron oxide(Roden2003).Sul-fate reduction may be more important to total anaerobic carbon mineralization than methane production(Vile et al.2003a,b);and sulfate deposition may even decrease methane produc-tion(Blodau et al.2002;Gauci et al.2004). Methane production and sulfate reduction may decrease with increased nitrate/denitrification rates(Westermann and Ahring1987).Studies of multiple processes indicate there may be impor-tant interactions that add to the complexity of wetland biogeochemistry.It is difficult,however, to synthesize the contribution of these studies from the small research base currently available.In conclusion,hydrology and substrate availability are the primary keys to understanding variability in process rate and occurrence.Areas that may be particularly important for future research are the importance of pH and plant community structure in process control.In addi-tion,while functional studies have provided a de-tailed empirical understanding of many wetland soil process rates and their controls,there are conceptual limitations to studying function alone that become apparent when results among studies are inconsistent or when more complex aspects of wetland ecosystem function are examined.In these cases,the research could provide more insight if it was approached not only from a process stand-point,but from a microbial standpoint as e of methods that combine these two aspects is unfortunately rare.Below,we briefly review microbiological(community and taxonomic) research to date in wetland ecosystems.Microbial communities and populationsin wetland soilsMicroorganisms have been characterized in freshwater wetland ecosystems using a variety of approaches and methods(Table5).General community structure(fingerprints)has been described using biochemical techniques such as phospholipids fatty acid analysis(PLFA)and less often with gene-basedfingerprinting techniques (such as terminal restriction fragment length polymorphisms,TRFLP).The majority of microbial studies in wetlands have been focused on bacterial groups that carryout processes of interest.In general,studies of wetland soil microbiology are few,and thus more difficult to synthesize.PLFA analysis has been used to report differ-ences in community composition in different wetlands wetlands(Borga et al.1994;Sundh et al. 1997;Boon et al.1996)as well as across gradients of nutrient stress in peatlands(Borga et al.1994). Lipids have also been used to assess rhizosphere effects on community composition(Halbritter and Mogyorossy2002).In general these studies suggest that microbial community composition varies between wetland sites and that differences may be due to differences in water level(Sundh et al.1997;Mentzer et al.2006).Wetland microbial research has focused far more on specific microorganisms responsible for key processes than on general microbial commu-nityfingerprints.Studies of methanotrophic bac-terial communities in wetlands indicate that acidic peatlands have unique genetic composition (Dedysh et al.1998a,b,2000,2003;Dedysh2002; Wartiainen et al.2003;Sizova et al.2003; Kemnitz et al.2004).Methanogenic communities may also be unique in peatlands(Utsumi et al. 2003).The importance of unique communities responsible for key processes has been demon-strated in upland areas(Schimel and Gulledge 1998;Cavigelli and Robertson2000).In these studies,rather than processes being controlled by environmental factors such as soil water content or methane availability,the physiology of the microorganisms themselves was uniquely adapted to extant conditions and could influence process rates independent of changes in the environment.。

/science?_ob=ArticleURL&_udi=B8SV2-4R7VJ06-1N8&_user=10&_coverDate=10％2F22％2F2009＆_alid = 1428302819＆_rdoc = 18＆_fmt =高＆_orig =搜索= 48642＆＆_cdi _st = 13＆_docanchor =＆_ct = 173＆_acct = C000050221＆_version = 1＆_urlVersion = 0＆_userid = 10＆md5的= e6e7a6940f3e1d0da9f5ae2bb4a4e6cf个人主页：10.1016/B978-008055232-3.62317-7 |奥曲肽布赖恩属FurmanaaStrathclyde生物医学科学研究所，格拉斯哥，英国抽象奥曲肽是一种合成的，生长抑素8肽模拟...命名法名称的临床表醋酸奥曲肽相关名称来源：EMTREE奥曲肽，醋酸奥曲肽，善宁（贸易）;长效奥曲肽站（贸易）;复合201995; dextrophenylalanylcysteinylphenylalanyl右旋; dextrophenylalanylcysteinylphenylalanyl右旋tryptophyllysylthreonyl ñ [2羟基; longastatin; longastatina，醋酸奥曲肽，善宁，sandostatina;奥曲肽拉尔; sandstatin，磺胺嘧啶201995;短信201 995;短信201-995，短信201995;右旋phenylalanylcysteinylphenylalanyl右旋tryptophyllysylthreonyl ñ [2羟基1（羟甲基）丙基二硫化物cysteinamide 2,7];右旋phenylalanylcysteinylphenylalanyl右旋tryptophyllysylthreonylcysteinylthreoninol二硫;奥曲肽pamoate; oncolar; samilstin; sdz201995，短信995; sms201 995; sms201 - 995; sms201995; sms995化学名称的D -苯丙- L型半胱氨酸- L型苯丙三维- tryptophyl - L型赖氨酰- L型threonyl -的N - [（1R的，2R的）-2羟基- 1 -（羟甲基）丙基] - L型cysteinamide循环（27 ）二硫化物醋酸CAS号83150-76-9/science?_ob=ArticleURL&_udi=B7582-4B9D7MN-2&_user=10&_coverDate=02％2F29％2F2004＆_alid =一十四亿二千八百三十万二千八百十九＆_rdoc = 46＆_fmt =高＆_orig =搜索= 12914＆＆_cdi _st = 13＆_docanchor =＆_ct = 173＆_acct = C000050221＆_version = 1＆_urlVersion = 0＆_userid = 10＆md5的= ca8cc63ee679cdc593d005cf182224f3个人主页：10.1016/j.dld.2003.11.018 |生长抑素受体：从基础科学到临床的方法，甲状腺米长扎泰利和E.长德利阿布鲁乌贝蒂，内分泌科，生物医学科学和先进疗法，费拉拉大学部，经萨沃纳罗拉9，44100，费拉拉，意大利可在线2003年12月24日。

花叶艳山姜叶片精油提取工艺优化陈建烟;李欣欣;余雪芳;刘鑫;苏永发;李永裕;吴少华【摘要】以花叶艳山姜叶片为材料,采用水蒸气蒸馏法,对其精油提取工艺进行优化.通过对蒸馏时间、浸泡时间、料液比和氯化钠浓度4个因素进行单因素试验和正交试验.结果表明,各因素对精油得率都有影响,影响大小顺序为:蒸馏时间＞料液比＞氯化钠浓度＞浸泡时间.同时确定了最优提取工艺条件为蒸馏时间1.5 h、料液比1∶6、浸泡时间30 min和氯化钠浓度1.5％,在此条件下精油得率为0.216％.【期刊名称】《热带作物学报》【年(卷),期】2014(035)005【总页数】4页(P1012-1015)【关键词】花叶艳山姜;精油;水蒸气蒸馏;工艺优化【作者】陈建烟;李欣欣;余雪芳;刘鑫;苏永发;李永裕;吴少华【作者单位】福建农林大学园艺学院,福建福州 350002;福建农林大学园艺植物天然产物研究所,福建福州 350002【正文语种】中文【中图分类】Q946.85;S682.36doi 10.3969/j.issn.1000-2561.2014.05.030植物精油是一类植物次生代谢产物的萃取物，可随水蒸气蒸出。

许多植物精油及其成分除具香气外，还有抗癌、抗炎症、抗病毒、抗氧化、抗真菌和抗虫等多种生物活性[1-8]，被广泛应用于医疗保健、果蔬保鲜、日化工业和农业等多个领域，具有极高的应用价值。

花叶艳山姜（Alpinia zerumbet‘Variegata’）是姜科（Zingiberaceae）山姜属（Alpinia）植物。

花叶艳山姜是艳山姜的主要园艺栽培品种，据报道艳山姜叶片精油具有多种生物活性[9-14]，但关于花叶艳山姜叶片精油的研究极少。

花叶艳山姜叶片具有抗氧化和抑菌活性[15-16]，并具独特香气，适应性强，资源丰富，可作为提取植物精油的优选材料。

花叶艳山姜叶片精油具有广阔的开发前景，不仅可应用于化妆品领域，并还有望应用于其它许多领域，因此对其进行提取并加以研究利用具有重要的实用意义。

线粒体靶向抗氧化剂研究进展樊鹏程;葛越;蒋炜;景临林;马慧萍;贾正平【摘要】线粒体是细胞呼吸的主要场所，在细胞的生命周期中扮演重要角色，三羧酸循环和氧化磷酸化都是在线粒体中进行。

线粒体功能障碍可导致一系列疾病，如缺血‐再灌注损伤、败血症和糖尿病等。

线粒体是神经退行性病变的治疗靶点，也是药物转运策略研究的引人注目的靶位。

虽然线粒体所介导的疾病进程的分子机制尚未完全阐明，但氧化应激是关键的环节。

开发线粒体靶向的抗氧化应激保护药物具有诱人的前景。

线粒体靶向抗氧化剂是指以线粒体为作用靶位的具有抗氧化作用的药物。

该文介绍了现有的线粒体靶向抗氧化剂的概念、分类及其疾病治疗研究进展。

%Mitochondria are the main places of cellular respiration as well as the citric acid cycle and oxidative phospho‐rylation .It plays an important role in controlling the life and death of cells .Mitochondrial dysfunction leads to a series of human diseases such a s ischemia‐reperfusioninjury ,sepsis and diabetes .Mitochondrial become an attractive target for drug transporters strategy and therapeutic targets for neurodegeneration .Although the molecular mechanisms responsible for mitochondria media‐ted disease pro cesses are not fully elucidated yet ,the oxidative stress appears to be critical .Accordingly ,strategies are being de‐veloped for the targeted delivery of antioxidants to mitochondria .The prospect of development of mitochondrial targeted drugs with anti‐oxidative stress protection is tempting .Mitochondrial targeting antioxidants were the antioxidant drugs which took mi‐tochondria as the target site .In this review ,weintroduced the conception and classificationof mitochondrial targeted antioxidants and the research progress of disease treatment by mitochondrial targeted antioxidants .【期刊名称】《药学实践杂志》【年(卷),期】2015(000)001【总页数】5页(P1-4,8)【关键词】线粒体靶向;抗氧化剂;活性氧;氧化应激【作者】樊鹏程;葛越;蒋炜;景临林;马慧萍;贾正平【作者单位】兰州军区兰州总医院药剂科，甘肃兰州 730050; 全军高原环境损伤防治研究重点实验室，甘肃兰州730050;延安市宝塔区妇幼保健院，陕西延安716000;兰州军区兰州总医院药剂科，甘肃兰州 730050; 全军高原环境损伤防治研究重点实验室，甘肃兰州730050;兰州军区兰州总医院药剂科，甘肃兰州730050; 全军高原环境损伤防治研究重点实验室，甘肃兰州730050;兰州军区兰州总医院药剂科，甘肃兰州 730050; 全军高原环境损伤防治研究重点实验室，甘肃兰州730050;兰州军区兰州总医院药剂科，甘肃兰州 730050; 全军高原环境损伤防治研究重点实验室，甘肃兰州730050【正文语种】中文【中图分类】R329.28·综述·樊鹏程1，2，葛越3，蒋炜1，2，景临林1，2，马慧萍1，2，贾正平1，2 (1.兰州军区兰州总医院药剂科，甘肃兰州 730050；2.全军高原环境损伤防治研究重点实验室，甘肃兰州 730050；3.延安市宝塔区妇幼保健院，陕西延安 716000)Research progress in mitochondria-targeted antioxidantsFAN Pengcheng1，2，GE Yue3，Jiang Wei1，2，JING Linlin1，2，MA Huiping1，2，JIA Zhengping1，2(1.Department of Pharmacy，Lanzhou General Hospital of Lanzhou Region，Lanzhou 730050，China;2.Key Laboratory of Entire Army Plateau Environment Injury Preventi on Research，Lanzhou 730050，China;3.Meternal and Child Health Care of Baota District，Yan′an 716000，China)[Key words] mitochondria-targeted;antioxidant;reactive oxygen species;oxidative stress线粒体在细胞的生命周期中扮演重要角色，是连接细胞应激信号通路和神经细胞凋亡的重要桥梁[1]，它参与了缺氧致神经细胞凋亡的过程[2，3]。

1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK 2Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA 3Broad Institute of MIT and Harvard, Cambridge,Massachusetts, USA 4Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands 5Division ofGastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA 6Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA 7Cedars-Sinai F.Widjaja Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, California, USA 8Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA9Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA10Inflammatory Bowel Disease Research Group, Addenbrooke’s Hospital, University ofCambridge, Cambridge, UK 11Department of Health Studies, University of Chicago, Chicago,Illinois, USA 12Department of Internal Medicine, Section of Digestive Diseases, Yale School of Medicine, New Haven, Connecticut, USA 13Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA 14University of Maribor,Faculty of Medicine, Center for Human Molecular Genetics and Pharmacogenomics, Maribor,Slovenia 15University Medical Center Groningen, Department of Genetics, Groningen, TheNetherlands 16Department of Pathophysiology, Gastroenterology section, KU Leuven, Leuven,Belgium 17Unit of Animal Genomics, Groupe Interdisciplinaire de Genoproteomique Appliquee (GIGA-R) and Faculty of Veterinary Medicine, University of Liege, Liege, Belgium 18Division of Gastroenterology, Centre Hospitalier Universitaire, Universite de Liege, Liege, Belgium19Department of Medical and Molecular Genetics, King’s College London School of Medicine,Guy’s Hospital, London, UK 20Division of Rheumatology Immunology and Allergy, Brigham and Women’s Hospital, Boston, Massachusetts, USA 21Program in Medical and Population Genetics,Broad Institute, Cambridge, Massachusetts, USA 22Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts, USA 23Université de Montréal and the Montreal Heart Institute,Research Center, Montréal, Québec, Canada 24Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA 25Department of Gastroenterology &Hepatology, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio 26Department of Pathobiology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA 27Peninsula College of Medicine and Dentistry, Exeter, UK 28Erasmus Hospital, Free University of Brussels,Department of Gastroenterology, Brussels, Belgium 29Massachusetts General Hospital, Harvard Medical School, Gastroenterology Unit, Boston, Massachusetts, USA 30Viborg Regional Hospital,Medical Department, Viborg, Denmark 31Inflammatory Bowel Disease Service, Department ofGastroenterology and Hepatology, Royal Adelaide Hospital, and School of Medicine, University of Adelaide, Adelaide, Australia 32Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany 33Department of Gastroenterology and Hepatology, Flinders Medical Centre and School of Medicine, Flinders University, Adelaide, Australia 34Division ofGastroenterology, McGill University Health Centre, Royal Victoria Hospital, Montréal, Québec,Canada 35Department of Medicine II, University Hospital Munich-Grosshadern, Ludwig-Maximilians-University, Munich, Germany 36Department of Gastroenterology, Charit, Campus Mitte, UniversitŠtsmedizin Berlin, Berlin, Germany 37Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York City, New York, USA 38Department of Genomics, Life & Brain Center, University Hospital Bonn, Bonn, Germany 39Department ofBiosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden 40Department of Pediatrics,Cedars Sinai Medical Center, Los Angeles, California, USA 41Torbay Hospital, Department ofGastroenterology, Torbay, Devon, UK 42School of Medical Sciences, Faculty of Medical & Health Sciences, The University of Auckland, Auckland, New Zealand 43University of Groningen,University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands 44Department of Medicine, University of Otago, Christchurch, New Zealand 45Department of $watermark-text $watermark-text $watermark-textGastroenterology, Christchurch Hospital, Christchurch, New Zealand 46Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for EnvironmentalHealth, Neuherberg, Germany 47St Mark’s Hospital, Watford Road, Harrow, Middlesex, HA1 3UJ 48Nottingham Digestive Diseases Centre, Queens Medical Centre, Nottingham NG7 1AW, UK 49Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway 50Kaunas University of Medicine, Department of Gastroenterology, Kaunas, Lithuania51Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA 52Unit of Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico-Casa Sollievo dellaSofferenza (IRCCS-CSS) Hospital, San Giovanni Rotondo, Italy 53Ghent University Hospital,Department of Gastroenterology and Hepatology, Ghent, Belgium 54School of Medicine andPharmacology, The University of Western Australia, Fremantle, Australia 55Gastrointestinal Unit,Molecular Medicine Centre, University of Edinburgh, Western General Hospital, Edinburgh, UK 56Department of Gastroenterology, The Townsville Hospital, Townsville, Australia 57Institute of Human Genetics, Newcastle University, Newcastle upon Tyne, UK 58Department of Medicine,Ninewells Hospital and Medical School, Dundee, UK 59Genetic Medicine, MAHSC, University of Manchester, Manchester, UK 60Academic Medical Center, Department of Gastroenterology,Amsterdam, The Netherlands 61University of Maribor, Faculty for Chemistry and Chemical Engineering, Maribor, Slovenia 62King’s College London School of Medicine, Guy’s Hospital,Department of Medical and Molecular Genetics, London, UK 63Royal Hospital for Sick Children,Paediatric Gastroenterology and Nutrition, Glasgow, UK 64Guy’s & St. Thomas’ NHS Foundation Trust, St. Thomas’ Hospital, Department of Gastroenterology, London, UK 65Department ofGastroenterology, Hospital Cl’nic/Institut d’Investigaci— Biomdica August Pi i Sunyer (IDIBAPS),Barcelona, Spain 66Centro de Investigaci—n Biomdica en Red de Enfermedades Hep‡ticas y Digestivas (CIBER EHD), Barcelona, Spain 67Christian-Albrechts-University, Institute of Clinical Molecular Biology, Kiel, Germany 68Department for General Internal Medicine, Christian-Albrechts-University, Kiel, Germany 69Inflammatory Bowel Diseases, Genetics and Computational Biology, Queensland Institute of Medical Research, Brisbane, Australia 70Norfolk and Norwich University Hospital 71Department of Gastroenterology, Leiden University Medical Center, Leiden,The Netherlands 72Child Life and Health, University of Edinburgh, Edinburgh, Scotland, UK 73Institute of Human Genetics and Department of Neurology, Technische Universität München,Munich, Germany 74Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts, USA 75Department for General Internal Medicine, Christian-Albrechts-University, Kiel, Germany 76Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, USA 78Mount Sinai Hospital Inflammatory Bowel Disease Centre, University of Toronto, Toronto, Ontario, Canada 79Azienda Ospedaliero Universitaria (AOU) Careggi, Unit of Gastroenterology SOD2, Florence, Italy 80Center for Applied Genomics,The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA 81Department of Pediatrics, Center for Pediatric Inflammatory Bowel Disease, The Children’s Hospital ofPhiladelphia, Philadelphia, Pennsylvania, USA 82Meyerhoff Inflammatory Bowel Disease Center,Department of Medicine, School of Medicine, and Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA 83Department of Gastroenterology, Royal Brisbane and Womens Hospital, and School of Medicine, University of Queensland, Brisbane, Australia 84Inflammatory Bowel Disease Research Group, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK 85Division of Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium AbstractCrohn’s disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory boweldisease (IBD), affect over 2.5 million people of European ancestry with rising prevalence in otherpopulations 1. Genome-wide association studies (GWAS) and subsequent meta-analyses of CD and UC 2,3 as separate phenotypes implicated previously unsuspected mechanisms, such as autophagy 4,$watermark-text $watermark-text $watermark-textin pathogenesis and showed that some IBD loci are shared with other inflammatory diseases 5.Here we expand knowledge of relevant pathways by undertaking a meta-analysis of CD and UC genome-wide association scans, with validation of significant findings in more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional and balancing selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe striking overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.We conducted an imputation-based association analysis using autosomal genotype level data from 15 GWAS of CD and/or UC (Supplementary Table 1, Supplementary Figure 1). We imputed 1.23 million SNPs from the HapMap3 reference set (Supplementary Methods),resulting in a high quality dataset with reduced genome-wide inflation (Supplementary Figures 2, 3) compared with previous meta-analyses of subsets of these data 2,3. The imputed GWAS data identified 25,075 SNPs that had association p < 0.01 in at least one of the CD,UC or all IBD analyses. A meta-analysis of GWAS data with Immunochip 6 validation genotypes from an independent, newly-genotyped set of 14,763 CD cases, 10,920 UC cases,and 15,977 controls was performed (Supplementary Table 1, Supplementary Figure 1).Principal components analysis resolved geographic stratification, as well as Jewish and non-Jewish ancestry (Supplementary Figure 4), and significantly reduced inflation to a level consistent with residual polygenic risk, rather than other confounding effects (from λGC =2.00 to λGC = 1.23 when analyzing all IBD samples, Supplementary Methods,Supplementary Figure 5).Our meta-analysis of the GWAS and Immunochip data identified 193 statistically independent signals of association at genome-wide significance (p < 5×10−8) in at least one of the three analyses (CD, UC, IBD). Since some of these signals (Supplementary Figure 6)probably represent associations to the same underlying functional unit, we merged thesesignals (Supplementary Methods) into 163 regions, of which 71 are reported here for the first time (Table 1, Supplementary Table 2). Figure 1A shows the relative contributions of each locus to the total variance explained in UC and CD. We have increased the total disease variance explained (variance being subject to fewer assumptions than heritability 7) from8.2% to 13.6% in CD and from 4.1% to 7.5% in UC (Supplementary Methods). Consistent with previous studies, our IBD risk loci seem to act independently, with no significantevidence of deviation from an additive combination of log odds ratios.Our combined genome-wide analysis of CD and UC enables a more comprehensive analysis of disease specificity than was previously possible. A model selection analysis(Supplementary Methods 1d) showed that 110/163 loci are associated with both disease phenotypes; 50 of these have an indistinguishable effect size in UC and CD, while 60 show evidence of heterogeneous effects (Table 1). Of the remaining loci, 30 are classified as CD-specific and 23 as UC-specific. However, 43 of these 53 show the same direction of effect in the non-associated disease (Figure 1B, overall p=2.8×10−6). Risk alleles at two CD loci,PTPN22 and NOD2, show significant (p < 0.005) protective effects in UC, exceptions that may reflect biological differences between the two diseases. This degree of sharing ofgenetic risk suggests that nearly all the biological mechanisms involved in one disease play some role in the other.The large number of IBD associations, far more than reported for any other complexdisease, increases the power of network-based analyses to prioritize genes within loci. We investigated the IBD loci using functional annotation and empirical gene network tools$watermark-text$watermark-text$watermark-text(Supplementary Table 2). Compared with previous analyses which identified candidate genes in 35% of loci 2,3 our updated GRAIL 8 -connectivity network identifies candidates in 53% of loci, including increased statistical significance for 58 of the 73 candidates from previous analyses. The new candidates come not only from genes within newly identified loci, but also integrate additional genes from previously established loci (Figure 1C). Only 29 IBD-associated SNPs are in strong linkage disequilibrium (r 2 > 0.8) with a missense variant in the 1000 Genomes Project data, which reinforces previous evidence that a large fraction of risk for complex disease is driven by non-coding variation. In contrast, 64 IBD-associated SNPs are in linkage disequilibrium with variants known to regulate gene expression (Supplementary Table 2). Overall, we highlighted a total of 300 candidate genes in 125 loci, of which 39 contained a single gene supported by two or more methods.Seventy percent (113/163) of the IBD loci are shared with other complex diseases or traits,including 66 among the 154 loci previously associated with other immune-mediated diseases 9, which is 8.6 times the number that would be expected by chance (Figure 2A, p <10−16, Supplementary Figure 7). Such enrichment cannot be attributed to the immune-mediated focus of the Immunochip, (Supplementary Methods 4a(i), Supplementary Figure 8), since the analysis is based on our combined GWAS-Immunochip data. Comparing overlaps with specific diseases is confounded by the variable power in studies of different diseases. For instance, while type 1 diabetes (T1D) shares the largest number of loci (20/39,10-fold enrichment) with IBD, this is partially driven by the large number of known T1D associations. Indeed, seven other immune-mediated diseases show stronger enrichment of overlap, with the largest being ankylosing spondylitis (8/11, 13-fold) and psoriasis (14/17,14-fold).IBD loci are also markedly enriched (4.9-fold, p < 10−4) in genes involved in primary immunodeficiencies (PIDs, Figure 2A), which are characterized by a dysfunctional immune system resulting in severe infections 10. Genes implicated in this overlap correlate with reduced levels of circulating T-cells (ADA , CD40, TAP1/2, NBS1, BLM, DNMT3B ), or of specific subsets such as Th17 (STAT3), memory (SP110), or regulatory T-cells (STAT5B ).The subset of PIDs genes leading to Mendelian susceptibility to mycobacterial disease(MSMD)10–12 is enriched still further; six of the eight known autosomal genes linked to MSMD are located within IBD loci (IL12B , IFNGR2, STAT1, IRF8, TYK2 and STAT3,46-fold enrichment, p = 1.3 × 10−6), and a seventh, IFNGR1, narrowly missed genome-wide significance (p = 6 × 10−8). Overlap with IBD is also seen in complex mycobacterial disease; we find IBD associations in 7/8 loci identified by leprosy GWAS 13, including 6cases where the same SNP is implicated. Furthermore, genetic defects in STAT314–15and CARD916, also within IBD loci, lead to PIDs involving skin infections with staphylococcus and candidiasis, respectively. The comparative effects of IBD and infectious diseasesusceptibility risk alleles on gene function and expression is summarized in Supplementary Table 3, and include both opposite (e.g. NOD2 and STAT3, Supplementary Figure 9) and similar (e.g., IFNGR2) directional effects.To extend our understanding of the fundamental biology of IBD pathogenesis we conducted searches across the IBD locus list: (i) for enrichment of specific GeneOntology (GO) terms and canonical pathways, (ii) for evidence of selective pressure acting on specific variants and pathways, and (iii) for enrichment of differentially expressed genes across immune cell types. We tested the 300 prioritized genes (see above) for enrichment in GO terms(Supplementary Methods) and identified 286 GO terms and 56 pathways demonstrating significant enrichment in genes contained within IBD loci (Supplementary Table 4,Supplementary Figure 10,11). Excluding high-level GO categories such as “immune system processes” (p = 3.5 × 10−26), the most significantly enriched term is regulation of cytokine production (p=2.7×10−24), specifically IFNG-γ, IL-12, TNF-α, and IL-10 signalling.$watermark-text$watermark-text$watermark-textLymphocyte activation was the next most significant (p=1.8 × 10−23), with activation of T-,B-, and NK-cells being the strongest contributors to this signal. Strong enrichment was also seen for response to molecules of bacterial origin (p=2.4 × 10−20), and for KEGG’s JAK-STAT signalling pathway (p = 4.8 × 10−15). We note that no enriched terms or pathways showed specific evidence of CD- or UC-specificity.As infectious organisms are known to be among the strongest agents of natural selection, we investigated whether the IBD-associated variants are subject to selective pressures (Supplementary Methods, Supplementary Table 5). Directional selection would imply that the balance between these forces shifted in one direction over the course of human history,whereas balancing selection would suggest an allele frequency dependent-scenario typified by host-microbe co-evolution, as can be observed with parasites. Two SNPs show Bonferroni-significant selection: the most significant signal, in NOD2, is under balancing selection (p = 5.2 × 10−5), and the second most significant, in the receptor TNFRSF18,showed directional selection (p = 8.9 × 10−5). The next most significant variants were in the ligand of that receptor, TNFSF18 (directional, p = 5.2 × 10−4), and IL23R (balancing, p =1.5 × 10−3). As a group, the IBD variants show significant enrichment in selection (Figure 2B) of both types (p = 5.5 × 10−6). We discovered an enrichment of balancing selection (Figure 2B) in genes annotated with the GO term “regulation of interleukin-17 production”(p = 1.4 × 10−4). The important role of IL17 in both bacterial defense and autoimmunity suggests a key role for balancing selection in maintaining the genetic relationship between inflammation and infection, and this is reinforced by a nominal enrichment of balancing selection in loci annotated with the broader GO term “defense response to bacterium” (p =0.007).We tested for enrichment of cell-type expression specificity of genes in IBD loci in 223distinct sets of sorted, mouse-derived immune cells from the Immunological Genome Consortium 17. Dendritic cells showed the strongest enrichment, followed by weaker signals that support the GO analysis, including CD4+ T, NK and NKT cells (Figure 2C). Notably,several of these cell types express genes near our IBD associations much more specifically when stimulated; our strongest signal, a lung-derived dendritic cell, had p stimulated < 1×10−6compared with p unstimulated = 0.0015, consistent with an important role for cell activation.To further our goal of identifying likely causal genes within our susceptibility loci and to elucidate networks underlying IBD pathogenesis, we screened the associated genes against 211 co-expression modules identified from weighted gene co-expression networkanalyses 18, conducted with large gene expression datasets from multiple tissues 19–21. The most significantly enriched module comprised 523 genes from omental adipose tissuecollected from morbidly obese patients 19, which was found to be 2.9-fold enriched for genes in the IBD-associated loci (p = 1.1 × 10−13, Supplementary Table 6, Supplementary Figure12). We constructed a probabilistic causal gene network using an integrative Bayesian network reconstruction algorithm 22–24 which combines expression and genotype data toinfer the direction of causality between genes with correlated expression. The intersection of this network and the genes in the IBD-enriched module defined a sub-network of genes enriched in bone marrow-derived macrophages (p < 10−16) and is suggestive of dynamic interactions relevant to IBD pathogenesis. In particular, this sub-network featured close proximity amongst genes connected to host interaction with bacteria, notably NOD2, IL10,and CARD9.A NOD2-focused inspection of the sub-network prioritizes multiple additional candidate genes within IBD-associated regions. For example, a cluster near NOD2 (Figure 2D)contains multiple IBD genes implicated in M.tb response, including SLC11A1, VDR and LGALS9. Furthermore, both SLC11A1 (also known as NRAMP1) and VDR have been$watermark-text$watermark-text$watermark-textassociated with M.tb infection by candidate gene studies 25–26, and LGALS9 modulates mycobacteriosis 27. Of interest, HCK (located in our new locus on chromosome 20 at 30.75Mb) is predicted to upregulate expression of both NOD2 and IL10, an anti-inflammatory cytokine associated with Mendelian 28 and non-Mendelian IBD 29. HCK has been linked to alternative, anti-inflammatory activation of monocytes (M2 macrophages)30;while not identified in our aforementioned analyses, these data implicate HCK as the causal gene in this new IBD locus.We report one of the largest genetic experiments involving a complex disease undertaken to date. This has increased the number of confirmed IBD susceptibility loci to 163, most of which are associated with both CD and UC, and is substantially more than reported for any other complex disease. Even this large number of loci explains only a minority of thevariance in disease risk, which suggests that other factors such as rarer genetic variation not captured by GWAS or environmental exposures make substantial contributions topathogenesis. Most of the evidence relating to possible causal genes points to an essential role for host defence against infection in IBD. In this regard the current results focus ever closer attention on the interaction between the host mucosal immune system and microbes both at the epithelial cell surface and within the gut lumen. In particular, they raise the question, in the context of this burden of IBD susceptibility genes, as to what triggers components of the commensal microbiota to switch from a symbiotic to a pathogenic relationship with the host. Collectively, our findings have begun to shed light on thesequestions and provide a rich source of clues to the pathogenic mechanisms underlying this archetypal complex disease.METHODS SUMMARY We conducted a meta-analysis of GWAS datasets after imputation to the HapMap3reference set, and aimed to replicate in the Immunochip data any SNPs with p < 0.01. We compared likelihoods of different disease models to assess whether each locus was associated with CD, UC or both. We used databases of eQTL SNPs and coding SNPs in linkage disequilibrium with our hit SNPs, as well as the network tools GRAIL andDAPPLE, and a co-expression network analysis to prioritize candidate genes in our loci.Gene Ontology, ImmGen mouse immune cell expression resource, the TreeMix selection software, and a Bayesian causal network analysis were used to functionally annotate these genes.Supplementary MaterialRefer to Web version on PubMed Central for supplementary material.AcknowledgmentsWe thank all the subjects who contributed samples and the physicians and nursing staff who helped withrecruitment globally. UK case collections were supported by the National Association for Colitis and Crohn’s disease, Wellcome Trust grant 098051 (LJ, CAA, JCB), Medical Research Council UK, the Catherine McEwan Foundation, an NHS Research Scotland career fellowship (RKR), Peninsular College of Medicine and Dentistry,Exeter, the National Institute for Health Research, through the Comprehensive Local Research Network and through Biomedical Research Centre awards to Guy’s & St. Thomas’ National Health Service Trust, King’s College London, Addenbrooke’s Hospital, University of Cambridge School of Clinical Medicine and to theUniversity of Manchester and Central Manchester Foundation Trust. The British 1958 Birth Cohort DNA collection was funded by Medical Research Council grant G0000934 and Wellcome Trust grant 068545/Z/02, and the UK National Blood Service controls by the Wellcome Trust. The Wellcome Trust Case Control Consortium projects were supported by Wellcome Trust grants 083948/Z/07/Z, 085475/B/08/Z and 085475/Z/08/Z. North American collections and data processing were supported by funds to the NIDDK IBD Genetics Consortium which is funded by the following grants: DK062431 (SRB), DK062422 (JHC), DK062420 (RHD), DK062432 (JDR), DK062423(MSS), DK062413 (DPM), DK076984 (MJD), DK084554 (MJD and DPM) and DK062429 (JHC). Additional$watermark-text$watermark-text$watermark-textfunds were provided by funding to JHC (DK062429-S1 and Crohn’s & Colitis Foundation of America, Senior Investigator Award (5-2229)), and RHD (CA141743). KYH is supported by the NIH MSTP TG T32GM07205training award. Cedars-Sinai is supported by USPHS grant PO1DK046763 and the Cedars-Sinai F. Widjaja Inflammatory Bowel and Immunobiology Research Institute Research Funds, National Center for Research Resources (NCRR) grant M01-RR00425, UCLA/Cedars-Sinai/Harbor/Drew Clinical and Translational Science Institute (CTSI) Grant [UL1 TR000124-01], the Southern California Diabetes and Endocrinology Research Grant (DERC) [DK063491], The Helmsley Foundation (DPM) and the Crohn’s and Colitis Foundation of America (DPM). RJX and ANA are funded by DK83756, AI062773, DK043351 and the Helmsley Foundation. TheNetherlands Organization for Scientific Research supported RKW with a clinical fellowship grant (90.700.281) and CW (VICI grant 918.66.620). CW is also supported by the Celiac Disease Consortium (BSIK03009). This study was also supported by the German Ministry of Education and Research through the National Genome Research Network, the Popgen biobank, through the Deutsche Forschungsgemeinschaft (DFG) cluster of excellence‘Inflammation at Interfaces’ and DFG grant no. FR 2821/2-1. S Brand was supported by (DFG BR 1912/6-1) and the Else-Kröner-Fresenius-Stiftung (Else Kröner-Exzellenzstipendium 2010_EKES.32). Italian case collections were supported by the Italian Group for IBD and the Italian Society for Paediatric Gastroenterology, Hepatology and Nutrition and funded by the Italian Ministry of Health GR-2008-1144485. Activities in Sweden were supported by the Swedish Society of Medicine, Ihre Foundation, Örebro University Hospital Research Foundation, Karolinska Institutet, the Swedish National Program for IBD Genetics, the Swedish Organization for IBD, and the Swedish Medical Research Council. DF and SV are senior clinical investigators for the Funds for Scientific Research (FWO/FNRS) Belgium. We acknowledge a grant from Viborg Regional Hospital, Denmark. VA was supported by SHS Aabenraa, Denmark. We acknowledge funding provided by the Royal Brisbane and Women’s Hospital Foundation,National Health and Medical Research Council, Australia and by the European Community (5th PCRDT). We gratefully acknowledge the following groups who provided biological samples or data for this study: theInflammatory Bowel in South Eastern Norway (IBSEN) study group, the Norwegian Bone Marrow Donor Registry (NMBDR), the Avon Longitudinal Study of Parents and Children, the Human Biological Data Interchange and Diabetes UK, and Banco Nacional de ADN, Salamanca. This research also utilizes resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the NIDDK, NIAID, NHGRI, NICHD,and JDRF and supported by U01 DK062418. The KORA study was initiated and financed by the HelmholtzZentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ.References 1. Molodecky NA, et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology. 2012; 142:46–54. [PubMed: 22001864]2. Anderson CA, et al. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing thenumber of confirmed associations to 47. Nat Genet. 2011; 43:246–252. [PubMed: 21297633]3. Franke A, et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat Genet. 2010; 42:1118–1125. [PubMed: 21102463]4. Khor BGA, Xavier RJ. Genetics pathogenesis of inflammatory bowel disease. Nature. 2011;474:307–317. [PubMed: 21677747]5. Cho JH, Gregersen PK. Genomics and the multifactorial nature of human autoimmune disease. N Engl J Med. 2011; 365:1612–1623. [PubMed: 22029983]6. Cortes A, Brown MA. Promise and pitfalls of the Immunochip. Arthritis Res Ther. 2011; 13:101.[PubMed: 21345260]7. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: Geneticinteractions create phantom heritability. Proc Natl Acad Sci USA. 2012; 109:1193–1198. [PubMed:22223662]8. Raychaudhuri S, et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 2009; 5:e1000534.10.1371/journal.pgen.1000534 [PubMed: 19557189]9. Hindorff LA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009; 106:9362–9367. [PubMed:19474294]10. International Union of Immunological Societies Expert Committee on Primary I et al. Primaryimmunodeficiencies: 2009 update. J Allergy Clin Immunol. 2009; 124:1161–1178. [PubMed:20004777]$watermark-text $watermark-text$watermark-text。

Runx2在骨形成中的作用及调控李娜;代晓霞【摘要】正常情况下,骨形成和骨吸收处于动态平衡.Runx2(runt-related transcription factor 2)是骨发育过程中重要的转录因子,对成骨细胞的分化、软骨细胞成熟、破骨细胞的分化及细胞外基质的分泌都有重要的调控作用.因此,研究Runx2基因的表达调控、传导通路十分必要,对治疗骨代谢疾病具有重要意义.本文将从Runx2的结构功能和表达调控对成骨细胞的影响进行阐述.【期刊名称】《国外医学（医学地理分册）》【年(卷),期】2018(039)004【总页数】4页(P353-356)【关键词】Runx2;成骨细胞;软骨细胞;表达调控【作者】李娜;代晓霞【作者单位】西安交通大学医学部公共卫生学院,陕西西安 710061;西安交通大学医学部公共卫生学院,陕西西安 710061【正文语种】中文【中图分类】R589.9骨形成是从骨髓间质干细胞(Bone marrow mesenchymal cells，MSCs)向成骨细胞和软骨细胞增殖分化以及细胞外基质的成熟和矿化的复杂生长发育过程，是在多种分子的调节下完成，Runx2就是其中的一个关键调节因子，在特定细胞的特定时期Runx2的表达水平有所不同。

在一些细胞中以G2/M达到最大值，在成骨细胞中，Runx2表达水平在G1期最高而在S期和M期最低，能调节成骨细胞G1期的转换[1]。

实验表明在前肥大软骨细胞和肥大软骨细胞中Runx2高度表达，能诱导软骨细胞成熟包括软骨细胞肥大化和软骨内血管生成。

Runx2能促进骨钙素(OCN)、骨桥素(OPN)、骨涎蛋白(BSP)、Ⅰ型胶原蛋白、纤维蛋白的转录和表达[2]。

1 Runx的结构Runx2是Runx转录因子家族的一份子，家族成员还包括Runx1和Runx3，能参与调节许多细胞的基因表达[3]。

Runx家族在N端都有一个由128个氨基酸组成的高度保守的结构域,因最先在果蝇中发现故命名为runt结构域[4]。

Antimicrobial efficacy of Syzygium antisepticum plant extract againstStaphylococcus aureus and methicillin-resistant S. aureus and itsapplication potential with cooked chicken 抗菌效能的植物提取物对antisepticum of蒲桃金黄色葡萄球菌和耐甲氧西林金黄色葡萄球菌and its应用电位以及煮熟的鸡For the past decades, there has been a growing demand for natural antimicrobials in the food industry.Plant extracts have attracted strong research interests due to their wide-spectrum antimicrobial activities, but only a limited number have been investigated thoroughly. The present study aimed at identifying a novel anti-staphylococcal plant extract, to validate its activity in a food model, and to investigateon its composition and antimicrobial mechanism. Fourplant extracts were evaluated against Staphylococcus aureus and methicillin-resistant S. aureus (MRSA) in vitro, with Syzygium antisepticum leaf extractshowing the strongest antimicrobial activity (MIC ¼ 0.125 mg/mL).在过去的几十年里，食品工业中对天然抗菌剂的需求越来越大。

P27蛋白与头颈鳞状细胞癌胡林平;滕尧树;陈漫漫;曾国辉;朱瑾【摘要】头颈鳞状细胞癌(HNSCCs)是人体常见恶性肿瘤之一,发病率呈逐年上升趋势,但其发病机制目前尚不清楚.近年来研究发现,细胞核内、外的P27蛋白异常表达,可通过不同的信号转导机制,在多种HNSCCs细胞的浸润和迁移等病理过程中发挥不同的调控作用,从而影响HNSCCs患者的预后.本文综述P27蛋白在HNSCCs临床病理特征及预后判断中的研究进展.【期刊名称】《中国眼耳鼻喉科杂志》【年(卷),期】2017(017)001【总页数】4页(P68-70,76)【关键词】P27蛋白;头颈鳞状细胞癌;病理机制;预后【作者】胡林平;滕尧树;陈漫漫;曾国辉;朱瑾【作者单位】浙江中医药大学第二临床医学院杭州310053;浙江省杭州市第一人民医院耳鼻喉科杭州310006;浙江中医药大学第二临床医学院杭州310053;浙江中医药大学第二临床医学院杭州310053;浙江省杭州市第一人民医院耳鼻喉科杭州310006【正文语种】中文头颈鳞状细胞癌(head and neck squamous cell carcinomas，HNSCCs)作为全球第六大常见的恶性肿瘤，发病率呈逐年上升趋势，恶性程度较高，预后与肿瘤局部复发和淋巴结转移密切相关，但其浸润、转移的分子病理机制目前尚不清楚。

P27 蛋白是由P27基因表达而来，其主要是通过抑制细胞周期蛋白依赖性激酶(cyclin dependent kinases，CDKs)复合物活性，以发挥限制性调节细胞周期进程的作用。

近来研究发现，P27蛋白的异常表达参与了HNSCCs细胞的浸润和迁移等病理过程，因而与HNSCCs临床病理特征密切相关。

本文综述P27蛋白在HNSCCs的临床病理特征及预后判断中的研究进展，以期为探索HNSCCs的分子病理机制及分子靶向治疗提供新的理论依据。

P27蛋白又称细胞周期依赖性激酶抑制蛋白B(cyclin dependent kinase inhibitors B, DKNIB)，属于CDKI中的Cip/Kip家族。