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【转载】揭开美国顶尖生物医学实验室成功的法宝

【转载】揭开美国顶尖生物医学实验室成功的法宝

【转载】揭开美国顶尖生物医学实验室成功的法宝来源:向骏的日志2005年3月我有幸加盟了哈佛医学院布里根妇女医院(Brigham and Women’s Hospital)Stephen Elledge 实验室,在Elledge 教授直接领导下工作了整整六个年头。

Stephen Elledge (后文皆称Steve)是美国生物医学界天才级科学家,他博士毕业于麻省理工学院(MIT)生物学系,在斯坦福大学生物化学系做的博士后。

1989年成为著名的贝勒医学院(Baylor Medical College )生物化学系的助理教授。

短短几年后便于1993年当选为霍华德休斯医学研究所的研究员(HHMI Investigator)。

2003 年他当选为美国科学院院士。

2003年初,他被美国哈佛医学院布里根妇女医院特聘为遗传学冠名终身教授。

他在多个研究领域,如细胞周期调控、DNA 损伤应答机制、肿瘤细胞生物学、泛素连接酶的组成与调控、新型生物技术的开发与利用以及病毒的感染机制方面均有杰出贡献。

他发现肿瘤抑癌基因TP53的直接下游靶点为P21,他发现DNA 损伤后ATM、ATR蛋白激酶激活下游CHK1、CHK2等信号传导通路,他发现抑癌基因REST、PTPN12等,揭示泛素连接酶F-box 家族,优化了酵母双杂交系统(Yeast two hybrid system)、Magic DNA 载体高通量转换系统、全蛋白组水平分析蛋白稳定性的GPS 系统及与Gregory Hannon 首创小发卡核苷酸干扰文库(shRNA library)。

50岁出头的他,光在Cell 、Nature 、Science 杂志上就已经发表了二十几篇文章,其数量与质量就是在哈佛医学院这样大师云集的地方也名列前茅。

Steve 的科研思维与科研能力当属超一流,他堪称科学家中的科学家。

在Steve 手下先后工作的中国同胞为数不少,其中很多人颇有成就,但能够回归祖国并将Steve 实验室成功经验总结出来的人不多。

学家揭示人类海马未成熟神经元在整个生命期的分子图谱

学家揭示人类海马未成熟神经元在整个生命期的分子图谱

学家揭示人类海马未成熟神经元在整个生命期的分子图谱
科学技术部2022-07-20 11:07:00
来源:科学技术部
由成年海马神经发生而产生的未成熟齿状颗粒细胞(imGCs)对啮齿动物大脑的可塑性和独特性具有一定的功能。

在多种人类神经系统疾病中,这种细胞表达会失调。

目前,对成年人类海马imGCs的分子特征知之甚少,甚至对其存在也具有争议。

美国宾夕法尼亚大学研究团队揭示了人类海马未成熟神经元在整个生命期的分子图谱。

该研究于近日发表在《Nature》上,题为:Molecular landscapes of human hippocampal immature neurons across lifespan。

研究人员利用基于机器学习且经过验证的分析方法进行了单核RNA 测序,以识别imGCs,并量化它们在人类海马生命周期不同阶段的丰度。

他们确定了人类imGCs在整个生命期的共同分子特征,并观察到人类imGCs与相关的转录动态。

结果表明,人类imGCs在细胞功能和疾病相关性等方面与小鼠不同。

进一步研究发现,在阿尔茨海默病中,imGCs基因表达改变的数量相比于正常有所减少。

最后,研究人员用罕见的齿状颗粒细胞脂肪特异性增殖的神经祖细胞培养,证明了成年人类海马的神经发生能力。

该研究表明,成年人类海马中存在大量的imGCs,并揭示了它们在整个生命期和阿尔茨海默病中的分子特性。

论文链接:
/articles/s41586-022-04912-w
注:此研究成果摘自《Nature》,文章内容不代表本网站观点和立场,仅供参考。

本内容由融合号“科学技术部”发布,仅代表作者观点。

人。

干细胞杂谈(2)

干细胞杂谈(2)

培养中的注意事项 二, 密度控制与接触抑制
密度抑制:因营养产物的枯竭,代谢废物的堆积而产生的 生长抑制的现象。
接触抑制:当细胞培养数量增多,细胞间相互接触使细 胞停顿运动和生长。肿瘤细胞无接触抑制,因此,肿瘤 细胞能堆积,是区别正常细胞与癌细胞的标志。
发生先后的讨论
培养中的注意事项 细胞密度控制的重要性
4.1,生长环境
细胞体外培养的生长特性
气体:代谢产物的CO2在培养环境中能与培养液中的 NaHCO2形成缓冲体系,调节PH的作用。
培养基的深度会影响氧气在细胞中的扩散率,应控制在 0.2~0.5ml/cm2之间,此外低氧培养有利于干细胞未分化 状态的保持。
光线:是一种诱导分化信号,也可使培养基中局部成分分 解
自己的想法
桑椹胚进一步发育,细胞开场出现分化,聚集在胚胎一侧, 个体较大的细胞,称为内细胞团〔ICM〕,将来发育成胎儿 的各种组织,而沿透明带内壁扩展和排列的,个体较小的细 胞,称为滋养层细胞,它们将来发育成胚膜和胎盘。
自己的想法 用滋养层细胞发育来的脐带,治疗由内细胞团发育来的个体。
细胞因子 诱导分化
自己的想法
一, 干细胞的不对称分裂
高等动物的成体干细胞通过不对称分裂产生非对称的细胞决定子 分割,使得一局部子代获得维持干细胞状态所必需的信息而成为子代 干细胞,另外一局部子代细胞那么不得不走向分化。也就是说,一个 干细胞的后代中,只有一局部子代细胞可能保持与父代细胞一样的干 细胞特征,另外一局部那么丧失了干细胞的功能。
影响; 快速复苏,迅速越过最大冰晶期。
培养中的注意事项
三, 细胞冻存
冻存保护剂——DMSO
使用浓度推荐在5%-15%,有诱导分化的作用; 使用前须预冷, 缓慢参加细胞悬液中,参加后须在4℃保持40-60min,让 甘油或DMSO等成分渗透到细胞内,在细胞内外到达平衡 以起到充分的保护作用。 可以使用条件培养基配置冻存液

肿瘤相关巨噬细胞外泌体调控KRAS信号通路影响胰腺癌细胞糖酵解功能

肿瘤相关巨噬细胞外泌体调控KRAS信号通路影响胰腺癌细胞糖酵解功能

肿瘤相关巨噬细胞外泌体调控KRAS信号通路影响胰腺癌细胞糖酵解功能迪里夏提·阿力木,郑坚江,多力坤·吐拉哈孜,阿木提江·马合木提 (新疆维吾尔自治区人民医院肝胆胰医学诊疗中心,新疆乌鲁木齐 830001)[摘要] 目的 探究肿瘤相关巨噬细胞外泌体对胰腺癌细胞糖酵解的影响及机制。

方法 THP-1细胞诱导分化为M0型和M2型巨噬细胞,并提取二者分泌的外泌体(M0 exo和M2 exo)。

将胰腺癌细胞CAPAN-2和ASPC-1分为PBS组、M0 exo组、M2 exo组、M2 exo+siKRAS组,分别与等体积PBS、10 μg/mL M0 exo、10 μg/mL M2 exo、转染KRAS siRNA+10 μg/mL M2 exo共孵育。

透射电镜观察外泌体结构;CCK-8法检测各组细胞增殖能力;试剂盒检测葡萄糖摄取率和乳酸生成水平;Western blot检测外泌体标志物、KRAS 蛋白表达和ERK1/2磷酸化水平。

结果 THP-1诱导分化为表达标志蛋白Arg-1和IL-10的M2型巨噬细胞,M0 exo和M2 exo具有双层膜结构,粒径100 nm左右,表达外泌体标志蛋白CD9、CD81、TSG101。

与PBS组比较,M2 exo组CAPAN-2和ASPC-1细胞增殖能力、葡萄糖摄取率力、乳酸生成水平显著增加(P<0.05),KRAS表达以及ERK1/2磷酸化水平显著增加(P<0.001)。

与M2 exo组比较,M2 exo+siKRAS组CAPAN-2和ASPC-1细胞增殖能力、葡萄糖摄取率以及乳酸生成水平均显著下降(P<0.05)。

结论 肿瘤相关巨噬细胞外泌体可通过激活KRAS信号通路促进胰腺癌细胞糖酵解。

[关键词]肿瘤相关巨噬细胞;外泌体;糖酵解;KRAS信号通路[中图分类号][;R392.2];A [收稿日期]2023-11-19Effect of tumor-associated macrophage exosomes on glycolysis of pancreatic cancer cells by regulating KRAS signal pathwayDilixiati Alimu,ZHENG Jian-jiang,Duolikun Tulahazi,Amutijiang Mahemuti (Center of Hepatobiliary Pancreatic Medicine Diagnosis and Treatment, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi Xinjiang 830001, China)Abstract: Objective To investigate the effect of tumor-associated macrophage exosomes on glycolysis of pancreatic cancer cells and its mechanism.Methods The THP-1 cells were induced to differentiate into the M0 and M2 macrophages, and the exosomes (M0 exo and M2 exo) were extracted. The pancreatic cancer cells CAPAN-2 and ASPC-1 were divided into the PBS group,the M0 exo group,the M2 exo group and the M2 exo+siKRAS group,and co-incubated with equal volumes of PBS,10 μg/mL of M0 exo,10 μg/mL of M2 exo,and transfection of KRAS siRNA and 10 μg/mL of M2 exo, respectively. Transmission electron microscopy was used to observe the structure of exosomes; CCK-8 was used to detect the cell proliferation capacity; the kit was used to detect the glucose uptake rate and production level of lactic acid, and Western blot was used to detect the exosome markers expression, KRAS protein expression and ERK1/2 phosphorylation level.Results THP-1 was induced to differentiate into M2 macrophages expressing Arg-1 and IL-10 marker proteins. M0 exo and M2 exo had a bilayer membrane structure with a particle size of about 100 nm and expressed exosomal marker proteins of CD9, CD81, and TSG101. Compared with the PBS group, the cell proliferation, glucose uptake rate, production level of lactic acid of CAPAN-2 and ASPC-1 cells in the M2 exo group increased significantly (P<0.05),and the KRAS expression and ERK1/2 phosphorylation level were significantly increased (P<0.001).Compared with the M2 exo group,the proliferation,glucose uptake rate and production level of lactic acid of CAPAN-2 and ASPC-1 cells in the M2 exo+siKRAS group decreased significantly (P<0.05).Conclusion Tumor-associated macrophage exosomes can promote the glycolysis of pancreatic cancer cells via the activation of KRAS signaling pathway.Keywords: tumor-associated macrophages; exosomes; glycolysis; KRAS signaling pathway胰腺癌是发病于胰腺导管上皮及腺泡细胞的恶性肿瘤,其早期发病隐匿,缺乏诊断的标志物,患者生存时间短,被称为“癌中之王”,因此探究胰腺癌诊断的标志物和治疗的新靶点一直是研究的重点领域[1-2]。

非编码RNA_调控铁死亡在肝细胞癌中作用的研究进展

非编码RNA_调控铁死亡在肝细胞癌中作用的研究进展

㊃消化专栏㊃[收稿日期]2023-03-02[基金项目]内蒙古自然科学基金(2022M S 08032);北京市海淀区卫生健康发展科研培育计划(H P 2021-19-50701);航天中心医院院级课题(Y N 202104);中国航天科工集团课题(2020-L C Y L -009)[作者简介]李林林(1981-),男,内蒙古赤峰人,赤峰学院附属医院副主任检验技师,医学硕士,从事肿瘤生物标志物研究㊂*通信作者㊂E -m a i l :h d d 2011yx @163.c o m 非编码R N A 调控铁死亡在肝细胞癌中作用的研究进展李林林1(综述),郝丹丹2*,王玉敏3(审校)(1.赤峰学院附属医院检验科,内蒙古赤峰024000;2.赤峰学院医学部基础医学院生理学教研室,内蒙古赤峰024000;3.航天中心医院,北京大学航天临床医学院呼吸与危重症医学科,北京100049) [摘要] 铁死亡是一种铁依赖介导的脂质过氧化诱导的调节细胞死亡形式,与肿瘤发生密切相关㊂越来越多证据表明,非编码R N A (n o n c o d i n g R N A s ,n c R N A )能够调节肝细胞癌(h e p a t o c e l l u l a r c a r c i n o m a ,H C C )中的铁死亡,进而参与H C C 恶性生物学表型㊂本文我们总结了铁死亡关的n c R N A 与肝癌进展之间的关系㊂本文将有助于我们理解n c R N A 在肝细胞铁死亡和肝细胞癌进展中的作用,并可能为未来探索新的肝细胞癌诊断和治疗生物标志物提供新的思路㊂[关键词] 肝肿瘤;铁死亡;R N A ,未翻译 d o i :10.3969/j.i s s n .1007-3205.2024.02.012 [中图分类号] R 735.7 [文献标志码] A [文章编号] 1007-3205(2024)02-0191-05肝细胞癌(h e pa t o c e l l u l a rc a r c i n o m a ,H C C )是最常见的原发性肝癌,是一种病死率极高的消化系统恶性肿瘤,全球发病率位居所有恶性肿瘤的前5位㊁病死率位列前3位[1]㊂中国是肝癌的高发区,发病率与病死率均位居世界首位,发病率呈逐年上升趋势[2]㊂H C C 发生发展机制仍有待于深入研究㊂尽管最近在治疗等方面取得了一些进展,肝癌的预后仍然很差[3]㊂2020年肝癌死亡例数为830180例,在癌症相关死亡例数中排名第3[1]㊂尽管在治疗病毒性肝炎(H C C 的最大病因)方面取得了显著进展,但随着脂肪性肝炎发病率的增加,肝细胞癌的发病率和病死率仍在增加㊂然而,肝癌复杂的病理生理学限制了有效诊断和治疗干预的发展,促使人们全面了解肝癌的发生机制[4]㊂铁死亡可通过介导耐药㊁放疗抵抗和调控免疫治疗抑制参与H C C[5-10]㊂非编码R N A (n o n -c o d i n g RN A s ,n c R N A s )通过调节铁死亡,在H C C 的发生和发展中起着重要作用[11-13]㊂因此,阐明n c R N A s 对铁死亡的调控作用有助于加深铁死亡在H C C 中的作用的理解㊂本文首先简要介绍了铁死亡,然后重点介绍了n c R N A s 调节铁死亡参与H C C 的分子机制最新进展,进而从n c R N A 角度阐述铁死亡参与H C C 提供理解和思路,并可能为未来探索新的肝细胞癌诊断和治疗生物标志物提供新的思路㊂1 铁死亡铁死亡是2012年提出的一种由铁依赖性㊁脂质过氧化引起的调节性细胞死亡形式[14]㊂生物化学上,细胞内谷胱甘肽(g l u t a t h i o n e ,G S H )的耗尽和谷胱甘肽过氧化物酶4(gl u t a t h i o n e p e r o x i d a s e4,G P X 4)的活性失活导致细胞铁死亡,因为G P X 4催化的还原反应不能消除过量产生的脂质过氧化物[15]㊂铁死亡的关键特征包括膜脂质过氧化(l i pi d pe r o x i d a t i o n ,L P O )㊁细胞内铁稳态失衡和抗氧化防御体系的丧失[5-6]㊂铁死亡的发生需要两个关键启动信号,即抑制抗氧化系统溶质载体家族7成员11(s o l u t e c a r r i e r f a m i l y 7m e m b e r 11,S L C 7A 11)/谷胱甘肽G S H /G P X 4通路受到抑制和游离铁的积累(图1)㊂在铁死亡过程中,多不饱和脂肪酸(p o l y u n s a t u r a t e d f a t t y a c i d ,P U F A )极易发生过氧化,从而破坏脂质双层,破坏膜功能㊂将P U F A 掺入细胞磷脂(尤其是磷脂酰乙醇胺)需要参与脂肪酸合成的特定酶即酯酰辅酶A 合成酶长链家族成员4(A c y l -C o As y n t h e t a s e l o n g -c h a i nf a m i l ym e m b e r 4,A C S L 4)的作用㊂A C S L 4使P U F A 酯化生成P U F A -C o A ,随后通过溶血磷脂酰胆碱酰基转移酶3㊃191㊃第45卷第2期2024年2月河北医科大学学报J O U R N A L O F H E B E I M E D I C A L U N I V E R S I T YV o l .45 N o .2F e b . 2024(l y s o p h o s p h a t i d y l c h o l i n e a c y l t r a n s f e r a s e3, L P C A T3)将P U F A-C o A掺入磷脂膜㊂铁死亡的最后一步是脂质过氧化或其次级产物(如4-H N E和M D A)直接或间接诱导血浆或细胞器膜上的孔隙形成,最终引发细胞死亡(图1)㊂目前研究显示,铁死亡的发生与以下三个因素密切相关[15-16]:①脂质过氧化物的过度产生:F e2+又可以与N A D P H氧化酶激活产生的过氧化氢通过芬顿反应产生脂质过氧化物的前体羟自由基㊂②细胞内二价铁离子(F e2+)的升高:铁在铁死亡中起着核心作用㊂与运输和结合铁相关的转铁蛋白受体1(t r a n s f e r r i n r e c e p t o r1,T f R1)的增加以及铁蛋白和铁转运蛋白(t r a n s f e r r i n,T f)的减少均会导致F e2+的增加从而诱发铁死亡㊂③脂质过氧化损伤修复机制的抑制:G P X4和胱氨酸/谷氨酸逆向转运体(s y s t e m X c-)对铁死亡过程中脂质过氧化损伤的修复具有重要的作用㊂s y s t e m X c-是细胞膜上的一种氨基酸转运体,由S L C7A11(又叫x C T)和S L C3A22组成,它负责细胞胱氨酸的输入和谷氨酸的输出,导致G S H的合成㊂x C T可将细胞外的胱氨酸转运到细胞内与谷氨酸合成G S H,进而G P X4利用产生的G S H将脂质过氧化物还原为相应的醇或水,对抗细胞的氧化应激完成脂质过氧化的修复㊂铁死亡激活剂e r a s t i n或R S L3可以通过抑制x C T 中G P X4活性,最终导致细胞铁死亡㊂2n c R N A调控铁死亡在H C C发病中作用n c R N A s是无蛋白编码功能的一类功能性转录本,其可分为二大类:即小于200个核苷酸的s m a l l n c R N A s和大于200核苷酸的l o n g n c R N A s[17]㊂n c R N A s作为调节分子在转录水平㊁翻译水平和翻译后水平改变基因表达,介导一系列细胞过程,如染色质重塑㊁转录以及转录后修饰等[17]㊂因此,某些n c R N A s能够作为癌基因或肿瘤抑制因子发挥作用㊂在肿瘤中发挥重要作用的主要调节性n c R N A s 包括小R N A(m i c r o R N A s,m i R N A s)㊁长链非编码R N A(l o n g n o n-c o d i n g R N A s,L n c R N A s)以及环状R N A s(c i r c u l a rR N A s,c i r c R N A s)㊂越来越多的研究表明,n c R N A s通过调节铁死亡,在H C C的发生和发展中起着重要作用(图1)㊂图1n c R N A通过调控铁死亡在肝细胞癌中作用2.1 m i R N A调控铁死亡参与H C C耐药和进展转录因子E T S原癌基因1(E T S p r o t o-o n c o g e n e1,E T S1)转录激活m i R-23a-3p,m i R-23a-3p在H C C 中表达增高介导索拉菲尼耐药,并与不良预后相关㊂㊃291㊃河北医科大学学报第45卷第2期索拉菲尼耐药的H C C细胞系中m i R-23a-3p增加,体内外研究显示敲低m i R-23a-3p后增加H C C对索拉菲尼的敏感性㊂m i R-23a-3p通过靶向抑制A C S L4进而抑制铁死亡发生,而m i R-23a-3p敲低后上调A C S L4,增强索拉菲尼诱导的H C C细胞铁死亡发生㊂A C S L4敲低后逆转m i R-23a-3p m i R-23a-3p敲低后索拉菲尼诱导的H C C细胞铁死亡发生,表明在H C C中E T S1上调m i R-23a-3p,m i R-23a-3p通过靶向抑制A C S L4进而抑制铁死亡发生,从而介导H C C对索拉菲尼耐药[18]㊂M i c r o R N A-214-3p在肝癌发生中起调节作用㊂在肝癌细胞系中m i R-214过表达增加细胞对铁死亡诱导剂e r a s t i n诱导细胞死亡的敏感性,这与其增加了e r a s t i n诱导的丙二醛和活性氧水平㊁上调了F e2+浓度和降低G S H水平有关,即表明m i R-214增强H C C细胞对铁死亡的敏感性㊂M i c r o R N A-214-3p通过抑制转录因子4(t r a n s c r i p t i o n f a c t o r4, A T F4)的激活,进而诱导铁死亡发生㊂进一步体内移植瘤研究显示,M i c r o R N A-214-3p过表达抑制了A T F4的表达,进而促进e r a s t i n的抗肿瘤效果,表明M i c r o R N A-214-3p在H C C中通过抑制A T F4进而诱导铁死亡,从而增强e r a s t i n的抗肿瘤效果[19]㊂乙型肝炎病毒(h e p a t i t i sB,H B V)诱导M1巨噬细胞铁死亡,而m i R-142-3p通过抑制,进而促进M1巨噬细胞铁死亡,加速H C C的侵袭和迁移[20]㊂H B V阳性肝细胞癌患者来源的外泌体中m i R-142-3p表达增加,通过上调转铁蛋白受体1(t r a n s f e r r i n r e c e p t o r1,T f R1),下调铁蛋白重链1(f e r r i t i n h e a v y c h a i n1,F T H1)㊁G P X4和A T F4诱导M1巨噬细胞铁死亡,进而促进H C C肿瘤发生,m i R-142-3p靶向抑制S L C3A2促进M1巨噬细胞铁死亡,进而在体内外抑制H C C肿瘤发生[21]㊂2.2 L n c R N A调控铁死亡参与H C C发生发展L n c R N A H E P F A L在肝癌组织中的表达减少,其通过降低S L C7A11表达,增加脂质活性氧和铁的水平来促进铁死亡发生㊂同时l n c R N A H E P F A L 增加了e r a s t i n诱导H C C细胞对铁死亡的敏感性,这可能与m T O R C1有关,并且l n c R N A H E P F A L 可以促进S L C7A11的泛素化并降低S L C7A1蛋白的稳定性,从而导致表达降低㊂表明,L n c R N A H E P F A L在H C C中通过促进S L C7A11泛素化降解,进而诱导H C C铁死亡,发挥其对肿瘤的抑制作用[22]㊂L I N C01134在H C C中表达增加进而促进肿瘤发生,并与不良临床预后相关㊂L I N C01134敲低后升高H C C细胞内R O S㊁脂质R O S㊁M D A水平和降低G S H/G S S G,进而增强对奥沙利铂(O x a l i p l a t i n)的化疗敏感性,表明敲低L I N C01134通过诱导铁死亡增加化疗敏感性[23]㊂机制研究发现,L I N C01134可以促进N r f2募集到G P X4启动子区,从而对G P X4进行转录调控,从抑制铁死亡发生㊂因此在H C C中L I N C01134作为癌基因,通过激活N r f2/ G P X4通路抑制铁死亡发生,进而促进肿瘤发生[23]㊂在H C C中,L n c R N A N E A T1能够通过诱导肌醇加氧酶(m y o-i n o s i t o l o x y g e n a s e,M I O X)的表达,促进H C C对铁死亡诱导剂e r a s t i n和R S L3的敏感性,从而增强它们的抗肿瘤效果[24]㊂铁死亡诱导剂e r a s t i n和R S L3通过促进p53与N E A T1启动子的结合来增加L n c R N A N E A T1的表达㊂诱导的L n c R N A N E A T1通过竞争性结合m i R-362-3p促进肌醇加氧酶M I O X的表达㊂M I O X是一种非血红素铁蛋白,M I O X的上调促进了R O S的产生,减少了烟酰胺腺嘌呤二核苷酸磷酸(n i c o t i n a m i d e a d e n i n ed i n u c l e o t i d e p h o s p h a t e,N A D P H)和G S H,导致细胞抗氧化能力下降㊂M I O X增加了R O S的产生,降低了细胞内N A D P H和G S H的水平,导致了e r a s t i n和R S L3诱导的铁死亡增强㊂L n c R N A N E A T1过表达促进铁死亡,提高了e r a s t i n和R S L3的抗肿瘤活性㊂总之,L n c R N A N E A T1通过调节m i R-362-3p和M I O X在铁死亡㊂因此,诱导铁死亡可能是L n c R N A N E A T1高表达的H C C患者的一种有前途的治疗策略[24]㊂L n c R N A HU L C在H C C中高表达,作为癌基因促进肿瘤发生[25]㊂敲低L n c R N A HU L C增加H C C细胞中的铁死亡和氧化应激㊂L n c R N A HU L C作为m i R-3200-5p的c e R N A发挥作用,并且m i R-3200-5p通过靶向A T F4调节铁死亡,从而抑制H C C细胞内的增殖和转移,表明下调L n c R N A HU L C能够通过靶向m i R-3200-5p/ A T F4轴来诱导H C C细胞铁死亡,抑制肿瘤进展[25]㊂在H C C中L n c R N A G A B P B1-A S1表达增高, L n c R N A-G A B P B1-A S1与G A B P B1m R N A形成R N A双链,然后抑制G A B P B1翻译,导致P R D X5表达减少,最终导致铁死亡[26]㊂E r a s t i n上调l n c R N A G A B P B1-A S1,l n c R N A G A B P B1-A S1通过阻断G A结合蛋白转录因子β1(G A b i n d i n g p r o t e i n t r a n s c r i p t i o n f a c t o r s u b u n i t b e t a1,㊃391㊃河北医科大学学报第45卷第2期G A B P B1)翻译下调G A B P B1蛋白水平,从而导致编码过氧化物酶原5(p e r o x i r e d o x i n-5,P R D X5)过氧化物酶的基因下调,并最终抑制细胞抗氧化能力㊂G A B P B1的高表达水平与H C C患者的不良预后相关,而H C C患者的高G A B P B1-A S1水平与总体生存率的提高相关㊂总之,这些数据证明了G A B P B1及其反义l n c R N A G A B P B1-A S1在e r a s t i n诱导的铁细胞凋亡中的机制联系,并将G A B P B1和G A B P B1-A S1确立为H C C有吸引力的治疗靶点㊂2.3 C i r c R N A调控铁死亡参与H C C发生和耐药c i r c0097009在H C C癌组织和细胞系中高表达,敲低c i r c0097009抑制H C C细胞增殖和侵袭,进一步发现c i r c0097009通过虹吸抑制m i R-1261进而上调S L C7A11从而抑制铁死亡发生,促进H C C发生[27]㊂C i r c I L4R在H C C组织和细胞中异常过表达,C i r c I L4R敲低后铁死亡增加,抑制H C C细胞增殖㊂C i r c I L4R可直接海绵虹吸抑制m i R-541-3p, m i R-541-3p抑制可减轻C i r c I L4R敲低对H C C细胞的影响㊂C i r c I L4R作为m i R-541-3p海绵调节其靶点G P X4㊂G P X4上调减轻了m i R-541-3p诱导的肿瘤抑制和铁死亡㊂表明,C i r c I L4R通过虹吸抑制m i R541-3p进而上调G P X4从而抑制铁死亡发生,促进H C C发生[28]㊂H s a_c i r c_0008367(c I A R S)在索拉非尼治疗后H C C细胞中表达最高,敲低c I A R S后显著抑制细胞对索拉非尼或E r a s t i n的敏感性㊂c I A R S与R N A结合蛋白A L K B H5相互作用,后者是H C C自噬的负调节因子㊂A L K B H5沉默介导的B C L-2/B E C N1复合物的解离被干扰c I A R S有效阻断㊂此外,A L K B H5下调显著地抑制干扰c I A R S引起的铁死亡㊁自噬和铁自噬㊂总之, c I A R S通过抑制A L K B H5介导的自噬抑制,积极调节索拉非尼诱导的铁死亡[29]㊂3问题与展望n c R N A在H C C的多个过程中扮演着重要角色,参与H C C的发生发展㊂近年来n c R N A在调控铁死亡进而在调控肿瘤恶性生物学中发挥着重要作用㊂从本文可以看出n c R N A可以调控铁死亡多个靶点进而参与H C C的发生发展和耐药等㊂然而对于n c R N A在H C C中调控铁死亡的研究目前处于起步阶段,探索其他n c R N A在H C C中调控铁死亡仍值得深入探索㊂因此,深入研究其他n c R N A在调控H C C中铁死亡进程也是未来的重要研究方向,值得我们进一步深入关注㊂另外部分n c R N A 通过铁死亡介导耐药等调控H C C恶性生物学作用,那么能否实现n c R N A的靶向输运在肿瘤部位精准积聚实现对肿瘤的抑制作用,值得思考㊂[参考文献][1]S u n g H,F e r l a y J,S i e g e lR L,e ta l.G l o b a lc a n c e rs t a t i s t i c s2020:G L O B O C A N e s t i m a t e s o fi n c i d e n c e a n d m o r t a l i t yw o r l d w i d e f o r36c a n c e r s i n185c o u n t r i e s[J].C A C a n c e rJC l i n,2021,71(3):209-249.[2]S u nH C,Z h o u J,W a n g Z,e t a l.C h i n e s ee x p e r t c o n s e n s u so nc o n v e r s i o n t h e r a p y f o r h e p a t o c e l l u l a r c a r c i n o m a(2021e d i t i o n)[J].H e p a t o b i l i a r y S u r g N u t r,2022,11(2):227-252.[3]翟来慧,陆海波.晚期原发性肝细胞癌的药物治疗[J].现代肿瘤医学,2020,28(2):326-329.[4] A l q a h t a n i A,K h a n Z,A l l o g h b i A,e t a l.H e p a t o c e l l u l a rc a r c i n o m a:m o l e c u l a rm e c h a n i s m s a nd t a r ge t e d t h e r a p i e s[J].M e d i c i n a(K a u n a s),2019,55(9):526.[5] Y u a nJ,L 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[17] B o l t o nE M,T u z o v aA V,W a l s hA L,e t a l.N o n c o d i n g R N A s i np r o s t a t e c a n c e r:t h e l o n g a n d t h e s h o r t o f i t[J].C l i nC a n c e rR e s,2014,20(1):35-43.[18] L u Y,C h a n Y T,T a n H Y,e ta l.E p i g e n e t i cr e g u l a t i o n o ff e r r o p t o s i s v i a E T S1/m i R-23a-3p/A C S L4a x i s m e d i a t e ss o r a f e n i b r e s i s t a n c e i nh u m a nh e p a t o c e l l u l a r c a r c i n o m a[J].JE x p C l i nC a n c e rR e s,2022,41(1):3.[19] B a iT,L i a n g R,Z h u R,e ta l.M i c r o R N A-214-3p e n h a n c e se r a s t i n-i n d u c e df e r r o p t o s i sb y t a rg e t i n g A T F4i nh e p a t o m ac e l l s[J].JC e l l P h y s i o l,2020,235(7/8):5637-5648.[20] H uZ,Y i nY,J i a n g J,e t a l.E x o s o m a lm i R-142-3p s e c r e t e db yh e p a t i t i sB v i r u s(H B V)-h e p a t o c e l l u l a rc a r c i n o m a(H C C)c e l l s p r o m o t e sf e r r o p t o s i so f M1-t y p e m a c r o p h a g e st h r o u g hS L C3A2a n d t h e m e c h a n i s m o f H C C p r o g r e s s i o n[J].JG a s t r o i n t e s tO n c o l,2022,13(2):754-767.[21] H uZ,Z h a n g H,L i u W,e ta l.M e c h a n i s m o f H B V-p o s i t i v el i v e r c a n c e r c e l l e x o s o m a lm i R-142-3p b y i n d u c i n g f e r r o p t o s i so fM1m a c r o p h a g e s t o p r o m o t e l i v e rc a n c e r p r o g r e s s i o n[J].T r a n s l C a n c e rR e s,2022,11(5):1173-1187.[22] Z h a n g B,B a o W,Z h a n g S,e t a l.L n c R N A H E P F A La c c e l e r a t e s f e r r o p t o s i s i n h e p a t o c e l l u l a r c a r c i n o m ab yr e g u l a t i n g S L C7A11u b i q u i t i n a t i o n[J].C e l lD e a t hD i s,2022,13(8):734.[23] K a n g X,H u o Y,J i aS,e ta l.S i l e n c e d L I N C01134e n h a n c e so x a l i p l a t i n s e n s i t i v i t y b y f a c i l i t a t i n g f e r r o p t o s i s t h r o u g hG P X4i nh e p a t o c a r c i n o m a[J].F r o n tO n c o l,2022,12:939605.[24] Z h a n g Y,L u oM,C u i X,e t a l.L o n g n o n c o d i n g R N A N E A T1p r o m o t e s f e r r o p t o s i sb y m o d u l a t i n g t h e m i R-362-3p/M I O Xa x i s a s a c e R N A[J].C e l lD e a t hD i f f e r,2022,29(9):1850-1863.[25] G u a nL,W a n g F,W a n g M,e ta l.D o w n r e g u l a t i o no fHU L CI n d u c e s F e r r o p t o s i s i n H e p a t o c e l l u l a r C a r c i n o m a v i aT a r g e t i n g o f t h em i R-3200-5p/A T F4A x i s[J].O x i dM e dC e l lL o n g e v,2022,2022:9613095.[26] Q iW,L i Z,X i aL,e t a l.L n c R N AG A B P B1-A S1a n dG A B P B1r e g u l a t e o x i d a t i v e s t r e s sd u r i n g e r a s t i n-i n d u c e d f e r r o p t o s i s i nH e p G2h e p a t o c e l l u l a rc a r c i n o m ac e l l s[J].S c iR e p,2019,9(1):16185.[27] L y uN,Z e n g Y,K o n g Y,e t a l.F e r r o p t o s i s i s i n v o l v e d i nt h ep r o g r e s s i o n o f h e p a t o c e l l u l a r c a r c i n o m a t h r o u g h t h ec i r c0097009/m i R-1261/S L C7A11a x i s[J].A n nT r a n s lM e d,2021,9(8):675.[28] X u Q,Z h o u L,Y a n g G,e t a l.C i r c I L4R f a c i l i t a t e s t h et u m o r i g e n e s i s a n d i n h i b i t s f e r r o p t o s i s i n h e p a t o c e l l u l a rc a r c i n o m ab y r e g u l a t i n g t h em i R-541-3p/G P X4a x i s[J].C e l lB i o l I n t,2020,44(11):2344-2356.[29] L i uZ,W a n g Q,W a n g X,e t a l.C i r c u l a rR N Ac I A R S r e g u l a t e sf e r r o p t o s i si n H C C c e l l s t h r o ughi n t e r a c t i n g w i t h R N Ab i n d i n gp r o t e i nA L K B H5[J].C e l lD e a t hD i sc o v,2020,6:72.(本文编辑:刘斯静)㊃591㊃李林林等非编码R N A调控铁死亡在肝细胞癌中作用的研究进展。

The Significance of Digital Gene Expression Profiles

The Significance of Digital Gene Expression Profiles

The Significance of Digital GeneExpression ProfilesSte´phane Audic and Jean-Michel Claverie1Laboratory of Structural and Genetic Information,Centre National de la Recherche Scientifique–E.P.91,Marseille13402,FranceGenes differentially expressed in different tissues,during development,or during specific pathologies are of foremost interest to both basic and pharmaceutical research.‘‘Transcript profiles’’or‘‘digital Northerns’’are generated routinely by partially sequencing thousands of randomly selected clones from relevant cDNA libraries.Differentially expressed genes can then be detected from variations in the counts of their cognate sequence tags.Here we present the first systematic study on the influence of random fluctuations and sampling size on the reliability of this kind of data.We establish a rigorous significance test and demonstrate its use on publicly available transcript profiles.The theory links the threshold of selection of putatively regulated genes (e.g.,the number of pharmaceutical leads)to the fraction of false positive clones one is willing to risk.Our results delineate more precisely and extend the limits within which digital Northern data can be used.Very large-scale,single-pass partial sequencing of cDNA clones from a large number of libraries has led to the identification of∼50,000human genes(Ad-ams et al.1995;Aaronson et al.1996;Hillier et al. 1996).However,a precise function or a complete transcript sequence are known for<5000of these (Adams et al.1995;Boguski and Schuler1995).In the absence of functional clues for most of the newly identified genes,evidence of differential ex-pression is the most important criteria to prioritize the exploitation of anonymous sequence data in both basic and pharmaceutical(Nowak1995;Ad-ams1996;Bains1996;Editorial1996)research.For example,the study of expression profiles in various tumors is central to the new Cancer Genome Anatomy project(Kuska1996;O’Brien1997).In contrast to functional assays,the quantitative analysis of gene expression level lends itself to large-scale implementation.Two main approaches have been proposed(1)‘‘analog’’methods based on hy-bridization to arrayed cDNA libraries(Lennon and Lehrach1991;Gress et al.1992;Nguyen et al.1995; Schena et al.1995;Zhao et al.1995)or oligonucleo-tide‘‘chips’’(Fodor et al.1991;Southern et al.1992; Guo et al.1994;Matson et al.1995);and(2)‘‘digi-tal’’methods,based on the generation of sequence tags.This paper focuses on the latter.The sequence tag-based method(Okubo et al.1992;Matsubara and Okubo1994)consists of generating a large number(thousands)of expressed sequence tags (ESTs)(Adams et al.1991;Wilcox et al.1991;Adams et al.1992;Khan et al.1992)from3Ј-directed re-gional non-normalized cDNA libraries.Recently, Velculescu et al.(1995)have introduced the serial analysis of gene expression(SAGE).Although tags are100–300nucleotides in length in the original EST approach,the SAGE method only requires nine nucleotides,therefore allowing a larger throughput. In both protocols,the number of tags is reported to be proportional to the abundance of cognate tran-scripts in the tissue or cell type used to make the cDNA library.The variation in the relative fre-quency of those tags,stored in computer databases, is then used to point out the differential expression of the corresponding genes:This is the concept of a ‘‘digital Northern’’comparison.In the absence of a sound theoretical framework,the validity of the method has only been verified for a handful of genes in the context of two cellular differentiation systems(Lee et al.1995;Okubo et al.1995)induc-ible in vitro.Yet,with a total number of human genes of∼80,000or more,it is not intuitive that sequencing a mere few thousand tags(a typical ex-periment)from highly redundant non-normalized cDNA libraries can produce a useful picture,or real-istic‘‘transcript profile,’’of a given tissue,develop-ment stage,or cell type.What variations in tag numbers allow for a reliable inference about differ-ential expression?How many tags should be gener-ated?Here we present the statistical framework re-quired to answer those questions and analyze tran-script profiles in a quantitative manner.1Corresponding author.E-MAIL jmc@rs-mrs.fr;FAX33491164549.RESEARCH 986RESULTSIn Methods we establish the probability distribution governing the occurrence of the same rare event in duplicate experiments.This probability distribution is a general result applicable to a wide variety of experimental situations,although this paper fo-cuses on its use to analyze digital gene expression patterns.The main and only mathematical assump-tion behind the derivation is that the observed events are rare and part of a large population of possible outcomes(the distribution of which is not specified).In the context of a digital Northern,one event is the observation of a given cDNA sequence tag,and the experiment consists of the random picking and partial sequencing of a number N of cDNA clones.Given the usual complexity(i.e.,the number of different genes expressed)of cDNA li-braries,observing a given cDNA qualifies as a rare event,as the abundance of most individual mes-sages is of the order of a few percents or less. Random Fluctuation vs.Significant Change in Tag Number:When to Infer Differential ExpressionLet us randomly pick N=1000clones from a cDNA library and generate the corresponding sequence tags;a given message(e.g.,interleukin-2)will be picked x(e.g.,two)times,with x in a typical(0–10) range.If we now redo this experiment,that is,again pick1000clones and generate the tags,the same message will now be picked y(e.g.,3)times.If the experiments have been duplicated correctly and the clones selected at random,we expect x and y to be close,albeit often different because of random fluc-tuations.In the Methods section,we show that the expected probability of observing y occurrences of a clone already observed x times is given by the simple formula:p(y|x)=͑x+y͒!x!y!2͑x+y+1͒(1)Equation1can be used to compute a confi-dence interval[y min,y max]⑀within which we expect to find y with a given probability,noted1–2⑀,where 2⑀is the significance level.For⑀small(e.g.,2.5%or less),y values falling outside the[y min,y max]⑀inter-val correspond to p(y|x)<<1,therefore pointing out very unlikely random fluctuations between the two experiments.The confidence intervals for the usual1%and5%significance levels are given in Table1.The same confidence intervals listed in Table1 can in fact be used to analyze the results of sampling N clones from two different libraries.Provided all experimental factors are well replicated,significant discrepancies between x(from one library)and y (from the other)will now characterize differentially expressed genes,for example,the relative abun-dance of which is unlikely to be the same in the two libraries.Simply reading Table1,we see that varia-tions in counts such as7→0,or2→12are signifi-cant(P<0.01)evidence of regulated gene expres-sion,whereas variations such as3→0or8→16are not(P>0.05).However,we do not advocate the use of rigid significance thresholds to analyze digital transcript profiles,as discussed below.Influence of the Sampling SizeSurprisingly at first,p(y|x)in Equation1does not involve the sampling size N,that is,the total num-ber of picked clones.The fluctuation probabilities, and confidence intervals,depend only on the values of the observed counts.To understand why,we must remember that Equation1governs the results of strictly duplicated experiments.Given N clones are sampled,the most likely tags to be picked up are, intuitively,those corresponding to cDNA,the abun-dance of which is of the order of1/N,or larger(ac-cording to Equation3,the probability of finding a given cDNA with1/N abundance while picking up N clones is0.63,see also Equation13).Choosing a sampling size therefore corresponds to targeting a given subset of genes,the level of expression of which allows their tags to occur at reasonable fre-quencies.As expected,more reliable inferences can be made on clones corresponding to larger absolute frequencies(i.e.,the ones more often picked up). For example(see Table1),a variation in counts from 1–3(threefold increase)is not indicative of a signifi-cant(P<0.05)increase,whereas a variation from 4–12is significant at P<0.05,and a variation from 7–21is significant at P<0.01.For a gene expressed at a given rate,increasing the sampling size N leads to higher tag counts,and allows more stringent sta-tistical inference to be made,for the same propor-tional variation.Most often in practice one wishes to compare digital Northerns or gene profiles that have been computed from the random picking of different numbers of clones,N1and N2.The mathematical problem is now to establish the probability for a given cDNA(e.g.,interleukin-2)to be picked up x times when the sampling size was N1and y times when the sampling size was N2.Equation1then becomes(see Methods):STATISTICAL ANALYSIS OF TRANSCRIPT PROFILESGENOME RESEARCHp͑y|x͒=ͩN2N1ͪy͑x+y͒!x!y!ͩ1+N2N1ͪ͑x+y+1͒(2)Whereas Equation1applied to the analysis of fluctuation in counts in strictly identical experi-ments,Equation2now applies to the analysis of counts in experiments only differing by the total number of clones randomly picked up.In practice, Equation2will be used to analyze experiments per-formed on two different libraries,using different sampling sizes.As for Equation1,small p(y|x)are expected to characterize the genes exhibiting regu-lated expression,the relative abundance of which is unlikely to be the same in the two libraries.Table1.Confidence Intervals in Function of the Value of xThe value of x(first column),one of the occurrence numbers.The intervals are given for the95%(2␧=0.05) and99%(2␧=0.01)confidence levels.Up to x=20,the exact boundaries,immediately outside the confi-dence interval(first significantly different values)are indicated.A star is used when none are possible.For larger values,the boundaries are given as percentages to be subtracted or added to x.Ricker’s confidence interval characterizes the value of␭,not y(see Methods).The use of a flat p(␭)prior distribution results in the most stringent test,as expected.Although the number(N)of clones sampled does not appear in the expres-sion of p(y|x)(Equation1),its influence shows in the fact that the confidence interval becomes proportionally smaller as x(and y)increases(e.g.,1¨7has the same statistical significance as40¨60).For the same expression level,larger N will result in larger absolute values for x and y,making the detection of significant differential expression more sensitive.Comparison with Fisher’s(2×2)Exact TestThe(2ן2)contingency tables arising from treat-ment versus control experiments are traditionally analyzed with Fisher’s exact test(Siegel1956; Agresti1996).Differential EST count data can be presented in a tabulated form so as to suggest the use of this test,as follows:Brain cDNA library Liver cDNA libraryNumber of actin ESTs211 Number of other ESTs9981189Total clones sampled10001200 The statistical significance according to Fisher’s exact test for such a result is4.6%(two-tail P-value,i.e.,the probability for such a table to occur in the hypothesis that actin EST frequencies are in-dependent of the cDNA libraries).In comparison, the P-value computed from the cumulative form (Equation9,see Methods)of Equation2(i.e.,for the relative frequency of actin ESTs to be the same in both libraries,given that at least11cognate ESTs are observed in the liver library after two were observed in the brain library)is1.6%.Fisher’s(2ן2)exact test is always more conservative than our test(e.g., Fisher’s P-value of1.6%requires a2→13EST count transition in the above setting).Besides being too conservative,there is a more fundamental difficulty in using this test to analyze EST count data.The sampling scheme assumed by Fisher’s exact test in principle requires the total number of data values in the contingency table to be fixed,as well as both the row marginal total and the column marginal totals. In our prospective experimental situation,only the column marginals(i.e.,the numbers of clones sampled from each library)are fixed.The extension of Fisher’s exact test to cases where only one set of marginal totals is fixed(Tocher1950)is still contro-versial.In the context of the above EST counting results,there is an additional problem with the lack of homogeneity in the definition of the‘‘other EST’’category.This category represents different subsets of transcripts for different libraries.The use of Fisher’s(2ן2)exact test is more natural for a different type of EST data analysis:the study of library-dependent alternative transcripts of the same gene(i.e.,splice or polyadenylation vari-ants)(D.Gautheret,O.Poirot,F.Lopez,S.Audic, and J.-M.Claverie,in prep.).Here,the results for an hypothetical gene G1may look as follows:G1-relatedtranscripts inbrain libraryG1-relatedtranscripts inliver library Long-form mRNA210Short-form mRNA83Total G1-relatedclones1013where the alternative categories are unambiguously defined and refer to the same objects.For example, the above results constitute good evidence that G1is expressed in different forms in those tissues(Fisher’s exact test two-tail P-value=1.2%).False Leads in the Selection of Candidate GenesA crucial measure of the power of statistical signifi-cance tests is their rate of false alarm,that is,how often random fluctuations are expected to be mis-taken for significant differences in the results.When analyzing the transcript profiles from two different libraries,a false alarm would cause a gene to be deemed differentially transcribed,whereas in fact it is not.The rate of false alarm is therefore a direct estimate of the fraction of false leads,when search-ing for differentially expressed genes on the basis of differences in tag counts.The rates of false alarm associated with the P<0.01and P<0.05confi-dence intervals listed in Table1have been com-puted by Monte-Carlo simulation on the basis of two experimental sequence tag distributions(Table 2;Fig.1).The rate of false alarms associated with the use of Equation1(in fact,its cumulative form Equa-tion9,see Methods)is very small for genes repre-sented by small tag counts and slowly increases for higher tag counts,without ever exceeding the se-lected significance level.Such good behavior vali-dates the use of the confidence intervals(Table1) computed from Equation1and Equation9to assess the statistical significance of variations in digital Northern data.The curves labeled‘‘window’’char-acterize the very similar behavior of a slightly less conservative derivation of the same test(see Meth-ods,Equation15).For comparison,Figure1also presents the behavior of another test,based on an inappropriate application of Ricker’s confidence in-tervals(Ricker1937)(see Methods).DISCUSSIONAn appropriate statistical test is now at our disposal to begin analyzing digital gene expression profiles STATISTICAL ANALYSIS OF TRANSCRIPT PROFILESGENOME RESEARCHin a more quantitative way.For example,the test can be used to determine how many genes appear regulated at various confidence levels using the data from a typical experiment(e.g.,sampling a thou-sand clones).We analyzed the data gathered by Okubo et al.(1995)on the human promyelocytic leukemia cell line HL60induced by dimethylsulfox-ide(DMSO)or tetradecanoylphorbolacetate(TPA). Table3shows the21EST classes the occurrences of which exhibit significant variations at the1%level. Most of the corresponding genes make biological sense in term of differentiation along the granulo-cyte or monocyte pathways.This example serves to discuss a subtle point in the interpretation of the P values computed from Equation1,2,and9.Rigorously,these equations apply to the case where a given gene(e.g.,lipocor-tin)would have been selected for scrutiny before looking at the differences in cognate tag counts be-tween libraries.When comparing two libraries with-out specifying in advance the transcripts we want to follow,and then focusing a posteriori on any of those exhibiting significant variations,the average number of expected false positive N f a l s e is N false=PN species,where N species is the number of dif-ferent transcript species encountered and p is a given significance level.For instance,in the experi-ment analyzed in Table3,N species is of the order of 600(Okubo et al.1995).It is therefore possible that up to four(600ן7ן10מ3)out of the21transcript species listed in Table3are not truly differentially expressed.Therefore,when two libraries are compared without prior gene selection,the use of a predeter-mined significance threshold is not advisable.The P values computed from Equation1,2,and9should simply be used to rank all observed variations by order of decreasing statistical significance(analo-gous to how‘‘similarity hits’’are listed after data-base searches).The end-users can then make their own choice about the number of candidate target genes to be retained from the top of the list,bearing in mind the corresponding number of expected false positives.Although the present interpretation of a digital Northern focuses on the genes exhibiting the most spectacular differential expressions,there is already ample evidence that small changes can cause drasticTable2.Publicly Available Distributions of Sequence Tags(Left)Data from Velculescu et al.(1995):Frequency of occurrence of each of the428transcriptspecies represented in840SAGE tags randomly generated from a3Ј-directed cDNA library fromhuman pancreas.(Right)Data from Okubo et al.(1992):Frequency of occurrence of each of641transcript species represented in982randomly sequenced clones from a3Ј-directed cDNAlibrary from human liver cell line HepG2.AUDIC AND CLAVERIE990effects.Disease states caused by haploinsufficiency and trisomy suggest that2→1or2→3propor-tional changes in expression level may be of biologi-cal significance.Table1shows that there is no theo-retical limit to the detection of such small variations from the comparison of digital expression patterns. Simply,the sampling size has to be increased enough for the required numbers of cDNA tags to reach a significance threshold(for instance 40→60,for a confidence level of95%).Analog hybridization-based methods(Fodor et al.1991;Lennon and Lehrach1991;Gress et al. 1992;Southern et al.1992;Guo et al.1994;Matson et al.1995;Nguyen et al.1995;Schena et al.1995; Zhao et al.1995)are traditionally opposed to digital tag-counting methods(Okubo et al.1992;Matsub-ara and Okubo1994;Lee et al.1995;Okubo et al.1995;Vel-culescu et al.1995)for theanalysis of differential geneexpression.Both types ofmethods are sensitive to thequality of the original messen-ger RNA preparation and/orcDNA libraries.Analog meth-ods promise higher through-put,lower cost,and have thecapacity of studying transcriptson a much wider scale of abun-dance.They are therefore ex-pected to supersede digitalmethods.On the down side,however,hybridization signalsare not easily reproducible,andcan be affected by many un-known properties such as thecDNA library complexity,aswell as clone and sequence spe-cific features(e.g.,insert size,nucleotide composition,pres-ence of repeats,secondarystructure,triple helix interac-tion,etc.).Therefore,the hy-bridization-based methods re-quire an estimation of the dis-persion of the signal associatedwith each clone(i.e.,enoughrepetitions of each experi-ment),and multiple standard-ization and calibration proce-dures to allow the meaningfulcomparison of hybridizationpatterns obtained from varioussources(tissues,cell types,etc.) or from different membranes or chips.This is far from routine and has yet to be worked out.In con-trast,and thanks to the unique properties of the Poisson distribution,digital methods have the ca-pacity of providing a quantitative assessment of dif-ferential expression without the repetition or the standardization of individual tag-counting experi-ments.The statistical analysis presented here pro-vides an objective method to analyze digital tran-script profile data,and adapts it to fit(1)the num-ber of leads one wants to be followed;(2)the fraction of false clues to be tolerated;and(3)the level of modulation in gene expression considered of biological interest.A program is available on our web site(http:// rs-mrs.fr)to compute the confidenceFigure1Rate of false alarm computed according to the confidence intervalslisted in Table1.(Top)Monte-Carlo simulation of the random sampling of840tags distributed according to the data from Velculescu et al.(1995;see Table2).(Bottom)Monte-Carlo simulation of the random sampling of982ESTs distrib-uted according to the data from Okubo et al.(1992;see Table2).The fre-quency of false alarm was computed for two significance levels(2⑀=5%,leftand2⑀=1%,right)and plotted in function of the tag class size(from1–64forVelculescu et al.,from1–22for Okubo et al.).In all cases,the rate of false alarmincreases up to a plateau for larger class sizes.The test(cumulative form ofEquation1)derived from the flat p(␭)prior shows perfect behavior with amaximal rate of false alarm always less than the significance levels(brokenlines).The test(cumulative form of Equation15)derived from the window p(␭)prior exhibits a slightly higher rate of false alarms.Both versions of the testexhibit conservative behaviors for class size<5,with a false alarm rate even lessthan expected.In contrast,Ricker’s confidence intervals(Equation12)aregrossly inadequate and lead to false alarm rates up to four times the significancelevel.Graphs are computed from the analysis of1000repetitions of each ex-periment.STATISTICAL ANALYSIS OF TRANSCRIPT PROFILESGENOME RESEARCHintervals corresponding to arbitrary significance lev-els and sampling size N1and N2.METHODSLet us denote p(x)the probability to observe x sequence tags of the same gene(i.e.,from the3Јend of the same transcript) when N cDNA clones are picked randomly.For each transcript representing a small(i.e.,less than5%)fraction of the library and Nജ1000,p(x)will closely follow the Poisson distribu-tion:p͑x͒=e−␭␭xx!(3)where␭is the actual(albeit unknown)number of transcript of this type per N clones in the library.If we duplicate this ex-periment(i.e.,once again randomly pick N clones of the same library and generate sequence tags),we will now observe y occurrences of the same transcript.What is the probability of the various y values?An approximate solution consists in us-ing x as the maximum likelihood estimate for␭and compute the probability for y occurrences given a Poisson distribution of mean␭=x:p͑y|x͒=e−x x yy!(4)Equation4is not symmetrical in x and y.This is an ob-vious flaw as the probability should not depend on which of the x or y values were observed first.p(y|x)=p(x|y)should hold provided that an equal number N of clones is sampled in both experiments.Equation4is not the correct formula,be-cause we have not yet taken into account the fluctuation of x around the unknown mean␭.To account for the fact that the actual value of␭is unknown,we have to integrate Equation4 over all possible␭values:p͑y|x͒=͐0ϱd␭p͑d=␭|x͒p͑y|d=␭͒(5) p(d=␭|x)in Equation5is the probability that the actualTable3.List of ESTs Exhibiting Significant(P<0.01)Differences inAbundance in the HL60Cell Line Induced by DMSO or TPAEST ID HL60HL60+TPA HL60+DMSO Significance418221013ן10מ7211241024ן10מ71982328ן10מ735616203ן10מ638012106ן10מ513541206ן10מ528514811ן10מ4201501102ן10מ424401143ן10מ429313613ן10מ429211015ן10מ465014525ן10מ433515339ן10מ444410412ן10מ316740814ן10מ31550834ן10מ38616107ן10מ33056207ן10מ318060607ן10מ318080607ן10מ317660607ן10מ3Only the probability(computed according to Equations7and8)corresponding to the most significanttransition(numbers in bold)is listed(Okubo et al.1995).The total EST numbers sampled from the HL60,HL60+TPA and HL60+DMSO cDNA libraries are845,845,and1058,respectively.ESTs418,211,356,285,293,292,650,335,444,861,305corresponding to ribosomal proteins,and EST380,a tag to an unkown gene,exhibit a marked reduction of expression level in the DMSO-and/or TPA-induced differentiated states.Inconstrast,ESTs135(ferritin),2015(LD78/macrophage inflammatory protein),1674(methionine adenosyl-transferase),155(thymosin␤-4),1806(lipocortin),1808(thymosin␤-10),and1766(a metallothionein)appear more abundant in the TPA-induced state,also highly enriched in EST19(the ubiquitous elongationfactor1-␣).␤-Actin(EST244),is the only markedly increased tag in the DMSO-induced state.EST numbers,abundance data,and protein assignments are from the‘‘body map’’public expression data repository athttp://www.imcb.osaka-u.ac.jp(K.Okubo and K.Matsubara).AUDIC AND CLAVERIE992abundance of a given transcript is␭given that x occurrences of a cognate tag have been observed in one experiment.The second term in the integral is the probability of drawing y occurrences given a Poisson distribution of mean␭:p͑y|d=␭͒=e−␭␭yy!(6)Using Bayes’theorem p(d=␭|x)can be written asp͑d=␭|x͒=p͑x|d=␭͒p͑d=␭͒͐0ϱd␭Јp͑x|d=␭Ј͒p͑d=␭Ј͒(7)To evaluate Equation7,we need to define the prior dis-tribution p(d=␭).The least constrained hypothesis(i.e.,with the least information content),is to attribute an equal a priori probability to all␭values in the[0,ϱ]range.Incorporating such a flat prior in Equation5leads top͑y|x͒=1x!y!͐0ϱd␭e−2␭␭͑x+y͒(8)From the definition of the⌫function for integer arguments we observe that͐0ϱd␭e−2␭␭͑x+y͒=͑x+y͒!2͑x+y+1͒and finally obtain the expression given in Results:p͑y|x͒=͑x+y͒!x!y!2͑x+y+1͒(1)This equation can be used in a wide variety of experi-mental situations.Equation1defines the probability of ob-serving x and y occurrences of the same rare event in dupli-cated experiments,regardless of the detailed probability dis-tribution of those events among the set of possible outcomes. In particular,in the context of transcription profiles,p(y|x) can be evaluated regardless of the distribution of each tran-script(provided it is rare)within a cDNA library.To compute the confidence intervals listed in Table1,we made use of the cumulative distributions:C͑yഛy min|x͒=͚y=0yഛy min p͑y|x͒(9a)D͑yജy max|x͒=͚y=y maxϱp͑y|x͒(9b) These equations allow the computation of an interval [y m i n,y m a x]⑀s u c h a s C(yഛy m i n|x)ഛ⑀a n d D(yജy max|x)ഛ⑀.Given that an event is observed x times in one experiment,the number y of occurrences of this event in a duplicate experiment is expected to fall within the interval [y min,y max]⑀with a probability of1–2⑀.Equation9,a and b, can therefore serve as a significance test when comparing,for instance,the results of sampling N clones from two different libraries.For2⑀small(e.g.,5%or less),y values falling outside the[y min,y max]⑀interval correspond to p(y|x)<<1,and point out significant differences between the two experiments. They should include differentially expressed genes,for ex-ample,for which␭is different in the two libraries.Generalization to Different Sampling SizesWhen different numbers of clones N1and N2are sequenced from the same library,Equation5becomesp͑y|x͒=͐0ϱd␭2͐0ϱd␭1p͑d1=␭1|x͒p͑y|d2=␭2͒␦ͩ␭2מN2N1␭1ͪ(10) where the two abundance values␭1and␭2are forced in the same ratio as N1and ing the same bayesian argument as before(Equation7)leads top͑y|x͒=1x!y!ͩN2N1ͪy͐0ϱd␭1e−␭1ͩ1+N2N1ͪ␭1͑x+y͒(11)the last integral is simply͑x+y͒!ͩ1+N2N1ͪ͑x+y+1͒leading to the formula presented in the Results section:p͑y|x͒=ͩN2N1ͪy͑x+y͒!x!y!ͩ1+N2N1ͪ͑x+y+1͒(2)Ricker’s Confidence IntervalThe confidence interval computed from Equation1(and its cumulative form,Equation9,a and b)is different from one introduced previously by Ricker(1937)although,at first,the two may appear to be related.Given x occurrences of a sequence tag,Ricker’s formula defines a confidence interval[␭min,␭max]x for␭(again the actual number of transcripts of this type per N clones in the library)such asp͑kഛx͒=͚k=0x e−␭max␭max kk!ഛ␣2(12a) andp͑kജx͒=͚k=xϱe−␭min␭min kk!ഛ␣2(12b) where␣is typically5%or1%.Ricker’s confidence intervals for various values of x are given in Table1.Those intervals are close to those computed from Equation1,but delineate the range of likely␭values,not y(the number of occurrences of the same event in a duplicated experiment).It is possible for x and y to fall outside each other’s Ricker’s confidence interval [␭min,␭max],while still being nonsignificant fluctuations around the same␭value.The confidence intervals computed from Equation12,a and b,are therefore too narrow to prop-erly define significant discrepancies between x and y.The false alarm rate associated with the use of Ricker’s confidence in-tervals is too high(Fig.1).However,an interesting use of Equation12,a and b,is the estimation of the range of possible frequencies[␭min,␭max]x=0for cDNAs not yet encountered after picking N clones.For example,the95%confidence interval is given by:0<N␭<3.7(13) That is,the abundance of a cDNA not picked up among STATISTICAL ANALYSIS OF TRANSCRIPT PROFILESGENOME RESEARCH。

群体感应-LASR

群体感应-LASR

Revisiting the quorum-sensing hierarchy inPseudomonas aeruginosa:the transcriptionalregulator RhlR regulates LasR-specific factorsVale´rie Dekimpe and Eric De´zielCorrespondenceEric De´zieleric.deziel@iaf.inrs.caINRS-Institut Armand-Frappier,Laval,Que´bec H7V1B7,CanadaReceived29July2008 Revised11November2008 Accepted13November2008Pseudomonas aeruginosa uses the two major quorum-sensing(QS)regulatory systems las and rhl to modulate the expression of many of its virulence factors.The las system is considered to stand at the top of the QS hierarchy.However,some virulence factors such as pyocyanin have been reported to still be produced in lasR mutants under certain conditions.Interestingly,such mutants arise spontaneously under various conditions,including in the airways of cystic fibrosis ing transcriptional lacZ reporters,LC/MS quantification and phenotypic assays,we have investigated the regulation of QS-controlled factors by the las system.Our results show that activity of the rhl system is only delayed in a lasR mutant,thus allowing the expression of multiple virulence determinants such as pyocyanin,rhamnolipids and C4-homoserine lactone(HSL)during the late stationary phase.Moreover,at this stage,RhlR is able to overcome the absence of the las system by activating specific LasR-controlled functions,including production of3-oxo-C12-HSL and Pseudomonas quinolone signal(PQS).P.aeruginosa is thus able to circumvent the deficiency of one of its QS systems by allowing the other to take over.This work demonstrates that the QS hierarchy is more complex than the model simply presenting the las system above the rhl system.INTRODUCTIONPseudomonas aeruginosa is a ubiquitous and versatile bacterium involved in numerous pathogenic infections affecting immunocompromised individuals and those suffering from cystic fibrosis(Marshall&Carroll,1991; Pier,1985;Speert,1985).This bacterium regulates most of its virulence determinants in a cell-density-dependent manner via a mechanism called quorum-sensing(QS). Such global regulatory systems are found in most bacterial species,and control several and diverse biological func-tions,such as virulence,bacterial conjugation,biolumin-escence and biofilm formation(de Kievit&Iglewski,2000; Donabedian,2003;Loh et al.,2002;Miller&Bassler,2001). QS is mediated by diffusible signalling molecules released into the external environment.These signals,when reach-ing specific concentrations correlated with specific popu-lation cell densities,bind to and activate their respective transcriptional regulators.In P.aeruginosa,two conven-tional complete QS systems are known:the synthases LasI and RhlI produce the N-acylhomoserine lactones3-oxo-C12-HSL and C4-HSL respectively,which induce their cognate LuxR-type transcriptional regulators LasR and RhlR,responsible for the activation of numerous QS-controlled genes(Juhas et al.,2005;Pesci et al.,1997). Among genes activated by these two regulators are those coding for the LasI and RhlI synthases.Since N-acyl-HSLs induce their own production,they are called autoinducers. More recently,a third,distinct QS system has been unveiled.It is composed of a transcriptional regulator from the LysR family,MvfR(PqsR),which directly activates two operons(phnAB and pqsABCDE)required for the biosynthesis of4-hydroxy-2-alkylquinolines (HAQs),including molecules involved in4-quinolone signalling(De´ziel et al.,2004;Le´pine et al.,2004;Pesci et al., 1999),and for the activation of many QS-controlled genes, via pqsE(De´ziel et al.,2005;Diggle et al.,2006;Farrow et al.,2008).Among the HAQs,4-hydroxy-2-heptylquino-line and the Pseudomonas quinolone signal(PQS)act as activators of the MvfR regulator,inducing a positive feedback loop typical of QS systems(Xiao et al.,2006a). QS regulation is a very complex and extensive network influencing,both positively and negatively,the transcrip-tion of perhaps5–10%of the P.aeruginosa genome (Hentzer et al.,2003;Schuster et al.,2003;Wagner et al., 2003).The LasR regulator is known to initiate the QS regulatory system,as it activates the transcription of a number of other regulators,such as rhlR,defining aAbbreviations:HAQ,4-hydroxy-2-alkylquinoline;HSL,homoserinelactone;PQS,Pseudomonas quinolone signal;QS,quorum sensing.Two supplementary figures are available with the online version of thispaper.Microbiology(2009),155,712–723DOI10.1099/mic.0.022764-0 712022764G2009SGM Printed in Great Britainhierarchical QS cascade from the las to the rhl regulons (Latifi et al.,1996;Pesci et al.,1997).Over the last few years, many whole-genome transcriptomic studies have been published with the aim of identifying genes that are under the control of LasR and/or RhlR(Hentzer et al.,2003; Rasmussen et al.,2005;Schuster et al.,2003;Wagner et al., 2003).Specific directly activated genes were clearly identified as belonging to the rhl regulon,such as rhlAB(rhamnolipid biosynthesis),lecA(lectin),hcnABC(HCN production)and both phzABCDEFG operons(phenazine biosynthesis)(Latifi et al.,1995;Schuster et al.,2004;Schuster&Greenberg, 2007;Whiteley et al.,1999;Winzer et al.,2000).However, the situation is not as clear for many LasR-controlled genes, for which it has not been possible to define a single consensus LasR binding site sequence in the promoter region,suggesting that some of these genes are activated indirectly(Schuster et al.,2004;Schuster&Greenberg, 2007).Actually,most QS-regulated factors are more or less influenced by both LasR and RhlR,as is the case for the proteases LasA(staphylolytic protease)and LasB(elastase) (Freck-O’Donnell&Darzins,1993;Hentzer et al.,2003; Nouwens et al.,2003;Schuster et al.,2003;Toder et al.,1994; Wagner et al.,2004).Thus QS plays a predominant role in the regulation of virulence determinants in P.aeruginosa. Surprisingly,however,there are increasing reports that lasR mutants occur frequently in the natural environment (Cabrol et al.,2003),in airways from individuals with cystic fibrosis(D’Argenio et al.,2007;Smith et al.,2006),in intubated patients(Denervaud et al.,2004)and in individuals suffering from bacteraemia,pneumonia or wound infection(Hamood et al.,1996).This is intriguing, since the LasR regulator is widely considered essential for full P.aeruginosa virulence(Preston et al.,1997;Rumbaugh et al.,1999;Storey et al.,1998).The LasR transcriptional regulator is generally considered to sit at the top of the QS hierarchy in P.aeruginosa(Latifi et al.,1996).However,we and others have observed that the phenazine pyocyanin is overproduced by lasR mutants at the late stationary phase(De´ziel et al.,2005;Diggle et al., 2003).As shown in Fig.1,a lasR mutant produces less pyocyanin during early growth phases,although at the end of exponential growth and during early stationary phase, pyocyanin begins to be produced.During late stationary phase,after24h of cultivation,the lasR mutant cultures contain much more pyocyanin than cultures of the wild-type strain(35mg l21compared to2.5mg l21,respect-ively).This is unexplained,since pyocyanin production is known to be regulated by QS(Latifi et al.,1995).The regulator of the pyocyanin biosynthesis genes(phz genes)is RhlR(Brint&Ohman,1995),whose transcription is considered to require LasR(de Kievit et al.,2002;Latifi et al.,1996;Pearson et al.,1997;Pesci et al.,1997).In theory,pyocyanin production is thus expected to be absent in lasR mutants,whereas experimental data show that it is actually only delayed(Fig.1).In order to better understand the specific role of LasR and its involvement in expression of virulence factors,we have characterized the expression of QS-controlled determinants in a lasR mutant and have observed that during stationary phase,many QS-regulated virulence factors are expressed. Our data show that at this stage of growth,the RhlR regulon is activated.Moreover,we found that RhlR is able to induce LasR-regulated genes(including some consid-ered specific such as lasI)in the absence of lasR,unveiling a new mechanism for the bacteria to bypass a defect in their QS regulation,allowing RhlR to induce the las system when LasR is non-functional.METHODSStrains,plasmids and growth conditions.Table1lists strains and plasmids.Bacteria were routinely grown in Tryptic Soy Broth(TSB) medium at37u C in a roller drum,with appropriate antibiotics when required(carbenicillin300mg l21and tetracycline75mg l21for P. aeruginosa;carbenicillin100mg l21and tetracycline15mg l21for Escherichia coli).TSB plates contained1.5%agar.For pyocyanin and rhamnolipid detection,King’s A medium was used(King et al.,1954). All measurements of optical density and absorbance were obtained with a Thermo Scientific NanoDrop1000spectrophotometer.An isogenic lasR rhlR double mutant was generated by allelic exchange of the rhlR gene in a lasR background with pSB224.10A using sucrose counterselection(Beatson et al.,2002).2040608010201101020Pyocyaninconcn(mgl_1)Time (h)Time (h)Growth(OD6)Fig.1.Expression of pyocyanin is delayed in alasR mutant:P.aeruginosa lasR mutant con-taining a constitutive rhlR(pUCPSK rhlR)orlasR(pUCPSK lasR)expression vector,or thesame vector without rhlR or lasR(pUCPSK),compared with the wild-type and the lasR rhlRmutant.Revisiting quorum sensing in P.aeruginosa 713Standard methods were used to manipulate DNA.Plasmid pDN19 (Nunn et al.,1990)was used to construct pVD1,containing the lasI gene under its own promoter.A region spanning from305bp upstream to170bp downstream of the lasI ORF was amplified and inserted between the Xba I and Hin dIII sites in the pDN19multiple cloning site.The gene fragment was generated from genomic DNA using PCR with primers59-GCTCTAGATTTTGGGGCTGTGTTC-TCTC-39and59-CCCAAGCTTACTCGAAGTACTGCGGGAAA-39. The construction was confirmed by effective complementation of a lasI mutant.Plasmids were introduced by electroporation(Choi et al., 2006).lasR mutant subcultures were carried out as follows:a first preculture was made at day1and used to inoculate fresh medium for day2;the latter was used to inoculate fresh medium for day3.Pyocyanin was measured during each day of culture.b-Galactosidase activity assay.Bacteria containing the gene reporter fusions were routinely grown overnight from frozen stocks in TSB with appropriate antibiotics,then subcultured in triplicate at a starting OD600of0.05without antibiotic.Culture samples were regularly taken for determination of growth(OD600)and b-galactosidase activity(Miller,1972).N-Butyryl-L-homoserine lactone (C4-HSL)was purchased from Sigma-Aldrich and the stock solution prepared in acetonitrile.Quantification of rhamnolipids,pyocyanin,AHLs and HAQs. Detection and measurements were performed by LC/MS.For pyocyanin,AHLs and HAQs,480m l culture samples were taken at regular intervals,used for determination of growth(OD600),and mixed with120m l acetonitrile containing50mg l215,6,7,8-tetradeutero-PQS for a final concentration of10mg l21as internal standard.After centrifugation,20m l aliquots of the supernatants were directly injected for LC separation on an Agilent HP1100HPLC system equipped with a36150mm C8Luna reverse-phase column (Phenomenex).A1%acidified water/acetonitrile gradient was used as the mobile phase at a flow rate of0.4ml min21,split to10%with a Valco Tee.A Quattro II(Waters)triple-quadrupole MS was used for molecule detection.Data acquisition was performed in full scan mode with a scanning range of130–350Da.Precise quantification of C4-HSL and3-oxo-C12-HSL was performed by MS/MS,as described previously(De´ziel et al.,2005).For rhamnolipid quantification, 500m l culture samples were taken at regular intervals,used for determination of growth(OD600),and diluted with an equivalent volume of methanol.After centrifugation,20m l aliquots of the supernatants were injected for LC/MS analysis as described pre-viously,using16-hydroxyhexadecanoic acid as internal standard (De´ziel et al.,1999;Le´pine et al.,2002).Elastase and protease enzymic assays.TSB plates supplemented with1%skim milk were inoculated with10m l from cultures at OD6003. Plates were incubated at37u C for3days.For specific LasB elastolytic activity,we used a protocol adapted from that of Bjorn et al.(1979). Briefly,filter-sterilized culture supernatant samples(100m l)from late stationary phase cultures were mixed with5mg elastin Congo red (Sigma)and300m l0.1M Tris/HCl pH7.2.Release of Congo red from degraded elastin was measured as A495after2h of incubation at37u C followed by centrifugation.For assessment of LasA staphylolytic activity, 4.5ml of Staphylococcus aureus overnight cultures were boiled for 15min.and100m l was mixed with300m l of filtered culture supernatants.The OD600was measured after2h of incubation at 37u C with agitation.All experiments were carried out in triplicate. RESULTSThe expression of RhlR-regulated factors is only delayed in the absence of LasRBased on previous observations reporting late pyocyanin production in lasR mutants,we decided to investigate theTable1.Bacterial strains and plasmids used in this studyStrain or plasmid Characteristics Source or reference BacteriaE.coli DH5a supE44D lacU169(w80lacZ D M15)hsdR17recA1endA1gyrA96thi-1relA1Hanahan(1983)P.aeruginosa/lab no.:PA14/ED14Clinical isolate UCBPP-PA14Rahme et al.(1995)PA14lasR/ED69lasR::Gm derivative of ED14De´ziel et al.(2004)PA14lasR rhlR/ED266rhlR::Tc derivative of ED69This studyS.aureus Newman Laboratory strain ATCC25904PlasmidspMIC61(pUCPSK-lasR)lasR in pUCPSK with lac promoter as a Hin dIII–Eco RI fragment(59–39lasR)John Mattick,Institute of Molecular Bioscience,University of Queensland, AustraliapMIC62(pUCPSK-rhlR)rhlR in pUCPSK with lac promoter as a Hin dIII–Eco RI fragment(59–39rhlR)John MattickpPCS1002pLP170containing rhlR-lacZ Pesci et al.(1997) pSB224.10A pRIC380suicide vector carrying rhlR::Tc Beatson et al.(2002) pVD1pDN19containing lasI with its native promoter,Tc r This studypME3853pME6010with a174bp lasI upstream fragment and translationallasI::lacZ fusion containing the first13lasI codons,Tc rPessi et al.(2001) pUCPSK E.coli–P.aeruginosa shuttle vector Watson et al.(1996)pLJR50lasB p-lacZ transcriptional reporter fusion;contains nt2190to+4of the lasB promoter region,Cb r Toder et al.(1994)V.Dekimpe and E.De´ziel714Microbiology155mechanism involved in this phenomenon,as an introduc-tion to exploring QS during the stationary phase.Since RhlR is the known regulator of the phz genes,we hypothesized that late pyocyanin production is due to RhlR activity.In the absence of lasR,RhlR should activate the expression of the phz genes in the late stationary phase,and in its absence,no pyocyanin should be produced.As shown in Fig.1,unlike the lasR mutant,the lasR rhlR double mutant does not produce this phenazine at all.Moreover, lasR(pUCPSK-rhlR),which constitutively expresses rhlR from a plasmid,produces pyocyanin at the same time as the wild-type,confirming that RhlR is responsible for the timing of pyocyanin production.As expected,continued expression of rhlR results in higher production of sR(pUCPSK)acts like the lasR mutant, confirming that the vector does not influence pyocyanin expression.Finally,lasR(pUCPSK lasR)does not over-produce pyocyanin,unlike the lasR mutant,showing that the lasR mutation is responsible for this phenotype.It is also noteworthy that a lasI mutant shows the same pyocyanin overproduction phenotype as the lasR mutant (data not shown).To ensure that optical density during all growth stages,and particularly during stationary phase, truly reflected the number of living bacterial cells,we also determined the viable cell counts.This showed that the growth rates and survival of the lasR mutant and the wild-type were essentially the same(see Supplementary Fig.S1a, available with the online version of this paper),thus confirming that the difference in pyocyanin production is not the result of variations in the number of viable cells. To ensure that this late pyocyanin production was not due to a spontaneous mutation that might have occurred in the lasR background,we subcultured a culture of the lasR mutant on three consecutive days in fresh medium,every time monitoring the production of pyocyanin. Consistently,the cultures had to reach the late stationary phase before producing pyocyanin,indicating that this phenotype in not due to accumulation of secondary mutations during cultivation(see Supplementary Fig.S1b). If RhlR is present and active during the late stationary phase in a lasR mutant,then we should be able to detect RhlR-regulated factors other than pyocyanin.The rhlAB and rhlC genes,coding for enzymes involved in rhamno-lipid biosynthesis,and rhlI,coding for the C4-HSL synthase,are all directly regulated by RhlR(de Kievit et al.,2002;Medina et al.,2003).We precisely quantified rhamnolipids and C4-HSL in lasR,lasR rhlR and lasR(pUCPSK-rhlR)cultures.As shown in Fig.2(a,b), the lasR rhlR double mutant was unable to synthesize rhamnolipids or C4-HSL,while the lasR mutant produced these molecules with a delay,essentially in late stationary phase.These results support the hypothesis thatexpression 1020304010201101020102011010200.40.30.20.1Rhamnolipidconcn(mgl_1)C4-HSLconcn(mgl_1)Growth(OD6)Growth(OD6)(a)(b)Time (h)Time (h)Time (h)Time (h)Fig.2.Expression of RhlR-controlled factorsis delayed in a lasR mutant.P.aeruginosawild-type and lasR mutant containing ornot a constitutive rhlR expression plasmid(pUCPSK-rhlR)are compared.Production of(a)rhamnolipids and(b)C4-HSL.Revisiting quorum sensing in P.aeruginosa 715of the rhl regulon is only delayed in a lasR mutant.The production of C4-HSL and rhamnolipids was restored to levels similar to wild-type when the lasR mutant was transformed with an rhlR expression vector,confirming that RhlR is responsible for these phenotypes.These results show that the delayed expression of RhlR-controlled phenotypes in a lasR background can be restored by expressing rhlR.In order to obtain additional evidence that RhlR is indeed expressed in a lasR mutant,we evaluated the transcription of rhlR with a lacZ fusion reporter.As shown in Fig.3, maximal rhlR transcription occurs at the early stationary phase in the wild-type strain.Furthermore,it follows a similar expression pattern in the lasR mutant background, but at lower levels.Still,during late stationary phase,level of rhlR expression slightly increases in the lasR mutant, while it decreases in the wild-type.These data support the significant presence of RhlR in lasR mutants during late stationary phase,as previously reported(Diggle et al., 2003).It is well established that the production of proteolytic enzymes such as LasA and LasB,responsible for staphylo-lytic and elastolytic activities respectively,is under LasR regulation(Rust et al.,1996;Storey et al.,1998;Toder et al., 1991).However,there are indications that production of these enzymes might also be under partial RhlR control (Brint&Ohman,1995;Diggle et al.,2003;Pearson et al., 1997).To evaluate global protease activity of the strains, the wild-type strain,and lasR and lasR rhlR mutants,were inoculated on solid medium containing skim milk. Protease activity was visible for the lasR mutants while the double mutant was unable to degrade milk proteins (see Supplementary Fig.S2).Since this test only indicates general proteolytic activity,it was interesting to target specific proteases.Fig.4(a)shows that the lasR mutant is able to activate lasB expression late in stationary phase, while the double lasR rhlR mutant cannot.Detection of LasB activity confirmed these results.During late stationary phase,the lasR mutant shows significant elastolytic activity, which is nearly as high as that in lasR(pUCPSK-rhlR) (Fig.4b).Finally,Fig.4(c)shows that the wild-type and the lasR mutant,complemented with rhlR or not,express LasA activity,while the lasR rhlR double mutant does not.Taken together,all these results indicate that the expression of many QS-controlled factors is only delayed when LasR is defective.RhlR controls factors generally considered to be solely regulated by LasRAnother observation we and others have made is that not only pyocyanin but also PQS is produced during late stationary phase by a lasR mutant(De´ziel et al.,2004; Diggle et al.,2003).This was unexpected,since the final step in PQS synthesis is catalysed by the lasR-dependent PqsH enzyme(De´ziel et al.,2004;Gallagher et al.,2002; Whiteley et al.,1999;Xiao et al.,2006b).It is of note that there is a close correlation between the timing of production of both PQS and pyocyanin in lasR mutant backgrounds(De´ziel et al.,2005;Diggle et al.,2002,2003). To test if RhlR might also be responsible for this effect,we quantified PQS production by the wild-type and the lasR, lasR rhlR and lasR(pUCPSK-rhlR)mutants.As shown in Fig.5(a),during the exponential and early stationary growth phases,PQS production is totally absent in the double mutant and barely detectable in the lasR mutant unless rhlR is expressed,which leads to a substantial reduction in the delay observed for that mutant.The same reduction of PQS is observed in a lasI mutant,and can also be restored by overexpressing RhlR in that mutant(data not shown).At the late stationary phase,however,the concentration of PQS in lasR mutant cultures is similar to the wild-type,while the double mutant still shows no detectable production.These data explain the late PQS production in a lasR mutant by the activity of RhlR.We then asked whether lasI,probably the most specific LasR-regulated gene,which codes for the autoinducer synthase producing3-oxo-C12-HSL,might also be regulated by RhlR.As expected from the above data,we found that3-oxo-C12-HSL production is greatly increased in lasR(pUCPSK-rhlR)compared to the wild-type strain,at the same optical density(Fig.5b).It also shows that3-oxo-C12-HSL is eventually produced in a lasR mutant at late stationary phase,but is totally absent if rhlR is alsodefective.16111621261020110Time (h)1020Time (h)13×b-Galactosidaseactivity(Millerunits)Growth(OD6)Fig. 3.rhlR transcription in a lasR mutantincreases during late stationary phase.b-Galactosidase activity using the pSC1002vector containing the rhlR-lacZ transcriptionalreporter.V.Dekimpe and E.De´ziel716Microbiology15514001200100080060040020010201020G r o w t h (O D 600)101lasB -lacZ654321E l a s t o l y t i c a c t i v i t y (A 495)S . a u r e u s l y s i s (%)80604020Time (h)Time (h)LasR PA14lasR rhlRWild-typelasRlasR rhlRlasR (pUCPSK-rhlR )Wild-type lasR lasR rhlRlasR (pUCPSK-rhlR )(a)(b)(c)b -G a l a c t o s i d a s e a c t i v i t y (M i l l e r u n i t s )sA and LasB are activated late in a lasR mutant but not in a lasR rhlR double mutant.(a)Transcription of the lasB gene;(b)elastolytic (LasB)activity;(c)staphylolytic (LasA)activity.50.40.30.20.11015202530102010201020110P Q S p r o d u c t i o n (m g l _1)3-O x o -C 12-H S L p r o d u c t i o n (m g l _1)G r o w t h (O D 600)G r o w t h (O D 600)Time (h)Time (h)Time (h)Time (h)(a)(b)Fig.5.Production of PQS (a)and 3-oxo-C 12-HSL (b)requires rhlR in the absence of lasR .LC/MS analysis from culture supernatants.Revisiting quorum sensing in P.aeruginosa717RhlR controls lasI in a heterologous system In order to further identify RhlR as an alternative activatorof lasI transcription in the absence of a functional LasR,we constructed a heterologous system in E.coli .A vector (pME3853)carrying the lasI-lacZ gene reporter was introduced into E.coli DH5a .In the presence of the rhlR gene constitutively expressed on another compatible plasmid,and with addition of its autoinducer C 4-HSL,b -galactosidase activity was greatly enhanced in the E.coli strain,while only basal expression was detected in absence of rhlR or C 4-HSL (Fig.6a).To confirm 3-oxo-C 12-HSL production through activation by RhlR,a vector contain-ing lasI under its native promoter was introduced into E.coli DH5a .3-Oxo-C 12-HSL was detected in this heterologous system only in the presence of both RhlR and its autoinducer C 4-HSL (Fig.6b).DISCUSSIONP.aeruginosa is an opportunistic pathogen that relies on its impressive ability to coordinate gene expression in order to compete against other species for nutrients or colonization.QS appears essential for this bacterium for competitiveness in clinical or environmental niches.The QS LasR transcriptional regulator is known to control a wide array of P.aeruginosa virulence-associated factors.Nevertheless,several reports mention the high frequency of lasR mutations among clinical and environmental isolates (Cabrol et al.,2003;D’Argenio et al.,2007).Most intriguingly,some lasR mutants still produce QS-regulated virulence factors such as pyocyanin (Heurlier et al.,2005),and naturally occurring lasR mutants have been isolated from wounds or intubated patients (Denervaud et al.,2004;Hamood et al.,1996).It was thus interesting to analyse the involvement of LasR in the expression of QS-regulated virulence determinants in more detail.This study provides new insights into the interplay between the las and the rhl QS systems in P.aeruginosa ,and demonstrates that a lasR mutation does not lead to loss of virulence factors.Expression of the rhl regulon is delayed until the late stationary phase in a lasR mutant,and is thus responsible for the late production of virulence factors in this background,such as pyocyanin,QS signalling molecules and proteases.These observations provide a solid basis allowing us to explain numerous inconsistencies in previous reports,and bring some clarifications to the P.aeruginosa QS model,as summarized in Fig.7.RhlR-regulated factors are expressed late in a lasR mutantThe delayed production of pyocyanin in a lasR mutant background has been anecdotally observed in numerousreports (De´ziel et al.,2005;Diggle et al.,2002,2003;Heurlier et al.,2005;Kohler et al.,2001;Lujan et al.,2007;Salunkhe et al.,2005).It has been suggested that RhlR might be involved in that production,although no evidence was presented (Diggle et al.,2003).Here we present evidence for the role of the RhlR regulator in pyocyanin production in a lasR mutant,since no production can be observed in a lasR rhlR double mutant and production is advanced in a lasR mutant comple-mented with rhlR.The activity of RhlR during stationary phase in a lasR mutant was confirmed by the delayed production of other RhlR-controlled factors,C 4-HSL and rhamnolipids.Others3530252015105b -G a l ac t o s id a se a c t i v i t y (M i l l e r u n i t s )3-O x o -C 12-H S L c o n c n (m g l _1)0.120.100.080.060.040.02DH 5a(p V D1)DH5a (pME 3853)DH5a(pM E 3853)+C4-H S LDH5a (pME 3853)(pU C P S K -r h l R )DH5a (p M E 3853)(p U C P S K -r h l R )+C 4-H S L D H 5a (p V D 1)(p U C P S K -r h l R )D H 5a (p V D 1)(p U C P S K -r h l R )+C 4-H S L(a)(b)sI is activated by RhlR in a heterologous E.coli DH5a system in the presence of either or both C 4-HSL (5mg l ”1)and rhlR (pUCPSK-rhlR ).(a)lasI-lacZ expression (pME3853);(b)3-oxo-C 12-HSL production in the presence of the lasI gene with its native promoter (pVD1).V.Dekimpe and E.De´ziel 718Microbiology 155。

体内注射治疗心脏损伤

体内注射治疗心脏损伤

体内注射治疗心脏损伤作者:来源:《科学中国人·下旬刊》2022年第07期《科学》封面:有针对性的T细胞。

《科学》杂志第6576期封面文章报道了向小鼠体内注射mRNA制剂,对患心衰的个体进行体内T细胞的重新编程,实现CAR-T治疗,成功减少了小鼠心脏的纤维化,修复了心脏的功能。

CAR-T疗法就是嵌合抗原受体T细胞免疫疗法,英文全称ChimericAntigenReceptorT-CellImmunotherapy。

研究团队将mRNA封装在气泡状的微型脂质纳米颗粒中,通过类似mRNA疫苗的方式注射至小鼠体内后,被封装的mRNA分子被T细胞捕获,使得T细胞获得特异性靶向攻击心肌成纤维细胞的能力。

鸟类和哺乳动物种群的减少阻碍植物适应气候变化《科学》封面:一只鸟在吃无花果。

《科学》杂志第6577期封面文章报道了植物处于种子散布损失的危险中。

大约一半的植物物种是由动物传播的,而种子传播是脊椎动物提供的最普遍的共生关系之一。

散播种子的动物通过将种子转移到合适的栖息地来帮助肉质水果植物适应气候变化。

然而,许多种子传播者正在减少,濒临灭绝或已经灭绝。

鸟类和哺乳动物种群的减少导致种子传播减少了60%左右,限制了植物追踪气候变化的能力。

研究表明,可以利用公开可用的数据准确预测物种相互作用,并在全球范围内描述生态系统有关情况。

流式细胞分选技术取得新突破《科学》封面:表达荧光标记蛋白(绿色)的靶细胞被蓝色激光照射,并从旋转的细胞池中分选。

《科学》杂志第6578期封面文章报道了支持高速荧光图像的细胞分选技术。

此项研究开发出全集成的成像细胞分选器,融合了基于射频发射的高速荧光成像技術、传统石英杯液滴分选和独创的无延迟信号处理及电子系统,实现了高速捕捉基因组筛选中瞬时动态变化的细胞表型,并进行单个目标的分选。

与传统流式细胞仪相比,新技术可以分析1000多倍的数据量,并根据图像以每秒15000个的速度对细胞进行分选。

玉米中配子体基因组激活时间研究《科学》封面:玉米丝(长链)上花粉(明亮物体)的荧光显微镜图像。

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increases in cellular chloride and potassium permeability that underlie secretion (petersen, 1992; Wilson et al., 1994). Adrenergic agonists evoke secretion in human sweat glands by interacting both with a- and fj-adrenoceptors, although these responses are generally smaller than the response to acetylcholine. o-Adrenoceptor agonists act by evoking phoshatidylinositol-4,5-biphosphate(pIP,) hydrolysis and so increasing intracellular free calcium ([Ca2 +]J , whereas fj-adrenergic agonists activate adenylate cyclase and hence the effects of these agents are thought to be mediated by cyclic AMP (Sato et al., 1991). ATP can evoke secretion from isolated sweat glands, although the pharmacology of this response is not known (Sato et al., 1991; Wilson et al., 1994). ATP can increase [Ca2 +);, the release of calcium from intracellular stores and subsequently sustained calcium influx. In equine sweat glands, it has been shown that extracellular ATP (at 100 JtM) can also
Department of HU11Uln Anatomy, University of Uppsala, Box 571, 5-75123 Uppsala, Sweden.
*Corresponding author.
ABSfRACT
Chloride secretion in primary cultures of cells originating from the secretory coil of human sweat glands was investigated by electron probe X-ray microanalysis. The total intracellular a concentration was lowered by muscarinic agonists (carbachol and acetylcholine), as well as by the calcium ionophore A23I87. The muscarinic agonists also lowered the cellular K concentration. a- secretion induced by these agonists could be inhibited by the chloride channel blocker NPPB. After cAMP stimulation, the frequency distribution of the Cl concentration changed from Gaussian to bimodal, indicating that cAMP induces Ct secretion only from a subpopulation of the cells. Also ATP stimulated Ct secretion, indicating the presence ofpurinergic receptors. The results suggest that some ofthe cells in addition to Ca"-regulated a- channels also possess cAMP-activated Ct channels. Hence, the primary cultures still possess the Ct transport mechanisms known to be present in intact glands. It can, however, not be excluded that some coil cells have acquired ductal characteristics during culture.
INTRODUCTION Ion transport in human sweat glands has recently received relatively much attention, mainly because sweat glands provide an easily accessible model organ for the study of epithelial chloride transport. This is of special relevance in the disease cystic fibrosis, where the activation of chloride transport by cAMP is defective, which results in elevated concentrations of NaCI in the sweat of these patients (Quinton, 1990). The secretory coil cells of the human sweat gland secrete an isotonic NaCI solution into the lumen. This primary sweat flows along the length of the duct where part of the salt is reabsorbed before the sweat reaches the skin surface. Sweating is primarily regulated by acetylcholine, which acts via muscarinic receptors that are functionally coupled to phospholipase C. This evokes the release of calcium from intracellular stores. This increase in free intracellular calcium is sustained by calcium influx from the extracellular compartment and results in
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© 1995 Academic Press Ltd
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raise the cellular cAMP content (Ko et al., 1994). Secretion of primary sweat generally conforms to a secondary active chloride (Cl) transport mechanism, where chloride enters the cell due to the combined activities of three basolateral membrane transport mechanisms: potassium (K+) channels, Na+-K+-2CIcotransporters and Nat/Ktpumps, ClIeaves the cell across the luminal membrane, presumably via CIchannels, down its electrochemical potential gradient. In the duct, chloride is reabsorbed via the so-called cystic fibrosis transmembrane conductance regulator (CFTR), a channel that is regulated by cAMp-{jependent phoshorylation and by intracellular ATP, and possibly, Ca2+ ions. Human sweat glands can be grown in primary cultures, either from whole sweat glands or from isolated ducts or coils (pedersen, 1984; Pedersen et al., 1985; Brayden and Cuthbert, 1990). Coil cells appear to have a pluripotential capacity which is revealed in culture since both whole-gland and secretory coil cell cultures exhibit some properties usually associated in vivo with duct cells (Brayden et al., 1991). By infection with SV40 virus an immortalized cell line (NCL-SG3) has been constructed where CI- transport can be activated by l3-adrenergic agonists (Lee and Dessi, 1989). Lee and Dessi (1989) found no activation by cholinergic agonists, in contrast to recent measurements in our laboratory (Ring, Mork and Roomans, submitted for publication) where cholinergic stimulation of Cl transport could be observed in cells grown on a permeable substrate. X-ray microanalysis can be used for the study of ion transport in cultured cells. The method has the advantage that changes in several different ions can be studied simultaneously, and that inhomogeneities in cell cultures can be easily revealed (von Euler et al., 1993). Most work of this kind has been carried out on cancer cells or transformed cell lines, but a few studies have been carried out on primary cultures (Sagstrorn et aI., 1992; Warley et al., 1994). Recently, we have studied ion transport in NCL-SG3 cells, and demonstrated that CI- transport can be activated both by cAMP analogues (Mork and
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