interneurons of the neocortical inhibitory system

第十章 神經系統：中樞神經系統中樞神經系統的構造〈General Anatomy of the Central Nervous System 〉心理一95135053廖于瑄神經膠細胞〈glial cells〉中樞神經系統中有90%是由神經膞細胞所構成，而神經交細胞又可分為5種，如圖10-1所示：1.星狀細胞〈astrocytes〉2.室管模細胞〈ependymal cell〉3.格子細胞〈microglia〉4.寡突膞細胞〈oligodendrcytes〉-組成髓磷脂5.許旺氏細胞〈schwann cells〉-組成髓磷脂所有的神經膞細胞皆會分泌生長因子促進中央神經系統的成長。

星狀細胞〈astrocytes〉：幫助神經元獲得養分，且刺激內皮細胞〈endothelial cells〉間形成緊密連結(tight junctions)，而幫助血腻屏障的形成。

格子細胞〈microglia〉：保護中央神經系統免於外來物質〈例如：細菌、受傷或死亡的細胞〉的傷害，另外，它也保護中央神經系統免於氧化壓力的造成的傷害神經膞細胞與神經退化疾病中樞神經系統的物理支撐（Physical Support of the CNS）˙頭蓋骨-顱骨˙腻膜-硬腻模：組成大腻及脊髓最外層堅韌的膜。

-蜘蛛網模：大腻的三層腻膜(meninges) 中的一層，位於硬膜(dura mater) 與軟膜(pia mater)。

此層腻膜較薄，緊附在硬膜下面。

其與硬膜之間的空隙稱為硬膜下腔(subdural space)，其與軟膜之間的空隙稱為蜘蛛膜下腔(subarachnoid space)-軟腻膜：纖細柔軟的血管膜，圍繞腻和脊髓的三層膜中的最內一層˙腻脊髓液：一種清透液體，成份與血漿相似，中樞神經系統浸於此液體中。

CSF可避免柔軟的神經組織與硬骨碰撞。

Physical Support of the CNS(protective structures)Bone–Cranium –Vertebrae Meninges–Dura mater–Arachnoid mater–Pia mater Cerebrospinal fluid（硬膜）（蜘蛛網膜）（軟膜）Figure 10.2b(no spac e)˙蜘蛛膜絨毛〈arachnoid villus 〉：其位於面臨靜脈竇區的蜘蛛膜上，通過硬膜伸入靜脈竇中。

·论著·右额下回岛盖部ｂｅｔａ事件相关去同步化与抗抑郁早期疗效相关性的脑磁图研究熊婷婷，夏逸，花玲玲，汤浩，尤为，阎锐，卢青，姚志剑摘要：　目的：通过探讨抑制控制网络关键脑区右额下回岛盖部ｂｅｔａ频段的事件相关去同步化（ＥＲＤ），寻找抗抑郁治疗早期疗效相关的生物学指标。

　方法：随机入组４６例抑郁症患者及２５名健康对照，在完成Ｇｏ／Ｎｏ Ｇｏ实验范式下采集脑磁图数据，计算基线期右额下回岛盖部ｂｅｔａ频段的ＥＲＤ，以抗抑郁治疗２周后ＨＡＭＤ１７总分减分率是否达５０％为标准划分为早期疗效有效组和无效组。

比较组间差异，分析其与ＨＡＭＤ１７减分率的相关性。

　结果：与对照组相比，抑郁症组右额下回岛盖部去同步化水平降低，且无效组弱于有效组，右额下回岛盖部（ｒ＝－０．３９７，Ｐ＝０．００６）ｂｅｔａ频段的ＥＲＤ与２周后ＨＡＭＤ１７减分率呈负相关。

　结论：右额下回岛盖部ｂｅｔａ频段的ＥＲＤ降低与抗抑郁早期疗效不佳相关，该区域ｂｅｔａ频段的活动强度可能是抑郁症早期疗效预测的生物学指标。

关键词：　抑郁症；　ｂｅｔａ能量；　疗效预测；　脑磁图；　事件相关去同步化中图分类号：　Ｒ７４９．４ 文献标识码：　Ａ 文章编号：　１００５ ３２２０（２０２３）０５ ０３３７ ０４Ａｍａｇｎｅｔｏｅｎｃｅｐｈａｌｏｇｒａｐｈｉｃｓｔｕｄｙｏｆｔｈｅｃｏｒｒｅｌａｔｉｏｎｂｅｔｗｅｅｎｂｅｔａｂａｎｄｅｖｅｎｔ ｒｅｌａｔｅｄｄｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎ（ＥＲＤ）ｉｎｔｈｅｏｐｅｒｃｕｌａｒｐａｒｔｏｆｒｉｇｈｔｉｎｆｅｒｉｏｒｆｒｏｎｔａｌｇｙｒｕｓｉｎｓｕｌａａｎｄｅａｒｌｙａｎｔｉｄｅｐｒｅｓｓａｎｔｅｆｆｉｃａｃｙ　ＸＩＯＮＧＴｉｎｇ ｔｉｎｇ，ＸＩＡＹｉ，ＨＵＡＬｉｎｇ ｌｉｎｇ，ＴＡＮＧＨａｏ，ＹＯＵＷｅｉ，ＹＡＮＲｕｉ，ＬＵＱｉｎｇ，ＹＡＯＺｈｉ ｊｉａｎ．ＤｅｐａｒｔｍｅｎｔｏｆＰｓｙｃｈｉａｔｒｙ，ＴｈｅＡｆｆｉｌｉａｔｅｄＢｒａｉｎＨｏｓｐｉｔａｌｏｆＮａｎｊｉｎｇＭｅｄｉｃａｌＵｎｉｖｅｒｓｉｔｙ，Ｎａｎｊｉｎｇ２１００２９，ＣｈｉｎａＡｂｓｔｒａｃｔ：　Ｏｂｊｅｃｔｉｖｅ：Ｔｏｅｎｅｒｇｙａｃｔｉｖａｔｉｏｎｏｆｔｈｅｒｉｇｈｔｉｎｆｅｒｉｏｒｆｒｏｎｔａｌｇｙｒｕｓｉｎｓｕｌａｉｎｔｈｅｋｅｙｂｒａｉｎａｒｅａｏｆｔｈｅｉｎｈｉｂｉｔｏｒｙｃｏｎｔｒｏｌｎｅｔｗｏｒｋ，ｉｎｏｒｄｅｒｔｏｓｅａｒｃｈｆｏｒｂｉｏｌｏｇｉｃａｌｉｎｄｉｃａｔｏｒｓｆｏｒｐｒｅｄｉｃｔｉｎｇｔｈｅｅａｒｌｙｅｆｆｉｃａｃｙｏｆａｎｔｉｄｅｐｒｅｓｓａｎｔｔｈｅｒａｐｙ．　Ｍｅｔｈｏｄ：４６ｄｅｐｒｅｓｓｉｏｎｐａｔｉｅｎｔｓａｎｄ２５ｈｅａｌｔｈｙｃｏｎｔｒｏｌｓｗｅｒｅｒａｎｄｏｍｌｙｅｎｒｏｌｌｅｄ，ａｎｄｍａｇｎｅｔｏｅｎｃｅｐｈａｌｏｇｒａｍｄａｔａｗｅｒｅｃｏｌｌｅｃｔｅｄｕｎｄｅｒｔｈｅＧｏ／ＮｏＧｏｅｘｐｅｒｉｍｅｎｔａｌｐａｒａｄｉｇｍｔｏｃａｌｃｕｌａｔｅｔｈｅｅｖｅｎｔｒｅ ｌａｔｅｄｄｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎ（β ＥＲＤ）ｉｎｔｈｅｂｅｔａｆｒｅｑｕｅｎｃｙｂａｎｄｏｆｔｈｅｒｉｇｈｔｉｎｆｅｒｉｏｒｆｒｏｎｔａｌｇｙｒｕｓｉｎｓｕｌａｄｕｒｉｎｇｔｈｅｂａｓｅｌｉｎｅｐｅｒｉｏｄ，ｄｉｖｉｄｅｄｉｎｔｏｅａｒｌｙｅｆｆｉｃａｃｙｅｆｆｅｃｔｉｖｅｇｒｏｕｐａｎｄｉｎｅｆｆｅｃｔｉｖｅｇｒｏｕｐｂａｓｅｄｏｎｔｈｅｔｏｔａｌｓｃｏｒｅｒｅｄｕｃ ｔｉｏｎｒａｔｅｏｆＨＡＭＤ１７ａｆｔｅｒ２ｗｅｅｋｓｏｆａｎｔｉｄｅｐｒｅｓｓａｎｔｔｒｅａｔｍｅｎｔ≥５０％．Ｔｈｅβ ＥＲＤｄｉｆｆｅｒｅｎｃｅｓｂｅｔｗｅｅｎｇｒｏｕｐｓｗｅｒｅｃｏｍｐａｒｅｄａｎｄｔｈｅｉｒｃｏｒｒｅｌａｔｉｏｎｗｉｔｈｔｈｅｒｅｄｕｃｔｉｏｎｒａｔｅｏｆＨＡＭＤ１７ｗａｓａｎａｌｙｚｅｄ．　Ｒｅｓｕｌｔｓ：Ｃｏｍ ｐａｒｅｄｗｉｔｈｔｈｅｃｏｎｔｒｏｌｇｒｏｕｐ，ｔｈｅｄｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｌｅｖｅｌｏｆｔｈｅｒｉｇｈｔｉｎｆｅｒｉｏｒｆｒｏｎｔａｌｇｙｒｕｓｉｎｓｕｌａｉｎｔｈｅｄｅｐｒｅｓ ｓｉｏｎｇｒｏｕｐｄｅｃｒｅａｓｅｄ，ａｎｄｔｈｅｉｎｅｆｆｅｃｔｉｖｅｇｒｏｕｐｗａｓｗｅａｋｅｒｔｈａｎｔｈｅｅｆｆｅｃｔｉｖｅｇｒｏｕｐ．Ｔｈｅｒｉｇｈｔｉｎｆｅｒｉｏｒｆｒｏｎｔａｌｇｙｒｕｓｉｎｓｕｌａｉｎｔｈｅｄｅｐｒｅｓｓｉｏｎｇｒｏｕｐ（ｒ＝－０．３９７，Ｐ＝０．００６）β ＥＲＤｗａｓｎｅｇａｔｉｖｅｌｙｃｏｒｒｅｌａｔｅｄｗｉｔｈｔｈｅＨＡＭＤ１７ｒｅｄｕｃｔｉｏｎｒａｔｅａｆｔｅｒ２ｗｅｅｋｓ．　Ｃｏｎｃｌｕｓｉｏｎ：ＴｈｅｄｅｃｒｅａｓｅｏｆＥＲＤｉｎｂｅｔａｂａｎｄｏｆｉｎｓｕｌａｒｃｏｒｔｅｘｏｆｉｎｆｅｒｉｏｒｆｒｏｎｔａｌｇｙｒｕｓｉｓａｓｓｏｃｉａｔｅｄｗｉｔｈｐｏｏｒｅａｒｌｙｅｆｆｉｃａｃｙｏｆａｎｔｉｄｅｐｒｅｓｓａｎｔｓ．Ｔｈｅｉｎｔｅｎｓｉｔｙｏｆｂｅｔａｂａｎｄａｃｔｉｖｉｔｙｉｎｔｈｉｓｒｅｇｉｏｎｍａｙｂｅａｂｉｏｌｏｇｉｃａｌｉｎｄｉｃａｔｏｒｆｏｒｐｒｅｄｉｃｔｉｎｇｅａｒｌｙｅｆｆｉｃａｃｙｏｆｄｅｐｒｅｓｓｉｏｎ．Ｋｅｙｗｏｒｄｓ：　ｄｅｐｒｅｓｓｉｏｎ；　ｂｅｔａｗａｖｅ；　ｍａｇｎｅｔｏｅｎｃｅｐｈａｌｏｇｒａｍ；　ｅｖｅｎｔ ｒｅｌａｔｅｄｄｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎ基金项目：基金项目：国家自然科学基金（８２１５１３１５，８２２７１５６８），国家自然科学青年基金（８２１０１５７３），江苏精神疾病医学创新中心（ＣＸＺＸ２０２２２６），江苏省重点研发计划专项（ＢＥ２０１９６７５），苏州市社会发展科技创新重点项目（２０２２ＳＳ０４），南京市科技发展计划一般项目（ＹＫＫ２２１４０），南京市科技发展计划重点项目（ＺＫＸ２２０４３），南京市医学科技发展重点项目（ＺＫＸ２１０３５）作者单位：２１００２９　南京医科大学附属脑科医院精神科（熊婷婷，夏逸，花玲玲，汤浩，尤为，阎锐，姚志剑）；东南大学生物科学与医学工程学院、东南大学儿童发展与学习科学教育部重点实验室（卢青）通信作者：姚志剑，Ｅ Ｍａｉｌ：ｚｉｙａｏ＠ｎｊｍｕ．ｅｄｕ．ｃｎＤＯＩ：１０．３９６９／ｊ．ｉｓｓｎ．１００５ ３２２０．２０２３．０５．００１抑郁症患者存在认知功能受损，且认知功能损害与抑郁症复发、自杀风险升高及疗效欠佳有着密切关联［１］。

现代电子技术Modern Electronics Technique2023年12月1日第46卷第23期Dec. 2023Vol. 46 No. 230 引 言脑机接口（Brain⁃Computer Interface, BCI ）是一种通过分析神经元电信号，促进人脑与外部电子设备直接通信的技术[1]。

BCI 系统最初是为帮助患有身体或认知障碍的患者而开发的，现已在神经医学、智能家居、自动驾驶和娱乐等领域得到广泛应用[2]。

BCI 系统包括收集大脑信号、对其进行解码和控制外部设备（例如计算机、智能轮椅或假肢）三部分组件[3]。

记录人脑活动意图的技术分为侵入式和非侵入式两种。

侵入性技术需要植入微电极阵列，存在一定的风险[4]；非侵入性技术如脑电图（Electroencephalography, EEG ）是主要采用的研究方基于通道选择的多尺度Inception 网络的脑电信号分类研究刘 培， 宋耀莲（昆明理工大学 信息工程与自动化学院， 云南 昆明 650500）摘 要： 基于运动想象脑电信号的脑机接口系统有可能在大脑和外部设备之间创建通信通道。

然而，特征提取的局限性、通道选择的复杂性和被试者之间的可变性使得脑电信号分类模型难以有效泛化。

在这项研究中，文中提出一种端到端的深度学习模型，该模型使用并行多尺度Inception 卷积神经网络在6个通道选择区域中进行多分类运动想象任务。

为了解决被试者间可变性，实验进行了跨被试和跨被试微调两种评估场景。

在BCI 竞赛IV 2a 数据集上的实验和测试结果表明：ROI F 达到了98.49%的最高分类精度，比最低准确率高17.26%；且跨被试微调场景分类性能优于被试内和跨被试场景，分类准确率分别提高了1.82%和1.69%。

此外，并行多尺度Inception 卷积神经网络模型的平均分类准确率比单尺度Inception CNN 模型高5.17%。

总之，文中提出一种基于通道选择的端到端的脑电信号分类框架，可以促进高性能和稳健的脑机接口系统的开发。

海马体中的GABA能神经元大脑抑制性调节的关键海马体是大脑中与学习和记忆密切相关的特殊结构，而GABA能神经元则起着重要的抑制性调节作用。

本文将探讨海马体中的GABA能神经元在大脑抑制性调节中的关键作用。

一、海马体的功能和结构海马体是人脑中的重要组成部分，常常被称为学习和记忆的中枢。

它位于颞叶内侧，由海马回和海马旁回组成。

海马体通过与其他脑区的连接，参与了许多认知功能的发挥，如空间记忆、情感调节和学习能力等。

二、GABA能神经元的基本特征GABA（γ-氨基丁酸）是一种神经递质，广泛存在于中枢神经系统中，并担任着重要的抑制性调节角色。

GABA能神经元指的是那些合成和释放GABA的神经元。

在海马体中，GABA能神经元数目相当丰富，特别是在海马体CA1和CA3区域。

三、GABA能神经元的抑制性调节作用1. 突触传递的抑制性调节GABA能神经元通过释放GABA这一抑制性神经递质，能够调节突触传递的过程。

当GABA分子结合到接受体上时，可以增加Cl-离子的进入，从而使细胞内的电位超级稳定化，抑制兴奋性信息的传递。

2. 神经网络的抑制性调节GABA能神经元还能够影响整个神经网络的活动。

通过抑制兴奋性神经元的活动，它们可以减少传递到神经网络其他区域的兴奋性冲动，以此来实现大脑的抑制性调节。

3. 学习和记忆的抑制性调节GABA能神经元对海马体的学习和记忆功能具有重要影响。

研究表明，GABA能神经元能够在学习过程中进行动态调节，在某些情况下增强兴奋性神经元的活动，并促进记忆形成。

四、GABA能神经元调节的疾病与治疗GABA能神经元的功能异常可能与多种脑部疾病相关，如癫痫、焦虑症和精神分裂症等。

因此，研究GABA能神经元的功能调控，对于治疗这些疾病具有重要意义。

目前，针对GABA能神经元抑制性调节的治疗方法主要包括药物和神经刺激技术。

药物治疗通过调整神经递质的浓度和活性来改善GABA能神经元的功能。

神经刺激技术则通过刺激或抑制特定脑区的活动来调节GABA能神经元的活动水平。

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。

· 93 ·中国疼痛医学杂志Chinese Journal of Pain Medicine 2021, 27 (2)[49] Teodor, Goroszeniuk. The effect of peripheral neuro-modulation on pain from the sacroiliac joint: A retro-spective cohort study[J]. Neuromodulation: Journal of the International Neuromodulation Society, 2019, 22(5):661-666.[50] Kim YH, Moon DE. Sacral nerve stimulation for thetreatment of sacroiliac joint dysfunction: A case re-port[J]. Neuromodulation, 2010, 13(4):306-310. [51] Dengler J, Kools D, Pflugmacher R, et al. Randomizedtrial of sacroiliac joint arthrodesis compared with con-servative management for chronic low back pain at-tributed to the sacroiliac joint[J]. J Bone Joint Surg Am, 2019, 101(5): 400-411.[52] Sachs D, Capobianco R. One year successful outcomesfor novel sacroiliac joint arthrodesis system[J]. Ann Surg Innovat Res, 2012, 6(1):13.[53] Smith AG, Capobianco R, Cher D, et al. Open versusminimally invasive sacroiliac joint fusion: A multi-center comparison of perioperative measures and clinical outcomes[J]. Ann Surg Innovat Res, 2013, 7(1):14.[54] Capobianco R, Cher D. Safety and effectiveness ofminimally invasive sacroiliac joint fusion in women with persistent post-partum posterior pelvic girdle pain: 12-month outcomes from a prospective, multi-center trial[J]. SpringerPlus, 2015, 4:570.•国际译文•杏仁核-海马环路参与抑郁症的发病机制慢性应激常可引发抑郁症。

Nerve Growth Factor Stimulates Proliferation and Survival of Human Breast Cancer Cells through Two Distinct Signaling Pathways*Received for publication,November 20,2000,and in revised form,February 14,2001Published,JBC Papers in Press,February 28,2001,DOI 10.1074/jbc.M010499200Simon Descamps‡§,Robert-Alain Toillon‡,Eric Adriaenssens§¶,Vale ´rie Pawlowski§ʈ,Simon M.Cool**,Victor Nurcombe**,Xuefen Le Bourhis‡,Be ´noni Boilly‡,Jean-Philippe Peyrat ʈ,and Hubert Hondermarck‡‡‡From the ‡Equipe Facteurs de Croissance,UPRES EA-1033Biologie du De ´veloppement,Universite ´des Sciences et Technologies de Lille,59655Villeneuve d’ASCQ France,the ¶Immunopathologie Cellulaire des Maladies Infectieuses,CNRS,UMR 8527,Institut de Biologie de Lille,59000France,the **Department of Anatomical Sciences,University of Queensland,St.Lucia,Queensland 4072,Australia,and the ʈLaboratoire d’Oncologie Mole ´culaire Humaine,Centre Oscar Lambret,59020Lille,FranceWe show here that the neurotrophin nerve growth factor (NGF),which has been shown to be a mitogen for breast cancer cells,also stimulates cell survival through a distinct signaling pathway.Breast cancer cell lines (MCF-7,T47-D,BT-20,and MDA-MB-231)were found to express both types of NGF receptors:p140trkA and p75NTR .The two other tyrosine kinase receptors for neu-rotrophins,TrkB and TrkC,were not expressed.The mitogenic effect of NGF on breast cancer cells required the tyrosine kinase activity of p140trkA as well as the mitogen-activated protein kinase (MAPK)cascade,but was independent of p75NTR .In contrast,the anti-apo-ptotic effect of NGF (studied using the ceramide ana-logue C2)required p75NTR as well as the activation of the transcription factor NF-kB,but neither p140trkA nor MAPK was necessary.Other neurotrophins (BDNF,NT-3,NT-4/5)also induced cell survival,although not proliferation,emphasizing the importance of p75NTR in NGF-mediated survival.Both the pharmacological NF-␬B inhibitor SN50,and cell transfection with IkBm,resulted in a diminution of NGF anti-apoptotic effect.These data show that two distinct signaling pathways are required for NGF activity and confirm the roles played by p75NTR and NF-␬B in the activation of the survival pathway in breast cancer cells.Nerve growth factor (NGF)1is the archetypal member of theneurotrophin superfamily,which also includes brain-derived neurotrophic factor (BDNF),neurotrophin-3(NT-3),NT-4/5,and NT-6(1).NGF interacts with two classes of membrane receptor:the TrkA proto-oncogene product p140trkA ,which pos-sesses intrinsic tyrosine kinase activity,and a secondary re-ceptor,p75NTR ,that belongs to the tumor necrosis factor (TNF)receptor family (2).The stimulation of cell survival and cell differentiation by NGF and other neurotrophins have been described primarily in neuronal cell systems (3).Although the neurotrophic effect through p140trkA is known to involve the MAPK cascade,the role of p75NTR is still controversial;there is evidence that it can both positively and negatively regulate neuronal cell death and differentiation,depending on the cell type examined (4).In some cases,p75NTR is an inducer of apoptosis,even without NGF stimulation (5),whereas in other cases the activation of p75NTR by NGF results in a protection from cell death (6).In addition to its neurotrophic function,other activities of NGF have been described.For example,NGF can modulate gene expression in monocytes (7),it is chemotac-tic for melanocytes (8),and its inhibition on p75NTR can block the migration of Schwann cells (9).NGF also stimulates the proliferation of chromaffin cells (10),lymphocytes (11),and keratinocytes (12).We have previously shown that NGF is mitogenic for cancerous but not normal human breast cells (13),and these data,as well as others showing a role for NGF in the stimulation of prostatic cancer cells (14–17),implicate NGF in non-neuronal carcinogenesis.Both cellular proliferation as well as tumor cell survival are crucial for malignant progression.The effect of NGF on the survival of cancer cells through the p75NTR receptor has been shown for neuroblastoma (18)and schwannoma (6).In prostate cancer,p75NTR has been shown to be a mediator of NGF’s effects during critical phases of developmental cell death and carcinogenic progression (19).To date only the mitogenic effect of NGF for breast cancer cells has been described (13),with its roles in the control of breast cancer cell survival unknown.In this study,we have shown that,in addition to its mito-genic effect,NGF is also an anti-apoptotic factor for breast cancer cells.These cells express mRNA for both p140trkA and p75NTR receptors.Our results indicate that the mitogenic effect of NGF requires p140trkA and the MAPK cascade,but not the p75NTR receptor,whereas the promotion of cell survival strictly requires p75NTR as well as NF-␬B,but not p140trkA and MAPK.Thus the mitogenic and anti-apoptotic effects of NGF on breast*This work was supported in part by a grant from the Ligue Na-tionale Contre le Cancer (Comite ´du Nord)and by the French Ministry of Research and Education.The costs of publication of this article were defrayed in part by the payment of page charges.This article must therefore be hereby marked “advertisement ”in accordance with 18U.S.C.Section 1734solely to indicate this fact.§Recipients of an Association pour la Recherche sur la Cancer fellowship.‡‡To whom correspondence should be addressed:EA-1033,batiment SN3,Universite ´des Sciences et Technologies de Lille,59655Villeneuve d’Ascq cedex,France.Tel.:33-3-20-43-40-97;Fax:33-3-20-43-40-38;E-mail:hubert.hondermarck@univ-lille1.fr.1The abbreviations used are:NGF,nerve growth factor;PAGE,poly-acrylamide gel electrophoresis;NF-␬B,nuclear factor-␬B;BDNF,brain-derived neurotrophic factor;NT,neurotrophin;PARP,polyADP-ribose polymerase;TNF,tumor necrosis factor;TBP,TATA box binding pro-tein;RT-PCR,reverse transcriptase-polymerase chain reaction;FCS,fetal calf serum;DTT,dithiothreitol;PBS,phosphate-buffered saline;ERK,extracellular signal-regulated kinase;GFP,green fluorescence protein;I ␬Bm,dominant-negative I ␬B ␣mutant;bp,base pair(s);PD98059,Park Davis 98059.T HE J OURNAL OF B IOLOGICAL C HEMISTRYVol.276,No.21,Issue of May 25,pp.17864–17870,2001©2001by The American Society for Biochemistry and Molecular Biology,Inc.Printed in U.S.A.This paper is available on line at 17864cancer cells are mediated through two different signaling pathways.EXPERIMENTAL PROCEDURESMaterials—Cell culture reagents were purchased from BioWhittaker (France)except insulin,which was obtained from Organon(France). Recombinant human nerve growth factor,brain derived growth factor (BDNF),and neurotrophins3(NT-3)and4(NT-4)were from R&D Systems(UK).K-252a(inhibitor of trk-tyrosine kinase activity)and PD98059(inhibitor of MAPK cascade)were from Calbiochem(France). The mouse monoclonal anti-NGF receptor(p75NTR)antibody was from Euromedex(France)and was previously described for its ability to block the interaction between p75NTR and NGF(20).The anti-lamin B(C-20), goat polyclonal IgG,and the polyclonal anti-p140trkA(trk763)were from Santa Cruz Biotechnology.C2ceramide analogue(N-acetyl-D-sphingo-sine),Hoechst33258,and electrophoresis reagents were from Sigma Chemical Co.(France).The SN50NF-␬B inhibitor peptide,the rabbit polyclonal anti-NF-␬B p65antibody,was obtained from TEBU(France). Anti-PARP antibody was from Oncogene Research Products(UK). Primers and probes for TrkA and p75NTR,probe for TATA box binding protein(TBP)were from Eurogentec(Belgium).RT-PCR reagents were from Applied Biosystems(France).Lipofectin reagent and Opti-MEM were provided by Life Technologies,Inc.(France).The green fluores-cence protein plasmid(EGFPC1)was purchased from CLONTECH,and the dominant-negative I␬B␣mutant(I␬Bm)expression vectors(in PCDNA3)containing a Ser to Ala substitution at residues32and36 were obtained from Dr.Jean Feuillard(UPRES EA1625,Bobigny, France).p65(rel-A)and c-rel cDNA were cloned at Eco RI site in PSVK3 expression plasmid.All vectors were obtained from Dr.Pascale Cre´pieux(McGill University,Montreal).The SY5Y subclone of SK-N-SH neuroblastoma cell line was a kind gift of Dr.Luc Bue´e(INSERM, U422,Lille,France).NT-2(Ntera/D1)human neural precursor cells (Stratagene)are derived from a clone of the NT-2teratocarcinoma.Cell Culture—Breast cancer cell lines(MCF-7,T47-D,BT-20,and MDA-MB-231)were obtained from the American Type Culture Collec-tion and routinely grown as monolayer cultures.Cells were maintained in minimal essential medium(Earle’s salts)supplemented with20m M Hepes,2g/liter sodium bicarbonate,2m M L-glutamine,10%fetal calf serum(FCS),100units/ml penicillin-streptomycin,50␮g/ml gentami-cin,1%of non-essential amino acids,and5␮g/ml insulin.Detection of Neurotrophin Receptors mRNA Expression—The reverse transcription reaction mixture contained2g of purified total RNA (extracted from breast cancer cell lines,NT-2cells,or SY5Y cells),1ϫreverse transcription reaction buffer,10m M DTT,400m M dNTP each, 2.5M oligo(dT)18primer,40units of RNasin,and200units of Moloney murine leukemia virus reverse transcriptase were added to25␮l of total reaction volume.All the reaction mixtures were incubated at37°C for1h and then inactivated at95°C for5min.Polymerase chain reaction was performed on cDNAs after RT or corresponding total RNA samples without the RT step for negative controls.The primers used for trkA and p75RT-PCR detection in breast cancer cell lines were as follows:trkA sense primer,5Ј(291)-CATCGTGAAGAGTGTCTCCG-3Ј(311)and antisense primer,5Ј(392)-GAGAGAGACTCCAGAGCGTT-GAA-3Ј(370)or p75sense primer,5Ј(442)-CCTACGGCTACTACCAG-GATGAG-3Ј(462)and antisense primer,5Ј(588)-TGGCCTCGTCG-GAATACG-3Ј(571).The primers used for RT-PCR comparative detection of trks in MCF-7cells were as follows:trkA sense primer,5Ј(118)-AGGCGGTCTGGTGACTTCGTTG-3Ј(139)and antisense primer, 5Ј(1162)-GGCAGCCAGCAGGGTGTAGTTC-3Ј(1141)or trkB sense primer,5Ј(134)-CGAGGTTGGAACCTAACAGCATTG-3Ј(157)and an-tisense primer,5Ј(1182)-GTCAGTTGGCGTGGTCCAGTCTTC-3Ј(1159)or trkC sense primer,5Ј(219)-CACGGACATCTCAAGGAAGA-GCA-3Ј(241)and antisense primer,5Ј(1078)-CTGAGAACTTCACCC-TCCTGGTAG-3Ј(1056).Each pair of primers was used in RT-PCR reaction to amplify trks or p75.To PCR tubes were added5␮l of PCRbuffer(200m M Tris-HCl,pH8.4,500m M KCl),10␮l of15m M MgCl2,1␮l of10m M dNTP mix,1␮l of cDNA or total mRNA(for negative control),1␮l of50m M respective primers,1␮l of2.5units/␮l Taq DNA polymerase,and water to a total volume of50␮l.The PCR conditions were as follows:after95°C for3min for denaturing cDNA,30cycles were run at94°C for1min,57°C for2min,and72°C for3min.The PCR tubes were incubated for a further10min at72°C for the exten-sion of cDNA fragments after the final cycle,and the PCR products were electrophoresed in an agarose gel.Cell Growth Assay—Experiments were performed as previously de-scribed(13).35-mm diameter dishes were inoculated with2ϫ104 cells/dish in2ml of medium containing10%FCS.After24h,cells were washed twice with serum-free medium.Next day,the medium wasreplaced with2ml of serum free medium containing100ng/ml NGF orvarious concentrations of other neurotrophins(BDNF,NT-3,NT-4/5).To study the effect of pharmacological inhibitors or blocking antibodies,various concentrations were added simultaneously with NGF(100ng/ml).After2days of NGF exposure,cells were harvested by trypsiniza-tion and counted using an hemocytometer.Determination of the Percentage of Apoptotic Cell Nuclei—Apoptosisof breast cancer cells was induced by the ceramide analogue C2,whichhas been described as a pro-apoptotic agent for human breast cancercells(21,22).Apoptosis was obtained by treatment with2␮M C2for 24h.To evaluate the anti-apoptotic activity of NGF,various concen-trations of this factor were tested;we found that the maximal effect wasobtained for100ng/ml.Consequently,this concentration was used in allexperiments with pharmacological inhibitors or blocking antibody.Fordetermination of apoptotic cell percentage,cells were fixed with coldmethanol(Ϫ20°C)for10min and washed twice with phosphate-buff-ered saline(PBS)before staining with1␮g/ml Hoechst33258for10min at room temperature in the dark.Cells were then washed with PBS andmounted with coverslips using Glycergel(Dako).The apoptotic cellsexhibiting condensed and fragmented nuclei were counted under anOlympus-BH2fluorescence microscope in randomly selected fields.Aminimum of500–1000cells was examined for each condition,andresults were expressed as a ratio of the total number of cells counted.Statistical Analysis and Software—The statistical analysis of thedata gathered from cell and apoptotic nuclei counting was performedusing SPSS version9.0.1(SPSS inc.,Chicago,IL).Analyses of variancewere followed by the Tukey’s test to determine the significance.NGF Receptors and PARP Immunoblotting—Subconfluent cell cul-tures were harvested by scraping in serum-free medium.After centrif-ugation(1000ϫg,5min),the pellet was treated with lysis buffer(0.3%SDS,200m M dithiothreitol)and boiled5min.In the case of PARP,thepellet was lysed with urea-rich buffer(62.5m M Tris-HCl,pH6.8,6Murea,10%glycerol,2%SDS),sonicated and incubated at65°C for15min.The lysates were subjected to SDS-PAGE,transferred onto anitrocellulose membrane(Immobilon-P,Millipore)by electroblotting(100V,75min),and probed with anti-trkA,anti-p75NTR or anti-PARPantibodies at4°C overnight.The membranes were then incubated atroom temperature for3h with biotin-conjugated anti-rabbit(TrkA)oranti-mouse(p75NTR and PARP)immunoglobulin G.After1h of incu-bation with extravidin,the reaction was revealed using the chemilumi-nescence kit ECL(Amersham Pharmacia Biotech)with Kodak X-OmatAR film.Detection of p140trkA and MAPK Activation—Proteins were extractedin lysis buffer(150m M NaCl,50m M Tris,pH7.5,0.1%SDS,1%NonidetP-40,100␮M sodium orthovanadate)prior to immunoprecipitation. Preclearing was done with protein A-agarose(10␮l/250␮l,60min, 4°C).After centrifugation(10,000ϫg,2min),the supernatant wasincubated with monoclonal anti-MAPK(anti-ERK2)antibody(10␮l/250␮l,60min,4°C).Protein A-agarose(10␮l)was added for60min(4°C) and then pelleted by centrifugation(10,000ϫg,2min).The pellet wasthen rinsed three times with lysis buffer and boiled for5min inLaemmli buffer.After SDS-PAGE and electroblotting,nitrocellulosemembranes were blocked with3%bovine serum albumin.Membraneswere then incubated with PY20anti-phosphotyrosine antibody over-night at4°C,rinsed,and incubated with a horseradish peroxidase-conjugated anti-mouse IgG for3h at room temperature.Membraneswere rinsed overnight at4°C before visualization with ECL.Cell Fractionation and NF-␬B Detection—Cell nuclear extracts were prepared as described by Herrmann et al.(23).Cells were trypsinized and then pelleted in minimal essential medium containing10%FCS. After washing with ice-cold PBS,cells were repelleted and resuspended in400␮l of ice-cold hypotonic buffer(10m M Hepes,pH7.8,10m M KCl,2m M MgCl2,0.1m M EDTA,10␮g/ml aprotinin,0.5␮g/ml leupeptin,3 m M phenylmethylsulfonyl fluoride,and3m M DTT).After10min on ice, 25␮l of10%Nonidet P-40was added and crude nuclei were collected by centrifugation for5min.The nuclear pellet was resuspended in high salt buffer(50m M Hepes,pH7.4,50m M KCl,300m M NaCl,0.1m M EDTA,10%(v/v)glycerol,3m M DTT,and3m M phenylmethylsulfonyl fluoride).After30min on ice with frequent agitation,the insoluble nuclear material was pelleted in a microcentrifuge for10min.Crude nuclear protein was collected from the supernatant and snap-frozen in a dry ice/ethanol bath.After thawing and boiling for5min in Laemmli buffer,the nuclear extracts were subjected to SDS-PAGE and probed with an anti-NF-␬B p65antibody.A control was established with anti-lamin B antibody.Transfection of I␬␤,c-rel,and rel-A—Cotransfection experiments were carried out using Lipofectin reagent,as described by the manu-Intracellular Signaling Pathways of NGF in Breast Cancer Cells17865facturer.Briefly,MCF-7cells were incubated for 5h in 1ml of Opti-MEM transfection medium containing 8␮l of Lipofectin reagent,0.8␮g of green fluorescence protein (GFP)-carrying vector and 0.2␮g of empty vector PCDNA3or 0.2␮g of I ␬Bm.In the case of c-rel or rel-A,cells were cotransfected with 0.8␮g of GFP-carrying GFP and 0.6␮g of PSVK 3(empty plasmid),c-rel,or rel-A.Cells were then grown for 24h with 10%FCS minimal essential medium and rinsed for 2h in serum-free me-dium before incubation in serum-free medium in the presence or ab-sence of 100ng/ml NGF and/or 2␮M C2for another 24h.Cells were then fixed with paraformaldehyde 4%(4°C)for 30min,and the per-centage of apoptotic cell nuclei in GFP-stained cells was determined as described above.RESULTSNGF Mitogenic and Anti-apoptotic Activity for Breast Cancer Cells—The effects of 100ng/ml NGF on cell proliferation and C2-induced apoptosis were evaluated by cell counting and Hoechst staining,respectively.The results show that NGF induces an increase in cell number for all breast cancer cell lines tested (Fig.1A ).We have previously demonstrated that NGF has a direct mitogenic effect on breast cancer cells by recruiting cells in G 0phase and by shortening the G 1length (Descamps et al.,1998).In addition,NGF rescued breast cancer cells undergoing C2-induced apoptosis;the maximum survival was observed at 200ng/ml (Fig.1B ).The morphology of cells undergoing this NGF-induced anti-apoptotic rescue was quite distinct (Fig.2A ).The induction of apoptosis by C2was found to involve cleavage of poly(A)DP-ribose polymerase (PARP);this cleavage was reversed by NGF (Fig.2B ).TrkA and p75NTR Expression—RT-PCR was used to show the expression of mRNA for both high and low affinity NGF recep-tors in MCF-7,T47-D,BT-20,and MDA-MB-231cells (Fig.3A );the 102-bp band for the TrkA transcript and a 147-bp band for the p75NTR transcript were readily detectable on 1%agarose gels.Moreover,Western blotting demonstrated that both p140trkA and p75NTR were present in all the breast cancer cell lines (Fig.3B ).Real-time quantitative RT-PCR indicated that there was no significant change in the levels of TrkA and p75NTR mRNAs in the presence of FCS,NGF,or C2(data not shown)and that the levels of mRNA for TrkA and p75NTR in breast cancer cells was between 5and 10times lower than the level observed in SY5Y neuroblastoma cells (data not shown).This indicates that NGF receptor expression in breast cancer cells is relatively limited.It should be noticed that,although mRNA levels of NGF receptors differ between breast cancer cells and SY5Y,the protein levels apparently do not.However,F IG .1.Effect of NGF on the growth and survival of breast cancer cells.A ,breast cancer cells were serum-deprived in minimum essential medium,and after 24h the NGF (100ng/ml)was added.After 48h,cells were harvested and counted.B ,cells were serum-deprived in minimum essential medium and treated with 2␮M C2with or without 100ng/ml NGF.After 24h,cells were fixed and the proportion of apoptotic nuclei were determined after Hoechst staining under an Olympus-BH2fluorescence microscope.For measurement of both cell number and apoptosis,results are expressed as the means ϮS.D.of five separate experiments.Significance was determined using the Tukey’s test (*,p Ͻ0.01).F IG .2.Anti-apoptotic effect of NGF.A ,Hoechst staining of apop-totic cell nuclei in control,C2and C2ϩNGF-treated MCF-7cells.Cells were serum-deprived in minimum essential medium and treated with C2.NGF was added at 100ng/ml.After 24h,cells were fixed and apoptotic nuclei were observed after Hoechst staining.B ,immunoblot detection of PARP cleavage.C2-induced PARP cleavage was reversed by p75NTR activation mediated by NGF.MCF-7cells were serum-de-prived in minimum essential medium for 24h and were then treated with 100ng/ml NGF in the presence or absence of 2␮M C2,10n M K-252a,10␮M PD98059,or 10␮g/ml anti-p75NTR -blocking antibody (Euromedex)for another 24-h period.Proteins were detected after SDS-PAGE of cell preparations from MCF-7breast cancer cells,electro-blotting onto nitrocellulose,and immunodetection with anti-PARP antibodies.Intracellular Signaling Pathways of NGF in Breast Cancer Cells17866it has been shown before that the level of a given cellular protein cannot be simply deduced from mRNA transcript level (24).One could hypothesize that the stability of mRNA and/or protein for NGF receptors,differs between breast cancer cells and neuroblastoma cells,leading to the observed disproportion-ality between mRNA and protein levels.Involvement of p140trkA and p75NTR in Mitogenic and Sur-vival Activities of NGF—We used a combination of specific antibodies and pharmacological inhibitors to study the puta-tive functions of p140trkA and p75NTR in the stimulation of proliferation and cell survival induced by NGF.The Trk tyro-sine kinase inhibitor K-252a,and the MEK inhibitor PD98059,both strongly inhibited the growth-stimulatory effect of NGF on MCF-7cells,but had no effect on its anti-apoptotic effects (Fig.4).Conversely,neither the anti-p75NTR blocking antibody nor the NF-␬B inhibitor SN50affected NGF-stimulated prolif-eration,although both strongly reduced the anti-apoptotic ef-fects (Fig.4).The tyrosine kinase activity of p140trkA was inhibited by K-252a but not by the anti-p75NTR or PD98059(Fig.5).On the other hand,the activity of the MAPKs was inhibited by K-252a and PD98059but not by the anti-p75NTR (Fig.5).It should be noted that the SN50peptidic inhibitor of NF-␬B,similarly to the anti-p75NTR ,inhibited the anti-apo-ptotic effect of NGF but neither its proliferative effect nor its activation of p140trkA and MAPKs.The effect of other neuro-trophins on MCF-7cell growth and survival was also evaluated (Fig.6A ).In contrast to NGF,no proliferative effect was pro-vided by BDNF,NT-3,or NT-4/5.However,all neurotrophins tested exhibited a rescue effect on C2-treated cells that was not altered in the presence of the trk inhibitor K-252a (Fig.6B ).These data suggest that trk receptors are not involved in NGF survival activity.Moreover,the participation of trkB and trkCin these events can be ruled out,because they are not expressed in these breast cancer cells (Fig.6C ).NF-␬B Involvement in the Anti-apoptotic Effect of NGF—The inhibitory effect of SN50on the NGF anti-apoptotic activity indicated the potential involvement of NF-␬B in the signaling leading to the protective activity of this growth factor.To fur-ther investigate this phenomenon,we studied the effect of NGF on the nuclear translocation of NF-␬B,as well as the conse-quence of transfection by IkBm (an inhibitor of NF-␬B)or by c-rel and rel-A (constitutively active subunits of NF-␬B)on the NGF-mediated anti-apoptotic activity in MCF-7cells.Western blotting revealed no change in the nuclear levels of NF-␬B (p65)during apoptosis induced by C2(Fig.7).In contrast,the addi-tion of NGF on C2-treated cells induced a translocation of NF-␬B from cytoplasm to puterized quantifica-tion revealed a doubling p65band intensity normalized to the total intensity of the lane (data not shown).Moreover,this NF-␬B nuclear translocation was inhibited by the presence of p75NTR -blocking antibody or SN50,but was not affected by K-252a and PD98059.Interestingly,in the absence of C2-in-duced apoptosis NGF was not able to induce the nuclear trans-location of NF-␬B,confirming previous observations that p75NTR -mediated NF-␬B activation requires cell stress (25).Transfection of MCF-7cells with IkBm,an inhibitor of NF-␬B,reversed the anti-apoptotic effect of NGF (Fig.8A ).As a control,we transfected MCF-7cells with an empty vector;no effect was observed.In addition,transfection with activators of the NF-␬B pathway,c-rel or rel-A (Fig.8B ),resulted in an inhibition of C2-induced apoptosis of MCF-7cells,even in absence ofNGF,F IG .3.TrkA and p75NTR expression in breast cancer cells.A ,agarose gel electrophoresis of RT-PCR products evidenced a 102-bp band and a 147-bp band,which are characteristic of TrkA and p75NTR ,respectively.Both NGF receptors were found in all cell types tested.B ,p140trkA and p75NTR were immunodetected after SDS-PAGE of breast cancer cell lines.The neuroblastoma cells SY5Y were used as positive control for the expression of NGFreceptors.F IG .4.Pharmacological modulation of the proliferative and anti-apoptotic effect of NGF.MCF-7cells were starved in minimum essential medium,and after 24h,100ng/ml NGF was added with or without inhibitors or antibody.A ,after 48h,cells were harvested and counted.B ,after 24h,cells were fixed and the proportion of apoptotic nuclei determined after Hoechst staining.The following concentrations were used:2␮M C2,10n M K-252a,10␮g/ml anti-p75NTR -blocking antibody (Euromedex),10␮M PD98059,18␮M SN50.For A and B ,results are expressed as the means ϩS.D.of five separate experiments.Significance was determined using the Tukey’s test (*,p Ͻ0.01).Intracellular Signaling Pathways of NGF in Breast Cancer Cells 17867confirming the involvement of NF-␬B family members in hu-man breast cancer cell survival.DISCUSSIONThis study shows that,in addition to its mitogenic activity,NGF is anti-apoptotic for breast cancer cells,and that these two biological effects are differentially mediated by the p140trkA and p75NTR receptors,respectively.The growth of breast cancer results from a balance between cell proliferation and apoptosis,both of which can be modulated by various regulatory peptides.For example,epidermal growth factor,fibroblast growth factors,and insulin-like growth factor-1can all stimulate the proliferation and survival of breast cancer cells (26).On the other hand,agents such as transforming growth factor-␤or tumor necrosis factor-␣can inhibit growth and induce apoptosis in these cells (27).Recently we have shown that NGF,which was primarily described for its neuro-trophic properties,is a strong mitogen for cancerous but not for normal human breast epithelial cells,suggesting a crucial func-tion for this factor in the initiation and progression of human breast tumors (13).In the present study,we have shown that the breast cancer cells express transcripts for both TrkA and p75NTR receptors.In contrast,no expression of TrkB and TrkC was found in any of the breast cancer cells tested,in accordance with the fact that BDNF,NT-3,or NT-4/5have no mitogenic effect for these cells.The presence of NGF receptors has been detected previously in breast cancer cells (28),and low levels of NGF receptor expression have recently been reported in other breast cancer cell lines (29),leading to the hypothesis of a recruitment and cooperation between p140trkA and p185Her-2for the induction of mitogenesis by NGF.Our results indicate a stimulation of p140trkA tyrosine kinase activity and of the MAPK cascade by NGF,and the use of the pharmacological inhibitors K-252a and PD98059demonstrate the requirementfor these signals in NGF-induced MCF-7cell proliferation.The induction of MAPK activity required p140trkA activation,but p75NTR did not appear to be involved,because p75NTR -blocking antibodies did not have any effect on NGF-inducedMAPKF IG .5.p140trkA and MAPK activation.MCF-7cells were treated with 100ng/ml NGF in the presence or absence of 10n M K-252a,10␮g/ml anti-p75NTR -blocking antibody,or 10␮M PD98059.p140trkA (A )and MAPK activation (B )were determined after immunoprecipitation using polyclonal anti-TrkA and monoclonal anti-ERK2antibodies,re-spectively.After SDS-PAGE and electroblotting,nitrocellulose mem-branes were counterprobed with the PY20anti-phosphotyrosine anti-body.For detection of TrkA (A )and MAPK (B )activation,the lower panel shows reprobing of the blots with the immunoprecipitatingantibody.F IG .6.Effect of different neurotrophins on MCF-7cells growth and survival.MCF-7cells were serum-deprived in minimum essential medium,and after 24h the neurotrophins (100ng/ml NGF,50ng/ml BDNF,50ng/ml NT-3,100ng/ml NT-4/5)were added.A ,after 48h,cells were harvested and counted.In contrast with NGF,neither BDNF,NT-3,nor NT-4/5displayed significant bioactivity (for concen-trations up to 400ng/ml).B ,MCF-7cells were serum-deprived in minimum essential medium and treated with 2␮M C2,with or without neurotrophins (100ng/ml NGF,50ng/ml BDNF,50ng/ml NT-3,100ng/ml NT-4/5).After 24h,cells were fixed and apoptotic nuclei percent-age was determined after Hoechst staining under an Olympus-BH2fluorescence microscope.For measurement of both cell number and apoptosis,results are expressed as the means ϮS.D.of five separate experiments.Significance was determined using the Tukey’s test (*,p Ͻ0.01).C ,TrkB and TrkC mRNA expression in MCF-7cells.Agarose gel electrophoresis of RT-PCR products reveals TrkA expression,but no TrkB or TrkC expression in MCF-7breast cancer cells.Human NT2cells were used as positive control for the expression of TrkB and ne 1,NT2-negative control without RT step;lane 2,NT2-positive control;lane 3,MCF-7cells-negative control without RT step;lane 4,MCF-7cells.Intracellular Signaling Pathways of NGF in Breast Cancer Cells17868。