Article(id=1198628667199947305, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198628666650493481, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2022-1286, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1669651200000, receivedDateStr=2022-11-29, revisedDate=1676390400000, revisedDateStr=2023-02-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1763704943704, onlineDateStr=2025-11-21, pubDate=1689091200000, pubDateStr=2023-07-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763704943704, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763704943704, creator=13701087609, updateTime=1763704943704, updator=13701087609, issue=Issue{id=1198628666650493481, tenantId=1146029695717560320, journalId=1189982191388893191, year='2023', volume='58', issue='7', pageStart='0', pageEnd='1980', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1763704943573, creator=13701087609, updateTime=1766137716668, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1208832456644490122, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198628666650493481, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1208832456644490123, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198628666650493481, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1725, endPage=1731, ext={EN=ArticleExt(id=1198628667451605546, articleId=1198628667199947305, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Taking the mechanism of glucose catabolism disorder in depression as an example to explore the research ideas and strategies of stable isotope tracer metabolomics, columnId=1190335348648547107, journalTitle=Acta Pharmaceutica Sinica, columnName=Reviews, runingTitle=null, highlight=null, articleAbstract=

Stable isotope tracer metabolomics tracks and analyzes the whole metabolic process of the body through the tracer atoms, which belongs to the frontier technology in the field of biomedicine. This technology is of great significance and value for explaining the pathogenesis of diseases, finding biomarkers of diseases and drug action targets. Taking the mechanism of glucose catabolism disorder in depression as an example, this paper systematically expounds the stable isotope tracer metabolomics technology and its application. The research idea of stable isotope tracer metabolomics based on unmarked metabolomics was put forward, and the research strategy of biological significance interpretation from four dimensions of metabolite isotope abundance, key metabolic enzymes, metabolic flow direction and metabolite flow was given, which broke through the bottleneck of stable isotope tracer metabolomics research technology based on overall animal experiment, and provided scientific basis for the promotion and application of this technology.

, correspAuthors=Xue-mei QIN, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2023 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Jun-sheng TIAN, Yun-hao ZHAO, Ting LING-HU, Wen-ze WU, Shao-bo LIU, Xian-xian WANG, Xue-mei QIN), CN=ArticleExt(id=1198628668869280306, articleId=1198628667199947305, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=以抑郁症葡萄糖分解代谢障碍机制解析为例探究稳定同位素示踪代谢组学的研究思路与策略, columnId=1190335349655180086, journalTitle=药学学报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

稳定同位素示踪代谢组学通过示踪标记的原子对整个机体代谢过程进行追踪分析, 属于生物医药领域前沿技术。该技术对于阐释疾病发生机制、发现疾病的生物标志物和药物作用靶点具有重要意义与价值。本文以抑郁症葡萄糖分解代谢障碍机制解析为例, 系统地阐述稳定同位素示踪代谢组学技术及其应用。提出了在非标记代谢组学基础上的稳定同位素示踪代谢组学研究思路, 给出了从代谢物同位素丰度、关键代谢酶、代谢物流向和代谢物流量四个维度展开生物学意义阐释的研究策略, 突破了基于整体动物实验的稳定同位素示踪代谢组学研究技术瓶颈, 为该技术的推广应用提供科学依据。

, correspAuthors=秦雪梅, authorNote=null, correspAuthorsNote=
*秦雪梅, Tel: 86-351-7011202, E-mail:
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以抑郁症葡萄糖分解代谢障碍机制解析为例探究稳定同位素示踪代谢组学的研究思路与策略
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田俊生 1, 2 , 赵云昊 1, 2 , 令狐婷 1, 2 , 武文泽 1, 2 , 刘少博 1, 2 , 王贤贤 1, 2 , 秦雪梅 1, 2, *
药学学报 | 综述 2023,58(7): 1725-1731
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药学学报 | 综述 2023, 58(7): 1725-1731
以抑郁症葡萄糖分解代谢障碍机制解析为例探究稳定同位素示踪代谢组学的研究思路与策略
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田俊生1, 2, 赵云昊1, 2, 令狐婷1, 2, 武文泽1, 2, 刘少博1, 2, 王贤贤1, 2, 秦雪梅1, 2, *
作者信息
  • 1.山西大学中医药现代研究中心, 山西 太原 030006
  • 2.地产中药功效物质研究与利用山西省重点实验室, 山西 太原 030006

通讯作者:

*秦雪梅, Tel: 86-351-7011202, E-mail:
Taking the mechanism of glucose catabolism disorder in depression as an example to explore the research ideas and strategies of stable isotope tracer metabolomics
Jun-sheng TIAN1, 2, Yun-hao ZHAO1, 2, Ting LING-HU1, 2, Wen-ze WU1, 2, Shao-bo LIU1, 2, Xian-xian WANG1, 2, Xue-mei QIN1, 2, *
Affiliations
  • 1. Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006, China
  • 2. Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Taiyuan 030006, China
出版时间: 2023-07-12 doi: 10.16438/j.0513-4870.2022-1286
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稳定同位素示踪代谢组学通过示踪标记的原子对整个机体代谢过程进行追踪分析, 属于生物医药领域前沿技术。该技术对于阐释疾病发生机制、发现疾病的生物标志物和药物作用靶点具有重要意义与价值。本文以抑郁症葡萄糖分解代谢障碍机制解析为例, 系统地阐述稳定同位素示踪代谢组学技术及其应用。提出了在非标记代谢组学基础上的稳定同位素示踪代谢组学研究思路, 给出了从代谢物同位素丰度、关键代谢酶、代谢物流向和代谢物流量四个维度展开生物学意义阐释的研究策略, 突破了基于整体动物实验的稳定同位素示踪代谢组学研究技术瓶颈, 为该技术的推广应用提供科学依据。

稳定同位素示踪代谢组学  /  抑郁症  /  能量代谢  /  葡萄糖分解代谢障碍  /  研究思路与策略

Stable isotope tracer metabolomics tracks and analyzes the whole metabolic process of the body through the tracer atoms, which belongs to the frontier technology in the field of biomedicine. This technology is of great significance and value for explaining the pathogenesis of diseases, finding biomarkers of diseases and drug action targets. Taking the mechanism of glucose catabolism disorder in depression as an example, this paper systematically expounds the stable isotope tracer metabolomics technology and its application. The research idea of stable isotope tracer metabolomics based on unmarked metabolomics was put forward, and the research strategy of biological significance interpretation from four dimensions of metabolite isotope abundance, key metabolic enzymes, metabolic flow direction and metabolite flow was given, which broke through the bottleneck of stable isotope tracer metabolomics research technology based on overall animal experiment, and provided scientific basis for the promotion and application of this technology.

stable isotope tracer metabolomics  /  depression  /  energy metabolism  /  glucose catabolism disorder  /  research ideas and strategy
田俊生, 赵云昊, 令狐婷, 武文泽, 刘少博, 王贤贤, 秦雪梅. 以抑郁症葡萄糖分解代谢障碍机制解析为例探究稳定同位素示踪代谢组学的研究思路与策略. 药学学报, 2023 , 58 (7) : 1725 -1731 . DOI: 10.16438/j.0513-4870.2022-1286
Jun-sheng TIAN, Yun-hao ZHAO, Ting LING-HU, Wen-ze WU, Shao-bo LIU, Xian-xian WANG, Xue-mei QIN. Taking the mechanism of glucose catabolism disorder in depression as an example to explore the research ideas and strategies of stable isotope tracer metabolomics[J]. Acta Pharmaceutica Sinica, 2023 , 58 (7) : 1725 -1731 . DOI: 10.16438/j.0513-4870.2022-1286
非标记代谢组学技术以代谢物轮廓分析为目标, 通过对生物体内的小分子代谢物进行定性及定量分析, 寻找代谢物与生理病理变化之间的内在联系, 具有客观性、系统性和整体性的特点[1]。但非标记代谢组学的检测只能明确机体的代谢物在体内完成复杂代谢后的总量, 无法明确具体代谢通路如何变化[2]。因此, 众多非标记代谢组学研究发现, 在不同的疾病发现相同的代谢物异常, 但无法区分疾病的代谢特异性[3, 4]
稳定同位素示踪代谢组学通过前体物质进行针对性地标记(如13C-葡萄糖、15N-谷氨酰胺等), 对示踪标记的原子的整个机体代谢过程进行追踪分析, 并依据中间代谢产物的同位素峰分布来判断被标记物质在相关代谢通路中的来龙去脉。稳定同位素示踪代谢组学的应用已经愈加广泛, 特别是对于特定代谢通路的追踪。Gu等[5]采用稳定同位素示踪代谢组学的方法明确其在细胞模型的代谢变化, 发现糖酵解代谢异常归因于丙酮酸脱氢酶激酶表达异常所致。Meiser等[6]通过稳定同位素示踪代谢组学发现, DJ-1基因的缺失会使神经细胞表现出谷氨酰胺通量的降低和色氨酸生物合成的减少。Hu等[7]通过稳定同位素示踪代谢组学发现, 二甲双胍可通过降低磷酸果糖激酶-1的代谢通量来降低肝癌组织糖酵解水平。由此可见, 在非标记代谢组学研究的基础上进行稳定同位素标记的示踪代谢组学的研究已经成为了一种趋势。但多数研究仍以细胞模型为研究对象, 失去了机体体内代谢的真实性。且研究思路较为单一, 仅局限于代谢物同位素丰度的分析, 缺乏整体研究思路与策略。
抑郁症是一种复杂的精神疾病, 基于临床患者的疲劳症状和模型动物的行为特征, 抑郁症的能量代谢紊乱假说受到广泛关注。目前的研究主要集中于抑郁症线粒体功能障碍, 重点关注线粒体结构与功能[8]。却忽视了能量底物的供应, 这对解读抑郁症中的异常代谢途径至关重要。本课题组长期从事抑郁症相关研究工作, 本文以抑郁症葡萄糖分解代谢障碍机制解析为例, 系统地阐述稳定同位素示踪代谢组学技术及其应用。提出了以非标记代谢组学为基础, 明确疾病与能量代谢网络的内在联系, 再进行稳定同位素示踪代谢组学的研究思路, 给出了从代谢物同位素丰度、关键代谢酶、代谢物流向和代谢物流量四个维度展开生物学意义阐释的研究策略。进一步设计了动物实验并构建了基于实验动物的稳定同位素示踪代谢组学技术, 最终确定了在非标记代谢组学基础上的稳定同位素示踪代谢组学研究思路与策略, 为稳定同位素示踪代谢组学技术应用研究提供参考。
稳定同位素示踪代谢组学研究不是盲目的, 人体内代谢网络的规模庞大且复杂, 多达几千种反应[9]。以能量代谢网络为例, 机体通过碳水化合物、脂肪和蛋白质的分解代谢获取能量。相关的代谢种类包括葡萄糖代谢、脂肪酸代谢和氨基酸代谢等。而每种物质代谢包含多条代谢途径, 如葡萄糖代谢包括, 糖酵解、糖异生和磷酸戊糖途径等。因此, 在稳定同位素示踪代谢组学研究前, 需要进行非标记代谢组学研究与分析, 从而确定研究目标, 明确某一类物质代谢或某一代谢途径, 使后续的稳定同位素示踪代谢组学研究更有针对性。
非标记代谢组学的研究要尽量全面, 保证所聚焦代谢途径的准确性。生物样本多来源(临床患者、模型动物和细胞等), 分析技术多种类(液质联用、气质联用、核磁共振), 研究水平多个层次(血液、尿液、粪便、组织、细胞)。研究中所有涉及到的临床患者, 须根据汉密尔顿抑郁量表、临床疗效总评量表、《中国疾病诊断疗效标准》和《中医证候量表》筛选得来保证临床样本的真实可靠性。动物及细胞模型需要验证筛选, 保证使用的动物模型较好地反映患者的状态, 细胞模型较好地反映病变器官的状态。
Ling-Hu等[10]使用液质联用技术(LC-MS) 对抑郁症临床患者的血浆样本进行测定分析, 选择了17位健康志愿者和22位抑郁症临床患者治疗前后的血浆样本。对多种差异代谢物进行富集分析, 主要聚焦于能量代谢相关的多条代谢途径, 如脂肪酸代谢、丙酮酸代谢、乙醛酸盐代谢等。
通过模型筛选验证, 慢性轻度不可预知应激抑郁模型(chronic unpredictable mild stress, CUMS) 是较为理想的抑郁症动物模型, 在行为学表现上较好地模拟了人类抑郁症 [11, 12]。皮质酮(corticosterone, CORT) 诱导PC12细胞损伤模型较好地模拟抑郁症患者脑部细胞的损伤。海马组织是认知、记忆和情绪相关的重要脑区, 抑郁情感障碍的病因和病理生理过程与海马结构有着密切的关系[13, 14]。肝脏是机体物质和能量代谢的中心场所及代谢中枢[15]。因此, 对海马及肝脏组织进行代谢组学分析是有必要的。
Liang[16]使用LC-MS更进一步发现了CUMS模型组相较于空白组的海马组织三羧酸循环中间体顺乌头酸含量下降。这表明CUMS模型海马组织内三羧酸循环能量代谢水平降低。Chen等[17]对CUMS模型组肝脏组织进行了分析测定。与空白组大鼠相比, CUMS模型组大鼠的富马酸、柠檬酸、琥珀酸等与能量代谢相关的代谢产物发生了显著的变化。
Shi[18]使用LC-MS对CORT损伤PC12细胞进行代谢物分析, 对比分析CORT损伤组与空白组的差异代谢物。筛选出19种差异代谢物, 其中谷氨酰胺、谷氨酸、乳酸、苹果酸、葡萄糖等代谢物与能量代谢相关。
通过对临床患者、动物和细胞样本的检测分析发现大量差异代谢物指向了与能量代谢相关的通路, 证实抑郁症患者的能量代谢发生障碍。通过Metabo Analyst网站对非标记代谢组学发现的差异代谢物进行整合分析, 发现差异代谢物聚焦于丙氨酸、天冬氨酸和谷氨酸代谢、D-谷氨酰胺和D-谷氨酸代谢、氨酰基-tRNA的生物合成等。富集后的代谢通路仍然相对较多, 因此需要进一步通过查阅文献、教科书和京都基因与基因组百科全书等明确研究目标。
葡萄糖、丙酮酸和乳酸三种代谢物指向了葡萄糖代谢中的糖酵解通路[19]。柠檬酸、α-酮戊二酸和琥珀酸三种差异代谢物均为三羧酸循环的中间产物[20]。丙氨酸虽然是氨基酸, 但在肝脏中主要发生葡萄糖-丙氨酸循环, 这也指向了葡萄糖代谢[21]。谷氨酰胺和谷氨酸间存在着谷氨酸-谷氨酰胺循环, 是三羧酸循环的旁支[22]。综上所述, 虽然差异代谢物包括葡萄糖代谢、脂肪酸代谢以及氨基酸代谢, 但这些差异代谢物都聚焦于葡萄糖分解代谢的糖酵解和三羧酸循环上(图 1)。抑郁情感障碍的病因和病理生理过程与海马结构有着密切的关系, 而葡萄糖又是脑内最主要的能量来源。因此分析得出抑郁症可能导致了机体的葡萄糖分解代谢障碍。本课题组通过整合分析, 将研究目标锁定到了葡萄糖分解代谢。
非标记代谢组学发现的代谢物处于生物系统中生化反应终端, 反映的是已发生的生物学事件, 无法精准阐明抑郁症能量代谢障碍的具体代谢途径, 这种分析终点代谢物总量的方法尚不能区分代谢物的来路与去向。稳定同位素示踪代谢组学技术通过示踪剂对目标代谢途径进行同位素记, 可以灵敏捕捉示踪原子在生物体内的动态过程。因此使用稳定同位素示踪代谢组学技术可以解决非标记代谢组学无法聚焦特异代谢途径的缺陷。明确具体代谢通路的受阻或通畅情况。已有相关文献[2, 23]对稳定同位素示踪代谢组学的原理进行了详细介绍。
由于稳定同位素示踪代谢组学存在着高成本、难引入动物体内等问题, 因此研究大都集中在细胞模型上。但疾病动物模型相较于细胞模型, 能更加全面体现出疾病对机体的影响。因此, 基于动物实验的相关技术急需突破。
本课题组先进行稳定同位素示踪代谢组学动物实验设计, 再构建基于实验动物的稳定同位素示踪代谢组学技术, 包括同位素引入技术、LC-MS分析条件和数据处理方法。提出从代谢物同位素丰度、关键代谢酶、代谢物流向和代谢物流量四个维度阐释生物学意义。首先明确代谢物同位素丰度和关键酶活性、含量变化, 再综合分析明确代谢流向的改变。最后通过常微分方程建立数学模型, 量化代谢物间的传递。从代谢物水平、酶水平、代谢流向和代谢流量水平全面表征疾病的代谢障碍机制。
完善的实验设计是实验成功的第一步。在常规动物实验设计的基础上, 还需要根据非标记代谢组学的分析结果, 选择合适的稳定同位素示踪剂、色谱柱和色谱质谱联用仪。稳定同位素示踪代谢组学研究动物实验设计不同于常规动物实验, 需要在常规动物实验的基础上, 分别设计引入同位素标记组和引入无同位素标记组, 从而实现消除示踪剂导致的质谱信息发生改变。针对不同的疾病模型和研究方向, 还需要对实验细节进行调整, 如研究能量代谢, 需要考虑到进食量改变对能量代谢的影响, 并设计相应的组别消除影响。本课题组根据非常规代谢组学结果, 将代谢障碍机制锁定于葡萄糖分解代谢, 因此, 选用13C6-葡萄糖作为示踪剂解析CUMS大鼠葡萄糖分解代谢。
基于实验动物的稳定同位素示踪代谢组学技术尚属于前沿技术领域, 其应用还存在一些技术难点需要突破。难点包括同位素示踪剂引入、液相色谱柱选择、液质条件设计、样品前处理和数据处理方法。
动物体内稳定同位素引入方法的确定。目前同位素引入动物体内的方法主要有灌胃、混入食物喂食、颈静脉注射、尾静脉注射等。灌胃和混入食物喂食给予同位素操作虽较为简单, 但多用于研究肠胃代谢功能, 且所需同位素量较大, 实验成本较高[24]。颈静脉注射可以实现长时间注射的同时动物还可以自由活动, 但其操作难度相对较大, 且对动物造成一定的创伤[25]。尾静脉注射可以实现较长时间且稳定的输注, 虽然会限制动物的活动, 但其可操作性较强[26]。引入同位素的用量也是需要考虑的一个关键因素, 该用量不仅要保证充分标记下游代谢产物, 同时要避免用量过多引起的代谢紊乱。
Ling-Hu等[27]设计了一种将稳定同位素示踪剂通过尾静脉注射引入大鼠体内的方法, 包括稳定同位素示踪溶液的配制、示踪引入装置的组装、示踪剂引入时间的确定及引入剂量的确定。引入装置由大鼠固定器、一次性静脉注射针、微量注射针和双通道微量注射泵组成, 可以实现缓速, 平稳注射。前期实验摸索确定以8 mg·kg-1·min-1为最佳, 引入剂量连续尾静脉推注6.0 h。
色谱柱是色谱分离系统的心脏, 需要根据目标代谢物的特性来选择合适的分离色谱柱。优先选择自带同位素分析软件的色谱质谱联用仪, 便于后续数据处理。并需要进行系统地条件摸索实验, 确定样本制备方法、样本复溶剂、液相流动相及其梯度和质谱分析条件等。
葡萄糖相关代谢产物极性强, 需选择分离强极性化合物的色谱柱。亲水作用液相色谱(hydrophilic interaction liquid chromatography, HILIC) 柱能充分保留强极性化合物, 多用于碳水化合物、氨基酸和多肽的分离[28]。LC-MS可以较好地分离相关代谢物, Thermo Q Exactive液质联用仪自带的Compound Discoverer 3.0软件可处理同位素数据。因此, 本课题组选用HILIC柱及Thermo Q Exactive液质联用仪进行样本分析。Ling-Hu等[27]等经过系统的条件摸索实验, 确定一套适用于实验动物样本的前处理条件以及分析葡萄糖代谢相关代谢物的分析方法。
采集到的原始图谱采用Compound Discoverer 3.0软件中“Stable Isotope Labeling w Metabolika Pathways and ID using Online Databases and Local Database”工作流程进行处理。经上述软件处理后的数据量依旧庞大, 手动筛选耗时耗力, 本课题组基于Python编写了同位素信息提取方法, 实现自动、快速和精准的数据分析与处理[29]
目前多数稳定同位素示踪代谢组学研究结果较为单一, 仅局限于对代谢物同位素丰度及代谢流向的分析, 没有对结果进一步深入挖掘。稳定同位素示踪代谢组学虽然能明确体内代谢物生成和分解的过程, 但机体内各种代谢反应均离不开酶的作用, 还需要酶学进一步验证稳定同位素示踪代谢组学结果。因此, 本课题组进一步提出了从代谢物同位素丰度、关键代谢酶、代谢物流向和代谢物流量四个方面逐步阐释生物学意义的研究策略。
对LC-MS测定结果进行数据分析与处理, 可以得到目标代谢物的同位素丰度。当同位素发生替换, 代谢物的质量会发生变化, 以13C同位素为例, 每当一个碳原子发生替换, 质量M都会升高1。在体内多次循环后, 会替换出n种不同质量的同位素代谢物(n为此代谢物的原子数)。可以计算出各标记代谢物的同位素丰度, 以丙酮酸为例, 丙酮酸有三个碳原子, m3峰的同位素丰度m3 (%) = [m3·(m0+m1+m2+m3)-1]×100%。根据各代谢物同位素的分布情况解析疾病特异代谢途径。
在对血清样本的研究中发现, CUMS模型组的丙酮酸m3同位素丰度显著增加, 而其下游三羧酸循环中间产物的m2同位素丰度显著降低, 这表明CUMS大鼠的三羧酸循环受阻导致了丙酮酸的异常增加。半胱氨酸、3-磷酸甘油胆碱m2同位素丰度和葡萄糖、二氢胸腺嘧啶的m3同位素丰度也显著增加, 这表明CUMS大鼠的糖异生途径、磷脂合成途径和氨基酸代谢途径被激活[27]
对海马组织的研究结果表明, CUMS大鼠海马的葡萄糖分解代谢发生异常, 丙酮酸和乳酸的m3同位素丰度显著降低, 而天冬氨酸及其下游代谢产物的同位素丰度显著增加。同时, 三羧酸循环中苹果酸和富马酸的m2同位素丰度显著下降。这表明抑郁症导致脑内葡萄糖分解代谢发生障碍, 糖异生途径被异常激活[29]
对肝脏组织的研究结果表明, CUMS大鼠肝脏的葡萄糖分解代谢发生障碍。丙酮酸m3同位素丰度及其下游代谢物的m2同位素丰度显著降低, 三羧酸循环中顺乌头酸和富马酸的m2同位素丰度也显著降低。因此, 抑郁症导致肝脏葡萄糖分解代谢发生障碍[8]
综上所述, 动物模型的代谢物同位素丰度均显示抑郁症导致丙酮酸进入三羧酸循环受阻, 糖异生代谢等途径激活。分析表明抑郁症导致了丙酮酸进入三羧酸循环受阻, 大量丙酮酸堆积, 机体为疏解丙酮酸, 激活了糖异生、磷脂合成途径和氨基酸代谢途径消耗堆积的丙酮酸。
代谢通路活性的改变是由代谢通路上一系列关键酶决定的。但现有的代谢相关研究内容较为单一, 代谢通路关键酶学研究与稳定同位素示踪研究并没有相结合。根据代谢物同位素丰度确定紊乱的代谢途径后, 进一步通过酶学验证寻找导致代谢途径紊乱的关键酶。将功能受损酶结果与代谢物同位素丰度结果整合分析, 阐释其生物学意义。
本课题组根据代谢物同位素丰度结果, 丙酮酸可能是抑郁症葡萄糖分解代谢受阻的关键节点, 需要以丙酮酸为中心对糖异生、三羧酸循环等代谢通路的关键酶进行验证。因此对磷酸烯醇式丙酮酸经丙酮酸激酶(pyruvate kinase, PK)、线粒体丙酮酸载体(mitochondrial pyruvate carrier, MPC)、丙酮酸脱氢酶(pyruvate dehydrogenase, PDH)、丙酮酸羧化酶(pyruvate carboxylase, PC) 和磷酸烯醇丙酮酸羧激酶(phosphoenolpyruvate carboxykinase, PEPCK) 进行了酶活性及酶含量的测定。
Ling-Hu等[8, 29]对大鼠海马和肝脏样本的MPC、PDH、PC、PK、PEPCK的酶活性、酶含量和腺嘌呤核苷三磷酸(adenosine triphosphate, ATP) 水平进行了测定。结果表明海马及肝脏组织内ATP含量显著降低。CUMS模型组海马及肝脏组织MPC酶活性、酶含量显著下降。
结合稳定同位素示踪代谢组学结果分析, 模型组大鼠三羧酸循环受阻可能是MPC的减少导致的, MPC有望成为抑郁症的潜在治疗靶点。在明确受阻的关键靶点后, 还需进一步对过度激活的疏解途径靶点进行验证, 如糖异生、磷酸戊糖、磷脂合成途径和氨基酸代谢途径的关键酶。
基于代谢物同位素丰度及关键酶验证得到的结果, 进行整合分析, 将代谢通路紊乱与酶功能受损结果相结合, 判断代谢流向。
在不同组织中, 丙酮酸进入三羧酸循环受阻的情况是一致的, 这是由于MPC活性及含量下降, 丙酮酸无法进入线粒体, 导致三羧酸循环底物供应不足。而在肝脏组织内模型组大鼠只有PK、PC活性增加, PDH、ATP、MPC活性降低。而海马组织内相关酶的酶活性均显著升高。肝脏作为机体代谢中枢, 可以通过葡萄糖、氨基酸及脂肪酸供能, 而海马组织只能利用葡萄糖作为单一的能量来源。因此, 海马为弥补能量缺失, 全面激活了葡萄糖代谢的酶。虽然机体激活了PDH、PC, 但是PDH与PC只存在于线粒体, 无法改善丙酮酸进入线粒体的受阻情况[8]
代谢流量是指底物分配在各代谢途径上单位时间的流量, 高酶表达或代谢物水平可能被错误地解释为高流量, 然而事实上恰恰相反[30]。代谢流量需要基于稳定同位素示踪代谢组学数据的进一步挖掘。
代谢流分析技术是目前测定代谢流量的主要手段, 首先通过矩阵对稳定同位素示踪代谢组学数据进行天然同位素丰度矫正, 再根据代谢通路设计较为简易的数学模型, 在此基础上建立常微分方程求得代谢物间的流量[31]。代谢流量分析需要引入多种示踪剂来测量, 例如, 研究葡萄糖代谢需要13C6-葡萄糖, 而更准确的代谢流量计算需要13C3-乳酸, 因为葡萄糖会快速转化为乳酸, 需要两种数据相互验证计算消除葡萄糖与乳酸间快速转换的误差[32]。在整体动物上的代谢流量分析研究, 还可明确不同组织之间的代谢转换。例如, 肌肉与肝脏间的可立氏循环, 肌内生成的乳酸不能通过糖异生产生葡萄糖, 需要透过细胞膜弥散进入血液, 再被肝脏吸收。
通过代谢流分析技术, 分析不同组织内的代谢流量, 通过量化的数据确定海马组织及肝脏组织的通路变化异同, 更具体、清晰地表现出代谢障碍。葡萄糖是脑部唯一的能量来源, 而肝脏是机体内最主要的产糖组织, 两组织之间代谢联系密切。可进一步分析抑郁症对肝脏和脑部代谢联系的改变。
Jang等[33]通过计算代谢流量对猪的器官间代谢产物交换进行量研究, 展示了器官间代谢产物交换的定量图谱。Dong等[34]通过计算代谢流量, 明确TetR家族转录因子缺乏导致的脂肪酸分解代谢的量的改变。Rahim等[35]通过计算代谢流量, 明确胞浆磷酸烯醇丙酮酸羧激酶敲除后, 小鼠肝脏葡萄糖代谢的量的改变。
对代谢物同位素丰度、酶学验证、代谢流向和代谢流量的结果整合分析, 从整体代谢通路受阻状态、异常的关键酶、代谢物之间反应的量和组织间代谢交换量的改变全面阐释疾病的代谢图景。
稳定同位素示踪代谢组学尚属于生物医药领域前沿技术, 广泛应用于基础代谢研究、疾病机制探究、药物作用机制的研究, 但目前尚无成体系的研究思路与策略。本文以抑郁症的葡萄糖分解代谢障碍机制解析为例, 系统地阐述稳定同位素示踪代谢组学技术及其应用, 提出了以非标记代谢组学为基础, 明确疾病与能量代谢网络的内在联系, 再进行稳定同位素示踪代谢组学的研究思路, 给出了从代谢物同位素丰度、关键代谢酶、代谢物流向和代谢物流量四个维度展开生物学意义阐释的研究策略。从整体代谢通路受阻状态、异常的关键酶、代谢物之间反应的量和组织间代谢交换量的改变去全面阐释疾病的代谢图景。
稳定同位素示踪代谢组学已广泛应用于糖、脂和氨基酸的代谢研究。但该技术还存在一些不足与挑战。如: 成本过高、示踪剂种类较少。因此, 示踪剂的开发和利用十分重要。此外, 在稳定同位素示踪代谢组学基础上, 结合转录组学、蛋白组学等, 多组学结合将对疾病的代谢研究产生重要影响。
作者贡献: 田俊生、秦雪梅负责文章框架思路及修改; 赵云昊负责文章撰写初稿; 令狐婷、武文泽、刘少博、王贤贤负责文献资料整理与收集。所有作者阅读并认可终稿。
利益冲突: 所有作者均声明不存在利益冲突。
  • 国家自然科学基金项目(82074147)
  • 山西省基础研究计划项目(202103021224026)
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2023年第58卷第7期
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doi: 10.16438/j.0513-4870.2022-1286
  • 接收时间:2022-11-29
  • 首发时间:2025-11-21
  • 出版时间:2023-07-12
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  • 收稿日期:2022-11-29
  • 修回日期:2023-02-15
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国家自然科学基金项目(82074147)
山西省基础研究计划项目(202103021224026)
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    1.山西大学中医药现代研究中心, 山西 太原 030006
    2.地产中药功效物质研究与利用山西省重点实验室, 山西 太原 030006

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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