脂肪组织的蛋白质组学:从反刍动物脂肪形成的分子驱动因素到分子表型

M. Bonnet
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The use of -omics methods, such as proteomics has begun to fill the gap of knowledge of the multifaceted regulations of AT growth and metabolism by providing numerous information on pathways and functions. Two examples illustrate the usefulness of proteomic approaches for ruminant issues by allowing knowledge acquisition and molecular phenotyping. In the first example, proteomics and measurements of chemical composition, cellularity, histology, enzyme activities, and gene expression were applied to fetal AT at 110, 180, 210 and 260 days post conception (dpc) in Blond d’Aquitaine and Charolais breeds. From 180 dpc in the two breeds, we identified (Taga et al., 2012) proteins declared to be hallmarks of brown and white adipocytes in mice, that were underscored by the histological characterization of a mix of multilocular and unilocular cells, putatively brown and white adipocytes, respectively. 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We merged available proteomics data to provide a list of candidate biomarkers (first step in the process of biomarker discovery) of muscular adiposity that we declared robust candidates because they were identified in at least two publications differing by the breed, the age and the nutrition of bovine (Ceciliani et al. , 2018, Bonnet et al ., unpublished). Very recently, targeted proteomics has become an approach of choice to validate and precisely/absolutely quantify protein biomarkers. We have evaluated and benchmarked three targeted methods (the selected reaction monitoring (SRM), parallel reaction monitoring (PRM) and sequential windowed acquisition of all theoretical spectral (SWATH-MS)) to precisely quantify adiposity biomarkers in muscle tissues of 64 cows (Bons et al. , 2018). The work in progress, is to use these absolute quantifications to bench test the quantification provided by much more rapide and cheaply methods for the the development of a final tool for bovine phenotyping. Finally, public and available -omics data are very valuable for research purposes, since they can be aggregated and repurposed to provide insights in a context that may be entirely different from the original study. We have recently integrated and mined -omics data to computationally predict the large-scale “secretome” of adipose tissues and muscles in ruminants (Bonnet et al. , 2016). 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The use of -omics methods, such as proteomics has begun to fill the gap of knowledge of the multifaceted regulations of AT growth and metabolism by providing numerous information on pathways and functions. Two examples illustrate the usefulness of proteomic approaches for ruminant issues by allowing knowledge acquisition and molecular phenotyping. In the first example, proteomics and measurements of chemical composition, cellularity, histology, enzyme activities, and gene expression were applied to fetal AT at 110, 180, 210 and 260 days post conception (dpc) in Blond d’Aquitaine and Charolais breeds. From 180 dpc in the two breeds, we identified (Taga et al., 2012) proteins declared to be hallmarks of brown and white adipocytes in mice, that were underscored by the histological characterization of a mix of multilocular and unilocular cells, putatively brown and white adipocytes, respectively. 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引用次数: 0

摘要

在反刍动物中,胎儿棕色脂肪组织和产后白色脂肪组织(AT)的数量和代谢的变化有助于新生儿(特别是绵羊)出生时的适应或生产特征,以及成年后奶牛雌性妊娠-哺乳期的生产效率或肉用动物的胴体产量和质量(Bonnet等,2010)。AT的生长(Louveau et al., 2016)和代谢适应(Sauerwein et al., 2014)所伴随的分子途径是复杂且不完全了解的。事实上,它们在很大程度上取决于动物的特性(年龄、品种、性别)、环境因素(营养……),而且它们是动态的(从胎儿到产后年龄或从妊娠晚期到哺乳期的过渡)。蛋白质组学等组学方法的应用,通过提供大量的途径和功能信息,填补了对AT生长和代谢多方面调控的认识空白。两个例子说明了蛋白质组学方法通过允许知识获取和分子表型分析来解决反刍动物问题的有用性。在第一个例子中,对布隆德阿基坦和夏洛莱品种妊娠后110、180、210和260天(dpc)的胎儿AT进行了蛋白质组学和化学组成、细胞结构、组织学、酶活性和基因表达的测定。从这两个品种的180个dpc中,我们发现了(Taga等,2012)被认为是小鼠棕色和白色脂肪细胞标志的蛋白质,多室和单室细胞混合的组织学特征强调了这一点,推测分别是棕色和白色脂肪细胞。这些细胞和分子特征挑战了牛胎儿AT主要是棕色性质的概念(基于先前报道的肾周AT出生前或出生后几天的组织学和代谢特征),并强烈表明胎牛AT与白色AT比棕色AT有更多的共同点。第二个例子是蛋白质组学应用于反刍动物生产性状的生物标志物发现(图1)。几年来,蛋白质组学已被用于鉴定两组牛之间脂肪蛋白的差异丰富程度,这两组牛主要因其肌肉或身体脂肪或能量平衡而不同。我们合并了现有的蛋白质组学数据,以提供肌肉肥胖的候选生物标志物列表(生物标志物发现过程的第一步),我们宣布这些候选生物标志物是可靠的,因为它们在至少两篇不同品种、年龄和牛营养的出版物中被识别出来(Ceciliani等人,2018年,Bonnet等人,未发表)。最近,靶向蛋白质组学已经成为验证和精确/绝对量化蛋白质生物标志物的首选方法。我们对三种有针对性的方法(选择反应监测(SRM)、平行反应监测(PRM)和所有理论光谱的顺序窗口采集(SWATH-MS))进行了评估和基准测试,以精确量化64头奶牛肌肉组织中的脂肪生物标志物(Bons等,2018)。正在进行的工作,是使用这些绝对定量的台架测试提供的定量更快速和廉价的方法,为牛表型的最终工具的发展。最后,公开和可用的组学数据对于研究目的非常有价值,因为它们可以被汇总和重新利用,以提供可能与原始研究完全不同的背景下的见解。我们最近整合并挖掘了组学数据,以计算预测反刍动物脂肪组织和肌肉的大规模“分泌组”(Bonnet et al., 2016)。从反刍动物物种的组学研究中获得的知识将促进知识的发展,并最大限度地减少研究工作中不必要的冗余
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proteomics of adipose tissue: from the molecular drivers of adipogenesis to the molecular phenotyping of ruminants
In ruminants, changes in the amount and metabolism of foetal brown and post-natal white adipose tissues (AT) contribute to adaptations or productive traits both at birth for neonate survival (especially in sheep), and in adult life for productive efficiency during the gestation-lactation cycle of dairy females or for carcass yield and quality of meat animals (Bonnet et al., 2010). The molecular pathways accompanying the growth (Louveau et al ., 2016) and metabolic adaptations (Sauerwein et al., 2014) of AT are complex and incompletely understood. Indeed, they strongly depend on animal peculiarities (age, breed, sex), environmental factors (nutrition...) and they are dynamic (transition from foetal to post-natal age or from late pregnancy to lactation). The use of -omics methods, such as proteomics has begun to fill the gap of knowledge of the multifaceted regulations of AT growth and metabolism by providing numerous information on pathways and functions. Two examples illustrate the usefulness of proteomic approaches for ruminant issues by allowing knowledge acquisition and molecular phenotyping. In the first example, proteomics and measurements of chemical composition, cellularity, histology, enzyme activities, and gene expression were applied to fetal AT at 110, 180, 210 and 260 days post conception (dpc) in Blond d’Aquitaine and Charolais breeds. From 180 dpc in the two breeds, we identified (Taga et al., 2012) proteins declared to be hallmarks of brown and white adipocytes in mice, that were underscored by the histological characterization of a mix of multilocular and unilocular cells, putatively brown and white adipocytes, respectively. These cellular and molecular features challenged the concepts on the largely brown nature of bovine foetal AT (based on histological and metabolic features previously reported a few days before or after birth for perirenal AT), and strongly suggested that fetal bovine AT have much more in common with white than with brown AT. The second example is an application of proteomics for biomarkers discovery of ruminant productive traits (Fig. 1). For several years, proteomics has been used to identify adipose proteins differentially abundant between two groups of bovine differing mainly by their muscular or body adiposity or by their energy balance. We merged available proteomics data to provide a list of candidate biomarkers (first step in the process of biomarker discovery) of muscular adiposity that we declared robust candidates because they were identified in at least two publications differing by the breed, the age and the nutrition of bovine (Ceciliani et al. , 2018, Bonnet et al ., unpublished). Very recently, targeted proteomics has become an approach of choice to validate and precisely/absolutely quantify protein biomarkers. We have evaluated and benchmarked three targeted methods (the selected reaction monitoring (SRM), parallel reaction monitoring (PRM) and sequential windowed acquisition of all theoretical spectral (SWATH-MS)) to precisely quantify adiposity biomarkers in muscle tissues of 64 cows (Bons et al. , 2018). The work in progress, is to use these absolute quantifications to bench test the quantification provided by much more rapide and cheaply methods for the the development of a final tool for bovine phenotyping. Finally, public and available -omics data are very valuable for research purposes, since they can be aggregated and repurposed to provide insights in a context that may be entirely different from the original study. We have recently integrated and mined -omics data to computationally predict the large-scale “secretome” of adipose tissues and muscles in ruminants (Bonnet et al. , 2016). The knowledge gained from -omics studies in ruminant species will foster knowledge and minimize unnecessary redundancy in research efforts
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