小柯机器人

科学家绘制出人类血清代谢组潜在决定因素的参考图谱
2020-11-15 00:52

以色列魏茨曼科学研究所的Eran Segal小组绘制出人类血清代谢组潜在决定因素的参考图谱。2020年11月11日,《自然》杂志在线发表了这项成果。

研究人员测量了来自491名健康个体血清样品中1,251种代谢物的水平。研究人员应用了机器学习算法,并根据宿主遗传学、肠道微生物组、临床参数、饮食、生活方式和人体测量学来预测了个体的代谢物水平,并获得了超过76%代谢产物的具有统计学意义的预测。饮食和微生物组具有最强的预测能力,它们各自解释了数百种代谢产物,在某些情况下,解释了超过50%的观察差异。
 
研究人员在两个不同地区的队列中显示出较高的重复性,并进一步验证了与微生物组相关的预测。研究人员使用特征归因分析揭示了特定的饮食和细菌相互作用。研究人员进一步证明,其中的某些相互作用可能存在因果关系,因为在一项随机临床试验的面包食用干预后,研究人员发现某些与面包正相关的代谢产物增加了。
 
总体而言,这些研究结果揭示了800多种代谢物的潜在决定因素,从而为机制性理解不同条件下代谢物变化的机理以及设计控制循环代谢物水平的干预措施铺平了道路。
 
据了解,血清代谢组包含多种疾病的生物标志物和病原体,其中一些是内源性产生的,而另一些则是从环境中吸收的。特定化合物的来源是已知的,包括高度可遗传的代谢物,或受肠道微生物组、生活方式(例如吸烟)或饮食影响的代谢物。但是,大多数代谢物的关键决定因素仍知之甚少。
 
附:英文原文

Title: A reference map of potential determinants for the human serum metabolome

Author: Noam Bar, Tal Korem, Omer Weissbrod, David Zeevi, Daphna Rothschild, Sigal Leviatan, Noa Kosower, Maya Lotan-Pompan, Adina Weinberger, Caroline I. Le Roy, Cristina Menni, Alessia Visconti, Mario Falchi, Tim D. Spector, Jerzy Adamski, Paul W. Franks, Oluf Pedersen, Eran Segal

Issue&Volume: 2020-11-11

Abstract: The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

DOI: 10.1038/s41586-020-2896-2

Source: https://www.nature.com/articles/s41586-020-2896-2

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html


本期文章:《自然》:Online/在线发表

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