科學家繪製出人類血清代謝組潛在決定因素的參考圖譜
作者：
小柯機器人發布時間：2020/11/15 0:52:57
以色列魏茨曼科學研究所的Eran Segal小組繪製出人類血清代謝組潛在決定因素的參考圖譜。2020年11月11日，《自然》雜誌在線發表了這項成果。
研究人員測量了來自491名健康個體血清樣品中1,251種代謝物的水平。研究人員應用了機器學習算法，並根據宿主遺傳學、腸道微生物組、臨床參數、飲食、生活方式和人體測量學來預測了個體的代謝物水平，並獲得了超過76％代謝產物的具有統計學意義的預測。飲食和微生物組具有最強的預測能力，它們各自解釋了數百種代謝產物，在某些情況下，解釋了超過50％的觀察差異。
研究人員在兩個不同地區的隊列中顯示出較高的重複性，並進一步驗證了與微生物組相關的預測。研究人員使用特徵歸因分析揭示了特定的飲食和細菌相互作用。研究人員進一步證明，其中的某些相互作用可能存在因果關係，因為在一項隨機臨床試驗的麵包食用幹預後，研究人員發現某些與麵包正相關的代謝產物增加了。
總體而言，這些研究結果揭示了800多種代謝物的潛在決定因素，從而為機制性理解不同條件下代謝物變化的機理以及設計控制循環代謝物水平的幹預措施鋪平了道路。
據了解，血清代謝組包含多種疾病的生物標誌物和病原體，其中一些是內源性產生的，而另一些則是從環境中吸收的。特定化合物的來源是已知的，包括高度可遺傳的代謝物，或受腸道微生物組、生活方式（例如吸菸）或飲食影響的代謝物。但是，大多數代謝物的關鍵決定因素仍知之甚少。
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