Nature Biotechnology: 利用血液代谢组预测肠道微生物组的多样性

2019-09-03 BioArt

肠道微生物组发挥着多种多样的功能,比如营养代谢、参与免疫调控等。研究发现肠道微生物组的改变与许多疾病都存在一定关联,且疾病人群的肠道微生物组的α-多样性往往会发生变化(α多样性指某个群落或生境内部的种的多样性,主要指群落内的物种多样性),比如炎症性肠病患者的α-多样性显着下降。最近的研究报道发现粪便代谢组可以解释肠道微生物组68%的差异,这意味着肠道微生物组和代谢产物密切相关。虽然粪便代谢组更为直

肠道微生物组发挥着多种多样的功能,比如营养代谢、参与免疫调控等。研究发现肠道微生物组的改变与许多疾病都存在一定关联,且疾病人群的肠道微生物组的α-多样性往往会发生变化(α多样性指某个群落或生境内部的种的多样性,主要指群落内的物种多样性),比如炎症性肠病患者的α-多样性显着下降。最近的研究报道发现粪便代谢组可以解释肠道微生物组68%的差异,这意味着肠道微生物组和代谢产物密切相关。虽然粪便代谢组更为直接地反映了微生物组的代谢情况,但是血液代谢组或许更利于揭示肠道微生物与宿主健康的关系,因为其能够反映这些代谢产物是否进入到循环系统并影响宿主的代谢等。

2019年9月2日,美国系统生物学研究所Nathan Price、Sean Gibbons与Leroy Hood团队在Nature Biotechnology杂志上发表了题为Blood metabolome predicts gut microbiome α-diversity in humans的论文。文章基于一组399个人的队列,从近1000种血液分析物(实验室检测、蛋白质组学和代谢组学)中筛选得到40种代谢物(其中13种来源于微生物)以预测肠道菌群的α-多样性,并且在另一组540个人的队列中验证了这40种代谢物的预测能力。

作者首先通过LASSO方法从659种血浆代谢物中筛选确定了40种代谢物,这40种代谢物主要来源于异生素(xenobiotics)、脂质、氨基酸超家族,可以解释肠道微生物组Shannon指数45%的差异。在这40种代谢物中,人与微生物的共同代谢物,即由宿主(微生物)合成然后由微生物(宿主)进行代谢的物质如胆汁酸等,能够更加有效的预测Shannon指数。进一步用这些代谢物预测其他用以评估α-多样性的指数——PD whole tree和Chao1,发现分别能够解释50%和36%的差异,还发现使用完整的代谢组数据也无法提高改进预测模型。另外,作者也使用了来源于血液临床检测的数据进行了相同的分析,但无法获得和血浆代谢组一样有效的预测模型 (平均LASSO R2=0.01,平均ridge regression R2 = 0.05) 。

在399名志愿者中有262名志愿者检测了263种独特的血浆蛋白,分析发现其中41种与Shannon指数相关,而相关性最高的为与机体代谢健康相关的蛋白质——对氧磷酶3、瘦素、低密度脂蛋白(LDL)受体。尽管如此,与血液临床检测的数据类似,蛋白质组学数据也无法构建出有力的预测模型。

为了证实上述发现,作在另一组队列(540人)中进行了验证。使用之前筛选得到的40个代谢物进行预测,虽然不如之前的结果,但是其仍可解释34%的差异。且即便使用全部的代谢组数据即659个代谢物,结果仅提高至38%。同时与之前的结果类似,血液临床检测数据与Shannon指数的相关性较低。由于该队列中仅有176人具有蛋白质组的数据,且其解释度比血液临床检测的更低,所以难以评估蛋白质组对Shannon指数的预测能力。不同组学数据对方差的可解释度。其中,代谢组学中的灰色为使用所有659个代谢物拟合的模型,而绿色为使用之前鉴定得到的40个代谢物拟合的模型。

总之,该研究进一步探索了宿主生理学与肠道微生物之间的关系,为其之间的密切关系提供了新的支持,同时为宿主代谢组或为肠道生态系统与宿主健康之间交互的桥梁提供了有力证据。

原始出处:
Tomasz Wilmanski, Noa Rappaport, John C. Earls, et al.Blood metabolome predicts gut microbiome α-diversity in humans.02 September 2019Nature Biotechnology (2019)

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    2020-07-31 shock_melon
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    2020-03-11 cathymary
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    2020-07-20 liye789132251
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  5. 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  6. 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    2020-03-13 sunylz
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