功能性菌群构建的研究进展

doi:10.12211/2096-8280.2021-074

摘要/Abstract

摘要：

功能性菌群构建作为一个新兴的研究方向，随着合成生物学、微生物组学技术的发展，逐渐成为研究热点。本文将从以下4个方面介绍功能性菌群的研究进展。第一，功能性菌群研究的初衷及其相对于单一生物体工程的优势和设计难点；第二，功能性菌群研究中自下而上（bottom-up）和自上而下（top-down）的设计策略；第三，功能性菌群的分析工具，包括“宏组学”和“多组学联用”手段以及相关的数据处理流程和软件。第四，分别从设计策略和分析工具方面出发，总结了功能性菌群构建过程中的主要挑战，并展望了未来以“智能设计”为核心的发展方向：①利用可解释的时空数据模型解析区域范围内功能性菌群的时空变化关系；②结合图神经网络与多模态学习方法建立多组学群落分析流程；③通过强化学习设计功能性菌群内分布式代谢回路。

关键词: 功能性菌群, 自下而上, 自上而下, 宏组学, 多组学, 智能设计

Abstract:

Study on functional microbial communities has become a hotspot with development of synthetic biology and microbiome. Comparing to single organism, functional microbial communities have several advantages in their robustness when facing environmental interference and for higher yields when producing complex products. This article introduces the research progress in studies on functional microbial communities from aspects: 1) the original motivation of functional microbial community research and the advantages and difficulties in design compared with single organism engineering, 2) the "bottom-up" and "top-down" strategies in the functional microbial community design process, and 3) analysis tools for functional microbial communities, such as "metagenomics", "multi-omics" and related data processing procedures and software. The main history of environmental microbial analysis is reviewed, and the main concepts of meta-omics is introduced. The major software which is used to process meta-omics data is also commented. In addition, challenges for the functional microbial community construction are highlighted based on the design strategy and analysis tools. The "bottom-up" strategy is not suitable for constructing a complicated microbial community, while the biosafety needs to be considered for developing the "top-down" strategy. Finally, the development of microbial communities with "intelligent design" as the core is prospected: first, using an interpretable spatiotemporal data model for temporal-spatial relationship mining of functional microbial communities; second, combining neural network and multi-modal learning methods to establish a multi-omics community analysis process; third, computational design of distributed metabolic circuits in the functional microbial community through reinforcement learning.

Key words: functional microbial community, bottom-up, top-down, meta-omics, multi-omics, intelligent design

中图分类号:

Q 816

黄佳城, 张瑷珲, 付友思, 方柏山. 功能性菌群构建的研究进展[J]. 合成生物学, 2022, 3(1): 155-167.

Jiacheng HUANG, Aihui ZHANG, Yousi FU, Baishan FANG. Research progress in construction of functional microbial communities[J]. Synthetic Biology Journal, 2022, 3(1): 155-167.

图/表 4

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软件名	对应处理的组学数据类型	功能
QIIME^[75]\QIIME2^[76]	扩增子测序	用于质量控制、OTU处理、物种分类、系统发育重建、可视化的扩增子数据处理流程
bioBakery^[77]	宏基因组/扩增子测序	基于比对的宏基因组数据处理流程
metaWRAP^[78]	宏基因组	基于比对的宏基因组数据处理流程
MetaQUBIC^[79]	宏基因组/宏转录组	基于双聚类的功能基因分类软件
MetaTrans^[80]	宏转录组	从RNA-Seq数据分析微生物群落结构和功能的数据处理流程
SAMSA^[81]	宏转录组	用于分析肠道微生物组数据的软件，重点关注样本中的生物体特异性活动或功能活动
MG-RAST^[82]	宏基因组/宏转录组	通过比较蛋白质和核苷酸数据库产生宏基因组序列功能分类的软件
IdentiPy^[83]	宏蛋白组	肽识别、搜索、验证、蛋白质推断和量化的数据处理流程
Trans-Proteomic Pipeline^[84]	宏蛋白组	大规模可重复的定量MS蛋白质组学数据处理软件
compleXView^[85]	宏蛋白组	根据蛋白质组数据计算丰度、再现性和特异性的度量以推断PPI的网络服务器
Pathos^[86]	宏代谢组	分析质谱数据并显示代谢物和代谢途径的网络服务器
MetaboAnalyst^[87]	宏代谢组/多组学整合	用于集成代谢组学数据分析、解释和与其他组学数据集成的网络服务器
Netome^[88]	宏代谢组/多组学整合	用于处理和分析代谢组学数据以及探索与其他组学数据和元数据关联的工具

软件名	对应处理的组学数据类型	功能
QIIME^[75]\QIIME2^[76]	扩增子测序	用于质量控制、OTU处理、物种分类、系统发育重建、可视化的扩增子数据处理流程
bioBakery^[77]	宏基因组/扩增子测序	基于比对的宏基因组数据处理流程
metaWRAP^[78]	宏基因组	基于比对的宏基因组数据处理流程
MetaQUBIC^[79]	宏基因组/宏转录组	基于双聚类的功能基因分类软件
MetaTrans^[80]	宏转录组	从RNA-Seq数据分析微生物群落结构和功能的数据处理流程
SAMSA^[81]	宏转录组	用于分析肠道微生物组数据的软件，重点关注样本中的生物体特异性活动或功能活动
MG-RAST^[82]	宏基因组/宏转录组	通过比较蛋白质和核苷酸数据库产生宏基因组序列功能分类的软件
IdentiPy^[83]	宏蛋白组	肽识别、搜索、验证、蛋白质推断和量化的数据处理流程
Trans-Proteomic Pipeline^[84]	宏蛋白组	大规模可重复的定量MS蛋白质组学数据处理软件
compleXView^[85]	宏蛋白组	根据蛋白质组数据计算丰度、再现性和特异性的度量以推断PPI的网络服务器
Pathos^[86]	宏代谢组	分析质谱数据并显示代谢物和代谢途径的网络服务器
MetaboAnalyst^[87]	宏代谢组/多组学整合	用于集成代谢组学数据分析、解释和与其他组学数据集成的网络服务器
Netome^[88]	宏代谢组/多组学整合	用于处理和分析代谢组学数据以及探索与其他组学数据和元数据关联的工具