微生物组学及其在厌氧消化中的研究进展

doi:10.13560/j.cnki.biotech.bull.1985.2020-1217

摘要/Abstract

摘要：

我国每年产生大量的有机废弃物,如果处置不当将会对生态、气候以及人类健康造成重大影响。厌氧消化是一种可靠的、绿色的、可持续的有机废弃物处理方式,但由于缺乏准确有效的监测手段,厌氧消化微观过程常常被视为“黑盒”。随着微生物组学的发展,学者们在菌群与运行参数关联性分析、代谢途径分析等方面有了更深入的认识。本文从“三阶段、四菌群”的厌氧消化过程出发,介绍了常用微生物组学的类型,包括：16S rRNA基因组、宏基因组、宏转录组和宏蛋白组;详细阐述了物种组成分析、α多样性分析、OTU相似性分析以及多元统计学分析等6种常用的微生物群落生物信息学分析方法;系统回顾了厌氧消化过程的微生物学研究进展,以期能为分析厌氧消化的微生物群落结构和功能、开发新的厌氧消化工艺和技术提供支持。

关键词: 厌氧消化, 高通量测序, 微生物组成, 生物信息学

Abstract:

A massive amount of organic waste is produced every year in China. The improper disposal of organic waste causes significantly negative impacts on ecology,climate,and human health. Anaerobic digestion has been recognized as a reliable,green and sustainable approach for the treatment of organic waste. However,due to the lack of precise and effective monitoring methods,the microscopic process of an anaerobic digestion is often regarded as a “black box”. With the development of microbiome,researchers have acquired a deeper understanding of the correlation analysis between microbial community and operating parameters,metabolic pathway analysis and other aspects. According to “three stages and four communities” in an anaerobic digestion,typical microbiome are introduced,including 16S rRNA genome,metagenome,metatranscriptome and metaproteome. In addition,the six commonly bioinformatics analysis methods for microbial community are described in detail,such as species composition analysis,α diversity analysis,OTU similarity analysis,and multivariate statistical analysis. Finally,the research progress of microbiology in anaerobic digestion process is systematically reviewed,aiming to provide support for the study of the structure and function of anaerobic digestion,and the development of new anaerobic digestion processes and technologies.

Key words: anaerobic digestion, high-throughput sequencing, microbial composition, bioinformatics

李叶青, 景张牧, 江皓, 徐泉, 周红军, 冯璐. 微生物组学及其在厌氧消化中的研究进展[J]. 生物技术通报, 2021, 37(1): 90-101.

LI Ye-qing, JING Zhang-mu, JIANG Hao, XU Quan, ZHOU Hong-jun, FENG Lu. Microbiome and Its Research Progress of Anaerobic Digestion[J]. Biotechnology Bulletin, 2021, 37(1): 90-101.

图/表 5

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技术	优势	缺点	主要应用	参考文献
16S rRNA基因组学	快速、经济的鉴定细菌和古菌	受到保守标记基因的限制; 病毒不能被鉴定	物种分类研究;系统功能猜测	[7,20,29-35]
宏基因组学	不受保守标记基因的限制;可以基因组重组获得新的物种信息	因需要组装,Read数量要求巨大;测序周期长	物种分类和系统发育学研究;代谢通路潜力研究;抗性基因、病原体检测	[36-41]
宏转录组学	鉴定活跃的、真正对群落有贡献的基因和通路	mRNA半衰期短,不稳定;多重的净化和扩增导致误差	代谢活性检测;代谢通路表达研究	[24-25,42]
蛋白组学	为代谢活动提供最直接的证据	难以提取合适的蛋白质部分进行分析;测试被核酸干扰,易产生误差	新功能基因筛选;代谢通路表达研究;环境质量动态监测;生物标记物筛选	[27-28,43-45]

技术	优势	缺点	主要应用	参考文献
16S rRNA基因组学	快速、经济的鉴定细菌和古菌	受到保守标记基因的限制; 病毒不能被鉴定	物种分类研究;系统功能猜测	[7,20,29-35]
宏基因组学	不受保守标记基因的限制;可以基因组重组获得新的物种信息	因需要组装,Read数量要求巨大;测序周期长	物种分类和系统发育学研究;代谢通路潜力研究;抗性基因、病原体检测	[36-41]
宏转录组学	鉴定活跃的、真正对群落有贡献的基因和通路	mRNA半衰期短,不稳定;多重的净化和扩增导致误差	代谢活性检测;代谢通路表达研究	[24-25,42]
蛋白组学	为代谢活动提供最直接的证据	难以提取合适的蛋白质部分进行分析;测试被核酸干扰,易产生误差	新功能基因筛选;代谢通路表达研究;环境质量动态监测;生物标记物筛选	[27-28,43-45]

指数	公式	符号意义	参考文献
Chao1	S_chao1=S_obs+n₁（n₁-1）/2（n₂+1）	S_chao1 和 S_obs分别为估计和观察的 OTU数目;n₁ 和 n₂分别是序列为1和2的OTU数目	[32,36,51]
ACE index	S_ACE= S_abund+S_rare/C_ACE+n₁/C_ACE·γ²_ACE （当γ_ACE <0.8） S_ACE= S_abund+S_rare/C_ACE+n₁/C_ACE·β²_ACE （当γ_ACE≥0.8） C_ACE=1-n₁/N_rare $\text { Nrare }=\sum_{i=1}^{a b u n d} \mathrm{i} \cdot \mathrm{n}_{i}$ $ \gamma^{2}{ }^{2} \mathrm{ACE}==\max \left[\frac{S_{\text {rare }}}{C_{A C E}} \frac{\sum_{i=1}^{\text {abund }} i(i-1) n_{i}}{N_{\text {rare }}\left(N_{\text {rare }}-1\right)}-1,0\right]$ $ \beta_{\mathrm{ACE}}^{2}=\max \left[\gamma_{\mathrm{ACE}}^{2}\left\{1+\frac{\mathrm{N}_{\mathrm{rare}}\left(1-\mathrm{C}_{\mathrm{ACE}}\right) \sum_{\mathrm{i}=1}^{\mathrm{abund}} \mathrm{i}(\mathrm{i}-1) \mathrm{n}_{\mathrm{i}}}{\mathrm{N}_{\mathrm{rare}}\left(\mathrm{N}_{\mathrm{rare}}-\mathrm{C}_{\mathrm{ACE}}\right)}\right\}, 0\right]$	n_i 是包含序列i的 OTU数目;S_rare 是包含 “abund”条或更少序列的OTU的数量;S_abund是包含多于 “abund”条序列的OTU的数量;‘abund’ 是主要OTU的阈值	[51]
Shannon- Wiener index	$ \mathrm{H}_{\text {shannon }}=-\sum_{i=1}^{S_{\text {ols }}} \frac{n_{i}}{N} \ln \frac{n_{i}}{N}$	S_obs是实际观察OTU数;n_i是第i个OTU的序列数;N是序列的总数。 n₁ 是序列1的OTU数目;N 是总的序列数	[31-32,35-36]
Simpson index	$ \mathrm{D}_{\text {simpson }}=\frac{\sum_{i=1}^{S_{\text {obs }}} n_{i}\left(n_{i}-1\right)}{N(N-1)}$	S_obs是实际观察OTU数;n_i是第i个OTU的序列数;N是序列的总数	[31,51,54]
Good’s coverage	C=1-n₁/N	n₁ 是序列1的OTU数目;N 是总的序列数	[51]

指数	公式	符号意义	参考文献
Chao1	S_chao1=S_obs+n₁（n₁-1）/2（n₂+1）	S_chao1 和 S_obs分别为估计和观察的 OTU数目;n₁ 和 n₂分别是序列为1和2的OTU数目	[32,36,51]
ACE index	S_ACE= S_abund+S_rare/C_ACE+n₁/C_ACE·γ²_ACE （当γ_ACE <0.8） S_ACE= S_abund+S_rare/C_ACE+n₁/C_ACE·β²_ACE （当γ_ACE≥0.8） C_ACE=1-n₁/N_rare $\text { Nrare }=\sum_{i=1}^{a b u n d} \mathrm{i} \cdot \mathrm{n}_{i}$ $ \gamma^{2}{ }^{2} \mathrm{ACE}==\max \left[\frac{S_{\text {rare }}}{C_{A C E}} \frac{\sum_{i=1}^{\text {abund }} i(i-1) n_{i}}{N_{\text {rare }}\left(N_{\text {rare }}-1\right)}-1,0\right]$ $ \beta_{\mathrm{ACE}}^{2}=\max \left[\gamma_{\mathrm{ACE}}^{2}\left\{1+\frac{\mathrm{N}_{\mathrm{rare}}\left(1-\mathrm{C}_{\mathrm{ACE}}\right) \sum_{\mathrm{i}=1}^{\mathrm{abund}} \mathrm{i}(\mathrm{i}-1) \mathrm{n}_{\mathrm{i}}}{\mathrm{N}_{\mathrm{rare}}\left(\mathrm{N}_{\mathrm{rare}}-\mathrm{C}_{\mathrm{ACE}}\right)}\right\}, 0\right]$	n_i 是包含序列i的 OTU数目;S_rare 是包含 “abund”条或更少序列的OTU的数量;S_abund是包含多于 “abund”条序列的OTU的数量;‘abund’ 是主要OTU的阈值	[51]
Shannon- Wiener index	$ \mathrm{H}_{\text {shannon }}=-\sum_{i=1}^{S_{\text {ols }}} \frac{n_{i}}{N} \ln \frac{n_{i}}{N}$	S_obs是实际观察OTU数;n_i是第i个OTU的序列数;N是序列的总数。 n₁ 是序列1的OTU数目;N 是总的序列数	[31-32,35-36]
Simpson index	$ \mathrm{D}_{\text {simpson }}=\frac{\sum_{i=1}^{S_{\text {obs }}} n_{i}\left(n_{i}-1\right)}{N(N-1)}$	S_obs是实际观察OTU数;n_i是第i个OTU的序列数;N是序列的总数	[31,51,54]
Good’s coverage	C=1-n₁/N	n₁ 是序列1的OTU数目;N 是总的序列数	[51]