生物技术通报 ›› 2021, Vol. 37 ›› Issue (1): 90-101.doi: 10.13560/j.cnki.biotech.bull.1985.2020-1217
李叶青1(), 景张牧1, 江皓1, 徐泉1, 周红军1, 冯璐2()
收稿日期:
2020-09-27
出版日期:
2021-01-26
发布日期:
2021-01-15
作者简介:
李叶青,男,副教授,博士生导师,研究方向:生物质和有机固废资源化利用;E-mail: 基金资助:
LI Ye-qing1(), JING Zhang-mu1, JIANG Hao1, XU Quan1, ZHOU Hong-jun1, FENG Lu2()
Received:
2020-09-27
Published:
2021-01-26
Online:
2021-01-15
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。
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.
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] |
表1 应用于厌氧消化的微生物组学类型
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 | Schao1=Sobs+n1(n1-1)/2(n2+1) | Schao1 和 Sobs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 | SACE= Sabund+Srare/CACE+n1/CACE·γ2ACE (当γACE <0.8) SACE= Sabund+Srare/CACE+n1/CACE·β2ACE (当γACE≥0.8) CACE=1-n1/Nrare $\text { Nrare }=\sum_{i=1}^{a b u n d} \mathrm{i} \cdot \mathrm{n}_{i}$ $ \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]$ | ni 是包含序列i的 OTU数目;Srare 是包含 “abund”条或更少序列的OTU的数量;Sabund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}$ | Sobs是实际观察OTU数;ni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)}$ | Sobs是实际观察OTU数;ni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] |
表2 α多样性分析参数的公式和符号意义[50]
指数 | 公式 | 符号意义 | 参考文献 |
---|---|---|---|
Chao1 | Schao1=Sobs+n1(n1-1)/2(n2+1) | Schao1 和 Sobs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 | SACE= Sabund+Srare/CACE+n1/CACE·γ2ACE (当γACE <0.8) SACE= Sabund+Srare/CACE+n1/CACE·β2ACE (当γACE≥0.8) CACE=1-n1/Nrare $\text { Nrare }=\sum_{i=1}^{a b u n d} \mathrm{i} \cdot \mathrm{n}_{i}$ $ \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]$ | ni 是包含序列i的 OTU数目;Srare 是包含 “abund”条或更少序列的OTU的数量;Sabund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}$ | Sobs是实际观察OTU数;ni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)}$ | Sobs是实际观察OTU数;ni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] |
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