分析方法对细菌群落16S rRNA基因扩增测序分析结果的影响

doi:10.13560/j.cnki.biotech.bull.1985.2021-1102

摘要/Abstract

摘要：

细菌16S rRNA基因扩增测序是当前环境微生物组学研究中应用最为广泛的方法之一。然而,测序序列最小分类单元的划分有多种方式,其对微生物多样性下游分析结果的影响还有待进一步探究。本研究通过提取5组环境样本（森林、农田、湿地土壤、湖泊沉积物和水体）的DNA进行16S rRNA基因扩增测序,对测序结果同时采用5种最小分类单元的划分方式（基于97%、98%、99%和100%序列相似性聚类的OTU以及基于DADA2算法得到的ASV）进行划分,比较分析最小分类单元划分方法对微生物群落多样性、组成、以及其与环境因子关联性分析造成的影响。结果表明,提高分类分辨率,能够获得更高的群落α多样性（Chao1和Shannon）和β多样性（P < 0.05）,而相对于按序列相似性聚类的OTU,ASV方法会在一定程度上降低Chao1和PD指数。对于群落组成,分类单元的划分方式主要影响微生物组一些低丰度属（< 0.2%）的占比,而对较高的分类学水平（门水平）组成的影响较小。此外,冗余分析的结果表明,提高分类分辨率水平,能够使得环境因子对微生物群落能够获得更高的解释度,同时也会影响各环境因子对群落组成的解释度排序。总之,本研究明晰了最小分类单元的不同划分方式会对微生物组多样性、组成以及与环境因子的关联性造成的影响,为后续环境微生物组学研究提供了理论指导。

关键词: 环境微生物组, 高通量测序, 16S rRNA基因, 最小分类单元, 细菌多样性

Abstract:

Bacterial 16S rRNA gene amplicon sequencing is one of the most widely used method in environmental microbiology. However,there are many ways to determine the minimum taxonomic unit of the sequencing reads,and its effect on the downstream analysis results of microbial diversity needs to be further explored. This study extracted DNA from 5 groups of environmental samples（forest soil,farmland soil,wetland soil,lake sediment,and lake water）for 16S rRNA gene amplicon sequencing,and based on different taxonomic resolutions,5 types of minimum taxonomic unit division method（Sequence clustering OTUs at 97%,98%,99%,100% similarity,and the DADA 2 algorithm to calculate ASVs）were used to compare and analyze the influences of minimum taxonomic unit division methods on microbial community diversity,composition and its correlation with environmental factors. The results showed that higher community α diversity（Chao1 and Shannon）and β diversity（P < 0.05）were obtained by increasing the taxonomic resolution. Compared with the OTU,which was clustering by sequence similarity,the ASV method reduced Chao1 and PD indices to some extent. For community composition,the division method of the taxonomic unit mainly affected the proportion of some low-abundance genera（< 0.2%）,but had a smaller effect on the composition of higher taxonomy level（phylum level）. In addition,the results of redundant analysis demonstrated that increasing the level of taxonomic resolution would allow the environmental factors to have a higher interpretation of the microbial community,and a concurrently it also affected the order of interpretation degree of community composition by environmental factors. In sum,this study clarified the effect of different division methods of the minimum taxonomy unit on the diversity,composition and association with environmental factors,which provides theoretical guidance for the subsequent study of environmental microbiomics.

Key words: environmental microbiomics, high-throughput sequencing, 16S rRNA gene, minimum taxonomy unit, bacterial diversity

钟辉, 刘亚军, 王滨花, 和梦洁, 吴兰. 分析方法对细菌群落16S rRNA基因扩增测序分析结果的影响[J]. 生物技术通报, 2022, 38(6): 81-92.

ZHONG Hui, LIU Ya-jun, WANG Bin-hua, HE Meng-jie, WU Lan. Effects of Analysis Methods on the Analyzed Results of 16S rRNA Gene Amplicon Sequencing in Bacterial Communities[J]. Biotechnology Bulletin, 2022, 38(6): 81-92.

图/表 7

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样本 Sample	样本类型 Sample type	采样时间 Sampling time	酸碱度 pH	总有机碳 Total organic carbon（TOC）/（g·kg^-1）	总氮 Total nitrogen（TN）/（g·kg^-1）	总磷 Total phosphorus（TP）/（g·kg^-1）	氨态氮 NH₄⁺N/ （mg·kg^-1）	硝态氮 NO₃^-N/ （mg·kg^-1）
FS1	森林土壤	2016年8月	4.57±0.24	48.07±1.17	3.98±0.26	0.49±0.09	9.01±0.64	45.14±11.26
FS2	森林土壤	2016年8月	4.64±0.1	58.29±9.12	3.86±0.62	0.68±0.17	46.73±4.21	12.19±2.08
FS3	森林土壤	2016年8月	4.70±0.12	55.17±4.91	3.93±0.83	0.56±0.11	13.24±1.48	27.90±3.41
FS4	森林土壤	2016年8月	5.35±0.67	22.96±0.86	2.68±0.54	0.80±0.14	9.06±1.05	32.48±7.65
WS1	湿地土壤	2015年1月	5.29±0.14	7.31±1.68	0.92±0.25	0.60±0.18	2.30±0.27	0.71±0.06
WS2	湿地土壤	2015年1月	4.95±0.58	6.89±1.78	0.97±0.26	0.64±0.19	2.85±0.42	0.42±0.04
WS3	湿地土壤	2015年1月	5.19±0.25	8.82±3.33	0.98±0.23	0.54±0.06	2.79±0.33	0.75±0.42
WS4	湿地土壤	2015年1月	5.72±0.39	6.64±2.81	1.02±0.24	0.41±0.06	2.65±0.47	1.07±0.36
CS1	农田土壤	2017年5月	4.71±0.54	36.60±4.66	3.95±0.15	0.45±0.19	0.91±0.11	2.54±1.18
CS2	农田土壤	2017年5月	5.22±0.02	66.34±3.13	7.86±1.15	0.55±0.09	1.05±0.11	5.81±0.25
CS3	农田土壤	2017年5月	5.17±0.17	41.11±1.62	4.13±0.36	0.26±0.01	0.87±0.12	3.37±0.80
CS4	农田土壤	2017年5月	5.06±0.07	44.49±2.11	4.97±0.13	0.52±0.13	0.96±0.08	6.52±0.32
LS1	湖泊沉积物	2018年5月	6.60±0.35	9.78±1.61	1.01±0.14	0.96±0.24	15.72±9.03	1.18±0.68
LS2	湖泊沉积物	2018年5月	7.30±0.22	9.97±5.50	14.40±2.07	1.15±0.16	22.10±7.40	1.75±0.57
LS3	湖泊沉积物	2018年5月	6.63±0.03	7.47±6.21	6.96±2.41	0.71±0.11	5.51±1.08	1.68±1.84
LS4	湖泊沉积物	2018年5月	8.08±0.08	5.32±3.72	14.37±8.57	0.90±0.22	16.83±2.47	1.79±1.04

样本 Sample	样本类型 Sample type	采样时间 Sampling time	酸碱度 pH	总有机碳 Total organic carbon（TOC）/（g·kg^-1）	总氮 Total nitrogen（TN）/（g·kg^-1）	总磷 Total phosphorus（TP）/（g·kg^-1）	氨态氮 NH₄⁺N/ （mg·kg^-1）	硝态氮 NO₃^-N/ （mg·kg^-1）
FS1	森林土壤	2016年8月	4.57±0.24	48.07±1.17	3.98±0.26	0.49±0.09	9.01±0.64	45.14±11.26
FS2	森林土壤	2016年8月	4.64±0.1	58.29±9.12	3.86±0.62	0.68±0.17	46.73±4.21	12.19±2.08
FS3	森林土壤	2016年8月	4.70±0.12	55.17±4.91	3.93±0.83	0.56±0.11	13.24±1.48	27.90±3.41
FS4	森林土壤	2016年8月	5.35±0.67	22.96±0.86	2.68±0.54	0.80±0.14	9.06±1.05	32.48±7.65
WS1	湿地土壤	2015年1月	5.29±0.14	7.31±1.68	0.92±0.25	0.60±0.18	2.30±0.27	0.71±0.06
WS2	湿地土壤	2015年1月	4.95±0.58	6.89±1.78	0.97±0.26	0.64±0.19	2.85±0.42	0.42±0.04
WS3	湿地土壤	2015年1月	5.19±0.25	8.82±3.33	0.98±0.23	0.54±0.06	2.79±0.33	0.75±0.42
WS4	湿地土壤	2015年1月	5.72±0.39	6.64±2.81	1.02±0.24	0.41±0.06	2.65±0.47	1.07±0.36
CS1	农田土壤	2017年5月	4.71±0.54	36.60±4.66	3.95±0.15	0.45±0.19	0.91±0.11	2.54±1.18
CS2	农田土壤	2017年5月	5.22±0.02	66.34±3.13	7.86±1.15	0.55±0.09	1.05±0.11	5.81±0.25
CS3	农田土壤	2017年5月	5.17±0.17	41.11±1.62	4.13±0.36	0.26±0.01	0.87±0.12	3.37±0.80
CS4	农田土壤	2017年5月	5.06±0.07	44.49±2.11	4.97±0.13	0.52±0.13	0.96±0.08	6.52±0.32
LS1	湖泊沉积物	2018年5月	6.60±0.35	9.78±1.61	1.01±0.14	0.96±0.24	15.72±9.03	1.18±0.68
LS2	湖泊沉积物	2018年5月	7.30±0.22	9.97±5.50	14.40±2.07	1.15±0.16	22.10±7.40	1.75±0.57
LS3	湖泊沉积物	2018年5月	6.63±0.03	7.47±6.21	6.96±2.41	0.71±0.11	5.51±1.08	1.68±1.84
LS4	湖泊沉积物	2018年5月	8.08±0.08	5.32±3.72	14.37±8.57	0.90±0.22	16.83±2.47	1.79±1.04

样本 Sample	样本类型 Sample type	采样时间 Sampling time	酸碱度 pH	总有机碳 TOC/（mg·kg^-1）	总氮 TN/（mg·kg^-1）	总磷 TP/（mg·kg^-1）	氨态氮 NH₄⁺N/（mg·kg^-1）	硝态氮 NO₃^--N/（mg·kg^-1）
LW1	湖泊水体	2017年7月	7.87±0.50	16.39±6.93	2.40±0.66	0.13±0.03	0.26±0.10	0.66±0.12
LW2	湖泊水体	2017年7月	7.3±0.16	12.40±5.44	3.10±0.63	0.13±0.02	0.14±0.05	0.26±0.04
LW3	湖泊水体	2017年7月	6.79±0.41	11.09±5.51	1.38±0.36	0.11±0.04	0.34±0.15	0.48±0.04
LW4	湖泊水体	2017年7月	7.12±0.13	14.06±6.00	1.14±0.67	0.11±0.04	0.25±0.06	0.02±0.03

样本 Sample	样本类型 Sample type	采样时间 Sampling time	酸碱度 pH	总有机碳 TOC/（mg·kg^-1）	总氮 TN/（mg·kg^-1）	总磷 TP/（mg·kg^-1）	氨态氮 NH₄⁺N/（mg·kg^-1）	硝态氮 NO₃^--N/（mg·kg^-1）
LW1	湖泊水体	2017年7月	7.87±0.50	16.39±6.93	2.40±0.66	0.13±0.03	0.26±0.10	0.66±0.12
LW2	湖泊水体	2017年7月	7.3±0.16	12.40±5.44	3.10±0.63	0.13±0.02	0.14±0.05	0.26±0.04
LW3	湖泊水体	2017年7月	6.79±0.41	11.09±5.51	1.38±0.36	0.11±0.04	0.34±0.15	0.48±0.04
LW4	湖泊水体	2017年7月	7.12±0.13	14.06±6.00	1.14±0.67	0.11±0.04	0.25±0.06	0.02±0.03

群落生境 Biotope	多样性指数 Diversity index	97 OTU	98 OTU	99 OTU	100 OTU	ASV	F	P
FS	Shannon	8.89±0.59c	9.44±0.57c	10.06±0.53b	11.35±0.34a	9.36±0.36c	3.76	0.01
	Faith’s phylog-enetic diversity	200.65±30.09a	206.76±30.33a	205.45±31.21a	189.98±30.05a	76.57±17.53b	3.59	0.01
	Chao1	6 197.73±681.64d	7 715.24±864.03c	9 783.86±1121.87b	15 783.82±1 896.16a	1 348.3±250.55e	64.05	<0.01
WS	Shannon	9.15±0.44cd	9.51±0.40c	9.93±0.33b	10.76±0.22a	9.10±0.25d	45.32	<0.01
	Faith’s phylog-enetic diversity	131.82±20.02ab	135.38±20.39a	131.70±21.02ab	112.17±17.56b	59.95±11.00c	46.78	<0.01
	Chao1	3 432.91±489.63c	4 119.94±523.16bc	4 834.80±605.21b	6 259.35±1 102.91a	909.03±166.82d	272.21	<0.01
CS	Shannon	8.99±0.88b	9.27±0.93b	9.60±1.01ab	10.40±1.00a	9.42±0.92ab	3.51	0.01
	Faith’s phylog-enetic diversity	180.30±33.33ab	169.08±31.10ab	202.30±38.74a	199.45±36.96a	159.09±30.77b	1.1	0.37
	Chao1	3 672.12±892.42c	4 271.85±1 029.13bc	4 993.04±1 280.13b	9 069.09±1 554.95a	2 186.44±570.72d	51.61	<0.01
LS	Shannon	6.82±1.06b	6.96±1.15b	7.21±1.24ab	8.32±1.09a	7.20±0.90ab	49.53	<0.01
	Faith’s phylog-enetic diversity	97.39±35.21a	97.87±34.88a	99.98±35.44a	110.06±32.85a	83.22±15.54a	35.66	<0.01
	Chao1	1 429.92±867.84bc	1 646.30±1 010.35bc	1 916.62±1 242.16b	5 343.72±481.48a	934.21±263.76c	110.54	<0.01
LW	Shannon	6.71±1.12b	6.98±1.14b	7.36±1.10b	9.04±0.71a	7.50±0.80b	10.01	<0.01
	Faith’s phylog-enetic diversity	79.77±30.70a	88.65±32.64a	91.25±32.18a	85.25±24.93a	59.01±18.95a	2.48	0.05
	Chao1	1 709.26±868.06bc	2 043.13±914.34bc	2 674.50±1 066.06b	8 847.05±1 696.64a	837.88±332.8c	108.4	<0.01