Effects of Analysis Methods on the Analyzed Results of 16S rRNA Gene Amplicon Sequencing in Bacterial Communities

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

Abstract

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

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.

Figures/Tables 7

References 48

[1]	Locey KJ, Lennon JT. Scaling laws predict global microbial diversity[J]. PNAS, 2016, 113(21):5970-5975. doi: 10.1073/pnas.1521291113 URL
[2]	高贵锋, 褚海燕. 微生物组学的技术和方法及其应用[J]. 植物生态学报, 2020, 44(4):395-408. doi: 10.17521/cjpe.2019.0222
	Gao GF, Chu HY. Techniques and methods of microbiomics and their applications[J]. Chin J Plant Ecol, 2020, 44(4):395-408. doi: 10.17521/cjpe.2019.0222 URL
[3]	Sanz JL, Köchling T. Molecular biology techniques used in wastewater treatment:an overview[J]. Process Biochem, 2007, 42(2):119-133. doi: 10.1016/j.procbio.2006.10.003 URL
[4]	Caruso V, Song XB, Asquith M, et al. Performance of microbiome sequence inference methods in environments with varying biomass[J]. mSystems, 2019, 4(1):e00163-e00118.
[5]	Yang B, Wang Y, Qian PY. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis[J]. BMC Bioinformatics, 2016, 17:135. doi: 10.1186/s12859-016-0992-y pmid: 27000765
[6]	Loman NJ, Misra RV, Dallman TJ, et al. Performance comparison of benchtop high-throughput sequencing platforms[J]. Nat Biotechnol, 2012, 30(5):434-439. doi: 10.1038/nbt.2198 URL
[7]	Allali I, Arnold JW, Roach J, et al. A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome[J]. BMC Microbiol, 2017, 17(1):194. doi: 10.1186/s12866-017-1101-8 pmid: 28903732
[8]	Pedrós-Alió C. Marine microbial diversity:can it be determined?[J]. Trends Microbiol, 2006, 14(6):257-263. pmid: 16679014
[9]	Lu HP, Yeh YC, Sastri AR, et al. Evaluating community-environment relationships along fine to broad taxonomic resolutions reveals evolutionary forces underlying community assembly[J]. Isme J, 2016, 10(12):2867-2878. doi: 10.1038/ismej.2016.78 URL
[10]	Capunitan DC, Johnson O, Terrill RS, et al. Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea[J]. Mol Ecol, 2020, 29(4):829-847. doi: 10.1111/mec.15354 pmid: 31943484
[11]	Edgar RC. UPARSE:highly accurate OTU sequences from microbial amplicon reads[J]. Nat Methods, 2013, 10(10):996-998. doi: 10.1038/nmeth.2604 URL
[12]	Gevers D, Cohan FM, Lawrence JG, et al. Re-evaluating prokaryotic species[J]. Nat Rev Microbiol, 2005, 3(9):733-739. pmid: 16138101
[13]	Erko S, Ebers J. Taxonomic parameters revisited:tarnished gold standards[J]. Microbiol Today, 2006, 33:152-155.
[14]	Edgar RC. Updating the 97% identity threshold for 16S ribosomal RNA OTUs[J]. Bioinformatics, 2018, 34(14):2371-2375. doi: 10.1093/bioinformatics/bty113 URL
[15]	Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2:High-resolution sample inference from Illumina amplicon data[J]. Nat Methods, 2016, 13(7):581-583. doi: 10.1038/NMETH.3869
[16]	Berg G, Rybakova D, et al. Microbiome definition re-visited:old concepts and new challenges[J]. Microbiome, 2020, 8(1):103. doi: 10.1186/s40168-020-00875-0 URL
[17]	Scarlett K, Denman S, Clark DR, et al. Relationships between nitrogen cycling microbial community abundance and composition reveal the indirect effect of soil pH on oak decline[J]. Isme J, 2021, 15(3):623-635. doi: 10.1038/s41396-020-00801-0 pmid: 33067585
[18]	Joos L, Beirinckx S, Haegeman A, et al. Daring to be differential:metabarcoding analysis of soil and plant-related microbial communities using amplicon sequence variants and operational taxonomical units[J]. BMC Genom, 2020, 21(1):733. doi: 10.1186/s12864-020-07126-4 URL
[19]	刘亚军, 蔡润发, 李赟璟, 等. 湿地土壤微生物碳源代谢活性对不同水分条件的动态响应——以鄱阳湖为例[J]. 土壤, 2018, 50(4):705-711.
	Liu YJ, Cai RF, Li YJ, et al. Functional response of wetland soil microbial carbon source metabolic activity to different water conditions—A case of lake Poyang[J]. Soils, 2018, 50(4):705-711.
[20]	何世耀. 鄱阳湖饶河入湖口水体和底泥微生物对氮、磷及重金属污染物输入的响应[D]. 南昌: 南昌大学, 2019.
	He SY. The response of microbial in sediment and water of Poyang lake to the inputs of N, P and heavy metals pollutions in Raohe river[D]. Nanchang: Nanchang University, 2019.
[21]	Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2[J]. Nat Biotechnol, 2019, 37(8):852-857. doi: 10.1038/s41587-019-0209-9 pmid: 31341288
[22]	Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads[J]. EMBnet J, 2011, 17(1):10.
[23]	Rognes T, Flouri T, Nichols B, et al. VSEARCH:a versatile open source tool for metagenomics[J]. PeerJ, 2016, 4:e2584.
[24]	Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project:improved data processing and web-based tools[J]. Nucleic Acids Res, 2013, 41(database issue):D590-D596.
[25]	Bokulich NA, Kaehler BD, Rideout JR, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin[J]. Microbiome, 2018, 6(1):90. doi: 10.1186/s40168-018-0470-z URL
[26]	Katoh K, Misawa K, Kuma K, et al. MAFFT:a novel method for rapid multiple sequence alignment based on fast Fourier transform[J]. Nucleic Acids Res, 2002, 30(14):3059-3066. doi: 10.1093/nar/gkf436 URL
[27]	Price MN, Dehal PS, Arkin AP. FastTree 2——approximately maximum-likelihood trees for large alignments[J]. PLoS One, 2010, 5(3):e9490.
[28]	Oksanen J, Blanchet FG, Friendly M, et al. vegan:Community Ecology Package[J]. R package version 2. 5-7, 2020.
[29]	Hothorn T, Bretz F, Westfall P. Simultaneous inference in general parametric models[J]. Biom J, 2008, 50(3):346-363. doi: 10.1002/bimj.200810425 URL
[30]	Wickham H. ggplot2[J]. WIREs Comp Stat, 2011, 3(2):180-185. doi: 10.1002/wics.147 URL
[31]	Shade A. Diversity is the question, not the answer[J]. Isme J, 2017, 11(1):1-6. doi: 10.1038/ismej.2016.118 URL
[32]	Louca S, Mazel F, Doebeli M, et al. A census-based estimate of Earth’s bacterial and archaeal diversity[J]. PLoS Biol, 2019, 17(2):e3000106.
[33]	Li XC, Huo SL, Xi BD. Updating the resolution for 16S rRNA OTUs clustering reveals the cryptic cyanobacterial genus and species[J]. Ecol Indic, 2020, 117:106695.
[34]	Kunin V, Engelbrektson A, et al. Wrinkles in the rare biosphere:pyrosequencing errors can lead to artificial inflation of diversity estimates[J]. Environ Microbiol, 2010, 12(1):118-123. doi: 10.1111/j.1462-2920.2009.02051.x URL
[35]	Washburne AD, Morton JT, Sanders J, et al. Methods for phylogenetic analysis of microbiome data[J]. Nat Microbiol, 2018, 3(6):652-661. doi: 10.1038/s41564-018-0156-0 pmid: 29795540
[36]	Barnes CJ, Rasmussen L, et al. Comparing DADA2 and OTU clustering approaches in studying the bacterial communities of atopic dermatitis[J]. J Med Microbiol, 2020, 69(11):1293-1302. doi: 10.1099/jmm.0.001256 URL
[37]	Zhou Z, Wang C, Luo Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality[J]. Nat Commun, 2020, 11(1):3072. doi: 10.1038/s41467-020-16881-7 URL
[38]	Berlow M, Phillips JN, Derryberry EP. Effects of urbanization and landscape on gut microbiomes in white-crowned sparrows[J]. Microb Ecol, 2021, 81(1):253-266. doi: 10.1007/s00248-020-01569-8 URL
[39]	Cottrell MT, David KL. Contribution of major bacterial groups to bacterial biomass production(thymidine and leucine incorporation)in the Delaware estuary[J]. Limnol Oceanogr, 2003, 48(1):168-178. doi: 10.4319/lo.2003.48.1.0168 URL
[40]	Dell’Anno F, Rastelli E, et al. Highly contaminated marine sediments can host rare bacterial taxa potentially useful for bioremediation[J]. Front Microbiol, 2021, 12:584850.
[41]	Grady EN, MacDonald J, Liu L, et al. Current knowledge and perspectives of Paenibacillus:a review[J]. Microb Cell Fact, 2016, 15(1):203. doi: 10.1186/s12934-016-0603-7 URL
[42]	McSpadden Gardener BB. Ecology of Bacillus and Paenibacillus spp. in agricultural systems[J]. Phytopathology®, 2004, 94(11):1252-1258. doi: 10.1094/PHYTO.2004.94.11.1252 URL
[43]	Monciardini P, Cavaletti L, Schumann P, et al. Conexibacter woesei gen. nov., sp. nov., a novel representative of a deep evolutionary line of descent within the class Actinobacteria[J]. Int J Syst Evol Microbiol, 2003, 53(Pt 2):569-576. doi: 10.1099/ijs.0.02400-0 URL
[44]	Dong L, Cheng R, Xiao L, et al. Diversity and composition of bacterial endophytes among plant parts of Panax notoginseng[J]. Chinese medicine, 2018, 13(1):1-9.
[45]	Berestovskaya JJ, Kotsyurbenko OR, Tourova TP, et al. Methylorosula polaris gen. nov., sp. nov., an aerobic, facultatively methylotrophic psychrotolerant bacterium from tundra wetland soil[J]. Int J Syst Evol Microbiol, 2012, 62(Pt 3):638-646. doi: 10.1099/ijs.0.007005-0 URL
[46]	Lozupone CA, Hamady M, Kelley ST, et al. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities[J]. Appl Environ Microbiol, 2007, 73(5):1576-1585. doi: 10.1128/AEM.01996-06 URL
[47]	Tromas N, Fortin N, Bedrani L, et al. Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course[J]. Isme J, 2017, 11(8):1746-1763. doi: 10.1038/ismej.2017.58 URL
[48]	An JX, Liu C, Wang Q, et al. Soil bacterial community structure in Chinese wetlands[J]. Geoderma, 2019, 337:290-299. doi: 10.1016/j.geoderma.2018.09.035 URL

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