Performance evaluation of a new custom, multi-component DNA isolation method optimized for use in shotgun metagenomic sequencing-based aerosol microbiome research
Tóm tắt
Aerosol microbiome research advances our understanding of bioaerosols, including how airborne microorganisms affect our health and surrounding environment. Traditional microbiological/molecular methods are commonly used to study bioaerosols, but do not allow for generic, unbiased microbiome profiling. Recent studies have adopted shotgun metagenomic sequencing (SMS) to address this issue. However, SMS requires relatively large DNA inputs, which are challenging when studying low biomass air environments, and puts high requirements on air sampling, sample processing and DNA isolation protocols. Previous SMS studies have consequently adopted various mitigation strategies, including long-duration sampling, sample pooling, and whole genome amplification, each associated with some inherent drawbacks/limitations. Here, we demonstrate a new custom, multi-component DNA isolation method optimized for SMS-based aerosol microbiome research. The method achieves improved DNA yields from filter-collected air samples by isolating DNA from the entire filter extract, and ensures a more comprehensive microbiome representation by combining chemical, enzymatic and mechanical lysis. Benchmarking against two state-of-the-art DNA isolation methods was performed with a mock microbial community and real-world air samples. All methods demonstrated similar performance regarding DNA yield and community representation with the mock community. However, with subway samples, the new method obtained drastically improved DNA yields, while SMS revealed that the new method reported higher diversity. The new method involves intermediate filter extract separation into a pellet and supernatant fraction. Using subway samples, we demonstrate that supernatant inclusion results in improved DNA yields. Furthermore, SMS of pellet and supernatant fractions revealed overall similar taxonomic composition but also identified differences that could bias the microbiome profile, emphasizing the importance of processing the entire filter extract. By demonstrating and benchmarking a new DNA isolation method optimized for SMS-based aerosol microbiome research with both a mock microbial community and real-world air samples, this study contributes to improved selection, harmonization, and standardization of DNA isolation methods. Our findings highlight the importance of ensuring end-to-end sample integrity and using methods with well-defined performance characteristics. Taken together, the demonstrated performance characteristics suggest the new method could be used to improve the quality of SMS-based aerosol microbiome research in low biomass air environments.
Tài liệu tham khảo
Yao M. Bioaerosol: a bridge and opportunity for many scientific research fields. J Aerosol Sci. 2018;115:108–12. https://doi.org/10.1016/j.jaerosci.2017.07.010.
King P, Pham LK, Waltz S, Sphar D, Yamamoto RT, Conrad D, Taplitz R, Torriani F, Forsyth RA. Longitudinal metagenomic analysis of hospital air identifies clinically relevant microbes. PLoS One. 2016;11(8):e0160124. https://doi.org/10.1371/journal.pone.0160124.
Choi JY, Zemke J, Philo SE, Bailey ES, Yondon M, Gray GC. Aerosol sampling in a hospital emergency room setting: a complementary surveillance method for the detection of respiratory viruses. Front Public Health. 2018;6:174. https://doi.org/10.3389/fpubh.2018.00174.
Nguyen TT, Poh MK, Low J, Kalimuddin S, Thoon KC, Ng WC, Anderson BD, Gray GC. Bioaerosol Sampling in Clinical Settings: A Promising, Noninvasive Approach for Detecting Respiratory Viruses. Open Forum Infect Dis. 2017;4(1):ofw259. https://doi.org/10.1093/ofid/ofw259.
Prost K, Kloeze H, Mukhi S, Bozek K, Poljak Z, Mubareka S. Bioaerosol and surface sampling for the surveillance of influenza a virus in swine. Transbound Emerg Dis. 2019. https://doi.org/10.1111/tbed.13139.
Osman S, La Duc MT, Dekas A, Newcombe D, Venkateswaran K. Microbial burden and diversity of commercial airline cabin air during short and long durations of travel. Int Soc Microb Ecol (ISME) J. 2008;2:482–97. https://doi.org/10.1038/ismej.2008.11.
Weiss H, Hertzberg VS, Dupont C, Espinoza JL, Levy S, Nelson K, Norris S, Team TFR. The airplane cabin microbiome. Microb Ecol. 2019;77(1):87–95. https://doi.org/10.1007/s00248-018-1191-3.
Zanni S, Lalli F, Foschi E, Bonoli A, Mantecchini L. Indoor air quality real-time monitoring in airport terminal areas: an opportunity for sustainable Management of Micro-Climatic Parameters. Sensors. 2018;18(11):3798. https://doi.org/10.3390/s18113798.
Wagar E. Bioterrorism and the role of the clinical microbiology laboratory. Clin Microbiol Rev. 2016;29(1):175–89. https://doi.org/10.1128/CMR.00033-15.
Amann RI, Ludwig W, Schleifer K-H. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol Mol Biol Rev. 1995;59(1):143–69.
Radosevich JL, Wilson WJ, Shinn JH, DeSantis TZ, Andersen GL. Development of a high-volume aerosol collection system for the identification of air-borne micro-organisms. Lett Appl Microbiol. 2002;34(3):162–7. https://doi.org/10.1046/j.1472-765x.2002.01048.x.
Toivola M, Alm S, Reponen T, Kolari S, Nevalainen A. Personal exposures and microenvironmental concentrations of particles and bioaerosols. J Environ Monit. 2002;4(1):166–74.
Eduard W, Heederik D. Methods for quantitative assessment of airborne levels of noninfectious microorganisms in highly contaminated work environments. Am Ind Hyg Assoc J. 1998;59(2):113–27. https://doi.org/10.1080/15428119891010370.
Leung MH, Wilkins D, Li EK, Kong FK, Lee PK. Indoor-air microbiome in an urban subway network: diversity and dynamics. Appl Environ Microbiol. 2014;80(21):6760–70. https://doi.org/10.1128/AEM.02244-14.
Triado-Margarit X, Veillette M, Duchaine C, Talbot M, Amato F, Minguillon MC, Martins V, de Miguel E, Casamayor EO, Moreno T. Bioaerosols in the Barcelona subway system. Indoor Air. 2017;27(3):564–75. https://doi.org/10.1111/ina.12343.
Cáliz J, Triadó-Margarit X, Camarero L, Casamayor EO. A long-term survey unveils strong seasonal patterns in the airborne microbiome coupled to general and regional atmospheric circulations. Proc Natl Acad Sci. 2018;115(48):12229–34. https://doi.org/10.1073/pnas.1812826115.
Hanson B, Zhou Y, Bautista EJ, Urch B, Speck M, Silverman F, Muilenberg M, Phipatanakul W, Weinstock G, Sodergren E, et al. Characterization of the bacterial and fungal microbiome in indoor dust and outdoor air samples: a pilot study. Environ Sci. 2016;18(6):713–24. https://doi.org/10.1039/c5em00639b.
The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14. https://doi.org/10.1038/nature11234.
Afshinnekoo E, Meydan C, Chowdhury S, Jaroudi D, Boyer C, Bernstein N, Maritz JM, Reeves D, Gandara J, Chhangawala S, et al. Geospatial resolution of human and bacterial diversity with City-scale Metagenomics. Cell Systems. 2015;1(1):72–87. https://doi.org/10.1016/j.cels.2015.01.001.
Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, Hogle SL, Coe A, Bergauer K, Bouman HA, et al. Marine microbial metagenomes sampled across space and time. Sci Data. 2018;5:180176. https://doi.org/10.1038/sdata.2018.176.
Yooseph S, Andrews-Pfannkoch C, Tenney A, McQuaid J, Williamson S, Thiagarajan M, Brami D, Zeigler-Allen L, Hoffman J, Goll JB, et al. A metagenomic framework for the study of airborne microbial communities. PLoS One. 2013;8(12):e81862. https://doi.org/10.1371/journal.pone.0081862.
Cao C, Jiang W, Wang B, Fang J, Lang J, Tian G, Jiang J, Zhu TF. Inhalable microorganisms in Beijing's PM2.5 and PM10 pollutants during a severe smog event. Environ Sci Technol. 2014;48(3):1499–507. https://doi.org/10.1021/es4048472.
Eisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R, Weyrich LS. Contamination in Low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 2019;27(2):105–17. https://doi.org/10.1016/j.tim.2018.11.003.
Dybwad M, Skogan G, Blatny JM. Comparative testing and evaluation of nine different air samplers: end-to-end sampling efficiencies as specific performance measurements for bioaerosol applications. Aerosol Sci Technol. 2014;48(3):282–95. https://doi.org/10.1080/02786826.2013.871501.
Behzad H, Gojobori T, Mineta K. Challenges and opportunities of airborne metagenomics. Genome Biol Evolution. 2015;7(5):1216–26. https://doi.org/10.1093/gbe/evv064.
Tringe SG, Hugenholtz P. A renaissance for the pioneering 16S rRNA gene. Curr Opin Microbiol. 2008;11(5):442–6. https://doi.org/10.1016/j.mib.2008.09.011.
Luhung I, Wu Y, Ng CK, Miller D, Cao B, Chang VW. Protocol improvements for Low concentration DNA-based bioaerosol sampling and analysis. PLoS One. 2015;10(11):e0141158. https://doi.org/10.1371/journal.pone.0141158.
Jiang W, Liang P, Wang B, Fang J, Lang J, Tian G, Jiang J, Zhu TF. Optimized DNA extraction and metagenomic sequencing of airborne microbial communities. Nat Protoc. 2015;10:768. https://doi.org/10.1038/nprot.2015.046.
Dommergue A, Amato P, Tignat-Perrier R, Magand O, Thollot A, Joly M, Bouvier L, Sellegri K, Vogel T, Sonke JE, et al. Methods to investigate the global atmospheric microbiome. Front Microbiol. 2019;10:00243. https://doi.org/10.3389/fmicb.2019.00243.
Tighe S, Afshinnekoo E, Rock TM, McGrath K, Alexander N, McIntyre A, Ahsanuddin S, Bezdan D, Green SJ, Joye S, et al. Genomic methods and microbiological Technologies for Profiling Novel and Extreme Environments for the extreme microbiome project (XMP). J Biomol Tech. 2017;28(1):31–9. https://doi.org/10.7171/jbt.17-2801-004.
Yuan S, Cohen DB, Ravel J, Abdo Z, Forney LJ. Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One. 2012;7(3):e33865. https://doi.org/10.1371/journal.pone.0033865.
Abusleme L, Hong BY, Dupuy AK, Strausbaugh LD, Diaz PI. Influence of DNA extraction on oral microbial profiles obtained via 16S rRNA gene sequencing. J Oral Microbiol. 2014;6:23990. https://doi.org/10.3402/jom.v6.23990.
Mbareche H, Brisebois E, Veillette M, Duchaine C. Bioaerosol sampling and detection methods based on molecular approaches: no pain no gain. Sci Total Environ. 2017;599–600:2095–104. https://doi.org/10.1016/j.scitotenv.2017.05.076.
The Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486:215–21. https://doi.org/10.1038/nature11209.
Gilbert JA, Jansson JK, Knight R. Earth Microbiome Project and Global Systems Biology. mSystems. 2018;3(3):e00217. https://doi.org/10.1128/mSystems.00217-17.
Kopf A, Bicak M, Kottmann R, Schnetzer J, Kostadinov I, Lehmann K, Fernandez-Guerra A, Jeanthon C, Rahav E, Ullrich M, et al. The ocean sampling day consortium. GigaScience. 2015;4(1):s13742. https://doi.org/10.1186/s13742-015-0066-5.
Lear G, Dickie I, Banks J, Boyer S, Buckley HL, Buckley TR, Cruickshank R, Dopheide A, Handley KM, Hermans S, et al. Methods for the extraction, storage, amplification and sequencing of DNA from environmental samples. N Z J Ecol. 2018;42(1):10–50. https://doi.org/10.20417/nzjecol.42.9.
Al-Hebshi NN, Baraniya D, Chen T, Hill J, Puri S, Tellez M, Hasan NA, Colwell RR, Ismail A. Metagenome sequencing-based strain-level and functional characterization of supragingival microbiome associated with dental caries in children. J Oral Microbiol. 2018;11(1):1557986. https://doi.org/10.1080/20002297.2018.1557986.
Honeyman AS, Day ML, Spear JR. Regional fresh snowfall microbiology and chemistry are driven by geography in storm-tracked events, Colorado, USA. PeerJ. 2018;6:e5961. https://doi.org/10.7717/peerj.5961.
Zaikova E, Goerlitz DS, Tighe SW, Wagner NY, Bai Y, Hall BL, Bevilacqua JG, Weng MM, Samuels-Fair MD, Johnson SS. Antarctic relic microbial mat Community revealed by Metagenomics and Metatranscriptomics. Front Ecol Evol. 2019;7(1):10.3389. https://doi.org/10.3389/fevo.2019.00001.
Trivedi CB, Lau GE, Grasby SE, Templeton AS, Spear JR. Low-temperature Sulfidic-ice microbial communities, Borup fiord pass, Canadian High Arctic. Front Microbiol. 2018;9:01622. https://doi.org/10.3389/fmicb.2018.01622.
Liu CM, Aziz M, Kachur S, Hsueh P-R, Huang Y-T, Keim P, Price LB. BactQuant: an enhanced broad-coverage bacterial quantitative real-time PCR assay. BMC Microbiol. 2012;12:56. https://doi.org/10.1186/1471-2180-12-56.
Minot SS, Krumm N, Greenfield NB. One Codex: A Sensitive and Accurate Data Platform for Genomic Microbial Identification. bioRxiv. 2015;027607. https://doi.org/10.1101/027607.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217.
Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17(3):377–86.
Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2(3):18–22.
Miller DN, Bryant JE, Madsen EL, Ghiorse WC. Evaluation and optimization of DNA extraction and purification procedures for soil and sediment samples. Appl Environ Microbiol. 1999;65(11):4715–24.
Griffiths LJ, Anyim M, Doffman SR, Wilks M, Millar MR, Agrawal SG. Comparison of DNA extraction methods for Aspergillus fumigatus using real-time PCR. J Med Microbiol. 2006;55(9):1187–91. https://doi.org/10.1099/jmm.0.46510-0.
Mbareche H, Veillette M, Teertstra W, Kegel W, Bilodeau GJ, Wösten HAB, Duchaine C. Fungal cells recovery from air samples: a tale of loss and gain. Appl Environ Microbiol. 2019;02941. https://doi.org/10.1128/aem.02941-18.