Microbial Communities in the Tropical Air Ecosystem Follow a Precise Diel Cycle
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Microbial communities in the tropical air ecosystem follow a precise diel cycle Elena S. Gusarevaa,1, Enzo Acerbia,1, Kenny J. X. Laua, Irvan Luhunga,1, Balakrishnan N. V. Premkrishnana, Sandra Kolundzijaa, Rikky W. Purbojatia, Anthony Wonga, James N. I. Houghtona, Dana Millera, Nicolas E. Gaultiera, Cassie E. Heinlea, Megan E. Clarea, Vineeth Kodengil Vettatha, Carmon Keea, Serene B. Y. Lima, Caroline Chénarda, Wen Jia Phunga, Kavita K. Kushwahaa, Ang Poh Neea, Alexander Putraa, Deepa Panickera, Koh Yanqinga, Yap Zhei Hweea, Sachin R. Lohara, Mikinori Kuwatab, Hie Lim Kima,b, Liang Yanga, Akira Uchidaa, Daniela I. Drautz-Mosesa, Ana Carolina M. Junqueirac, and Stephan C. Schustera,2 aSingapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551 Singapore; bAsian School of the Environment, Nanyang Technological University, 637459 Singapore; and cDepartamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-590 Brazil Edited by Edward F. DeLong, University of Hawaii at Manoa, Honolulu, HI, and approved October 3, 2019 (received for review June 14, 2019) The atmosphere is vastly underexplored as a habitable ecosystem well as absence of large day-to-day fluctuations in temperature and for microbial organisms. In this study, we investigated 795 time- relative humidity are factors making the tropical region an ideal air resolved metagenomes from tropical air, generating 2.27 terabases microbiome research site. Using deep metagenomics sequencing of data. Despite only 9 to 17% of the generated sequence data and analysis, we describe the air microbiome variation within time currently being assignable to taxa, the air harbored a microbial di- intervals of hours, days, and up to months. The resulting dataset of versity that rivals the complexity of other planetary ecosystems. 795 air samples provided a high level of reproducibility and en- The airborne microbial organisms followed a clear diel cycle, possi- abled extensive statistical analysis. Models developed using bly driven by environmental factors. Interday taxonomic diversity Bayesian network analysis implicated specific environmental pa- exceeded day-to-day and month-to-month variation. Environmental rameters as drivers for ecosystem diversity and composition. En- time series revealed the existence of a large core of microbial taxa vironmental studies have reported a microbial cellular density of that remained invariable over 13 mo, thereby underlining the long- ∼1012 per L of soil and ∼109 perLofwater(14–17). In contrast, MICROBIOLOGY term robustness of the airborne community structure. Unlike terres- air, being a much less dense medium, is reported to harbor ∼102 trial or aquatic environments, where prokaryotes are prevalent, the cells per L sampled, based on CFU counts and amplification of tropical airborne biomass was dominated by DNA from eukaryotic rRNA markers (5). The 7 to 10 log difference in cell density per phyla. Specific fungal and bacterial species were strongly correlated volume represents a significant technological challenge for ana- with temperature, humidity, and CO2 concentration, making them lyzing the biological content of air, as it mandates very large vol- suitable biomarkers for studying the bioaerosol dynamics of umes to be collected over extended periods of time (2). We have the atmosphere. devised an air-sampling approach that allows for 2-h or shorter intervals to be sampled, generating highly reproducible results for microbial ecology | bioaerosols | air microbiome | temperature | tropics f the 3 planetary ecosystems, the lithosphere, hydrosphere, Significance Oand atmosphere, only soil and aquatic environments have been extensively investigated for their microbial content (1). In This manuscript describes a precise diel cycle carried out by contrast, the study of the atmosphere as a microorganism-harboring airborne microbiota in the tropics. 795 metagenomes from air ecosystem has been technologically challenging, rendering it an un- samples taken from a single site show that fungi, bacteria, and derstudied biosphere (2, 3). The atmosphere is assumed to contain plants all adhere to a specific timing for their presence in the microorganisms from local and remote sources, being dispersed for near-surface atmosphere. The airborne community composi- as long as they remain suspended in air (4–6). Previously reported tion thereby shows an unexpected robustness, with the ma- numbers of cells found in a defined volume of air are based either jority of the dynamics in taxa composition occurring within on cultivation (colony-forming units; CFUs) or are estimated by 24 h, but not across days, weeks, or months. Environmental amplified ribosomal molecular markers (2, 7). Furthermore, research parameters are the main drivers for the observed phenome- into airborne microbial organisms is predominantly carried out non, with temperature being the most important one. in countries located within temperate climate zones or as global surveys with varying geographic and climatic parameters (8–10). Author contributions: S.C.S. designed research; K.J.X.L., I.L., S.K., A.W., J.N.I.H., D.M., Here, we investigate the microbial composition of the outdoor N.E.G., C.E.H., M.E.C., V.K.V., C.K., S.B.Y.L., C.C., W.J.P., K.K.K., A.P.N., A.P., D.P., K.Y., Y.Z.H., S.R.L., M.K., H.L.K., L.Y., A.U., D.I.D.-M., A.C.M.J., and S.C.S. performed research; air microbiome at a single site in the tropics (Singapore, N1.334731; E.S.G., E.A., K.J.X.L., I.L., B.N.V.P., R.W.P., D.I.D.-M., A.C.M.J., and S.C.S. analyzed data; and E103.680996, sea-surface level) through a metagenomics time E.S.G., E.A., I.L., D.I.D.-M., A.C.M.J., and S.C.S. wrote the paper. course study of unprecedented temporal and taxonomic resolu- The authors declare no competing interest. tion. Singapore is located in the intertropical convergence zone This article is a PNAS Direct Submission. and experiences a biannual reversal of wind directions, which de- This open access article is distributed under Creative Commons Attribution-NonCommercial- fines the 2 monsoon seasons (11, 12). The biannually alternating NoDerivatives License 4.0 (CC BY-NC-ND). wind directions of the study site could result in vastly disparate Data deposition: The raw metagenomic sequencing data reported in this paper have been sources of transported microorganisms. Hindcast estimations over deposited in the National Center for Biotechnology Information (NCBI) database (sub- 48 h using the HYSPLIT Trajectory Model package (13) indicate mission ID code SUB3630161; BioProject ID code PRJNA436039). that during the northeast monsoon season, the prevailing airflow 1E.S.G., E.A., and I.L. contributed equally to this work. is transporting air masses from the general area of Taiwan and 2To whom correspondence may be addressed. Email: [email protected]. the Philippines. Wind trajectories during the southwest monsoon This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. season identify the Timor and Arafura seas as possible sites of 1073/pnas.1908493116/-/DCSupplemental. origin. Little variation in length of daylight across the seasons as First published October 28, 2019. www.pnas.org/cgi/doi/10.1073/pnas.1908493116 PNAS | November 12, 2019 | vol. 116 | no. 46 | 23299–23308 Downloaded by guest on October 2, 2021 downstream metagenomics analyses. In this regard, we can show superkingdom level (Fig. 1A) (19). The unidentifiable fraction of that collected biomass contains sufficient quantity and complexity the air microbiome contained reads that were either not specific to of DNA to enable ultradeep sequencing, generating more than a known taxon (35%, unassigned) or did not show any similarity to 300 million reads from a single air sample. More importantly, the the nonredundant sequence database (48%, no hits). The “no hits” robustness and precision of our sampling technique, as illustrated category likely consists of 1 of the following possibilities: 1) inter- by its replicability, allowed for the undertaking of experimen- genic regions of eukaryotic organisms; 2) extracellular DNA that tal time-series surveys across days, weeks, and months (i.e., day has been highly mutagenized by the exposure to harsh environ- variation experiment [DVE] 1 to 5). Air samples were collected mental conditions; or 3) as-yet undiscovered microorganisms that 12 times per d, every 2 h, for 5 consecutive days, in 3-mo intervals currently have no representation in sequence databases. Hence, (DVE2 to 5). This 13-mo schedule of sampling events covered compared with soil and seawater, air is currently the most under- ’ Singapore s wet and dry seasons, including 2 monsoon seasons with explored ecosystem on a planetary scale. wind directions from the northeast and southwest. Each time series Despite the low percentage of assigned reads, the air samples (DVE2 to 5) resulted in 180 sequencing libraries. Thus, all 5 DVEs displayed species richness comparable to the other 3 ecosystems combined represent 795 air samples from 265 time points, which (Fig. 1B). Unlike these environments, which were dominated by resulted in 9.11 billion reads of 250 bp, or 2.27 terabase pairs, the bacterial phyla, the tropical air samples showed that a large equivalent of ∼760 human genomes (SI Appendix,TableS1). proportion of the assigned reads belong to fungi (82%), while Results only 14.5% were assigned to bacteria, 2.6% to plant species, and Taxonomic Diversity of a Single Tropical Air Sample. We first assessed 0.087% to archaea (Fig. 1C). As our sequencing data and meta- the taxonomic diversity of a random air sample (DVE1011) in genomics analysis allowed for species-level identification, we DVE1 relative to seawater, soil, and human gut microbiome sam- used known genome sizes of the top 40 fungal and bacterial ples from the same geographic region (Singapore, each normalized genomes (SI Appendix, Fig.