Skip to main content

Advertisement

SpringerLink
Log in

Airborne Bacterial Diversity from the Low Atmosphere of Greater Mexico City

  • Environmental Microbiology
  • Published:
Microbial Ecology Aims and scope Submit manuscript

An Erratum to this article was published on 04 August 2017

This article has been updated

Abstract

Greater Mexico City is one of the largest urban centers in the world, with an estimated population by 2010 of more than 20 million inhabitants. In urban areas like this, biological material is present at all atmospheric levels including live bacteria. We sampled the low atmosphere in several surveys at different points by the gravity method on LB and blood agar media during winter, spring, summer, and autumn seasons in the years 2008, 2010, 2011, and 2012. The colonial phenotype on blood agar showed α, β, and γ hemolytic activities among the live collected bacteria. Genomic DNA was extracted and convenient V3 hypervariable region libraries of 16S rDNA gene were high-throughput sequenced. From the data analysis, Firmicutes, Proteobacteria, and Actinobacteria were the more abundant phyla in all surveys, while the genera from the family Enterobacteriaceae, in addition to Bacillus spp., Pseudomonas spp., Acinetobacter spp., Erwinia spp., Gluconacetobacter spp., Proteus spp., Exiguobacterium spp., and Staphylococcus spp. were also abundant. From this study, we conclude that it is possible to detect live airborne nonspore-forming bacteria in the low atmosphere of GMC, associated to the microbial cloud of its inhabitants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Change history

  • 04 August 2017

    An erratum to this article has been published.

References

  1. Konstantinidis KT (2014) Do airborne microbes matter for atmospheric chemistry and cloud formation? Environ Microbiol 16:1482–1484

    Article  PubMed  Google Scholar 

  2. Cho I, Blaser MJ (2012) The human microbiome: at the interface of health and disease. Nat Rev Genet 13:260–270

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Meadow JF, Altrichter AE, Bateman AC, Stenson J, Brown GZ, Green JL, Bohannan BJ (2015) Humans differ in their personal microbial cloud. Peer J 3:e1258

    Article  PubMed  PubMed Central  Google Scholar 

  4. Demographia World Urban Areas: 11th annual edition: 2015.01. http://www.demographia.com/

  5. Rosas I, McCartney HA, Payne RW, Calderon C, Lacey J, Chapela R et al (1998) Analysis of the relationship between environmental factors (aeroallergens, air polution, and weather) and asthma emergency admissions to a hospital in Mexico City. Allergy 53:394–401

    Article  CAS  PubMed  Google Scholar 

  6. Valverde M, Lopez M, Lopez I, Sanchez I, Fortoul T, Ostroky-Wegman P et al (1997) DNA damage in leukocytes and buccal nasal epithelial cells of individuals exposed to air pollution in Mexico City. Environ Mol Mutagen 30:147–152

    Article  CAS  PubMed  Google Scholar 

  7. Borja-Aburto VH, Loomis DP, Bangdiwala SI, Shy CM, Rascon-Pacheco RA (1997) Ozone, suspended particulates, and daily mortality in Mexico City. Am J Epidemiol 145:258–268

    Article  CAS  PubMed  Google Scholar 

  8. Hernández-Garduño E, Pérez-Neira J, Paccagnella A, Piña-García M, Munguía-Castro M, Catalan-Vazquez M et al (1997) Air pollution and respiratory health in Mexico City. J Occup Environ Med 39:299–307

    Article  PubMed  Google Scholar 

  9. SUIVE/DGE/SALUD/Información Epidemiológica de Morbilidad, Anuario 2011. Versión Ejecutiva

  10. Eduard W, Heederik D, Duchaine C, Green BJ (2012) Bioaerosol exposure assessment in the workplace: the past, present and recent advances. J Environ Monit 14:334–339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Stetzenbach DL, Buttner PM, Cruz P (2004) Detection and enumeration of airborne biocontaminants. Curr Opin Biotechnol 15:170–174

    Article  CAS  PubMed  Google Scholar 

  12. Rivera-González LO, Zhang Z, Sánchez BN, Zhang K, Brown DG, Rojas-Bracho L et al (2015) An assessment of air pollutant exposure methods in Mexico City, Mexico. J Air Waste Manag Assoc 65:581–591

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sánchez-Monedero AM, Roig A, Cayuela LM, Stentiford IE (2006) Emisión de Bioaerosoles Asociada a la Gestión de Residuos Orgánicos. Ingeniería 10:39–47

    Google Scholar 

  14. MacNeil L, Kauri T, Robertson W (1995) Molecular techniques and their potential application in monitoring the microbiological quality of indoor air. Can J Microbiol 41:657–665

    Article  CAS  PubMed  Google Scholar 

  15. Walter MV, Marthi B, Fieland VP, Ganio LM (1990) Effect of aerosolization on subsequent bacterial survival. Appl Environ Microbiol 56:3468–3472

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Marthi B, Fieland VP, Walter M, Seidler RJ (1990) Survival of bacteria during aerosolization. Appl Environ Microbiol 56:3463–3467

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Domínguez-Malfavón L, Cervantes-González E, García-Romero JR, Torres-Mota AM, Rojas-Avelizapa NG, García-Mena J (2007) Study of the microbial diversity of outdoor bioaerosols of Mexico City. In: Wang Y, Li S, Huang P, Yang Y, An Y, Sun X (eds) Progress in environmental science and technology (vol. I). Proceedings of the 2007 International Symposium on Environmental Science and Technology. Beijing, China, November 13–16, 2007. ISBN 978-7-03-020403-5. Published by Science Press 16 Donghuangchenggen North Street, Beijing, 100717, P. R. China pp 502–509.

  18. Kawasaki T, Kyotani T, Ushiogi T, Lee H (2013) Distribution of airborne bacteria in railway stations in Tokyo, Japan. J Occup Health 55:495–502

    Article  PubMed  Google Scholar 

  19. Gangamma S (2014) Characteristics of airborne bacteria in Mumbai urban environment. Sci Total Environ 488–489:70–74

    Article  PubMed  Google Scholar 

  20. Brooks PJ, Gerba PC, Pepper LI (2004) Aerosol emission, fate, and transport from municipal and animal wastes. J Residuals Sci Technol 1:13–25

    Google Scholar 

  21. Tanner BD, Brooks JP, Haas CN, Gerba CP, Pepper IL (2005) Bioaerosol emission rate and plume characteristics during land application of liquid class B biosolids. Environ Sci Technol 39:1584–1590

    Article  CAS  PubMed  Google Scholar 

  22. Paez-Rubio T, Viau E, Romero-Hernández S, Peccia J (2005) Source bioaerosol concentration and rRNA gene-based identification of microorganisms aerosolized at a flood irrigation wastewater reuse site. Appl Environ Microbiol 71:804–810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Thorne PS, Kiekhaefer MS, Whitten P, Donham KJ (1992) Comparison of bioaerosol sampling methods in barns housing swine. Appl Environ Microbiol 58:2543–2551

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Dybwad M, Granum PE, Bruheim P, Blatny JM (2012) Characterization of airborne bacteria at an underground subway station. Appl Environ Microbiol 78:1917–1929

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Okten S, Asan A (2012) Airborne fungi and bacteria in indoor and outdoor environment of the Pediatric Unit of Edirne Government Hospital. Environ Monit Assess 184:1739–1751

    Article  PubMed  Google Scholar 

  26. Fang Z, Ouyang Z, Zheng H, Wang X, Hu L (2007) Culturable airborne bacteria in outdoor environments in Beijing, China. Microb Ecol 54:487–496

    Article  PubMed  Google Scholar 

  27. Rodríguez-Gómez S, Sauri MR, Peniche I, Pacheco J, Ramírez JM (2005) Aerotransportables viables en el área de tratamiento y disposición final de residuos sólidos municipales de Mérida, Yucatán. Ingeniería 9:19–29

    Google Scholar 

  28. CONAPO (2012) Delimitación de las zonas metropolitanas de México 2010. http://www.conapo.gob.mx/es/CONAPO/Zonas_metropolitanas_2010

  29. Buttner MP, Willeke K, Grinshpun SA (1997) Sampling and analysis of airborne microorganisms. In Section VII, Aerobiology. Stetzenbach, LD (vol. ed.). Manual of environmental microbiology. Hurst CJ (ed. in chief). ASM Press, Washington. pp 629–640.

  30. Lohmann C, Sabou M, Moussaoui W, Prévost G, Delarbre JM, Candolfi E et al (2013) Comparison between the Biflex III-Biotyper and the Axima-SARAMIS systems for yeast identification by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol 51:1231–1236

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Brosius J, Palmer ML, Poindexter JK, Noller HF (1978) Complete nucleotide sequence of a 16S Ribosomal RNA gene from Escherichia coli. Proc Natl Acad Sci U S A 75:4801–4805

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ehresmann C, Stiegler P, Fellner P, Ebel JP (1972) The determination of the primary structure of the 16S ribosomal RNA of Escherichia coli. 2. Nucleotide sequences of products from partial enzymatic hydrolysis. Biochimie 54:901–967

    Article  CAS  PubMed  Google Scholar 

  33. Hall TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser 41:95–98

    CAS  Google Scholar 

  34. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  CAS  PubMed  Google Scholar 

  35. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL (2008) NCBI BLAST: a better web interface. Nucleic Acids Res 36:W5–W9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425

    CAS  PubMed  Google Scholar 

  38. Felsenstein J (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39:783–791

    Article  PubMed  Google Scholar 

  39. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 30:2725–2729

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Fierer N, Hamady M, Lauber CL, Knight R (2008) The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc Natl Acad Sci U S A 105:17994–17999

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Whiteley AS, Jenkins S, Waite I, Kresoje N, Payne H, Mullan B et al (2012) Microbial 16S rRNA Ion Tag and community metagenome sequencing using the Ion Torrent (PGM) Platform. J Microbiol Meth 91:80–88

    Article  CAS  Google Scholar 

  42. Murugesan S, Ulloa-Martínez M, Martínez-Rojano H, Galván-Rodríguez FM, Miranda-Brito C, Romano MC et al (2015) Study of the diversity and short-chain fatty acids production by the bacterial community in overweight and obese Mexican children. Eur J Clin Microbiol Infect Dis 34:1337–1346

    Article  CAS  PubMed  Google Scholar 

  43. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461

    Article  CAS  PubMed  Google Scholar 

  45. Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G et al (2011) Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res 21:494–504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K et al (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R (2013) EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2:16

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bartram AK, Lynch MD, Stearns JC, Moreno-Hagelsieb G, Neufeld JD (2011) Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end Illumina reads. Appl Environ Microbiol 77:3846–3852

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Huse SM, Dethlefsen L, Huber JA, Welch DM, Relman DA, Sogin ML (2008) Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. PLoS Genet 4(11), e1000255

    Article  PubMed  PubMed Central  Google Scholar 

  50. Yamaguchi N, Park J, Kodama M, Ichijo T, Baba T, Nasu M (2014) Changes in the airborne bacterial community in outdoor environments following Asian dust events. Microbes Environ 29:82–88

    Article  PubMed  PubMed Central  Google Scholar 

  51. Bowers RM, McCubbin MB, Hallar AG, Fierer N (2012) Seasonal variability in airborne bacterial communities at a high-elevation site. Atmos Environ 50:41–49

    Article  CAS  Google Scholar 

  52. Tanaka D, Terada Y, Nakashima T, Sakatoku A, Nakamura S (2015) Seasonal variations in airborne bacterial community structures at a suburban site of central Japan over a 1-year time period using PCR-DGGE method. Aerobiologia 31:143–157

    Article  Google Scholar 

  53. McDonald LC, Banerjee SN, Jarvis WR (1999) Seasonal variation of Acinetobacter infections: 1987–1996. Nosocomial Infections Surveillance System. Clin Infect Dis 29:1133–1137

    Article  CAS  PubMed  Google Scholar 

  54. Eber MR, Shardell M, Schweizer ML, Laxminarayan R, Perencevich EN (2011) Seasonal and temperature-associated increases in gram-negative bacterial bloodstream infections among hospitalized patients. PLoS One 6, e25298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Rule AM, Schwab KJ, Kesavan J, Buckley TJ (2009) Assessment of bioaerosol generation and sampling efficiency based on Pantoea agglomerans. Aerosol Sci Technol 43:620–628

    Article  CAS  Google Scholar 

  56. Środowiskai O, Naturalnych Z (2014) Bioaerosol concentration in the air surrounding municipal solid waste landfill. Environ Protect Nat Res 25:17–25

    Google Scholar 

  57. Eskin N, Vessey K, Tian L (2014) Research progress and perspectives of nitrogen fixing bacterium, Gluconacetobacter diazotrophicus, in monocot plants. Int J Agron 2014:208383

    Article  Google Scholar 

  58. Bahashwan SA, El Shafey HM (2013) Antimicrobial resistance patterns of Proteus isolates from clinical specimens. Eur Sci J 9:188–202

    Google Scholar 

  59. Rodrigues DF, Tiedje JM (2007) Multi-locus real-time PCR for quantitation of bacteria in the environment reveals Exiguobacterium to be prevalent in permafrost. FEMS Microbiol Ecol 59:489–499

    Article  CAS  PubMed  Google Scholar 

  60. Tena D, Martínez NM, Casanova J, García JL, Román E, Medina MJ et al (2014) Possible Exiguobacterium sibiricum skin infection in human [letter]. Emerg Infect Dis. doi:10.3201/eid2012.140493 [Internet]

    PubMed  PubMed Central  Google Scholar 

  61. Klein EY, Sun L, Smith DL, Laxminarayan R (2013) The changing epidemiology of methicillin-resistant Staphylococcus aureus in the United States: a national observational study. Am J Epidemiol 7:666–674

    Article  Google Scholar 

Download references

Acknowledgments

This work was financed by Cinvestav, REMAS-CONACyT 0123119, CONACyT AP LIC 104881, ICYTDF/324/2009 FOLIO: 0649, CONACyT 163235 INFR-2011-01, and FONSEC SS/IMSS/ISSSTE-CONACYT-233361 granted to JGM. We thank a Postdoctoral Fellowship from FONSEC SS/IMSS/ISSSTE-CONACYT-233361 granted to SM. We also thank Mrs. Antonia López Salazar for clerical assistance and Mr. José Rodrigo García Gutiérrez for technical assistance.

Author information

Author notes

  1. Marlenne Gómez-Ramírez

    Present address: CICATA-Querétaro, Cerro Blanco 141, Colinas del Cimatario, 76090, Santiago de Querétaro, Querétaro, Mexico

  2. Elsa Cervantes-González

    Present address: Departamento de Ingeniería Química, Universidad Autónoma de San Luis Potosí, 78700, Matehuala, Mexico

Authors and Affiliations

  1. Departamento de Genética y Biología Molecular, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, 07360, Ciudad de México, Mexico

    Jaime García-Mena, Selvasankar Murugesan, Ashael Alfredo Pérez-Muñoz, Otoniel Maya, Monserrat Jacinto-Montiel, Giselle Monsalvo-Ponce, Alberto Piña-Escobedo, Lilianha Domínguez-Malfavón, Marlenne Gómez-Ramírez & Elsa Cervantes-González

  2. Escuela Nacional de Medicina y Homeopatía del IPN, Guillermo Massieu Helguera 239, 07320, Ciudad de México, Mexico

    Matilde García-Espitia

  3. Departamento El Hombre y su Ambiente, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, 04960, Ciudad de México, Mexico

    María Teresa Núñez-Cardona

Authors
  1. Jaime García-Mena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selvasankar Murugesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashael Alfredo Pérez-Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matilde García-Espitia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Otoniel Maya
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Monserrat Jacinto-Montiel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Giselle Monsalvo-Ponce
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alberto Piña-Escobedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lilianha Domínguez-Malfavón
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Marlenne Gómez-Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Elsa Cervantes-González
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. María Teresa Núñez-Cardona
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaime García-Mena.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflicts of interest. The authors alone are responsible for the content and writing of the paper. The authors declared that they have no financial and personal relationships with other people or organizations that can inappropriately influence their work. There is no professional or other personal interest of any nature or kind in any product, service, and/or company.

Additional information

An erratum to this article is available at https://doi.org/10.1007/s00248-017-1046-3.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 14 kb)

Fig. S1

LB and Blood Agar plates from gravity sampling. The figure shows digital images of representative sampling plates of the surveys. a) Typical bacterial colony growth on Luria-Bertani (LB) and b) Typical bacterial colony growth on Blood-Agar (BA); hemolysis halos can be observed around some colonies (GIF 158 kb)

High resolution image (TIF 2644 kb)

Fig. S2

Escherichia coli 16S rDNA gene and primers. Figure shows a representation of the Brosius 1,541 bp rrnB 16S ribosomal gene of E. coli. Primers used for PCR are shown: First Set, forward FBac 5′-ATC ATG GCT CAG ATT GAA CGC-3′ (complementary positions 16-36), reverse RBAc 5′-ACT CCT ACG GGA GGC AGC AG-3′ (position 337-356); Second Set, CGO Forward 465 5′-CTC CTA CGG GAG GCA GCA G-3′ (positions 338-356), CGO Reverse 465 5′-CAG GAT TAG ATA CCC TGG TAG-3′, (positions 782-802); and Third Set, CGO Forward 605 5′-CAG GAT TAG ATA CCC TGG TAG-3′ (positions 782-802), CGO Reverse 605 5′-CGG TGA ATA CGT TCC CGG G-3′ (1368-1386). The primer V3-341F which indicates the complementary sequence for the seven forward primers used for 16S rDNA based high-throughput sequencing (V3-341F2, V3-341F4, V3-341F7, V3-341F8, V3-341F9, V3-341F18, V3-341F49) (position 334-354), and the reverse V3-518R primer (position 506-529) have described elsewhere [42]. Solid arrows indicate complementary regions for primers; larger gray color filled arrows from V1 to V9, indicate the polymorphic variable sequences in the molecule: GenBank: J01859.1 [31, 32] (GIF 15 kb)

High resolution image (TIF 818 kb)

Fig. S3

Mass spectroscopy identification of selected bacteria. The figure shows the spectra of selected isolated bacteria in the 2008 survey in GMC, generated using the Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry, technology AXIMA@SARAMIS from Anagnostec as described in Material and methods [30]. Identified bacteria using this method are: a) Bacillus subtilis; b) Staphylococcus epidermidis; c) Bacillus subtilis; d) Bacillus pumilus; e) Bacillus subtilis; f) Staphylococcus xylosus; g) Bacillus cereus; h) Not analyzed by Anagnostec; i) Bacillus subtilis; j) Staphylococcus epidermidis; k) Bacillus pumilus; l) Bacillus pumilus; m) Bacillus pumilus; n) Staphylococcus saprophyticus; o) Bacillus pumilus; p) Psychrobacter phenylpyruvicus; q) Not identified by the AXIMA@SARAMIS database; r) Psychrobacter phenylpyruvicus; s) Bacillus megaterium; t) Bacillus sp.; u) Bacillus subtilis; v) Bacillus coagulans; w) Psychrobacter phenylpyruvicus; x) Bacillus sp. See bacteria in Table 3 (GIF 224 kb)

(GIF 262 kb)

High resolution image (TIF 700 kb)

High resolution image (TIF 814 kb)

Fig. S4

Beta diversity. Unweighted UniFrac analyses were used to calculate distances between samples obtained from the seven surveys, determined by massive sequencing of V3-16S rDNA libraries prepared from DNA from bacterial growth on LB plates as described in Materials and methods. Three-dimensional scatter plots were generated by principal coordinates analysis (PCoA). (GIF 33 kb)

High resolution image (TIF 2190 kb)

Fig. S5

Alpha diversity Rarefaction curves. Rarefaction curves were generated using massive sequencing data of V3-16S rDNA libraries from the seven surveys, as described in Materials and methods. The vertical axes display the diversity of the community, while the horizontal axes display the number of sequences considered in the diversity calculation. Each color indicates diversity of community for each survey. Panel A, displays rarefaction curves of the number of observed species was calculated at a similarity threshold of 97 %; Panel B, represents the rarefaction curves using the Shannon index; Panel C, shows rarefaction curves using the Chao1. (GIF 61 kb)

High resolution image (TIF 3992 kb)

Fig. S6

Jacknifed beta diversity. Unweighted UniFrac analyses were used to calculate distances among sequencing data of the seven surveys, determined by massive sequencing of V3-16S rDNA libraries prepared as described in Materials and methods; tree chart was generated by using Jacknifing method. (GIF 16 kb)

High resolution image (TIF 1392 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

García-Mena, J., Murugesan, S., Pérez-Muñoz, A.A. et al. Airborne Bacterial Diversity from the Low Atmosphere of Greater Mexico City. Microb Ecol 72, 70–84 (2016). https://doi.org/10.1007/s00248-016-0747-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00248-016-0747-3

Keywords

Search

Navigation