Advertisement

Microbial Ecology

, Volume 76, Issue 1, pp 125–143 | Cite as

Diversity and Cyclical Seasonal Transitions in the Bacterial Community in a Large and Deep Perialpine Lake

  • Nico SalmasoEmail author
  • Davide Albanese
  • Camilla Capelli
  • Adriano Boscaini
  • Massimo Pindo
  • Claudio Donati
Microbiology of Aquatic Systems

Abstract

High-throughput sequencing (HTS) was used to analyze the seasonal variations in the bacterioplankton community composition (BCC) in the euphotic layer of a large and deep lake south of the Alps (Lake Garda). The BCC was analyzed throughout two annual cycles by monthly samplings using the amplification and sequencing of the V3–V4 hypervariable region of the 16S rRNA gene by the MiSeq Illumina platform. The dominant and most diverse bacterioplankton phyla were among the more frequently reported in freshwater ecosystems, including the Proteobacteria, Cyanobacteria, Bacteroidetes, Verrucomicrobia, Actinobacteria, and Planctomycetes. As a distinctive feature, the development of the BCC showed a cyclical temporal pattern in the two analyzed years and throughout the euphotic layer. The recurring temporal development was controlled by the strong seasonality in water temperature and thermal stratification, and by cyclical temporal changes in nutrients and, possibly, by the remarkable annual cyclical development of cyanobacteria and eukaryotic phytoplankton hosting bacterioplankton that characterizes Lake Garda. Further downstream analyses of operational taxonomic units associated to cyanobacteria allowed confirming the presence of the most abundant taxa previously identified by microscopy and/or phylogenetic analyses, as well as the presence of other small Synechococcales/Chroococcales and rare Nostocales never identified so far in the deep lakes south of the Alps. The implications of the high diversity and strong seasonality are relevant, opening perspectives for the definition of common and discriminating patterns characterizing the temporal and spatial distribution in the BCC, and for the application of the new sequencing technologies in the monitoring of water quality in large and deep lakes.

Keywords

Bacterioplankton Cyanobacteria High-throughput sequencing Metagenomic Temporal cyclical patterns Deep lakes 

Notes

Acknowledgements

Investigations were carried out in the framework of the LTER (Long Term Ecological Research) Italian network, site Southern Alpine lakes, IT08-000-A (http://www.lteritalia.it/), with the support of the ARPA Veneto (Giorgio Franzini and colleagues). We thank our colleagues in FEM, in particular Lorena Ress, Milva Tarter and Andrea Zampedri, for their support in the field and/or laboratory activities. We are grateful to Veronica De Sanctis and Roberto Bertorelli (NGS Facility at the Centre for Integrative Biology and LaBSSAH, University of Trento) for HTS analyses and helpful discussions. The activity was supported by a PhD fellowship (FIRS>T) to C.C. from the E. Mach Foundation – Istituto Agrario di S. Michele all’Adige. We thank the European Cooperation in Science and Technology COST Action ES1105 CYANOCOST for networking and knowledge transfer support. We are grateful to three anonymous reviewers for valuable comments and suggestions on an earlier version of the manuscript.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

248_2017_1120_MOESM1_ESM.docx (3.2 mb)
ESM 1 (DOCX 3270 kb)

References

  1. 1.
    Falkowski PG, Fenchel T, Delong EF (2008) The microbial engines that drive Earth’s biogeochemical cycles. Science (New York, NY) 320:1034–1039.  https://doi.org/10.1126/science.1153213 CrossRefGoogle Scholar
  2. 2.
    Fenchel T (2008) The microbial loop—25 years later. J Exp Mar Biol Ecol 366:99–103.  https://doi.org/10.1016/j.jembe.2008.07.013 CrossRefGoogle Scholar
  3. 3.
    Weisse T (2004) Pelagic microbes—protozoa and the microbial food web. In: O’Sullivan PE, Reynolds CS (eds) The lakes handbook. Volume 1. Limnology and limnetic ecology. Blackwell Publishing, Malden, pp 417–460Google Scholar
  4. 4.
    De Wever A, Muylaert K, Van der Gucht K et al (2005) Bacterial community composition in Lake Tanganyika: vertical and horizontal heterogeneity. Appl. Environ. Microbiol. 71:5029–5037.  https://doi.org/10.1128/AEM.71.9.5029-5037.2005 PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Plasencia A, Gich F, Fillol M, Borrego CM (2013) Phylogenetic characterization and quantification of ammonia-oxidizing archaea and bacteria from Lake Kivu in a long-term microcosm incubation. Int Microbiol 16:177–189.  https://doi.org/10.2436/20.1501.01.192 PubMedCrossRefGoogle Scholar
  6. 6.
    Brown JW (2015) Principles of microbial diversity.  https://doi.org/10.1128/9781555818517
  7. 7.
    Pessi IS, Maalouf PDC, Laughinghouse HD et al (2016) On the use of high-throughput sequencing for the study of cyanobacterial diversity in Antarctic aquatic mats. J Phycol 52:356–368.  https://doi.org/10.1111/jpy.12399 PubMedCrossRefGoogle Scholar
  8. 8.
    Tytgat B, Verleyen E, Obbels D et al (2014) Bacterial diversity assessment in antarctic terrestrial and aquatic microbial mats: a comparison between bidirectional pyrosequencing and cultivation. PLoS One 9:e97564.  https://doi.org/10.1371/journal.pone.0097564 PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Venter JC, Remington K, Heidelberg JF et al (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science (New York, NY) 304:66–74.  https://doi.org/10.1126/science.1093857 CrossRefGoogle Scholar
  10. 10.
    Sunagawa S, Coelho LP, Chaffron S et al (2015) Ocean plankton. Structure and function of the global ocean microbiome. Science (New York, N.Y.) 348:1261359.  https://doi.org/10.1126/science.1261359 CrossRefGoogle Scholar
  11. 11.
    Tringe SG, Hugenholtz P (2008) A renaissance for the pioneering 16S rRNA gene. Curr. Opin. Microbiol. 11:442–446.  https://doi.org/10.1016/j.mib.2008.09.011 PubMedCrossRefGoogle Scholar
  12. 12.
    Oulas A, Pavloudi C, Polymenakou P et al (2015) Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinf Biol Insights 9:75–88.  https://doi.org/10.4137/BBI.S12462 CrossRefGoogle Scholar
  13. 13.
    Tammert H, Tšertova N, Kiprovskaja J et al (2015) Contrasting seasonal and interannual environmental drivers in bacterial communities within a large shallow lake: evidence from a seven year survey. Aquat. Microb. Ecol. 75:43–54.  https://doi.org/10.3354/ame01744 CrossRefGoogle Scholar
  14. 14.
    Beall BFN, Twiss MR, Smith DE et al (2016) Ice cover extent drives phytoplankton and bacterial community structure in a large north-temperate lake: implications for a warming climate. Environ. Microbiol. 18:1704–1719.  https://doi.org/10.1111/1462-2920.12819 PubMedCrossRefGoogle Scholar
  15. 15.
    Salmaso N, Mosello R (2010) Limnological research in the deep southern subalpine lakes: synthesis, directions and perspectives. Adv. Oceanogr. Limnol. 1:29–66.  https://doi.org/10.1080/19475721003735773 CrossRefGoogle Scholar
  16. 16.
    Bertoni R, Callieri C, Corno G et al (2010) Long-term trends of epilimnetic and hypolimnetic bacteria and organic carbon in a deep holo-oligomictic lake. Hydrobiologia 644:279–287.  https://doi.org/10.1007/s10750-010-0150-x CrossRefGoogle Scholar
  17. 17.
    Callieri C, Cronberg G, Stockner JG (2012) Freshwater picocyanobacteria: single cells, microcolonies and colonial forms. Springer, Netherlands, pp 229–269Google Scholar
  18. 18.
    Callieri C, Amalfitano S, Corno G, Bertoni R (2016) Grazing-induced Synechococcus microcolony formation: experimental insights from two freshwater phylotypes. FEMS Microbiol Ecol 92:fiw154.  https://doi.org/10.1093/femsec/iw154 PubMedCrossRefGoogle Scholar
  19. 19.
    Coci M, Odermatt N, Salcher MM, et al (2015) Ecology and distribution of Thaumarchaea in the deep hypolimnion of Lake Maggiore. Archaea 2015/59043:11 pp.  https://doi.org/10.1155/2015/590434
  20. 20.
    Callieri C, Hernández-Avilés S, Salcher MM et al (2016) Distribution patterns and environmental correlates of Thaumarchaeota abundance in six deep subalpine lakes. Aquat. Sci. 78:215–225.  https://doi.org/10.1007/s00027-015-0418-3 CrossRefGoogle Scholar
  21. 21.
    Salmaso N, Morabito G, Mosello R et al (2003) A synoptic study of phytoplankton in the deep lakes south of the Alps (lakes Garda, Iseo, Como, Lugano and Maggiore). J. Limnol. 62:207.  https://doi.org/10.4081/jlimnol.2003.207 CrossRefGoogle Scholar
  22. 22.
    Salmaso N, Padisák J (2007) Morpho-functional groups and phytoplankton development in two deep lakes (Lake Garda, Italy and Lake Stechlin, Germany). Hydrobiologia 578:97–112.  https://doi.org/10.1007/s10750-006-0437-0 CrossRefGoogle Scholar
  23. 23.
    Salmaso N (2011) Interactions between nutrient availability and climatic fluctuations as determinants of the long-term phytoplankton community changes in Lake Garda, Northern Italy. Hydrobiologia 660:59–68.  https://doi.org/10.1007/s10750-010-0394-5 CrossRefGoogle Scholar
  24. 24.
    Meriluoto J, Blaha L, Bojadzija G et al (2017) Toxic cyanobacteria and cyanotoxins in European waters – recent progress achieved through the CYANOCOST. Action and challenges for further research. Adv Oceanogr Limnol 8:161–178.  https://doi.org/10.4081/aiol.2017.6429 CrossRefGoogle Scholar
  25. 25.
    Savela H, Spoof L, Perälä N et al (2017) First report of cyanobacterial paralytic shellfish toxin biosynthesis genes and paralytic shellfish toxin production in Polish freshwater lakes. Adv Oceanogr Limnol 8:61–70.  https://doi.org/10.4081/aiol.2017.6319 CrossRefGoogle Scholar
  26. 26.
    Sukenik A, Quesada A, Salmaso N (2015) Global expansion of toxic and non-toxic cyanobacteria: effect on ecosystem functioning. Biodivers Conserv 24:889–908.  https://doi.org/10.1007/s10531-015-0905-9 CrossRefGoogle Scholar
  27. 27.
    Shams S, Capelli C, Cerasino L et al (2015) Anatoxin-a producing Tychonema (Cyanobacteria) in European waterbodies. Water Res. 69:68–79.  https://doi.org/10.1016/j.watres.2014.11.006 PubMedCrossRefGoogle Scholar
  28. 28.
    Salmaso N, Cerasino L (2012) Long-term trends and fine year-to-year tuning of phytoplankton in large lakes are ruled by eutrophication and atmospheric modes of variability. Hydrobiologia 698:17–28.  https://doi.org/10.1007/s10750-012-1068-2 CrossRefGoogle Scholar
  29. 29.
    Read JS, Hamilton DP, Jones ID et al (2011) Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environ. Model Softw. 26:1325–1336.  https://doi.org/10.1016/j.envsoft.2011.05.006 CrossRefGoogle Scholar
  30. 30.
    Cerasino L, Salmaso N (2012) Diversity and distribution of cyanobacterial toxins in the Italian subalpine lacustrine district. Oceanol. Hydrobiol. Stud. 41:54–63.  https://doi.org/10.2478/s13545-012-0028-9 CrossRefGoogle Scholar
  31. 31.
    R Core Team (2017) R: A language and environment for statistical computing (v. 3.4.1). R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
  32. 32.
    Yuan S, Cohen DB, Ravel J et al (2012) Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One 7:e33865.  https://doi.org/10.1371/journal.pone.0033865 PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Bag S, Saha B, Mehta O et al (2016) An improved method for high quality metagenomics DNA extraction from human and environmental samples. Sci. Rep. 6:26775.  https://doi.org/10.1038/srep26775 PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Herlemann DP, Labrenz M, Jürgens K et al (2011) Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J 5:1571–1579.  https://doi.org/10.1038/ismej.2011.41 PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Klindworth A, Pruesse E, Schweer T et al (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41:e1.  https://doi.org/10.1093/nar/gks808 PubMedCrossRefGoogle Scholar
  36. 36.
    Apprill A, McNally S, Parsons R, Weber L (2015) Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75:129–137CrossRefGoogle Scholar
  37. 37.
    Albanese D, Fontana P, De Filippo C et al (2015) MICCA: a complete and accurate software for taxonomic profiling of metagenomic data. Sci. Rep. 5:9743.  https://doi.org/10.1038/srep09743 PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41:D590–D596.  https://doi.org/10.1093/nar/gks1219 PubMedCrossRefGoogle Scholar
  39. 39.
    Rognes T, Flouri T, Nichols B et al (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584.  https://doi.org/10.7717/peerj.2584 PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    DeSantis TZ, Hugenholtz P, Keller K et al (2006) NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Res. 34:W394–W399.  https://doi.org/10.1093/nar/gkl244 PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Price MN, Dehal PS, Arkin AP (2010) FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS One 5:e9490.  https://doi.org/10.1371/journal.pone.0009490 PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217.  https://doi.org/10.1371/journal.pone.0061217 PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71:8228–8235.  https://doi.org/10.1128/AEM.71.12.8228-8235.2005 PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Legendre P, Gallagher E (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129:271–280.  https://doi.org/10.1007/s004420100716 PubMedCrossRefGoogle Scholar
  45. 45.
    Legendre P, Legendre L (1998) Numerical ecology, Second Eng. Elsevier Science BV, AmsterdamGoogle Scholar
  46. 46.
    Jackson DA (1995) PROTEST: a PROcrustean randomization TEST of community environment concordance. Écoscience 2:297–303.  https://doi.org/10.1080/11956860.1995.11682297 CrossRefGoogle Scholar
  47. 47.
    Oksanen J, Blanchet FG, Friendly M, et al (2016) vegan: Community Ecology Package. 285Google Scholar
  48. 48.
    Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46.  https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x CrossRefGoogle Scholar
  49. 49.
    Flores GE, Bates ST, Knights D et al (2011) Microbial biogeography of public restroom surfaces. PLoS One 6:e28132.  https://doi.org/10.1371/journal.pone.0028132 PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer-Verlag, New YorkCrossRefGoogle Scholar
  51. 51.
    Garnier S (2017) Viridis: default color maps from “matplotlib”. R package version 0.4.0Google Scholar
  52. 52.
    Yoon S-H, Ha S-M, Kwon S et al (2017) Introducing EzBioCloud: a taxonomically united database of 16S rRNA and whole genome assemblies. Int. J. Syst. Evol. Microbiol.  https://doi.org/10.1099/ijsem.0.001755
  53. 53.
    Cole JR, Wang Q, Fish JA et al (2014) Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42:D633–D642.  https://doi.org/10.1093/nar/gkt1244 PubMedCrossRefGoogle Scholar
  54. 54.
    Eren AM, Maignien L, Sul WJ et al (2013) Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4:1111–1119.  https://doi.org/10.1111/2041-210X.12114 PubMedCentralCrossRefGoogle Scholar
  55. 55.
    Fisher JC, Levican A, Figueras MJ, McLellan SL (2014) Population dynamics and ecology of Arcobacter in sewage. Front. Microbiol. 5:525.  https://doi.org/10.3389/fmicb.2014.00525 PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Berry MA, White JD, Davis TW et al (2017) Are oligotypes meaningful ecological and phylogenetic units? A case study of Microcystis in freshwater lakes. Front. Microbiol. 8:365.  https://doi.org/10.3389/fmicb.2017.00365 PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Eren AM, Morrison HG, Lescault PJ et al (2015) Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J 9:968–979.  https://doi.org/10.1038/ismej.2014.195 PubMedCrossRefGoogle Scholar
  58. 58.
    Ercolini D (2013) High-throughput sequencing and metagenomics: moving forward in the culture-independent analysis of food microbial ecology. Appl. Environ. Microbiol. 79:3148–3155.  https://doi.org/10.1128/AEM.00256-13 PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Boone DR, Castenholz RW (2001) Bergey’s manual of systematic bacteriology. Volume one—the archaea and the deeply branching and phototrophic bacteria. Springer Verlag, New YorkGoogle Scholar
  60. 60.
    Salmaso N, Capelli C, Shams S, Cerasino L (2015) Expansion of bloom-forming Dolichospermum lemmermannii (Nostocales, Cyanobacteria) to the deep lakes south of the Alps: colonization patterns, driving forces and implications for water use. Harmful Algae 50:76–87.  https://doi.org/10.1016/j.hal.2015.09.008 CrossRefGoogle Scholar
  61. 61.
    Salmaso N, Cerasino L, Boscaini A, Capelli C (2016) Planktic Tychonema (Cyanobacteria) in the large lakes south of the Alps: phylogenetic assessment and toxigenic potential. FEMS Microbiol. Ecol.  https://doi.org/10.1093/femsec/fiw155
  62. 62.
    Capelli C, Ballot A, Cerasino L et al (2017) Biogeography of bloom-forming microcystin producing and non-toxigenic populations of Dolichospermum lemmermannii (Cyanobacteria). Harmful Algae 67:1–12.  https://doi.org/10.1016/j.hal.2017.05.004 PubMedCrossRefGoogle Scholar
  63. 63.
    Rieck A, Herlemann DPR, Jürgens K, Grossart H-P (2015) Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front. Microbiol. 6:1297.  https://doi.org/10.3389/fmicb.2015.01297 PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Merkel AY, Korneeva VA, Tarnovetskii IY et al (2015) Structure of the archaeal community in the Black Sea photic zone. Microbiology 84:570–576.  https://doi.org/10.1134/S0026261715040128 CrossRefGoogle Scholar
  65. 65.
    Milici M, Deng Z-L, Tomasch J et al (2016) Co-occurrence analysis of microbial taxa in the Atlantic Ocean reveals high connectivity in the free-living bacterioplankton. Front. Microbiol. 7:649.  https://doi.org/10.3389/fmicb.2016.00649 PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Doherty M, Yager PL, Moran MA et al (2017) Bacterial biogeography across the Amazon River-ocean continuum. Front. Microbiol. 8:882.  https://doi.org/10.3389/fmicb.2017.00882 PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Yang C, Wang Q, Simon PN et al (2017) Distinct network interactions in particle-associated and free-living bacterial communities during a Microcystis aeruginosa bloom in a plateau lake. Front. Microbiol. 8:1202.  https://doi.org/10.3389/fmicb.2017.01202 PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Kurilkina MI, Zakharova YR, Galachyants YP et al (2016) Bacterial community composition in the water column of the deepest freshwater Lake Baikal as determined by next-generation sequencing. FEMS Microbiol. Ecol. 92:fiw094.  https://doi.org/10.1093/femsec/fiw094 PubMedCrossRefGoogle Scholar
  69. 69.
    Llirós M, Inceoğlu Ö, García-Armisen T et al (2014) Bacterial community composition in three freshwater reservoirs of different alkalinity and trophic status. PLoS One 9:e116145.  https://doi.org/10.1371/journal.pone.0116145 PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Kara EL, Hanson PC, Hu YH et al (2013) A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J 7:680–684.  https://doi.org/10.1038/ismej.2012.118 PubMedCrossRefGoogle Scholar
  71. 71.
    Pollet T, Tadonleke RD, Humbert JF (2011) Spatiotemporal changes in the structure and composition of a less-abundant bacterial phylum (Planctomycetes) in two perialpine lakes. Appl. Environ. Microbiol. 77:4811–4821.  https://doi.org/10.1128/AEM.02697-10 PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Pollet T, Humbert J-F, Tadonléké RD (2014) Planctomycetes in lakes: poor or strong competitors for phosphorus? Appl. Environ. Microbiol. 80:819–828.  https://doi.org/10.1128/AEM.02824-13 PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Logue JB, Langenheder S, Andersson AF et al (2012) Freshwater bacterioplankton richness in oligotrophic lakes depends on nutrient availability rather than on species-area relationships. ISME J 6:1127–1136.  https://doi.org/10.1038/ismej.2011.184 PubMedCrossRefGoogle Scholar
  74. 74.
    Walsby AE (2005) Stratification by cyanobacteria in lakes: a dynamic buoyancy model indicates size limitations met by Planktothrix rubescens filaments. New Phytol 168:365–376.  https://doi.org/10.1111/j.1469-8137.2005.01508.x PubMedCrossRefGoogle Scholar
  75. 75.
    Padisák J, Soróczki-Pintér É, Rezner Z (2003) Sinking properties of some phytoplankton shapes and the relation of form resistance to morphological diversity of plankton—an experimental study. Hydrobiologia 500:243–257.  https://doi.org/10.1023/A:1024613001147 CrossRefGoogle Scholar
  76. 76.
    Kouzuma A, Watanabe K (2015) Exploring the potential of algae/bacteria interactions. Curr. Opin. Biotechnol. 33:125–129.  https://doi.org/10.1016/j.copbio.2015.02.007 PubMedCrossRefGoogle Scholar
  77. 77.
    Ramanan R, Kim B-H, Cho D-H et al (2016) Algae–bacteria interactions: evolution, ecology and emerging applications. Biotechnol. Adv. 34:14–29.  https://doi.org/10.1016/j.biotechadv.2015.12.003 PubMedCrossRefGoogle Scholar
  78. 78.
    Secker NH, Chua JPS, Laurie RE et al (2016) Characterization of the cyanobacteria and associated bacterial community from an ephemeral wetland in New Zealand. J. Phycol. 52:761–773.  https://doi.org/10.1111/jpy.12434 PubMedCrossRefGoogle Scholar
  79. 79.
    Parveen B, Mary I, Vellet A et al (2013) Temporal dynamics and phylogenetic diversity of free-living and particle-associated Verrucomicrobia communities in relation to environmental variables in a mesotrophic lake. FEMS Microbiol. Ecol. 83:189–201.  https://doi.org/10.1111/j.1574-6941.2012.01469.x PubMedCrossRefGoogle Scholar
  80. 80.
    Seymour JR, Amin SA, Raina J-B, Stocker R (2017) Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat Microbiol 2:17065.  https://doi.org/10.1038/nmicrobiol.2017.65 PubMedCrossRefGoogle Scholar
  81. 81.
    Sigee DC (2005) Freshwater microbiology: biodiversity and dynamic interactions of microorganisms in the aquatic environment. J. WileyGoogle Scholar
  82. 82.
    Friedman J, Alm EJ (2012) Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8:e1002687.  https://doi.org/10.1371/journal.pcbi.1002687 PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Dorado-Morales P, Vilanova C, Garay PC et al (2016) Unveiling bacterial interactions through multidimensional scaling and dynamics modeling. Sci. Rep. 5:18396.  https://doi.org/10.1038/srep18396 CrossRefGoogle Scholar
  84. 84.
    Newton RJ, Jones SE, Eiler A et al (2011) A guide to the natural history of freshwater lake bacteria. Microbiol Mol Biol Rev 75:14–49.  https://doi.org/10.1128/MMBR.00028-10 PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Eiler A, Ollson JA, Bertillson S (2006) Diurnal variations in the auto- and heterotrophic activity of cyanobacterial phycospheres (Gloeotrichia echinulata) and the identity of attached bacteria. Freshw. Biol. 51:298–311.  https://doi.org/10.1111/j.1365-2427.2005.01493.x CrossRefGoogle Scholar
  86. 86.
    Hahn MW, Kasalický V, Jezbera J et al (2010) Limnohabitans curvus gen. nov., sp. nov., a planktonic bacterium isolated from a freshwater lake. Int. J. Syst. Evol. Microbiol. 60:1358–1365.  https://doi.org/10.1099/ijs.0.013292-0 PubMedCrossRefGoogle Scholar
  87. 87.
    Hutalle-Schmelzer KML, Zwirnmann E, Krüger A, Grossart H-P (2010) Enrichment and cultivation of pelagic bacteria from a humic lake using phenol and humic matter additions. FEMS Microbiol. Ecol. 72:58–73.  https://doi.org/10.1111/j.1574-6941.2009.00831.x PubMedCrossRefGoogle Scholar
  88. 88.
    Corno G (2006) Effects of nutrient availability and Ochromonas sp. predation on size and composition of a simplified aquatic bacterial community. FEMS Microbiol. Ecol. 58:354–363.  https://doi.org/10.1111/j.1574-6941.2006.00185.x PubMedCrossRefGoogle Scholar
  89. 89.
    Salcher M, Pernthaler J, Psenner R, Posch T (2005) Succession of bacterial grazing defense mechanisms against protistan predators in an experimental microbial community. Aquat. Microb. Ecol. 38:215–229.  https://doi.org/10.3354/ame038215 CrossRefGoogle Scholar
  90. 90.
    Brenner DJ, Krieg NR, Staley JT, Garrity GM (2005) Bergey’s manual of systematic bacteriology—volume two the Proteobacteria. Springer, BerlinGoogle Scholar
  91. 91.
    Eiler A, Bertilsson S (2004) Composition of freshwater bacterial communities associated with cyanobacterial blooms in four Swedish lakes. Environ. Microbiol. 6:1228–1243.  https://doi.org/10.1111/j.1462-2920.2004.00657.x PubMedCrossRefGoogle Scholar
  92. 92.
    Zeder M, Peter S, Shabarova T, Pernthaler J (2009) A small population of planktonic Flavobacteria with disproportionally high growth during the spring phytoplankton bloom in a prealpine lake. Environ. Microbiol. 11:2676–2686.  https://doi.org/10.1111/j.1462-2920.2009.01994.x PubMedCrossRefGoogle Scholar
  93. 93.
    Pernthaler J, Zollner E, Warnecke F, Jurgens K (2004) Bloom of filamentous bacteria in a mesotrophic lake: identity and potential controlling mechanism. Appl. Environ. Microbiol. 70:6272–6281.  https://doi.org/10.1128/AEM.70.10.6272-6281.2004 PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Krieg NR, Staley JT, Brown DR, et al (2010) Bergey’s manual of systematic bacteriology, Vol. 4, 2nd Ed —The Bacteroidetes, Spirochaetes, Tenericutes (Mollicutes), Acidobacteria, Fibrobacteres, Fusobacteria, Dictyoglomi, Gemmatimonadetes, Lentisphaerae, Verrucomicrobia, Chlamydiae, Planctomycetes.  https://doi.org/10.1007/978-0-387-68572-4
  95. 95.
    Eiler A, Bertilsson S (2007) Flavobacteria blooms in four eutrophic lakes: linking population dynamics of freshwater bacterioplankton to resource availability. Appl. Environ. Microbiol. 73:3511–3518.  https://doi.org/10.1128/AEM.02534-06 PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    J-H Q, Yuan H-L (2008) Sediminibacterium salmoneum gen. nov., sp. nov., a member of the phylum Bacteroidetes isolated from sediment of a eutrophic reservoir. Int. J. Syst. Evol. Microbiol. 58:2191–2194.  https://doi.org/10.1099/ijs.0.65514-0 CrossRefGoogle Scholar
  97. 97.
    Kang H, Kim H, Lee B-I et al (2014) Sediminibacterium goheungense sp. nov., isolated from a freshwater reservoir. Int. J. Syst. Evol. Microbiol. 64:1328–1333.  https://doi.org/10.1099/ijs.0.055137-0 PubMedCrossRefGoogle Scholar
  98. 98.
    Birtel J, Walser J-C, Pichon S et al (2015) Estimating bacterial diversity for ecological studies: methods, metrics, and assumptions. PLoS One 10:e0125356.  https://doi.org/10.1371/journal.pone.0125356 PubMedPubMedCentralCrossRefGoogle Scholar
  99. 99.
    Ávila MP, Staehr PA, Barbosa FAR et al (2017) Seasonality of freshwater bacterioplankton diversity in two tropical shallow lakes from the Brazilian Atlantic Forest. FEMS Microbiol. Ecol. 93:fiw218.  https://doi.org/10.1093/femsec/fiw218 CrossRefGoogle Scholar
  100. 100.
    Chin KJ, Liesack W, Janssen PH (2001) Opitutus terrae gen. nov., sp. nov., to accommodate novel strains of the division “Verrucomicrobia” isolated from rice paddy soil. Int. J. Syst. Evol. Microbiol. 51:1965–1968.  https://doi.org/10.1099/00207713-51-6-1965 PubMedCrossRefGoogle Scholar
  101. 101.
    Glöckner FO, Zaichikov E, Belkova N et al (2000) Comparative 16S rRNA analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of Actinobacteria. Appl. Environ. Microbiol. 66:5053–5065.  https://doi.org/10.1128/AEM.66.11.5053-5065.2000 PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Ghylin TW, Garcia SL, Moya F et al (2014) Comparative single-cell genomics reveals potential ecological niches for the freshwater acI Actinobacteria lineage. ISME J 8:2503–2516.  https://doi.org/10.1038/ismej.2014.135 PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Garcia SL, McMahon KD, Martinez-Garcia M et al (2013) Metabolic potential of a single cell belonging to one of the most abundant lineages in freshwater bacterioplankton. ISME J 7:137–147.  https://doi.org/10.1038/ismej.2012.86 PubMedCrossRefGoogle Scholar
  104. 104.
    Zeng D-N, Fan Z-Y, Chi L et al (2013) Analysis of the bacterial communities associated with different drinking water treatment processes. World J. Microbiol. Biotechnol. 29:1573–1584.  https://doi.org/10.1007/s11274-013-1321-5 PubMedCrossRefGoogle Scholar
  105. 105.
    Hahn MW, Lünsdorf H, Wu Q et al (2003) Isolation of novel ultramicrobacteria classified as Actinobacteria from five freshwater habitats in Europe and Asia. Appl. Environ. Microbiol. 69:1442–1451.  https://doi.org/10.1128/AEM.69.3.1442-1451.2003 PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Okazaki Y, Fujinaga S, Tanaka A et al (2017) Ubiquity and quantitative significance of bacterioplankton lineages inhabiting the oxygenated hypolimnion of deep freshwater lakes. ISME J.  https://doi.org/10.1038/ismej.2017.89
  107. 107.
    Clum A, Tindall BJ, Sikorski J et al (2009) Complete genome sequence of Pirellula staleyi type strain (ATCC 27377). Stand. Genomic Sci. 1:308–316.  https://doi.org/10.4056/sigs.51657 PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Wilmotte A, Laughinghouse HDI, Capelli C et al (2017) Taxonomic identification of cyanobacteria by a polyphasic approach. In: Kurmayer R, Sivonen K, Wilmotte A, Salmaso N (eds) Molecular tools for the detection and quantification of toxigenic cyanobacteria. John Wiley, Hoboken, pp 79–119CrossRefGoogle Scholar
  109. 109.
    Plummer E, Twin J, Bulach DM et al (2015) A comparison of three bioinformatics pipelines for the analysis of preterm gut microbiota using 16S rRNA gene sequencing data. J Proteomics Bioinformatics.  https://doi.org/10.4172/jpb.1000381
  110. 110.
    Xiao X, Sogge H, Lagesen K et al (2014) Use of high throughput sequencing and light microscopy show contrasting results in a study of phytoplankton occurrence in a freshwater environment. PLoS One 9:e106510.  https://doi.org/10.1371/journal.pone.0106510 PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Kleinteich J, Hildebrand F, Wood SA et al (2014) Diversity of toxin and non-toxin containing cyanobacterial mats of meltwater ponds on the Antarctic Peninsula: a pyrosequencing approach. Antarct. Sci. 26:521–532.  https://doi.org/10.1017/S0954102014000145 CrossRefGoogle Scholar
  112. 112.
    Jasser I, Callieri C (2016) Picocyanobacteria—the smallest cell-size cyanobacteria. In: Meriluoto J, Spoof L, Codd GA (eds) Handbook on cyanobacterial monitoring and cyanotoxin analysis1st edn. Wiley, Chichester, pp 19–27Google Scholar
  113. 113.
    Sivonen K, Carmichael WW, Namikoshi M et al (1990) Isolation and characterization of hepatotoxic microcystin homologs from the filamentous freshwater cyanobacterium Nostoc sp. strain 152. Appl Environ Microbiol 56:2650–2657PubMedPubMedCentralGoogle Scholar
  114. 114.
    Bernard C, Ballot A, Thomazeau S et al (2017) Appendix 2. Cyanobacteria associated with the production of cyanotoxins. In: Meriluoto J, Spoof L, Codd GA (eds) Handbook on cyanobacterial monitoring and cyanotoxin analysis. Wiley, Hoboken, pp 501–525CrossRefGoogle Scholar
  115. 115.
    D’Alelio D, Salmaso N, Gandolfi A (2013) Frequent recombination shapes the epidemic population structure of Planktothrix (Cyanoprokaryota) in Italian subalpine lakes. J. Phycol. 49:1107–1117.  https://doi.org/10.1111/jpy.12116 PubMedCrossRefGoogle Scholar
  116. 116.
    Shih PM, Hemp J, Ward LM et al (2017) Crown group Oxyphotobacteria postdate the rise of oxygen. Geobiology 15:19–29.  https://doi.org/10.1111/gbi.12200 PubMedCrossRefGoogle Scholar
  117. 117.
    Di Rienzi SC, Sharon I, Wrighton KC et al (2013) The human gut and groundwater harbor non-photosynthetic bacteria belonging to a new candidate phylum sibling to Cyanobacteria. eLife 2:e01102.  https://doi.org/10.7554/eLife.01102 PubMedPubMedCentralCrossRefGoogle Scholar
  118. 118.
    Monchamp M-E, Walser J-C, Pomati F, Spaak P (2016) Sedimentary DNA reveals cyanobacterial community diversity over 200 years in two perialpine lakes. Appl. Environ. Microbiol. 82:6472–6482.  https://doi.org/10.1128/AEM.02174-16 PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Research and Innovation Centre, Fondazione Edmund Mach (FEM)San Michele all’AdigeItaly

Personalised recommendations