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Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

邮发代号 80-971

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Quantitative Biology  2023, Vol. 11 Issue (3): 332-342   https://doi.org/10.15302/J-QB-023-0329
  本期目录
High-throughput metabarcoding of SAR11 assemblages from the southwest Atlantic shelf and arid Patagonia: richness and associated rank abundance distributions
Leandro R. Jones(), Julieta M. Manrique
Laboratorio de Virología y Genética Molecular, Universidad Nacional dela Patagonia San Juan Bosco, Trelew CP 9100, Argentina
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Abstract：

Background: Massively parallel sequencing of environmental DNA allows microbiological studies to be performed in greater detail than was possible with first-generation sequencing. For example, it facilitates the use of approaches hitherto largely applied to flora and fauna, such as rank abundance distribution (RAD) analyses.

Methods: Here, we set out to advance the knowledge on Ca. Pelagibacterales (SAR11) communities from southern South America using environmental sequences from the open ocean in the Argentine sea, the uncharted Engaño Bay, as well as a river and an oligohaline shallow lake from the Patagonian Steppe ecoregion. The structures of the SAR11 assemblages present in these ecosystems were dissected by direct and rarefaction-based estimates of species richness, and evaluations of the corresponding abundance distributions (ADs), which was addressed by RAD analyses.

Results: Microbial community composition analyses revealed that the studied SAR11 assemblages coexist with 27 bacterial phyla. SAR11 richness was in general very high, but ADs turned out to be highly uneven. The results were compatible with prior knowledge, and similar to that derived from point estimates of diversity. However, our comprehensive dissection allowed for more detailed quantitative comparisons to be made between the environments surveyed, and revealed differences regarding both richness and the underlying ADs.

Conclusions: Despite SAR11 assemblages being extremely rich, their ADs are very uneven. Richness and ADs can vary, not only between fresh and salt water, but also between oceanic and coastal marine environments. The obtained results provide insights on general topics such as adaptation and the contrast between marine and freshwater radiations.
Key words： SAR11    richness    species abundance distribution    rank abundance distribution    Patagonia    Argentina
收稿日期: 2022-12-01      出版日期: 2023-10-08
Corresponding Author(s): Leandro R. Jones   
 引用本文:   
. [J]. Quantitative Biology, 2023, 11(3): 332-342.
Leandro R. Jones, Julieta M. Manrique. High-throughput metabarcoding of SAR11 assemblages from the southwest Atlantic shelf and arid Patagonia: richness and associated rank abundance distributions. Quant. Biol., 2023, 11(3): 332-342.
 链接本文:  
https://academic.hep.com.cn/qb/CN/10.15302/J-QB-023-0329
https://academic.hep.com.cn/qb/CN/Y2023/V11/I3/332
Fig.1  
Sample HQSs s11HQS G eG D eD H eH SV eSV HR J eJ
C1 15,121 9638 0.86 0.72 0.96 0.97 5.17 4.68 1985 424 445(14) 0.68 0.76
C2 9348 7176 0.87 0.75 0.95 0.95 4.87 4.48 1431 394 404(13) 0.67 0.74
C3 11,820 9549 0.91 0.81 0.88 0.88 4.00 3.83 1332 332 319(12) 0.55 0.65
C4 19,112 14,797 0.89 0.74 0.92 0.91 4.89 4.35 2648 456 435(14) 0.62 0.70
O1 14,979 8558 0.84 0.67 0.95 0.95 5.33 4.84 2076 537 513(14) 0.69 0.77
O2 6952 5149 0.83 0.69 0.97 0.97 5.42 5.05 1332 531 503(13) 0.75 0.78
O3a/b 22,147 11,358 0.83 0.60 0.96 0.95 5.99 5.33 3082 674 632(15) 0.74 0.82
ChR1/2 19,069 1368 0.86 − 0.74 − 2.88 − 271 − 261(2) 0.51 −
LCa/b 37,054 1 − − − − − − 1 − − − −
Tab.1  
Comparison Δj2 p(Δj)3 nΔj4 p(nΔj)5
Coast vs. ocean 0.102 p = 0.034 0.113 p < 0.001
Coast vs. river 0.245 p = 0.085 0.283 p < 0.001
Ocean vs. river 0.329 p = 0.005 0.373 p < 0.001
Sea vs. river 0.307 p = 0.022 0.348 p < 0.001
Tab.2  
Fig.2  
Fig.3  
Sample Env. Lat/Lon Depth PSU Temp pH
O1 OC –39,95/–55,68 1.95 33.92 12.39 8.13
O2 OC –45,93/–57,7 0.53 34.14 12.95 8.24
O3a OC –46/–59,39 1.03 34.57 12.83 8.27
O3b OC –46/–59,39 1.03 34.58 12.83 8.27
C1 CO –43,44/–65,11 ~1.00 32.96 16.13 8.34
C2 CO –43,44/–65,11 ~1.00 32.88 7.52 8.32
C3 CO –43,44/–65,11 ~1.00 32.84 15.71 8.19
C4 CO –43,44/–65,11 ~1.00 34.86 9.05 8.14
ChR1 Rv –43,45/–65,92 ~0.6 0.13 9.66 8.11
ChR2 Rv –43,45/–65,92 ~0.6 0.14 9.66 8.14
LC1 SL –43,24/–65,29 ~0.6 2.43 12.23 8.28
LC2 SL –43,24/–65,29 ~0.6 2.42 12.23 8.28
Tab.3  
1 P. G., Falkowski, T. Fenchel, E. Delong, (2008). The microbial engines that drive earth’s biogeochemical cycles. Science, 320: 1034–1039
https://doi.org/10.1126/science.1153213
2 P., Falkowski, R. J., Scholes, E., Boyle, J., Canadell, D., Canfield, J., Elser, N., Gruber, K., Hibbard, P., gberg, S. Linder, et al.. (2000). The global carbon cycle: a test of our knowledge of earth as a system. Science, 290: 291–296
https://doi.org/10.1126/science.290.5490.291
3 L. Pomeroy, P. Williams, F. Azam, (2007). The Microbial Loop. Oceanography (Wash. D.C.), 20: 28–33
https://doi.org/10.5670/oceanog.2007.45
4 N., Jiao, G. J., Herndl, D. A., Hansell, R., Benner, G., Kattner, S. W., Wilhelm, D. L., Kirchman, M. G., Weinbauer, T., Luo, F. Chen, et al.. (2010). Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat. Rev. Microbiol., 8: 593–599
https://doi.org/10.1038/nrmicro2386
5 S. J., Giovannoni, T. B., Britschgi, C. L. Moyer, K. Field, (1990). Genetic diversity in Sargasso Sea bacterioplankton. Nature, 345: 60–63
https://doi.org/10.1038/345060a0
6 R. I., Amann, W. Ludwig, K. Schleifer, (1995). Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev., 59: 143–169
https://doi.org/10.1128/mr.59.1.143-169.1995
7 H., Cui, Y. Li, (2016). An overview of major metagenomic studies on human microbiomes in health and disease. Quant. Biol., 4: 192–206
https://doi.org/10.1007/s40484-016-0078-x
8 K. G., Lloyd, A. D., Steen, J., Ladau, J. Yin, (2018). Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems, 3: e00055–e18
https://doi.org/10.1128/mSystems.00055-18
9 P. Legendre. and Legendre, L. (1998) Numerical Ecology. Amsterdam: Elsevier
10 S. Giovannoni, (2017). SAR11 bacteria: the most abundant plankton in the oceans. Annu. Rev. Mar. Sci., 9: 231–255
https://doi.org/10.1146/annurev-marine-010814-015934
11 J. M., Haro-Moreno, F., Rodriguez-Valera, R., Rosselli, F., Martinez-Hernandez, J. J., Roda-Garcia, M. L., Gomez, O., Fornas, M. Martinez-Garcia, (2019). Ecogenomics of the SAR11 clade. Environ. Microbiol., 22: 1748–1763
https://doi.org/10.1111/1462-2920.14896
12 L. J., Wilhelm, H. J., Tripp, S. A., Givan, D. P. Smith, S. Giovannoni, (2007). Natural variation in SAR11 marine bacterioplankton genomes inferred from metagenomic data. Biol. Direct, 2: 27
https://doi.org/10.1186/1745-6150-2-27
13 T. O., Delmont, E., Kiefl, O., Kilinc, O. C., Esen, I., Uysal, S. Giovannoni, A. Eren, (2019). Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife, 8: e46497
https://doi.org/10.7554/eLife.46497
14 M., rez, J. M., Haro-Moreno, F. H., Coutinho, M. Martinez-Garcia, (2020). The evolutionary success of the marine bacterium SAR11 analyzed through a metagenomic perspective. mSystems, 5: e00605–e00620
https://doi.org/10.1128/mSystems.00605-20
15 S., Kraemer, A., Ramachandran, D., Colatriano, C. Lovejoy, D. Walsh, (2020). Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J., 14: 79–90
https://doi.org/10.1038/s41396-019-0499-4
16 D. K. Ngugi, (2012). Combined analyses of the ITS loci and the corresponding 16S rRNA genes reveal high micro- and macrodiversity of SAR11 populations in the Red Sea. PLoS One, 7: e50274
https://doi.org/10.1371/journal.pone.0050274
17 J., Grote, J. C., Thrash, M. J., Huggett, Z. C., Landry, P., Carini, S. J. Giovannoni, (2012). Streamlining and core genome conservation among highly divergent members of the SAR11 clade. MBio, 3: e00252–e12
https://doi.org/10.1128/mBio.00252-12
18 M. W., Henson, V. C., Lanclos, B. C. Faircloth, J. Thrash, (2018). Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J., 12: 1846–1860
https://doi.org/10.1038/s41396-018-0092-2
19 J. C., Cameron Thrash, B., Temperton, B. K., Swan, Z. C., Landry, T., Woyke, E. F., DeLong, R. Stepanauskas, S. Giovannoni, (2014). Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J., 8: 1440–1451
https://doi.org/10.1038/ismej.2013.243
20 P., Carini, B. A. S. V., Van Mooy, J. C., Thrash, A., White, Y., Zhao, E. O., Campbell, H. F. Fredricks, S. Giovannoni, (2015). SAR11 lipid renovation in response to phosphate starvation. Proc. Natl. Acad. Sci. USA, 112: 7767–7772
https://doi.org/10.1073/pnas.1505034112
21 S. F., Paver, D., Muratore, R. J. Newton, M. Coleman, (2018). Reevaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems. mSystems, 3: e00232–e18
https://doi.org/10.1128/mSystems.00232-18
22 D. P., Herlemann, J., Woelk, M. Labrenz, (2014). Diversity and abundance of “Pelagibacterales” (SAR11) in the Baltic Sea salinity gradient. Syst. Appl. Microbiol., 37: 601–604
https://doi.org/10.1016/j.syapm.2014.09.002
23 S., Oh, R., Zhang, Q. L. Wu, W. Liu, (2014). Draft genome sequence of a novel SAR11 clade species abundant in a Tibetan Lake. Genome Announc., 2: e01137–e14
https://doi.org/10.1128/genomeA.01137-14
24 S., Oh, R., Zhang, Q. L. Wu, W. Liu, (2016). Evolution and adaptation of SAR11 and Cyanobium in a saline Tibetan lake. Environ. Microbiol. Rep., 8: 595–604
https://doi.org/10.1111/1758-2229.12408
25 R., Logares, J., Brate, F., Heinrich, K. Shalchian-Tabrizi, (2009). Infrequent transitions between saline and fresh waters in one of the most abundant microbial lineages (SAR11). Mol. Biol. Evol., 27: 347–357
https://doi.org/10.1093/molbev/msp239
26 A., Eiler, R., Mondav, L., Sinclair, L., Fernandez-Vidal, D. G., Scofield, P., Schwientek, M., Martinez-Garcia, D., Torrents, K. D., McMahon, S. G. Andersson, et al.. (2016). Tuning fresh: radiation through rewiring of central metabolism in streamlined bacteria. ISME J., 10: 1902–1914
https://doi.org/10.1038/ismej.2015.260
27 A. M., Latimer, J. A. Silander, R. Cowling, (2005). Neutral ecological theory reveals isolation and rapid speciation in a biodiversity hot spot. Science, 309: 1722–1725
https://doi.org/10.1126/science.1115576
28 N. J., West, C., re, C. Manes, P., Catala, D. J. Scanlan, (2016). Distinct spatial patterns of SAR11, SAR86, and actinobacteria diversity along a transect in the ultra-oligotrophic South Pacific Ocean. Front. Microbiol., 7: 234
https://doi.org/10.3389/fmicb.2016.00234
29 F. L., Hellweger, E. van Sebille, N. Fredrick, (2014). Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science, 345: 1346–1349
https://doi.org/10.1126/science.1254421
30 J. M. ManriqueL. Jones. (2017) Are ocean currents too slow to counteract SAR11 evolution? A next-generation sequencing, phylogeographic analysis. Mol. Phylogenet. Evol., 107, 324–337
31 K., Vergin, N., Jhirad, J., Dodge, C. Carlson, (2017). Marine bacterioplankton consortia follow deterministic, non-neutral community assembly rules. Aquat. Microb. Ecol., 79: 165–175
https://doi.org/10.3354/ame01824
32 A., Dogliotti, V. Lutz, (2014). Estimation of primary production in the southern Argentine continental shelf and shelf-break regions using field and remote sensing data. Remote Sens. Environ., 140: 497–508
https://doi.org/10.1016/j.rse.2013.09.021
33 M. C. PiccoloG. M. Perillo. (1999) The Argentina Estuaries: A Review. In: Estuaries of South America, Piccolo, M. C. & Perillo, G.M.E. (Ed.), Heidelberg: Springer
34 L., Carbonell-Silletta, A., Cavallaro, D. A., Pereyra, J. O., Askenazi, G., Goldstein, F. G. Scholz, S. Bucci, (2022). Soil respiration and N-mineralization processes in the Patagonian steppe are more responsive to fertilization than to experimental precipitation increase. Plant Soil, 479: 405–422
https://doi.org/10.1007/s11104-022-05531-0
35 M. R., Derguy, S. Martinuzzi, (2022). Bioclimatic changes in ecoregions of southern South America: trends and projections based on Holdridge life zones. Austral Ecol., 47: 580–589
https://doi.org/10.1111/aec.13142
36 G. MataloniR. D., Quintana. (2022) Freshwaters and Wetlands of Patagonia. Springer International Publishing
37 R. P., Matano, E. D. Palma, A. Piola, (2010). The influence of the Brazil and Malvinas Currents on the Southwestern Atlantic Shelf circulation. Ocean Sci., 6: 983–995
https://doi.org/10.5194/os-6-983-2010
38 M. L., Torres Alberto, N., Bodnariuk, M., Ivanovic, M. Saraceno, E. Acha, (2020). Dynamics of the confluence of Malvinas and Brazil currents, and a southern Patagonian spawning ground, explain recruitment fluctuations of the main stock of Illex argentinus. Fish. Oceanogr., 30: 127–141
https://doi.org/10.1111/fog.12507
39 L. I., Giaccardi, M. A., Badenas, L. R. Jones, J. Manrique, (2022). Abundant microbes of surface sea waters of the uncharted Engaño Bay at the Atlantic Patagonian Coast: relevance of bacteria-sized photosynthetic eukaryotes. Aquat. Ecol., 56: 1217–1230
https://doi.org/10.1007/s10452-022-09962-w
40 P. D., Schloss, S. L., Westcott, T., Ryabin, J. R., Hall, M., Hartmann, E. B., Hollister, R. A., Lesniewski, B. B., Oakley, D. H., Parks, C. J. Robinson, et al.. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol., 75: 7537–7541
https://doi.org/10.1128/AEM.01541-09
41 Z. S. L., Foster, T. J. Sharpton, N. nwald, (2017). Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLOS Comput. Biol., 13: e1005404
https://doi.org/10.1371/journal.pcbi.1005404
42 J., OksanenF. G., BlanchetM., FriendlyR., KindtP., Legendre D., McGlinnP. R., MinchinR. B., HaraG. L., SimpsonP., Solymos. (2020) vegan: community ecology package, available on the website of cran.r-project
43 R Core Team. (2022) R: a language and environment for statistical computing, R foundation for statistical computing, Vienna, Austria, available on the website of R-project
44 J. Quensen. (2019) QsRutils: R functions useful for community ecology, available on the website of GitHub
45 S. Hurlbert, (1971). The nonconcept of species diversity: a critique and alternative parameters. Ecology, 52: 577–586
https://doi.org/10.2307/1934145
46 R. Kindt. (2005) Tree Diversity Analysis: A Manual and Software for Common Statistical Methods for Ecological and Biodiversity Studies. Nairobi: World Agroforestry Centre (ICRAF)
47 M., Saeedghalati, F., Farahpour, B., Budeus, A., Lange, A. M., Westendorf, M., Seifert, R. ppers, (2017). Quantitative comparison of abundance structures of generalized communities: from B-cell receptor repertoires to microbiomes. PLOS Comput. Biol., 13: e1005362
https://doi.org/10.1371/journal.pcbi.1005362
48 M., SaeedghalatiF. Farahpour. (2016) RADanalysis: normalization and study of rank abundance distributions. Available on the website of cran.R-project
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