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Microbial Ecology

, Volume 70, Issue 3, pp 724–740 | Cite as

Conversion of Uric Acid into Ammonium in Oil-Degrading Marine Microbial Communities: a Possible Role of Halomonads

  • Christoph Gertler
  • Rafael Bargiela
  • Francesca Mapelli
  • Xifang Han
  • Jianwei Chen
  • Tran Hai
  • Ranya A. Amer
  • Mouna Mahjoubi
  • Hanan Malkawi
  • Mirko Magagnini
  • Ameur Cherif
  • Yasser R. Abdel-Fattah
  • Nicolas Kalogerakis
  • Daniele Daffonchio
  • Manuel Ferrer
  • Peter N. Golyshin
Environmental Microbiology

Abstract

Uric acid is a promising hydrophobic nitrogen source for biostimulation of microbial activities in oil-impacted marine environments. This study investigated metabolic processes and microbial community changes in a series of microcosms using sediment from the Mediterranean and the Red Sea amended with ammonium and uric acid. Respiration, emulsification, ammonium and protein concentration measurements suggested a rapid production of ammonium from uric acid accompanied by the development of microbial communities containing hydrocarbonoclastic bacteria after 3 weeks of incubation. About 80 % of uric acid was converted to ammonium within the first few days of the experiment. Microbial population dynamics were investigated by Ribosomal Intergenic Spacer Analysis and Illumina sequencing as well as by culture-based techniques. Resulting data indicated that strains related to Halomonas spp. converted uric acid into ammonium, which stimulated growth of microbial consortia dominated by Alcanivorax spp. and Pseudomonas spp. Several strains of Halomonas spp. were isolated on uric acid as the sole carbon source showed location specificity. These results point towards a possible role of halomonads in the conversion of uric acid to ammonium utilized by hydrocarbonoclastic bacteria.

Keywords

Crude oil degradation Bioremediation Alcanivorax 

Notes

Acknowledgments

The authors would like to thank Anna Foster, Sarah Chesworth and Gordon Turner for their help with photometric and respiration measurements. With exception of XH and JC, all authors were supported by the FP7 Project ULIXES (FP7-KBBE-2010-266473). This work was further funded by grant BIO2011-25012 from the Spanish Ministry of the Economy and Competitiveness. FM was supported by Università degli Studi di Milano, European Social Fund (FSE) and Regione Lombardia (contract “Dote Ricerca”). DD acknowledges support of KAUST, King Abdullah University of Science and Technology. PG acknowledges the support of the European Commission through the project Kill-Spill (FP7, Contract Nr 312139). CG would like to thank Mr. Kyungsun Lee of Macrogen Inc. for his courtesy regarding sequencing services, J. Cans, B. Strid and C. Hudson for continued advice and inspiration as well as Delphine Lallias for her help with multivariate statistics and John Flannery for proofreading this manuscript.

Supplementary material

248_2015_606_MOESM1_ESM.xlsx (9 kb)
Supplementary Table 1a ANOSIM results of bacterial community RISA fingerprinting profiles on the basis of Bray-Curtis distance measure, showing the univariate partition of factors “Location”, “Nitrogen source” and “Sampling day” and the multivariate partition of factors “Location” within the factor “Nitrogen source”. The R-value was calculated based on the Null hypothesis of no similarity between samples. The values highlighted in bold are statistically significant (P < 0.05). (XLSX 9 kb)
248_2015_606_MOESM2_ESM.xlsx (9 kb)
Supplementary Table 1b R-values derived from ANOSIM pairwise comparisons of factor “(sampling) Location” values using Bray-Curtis values. The R-value was calculated based on the Null hypothesis of no similarity between samples. The values highlighted in bold are statistically significant (P < 0.05). (XLSX 9 kb)
248_2015_606_MOESM3_ESM.xlsx (9 kb)
Supplementary Table 1c R-values derived from ANOSIM pairwise comparisons of factor “Sampling day” values using Bray-Curtis values. The R-value was calculated based on the Null hypothesis of no similarity between samples. The values highlighted in bold are statistically significant (P < 0.05). (XLSX 9 kb)
248_2015_606_MOESM4_ESM.xlsx (10 kb)
Supplementary Table 2a Data statistics for samples as obtained by Illumina sequencing (XLSX 10 kb)
248_2015_606_MOESM5_ESM.xlsx (11 kb)
Supplementary Table 2b Data statistics for the best assembly results (XLSX 10 kb)
248_2015_606_MOESM6_ESM.xlsx (10 kb)
Supplementary Table 2c Gene prediction results (XLSX 9 kb)
248_2015_606_MOESM7_ESM.xlsx (11 kb)
Supplementary Table 3a Annotation of the metagenomic data from the Aqaba UA sample from day 21, displayed in Fig. 3 of the main text. The subset consists of 30 genes involved in uric acid metabolism from a total of 26,866 ORFs identified in the metagenome. Gene sequences with similarity to Halomonas spp. are printed in bold. (XLSX 10 kb)
248_2015_606_MOESM8_ESM.xlsx (10 kb)
Supplementary Table 3b Annotation of the metagenomic data from the Ancona UA sample from day 21, displayed in Fig. 3 of the main text. The subset consists of 15 genes involved in uric acid metabolism from a total of 27,893 ORFs identified in the metagenome. Gene sequences with similarity to Halomonas spp. are printed in bold. (XLSX 10 kb)
248_2015_606_MOESM9_ESM.xlsx (10 kb)
Supplementary Table 3c Annotation of the metagenomic data from the Ancona NP sample from day 21, displayed in Fig. 3 of the main text. The subset consists of 18 genes involved in uric acid metabolism from a total of 32,180 ORFs identified in the metagenome. Gene sequences with similarity to Halomonas spp. are printed in bold. (XLSX 10 kb)
248_2015_606_MOESM10_ESM.xlsx (10 kb)
Supplementary Table 3d Annotation of the metagenomic data from the Bizerte NP sample from day 21, displayed in Fig. 3 of the main text. The subset consists of six genes involved in uric acid metabolism from a total of 28,698 ORFs identified in the metagenome. Gene sequences with similarity to Halomonas spp. are printed in bold. (XLSX 9 kb)
248_2015_606_MOESM11_ESM.xlsx (11 kb)
Supplementary Table 3e Annotation of the metagenomic data from the El Max UA sample from day 21, displayed in Fig. 3 of the main text. The subset consists of 29 genes involved in uric acid metabolism from a total of 61,277 ORFs identified in the metagenome. Gene sequences with similarity to Halomonas spp. are printed in bold. (XLSX 10 kb)
248_2015_606_MOESM12_ESM.xlsx (11 kb)
Supplementary Table 4 Closest relatives of 16S rRNA gene sequences of uric acid-degrading isolates. The first letter of the isolate code designates the sediment sampling location (A—Ancona; Q—Aqaba; X—El Max; B—Bizerte), and the second letter indicates the nitrogen source (N—Ammonium; U—Uric Acid). The first number in the isolate code represents the sampling date, and the last number indicates the individual isolate. (XLSX 10 kb)
248_2015_606_Fig5_ESM.gif (305 kb)
Supplementary Fig. 1

RISA fingerprinting profiles used for the multivariate analysis presented in Fig. 2 of the main text. Full DNA profiles of two out of three microcosm replicates were used to avoid mismatching in the band matching required for statistical analysis. All RISA fingerprints were run using the O’gene Ruler Plus (Thermo Scientific, Lutterworth, UK) as indicated by the letter M under the marker lanes. Gel images show DNA fragments of 300 to 5000 bp in length. Numbers under individual lanes represent sampling days. Fingerprints from microcosm replicates are displayed as blocks of profiles with increasing sampling time (0 to 28 days). (GIF 304 kb)

248_2015_606_MOESM13_ESM.tif (15.4 mb)
High Resolution Image (TIFF 15722 kb)

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Christoph Gertler
    • 1
    • 11
  • Rafael Bargiela
    • 2
  • Francesca Mapelli
    • 3
    • 10
  • Xifang Han
    • 4
  • Jianwei Chen
    • 4
  • Tran Hai
    • 1
  • Ranya A. Amer
    • 5
  • Mouna Mahjoubi
    • 6
  • Hanan Malkawi
    • 7
  • Mirko Magagnini
    • 8
  • Ameur Cherif
    • 6
  • Yasser R. Abdel-Fattah
    • 5
  • Nicolas Kalogerakis
    • 9
  • Daniele Daffonchio
    • 3
    • 10
  • Manuel Ferrer
    • 2
  • Peter N. Golyshin
    • 1
  1. 1.School of Biological Sciences, Environment Centre WalesBangor UniversityBangorUK
  2. 2.Consejo Superior de Investigaciones Científicas (CSIC)Institute of CatalysisMadridSpain
  3. 3.Department of Food, Environment and Nutritional Sciences (DeFENS)University of MilanMilanItaly
  4. 4.BGI Tech Solutions Co., LtdShenzhenChina
  5. 5.Genetic Engineering and Biotechnology Research Institute, City for Scientific Research & Technology ApplicationsAlexandriaEgypt
  6. 6.Highe Higher Institute for Biotechnology, Biotechpole of Sidi ThabetUniversity of ManoubaSidi ThabetTunisia
  7. 7.Deanship of Research & Doctoral StudiesHamdan Bin Mohammad Smart UniversityDubaiUnited Arab Emirates
  8. 8.EcoTechSystems Ltd.AnconaItaly
  9. 9.School of Environmental EngineeringTechnical University of CreteChaniaGreece
  10. 10.BESE DivisionKing Abdullah University of Science and TechnologyThuwalKingdom of Saudi Arabia
  11. 11.Friedrich-Loeffler-Institut - Federal research Institute for Animal Health, Institute of Novel and Emerging DiseasesGreifswaldGermany

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