Advertisement

Microbiota adaptation after an alkaline pH perturbation in a full-scale UASB anaerobic reactor treating dairy wastewater

  • Cecilia CallejasEmail author
  • Alfonsina Fernández
  • Mauricio Passeggi
  • Jorge Wenzel
  • Patricia Bovio
  • Liliana Borzacconi
  • Claudia Etchebehere
Research Paper

Abstract

The aim of this study was to understand how the microbial community adapted to changes, including a pH perturbation, occurring during the start-up and operation processes in a full-scale methanogenic UASB reactor designed to treat dairy wastewater. The reactor performance, prokaryotic community, and lipid degradation capacity were monitored over a 9-month period. The methanogenic community was studied by mcrA/mrtA gene copy-number quantification and methanogenic activity tests. A diverse prokaryotic community characterized the seeding sludge as assessed by sequencing the V4 region of the 16S rRNA gene. As the feeding began, the bacterial community was dominated by Firmicutes, Synergistetes, and Proteobacteria phyla. After an accidental pH increase that affected the microbial community structure, a sharp increase in the relative abundance of Clostridia and a decrease in the mcrA/mrtA gene copy number and methanogenic activity were observed. After a recovery period, the microbial population regained diversity and methanogenic activity. Alkaline shocks are likely to happen in dairy wastewater treatment because of the caustic soda usage. In this work, the plasticity of the prokaryotic community was key to surviving changes to the external environment and supporting biogas production in the reactor.

Keywords

UASB Full-scale Dairy wastewater 16S rRNA gene sequencing qPCR mcrA gene 

Abbreviations

AD

Anaerobic digestion

COD

Chemical oxygen demand

EGSB

Expanded granular sludge bed

FOG

Fat, oil and grease

HRT

Hydraulic retention time

LCFA

Long-chain fatty acid

OLR

Organic loading rate

OUT

Operational taxonomic unit

SFDA

Specific fatty-acid degradation activity

SLR

Specific loading rate

SMA

Specific methanogenic activity

SRT

Sludge retention time

UASB

Upflow anaerobic sludge blanket

VSS

Volatile suspended solids

Notes

Acknowledgements

This work was supported by the project grant ANII FSE 17. Cecilia Callejas was funded by ANII (PhD thesis grant).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

449_2019_2198_MOESM1_ESM.docx (2.4 mb)
Supplementary file1 (DOCX 2487 kb)

References

  1. 1.
    Ramasamy EV, Gajalakshmi S, Sanjeevi R et al (2004) Feasibility studies on the treatment of dairy wastewaters with upflow anaerobic sludge blanket reactors. Bioresour Technol 93:209–212.  https://doi.org/10.1016/j.biortech.2003.11.001 CrossRefGoogle Scholar
  2. 2.
    Karadag D, Köroʇlu OE, Ozkaya B, Cakmakci M (2015) A review on anaerobic biofilm reactors for the treatment of dairy industry wastewater. Process Biochem 50:262–271.  https://doi.org/10.1016/j.procbio.2014.11.005 CrossRefGoogle Scholar
  3. 3.
    Instituto Nacional de la Leche (2019) Uruguay Lechero. https://www.inale.org/uruguay-lechero/
  4. 4.
    Hemalatha M, Sravan JS, Min B, Venkata Mohan S (2019) Microalgae-biorefinery with cascading resource recovery design associated to dairy wastewater treatment. Bioresour Technol 284:424–429.  https://doi.org/10.1016/j.biortech.2019.03.106 CrossRefGoogle Scholar
  5. 5.
    Chandra R, Castillo-Zacarias C, Delgado P, Parra-Saldívar R (2018) A biorefinery approach for dairy wastewater treatment and product recovery towards establishing a biorefinery complexity index. J Clean Prod 183:1184–1196.  https://doi.org/10.1016/j.jclepro.2018.02.124 CrossRefGoogle Scholar
  6. 6.
    Hernández-Padilla F, Margni M, Noyola A et al (2017) Assessing wastewater treatment in Latin America and the Caribbean: enhancing life cycle assessment interpretation by regionalization and impact assessment sensibility. J Clean Prod 142:2140–2153.  https://doi.org/10.1016/j.jclepro.2016.11.068 CrossRefGoogle Scholar
  7. 7.
    van Lier JB, van der Zee FP, Frijters CTMJ, Ersahin ME (2015) Celebrating 40 years anaerobic sludge bed reactors for industrial wastewater treatment. Rev Environ Sci Biotechnol 14:681–702.  https://doi.org/10.1007/s11157-015-9375-5 CrossRefGoogle Scholar
  8. 8.
    Alves MM, Pereira MA, Sousa DZ et al (2009) Waste lipids to energy: how to optimize methane production from long-chain fatty acids (LCFA). Microb Biotechnol 2:538–550.  https://doi.org/10.1111/j.1751-7915.2009.00100.x CrossRefGoogle Scholar
  9. 9.
    Vidal G, Carvalho A, Mendez R, Lema JM (2000) Influence of the content in fats and proteins on the anaerobic biodegradability of dairy wastewaters. Bioresour Technol 74:231–239.  https://doi.org/10.1016/S0960-8524(00)00015-8 CrossRefGoogle Scholar
  10. 10.
    Cammarota MC, Freire DMG (2006) A review on hydrolytic enzymes in the treatment of wastewater with high oil and grease content. Bioresour Technol 97:2195–2210.  https://doi.org/10.1016/j.biortech.2006.02.030 CrossRefGoogle Scholar
  11. 11.
    Passeggi M, López I, Borzacconi L (2009) Integrated anaerobic treatment of dairy industrial wastewater and sludge. Water Sci Technol 59:501–506.  https://doi.org/10.2166/wst.2009.010 CrossRefGoogle Scholar
  12. 12.
    Passeggi M, López I, Borzacconi L (2012) Modified UASB reactor for dairy industry wastewater: performance indicators and comparison with the traditional approach. J Clean Prod 26:90–94.  https://doi.org/10.1016/j.jclepro.2011.12.022 CrossRefGoogle Scholar
  13. 13.
    Ziels RM, Beck DAC, Stensel HD (2017) Long-chain fatty acid feeding frequency in anaerobic codigestion impacts syntrophic community structure and biokinetics. Water Res 117:218–229.  https://doi.org/10.1016/j.watres.2017.03.060 CrossRefGoogle Scholar
  14. 14.
    Nelson MC, Morrison M, Yu Z (2011) A meta-analysis of the microbial diversity observed in anaerobic digesters. Bioresour Technol 102:3730–3739.  https://doi.org/10.1016/j.biortech.2010.11.119 CrossRefGoogle Scholar
  15. 15.
    Werner JJ, Knights D, Garcia ML et al (2011) Bacterial community structures are unique and resilient in full-scale bioenergy systems. Proc Natl Acad Sci 108:4158–4163.  https://doi.org/10.1073/pnas.1015676108 CrossRefGoogle Scholar
  16. 16.
    Sundberg C, Al-Soud WA, Larsson M et al (2013) 454 Pyrosequencing analyses of bacterial and archaeal richness in 21 full-scale biogas digesters. FEMS Microbiol Ecol 85:612–626.  https://doi.org/10.1111/1574-6941.12148 CrossRefGoogle Scholar
  17. 17.
    Vanwonterghem I, Jensen PD, Dennis PG et al (2014) Deterministic processes guide long-term synchronised population dynamics in replicate anaerobic digesters. ISME J 8:2015–2028.  https://doi.org/10.1038/ismej.2014.50 CrossRefGoogle Scholar
  18. 18.
    Lucas R, Kuchenbuch A, Fetzer I et al (2015) Long-term monitoring reveals stable and remarkably similar microbial communities in parallel full-scale biogas reactors digesting energy crops. FEMS Microbiol Ecol 91:fiv004CrossRefGoogle Scholar
  19. 19.
    Luo G, Fotidis IA, Angelidaki I (2016) Comparative analysis of taxonomic, functional, and metabolic patterns of microbiomes from 14 full-scale biogas reactors by metagenomic sequencing and radioisotopic analysis. Biotechnol Biofuels 9:51.  https://doi.org/10.1186/s13068-016-0465-6 CrossRefGoogle Scholar
  20. 20.
    Thauer RK, Kaster A-K, Seedorf H et al (2008) Methanogenic archaea: ecologically relevant differences in energy conservation. Nat Rev Microbiol 6:579–591.  https://doi.org/10.1038/nrmicro1931 CrossRefGoogle Scholar
  21. 21.
    Lang K, Schuldes J, Klingl A et al (2015) New mode of energy metabolism in the seventh order of methanogens as revealed by comparative genome analysis of “Candidatus Methanoplasma termitum”. Appl Environ Microbiol 81:1338–1352.  https://doi.org/10.1128/AEM.03389-14 CrossRefGoogle Scholar
  22. 22.
    Luton PE, Wayne JM, Sharp RJ, Riley PW (2002) The mcrA gene as an alternative to 16S rRNA in the phylogenetic analysis of methanogen populations in landfill. Microbiology 148:3521–3530.  https://doi.org/10.1099/00221287-148-11-3521 CrossRefGoogle Scholar
  23. 23.
    Traversi D, Villa S, Lorenzi E et al (2012) Application of a real-time qPCR method to measure the methanogen concentration during anaerobic digestion as an indicator of biogas production capacity. J Environ Manage 111:173–177.  https://doi.org/10.1016/j.jenvman.2012.07.021 CrossRefGoogle Scholar
  24. 24.
    Morris R, Schauer-Gimenez A, Bhattad U et al (2014) Methyl coenzyme M reductase (mcrA) gene abundance correlates with activity measurements of methanogenic H2/CO2-enriched anaerobic biomass. Microb Biotechnol 7:77–84.  https://doi.org/10.1111/1751-7915.12094 CrossRefGoogle Scholar
  25. 25.
    Fykse EM, Aarskaug T, Madslien EH, Dybwad M (2016) Microbial community structure in a full-scale anaerobic treatment plant during start-up and first year of operation revealed by high-throughput 16S rRNA gene amplicon sequencing. Bioresour Technol 222:380–387.  https://doi.org/10.1016/j.biortech.2016.09.118 CrossRefGoogle Scholar
  26. 26.
    Goux X, Calusinska M, Fossépré M et al (2016) Start-up phase of an anaerobic full-scale farm reactor—appearance of mesophilic anaerobic conditions and establishment of the methanogenic microbial community. Bioresour Technol 212:217–226.  https://doi.org/10.1016/j.biortech.2016.04.040 CrossRefGoogle Scholar
  27. 27.
    Zhu J, Chen L, Zhang Y, Zhu X (2017) Revealing the anaerobic acclimation of microbial community in a membrane bioreactor for coking wastewater treatment by Illumina Miseq sequencing. J Environ Sci.  https://doi.org/10.1016/j.jes.2017.06.003 Google Scholar
  28. 28.
    Trego AC, Morabito C, Bourven I et al (2018) Diversity converges during community assembly in methanogenic granules, suggesting a biofilm life-cycle. bioRxiv.  https://doi.org/10.1101/484642 Google Scholar
  29. 29.
    Gao WJJ, Lin HJ, Leung KT, Liao BQ (2010) Influence of elevated pH shocks on the performance of a submerged anaerobic membrane bioreactor. Process Biochem 45:1279–1287.  https://doi.org/10.1016/j.procbio.2010.04.018 CrossRefGoogle Scholar
  30. 30.
    Nadais MHGAG, Capela MIAPF, Arroja LMGA, Hung Y-T (2010) Anaerobic treatment of milk processing wastewater. In: Wang LK, Tay J-H, Tay STL, Hung Y-T (eds) Environmental bioengineering, vol 11. Humana Press, Totowa, pp 555–627CrossRefGoogle Scholar
  31. 31.
    Eaton AD, Clesceri LS, Greenberg AE, Franson MAH (1998) Standard methods for the examination of water and wastewater, 20th edn. American Public Health Association, WashingtonGoogle Scholar
  32. 32.
    Soto M, Méndez R, Lema JM (1993) Methanogenic and non-methanogenic activity tests. Theoretical basis and experimental set up. Water Res 27:1361–1376.  https://doi.org/10.1016/0043-1354(93)90224-6 CrossRefGoogle Scholar
  33. 33.
    Fernández A (2016) Puesta en marcha de un ractor UASB para el tratamiento de efluente lácteo. Evaluación de desmpeño del reactor yadaptación del inóculo. (Master thesis). Universidad de la RepúblicaGoogle Scholar
  34. 34.
    Claesson MJ, O’Sullivan O, Wang Q et al (2009) Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial community structures in the human distal intestine. PLoS ONE.  https://doi.org/10.1371/journal.pone.0006669 Google Scholar
  35. 35.
    Bolyen E, Rideout JR, Dillon MR et al (2018) QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Peer J Prepr 6:e27295v2.  https://doi.org/10.7287/peerj.preprints.27295v2 Google Scholar
  36. 36.
    Callahan BJ, McMurdie PJ, Rosen MJ et al (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583.  https://doi.org/10.1038/nmeth.3869 CrossRefGoogle Scholar
  37. 37.
    Katoh K, Misawa K, Kuma K, Miyata T (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30:3059–3066.  https://doi.org/10.1093/nar/gkf436 CrossRefGoogle Scholar
  38. 38.
    Price MN, Dehal PS, Arkin AP (2010) FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5:1–10.  https://doi.org/10.1371/journal.pone.0009490 Google Scholar
  39. 39.
    Lozupone C, Lladser ME, Knights D et al (2011) UniFrac: an effective distance metric for microbial community comparison. ISME J 5:169–172.  https://doi.org/10.1038/ismej.2010.133 CrossRefGoogle Scholar
  40. 40.
    Bokulich NA, Rideout JR, Mercurio WG et al (2016) mockrobiota: a public resource for microbiome bioinformatics benchmarking. mSystems 1:e00062–162.  https://doi.org/10.1128/mSystems.00062-16 CrossRefGoogle Scholar
  41. 41.
    Quast C (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res.  https://doi.org/10.1093/nar/gks1219 Google Scholar
  42. 42.
    Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410.  https://doi.org/10.1016/S0022-2836(05)80360-2 CrossRefGoogle Scholar
  43. 43.
    Denman SE, Tomkins NW, McSweeney CS (2007) Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane. FEMS Microbiol Ecol 62:313–322.  https://doi.org/10.1111/j.1574-6941.2007.00394.x CrossRefGoogle Scholar
  44. 44.
    Hammer Ø, Harper DAT, Ryan PD (2001) PAST: Paleontological statistics software package for education and data analysis. Palaeontol Electron 4:9Google Scholar
  45. 45.
    Borja R, Banks CJ (1995) Response of an anaerobic fluidized bed reactor treating ice-cream wastewater to organic, hydraulic, temperature and pH shocks. J Biotechnol 39:251–259.  https://doi.org/10.1016/0168-1656(95)00021-H CrossRefGoogle Scholar
  46. 46.
    Goux X, Calusinska M, Lemaigre S et al (2015) Microbial community dynamics in replicate anaerobic digesters exposed sequentially to increasing organic loading rate, acidosis, and process recovery. Biotechnol Biofuels 8:122.  https://doi.org/10.1186/s13068-015-0309-9 CrossRefGoogle Scholar
  47. 47.
    Rinke C, Schwientek P, Sczyrba A et al (2013) Insights into the phylogeny and coding potential of microbial dark matter. Nature 499:431–437.  https://doi.org/10.1038/nature12352 CrossRefGoogle Scholar
  48. 48.
    Youssef NH, Couger MB, McCully AL et al (2015) Assessing the global phylum level diversity within the bacterial domain: a review. J Adv Res 6:269–282.  https://doi.org/10.1016/j.jare.2014.10.005 CrossRefGoogle Scholar
  49. 49.
    Ito T, Yoshiguchi K, Ariesyady HD, Okabe S (2011) Identification of a novel acetate-utilizing bacterium belonging to Synergistes group 4 in anaerobic digester sludge. ISME J 5:1844–1856.  https://doi.org/10.1038/ismej.2011.59 CrossRefGoogle Scholar
  50. 50.
    Whiteley AS, Jenkins S, Waite I et al (2012) Microbial 16S rRNA ion tag and community metagenome sequencing using the Ion Torrent (PGM) Platform. J Microbiol Methods 91:80–88.  https://doi.org/10.1016/j.mimet.2012.07.008 CrossRefGoogle Scholar
  51. 51.
    Jumas-bilak E, Pathoge E, Re CH et al (2014) The prokaryotes. Springer, HeidelbergGoogle Scholar
  52. 52.
    Militon C, Hamdi O, Michotey V et al (2015) Ecological significance of synergistetes in the biological treatment of tuna cooking wastewater by an anaerobic sequencing batch reactor. Environ Sci Pollut Res 22:18230–18238.  https://doi.org/10.1007/s11356-015-4973-x CrossRefGoogle Scholar
  53. 53.
    Ganesan A, Chaussonnerie S, Tarrade A et al (2008) Cloacibacillus evryensis gen. nov., sp. nov., a novel asaccharolytic, mesophilic, amino-acid-degrading bacterium within the phylum “Synergistetes”, isolated from an anaerobic sludge digester. Int J Syst Evol Microbiol 58:2003–2012.  https://doi.org/10.1099/ijs.0.65645-0 CrossRefGoogle Scholar
  54. 54.
    Díaz C, Baena S, Patel BKC, Fardeau ML (2010) Peptidolytic microbial community of methanogenic reactors from two modified uasbs of brewery industries. Br J Microbiol 41:707–717.  https://doi.org/10.1590/S1517-83822010000300022 CrossRefGoogle Scholar
  55. 55.
    Qin Q-L, Xie B-B, Zhang X-Y et al (2014) A proposed genus boundary for the prokaryotes based on genomic insights. J Bacteriol 196:2210–2215.  https://doi.org/10.1128/JB.01688-14 CrossRefGoogle Scholar
  56. 56.
    Nobu MK, Narihiro T, Rinke C et al (2015) Microbial dark matter ecogenomics reveals complex synergistic networks in a methanogenic bioreactor. Isme J 9:1710CrossRefGoogle Scholar
  57. 57.
    Hamilton TL, Bovee RJ, Sattin SR et al (2016) Carbon and sulfur cycling below the chemocline in a meromictic lake and the identification of a novel taxonomic lineage in the FCB superphylum. Candidatus Aegiribacteria Front Microbiol.  https://doi.org/10.3389/fmicb.2016.00598 Google Scholar
  58. 58.
    Fischer MA, Güllert S, Neulinger SC et al (2016) Evaluation of 16S rRNA gene primer pairs for monitoring microbial community structures showed high reproducibility within and low comparability between datasets generated with multiple archaeal and bacterial primer pairs. Front Microbiol.  https://doi.org/10.3389/fmicb.2016.01297 Google Scholar
  59. 59.
    Cabezas A, Bovio P, Etchebehere C (2019) Commercial formulation amendment transiently affects the microbial composition but not the biogas production of a full scale methanogenic UASB reactor. Environ Technol.  https://doi.org/10.1080/09593330.2019.1600042 Google Scholar
  60. 60.
    Yang Y, Yu K, Xia Y et al (2014) Metagenomic analysis of sludge from full-scale anaerobic digesters operated in municipal wastewater treatment plants. Appl Microbiol Biotechnol 98:5709–5718.  https://doi.org/10.1007/s00253-014-5648-0 CrossRefGoogle Scholar
  61. 61.
    Sayed S (1987) Anaerobic treatment of slaughterhouse wastewater using the UASB process. Wageningen Agricultural UnivesityGoogle Scholar
  62. 62.
    Sousa DZ, Smidt H, Alves MM, Stams AJM (2009) Ecophysiology of syntrophic communities that degrade saturated and unsaturated long-chain fatty acids. FEMS Microbiol Ecol 68:257–272.  https://doi.org/10.1111/j.1574-6941.2009.00680.x CrossRefGoogle Scholar
  63. 63.
    Kim S-H, Han S-K, Shin H-S (2004) Kinetics of LCFA inhibition on acetoclastic methanogenesis, propionate degradation and β-Oxidation. J Environ Sci Heal Part A 39:1025–1037.  https://doi.org/10.1081/ESE-120028411 CrossRefGoogle Scholar
  64. 64.
    Novak JT, Carlson DA (1970) The Kinetics of anaerobic long chain fatty acid degradation. Water Pollut Control Fed 42:1932–1943Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.BIOPROA, Faculty of Engineering, Institute of Chemical EngineeringUniversidad de La RepúblicaMontevideoUruguay
  2. 2.Microbial Ecology Laboratory, BioGem DepartmentBiological Research Institute Clemente Estable, Ministry of EducationMontevideoUruguay

Personalised recommendations