Evaluation of bacterial association in methane generation pathways of an anaerobic digesting sludge via metagenomic sequencing

  • Nasir Ali
  • Hui Gong
  • Xiang Liu
  • Abdulmoseen Segun Giwa
  • Kaijun WangEmail author
Original Paper


Anaerobic digestion, a recently hot technology to produce biogases especially methane generation for biofuel from wastewater, is considered an effective explanation for energy crisis and global pollution threat. A complex microbiome population is present in sludge, which plays an important role in the digestion of complex polymer into simple monomers. 16S rRNA approaches simply are not enough for amplification due to the involvement of extreme complex population. However, Illumina sequencing is a recent powerful technology to reveal the entire microbiome structure and methane generation pathways in anaerobic digestion. Metagenomic sequencing was tested to reveal the microbial structure of a digested sludge from a local wastewater treatment plant in Beijing. The Illumina HiSeq program was used to extract about 5 GB of data for metagenomic analysis. The classification investigation revealed about 97.64% dominancy of bacteria while 1.78% were detected to be archaea using MG-RAST server. The most abundant bacterial communities were reported to be Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria. Furthermore, the important microbiome involved in methane generation was revealed. The dominant methanogens were detected (Methanosaeta and Methanosarcina), with affiliation of dominant genes involved in acetoclastic methanogenesis in a digesting sludge. The metagenomic analysis showed that microbial structure and methane generation pathways were successfully dissected in an anaerobic digester.


Anaerobic digestion Metagenomic evaluation Bacterial association Methanogenic pathways Wastewater treatment plant 



This work was supported by Major Science and Technology Program for Water Pollution Control and Treatment of China (Grant no. 2017ZX07102-004), and National Natural Science Foundation of China (Grant no. 21206084). We are grateful to School of Medicine, Tsinghua University for providing the tools of metagenomic Matlab.

Author contributions

All the authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declared that there is no conflict of interest.

Supplementary material

203_2019_1716_MOESM1_ESM.docx (735 kb)
Supplementary material 1 (DOCX 734 kb)


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Copyright information

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

Authors and Affiliations

  1. 1.State Key Joint Laboratory of Environment Simulation and Pollution Control, School of EnvironmentTsinghua UniversityBeijingPeople’s Republic of China
  2. 2.Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdaoPeople’s Republic of China
  3. 3.Green Intelligence Environmental SchoolYangtze Normal UniversityChongqingPeople’s Republic of China

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