Metatranscriptomics reveals the molecular mechanism of large granule formation in granular anammox reactor
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Environmental Science and Engineering Program
Water Desalination and Reuse Research Center (WDRC)
KAUST Grant NumberCRG_R2_13_SAIK_KAUST_1
Permanent link to this recordhttp://hdl.handle.net/10754/614405
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AbstractGranules enriched with anammox bacteria are essential in enhancing the treatment of ammonia-rich wastewater, but little is known about how anammox bacteria grow and multiply inside granules. Here, we combined metatranscriptomics, quantitative PCR and 16S rRNA gene sequencing to study the changes in community composition, metabolic gene content and gene expression in a granular anammox reactor with the objective of understanding the molecular mechanism of anammox growth and multiplication that led to formation of large granules. Size distribution analysis revealed the spatial distribution of granules in which large granules having higher abundance of anammox bacteria (genus Brocadia) dominated the bottom biomass. Metatranscriptomics analysis detected all the essential transcripts for anammox metabolism. During the later stage of reactor operation, higher expression of ammonia and nitrite transport proteins and key metabolic enzymes mainly in the bottom large granules facilitated anammox bacteria activity. The high activity resulted in higher growth and multiplication of anammox bacteria and expanded the size of the granules. This conceptual model for large granule formation proposed here may assist in the future design of anammox processes for mainstream wastewater treatment.
CitationMetatranscriptomics reveals the molecular mechanism of large granule formation in granular anammox reactor 2016, 6:28327 Scientific Reports
SponsorsThis research was funded by Competitive Research Grant (CRG_R2_13_SAIK_KAUST_1) from King Abdullah University of Science and Technology (KAUST). Special thanks are extended to Shan Sun for assisting with the qPCR analysis and Academic Writing Service Team at KAUST for making illustration figure and animation. We also thank Shahjahan Ali and colleagues in the Bioscience Core Laboratory at KAUST for their support in metatranscriptome sequencing on the Illumina HiSeq and in 16S rRNA gene sequencing on the Ion Torrent PGM. A portion of the bioinformatics analyses was supported by a grant to Juniata College from the Howard Hughes Medical Institute (http://www.hhmi.org) through the Precollege and Undergraduate Science Education Program.
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