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Metatranscriptomics analysis of cyanobacterial aggregates during cyanobacterial bloom period in Lake Taihu, China.

Zhenzhu ChenJunyi ZhangRui LiFei TianYanting ShenXueying XieQinyu GeZuhong Lu
Published in: Environmental science and pollution research international (2017)
Molecular mechanism of interaction between the bloom-forming cyanobacterial species and attached microbios within cyanobacterial aggregates has not been elucidated yet and understanding of which would help to unravel the cyanobacteria bloom-forming mechanism. In this study, we profiled the metabolically active community by high-throughput metatranscriptome sequencing from cyanobacterial aggregates during cyanobacterial bloom period in Lake Taihu, China. A total of 308 million sequences were obtained using the HiSeq 2500 sequencing platform, which provided a great sequence coverage to carry out the in-depth taxonomic classification, functional classification, and metabolic pathway analysis of the cyanobacterial aggregates. The results show that bacteria dominated in cyanobacterial aggregates, accounting for more than 96.66% of total sequences. Microcystis was the most abundant genus, accounted for 26.80% of total assigned sequences at the genus level in cyanobacterial aggregates community; however, Proteobacteria (46.20%) was found to be as the most abundant active bacterial populations at the phylum level. More importantly, nitrogen, phosphonate, and phosphinate metabolism which associated with eutrophication were found in this study. Especially, the enzymes and organisms relating to denitrification and anammox of nitrogen metabolism, which reduced nitrogen concentration by reducing nitrate to nitrogen to inhibit the eutrophication, were first discovered in Lake Taihu during cyanobacterial bloom period. The present study provides a snapshot of metatranscriptome for cyanobacterial aggregates in Lake Taihu and demonstrates that cyanobacterial aggregates could play a key role in the nitrogen cycle in eutrophic water.
Keyphrases
  • high throughput
  • healthcare
  • mental health
  • single cell
  • deep learning
  • microbial community
  • wastewater treatment
  • water quality