Scientific ReportsGenomicsPeer-reviewed journal article
Sequencing and comparative analysis of three Chlorella genomes provide insights into strain-specific adaptation to wastewater
Tian WuiD, Linzhou LiiD, Xiaosen JiangiD, Yang YongiD, Yanzi Song, Liang CheniD, Xun XuiD, Yue SheniD, Ying GuiD
Abstract
Microalgal Chlorella has been demonstrated to process wastewater efficiently from piggery industry, yet optimization through genetic engineering of such a bio-treatment is currently challenging, largely due to the limited data and knowledge in genomics. In this study, we first investigated the differential growth rates among three wastewater-processing Chlorella strains: Chlorella sorokiniana BD09, Chlorella sorokiniana BD08 and Chlorella sp. Dachan, and the previously published Chlorella sorokiniana UTEX 1602, showing us that BD09 maintains the best tolerance in synthetic wastewater. We then performed genome sequencing and analysis, resulting in a high-quality assembly for each genome with scaffold N50 >2 Mb and genomic completeness [≥] 91%, as well as genome annotation with 9,668, 10,240, 9,821 high-confidence gene models predicted for BD09, BD08, and Dachan, respectively. Comparative genomics study unravels that metabolic pathways, which are involved in nitrogen and phosphorus assimilation, were enriched in the faster-growing strains. We found that gene structural variation and genomic rearrangement might contribute to differential capabilities in wastewater tolerance among the strains, as indicated by gene copy number variation, domain reshuffling of orthologs involved, as well as a [~]1 Mb-length chromosomal inversion we observed in BD08 and Dachan. In addition, we speculated that an associated bacterium, Microbacterium chocolatum, which was identified within Dachan, play a possible role in synergizing nutrient removal. Our three newly sequenced Chlorella genomes provide a fundamental foundation to understand the molecular basis of abiotic stress tolerance in wastewater treatment, which is essential for future genetic engineering and strain improvement.
- assay: whole-genome sequencing
- research focus: genome assembly
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- approach: experimental assay
- Citations
- 49
- FWCI
- 1.06
- Field percentile
- top 28%
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