Stimulating carbon and nitrogen metabolism of Chlorella pyrenoidosa to treat aquaculture wastewater and produce high-quality protein in plate photobioreactors
Abstract
Wastewater treatment by microalgae is the economical and environmentally friendly strategy, but is still challenged with the strict discharge standards and valuable biomass exploitations. The carbon and nitrogen metabolism of Chlorella pyrenoidosa was improved by the red LED light and starch addition to treat Tilapia aquaculture wastewater (T-AW) and produce protein simultaneously in a plate photobioreactor. The red LED light was applied to improve the nutrient removals at an outdoor temperature, but the concentrations except total nitrogen did not satisfy the discharge standards. After starch addition, the removal efficiencies of total phosphorus, total nitrogen, chemical oxygen demand, and total ammonia nitrogen were 85.15, 96.96, 88.53, and 98.01 % in a flat-plate photobioreactor, respectively, which met the discharge standards and the protein production reached 0.60 g/L. At a molecular level, the metabolic flux and transcriptome analyses showed that red light promoted carbon flux of the Embden-Meyerhof-Pranas pathway and tricarboxylic cycle, and upregulated the levels of genes encoding α-amylase, glutamine synthetase, glutamate dehydrogenase, nitrate transporter, and ammonium transporter, which facilitated nutrients removal and provided nitrogen sources for protein biosynthesis. The harvesting C. pyrenoidosa possessed the 62 % essential amino acids and great lipid composition for biofuels. This study provided a new orientation for outdoor wastewater treatment and protein…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
By leveraging genomic data and advanced computational techniques, researchers can identify genetic modifications that improve production of specific metabolites.
“By leveraging genomic data and advanced computational techniques, researchers can identify genetic modifications that improve the production of specific metabolites [61].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [61]
Cites
Juneja, A., Ceballos, R., & Murthy, G. (2013) · Energies · doi:10.3390/en6094607 · Cited by 835 · see citing works
Li, Y., Chen, Y.-F., Chen, P., Min, M., Zhou, W., Martinez, B., Zhu, J., & Ruan, R. (2011) · Bioresource Technology · doi:10.1016/j.biortech.2011.01.091 · Cited by 724 · see citing works
Trends and novel strategies for enhancing lipid accumulation and quality in microalgae
Singh, P., Kumari, S., Guldhe, A., Misra, R., Rawat, I., & Bux, F. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.001 · Cited by 309 · see citing works
Moheimani, N. R., Vadiveloo, A., Ayre, J. M., & Pluske, J. R. (2018) · Algal Research · doi:10.1016/j.algal.2018.09.007 · Cited by 100 · see citing works
Photobioreactor scale-up for a shear-sensitive dinoflagellate microalga
Camacho, F. G., Rodríguez, J. J. G., Mirón, A. S., Belarbi, E. H., Chisti, Y., & Grima, E. M. (2011) · Process Biochemistry · doi:10.1016/j.procbio.2011.01.005 · Cited by 72 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wu, Y., Shan, L., Zhao, W., & Lu, X. (2025) · Marine Drugs · doi:10.3390/md23050184 · Cited by 59 · see citing works
Zheng, J., Geng, J., Li, J., Li, D., Yang, S., Lu, X., Liu, J., Zhang, C., & Sun, H. (2025) · Future Foods · doi:10.1016/j.fufo.2025.100776 · Cited by 11 · see citing works
Self-citationFlat-plate photobioreactors for renewable resources production
Rahman, M. R., & Hellgardt, K. (2025) · Algal Bioreactors · doi:10.1016/b978-0-443-14058-7.00019-1 · Cited by 6 · see citing works
- Authors
- Wang, J., Wang, Y., Gu, Z., Mou, H., & Sun, H.
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 878
- Pages
- 163061
- ISSN
- 0048-9697
- Cited by
- 49
- References
- 5
- Language
- en
- Licence
- Funders
- Science, Technology and Innovation Commission of Shenzhen Municipality (KQTD20180412181334790)
- National Key Research and Development Program of China (2018YFD0900705, 2019YFD0900201)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
