Literature DB >> 34580746

Secretomics: a biochemical footprinting tool for developing microalgal cultivation strategies.

Rakhi Bajpai Dixit1, Balu Raut1, Suvarna Manjre1, Mitesh Gawde1, Chandra Gocher1, Manish R Shukla1, Avinash Khopkar1, Venkatesh Prasad1, Thomas P Griffin1,2, Santanu Dasgupta3.   

Abstract

Microalgae offer a promising source of biofuel and a wide array of high-value biomolecules. Large-scale cultivation of microalgae at low density poses a significant challenge in terms of water management. High-density microalgae cultivation, however, can be challenging due to biochemical changes associated with growth dynamics. Therefore, there is a need for a biomarker that can predict the optimum density for high biomass cultivation. A locally isolated microalga Cyanobacterium aponinum CCC734 was grown with optimized nitrogen and phosphorus in the ratio of 12:1 for sustained high biomass productivity. To understand density-associated bottlenecks secretome dynamics were monitored at biomass densities from 0.6 ± 0.1 to 7 ± 0.1 g/L (2 to 22 OD) in batch mode. Liquid chromatography coupled with mass spectrometry identified 880 exometabolites in the supernatant of C. aponinum CCC734. The PCA analysis showed similarity between exometabolite profiles at low (4 and 8 OD) and mid (12 and 16 OD), whereas distinctly separate at high biomass concentrations (20 and 22 OD). Ten exometabolites were selected based on their role in influencing growth and are specifically present at low, mid, and high biomass concentrations. Taking cues from secretome dynamics, 5.0 ± 0.5 g/L biomass concentration (16 OD) was optimal for C. aponinum CCC734 cultivation. Further validation was performed with a semi-turbidostat mode of cultivation for 29 days with a volumetric productivity of 1.0 ± 0.2 g/L/day. The secretomes-based footprinting tool is the first comprehensive growth study of exometabolite at the molecular level at variable biomass densities. This tool may be utilized in analyzing and directing microalgal cultivation strategies and reduction in overall operating costs.
© 2021. The Author(s), under exclusive licence to Springer Nature B.V.

Entities:  

Keywords:  Biochemical tool; Bioprocess monitoring; Microalgal cultivation; Operation cost; Principal component biplot

Mesh:

Substances:

Year:  2021        PMID: 34580746     DOI: 10.1007/s11274-021-03148-6

Source DB:  PubMed          Journal:  World J Microbiol Biotechnol        ISSN: 0959-3993            Impact factor:   3.312


  20 in total

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Authors:  Richard Baran; Eoin L Brodie; Jazmine Mayberry-Lewis; Eric Hummel; Ulisses Nunes Da Rocha; Romy Chakraborty; Benjamin P Bowen; Ulas Karaoz; Hinsby Cadillo-Quiroz; Ferran Garcia-Pichel; Trent R Northen
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