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. 2025 May 21:16:1601691.
doi: 10.3389/fmicb.2025.1601691. eCollection 2025.

Enhancing lipid production in Nannochloropsis salina via RNAi-mediated downregulation of carbohydrate biosynthesis

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Enhancing lipid production in Nannochloropsis salina via RNAi-mediated downregulation of carbohydrate biosynthesis

Hyun Gi Koh et al. Front Microbiol. .

Abstract

Microalgae are promising platforms for sustainable biofuel production owing to their high photosynthetic efficiency and carbon fixation capacity. Nannochloropsis salina is particularly valued for its robust growth and lipid accumulation. However, redirecting carbon flux from carbohydrate to lipid biosynthesis remains a key challenge in microalgal metabolic engineering. In this study, RNA interference (RNAi) was employed to downregulate uridine diphosphate-glucose pyrophosphorylase (UGPase), a central enzyme in chrysolaminarin biosynthesis. After confirming the presence of core RNAi machinery (Argonaute, Dicer, and RDR) in N. salina, an RNAi construct targeting UGPase was introduced. Two transformants, NsRiUGPase 5 and NsRiUGPase 26, were selected through McrBC-PCR and qRT-PCR screening based on reduced methylation-sensitive PCR band intensity and UGPase transcript levels. These RNAi mutants exhibited significantly enhanced growth compared to wild-type. On day 12, dry cell weight (DCW) reached 4.77 g/L in NsRiUGPase 5 and 6.37 g/L in NsRiUGPase 26, representing 32.4% and 76.9% increases, respectively, compared to WT (3.60 g/L). Despite similar lipid contents per biomass, lipid productivity was markedly improved. On day 12, NsRiUGPase 26 achieved 196.3 mg/L/day, a 71.0% increase over WT (114.8 mg/L/day). Fatty acid methyl ester (FAME) analysis showed no significant difference in lipid composition among strains, indicating that UGPase knockdown did not affect lipid quality. These results demonstrate that RNAi-mediated suppression of UGPase successfully redirected carbon flux away from carbohydrate storage toward growth, thereby enhancing overall lipid productivity. This study provides new insights into carbon partitioning in N. salina and underscores RNAi as a powerful tool for microalgal biofuel optimization.

Keywords: Nannochloropsis salina; RNAi; UGPase; carbohydrate; lipid; microalgae.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Uridine diphosphate-glucose pyrophosphorylase (UGPase) RNA interference (RNAi) knockdown validation using McrBC-PCR and quantitative real-time PCR (qRT-PCR). (A) Schematic representation of the RNAi knockdown vector targeting UGPase. The construct includes a sense and antisense region separated by a linker, driven by the TUB promoter and terminator. The Shble gene provides selection. (B) UGPase gene region and the locations of primers used for McrBC-PCR (McrBC-FWD, McrBC-REV) and qRT-PCR (Q1, Q2). (C) McrBC-PCR results. In the presence of GTP, methylated DNA is cleaved, leading to weaker PCR amplification. This indirectly suggests successful RNAi-mediated methylation-dependent degradation. (D) qRT-PCR analysis of mRNA expression levels in RNAi-targeted regions using Q1 and Q2 primers. The relative expression levels indicate the effectiveness of RNAi-mediated knockdown. The data points represent the average of samples and error bars indicate standard error (n = 3). Significant differences against wild-type (WT) for the same conditions and same time points, as determined by Student’s t-test, are indicated by asterisks (*p < 0.05, **p < 0.01, ***p < 0.001).
FIGURE 2
FIGURE 2
Growth performance of N. salina wild-type (WT) and the NsRiUGPase transformants. (A) Growth curves of WT and RNA interference (RNAi) transformants (NsRiUGPase 5 and NsRiUGPase 26) were measured as cell concentration (cells/mL) over time under photoautotrophic conditions. (B) Dry cell weight (DCW) of WT, NsRiUGPase 5, and NsRiUGPase 26 at days 8 and 12 of cultivation. Dry cell weight (DCW) was determined by filtering and drying biomass. All data represent the mean ± standard error (n = 3). Significant differences against WT for the same conditions and same time points, as determined by Student’s t-test, are indicated by asterisks (*p < 0.05, **p < 0.01, ***p < 0.001).
FIGURE 3
FIGURE 3
Carbohydrate analysis of N. salina wild-type (WT) and the NsRiUGPase transformants. (A) Total carbohydrate content of WT, NsRiUGPase 5, and NsRiUGPase 26 at days 8 and 12 of cultivation. (B) Total carbohydrate productivity (g/L/day) of WT, NsRiUGPase 5, and NsRiUGPase 26 at days 8 and 12. All data represent the mean ± standard error (n = 3). Significant differences against WT for the same conditions and same time points, as determined by Student’s t-test, are indicated by asterisks (*p < 0.05, **p < 0.01, ***p < 0.001).
FIGURE 4
FIGURE 4
Lipid and fatty acid methyl ester (FAME) analysis in N. salina wild-type (WT) and the NsRiUGPase transformants. (A) Lipid content and (B) lipid productivity of WT, NsRiUGPase 5, and NsRiUGPase 26 at days 8 and 12 of cultivation, determined by the gravimetric method (Folch extraction). (C) FAME content and (D) FAME productivity were measured on days 8 and 12 and analyzed by gas chromatography (GC) following transesterification. All data represent the mean ± standard error (n = 3). Significant differences against WT for the same conditions and same time points, as determined by Student’s t-test, are indicated by asterisks (*p < 0.05, **p < 0.01, ***p < 0.001).

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References

    1. Agrawal N., Dasaradhi P. V. N., Mohmmed A., Malhotra P., Bhatnagar Raj K., Mukherjee Sunil K. (2003). RNA interference: Biology, mechanism, and applications. Microbiol. Mol. Biol. Rev. 67 657–685. 10.1128/MMBR.67.4.657-685.2003 - DOI - PMC - PubMed
    1. Babu S. S., Gondi R., Vincent G. S., Johnsamuel G. C., Jeyakumar R. B. (2022). Microalgae biomass and lipids as feedstock for biofuels: Sustainable biotechnology strategies. Sustainability 14:15070. 10.3390/su142215070 - DOI
    1. Banerjee A., Banerjee C., Negi S., Chang J. S., Shukla P. (2018). Improvements in algal lipid production: A systems biology and gene editing approach. Crit. Rev. Biotechnol. 38 369–385. 10.1080/07388551.2017.1356803 - DOI - PubMed
    1. Bartley M. L., Boeing W. J., Corcoran A. A., Holguin F. O., Schaub T. (2013). Effects of salinity on growth and lipid accumulation of biofuel microalga Nannochloropsis salina and invading organisms. Biomass Bioenergy 54 83–88. 10.1016/j.biombioe.2013.03.026 - DOI
    1. Chan S. W. (2008). Inputs and outputs for chromatin-targeted RNAI. Trends Plant Sci. 13 383–389. 10.1016/j.tplants.2008.05.001 - DOI - PubMed

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