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SUMMARY Microalgae modulate lipid metabolism in response to nutrient stress, offering a promising avenue for sustainable biofuel production. However, a mechanistic understanding of the transcriptional programs driving triacylglycerol (TAG) accumulation remains limited, particularly in non‐model species. Here, we employ a systems‐level approach to dissect the regulatory basis of TAG biosynthesis in twoChlorella sorokinianastrains exhibiting contrasting lipid accumulation phenotypes under nitrogen (N) and phosphorus (P) deprivation. Through physiological, metabolic, and transcriptomic analyses, we confirmedC. sorokinianaDOE1412 (CsDOE1412) as a high TAG‐accumulator andC. sorokinianaUTEX1228 (Cs1228) as a low TAG‐accumulator, providing a comparative framework for inferring transcriptional regulatory networks (TRNs). Both stressors induced rapid TAG accumulation within 6 h, withCsDOE1412 reaching 40% TAG content by 48 h under N conditions. While N deprivation primarily promoted TAG accumulation, P starvation favored diacylglyceryl trimethylhomoserine biosynthesis, reaching up to 21 and 30% of the lipid composition inCs1228 andCsDOE1412, respectively. TRNs analysis revealed a distinct regulatory logic between strains:CsDOE1412 exhibited a stress‐specific, narrowly focused transcriptional response, with five transcription factors (TFs) identified as leading regulators based on centrality measures, whereasCs1228 mounted a broader, overlapping response, with 30 key TFs across conditions. A detailed analysis of the inferred TRNs identified 15 and 14 candidate TFs inCsDOE1412 andCs1228, respectively, with predicted interactions involving key steps in carbon metabolism and lipid biosynthesis, suggesting their involvement in metabolic rewiring during nutrient stress. Among them, we found two CH3‐type ortholog pairs,Cs1228_21g10473/CsDOE1412_2079g07848andCs1228_02g00899/CsDOE1412_2296g01133, showing upregulation in TAG‐accumulating conditions; and one AP2‐type ortholog pair,Cs1228_04g03113/CsDOE1412_2160g02163, with contrasting transcription profiles, pointing to transcriptional regulatory pathways with shared and unique regulators between strains. These findings expand the repertoire of regulatory components associated with algal lipid metabolism and highlightC. sorokinianaas a robust model for elucidating complex transcriptional responses to environmental cues. Furthermore, this study provides candidate TFs for engineering enhanced lipid productivity in microalgae.
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Oleaginous Yeast Biology Elucidated With Comparative Transcriptomics
Extremophilic yeasts have favorable metabolic and tolerance traits for biomanufacturing‐ like lipid biosynthesis, flavinogenesis, and halotolerance – yet the connection between these favorable phenotypes and strain genotype is not well understood. To this end, this study compares the phenotypes and gene expression patterns of biotechnologically relevant yeasts Yarrowia lipolytica, Debaryomyces hansenii, and Debaryomyces subglobosus grown under nitrogen starvation, iron starvation, and salt stress. To analyze the large data set across species and conditions, two approaches were used: a “network‐first” approach where a generalized metabolic network serves as a scaffold for mapping genes and a “cluster‐first” approach where unsupervised machine learning co‐expression analysis clusters genes. Both approaches provide insight into strain behavior. The network‐first approach corroborates that Yarrowia upregulates lipid biosynthesis during nitrogen starvation and provides new evidence that riboflavin overproduction in Debaryomyces yeasts is overflow metabolism that is routed to flavin cofactor production under salt stress. The cluster‐first approach does not rely on annotation; therefore, the coexpression analysis can identify known and novel genes involved in stress responses, mainly transcription factors and transporters. Therefore, this work links the genotype to the phenotype of biotechnologically relevant yeasts and demonstrates the utility of complementary computational approaches to gain insight from transcriptomics data across species and conditions.
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- PAR ID:
- 10592652
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Biotechnology and Bioengineering
- Volume:
- 122
- Issue:
- 3
- ISSN:
- 0006-3592
- Page Range / eLocation ID:
- 677 to 697
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/bit.28891
