An integrated transcriptomic and metabolic phenotype analysis to uncover the metabolic characteristics of a genetically engineered Candida utilis strain expressing δ-zein gene

Abstract

Introduction: Candida utilis (C. utilis) has been extensively utilized as human food or animal feed additives. With its ability to support heterologous gene expression, C. utilis proves to be a valuable platform for the synthesis of proteins and metabolites that possess both high nutritional and economic value. However, there remains a dearth of research focused on the characteristics of C. utilis through genomic, transcriptomic and metabolic approaches.

Methods: With the aim of unraveling the molecular mechanism and genetic basis governing the biological process of C. utilis, we embarked on a de novo sequencing endeavor to acquire comprehensive sequence data. In addition, an integrated transcriptomic and metabolic phenotype analysis was performed to compare the wild-type C. utilis (WT) with a genetically engineered strain of C. utilis that harbors the heterologous δ-zein gene (RCT).

Results: δ-zein is a protein rich in methionine found in the endosperm of maize. The integrated analysis of transcriptomic and metabolic phenotypes uncovered significant metabolic diversity between the WT and RCT C. utilis. A total of 252 differentially expressed genes were identified, primarily associated with ribosome function, peroxisome activity, arginine and proline metabolism, carbon metabolism, and fatty acid degradation. In the experimental setup using PM1, PM2, and PM4 plates, a total of 284 growth conditions were tested. A comparison between the WT and RCT C. utilis demonstrated significant increases in the utilization of certain carbon source substrates by RCT. Gelatin and glycogen were found to be significantly utilized to a greater extent by RCT compared to WT. Additionally, in terms of sulfur source substrates, RCT exhibited significantly increased utilization of O-Phospho-L-Tyrosine and L-Methionine Sulfone when compared to WT.

Discussion: The introduction of δ-zein gene into C. utilis may lead to significant changes in the metabolic substrates and metabolic pathways, but does not weaken the activity of the strain. Our study provides new insights into the transcriptomic and metabolic characteristics of the genetically engineered C. utilis strain harboring δ-zein gene, which has the potential to advance the utilization of C. utilis as an efficient protein feed in agricultural applications.

Keywords: Candida utilis; genome; metabolic phenotype; transcriptome; δ-zein.

Figure 1

GO and KEGG functional classification of the C. utilis genome. (A) GO functional classification. (B) KEGG functional classification. GO and KEGG database were used to classify the functions of the predicted genes.

Figure 2

Differential expression analysis of the transcriptome between the WT and RCT C. utilis. (A) Statistics of the alternative splicing types of the two groups. The Abscissa represents the number of transcripts in specific alternative splicing type. The ordinate indicates 12 alternative splicing types. (B) Saturation test on RNA-seq data of WT. (C) Saturation test on RNA-seq data of RCT. The abscissa represents the percentage of the number of reads located on the genome to the total number of reads localized, and the ordinate represents the percentage of gene in each FPKM range. The expression becomes more saturated as the value gets closer to 1, and each color line depicts the saturation curve of gene expression at different levels within the sample. (D) MA plot of DEGs between the WT and RCT C. utilis. The green dots represent down-regulated DEGs, the red dots represent up-regulated DEGs, and the black dots represent non-differentially expressed genes.

Figure 3

GO enrichment analysis. (A) In cellular components, DEGs were associated with “integral component of membrane,” “ribosome,” and “mitochondrial large ribosomal subunit.” (B) In molecular functions, DEGs were mainly involved in “structural constituent of ribosome,” “transmembrane transporter activity,” and “pyridoxal phosphate binding.” (C) In biological processes, the most highly enriched terms were “translation,” “mitochondrial translation,” and “oxidation–reduction process.”

Figure 4

KEGG enrichment analysis. (A) Substrates with different utilization rates in PM experiments related to KEGG enrichment pathway. (B) KEGG pathway analysis revealed that DEGs were significantly correlated with Ribosome, Peroxisome, Arginine and proline metabolism, Carbon metabolism, and Fatty acid degradation.

Figure 5

GO and KEGG enrichment analyses of the module genes identified by WGCNA analysis. (A) Molecular function GO terms for genes in the turquoise module. (B) KEGG analysis for genes in the turquoise module.

Figure 6

Utilization of different carbon, phosphorus and sulfur sources in the WT and RCT C. utilis. In the phenotype microarray assay, the redox signal intensity is used to define the phenotypic characteristics. The data from the WT strain is represented in red, while the data from the RCT strain is represented in green. Any similarities in the metabolic output between the two strains are depicted in yellow.

Figure 7

Schematic of the metabolic pathways related to differentially expressed genes. Red arrows denote up-regulated genes, while green arrows represent down-regulated genes in these metabolic pathways.