Data-Independent-Acquisition-Based Proteomic Approach towards Understanding the Acclimation Strategy of Oleaginous Microalga Microchloropsis gaditana CCMP526 in Hypersaline Conditions

Abstract

Salinity is one of the significant factors that affect growth and cellular metabolism, including photosynthesis and lipid accumulation, in microalgae and higher plants. Microchloropsis gaditana CCMP526 can acclimatize to different salinity levels by accumulating compatible solutes, carbohydrates, and lipids as energy storage molecules. We used proteomics to understand the molecular basis for acclimation of M. gaditana to increased salinity levels [55 and 100 PSU (practical salinity unit)]. Correspondence analysis was used for the identification of salinity-responsive proteins (SRPs). The highest number of salinity-induced proteins was observed in 100 PSU. Gene ontology enrichment analysis revealed a separate path of acclimation for cells exposed to 55 and 100 PSU. Osmolyte and lipid biosynthesis were upregulated in hypersaline conditions. Concomitantly, lipid oxidation pathways were also upregulated in hypersaline conditions, providing acetyl-CoA for energy metabolism through the tricarboxylic acid cycle. Carbon fixation and photosynthesis were tightly regulated, while chlorophyll biosynthesis was affected in hypersaline conditions. Importantly, temporal proteome analysis of salinity-induced M. gaditana revealed vital SRPs which could be used for engineering salinity resilient microalgal strains for improved productivity in hypersaline culture conditions.

Figure 1

Venn diagram showing the number of differentially expressed proteins at different time points in salinity-stressed M. gaditana.

Figure 2

Volcano plot showing significantly regulated proteins [p-value < 0.05 and absolute log₂(fold change) > 1] as green dots in two different salinity levels (A). 55 PSU vs control and (B) 100 PSU vs control. Values on either side of the plot represent the number of significantly up- and downregulated proteins. The cultivation time point for proteomic analysis is defined as red text in the corresponding plots.

Figure 3

Interaction map of enriched GO terms at different cultivation time points and salinities in M. gaditana. Bubble color indicates the p-value; bubble size indicates the generality of the GO terms in the underlying GOA (Gene Ontology Annotation) database. p-values of enriched GO terms for 55 and 100 PSU at different time points are provided in Supporting Information, Tables S2 and S3, respectively.

Figure 4

CA of differentially expressed proteins in M. gaditana at different time points in 55 and 100 PSU. Biplot showing the association between time points and differentially expressed proteins (numbers along the red arrow represent time points).

Figure 5

Heatmap of the top 10 differentially expressed proteins in M. gaditana based on the contribution toward the dimensions in the biplot. Color scale indicates the log₂ FC value. Significant levels of protein abundance are marked with an asterisk.

Figure 6

Key metabolic events in salinity-stressed M. gaditana. The regulation of proteins involved in various biosynthetic processes is indicated by green (upregulation) and red arrows (downregulation). LD: lipid droplet, N: nucleus, Mt: mitochondria, Ch: chloroplast, SOD: superoxide dismutase, CAT: catalase, GPDH: glycerol-6-phosphate dehydrogenase, DHAP: dihydroxy acetone phosphate, G3P: glycerol-3-phosphate, TAG: triacylglycerol, LACS: long-chain acyl-CoA synthetase, MDGS: monogalactosyldiacylglycerol synthase, DGAT: diacylglycerol acyltransferase, PPP: pentose phosphate pathway, and LHC: light-harvesting complex.

Figure 7

Salinity-induced temporal dynamics of proteins involved in trehalose biosynthesis in M. gaditana. Significant levels of protein abundance are marked with an asterisk.