Functional Genomic Analyses of Exopolysaccharide-Producing Streptococcus thermophilus ASCC 1275 in Response to Milk Fermentation Conditions

Abstract

Exopolysaccharide (EPS) produced from dairy bacteria improves texture and functionalities of fermented dairy foods. Our previous study showed improved EPS production from Streptococcus thermophilus ASCC1275 (ST1275) by simple alteration of fermentation conditions such as pH decrease (pH 6.5 → pH 5.5), temperature increase (37°C → 40°C) and/or whey protein isolate (WPI) supplementation. The iTRAQ-based proteomics in combination with transcriptomics were applied to understand cellular protein expression in ST1275 in response to above shifts during milk fermentation. The pH decrease induced the most differentially expressed proteins (DEPs) that are involved in cellular metabolic responses including glutamate catabolism, arginine biosynthesis, cysteine catabolism, purine metabolism, lactose uptake, and fatty acid biosynthesis. Temperature increase and WPI supplementation did not induce much changes in global protein express profiles of ST1275 between comparisons of pH 5.5 conditions. Comparative proteomic analyses from pairwise comparisons demonstrated enhanced glutamate catabolism and purine metabolism under pH 5.5 conditions (Cd2, Cd3, and Cd4) compared to that of pH 6.5 condition (Cd1). Concordance analysis for differential expressed genes (DEGs) and DEPs highlighted down-regulated glutamate catabolism and up-regulated arginine biosynthesis in pH 5.5 conditions. Down regulation of glutamate catabolism was also confirmed by pathway enrichment analysis. Down-regulation of EpsB involved in EPS assembly was observed at both mRNA and protein level in pH 5.5 conditions compared to that in pH 6.5 condition. Medium pH decreased to mild acidic level induced cellular changes associated with glutamate catabolism, arginine biosynthesis and regulation of EPS assembly in ST1275.

Keywords: Streptococcus thermophilus; exopolysaccharide; pH; proteome; transcriptome.

FIGURE 1

Experimental design for iTRAQ-based proteomic and matched transcriptome analysis on ST1275 under four milk fermentations. Duplicate batches of milk fermentation were carried out for each condition.

FIGURE 2

Summary of proteomic analysis with ProteinPilot software. (A) False discovery rate analysis based on the target-decoy database search strategy. (B) FDR levels and ProteinPilot reported confidences. A critical FDR with 1% which corresponding to Unused Protscore of 1.46 from global FDR (fit) was used for filtering protein.

FIGURE 3

Comparisons between optimized conditions (Cd2, Cd3, and Cd4) and pH 6.5 condition (Cd1) based on proteome analysis. (A) DEPs from the comparison (Cd2 vs. Cd1). (B) DEPs from the comparison (Cd3 vs. Cd1). (C) DEPs from the comparison (Cd4 vs. Cd1). (D,E) Common DEPs shared by three comparisons. Dot (detected proteins) highlighted in blue indicates down-DEPs, dot (detected proteins) highlighted in red indicates up-DEPs.

FIGURE 4

Comparisons between conditions (Cd3 and Cd4) and Cd2 based on proteome analysis. (A) DEPs from the comparison (Cd3 vs. Cd2). (B) DEPs from the comparison (Cd4 vs. Cd2); (C,D) Common DEPs shared by two comparisons. Dot (detected proteins) highlighted in blue indicates down-DEPs, dot (detected proteins) highlighted in red indicates up-DEPs.

FIGURE 5

Comparisons between optimized conditions (Cd2, Cd3, and Cd4) and pH 6.5 condition (Cd1) based on paired analysis on both proteome and transcriptome data. Only DEPs and DEGs (both up and down) were used to find common differential expressed targets.

FIGURE 6

Arginine biosynthesis in ST1275 was enhanced in condition 2 as compared to that of condition 1. Gene IDs highlighted in red indicate significantly up-regulated (at least 2-fold); gene IDs (locus tag) highlighted in green indicate significantly down-regulated (at least 2-fold); gene IDs highlighted in black indicate no significantly change.