3.1 The Effects of CFS and LA on Morphology of G. anatis
Transmission electron microscopy (TEM)was used to observe the ultrastructural changes in G. anatis (GAC026) after treated with CFS or LA. In the control group, the bacterial had a normal shape (Fig. 2A, B). The CFS treated bacteria were characterized by cell membrane rupture (Figure C and D). Similarly, the LA treated bacteria were smaller without membrane integrity (Figure E and F). It needs to be noted that LA was more detrimental to the G. anatis evidenced by the fact that nearly all the cells lost their membranes. The only explanation for this difference was the CFS content other than acids were somehow protective to the cells.
3.2 Different expressed genes for G. anatis under acid stress
The effect of acid stress on the GAC026 transcriptome was first investigated. Differentially transcribed genes at the mRNA level were evaluated by comparing acid-challenged samples harvested at two time points (30 and 60 min) with the 0-min time point (set as the control). The transcriptomic data analysis indicated that 1872 coding genes and 1872 genes at 30 min under the CFS and LA, 1876 at 60 min under the CFS, 1893 at 60 min under the LA and 1874 at 0 min were detected. After filtering (≥ 2-fold change, adjusted p ≤ 0.05), 677 genes were considered significantly differentially expressed (264 up- and 413 down-regulated) under the acid stress condition (supplemental Table S-1): including 174 at 30 min under the CFS (94 up- and 90 down-regulated) and 49 at 60 min under the CFS (5 up- and 44 down-regulated) (Fig. 3A). Taking the F0min and R0min samples as the control group, there were 73 differentially expressed genes (DEGs) identified between F30min and R30min was major (Figure.3B). However, the DEGs were reduced to 17 after 60min treatment (Figure.3C). Consistent with the TEM results, acid stress was the main cause for the antimicrobial effect of CFS within 30 min, while the other components in CFS tended to antagonize this effect with unknown mechanisms.
3.3 Gene Expression Pattern Analysis, Clustering, and Functional Enrichment
The genes displayed a considerable difference in expression profiles in response to acid stress between different exposure times (Fig. 4). The total DEGs at varying exposure times were clustered into different profiles based on the expression patterns of genes using the Short Time-series Expression Miner (STEM) software. The most reprehensive clusters are profile 2 and profile 5 in CFS, and profile 3 and profile 4 in LA (p < 0.05). In profile 2, the expression of 67 gene transcripts decreased and then increased with the duration of CFS treatment (Fig. 4A), and in profile 5, the expression of 126 gene transcripts was increased and then decreased with the duration of CFS (Fig. 4B). In profile 3, the expression of 211 gene transcripts remained unchanged and then decreased with the duration of LA (Fig. 4C), and in profile 5, the expression of 101 gene transcripts remained unchanged and then increased with the duration of LA (Fig. 4D).
To define the functional annotation of the changes during transcription, KO classifications were implemented for the genes belonging to these profiles. As shown in Fig. 2A, the DEGs of Profile2 (gene expression first decreased and then increased under fermentation stress) were mainly enriched in KEGG pathways: ribosome including rpmH, rpmF, rplQ, rpsD, rplX, rpsQ, rpmC, rplW, rplD and rplC; ABC transporters including oppB, potC, HI_0359, xylF, HI_0036. As shown in Fig. 2B, the DEGs of Profile5 (gene expression first increased and then decreased under fermentation stress) were mainly enriched in KEGG pathways: Amino sugar and nucleotide sugar metabolism including manA, manZ, manY, manX, nagA, nagE, galT, nanK and nanA; Inositol phosphate metabolism including iolB, iolA, iolG, iolE and iolD ; Phosphotransferase system including manZ, manY, manX, fruB, unknown and nagE; Fructose and mannose metabolism including manA, manZ, manY, manX, fruB and fruK. As shown in Fig. 2C, the DEGs of Profile3 (gene expression remained unchanged and then decreased under LA stress) were mainly enriched in KEGG pathways: Amino sugar and nucleotide sugar metabolism including galE, manY, manX, nagB, nagA, scrK, manB, galK, galT, nanEK and nanK. Inositol phosphate metabolism including tpiA, iolB, iolA, iolD and iolC. Microbial metabolism in diverse environments including 26 genes. Sulfur metabolism including dmsC, dmsB, dmsA, ttrB and ttrA. Valine, leucine and isoleucine biosynthesis including ilvI, ilvE, ilvG, leuC2 and alaA. As shown in Fig. 2D, the DEGs of Profile4 (gene expression remained unchanged and then increased under LA stress) were mainly enriched in KEGG pathways: Oxidative phosphorylation including atpC, atpD, atpG, atpF, cyoC, cyoD and cyoE; Photosynthesis including atpC, atpD, atpG and atpF. Propanoate metabolism including sucD, sucC, prpF, acnD, acsA and puuE; Ribosome including rpmF, rplQ, rpsD, rpmJ, rpmD, rplX, rpmG, rplS and rpmE.
The results indicate that the expression level of most of genes related to ribosome and ABC transporters were first decreased and then increased under fermentation stress, but the transcription level of genes involved in Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, Phosphotransferase system, Fructose and mannose metabolism and Sulfur metabolism were first increased and then decreased after fermentation exposure. The expression level of most of genes related to Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, Microbial metabolism in diverse environments, Sulfur metabolism and Valine, leucine and isoleucine biosynthesis remained unchanged and then decreased under LA stress, but the transcription level of genes involved in Oxidative phosphorylation, Photosynthesis, Propanoate metabolism and Ribosome were remained unchanged and then increased after LA exposure. The similar pattern between gene expression of CFS and LA was involved in ribosome, inositol phosphate metabolism, amino sugar and nucleotide sugar metabolism, all of which play critical roles in the structural integrity and energy balance. These indicated that both of CFS and LA inhibit cell growth or even cause cell death by destroying the cell structure including the membrane and disturbing energy homeostasis.
3.4 qRT-PCR validation of DEGs in RNA-Seq analysis
To confirm the reproducibility and accuracy of DEGs identified by RNA-seq analysis, we selected 16 targeted genes (purM, metK, copA, metN, fruK, htpG, ldh1, rplQ, rpsS, hcp, iolA, rpoE, norB, dcuB, HI_0227, nanA) that are involved in structural and energy metabolism for qRT-PCR measurement. The primer sequences for the analyzed genes were shown in supplemental Table S-2. In Fig. 5, the gene expression between CFS and LA acid stress-treated G. anatis (GAC026) by qRT-PCR and RNA-seq were compared. The fold changes for gene regulation predicated from transcriptome were verified by qRT-PCR as both of them showed a similar or nearly synchronized pattern.
3.5 Non-targeted metabolome analysis and validation of targeted energy metabolites
Non-targeted metabolomic analysis was used for differential metabolic profiling of G. anatis (GAC026) treated with CFS and LA to identify potentially impactful molecules. The CFS and the LA treated samples were differentiated using orthogonal partial least-squares discriminant analysis (OPLS-DA) (Fig. 6A and 6B). In total, 374 metabolites were identified. Intracellular metabolites, which were changed significantly under CFS at 60min, were mainly involved in Arachidonic acid metabolism, Linoleic acid metabolism, Metabolic pathways and alpha-Linolenic acid metabolism (P < 0.05) (Fig. 6C). Intracellular metabolites, which were changed significantly under LA at 60min, were mainly involved in Arginine and proline metabolism, Metabolic pathways, Biosynthesis of antibiotics, Biosynthesis of amino acids, Arachidonic acid metabolism, Aminoacyl-tRNA biosynthesis, 2-Oxocarboxylic acid metabolism, Cysteine and methionine metabolism and Sulfur relay system (P < 0.05) (Fig. 6D). The shared pathway which explained the antimicrobial effect of CFS due to acid stress include Arachidonic acid metabolism and Metabolic pathways. The KEGG pathway mainly involved 7 metabolites with CFS treatment, such as phospholipids, adenosine, D-Glutamate, L-Citrulline, creatine, creatinine, Thiamine, etc. The KEGG pathway mainly involved 14 metabolites with LA treatment, such as S-Adenosyl-L-methionine, Putrescine, L-Arginine, L-Proline, creatine, creatinine, 1,3-Diaminopropane, L-Phenylalanine, L-Tyrosine, phospholipids, adenosine, L-Isoleucine, 2-Dehydro-3-deoxy-D-galactonate, Thiamine, etc. These results suggest that structural and metabolic pathways are involved in the process of acid stress. In addition, the metabolites, citrate, lactic acid, L-malic acid and oxaloacetate (Fig. 7), were confirmed by targeted energy metabolomics.
3.6 Transcript-Metabolite Correlation Network
Based on data from differentially accumulating metabolites (DAMs) and DEGs, a subnetwork was constructed for some hub genes to determine transcript-metabolite correlations. Pearson’s correlation tests were carried out between relative quantitative changes of metabolites and related transcripts, and we set correlation coefficient > 0.8 as cut-off in the analysis. Meanwhile, the pathways involved in DAMs and DEGs were shown by the pie chart. In addition to “metabolic pathways”, DAMs and DEGs were also involved in “Aminoacyl-tRNA biosynthesis” and "Biosynthesis of secondary metabolites" (Fig. 8). These results indicated that the hub genes were highly correlated with their corresponding metabolites. Citric acid, Guanosine, D-Glutamic acid, L-Arginine, Taurocholic acid, Stearidonic acid, D-Xylulose, Galactose 1-phosphate and 2-Keto-3-deoxy-D-gluconic acid were identified in these biological processes, which reconfirmed the large accumulation of metabolites and their participance under acid stress.