Global transcriptomic analysis of ethanol tolerance response in Salmonella Enteritidis.

Adaptation to sublethal amounts of ethanol enables Salmonella Enteritidis to survive under normally lethal ethanol conditions, which is referred to as the ethanol tolerance response (ETR). To uncover mechanisms underlying this adaptative response, RNA-seq and RT-qPCR techniques were employed to reveal global gene expression patterns in S. Enteritidis after sublethal ethanol treatment. It was observed that 811 genes were significantly differentially expressed in ethanol-treated cells compared with control cells, among which 328 were up-regulated and 483 were down-regulated. Functional analysis revealed that these genes were enriched in different pathways, including signal transduction, membrane transport, metabolism, transcription, translation, and cell motility. Specifically, a couple of genes encoding histidine kinases and response regulators in two-component systems were up-regulated to activate sensing and signaling pathways. Membrane function was also influenced by ethanol treatment since ABC transporter genes for transport of glutamate, phosphate, 2-aminoethylphosphonate, and osmoprotectant were up-regulated, while those for transport of iron complex, manganese, and ribose were down-regulated. Accompanied with this, diverse gene expression alterations related to the metabolism of amino acids, carbohydrates, vitamins, and nucleotides were observed, which suggested nutritional requirements for S. Enteritidis to mount the ETR. Furthermore, genes associated with ribosomal units, bacterial chemotaxis, and flagellar assembly were generally repressed as a possible energy conservation strategy. Taken together, this transcriptomic study indicates that S. Enteritidis employs multiple genes and adaptation pathways to develop the ETR.

Introduction

Ethanol has long been employed for chemical disinfection, food preservation, and colorant dissolution in food industries. In food processing plants, ethanol can be utilized for the disinfection of food processing tools, conveyor belts, and food contact surfaces (Dev Kumar et al., 2020; Fagerlund et al., 2017; Shen et al., 2016). In addition, direct addition of ethanol (0.5–5%) is beneficial for prolonging shelf life of foods (Doulia et al., 2000; Katsinis et al., 2008; Shibasaki, 1982), while immersion in ethanol (2.5–70%) is effective in controlling postharvest decay of fruits (Dao and Dantigny, 2011). Ethanol is also a common component in fermented beverages, fruit products and other foods at major or minor levels (He et al., 2021a). Therefore, there exist opportunities for pathogenic bacteria to adapt to sublethal concentrations of ethanol during food processing.

Adaptation to sublethal levels of ethanol is able to enhance bacterial tolerance to subsequent lethal ethanol challenges, which is termed the ethanol tolerance response (ETR) (He et al., 2016). This adaptive response has been observed in a number of pathogenic bacteria such as Salmonella Enteritidis, Cronobacter sakazakii, Bacillus cereus, Vibrio parahaemolyticus, and Listeria monocytogenes (Browne and Dowds, 2001, 2002; Chiang et al., 2006; He et al., 2021b; Huang et al., 2013; Lou and Yousef, 1997). For example, exposure to a sublethal level of 5% ethanol has been demonstrated to induce bacterial tolerance to 15% ethanol challenge in S. Enteritidis (He et al., 2016). The development of ETR in pathogenic bacteria represents a concern to food safety since it may counteract the effectiveness of currently employed food control measures (He et al., 2021a). Therefore, it is crucial to uncover why pathogenic bacteria mount ethanol tolerance.

Physiological and proteomic approaches have been employed to explore the ETR mechanisms in pathogenic bacteria. Physiological analysis revealed that cell membrane permeability and fatty acid composition were involved in the ETR of V. parahaemolyticus (Chiang et al., 2006, 2008). By means of the 2-DE technique, Yeh (2012) found that a total of 16 proteins were differentially expressed after exposure of C. sakazakii to 5% ethanol for 60 min. Moreover, the same sublethal ethanol treatment resulted in the differential expression of 138 proteins belonging to metabolism, enterobactin biosynthesis, virulence, and other pathways in S. Enteritidis (He et al., 2019). To our best knowledge, however, there is no available literature on elucidating the ETR mechanisms in pathogenic bacteria at the transcriptome level.

RNA-seq-based transcriptomics is a powerful tool for exploring stress response mechanisms in pathogenic bacteria (Lamas et al., 2019). This approach has been extensively used to characterize bacterial response to food processing-related stress factors such as acid, low temperature, erythorbyl laurate, and acidified sodium chlorite (Hingston et al., 2017; Hu et al., 2018; Park et al., 2019; Weerasooriya et al., 2021; Zhou et al., 2020). In addition, RNA-seq technology was successfully applied in revealing genes and pathways responsible for the survival of Salmonella enterica in different foods, including peanut oil, powdered milk, milk chocolate, black pepper, and egg white (Crucello et al., 2019; Deng et al., 2012; Huang et al., 2019). It is thus expected that RNA-seq will be helpful in elucidating the ETR mechanisms in foodborne pathogens.

In our previous work, S. Enteritidis was found to mount the ETR upon adaptation to a sublethal level (5%) of ethanol (He et al., 2016; 2021b). The current work aimed to unravel mechanisms of ETR in this pathogen by RNA-seq analysis, which might be useful in designing effective food control measures.

Materials and methods

Bacterial strains

S. Enteritidis ATCC 13076 was stocked at −80 °C in Luria-Bertani broth (LB) (Oxoid, Hampshire, UK) supplemented with 50% glycerol (Aladdin, Shanghai, China). Prior to each test, this bacterium was resuscitated by two successive transfers in 5 mL LB broth at 37 °C for 24 h. An aliquot (500 μL) of activated cultures was then inoculated into 50 mL LB broth, followed by incubation at 37 °C/200 rpm for 5 h to reach the late log phase (He et al., 2016).

Ethanol treatments

Sublethal ethanol treatment was carried out by exposure of S. Enteritidis to 5% ethanol for 60 min, which was previously identified as an optimal condition to induce bacterial ETR (He et al., 2016). Briefly, 1 mL of late-log-phase culture was centrifugated at 8000 g for 10 min and resuspended in 10 mL fresh LB broth with or without 5% ethanol (Changshu Yangyuan Chemical Co. Ltd., Jiangsu, China), respectively. Subsequently, these samples were incubated at 25 °C/170 rpm for 60 min to produce ethanol-treated and control cultures for RNA sequencing.

Total RNA isolation

Total RNA was extracted from ethanol-treated and control cultures of S. Enteritidis using the Trizol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's instructions. The Nanodrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA) and Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) were then utilized to verify RNA quantity and quality. The RNA integrity number was between 9.7 and 9.9, indicating that RNA was undegraded.

cDNA library construction and RNA sequencing

The cDNA library was constructed and sequenced at BGI Group (Shenzhen, Guangdong, China). Briefly, specific biotinylated oligonucleotides were used to remove rRNA from total RNA. The Illumina TruSeq Stranded Kit (Illumina, Inc., USA) was then utilized to construct a strand-specific cDNA library. The resulting cDNA library was sequenced using the Illumina HiSeq 4000 platform (Illumina, Inc., USA). The quality of sequencing data was evaluated after removing the adaptors by the SOAP software (v2.21) with optimized parameters (-m 0-x 1000-s 28-l 32-v 5-r 1-p 3). Subsequently, the HISAT software (v2.0.1-beta) was used to map high-quality reads to the genome of S. Enteritidis str. P125109. The relative expression level of each gene in ethanol-treated samples compared with control samples was calculated by the FPKM method using the RSEM (v1.2.12) and Bowtie2 (v2.2.5) softwares. Differential gene expression analysis was carried out based on the DESeq2 method. The criteria for the selection of significant gene expression were set as follows: fold change ≥2 and P-value (Padj) ≤ 0.05. Raw sequences have been deposited in the National Microbiology Data Center (NMDC) database under BioProject number NMDC10018093.

Bioinformatic analysis of differentially expressed genes

Differentially expressed genes were mapped to the items in Gene Ontology (GO) database (http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/pathway.html) to identify their biological functions and molecular pathways. A Q-value ≤ 0.05 was utilized to recognize significantly enriched GO terms and KEGG pathways.

Quantitative real-time PCR (RT-qPCR) analysis

Total RNA was extracted from ethanol-treated and control cells of S. Enteritidis by the Trizol reagent (Invitrogen, Carlsbad, CA, USA), followed by reversed transcription into cDNA by PrimeScript™ RT reagent kits with gDNA Eraser (TaKaRa, Dalian, China). Primers were designed by the software Primer 5 and synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). Subsequently, PCR reactions were carried out using primers listed in Table 1 with the following programs: 1 cycle at 95 °C for 5 min, and 40 cycles at 95 °C, 55 °C, and 68 °C for 5 s, 15 s and 30 s, respectively. Relative gene expression levels in ethanol-treated samples compared with control samples were calculated by the 2−ΔΔCt method with 16 S rRNA as the reference gene (Livak and Schmittgen, 2001).

Table 1

Primers used for RT-qPCR analysis.

Gene	Forward primer sequence (5′–3′)	Reverse primer sequence (5′–3′)
asr	CTCTTCCGCAGCATTCGC	GCCGGTTGAGTAGTTGGTTT
srlE	ATCACCGAGCAGAGCG	ACAGCAGCGGGAAAGA
rbfA	AAAAGCGGGCATCAAA	ACGCATCCCTTCCACC
SEN1805	TGATTGTAAGAGCGGTAA	CGCTTCTGTTTCGTGT
SEN1383	GGCTGGCGAATGGTGA	AGCGAGCATGTTCTGGAAAG
16 S rRNA	CAGAAGAAGCACCGGCTAAC	GACTCAAGCCTGCCAGTTTC

Statistical analysis

RNA sequencing was carried out with three biological replicates. RT-qPCR test was performed in duplicate, and the resulting data were presented as mean ± standard deviation. Statistical comparison of gene expression levels in RT-qPCR tests was determined by Student's t-test (P < 0.05).

Results and discussion

Identification of differentially expressed genes

In the current work, S. Enteritidis was adapted with 5% ethanol for 60 min, which was previously confirmed as an optimal sublethal treatment that induced the highest magnitude of ETR (He et al., 2016). Ethanol-treated and control cells were then subjected to RNA-seq using the high-throughput Illumina sequencing platform. A large number of raw reads were generated and after rigorous data filtration, an average of 14,436,838 and 14,418,735 clean reads were collected from ethanol-treated and control groups, respectively. A total of 98.2% of clean reads were mapped to the reference genome for both groups. These data suggested that RNA-seq quality was confidential for further analysis.

Gene expression levels were then compared between ethanol-treated and control samples. The global transcript profile of S. Enteritidis in the ethanol-treated group compared with the control group was shown in Fig. 1. According to the cutoff criteria of fold-change ≥ 2 and Padj ≤ 0.05, a total of 811 genes were significantly differentially expressed in S. Enteritidis in response to sublethal ethanol treatment, of which 328 were up-regulated and 483 were down-regulated. Information on these differentially expressed genes was provided in Supplementary Table 1.

Fig. 1

Scatter plot of differentially expressed genes in S. Enteritidis under sublethal ethanol stress.

Functional analysis of differentially expressed genes

Differentially expressed genes in S. Enteritidis under sublethal ethanol stress were annotated to three GO categories (Fig. 2). In the biological process category, a large number of differentially expressed genes were related to cellular processes, metabolic processes, localization, and biological regulation. Within the cellular component group, differentially expressed genes associated with the cell, cell part, membrane, and membrane part represented the largest clusters. In terms of molecular function, most differentially expressed genes were responsible for catalytic activity, binding, transporter activity, and structural molecule activity.

Fig. 2

GO functional analysis of differentially expressed genes in S. Enteritidis under sublethal ethanol stress.

KEGG pathway analysis was also performed to reveal the interaction of different pathways during the process of ETR in S. Enteritidis. As presented in Fig. 3, differentially expressed genes were principally enriched in pathways of metabolism (e.g., carbohydrate metabolism, amino acid metabolism), environmental information processing (e.g., signal transduction, membrane transport), genetic information processing (e.g., transcription, translation), and cellular processes (e.g., cell motility). The top 20 enriched KEGG pathways were shown in Fig. 4 as a scatter plot. Moreover, a proposed model for the regulation of ETR in S. Enteritidis was illustrated in Fig. 5, of which the major metabolic pathways were discussed herein.

Fig. 3

KEGG pathway analysis of differentially expressed genes in S. Enteritidis under sublethal ethanol stress.

Fig. 4

Top 20 enriched KEGG pathways in S. Enteritidis under sublethal ethanol stress. Note: Plot size indicates the number of differentially expressed genes in a pathway. Plot color indicates Qvalue, and means more obvious pathway enrichment as it becomes closer to bule. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Schematic diagram of main metabolic regulations in S. Enteritidis under sublethal ethanol stress.

Metabolism

A considerable proportion of differentially expressed genes were associated with cellular metabolism in the current work (Fig. 3). These genes were mainly distributed in metabolic pathways for carbohydrates, amino acids, energy, cofactors, vitamins, nucleotide, xenobiotics, lipids, terpenoids, polyketides, and other secondary metabolites (Fig. 5). Such diverse gene expression alterations indicated that S. Enteritidis could coordinately regulate the metabolic processes of many macromolecules to adapt to sublethal ethanol stress.

It was noted that the expression of many genes related to carbohydrate metabolism showed differential expression in the current work. In particular, most fructose and mannose metabolic genes were up-regulated in response to sublethal ethanol stress (Table 2). Fructose and mannose have been suggested to confer resistance to lactic acid in Escherichia coli O157:H7 (Lan et al., 2022). These carbohydrates are required by foodborne pathogens as the main source of nutrients and energy, thus contributing to bacterial survival under stressful conditions.

Table 2

Selected differentially expressed genes mentioned in the Results and discussion section.

Gene ID	Gene name	Fold change	Description
Fructose and mannose metabolism
SEN1205	manZ	6.52	PTS system mannose-specific transporter subunit IID
SEN1206	manY	5.13	Phosphotransferase enzyme II, C component
SEN2673	srlA	2.83	PTS system glucitol/sorbitol-specific transporter subunit IIBC
SEN2197	fruA	3.09	Fructose PTS system EIIA component
SEN2675	slrB	5.09	PTS system glucitol/sorbitol-specific transporter subunit IIA
SEN2674	srlE	4.91	PTS system glucitol/sorbitol-specific transporter subunit IIBC
SEN3875	glpX	−2.11	Fructose-1,6-bisphosphatase I
SEN2676	srlD	10.51	Sorbitol-6-phosphate 2-dehydrogenase
SEN1207	manX	2.61	PTS system mannose-specific transporter subunit IIAB
SEN2137	fbaB	2.60	Fructose-bisphosphate aldolase, class I
SEN1717	pfkB	3.33	6-phosphofructokinase 1
Two-component systems
SEN0381	phoR	2.98	Phosphate regulon sensor protein
SEN0380	phoB	2.56	Transcriptional regulator PhoB
SEN1653	ssrA	2.76	Two-component sensor kinase
SEN1654	ssrB	3.07	Two-component response regulator
SEN2126	baeS	3.07	Signal transduction histidine-protein kinase
SEN3794	glnL	2.04	Nitrogen regulation protein NR (II)
SEN1659	ttrS	2.01	Histidine kinase, two component regulatory protein
ABC transporters
SEN0411	phnS	4.17	Periplasmic binding component of 2-aminoethylphosphonate transporter
SEN0408	phnV	2.38	Membrane protein of 2-aminoethylphosphonate transporter
SEN0409	phnU	3.66	Membrane protein of 2-aminoethylphosphonate transporter
SEN0410	phnT	2.21	2-aminoethylphosphonate transporter ATP-binding protein
SEN3671	pstS	15.88	Phosphate ABC transporter substrate-binding protein
SEN3670	pstC	7.48	Phosphate transporter permease subunit PstC
SEN3669	pstA	7.20	Phosphate transporter permease subunit PtsA
SEN3668	pstB	3.92	Phosphate transporter ATP-binding protein
SEN1556	SEN1556	2.93	ABC transporter membrane protein
SEN1557	SEN1557	2.32	ABC transporter substrate-binding protein
SEN1289	oppA	2.16	Periplasmic oligopeptide-binding protein OppA
SEN1290	oppB	2.26	Oligopeptide transporter permease
SEN1291	oppC	2.10	Oligopeptide transport system permease OppC
SEN1292	oppD	2.08	Oligopeptide transporter ATP-binding protein
SEN0634	gltI	3.30	Glutamate and aspartate transporter subunit
SEN0632	gltK	2.44	Glutamate/aspartate transport system permease GltK
SEN0633	gltJ	2.31	Glutamate/aspartate transport system permease GltJ
SEN0199	fhuB	−3.21	Iron-hydroxamate transporter permease subunit
SEN0197	fhuC	−2.55	Iron-hydroxamate transporter ATP-binding subunit
SEN0198	fhuD	−2.88	Iron-hydroxamate transporter substrate-binding subunit
SEN2703	sitA	−3.32	Iron transport protein periplasmic-binding protein
SEN2704	sitB	−2.39	Iron transport protein ATP-binding protein
SEN2705	sitC	−2.22	Iron transport protein inner membrane protein
SEN3696	rbsA	−9.10	D-ribose transporter ATP-binding protein
SEN3695	rbsD	−15.11	D-ribose pyranase
Phosphotransferase systems
SEN1207	manX	2.16	PTS system mannose-specific transporter subunit IIAB
SEN1206	manY	5.13	Phosphotransferase enzyme II, C component
SEN1205	manZ	6.52	PTS system mannose-specific transporter subunit IID
SEN2197	fruA	3.09	Fructose PTS system EIIA component
SEN2673	srlA	2.83	PTS system glucitol/sorbitol-specific transporter subunit IIBC
SEN2675	slrB	5.09	PTS system glucitol/sorbitol-specific transporter subunit IIA
SEN2674	srlE	4.91	PTS system glucitol/sorbitol-specific transporter subunit IIBC
Bacterial secretion systems
SEN1636	ssaJ	2.29	Pathogenicity island lipoprotein
SEN1630	ssaN	2.22	Type III secretion system ATPase
SEN1635	SEN1635	2.80	Pathogenicity island protein
SEN3931	secE	−2.11	Preprotein translocase subunit SecE
SEN3248	secY	−3.85	Preprotein translocase subunit SecY
SEN3659	yidC	−3.20	Inner membrane protein translocase component YidC
SEN1970	SEN1970	−4.56	Phage integrase
Transcription
SEN3243	rpoA	−3.97	DNA-directed RNA polymerase subunit alpha
SEN3937	rpoB	−2.39	DNA-directed RNA polymerase subunit beta
SEN3938	rpoC	−2.62	DNA-directed RNA polymerase subunit beta'
Translation
SEN3254	rpsH	−4.10	30 S ribosomal protein S8
SEN3247	rpmJ	−4.49	50 S ribosomal protein L36
SEN3178	rplM	−3.19	50 S ribosomal protein L13
SEN3258	rplN	−2.48	50 S ribosomal protein L14
SEN2597	rpsP	−3.06	30 S ribosomal protein S16
SEN3934	rplA	−3.52	50 S ribosomal protein L1
SEN3275	rpsG	−3.56	30 S ribosomal protein S7
SEN3260	rpmC	−3.70	50 S ribosomal protein L29
SEN3253	rplF	−3.02	50 S ribosomal protein L6
SEN3886	rpmE	−3.41	50 S ribosomal protein L31
SEN3266	rplW	−3.64	50 S ribosomal protein L23
SEN3276	rpsL	−3.01	30 S ribosomal protein S12
SEN3118	rpsO	−4.51	30 S ribosomal protein S15
SEN4160	rplL	−3.54	50 S ribosomal protein L9
SEN3257	rplX	−3.09	50 S ribosomal protein L24
SEN3136	rpmA	−3.44	50 S ribosomal protein L27
SEN4157	rpsF	−3.30	30 S ribosomal protein S6
SEN3549	rpmG	−2.16	50 S ribosomal protein L33
SEN3246	rpsM	−3.41	30 S ribosomal protein S13
SEN3259	rpsQ	−4.77	30 S ribosomal protein S17
SEN0450	rpmE2	−5.27	50 S ribosomal protein L31
SEN3137	rplU	−2.89	50 S ribosomal protein L21
SEN3269	rpsJ	−3.67	30 S ribosomal subunit protein S10
SEN3245	rpsK	−3.34	30 S ribosomal protein S11
SEN3177	rpsL	−2.83	30 S ribosomal protein S9
SEN4159	rpsR	−3.70	30 S ribosomal protein S18
SEN3244	rpsD	−3.96	30 S ribosomal protein S4
SEN3264	rpsS	−4.08	30 S ribosomal protein S19
SEN3267	rplD	−3.17	50 S ribosomal protein L4
SEN3550	rpmB	−2.40	50 S ribosomal protein L28
SEN3263	rplV	−4.37	50 S ribosomal protein L22
SEN0043	rpsT	−3.49	30 S ribosomal protein S20
SEN3935	rplJ	−3.32	50 S ribosomal protein L10
SEN2594	rplS	−3.71	50 S ribosomal protein L19
SEN3250	rpmD	−4.40	50 S ribosomal protein L30
SEN1708	rplT	−2.80	50 S ribosomal protein L20
SEN0223	rpsB	−2.97	30 S ribosomal protein S2
SEN3242	rplQ	−4.30	50 S ribosomal protein L17
SEN3262	rpsC	−3.69	30 S ribosomal protein S3
SEN3656	rpmH	−3.17	50 S ribosomal protein L34
SEN3265	rplB	−3.80	50 S ribosomal protein L2
SEN3256	rplE	−3.10	50 S ribosomal protein L5
SEN3936	rplL	−3.73	50 S ribosomal protein L7/L12
SEN1709	rpmI	−2.99	50 S ribosomal protein L35
SEN0885	rpsA	−2.85	30 S ribosomal protein S1
SEN3252	rplR	−3.90	50 S ribosomal protein L18
SEN3261	rplP	−3.83	50 S ribosomal protein L16
SEN3249	rplO	−3.40	50 S ribosomal protein L15
SEN3255	rpsN	−3.96	30 S ribosomal protein S14
SEN3933	rplK	−2.87	50 S ribosomal protein L11
SEN3268	rplC	−2.95	50 S ribosomal protein L3
SEN2217	rplY	−2.95	50 S ribosomal protein L25
SEN3251	rpsE	−3.72	30 S ribosomal protein S5
Cell motility
SEN3059	aer	−2.01	Aerotaxis receptor protein
SEN3058	SEN3058	−2.68	Methyl-accepting chemotaxis protein II
SEN1033	fliM	−2.86	Flagellar motor switch protein FliM
SEN1032	fliN	−2.11	Flagellar motor switch protein FliN
SEN1039	fliG	−2.62	Flagellar motor switch protein G
SEN1031	fliO	−2.17	Flagellar biosynthesis protein FliO
SEN1868	flgH	−2.20	Flagellar basal body L-ring protein
SEN1871	flgE	−2.05	Flagellar hook protein FlgE
SEN1029	fliQ	−2.40	Flagellar biosynthesis protein FliQ
SEN1028	fliR	−5.74	Flagellar biosynthesis protein FliR
SEN1875	flgA	−2.55	Flagellar basal body P-ring biosynthesis protein FlgA
SEN1872	flgD	−2.10	Flagellar basal body rod modification protein
SEN1040	fliF	−2.50	Flagellar MS-ring protein
SEN1873	flgC	−2.30	Flagellar basal body rod protein FlgC
SEN1036	fliJ	−3.32	Flagellar biosynthesis chaperone
SEN1037	fliL	−2.48	Flagellum-specific ATP synthase
SEN1869	flgG	−2.21	Flagellar basal body rod protein FlgG
SEN1038	fliH	−2.22	Flagellar assembly protein H
SEN1030	fliP	−2.39	Flagellar biosynthesis protein FliP
SEN1870	flgF	−2.20	Flagellar basal body rod protein FlgF

Signal transduction

Bacterial two-component system is a crucial signal transduction pathway that consists of a histidine kinase that senses external stimulus and a cytoplasmic response regulator protein that modulates gene expression. In the current work, a total of 47 genes belonging to two-component systems showed altered expression in S. Enteritidis, and most of them were up-regulated in response to sublethal ethanol stress. Practically, the expression of several histidine kinase genes (e.g., phoR, ssrA, baeS, glnL, ttrS) and response regulator genes (e.g., phoB, ssrB) was enhanced by sublethal ethanol treatment (Table 2). These genes have been recognized as important sensing and signaling elements employed by S. enterica to survive under harsh conditions (de Pina et al., 2021). Interestingly, deletion of some two-component system genes (e.g., phoP, degU, virS, yycG, agrC, liaS) significantly impaired the growth of L. monocytogenes under ethanol stress (Pöntinen et al., 2017). Hence, it is reasonable to speculate that two-component systems play a role in the ETR of S. Enteritidis.

Membrane transport

In the membrane transport category, all three pathways (i.e., ABC transporters, phosphotransferase systems, bacterial secretion systems) had differentially expressed genes in response to sublethal ethanol treatment (Fig. 5). The largest number of differentially expressed genes was observed in the ABC transporter pathway, followed by the phosphotransferase system, and the bacterial secretion system. In terms of ABC transporters, up-regulated genes were mainly associated with transport of 2-aminoethylphosphonate (phnS, phnV, phnU, phnT), phosphate (pstS, pstC, pstA, pstB), osmoprotectant (SEN1556, SEN1557), oligopeptide (oppA, oppB, oppC, oppD), and glutamate/aspartate (gltI, gltK, gltJ), while down-regulated genes were mainly related to transport of iron complex (fhuB, fhuC, fhuD), manganese (sitA, sitB, sitC), and ribose (rbsA, rbsD) (Table 2). Such a sizable fraction of differentially expressed genes certainly highlights the importance of ABC transporters to the ETR in S. Enteritidis. Similarly, 2-aminoethylphosphonate transporter genes phnSTUV and phosphate transporter genes pstSCAB were overexpressed in S. Typhimurium during acid adaptation (Ryan et al., 2015). Therefore, it would be interesting to explore the role of ABC transporters in the ETR of S. Enteritidis in future studies.

Sublethal ethanol treatment also led to the differential expression of phosphotransferase system (PTS)-related genes in the current work (Table 2). In particular, mannose-PTS genes (e.g., manX, manY, manZ), fructose-PTS genes (e.g., fruA), and glucitol/sorbitol-PTS genes (e.g., srlA, slrB, srlE) went through an increased expression during the induction of ETR (Table 2). It was noted that the expression of many genes related to mannose and fructose metabolism was also activated by sublethal ethanol stress (Table 2). These observations suggested the importance of mannose- and fructose-PTS to the ETR in S. Enteritidis. Similarly, mannose-PTS genes manXYZ were involved in the response of S. enterica and E. coli to sodium hypochlorite and organic solvent, respectively (Okochi et al., 2007; Wang et al., 2010).

Several genes in the bacterial secretion pathway also showed altered expression, with ssaJ, ssaN and SEN1635 in the type III secretion system (T3SS) up-regulated as well as secE, secY, and yidC in the type II secretion system, and SEN1970 in the type VI secretion system down-regulated, respectively (Table 2). Similarly, the expression of many T3SS-related genes was also triggered in S. Enteritidis by acid adaptation (Hu et al., 2018). T3SS and other secretion systems are fundamental to the colonization and survival of S. enterica during infection in the animal host (Bao et al., 2020). It is thus indicative that S. Enteritidis may alter its virulence in stressful conditions such as ethanol exposure, which can be explored in future studies.

Transcription and translation

Modulation of gene transcription and translation is necessary for foodborne pathogens to respond to environmental changes. In the current work, many genes related to transcription and translation were significantly differentially expressed in S. Enteritidis in response to sublethal ethanol treatment (Fig. 5). In the transcription pathway, three RNA polymerase genes (i.e., rpoA, rpoB, rpoC) were down-regulated by 3.97-, 2.39- and 2.62-fold, respectively, indicating RNA transcribing function might be weakened under ethanol stress (Table 2). In terms of translation function, a total of 53 ribosome-related genes were repressed, including 33 large ribosomal subunit genes (e.g., rplA, rpmA) and 20 small ribosomal subunit genes (e.g., rpsA, rpsB) (Table 2). It thus seemed that the synthesis ability of ribosomes in S. Enteritidis was reduced in face of ethanol stress. In a similar vein, regulation of ribosome-related gene expression was also a strategy by which E. coli responds to ultrasonic stress and C. sakazakii copes with the combination of carvacrol and citral (Cao et al., 2020; Li et al., 2021).

Cell motility

In the case of cell motility function, the expression of genes responsible for bacterial chemotaxis and flagellar assembly was repressed by sublethal ethanol stress (Fig. 5). These bacterial chemotaxis genes mainly included aerotaxis receptor genes (e.g., aer), and methyl-accepting chemotaxis genes (e.g., SEN3058). Moreover, a total of 18 genes related to flagellar assembly were down-regulated in S. Enteritidis, such as flagellar basal body gene (e.g., flgC), flagellar hook gene (e.g., flgE), flagellar L-ring gene (e.g., flgH), flagellar M-ring gene (e.g., fliF), flagellar motor switch gene (e.g., fliG), and flagellar biosynthesis gene (e.g., fliQ) (Table 2). Similarly, flagellar assembly-related genes were also down-regulated in S. enterica in the presence of many other food processing-related stress factors such as chlorine, acid and heat (Ryan et al., 2015; Sirsat et al., 2011, Wang et al., 2010). It has been suggested that inhibition of flagellar assembly might be an energy conservation strategy that enabled bacterial survival under stressful conditions by reducing energy-consuming processes (Hu et al., 2018).

It should be noted that some of the aforementioned pathways such as metabolism, ABC transporters, and translation were also significantly differentially expressed in S. Enteritidis in response to sublethal ethanol treatment as revealed by proteomic analysis in our previous work (He et al., 2019). On the contrary, the involvement of two-component systems, PTS systems, and RNA polymerases in the ETR of S. Enteritidis was principally uncovered by transcriptomic analysis in the current work. Thus, transcriptomics may be a powerful tool for elucidating stress resistance mechanisms of pathogenic bacteria during food processing (Lamas et al., 2019).

Validation of differentially expressed genes by RT-qPCR

Several differentially expressed genes were subjected to RT-qPCR analysis in the current work. Gene expression profile in the RNA-seq and RT-qPCR tests was then compared. As shown in Fig. 6, the overall trend of the differential expression pattern for asr, srlE, rbfA, SEN1805, and SEN1383 genes was similar as determined by both techniques. This finding provided evidence that the RNA-seq test was properly conducted and the resulting data were reliable.

Fig. 6

Comparison of gene expression patterns revealed by the RNA-seq and RT-qPCR tests.

Conclusion

Transcriptome sequencing revealed that multiple genes and adaptation pathways were involved in the ETR of S. Enteritidis. In total, 811 genes were significantly differentially expressed in response to sublethal ethanol treatment. A couple of two-component sensor and response genes were up-regulated to activate signaling pathways. The expression of ABC transporter genes responsible for transport of osmoprotectant, phosphate, and 2-aminoethylphosphonate were also induced as a membrane transport strategy. On the other hand, genes related to ribosomal units, bacterial chemotaxis, and flagellar assembly were repressed to serve as a possible energy conservation strategy. These findings offer new insight into the ETR mechanisms in S. Enteritidis at the transcriptome level. Future work can be focused on the functional characterization of differentially expressed genes to further identify key regulatory networks governing bacterial ETR.

CRediT authorship contribution statement

Shoukui He: Writing – original draft, Methodology, Investigation, Funding acquisition. Yan Cui: Formal analysis, Writing – review & editing. Rui Dong: Methodology, Validation. Jiang Chang: Data curation, Visualization. Hua Cai: Data curation. Hong Liu: Writing – review & editing. Xianming Shi: Conceptualization, Supervision, Writing – review & editing, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.