Proteomic alteration of porcine intestinal epithelial cells after pretreatment with Lactobacillus plantarum followed by infection with enterotoxigenic Escherichia coli F4

Journal Pre-proof Proteomic alteration of porcine intestinal epithelial cells after pretreatment with Lactobacillus plantarum followed by infection with enterotoxigenic Escherichia coli F4 Cui Zhu, Yantao Lv, Jun Yang, Yinshan Bai, Jinling Ye, Zhilin Wang, Zhuang Chen, Zongyong Jiang

PII:

S0165-2427(19)30089-3

DOI:

https://doi.org/10.1016/j.vetimm.2019.109943

Reference:

VETIMM 109943

To appear in:

Veterinary Immunology and Immunopathology

Received Date:

13 March 2019

Revised Date:

12 September 2019

Accepted Date:

13 September 2019

Please cite this article as: Zhu C, Lv Y, Yang J, Bai Y, Ye J, Wang Z, Chen Z, Jiang Z, Proteomic alteration of porcine intestinal epithelial cells after pretreatment with Lactobacillus plantarum followed by infection with enterotoxigenic Escherichia coli F4, Veterinary Immunology and Immunopathology (2019), doi: https://doi.org/10.1016/j.vetimm.2019.109943

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Proteomic alteration of porcine intestinal epithelial cells after pretreatment with Lactobacillus plantarum followed by infection with enterotoxigenic Escherichia coli F4 Cui Zhua, b, 1, Yantao Lva, 1, Jun Yanga,1, Yinshan Baib, Jinling Yea, Zhilin Wanga, Zhuang Chena,*, Zongyong Jianga, c, *

Agro-biological Gene Research Center, Guangdong Academy of Agricultural

Sciences, Guangzhou 510640, China;

b

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a

School of Life Science and Engineering,

Foshan University, Foshan 528225, China; c Institute of Animal Science, Guangdong

1

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Academy of Agricultural Sciences, Guangzhou 510640, China

These authors have contributed equally to this work.

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*Correspondence: Dr. Zhuang Chen ([email protected]) or Dr.

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Zongyong Jiang ([email protected])

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Running title: Proteomic changes in IPEC-J2 cells

ABSTRACT Enterotoxigenic Escherichia coli (ETEC) F4 causes diarrhea in infants and weaned piglets. The technique of isobaric tags for relative and absolute quantitation (iTRAQ) was used in this study to determine the differentially expressed proteins in porcine

intestinal epithelial cells (IPEC-J2) after pretreatment with Lactobacillus plantarum (LP) followed by challenge with ETEC F4. A total of 4,771 proteins were identified in IPEC-J2 cells, with 90, 105, and 134 differentially expressed proteins in cells exposed to ETEC, LP, and LP+ETEC, respectively. The COG analysis divided the identified proteins into 20 categories. The GO and KEGG annotation indicated that most of the differentially expressed proteins were enriched in various biological

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metabolism including cell cycle control, cell division and differentiation. Additionally, western blotting analyses confirmed the reduced abundance of selected

proteins of the mTOR and MAPK signal pathways affected by ETEC F4. Moreover,

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LP pretreatment increased JNK activation in IPEC-J2 cells infected with ETEC F4.

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These results may provide further insights into the mechanisms involved in the interaction between ETEC F4 and intestinal epithelial cells, and broaden the

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understanding of the protective effects of LP in alleviating ETEC-provoked diarrhea

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of piglets.

Keywords:

Proteomic

analysis;

IPEC-J2

cells;

Lactobacillus

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Enterotoxigenic Escherichia coli; Weaned piglet; Diarrhea.

1. Introduction

plantarum;

Enterotoxigenic Escherichia coli (ETEC) F4 is an important cause of post-weaning diarrhea, leading to important economic loss in swine production (Dubreuil et al., 2016). The common use of antibiotics for the control and prevention of these disorders has been criticized in recent years, due to their potential risks to human health including the possibility of antibiotics residues in meat, unapparent carriage of anti-microbial drug-resistant bacteria, and exchange of plasmids from

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antibiotics-resistant bacteria of swine to human pathogens (Gallois et al., 2009).

Probiotics are potentially effective alternatives for controlling and preventing ETEC-induced post-weaning diarrhea, and could regulate the host cell response and

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thereby influence intestinal health of the neonatal piglets and weaned piglets

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(Dubreuil, 2017). Optimal gut development is of vital importance for young piglets to maintain the normal functions including nutrient digestion and absorption, and

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(Jacobi and Odle, 2012).

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selective barrier function, which protects the host from luminal pathogens and toxins

Lactobacillus plantarum (L. plantarum, LP) modulates gut microbial populations,

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intestinal morphology, expression of tight junction proteins, and cytokine production (Guerra-Ordaz et al., 2014; Suo et al., 2012; Wang et al., 2019; Wu et al., 2016; Yang

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et al., 2014). There is little known about the underlying global changes of proteins affected by ETEC F4 challenge in IPEC-J2 cells nor how these are affected by prior treatment with LP (Lin et al., 2017; Xia et al., 2017). Previous intestinal proteomics studies have revealed potential biomarkers in the pathophysiology and diagnosis of necrotizing enterocolitis, short bowel syndrome

and intrauterine growth restriction in pig models (Jiang and Sangild, 2014). The application of proteomics in animal health, welfare, and production has been increasingly recognized in recent decades (Almeida et al., 2015). Isobaric tags for relative and absolute quantitation (iTRAQ) technology is a powerful proteomics technique, which utilizes isobaric reagents to label the primary amines of peptides and proteins for quantitative analysis of proteomes via tandem mass spectrometry

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(Vélez-Bermúdez et al., 2016). It allows sensitive and accurate protein quantification, and represents an important research tool for identifying a sufficient number of differentially expressed proteins and discovering mechanisms involved in health and

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diseases during pig production (De Almeida et al., 2012; Liu et al., 2016).

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The aim of this study therefore was to investigate the global alterations of proteins in porcine intestinal epithelial cells (IPEC-J2) after pretreatment with LP followed

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by challenge with ETEC F4 using an iTRAQ quantitative proteomic technique.

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2. Materials and methods

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2.1. Cells and bacteria

The porcine intestinal epithelial cell line (IPEC-J2) was purchased from the cell

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bank of the Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. The IPEC-J2 cells were grown in a basal Dulbecco's modified Eagle's medium (DMEM)/F12 (Gibco, Grand Island, NY), supplemented with 5% fetal bovine serum (FBS) (Gibco), Insulin-Transferrin Selenium (ITS) (5 μg/mL, Gibco), and epidermal growth factor (EGF) (5 ng/mL, Sigma-Aldrich, St. Louis, MO). Cells

were incubated in 100 mL flasks at 37 °C under 5% CO2. The medium was changed every 24hours (h). The L. plantarum (strain CGMCC No. 1258) used in this study was kindly provided by Dr. Hang Xiaomin (Institute of Science Life, Shanghai JiaoTong University, Shanghai, China). L. plantarum was prepared exactly as described previously (Wu et al., 2016). The ETEC F4 (strain serotype O149: K91: F4ac) was

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obtained from China Institute of Veterinary Drug Control (Beijing, China). Similarly, ETEC F4 was prepared exactly as described previously (Wu et al., 2016).

The IPEC-J2 cell monolayers were subjected to 1 of 4 treatments, performed in

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triplicate, as described by Wu et al. (2016): (1) non-infected cells served as the

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Control; (2) cells pretreated with LP for 6 h; (3) cells infected for 3 h with ETEC F4; (4) cells pretreated with LP as above then followed by ETEC F4 infection for 3 h.

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used as MOI of 100.

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The ETEC F4 was used at a multiplicity of infection (MOI) of 50, while LP was

2.2. Protein isolation, labeling with iTRAQ reagents, and LC-MS/MS analysis

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After treatments, IPEC-J2 cells were washed twice with phosphate buffered saline (PBS) and collected using a cell scraper. Cells were then centrifuged at 400 × g for

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10 min at 4 °C, and washed twice with ice-cold PBS containing 1 mM sodium fluoride and 1 mM pervanadate. The collected cells were lysed using radioimmunoprecipitation

assay

(RIPA)

lysis

buffer

containing

1

mM

phenylmethylsulfonyl fluoride (PMSF), and the soluble protein fraction was harvested by centrifugation at 10,000 × g for 15 min at 4°C. The protein

concentration in the supernatant was determined using a Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). Proteins were analyzed commercially (AB Sciex, Concord, ON, Canada) using their iTRAQ protocol. Protein solutions (100 μg) were reduced, cysteine blocked and then digested overnight at 37 °C with sequence grade modified trypsin (Promega, V5111). After the digestion with trypsin, peptides were then labeled individually with iTRAQ tags

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as follows: iTRAQ 114, and iTRAQ 115, iTRAQ 116 and iTRAQ 117 for Control,

ETEC F4, LP, LP+ETEC F4 samples. The labeled samples were mixed and dried using a rotary vacuum concentrator.

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The liquid chromatogram tandem mass spectrometry (LC-MS/MS) was conducted

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with the following program. Briefly, the fractionated peptides were performed using a Q-Exactive mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA)

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controlled by the Thermo Scientific EASY-nLC 1000 System (Thermo Fisher

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Scientific, Waltham, MA, USA). Typical mass spectrometer settings used were spray voltage = 2.1 kV, capillary temperature = 250°C, and declustering potential = 100 V.

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The mass spectrometer was operated in data-dependent Top 20 mode with automatic switching between MS and MS/MS. Full-scan MS mode (350-1800 m/z) was

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operated at a resolution of 70,000, automatic gain control (AGC) target was 1 × 106 ions, and maximum ion transfer time (IT) was 60 ms. Ions selected for MS/MS were subjected to the following parameters: resolution, 17500; AGC, 5 × 106 ions; maximum IT, 70 ms; intensity threshold, 5000; and normalized collision energy, 29%.

2.3. Bioinformatics analysis Functional annotations of differentially expressed proteins were conducted using the Gene Ontology (GO) annotation software. The GO annotation comparison was conducted to show the characteristics of all differentially expressed proteins identified in IPEC-J2 cells. For the pathway analysis, the Kyoto Encyclopedia of

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Genes and Genomes (KEGG) was applied to show the details of pathway identified. 2.4. Western blotting analysis

Western blotting analysis was conducted according as previously described (Zhu

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et al., 2015). In brief, equal amounts of proteins obtained from the cells were

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separated by SDS-PAGE. Separated proteins were transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore, MA), blocked with 5% non-fat milk in

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Tris-buffered saline containing 0.1% Tween-20 for 3 h, and then probed with primary antibodies overnight at 4°C. After washing, membranes were incubated with

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HRP-conjugated secondary antibodies for 1 h at room temperature before analysis using Alpha Imager 2200 software (Alpha Innotech Corporation, CA). Signal

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intensity of protein abundance was digitally quantified and normalized to β-actin.

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The antibodies and dilutions used here are provided in Supplementary Table S1. 2.5. RNA extraction and real-time quantitative PCR The abundance of the target transcripts was determined by quantitative PCR as

described previously (Lv et al., 2018). In brief, total RNA was extracted from the cells using TRIzol reagent (Invitrogen, Carlsbad, CA) following the manufacturer’s

protocol. RNA purification and first strand cDNA synthesis was performed by using a PrimeScript RT reagent kit with gDNA eraser (Takara, Tokyo, Japan). Quantitative PCR was performed in a CFX Connect Real-Time PCR Detection System (Bio-Rad, Hercules, CA) using SYBR green master mix (Bio-Rad). All the primer sequences are shown in Supplementary Table S2. The reference gene β-actin was used for normalization of expression of the targeted genes. Efficiency of quantitative PCR

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amplification for each gene was calculated using the standard curve method

(Supplemental Table 3). All data were expressed as fold changes of threshold cycle

(Ct) value relative to the control using the 2-ΔΔCt method (Livak and Schmittgen,

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2001).

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2.6. Statistical analyses

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The statistical analyses were performed in SPSS 18.0 software (Chicago, IL). Variances of the mRNA and protein expression were analyzed by one-way ANOVA,

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and significant differences were compared by Duncan’s multiple test. The statistical significance for proteomic data between pairs of treatments (ETEC vs. Control, LP

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vs. Control, and LP+ETEC vs. ETEC) was determined using Student’s t-test. Data

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are expressed as means ± SD. Differences at P < 0.05 were considered to be statistically significant.

3. Results

3.1. Global protein profiles identified by iTRAQ and LC-MS/MS analysis In the present study, a total of 4,771 proteins were quantitatively identified among

3 replicate experiments, with 750, 428, and 503 proteins occurring uniquely in the 3 individual runs (Fig. 1A). The GO annotation analyses showed that the global proteins were categorized into biological processes, cellular components, and molecular function. For the biological processes, these proteins were mainly involved in translation, intracellular protein transport, positive and negative regulation of transcription from RNA polymerase promoter, regulation of

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transcription, small GTPase mediated signal transduction, protein binding, signal

transduction, negative regulation of apoptotic process, intracellular signal

transduction, and mRNA splicing (Fig. 1B). For cellular localizations annotation, the

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predominant cellular components were extracellular exosome, cytoplasm, nucleus,

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integral component of membrane, nucleoplasm, mitochondrion, membrane, cytosol, plasma membrane, and endoplasmic reticulum (Fig. 1C). For the molecular function,

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these proteins were mainly associated with poly (A) RNA binding, ATP binding, zinc

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ion binding, metal ion binding, nucleotide binding, DNA binding, GTP binding, structural constituent of ribosome, calcium ion binding, and RNA binding (Fig. 1D).

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3.2. Bioinformatics analysis for differentially expressed proteins of IPEC-J2 cell

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proteome

Based on a 95% confidence level, cutoff values of 2.0, 1.3, and 1.3 fold for

up-regulated proteins for ETEC vs. Control, LP vs. Control, and LP+ETEC vs. ETEC, respectively, while cutoff value of 0.80-fold for down-regulated proteins were used to define differentially expressed proteins. A total of 90 differentially expressed proteins, consisted of 48 increased and 42 decreased proteins between the

ETEC F4 treatment and Controls. Table 1 showed the main differentially expressed proteins in IPEC-J2 cells infected with ETEC F4. For the comparison between the LP treatment and Controls, 105 differentially expressed proteins (95 increased, 10 decreased) were found. A total of 134 differentially expressed proteins were found between LP+ETEC treatment and cells exposed only to ETEC F4, with 73 proteins up-regulated and 61 proteins down-regulated (Fig. 2).

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According to cluster of orthologous groups (COG) analyses (Fig. 3), these differently expressed proteins in IPEC-J2 were classified into 22 groups based on

their functions. The leading group was [R] general function prediction only (11

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differently expressed proteins for ETEC vs. Control; 15 for LP vs. Control; 15 for

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LP+ETEC vs. ETEC, respectively), followed by [O] postranslational modification, protein turnover, chaperones (6; 9; 16, differences as above). Other groups had fewer

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differently expressed proteins, including [G] carbohydrate transport and metabolism

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(4; 6; 14); [C] energy production and conversion (5; 4; 7); [J] translation, ribosomal structure and biogenesis (1; 5; 9); [K] transcription (3; 3; 7); then many with < 7

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differences within the comparisons – [F], [E], [T], [L], [H], [Q], [I], [Z], [M], [S], [P], [B], [D], [N], [A] and [U]. See Figure 3 for explanation of these categories.

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The differently expressed proteins were categorized into biological processes,

cellular components and molecular functions based on GO analysis (Fig. 4). The most predominant biological processes were cellular process, biological regulation, metabolic process, response to stimulus, and single-organism process. The GO function classification revealed that the most predominant cellular components were

located in the cell part, cell, organelle, extracellular region, and macromolecular complex. The most predominant molecular function was binding activitiy, catalytic activity, molecular function regulator, signal transducter activity, and transporter activity. Further KEGG pathway analysis (Fig. 5) showed that most of the differentially expressed proteins between ETEC F4 infected and Control cells, were associated

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with human diseases, mTOR signaling pathway, HIF-1 signaling pathway, MAPK

signaling pathway, biosynthesis of amino acids, and carbon metabolism (Fig. 5A).

The differentially expressed proteins between LP treated and Control cells were

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mainly involved in metabolism and biosynthesis, citrate cycle, AMPK signaling

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pathway, and MAPK signaling pathway (Fig. 5B). The differentially expressed proteins between LP+ETEC cells and those that were ETEC-infected without

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pre-treatment were mainly associated with cysteine and methionine metabolism,

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amino sugar and nucleotide sugar metabolism, pyruvate metabolism, glucagon signaling pathway, estrogen signaling pathway, regulation of actin cytoskeleton,

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neurotrophin signaling pathway, antigen processing and presentation, MAPK signaling pathway, ribosome, glycolysis and gluconeogenesis, and spliceosome (Fig.

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5C).

To explicitly present the voluminous protein data set consisting of all significantly

induced proteins in ETEC-infected and Control IPEC-J2 cells, the heatmap (Fig. 6) was created by clustering all induction values of proteins of similar physiological functions both for major and sub-categories. This showed the overrepresentation of

significantly altered proteins in several biological processes, including cellular process, biological regulation, metabolic process, response to stimulus, and single-organism process. The interaction network was then extracted using Cytoscape to further explore the protein interaction networks in IPEC-J2 cells that were infected with ETEC F4, without or with LP pretreatment. As shown in Fig. 7, 84 proteins were involved in

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the interaction network, suggesting their likely importance. The analysis revealed

several proteins with interesting interactions, including Phosphoglycerate kinase (PGK) - Glucose-6-phosphate isomerase (GPI) - L-lactate dehydrogenase B chain

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(LDHB) - 6-phosphogluconate dehydrogenase (PGD) - S-(hydroxymethyl)

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glutathione dehydrogenase (ADH5). The expression of these key proteins was up-regulated by ETEC F4 after pretreatment with LP as compared to ETEC infection

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alone. These energy metabolism enzymes may play some functions in LP conferring

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some protection on IPEC-J2 cells.

3.3. Validation of protein quantification by Western blotting assay

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As shown in Fig. 8, ETEC F4 induced significant decrease of activation of JNK

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MAPK pathway and mTOR signaling pathway, with decreased levels of total mTOR, p-mTOR, total 4EBP1, p-4EBP1, total S6K, p-S6K, total JNK, and p-JNK in IPEC-J2 cells after infection with ETEC F4. Pretreatment with LP did not significantly reverse the decreased level of mTOR-signaling associated proteins induced by ETEC F4. The pretreatment of IPEC-J2 cells with LP, however, did improve the JNK activation after subsequent infection with ETEC F4.

3.4. Profiles of solute carrier transporters expression In the present study, 42 solute carrier transporter proteins were quantitatively identified in the iTRAQ analyses (Table 2). Protein expression of solute carrier transporters did not differ between cells exposed to LP and the Controls. In the comparison between cells infected with ETEC and the Controls, 34 differentially expressed solute carrier transporters proteins (1 increased, 33 decreased) were found.

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There were 3 differentially expressed proteins (SLC1A5, decreased, SLC25A15,

decreased, and SLC25A29, increased) in LP+ETEC cells compared with those

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exposed to ETEC F4 alone.

Gene expression analysis found that expression transcript abundance of 33 solute

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carrier transporters genes was down-regulated in IPEC-J2 cells after infection with

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ETEC F4, and another 9 solute carrier transporters genes were un-affected (Fig. 9). Pretreatment with LP significantly reversed the down-regulated expression of 4 of

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the solute carrier transporters (SLC3A2, SLC4A7, SLC5A3 and SLC25A29) otherwise induced by infection with ETEC F4.

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4. Discussion

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ETEC is a common type of Gram-negative bacteria that causes severe diarrhea of newborn and weaning piglets as well as children and travelers, through its virulence factors including enterotoxins and fimbriae (Dubreuil et al., 2016; Roussel et al., 2017). Most of the ETEC-induced diarrhea in the swine industry is caused by ETEC F4+ (Jin and Zhao, 2000; Xia et al., 2015), and is commonly controlled with

antibiotic growth promoters. Probiotics, however, represent a promising alternative to prophylactic use of antibiotics in farm animals. Although probiotics displayed strain-dependent and dose-dependent effects in preventing or reducing the duration of diarrhea (Jacobi and Odle, 2012), increasing evidence exists for probiotics as beneficial adjunctive antidiarrheal therapies (Thiagarajah et al., 2015). Dietary probiotics supplementation could shape the microenvironment of the intestinal

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lumen and to favor optimal gastrointestinal growth and function in neonates (Jacobi and Odle, 2012). Many studies have demonstrated the protective effects of

Lactobacillus on the integrity of the intestinal epithelial barrier compromised by

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pathogenic bacteria, including ETEC (Yang et al., 2014; Yu et al., 2015), and help

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control postweaning colibacillosis in piglets (Guerra-Ordaz et al., 2014). Addition of L. plantarum to the young piglets’ diet improved growth performance and effectively

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prevented ETEC-associated diarrhea by improving the intestinal barrier function

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(Yang et al., 2014). Thorough characterization of the host-pathogen interaction is essential to understand the pathogenicity of the bacteria, and help develop

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therapeutic strategies to prevent this illness (Xia et al., 2015). The F4 fimbriae of ETEC binds to IPEC-J2 cells (Rasschaert et al., 2010), thus

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providing a recognized in vitro model system for studying the interactions between intestinal bacteria and the animal host (Koh et al., 2008). The enterotoxins produced by ETEC infection are responsible for the loss of electrolytes and water and a recent study in this laboratory indicated that the intestinal ion transporters and water channel aquaporins were differentially affected by ETEC F4 infection (Zhu et al.,

2017). Previous study reported the transcriptomic profiles of IPEC-J2 cells infected by 3 ETEC strains (F4ab, F4ac and F18ac ETEC) for 3 h, with ETEC F4ac (K88) being the most virulent (Zhou et al., 2012). The F4ac ETEC induced a total of 3,493 differentially expressed genes in IPEC-J2 cells, most of which were involved in immune, inflammation, wounding response, apoptosis, cell cycle progress, and

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proteolysis (Zhou et al., 2012). Proteomic study provides an in-depth analysis of

proteins defining enterocyte differentiation of Caco-2 cells (Olander et al., 2016),

and identified the interaction of that cell line with Bifidobacterium longum (Wei et

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al., 2014). The recent proteomic analysis of ETEC in neutral and alkaline conditions

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has revealed the up-regulation of membrane proteins and secretion of heat-labile LT toxin of E. coli (Gonzales-Siles et al., 2017). There has been no description of the

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proteomic alterations of a porcine intestinal epithelial cell line when pretreated with

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L. plantarum followed by challenge with ETEC F4. The present study detected 90 IPEC-J2 proteins with significant alterations

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post-infection with ETEC F4, and 134 proteins in IPEC-J2 challenged with ETEC F4 after pretreatment with L. plantarum. These proteins may serve as potential

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biomarkers of the interactions between ETEC and IPEC-J2 cells. The protein profile of jejunal tissues in ETEC-infected piglets had been characterized by quantitative proteomics, and the differentially expressed proteins were mainly involved in intestinal function of binding, metabolic process, catalytic activity and immune responses (Ren et al., 2016). In addition, previous study has demonstrated the

proteomic analysis of IPEC-J2 cells in response to co-infection with porcine transmissible gastroenteritis virus and ETEC F4 (Xia et al., 2017). The virulence factors of ETEC can induce the host inflammatory response by modulating NF-κB and MAPK pathways (Sanchez-Villamil and Navarro-Garcia, 2015; Zanello et al., 2011a). The current proteomic results indicate several pathways related to immune function and inflammation appear to be involved. Here, the

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mTOR, JNK and MAPK pathways were inhibited by ETEC F4 infection in IPEC-J2 cells. Indeed, it has been shown that ETEC F4 increased the phosphorylation level of

ERK and JNK in Caco-2 cells, and L. fructosus C2 reduced the expression of p-ERK

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and p-JNK (Yu et al., 2015). ETEC F4 strains could induce pro-inflammatory

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responses and enhance the phosphorylation level of MAPK ERK1/2 and p38 in porcine IECs, and Saccharomyces cerevisiae inhibited ETEC-mediated the

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phosphorylation level of MAPK ERK1/2 and p38 in IPEC-1(Zanello et al., 2011 a,

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b). Previous studies showed that L. plantarum may improve epithelial barrier function of IPEC-J2 cells by modulation of toll-like receptors (TLRs), NFκB and

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MAPK pathways (Wu et al., 2016); NF-kB and MAPK signaling pathways were shown to be involved in ETEC colonization (Sanchez-Villamil and Navarro-Garcia,

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2015) and ETEC-induced diarrhea may inhibit the NF-κB and MAPK pathways in the jejunum of weaned piglets (Ren et al., 2016). Previous study has also shown that ETEC inhibits mTOR phosphorylation through the AMPK and ERK MAPK signaling pathways and induces intestinal epithelial cell autophagy in IPEC-1 cells (Tang et al., 2014).

Solute carrier transporters play an important role in mediating the transport of ions and other solutes, such as glucose and amino acids. In the present study, 33 solute carrier transporters proteins in IPEC-J2 cells were decreased after infection with ETEC F4; most of these mediate the transport of ions, including sodium, potassium, calcium, etc. These results are consistent with previous studies showing that a significant component or subset of diarrhea caused by enteric infections was related

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to inhibition of sodium ions and fluid absorption (Gurney et al., 2017). Interestingly,

the reduced expression of SLC25A29 induced by ETEC F4 was reversed here by pretreatment with LP. SLC25A29 is a mitochondrial transporter for basic amino

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acids (Porcelli et al., 2014), and plays a role in transporting arginine into

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mitochondria and synthesis of nitric oxide in cancer cells (Zhang et al., 2018). Therefore, it can be speculated that L. plantarum may transport more arginine into

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mitochondria for NO synthesis, thereby modulating energy supply and metabolic

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processes to offset ETEC-induced disruption.

5. Conclusions

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This study explored the proteomic profiles of IPEC-J2 cells after pretreatment

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with LP followed by challenge with ETEC F4. This proteomic analysis based on these differentially expressed proteins might provide insights for future studies concerning the pathogenesis and host responses to ETEC infections and broaden our understanding of the protective effects of LP in alleviating the ETEC diarrhea of piglets.

Conflict of interest statement None.

Acknowledgements This work was supported by the grants from the National Science and Foundation of China (No. 31472112, 31501967, and 31702150), the Science and

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Technology Program of Guangzhou City, China (No. 201607020035), the China Agriculture Research System (CARS-35), the Guangdong Provincial Department of Education (No. 2018KTSCX244), and the Presidential Foundation of Guangdong

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Academy of Agricultural Sciences, China (No. 201703001). The authors gratefully

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revise and edit the manuscript.

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acknowledge Professor W. B. Currie (Cornell University, Ithaca, NY) for helping

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5240-5252.

Figure legends Fig. 1. Venn diagram and gene ontology (GO) annotation of all identified proteins from experiments. The samples were collected after treatment, fractionated to extract protein, digested with trypsin, and labeled with different iTRAQ reagents containing reporter groups of different masses (115, 116, 117, and 118). The peptides were separated by liquid chromatography (LC) and analyzed by

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mass spectrometry (MS). (A) Venn diagram for the distribution of proteins

quantification among the 3 iTRAQ experiments. Based on functional annotations with GO analysis, the global proteins of IPEC-J2 were categorized into biological

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processes (B), cellular components (C), and molecular function (D). (B), For the

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biological processes, these proteins were mainly involved in translation, intracellular protein transport, positive and negative regulation of transcription from RNA

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polymerase promoter, etc. (C), the predominant cellular components were

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extracellular exosome, cytoplasm, nucleus, integral component of membrane, etc. (D), these proteins were mainly associated with poly (A) RNA binding, ATP binding,

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etc.

Fig. 2. Differentially expressed proteins identified among treatments. (A) Total

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differentially expressed, up or down-regulated proteins among treatments. (B) Volcano plots of differentially expressed proteins among treatments. The red dots are up-regulated differentially expressed proteins, while the green dots

are

down-regulated differentially expressed proteins, based on the criteria of fold change >2.0 or 1.3 and P value <0.05.

Fig. 3. The COG function classification of the differentially expressed proteins in IPEC-J2 cells. Classification of identified proteins based on functional annotations with COG analysis between ETEC treatment and (A), LP treatment and Controls (B), and LP+ETEC treatment and ETEC alone (C). These differently expressed proteins were classified into 25 groups based on their functions. Fig. 4. The GO (gene ontology) analysis of the differentially expressed proteins

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in IPEC-J2 cells. Classification of identified proteins based on functional

annotations using GO analysis are shown for comparisons between the ETEC treatment and Controls (A), LP treatment and Controls (B), LP+ETEC treatment and

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ETEC alone (C). The differently expressed proteins were categorized into biological

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processes, cellular components and molecular functions based on GO analysis. The most predominant GO terms for biological processes, cellular components and

in

cellular

process,

biological

regulation,

metabolic

process,

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enriched

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molecular function showed that the differentially expressed proteins were mainly

single-organism process, cell part, cell, organelle, binding activity and catalytic

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activity.

Fig. 5. The KEGG pathway analysis of the differentially expressed proteins in

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IPEC-J2 cells. Classification of identified proteins based on functional annotations using KEGG pathway analysis between ETEC treatment and (A), LP treatment and Controls (B), and LP+ETEC treatment and ETEC alone (C). Fig. 6. Heatmap of the differentially expressed proteins in ETEC-infected and Control IPEC-J2 cells. Protein expression is shown using a pseudocolor scale, with

the decreased (green) and increased (red) proteins indicated. Fold change = 2, P = 0.05. Fig. 7. The network of the differentially expressed proteins in ETEC infected IPEC-J2 cells after pretreatment with LP or not. Protein-protein interaction network analyzed via STRING software. An edge was drawn with up to 7 different colored lines representing the existence of 7 lines of evidence used in predicting

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associations. A red line indicates the presence of fusion evidence, a green line

indicates neighborhood evidence, a blue line indicates co-occurrence evidence, a

purple line indicates experimental evidence, a yellow line indicates text mining

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evidence, a light blue line indicates database evidence, and a black line indicates

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coexpression evidence.

Fig. 8. Western blotting analyses of mTOR and JNK pathway proteins

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expression. Results are means ± SD (n = 3). Different letters indicate statistical

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significance among different treatment groups (P < 0.05). CON, control; LP, Lactobacillus plantarum; ETEC, ETEC F4; LP+ETEC, LP + ETEC F4.

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Fig. 9. The relative transcript abundance of solute carrier transporters (SLC) in IPEC-J2 cells. The relative mRNA expression of SLC genes among different

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treatment groups (results are means ± SD, n = 6; different letters used to indicate statistical significance among different treatment groups, P < 0.05). CON, control; LP, Lactobacillus plantarum; ETEC, ETEC F4; LP + ETEC, LP + ETEC F4.

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Table 1. Differentially expressed proteins in IPEC-J2 cells infected with ETEC K88 Description

Accession

Regulation

TNKS2

Poly [ADP-ribose] polymerase

PHPT1

Ratio of

P value

expression level (K88/Control) *

F1RPW2

Up

3.45

0.00

F1SCW9

Up

2.55

0.01

14 kDa phosphohistidine phosphatase

F1RWN4

Up

2.42

0.00

PLG

Plasminogen

F1SB81

Up

2.28

0.00

MAP2K1

Mitogen-activated protein kinase kinase 1

B8XSJ8

Up

2.12

0.00

ALB

Serum albumin

F1RUN2

Up

2.11

0.01

PPME1

Protein phosphatase methylesterase 1

I3LJT9

Up

2.04

0.01

ITIH2

Inter-alpha-trypsin inhibitor heavy chain H2

F1RUM4

Up

2.03

0.00

HBA

Hemoglobin subunit alpha

P01965

Up

2.03

0.01

Gelsolin (Fragment)

P20305

Up

2.02

0.00

Peptidylprolyl isomerase

F1S3G5

Up

2.02

0.00

Eukaryotic translation initiation factor 3 subunit K

F1RI41

Up

2.02

0.00

ADP-ribosylation factor 4

Q56P20

Up

2.01

0.00

Carbonyl reductase 3

A0A0K1TQQ7

Up

2.01

0.00

Nucleoside diphosphate kinase B

Q2EN76

Down

0.50

0.00

PIN1 EIF3K ARF4 CBR3 NME2

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GSN

Pr

Coagulation factor V

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F5

e-

pr

Protein name

FZD6

Frizzled class receptor 6

TALDO1

Transaldolase

FDPS

Farnesyl diphosphate synthase

NDUFA4

Mitochondrial NDUFA4

TNFRSF12A

Fibroblast growth factor-inducible 14

HMGN2

f

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Na(+)/H(+) exchange regulatory cofactor NHE-RF

B8XH67

Down

0.50

0.00

F1S0R5

Down

0.49

0.00

F1RYY6

Down

0.49

0.01

D0G6X4

Down

0.49

0.00

A1XQS3

Down

0.48

0.00

B7SLY2

Down

0.48

0.04

Non-histone chromosomal protein HMG-17

P80272

Down

0.47

0.00

GAPDH

Glyceraldehyde-3-phosphate dehydrogenase

P00355

Down

0.46

0.01

ATP5J2

ATP synthase subunit F, mitochondrial

F1REX4

Down

0.46

0.00

PRDX6

Peroxiredoxin-6

Q9TSX9

Down

0.45

0.00

RPIA

Ribose-5-phosphate isomerase

A2TLM1

Down

0.45

0.00

STMN1

Stathmin

Q6DUB7

Down

0.44

0.02

SDC4

Syndecan-4

J9JIL6

Down

0.42

0.00

PICALM

Phosphatidylinositol-binding clathrin assembly protein

F8T0U2

Down

0.39

0.00

e-

Pr

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pr

SLC9A3R1

* The ratio represents the protein levels in ETEC K88-infected cells relative to the levels in the control.

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Description

Protein

Accession

name

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Table 2. Proteins of solute carrier transporters were quantitatively identified in K88 infected IPEC-J2 cells after pretreatment with LP or not. Ratios of expression level

K88/Control

LP/Control

LP+K88/K88

P value

Neutral amino acid transporter B(0)

F1RM08

0.57

1.02

0.76

0.00

SLC3A2

4F2 cell-surface antigen heavy chain

B2D2K7

0.69

1.05

0.84

0.00

SLC4A1AP

Kanadaptin

F1SED8

1.49

1.09

0.89

0.00

SLC4A2

Anion exchange protein 2

I3L6Z1

0.71

0.92

0.90

0.00

SLC4A7

Sodium bicarbonate cotransporter 3

M3V864

0.79

1.07

0.88

0.00

SLC5A3

Sodium/myo-inositol cotransporter

D3K5N9

0.70

0.99

0.87

0.00

SLC7A5

Large neutral amino acids transporter small subunit 1

K9IVK6

0.81

1.01

0.88

0.01

SLC9A3R1

Na(+)/H(+) exchange regulatory cofactor NHE-RF1

B8XH67

0.50

1.09

1.09

0.00

SLC9A3R2

Na(+)/H(+) exchange regulatory cofactor NHE-RF2

F1RFB4

0.75

0.89

0.97

0.07

SLC12A4

Electroneutral potassium-chloride cotransporter 1

O18887

0.85

0.98

0.87

0.00

SLC12A7

Electroneutral potassium-chloride cotransporter 4

K9J6L1

0.77

0.88

0.95

0.00

Monocarboxylate transporter 4

I3LQ25

0.64

0.95

0.85

0.00

Sodium-dependent phosphate transporter 2

F1SE25

0.80

1.03

0.89

0.01

Tricarboxylate transport protein, mitochondrial

F1RK74

0.58

0.85

0.92

0.00

Phosphate carrier protein, mitochondrial

F1SQT3

0.69

0.96

0.92

0.00

ADP/ATP translocase 1

F1RZQ6

0.76

0.98

0.91

0.00

ADP/ATP translocase 2

F2Z565

0.74

0.92

1.02

0.00

SLC20A2 SLC25A1 SLC25A3 SLC25A4 SLC25A5

ePr

na l

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SLC16A3

pr

SLC1A5

f 1.06

0.86

0.00

0.72

0.94

0.92

0.00

0.79

0.98

0.94

0.00

0.69

0.88

0.89

0.00

F1RME0

0.78

0.99

0.80

0.00

M3UZ90

0.63

0.89

1.03

0.00

Mitochondrial carnitine/acylcarnitine carrier protein

F1SKK1

0.80

1.00

0.85

0.00

SLC25A21

Mitochondrial 2-oxodicarboxylate carrier

F1SHK6

0.67

0.93

0.89

0.00

SLC25A22

Mitochondrial glutamate carrier 1

F1RYY8

0.67

0.92

0.90

0.00

SLC25A25

Calcium-binding mitochondrial carrier protein

C8C417

0.89

0.98

0.90

0.00

Q6QRN9

SLC25A11

Mitochondrial 2-oxoglutarate/malate carrier protein

F1RFX9

SLC25A12

Calcium-binding mitochondrial carrier protein Aralar1

M3VK21

SLC25A13

Calcium-binding mitochondrial carrier protein Aralar2

M3VH80

SLC25A15

Mitochondrial ornithine transporter 1

SLC25A16

Graves disease carrier protein

SLC25A20

0.72

oo

ADP/ATP translocase 3

Pr

e-

pr

SLC25A6

na l

SCaMC-2 Mitochondrial basic amino acids transporter

F1SAP1

0.69

1.01

1.20

0.08

SLC25A30

Kidney mitochondrial carrier protein 1

M3UZ57

0.83

1.08

0.85

0.00

SLC25A46

Solute carrier family 25 member 46

F1RLI6

0.76

0.89

0.91

0.00

Equilibrative nucleoside transporter 1

F1RQU4

0.61

1.01

0.86

0.00

Zinc transporter 1

B6VCB0

0.59

0.95

1.04

0.00

Zinc transporter 7

B6V6V4

0.98

1.00

0.86

0.45

Zinc transporter 9

B5U335

0.64

0.87

0.97

0.00

Acetyl-coenzyme A transporter 1

I3LE25

0.97

1.05

0.89

0.02

Adenosine 3'-phospho 5'-phosphosulfate transporter 1

F1RQU1

0.77

0.97

0.92

0.00

SLC29A1 SLC30A1 SLC30A7 SLC30A9 SLC33A1 SLC35B2

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SLC25A29

0.98

0.00

1.18

1.11

0.85

0.35

0.91

1.00

0.96

0.17

0.79

0.91

0.94

0.01

I3LTT7

0.66

0.86

1.00

0.00

F1RGR5

0.50

0.86

0.83

0.00

SLC38A2

Sodium-coupled neutral amino acid transporter 2

K9J4Q4

SLC38A5

Sodium-coupled neutral amino acid transporter 5

I3LQM7

SLC38A10

Putative sodium-coupled neutral amino acid transporter

I3LB54

SLC50A1

Sugar transporter SWEET1

e-

Zinc transporter ZIP6

Jo ur

na l

Pr

SLC39A6

0.68

oo

F1SG45

pr

Glycerol-3-phosphate transporter

10

f 0.84

SLC37A1

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