BioMed Research International

Volume 2014 (2014), Article ID 154946, 14 pages

http://dx.doi.org/10.1155/2014/154946

Research Article

Identification of Key Genes in the Response to Salmonella enterica Enteritidis, Salmonella enterica Pullorum, and Poly(I:C) in Chicken Spleen and Caecum

Teng Ma, Guobin Chang, Rong Chen, Zhongwei Sheng, Aiqin Dai, Fei Zhai, Jianchao Li, Mingxiu Xia, Dengke Hua, Lu Xu, Hongzhi Wang, Jing Chen, Lu Liu, and Guohong Chen

Animal Genetic Resources Laboratory, College of Animal Science and Technology, Yangzhou University, 88 South of University Avenue, Yangzhou, Jiangsu 225009, China

Received 10 September 2013; Revised 20 November 2013; Accepted 5 January 2014; Published 23 February 2014

Academic Editor: Thomas Schmitz-Rixen

Copyright © 2014 Teng Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Salmonella enterica Enteritidis (S. Enteritidis) and Salmonella enterica Pullorum (S. pullorum) are regarded as a threat to poultry production. This study’s aim is to characterize the expression profiles in response to three different challenges and to identify infection-related genes in the chicken spleen and caecum. Groups of the Chinese chicken breed Langshan were challenged with either S. Enteritidis, S. pullorum, or poly(I:C). The concentrations of cytokines and antibodies and the Salmonella colonization level of the caecum and liver were detected in each group at 7 days postinfection. Expression microarray experiments were conducted using mRNA isolated from both spleen and caecum. Crucial differentially expressed genes (DEGs) associated with immunity were identified. Four DEGs were identified in spleen of all three challenge groups (RBM16, FAH, SOX5, and RBM9) and different four genes in caecum (SOUL, FCN2, ANLN, and ACSL1). Expression profiles were clearly different among the three challenged groups. Genes enriched in the spleen of birds infected with S. pullorum were enriched in lymphocyte proliferation related pathways, but the enriched genes in the caecum of the same group were primarily enriched in innate immunity or antibacterial responses. The DEGs that appear across all three challenge groups might represent global response factors for different pathogens.

1. Introduction

Salmonella is frequently detected during the process of poultry production, especially Salmonella enterica Enteritidis (S. Enteritidis) and Salmonella enterica Pullorum (S. pullorum). Chicks over one-month of age have a low mortality rate when infected with S. Enteritidis [1], but in chicks less than one month old, this infection causes symptoms such as diarrhea, intestinal lesions, and an influx of heterophils into the gut accompanied by inflammation and damage to villi, leading to high mortality [2]. S. pullorum can cause young chicken cachexia, drop-head, wing prolapse, white fecal, loose stool, and anal blockage by excrement, with high mortality rates until two or three weeks of age [1].

Salmonella activates the innate immune system in the early stage of infection, particularly by LPS the TLR4 pathway after stimulation by lipopolysaccharide (LPS) [3]. Molecules involved in this process have been widely studied, including the TLR family and intermediate signal factors such as MyD88 [4]. However, compared with human or mouse, the studies of immune regulation mechanism on chicken were not sufficient. Most of these studies on chicken mainly focused on comprehensively investigating the expression patterns of a few crucial regulative genes after a specific infection [5].

To investigate the whole expression profiles of chicks infected with Salmonella, some researchers have performed expression microarray experiments and identified genes related to the innate immune response [6–11]. For instance, van Hemert et al. identified differential expression of genes by microarray experiments in intestinal tissue sampled from chicks infected with S. Enteritidis and uninfected individuals [8–10]. Similarly, Schokker et al. identified 20 key hub genes by analyzing a time series of microarray data of intestinal tissues collected at different time points after day-old chicks were infected with S. Enteritidis [11]. van Hemert et al. collected samples at the 1st, 3rd, 5th, 7th, 9th, 11th, 15th, and 21st day postinfection, and Schokker et al. sampled six consecutive time points postinfection, which were the 1st, 2nd, 4th, 8th, 12th, and 21st day postinfection. Considering the features of pathogenesis caused by Salmonella, we choose the 7th day postinfection as the sampling time point at which to carry out microarray experiments to investigate the immune response of the spleen and caecum to different types of Salmonella as well as Poly(I:C).

Poly(I:C) is regarded as an analog of RNA viruses such as avian influenza virus and is normally used as an immune-system activator, specifically activating the TLR3 pathway or the MDA5 pathway, which is related to antiviral function [12]. Its effect is different from LPS, which triggers the TLR4 pathway causing an inflammatory reaction. Therefore, the immune response to poly(I:C) might be extremely different from the response to these two serotypes of Salmonella, which could lead to a highly distinct gene expression profile [3, 8, 11, 13, 14].

We chose to study Langshan chickens in the current experiment. The Langshan breed has the good meat quality but a longer growth period than commercial broilers [15–17]. Te Pas et al. carried out a meta-analysis to integrate several individual artificial infection experiments and found that different chicken lines exhibited that different expression profiles of immune response in each line were varied [18], which was validated via Schokker et al.’s study as well [19]. Therefore, we hypothesized that because of different genetic backgrounds, the Langshan breed (Chinese indigenous) and commercial broiler breeds would have different expression profiles of antimicrobial response.

This study used three antigens to challenge day-old Langshan chicks. By comparing the gene expression profiles of the spleen and caecum of the challenged chicks at seventh days postinfection with the expression profiles of the control chicks at the same time, we identified DEGs that were involved in the immune response. These genes are candidates for breeding disease-resistant lines.

2. Materials and Methods

2.1. Preparation and Testing of Pathogenic Strains

S. Pullorum and S. Enteritidis, isolated from a local farm in Yangzhou, China, and then cultured for several generations, were used as pathogens to challenge day-old chicks. Bacteria were cultured in Luria-Bertani (LB) broth at 37°C for 12 h in a shaking incubator at 200 rpm/min before infection. The concentration of bacteria was diluted to 10⁷ cfu/mL.

Cloaca swabs were used to collect samples from two groups to test for the presence of Salmonella before the challenge. Each sample was cultured in 10 mL of LB broth at 37°C for 10 h and then plated on SS (Salmonella Shigella) agar at 37°C for 24 h before testing colonies with Salmonella-specific polyclonal antibodies (SSPA).

2.2. Challenging and Sample Collection

All experimental procedures were performed in accordance with the Guidelines for Experimental Animals established by the Ministry of Science and Technology (Beijing, China). Ninety Langshan chickens from the Rugao Langshan Chicken Genetic Resources Preservation Field were used. S. Enteritidis strains and S. pullorum strains were cultured in the liquid LB medium for 12 hours, and the bacterial concentration was determined via spectrophotometry before orally challenging the chicks. 10 mg/vial of poly(I:C) was purchased (Invivogen, San Diego, California, USA, CAS number: 31852-29-6 tlrl-pic). Chicks were fed with autoclave sterilizing chick feeds and divided into four groups, which were maintained in biological isolation. Twenty chicks were assigned to each of the three infected groups and 30 to the control group. All were maintained under uniform conditions of feeding and drinking ad libitum, at a temperature of approximately 33°C and 24-hour illumination. Twenty chicks were challenged with inoculum containing 10⁷ of S. Enteritidis via oral administration (“group SE”). Another 20 chicks were challenged with 10⁷  S. pullorum via oral administration (“group SP”). Another 20 chicks were challenged with 1 mL of poly(I:C) in diluent (1 mg/mL) via intramuscular injection into the thigh (“group poly(I:C)”). The control group of 30 chicks was orally perfused with 1 mL normal saline.

All surviving chicks were sacrificed at 7 days postinfection (DPI). Serum samples of those chicks were collected to determine the concentration of IgG, IgM, IL-1α, and IFN-γ. The spleen and caecum were isolated. After collecting the caecum content, they were placed into liquid nitrogen. Subsequently, spleen and caecum samples of four individuals from each group with intact experimental records were randomly selected and sent to Shanghai Biotechnology Co., Ltd. (SBC), which performed the microarray test.

2.3. Calculating Salmonella Load Counts in Caecum Content

After chicks were sacrificed, the caeca were completely scraped to collect the contents. After measuring the weight of the caecum contents, it was diluted in 100 mL normal saline. 1 mL diluent was further diluted 10-, 10²-, 10³-, 10⁴-, 10⁵-, and 10⁶-fold in 10 mL normal saline. Subsequently, 1 mL of the diluent of each concentration was directly plated on three plates of SS (Salmonella Shigella) agar at 37°C for 24 h. The appropriate concentration between 30 and 300 cfu/plate was generated from the 10⁴ dilution. The spread plate count method was used to count the number of S. Pullorum or S. Enteritidis colonies. The intestinal load capacity of S. Pullorum or S. Enteritidis was calculated from the formula:

2.4. Determination and Statistical Analysis of the IL-1α, IFNγ, IgG, and IgM Content in Serum

Chicks were first sacrificed by cervical dislocation, then blood was immediately collected from heart. Subsequently; the bodies were dissected for collection of tissues. The collected blood without anticoagulant was incubated at 37°C for 2 h. Then, the blood was kept at 4°C overnight. After the serum naturally separated from the whole blood, this mixture was centrifuged at 3000 rpm/min for 10 min at 4°C. The upper serum layer was transferred into a new, sterile 1.5 mL centrifuge tube and stored at −80°C, and the blood cell layer was discarded. The IL-1α, IFNγ, IgG, and IgM content in the serum was determined with an enzyme-linked immunosorbent assay (ELISA) kit (Shanghai Yueyan Biotech Co., Ltd. Shanghai, China) according to the manufacturer’s instructions. The absorbance in each well was determined via a microplate reader (NanoQant Infinite M200 PRO, TECAN, Männedorf, Switzerland). These raw results were used to fit a binomial regression line as a standard curve to calculate the IL-1α, IFNγ, IgG, and IgM content in serum samples. A one-way ANOVA was used to analyze all of the concentration data for each immunity index. The LSD algorithm was used for multiple comparisons. All analyses were carried out in Microsoft Excel 2010 (Microsoft Corporation (China), Beijing, China).

2.5. Identification of Salmonella in Spleen via Immunofluorescence

To investigate Salmonella invasion of tissues, spleens were collected for making frozen sections and stained with a fluorochrome-labeled polyclonal antibody. The chicken spleens were cut into 6 μm thick slices by a freezing microtome (CM1950, Leica, Wetzlar, Germany), which were fixed with −20°C acetone for 10 min followed by washing with phosphate buffered saline (PBS) for 5 min three times and subsequently were incubated with 10% goat serum to block the unspecific antigens for 1 h then loading the diluent of Salmonella-specific polyclonal antibodies and incubating overnight. At the second day, after washing with PBS 5 min × 3 times, the secondary goat anti-mouse immunoglobulin (BOSTER, Wuhan, China) tagged with Cy3 fluorophore was added, incubated at 37°C for 1 h, and then washed with PBS 5 min × 3 times again. Subsequently, sections were incubated with 4′,6-diamidino-2-phenylindole (DAPI) (BOSTER, Wuhan, China) for 15 min at room temperature. Finally, the fluorescence anti-quenching agent (BOSTER, Wuhan, China) was added to sections, which were observed under an Inverted Fluorescence Microscopy system (IX71, OLYMPUS, Tokyo, Japan).

2.6. Microarray Hybridization Experiment

Four spleen samples and four caecum samples (from the same 4 individuals within each group) were sent to Shanghai Biotechnology Co., Ltd. (SBC, Shanghai, China) for the microarray hybridization. After strict total RNA extraction and quality control for each tissue sample following the standard protocol of SBC, two RNA samples from the same group were mixed and hybridized with one Agilent Chicken Whole Genome Expression chip (Agilent. SingleColor.26441). As a result, in each group there were two biological replications. All raw microarray data were normalized with the Quantile algorithm [20] via Gene Spring Software 11.5.1 (Agilent Technologies, Santa Clara, CA, US).

2.7. Filtering and Analyzing DEGs

Gene Spring Software 11.5.1 was adopted for further filtering of DEGs from normalized data through a series of quality control steps. At first, all detected probes were kept. The QC probes were removed. Two chips, one for male and the other for female, were used for each group as biological duplications. The following filtered data were created from the Advanced Analysis operation: significance analysis, corrected value cut-off: 0.05; post hoc test: Tukey HSD; selected test: one-way ANOVA; value computation: asymptotic; multiple testing correction: Benjamini-Hochberg. The expression data from the control group were used as the control. Primary DEGs with fold changes ≥1.5 and ≤0.75 were retained. Then those with fluorescence intensity more than 100 were kept as candidate genes from the results of clustering analysis carried out for all DEGs, and comparative analysis was performed with Gene Spring Software 11.5.1. Subsequently, DAVID (http://david.abcc.ncifcrf.gov/) [21] was used in the enrichment analysis of gene ontology (GO) and KEGG pathways.

2.8. qRT-PCR Validation of Microarray Data

Five genes with significant differential expression folds were selected to validate the reliability of the microarray data: RSFR, VPREB3, CD72, IL15, and GAL1 (see Table S7, in Supplementary Material available online at http://dx.doi.org/10.1155/2014/154946). The experimental design for the qRT-PCR was the same as that used for the chips, but each sample had four technical replicates. The same RNA samples that were used in the microarray experiment were used for qRT-PCR. The relative quantification of the RT-PCR experiment was performed according to the instructions of the PrimeScript RT Master Mix kit and SYBR Premix Ex Taq II kit (PrimeScript RT Master Mix (Perfect Real Time, DRR036); SYBR Premix Ex Taq II (Tli RNase H Plus, DRR820). The Applied Biosystems 7500 Real-Time PCR System was used for this experiment. REST was used to analyze the experimental results [22, 23].

3. Results and Analysis

3.1. Infection Status

By 7 days postinfection (7 DPI), when all chicks were sacrificed and sampled, the mortality of group SE was 15%, and the mortality of group SP was 10%. No chicks died in group poly(I:C) before they were sacrificed, which suggests that the dose of bacteria used to challenge day-old chicks was appropriate to stimulate immunoreaction but did not cause high mortality. Necropsy was carried out, and no obvious pathological changes were found. We inferred that these results were probably caused by bacteremia, which led to multiple organ failure. This study aimed to investigate the immunoreaction to Salmonella in vivo. Thus, dead individuals were removed and only live chicks were used for follow-up experiments. Chicks whose serum was not collected were also removed from this study.

3.2. Results of Determining Salmonella Counts in Caecum Content

The results (Table 1) indicate that Salmonella was identified in groups SE and SP. The microbial load of Salmonella in groups SE and SP was nearly 10⁷ cfu/g; there was no significant difference between the two groups. No Salmonella was found in the control and poly(I:C) group. Salmonella remained in the caecum until 7 DPI, which mean that immunoreaction might continue to occur.

Table 1: The Salmonella load in each group.

Groups
Microbial load of Salmonella in caecum (10⁶ cfu/g)

Group SE

Group SP

Poly(I:C)
0

Control
0

3.3. IL-1α, IFNγ, IgG, and IgM Content in Serum

The level of two cytokines (IL-1α, IFNγ) and two antibodies (IgG, IgM) was determined via ELISA (Table 2). The mean IL-1α concentration of the control group was significantly lower than the poly(I:C) group. The mean IFNγ concentration of the control group was significantly lower than the SE group. Thus, a significant, though moderate, immune response could be detected at 7 DPI.

Table 2: Immune indices of the experimental groups.

Groups
IL1- (ng/L)
IFN- (ng/L)
IgG (g/L)
IgM (mg/L)

Control (30)²

Group SE (17)

Group SP (18)

Poly(I:C) (20)

Mean ± standard error; ²number of individuals in groups. In a given column, the same letter indicates that there is no significant difference ().

In terms of the adaptive immune response, the IgG level of group SP was significantly higher than that of the other groups. Although the average IgM level of the SP group was not significantly higher than the other groups, it was at least 10 mg/L more than other groups. The results show that chicks had different Ig response patterns to S. Enteritidis and the adaptive immune system of chicks challenged with S. pullorum was activated. To investigate the expression changes of this phenomenon, the microarray experiment was performed to identify differences at the mRNA level.

3.4. Salmonella Still Exists in Spleen at 7 DPI

We also carried out immunofluorescence experiments on frozen sections of the chicken spleens. At 7 DPI Salmonella still could be found in the spleens of groups SP and SE (Figure 1). The number of bacteria in the SP group (total 3882.92 positive spots/mm³) was significantly greater than that of the SE group (total 1244.52 positive spots/mm³). The control group and the poly(I:C) group were free from Salmonella. This result suggests that S. Pullorum has a stronger tissue invasion ability than S. Enteritidis for the strains used in this experiment.

Figure 1: Typical immunofluorescence pictures of frozen sections. DAPI was used to stain the cellular nucleus, and polyclonal antibodies labeled by Cy3 were used to stain the bacteria body of Salmonella.

3.5. Statistical Description of DEGs of Spleen and Caecum in the SE, SP, and Poly(I:C) Groups

DEGs in the spleen with a value < 0.05 and a FC (Fold Change) ≥1.5 for each group are shown in Table S8. The proportion of upregulated genes in group SP was greater than that in the other two treatment groups, which suggests that the immune response might be still active at 7 DPI, in agreement with the immune index. In contrast, the proportion of downregulated genes in the poly(I:C) group was slightly higher than the other two groups.

The DEGs in the caecum for each group were identified, and the statistics are shown in Table S9. Because poly(I:C), which was not administered orally, initiated immune stimulation through the blood rather than the intestinal track, we hypothesized that there would be fewer DEGs in the caecum than those of the other two groups, which was confirmed by the results (Table S9). In addition, the identified DEGs were not primarily enriched in immune processes. The proportion and the absolute quantity of DEGs in group SP were 16.21% greater than those in group SE, which indicates that immune response in the SP group was still active and the pathogenesis mechanisms were distinct between SP and SE groups.

3.6. Filtering and Analyzing DEGs in the Spleen

Through a series of filtering steps, the DEGs with high expression and high differential expression were obtained in group SP. There were 16 upregulated probes with , Fluorescence Signal, and Raw_Data > 100 (Table S1-1), of which 7 probes had annotation information (Table 3(a)). Some of these genes are reported to be correlated with immune regulation. RANGAP1 is a GTPase activator of RAN, and SLCO1B3 is an important membrane transport protein. VAV2 is an oncogene family member that is expressed in multiple tissues, unlike VAV1, which is only expressed in hematopoietic cells. Boettcher et al. found that, at early stages of the immune response, P+GC triggered the amplification cascade of the Cav1-Vav2-RhoA signal pathway dependent on phosphotyrosine, leading to the rearrangement of the cytoskeleton and blocking host cell phagocytosis of bacteria [24]. However, the function of the rest of the probes was unknown.

Table 3: (a) Upregulated differentially expressed genes in the spleen in group SP with Fold Change (FC) ≥ 2 and signal density of raw data ≥ 100. (b) Downregulated differentially expressed genes in the spleen in group SP with FC ≥ 2 and signal density of raw data ≥ 100.

(a)

Probe name
value

Group SP (raw_data)
Control (raw_data)
Gene symbol

A_87_P135073
0.0273
2.6414
38479.96
21784.10
RANGAP1

A_87_P068266
0.0299
2.7656
4204.96
2270.40
OGCHI

A_87_P063991
0.0130
2.4764
2527.43
1473.75
AIM1L

A_87_P158638
0.0320
2.9393
1893.88
972.62
SLCO1B3

A_87_P034912
0.0135
2.4772
1504.71
855.34
ITM2C

A_87_P307413
0.0331
2.5736
247.74
141.09
VAV2

A_87_P152483
0.0188
3.7106
244.17
96.53
FREM1

(b)

Probe name
value

Group SP (raw_data)
Control (raw_data)
Gene symbol

A_87_P263263
0.0337
3.7427
15599.45
72900.14
RSFR

A_87_P009488
0.0477
2.6393
1483.44
4893.00
MERTK

A_87_P021714
0.0162
2.3141
467.72
1469.43
RAB32

A_87_P155463
0.0420
2.1985
1043.95
3037.73
TPRN

A_87_P106828
0.0105
2.1289
685.27
1982.21
TMEM140

A_87_P036207
0.0202
2.0998
3873.45
10873.39
C3AR1

Ten downregulated genes (Table S1-2) were obtained from group SP, six of which were annotated (Table 3(b)). Some of these have been observed to play an important role in immune function. Nitto et al. reported that RSFR (leukocyte ribonuclease A-2) not only acted as a nuclease but also had a antibacterial activity [25]. The mutation of MERTK (Proto-oncogene tyrosine-protein kinase MER) was associated with blocking phagocytosis by retinal pigment epithelial cells and with retinitis pigmentosa [26]. Linger et al. found that MerTK, Nyk, and Tyro12 mediated pathologic platelet aggregation, macrophage-mediated clearance of apoptotic cells, release of cytokines, and differentiation and proliferation [27]. RAB32 is an important factor in autophagy in fruit flies and also in lipid deposition [28, 29], and the mutation of specific sites was linked to susceptibility to lepriasis [30]. In addition, it also mediated protein trafficking to lysosome-like organelles. Mutation of TPRN was associated with recessive progressive autosomal deafness syndrome [31]. TMEM140 may be involved in maintaining the activity of stem cells [32].

After filtering the array results of other two groups, there were 67 DEGs in group SE and 66 DEGs in the poly(I:C) group (Tables S2-1, S2-2, S3-1, and S3-2). Except for VPREB3 (upregulated) and RSFR (downregulated), the other genes were mainly involved in the circulatory system and hemocyte development. RSFR is an antimicrobial protein that was downregulated in the SE and poly(I:C) groups, which might reflect the repression of innate immunity system. VPREB3 was the most highly differentially expressed gene in the poly(I:C) group, about 7.5 times more than the control. It was reported that this gene encoded a protein that is homologous to mouse VpreB3 (8HS20), which is specifically expressed in every stage of B lymphocyte differentiation, and correlated with inchoate μ heavy chain, which was produced in the process of the generation of the B lymphocyte receptor. Rosnet et al. found that covalent VpreB3 binding with free IgLC results in the maintenance of IgLC in the endoplasmic reticulum instead of secreting it, but this did not influence the production of IgM, thereby modulating the immune reaction [33].

3.7. Filtering and Analyzing DEGs in the Caecum

Expression profiles in the caecum of group SP were determined using the procedures described above. Fifty-two upregulated probes (34 were annotated) and 18 downregulated probes (10 were annotated) were identified (Tables 4(a) and 4(b), Tables S4-1 and S4-2). Colonization of the caecum content was observed when the challenged group was sampled, indicating that the expression profile of that tissue should reflect the immune response. Wong et al. showed that FHL2, which upregulated in the current study, could regulate the expression of IL6 via the p38 and NF-κB pathways and was involved in the protection of muscle cells and inflammatory response after damage [34]. However, other DEGs in the caecum were mainly involved in muscle development according to their annotation. For example, PDLIM3 is highly expressed in developing human skeletal muscle [35].

Table 4: (a) Upregulation of differentially expressed genes in the caecum in group SP with FC ≥ 2 and signal density of raw data ≥ 100. (b) Downregulation of differentially expressed genes in the caecum in group SP with FC ≥ 2 and signal density of raw data ≥ 100.

(a)

Probe name
value

Group SP (raw_data)
Control (raw_data)
Gene symbol

A_87_P017067
0.014833
2.241353
27158.97
10506.86
CLEC3B

A_87_P055986
0.026444
2.048135
9172.174
3898.472
PDLIM3

A_87_P110393
0.044918
2.134904
8211.222
3363.067
FHL2

A_87_P193418
0.046498
2.219647
7528.244
2944.842
FHL2

A_87_P213848
0.026094
2.937232
6073.972
1757.178
LOC423423

A_87_P265788
0.047425
2.028906
5994.927
2552.497
BAG2

A_87_P055721
0.004181
2.57088
5538.785
1861.226
HOXD11

A_87_P009294
0.004336
2.365375
3699.251
1356.083
HOXD11

A_87_P080001
0.006183
2.206574
2488.212
976.5921
CISD1

A_87_P245708
0.033453
2.915283
2122.973
633.1538
ANLN

A_87_P034042
0.040937
4.771468
2106.639
398.8331
ACTC1

A_87_P051766
0.046243
2.132916
1784.825
733.5394
LOC418424

A_87_P151068
0.008562
2.248188
1701.529
656.2959
MAMDC2

A_87_P037492
0.021
2.189082
1638.75
647.6197
PDLIM3

A_87_P009048
0.026677
2.046262
1590.921
674.5408
CENPF

A_87_P085056
0.046133
2.137321
1406.97
564.0237
SCG5

A_87_P092561
0.009984
2.567488
1324.834
446.0624
MAMDC2

A_87_P152008
0.004968
2.519559
775.9677
266.9718
KLHDC8A

A_87_P027905
0.019624
2.050455
704.9199
299.1262
COMP

A_87_P054746
0.021351
2.122426
545.0016
223.2091
CENPF

A_87_P102401
0.003087
2.933887
352.884
104.1954
ANLN

A_87_P236533
0.046516
7.077659
318.3763
37.00138
C20H20orf85

A_87_P104513
0.002662
3.046689
315.2515
89.91791
MYBL1

A_87_P179618
0.018637
3.150793
305.2546
84.68122
LOC768920

A_87_P319922
0.024433
3.271542
299.3379
78.5472
LOC768920

A_87_P022300
0.046964
2.042768
291.6057
122.5599
RIC3

A_87_P145383
0.039066
2.329995
282.5269
105.3242
SLIT1

A_87_P104908
0.009377
2.041087
208.8738
88.86916
OSR2

A_87_P021436
0.04428
2.539008
183.9284
63.00039
TUB

A_87_P037697
0.043356
3.221175
163.8235
45.27623
COL20A1

A_87_P296773
0.039883
2.340923
156.9485
58.23907
ALDH1L2

A_87_P037881
0.00596
3.139463
155.8588
43.17721
SOUL

A_87_P053201
0.026585
2.044309
141.0832
59.69648
SYNPR

A_87_P037934
0.034472
8.460807
139.0743
15.11028
GDF5

A_87_P056771
0.021345
2.403871
131.1553
47.09236
GLP1R

A_87_P106548
0.033798
2.003954
122.4247
53.13174
TRHDE

A_87_P101681
0.006572
2.674664
117.2865
37.99509
DGKB

A_87_P054406
0.029114
2.063656
110.2237
46.68424
OXTR

(b)

Probe name
value

Group SP (raw_data)
Control (raw_data)
Gene symbol

A_87_P081341
0.008753
2.100018
5147.149
9383.597
LOC423942

A_87_P124573
0.018046
2.063123
4147.323
7464.053
MOV10

A_87_P034814
0.007111
2.76483
2803.302
6681.683
IFIT5

A_87_P081336
0.008268
2.142918
2141.293
3960.148
LOC423942

A_87_P275528
0.028744
2.018683
1590.584
2771.403
PSAP

A_87_P094106
0.046616
2.009193
998.6245
1720.768
GPR112

A_87_P315787
0.01107
2.017383
404.4446
704.7305
PARP14

A_87_P129690
0.012764
2.245596
218.1902
424.9018
CATHL3

A_87_P263168
0.027271
2.157093
171.706
317.8527
PLBD1

A_87_P018024
0.028947
12.86841
147.5546
1823.653
CD72

A_87_P022808
0.042314
2.469146
122.4936
258.4686
MOG

A_87_P016668
0.039132
2.424182
118.4504
252.0765
CILP

Among the downregulated genes, IFIT5 (interferon-induced protein with tetratricopeptide repeats 5) is a protein involved in immune response. Vanderven et al. studied the immune response to duck influenza and found that IFIT5 was upregulated 1000 times as much as the control [36]. In addition, other researchers found that this gene was associated with antivirus and antibacterial activity [37, 38]. Downregulation of this gene at 7 DPI suggests that the immune process was being downregulated and innate immune activity repressed. CATHL3, an antimicrobial peptide, was also identified. CD72, which was identified as the gene downregulated the most, was downregulated 12.87-fold. Alcón et al. reported that CD72 was a repressive receptor on the surface of the NK cells, which regulates the production of cytokines [39]. Downregulation of this gene might facilitate production of more cytokines in NK cells. There is minimal information regarding the effect of the other downregulated genes on the immune system. Given the fact that the IgG level of group SP was significantly higher than the other two treatment groups, it may be inferred that the intestine was still infected, but the chief mode of immune protection had shifted from cellular immunity to adaptive immunity.

The upregulated genes of the SE and poly(I:C) groups were not enriched in immune function; however, some downregulated genes in the SE and poly(I:C) groups were linked to immune response (Tables S5-1, S5-2, S6-1, and S6-2). For instance, RSFR (fold change = −5.33) and B-G (fold change = −10.26) were identified as downregulated genes in group SE. The B-G segment was identified by Miller et al. as an important variable area of chicken MHC [40], which is involved with antigen presentation. The downregulation of this gene indicates that the antigen presentation was repressed, potentially leading to lower expression than in the control group, while tissue reconstruction activity had begun.

3.8. Common DEGs in All Three Experimental Groups

A Venn diagram illustrates the differences and similarities among the three treatment groups (Figure 2). There were four annotated genes out of 11 DEGs present in all three groups in the spleen (Table 5). These four genes were: SOX5; an enhancement element of COL2A1, which modulated the activity of the promoter of the reverse transcription transposon known as LINE; RBM16 and RBM9, which are involved in mRNA editing; and FAH, which participates in the catabolism of tyrosine. Transcription was obviously active in the spleen, inferring that these four genes might play a role in immune response and development of the spleen.

Table 5: Four annotated DEGs found in the spleen in all three treatment groups.

ID
Gene name
Gene symbol
Regulation

A_87_P014094
RNA binding motif protein 16
RBM16
Up

A_87_P022310
fumarylacetoacetate hydrolase (fumarylacetoacetase)
FAH
Up

A_87_P037218
SRY (sex determining region Y)-box 5
SOX5
Up

A_87_P150753
RNA binding protein, fox-1 homolog (C. elegans)2
RBM9
Up

(a)

(b)

Figure 2: (a) Venn diagram of DEGs in the spleen of all three treatment groups. (b) Venn diagram of DEGs in the caecum of all three treatment groups.

There were four annotated genes of the six DEGs found in the caecum of the three treatment groups (Table 6). Two of these four genes, SOUL and ANLN, were upregulated. The SOUL protein is a new member of the BH3 protein family that triggers apoptosis via initiating the mitochondrial permeability transition [41]. The two downregulated genes were FCN2 and ACSL1. FCN2 indirectly activates the innate immune system via enhancing the activity of neutrophil granulocyte phagocytosis of Salmonella Typhimurium and is also regarded as an immune opsonin [42]. The downregulation of FCN2 indicated that innate immune activity might be repressed in the caecum 7 DPI.

Table 6: Four annotated DEGs found in the caecum of all three treatment groups.

ID
Gene name
Gene symbol
Regulation

A_87_P037881
SOUL protein
SOUL
Up

A_87_P012535
ficolin (collagen/fibrinogen domain containing lectin) 2 (hucolin)
FCN2
Down

A_87_P102401
anillin, actin binding protein
ANLN
Up

A_87_P056446
Gallus gallus acyl-CoA synthetase long-chain family member 1 (ACSL1)
ACSL1
Down

Most DEGs were only found in one group, which demonstrated that, at 7 DPI, the three groups had very different expression profiles, reflecting the different physiological status of each treatment group.

3.9. GO and Pathway Enrichment Analysis of DEGs

GO and Pathway enrichment analyses of DEGs were carried out by using the online tool DAVID. DEGs in the SP group were clustered into 11 classes under medium parameters for Biology Process (BP) in GO (Table 7). The class with the highest enrichment value included immune response, development, and differentiation of lymphoid tissue and differentiation and activation of lymphoid cells, which was specific to the differentiation of T-cells and positive regulation of T-cell activity, but other classes were enriched in material and energy metabolism.

Table 7: Results of GO biology process enrichment analysis of DEGs in the spleen of group SP ( value ≤ 0.05).

Term
Count
value

Hemopoiesis
7

Hemopoietic or lymphoid organ development
7
0.001563043

Immune system development
7
0.001998123

Cell activation
6
0.003897752

Lymphocyte differentiation
4
0.015888222

Positive regulation of cell proliferation
6
0.025606698

Leukocyte differentiation
4
0.030403458

Positive regulation of mononuclear cell proliferation
3
0.037956297

Positive regulation of leukocyte proliferation
3
0.037956297

Positive regulation of lymphocyte proliferation
3
0.037956297

Lymphocyte activation
4
0.046396956

Regulation of mononuclear cell proliferation
3
0.046703876

Regulation of leukocyte proliferation
3
0.046703876

Regulation of lymphocyte proliferation
3
0.046703876

T-cell differentiation
3
0.05611028

The same analyses were carried out on the SE and poly(I:C) groups. Clustering DEGs in group SE using the GO Biology Process showed enrichment in coagulation and wound-healing processes, which might be correlated with immune response leading to injury to the spleen. DEGs in the poly(I:C) group were primarily enriched in nucleic acid metabolism processes, which may indicate that poly(I:C) promotes the activity of nucleic acid metabolism-related pathways.

Furthermore, caecal DEGs of group SP were enriched in a cluster similar to the splenic DEGs of group SP (Table 8), mainly antibacterial processes and activation of antibacterial-related innate immune pathways for GO BP, membrane system and cytoskeletal components for GO CC, nucleic acid metabolism and ion-binding proteins for GO molecular function (MF).

Table 8: Results of GO biology process enrichment analysis of DEGs in the caecum of group SP ( value ≤ 0.05).

Term
Count
%
value

Enrichment score: 2.9222

    Response to bacteria
5
5.154639

    Defense response to bacteria
4
4.123711
0.001003

    Defense response
5
5.154639
0.004978

Enrichment score: 2.1436

    Circulatory system process
4
4.123711
0.005254

    Blood circulation
4
4.123711
0.005254

    Regulation of blood pressure
3
3.092784
0.013437

Enrichment score: 1.3467

    Positive regulation of I-kappaB kinase/NF-kappaB cascade
3
3.092784
0.012329

    Regulation of I-kappaB kinase/NF-kappaB cascade
3
3.092784
0.013437

    Positive regulation of protein kinase cascade
3
3.092784
0.039886

As seen from Table 9, 11 DEGs were involved in the splenic immune response in group SP. According to the enrichment analysis of the caecum of the group SP, there were eight genes (Table 10) involved in the immune response to bacteria. However, not all of these genes had the same direction of expression. Some of them were upregulated in the spleen of group SP, whereas those DEGs in the caecum of group SP were primarily downregulated.

Table 9: DEGs involved in immune process in the spleen of group SP.

Probe IDs
Gene names
Regulation

A_87_P009188
paired box gene 6
Down

A_87_P037253
cholecystokinin B receptor
Up

A_87_P037851
interleukin 15
Down

A_87_P008861
CD28 molecule
Down

A_87_P035516
interleukin 7
Up

A_87_P009462
interferon regulatory factor 8
Down

A_87_P021863
Notch homolog 1, translocation-associated (Drosophila)
Up

A_87_P013809
general transcription factor IIH, polypeptide 4, 52 kDa
Up

A_87_P038093
mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli)
Up

A_87_P037741
similar to twisted gastrulation; twisted gastrulation homolog 1 (Drosophila)
Up

A_87_P013467
homeobox A9
Up

Table 10: DEGs involved in immune process in the caecum of group SP.

Probe IDs
Gene name
Regulation

A_87_P035105
Gal 7
Down

A_87_P024004
gallinacin 1
Down

A_87_P009700
myeloid antimicrobial peptide 27
Down

A_87_P036472
receptor-interacting serine-threonine kinase 2
Down

A_87_P038151
toll-like receptor 2
Down

A_87_P037776
5-Hydroxytryptamine (serotonin) receptor 2B
Up

A_87_P035041
adiponectin, C1Q and collagen domain containing
Up

A_87_P036472
receptor-interacting serine-threonine kinase 2
Down

3.10. Results of qRT-PCR

Five genes selected from the above results (RSFR, IL15, VPREB3, CD72, and GAL1) were used for qRT-PCR to validate the reliability of microarray data. As depicted in Table 11, the results of qRT-PCR showed the same expression trend as the microarray data. VPREB3 was expressed in the spleen, and the transcription abundance in the challenged group at 7 DPI was significantly higher than the control group. This result suggests that VPREB3 expression is correlated with B cells migrating into the spleen, experiencing clonal selection and reaching maturity.

Table 11: qRT-PCR results of five DEGs selected from microarray data. ( value ≤ 0.05).

Gene symbols
of microarray data
Expression trend of microarray data
Relative expression level of qRT-PCR
Significance ()
Expression trend of qRT-PCR

RSFR
0.27
Down
0.00
0.04
Down

IL15
0.54
Down
4.51
0.10
No difference

VPREB3
7.55
Up
535.57
0.00
Up

CD72
0.08
Down
0.00
0.02
Down

GAL1
0.02
Down
0.00
0.00
Down

4. Discussion

Salmonella were present in the content of the caecum, and, therefore, bacterial colonization still existed in the intestine in challenged groups at 7 DPI. This experiment simulated the oral infection route of Salmonella in a chicken farming operation and suggests that a persistent infection could effectively stimulate the immune response.

According to the results of the serological Ig test, S. pullorum effectively activated adaptive immunity by the 7th day after the challenge, while the innate immune system had been repressed. Furthermore, the adaptive immunity index of the poly(I:C) group was the same as the control, whereas the innate immunity index of interleukin and interferon were higher than those of control. Typically, at the 7th day after Salmonella infection chicks mortalities would reach its peak, but in the present study, its infectious ability was not severe enough to lead to high mortality but did effectively activate the immune system [43]. Because the bacterial strains we chose were not the original field wild strains but strains cultured for several generations, their virulence had been weakened. The splenic expression profile of group SE did not reflect typical bacterial infection response and did not show relatively high level of IFNγ, which is involved in a complex regulation network including the response to highly pathogenic avian influenza viruses [44].

The spleen is a peripheral immune organ that plays a major role in immune response. The number of DEGs in the poly(I:C) group was greater than that of groups SE and SP. This suggested that poly(I:C) was an efficient immune activator when it was injected into the chick’s thigh muscle, because it directly stimulated the spleen via transporting the poly(I:C) from the thigh muscle through the circulation.

It is of interest that the number of DEGs in the caecum was less than the number of DEGs found in the spleen. We inferred that, at 7 DPI, the normal function of the caecum was recovering from the active immune response. Because the poly(I:C) group did not directly stimulate the caecum, the number of DEGs was the lowest of all treatment groups. The 296 DEGs identified in the poly(I:C) group in the caecum were likely caused by differences in normal physiological status between individuals.

Both significantly upregulated genes and enriched clusters revealed the activity of the immune system in the spleen of group SP. At 7 DPI, group SP displayed effective activation of adaptive immunity given that the genes activating adaptive immunity were identified. Most upregulated genes were involved in the activation of B lymphocytes and the related adaptive immune function. The downregulated genes correlated with repression of the innate immune system and enhancement of the adaptive immune system in the SP group.

Based on the functions of those DEGs, identified in the spleen of the SP group, it may be inferred that, at 7 DPI, VAV2 was upregulated to reduce antigen uptake, and the transcription abundance of other genes involved in phagocytosis was downregulated. At early stages of response by the innate immune system, downregulation of these innate immunity-related genes suggests that they might have important functions and be highly expressed. Genes repressing innate immune activity may be crucial regulators of activating the adaptive immune system because the expression was evidently associated with both the innate and adaptive immune systems [45]. However, according to Boettcher’s study, VpreB3 (8HS20) was not expressed in the spleen, which was contradictory to our result, which was validated by qRT-PCR. We hypothesize that this inconsistency may be due to differences in the mechanism of activation of the immune system in the chicken and mouse.

In the poly(I:C) group, the result of the serological test was not consistent with the characteristics of expression profile of the spleen and caecum. We hypothesize that because poly(I:C) was injected directly into the thigh muscle, it stimulated not only immune organs but also other tissues to produce an extended immune response and release more cytokines. This was an expected result, because poly(I:C) is known as a viral analog that mainly stimulates the antiviral pathway [14]. It was a different type of antigen from the two serotypes of Salmonella, which were live bacteria that produced a complex immune response, activating the innate immune system and subsequent adaptive immunity. Furthermore, the disease symptoms of S. Enteritidis and S. pullorum are distinct from each other [1], which means that some differences exist in the immune process, which were reflected in the microarray profiles.

The IgG level of group SP was higher than the other two treatment groups. Considering the results of the enrichment analysis of the expression profiles of each group, it was deduced that group SP remained in an immune reactive state, but group SE seemed to be recovering from the S. Enteritidis infection at 7 DPI. This distinction has been reported to be correlated with the different measures of pathogenic invasion and colonization [46]. S. Enteritidis mainly colonizes in the intestine, but S. pullorum can invade organs such as the liver and the spleen. This distinction makes eliminating S. pullorum from the internal tissue environment more difficult and makes S. pullorum tend to reinfect chickens more easily. The activity of nucleic acid metabolism due to a high level of cytidylate in the poly(I:C) group was easier to be observed in the microarray profile at 7 DPI.

According to enrichment results of group SP, innate immunity was still active at 7 DPI, especially T-cell maturity and differentiation processes or pathways. The GO MF of DEGs was enriched in membrane and cytoplasmic protein. Given that most immune cells possess many membrane signal receptors or adaptors, the enrichment in membrane proteins may suggest immune cell activity, which is also supported by the result from the enrichment analysis of the GO cell component (CC), which is chiefly enriched in metallopeptidases and ion-binding proteins relevant clusters. Interestingly, the results of enrichment clustering of MF and CC of DEGs of both the SE and the poly(I:C) groups were similar to that of the SP group. However, gene expression in the caecum of the SE and poly(I:C) groups was not enriched in the immune processes in spite of the similar enrichment results for CC and MF as compared to group SP.

The infection of S. pullorum was still active in the intestine and spleen, where active immune response could be identified via the microarray expression profiles. However, the other two groups seemed to have weaker immune responses given that the DEGs of those groups were enriched in tissue function-related pathways or processes. Therefore, the genes involved in the immune response of group SP according to the enrichment analysis became the focus of the study. Among those genes, several genes were reported to participate in an immune-related process or pathway. For instance, CD28 combines with CD80 to coactivate T-cells [47]; IL15 is an activation factor for lymphocytes and T-cells, activating the cytotoxic effect of NK cells [48]. However, both CD28 and IL15 were downregulated, which indicated that the differentiation of T-cells or mature of NK cells was repressed.

In our study, the expression profile in the spleen did not reflect the activation of T-cells and NK cells at 7 DPI. However, it has been observed by other researchers that upregulated IL7 promoted the proliferation of pro-B-cells [49]. Therefore, it was inferred that the reason that there was high IgG level in group SP but a low cytokine level (Table 2) was mainly because upregulated IL7 induced the maturation and proliferation of B lymphocytes to produce antibodies to protect against S. pullorum. Although other upregulated genes also promoted cell differentiation and proliferation, downregulated IRF8, which mediates apoptosis of cancerous nonhematopoietic cells via Fas, Bax, FLIP, Jak1, and STAT1, availed the proliferation of stem cells in the spleen [50, 51]. NF-kappaB is an important transcription factor in the innate immune system; its expression level is regulated by Notch1 through an intricate regulation pathway [52], and it is regulated by several upstream regulatory factors of MyD88 and regulates the transcription abundance of many cytokines [53]. The upregulation of Notch1 in our experiment suggested that the NF-kappaB pathway might be involved in cytokine release.

We also identified several immune-related DEGs from the enrichment results of the caecum in the group SP. However, only the 5-hydroxytryptamine (serotonin) receptor 2B, a neuropeptide, and adiponectin, a metabolism-regulating hormone [54], were upregulated. The other six genes were all downregulated. This situation indicated that the innate immune system was repressed, whereas adaptive and mucosal immunities might be enhanced in a challenged caecum at 7 DPI compared to normal caecal tissue. In addition, neither the 5-hydroxytryptamine (serotonin) receptor 2B nor adiponectin has been reported to be directly associated with immune response, but they might regulate immune function by an unknown mechanism via indirect signal transduction.

Some immune-related genes that were identified lacked functional information. Because of the differences between the immune systems of poultry and mammals, immune-related genes that exist in both might not be regulated in the same way [55–57]. The microarray analysis will be helpful for further study of the immune response of poultry to specific pathogens. At the same time, the identified genes could be used as candidates for breeding disease-resistant poultry.

Finally, we found that some highly expressed genes were not annotated, but some probes were aligned with some segments of bacterial genes with E-value ≤ 0.1 via the tBLASTx program, which hinted that these probes might correspond to some unknown immune response mechanism. Whether the sequences corresponding to those probes are correlated with coevolution of the immune system and pathogens or horizontal transmission of sequence segments of pathogens requires further research.

5. Conclusion

The expression profiles were distinctly different in each challenged group, indicating different pathologic processes. The DEGs of the group infected with S. pullorum in the spleen were enriched in lymphocyte proliferation-related pathways, which suggested that T cells or B cells were activated at 7 DPI. However, the DEGs in the same group in the caecum were enriched for innate immunity or antibacterial response, indicating that inflammation of the gut was still present. Some of these enriched genes are involved in promoting the immune response. Furthermore, the DEGs found across all three groups may represent global response factors to different pathogens. All identified DEGs warrant further investigation into their roles in the immune system and may potentially be utilized as candidate genes to assist in the breeding of disease-resistant lines, thereby reducing the need for use of antibiotics or medicines.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (31301966, 31172199) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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