Temporal Transcriptome Profiling in the response to Salmonella enterica serovar Enteritidis Infection in Chicken Cecum

Abstract

Salmonella enterica serovar Enteritidis (S. Enteritidis) is a common zoonotic pathogen that not only causes gastroenteritis or death of livestock and poultry but also poses a serious threat to human health, causing severe economic losses to the poultry industry and society. Herein, RNA-sequencing (RNA-seq) was used to analyze the transcriptome variation of chicken cecum at four different time points (1, 3, 7, and 14 days) following S. Enteritidis infection. There were 529, 1477, 476, and 432 differentially expressed genes (DEGs) in the cecum at four different days post-infection (dpi), respectively. The DEGs were significantly enriched in various immune-related pathways on 3 dpi and 7 dpi, such as cytokine-cytokine-receptor interaction and Toll-like receptor signaling pathway. DEGs were significantly enriched in several metabolic pathways on 14 dpi. Gene ontology (GO) enrichment of DEGs showed that up-regulated genes were significantly enriched in immune-related terms on 3 and 7 dpi. On 14 dpi, up-regulated genes were mainly enriched in the signaling-related terms, while the down-regulated genes were primarily enriched in the metabolic-related terms. Based on weighted gene co-expression network analysis (WGCNA), the key modules related to energy, non-coding processes, immunity, and development-related functions were identified at 1, 3, 7, and 14 dpi, respectively, and 5, 8, 6, and 5 hub genes were screened out, respectively. This study demonstrated that the chicken cecal transcriptome regulation responding to S. Enteritidis infection is time-dependent. The regulation of S. Enteritidis infection in chickens is coordinated by multiple systems, mainly involving immunity, metabolism, and signal transduction. Both 3 and 7 dpi are key time points for immune response. As the infection progresses, metabolism-related pathways were increasingly identified. This change reflects the dynamic adjustment between immune response and metabolism in Jining Bairi chickens following S. Enteritidis infection. These results suggested that starting from 3 dpi, the chickens gradually transition from an immune response triggered by S. Enteritidis infection to a state where they adapt to the infection by modulating their metabolism.

Full text

Full-Length Article
Temporal transcriptome proling in the response to Salmonella enterica
serovar enteritidis infection in chicken cecum
Yanan Peng
a
, Huilong Li
a
, Jingchao Yang
c
, Xiaohua Yang
d
, Xiuxiu Miao
a
, Xinzhong Fan
a
,
Liying Liu
b
, Xianyao Li
a,*
a
Shandong Provincial Key Laboratory for Livestock Germplasm Innovation Utilization, College of Animal Science and Technology, Shandong Agricultural University,
Tai’an 271018 China
b
College of Life Sciences, Shandong Agricultural University, Tai’an 271018 China
c
Shandong Animal Husbandry General Station, Jinan 250010, China
d
Animal Husbandry and Veterinary Development Center of Zhangqiu District, Jinan 250200, China
ARTICLE INFO
Keywords:
Chicken
S. Enteritidis
RNA-seq
Cecum
Weighted gene co-expression network analysis
ABSTRACT
Salmonella enterica serovar Enteritidis (S. Enteritidis) is a common zoonotic pathogen that not only causes
gastroenteritis or death of livestock and poultry but also poses a serious threat to human health, causing severe
economic losses to the poultry industry and society. Herein, RNA-sequencing (RNA-seq) was used to analyze the
transcriptome variation of chicken cecum at four different time points (1, 3, 7, and 14 days) following S.
Enteritidis infection. There were 529, 1477, 476, and 432 differentially expressed genes (DEGs) in the cecum at
four different days post-infection (dpi), respectively. The DEGs were signicantly enriched in various immune-
related pathways on 3 dpi and 7 dpi, such as cytokine-cytokine-receptor interaction and Toll-like receptor
signaling pathway. DEGs were signicantly enriched in several metabolic pathways on 14 dpi. Gene ontology
(GO) enrichment of DEGs showed that up-regulated genes were signicantly enriched in immune-related terms
on 3 and 7 dpi. On 14 dpi, up-regulated genes were mainly enriched in the signaling-related terms, while the
down-regulated genes were primarily enriched in the metabolic-related terms. Based on weighted gene co-
expression network analysis (WGCNA), the key modules related to energy, non-coding processes, immunity,
and development-related functions were identied at 1, 3, 7, and 14 dpi, respectively, and 5, 8, 6, and 5 hub
genes were screened out, respectively. This study demonstrated that the chicken cecal transcriptome regulation
responding to S. Enteritidis infection is time-dependent. The regulation of S. Enteritidis infection in chickens is
coordinated by multiple systems, mainly involving immunity, metabolism, and signal transduction. Both 3 and 7
dpi are key time points for immune response. As the infection progresses, metabolism-related pathways were
increasingly identied. This change reects the dynamic adjustment between immune response and metabolism
in Jining Bairi chickens following S. Enteritidis infection. These results suggested that starting from 3 dpi, the
chickens gradually transition from an immune response triggered by S. Enteritidis infection to a state where they
adapt to the infection by modulating their metabolism.
Introduction
Salmonella enterica serovar Enteritidis (S. Enteritidis) is one of the
most important foodborne pathogens worldwide. It causes signicant
economic loss to the poultry industry, and it also poses a serious threat to
the public health through consumption of poultry products. Chicken
meat and eggs are most commonly contaminated with S. Enteritidis
(Gantois et al., 2009; Li et al., 2020; Te Pas et al., 2012). People are often
infected with S. Enteritidis from consuming the contaminated products
(Chea et al., 2022; Fall-Niang et al., 2019; Milkievicz et al., 2021). Most
farms face the challenge of preventing S. Enteritidis infection in
chickens, but prevention is often impossible (Fagbamila et al., 2017;
Fall-Niang et al., 2019; Vandeplas et al., 2010; Xin et al., 2021). In young
chickens it can lead to severe disease and death, whereas adult chickens
are often subclinically infected with S. Enteritidis, carrying the bacteria
in their intestines (Xin et al., 2021). Prevention of S. Enteritidis in
poultry is crucial for safeguarding human health and well-being, as well
as for minimizing productivity losses and healthcare costs. Therefore, to
* Corresponding author.
E-mail address: xyli@sdau.edu.cn (X. Li).
Contents lists available at ScienceDirect
Poultry Science
journal homepage: www.elsevier.com/locate/psj
https://doi.org/10.1016/j.psj.2025.104773
Received 23 September 2024; Accepted 3 January 2025
104 (2025) 104773
Available online 4 January 2025
0032-5791/© 2025 The Authors. Published by Elsevier Inc. on behalf of Poultry Science Association Inc. This is an open access article under the CC BY-NC-ND
license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

effectively mitigate these risks and losses, it is essential to conduct
comprehensive research into the infection mechanisms of Salmonella,
with particular focus on its interactions with the host immune system.
Such research will provide a solid theoretical basis for the development
of more effective prevention and control strategies.
The early response of the innate immune system in chickens within 1
week post-S. Enteritidis infection has been characterized by the upre-
gulation of genes associated with “defense/pathogen response”
(Schokker et al., 2011), inammation (Matulova et al., 2013; Swaggerty
et al., 2004), NK cell-mediated cytotoxicity (Luan et al., 2012) and
production and secretion of the cytokine IFN-γ (Abasht et al., 2008). It is
widely believed that the intestinal immune system of newly hatched
chicks is not fully mature and is highly sensitive to Salmonella infection
(Beal et al., 2004; Ijaz et al., 2021; Juricova et al., 2013). The cecum is
main reservoir of S. Enteritidis. Host immune defense mechanisms are
activated after S. Enteritidis invasion of the intestinal mucosa. This local
immune response is important to remove S. Enteritidis
(Berthelot-H´
erault et al., 2003; Desmidt et al., 1998). This local immune
response of the host is always accompanied by signicant changes in
gene expression. (Hern´
andez-Ramírez et al., 2020; Khilji et al., 2018).
Proinammatory cytokines such as IL-6, IL-1β, IL-22, and IL-17 are
increased in the cecum following Salmonella infection (Crhanova et al.,
2011; Luan et al., 2012; Packialakshmi et al., 2016). TLR1A, TLR2 and
TLR4 were signicantly up-regulated following S. enteritidis infection,
and TLR5 was down-regulated which is benecial to protect host cells
from overstimulation by bacterial agellin in the cecum (Abasht et al.,
2008; Ijaz et al., 2021).The up-regulated mRNAs were mainly enriched
in KEGG pathways of immune-related processes such as immune
response and Toll-like receptor signaling pathway following S. Enter-
itidis infection (Miao et al., 2022).
Immune response and time of infection are highly correlated with the
interaction of Salmonella and avian tissues. Proinammatory cytokines
such as IL-1 and IL-6 are characterized by increased levels in the early
period of infection, accompanied by up-regulation of IFN-γ-encoding
mRNAs (Packialakshmi et al., 2016). Approximately 2 weeks after
infection with S. Enteritidis, the expression of IgY and IgA in the cecum
increases to relieve the effects of Salmonella infection (Rychlik et al.,
2014). Multiple host immune and metabolic pathways such as T cell
receptor signaling pathway, NOD-like receptor signaling pathways, the
mTOR and AMPK metabolic signaling pathways are activated following
Salmonella infection (Kogut et al., 2016c). Those studies have shown that
the host response to Salmonella infection in chickens at different stages is
variable and complex. This implies a need to understand the temporal
changes following Salmonella infection.
In this study, transcriptome sequencing was used to reveal the tem-
poral transcriptome prole in the cecum of Jining Bairi chickens at 1, 3,
7, and 14 days post S. Enteritidis infection. Genes and signaling path-
ways related to S. Enteritidis infection in chickens were identied. Our
results will offer novel evidence for resistance to S. enteritidis infection
in Jining Bairi chicken.
Materials and methods
The current study was carried out in compliance with the ARRIVE
guidelines
All animal procedures were approved by the Shandong Agricultural
University Animal Care and Use Committee (Permit Number: SDAUA-
2017-041) and performed in accordance with China animal welfare
laws.
Experimental design and sample collection
Jining Bairi chicken is one of the local chicken breeds in China,
known for its strong disease resistance. The one-day-old Jining Bairi
chicken used in this study was provided by Shandong Bairi Chicken
Breeding Co., Ltd. (Shandong, China). The S. enteritidis strain
(CVCC3377) was purchased from the China Veterinary Microbial Cul-
ture Collection Center (Beijing, China).
The animal inoculation was described in detail previously (Liu et al.,
2018). During the experiment, 68 Jining Bairi chickens were randomly
divided into the treat group (n =40) and the control group (n =28) and
raised in two separate isolators, respectively, with free access to feed and
water (Fig. S1). Each chicken in the treat group was orally inoculated
with 0.3 ml 10
9
colony-forming units (cfu)/mL S. Enteritidis inoculant
and each chicken in the control group was mock inoculated with 0.3 ml
sterile phosphate buffer saline. Ten chickens from the treat group and
seven chickens from the control group were euthanized by cervical
dislocation for the cecum sample collection at 1, 3, 7, and 14 dpi,
respectively. The cecum samples were snap frozen in liquid nitrogen and
stored at −80 ◦C for further RNA isolation.
RNA extraction, library preparation, and sequencing
Three individual cecum samples from each of the treat and control
groups at 1, 3, 7, and 14 dpi were randomly selected for RNA extraction
based on our previous study (Wang et al., 2020b). The cecum not used
for sequencing was used for other studies in our laboratory. The total
RNA of the cecum was extracted using TRIzol (Thermo Fisher Scientic,
US) following the manufacturer’s instructions. In total, 24 RNA samples
were used in the current study. Total RNA was further puried using
RNA Clean Kit (Tiangen, Beijing, China). RNA purity and concentration
were measured with Nanodrop instruments (Thermo Scientic, US).
RNA integrity was assessed using 1 % agarose gel electrophoresis. A
cDNA library was constructed according to the specications of the
TruSeq RNA Sample Prep Kit (Illumina, US), and 2 ×100 bp paired-end
sequencing was performed using the Illumina HiSeq 2500 platform
(Illumina, US) at Biomarker Technology Co., Ltd. (Beijing, China).
Quality control of sequencing data
The raw reads were checked using FastQC (v0.11.8) and ltered
using Trimmomatic (v0.36) to remove adapter sequences, ploy-N, and
low-quality reads (more than 50 % of base quality scores ≤10) and
obtain the clean reads. Q20, Q30, and GC content and sequence dupli-
cation level of the clean data were calculated. All the downstream an-
alyses were based on clean data.
Mapping reads to the reference genome and screening of differentially
expressed genes (DEGs)
Clean reads were aligned to the chicken reference genome of Galgal5
(ftp://ftp.ensembl.org/pub/release-93/fasta/gallus_gallus/dna/). The
Spliced Transcripts Alignment to a Reference (STAR) aligner (v2.5.3a)
(Dobin et al., 2013) was used to map reads to the genomic sequences.
The counts of read mapping to each known gene were summarised at the
gene level using the featureCounts function of the Subread package
(v2.0.0) (Liao et al., 2014). The Principal Component Analysis (PCA) of
the samples was performed using the prcomp function in R software, and
the PCA plots were drawn using the ggplot2. Differential expression
analysis was performed using the DESeq2 (v1.38.2) and genes were
considered differentially expressed when P-value <0.05 and |log2-
FoldChange| >1.
Functional enrichment of DEGs
The Gene Ontology (GO) enrichment analysis of DEGs was imple-
mented using g: Proler (https://biit.cs.ut.ee/gproler/) (Raudvere
et al., 2019). The Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathways (Kanehisa et al., 2017) were carried out using OmicShare tools
(https://www.omicshare.com/tools), in which the DEGs were compared
with the KEGG database (http://www.genome.jp/kegg/). P <0.05 was
Y. Peng et al.
Poultry Science 104 (2025) 104773
2

considered signicant.
WGCNA analysis and time series analysis
Weighted gene co-expression network analysis (WGCNA)
(Langfelder and Horvath, 2008) was performed to determine gene
co-expression in highly correlated modules. The WGCNA package was
applied to construct the weight co-expression network based on the
normalized count matrix from DEseq2. When the soft threshold β =8
and the correlation coefcient was 0.81, all transcripts and their
normalized values were clustered into modules based on similarities in
gene expression. Modules were merged and assigned with different
colors. Cytoscape (Shannon et al., 2003) was used to visualize the
Protein-Protein interaction (PPI) network of the key DEGs, and its
plug-in cytoHubba (Chin et al., 2014) was used to extract the hub genes
of high-relevance modules. Time series analysis of all genes was per-
formed using MaSigPro (Conesa et al., 2006) to distinguish genes with
different expression trends.
Quantitative real time PCR (qRT-PCR) validation
The 24 RNA samples used for RNA-seq were used for validation. 1
μ
g
total RNA was reverse-transcribed using the Primer Script
TM
RT Reagent
Kit (Perfect Real Time, TaKaRa) with a 20
μ
l reaction system. The spe-
cic primers were designed using the PRIMER3-BLAST program
(http://www.ncbi.nlm.nih.gov/tools/primer-blast/) and listed in
Table S8. The SYBR Green Master Mix and an ABI Prism 7500 system
(Applied Biosystems) were used for amplication reactions. Beta-actin
(β-actin) gene was used as the internal control to correct the input of
cDNA. The thermal cycling conditions were as follows: 90 ◦C for 30 s, 40
cycles at 95 ◦C for 5 s, 60 ◦C for 30 s, and melting curve at 95 ◦C for 1
min, 62 ◦C for 30 s and 95 ◦C for 30 s. The qRT-PCR was performed in
triplicate for each cDNA sample. The relative expression levels were
estimated using the 2
−ΔΔCt
method.
Results
Sequencing data quality assessment
By quality evaluation of the raw data, the reads were characterized
by more than 85 % of Q30 with a GC content of approximately 50 % ~
55 % (Table S1). The quality of the raw data was satisfactory. Raw data
was ltered to remove adapters, low-quality, and N-containing reads. A
total of 189.7GB of the clean data were obtained from 24 sequenced
samples, with an average of 7.9GB of the clean data per sample. Over 82
% of the total sequencing reads were mapped to the chicken reference
genome of Galgal5 (ftp://ftp.ensembl.org/pub/release93/fasta/ga
llus_gallus/dna/) for all samples (Table S2).
Identication of differentially expressed genes
The results of PCA showed that chicken cecum samples from
different time points could be differentiated between the treat and
control groups based on PC 1 and PC 2 (Fig. S2). DEGs between control
and treat groups were identied at 1 day post-infection (dpi), 3 dpi, 7
dpi, and 14 dpi, respectively. At 1 dpi, 529 genes were differentially
expressed (P <0.05 and |log2FoldChange| >1), including 258 up-
regulated genes and 271 down-regulated genes. At 3 dpi, 1,477 DEGs
(P <0.05 and |log2FoldChange| >1) were identied, including 782 up-
regulated genes and 695 down-regulated genes. At 7 dpi, 476 DEGs were
identied (P <0.05 and |log2FoldChange| >1), including 264 up-
regulated genes and 212 down-regulated genes. At 14 dpi, 432 DEGs
were identied (P <0.05 and |log2FoldChange| >1), including 283 up-
regulated genes and 149 down-regulated genes (Fig. 1A). The number of
overlapped DEGs across different time points were listed in Fig. 1B.
There were 113 overlapped DEGs found between 3 and 7 dpi, which was
the most in the comparison between any other two time points. In
contrast, only 25 DEGs were found between 1 and 14 dpi. The number of
specic DEGs varied across different time points. The highest and lowest
number of uniquely expressed genes were at 3 dpi and 14 dpi which
were 1,102 DEGs and 246 DEGs, respectively. Table S3 and Table S4
provided the read count per gene and the results of the analysis of dif-
ferential expression, respectively.
KEGG enrichment analysis of DEGs
The number of KEGG pathways enriched for DEGs were 6, 21, 13,
and 10 at 1, 3,7, and 14 dpi, respectively (P <0.05) (Fig. 2). Enriched
pathways at 3 and 7 dpi were mainly divided into immune and meta-
bolic groups that include toll-like receptor signaling pathway, cytokine-
cytokine receptor signaling pathway, cell adhesion molecules (CAMs),
MAPK signaling pathway and cellular hormone metabolic process. The
phagosomes were signicantly enriched at 1, 3, and 7 dpi. There were
three KEGG pathways signicantly enriched at 3, 7, and 14 dpi: retinol
metabolism, linoleic acid metabolism, and arachidonic acid metabolism.
Furthermore, MAPK signaling pathway, histidine metabolism, trypto-
phan metabolism, and gluconeogenesis were signicantly enriched at 14
dpi. The immune-related pathways, including NOD-like receptor
Fig. 1. Number and relationship of differentially expressed genes
(A) The number of differentially expressed genes between the control and treat chickens at each time point. Red plots represented signicant up-regulated genes and
blue plots represented signicant down-regulated genes (|log2-fold change| >1, P <0.05). (B) Venn diagram showed the number of differentially expression genes
at different time points after S. Enteritidis infection.
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Poultry Science 104 (2025) 104773
3

signaling pathway, salmonella infection, and ECM-receptor interaction,
were enriched at 3 dpi.
GO enrichment analysis of DEGs
GO enrichment analysis was used to explore the functions of the
DEGs at different time points. The DEGs at 1 dpi were enriched in the
cellular component terms including plasma membrane part, cell pe-
riphery, plasma membrane, and in the biological process ion transport,
ion transmembrane transport and transmembrane transport (Fig. 3,
Table S5A). As shown in Fig. S3, biological processes associated with the
DEGs at 3 dpi were immune response, immune system process, positive
regulation of immune system process, and in the cellular component
extracellular region, extracellular space and intrinsic component of
Fig. 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs at four time points
The x-axis was –log10 (P value), and the y-axis was the KEGG pathway.
Fig. 3. GO enrichment of the DEGs at 1(A), 3(B), 7(C), and 14(D) dpi
Twenty-ve signicantly enriched GO terms were shown. From outer to inner, the outermost circle represented the IDs of enriched GO terms. The names of GO ID in
orange, blue and green represented biological process, molecular function and cell composition respectively. The second circle indicated shared genes enriched in GO
terms. In the third circle, the piece in dark purple and light purple represented up- and down-regulated genes, respectively. In the innermost circle, each bar rep-
resented one GO term, and the size represents the rich factor.
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Poultry Science 104 (2025) 104773
4

membrane (Table S5B). Enriched GO of DEGs at 3 dpi and 7dpi was
similar in biological process and cellular component (Fig. S4,
Table S5C). Meanwhile, the enriched molecular function associated with
DEGs at 7dpi were chemokine activity, and chemokine receptor binding.
The enriched biological processes associated with DEGs at 14 dpi were
regulation of signaling receptor activity, regulation of hormone levels,
ion transport. The term enriched in cellular component was the same as
1dpi (Fig. S5, Table S5D). The categories of immunity, metabolism, and
transport were increased. It was also found that there was a difference in
the number of up- and down-regulated genes in some of the GO terms.
To further investigate whether the up- and down-regulated genes are
enriched in different GO terms, GO enrichment analysis was performed
for the up- and down-regulated genes at each time point separately. The
up-regulated genes at 1 dpi were signicantly enriched in transport,
transporter activity, and cellular anatomical entity (Fig. S6A, Table S6).
The down-regulated genes at 1 dpi were signicantly enriched in the
cellular anatomical entity, cell junction, and cell projection. There were
also 7 terms related to channel activities, such as ion channel activity,
cation channel activity, and gate channel activity (Fig. S6B, Table S6).
The up-regulated genes at 3 dpi, in terms of biological process, were
signicantly enriched in many immune-related terms, such as immune
system process and response to stimulus. In terms of cellular composi-
tion, they were signicantly enriched in 9 terms, such as extracellular
region, extracellular space, and cell projection. In terms of molecular
function, they were signicantly enriched in cytokine activity, binding,
and molecular transducer activity (Fig. S7A, Table S6). The down-
regulated genes at 3 dpi, in terms of biological process, were signi-
cantly enriched in transport, signaling receptor activity, and cellular
anatomical entity. In terms of molecular function, the signicantly
enriched terms were related to transporter activity, and extracellular
region (Fig. S7B, Table S6). The up-regulated genes at 7 dpi were
signicantly enriched in immune response, immune system process,
response to stimulus, and cellular process. In terms of cellular compo-
sition, they were signicantly enriched regarding the cell membrane,
cell periphery, and extracellular region. In terms of molecular function,
they were mainly related to the oxygenation processes such as heme
binding, oxygen binding, and iron ion binding (Fig. S8A, Table S6). The
down-regulated genes at 7 dpi were signicantly enriched in cell
membrane, cell periphery, and extracellular region related terms
(Fig. S8B, Table S6). The up-regulated genes at 14 dpi, in terms of bio-
logical processes, were signicantly enriched in cellular process, bio-
logical regulation, and response to stimulus (Fig. S9A, Table S6). The
Fig. 4. The enriched biological process terms of up-regulate genes (A) and down-regulate genes (B)
Each row represented the biological process. The left side of each row was the -log10 (P-value), and the shapes on the right side of each row represented the genes
involved in that biological process. The different time point was represented by 4 different colors and the genes was represented by different shapes.
Y. Peng et al.
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down-regulated genes at 14 dpi were signicantly enriched in metabolic
process, multicellular organismal process, and catalytic activity
(Fig. S9B, Table S6).
In terms of biological process, the up-regulated or down-regulated
genes were signicantly enriched were summarized (Fig. 4). The
terms associated with the down-regulated genes varied greatly. These
genes were signicantly enriched in some terms related to ion transport
at both 1 and 3 dpi (Fig. 4B). There seems to be a subtle relationship in
terms of up-regulated genes enrichment (Fig. 4A). The terms of up-
regulated genes enrichment in 1 dpi were different from other time
points. Compared with that between the other time points, the associ-
ation between 1 dpi and the other time points was weaker. Primarily,
OVAL was found in 2 terms: ion transport at 1 dpi and response to the
stimulus at 7 dpi; ACE was enriched in terms related to ion transport and
acid transport at 1 dpi and repeatedly found in the system process and
cellular developmental process at 14 dpi. Some immune-related terms,
including immune system process, immune response, regulation of im-
mune system process, regulation of immune response, leukocyte acti-
vation, and T cell activation, were signicantly enriched at 3 and 7 dpi.
TLR2A and CD28 were involved in most of the enriched immune-related
terms. Regulation of response to the stimulus was the only terms shared
between 3 and 14 dpi. NOS2 and CSF3 were involved in some terms at 3
and 14 dpi. They were mainly involved in immune-related terms at 3 dpi
but related to the regulation of signaling at 14 dpi.
Weighted gene co-expression network analysis
WGCNA was used to identify core modules and hub genes related at
different time points after S. Enteritidis infection in chickens. The most
signicant expression changes (the top 25 % of rank genes with the
largest variance) were selected for further analysis. A total of 27 co-
expression modules were identied. The genes with similar gene
expression proles were clustered (Fig. 5A). Bright yellow (r =0.57, P =
0.004), pink (r =0.82, P =0.000001), green (r =0.5, P =0.01), and
midnight blue (r =0.59, P =0.002) had the highest positive correlations
with the days post-infection feature among all modules (Fig. 5B).
GO enrichment analysis was performed for the genes in the four
modules with the highest positive correlations with the days post-
infection (Fig. 5C). Genes in the ME bright yellow module were signif-
icantly enriched in the energy-related terms, and few immune-related
terms. Genes in the ME pink module were signicantly enriched in
terms related to noncoding RNAs, and ncRNA processing. In the ME
green module, the genes were signicantly enriched in immune-related
terms, such as immune system process, regulation of response to stim-
ulus, and defense response. In the ME midnight blue module, the genes
were signicantly enriched in development-related, and signaling-
Fig. 5. Cluster dendrogram and module-feature relationships from WGCNA
(A) A correlation cluster analysis showed the genes and their corresponding module. Each module was marked with 1 color, and “ME-grey” modules were not co-
expressed. The rows and columns in the image corresponded to specic genes. (B) Each module (y-axis) was correlated to each day (x-axis) the correlation and P-
value were reported for each comparison. Strong positive correlations were colored in red, and strong negative correlations in blue. (C) Terms of biological processes
in which genes were signicantly enriched in four modules. GO-term of BP enrichment analysis for the genes in four modules.
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related.
Five, 6, 8, and 5 hub genes were found in the ME light yellow, ME
pink, ME green, and ME midnight blue modules (Table 1, Fig. S10-S13).
CHEK1 involved in cell cycle processes was signicantly differentially
expressed at 3 dpi. TLR7 was a vital gene against Salmonella infection,
which was signicantly differentially expressed at 7 dpi.
Temporal expression analysis
To capture variations in the transcriptional dynamics between con-
trol group and treat group, the maSigPro R package was used to divide
genes with different expression trends changes over time.
The genes were grouped into ve clusters (Fig. 6A). There was 674,
99, 90, 209, and 217 genes in Clusters 1-5, respectively (Table S7). In
Cluster 1, the expression of genes increased from 1 to 14 days in the
control group, and down-regulated in the treat group. In Cluster 2, the
expression of genes decreased from 7 to 14 days in the control group and
down-regulated in the treat group. In Cluster 3, the expression of genes
decreased from 3 to 7 days and up-regulated in the treat group. In
Cluster 4, the expression of genes decreased among 1, 3, 7 days and
slightly increased from 7 to 14 days after infection. In Cluster 5, the
expression of genes decreased from 1 to 7 days in the control group and
up-regulated in the treat group, while the expression of genes increased
from 7 to 14 days in the control group and down-regulated in the treat
group. As shown in Fig. 6B, Clusters 1 and 2 were most signicantly
enriched in immune-related terms, such as immune system process and
response to cytokine. This showed that the immune system of chickens
was enhanced over time whether they were infected or not, and the
immune response of the treat group was more pronounced. The genes in
Cluster 3 were enriched in 4 terms: oligopeptide transport, oligopeptide
transmembrane transport, negative regulation of bile acid biosynthetic
process, and negative regulation of bile acid metabolic process. The
genes in Clusters 4 and 5 were signicantly enriched only in 1 term:
synaptic signaling and negative regulation of sodium ion trans-
membrane transporter activity, respectively.
Validation of RNA-Seq results by qRT-PCR
To verify the expression levels of DEGs at different time points, the
expression levels of six DEGs (NCF1C, SOUL, TLR7, FKBP5, CCL1, and
CXCL13) were quantied using qRT-PCR (Fig. 7). Except for CCL1, the
results of qRT-PCR and RNA-Seq were consistent. Overall, 5/6 (83.3 %)
were compatible concerning expression between the transcriptomic and
qRT-PCR data.
Discussion
The transcriptome prole in the cecum at different time points
following S. Enteritidis infection in Jining Bairi chickens were analyzed
in the current study, which can help understand the temporal regulation
of chicks in response to S. Enteritidis infection at transcriptome level.
The cecum of chickens is a major site of colonization by S. Enteritidis
(Sadler et al., 1969).
The number of DEGs changed with time following S. Enteritidis
infection. At 3 dpi, the number of DEGs was higher than that in all other
time points. Previous studies have revealed that the content of S.
Enteritidis in the cecum of chicks infected with S. Enteritidis changed
with time (Pal et al., 2021; Wang et al., 2020b). The population of S.
Enteritidis in the cecal contents peaks at 3 dpi and then decreases pro-
gressively at 7, 14, 21, 28, and 35 dpi (Liu et al., 2018). The trend of S.
Enteritidis number in cecal contents was consistent with that of DEGs in
the current results. These results indicated that 3 dpi is the key time
point in the response to S. Enteritidis infection in Jining Bairi chicken.
Baseline ion transport was increased at 1 h following Salmonella
infection (Bertelsen et al., 2003). Salmonella Typhimurium infection
rapidly increases both basal and Ca
2+
- and cAMP-stimulated ion trans-
port (Bertelsen et al., 2003; Vajanaphanich et al., 1995). Enhanced ion
transport activity may indicate intestinal barrier damage, accompanied
by an inammatory response (Marchelletta et al., 2013; Quach et al.,
2022). In the current study, the DEGs at 1 dpi were mainly associated
with ion transport-related terms. Ion transport is essential for phag-
osome maturation during phagocytosis (Schappe et al., 2022; Wu et al.,
2023). Ca
2+
signals play a major role in the nely controlled
Table 1
Hub genes in 4 selected modules.
Module Ensembl gene ID Hub genes Description GO_term
ME light yellow ENSGALG00000000685 MAP3K14 mitogen-activated protein kinase kinase kinase 14 cellular response to mechanical stimulus
ENSGALG00000014121 COX5A cytochrome c oxidase subunit 5A mitochondrial respiratory chain complex IV
ENSGALG00000028302 UQCRQ ubiquinol-cytochrome c reductase complex III subunit
VII
mitochondrial respiratory chain complex III
ENSGALG00000033513 ELL elongation factor for RNA polymerase II Enables phosphatase binding activity
ENSGALG00000038608 HSPB11 heat shock protein family B (small) member 11 skeletal system development
ME pink ENSGALG00000000518 KLHL12 kelch like family member 12 Golgi membrane
ENSGALG00000000650 NIP7 NIP7, nucleolar pre-rRNA processing protein RNA binding|nucleolus
ENSGALG00000000693 NOB1 NIN1/PSMD8 binding protein 1 homolog cleavage involved in rRNA processing
ENSGALG00000003398 ADAM9 ADAM metallopeptidase domain 9 SH3 domain binding and integrin binding
ENSGALG00000032843 AAAS aladin WD repeat nucleoporin mRNA transport|nucleocytoplasmic transport
ENSGALG00000011499 SCARB2 scavenger receptor class B member 2 action potential|receptor activity
ENSGALG00000026809 SARS seryl-tRNA synthetase serine-tRNA ligase activity
ENSGALG00000042418 CHEK1 checkpoint kinase 1 DNA damage checkpoint|G2/M transition of mitotic cell
cycle
ME green ENSGALG00000012208 GMFB glia maturation factor beta signal transducer activity|Arp2/3 complex binding
ENSGALG00000014324 SLC2A14 solute carrier family 2 member 14 glucose transmembrane transporter activity|glucose
binding
ENSGALG00000016590 TLR7 toll like receptor 7 defense response to virus|cellular response to mechanical
stimulus
ENSGALG00000016986 LCP1 lymphocyte cytosolic protein 1 calcium ion binding and actin binding
ENSGALG00000029244 SMAP2 small ArfGAP2 GTPase activator activity|metal ion binding
ENSGALG00000042838 TRAF2 TNF receptor associated factor 2 identical protein binding|signaling receptor binding
ME midnight
blue
ENSGALG00000001561 MXRA8 matrix remodeling associated 8 cell surface|integral component of membrane
ENSGALG00000006346 CXCL14 C-X-C motif chemokine ligand 14 cell-cell signaling|chemokine activity
ENSGALG00010022353 SLC39A13 solute carrier family 39 member 13 Golgi membrane|cellular zinc ion homeostasis
ENSGALG00000021869 PODN podocan negative regulation of JAK-STAT cascade
ENSGALG00000033157 ADAMTS10 ADAM metallopeptidase with thrombospondin Type 1
motif 10
peptidase activity|metalloendopeptidase activity
Y. Peng et al.
Poultry Science 104 (2025) 104773
7

rearrangement of the actin cytoskeleton which is the key to all phago-
cytic processes (Melendez and Tay, 2008; Schappe et al., 2022). The
Calcium signaling pathway was signicantly increased at 1 dpi in the
current study. Phagocytosis is an important process for the organism to
eliminate microbial pathogens and is essential in immune response (Lee
et al., 2020). Phagocytes recognize foreign pathogens and internalize
them into phagosomes through phagocytosis (Roberts et al., 2006).
Phagosomes was signicantly increased at 1, 3, and 7 dpi. These results
indicated the initial immune response to S. Enteritidis infection in Jining
Bairi chickens may be induced through ion transport activated phago-
cytosis at 1 dpi.
TLRs recognize lipopolysaccharide (LPS), a key component of Sal-
monella, thereby triggering the host immune response (Higuchi et al.,
2008; Keestra et al., 2013; Nawab et al., 2019). Inhibition of TLR4,
TLR2, and TLR21 expressions in chicken leukocytes reduce the resis-
tance ability to S. Enteritidis infection in young chickens (Huang et al.,
2017). After infected with Newcastle disease viruses (NDV), the
expression of the chicken TLR7 gene was increased in different tissues
(Yan et al., 2017). The expression of TLR7 was signicantly up-regulated
following avian reoviruses (ARV) infection (Wang et al., 2021). TLR1A,
TLR2B, TLR4, TLR5, TLR7, TLR15, and TLR2 were induced in the
chicken cecum at day 7 post S. Enteritidis infection (Jiang et al., 2023).
In this study, the TLRs signaling pathway was signicantly increased at 3
and 7 dpi, and the genes TLR7, TLR1A, TLR1B and TLR2B involved in
this pathway was up-regulated. TLR7 was a hub gene of the
immune-related ME green module. Our ndings conrmed that S.
Enteritidis infection activated TLRs-mediated immune response in
chicks. The ndings indicated S. Enteritidis infection induced
Fig. 6. Gene expression trend patterns and GO enrichment analysis of time series
(A) Genes grouped into ve clusters showed distinct expression proles during the time of the experiment. For each plot, the expression values of the clustered genes
were represented in either the control group (green) or the treat group (red), respectively. (B) GO enrichment analysis was performed on genes within each of the ve
clusters independently.
Y. Peng et al.
Poultry Science 104 (2025) 104773
8

TLRs-mediated immune response in Jining Bairi chickens at 3 and 7 dpi.
Activation of the TLRs signaling pathway stimulates the expression
of cytokines and chemokines (Dar et al., 2022; Khan and Chousalkar,
2020). Studies have shown that day-old chicks infected with S. Enter-
itidis present a peak of intestinal inammation from 2 to 4 d after
inoculation. Previous studies showed that cytokine-cytokine receptor
interactions were increased in spleen and cecum following S. Enteritidis
infection (Li et al., 2018; Wang et al., 2020a). Salmonella infection can
result in the production of a large number of pro-inammatory cyto-
kines, or a “cytokine storm,” leading to endotoxin shock or sepsis-related
deaths (Clark and Vissel, 2017; Netea et al., 2017). S. Enteritidis infec-
tion caused upregulation of IL-1β, IL-6, IL-18, TNF-
α
, IL-2, TGF-β, and
IL12 in chicken (Berndt et al., 2007; Guan et al., 2024; Shanmuga-
sundaram et al., 2021). IL-1β, TNF-
α
, IFN-γ, IL-12, and IL-2 play pro-
tective roles in the host’s defense against Salmonella infection
(Mastroeni et al., 1999; Rosenberger et al., 2000). In this study,
cytokine-cytokine receptor interactions were also increased at 3 and 7
dpi. The expression levels of IL-18, IL-1β, IL-6, and TGF-β1 were
up-regulated at 3dpi. This suggests that they may be actively working to
prevent S. Enteritidis colonization. Meanwhile, our study further
conrmed that 3 dpi was an important time point for interaction be-
tween the Jining Bairi chickens and S. Enteritidis.
S. Enteritidis infection inuences both lipid metabolism and amino
acid metabolism (Lu et al., 2023; Wang et al., 2023). Both linoleic acid
and arachidonic acid are long-chain unsaturated fatty acid (Blasbalg
et al., 2011; Carabajal et al., 2020). Long-chain unsaturated fatty acid
are powerful inhibitors of Salmonella invasion (Chowdhury et al., 2021;
Golubeva et al., 2016) and display anti-inammatory actions (Yan et al.,
2023). Amino acid metabolism-related pathways like histidine and
beta-alanine metabolism were increased at 14 dpi. Amino acids play a
crucial role in the intricate process of bacterial infection, involving
immune regulation, metabolic balance, and other biological functions
(Zhang et al., 2023). Injection of L-histidine prior to administration of S.
Typhimurium inhibited the inammation (Choudhary et al., 2005).
His27 is an essential component of HD6, an
α
-defensin, and its substi-
tution impairs HD6
′
s ability to inhibit the invasion of S. Typhimurium
into intestinal epithelial cells (Chu et al., 2012). Supplementation of
histidine for 12 weeks signicantly decreased the expression of TNF-
α
and IL-6 mRNA in human (Du et al., 2017; Watanabe et al., 2008). In this
study, lipid metabolism-related pathways, including linoleic acid and
arachidonic acid metabolism, were increased at 3, 7, and 14 dpi, with
amino acid metabolism-related pathways increased at 14 dpi during S.
Enteritidis infection in Jining Bairi chickens. Moreover, as the infection
progressed, more metabolism-related pathways were signicantly
enriched. These results suggest that starting from 3 dpi, the chickens
gradually transition from an immune response triggered by S. Enteritidis
infection to a state where they adapt to the infection by modulating their
metabolism. We propose that this transition can be characterized as an
immunometabolic reprogramming. This immunometabolic reprogram-
ming enables chickens to exhibit disease tolerance, meaning that the
chickens allowed the bacteria to establish a long-term, persistent
infection in the cecum (Kogut and Arsenault, 2017a; Kogut et al.,
2022b). Our results provide new insights for the relationship between
immune response and metabolism in Jining Bairi chickens following S.
Enteritidis infection.
Criteria is important for transcriptome study to identify DEGs. Both P
Fig. 7. Validation of DGEs by qRT-PCR
* P ≤0.05, ** P ≤0.01
Y. Peng et al.
Poultry Science 104 (2025) 104773
9

value and adjusted P value were commonly used (Contriciani et al.,
2024; Duan et al., 2023; Mantilla Valdivieso et al., 2024). There is no
golden standard for cutoff selection to identify DEGs. Both statistical and
biological meaning should be considered. P <0.05 and |log2Fold-
Change| >1 was used in several previous studies (Bonet-Rossinyol et al.,
2023; Duan et al., 2023; Turdo et al., 2023; Yadav et al., 2024). In this
study, DEGs responding to S. Enteritidis at four different time points
were identied using a cutoff value of P <0.05 and |log2FoldChange| >
1. Due to the lack of application of adjusted P - values, the current results
may include false positive genes, even though they still hold biological
signicance. Further studies, such as gene functional analysis, will be
required to conrm the transcriptome ndings.
Conclusions
This study used the Chinese local chicken breed, Jining Bairi chicken,
to explore the temporal transcriptome regulation following S. Enteritidis
infection in the chicken cecum. The regulation of S. Enteritidis infection
in chickens is coordinated by multiple systems, mainly involving im-
munity, metabolism, and ion transport. Both 3 and 7 dpi are key time
points for immune response. The dynamic adjustment between immune
response and metabolism plays important role in the response to S.
Enteritidis infection in Jining Bairi chickens. Cytokine-cytokine receptor
interaction, and TLRs signaling pathway play vital roles in S. Enteritidis
infection. TLR2A, TLR7, IL-18, IL-1β and IL-6 can be used as molecular
markers for selection of S. Enteritidis infection resistant chickens. The
results will provide a theoretical basis for the future breeding of poultry
resistant to S. Enteritidis infection.
Availability of data and materials
The raw sequence data of RNA-seq have been deposited in National
Genomics Data Center, accession number CRA011068, publicly acces-
sible at https://ngdc.cncb.ac.cn/gsa.
Declaration of interests
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgements
This study was supported by the National Key Research and Devel-
opment Program of China (2022YFD1300102, 2021YFD1300102),
Shandong Provincial Poultry Industry & Technology System (SDAIT-11-
02), and Key R&D Program of Shandong Province, China
(2022LZGC013-04, 2023LZGC018).
Disclosures
The authors declare no conicts of interest.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.psj.2025.104773.
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