Skip to main content
NCBI home page
Poultry Science logoLink to Poultry Science
. 2025 Jan 4;104(2):104773. doi: 10.1016/j.psj.2025.104773

Temporal transcriptome profiling 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,⁎
PMCID: PMC11782854  PMID: 39813862

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.

Keywords: Chicken, S. Enteritidis, RNA-seq, Cecum, Weighted gene co-expression network analysis

Introduction

Salmonella enterica serovar Enteritidis (S. Enteritidis) is one of the most important foodborne pathogens worldwide. It causes significant 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 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 upregulation of genes associated with “defense/pathogen response” (Schokker et al., 2011), inflammation (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-Hérault et al., 2003; Desmidt et al., 1998). This local immune response of the host is always accompanied by significant changes in gene expression. (Hernández-Ramírez et al., 2020; Khilji et al., 2018). Proinflammatory 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 significantly up-regulated following S. enteritidis infection, and TLR5 was down-regulated which is beneficial to protect host cells from overstimulation by bacterial flagellin 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. Enteritidis infection (Miao et al., 2022).

Immune response and time of infection are highly correlated with the interaction of Salmonella and avian tissues. Proinflammatory 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 temporal transcriptome profile in the cecum of Jining Bairi chickens at 1, 3, 7, and 14 days post S. Enteritidis infection. Genes and signaling pathways related to S. Enteritidis infection in chickens were identified. 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 Culture 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 109 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 Scientific, US) following the manufacturer's instructions. In total, 24 RNA samples were used in the current study. Total RNA was further purified using RNA Clean Kit (Tiangen, Beijing, China). RNA purity and concentration were measured with Nanodrop instruments (Thermo Scientific, US). RNA integrity was assessed using 1 % agarose gel electrophoresis. A cDNA library was constructed according to the specifications 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 filtered 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 duplication level of the clean data were calculated. All the downstream analyses 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 |log2FoldChange| > 1.

Functional enrichment of DEGs

The Gene Ontology (GO) enrichment analysis of DEGs was implemented using g: Profiler (https://biit.cs.ut.ee/gprofiler/) (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 considered significant.

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 coefficient 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 performed 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 μg total RNA was reverse-transcribed using the Primer ScriptTM RT Reagent Kit (Perfect Real Time, TaKaRa) with a 20 μl reaction system. The specific 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 amplification 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 filtered 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/gallus_gallus/dna/) for all samples (Table S2).

Identification 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 identified 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 identified, including 782 up-regulated genes and 695 down-regulated genes. At 7 dpi, 476 DEGs were identified (P < 0.05 and |log2FoldChange| > 1), including 264 up-regulated genes and 212 down-regulated genes. At 14 dpi, 432 DEGs were identified (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 specific 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 differential expression, respectively.

Fig. 1.

Fig 1

Open in a new tab

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 significant up-regulated genes and blue plots represented significant 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.

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 metabolic 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 significantly enriched at 1, 3, and 7 dpi. There were three KEGG pathways significantly enriched at 3, 7, and 14 dpi: retinol metabolism, linoleic acid metabolism, and arachidonic acid metabolism. Furthermore, MAPK signaling pathway, histidine metabolism, tryptophan metabolism, and gluconeogenesis were significantly enriched at 14 dpi. The immune-related pathways, including NOD-like receptor signaling pathway, salmonella infection, and ECM-receptor interaction, were enriched at 3 dpi.

Fig. 2.

Fig 2

Open in a new tab

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.

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 periphery, 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 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.

Fig. 3.

Fig 3

Open in a new tab

GO enrichment of the DEGs at 1(A), 3(B), 7(C), and 14(D) dpi

Twenty-five significantly 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 represented one GO term, and the size represents the rich factor.

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 significantly enriched in transport, transporter activity, and cellular anatomical entity (Fig. S6A, Table S6). The down-regulated genes at 1 dpi were significantly 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 significantly enriched in many immune-related terms, such as immune system process and response to stimulus. In terms of cellular composition, they were significantly enriched in 9 terms, such as extracellular region, extracellular space, and cell projection. In terms of molecular function, they were significantly 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 significantly enriched in transport, signaling receptor activity, and cellular anatomical entity. In terms of molecular function, the significantly enriched terms were related to transporter activity, and extracellular region (Fig. S7B, Table S6). The up-regulated genes at 7 dpi were significantly enriched in immune response, immune system process, response to stimulus, and cellular process. In terms of cellular composition, they were significantly 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 significantly 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 biological processes, were significantly enriched in cellular process, biological regulation, and response to stimulus (Fig. S9A, Table S6). The down-regulated genes at 14 dpi were significantly 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 significantly enriched were summarized (Fig. 4). The terms associated with the down-regulated genes varied greatly. These genes were significantly 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 association 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 immune system process, regulation of immune response, leukocyte activation, and T cell activation, were significantly 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.

Fig. 4.

Fig 4

Open in a new tab

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.

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 significant 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 identified. The genes with similar gene expression profiles 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).

Fig. 5.

Fig 5

Open in a new tab

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 specific 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 significantly enriched in four modules. GO-term of BP enrichment analysis for the genes in four modules.

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 significantly enriched in the energy-related terms, and few immune-related terms. Genes in the ME pink module were significantly enriched in terms related to noncoding RNAs, and ncRNA processing. In the ME green module, the genes were significantly enriched in immune-related terms, such as immune system process, regulation of response to stimulus, and defense response. In the ME midnight blue module, the genes were significantly enriched in development-related, and signaling-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 significantly differentially expressed at 3 dpi. TLR7 was a vital gene against Salmonella infection, which was significantly differentially expressed at 7 dpi.

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
Open in a new tab

Temporal expression analysis

To capture variations in the transcriptional dynamics between control 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 five 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 significantly 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 significantly enriched only in 1 term: synaptic signaling and negative regulation of sodium ion transmembrane transporter activity, respectively.

Fig. 6.

Fig 6

Open in a new tab

Gene expression trend patterns and GO enrichment analysis of time series

(A) Genes grouped into five clusters showed distinct expression profiles 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 five clusters independently.

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 quantified 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.

Fig. 7.

Fig 7

Open in a new tab

Validation of DGEs by qRT-PCR

* P ≤ 0.05, ** P ≤ 0.01

Discussion

The transcriptome profile 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 progressively 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 Ca2+- and cAMP-stimulated ion transport (Bertelsen et al., 2003; Vajanaphanich et al., 1995). Enhanced ion transport activity may indicate intestinal barrier damage, accompanied by an inflammatory 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 phagosome maturation during phagocytosis (Schappe et al., 2022; Wu et al., 2023). Ca2+ signals play a major role in the finely controlled rearrangement of the actin cytoskeleton which is the key to all phagocytic processes (Melendez and Tay, 2008; Schappe et al., 2022). The Calcium signaling pathway was significantly 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 significantly 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 phagocytosis at 1 dpi.

TLRs recognize lipopolysaccharide (LPS), a key component of Salmonella, 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 resistance 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 significantly 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 significantly 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 findings confirmed that S. Enteritidis infection activated TLRs-mediated immune response in chicks. The findings indicated S. Enteritidis infection induced 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. Enteritidis present a peak of intestinal inflammation 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-inflammatory cytokines, or a “cytokine storm,” leading to endotoxin shock or sepsis-related deaths (Clark and Vissel, 2017; Netea et al., 2017). S. Enteritidis infection 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; Shanmugasundaram et al., 2021). IL-1β, TNF-α, IFN-γ, IL-12, and IL-2 play protective 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 confirmed that 3 dpi was an important time point for interaction between the Jining Bairi chickens and S. Enteritidis.

S. Enteritidis infection influences 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-inflammatory 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 inflammation (Choudhary et al., 2005). His27 is an essential component of HD6, an α-defensin, and its substitution 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 significantly 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 significantly 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 reprogramming 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 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 |log2FoldChange| > 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 identified 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 significance. Further studies, such as gene functional analysis, will be required to confirm the transcriptome findings.

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 immunity, 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 accessible at https://ngdc.cncb.ac.cn/gsa.

Declaration of interests

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

Acknowledgments

Acknowledgements

This study was supported by the National Key Research and Development 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 conflicts of interest.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2025.104773.

Appendix. Supplementary materials

mmc1.pdf (122.4KB, pdf)
mmc2.docx (21.2MB, docx)
mmc3.docx (14.8KB, docx)
mmc4.xlsx (11.2KB, xlsx)
mmc5.xlsx (12.2KB, xlsx)
mmc6.xlsx (354KB, xlsx)
mmc7.xlsx (3.2MB, xlsx)
mmc8.xlsx (20.6KB, xlsx)
mmc9.xlsx (20.5KB, xlsx)
mmc10.xlsx (25.6KB, xlsx)
mmc11.xlsx (10.7KB, xlsx)

References

  1. Abasht B., Kaiser M.G., Lamont S.J. Toll-like receptor gene expression in cecum and spleen of advanced intercross line chicks infected with Salmonella enterica serovar Enteritidis. Vet. Immunol. Immunop. 2008;123:314–323. doi: 10.1016/j.vetimm.2008.02.010. [DOI] [PubMed] [Google Scholar]
  2. Beal R.K., Wigley P., Powers C., Hulme S.D., Barrow P.A., Smith A.L. Age at primary infection with Salmonella enterica serovar Typhimurium in the chicken influences persistence of infection and subsequent immunity to re-challenge. Vet. Immunol. Immunop. 2004;100:151–164. doi: 10.1016/j.vetimm.2004.04.005. [DOI] [PubMed] [Google Scholar]
  3. Berndt A., Wilhelm A., Jugert C., Pieper J., Sachse K., Methner U. Chicken cecum immune response to Salmonella enterica serovars of different levels of invasiveness. Infect. Immun. 2007;75:5993–6007. doi: 10.1128/IAI.00695-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bertelsen L.S., Paesold G., Eckmann L., Barrett K.E. Salmonella infection induces a hypersecretory phenotype in human intestinal xenografts by inducing cyclooxygenase 2. Infect. Immun. 2003;71:2102–2109. doi: 10.1128/IAI.71.4.2102-2109.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Berthelot-Hérault F., Mompart F., Zygmunt M.S., Dubray G., Duchet-Suchaux M. Antibody responses in the serum and gut of chicken lines differing in cecal carriage of Salmonella enteritidis. Vet. Immunol. Immunop. 2003;96:43–52. doi: 10.1016/s0165-2427(03)00155-7. [DOI] [PubMed] [Google Scholar]
  6. Blasbalg T.L., Hibbeln J.R., Ramsden C.E., Majchrzak S.F., Rawlings R.R. Changes in consumption of omega-3 and omega-6 fatty acids in the United States during the 20th century. Am. J. Clin. Nutr. 2011;93:950–962. doi: 10.3945/ajcn.110.006643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bonet-Rossinyol Q., Camprubí-Font C., López-Siles M., Martinez-Medina M. Identification of differences in gene expression implicated in the adherent-invasive Escherichia coli phenotype during in vitro infection of intestinal epithelial cells. Front. Cell. Infect. Microbiol. 2023;13 doi: 10.3389/fcimb.2023.1228159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Carabajal M.A., Viarengo G., Yim L., Martínez-Sanguiné A., Mariscotti J.F., Chabalgoity J.A., Rasia R.M., Véscovi E.G. PhoQ is an unsaturated fatty acid receptor that fine-tunes Salmonella pathogenic traits. Sci. Signal. 2020;13(628):eaaz3334. doi: 10.1126/scisignal.aaz3334. [DOI] [PubMed] [Google Scholar]
  9. Chea R., Nguyen-Viet H., Tum S., Unger F., Lindahl J., Grace D., Ty C., Koam S., Sina V., Sokchea H., Pov S., Heng T., Phirum O., Dang-Xuan S. Experimental cross-contamination of chicken salad with Salmonella enterica serovars Typhimurium and London during food preparation in Cambodian households. PLoS One. 2022;17 doi: 10.1371/journal.pone.0270425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chin C.H., Chen S.H., Wu H.H., Ho C.W., Ko M.T., Lin C.Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014;8(Suppl 4):S11. doi: 10.1186/1752-0509-8-S4-S11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Choudhary A., Tiwari R.P., Koul A., Chanana V., Gupta S., Rishi P. Role of Salmonella surface components in immunomodulation of inflammatory mediators. Mol. Cell. Biochem. 2005;270:167–175. doi: 10.1007/s11010-005-4506-x. [DOI] [PubMed] [Google Scholar]
  12. Chowdhury R., Pavinski Bitar P.D., Keresztes I., Condo A.M., Jr., Altier C. A diffusible signal factor of the intestine dictates Salmonella invasion through its direct control of the virulence activator HilD. PLoS Pathog. 2021;17 doi: 10.1371/journal.ppat.1009357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chu H., Pazgier M., Jung G., Nuccio S.P., Castillo P.A., de Jong M.F., Winter M.G., Winter S.E., Wehkamp J., Shen B., Salzman N.H., Underwood M.A., Tsolis R.M., Young G.M., Lu W., Lehrer R.I., Bäumler A.J., Bevins C.L. Human α-defensin 6 promotes mucosal innate immunity through self-assembled peptide nanonets. Science. 2012;337:477–481. doi: 10.1126/science.1218831. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Clark I.A., Vissel B. The meteorology of cytokine storms, and the clinical usefulness of this knowledge. Semin. Immunopathol. 2017;39:505–516. doi: 10.1007/s00281-017-0628-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Conesa A., Nueda M.J., Ferrer A., Talón M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics. 2006;22:1096–1102. doi: 10.1093/bioinformatics/btl056. [DOI] [PubMed] [Google Scholar]
  16. Contriciani R.E., Grade C.V.C., Buzzatto-Leite I., da Veiga F.C., Ledur M.C., Reverter A., Alexandre P.A., Cesar A.S.M., Coutinho L.L., Alvares L.E. Phenotypic divergence between broiler and layer chicken lines is regulated at the molecular level during development. BMC Genom. 2024;25:168. doi: 10.1186/s12864-024-10083-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Crhanova M., Hradecka H., Faldynova M., Matulova M., Havlickova H., Sisak F., Rychlik I. Immune response of chicken gut to natural colonization by gut microflora and to Salmonella enterica serovar enteritidis infection. Infect. Immun. 2011;79:2755–2763. doi: 10.1128/IAI.01375-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dar M.A., Ahmad S.M., Bhat B.A., Dar T.A., Haq Z.U., Wani B.A., Shabir N., Kashoo Z.A., Shah R.A., Ganai N.A., Heidari M. Comparative RNA-Seq analysis reveals insights in Salmonella disease resistance of chicken; and database development as resource for gene expression in poultry. Genomics. 2022;114 doi: 10.1016/j.ygeno.2022.110475. [DOI] [PubMed] [Google Scholar]
  19. Desmidt M., Ducatelle R., Mast J., Goddeeris B.M., Kaspers B., Haesebrouck F. Role of the humoral immune system in Salmonella enteritidis phage type four infection in chickens. Vet. Immunol. Immunop. 1998;63:355–367. doi: 10.1016/s0165-2427(98)00112-3. [DOI] [PubMed] [Google Scholar]
  20. Dobin A., Davis C.A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., Gingeras T.R. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. doi: 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Du S., Sun S., Liu L., Zhang Q., Guo F., Li C., Feng R., Sun C. Effects of histidine supplementation on global serum and urine (1)H NMR-based metabolomics and serum amino acid profiles in obese women from a randomized controlled study. J. Proteome Res. 2017;16:2221–2230. doi: 10.1021/acs.jproteome.7b00030. [DOI] [PubMed] [Google Scholar]
  22. Duan G., Liu W., Han H., Li D., Lei Q., Zhou Y., Liu J., Wang J., Du Y., Cao D., Chen F., Li F. Transcriptome and histological analyses on the uterus of freckle egg laying hens. BMC Genom. 2023;24:738. doi: 10.1186/s12864-023-09828-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fagbamila I.O., Barco L., Mancin M., Kwaga J., Ngulukun S.S., Zavagnin P., Lettini A.A., Lorenzetto M., Abdu P.A., Kabir J., Umoh J., Ricci A., Muhammad M. Salmonella serovars and their distribution in Nigerian commercial chicken layer farms. PLoS One. 2017;12 doi: 10.1371/journal.pone.0173097. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Fall-Niang N.K., Sambe-Ba B., Seck A., Deme S.N., Wane A.A., Bercion R., Alambedji-Bada R., Gassama-Sow A. Antimicrobial Resistance Profile of Salmonella Isolates in Chicken Carcasses in Dakar. Senegal. Foodborne Pathog. Dis. 2019;16:130–136. doi: 10.1089/fpd.2018.2459. [DOI] [PubMed] [Google Scholar]
  25. Gantois I., Ducatelle R., Pasmans F., Haesebrouck F., Gast R., Humphrey T.J., Van Immerseel F. Mechanisms of egg contamination by Salmonella Enteritidis. FEMS Microbiol. Rev. 2009;33:718–738. doi: 10.1111/j.1574-6976.2008.00161.x. [DOI] [PubMed] [Google Scholar]
  26. Golubeva Y.A., Ellermeier J.R., Cott Chubiz J.E., Slauch J.M. Intestinal long-chain fatty acids act as a direct signal to modulate expression of the salmonella pathogenicity island 1 Type III secretion system. mBio. 2016;7 doi: 10.1128/mBio.02170-15. e02170-02115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Guan L., Hu A., Ma S., Liu J., Yao X., Ye T., Han M., Yang C., Zhang R., Xiao X., Wu Y. Lactiplantibacillus plantarum postbiotic protects against Salmonella infection in broilers via modulating NLRP3 inflammasome and gut microbiota. Poult. Sci. 2024;103 doi: 10.1016/j.psj.2024.103483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hernández-Ramírez J.O., Nava-Ramírez M.J., Merino-Guzmán R., Téllez-Isaías G., Vázquez-Durán A., Méndez-Albores A. The effect of moderate-dose aflatoxin B(1) and Salmonella Enteritidis infection on intestinal permeability in broiler chickens. Mycotoxin Res. 2020;36:31–39. doi: 10.1007/s12550-019-00367-7. [DOI] [PubMed] [Google Scholar]
  29. Higuchi M., Matsuo A., Shingai M., Shida K., Ishii A., Funami K., Suzuki Y., Oshiumi H., Matsumoto M., Seya T. Combinational recognition of bacterial lipoproteins and peptidoglycan by chicken Toll-like receptor 2 subfamily. Dev. Comp. Immunol. 2008;32:147–155. doi: 10.1016/j.dci.2007.05.003. [DOI] [PubMed] [Google Scholar]
  30. Huang X.Y., Ansari A.R., Huang H.B., Zhao X., Li N.Y., Sun Z.J., Peng K.M., Zhong J., Liu H.Z. Lipopolysaccharide mediates immuno-pathological alterations in young chicken liver through TLR4 signaling. BMC Immunol. 2017;18:12. doi: 10.1186/s12865-017-0199-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Ijaz A., Veldhuizen E.J.A., Broere F., Rutten V., Jansen C.A. The interplay between salmonella and intestinal innate immune cells in chickens. Pathogens. 2021;10:1512. doi: 10.3390/pathogens10111512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jiang X., Zhang X., Sun Y., Sun Z., Li X., Liu L. Effects of Salmonella Enteritidis infection on TLRs gene expression and microbial diversity in cecum of laying hens. Heliyon. 2023;9:e16414. doi: 10.1016/j.heliyon.2023.e16414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Juricova H., Videnska P., Lukac M., Faldynova M., Babak V., Havlickova H., Sisak F., Rychlik I. Influence of Salmonella enterica serovar enteritidis infection on the development of the cecum microbiota in newly hatched chicks. Appl. Environ. Microbiol. 2013;79:745–747. doi: 10.1128/AEM.02628-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kanehisa M., Furumichi M., Tanabe M., Sato Y., Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucl. Acids Res. 2017;45:D353–d361. doi: 10.1093/nar/gkw1092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Keestra A.M., de Zoete M.R., Bouwman L.I., Vaezirad M.M., van Putten J.P. Unique features of chicken Toll-like receptors. Dev. Comp. Immunol. 2013;41:316–323. doi: 10.1016/j.dci.2013.04.009. [DOI] [PubMed] [Google Scholar]
  36. Khan S., Chousalkar K.K. Transcriptome profiling analysis of caeca in chicks challenged with Salmonella Typhimurium reveals differential expression of genes involved in host mucosal immune response. Appl. Microbiol. Biotechnol. 2020;104:9327–9342. doi: 10.1007/s00253-020-10887-3. [DOI] [PubMed] [Google Scholar]
  37. Khilji M.S., Sandhu M.A., Yousaf M.S., Saeed A.A., Rehman H.U., Zaneb H., Rashid M.A. Differential effects of experimental hyperthyroidism on declined immunity of broiler chicken. J. Anim. Physiol. Anim. Nutr. 2018;102:e948–e956. doi: 10.1111/jpn.12861. [DOI] [PubMed] [Google Scholar]
  38. Kogut M.H., Arsenault R.J. Immunometabolic phenotype alterations associated with the induction of disease tolerance and persistent asymptomatic infection of salmonella in the chicken intestine. Front. Immunol. 2017;8:372. doi: 10.3389/fimmu.2017.00372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kogut M.H., Genovese K.J., Byrd J.A., Swaggerty C.L., He H., Farnell Y., Arsenault R.J. Chicken-specific kinome analysis of early host immune signaling pathways in the cecum of newly hatched chickens infected with salmonella enterica serovar enteritidis. Front. Cell. Infect. Microbiol. 2022;12 doi: 10.3389/fcimb.2022.899395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kogut M.H., Genovese K.J., He H., Arsenault R.J. AMPK and mTOR: sensors and regulators of immunometabolic changes during Salmonella infection in the chicken. Poult. Sci. 2016;95:345–353. doi: 10.3382/ps/pev349. [DOI] [PubMed] [Google Scholar]
  41. Langfelder P., Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559. doi: 10.1186/1471-2105-9-559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lee H.J., Woo Y., Hahn T.W., Jung Y.M., Jung Y.J. Formation and maturation of the phagosome: a key mechanism in innate immunity against intracellular bacterial infection. Microorganisms. 2020;8:1298. doi: 10.3390/microorganisms8091298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Li P., Fan W., Everaert N., Liu R., Li Q., Zheng M., Cui H., Zhao G., Wen J. Messenger RNA sequencing and pathway analysis provide novel insights into the susceptibility to salmonella enteritidis infection in chickens. Front. Genet. 2018;9:256. doi: 10.3389/fgene.2018.00256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Li Y., Yang X., Zhang H., Jia H., Liu X., Yu B., Zeng Y., Zhang Y., Pei X., Yang D. Prevalence and antimicrobial susceptibility of Salmonella in the commercial eggs in China. Int. J. Food Microbiol. 2020;325 doi: 10.1016/j.ijfoodmicro.2020.108623. [DOI] [PubMed] [Google Scholar]
  45. Liao Y., Smyth G.K., Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–930. doi: 10.1093/bioinformatics/btt656. [DOI] [PubMed] [Google Scholar]
  46. Liu L., Lin L., Zheng L., Tang H., Fan X., Xue N., Li M., Liu M., Li X. Cecal microbiome profile altered by Salmonella enterica, serovar Enteritidis inoculation in chicken. Gut Pathog. 2018;10:34. doi: 10.1186/s13099-018-0261-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Lu Y., Ge S., Zhang H., Lu W., Bao X., Pan S., Pang Q. Metabolomic and microbiome analysis of the protective effects of Puerarin against Salmonella Enteritidis Infection in chicks. BMC Vet. Res. 2023;19:242. doi: 10.1186/s12917-023-03806-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Luan D.Q., Chang G.B., Sheng Z.W., Zhang Y., Zhou W., Li Z.Z., Liu Y., Chen G.H. Analysis of gene expression responses to a salmonella infection in rugao chicken intestine using GeneChips. Asian-Austr. J. Anim. Sci. 2012;25:278–285. doi: 10.5713/ajas.2011.11174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Mantilla Valdivieso E.F., Ross E.M., Raza A., Nguyen L., Hayes B.J., Jonsson N.N., James P., Tabor A.E. Expression network analysis of bovine skin infested with Rhipicephalus australis identifies pro-inflammatory genes contributing to tick susceptibility. Sci. Rep. 2024;14:4419. doi: 10.1038/s41598-024-54577-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Marchelletta R.R., Gareau M.G., McCole D.F., Okamoto S., Roel E., Klinkenberg R., Guiney D.G., Fierer J., Barrett K.E. Altered expression and localization of ion transporters contribute to diarrhea in mice with Salmonella-induced enteritis. Gastroenterology. 2013;145:1358–1368. doi: 10.1053/j.gastro.2013.08.054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Mastroeni P., Clare S., Khan S., Harrison J.A., Hormaeche C.E., Okamura H., Kurimoto M., Dougan G. Interleukin 18 contributes to host resistance and gamma interferon production in mice infected with virulent Salmonella typhimurium. Infect. Immun. 1999;67:478–483. doi: 10.1128/iai.67.2.478-483.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Matulova M., Varmuzova K., Sisak F., Havlickova H., Babak V., Stejskal K., Zdrahal Z., Rychlik I. Chicken innate immune response to oral infection with Salmonella enterica serovar Enteritidis. Vet. Res. 2013;44:37. doi: 10.1186/1297-9716-44-37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Melendez A.J., Tay H.K. Phagocytosis: a repertoire of receptors and Ca(2+) as a key second messenger. Biosci. Rep. 2008;28:287–298. doi: 10.1042/BSR20080082. [DOI] [PubMed] [Google Scholar]
  54. Miao X., Liu L., Liu L., Hu G., Wu G., Wang Y., Zhao Y., Yang J., Li X. Regulation of mRNA and miRNA in the response to Salmonella enterica serovar Enteritidis infection in chicken cecum. BMC Vet. Res. 2022;18:437. doi: 10.1186/s12917-022-03522-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Milkievicz T., Badia V., Souza V.B., Longhi D.A., Galvão A.C., Robazza W.D.S. Modeling Salmonella spp. inactivation in chicken meat subjected to isothermal and non-isothermal temperature profiles. Int. J. Food Microbiol. 2021;344 doi: 10.1016/j.ijfoodmicro.2021.109110. [DOI] [PubMed] [Google Scholar]
  56. Nawab A., An L., Wu J., Li G., Liu W., Zhao Y., Wu Q., Xiao M. Chicken toll-like receptors and their significance in immune response and disease resistance. Int. Rev. Immunol. 2019;38:284–306. doi: 10.1080/08830185.2019.1659258. [DOI] [PubMed] [Google Scholar]
  57. Netea M.G., Balkwill F., Chonchol M., Cominelli F., Donath M.Y., Giamarellos-Bourboulis E.J., Golenbock D., Gresnigt M.S., Heneka M.T., Hoffman H.M., Hotchkiss R., Joosten L.A.B., Kastner D.L., Korte M., Latz E., Libby P., Mandrup-Poulsen T., Mantovani A., Mills K.H.G., Nowak K.L., O'Neill L.A., Pickkers P., van der Poll T., Ridker P.M., Schalkwijk J., Schwartz D.A., Siegmund B., Steer C.J., Tilg H., van der Meer J.W.M., van de Veerdonk F.L., Dinarello C.A. A guiding map for inflammation. Nat. Immunol. 2017;18:826–831. doi: 10.1038/ni.3790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Packialakshmi B., Liyanage R., Lay J.O., Jr., Makkar S.K., Rath N.C. Proteomic changes in chicken plasma induced by salmonella typhimurium lipopolysaccharides. Proteomics. 2016;7:1–9. doi: 10.4137/PRI.S31609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Pal A., Riggs M.R., Urrutia A., Osborne R.C., Jackson A.P., Bailey M.A., Macklin K.S., Price S.B., Buhr R.J., Bourassa D.V. Investigation of the potential of aerosolized salmonella enteritidis on colonization and persistence in broilers from day 3 to 21. Poult. Sci. 2021;100 doi: 10.1016/j.psj.2021.101504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Quach A., Jayaratne R.R., Lee B.J., Ibeawuchi S.R., Lim E., Das S., Barrett K.E. Diarrhoeal pathogenesis in Salmonella infection may result from an imbalance in intestinal epithelial differentiation through reduced Notch signalling. J. Physiol. 2022;600:1851–1865. doi: 10.1113/JP282585. [DOI] [PubMed] [Google Scholar]
  61. Raudvere U., Kolberg L., Kuzmin I., Arak T., Adler P., Peterson H., Vilo J. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) Nucl. Acids Res. 2019;47:W191–w198. doi: 10.1093/nar/gkz369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Roberts E.A., Chua J., Kyei G.B., Deretic V. Higher order Rab programming in phagolysosome biogenesis. J. Cell Biol. 2006;174:923–929. doi: 10.1083/jcb.200603026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Rosenberger C.M., Scott M.G., Gold M.R., Hancock R.E., Finlay B.B. Salmonella typhimurium infection and lipopolysaccharide stimulation induce similar changes in macrophage gene expression. J. Immunol. 2000;164:5894–5904. doi: 10.4049/jimmunol.164.11.5894. [DOI] [PubMed] [Google Scholar]
  64. Rychlik I., Elsheimer-Matulova M., Kyrova K. Gene expression in the chicken caecum in response to infections with non-typhoid Salmonella. Vet. Res. 2014;45:119. doi: 10.1186/s13567-014-0119-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sadler W.W., Brownell J.R., Fanelli M.J. Influence of age and inoculum level on shed pattern of Salmonella typhimurium in chickens. Avian Dis. 1969;13:793–803. [PubMed] [Google Scholar]
  66. Schappe M.S., Stremska M.E., Busey G.W., Downs T.K., Seegren P.V., Mendu S.K., Flegal Z., Doyle C.A., Stipes E.J., Desai B.N. Efferocytosis requires periphagosomal Ca(2+)-signaling and TRPM7-mediated electrical activity. Nat. Commun. 2022;13:3230. doi: 10.1038/s41467-022-30959-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Schokker D., de Koning D.J., Rebel J.M., Smits M.A. Shift in chicken intestinal gene association networks after infection with Salmonella. comparative biochemistry and physiology. Comp. Biochem. Physiol. Part D Genom. Proteom. 2011;6:339–347. doi: 10.1016/j.cbd.2011.07.004. [DOI] [PubMed] [Google Scholar]
  68. Shanmugasundaram R., Acevedo K., Mortada M., Akerele G., Applegate T.J., Kogut M.H., Selvaraj R.K. Effects of Salmonella enterica ser. Enteritidis and Heidelberg on host CD4+CD25+ regulatory T cell suppressive immune responses in chickens. PLoS One. 2021;16 doi: 10.1371/journal.pone.0260280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–2504. doi: 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Swaggerty C.L., Kogut M.H., Ferro P.J., Rothwell L., Pevzner I.Y., Kaiser P. Differential cytokine mRNA expression in heterophils isolated from Salmonella-resistant and -susceptible chickens. Immunology. 2004;113:139–148. doi: 10.1111/j.1365-2567.2004.01939.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Te Pas M.F., Hulsegge I., Schokker D., Smits M.A., Fife M., Zoorob R., Endale M.L., Rebel J.M. Meta-analysis of chicken–salmonella infection experiments. BMC Genom. 2012;13:146. doi: 10.1186/1471-2164-13-146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Turdo A., Gaggianesi M., D'Accardo C., Porcelli G., Bella S.D., Cricchio D., Pillitteri I., Porcasi R., Lo Iacono M., Verona F., Modica C., Roozafzay N., Florena A.M., Stassi G., Mancuso S., Todaro M. EBF1, MYO6 and CALR expression levels predict therapeutic response in diffuse large B-cell lymphomas. Front. Immunol. 2023;14 doi: 10.3389/fimmu.2023.1266265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Vajanaphanich M., Schultz C., Tsien R.Y., Traynor-Kaplan A.E., Pandol S.J., Barrett K.E. Cross-talk between calcium and cAMP-dependent intracellular signaling pathways. Implications for synergistic secretion in T84 colonic epithelial cells and rat pancreatic acinar cells. J. Clin. Investig. 1995;96:386–393. doi: 10.1172/JCI118046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Vandeplas S., Dubois Dauphin R., Beckers Y., Thonart P., Théwis A. Salmonella in chicken: current and developing strategies to reduce contamination at farm level. J. Food Prot. 2010;73:774–785. doi: 10.4315/0362-028x-73.4.774. [DOI] [PubMed] [Google Scholar]
  75. Wang D., Ma B., Liao Z., Li W., Zhang T., Lei C., Wang H. Flaxseed supplementation in chicken feed accelerates salmonella enterica subsp. enterica serovar enteritidis clearance, modulates cecum microbiota, and influences ovarian gene expression in laying hens. Biomolecules. 2023;13:1353. doi: 10.3390/biom13091353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Wang S., Xie L., Xie Z., Wan L., Huang J., Deng X., Xie Z.Q., Luo S., Zeng T., Zhang Y., Zhang M., Zhou L. Dynamic changes in the expression of interferon-stimulated genes in joints of SPF chickens infected with avian reovirus. Front. Vet. Sci. 2021;8 doi: 10.3389/fvets.2021.618124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Wang Y., Liu L., Li M., Lin L., Su P., Tang H., Fan X., Li X. Chicken cecal DNA methylome alteration in the response to Salmonella enterica serovar Enteritidis inoculation. BMC Genom. 2020;21:814. doi: 10.1186/s12864-020-07174-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Wang Y., Miao X., Li H., Su P., Lin L., Liu L., Li X. The correlated expression of immune and energy metabolism related genes in the response to Salmonella enterica serovar Enteritidis inoculation in chicken. BMC Vet. Res. 2020;16:257. doi: 10.1186/s12917-020-02474-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Watanabe M., Suliman M.E., Qureshi A.R., Garcia-Lopez E., Bárány P., Heimbürger O., Stenvinkel P., Lindholm B. Consequences of low plasma histidine in chronic kidney disease patients: associations with inflammation, oxidative stress, and mortality. Am. J. Clin. Nutr. 2008;87:1860–1866. doi: 10.1093/ajcn/87.6.1860. [DOI] [PubMed] [Google Scholar]
  80. Wu J.Z., Zeziulia M., Kwon W., Jentsch T.J., Grinstein S., Freeman S.A. ClC-7 drives intraphagosomal chloride accumulation to support hydrolase activity and phagosome resolution. J. Cell Biol. 2023;222 doi: 10.1083/jcb.202208155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Xin S., Zhu H., Tao C., Zhang B., Yao L., Zhang Y., Afayibo D.J.A., Li T., Tian M., Qi J., Ding C., Yu S., Wang S. Rapid detection and differentiating of the predominant salmonella serovars in chicken farm by TaqMan multiplex real-time PCR assay. Front. Cell. Infect. Microbiol. 2021;11 doi: 10.3389/fcimb.2021.759965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Yadav R., Das S.K., Ali M., Shetake N.G., Pandey B.N., Kumar A. Mechanistic insights into Thorium-232 induced liver carcinogenesis: the driving role of Wnt/β-catenin signaling pathway. Sci. Total Environ. 2024;907 doi: 10.1016/j.scitotenv.2023.168065. [DOI] [PubMed] [Google Scholar]
  83. Yan B., Zhang J., Zhang W., Wang M., Jia R., Zhu D., Liu M., Yang Q., Wu Y., Sun K., Chen X., Cheng A., Chen S. GoTLR7 but not GoTLR21 mediated antiviral immune responses against low pathogenic H9N2 AIV and newcastle disease virus infection. Immunol. Lett. 2017;181:6–15. doi: 10.1016/j.imlet.2016.11.001. [DOI] [PubMed] [Google Scholar]
  84. Yan D., Ye S., He Y., Wang S., Xiao Y., Xiang X., Deng M., Luo W., Chen X., Wang X. Fatty acids and lipid mediators in inflammatory bowel disease: from mechanism to treatment. Front. Immunol. 2023;14 doi: 10.3389/fimmu.2023.1286667. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Zhang X., Xie T., Li X., Feng M., Mo G., Zhang Q., Zhang X. Transcriptome sequencing reveals that intact expression of the chicken endogenous retrovirus chERV3 in vitro can possibly block the key innate immune pathway. Animals (Basel) 2023;13:2720. doi: 10.3390/ani13172720. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.pdf (122.4KB, pdf)
mmc2.docx (21.2MB, docx)
mmc3.docx (14.8KB, docx)
mmc4.xlsx (11.2KB, xlsx)
mmc5.xlsx (12.2KB, xlsx)
mmc6.xlsx (354KB, xlsx)
mmc7.xlsx (3.2MB, xlsx)
mmc8.xlsx (20.6KB, xlsx)
mmc9.xlsx (20.5KB, xlsx)
mmc10.xlsx (25.6KB, xlsx)
mmc11.xlsx (10.7KB, xlsx)

Data Availability Statement

The raw sequence data of RNA-seq have been deposited in National Genomics Data Center, accession number CRA011068, publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Articles from Poultry Science are provided here courtesy of Elsevier

RESOURCES