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Article

Liver Transcriptome Analysis Reveals a Potential Mechanism of Heat Stress Increasing Susceptibility to Salmonella Typhimurium in Chickens

by
Qi Zhang
†,
Yvqing Zhu
†,
Zixuan Wang
,
Qinghe Li
,
Guiping Zhao
and
Qiao Wang
*
State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No2 Yuanmingyuan W Rd., Beijing 100193, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2025, 14(6), 720; https://doi.org/10.3390/biology14060720
Submission received: 11 May 2025 / Revised: 10 June 2025 / Accepted: 12 June 2025 / Published: 18 June 2025
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Simple Summary

Salmonella infection causes severe disease in chickens and major economic losses for the poultry industry. Heat stress, which is becoming more common with global warming, makes chickens more vulnerable to infections, but how it does so remains incompletely understood. This study investigated how heat stress affects the ability of chickens to resist Salmonella infection. We compared healthy chickens to those infected with Salmonella Typhimurium, and to chickens exposed to both heat stress and Salmonella Typhimurium. The results showed that heat stress caused infected chickens to gain less weight, develop stronger inflammation, and experience higher mortality. Transcriptome analysis of the liver revealed that heat stress triggered excessive inflammatory responses and impaired antioxidant defenses. By integrating differential expression analysis, weighted gene co-expression network analysis, and cellular-level validation, we identified three key candidate genes (PTGDS, SLC6A9, and WISP2) that may play important roles in the host immune response during Salmonella Typhimurium infection under heat stress. These findings are important for disease-resistant poultry breeding and improving prevention strategies for poultry farming during hot weather.

Abstract

Salmonella infection poses a serious threat to the poultry industry, causing significant economic losses. Under global warming conditions, the underlying molecular mechanisms by which heat stress affects bacterial infections in poultry remain unclear. This study conducted a Salmonella Typhimurium infection under heat stress in Guang Ming broilers. A total of 100 chickens were randomly divided into three groups: control group (CTL), Salmonella Typhimurium (ST) infection group, and heat stress and Salmonella Typhimurium (HS + ST) co-stimulation group. By integrating inflammatory phenotypes, liver transcriptome profiles, and weighted gene co-expression network analysis (WGCNA), we systematically investigated the key regulatory factors through which heat stress affects host susceptibility to Salmonella. The results demonstrated that heat stress reduced body weight gain, exacerbated Salmonella Typhimurium-induced inflammatory responses, and increased mortality. Transcriptome results revealed that heat stress led to excessive inflammatory responses and antioxidant defense imbalances. Combined differential expression analysis and WGCNA identified three hub regulatory genes: PTGDS and WISP2 showed significant correlations with the heterophil/lymphocyte ratio, while SLC6A9 was significantly correlated with serum IL-8 levels. Validation in HD11 cell infection models confirmed the differential expression of these genes under heat stress and Salmonella Typhimurium co-stimulation, indicating their critical roles in host immune regulation. This study elucidates the intrinsic regulatory relationships through which heat stress promotes Salmonella pathogenicity and inflammatory responses, providing important insights for disease-resistant poultry breeding and prevention strategies.

1. Introduction

The poultry industry plays a vital role in providing high-quality and nutritious meat to meet the growing demands of the rapidly increasing global population [1]. However, beyond their nutritional and economic contributions, chickens serve as natural reservoirs for Salmonella, a major causative agent of human salmonellosis and one of the most significant foodborne diseases worldwide [2,3]. Industrialized and intensive farming practices, reduced trade barriers, and global warming collectively contribute to the increased prevalence and severity of Salmonella infections in poultry [4].
Climate change represents one of the greatest existential challenges to planetary and human health [5], with profound implications for agriculture [6]. Rising global temperatures have led to heat stress (HS), which adversely affects livestock health and has emerged as a critical concern for the global poultry industry [7]. Extensive research indicates that HS disrupts broiler physiology, leading to growth retardation, metabolic disorders, increased mortality, and immune imbalance [8,9]. Notably, HS has been shown to compromise avian immunity [10], thereby enhancing susceptibility to Salmonella. This effect may be linked to intestinal immune dysfunction, as Salmonella Enteritidis infection under HS has been reported to impair gut barrier integrity and promote inflammatory infiltration [11]. Furthermore, a recent study demonstrated that HS suppresses intestinal immune responses in Salmonella Typhimurium (ST)-infected broilers by downregulating the TLR4-NFκB-NLRP3 and TLR4-TBK1 signaling pathways [12]. Conversely, another study elucidated that the presence of pathogens or PAMPs under HS can result in cytokine storms and inflammatory cell death [13].
While it is evident that HS impairs broiler resistance to Salmonella infection, the underlying genetic mechanisms remain poorly understood. Transcriptomic analysis offers a powerful approach to elucidate the global gene expression profiles and complex biological processes controlling host immune responses. Therefore, this study aims to evaluate the phenotypic, physiological, and immunological responses of chickens to Salmonella infection under HS conditions, and to identify key genes and signaling pathways associated with disease resistance in the liver transcriptomes. Elucidating these mechanisms is essential for understanding the molecular basis of poultry immune responses to Salmonella infection under heat stress.

2. Materials and Methods

2.1. Experimental Population and Design

This study utilized Guang Ming (GM) broilers, a new white-feather broiler bred with the participation of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (IAS-CAAS, Beijing, China). A total of 100 1-day-old chickens were obtained from the Changping Experimental Base of IAS-CAAS.
From 1 to 24 days of age, the chickens were maintained under the recommended environmental temperatures (33 ± 1 °C for days 1–7, 30 ± 1 °C for days 8–14, 26 ± 1 °C for days 15–21, and 22 ± 1 °C for days 22–24). At 25 days of age, the chickens were randomly divided into three groups: the control group (CTL group, n = 30), Salmonella Typhimurium-infected group (ST group, n = 30), and heat stress and Salmonella Typhimurium co-stimulation group (HS + ST group, n = 40). The chickens were then transferred to three separate isolation rooms (one room for the CTL group, one for the ST group, and one for the HS + ST group). Within their respective isolation rooms, all chickens were individually housed in cages. The environmental conditions (the temperature was controlled according to the design plan) within each isolation room were strictly controlled to minimize cross-contamination and environmental variability between groups. The CTL and ST groups were kept at 22 °C, while the HS + ST group was exposed to 33 °C for 8 days. On the 4th day of heat stress (at 28 days of age), the ST and HS + ST groups were orally challenged with 1 mL of PBS containing 2.5 × 1010 CFU ST, while the CTL group was given 1 mL of PBS orally. At 24 h post infection (hpi) (29 days of age), blood and livers were collected from 8 randomly selected chickens per group. The remaining chickens continued under their original conditions, and the survival rates were monitored throughout the study.

2.2. Bacteria Culture

Salmonella enterica serovar Typhimurium strain 21,484 (CICC) was cultured in Luria–Bertani (LB) broth at 37 °C with shaking (150 rpm) overnight, recovered, and then subcultured for an additional 12 h. The third-generation culture was used for infection experiments.

2.3. Weight Measurement

We measured the body weight of each group (CTL, ST, and HS + ST groups) at 8:00 a.m. at 25 days of age (before heat stress), 28 days of age (day 4 of heat stress, before Salmonella infection), and 29 days of age (1 day after Salmonella infection).

2.4. Phenotype Determination

Peripheral blood samples were collected from 8 chickens per group. Each sample was divided into two aliquots: one for blood smears and the other for serum isolation via centrifugation. After the blood smears were air-dried, they were stained with Wright–Giemsa. The heterophils (H), lymphocytes (L), and monocytes (M) were counted under an optical microscope at 100× magnification, with a total of 100 cells enumerated per sample [14]. The activities of the total antioxidant capacity (T-AOC) and total superoxide dismutase (SOD) serum levels were determined using commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). The concentrations of three inflammatory cytokines—interferon-γ (IFN-γ), interleukin-1β (IL-1β), and interleukin-8 (IL-8)—were measured in serum using the chicken enzyme-linked immunosorbent assay (ELISA) kit (Cusabio Biotech Co., Wuhan, China), following the manufacturer’s protocol.

2.5. Total RNA Isolation, cDNA Library Construction, and Sequencing

We extracted the total RNA from the liver samples using the QIAGEN RNeasy Kit (QIAGEN, Hilden, Germany) and removed the genomic DNA via the TIANGEN DNase Kit (TIANGEN Biotech, Beijing, China). The RNA purity, integrity, and concentration were checked with the Agilent Bioanalyzer 2100 System (RNA Nano 6000 assay kit, Agilent Technologies, Santa Clara, CA, USA).
For cDNA library construction, the purified mRNA was fragmented, and first-strand cDNA synthesis was initiated with 6-base random primers. Second-strand cDNA was generated using buffer, dNTPs, RNase H, and DNA polymerase I, followed by purification with the QIAQuick PCR kit (QIAGEN, Hilden, Germany) and elution in EB buffer. The double-stranded cDNA underwent end repair, A-tailing, adapter ligation, and size selection via agarose gel electrophoresis. Target fragments were PCR-amplified to finalize the library.
The libraries were quantified (Qubit 3.0, Thermo Fisher Scientific, Waltham, MA, USA), diluted to 1 ng/μL, and analyzed for insert size distribution (Agilent 2100 Bioanalyzer, Agilent Technologies, Santa Clara, CA, USA). The effective library concentration (>10 nM) was validated using the Bio-RAD CFX 96 system (Bio-Rad, Hercules, CA, USA) with the KIT-IQ SYBR GRN Q-PCR kit (Bio-Rad, Hercules, CA, USA). Qualified libraries were sequenced on the Illumina platform (Illumina Inc., San Diego, CA, USA) with a paired-end 150 bp (PE150) strategy.

2.6. Screening of Differentially Expressed Genes (DEGs) and Functional Enrichment Analysis

Read quality control was performed using FastQC v0.10.1 [15]. Adapters and low-quality reads were trimmed using Trimmomatic v0.39 [16]. Filtered reads were aligned to the Gallus gallus 6.0 reference genome using HISAT2 v2.1.0 [17]. Transcript abundance was quantified as FPKM (fragments per kilobase per million mapped reads) using RSEM v1.3.0 [18,19]. Differential expression analysis was conducted with DESeq2 v1.4.0 [20]. DEGs were screened with the threshold of |log2(Fold Change)| ≥ 1 and a p-value < 0.05. Gene expression values were provided in Supplementary Table S1. To investigate the function of DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed [21,22]. Terms or pathways with a p-value < 0.05 were regarded as significantly enriched.

2.7. Weighted Gene Co-Expression Network Analysis (WGCNA)

WGCNA was used to identify functional modules by constructing scale-free gene co-expression networks [23]. For preprocessing the data, low-expression genes (mean expression < 1) and outlier samples (based on abnormal branches in sample clustering dendrograms) were filtered. The Pearson correlation coefficient matrix between genes was calculated, and a suitable soft threshold β (based on a scale-free topology fitting index of R2 > 0.85) was selected to transform the correlation coefficients into an adjacency matrix using a power function. A topological overlap matrix (TOM) was subsequently computed to quantify gene co-expression similarity. Hierarchical clustering based on the TOM, combined with the dynamic tree-cutting algorithm, was used to delineate initial modules, which were merged based on correlations between module eigengenes. Finally, module eigengenes (MEs) were extracted, and their associations with target phenotypes were analyzed via Pearson correlation. Modules were selected for downstream analysis using thresholds of ∣correlation coefficient∣ ≥ 0.3∣ and a p-value < 0.05. Within the candidate modules, genes with |Module Membership| (|MM|) > 0.85 and |Gene Significance| (|GS|) >  0.30 were filtered as key genes. Gene interaction networks were exported using the exportNetworkToCytoscape function and visualized using Cytoscape v3.10.2 to identify the hub genes [24].

2.8. Cell Stimulation and Infection

HD11 cells (a chicken macrophage cell line) were seeded into culture plates and incubated at 37 °C with 5% CO2. The experiment included three groups: the CTL group (control group) and ST group (Salmonella Typhimurium-infected group) were kept at 37 °C, while the HS + ST group (heat stress and Salmonella Typhimurium co-stimulation group) was exposed to 41 °C for 3 days [25,26]. Subsequently, HD11 cells in the ST and HS + ST groups were infected with ST at 100 MOI. To promote bacterial–cell contact, the culture plates were centrifuged at 3000 rpm for 5 min, followed by continued incubation at the respective temperatures for 1 h to ensure bacterial invasion. Then, the cells were washed three times with PBS, and fresh medium containing 100 μg/mL gentamicin (Inalco Inc., San Luis Obispo, CA, USA) was added to continue culturing for 1 h. The cells were washed three times again with PBS to thoroughly remove extracellular bacteria. And the medium was replaced with fresh medium containing 10 μg/mL gentamicin and 10% FBS for 3 h [27]. Finally, cells were collected for subsequent experimental analysis.

2.9. Quantitative Real-Time PCR (qPCR) Analysis

The qPCR was performed using the ABI 7500 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) under the following conditions: for 10 min at 95 °C (initial denaturation), followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The relative mRNA expression was calculated using the comparative 2−ΔΔCt method [28], with β-actin as the endogenous control. The primer sequences are detailed in Supplementary Table S2. Three biological replicates (with technical triplicates) were analyzed per condition.

2.10. Statistical Analysis

All statistical analyses were performed using GraphPad Prism v9.0.0. The data are presented as the mean ± standard deviation (mean ± SD). Intergroup comparisons were analyzed by one-way analysis of variance (ANOVA). When ANOVA indicated significance (p-value < 0.05), Tukey’s post hoc multiple comparison test was further applied. Significant differences in body weight data between groups are indicated by different lowercase letters (a, b; p-value < 0.05) in the tables. Significant differences for other phenotypic indicators between groups—including serum biochemical parameters (T-AOC and SOD), H/L ratio, serum cytokine concentrations (IFN-γ, IL-1β, and IL-8), and mRNA expression levels in HD11 cells—are denoted by asterisks in the figures (* p-value < 0.05 and ** p-value < 0.01).

3. Results

3.1. HS Exacerbated the Negative Impact of ST Infection on Weight Gain

Before exposure to HS (at 25 days of age), the initial body weights showed no significant differences among the groups (CTL, ST, and HS + ST group), indicating a balanced baseline body weight in the experimental animals (Table 1). After 3 days of HS (at 28 days of age), the body weight of the HS + ST group was significantly lower than that of both the ST and CTL groups (p-value < 0.05), while no significant difference existed between the ST and CTL groups, indicating that HS significantly suppressed weight gain in the HS + ST group. Following subsequent ST infection, at 24 hpi (at 29 days of age), the body weight of the ST group was slightly lower than the CTL group. The HS + ST group’s body weight remained significantly lower than that of the ST group (p-value < 0.05), and also showed a highly significant reduction compared to the CTL group (p-value < 0.01). In summary, HS significantly impeded animal growth and markedly exacerbated the negative impact of ST infection on weight gain.

3.2. HS Exacerbates Mortality in ST-Infected Chickens

To investigate the impact of HS on host responses to ST infection, we recorded the survival curves of the ST and HS + ST groups over a 5-day post-infection period. The results demonstrated that HS significantly enhanced the lethal effects of ST infection. The ST group showed a 95.45% survival rate, whereas the HS + ST group exhibited a markedly lower survival rate of 84.38% (Figure 1). These data indicated that HS substantially increases poultry susceptibility to ST infection.

3.3. HS Enhances Immune Factor Expression in ST-Infected Chickens

Assessment of HS markers revealed significantly elevated serum levels of T-AOC and SOD in the HS + ST group compared to both the CTL and ST groups (Figure 2A,B), confirming successful HS induction.
To examine ST-induced immune responses under HS, we analyzed the heterophil/lymphocyte (H/L) ratio and serum inflammatory cytokines. The H/L ratio, a well-established indicator for selecting ST-resistant chickens (lower ratios indicate higher resistance), progressively increased across groups (CTL < ST < HS + ST) (Figure 2C). These patterns suggested that HS may compromise host defenses by disrupting heterophil/lymphocyte homeostasis. At the serum inflammatory level, ST infection (ST group) upregulated IFN-γ, IL-1β, and IL-8 expression compared to in the CTL group. Notably, IFN-γ and IL-8 exhibited a stepwise increase (CTL < ST < HS + ST), with the HS + ST group displaying the peak expression (Figure 2D–F). These results clearly demonstrated that HS exacerbates inflammatory responses during ST infection.

3.4. Dynamic Regulation of DEGs in ST-Infected Chickens Under HS

To investigate the molecular mechanisms by which HS enhanced the host immune response to ST infection, we performed transcriptome sequencing on livers from the CTL, ST, and HS + ST groups. With screening thresholds of |log2(Fold Change)| ≥ 1 and p-value < 0.05, we identified DEGs in three comparison groups: ST vs. CTL, HS + ST vs. CTL, and HS + ST vs. ST (Figure 3A). Specifically, 329 DEGs were identified in ST vs. CTL (173 upregulated and 156 downregulated), 616 DEGs were identified in HS + ST vs. CTL (341 upregulated and 275 downregulated), and 305 DEGs were identified in HS + ST vs. ST (161 upregulated and 144 downregulated).
Gene expression analysis revealed gradient upregulation of inflammatory regulatory factors (IL1R1, MAP3K9), antimicrobial peptide gene (AvBD9), and prostaglandin synthesis gene (PTGDS) across the CTL, ST, and HS + ST groups. Conversely, tumor necrosis factor receptor (TNFRSF13C), WNT signaling gene (WISP2), and glycine transporter (SLC6A9) showed gradient downregulation. The chemokine (CXCL13), pattern recognition receptor (TLR1A), and superoxide dismutase (SOD3) peaked in the ST group but decreased in HS + ST group. Additionally, the pro-inflammatory factor IL8L1 and heat shock proteins (HSP90AA1, HSP25) exhibited the highest expression levels in the HS + ST group (Figure 3B). Among these, 497 DEGs were specific to HS + ST vs. CTL, 210 DEGs were specific to ST vs. CTL, and 119 DEGs were shared (Figure 3C). We carried out GO (biological process) enrichment on the respective DEG sets. The specific genes of the ST vs. CTL comparison group were primarily enriched in immune response, leukocyte chemotaxis, and defense response to bacterium. The specific genes of the HS + ST vs. CTL comparison group were closely associated with oxygen transport, phospholipid homeostasis, and plasma membrane organization. And the shared genes of both comparison groups were significantly enriched in lipid transport and innate immune response (Figure 3D).

3.5. HS Activates Pro-Inflammatory Immune Pathways While Impairing Antioxidant Defenses in ST-Infected Chickens

We conducted GO and KEGG enrichment analysis on upregulated and downregulated DEGs in the HS + ST vs. ST comparison group.
GO term enrichment results (Figure 4A) showed that the upregulated DEGs were primarily enriched in heme binding, macrophage chemotaxis, the positive regulation of inflammatory response, positive regulation of apoptotic process, heat shock protein binding, and positive regulation of NF-κB transcription factor activity. The downregulated genes were mainly involved in the positive regulation of phagocytosis, phagocytosis recognition, and positive regulation of MAP kinase activity. These results indicated that HS enhanced inflammatory responses through the activation of the NF-κB signaling pathway, and induced the activation of genes related to heme binding and macrophage chemotaxis, which may explain the observed excessive inflammatory response in our experiments. Furthermore, the activation of heat shock protein binding pathways and the acceleration of apoptotic processes suggest that HS may participate in host immune responses by regulating programmed cell death pathways.
KEGG pathway enrichment results (Figure 4B) revealed that upregulated DEGs were mainly enriched in the NOD-like receptor signaling pathway and Toll-like receptor signaling pathway, while downregulated DEGs primarily participated in pentose and glucuronate interconversions and glutathione metabolism. Comprehensive analysis suggested that HS may enhance host immune responses to ST infection by coordinately activating multiple innate immune signaling pathways including the NLR, TLR, and mTOR signaling pathways. Conversely, inhibiting glutathione pathways suggested that HS impairs host antioxidant defenses, causing oxidative stress imbalance.

3.6. Weighted Gene Co-Expression Network Analysis and Identification of Key Module Genes

WGCNA was performed to identify modules correlated with the phenotype of the H/L ratio and serum inflammatory cytokine levels. Based on raw transcriptome data from 24 samples (8 CTL, 8 ST, and 8 HS + ST samples; 23,405 genes), we filtered stably expressed genes (SD ≤ 0.5), retaining 1702 genes for network construction. The scale-free topology fitting index reached 0.85, with the optimal soft threshold power set to 14 (Figure 5A). This threshold clustered the genes into 18 functional modules (the gray module containing unclustered genes was excluded from subsequent analysis) (Figure 5B).
Pearson correlation analysis evaluated correlations between module eigengenes and phenotypic traits, with |correlation coefficient| ≥ 0.3 and a p-value < 0.05 as significance thresholds. Notably, the yellow module (cor = 0.67, p-value < 0.001), blue module (cor = 0.50, p-value < 0.05), and purple module (cor = 0.54, p-value < 0.01) showed significant correlations with H/L ratio. The tan module (cor = −0.52, p-value < 0.01) demonstrated a significant correlation with the IFN-γ serum level. The turquoise module (cor = 0.44, p-value < 0.05) and green module (cor = −0.41, p-value < 0.05) were significantly correlated with the IL-8 serum level (Figure 5C).
With the thresholds of GS > 0.3 and MM > 0.8, we identified key genes associated with corresponding phenotypes in the significant modules (Supplementary Table S3). Analysis revealed that the blue module significantly correlated with the H/L ratio was enriched with multiple collagen genes (e.g., COL1A1, COL8A1, COL3A1, COL1A2, COL4A2, and COL4A1), primarily involved in tissue repair and fibrosis [29,30]. The purple module significantly associated with the H/L ratio was enriched with the lipid metabolism-related gene APOA4, potentially participating in cellular stress response and thereby profoundly influencing immune response processes [31]. In the tan module significantly correlated with IFN-γ, the key gene CYP51A1 was identified, which participates in the biosynthesis of sterols (including cholesterol and potentially immunomodulatory sterols) [32], and whose metabolism is closely linked to immune cell functions (such as T-cell activation) and the regulation of inflammatory responses [33,34]. Among the modules significantly associated with IL-8, the turquoise module was enriched with the cytokine receptor gene IL1R1. As the primary receptor for the pro-inflammatory cytokine IL-1, the activation of IL1R1 constitutes a core pathway for initiating and amplifying inflammatory responses [35]. Meanwhile, within the green module, also associated with IL-8, the gene SLC6A9 was enriched; this gene encodes glycine transporter 1 (GLYT1) and exhibits anti-inflammatory and cytoprotective effects [36]. These enriched key hub genes indicate their crucial roles in the combined stimulation of heat stress and Salmonella infection. Among all modules, the yellow module showed the strongest correlation (Figure 5C,D). We constructed a co-expression network for key genes in the yellow module and visualized it using Cytoscape (Figure 5E). This network included metallothionein genes (MT4L and MT3), inflammatory regulator (IL1R2), fatty acid transporter (EXFABP), growth factor (FGF7), prostaglandin synthase (PTGDS), and coagulation factor (F13A1). Notably, MT4L was identified as a potential hub gene.

3.7. Effect of PTGDS, WISP2, and SLC6A9 on Inflammatory Responses During ST Infection Under HS

By integrating differential expression analysis (HS + ST vs. ST) with WGCNA results, we identified three key hub genes: PTGDS (in the yellow module, significantly correlated with H/L ratio), WISP2 (in the blue module, significantly correlated with H/L ratio), and SLC6A9 (in the green module, significantly correlated with serum IL-8 levels), which showed differential expression in HS + ST vs. CTL comparisons.
To validate their functions, we established a HD11 cell stimulation model combining HS and ST infection. Experimental results demonstrated that ST significantly activated macrophage inflammatory responses, as evidenced by the markedly increased expression of inflammatory factors including IL-1β, IL-8, and IFN-γ (Figure 6A–C). Analysis of candidate gene expression patterns revealed that PTGDS was significantly upregulated in the ST group compared to the CTL group, and was further elevated in the HS + ST group (Figure 6D). In contrast, WISP2 and SLC6A9 exhibited suppressive expression in the ST group compared to the CTL group and remained downregulated in the HS + ST group (Figure 6E,F). Based on these distinct expression patterns, we propose that PTGDS acts as a positive regulator amplifying inflammatory responses, while WISP2 and SLC6A9 may participate in negative feedback mechanisms controlling excessive immune activation during ST infection and HS.

4. Discussion

Salmonellosis is a major infectious disease in the poultry industry, resulting in substantial economic losses [37,38]. Chickens’ resistance to Salmonella is influenced by multiple factors. Previous studies have reported increased splenic Salmonella colonization and intestinal inflammation in Salmonella-infected chickens under HS [11]. With the unequivocal trend of global warming, HS has emerged as a major environmental challenge in the poultry industry due to its adverse effects on health and production performance. Recent evidence has demonstrated that HS exacerbates infection-induced tissue damage, leading to severe pathological changes and elevated mortality [19,39]. However, the molecular mechanisms by which HS affects Salmonella-driven immune dysregulation remain incompletely understood. Through systematic phenotypic analysis, immune factor detection, and transcriptome analysis, this study provides a comprehensive elucidation of the molecular mechanisms underlying HS-aggravated ST infection in poultry. Our research provides novel theoretical perspectives for understanding the effects of environmental stress in pathogenic infections.
Our study revealed that HS alters host immune resilience through a dual mechanism: significantly exacerbating pro-inflammatory reactions while suppressing antioxidant defense capabilities. Experimental observations demonstrated decreased body weight gain, significantly higher mortality, and an abnormally elevated H/L ratio compared to the ST group. Extensive research has confirmed that the H/L ratio serves as a reliable and practical indicator for assessing poultry stress levels and health status [40,41,42,43]. Our previous studies established that chickens with lower H/L ratios exhibit stronger resistance to Salmonella [44]. These findings suggest that HS disrupts immune homeostasis by altering granulocyte proportions and impairs the host’s pathogen clearance capacity. Further investigation revealed that HS promoted a progressive upregulation of pro-inflammatory factors (IL-8, IFN-γ) in ST-infected chickens (CTL < ST < HS + ST). These expression patterns were correlated with the activation of NF-κB, TLR, and NLR pathways in liver transcriptomes, indicating that HS amplifies inflammatory phenotypes by enhancing innate immune signaling. These results align with previous poultry HS studies reporting increased infection susceptibility and excessive inflammatory responses. Both acute and chronic HS treatments significantly elevated serum inflammatory cytokine levels while increasing Salmonella load in host tissues [39]. Notably, the specific DEGs of the HS + ST group were predominantly enriched in oxygen transport and plasma membrane organization pathways, potentially reflecting HS-induced alterations in cell membrane permeability and oxygen radical accumulation. The concurrent activation of TLR/NLR pathways and suppression of glutathione metabolism suggested that the increased mortality of the HS + ST group is caused by the combined effects of hyperactivated innate immunity and compromised antioxidant defenses. Additionally, parallel studies in fish models demonstrated that HS increases pro-inflammatory cytokines, apoptosis regulators, and immune-related genes while disrupting the energy supply and oxidative metabolism [45,46]. Intriguingly, these fish studies suggested that prolonged and repeated heat exposure may conversely enhance HS recovery capacity—a discrepancy mainly attributable to fundamental differences in the thermoregulatory and immune systems between homeothermic and ectothermic animals.
The liver, as a pivotal immunometabolic hub, plays a central role in regulating gut barrier function and pathogen clearance. Through differential gene expression analysis and WGCNA network construction, this study successfully elucidated the molecular network characteristics of the synergistic effect of host responses to HS and ST infection. Our findings revealed that PTGDS, the hub gene of the yellow module (significantly correlated with the H/L ratio), showed consistent upregulation across the CTL, ST, and HS + ST groups, suggesting its pivotal role in the host immune response. The PTGDS-encoded prostaglandin D2 synthase (PGD2 synthase) catalyzes PGD2 production, exhibiting unique dual immunomodulatory properties. PGD2 can enhance macrophage chemotaxis to improve pathogen clearance [47]. Moreover, its metabolite 15d-PGJ2 might exacerbate tissue oxidative damage by activating pathways like NF-κB [39,48,49]. This dual function potentially explains why the HS + ST group demonstrated more severe inflammation and higher mortality compared with the ST group. Furthermore, excessive inflammatory factors (IL8L1) and prostaglandin mediators (such as PGD2, catalyzed by upregulated PTGDS) in the liver can directly act on intestinal tissues via portal circulation. These systemic inflammatory signals may disrupt intestinal epithelial tight junctions, increase intestinal mucosal permeability, create favorable conditions for Salmonella translocation and systemic dissemination, while further exacerbating local inflammatory damage in the gut. The WGCNA further identified significant downregulation of SLC6A9 (glycine transporter) and WISP2 (WNT signaling regulator) in ST-infected chickens under HS conditions, which may serve as a crucial regulator. Decreased expression of SLC6A9 in the liver may block glycine utilization, disrupting glutathione synthesis and consequently weakening the host’s antioxidant defense capacity. This not only causes oxidative stress in the liver itself, but may also lead to a reduction in circulating antioxidant substances. Consequently, intestinal epithelial cells become more vulnerable to infection and reactive oxygen species (ROS) attacks induced by heat stress, further impairing barrier function [50,51,52]. Concurrently, WISP2 downregulation may reduce its inhibitory effect on the WNT/β-catenin pathway, promoting excessive intestinal epithelial cell proliferation and compromising barrier integrity [53,54]. This process could both facilitate pathogen invasion and trigger inflammatory storms through abnormal immune cell activation, particularly through Th17 lymphocyte differentiation [55,56]. The dysregulation of these two pathways, combined with PTGDS-mediated inflammatory imbalance, forms an intricate cross-regulatory network that collectively drives a vicious cycle of “metabolic disorder–barrier damage–inflammation amplification”, ultimately leading to significantly increased host susceptibility to Salmonella.

5. Conclusions

This study elucidates the key pathways by which HS exacerbates ST infection in poultry. Our findings demonstrate that HS significantly compromises poultry disease resistance through inducing excessive inflammatory responses and disrupting antioxidant defense systems. Through transcriptome analysis and WGCNA network construction, we identified the key candidate genes (PTGDS, SLC6A9, and WISP2) that exhibited differential expression in the HS and ST co-exposed HD11 cell model, suggesting their pivotal regulatory roles in immune modulation. However, the precise mechanisms through which these genes mediate inflammatory responses require further investigation. Collectively, this work provides important insights and identifies critical molecular targets for enhancing disease resistance in poultry under heat stress conditions, offering valuable directions for future research in heat stress management and infectious disease control.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology14060720/s1. Supplementary Table S1. Gene expression in 18 liver samples.xlsx, Supplementary Table S2. The qPCR primers used in this study.xlsx. Supplementary Table S3. The key genes identified by WGCNA analysis.xlsx.

Author Contributions

Conceptualization, G.Z. and Q.W.; Methodology, Q.Z., Y.Z. and Z.W.; Project administration, Q.W.; Validation, Q.Z., Y.Z. and Z.W.; Writing—original draft, Q.Z. and Y.Z.; Writing—review and editing, Q.L., G.Z. and Q.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the National Key Research and Development Program (2022YFF1000203), the Biological Breeding-National Science and Technology Major Project (2023ZD0405302), and the Central Public-Interest Scientific Institution Basal Research Fund (No. 2023-YWF-ZYSQ-07).

Institutional Review Board Statement

The animal study protocol was approved by the Animal Ethics Committee of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (Approval number: IAS2021-31, date: 10 March 2021).

Informed Consent Statement

Not applicable.

Data Availability Statement

All data supporting our findings are included in the manuscript. The Raw RNA-seq data can been downloaded in NCBI under the accession number PRJNA1178338 (Transcriptome data of chicken liver response to ST + HS) and are publicly accessible at https://www.ncbi.nlm.nih.gov/home/download/ (accessed on 27 October 2024).

Acknowledgments

The authors would like to thank all colleagues at the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, for their help in the process of the experiment.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

HSHeat stress
GMGuang Ming broilers
CTLThe control group
STSalmonella Typhimurium
hpiHours post infection
LBLuria–Bertani
HHeterophils
LLymphocytes
MMonocytes
T-AOCThe total antioxidant capacity
SODSuperoxide dismutase
IFN-γInterferon-γ
IL-1βInterleukin-1β
IL-8Interleukin-8
ELISAEnzyme-linked immunosorbent assay
DEGsDifferentially expressed genes
FPKMFragments per kilobase per million mapped reads
GOGene Ontology
KEGGKyoto Encyclopedia of Genes and Genomes
WGCNAWeighted gene co-expression network analysis
TOMTopological overlap matrix
MEsModule eigengenes
MMModule Membership
GSGene Significance
qPCRQuantitative Real-Time PCR
SDStandard deviation
ANOVAOne-way analysis of variance
GLYT1Glycine transporter 1
SLC6A9Solute carrier family 6 member 9
WISP2WNT1-inducible signaling pathway protein 2
PTGDSprostaglandin synthase
PGD2 synthaseProstaglandin D2 synthase
ROSReactive oxygen species

References

  1. Grzinic, G.; Piotrowicz-Cieslak, A.; Klimkowicz-Pawlas, A.; Górny, R.L.; Lawniczek-Walczyk, A.; Piechowicz, L.; Olkowska, E.; Potrykus, M.; Tankiewicz, M.; Krupka, M.; et al. Intensive poultry farming: A review of the impact on the environment and human health. Sci. Total Environ. 2023, 858 Pt 3, 160014. [Google Scholar] [CrossRef] [PubMed]
  2. Wang, Z.; Jiang, Y.; Xu, H.; Jiao, X.; Wang, J.; Li, Q. Poultry production as the main reservoir of ciprofloxacin- and tigecycline-resistant extended-spectrum beta-lactamase (ESBL)-producing Salmonella enterica serovar Kentucky ST198.2-2 causing human infections in China. Appl. Environ. Microbiol. 2023, 89, e0094423. [Google Scholar] [CrossRef] [PubMed]
  3. Antunes, P.; Mourao, J.; Campos, J.; Peixe, L. Salmonellosis: The role of poultry meat. Clin. Microbiol. Infect. 2016, 22, 110–121. [Google Scholar] [CrossRef] [PubMed]
  4. Magwedere, K.; Rauff, D.; De Klerk, G.; Keddy, K.H.; Dziva, F. Incidence of Nontyphoidal Salmonella in Food-Producing Animals, Animal Feed, and the Associated Environment in South Africa, 2012–2014. Clin. Infect. Dis. 2015, 61 (Suppl. S4), S283–S289. [Google Scholar] [CrossRef]
  5. Khraishah, H.; Alahmad, B.; Ostergard, R.L., Jr.; AlAshqar, A.; Albaghdadi, M.; Vellanki, N.; Chowdhury, M.M.; Al-Kindi, S.G.; Zanobetti, A.; Gasparrini, A.; et al. Climate change and cardiovascular disease: Implications for global health. Nat. Rev. Cardiol. 2022, 19, 798–812. [Google Scholar] [CrossRef]
  6. Springmann, M.; Mason-D’Croz, D.; Robinson, S.; Garnett, T.; Godfray, H.C.; Gollin, D.; Rayner, M.; Ballon, P.; Scarborough, P. Global and regional health effects of future food production under climate change: A modelling study. Lancet 2016, 387, 1937–1946. [Google Scholar] [CrossRef]
  7. Onagbesan, O.M.; Uyanga, V.A.; Oso, O.; Tona, K.; Oke, O.E. Alleviating heat stress effects in poultry: Updates on methods and mechanisms of actions. Front. Vet. Sci. 2023, 10, 1255520. [Google Scholar] [CrossRef]
  8. Hirakawa, R.; Nurjanah, S.; Furukawa, K.; Murai, A.; Kikusato, M.; Nochi, T.; Toyomizu, M. Heat Stress Causes Immune Abnormalities via Massive Damage to Effect Proliferation and Differentiation of Lymphocytes in Broiler Chickens. Front. Vet. Sci. 2020, 7, 46. [Google Scholar] [CrossRef]
  9. Li, G.M.; Liu, L.P.; Yin, B.; Liu, Y.Y.; Dong, W.W.; Gong, S.; Zhang, J.; Tan, J.H. Heat stress decreases egg production of laying hens by inducing apoptosis of follicular cells via activating the FasL/Fas and TNF-alpha systems. Poult. Sci. 2020, 99, 6084–6093. [Google Scholar] [CrossRef]
  10. Oluwagbenga, E.M.; Fraley, G.S. Heat stress and poultry production: A comprehensive review. Poult. Sci. 2023, 102, 103141. [Google Scholar] [CrossRef]
  11. Quinteiro-Filho, W.M.; Gomes, A.V.; Pinheiro, M.L.; Ribeiro, A.; Ferraz-de-Paula, V.; Astolfi-Ferreira, C.S.; Ferreira, A.J.; Palermo-Neto, J. Heat stress impairs performance and induces intestinal inflammation in broiler chickens infected with Salmonella Enteritidis. Avian Pathol. 2012, 41, 421–427. [Google Scholar] [CrossRef] [PubMed]
  12. Li, W.H.; Liu, Y.L.; Lun, J.C.; He, Y.M.; Tang, L.P. Heat stress inhibits TLR4-NF-kappaB and TLR4-TBK1 signaling pathways in broilers infected with Salmonella Typhimurium. Int. J. Biometeorol. 2021, 65, 1895–1903. [Google Scholar] [CrossRef]
  13. Han, J.H.; Karki, R.; Malireddi, R.K.S.; Mall, R.; Sarkar, R.; Sharma, B.R.; Klein, J.; Berns, H.; Pisharath, H.; Pruett-Miller, S.M.; et al. NINJ1 mediates inflammatory cell death, PANoptosis, and lethality during infection conditions and heat stress. Nat. Commun. 2024, 15, 1739. [Google Scholar] [CrossRef] [PubMed]
  14. Fidan, E.D.; Nazligül, A.; Türkyilmaz, M.K.; Aypak, S.Ü.; Kilimci, F.S.; Karaarslan, S.; Kaya, M. Effect of photoperiod length and light intensity on some welfare criteria, carcass, and meat quality characteristics in broilers. Rev. Bras. Zootecn. 2017, 46, 202–210. [Google Scholar] [CrossRef]
  15. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef] [PubMed]
  16. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  17. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef]
  18. Li, B.; Dewey, C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef]
  19. Trapnell, C.; Williams, B.A.; Pertea, G.; Mortazavi, A.; Kwan, G.; van Baren, M.J.; Salzberg, S.L.; Wold, B.J.; Pachter, L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 2010, 28, 511–515. [Google Scholar] [CrossRef]
  20. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
  21. Dennis, G., Jr.; Sherman, B.T.; Hosack, D.A.; Yang, J.; Gao, W.; Lane, H.C.; Lempicki, R.A. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4, P3. [Google Scholar] [CrossRef]
  22. Xie, C.; Mao, X.; Huang, J.; Ding, Y.; Wu, J.; Dong, S.; Kong, L.; Gao, G.; Li, C.Y.; Wei, L. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011, 39, W316–W322. [Google Scholar] [CrossRef] [PubMed]
  23. Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 2008, 9, 559. [Google Scholar] [CrossRef] [PubMed]
  24. 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. [Google Scholar] [CrossRef]
  25. Yan, L.; Hu, M.; Gu, L.; Lei, M.; Chen, Z.; Zhu, H.; Chen, R. Effect of Heat Stress on Egg Production, Steroid Hormone Synthesis, and Related Gene Expression in Chicken Preovulatory Follicular Granulosa Cells. Animals 2022, 12, 1467. [Google Scholar] [CrossRef] [PubMed]
  26. Siddiqui, S.H.; Subramaniyan, S.A.; Kang, D.; Park, J.; Khan, M.; Choi, H.W.; Shim, K. Direct exposure to mild heat stress stimulates cell viability and heat shock protein expression in primary cultured broiler fibroblasts. Cell Stress Chaperones 2020, 25, 1033–1043. [Google Scholar] [CrossRef]
  27. Lai, H.Y.; Chen, Q.; Li, H.; Zhu, C.H.; Yi, L.J.; Zhou, J.; Hu, Q.H.; Yu, X.J. Role of p38MAPK signaling pathway in autophagy of Henle-407 cells induced by spvB of Salmonella typhimurium. Nan Fang Yi Ke Da Xue Xue Bao 2018, 38, 268–273. [Google Scholar]
  28. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT Method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef]
  29. Yoo, J.H.; Ho, S.; Tran, D.H.; Cheng, M.; Bakirtzi, K.; Kukota, Y.; Ichikawa, R.; Su, B.; Tran, D.H.; Hing, T.C.; et al. Anti-fibrogenic effects of the anti-microbial peptide cathelicidin in murine colitis-associated fibrosis. Cell. Mol. Gastroenterol. Hepatol. 2015, 1, 55–74.e1. [Google Scholar] [CrossRef]
  30. Lo, B.C.; Shin, S.B.; Messing, M.; McNagny, K.M. Chronic Salmonella Infection Induced Intestinal Fibrosis. J. Vis. Exp. 2019, e60068. [Google Scholar] [CrossRef]
  31. Liu, C.C.; Wang, N.; Chen, Y.; Inoue, Y.; Shue, F.; Ren, Y.; Wang, M.; Qiao, W.; Ikezu, T.C.; Li, Z.; et al. Cell-autonomous effects of APOE4 in restricting microglial response in brain homeostasis and Alzheimer’s disease. Nat. Immunol. 2023, 24, 1854–1866. [Google Scholar] [CrossRef] [PubMed]
  32. van de Weijer, M.L.; Krshnan, L.; Liberatori, S.; Guerrero, E.N.; Robson-Tull, J.; Hahn, L.; Lebbink, R.J.; Wiertz, E.; Fischer, R.; Ebner, D.; et al. Quality Control of ER Membrane Proteins by the RNF185/Membralin Ubiquitin Ligase Complex. Mol. Cell. 2020, 79, 768–781.e7. [Google Scholar] [CrossRef] [PubMed]
  33. Yin, J.; Fu, J.; Shao, Y.; Xu, J.; Li, H.; Chen, C.; Zhao, Y.; Zheng, Z.; Yu, C.; Zheng, L.; et al. CYP51-mediated cholesterol biosynthesis is required for the proliferation of CD4+ T cells in Sjogren’s syndrome. Clin. Exp. Med. 2023, 23, 1691–1711. [Google Scholar] [CrossRef] [PubMed]
  34. Crites, B.R.; Carr, S.N.; Matthews, J.C.; Bridges, P.J. Form of dietary selenium affects mRNA encoding cholesterol biosynthesis and immune response elements in the early luteal phase bovine corpus luteum. J. Anim. Sci. 2022, 100, skac135. [Google Scholar] [CrossRef]
  35. Sims, J.E.; Smith, D.E. The IL-1 family: Regulators of immunity. Nat. Rev. Immunol. 2010, 10, 89–102. [Google Scholar] [CrossRef]
  36. Howard, A.; Hirst, B.H. The glycine transporter GLYT1 in human intestine: Expression and function. Biol. Pharm. Bull. 2011, 34, 784–788. [Google Scholar] [CrossRef]
  37. Fan, H.H.; Fang, S.B.; Chang, Y.C.; Huang, S.T.; Huang, C.H.; Chang, P.R.; Chang, W.C.; Yang, L.T.; Lin, P.C.; Cheng, H.Y. Effects of colonization-associated gene yqiC on global transcriptome, cellular respiration, and oxidative stress in Salmonella Typhimurium. J. Biomed. Sci. 2022, 29, 102. [Google Scholar] [CrossRef]
  38. Akil, L.; Ahmad, H.A.; Reddy, R.S. Effects of climate change on Salmonella infections. Foodborne Pathog. Dis. 2014, 11, 974–980. [Google Scholar] [CrossRef]
  39. Alhenaky, A.; Abdelqader, A.; Abuajamieh, M.; Al-Fataftah, A.R. The effect of heat stress on intestinal integrity and Salmonella invasion in broiler birds. J. Therm. Biol. 2017, 70 Pt B, 9–14. [Google Scholar] [CrossRef]
  40. Gross, W.B.; Siegel, H.S. Evaluation of the heterophil/lymphocyte ratio as a measure of stress in chickens. Avian Dis. 1983, 27, 972–979. [Google Scholar] [CrossRef]
  41. Al-Murrani, W.K.; Al-Rawi, A.J.; Al-Hadithi, M.F.; Al-Tikriti, B. Association between heterophil/lymphocyte ratio, a marker of ‘resistance’ to stress, and some production and fitness traits in chickens. Br. Poult. Sci. 2006, 47, 443–448. [Google Scholar] [CrossRef] [PubMed]
  42. Scanes, C.G. Biology of stress in poultry with emphasis on glucocorticoids and the heterophil to lymphocyte ratio. Poult. Sci. 2016, 95, 2208–2215. [Google Scholar] [CrossRef] [PubMed]
  43. Thiam, M.; Barreto Sanchez, A.L.; Zhang, J.; Wen, J.; Zhao, G.; Wang, Q. Investigation of the Potential of Heterophil/Lymphocyte Ratio as a Biomarker to Predict Colonization Resistance and Inflammatory Response to Salmonella enteritidis Infection in Chicken. Pathogens 2022, 11, 72. [Google Scholar] [CrossRef] [PubMed]
  44. Thiam, M.; Wang, Q.; Barreto Sanchez, A.L.; Zhang, J.; Ding, J.; Wang, H.; Zhang, Q.; Zhang, N.; Wang, J.; Li, Q.; et al. Heterophil/Lymphocyte Ratio Level Modulates Salmonella Resistance, Cecal Microbiota Composition and Functional Capacity in Infected Chicken. Front. Immunol. 2022, 13, 816689. [Google Scholar] [CrossRef]
  45. Duan, C.; Yang, L.; Chen, W.; Zhou, D.; Cao, S.; Zhang, Y.; Li, G.; Chen, H.; Tian, C. Long-term thermal stress reshapes the tolerance of head kidney of Hong Kong catfish (Clarias fuscus) to acute heat shock by regulating energy metabolism and immune response. Comp. Biochem. Physiol. Part D Genom. Proteom. 2025, 54, 101437. [Google Scholar] [CrossRef]
  46. Pandey, A.; Rajesh, M.; Baral, P.; Sarma, D.; Tripathi, P.H.; Akhtar, M.S.; Ciji, A.; Dubey, M.K.; Pande, V.; Sharma, P.; et al. Concurrent changes in thermal tolerance thresholds and cellular heat stress response reveals novel molecular signatures and markers of high temperature acclimation in rainbow trout. J. Therm. Biol. 2021, 102, 103124. [Google Scholar] [CrossRef]
  47. Wu, W.; Li, H.; Wang, Z.; Dai, Z.; Liang, X.; Luo, P.; Liu, K.; Zhang, H.; Zhang, N.; Li, S.; et al. The tertiary lymphoid structure-related signature identified PTGDS in regulating PD-L1 and promoting the proliferation and migration of glioblastoma. Heliyon 2024, 10, e23915. [Google Scholar] [CrossRef]
  48. Ma, S.X.; Xie, G.F.; Fang, P.; Tang, M.M.; Deng, Y.P.; Lu, Y.J.; Cao, W.; Fu, L. Low 15d-PGJ2 status is associated with oxidative stress in chronic obstructive pulmonary disease patients. Inflamm. Res. 2023, 72, 171–180. [Google Scholar] [CrossRef]
  49. Ishii, T. Close teamwork between Nrf2 and peroxiredoxins 1 and 6 for the regulation of prostaglandin D2 and E2 production in macrophages in acute inflammation. Free Radic. Biol. Med. 2015, 88 Pt B, 189–198. [Google Scholar] [CrossRef]
  50. Xia, J.; Zhang, J.; Wu, X.; Du, W.; Zhu, Y.; Liu, X.; Liu, Z.; Meng, B.; Guo, J.; Yang, Q.; et al. Blocking glycine utilization inhibits multiple myeloma progression by disrupting glutathione balance. Nat. Commun. 2022, 13, 4007. [Google Scholar] [CrossRef]
  51. Hayes, J.D.; Flanagan, J.U.; Jowsey, I.R. Glutathione transferases. Annu. Rev. Pharmacol. Toxicol. 2005, 45, 51–88. [Google Scholar] [CrossRef] [PubMed]
  52. Rhen, M. Salmonella and Reactive Oxygen Species: A Love-Hate Relationship. J. Innate Immun. 2019, 11, 216–226. [Google Scholar] [CrossRef] [PubMed]
  53. Fang, Y.X.; Lu, E.Q.; Cheng, Y.J.; Xu, E.; Zhu, M.; Chen, X. Glutamine Promotes Porcine Intestinal Epithelial Cell Proliferation through the Wnt/beta-Catenin Pathway. J. Agric. Food Chem. 2024, 72, 7155–7166. [Google Scholar] [CrossRef] [PubMed]
  54. Clevers, H. Wnt/β-catenin signaling in development and disease. Cell 2006, 127, 469–480. [Google Scholar] [CrossRef]
  55. Cheng, L.; Zhao, Y.; Qi, D.; Li, W.; Wang, D. Wnt/beta-catenin pathway promotes acute lung injury induced by LPS through driving the Th17 response in mice. Biochem. Biophys. Res. Commun. 2018, 495, 1890–1895. [Google Scholar] [CrossRef]
  56. Dong, Y.; Fan, H.; Zhang, Z.; Jiang, F.; Li, M.; Zhou, H.; Guo, W.; Zhang, Z.; Kang, Z.; Gui, Y.; et al. Berberine ameliorates DSS-induced intestinal mucosal barrier dysfunction through microbiota-dependence and Wnt/beta-catenin pathway. Int. J. Biol. Sci. 2022, 18, 1381–1397. [Google Scholar] [CrossRef]
Biology 14 00720 g001
Figure 1. Survival curves for ST (upper curve, n = 22) and HS + ST (lower curve, n = 32) chickens during the first 5 days post infection.
Figure 1. Survival curves for ST (upper curve, n = 22) and HS + ST (lower curve, n = 32) chickens during the first 5 days post infection.
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Figure 2. Phenotypic data in CTL (n = 8), ST (n = 8), and HS + ST (n = 8) groups. (A) Comparison of the total antioxidant capacity (T-AOC) blood serum concentration between CTL, ST, and HS + ST chickens. (B) Comparison of the superoxide dismutase (SOD) blood serum concentration between CTL, ST, and HS + ST chickens. (C) Comparison of the heterophil/lymphocyte (H/L) ratio in blood between CTL, ST, and HS + ST chickens. (D) Comparison of the interferon-γ (IFN-γ) blood serum concentration between CTL, ST, and HS + ST chickens. (E) Comparison of the interleukin-1β (IL-1β) blood serum concentration between CTL, ST, and HS + ST chickens. (F) Comparison of the interleukin-8 (IL-8) blood serum concentration between CTL, ST, and HS + ST chickens. * p-value < 0.05; ** p-value < 0.01.
Figure 2. Phenotypic data in CTL (n = 8), ST (n = 8), and HS + ST (n = 8) groups. (A) Comparison of the total antioxidant capacity (T-AOC) blood serum concentration between CTL, ST, and HS + ST chickens. (B) Comparison of the superoxide dismutase (SOD) blood serum concentration between CTL, ST, and HS + ST chickens. (C) Comparison of the heterophil/lymphocyte (H/L) ratio in blood between CTL, ST, and HS + ST chickens. (D) Comparison of the interferon-γ (IFN-γ) blood serum concentration between CTL, ST, and HS + ST chickens. (E) Comparison of the interleukin-1β (IL-1β) blood serum concentration between CTL, ST, and HS + ST chickens. (F) Comparison of the interleukin-8 (IL-8) blood serum concentration between CTL, ST, and HS + ST chickens. * p-value < 0.05; ** p-value < 0.01.
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Figure 3. Liver transcriptome profiles in CTL (n = 8), ST (n = 8), and HS + ST (n = 8) groups. (A) Volcano plots of DEGs. Red spots represent the upregulated DEGs, and blue spots represent the downregulated DEGs. (B) Gene expression heatmaps in CTL, ST, and HS + ST groups. (C) Venn diagram of DEGs between “ST vs. CTL” and “HS + ST vs. CTL” comparison groups. (D) The GO (biological process) enrichment analysis of DEGs in three parts based on Venn diagram distribution. The red box represented the specific DEGs in “ST vs. CTL” comparison group, the blue box represented the specific DEGs in “HS + ST vs. CTL” comparison group, and the green box represented the common DEGs between “ST vs. CTL” and “HS + ST vs. CTL” comparison groups.
Figure 3. Liver transcriptome profiles in CTL (n = 8), ST (n = 8), and HS + ST (n = 8) groups. (A) Volcano plots of DEGs. Red spots represent the upregulated DEGs, and blue spots represent the downregulated DEGs. (B) Gene expression heatmaps in CTL, ST, and HS + ST groups. (C) Venn diagram of DEGs between “ST vs. CTL” and “HS + ST vs. CTL” comparison groups. (D) The GO (biological process) enrichment analysis of DEGs in three parts based on Venn diagram distribution. The red box represented the specific DEGs in “ST vs. CTL” comparison group, the blue box represented the specific DEGs in “HS + ST vs. CTL” comparison group, and the green box represented the common DEGs between “ST vs. CTL” and “HS + ST vs. CTL” comparison groups.
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Figure 4. GO and KEGG enrichment analysis of upregulated and downregulated DEGs. (A) GO term analysis of upregulated DEGs (the left) and downregulated DEGs (the right). (B) KEGG signaling pathway enrichment analysis of upregulated DEGs (the left) and downregulated DEGs (the right).
Figure 4. GO and KEGG enrichment analysis of upregulated and downregulated DEGs. (A) GO term analysis of upregulated DEGs (the left) and downregulated DEGs (the right). (B) KEGG signaling pathway enrichment analysis of upregulated DEGs (the left) and downregulated DEGs (the right).
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Figure 5. Implementation of WGCNA and identification of key module genes in CTL (n = 8), ST (n = 8), and HS + ST (n = 8) groups. (A) Analysis of network topology for various soft thresholding powers (1–30). (B) Clustering dendrogram of genes, with dissimilarity based on topological overlap, together with assigned module colors. (C) Module–trait associations. Each row corresponds to a module, and each column corresponds to a trait. Each cell contains the corresponding correlation and p-value. Color scale: Red represents positive correlation (max = 1), blue represents negative correlation (min = −1). (D) The scatterplot of the relevance between heterophil/lymphocyte (H/L) ratio and yellow module genes. (E) The regulatory network diagram of key genes in the yellow module. Node degree indicates the number of direct connections (edges) to other genes.
Figure 5. Implementation of WGCNA and identification of key module genes in CTL (n = 8), ST (n = 8), and HS + ST (n = 8) groups. (A) Analysis of network topology for various soft thresholding powers (1–30). (B) Clustering dendrogram of genes, with dissimilarity based on topological overlap, together with assigned module colors. (C) Module–trait associations. Each row corresponds to a module, and each column corresponds to a trait. Each cell contains the corresponding correlation and p-value. Color scale: Red represents positive correlation (max = 1), blue represents negative correlation (min = −1). (D) The scatterplot of the relevance between heterophil/lymphocyte (H/L) ratio and yellow module genes. (E) The regulatory network diagram of key genes in the yellow module. Node degree indicates the number of direct connections (edges) to other genes.
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Figure 6. qPCR analysis of candidate gene expression was conducted in HD11 cells from CTL (n = 3), ST (n = 3), and HS + ST (n = 3) groups. (A) Interferon-γ (IFN-γ) mRNA expression. (B) Interleukin-1β (IL-1β) mRNA expression. (C) Interleukin-8 (IL-8) mRNA expression. (D) Prostaglandin D2 Synthase (PTGDS) mRNA expression. (E) Solute carrier family 6 member 9 (SLC6A9) mRNA expression. (F) WNT1-inducible signaling pathway protein 2 (WISP2) mRNA expression. * p-value < 0.05; ** p-value < 0.01.
Figure 6. qPCR analysis of candidate gene expression was conducted in HD11 cells from CTL (n = 3), ST (n = 3), and HS + ST (n = 3) groups. (A) Interferon-γ (IFN-γ) mRNA expression. (B) Interleukin-1β (IL-1β) mRNA expression. (C) Interleukin-8 (IL-8) mRNA expression. (D) Prostaglandin D2 Synthase (PTGDS) mRNA expression. (E) Solute carrier family 6 member 9 (SLC6A9) mRNA expression. (F) WNT1-inducible signaling pathway protein 2 (WISP2) mRNA expression. * p-value < 0.05; ** p-value < 0.01.
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Table 1. The mean ± standard deviation for the effect of heat stress on the growth performance of Salmonella Typhimurium-infected chickens.
Table 1. The mean ± standard deviation for the effect of heat stress on the growth performance of Salmonella Typhimurium-infected chickens.
ParametersGroups
CTLSTHS + ST
Before the start of heat exposure, 25 dpi691.31 ± 121.79688.64 ± 106.71710.18 ± 73.45
One day after heat exposure, 28 dpi1048.24 ± 156.74 a1048.47 ± 114.05 a936.24 ± 166.13 b
One day after ST infection, 29 dpi1115.74 ± 82.90 a1077.28 ± 113.78 a951.75 ± 140.60 b
Note: Total sample size N = 100 (CTL: n = 30; ST: n = 30; HS + ST: n = 40). Within a row, means with different superscript letters (a, b) differ significantly at p-value < 0.05.
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Zhang, Q.; Zhu, Y.; Wang, Z.; Li, Q.; Zhao, G.; Wang, Q. Liver Transcriptome Analysis Reveals a Potential Mechanism of Heat Stress Increasing Susceptibility to Salmonella Typhimurium in Chickens. Biology 2025, 14, 720. https://doi.org/10.3390/biology14060720

AMA Style

Zhang Q, Zhu Y, Wang Z, Li Q, Zhao G, Wang Q. Liver Transcriptome Analysis Reveals a Potential Mechanism of Heat Stress Increasing Susceptibility to Salmonella Typhimurium in Chickens. Biology. 2025; 14(6):720. https://doi.org/10.3390/biology14060720

Chicago/Turabian Style

Zhang, Qi, Yvqing Zhu, Zixuan Wang, Qinghe Li, Guiping Zhao, and Qiao Wang. 2025. "Liver Transcriptome Analysis Reveals a Potential Mechanism of Heat Stress Increasing Susceptibility to Salmonella Typhimurium in Chickens" Biology 14, no. 6: 720. https://doi.org/10.3390/biology14060720

APA Style

Zhang, Q., Zhu, Y., Wang, Z., Li, Q., Zhao, G., & Wang, Q. (2025). Liver Transcriptome Analysis Reveals a Potential Mechanism of Heat Stress Increasing Susceptibility to Salmonella Typhimurium in Chickens. Biology, 14(6), 720. https://doi.org/10.3390/biology14060720

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