Introduction

Pigs infected with Salmonella spp. are significant zoonotic reservoirs for the transmission of salmonellosis to humans [1]. Among the various serovars of Salmonella enterica, Salmonella enterica serovar Typhimurium (ST) is particularly prevalent in pigs, affecting all age groups [2]. It is particularly prevalent in post-weaned pigs, in which it often leads to enterocolitis, manifesting as diarrhea. Post-weaned pigs are vulnerable to ST infection because of a temporary decrease in feed intake and reduced maternal antibody levels [3]. Several strategies, including vaccination, antibiotic administration, and regular health examinations, have been employed to mitigate Salmonella infection in post-weaned pigs [4].

Building on the efforts to reduce or eliminate Salmonella infection in post-weaned pigs, identifying key genes involved in the initial immune responses following ST infection represents a proactive strategy to control salmonellosis. Pathogenesis of ST has been reported in quantitative reverse transcription PCR (RT-qPCR) studies using specific immune tissues, such as Peyer’s patches [5, 6] and mesenteric lymph nodes [7, 8]. Salmonella spp. typically enters the host through fecal-oral route, and this pathogen is detected by pattern recognition receptors (PRRs) of innate immune cells. Some bacteria penetrate the epithelium and M cells, while others are phagocytosed by macrophages in the submucosal layer [9, 10]. If these bacteria escape due to macrophage death triggered by the caspase-1 pathway, they can spread either directly or via lymphatic vessels and mesenteric lymph nodes into the bloodstream, causing systemic infection [11]. During this systemic infection phase, the immune response is mediated largely by white blood cells, such as macrophages, dendritic cells, and natural killer cells. In addition, peripheral blood mononuclear cells (PBMCs) contain a mixed population of various immune cells [12]. Therefore, the investigation of gene expression in blood samples from pigs could serve as a valuable window into understanding the immune responses in ST infection. Furthermore, recent studies based on advanced Next-Generation Sequencing technologies have established a foundational understanding of the peripheral blood transcriptomic responses to ST infections, providing critical insights into the dynamics of host–pathogen interactions. Huang et al. highlighted the roles of specific regulators, such as TGFB1 and TRP53, in modulating intracellular ST replication, demonstrating the complexity of immune response modulation by these genes [13]. Another study by Huang et al. further outlined the influence of microRNA on the host immune system during ST infection, emphasizing the intricate network of gene regulation and signaling pathways involved in the response to ST [14].

However, previous studies have primarily focused on the roles of specific annotated pathway factors; other potential regulators and mechanisms that may influence Salmonella–host interactions remain unclear. This gap in literature underscores the need for continued research regarding the mechanisms underlying Salmonella infection and host immune responses. Thus, in the present study, we aimed to elucidate the important immune mechanisms associated with the early stages of ST infection in post-weaned piglets. To this end, we comprehensively analyzed blood parameters and peripheral blood transcriptome in ST-infected piglets at 0 and 2 days post-infection (dpi); these results were validated using RT-qPCR in PBMCs samples. Our findings will greatly enhance our understanding regarding the mechanisms underlying host responses to ST and aid the development of effective strategies to control ST infection in post-weaned piglets.

Materials and methods

Animals and sample collection

The animal experimental procedures were approved by the Animal Ethics Committee of National Institute of Animal Science in Republic of Korea (approval No. NIAS 2021 − 503). This study was part of a larger project aimed at developing novel alternative antibiotics for preventing Salmonella infection in piglets. The goal of the current study was to specifically elucidate the Salmonella–host interaction in ST-infected piglets using transcriptomic approaches. The analytical samples used in this study were obtained in our previous study [15], but the present study focused on elucidating the peripheral blood transcriptome in ST-infected piglets. In brief, castrated piglets (Landrace × Yorkshire, 25 days of age) were randomly assigned to an ST infection group (ST, n = 6) and a negative control group (NC, n = 4). To prevent cross-contamination, animals of each group were housed in a separate, sanitized room. At the age of 49 days, the piglets in the ST group were orally administered 1 × 108 colony-forming units of ST strain LT2 (ATCC 19585; American Type Culture Collection, Manassas, VA, USA), whereas those in the NC group received an equivalent volume of sterile phosphate-buffered saline, with the dosage selected based on previous studies [16, 17]. Comprehensive details on the methodologies used in the animal experiments, including fecal sampling, bacterial isolation, post-mortem examination at 14 dpi, and histopathological analysis, are provided in our previous study [15].

At 0, 2, 5, 8, 11, and 14 dpi, blood samples were collected from the jugular vein of each piglet before the morning feeding. Blood was collected in EDTA-vacutainer tubes (BD Biosciences, Franklin Lakes, NJ, USA) for complete blood counts (CBCs) and in Tempus Blood RNA Tubes (Applied Biosystems, Seoul, Korea) for transcriptome analysis of blood samples obtained at 2 dpi.

Analysis of blood parameters

The CBC test was conducted according to a previous study [18]. In brief, blood samples were analyzed using a ProCyte Dx hematology analyzer (IDEXX Laboratories, Westbrook, ME, USA) to quantify white blood cells (WBCs), neutrophils, monocytes, and lymphocytes.

RNA extraction, library construction, and sequencing

Total RNA was isolated from peripheral blood collected at 2 dpi from pigs of the ST and NC groups using an RNeasy Mini Kit (Qiagen, Hilden, Germany) per the manufacturer’s guidelines. Potential genomic DNA contaminants were removed using DNase I (Qiagen) for on-column DNase digestion during RNA purification. RNA purity and structural integrity were validated using a NanoPhotometer (Implen, Munich, Germany). RNA quality was evaluated using a Nano 6000 Assay kit in a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Libraries were constructed using a TruSeq Stranded Total RNA Library Prep Globin kit (Illumina, San Diego, CA, USA) per the manufacturer’s instructions. In brief, the total RNA was fragmented, and cDNA was synthesized. The cDNA fragments were subjected to end repair, A-tailing, and indexed adapter ligation. The ligated fragments were PCR-amplified, followed by gel purification to eliminate leftover primers and adapters. Transcriptome sequencing was executed using the Illumina HiSeq 2000 system, producing 100-bp paired-end unprocessed reads.

Raw data processing

Raw Illumina sequencing data were obtained after base calling and stored in FASTQ format. A series of data preprocessing steps were undertaken to ensure sequencing data quality and reliability. FastQC v0.11.7 (www.bioinformatics.babraham.ac.uk) was used to assess data quality. Trimmomatic v0.38 (www.usadellab.org) was used to eliminate N bases from both 5′- and 3′-end reads. Reads shorter than 36 bp were removed. The trimmed sequences were aligned to the pig reference genome (S scrofa11.1) using the Hisat2 v2.1.0 mapping program (www.ccb.jhu.edu/software/hisat2). Using the StringTie program, we conducted transcript assembly based on a reference gene model for known transcripts. Read count values for known genes were used for subsequent analysis of differentially expressed genes (DEGs).

Partial least squares-discriminant analysis (PLS-DA)

PLS-DA is a supervised classification method that builds on the PLS algorithm to determine axes (latent variables) that explain the most variance in both predictors and response variables. We conducted PLS-DA using the R package (v4.3.1). Processed transcriptome data were scaled to ensure equal variance across all measured transcripts. Then, a PLS-DA model was constructed to differentiate between the ST and NC groups.

DEG analysis and hierachial clustering analysis (HCA)

Prior to DEG analysis, low-quality genes across samples were removed and the read counts were normalized. DEGs were identified using the DESeq2 R package tool, which assumes that no genes are differentially expressed. The data were normalized using the median of ratios, which is calculated as counts divided by sample-specific size factors determined by median ratio of gene counts relative to the geometric mean per gene. P-values were adjusted using the Benjamini & Hochberg method. DEGs were identified using a false discovery rate (FDR)-adjusted P < 0.05 and log2 (fold change) ≥ 2.0 as thresholds. In total, 495 DEGs were identified and used in subsequent analyses.DEGs between the ST and NC groups were visualized in a heatmap generated using HCA.

Bioinformatics analyses

Initial exploratory steps involved enrichment analyses of the DEGs, using the Gene Ontology (GO) framework. GO provides a systematic nomenclature that uniformly characterizes gene functions, along with the properties of their protein products, across multiple databases. We assessed enrichment in the three primary GO categories: biological processes (BP), cellular components (CC), and molecular functions (MF). In addition, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

It is noteworthy that variations may arise in pathway enrichment across different databases, even for the same DEGs, because of the unique datasets and algorithmic processes employed in each database. Therefore, the same set of DEGs was analyzed using the Ingenuity Pathway Analysis (IPA) platform (Qiagen) to identify potential upstream regulators and corresponding canonical pathways affected by ST inoculation. The upstream regulators were predicted based on the well-curated relationships documented in the Ingenuity Knowledge Base. The significance of associations identified by Upstream Regulator Analysis in IPA was determined using Fisher’s exact test, providing statistical rigor to the inferred biological relationships.

PBMC isolation

At 2 dpi, blood samples were collected from animals of the ST and NC groups in EDTA tubes and mixed at a 1:1 ratio with Dulbecco’s PBS (Life Technologies, Carlsbad, CA, USA). The mixture was deposited on the porous membrane in a Leucosep tube (Greiner Bio-One, Kremsmünste, Austria) pre-filled with Ficoll-Paque (GE Healthcare, Chicago, IL, USA). Following centrifugation at 800 × g for 15 min, the PBMCs were carefully harvested from the Ficoll-Paque interface, washed twice with PBS, and stored at − 80 °C until use in RT-qPCR analysis.

RT-qPCR validation

We used a combination of literature-derived and custom-designed primers to target a comprehensive panel of genes of interest (Additional file 1: Table S1). For genes, such as TLR2, TLR4, and CXCL10, primer sequences were adopted from previously published studies and normalized by ACTB [19,20,−21]. Primers for IL15 and IL27 were designed using the IDT Oligo Primer Design Tool (Integrated DNA Technologies, Coralville, IA, USA) and synthesized at TNT Biotech Co., Ltd. (Jeonju, Korea). RT-qPCR assays were conducted to validate the transcriptomic findings. Total RNA was extracted from PBMCs using a RNeasy Mini Kit (Qiagen) following the manufacturer’s instructions. Five hundred nanograms of total RNA was reverse transcribed into cDNA using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) per the supplier’s instructions. qPCRs were run in a 7500 Real-Time PCR System (Applied Biosystems). The amplification protocol was as follows: initial denaturation at 95 °C for 5 min, followed by 40 cycles of denaturation at 95 °C for 15 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s. Target gene expression levels were quantified relative to the ACTB reference gene using the comparative ΔΔCT method [22] and analyzed using the StepOne software v2.1 (Applied Biosystems).

Statistical analysis

Statistical analyses were performed using the SPSS software version 25.0 (IBM, Armonk, NY, USA). A generalized linear mixed model with Bonferroni correction was used to evaluate repeated measurements of hematological and serum biochemical parameters. Within this model, “time” and “group” were treated as fixed effects, whereas “piglets nested within groups” was considered a random effect. The Mann–Whitney U test with Bonferroni correction was employed for non-parametric comparisons and to avoid type-1 errors. Data are presented as mean ± standard deviation. Results with P-values < 0.05 were considered statistically significant. RT-qPCR results were analyzed using Welch’s t-test to accommodate any potential variance in gene expression between the ST and NC groups.

Results and discussion

Confirmation of phenotypic observations and blood parameters in ST-infected piglets

In our previous study [15], we reported ST isolation from all fecal samples (6 out of 6 piglets, 100%) at 2 dpi, along with histopathological changes indicative of ST infection at 14 dpi. Piglets in the ST group showed moderate to severe lesions representative of Salmonella infection, including villous atrophy, inflammatory cell infiltration, and ST presentation, in immunohistochemical staining tissue sections (Additional file 1: Fig. S1). To evaluate the cellular immune responses to ST infection in piglets, the WBC, neutrophil, monocyte, and lymphocyte counts were determined at 0, 2, 5, 8, 11, and 14 dpi (Fig. 1). The WBC, neutrophil and lymphocyte counts at 5 dpi tended to be higher in the piglets from ST group than in those from NC group; this finding is consistent with the results of a previous study [23]. This previous study reported that the WBC and neutrophil counts in the ST-infected piglets increased significantly by 7 dpi, and a genetic variant in the CCT7 gene was associated with higher levels of these immune cells. Our findings also revealed that ST-infected piglets showed significantly higher monocyte counts (1.30 ± 0.22 K/gL and 1.70 ± 0.53 K/gL, respectively) at 2 and 5 dpi than the piglets in NC group (0.88 ± 0.21 K/gL and 0.83 ± 0.35 K/gL, respectively). These results implied that the monocyte count in ST-infected piglets may serve as a valuable hematology-based marker for ST infection. Monocytes play a crucial role in controlling ST infection in the early and late phases and are key to both innate and adaptive immunity. They originate from progenitors in the bone marrow and migrate via the bloodstream to peripheral tissues, where they can differentiate into macrophages and dendritic cells, which are essential for combating ST infection [24, 25]. The recruitment of monocytes into tissues during persistent ST infection is critical; the depletion of monocytes during early infection increases the susceptibility to salmonellosis, underscoring their importance in the host defense mechanism against ST infection.

Fig. 1
figure 1

Blood parameter analysis in ST-infected and NC piglets. WBC, neutrophil, monocyte, and lymphocyte counts at 0, 2, 5, 8, 11, and 14 dpi are shown. Red squares represent the average value of ST group, blue squares denote the NC group. *P < 0.05

Full size image

RNA-sequencing quality control

To assess the quality of our sequencing data, the sequence error rate was calculated based on the Phred score, with Phred scores 10, 20, 30, and 40 corresponding to 10%, 1%, 0.1%, and 0.01% error rates, respectively. Following paired-end transcriptome sequencing analysis of seven samples, the outcomes for all samples, except three from the ST group, aligned with the anticipated range. Additional file 1: Tables S2 and S3 show the raw and processed reads for each sample, categorized by total data volume and Q30 (Phred score for base quality), highlighting metrics that exceeded a value of 30 (Additional file 1: Tables S2–S4).

PLS-DA was used to reduce the dimensionality of the transcriptome dataset. The PLS-DA plot in Fig. 2A shows a clear separation of the samples from the NC and ST groups, with dimensions 1 and 2 explaining 42.48% and 25.53% of the classification, respectively. The distinct clustering of the ST and NC groups indicates the presence of pronounced differences between their respective RNA expression profiles. This separation, with dimensions 1 and 2 explaining a substantial portion of the variance, suggests that the identified biomarkers are potentially strong predictors of the respective group characteristics. The robustness of these findings points to the utility of PLS-DA in discerning subtle yet crucial biological variations in understanding ST infections.

Fig. 2
figure 2

(A) PLS-DA plot (supervised classification) and (B) heatmap of the DEGs. The PLS-DA plot shows a robust separation of the groups (NC vs. ST). HCA of the DEGs between the two study groups is represented in the heatmap (red color indicates high abundance, blue indicates low abundance). NC (n = 4), ST (n = 3)

Full size image

DEGs in Salmonella-infected piglets

Using the DESeq2 package in R, we identified a total of 495 DEGs between the ST and NC groups based on thresholds of P < 0.05 and log2 (fold change) ≥ 2.0. Among the 495 DEGs, 247 genes were upregulated, and 248 genes were downregulated in the ST group compared to the NC group (Additional file 1: Table S5). A hierarchical clustering-based heatmap is shown in Fig. 2B. In the heatmap, the ST and NC groups are clearly delineated, reflecting the differences between the groups as well as the homogeneity within each group. This pattern indicates a consistent biological response to ST infection, as evidenced by the specific gene expression alterations.

Enrichment analyses

GO term enrichment

Table 1 lists the significantly (FDR < 0.05) enriched GO terms specifics for BPs, CCs, and MFs. In the BP category, “inflammatory response,” “cellular response to cytokine stimulus,” “positive regulation of NIK/NF-κB signaling,” “immune response,” “positive regulation of interferon-gamma production,” “innate immune response,” “toll-like receptor 2 signaling pathway,” MyD88-dependent toll-like receptor signaling pathway,” “positive regulation of T cell proliferation,” and “positive regulation of interleukin-6 production” were enriched.

Table 1 GO terms enriched in peripheral blood samples from Salmonella Typhimurium-infected piglets
Full size table

The above mentioned terms have strong implications in the immune response and inflammatory processes. Particularly, the enrichment of DEGs related to pathway associated with toll-like receptors (TLRs), such as “toll-like receptor 2 signaling pathway” and “MyD88-dependent toll-like receptor signaling pathway”, indicates that TLRs are crucial PRRs in the initial immune response against ST infection in pigs. Huang et al. [13] also reported the upregulation of the genes associated with Toll-like receptors and the MyD88 pathway in the innate immune system at 2 dpi of ST infection and the attenuation of these gene expression at 7 dpi. Our findings indicated that the specific genes associated with the TLR2 signaling pathway were highly expressed during ST infection. In general, the lipopolysaccharides (LPSs) of gram-negative bacteria, such as Salmonella spp., are primarily recognized by TLR4, which utilizes MyD88 as a key adaptor protein for signal transduction [26, 27]. In contrast, TLR2 could recognize pathogen-associated molecular patterns (PAMPs), such as lipoteichoic acid and lipoproteins from gram-positive bacteria [28]. However, a recent study suggested that low-endotoxic atypical LPSs can induce TLR4/TLR2 interaction and heterodimer formation [29]. Therefore, our results suggested the possibility that atypical LPSs or other components of Salmonella spp. (i.e., cell wall constituents or lipoproteins) stimulate TLR2. The observed increase in NF-κB expression is attributed to the stimulation of TLRs by the PAMPs of Salmonella spp. [30]. The results a study by Huang et al. [13] also support our results, which highlight the overexpression of the NF-κB regulon and inhibitory subunit IκB (NFKB1A) in ST-infected pigs classified as persistent shedders [13]. Activated NF-κB translocates to the nucleus, regulating the expression of various immune-related genes and generating pro-inflammatory cytokines. Our study also reported the enrichment of specific terms, such as “cellular response to cytokine stimulus,” “positive regulation of interferon-gamma production,” and “positive regulation of interleukin-6 production.” These findings were also agreement with the aforementioned previous study by Huang et al. [13], who analyzed the alterations of both microRNAs and mRNAs in the peripheral blood from ST-infected pigs at 2 dpi. The findings of this previous study suggested that the miR-146a regulates the levels of the targets of IFN-γ and IL-6, thereby increasing fecal bacterial shedding; overall, these findings underscore the significance of IL-6 and IFN-γ in response to Salmonella infection [14]. The term “positive regulation of T cell proliferation” indicates a potential activation of adaptive immunity [31]. Collectively, these terms highlight a profound involvement of both innate and adaptive immune responses in ST infection, with potential crosstalk among cytokine signaling, TLRs, and T cell activation. The presence of these interconnected terms underscores the complexity and integrated nature of the immune response to ST infection captured in the dataset.

In the CC category, the significantly enriched terms included “external side of the plasma membrane,” “extracellular space,” “cell surface,” “phagocytic vesicle membrane,” and “membrane raft.” All these terms are primarily associated with the cellular boundary or its immediate surroundings.

The enrichment of “external side of the plasma membrane,” “extracellular space,” and “cell surface” were in agreement with the findings of previous study in piglets [32]; this previous study reported a significant upregulation of genes (BGN, DCN, ZFPM2, and BPI) associated with the extracellular mechanisms underlying the adhesion of Salmonella spp. to host cells. Our results showed that these interactions are not confined to intestinal epithelial cells, but also involve blood cellular components, indicating a systemic response to ST infection. The term “phagocytic vesicle membrane” suggests the involvement of immune cells, such as macrophages [33]. Membrane rafts are specialized microdomains in the plasma membrane that are rich in cholesterol and sphingolipids and play essential roles in signal transduction and cellular trafficking [34]. Overall, these results suggest a critical role of cell surface interactions in the initial stages of the colonization of, and infection with, Salmonella spp. The term “phagocytic vesicle membrane” underscores the importance of cellular boundaries and interactions with the external environment in the response to ST infection.

In the MF category, the significantly enriched terms included “transmembrane signaling receptor activity” and “NAD+ nucleosidase activity.” “Transmembrane signaling receptor activity” is associated with proteins that span the cell membrane and transmit signals from the external environment to the cell’s interior. These receptors play pivotal roles in immune responses and cell-to-cell communication [35]. These findings were agreement with the terms enriched under the BP category, suggesting that ST infection could lead to the activation of specific genes associated with intracellular immune responses through the stimulation of cell membrane receptors. “NAD+ nucleosidase activity” pertains to the enzymatic breakdown of nicotinamide adenine dinucleotide (NAD+), a crucial coenzyme in cellular metabolism. The activity of NAD+ is responsible for various cellular processes, including energy production, DNA repair, and cell signaling. While this term appears to be more related to metabolism, NAD+ and NAD+-associated pathways are being increasingly recognized for their roles in immune responses. For instance, the role of NAD+ in immune responses was evidenced by a study on de novo NAD+ synthesis in macrophages [36], revealing that the cell-autonomous generation of NAD+ via the kynurenine pathway regulates macrophage immune function in aging and inflammation. This underscores the crucial role of NAD+ in mediating macrophage effector responses and suggests that a decrease in de novo NAD+ synthesis underlies the decline NAD+ levels and enhancement of innate immune dysfunction in aging and age-associated diseases.

Overall, the GO enrichment results emphasized the roles of immune responses and inflammatory processes in ST infection, particularly highlighting the recognition of PAMPs by TLRs, including key innate immune components, such as TLR2 and MyDD88-dependent toll-like receptors. This recognition is followed by NIK/NF-κB signaling and cytokine signaling, leading to T cell proliferation as a part of adaptive immunity. These findings highlight the involvement of both innate and adaptive immune mechanisms in the response to ST infection. The enrichment of the MF-related terms “transmembrane signaling receptor activity” and “NAD+ nucleosidase activity” underscored the importance of signal transduction and metabolic processes in cellular health and immune function, and particularly highlighting the role of NAD+ role in immune responses.

KEGG pathway enrichment

The significantly (FDR < 0.05) DEG-enriched KEGG pathways included “Cytokine-cytokine receptor interaction,” “Intestinal immune network for IgA production, “Rheumatoid arthritis,” and “Pertussis,” shedding light on the comprehensive impact of ST infection on host immune mechanisms (Table 2). The “Cytokine-cytokine receptor interaction” pathway underscores the pivotal role of cytokines in mediating immune responses to ST infection, aligning with the emphasis on the term “cellular response to cytokine stimulus” in the BP category. This suggests that ST infection triggers a cytokine-mediated signaling cascade crucial for initiating and regulating both innate and adaptive immune responses [37]. The identification of the “Intestinal immune network for IgA production” pathway emphasizes the specific impact of ST infection on mucosal immunity. IgA is the predominant immunoglobulin class in mucosal areas, such as the gut, playing a crucial role in neutralizing pathogens and toxins; this indicates that ST infection triggers an adaptive immune response aimed at boosting mucosal defenses [38]. Further, a previous study reported that the genes associated with the intestinal immune network for IgA production were upregulated in chickens after ST infection [39], demonstrating the significant activation of pathways involved in Toll-like receptor signaling, cytokine–receptor interactions, and IgA production. These common pathways highlight the fundamental mechanisms underlying innate immunity that are preserved across various species to combat bacterial infections. The identification of disease-specific pathways, such as “Rheumatoid arthritis” and “Pertussis”, suggests that the immune response to ST infection shares common pathways with these conditions. Rheumatoid arthritis is characterized by chronic inflammation and autoimmunity; therefore, the activation of this pathway may suggest that ST infection may induce or exacerbate inflammatory and autoimmune processes [40]. The enrichment of the “Pertussis” pathway underscores the relevance of general mechanisms underlying pathogen recognition, immune activation, and defense mounted by the host against bacterial challenges, suggesting that a broad immune response is elicited against bacterial infections [41].

Table 2 KEGG pathways enriched in peripheral blood samples from Salmonella Typhimurium-infected piglets
Full size table

Canonical pathway enrichment

The IPA analysis results were consistent with the findings of the GO term and KEGG analyses, emphasizing the importance of PRRs, such as TLRs and cytokine interactions. The top 10 pathways identified by IPA canonical pathway analysis encompassed various biological processes (Table 3). The most significant pathway was “Pathogen-induced cytokine storm signaling pathway” (Fig. 3), underscoring the role of genes involved in inflammatory responses during pathogenic infections. Similarly, the “Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses” (Fig. 4) highlighted specific genes that play critical roles in the initial immune response to microbial pathogens, thereby emphasizing the importance of innate immunity. Overall, these findings underscore the significant role of immune responses, particularly those triggered by pathogenic challenges, and suggest the involvement of a complex interplay of genes in the immediate response to pathogens.

Table 3 Top 10 predicted canonical pathways in peripheral blood samples from Salmonella Typhimurium-infected piglets
Full size table
Fig. 3
figure 3

Predicted most significantly activated canonical pathway: Pathogen-induced cytokine storm signaling pathway

Full size image
Fig. 4
figure 4

Predicted activated canonical pathway that is possibly relevant to the study design: Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses

Full size image

Upstream regulators and target molecules

The activated upstream regulators (Table 4) suggest a significant influence of external chemical entities, particularly those associated with bacterial components, which elicit strong immune responses. The inhibited upstream regulators included diverse entities, ranging from transcription factors (e.g., MRTFB) to responses on drugs (e.g., fontolizumab), indicating the potential downregulating of a range of processes, including transcriptional activities and specific drug responses. Specifically, the activated regulators indicated a pronounced influence of bacterial components (i.e., lipopolysaccharides derived from Escherichia coli and Salmonella), suggesting the importance of immune responses, particularly those elicited by bacterial challenges. Several of the inhibited regulators (e.g., fontolizumab) were related to therapeutic interventions, hinting at the potential impacts of drugs or treatments on these genes. Responses to chemicals, such as hydrocortisone and cholesterol, further underscored the diversity of the dataset, emphasizing both endogenous processes (e.g., lipid metabolism) and external interventions (e.g., hydrocortisone treatment). Together, these findings suggested that our dataset captured a wide range of biological processes, from transcriptional regulation to immune responses to therapeutic interventions.

Table 4 Predicted activated and inhibited upstream regulators with target molecules in peripheral blood samples from Salmonella Typhimurium-infected piglets
Full size table

RT-qPCR validation using PBMCs

Analyzing genes in PBMCs can be highly informative for several reasons, primarily because of the diverse roles and origins of PBMCs in the immune system. As the transcriptomic signatures collectively indicated significant enrichment of immune-related signaling pathways, we analyzed the expression of a few key genes in PBMCs isolated from the piglets. Specifically, major players involved in the aforementioned GO terms and KEGG pathways were included: TLR2, TLR4, CXCL10, IL-15, and IL-27. While TLR2 and TLR4 transcript levels did not significantly differ between the groups, CXCL10, IL15, and IL27 mRNA levels were all consistently higher in the ST group than in the NC group (Fig. 5).

Fig. 5
figure 5

RT-qPCR validation of key genes. Transcript levels of TLR2, TLR4, CXCL10, IL15, and IL27 in peripheral blood mononuclear cells isolated from experimental piglets at 2 dpi relative to ACTB expression are shown. *P < 0.05

Full size image

Our findings that TLR2 and TLR4 expression did not differ between the study groups is in line with previous findings that the baseline levels of these receptors do not markedly fluctuate in response to infection or disease states, indicating a possible steady state of readiness for pathogen recognition [42]. In contrast, CXCL10, IL15, and IL27 transcript levels were significantly higher in the ST group than in the NC group. These cytokines and chemokines are critical in the recruitment and activation of immune cells, indicating a more pronounced immune response in the ST group. CXCL10 has chemotactic properties, directing the migration of immune cells to sites of inflammation or infection [43]. IL-15 and IL-27 are pivotal for T cell proliferation and differentiation and inflammatory response regulation, respectively [44]. The elevated levels of these molecules underscore a heightened state of immune activation, potentially reflecting the piglets’ response to ST infection. In summary, the differential expression of CXCL10, IL-15, and IL-27 in contrast to the stable expression of TLR2, and TLR4 reflects the dynamic nature of the immune response to Salmonella infection in piglets

Our study revealed the complex interactions between ST infection and the immune response in piglets, advancing our understanding of the mechanisms underlying host–pathogen dynamics. We discovered significant alterations in both innate and adaptive immune strategies through the identification of DEGs and GO term and KEGG pathway enrichment analyses. Notably, the genes associated with Toll-like receptors, NIK/NF-κB signaling, cytokine signaling, and T cell proliferation pathways were upregulated, consistent with the elevated expression of CXCL10, IL-15, and IL-27, indicating robust immune activation. Furthermore, the increase in the monocyte counts at the early stages of infection suggested its potential to serve as as a hematological marker for ST infection in post-weaned piglets. Our integrative approach combined phenotypic, biochemical, and molecular data to provide a holistic view of the response of piglets to ST infection. However, this study had a few limitations. We utilized RNA-seq technology, which may not adequately reflect the genetic profiles of rare cell populations, compared to single-cell sequencing, which is a more advanced. In addition, the present study’s findings do not reflect about the clinical stage-dependent effects of Salmonella infection. Therefore, future studies are essential to explore the subtle changes in cells across different phases of disease progression. Despite these limitations, the overall results of this study could enhance our understanding of the mechanisms underlying, and contribute to the development of novel therapeutic strategies for, ST infection in post-weaned piglets.