Introduction

Salmonella enterica serovar Typhimurium (ST) is a gram-negative facultative intracellular bacterium that is capable of causing disease in a range of hosts (He et al., 2018). Except for very young chickens, ST rarely causes clinical disease. However, the bacterium can colonize the poultry intestinal tract (Barrow et al., 1988; Barrow et al., 1990). Poultry products contaminated with ST are one of the major causes of zoonotic foodborne diseases in humans (Scallan et al., 2011). Extensive epidemiological surveys focused on the prevalence of Salmonella in poultry have been performed and well documented (Foley et al., 2011). However, our understanding of immunological mechanisms contributing to the invasion, colonization, and intracellular persistence of Salmonella in chickens is still limited (Boyd et al., 2007; Lillehoj et al., 2007; Chappell et al., 2009; Wisner et al., 2010; Wisner et al., 2011).

Pathogenesis of ST is facilitated by two distinct forms of type III secretion system (T3SS) encoded by the genes of Salmonella pathogenicity islands 1 and 2 (SPI-1 and -2) (Balan and Babu, 2017). The T3SS plays a key role in targeting host cell and triggers the production of pro-inflammatory cytokines during the invasion of mammalian hosts. The T3SS produces about 40 distinct effector proteins to enable Salmonella invasion, survival, and replication within the host cells (Haraga et al., 2008; Ibarra and Steele‐Mortimer, 2009; Malik Kale et al., 2011). The SPI-1 T3SS is primarily expressed in the gut lumen and promotes Salmonella invasion to the gut epithelial cells. Alternatively, SPI-2 T3SS is mainly expressed when the bacterium lives within the vacuolar compartment of macrophages and epithelial cells. Therefore, SPI-2 T3SS is important for intracellular replication and survival of the bacterium and causes a systemic disease (Miao and Rajan, 2011; Braukmann et al., 2015; Balan and Babu, 2017).

After intestinal colonization, macrophages are the primary immune defender cells that detect the existence of microorganisms and secrete cytokines and chemokines responsible for the recruitment of other immune cells to the site of infection. Therefore, it regulates the inflammatory response (Babu et al., 2006; Chappell et al., 2009; Miao et al., 2010; Setta et al., 2012). Intracellular survival of the bacteria depends on the expression of cytokines and their subsequent inflammation, which defines macrophage cell fates (Ibarra and Steele‐Mortimer, 2009). Consequently, different strains of Salmonella have developed various mechanisms to avoid immune reactions or subvert immunity to their benefit and there is an interplay between the detection and evasion of Salmonella in the host (Miao and Rajan, 2011).

A better understanding of the underlying immunological mechanisms involved in Salmonella pathogenicity, at cellular and molecular levels, is crucial to improve the existing control measures against poultry salmonellosis, including vaccination and molecular-based immunotherapeutic strategies. This study sought to provide a snapshot of the immune responses against ST challenge in primary chicken monocyte-derived macrophages (MDMs) by evaluating the transcriptional changes in inflammatory cytokine interleukin (IL)-1 β. Possible immunomodulatory impacts of the bacterium on chicken MDMs were revealed, which may partially explain how the bacterium survives in the environment of host immune cells and uses such mechanisms to further disseminate within the host body.

Materials and Methods

Bacterial Strains

 Overnight Luria-Bertani broth (Merck, Darmstadt, Germany) culture of Salmonella enterica subsp. enterica (Salmonella enterica serovar Typhimurium) ATCC^® 14028 was diluted and grown in Mueller-Hinton agar (Merck, Darmstadt, Germany) at 37°C for 12 h. The corresponding dilution with 4×10³ colony forming units (CFU)/ml of ST ATCC^® 14028 was used to induce infection as described later in this paper.

Chicken MDMs

Blood MDMs were prepared as described in human and porcine model systems (Bahari et al., 2014). Briefly, peripheral blood mononuclear cells were isolated from the blood obtained from 3-week-old broiler chickens (Ross 308) using the Ficoll method. To obtain monocytes, mononuclear-containing cells isolated from chickens were cultured in 24-well tissue culture plates in RPMI medium and incubated for 2 h at 37°C, 5% CO₂, and 95% humidity. The purity of the monocyte cultures was confirmed by Giemsa staining under a light microscope. The number of viable cells was counted using Trypan Blue vital staining.

Salmonella Challenge

Twenty-four-well plates were seeded with 2×10⁶ million cells per well and incubated for 16-18 h. Cell monolayers were challenged with ST at a multiplicity of infection of 50 for 2 h at 40°C, 5% CO₂, and 95% humidity. Cell suspensions were obtained by centrifugation at 1000 g for 5 min at 4°Cand were stored at -70°C prior to RNA extraction.

Reverse Transcription-Quantitative Polymerase Chain Reaction

Transcriptional analysis of inflammatory IL-1β was performed by reverse transcription-quantitative polymerase chain reaction (RT-PCR) using SYBR Green dye. Briefly, RNA was extracted using the Favorgen kit (Ambion, Thermo Fisher Scientific Inc., Waltham, MA, USA) according to the manufacturer’s instructions. A two-step RT-qPCR was initiated by cDNA synthesis utilizing the RevertAid First Strand cDNA Synthesis Kit and oligo(dT) primer (Fermentas, Finland). Relative quan-tification of the mRNA copies of the respective genes was performed by Rotor-Gene Q (Qiagen, Valencia, CA) real-time PCR machine using YTA SYBR Green qPCR Kit (Yekta Tajhiz Azma, Tehran, Iran) with the cDNAs synthesized in the previous step. Table 1 provides a detailed description of the primer sequences, amplicon sizes, and annealing temperatures used for the quantification of IL-1β and glyceraldehyde-3-phosphate dehyd-rogenase (GAPDH) genes in this study. Primer specificities were verified by NCBI BLAST analysis against the chicken genome and were confirmed by observing specific amplified PCR products on 1.5% agarose gel and melting curve analysis after RT-qPCR. The most optimal annealing temperatures were determined by performing gradient PCR as shown in Table 1. Afterwards, each primer pair was tested by drawing a standard curve based on the cycle threshold (Ct) values obtained from serially diluted template RNAs to ensure optimal PCR amplification efficiencies for the primer sets. The RT-qPCR samples were run in triplicate where each 20 µL reaction solution contained 1 μL (500 ng) of the template cDNAs. The thermal program used for RT-qPCR included 10 min pre-denaturation at 95°C followed by 40 cycles of PCR, including 15 sec of denaturation at 95°C, 20 sec of annealing at the temperature specific for each primer set, and 20 sec of extension at 72°C prior to melting curve analysis. Normalization of target genes was performed using GAPDH as an endogenous standard for gene expression in chicken cells. The relative PCR amplicon concentration was determined by fluorescence signals detected at the end of each PCR cycle, and their logarithmic values were plotted against the cycle number as Ct values. The corresponding Ct values for each sample were used to calculate the fold-changes in gene expression using the ΔΔCt method.

Table 1. Detailed description of primers used for transcriptional analysis Statistical Analysis

Statistical differences between transcriptional fold-change values were determined by the independent-samples t-test using the SPSS software version 25 (IBM, Chicago, Ill., USA). P-values equal to or less than0.05 (P≤0.05) were considered statistically significant.

Results

The specificity of the primers for their target genes was confirmed by observing a single peak in melting curve analysis (Figure 1), as well as visualizing a single band within its expected length on the agarose gel (Figure 2). The accuracy of RT-qPCR for determining gene copy numbers was confirmed based on the presence of a linear relationship between the extrapolated Ct values and the concentration of cDNA used in the reaction (Figure 3). A gradual elevation in the sample Ct values was also evident in the replication curve using the decreasing amounts of the input templates (Figure 4).

The Ct values acquired from the RT-qPCR were used to assess the fold changes in the transcription level of the IL-1β gene in chicken MDMs in the presence or absence of Salmonella infection. Interestingly, the ST challenge in chicken MDMs resulted in a significant downregulation of IL-1β mRNA expression (0.4 or -1.3 Log₂ mean fold change), compared to the non-infected controls (P≤0.05) (Figure 5).

Figure 1. Melting curve was drawn for proinflammatory IL-1β and GAPDH genes to evaluate the specificity of the qRT-PCR in replication of the target genes.

Figure 2. Gene products visualized on 1.5% agarose gel to confirm the specificity of replicated amplicons in qPCR.

Figure 3. Presence of a linear relationship between qRT-PCR Ct values and concentration of input cDNA confirms the accuracy of qRT-PCR reaction in determination of gene copy numbers.

Figure 4. Replication curves related to decreasing concentrations of IL-1β and GAPDH genes.