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. 2025 Apr 17;15:13264. doi: 10.1038/s41598-025-95004-y

Immune response against antibiotic-resistant and antibiotic-sensitive staphylococcus aureus in a rat model of implant infection

Yingfang Fan 1,2,#, Amita Sekar 1,2,#, Madeline McCanne 1, Jean Yuh 1, Devika Dutta Kannambadi 1, Sashank Lekkala 1, Orhun K Muratoglu 1,2, Ebru Oral 1,2,✉
PMCID: PMC12006483  PMID: 40246912

Abstract

Little is known about the in-vivo dynamics of biofilms associated with medical-device infections and their interplay with systemic inflammation, local immune responses, and tissue healing processes. There may be an opportunity to tailor therapeutic strategies to target these dynamics to improve treatment outcomes. We investigated immune responses to a methicillin-susceptible (ATCC 12600) and a multi-drug resistant (L1101) S. aureus strain using a rat subcutaneous implant model, analyzing local and systemic inflammation through 19 gene expressions over 21 days. Our goals were to identify differences in the immune response due to infection and also with respect to the two strains. We observed that systemic inflammation, indicated by α-2-macroglobulin, was elevated in the initial stages (up to day 7). Local inflammatory cytokine levels (IL-6, TNF-α, IL-6, TNF-α, IL-1β, IL10, IL-17, IL12a, IL12b, IFNG) varied by strain, typically higher against the clinical strain. Infections generally hindered early macrophage (MCSF1) and T-cell (CD4, CD5, CD6, CD8A) recruitment, particularly in cases involving the clinical strain. Conversely, a better healing response was observed in the infection of the more susceptible ATCC 12600 strain (VEGF, CXCR1, CXCR2, MMP-1, MMP-3, MMP-13). These results are crucial for understanding immune responses to such infections, guiding therapeutic strategies.

Keywords: Immune response, Staphylococcus aureus, Inflammation, Infection, Antibiotic resistance

Subject terms: Immunology, Microbiology, Molecular biology, Biomarkers

Introduction

Medical-device-associated infections (MDAI) are a significant health concern1. Left ventricular assist devices report infection rates between 16 and 36%2–4, prosthetic joints between 1 and 5%5–7, urinary devices from 3 to 8%8, and peritoneal dialysis catheters around 20%9. Other implanted devices, such as central venous catheters, pacemakers, cerebrospinal fluid shunts, breast implants, and subcutaneous (s.c.) implants including surgical meshes, tissue fillers, and medical device coatings are also susceptible to infection, often leading to severe complications and device failure10 Infections associated with these implants remain a major challenge, often leading to fibrotic response of the peri-implant tissues, chronic inflammation, and location-specific symptoms such as variable tissue integration11. Colonizing bacteria are persistent due to the formation of biofilm that protects the bacteria from host defenses and antibiotics12. Infections are often characterized by rapid evolution of clinical symptoms, including severe pain and bone loss, and necessitate prompt medical intervention13. It is not possible to treat biofilms only by administering antibiotics; definitive treatment requires debridement and complete excision of the infected tissues as well as the removal of infected implants14. It is desirable to study the extent and timeline of the host–pathogen interaction and the key determinants for the success of an immune response.

The immune response to bacterial infection is initiated with the recognition of pathogens by innate immune cells, primarily monocytes and macrophages, via pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs), which detect foreign entities and trigger the immune response15. Upon activation, monocytes and macrophages secrete cytokines and chemokines that recruit additional phagocytic cells to the site of the infection, facilitating the transition to an adaptive immune response by presenting antigens derived from the pathogens to T cells16. Activation of T cells leads to the differentiation into various subtypes, including helper T cells that assist in B cell activation and cytotoxic T cells that target infected cells17. Concurrently, B cells produce specific antibodies that target the bacteria for opsonization and facilitate further phagocytosis18. This interactive response of innate and adaptive immunity not only controls and eradicates the infection but also initiates tissue remodeling processes for healing and restoration of tissue integrity19. While the innate system offers immediate defense15, the adaptive system provides a more targeted response over time, facilitated by T lymphocytes which recognize and eliminate infected host cells and trigger further immune reactions through cytokine production17. Dendritic cells (DCs) are known to migrate from infected tissues to the draining lymph nodes through distinct lymphatic pathways, where they present antigens to immune cells, supporting the adaptive response20. T helper 17 (Th17) cells are implicated in promoting fibrosis, a significant role for adaptive immune cells in wound repair21. The immune profiling of peri-implant tissues in periprosthetic infections (PJI), which are associated with the implantation of joint implants using the ‘sonicate fluid’ obtained at the time of revision from infected joints suggested that there are distinctions between joints failed due to infection compared to those failed for other reasons22,23. However, immune profiling during the acute stages of an infection as well as the interplay between innate and adaptive immune responses during infection progression remain underexplored.

Staphylococcal species; Staphylococcus aureus, Staphylococcus epidermidis and other Coagulase negative Staphylococci cause the majority of medical-device infections24. The epidemiology of device infections can include the prevalence of location-specific organisms such as C. acnes in infections associated with shoulder implants25 and Gram-negative organisms in catheter infections26; however, S. aureus remains the principal acute causative microorganism of implantable devices27–29. While methods such as shotgun metagenomic sequencing (SMS) have been proposed for diagnosis of device-associated infections, there is no standard method for the use of immune profiling in the diagnosis or treatment of these infections.

There are significant knowledge gaps in the correlation between the antibacterial efficacy testing in vitro, in preclinical animal models in vivo and the clinical outcome. A significant challenge is the identification of common outcome measures relevant to each system to harmonize the results and to obtain higher predictability for drugs, devices and treatments individually or in combination in a clinically relevant manner. The investigation of the immune landscape in a time-dependent manner in medical device implantation models in vivo can enable the identification of such measures. This study aims to delineate the innate and adaptive immune responses against two strains of S aureus, hypothesizing that significant variances in the immune response will be observed. These differences could be pivotal in determining the timing for prophylactic and therapeutic interventions.

Results

Animal well-being and systemic inflammation

Animal wellbeing assessments, including weight normalization and core body temperature measurements, showed consistent trends across all groups and time points, with minimal impact of infection (Fig. S1). Systemic inflammation, quantified by alpha-2-macroglobulin (α2M) concentrations, persisted until POD 7, with no significant difference between bacterial strains in high inoculation groups (108 CFU) (Fig. 1). In low inoculation groups (105 CFU), α2M levels remained elevated (Fig. S2).

Fig. 1.

Fig. 1

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Systemic response to S. aureus infections in rats. Concentrations of alpha-2-macroglobulin in serum measured in ng/mL for rats inoculated with high inoculum groups (108 CFU) methicillin-susceptible S. aureus (ATCC 12600, gray bars), multi-drug resistant S. aureus (L1101, black bars), and uninfected controls (white bars). Measurements were taken at days 1, 3, 7, 14, and 21 post-operation (POD). Data are presented as mean ± 1 standard deviation (SD). The dotted red line indicates the average baseline level. Statistical significance compared to baseline is indicated (*p < 0.05, **p < 0.01, ***p < 0.001).

Inflammatory response dynamics in subcutaneous tissue

All gene expression data presented in results and interpreted in discussion are presented longitudinally as log2(ΔΔCt) data normalized to the expression observed in the NIC animals (Figs. 2, 4, 5, 6, 9, 10, and S3, S5, S8, S9) or alternatively as log2(ΔCt) data (without ΔΔCt conversion) directly compared to the expression in NIC animals (Fig. S10). The corresponding P values indicating statistical significance are provided in Fig. S10.

Fig. 2.

Fig. 2

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Dynamic cytokine expression in response to S. aureus infections in rats. Differential expression of TNF-α, IL-6, IL-1β, and IL-10 in rats inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600, dashed lines) and multi-drug resistant S. aureus (L1101, solid lines), relative to uninfected controls (NIC, gray lines). Data are shown as mean ± 1 SD. Statistical significance was found between infected groups and NIC day 0, or between groups as indicated (*p < 0.05, **p < 0.01).

Fig. 4.

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Analysis of cell-mediated inflammatory responses to S. aureus infection. Graphs depict the log2 fold change in gene expression of CD4 and IL-17 in rats inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600, dashed lines) and multi-drug resistant S. aureus (L1101, solid lines), compared to uninfected controls (NIC, gray lines). Data points represent measurements on post-operation days (POD) 1, 3, 7, and 21. Error bars indicated mean ± 1 SD. Statistical significance noted where CD4 and IL-17 expression changes significantly from the NIC day 0 (*p < 0.05).

Fig. 5.

Fig. 5

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Gene expression dynamics of macrophages and T-cells in rat models of S. aureus infection. Graphs depict log2 fold changes in expression of macrophage colony-stimulating factor 1 (CSF-1) and T-cell markers (CD5, CD6, CD8a) in rats inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600, dashed lines) and multi-drug resistant S. aureus (L1101, solid lines), relative to uninfected controls (NIC, gray lines). Measurements were taken on post-operation days (POD) 1, 3, 7, 14, and 21. Error bars represented mean ± 1 SD. Significant differences between groups or compared to NIC day 0 at specified time points are marked (*p < 0.05).

Fig. 6.

Fig. 6

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Longitudinal analysis of cytokine gene expression in S. aureus infected rats. This figure presents the log2 fold change in expression of key cytokines involved in cell-mediated immunity: IL-12a, IL-12b, and IFNG. Rats were inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600, dashed lines) and multi-drug resistant S. aureus (L1101, solid lines), and compared with uninfected controls (NIC, gray lines). Measurements were recorded on post-operation days (POD) 1, 3, 7, 14, and 21. Error bars represented mean ± 1 SD. Statistical significance indicated as *p < 0.05, ***p < 0.001 vs. NIC day 0.

Fig. 9.

Fig. 9

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Temporal expression profiles of MMPs in response to S. aureus infection. Graphs display the log2 fold change in gene expression of MMP-1, MMP-3, and MMP-13 in rats inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600, dashed lines) and multi-drug resistant S. aureus (L1101, solid lines), alongside uninfected controls (NIC, gray lines). Measurements were taken on post-operation days (POD) 1, 3, 7, 14, and 21 to assess changes in tissue remodeling and inflammatory response. Error bars represented mean ± 1 SD. Statistical significance was found between infected groups and NIC day 0, or between groups as indicated (*p < 0.05, ***p < 0.001).

Fig. 10.

Fig. 10

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Expression profiles of angiogenic and chemotactic factors in rat skin following S. aureus infection. This graph presents the log2 fold changes in gene expression of vascular endothelial growth factor (VEGF) and chemokine receptors CXCR1 and CXCR2 in rats inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600, dashed lines) and multi-drug resistant S. aureus (L1101, solid lines), compared to uninfected controls (NIC, gray lines). Measurements were taken on post-operation days (POD) 1, 3, 7, 14, and 21 to evaluate changes in tissue repair and inflammatory signaling. Error bars represented mean ± 1 SD. Statistical significance noted where changes in expression are significant between groups (*p < 0.05).

Investigating inflammatory response dynamics in subcutaneous tissue post-S. aureus inoculation revealed distinct patterns in TNFα, IL6, IL-1β, and IL-10 gene expressions, analyzed via RT-PCR and normalized against no infection control (NIC) values across various post-operative days. High-dose infection (Fig. 2) showed that TNFα expression peaked early in the L1101 group, with a subsequent decline, contrasting with the 12600 group’s initial surge and later decrease by day 21. Similarly, IL6 levels in the L1101 group initially decreased, then increased by POD 7, differing from the steady decrease seen in the 12600 group. IL-1β and IL-10 expressions also varied, showing dose-dependent and strain-specific responses (Figs. 2 and S3). The immunofluorescent analysis (Figs. 3 and S4) showed different temporal expression patterns for TNFα and IL-6 between the two groups.

Fig. 3.

Fig. 3

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Immunofluorescent analysis of cytokine expression in rat tissue following S. aureus infection. Panels show the expression of TNFα (panel a, red fluorescence) and IL-6 (panel b, yellow fluorescence) in tissue sections from rats inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600), multi-drug resistant S. aureus (L1101), and uninfected controls (NIC). Images were captured on days 0, 1, 3, 7, and 21 post-operation. The scale bar represents 25 µm, indicating the cytokine localization and intensity changes over the time course of the infection.

Cell-mediated inflammation gene expression insights

Gene expression analysis of cell-mediated inflammation markers (CD4 and IL-17) in the high dose S. aureus infection (Fig. 4) indicated similar immune responses to strains 12600 and L1101. CD4 levels decreased significantly by POD 7 with partial recovery by POD 21. IL-17 expression remained constant.

Macrophage and T-cell gene expression in skin tissue

High-dose S. aureus infection led to a reduction in CSF-1 and CD4 by POD 3 compared to NIC with levels returning to baseline by POD 21 (Fig. 5). In contrast, there was a general increase in CD5, CD6 and CD8a levels on POD3. CD5 and CD6 exhibited strain-specific variations, with the 12600 group showing a significantly higher early increase in CD5 expression (POD1-3) and the L1101 group showing more depressed CD6 levels on POD7. CD8a levels were higher in the 12600 group on POD1 and POD7 but the levels for both strains normalized by POD 21. Low-dose exposure (Fig. S5) mirrored these trends but highlighted a more pronounced decline in CSF-1 and CD4 levels in the L1101 group.

In high-dose S. aureus infections, IL12a significantly decreased by POD 3 in the L1101 group compared to baseline (Fig. 6). Conversely, IL12b levels rose significantly in both strains by POD 1. IFNG expression diverged between strains; it significantly increased by POD 1 in the 12600 group, whereas it decreased in the L1101 group by POD 3.

Capsule thickness and bacterial analysis

Histological evaluations using Hematoxylin and Eosin (H&E) and Brown and Brenn staining elucidated the tissue response and bacterial presence in high (Figs. 7 and 8) and low dose (Figs. S6 and S7) S. aureus infections over time. Capsule thickness increased significantly in both the 12600 and L1101 groups by POD 1, with more pronounced thickening in the L1101 group. By POD 21, both groups showed reduced capsule thickness, which remained thicker than that of the NIC group. Bacterial distribution analysis revealed a moderate to high presence initially, with a noticeable decrease by POD 7. The L1101 strain exhibited more persistent colonization.

Fig. 7.

Fig. 7

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Histological assessment of tissue response to S. aureus infection over time. This panel displays Hematoxylin and Eosin (H&E) stained sections from rat models inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600) and multi-drug resistant S. aureus (L1101), alongside uninfected controls (NIC). Images were captured on post-operation days (POD) 0, 1, 3, 7, and 21 to monitor the progression of capsule thickness and inflammatory response. The scale bar represented 500 µm.

Fig. 8.

Fig. 8

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Bacterial localization in tissue sections over time following S. aureus infection. Brown and Brenn staining highlights the presence of bacteria (indicated by black arrows) in tissue samples from rat models inoculated with high inoculum groups (108 CFU) of methicillin-susceptible S. aureus (ATCC 12600) and multi-drug resistant S. aureus (L1101), compared to uninfected controls (NIC). Images were captured on post-operation days (POD) 0, 1, 3, 7, and 21 to assess the bacterial distribution and tissue response. The scale bar represented 100 µm.

Gene expression patterns and tissue remodeling

In high and low-dose S. aureus infection (Figs. 9 and S8), matrix metalloproteinase (MMP-1, MMP-3, and MMP-13) gene expressions were analyzed. MMP-1 levels showed a decrease until POD7, normalizing thereafter with no difference for the two strains. MMP-3 expression was relatively stable, with minimal decrease on POD7 and 21 and no difference between the two strains. MMP-13 levels were significantly increased on POD1 due to infection, returning to a slightly decreased response at POD3 and thereafter. The MMP-13 levels for the 12600 group were significantly higher than that of the L1101 group on POD1 and POD7.

Angiogenesis and wound healing

The VEGFα-CXCR pathway was analyzed in high (Fig. 10) and low (Fig. S9) dose S. aureus infections. In high dose groups, the time-dependent profiles of MCSF-1 (Fig. 5), VEGFα, and CXCR1/CXCR2 expression followed a contrasting pattern to the NIC with increases on POD1, decreases on POD 3 and a gradual recovery to baseline by POD21. There were no differences between the groups except for CRCX1 expression on POD3 and POD7. The low dose groups showed similar trends, with rapid modulation of VEGFα signaling post-infection and a strain-specific regulation of CXCR1/CXCR2.

Discussion

S. aureus is well-known for its role in infections from superficial skin conditions to severe, life-threatening diseases, and is particularly challenging to treat in medical device associated infections30–32. We selected the strains in this study based on the prevalence of MSSA and MRSA in medical-device infections33. MSSA is more common with a better response to β-lactam therapy with lower recurrence and improved prognosis whereas MRSA leads to worse clinical outcomes, higher treatment failure rates, and greater surgical burden due to antibiotic resistance34.

We have observed significant differences in the evolution of gentamicin susceptibility of both strains in this model even in the absence of gentamicin/vancomycin exposure25, differentiating acquired and inherent resistance in vivo, leading us to question the differential host responses to these distinct S. aureus risk profiles. Our study aims to identify strain-specific variations in inflammatory cytokine expression, immune cell recruitment, and tissue remodeling. Our hypothesis is that these two strains present a ‘best’ and ‘worst’ case for the prognosis of a medical-device infection. Profiling the immune responses to bacterial infections may enable an additional diagnostic tool; however, there is currently no established method to identify bacterial pathogens via immune response profiling. This knowledge is important in answering several questions regarding medical device-associated infections: (1) is there a distinct immune response for susceptible and resistant strains? (2) is there a difference in the timeline of the immune response in responding to infections? (3) what are the contributions of the different signaling pathways to the immune response? (4) how do any of these findings correlate with known immune patterns in humans?

The rat model provides a strong platform for investigating robust immune responses to S. aureus infections in the context of medical device implantations despite various differences from humans35. It provides the opportunity to test multiple hypotheses in a timely manner at lower cost. Although mice models are more widely used for testing biological hypotheses due to their high level of genetic manipulation potential, we have chosen the rat model due to its size, providing sufficient capability for studying medical device material implantations. The model’s well-characterized immune system36 enables a comprehensive analysis of systemic inflammation, localized immune reactions, and the evaluation of therapeutic strategies. Although our immune profiling comprises a set of limited genes, the expression of these groups of genes supports the preliminary evaluation of the immune response as it relates to inflammation, immune cell recruitment, cellular immunity, and wound healing, respectively.

In our previous work studying the differences between systemic and local inflammation in rat models of device implantation, the magnitude of the systemic inflammation, as measured by α2M, was only heightened immediately after surgery and was not enhanced by the local infection in the longer term37. Here, we also observed in this subcutaneous implantation model that systemic inflammation was acutely and drastically elevated (Fig. 1), which we mainly attributed to the reaction to the infectious species due to the lower amount of trauma experienced in the subcutaneous implantation compared to that in the joint. Other factors that may be influencing the systemic response to the subcutaneous infection compared to that in the joint infection may be the composition of the surrounding tissue (higher adipose tissue subcutaneously), the concentration of oxygen (the joint environment is hypoxic) and the differences in the vascular structure (vessel size and distribution)38,39. However, the fibrous encapsulation of implanted materials subcutaneously can also form a ‘pseudo-capsule’ and exhibit a similar local environment to that of the joint40. The early systemic inflammation was dose-dependent in this model as well (Figs. 1 and S1); but systemic inflammation was resolved by day 14 and was confirmed not to be a good indicator of the progression of the local infection.

By studying the pro-inflammatory (TNFα, IL-6, IL-1β) and anti-inflammatory (IL-10) markers in the skin/subcutaneous tissue, we aimed to capture the local inflammation from the immediate post-infection phase through to recovery (Fig. 2). TNFα is mainly expressed by macrophages, which are recruited early in the immune response41. Its expression levels are expected to increase early as a means of triggering inflammation as a tool in fighting the infection41. Another early phase pro-inflammatory cytokine, IL-6, can also be expressed by cell types other than macrophages such as T- and B-cells. The differential expression patterns between TNFα and IL-6 in surgical vs. infection contexts suggest that TNFα might be more rapidly and acutely responsive to surgical trauma42, whereas IL-6 elevation profile can be attributed to its role in driving both the acute and chronic phases of the infectious immune response43. The expression levels of IL-6 by the control and resistant strains were different, presumably supporting the preferred interaction of the different strains with different cell types. Macrophages are primary producers of IL-6, especially in response to infection and inflammation38. The differences in IL-6 levels might indicate varying degrees of macrophage activation or differences in macrophage populations responding to the control and resistant strains. IL-1β is a pro-inflammatory cytokine involved in initiating and amplifying the immune response. Although its expression was not differentiated against the different strains, there was a universal pattern of initial increase followed by suppression of its expression due to infection, clearly suggesting its involvement in bacterial interactions across various cell types. IL-10 is an anti-inflammatory cytokine, whose expression can be associated with the regulation of inflammatory amplification but also of immune tolerance44. The clear early suppression of IL10 expression due to infection combined with a late spike suggests its involvement in the bacterial effort in engendering immune tolerance. A subset analysis of inflammatory proteins by the immunofluorescent staining for TNF-α agreed with the gene expression analysis whereas the levels for IL-6 protein were different from its gene expression profile (Fig. 3). This result suggests the need for complementing gene expression levels with translation analysis of these genes to gain a comprehensive understanding of the factors affecting the immune landscape.

Innate and adaptive responses were both altered in the presence of S. aureus. The Th1/Th17 pathway and the presence of regulatory T cells (T-regs) are strongly correlated with an efficient long-term response to injury and infection by orchestrating a complementary and optimized transition between acute and adaptive responses45. The absence of key components (such as IFN-gamma and IL-17) associated with the Th1/Th17 pathway is strongly associated with delayed and deficient healing in the presence of infection46. Th1 responses, characterized by cytokines such as IL-12 and IFN-gamma, are crucial for activating macrophages, which present antigens to T- and B-cells and support microbial clearance via phagocytosis. Th17 cells, through IL-17 production, facilitate neutrophil recruitment to infection sites, bolstering the defense against extracellular pathogens46. Th17 cells can transdifferentiate into Tregs and begin to secrete IL-10, acquiring regulatory and anti-inflammatory functions47. This transformation helps balance the immune response, ensuring effective pathogen clearance while preventing excessive inflammation. A notable decrease in CD4 expression on days 3 and 7 post-operation suggested a strategic suppression of T-helper cell function. This phenomenon could be attributed to specific bacterial evasion tactics such as the secretion of virulence factors that directly inhibit T-cell activation or induce regulatory T cells, thereby allowing the bacteria to evade immune surveillance while the host attempts to temper the inflammatory response to minimize collateral tissue damage48. The observed pattern of IL-1β and IL-17 levels, with downregulation in the 3–7 day period suggested the delay of the adaptive response by the bacteria (Figs. 2 and 4). At the same time, a longer-term increase suggests an eventual transition from a state of inflammation, which helps fight off infection, to a subsequent focus on tissue repair and healing.

IL-12a (p35 subunit) plays a crucial role in promoting Th1 cell differentiation, which is instrumental in cellular immunity against intracellular pathogens49. The observed decrease in IL-12a by post-operative day 3 might reflect an adaptive response where the immune system potentially shifts focus from a Th1-type response. IL-12b (p40 subunit), which also forms a part of IL-23 when paired with p19, is crucial for both Th1 and Th17 cell responses49. The rapid increase in IL-12b levels on POD1 across both strains indicates an immediate and robust immune activation, recruiting inflammatory cells and amplifying their responses. This surge is likely a direct reaction to the detection of bacterial components, such as MSCRAMMs (microbial surface components recognizing adhesive matrix molecules) and leukotoxins50. The universality of this response across strains suggests that IL-12b plays a fundamental role in the initial immune defense. The drop on POD3 suggests a bacterial adaptation mirroring the expression levels of IL12a, suggesting an effort to minimize the Th1 response. In contrast, the expression profiles of IFNG, which is a critical cytokine for potent antimicrobial actions, enhancing phagocytosis and promoting the activation of macrophages and NK cells51, also follows a similar early increase without the subsequent decrease on POD3. The marked increase in IFNG on POD1 represents a direct and aggressive response followed by an intensifying Th1 response (Fig. 6). The distinct changes in IL-12a, IL-12b, and IFNG levels by POD3 may also be indicative of a refined immune strategy: reducing IL-12a and IL-12b possibly mitigates prolonged inflammatory damage, while maintaining IFN-gamma supports ongoing antimicrobial defenses without exacerbating tissue injury, demonstrating a delicate balance between controlling infection and preventing immune overreaction. Overall, the time-dependent changes in the inflammation, cell recruitment and cellular immunity suggest that the initial response is dominated by the host, after which the effects of the bacterial adaptation are dominant until about POD7. The longer-term immune profile could thereafter be indicative of a ‘failed’ or ‘successful’ eradication.

The post-operative downregulation of CSF-1 by POD3 (Fig. 5) points to dampening of macrophage function by the bacteria. Combined with the transient suppression of CD4 expression in the same time frame, this suggests a stunting of T-helper cell activity and a significant reduction in immune cell function engendered by the bacteria. The macrophage function is largely recovered by POD7; however, the long-term dampening of T-cell recruitment and function (CD5, CD6, CD8a; Fig. 5), especially in the response to the resistant strain, suggests a weakened immune response48.

The histological findings (Figs. 7 and 8) provide critical insights into the tissue-level response to high bacterial loads and the associated changes in tissue architecture. Although we would like to interpret the immune response in this subcutaneous implantation model across other device implantation and infection models, there are expected differential factors that influence wound healing in the skin including the formation of a fibrous capsule, which can be a physical barrier to limit infection spread, but can also cause delayed healing52. There is a significantly thick capsule formed by POD3 in all groups; but the lower thickness of the capsule in L1101 infections, combined with the persistent bacterial load in the wound (Brown and Brenn staining; Fig. 8) suggests a reduction in the fibrotic response and a delay in clearance presumably due to better evasion. We have previously reported the quantitative bacterial burden in this model53. Early high bacterial loads (POD1 and POD3) corresponded to dense bacterial staining, followed by a reduction at later time points (POD21), reflecting bacterial clearance. However, a key difference was that the MRSA group exhibited persistently high bacterial staining at POD7, which was not observed in the tissue culture-based CFU quantification. This interesting discrepancy may be attributed to the stronger biofilm formation and enhanced immune evasion by MRSA, leading to bacterial aggregates that persist in tissue despite reduced CFU recovery in tissue culture-based assays.

Successful healing requires the host to effectively clear infections while minimizing prolonged inflammatory damage. Matrix metalloproteinases (MMPs), particularly MMP-13, play a key role in tissue repair by regulating extracellular matrix remodeling, as evidenced by its acute upregulation and differential expression between bacterial strains on POD7 (Fig. 9). Simultaneously, vascular endothelial growth factor (VEGF) and its receptors (CXCR) drive angiogenesis, facilitating immune cell migration and tissue regeneration. The initial VEGFα surge by POD3 in high-dose groups aligned with immune recruitment, while its resurgence at POD7 signaled a transition from inflammation to healing through enhanced blood vessel formation (Fig. 10). CXCR1 and CXCR2, which mediate neutrophil recruitment54, showed a marked decline by POD3 in infected tissue, likely reflecting receptor utilization and neutrophil apoptosis post-infection. Unlike CXCRs, VEGF remains active longer, supporting sustained angiogenesis and tissue repair beyond the initial immune response.

Our choice of inoculum doses; 108 CFU for robust and 105 CFU for moderate infection, is informed by the development of a periprosthetic joint infection model in the rat53,55. At the same time, it has been shown that as little as 100 CFU of bacteria is sufficient to initiate an infection56. All discussions related to the low-dose infection groups (105 CFU) are shown in the Supplementary Information (Figs. S1 to S9). Comprehensive group curves for the non-normalized data of each studied gene are shown in the supplementary information (Fig. S10).

A major conclusion from our study is the definition of the time frame up to 7 days as a prophylactic period, after which the maintenance or the resolution of the infection reaches equilibrium. While we will continue to study this time frame and search for connections to in-vitro models of bacteria behavior in the context of medical device-associated infections, the translation of our results regarding the immune response differences against the two different strains and the timeline of the response to the human context is limited yet.

A major limitation of the current study is the lack of exposure to antibiotic treatment to which the bacteria have different resistance. It is expected that the immune response to these strains will be more distinct under these conditions which present higher pressure for the bacteria. While our findings highlighted the complexity of host–pathogen interactions with some preliminary insight into affected mechanisms, we recognize the limitations of investigating only two S. aureus isolates and using a subcutaneous model of implantation. In fact, our group’s primary interest is in addressing PJI and the nutrient-starved, hypoxic joint likely presents a very different environment for the bacteria. Thus, future research will incorporate antibiotic treatment and will study the antigen-specific response in more clinically specific implant-associated infection models. Future research will also aim to validate these observations of the time-dependent analysis of gene expression in human infection and explore the immune response to a broader array of S. aureus strains, enhancing our understanding of host–pathogen dynamics and informing more effective infection control strategies in clinical settings.

Conclusion

Our study advances the understanding of the immune response to S. aureus medical device infections. By studying the systemic and especially the localized immune response across different bacterial loads and strains and a time frame up to 21 days after infection in a subcutaneous device implantation model, we identified both common and strain-specific immune response patterns. The early inflammatory phase, immune cell recruitment, and transition to an adaptive immune response were consistent across infections, indicating a shared immune activation pathway regardless of bacterial strain. However, strain-specific differences emerged in the magnitude of inflammatory cytokine expression, macrophage and T-cell recruitment, and tissue remodeling, suggesting that bacterial virulence factors modulate the host response in distinct ways. Our findings highlighted the potential of stratifying the progression of an infection using immune profiling and the development of personalized medical approaches.

Materials and methods

Bacteria culture and preparation

Methicillin-sensitive S. aureus ATCC 12600 (MSSA) and methicillin-resistant S. aureus L1101(MRSA) were used to establish low-risk and high-risk infections, respectively. The bacterial stocks stored at −80 °C were thawed and recovered in tryptic soy agar (TSA) for 16–18 h at 35 °C. To optimize bacterial growth, the bacterial colonies were grown further in tryptic soy broth (TSB) at 35 °C overnight. The turbidity of overnight-grown S. aureus in TSB was spectrophotometrically determined at OD600 and enumerated using a strain-specific standard curve. The bacteria were pelleted by centrifugation at 10,000× g, 5 min, resuspended in sterile PBS, and further adjusted to 108 and 105 CFU in sterile PBS for animal infection experiments.

Animal subjects and experimental design

This study was approved by the Institutional Animal Care and Use Committee (IACUC) of Massachusetts General Hospital (2021N000127). All experiments were performed in accordance with the IACUC guidelines and regulations. This study is reported in accordance with ARRIVE guidelines. We used 117 adult male Sprague–Dawley rats (Charles River, Wilmington, MA) weighing 350–400 g. The rats were randomly assigned to five groups: Non-infected control (NIC) group: n = 15; 21 rats received a total of 105 CFU of gentamicin-sensitive MSSA (ATCC 12600); 30 rats received a total of 108 CFU of ATCC 12600; 21 rats received a total of 105 CFU of gentamicin-resistant MRSA (L1101); and 30 rats received a total of 108 CFU of L1101. All rats were given facility chow and water ad libitum.

Rats were anesthetized with 1–3% isoflurane in 1 L of O2/min, and 0.05 mg/kg IP buprenorphine was administered 30 min before surgery. We implanted six 3 × 1 × 10 mm stainless steel strips subcutaneously on the dorsum of each rat. Each strip pocket was injected with 10 µl containing 1.7 × 107 or 1.7 × 104 S. aureus. Rats received post-operative buprenorphine at 12-h intervals for 72 h.

All groups were sacrificed on postoperative days (POD) 1, 3, 7, or 21, and comparisons were made relative to the NIC group to assess the immune response over time (Fig. S11). Euthanasia was performed using pentobarbital euthanasia solution (Euthasol®) at a dose of 100 mg/kg intraperitoneally (0.22 mL/kg IP). Implants and skin tissues were collected for bacterial culture, RT-PCR, and histological analysis.

Weight and temperature monitoring

We calculated the percentage change in weight based on the pre-operative weight. For core temperature measurement, we inserted a rectal thermometer for 5 s. The normal core temperature range was 96.8–99.5 F.

Blood collection and analysis

We collected blood (~ 150 µL) from the lateral tail vein and centrifuged it at 1500 g for 15 min to obtain serum, which was stored at − 20 °C. We determined the concentration of Alpha 2 Macroglobulin (α2M) in serum using an ELISA kit (ab157730, Abcam). We multiplied the concentration readings by 1500x, determined by a dilution test, to determine the blood concentration of α2M in ug/mL. We also calculated the concentration ratios using the baseline concentration for each rat.

Immune response-associated gene expression

We performed reverse transcriptase-quantitative-PCR (RT-qPCR) to analyze the cytokine expression profile of the samples from the skin tissue. All the tissues were preserved in the RNAprotect Reagents (QIAGEN) and stored at − 20 °C before analysis. We isolated total RNA from tissues using the RNeasy Mini Kit (QIAGEN) and synthesized cDNA using the iScript reverse transcription supermix for RT-qPCR (Bio-Rad) according to the manufacturer’s instructions. We performed qPCR using the TaqMan® Gene expression assays or SYBR Green assay following the manufacturer’s protocol, and all PCR assays were performed in duplicate. Gene expression levels were normalized to those of GAPDH, a housekeeping gene, and the relative gene expression was calculated using the 2−ΔΔCt method57. For the bacterial groups, comparisons were made relative to the corresponding time points in the NIC groups. For the NIC groups, comparisons were made relative to values from day 0 (NIC POD0).The following TaqMan assay and specific primers were used in the present experiments: IL-1β, Rn00580432_m1; MMP-1, Rn01486634_m1; MMP-3, Rn00591740_m1; MMP-13, Rn01448194_m1; IL-10, Rn01483988_g1; VEGF-α, Rn01511602_m1; CXCR1, Rn00570857_s1; CXCR2, Rn02130551_s1; Macrophage CSF-1, Rn01522726_m1; CD4, Rn00562286_m1; CD5, Rn00570713_m1; CD6, Rn00598047_m1; CD8α, Rn00580577_m1; GAPDH, Rn01775763_g1; IFNG, Rn00594078_m1; IL-12a, Rn00584538_m1; IL-12b, Rn00575112_m1; IL-17a, Rn01757168_m1; TNF-α, F-CAGACCCTCACACTCAGATCATC, R-AGCCTTGTCCCTTGAAGAGAAC; IL-6, F- TCTGCTCTGGTCTTCTGGAGT, R-GGACGCACTCACCTCTTGTT; β-actin, F- GACGTTGACATCCGTAAAGACC, R-CTAGGAGCCAGGGCAGTAATCT.

Histological analysis

Following euthanasia of the rats with pentobarbital, skin samples were harvested and fixed in 4% formalin for 24–48 h. After histological processing and embedding in paraffin, transverse cross-Sects. (5 µm) were obtained from the plate region and stained with hematoxylin and eosin (H&E) and Brown-Brenn stain58,59. The representative images were scanned and analyzed using image analysis software (NanoZoomer Digital Pathology, Meyer Instruments, Inc., Houston, TX).

Immunofluorescent staining

Immunohistochemical staining for IL6 and TNF-α was performed on skin tissue sections from each group using IL-6 Polyclonal antibody (#ARC0062, ThermoFisher, Cambridge, MA) and Anti-TNF alpha antibody (#Ab269772, Abcam, Cambridge, MA), respectively. Before use, all antibodies were diluted at 1:200 in DAKO Antibody Diluent (#S0809, DAKO). Homemade antigen retrieval solution (pH 8.5) was used for antigen extraction. After blocking for 30 min, the slides were incubated with primary antibody overnight at 4 °C in a moisture chamber. Negative control slides were prepared without primary antibody. The slides were washed extensively in TBS and then incubated in the dark for 2 h with fluorescent-conjugated secondary antibody (Goat Anti Rabbit Alexa Fluor 568 Rabbit (#: A11011, ThermoFisher) and Goat Anti Mouse Alexa Fluor 647 Mouse (#A21236, ThermoFisher)). All slides were washed in TBS (4 × 5 min) and then in distilled water. The sections were mounted with coverslips using DAPI mounting media (#P36935, SlowFade® Gold Antifade Reagent with DAPI, Invitrogen, Cambridge, MA).

Statistical analyses

We performed two-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test when comparing three or more groups. Paired t-tests were used to compare each group’s data on each measurement day with respect to baseline, and unpaired t-tests were used to compare intergroup differences on each measurement day. A significance level of p < 0.05 was used to indicate statistically significant differences.

Supplementary Information

Supplementary Information. (686KB, pdf)
Supplementary Information. (28.7KB, docx)

Acknowledgements

This work was supported by the National Institutes of Health Grant R01AR077023 and Harris-Muratoglu Endowment. We thank Dr. Kerry Laplante at the University of Rhode Island for providing the clinical MRSA strain L1101. We acknowledge the Wellman Center Photopathology Core for performing immunofluorescence, Center for Skeletal Research Cores (CSR) for the H&E and Brown &Brenn staining. The CSR is supported by funding from the NIH.

Author contributions

Y.F., E.O., and A.S. conceptualized and designed the experiments. Y.F., M.M., J.Y., and S.L. performed the animal surgeries. Y.F., A.S., M.M., J.Y., and D.K. conducted the data acquisition. Y.F. analyzed and interpreted the data. Y.F. and E.O. wrote the manuscript. E.O. and O.K.M. provided funding. Y.F., A.S., M.M., J.Y., D.K., S.L., O.K.M., and E.O. reviewed and approved the final manuscript.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Declarations

Competing interests

O.K.M. discloses the following: Royalties–Corin, Mako, Iconacy, Renovis, Arthrex, ConforMIS, Meril Healthcare, Exactech, Cambridge Polymer Group; Stake/Equity-Cambridge Polymer Group, Orthopedic Technology Group, Alchimist. E.O. discloses the following: Royalties-Corin, Iconacy, Renovis, Arthrex, ConforMIS, Meril Healthcare, Exactech; Paid consultant – WL Gore & Assoc; Editorial Board – JBMR; Officer/Committee- SFB, ISTA. None of these disclosures present a direct conflict with this study. All the remaining authors declare no conflict of interest.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yingfang Fan and Amita Sekar contributed equally.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-95004-y.

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Supplementary Materials

Supplementary Information. (686KB, pdf)
Supplementary Information. (28.7KB, docx)

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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