Reprogramming aerobic metabolism mitigates Streptococcus pyogenes tissue damage in a mouse necrotizing skin infection model

Xu, Wei; Bradstreet, Tara R.; Zou, Zongsen; Hickerson, Suzanne; Zhou, Yuan; He, Hongwu; Edelson, Brian T.; Caparon, Michael G.

doi:10.1038/s41467-025-57348-x

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Published: 15 March 2025

Reprogramming aerobic metabolism mitigates Streptococcus pyogenes tissue damage in a mouse necrotizing skin infection model

Nature Communications volume 16, Article number: 2559 (2025) Cite this article

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Abstract

Disease tolerance is a host response to infection that limits collateral damage to host tissues while having a neutral effect on pathogen fitness. Previously, we found that the pathogenic lactic acid bacterium Streptococcus pyogenes manipulates disease tolerance using its aerobic mixed-acid fermentation pathway via the enzyme pyruvate dehydrogenase, but the microbe-derived molecules that mediate communication with the host’s disease tolerance pathways remain elusive. Here we show in a murine model that aerobic mixed-acid fermentation inhibits the accumulation of inflammatory cells including neutrophils and macrophages, reduces the immunosuppressive cytokine interleukin-10, and delays bacterial clearance and wound healing. In infected macrophages, the aerobic mixed-acid fermentation end-products acetate and formate from streptococcal upregulate host acetyl-CoA metabolism and reduce interleukin-10 expression. Inhibiting aerobic mixed-acid fermentation using a bacterial-specific pyruvate dehydrogenase inhibitor reduces tissue damage during murine infection, correlating with increased interleukin-10 expression. Our results thus suggest that reprogramming carbon flow provides a therapeutic strategy to mitigate tissue damage during infection.

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Introduction

Resistance to infection requires immune functions that reduce the pathogen burden. Equally crucial within the realm of immunity is a function that remains neutral with respect to pathogen fitness, known as “Disease Tolerance” (DT). This aspect of immunity is often coupled to the induction of cellular and tissue regenerative processes and functions to balance tissue immunopathology against the anti-pathogen response¹. Overall, the severity of an infectious disease is influenced by the balance between the effector and DT arms of the immune system^2,3. It is well-established that bacterial pathogens can actively manipulate the host’s anti-pathogen response⁴. Whether they can also manipulate DT to promote fitness is less well-understood.

Previously, we found that the pathogenic lactic acid bacterium Streptococcus pyogenes (group A streptococcus) can manipulate DT in patterns that reflect how carbon is distributed among its several pathways of central carbon metabolism⁵. This Gram-positive bacterium is responsible for over 2.5 million cases of pharyngitis (“strep throat”) and 20,000 invasive infections annually in the USA and over 500,000 deaths worldwide, despite its sensitivity to beta-lactam antibiotics⁶. In addition, it causes several post-infection sequelae, including rheumatic fever, rheumatic heart disease, and acute glomerulonephritis⁷. Its ability to cause this wide range of pathologies relies on its ability to exploit a diverse array of host niches⁶ using the metabolic plasticity of its central carbon metabolism⁸. As a lactic acid bacterium, S. pyogenes exclusively relies on fermentative metabolism to reduce pyruvate for regeneration of NAD⁺ consumed by glycolysis. Its canonical pathway uses homolactic fermentation and the enzyme lactate dehydrogenase (LDH) to reduce pyruvate into lactate. However, S. pyogenes can also reduce pyruvate via two parallel arms of mixed-acid fermentation. The first functions only under anaerobic conditions and involves the enzyme pyruvate-formate lyase (PFL) and the second functions only under aerobic conditions using the pyruvate dehydrogenase enzyme complex (PDH). Carbon flux through these pathways dictates the composition of the short-chain fatty end-products of fermentation, including lactate, acetate, and formate, which each have immunoregulatory properties^9,10, and can influence S. pyogenes virulence factor expression through metabolic-sensing transcription factors such as Mga and CcpA¹¹.

For example, virulence factor regulation involving CcpA is tied to its central role in regulating carbon-catabolite repression, the process through which bacteria fine-tune carbon flux to make the most efficient use of available carbon source substrates^12,13. CcpA functions as a transcriptional repressor of metabolic and virulence genes and mutants de-repressed by deletion of CcpA had a diminished ability to grow in tissue, leading to less damage and accelerated bacterial clearance¹³. Mutants expressing a CcpA variant engineered to constitutively repress gene expression also had a reduced ability to damage tissue¹³. However, these mutants were not attenuated for their ability to grow in tissue¹³, showing that growth and tissue damage are not always directly proportional and implying that S. pyogenes carbon flux regulates the host’s DT response¹³.

To further dissect this relationship, we used a murine model of soft tissue infection to examine single and pair-wise mutants that constrained carbon flux through all combinations of the three major S. pyogenes fermentation pathways. While the canonical lactic acid pathway (via LDH) had a minimal contribution to virulence, the anaerobic mixed-acid fermentation (via PFL) was essential for growth in tissue. In contrast, its aerobic mixed-acid fermentation (ARMAF) had no impact on bacterial growth but did alter the severity of tissue damage through altering the balance between the proinflammatory cytokine TNFα and the immunosuppressive cytokine IL-10⁵. These data support a model in which central carbon flux, influenced by the availability of oxygen and specific carbon sources, is used by S. pyogenes to regulate its disease-causing abilities. The recent development of synthetic PDH inhibitors with selectivity demonstrated through molecular docking, mutagenesis, enzymatic assays, and inhibition kinetic analysis^14,15,16,17 suggests that it will be possible to therapeutically manipulate bacterial carbon flux during infection.

Here, in the present study, we examine the mechanisms by which ARMAF alters DT, including how the cellular innate immune response is reshaped by PDH to alter IL-10 expression, how PDH changes the production of short-chain fatty acid end-products to alter IL-10 expression, and how these fermentation end-products regulate DT via altering acetyl-CoA metabolism. Finally, we show how reprogramming central carbon flux using a prokaryotic-specific PDH inhibitor mitigates tissue damage during a necrotizing skin infection. These data help explain how environmental and nutritional factors in different host tissues influence the severity of disease and provide a therapeutic strategy to mitigate tissue damage by regulating DT during invasive S. pyogenes infection.

Results

PDH is required to promote ulcer progression and late-stage infection

Using an established murine model of subcutaneous ulcer infection that allows an independent assessment of tissue damage (ulcer area) vs. bacterial burden (colony forming units [CFUs]), we previously reported that by 3 days post-infection (dpi), ulcers caused by wild type (WT) streptococci were significantly larger than those caused by the ΔPdh mutant, despite the latter yielding a bacterial burden that was not significantly different from WT (ref. ⁵, see Fig. 1A, B). To examine the influence of ARMAF on late-stage infection and resolution, we compared WT to ΔPdh bacteria through 12 days of infection. By 1-dpi for both WT and ΔPdh, an initial region of induration at the site of inoculation ulcerated and developed an eschar. WT lesions expanded continuously to reach a peak on day 6 (Fig. 1A, B). In contrast, the progression of ΔPdh lesions stalled and did not increase over their 1-dpi size (Fig. 1A, B). Around 9-dpi, eschars began to slough off, revealing newly generated tissue, and by 12-dpi hair had re-grown to complete the healing process. Clearance of bacterial burdens initiated at 4-5-dpi. However, despite obtaining the same peak burden to WT at 3-dpi, ΔPdh was cleared at a faster rate with significantly lower burdens apparent by 6-dpi, which continued over the 12-day period (Fig. 1C). The distinct growth/damage profiles of WT vs. ΔPdh at both the late and early stages of infection suggests that PDH is affecting DT possibly through manipulation of the immune response.

ARMAF alters composition of the cellular immune response

To test this hypothesis, we compared WT and ΔPdh strains during the very initial stages of infection using luminol-bioluminescence imaging^18,19 to track reactive oxygen species (ROS)-producing cells at the site of infection. At 1 h post-infection (hpi), significantly more ROS-producing cells were elicited by WT as compared to ΔPdh (Fig. 2A, B). Depletion of neutrophils using an anti-Gr1 antibody significantly reduced luminol bioluminescence for both WT and mutant strains, indicating that this early response was predominantly due to neutrophils (Fig. 2A, B). To examine other immune populations, single-cell suspensions from the lesions and surrounding tissue were analyzed by flow cytometry (for gating strategies, see Supplementary Fig. 1). By 3-dpi, the number of CD11b⁺F4/80⁺ macrophages was 5-fold higher in ΔPdh-infected vs. WT-infected skin (Fig. 2C). At this time-point neutrophils were the predominant CD45⁺ cell type and were 1.5-fold higher in ΔPdh- vs. WT-infected cutaneous tissue. There were no significant differences in the population sizes of other myeloid or lymphoid cell types analyzed, including T cells, monocytes, and dendritic cells (Fig. 2C). Single-cell RNA sequencing of one million cells from 3-dpi WT and ΔPdh-infected skins identified 15 distinct cell clusters which were assigned to designated cell types by their canonical gene markers (Supplementary Fig. 2). By collapsing cell clusters based on t-distributed stochastic neighbor embedding (t-SNE) we obtained 9 subcategories of immune cells (Fig. 2D). All immune cells were found in both WT- and ΔPdh-infected skins samples; however, ΔPdh-infection results in much higher levels of macrophages and neutrophils (Fig. 2E). ARMAF appears to affect not only neutrophil and macrophage populations but also B and T cell populations (Fig. 2E). In addition, infection by WT vs ΔPdh induced differential gene expression within each cell population (Supplementary Data 1–4), with B cells showing downregulation of interferon signaling and IL-6 family signaling (Supplementary Table 1), and T cells showing downregulation of cytokine signaling, adaptive immune response, neutrophil degranulation and interferon gamma signaling in ΔPdh-infected tissue (Supplementary Table 2). Differential gene expression in neutrophil and macrophage populations is described below. Although the precise mechanisms underlying these changes remain to be elucidated, these observations suggest that ARMAF-mediated alterations to the immune response may involve broader effects on multiple immune cell subsets. Taken together, these data suggest that ARMAF affects early-stage ROS induction and the numbers of macrophages and neutrophils continuously present at the site of infection.

**Fig. 2: Lack of PDH induces low levels of ROS and causes a macrophage/neutrophil-dominated immune response.**

ARMAF is required to avoid immune surveillance

Immunofluorescence was then used to directly observe interactions between streptococcal bacteria and recruited immune cells in infected tissue. Consistent with the data presented above, neutrophils were the predominant cell in WT lesions at 1-dpi, which surrounded a central core of the lesion where streptococcal bacteria were growing in an extra-cellular niche. The central core itself consisted mostly of bacteria and contained few intact inflammatory cells (bacteria appear green, neutrophils appear purple, Fig. 3A and Supplementary Fig. 3). While neutrophils also surrounded ΔPdh bacteria in a central region, considerably more macrophages were observed in the surrounding tissue (red arrows, Fig. 3B). At 3-dpi, WT streptococci continued to be predominantly extra-cellular and partially breached into deeper tissues (Fig. 3C). In contrast, while the ΔPdh mutant had a similar tissue burden as WT at this time point (see Fig. 1A) and were constrained to a defined lesion, the streptococci were mostly located intracellularly in macrophages and/or neutrophils (Fig. 3D), as indicated by the white appearance of the merged macrophage, neutrophil, and bacterial fluorescent channels (see Supplementary Fig. 3). Consistent with our prior study that WT and ΔPdh streptococci could persist intracellularly in macrophages, examination of tissue sections using transmission electron microscopy (TEM) revealed macrophages containing bacteria in membrane-bound compartments, with ΔPdh-infected macrophages typically containing more bacteria per cell (black arrow) with fewer signs of cell death, including nucleoli with a translucent (ΔPdh) vs opaque (WT) appearance (indicated by the white arrows, Fig. 3E). Previously, we observed that the ΔPdh strain exhibited lower survival compared to WT, correlating with its increased sensitivity to acid stress, as demonstrated by vacuole acidification and enhanced survival with Bafilomycin A1 treatment⁵. To test whether the influx of macrophages or neutrophils contributed to host resistance and DT, mice were treated to deplete macrophages (with clodronate liposomes) or neutrophils (with anti-Gr1 antibody) at 18 h prior to bacterial challenge. The efficacy of depletion was confirmed by assessing cell percentages in the spleens of all mice using flow cytometry (Supplementary Fig. 4), with the potential existence of an anti-inflammatory effect²⁰. Mice depleted of macrophages were highly susceptible to infection with WT S. pyogenes, showing significantly reduced survival by 3-dpi (70% mortality) as compared to ΔPdh-infected mice (10% mortality), while mice depleted of neutrophils all survived infection with both strains of bacteria (Fig. 3F). Analysis of DT revealed that depletion of neutrophils exacerbated tissue damage caused by WT bacteria (Fig. 3G, H), while depletion of macrophages increased WT bacterial burdens (Fig. 3I). In contrast, neither tissue damage nor bacterial burdens increased when mice depleted of either neutrophils or macrophages were infected by ΔPdh (Fig. 3G–I). Thus, consistent with prior analyses showing that PDH supports intracellular persistence and altered cytokine expression of infected macrophages in vitro⁵, these results add additional support for PDH in the regulation of DT by controlling how S. pyogenes influences the behaviors of recruited myeloid cells. In addition, these data reveal unique roles for macrophages and neutrophils in the regulation of DT.

**Fig. 3: PDH contributes to extracellular growth and immune cell-dependent bacterial clearance.**

ARMAF blocks anti-inflammatory and wound-healing responses

In IL-10⁻/⁻ mice, tissue damage caused by ΔPdh resembles WT, showing that ARMAF via PDH is required for S. pyogenes to selectively repress production of the anti-inflammatory cytokine IL-10 without altering expression of the pro-inflammatory cytokine TNFα⁵. To identify the cell types responsible for increased IL-10 expression, we used Tg IL-10 bacterial artificial chromosome (BAC)-in transgene (10BiT) IL-10 reporter mice, where IL-10-expressing cells display Thy1.1 on their surface²¹. Using Thy1.1 expression as a proxy for IL-10 expression, we observed both a higher frequency of Thy1.1⁺ macrophages, monocytes, and neutrophils in ΔPdh- vs. WT-infected skin (Fig. 4A) and that these macrophages and neutrophils expressed more Thy1.1 on a per-cell basis (Fig. 4B). In contrast, Thy1.1 expression and numbers of Thy1.1-expressing T cells were similar between WT- and ΔPdh-infected cutaneous tissue (Fig. 4A, B). Consistent with this, examination of the scRNAseq dataset (Supplementary Data 1, 2) showed that the IL-10 signaling pathway is one of the most differentially up-regulated pathways in neutrophils by infection of ΔPdh vs. WT (Supplementary Table 3). In macrophages, expression of IL-10 is associated with the M2-like phenotype that promotes anti-inflammatory responses²². Comparison of markers for the M2 vs. the pro-inflammatory M1macrophage phenotype showed higher expression of M1 markers during WT infection (IL1β, CCL3, etc. Supplementary Fig. 5) and higher expression of M2 markers during ΔPdh infection (Tgm2, Vegfa, etc, Supplementary Fig. 5). The M2 phenotype is associated with the wound-healing response²³ and the scRNAseq dataset revealed higher expression of several signaling genes involved in tissue repair during ΔPdh infection, including S100a9 and S100a4 in myeloid cells (Fig. 4C). Elevated expression of IL-10 regulatory genes was discovered in ΔPdh-infected tissue, including STAT3, Hmox1, and Cdkn1a (Supplementary Fig. 6). Immunofluorescent microscopy analysis of RAW 264.7 macrophages infected in vitro revealed enhanced expression of the M2-associated markers CD163 and CCL22²⁴ in ΔPdh-infected compared to WT-infected cells (Fig. 4D–G, and Supplementary Fig. 7). Together, these data show: 1. Myeloid, not lymphoid cells, are the source of increased IL-10 in ΔPdh infection; and 2. That carbon flux through PDH is required to depress anti-inflammatory and wound-healing responses.

**Fig. 4: PDH blocks the anti-inflammatory and wound-healing responses to infection.**

SCFA’s produced by ARMAF alter levels of host acetyl-CoA to regulate IL-10 expression

Constraining carbon flux through mutation of PDH alters the composition of the short-chain fatty acid (SCFA) end-products of fermentation, most notably a significant reduction in levels of acetate and formate⁵. Similar trends of low acetate and formate levels were found in the supernatant of infected macrophages (Fig. 5A). Since SCFAs have immunoregulatory properties⁹, the effect of these SCFAs on DT was evaluated by complementing ΔPdh infection of RAW 264.7 macrophages or bone marrow-derived macrophages (BMDM) in vitro by addition of exogenous acetate and formate. Supplementation restored a WT phenotype to ΔPdh infection, with WT levels of both persistence and inhibition of IL-10 expression (Fig. 5B-C and Supplementary Fig. 8). While the effect of formate on host cells is not well understood, acetate can affect macrophage gene expression by a number of sensing pathways, including Toll-like receptors, G protein-coupled receptors, and adenosine receptors^25,26,27. However, several antagonists targeting these pathways had no effect on IL-10 expression in response to ΔPdh infection (Supplementary Figs. 9, 10). Alternatively, acetate can be utilized by host cells by conversion to acetyl-CoA, which then serves as a co-factor for a wide range of cellular functions including gene regulation²⁸. To determine whether acetyl-CoA contributes to the regulation of IL-10 expression, acetyl-CoA levels were examined in infected RAW 264.7 macrophages, which revealed that macrophages infected by ΔPdh mutant had significantly lower levels of acetyl-CoA as compared to WT infection (Fig. 5D). Supplementation with acetate and formate significantly enhanced acetyl-CoA levels for infection by ΔPdh, but not for WT bacteria (Fig. 5D). Additional evidence for alteration to acetyl-CoA metabolism comes from the scRNAseq dataset, which shows elevated expression of genes for acetyl-CoA synthesis and metabolism (Supplementary Fig. 11). Acetyl-CoA acts to regulate gene expression as a co-factor for acetylation of regulatory lysine residues on a broad range of proteins, including histones, transcription factors, chaperones, and other regulatory factors²⁹. Regulation is influenced both by levels of acetyl-CoA and the activity of histone deacetylases (HDACs), which remove acetyl groups on a broad range of regulatory proteins in addition to histones³⁰. To determine if acetylation contributes to the regulation of IL-10 expression infected RAW 264.7 macrophages were treated with Trichostatin A (TSA), a broad-spectrum inhibitor of zinc-dependent HDACs³¹. While having no significant effect on WT or ΔPdh persistence in RAW 264.7 macrophages or bone marrow-derived macrophages (BMDM) (Fig. 5E and Supplementary Fig. 8), TSA treatment inhibited IL-10 expression to below the level of detection by infection of either WT or ΔPdh (Fig. 5F and Supplementary Fig. 8). There are 18 HDAC enzymes, which are organized into 5 distinct classes of which classes I, IIa, IIb, and IV are zinc-dependent³². Treatment with inhibitors selective for several zinc-dependent classes, including Entinostat (class I; HDACs 1, 2, 3, 8), TMP195 (class IIa; HDACs 4, 5, 7, 9) and Tubastatin A (class IIb; HDAC 6), did not affect bacterial persistence alone or when combined (Fig. 5G) and did not significantly affect IL-10 expression on an individual basis (Fig. 5H). However, the combination of all three inhibitors phenocopied TSA treatment, inhibiting IL-10 expression to near-background levels (Fig. 5H). Together, these data suggest a model where acetate production by S. pyogenes via PDH enhances levels of acetylation in macrophages, influencing gene expression and IL-10 regulation.

**Fig. 5: IL-10 expression is influenced by acetyl-CoA levels and the activity of histone deacetylases (HDACs).**

According to the model, the acetate and formate produced by WT bacteria increases host cell acetylation, resulting in partial suppression of IL-10. TSA treatment significantly enhances acetylation in WT-infected cells, leading to a complete inhibition of IL-10 expression. In ΔPdh-infected cells, TSA elevates acetylation levels sufficiently to compensate for the diminished production of bacterial acetate, thus effectively inhibiting IL-10 expression. Together, these observations suggest that acetylation levels are a critical regulator of IL-10. The model predicts: 1. That the loss of PDH activity in ΔPdh results in lower levels of acetylation that can be counteracted by treatment with HDAC inhibitors to increase overall levels of acetylation, and 2. The inhibition of deacetylases would have a minor effect on the already high levels of acetylation induced by WT infection compared to ΔPdh infection. To test this model, an RNA-seq analysis was conducted on WT- and ΔPdh-infected Raw 264.7 macrophages in the presence or absence of TSA treatment. As predicted, TSA treatment of WT-infected macrophages did not result in a large difference in global gene expression (0 genes upregulated, 65 genes downregulated; WT vs WT + TSA, Supplementary Fig. 12 and Supplementary Table 4). However, TSA treatment had a much greater impact on gene expression in ΔPdh-infected macrophages (220 genes upregulated, 175 genes downregulated; ΔPdh vs ΔPdh + TSA, Supplementary Fig. 12 and Supplementary Table 5). In addition to IL-10, cytokines including CSF2, IL-11, TNF, CXCL2, Ccl3, CXCL3, and IL-1α were also downregulated by TSA treatment (Supplementary Data 5, 6 and Supplementary Table 4–6). Taken together, these data indicate that: 1. S. pyogenes manipulates disease tolerance using SCFAs by influencing host acetyl-CoA metabolism; 2. Elevated levels of acetylation correlate with inhibition of IL-10 expression; and 3. The observation that suppression of IL-10 expression requires the combined action of inhibitors from several distinct HDAC classes implies that diverse regulatory pathways act redundantly to regulate IL-10. Future studies aimed at elucidating the specific histone or non-histone targets of HDACs involved in regulating IL-10 expression, as well as the downstream signaling pathways involved in this process, will provide a more comprehensive understanding of the molecular mechanisms underlying PDH-mediated modulation of IL-10 expression.

Reprogramming carbon flow using the PDH inhibitor H8 attenuates tissue damage

To test the hypothesis that therapeutic inhibition of PDH can attenuate tissue damage during S. pyogenes infection of soft tissue, we examined a library of 20 compounds with high inhibitory activity against bacterial, but not human, PDH^14,16,17. Since PDH is required for aerobic growth⁵, the various compounds’ ability to inhibit S. pyogenes PDH was assessed under aerobic growth conditions, identifying compound H8 (2-methyl-5-((4-(((4-(trifluoromethyl)phenyl)amino)methyl)-1H-1,2,3-triazol-1-yl)methyl)pyrimidin-4-amine) (Fig. 6A) as most effective in inhibition of aerobic growth (Fig. 6B). H8 effectively inhibited WT bacteria growth during macrophage infection (Fig. 6C), mirroring the effects seen during ΔPdh infection, and elicited significantly higher levels of IL-10 in WT-infected macrophages vs. untreated macrophages (Fig. 6D). To test H8 in vivo, mice challenged subcutaneously with WT S. pyogenes (0 hr) immediately received a subcutaneous injection of H8 at the site of infection, followed by daily injections of H8 at the same site (Fig. 6E). When analyzed at 3-dpi, H8-treated mice had a similar burden of infection as compared to untreated (vehicle-treated) or ΔPdh-infected mice (Fig. 6F). However, lesions areas of H8-treated mice were significantly reduced relative to untreated mice (Fig. 6G) with less tissue damage (Fig. 6H).

**Fig. 6: Small molecule H8 mitigates tissue damage during *S. pyogenes* infection.**

Discussion

In this study, we have determined the mechanism through which a pathogenic bacterium can manipulate the host’s DT response in patterns influenced by oxygen and carbon flow through its central carbon metabolism. We show how a small molecule inhibitor of a fermentative enzyme can reprogram carbon flow to exploit DT control mechanisms to mitigate tissue damage during soft tissue infection. Further studies into how pathogens can actively manipulate DT responses may provide approaches for the therapeutic management of tissue-destructive infections.

Comparison of WT to ΔPdh S. pyogenes in a murine model of infection supports the following model for how central carbon metabolism manipulates DT. For WT, streptococcal bacteria are predominantly extracellular. However, a minor population of bacteria is phagocytosed by monocyte-derived macrophages recruited to the site of infection. Using ARMAF, bacteria persist in these cells to produce high levels of acetate and formate, which are metabolized by macrophages^33,34, resulting in elevated levels of acetyl-CoA. High levels of acetyl-CoA enhance the acetylation of key regulatory proteins, which in turn contributes to the repression of IL-10 expression. Consequently, the ratio of IL-10 relative to the pro-inflammatory cytokine TNFα is low, delaying the wound healing response to promote greater damage to tissue. In the absence of PDH activity, either through mutation or from chemical inhibition, only low levels of acetate and formate are produced by the streptococcal bacteria, macrophage acetyl-CoA levels are not elevated, and acetylation of IL-10 regulatory protein(s) is reduced. As a result, when the IL-10/TNFα ratio is elevated, it leads to the recruitment of a higher number of macrophages and neutrophils that produce IL-10 at the infection site. This, in turn, triggers a wound-healing program aimed at halting the advancement of tissue damage and expediting the kinetics of wound healing. This model that links the host DT response to bacterial metabolic carbon flow can also help to explain how S. pyogenes infections can dramatically differ in severity depending on the site of infection in response to host-derived cues, as tissue-specific differences in the availability of carbon sources and oxygen can influence carbon flow and thus, how the bacterium manipulates host DT responses.

Due to its broad-spectrum anti-inflammatory activity and its expression by a variety of immune cells, regulation of IL-10 expression plays a key role in balancing DT against the pro-inflammatory anti-pathogen response². However, the factors involved are poorly understood. It is known that regulation is multi-factorial and includes numerous transcription factors and post-transcriptional mechanisms (reviewed³⁵). An example of the former is Bhlhe40, which acts to repress IL-10 expression during Mycobacterium tuberculosis infection³⁶. Here, we show that HDAC activity plays an important role in the regulation of IL-10 expression. In the best-characterized mechanism of HDAC regulation of gene expression, histone acetylation leads to an unwinding of chromatin structure to stimulate transcription (reviewed²⁹). HDAC inhibitors elevate levels of histone acetylation resulting in enhanced transcription²⁹. However, HDAC inhibitors had the opposite effect during S. pyogenes infection, indicating that elevated levels of acetylation acts to suppress, rather than stimulate, IL-10 expression. This suggests that the PDH-dependent regulatory mechanism does not involve acetylation of histones. Consistent with this, we found that infection with WT or ΔPdh did not result in elevated acetylation of histone H3, nor did supplementation with acetate + formate. Although, as expected, higher levels of acetylation were observed upon treatment with the HDAC inhibitor TSA (Supplementary Fig. 13). Consistent with this, it has been observed that in macrophages, histone phosphorylation rather than acetylation plays an important role in the control of IL-10 gene expression³⁷. Since HDACs also have multiple non-histone protein targets, including transcription factors, signal transduction mediators, DNA repair enzymes, nuclear import regulators, chaperone proteins, and other inflammation mediators³⁰, it is likely that S. pyogenes is targeting a non-histone regulator. Furthermore, since a combination of inhibitors specific for several different classes of zinc-dependent HDAC families was required to phenocopy the broad-spectrum inhibitor TSA, it is likely that HDACs downstream of multiple regulatory pathways act redundantly in IL-10 regulation.

By providing a mechanism for how S. pyogenes can actively manipulate the DT response and how this affects the cellular characteristics of the innate immune response, the data presented here extend studies showing that cooperation between macrophages and neutrophils is required for innate defense against S. pyogenes during infection of soft tissue. It has been shown that resident macrophages effectively phagocytose and kill S. pyogenes in the absence of opsonic antibodies, while monocytes migrate to the site of infection and differentiate into macrophages assisting with bacterial clearance^38,39,40. Monocyte-derived macrophages play an important role in regulating the neutrophilic response, which acts to compartmentalize the infection to prevent systemic spread^38,39,40. Macrophages also must balance the expression of pro- and anti-inflammatory cytokines to regulate the wound healing response²⁴. How macrophages achieve this balance during infection is unknown; however, our analysis suggests that disruption of bacterial aerobic metabolism can significantly interfere with this balance. Consistent with our prior report⁵, the data suggest that while S. pyogenes is predominantly an extracellular pathogen in soft tissue, that DT is regulated by a minor population of bacteria that are internalized by monocyte-derived macrophages. These intracellular bacteria manipulate macrophage immunoregulatory activities to regulate macrophage polarization and macrophage-neutrophil cooperation such that ΔPdh-infected tissue contains twice as many macrophages as WT-infected tissue, which also become infected by intracellular bacteria, leading to higher expression of IL-10 and an accelerated wound-healing response.

Studies of the microbiome space have shown that microbial SCFAs play an important role in shaping health and disease through immunomodulatory effects⁴¹. For example, acetate can enhance innate immune responses through signaling via the membrane-associated free fatty acid receptor 2⁴². Formate can manipulate patterns of methylation to regulate mitophagy and inhibit the polarization of macrophages to pro-inflammatory phenotypes⁴³. Lactate produced by Staphylococcus aureus biofilm can inhibit HDAC11 to reprogram host immunity to promote persistent infection^44,45. In this study, we show a mechanism through which SCFAs regulate immunity by manipulation of host cell levels of acetyl-CoA, which impacts acetylation metabolism. This is consistent with studies indicating that there is compartmentalization of the production of acetyl-CoA in the mitochondria and cytoplasm vs. the nucleus^28,46,47,48. Identification of the pathway(s) targeted by streptococcal SCFA metabolism will be important for understanding how a pathogen can manipulate the DT response.

Due to its role in the regulation of DT, PDH becomes an attractive target for therapeutic interventions in severe invasive diseases. In addition to S. pyogenes, PDH is critical for the growth of numerous pathogenic bacteria^49,50. PDH is an enzyme complex that converts pyruvate into acetyl-CoA, which consists of three enzymes⁵. Of these, component E1a catalyzes the initial irreversible reaction of the complex and is highly conserved between eukaryotic and prokaryotic organisms. However, using the structure of the Escherichia coli PDH E1a enzyme, a combination of molecular docking, site-specific mutagenesis, and enzymatic assays has resulted in the synthesis of a series of compounds with highly selective binding to the prokaryotic enzyme that has significant anti-bacterial-activity-against-several Gram-negative species^14,16,17. We have shown that S. pyogenes growth in soft tissue is primarily supported by mixed-acid fermentation through PFL⁵, so the failure of chemical inhibition to reduce CFUs during soft-tissue infection was not unexpected. However, carbon flux through the PDH arm of mixed acid fermentation does influence DT to regulate levels of tissue damage⁵. Here we show that a selective PDH inhibitor was effective in reprogramming carbon flux to alter patterns of tissue damage. Further refinement to increase the efficacy of targeting the Gram-positive PDH E1a enzyme may result in effective therapies for tissue destructive infections where rapid tissue damage limits perfusion of conventional antibiotics to the site of infection and retards the healing process, resulting in unwanted surgical interventions^51,52,53,54. Therapies that reduce tissue destruction in combination with standard-of-care antibiotics could improve treatment outcomes.

Methods

Ethics

All animal experiments were approved by the Institutional Animal Care and Use Committee (protocols #16-1119 and #22-0307). Procedures were performed according to all institutional policies, the Animal Welfare Act, NIH guidelines, and American Veterinary Medical Association guidelines on euthanasia. Adult (6–8-week-old) female C57BL/6 J and derivative mice, were maintained at Washington University School of Medicine animal facilities. 10BiT IL-10 reporter mice were maintained in a specific pathogen-free facility²¹.

Bacterial strains

Where indicated, S. pyogenes HSC5⁵⁵ and isogenic mutant ΔPdh⁵ were cultured in Todd Hewitt + 1% Yeast Extract (THY) broth⁵⁶ or C medium supplemented with 0.2% glucose⁵⁶. Quantitation of S. pyogenes CFUs utilized THY medium solidified by the addition of 1.4% agar⁵⁶, which was incubated at 37 °C anaerobically using a commercial atmospheric container (GasPak™ EZ, catalog #BD 260001). All cultures were seeded from overnight cultures in C medium to an initial OD₆₀₀ = 0.05, which were then incubated for the various times indicated in the text.

Mouse subcutaneous ulcer model of infection

Infection of 6–8-week-old C57BL/6 and 10BiT⁺ mice followed a well-established protocol⁵. As sex does not influence infection outcomes⁵⁷, only female mice were utilized in order to minimize the number of mice used⁵⁷. Briefly, mice received a subcutaneous injection of ~ 10⁷ CFU of the indicated bacterial strains into the thigh, and the areas of the resulting ulcers determined following 72 h of infection from analysis of digital images using ImageJ as described⁵⁷. Mice were euthanized by CO₂ with a flow rate of 30–70% of the chamber’s volume per minute, followed by cervical dislocation. Bacterial burdens in lesions were assessed by excision and homogenization of infected tissues and spot-plating aliquots of the tissue homogenate⁵⁸. CFU counts were normalized based on the entire infected tissue volume. Procedures of H8 treatment were shown in Fig. 6E.

Macrophage infection

RAW 264.7 macrophages were grown in Dulbecco’s Modified Eagles Medium (DMEM, Sigma) with 10% fetal bovine serum supplemented with antibiotics (1% Penicillin-Streptomycin) at 37 °C in an atmosphere of 5% CO₂. For infection, the bacterial inoculum was prepared as described above for murine infection, and after replacing media with antibiotic-free media, bacteria were added to macrophages cultured in 12-well dishes at an MOI of 10:1 which were then immediately subjected to centrifugation at 500 × g for 1 min. and placed in an incubator for 1 h. Wells were then washed twice with Dulbecco’s PBS, then DMEM containing antibiotics (100 μg/ml Gentamicin) was added for 5 minutes and washed 3 x with PBS before the cells were incubated for an additional 2–4 h in antibiotic-free DMEM. For supplementation, acetate/formate (Sigma-Aldrich, #32319, 71539) or acetylation inhibitors (TSA: Sigma-Aldrich, T1952; TMP195: Selleck, #S8502; Tub A: Selleck, #S8049; Ent: Selleck, #S1053; H8 is custom-synthesized using the formula from Fig. 6A^14,16,17) were added at the beginning of infection. Cells were harvested using a cell scraper and collected by centrifugation at 1000 × g for 5 min, were resuspended in 1 ml dH₂O at pH 11, and lysed using a cup sonicator for 20 sec. Bacteria in the resulting suspension were collected by centrifugation (6000 × g, 5 min) and resuspended in 100 μl of C-media for determination of CFUs. Net fold-change in CFU was determined by comparison to cells harvested after the addition of an antibiotic-containing medium.

Luminol assay

Bioluminescence images were captured using the In Vivo Imaging System (IVIS) Spectrum (Perkin Elmer, Santa Clara, CA, USA) and analyzed using IVIS imaging software (Perkin Elmer, version 2.6.1). Ten minutes before imaging, animals received an intraperitoneal (IP) injection of 200 mg/kg luminol sodium salt (Sigma-Aldrich, A4685).

Flow cytometry

To prepare single-cell suspensions, infected ulcer with minimal surrounding tissue were removed aseptically and placed in ice-cold PBS. Samples were mechanically dissociated using a GentleMACS^TM dissociator in C Tubes (Miltenyi Biotec, 130-096-334) according to the manufacturer’s protocol⁵⁹. The resulting cell suspension was filtered through a 40 µm cell strainer (Corning Falcon, 352340). For staining, all antibodies were used at a dilution of 1:200. Single-cell suspensions were preincubated with anti-CD16/CD32 Fc Block antibody (BioXCell, BE0307) in PBS for 10 min at RT before staining with the following antibodies. Antibodies were obtained from BioLegend and included: FITC anti-CD45.2 (109806), BV605 anti-MHC II (107639), APC anti-CD64 (139306), PB anti-Ly6C (128014), and BV510 anti-Thy1.1 (202535). The following anti-mouse antibodies were obtained from Tonbo Biosciences: PE anti-F4/80 (50-4801-U100), PercpCy5.5 anti-Ly6G (65-1276-U025), APC-Cy7 anti-CD11c (25-0114-U025), PE-Cy7 anti-CD11b (60-0112-U100). Alexa Fluor^TM 700 anti-CD3ε was from Invitrogen (56-0033-82). Validation information for these antibodies is available at vendor websites. Cells were stained for 20 min at 4 °C, washed, and fixed in 4% paraformaldehyde (Electron Microscopy Sciences) in PBS for 20 min at 4 °C. Cell counts were determined by hemocytometer³⁶. Flow cytometry data were acquired on an LSR Fortessa cytometer (BD) and analyzed using FlowJo software (TreeStar, version 10.8.1). Gating strategies are depicted in Supplementary Fig. 1.

Transmission electron microscopy

For ultrastructural analyses, samples were fixed in 2% paraformaldehyde/2.5% glutaraldehyde (Ted Pella Inc, Redding, CA) in 100 mM sodium cacodylate buffer for 2 hr at room temperature and then overnight at 4 °C. Samples were washed in sodium cacodylate buffer and postfixed in 1% osmium tetroxide (Ted Pella Inc.) for 1 hr at room temperature. After three washes in dH₂O, samples were stained en bloc in 1% aqueous uranyl acetate (Electron Microscopy Sciences, Hatfield, PA) for 1 h. Samples were then rinsed in dH₂0, dehydrated in a graded series of ethanol, and embedded in Eponate 12 resin (Ted Pella Inc). Ultrathin sections were cut to a thickness of 95 nm with a Leica Ultracut UCT ultramicrotome (Leica Microsystems Inc., Bannockburn, IL), stained with uranyl acetate and lead citrate, and viewed on a JEOL 1200 EX transmission electron microscope (JEOL USA Inc., Peabody, MA) equipped with an AMT 8-megapixel digital camera and AMT Image Capture Engine V602 software (Advanced Microscopy Techniques, Woburn, MA).

Macrophage or neutrophil depletion

To deplete macrophages, mice were intraperitoneally injected with 1 mg liposomal clodronate or control liposomes (Fisher Scientific, NC1488571)⁶⁰. To deplete neutrophils, mice were intraperitoneally injected with 500 µg monoclonal anti-Ly6G (BioXCell, BE0075-1) or anti-Gr1 (BioXCell, BE0075) antibody, or rat IgG2a isotype control (BioXCell, BE0089) diluted in sterile PBS 18 h before infection⁶¹. Depletion efficiency was assessed by FACS as described in the text and Supplementary Fig. 4.

Western blot analyses

RAW 264.7 macrophages were infected for 4 hours before being collected by gentle scraping, along with uninfected controls. Total cell lysates were heated at 100 °C for 10 min, then centrifuged at 13,000 × g for 10 min at 4 °C. Equal amounts of protein (30 µg per lane) were mixed with Laemmli sample buffer containing β-mercaptoethanol, separated on 10% SDS-polyacrylamide gels, and transferred to nitrocellulose membranes. Membranes were blocked for 1 h at room temperature in 5% non-fat milk in Tris-buffered saline with 0.1% Tween-20 (TBS-T) and incubated overnight at 4 °C with primary antibodies anti-H3K9 (Cell Signaling Technology, cat.#9649 T, 1:1000) or anti-H3 (Cell Signaling Technology, cat.#4499 T, 1:1000). Blots were washed in TBS-T and incubated with horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG (1:5000) for 1 h at room temperature. Protein bands were detected using enhanced chemiluminescence (Thermo Fisher Scientific, cat.#32106) and imaged with a ChemiDoc MP System (Bio-Rad)⁶².

Cytokine quantification

At the 4 h time point, supernatants from infected and uninfected RAW 264.7 cells were harvested by gentle scrape and centrifuge at 1000 × g for 5 mins, and stored at − 80 °C. The following day, the concentration of IL-10 and TNFα in supernatants were determined by ELISA according to the manufacturer’s protocol (R&D Systems, cat.#DY417, #DY410). Concentrations were determined by comparison to a standard curve generated using purified proteins provided in the kit.

Measurement of SCFAs and Acetyl-CoA

Following 4 h of infection, RAW 264.7 cells were harvested by gentle scrape, subjected to centrifugation (1000 × g, 5 min), and the resulting supernatant fluids and cell pellets stored separately at − l80 °C. The concentrations of acetate (Sigma-Aldrich, MAK086), lactate (Sigma-Aldrich, MAK064), and formate (Sigma-Aldrich, MAK059) were then determined in cell supernatants. The concentration of acetyl-CoA from cell pellets was measured by a colorimetric assay using commercial kits (Sigma-Aldrich, MAK039) following the manufacturer’s protocol. SCFA and acetyl-CoA levels were compared to uninfected RAW 264.7 cells.

Fluorescent microscopy

Infected skin ulcers were fixed in 10% buffered formalin (Thermo Fisher Scientific) for 24 h at room temperature, followed by overnight storage in 70% ethanol at 4 °C. Samples were paraffin-embedded and sectioned (5 µm) by the Anatomic and Molecular Pathology Core Labs at Washington University. Sections were deparaffinized in xylene, rehydrated through graded ethanol, and subjected to antigen retrieval in citrate buffer (pH 6.0) at 95 °C for 20 min. After cooling, sections were blocked in 5% goat serum in PBS with 0.1% Tween-20 (PBS-T) for 1 h to prevent nonspecific binding. Primary antibodies diluted in PBS-T with 1% BSA were applied overnight at 4 °C. After washing, fluorescently labeled secondary antibodies (1:500 in PBS-T) were added for 1 h at room temperature. Nuclei were counterstained with DAPI, and sections were mounted with ProLong Gold Antifade Mountant (Thermo Fisher Scientific)⁶³. PE anti-F4/80 (Tonbo, 50-4801-U100), FITC anti-Streptococcus Group A (Invitrogen, PA1-73056), Alexa Fluor^TM 647 anti-Ly6G (BioLegend, 127609), CD163 (Invitrogen, #16646-1-AP), MDC (Invitrogen, #57226) antibodies were used. Fluorescent images were acquired using a Zeiss Axio Imager 2 microscope (Jena, Germany) equipped with a digital camera. Fluorescent intensity was measured with ImageJ software (https://imagej.nih.gov/ij/).

RNA sequencing

Total RNA from infected and treated RAW 264.7 cells (4-hpi) were isolated using a Direct-Zol RNA MiniPrep Plus kit (Zymo Research, cat.#R2071) per the manufacture’s protocol. RNA was quantified using a Qubit 2.0 Fluorometer, and integrity was checked on an Agilent TapeStation 4200. RNA sequencing libraries were prepared with the NEBNext Ultra II RNA Library Prep Kit for Illumina (cat.#E7760S). mRNAs were enriched using Oligo(dT) beads, fragmented, and converted into cDNA, which was end-repaired, adenylated, adapter-ligated, indexed, and enriched via PCR. Libraries were validated on a TapeStation, quantified with Qubit and qPCR, and sequenced on an Illumina HiSeq 4000 (2x150bp PE). Raw data were converted to fastq files and de-multiplexed using bcl2fastq (Illumina, v2.17) with one allowed index mismatch. Reads were trimmed with Trimmomatic (v0.36) and mapped to the Mus musculus genome (ENSEMBL) using a STAR aligner (v2.5.2b). Unique gene hit counts were generated with Subread (v1.5.2). Differential expression was analyzed with DESeq2 (v3.15) using Wald tests, identifying genes with adjusted P-values < 0.05 and absolute log2 fold-changes > 1. Pathway enrichment analysis was performed using Reactome, with P-values corrected for multiple testing via the Benjamini–Hochberg procedure⁵.

Single cell RNA-seq

DNA was prepared after the Gel Beads in Emulsion (GEM) generation and barcoding, followed by the GEM-RT reaction and bead cleanup steps. Purified cDNA was amplified for 11–16 cycles before being size-selected using SPRIselect beads (Beckman Colter, B23319). Samples were then run on a Bioanalyzer (Agilent, #2100) to determine the cDNA concentration. Gene Expression libraries were prepared as recommended by the 10x Genomics Chromium Single Cell 5’ Reagent Kits User Guide (v2 Chemistry Dual Index), with appropriate modifications to the PCR cycles based on the calculated cDNA concentration. For sample preparation on the 10x Genomics platform, the Chromium Next GEM Single Cell 5’ Kit v2, 16 rxns (PN-1000263), Chromium Next GEM Chip K Single Cell Kit, 48 rxns (PN-1000286), Dual Index Kit TT Set A, 96 rxns (PN-1000215) were used. The concentration of each library was determined through qPCR utilizing the KAPA library Quantification Kit according to the manufacturer’s protocol (KAPA Biosystems/Roche) to produce cluster counts appropriate for the Illumina NovaSeq6000 instrument. Normalized libraries were sequenced on a NovaSeq6000 S4 Flow Cell using the XP workflow and a 151 × 10 × 10 × 151 sequencing recipe according to the manufacturer’s protocol. A median sequencing depth of 50,000 reads/cell was targeted for each Gene Expression library. Transcript alignment, counting, and inter-library normalization were performed using the Cell Ranger pipeline (10x Genomics, default settings, Version 2.1.1, GRCh38 reference). Data were analyzed using the Biomage online tool (https://www.biomage.net/).

Statistical analyses

Data are derived from at least 3 independent experiments, with values presented representing the mean ± SEM for each group. The difference between experimental groups were tested for significance using a non-parametric statistical test (two-tailed Mann-Whitney U test) available in GraphPad Prism software where *, **, and *** indicates p < 0.05, < 0.01, and < 0.001, respectively. For all tests, the null hypothesis was rejected for p > 0.05.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw files for ScRNASeq data generated in this study have been deposited in the NCBI database under accession code BioProject ID PRJNA917702. The raw files for RNAseq data generated in this study have been deposited in the NCBI database under accession code BioProject ID PRJNA1145018. The raw data generated in this study are provided in the Supplementary Information/Source Data file. Source data are provided in this paper.

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Acknowledgements

We thank Wandy Beatty for assistance with electron microscopy. We thank Dr. Casey Weaver from University of Alabama at Birmingham, Birmingham, AL for providing the 10BiT mice. This work was supported by National Institutes of Health Grants R56 AI070759, R21 AI163825 (to M.G.C.) and R01 AI132653 (to B.T.E.), National Natural Science Foundation of China 21472062 and 21907035 (to Y.Z. & H.H.).

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Wei Xu
Present address: Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, USA

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Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO, USA
Wei Xu, Zongsen Zou, Suzanne Hickerson & Michael G. Caparon
Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
Tara R. Bradstreet & Brian T. Edelson
Key Laboratory of Chemical Biology, Jiangxi Normal University, Nanchang, PR China
Yuan Zhou
Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, Central China Normal University, Wuhan, PR China
Yuan Zhou & Hongwu He

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Contributions

Conceptualization. W.X., B.T.E., and M.G.C. Investigation. W.X., T.B., Z.Z., and S.H.. Writing-original draft. W.X. and MGC. Resources. W.X., Y.Z., H.H., B.T.E., and M.G.C. Supervision and acquisition of grant support. B.T.E. and M.G.C.

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Correspondence to Michael G. Caparon.

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Xu, W., Bradstreet, T.R., Zou, Z. et al. Reprogramming aerobic metabolism mitigates Streptococcus pyogenes tissue damage in a mouse necrotizing skin infection model. Nat Commun 16, 2559 (2025). https://doi.org/10.1038/s41467-025-57348-x

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Received: 14 December 2023
Accepted: 20 February 2025
Published: 15 March 2025
DOI: https://doi.org/10.1038/s41467-025-57348-x