Candesartan cilexetil disrupts methicillin-resistant Staphylococcus aureus membrane and potentiates gentamicin and polymyxin B activity

Domain: biology
Published: 2026-03-15
Editorial outcome: major_revision

Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of bacterial infections, but treatment options are limited due to MRSA multi-drug resistance. The anti-hypertensive drug Candesartan cilexetil (CC) exhibits potent anti-MRSA activity. It permeabilizes the membrane of MRSA cells and potentiates the activity of aminoglycoside antibiotics. Here we used a variety of methods to elucidate the mechanism by which CC disrupts membrane homeostasis. We show that CC binds to bilayer lipid molecules, decreases membrane fluidity, down-regulates cell membrane and cell wall related genes and related metabolites, and decreases C20 fatty acids (C20:0). Decreasing C20:0 fatty acids confers CC- resistance, which can be reversed by C20:0 supplementation. Structural activity relationship analysis shows that the tetrazole ring and ester carbonic acid of CC are critical for antibacterial activity. Finally, CC reduces MRSA-MW2 replication in a murine MRSA abscess model, supporting a potential role of CC as a lead antimicrobial compound/potentiator against MRSA.

Introduction
Staphylococcus aureus is a leading cause of hospital- and community-acquired infections 1, 2, 3, 4. The broad repertoire of S. aureus virulence factors contributes to the wide spectrum of clinical manifestations and infection types, while persistence and tolerance mechanisms allow the pathogen to survive or continue limited growth in the presence of antibiotics, complicating treatment 5, 6. Increasing levels of antibiotic resistance further limits treatment options for S. aureus. The Centers for Disease Control and Prevention estimated that methicillin-resistant S. aureus (MRSA) is associated with more than 300,000 life-threatening infections and more than 10,000 fatalities per year in the United States 7.

The high cost of antibiotic development has engendered interest in repurposing existing Food and Drug Administration (FDA)-approved medications to treat bacterial infections. Our laboratory is particularly interested in antibacterial agents that affect key physical properties of the bacterial membrane, such as curvature and fluidity, thereby disrupting membrane architecture and compromising its functional integrity 8, 9, 10, 11, 12, 13, 14, 15. Membrane-disrupting compounds can also interact with membrane-embedded proteins or alter the proton motive force 16 . The particular effects of membrane-disrupting compounds are highly dependent upon the specific composition of the lipid bilayer 17, key components of which include cardiolipins and negatively charged phospholipids, such as phosphatidylglycerol 18. In MRSA, alterations in membrane lipid composition, particularly reduced phosphatidylglycerol or altered cardiolipin distribution, have been associated with decreased drug penetration and increased tolerance to membrane-active antibiotics. These adaptive changes stabilize the bilayer and create a barrier to antibiotic entry, thereby contributing directly to drug resistance 19, 20, 21 . Thus, targeting membrane lipid composition and fluidity may represent an effective strategy to overcome intrinsic and acquired resistance mechanisms in MRSA.

Here we show, that candesartan cilexetil (CC), an angiotensin II receptor blocker (ARB) used for treating hypertension 22, exhibited anti-staphylococcal activity in an MRSA-C. elegans high-throughput screen of ~82,000 synthetic compounds 23, consistent with published work by Xu et al. 24. Here, we explored the mechanism of action of CC using a variety of assays, microscopic techniques including cryo- computed tomography, multi-OMICS analysis, as well as in silico all-atom molecular dynamics (MD) simulations, and gas chromatography-mass spectrometry (GC-MS) analysis. We also evaluated the efficacy of CC in a murine S. aureus infection model. A key finding of our work is that CC treatment unexpectedly decreased S. aureus membrane fluidity and reduced the levels of C20 fatty acids (C20:0). Importantly, decreasing the levels of C20:0 fatty acids by cerulenin treatment greatly decreased CC susceptibility and supplementation of the cerulenin-treated cells with C20:0 fatty acids significantly restored CC susceptibility, suggesting that C20:0 fatty acids play an important mechanistic role in determining the susceptibility of MRSA to CC treatment. Our data suggest that CC has potential for development as S. aureus antibiotic potentiator.

Results
CC inhibits the growth of MRSA
The Microsource-1 library of chemical compounds was among the chemical libraries that our laboratory previously screened for anti-MRSA compounds using a C. elegans–MRSA infection model 11, 23. An antimicrobial compound identified in this screen was the antihypertensive drug Candesartan Cilexetil (CC; Fig. 1A) 23.

We found that the minimal inhibitory concentration (MIC) of CC is 8 mg/L against MRSA-MW2 and several S. aureus clinical strains and 16 mg/L against Enterococcus faecium (E007) (Supplementary Tables 1 and 2). CC is also active against vancomycin-intermediate resistant strains (Supplementary Table 3). In humans, CC is a prodrug 22. The active moiety, candesartan (Fig. 1A-inner panel), which is generated in gastric epithelial cells 25, did not exhibit antibacterial activity (Supplementary Table 4). Killing kinetics analysis showed that CC (≥2xMIC) was bactericidal against exponential-phase MRSA-MW2 cells (5-log -CFU/mL reduction to limit of detection, Fig. 1B) and stationary-phase MRSA-MW2 cells with a persister-like phenotype 26 (4-log -CFU reduction over a 4-hour treatment period at 4xMIC and 8xMIC, Fig. 1C). Notably, different inocula were used to represent distinct bacterial growth phases: ~10⁷ CFU/ml for log-phase cultures (actively dividing conditions; Fig. 1B) and ~10⁸ CFU/ml for stationary-phase cultures (metabolically less active and tolerant to antibiotics; Fig. 1C). In contrast, gentamicin (25xMIC) had no effect on persisters (Fig. 1C), consistent with published results 9, 26. Biofilm studies showed that CC treatment resulted in a dose-dependent reduction in biofilm formation and a decrease in metabolically active cells within mature biofilms, as assessed by Alamar Blue fluorescence (Fig. 1D). We found that CC inhibited bacterial biofilm formation at concentrations as low as 0.5xMIC (4 mg/L) (Fig. 1D-right) and disrupted mature biofilms at concentrations as low as 2xMIC (16 mg/L, Fig. 1D-left) as demonstrated by using Alamar Blue to monitor the levels of metabolically active cells. While the reduction at sub-MIC concentrations was modest, these results indicate that CC can affect biofilm metabolic activity.

CC disrupts the S. aureus membrane
Consistent with its rapid killing kinetics, CC (≥0.5xMIC) permeabilized MRSA-MW2 to Sytox Green (Fig. 1E) Experiments using the fluorescent probe DiSC (5) showed that 1xMIC and 2xMIC CC decreased MRSA-MW2 membrane potential similar to the positive control carbonyl cyanide m-chlorophenyl hydrazone (CCCP) (Fig. 1F). Unexpectedly, however, experiments with the fluorescent probe BCECF-AM showed that CC caused a rapid dose-dependent increase in intracellular pH in contrast with the positive control CCCP, which decreased intracellular pH (Fig. 1G). These latter results suggest that rapid intracellular alkalization may be a key feature underlying the mechanism by which CC disrupts the S. aureus membrane.

Unbiased all-atom molecular dynamic (MD) simulations (500 ns) of equilibrated bilayer-drug interactions using a model DOPC:DOPG (7:3) (1,2-dioleoyl-sn-glycero-3-phosphocholine/glycerol) lipid bilayer, showed that the tetrazole group of CC anchors the drug to the membrane surface where it binds to hydrophilic head groups of membrane phospholipids (glycerol moiety of DOPG), allowing CC to penetrate and rapidly perturb the membrane (Fig. 1H; Supplementary movie S1). The all-atom MD simulations of CC interacting with a model S. aureus membrane were carried out independently at Vanderbilt University and the University of Miami with similar results (compare Fig. 1H with Supplementary Figure 1). Although candesartan also penetrates DOPC/DOPG-simulated S. aureus membranes in these simulations, it does not interact with membrane lipids and does not appear to perturb the membrane (Supplementary Figure 1F).

Scanning electron microscopy (SEM) studies (Fig. 1I) showed normal morphology in DMSO but that multiple blisters formed on the MRSA-MW2 cell surface after CC treatment at 4xMIC, 2xMIC, 1xMIC (right to left respectively). Atomic force microscopy (AFM) confirmed the SEM observation that CC at 10xMIC caused changes in membrane topography and mechanical properties followed by cell-leakage (Fig. 1J). Transmission electron microscopy (TEM) studies showed that CC treatment caused morphological changes including mesosome-like structural deformities at 8xMIC or membrane deformation and cell shrinkage or condensation at 10xMIC (Fig. 1K). Cryo-computed tomography was used to generate a 3D model of CC-damaged membranes as well as mesosome-like formation (Fig. 1L). Cryo-computed tomography with 3D segmentation revealed that, compared to untreated (Supplementary movie S2), CC treatment caused membrane-associated irregularities such as membrane thinning, disruption of the bilayer structure, and the formation of perforations or gaps (Fig. 1L; Supplementary movies S3 and S4), consistent with MDS analysis.

Comprehensive molecular multi-OMICS analysis
To gain further insights into the MOA by which CC kills S. aureus and to identify potential CC targets, we carried out comprehensive transcriptomic, metabolomic, and lipidomic analyses. Using the KEGG database, an integrated analysis of significantly downregulated genes, metabolites, and lipids from multi-omics data identified several key metabolic pathways that were altered by treatment of MRSA-MW2 with a sublethal concentration of CC (4 mg/L), including pathways leading to fatty acid precursors, as well as cell membrane and cell wall components (Fig. 2A). Volcano plots of the RNA-seq data revealed upregulated (left) and downregulated (right) genes (Supplementary Figure 2A) while lollipop plots highlighted the number of genes modulated in particular pathways (Supplementary Figure 2B). Downregulated genes included ones involved in fatty acid synthesis (lip1, lip2), cell wall biosynthesis (wecB, walk, glmU, murA, murB, murC), and cell membrane integrity (fakB2, uhpT) (Fig. 2B). In addition, the transcriptomics analysis showed down-regulation of genes responsible for purine biosynthesis (purN, purM, purF, purH), amino acid metabolism (bioA, bioB, ilvC), stress responses (arcA, arcD, sigS), and energy metabolism (atpA, kdpA), indicating that CC has a broad impact on cellular physiology (Fig. 2B, Supplementary Figure 2C-D).

Metabolomic studies of MRSA-MW2 treated with 4 mg/L CC identified several enriched pathways (Supplementary Figure 2E-right) and a Volcano plot (Supplementary Figure 2E left) shows metabolites significantly altered by CC treatment, with a fold change (FC) > 2 (or < 0.5 for decreases) and a p-value < 0.05 (Extended Data 2E-left). The metabolomic data are consistent with the transcriptomics data, which showed that CC causes downregulation of metabolites such as mevalonic acid (likely influenced by menD, and menH in menaquinone biosynthesis) and UDP-N-acetylglucosamine (associated with wecA, glmU, murA, murB, murC) (Fig. 2C). A heatmap of the top 25 affected metabolites (Fig. 2C) shows distinct metabolic shifts driven by CC, with downregulated metabolites including guanosine, mevalonic acid, L-lactic acid, as well as intermediates in glycolysis and pentose phosphate pathways (G6P, fructose-1,6-diphosphate, sedoheptulose 7-phosphate, D-ribose-5-phosphate). These metabolites are crucial for energy metabolism, nucleotide biosynthesis, and stress responses.

PLS-DA analysis of the lipidomics data detected an average of 132 lipids, with full separation between control and treated groups, and 20 lipids showing significant differences (p < 0.05) (Supplementary Figure 2F-right) and a volcano plot highlights lipids significantly altered by CC treatment, with a fold change (FC) > 2 (or < 0.5 for decreases) and a p-value < 0.05 (Extended Data 2F-left). We found that reduction of key membrane lipids including phosphatidylglycerol (PG(15:0_15:0), PG(15:0_16:0), PG(15:0_20:2), PG(15:0_17:0), PG(14:0_18:0),) and phosphatidylethanolamine (PE(15:0_16:0)) (Fig. 2D), which are crucial for maintaining membrane integrity and function 27. This reduction may be linked to the metabolic activity of ilvC, bioA, bioB, and bioD, although the precise mechanism remains to be fully elucidated. Lipidomic profiling identified 15 lipids (Supplementary Figure 2E; Fig. 2D) and two key lipids, phosphatidylglycerol (PG) (15:0_15:0) and diacylglycerol (DAG) (15:0_17:0) that were significantly decreased. Heatmaps of the top 25 lipids (Fig. 2D) illustrated distinct lipid expression patterns, highlighting a reduction in specific phospholipids (PG(15:0_15:0), PG(15:0_16:0), PE(15:0_20:4)) essential for membrane structure and function. These findings suggest altered membrane integrity and lipid metabolism following CC treatment.

The multi-omics data indicate that CC disrupts S. aureus cell wall and membrane integrity and suggests that CC even at concentrations below its MIC may have deleterious effects on the viability and pathogenicity of S. aureus, in addition to its ability to rapidly kill S. aureus cells. The downregulation of genes critical for the synthesis of cell wall precursors and membrane components appears to lead to a reduction in metabolites that are essential for these processes. Lipidomic data further confirms the impact on membrane structure by showing a decrease in key phospholipids needed for membrane integrity. This disruption in cellular structure is likely to compromise cell function, and the findings suggest that targeting these pathways may identify additional therapeutic strategies.

Transcriptomics showed down-regulation of wecA, which is involved in the synthesis of cell wall constituents and metabolomics analysis showed that CC treatment reduced levels of UDP-NAG, a key precursor in peptidoglycan biosynthesis. Lipidomics further revealed decreases in phospholipid abundance and changes in fatty acid composition, consistent with impaired cell-wall and membrane homeostasis. To test whether depletion of cell-wall precursors contributes to CC-elicited membrane defects, we supplemented cultures with UDP-N-acetylglucosamine (UDP-NAG) or N-acetylglucosamine (NAG). CC-treated MRSA-MW2 show impaired growth and membrane permeability to CC. However, both supplements partially rescued CC-inhibited growth (Fig. 2E; p<0.0001; 2way ANOVA) and reduced Sytox Green permeability (Fig. 2F–G; p<0.0001; 2way ANOVA), supporting a functional link between CC-mediated depletion of UDP-NAG and loss of cell-wall/membrane integrity (Fig. 2H).

CC synergize with gentamicin, kanamycin, streptomycin, & polymyxin B
Testing various classes of antibiotics (including beta-lactams, aminoglycosides, tetracycline, macrolides, glycopeptides, and cyclic peptides) using checkerboard MIC analysis 13, 28, showed that CC synergizes well with aminoglycosides (gentamicin, kanamycin, streptomycin), as well as with the cyclic peptide (polymyxin B) with respect to inhibiting the growth of MRSA-MW2 (Fractional Inhibitory Concentration Index  0.5, Fig. 3A). Killing kinetics analysis demonstrated that CC+gentamicin (1xMIC for both) eradicated MRSA-MW2 logarithmic cells in 60 minutes (Fig. 3B), whereas CC alone at 1xMIC did not eradicate logarithmic-cells and gentamicin alone at 1xMIC eradicated them only after 240 minutes. Furthermore, gentamicin (10xMIC) + CC (2xMIC) decreased a persister cell population by 4-log -CFU, whereas CC or gentamicin alone at these concentrations had no effect (Fig. 3C). Similar to CC + gentamicin synergism, CC at 1xMIC + polymixin B at 1xMIC eradicated log phase MRSA-MW2 within 30 minutes, whereas CC (1xMIC) or polymyxin B (1xMIC) alone exhibited modest killing activity (Fig. 3B). Likewise, CC (2xMIC) + polymyxin B (1xMIC) enhanced the killing of stationary-phase MRSA-MW2 cells (1.5-log -CFU) compared to CC (2xMIC) or polymyxin B (1xMIC) alone which had no observable effect (Fig. 3C).

Since both CC and aminoglycosides disrupt membrane homeostasis 29, 30, we tested CC+aminoglycoside combinations using DiSC (5) to determine the effect of the combination on MRSA-MW2 membrane potential. The combinations decreased membrane potential more rapidly than CC alone or the aminoglycosides alone. Valinomycin/CCCP 31 was used as a positive control (Supplementary Figure 3A-D). Interestingly, MD analyses of CC+polymyxin B (Fig. 3D) showed that CC and polymyxin B spontaneously formed defined and stable (<-5 kcal mol-1 ΔG ) bi- and tri-molecular complexes at 1:1, 1:2 and 2:1 ratios of CC to polymyxin B moieties. Further simulations of the 1:1 complex interacting with a simulated S. aureus bilayer revealed similar antimicrobial behavior to polymyxin B, including similar free energy minima of penetration within the membrane’s headgroup region (Fig. 3D; Supplementary movie S5). However, the added bulk from the complexation of CC (2:1) enabled immediate large-scale membrane deformation during membrane permeabilization, particularly at the distal face to the drug penetration (the side of the membrane away from where the drug is entering), which was not observed in the simulations with CC or polymyxin B alone. Analysis of the 1:2 complex revealed a slightly shallower penetration depth than polymyxin alone or the 1:1 complex (Fig 3D; Supplementary movie S6). The 1:2 complex caused 170% more membrane disruption at the proximal face and 270% more disruption at the distal face compared to the 1:1 complex, indicating greater long-range effects on bilayer integrity. The dramatic increase in deformation at the distal face indicates long-range reorganization of the membrane, reducing the bilayer integrity far away from the penetration site. These findings point to an interesting steric-driven, on-contact mechanism for membrane disruption, occurring on the nanosecond and single-molecule scales. In contrast, the antibacterial action of polymyxin B involves the gradual amphiphilic aggregation of several penetrated molecules (Supplementary movie S7)32. This proposed mechanism is consistent with the combinatorial action of CC + polymyxin enhancing cellular damage using AFM (Figs. 1J and 3E) or membrane permeability to Sytox Green (Supplementary Figure 3E-H). Note that the AFM data in Fig. 1J and Fig. 3E were collected in the same experiment.

Mechanism by which CC disrupts the S. aureus membrane
To further explore the mechanism by which CC disrupts the S. aureus membrane, we quantified membrane fluidity using the Laurdan generalized polarization (L-GP) metric in which a decrease in L-GP correlates with an increase in membrane fluidity 33. Unexpectedly, we found that CC at 32 mg/L (4xMIC) or 64 mg/L (8xMIC) caused a significant increase in the L-GP in MRSA-MW2-treated cells (Fig. 4A), suggesting that CC makes the membrane more rigid, potentially retarding the rate of cell division 34.

Reasoning that CC may target membrane lipids, we studied the effect of CC on MRSA-MW2 membranes containing different lipid compositions. Because bacterial membrane lipid composition can be affected by growing bacteria in the presence of different lipids 35, we cultured MRSA-MW2 cells in the presence of 25 mg/L oleic acid (long-chain unsaturated fatty acid) or 25 mg/L stearic acid (long-chain saturated fatty acid), treated them with CC (4xMIC or 8xMIC), and measured membrane fluidity. Fig. 4B shows that growth in stearic acid increased L-GP (increase in rigidity) and that this level of rigidity was modestly increased by treatment with CC. In contrast, Fig. 4C shows that growth in oleic acid increased membrane fluidity but that treatment with CC increased rigidity to about the same levels as in cells grown in the absence of long-chain fatty acids or the presence of stearic acid. Consistent with these results, we found that MRSA-MW2 cell growth in oleic acid (Fig. 4D and H) behaved like wild type cells (Fig. 4F), but that MRSA-MW2 cell growth in stearic acid decreased the ability of CC to permeabilize MW2 cells to Sytox Green (Figs. 4E) and decreased the susceptibility of MRSA-MW2 to CC-mediated killing (Figs. 4G). Fluorescence microscopy of Laurdan dye-treated MRSA-MW2 cells 36 revealed that dimethyl sulfoxide (DMSO, Fig. 4K) or vancomycin (Fig. 4L) or CCCP-treated cells (Fig. 4M) only exhibited fluorescence in the dividing septum, whereas CC-treated cells showed a fluorescence signal all over the membrane at 4xMIC (Fig. 4N) and 8xMIC (Fig. 4O).

To identify potential specific lipid targets of CC in the bacterial bilayer of MRSA-MW2 cells, we used cerulenin 37, which is known to modify bilayer fatty acid composition 38, 39, 40. MRSA-MW2 cells grown for four hours with 40 mg/L cerulenin were dramatically more resistant to CC-mediated killing (Fig. 4J) than control cells (Fig. 4I). Since cerulenin modifies the bacterial membrane bilayer lipid composition, we hypothesized that the target of CC is either missing in these cells or another membrane lipid blocks CC activity. Hence, we analyzed the lipid compositions of untreated and cerulenin-treated MRSA-MW2 cells via GC-MS using the MIDI protocol 41. We also analyzed lipid composition of sub-MIC CC-treated MRSA-MW2 cells, reasoning from the “omics” data presented in Fig. 2 that such treatment may affect lipid biosynthetic pathways.

Cerulenin treatment showed a significant shift in the MRSA-MW2 membrane fatty acid profile, including a significant decrease in 20 carbon atom (C20:0) fatty acids (Fig. 4P), suggesting that C20:0 lipids plays a critical role in membrane susceptibility to CC. Consistent with this conclusion, cerulenin-treated cells were less susceptible to CC-mediated permeabilization (five-fold increase in CC-mediated Sytox Green uptake) compared to untreated cells (10-fold increase in CC-mediated Sytox Green uptake, Figs. 4Q, R). Moreover, we found that supplementing cerulenin-grown cells with arachidic acid, a C20:0 lipid, at least partially rescued the ability of CC to permeabilize the MRSA-MW2 cells (15-fold increase in CC-mediated Sytox Green uptake, Fig. 4S).

To provide additional data in support of the hypothesis that C20:0 lipids are required for CC susceptibility, we cultured the MRSA-MW2 cells in the presence of cerulenin with or without C20:0 supplementation. Killing kinetics analysis showed that compared to DMSO-treated cells (Fig. 4T), cerulenin-treated cells lacking C20:0 were less susceptible to CC (Fig. 4U) and that C20:0 supplementation partially (p<0.001; 2-way ANOVA) restored the susceptibility of MRSA-MW2 cells to CC at 30 and 60 minutes (Fig. 4V). In addition, we found a similar pattern for treatment with sub-MIC levels of CC, with a significant decrease in C20:0 fatty acids in the MRSA-MW2 membrane (Fig. 4P), resulting in the cells being less susceptible to CC (Fig. 4W), similar to cerulenin-treated cells. As was the case with cerulenin, C20:0 supplementation of the sub-MIC CC-treated cells, partially (p<0.001; 2-way ANOVA) restored CC-sensitivity at 60 minutes (Fig. 4X). Finally, consistent with the hypothesis that CC directly interacts with C20:0 fatty acids, we found that CC displaces the lipid-interacting fluorescent probe BODIPY TR cadaverine 42 coupled to C20:0 (Fig. 4Y). Using the BODIPY TR cadaverine probe 42, we found that CC interacts with a variety of lipid-containing membrane components including lipoteichoic acid, lipopolysaccharide, L-alpha phosphatidylglycerol, 1,2-dipalmitoyl-sn-glycero-3-phospho-rac-(1-glycerol), and cardiolipin (Supplementary Figure 4A-E).

We next tested different pH levels to assess how variations in environmental acidity or alkalinity influence antibacterial activity of CC and membrane interactions, as pH can significantly impact drug efficacy, bacterial physiology, and membrane properties43 44, 45. Specifically, we found that CC significantly enhanced membrane rigidity only at pH 7.0 and pH 8.0, but permeabilized the membrane at both alkaline and mildly acidic conditions (pH 6), but not at lower pH levels (Supplementary Figure 5A, B). The anti-S. aureus activity of CC was highest at pH 4 with an MIC of 2 µg/mL, even though CC did not permeabilize the membrane at pH 4, and CC retained significant antibacterial activity from pH 5 to 9 (Supplementary Table 5). At pH 4.0, the protonation of CC may enhance its interaction with intracellular targets, increasing its potency without significant membrane disruption. CC was bactericidal at pH 7 and 8 but bacteriostatic at pH <6.0 and >8.0 (Supplementary Figure 5C), correlating with its ability to enhance membrane rigidity at neutral to alkaline pH and permeabilize cells at pH 6.0 and higher (Supplementary Figure 5A–C). Interestingly, despite the absence of detectable membrane permeabilization at pH 4, CC displayed its highest potency (MIC= 2 mg/L) under these acidic conditions. This suggests that CC may exert antibacterial effects through membrane-independent mechanisms at low pH. Together, these observations suggest that the mechanisms by which CC kills S. aureus may be complicated and involves more than simple physical disruption of the membrane.

Isolation of CC-resistant mutants
Our observations showing that a cerulenin-induced decrease in C20:0 membrane lipids results in significantly enhanced resistance to CC, led us to hypothesize that MRSA MW2 mutants with decreased C20:0 levels should exhibit at least some level of resistance to CC-mediated killing. To isolate such C20:0 mutants and in general to identify MRSA-MW2 CC-resistant mutants that might help identify the target(s) of CC, we carried out a 50-day serial passage experiment using sub-lethal concentrations of CC to identify CC-resistant mutants as described in previous publications from our laboratory 26, 46. The MIC of CC remained unchanged until about day 25, with an approximately 32-fold increase by day 50 (Supplementary Figure 4F). Whole-genome sequencing of purified clones from different days of passage identified single nucleotide polymorphisms (SNP) in genes purR, folC, farR, and tarS (Supplementary Table 6). One particular CC-resistant mutant clone carrying mutations in purR, folC, farR, and tarS (strain MRSA-MW2-S50 from day 50 of serial passage (Supplementary Tables 6)) was chosen for further analysis (as described in the section below on structure-activity relationship studies). Interestingly, however, MRSA-MW2-S50 did not exhibit decreased levels of C20:0 lipids (Supplementary Figure 4G), suggesting that a variety of mechanisms could lead to CC-resistance. In this regard, the observation that the CC-resistant strain MRSA-MW2-S50 contained a mutation in the tarS gene, which is required for cell-wall teichoic acid biosynthesis, prompted us to test whether a well-studied S. arueus tar mutant exhibits CC resistance. Specifically, we tested a previously constructed deletion of the tarO gene, an isogenic derivative of the tarO mutant carrying wild-type tarO expressed on plasmid pTP63, and S. aureus strain Newman, the parent of the tarO mutants. As shown in Supplementary Figure 4H, the MICs of the Newman strain, the tarO mutant, and the complemented mutants were unexpectedly 8 mg/L, 4 mg/L and 8 mg/L, respectively. That is, the tarO mutant was more susceptible to CC than the parent strain, in contrast to our expectation.

Since the ΔtarO mutant was more sensitive to CC, it is unlikely that TarO or TarS are direct molecular targets of the compound. Instead, these results indicate that TarO–TarS–dependent pathways preserve cell wall architecture and mitigate CC-induced stress. Mutations in tarS may alter WTA composition or surface charge, potentially reducing CC binding or penetration. Collectively, these findings suggest that CC activity is modulated by the integrity and glycosylation status of wall teichoic acids.

On the basis of these tarO data and the fact that our passaging experiment did not yield mutants with reduced levels of C20:0 fatty acids, we decided that comprehensive genetic analysis of CC resistance is likely to be complicated and therefore beyond the scope of this current publication.

Structure-activity relationship (SAR) studies
We sought to carry out a limited SAR study using commercially available analogs of CC as well as a few custom synthesized CC analogs. First, the Microsource 1 library that was used in the C. elegans high-throughput screening platform that identified CC contained several other angiotensin receptor blocking compounds with structures related to CC, including irbesartan, losartan, olmesartan medoxomil, telmisartan, and valsartan. These CC-related compounds possess one or more combinations of benzimidazole, biphenyl or tetrazole moieties. However, none of these compounds were identified in the screen for compounds that blocked the ability of MRSA-MW2 to kill C. elegans and none of them exhibited anti-MRSA activity (Supplementary Figure 6A).

Second, we tested a limited set of commercially available tetrazole-containing molecules, but none of these exhibited an anti-MRSA activity (Supplementary Figure 6B), indicating that an intact tetrazole ring alone is insufficient for antimicrobial activity. Third, we tested five commercial (Supplementary Figure 6C) and six custom-synthesized analogs of CC (Supplementary Figure 6D) that have benzimidazole, biphenyl, tetrazole, and cilexetil moieties. Among the five commercially available CC-analogs (Supplementary Figure 6C), two have unmodified tetrazole rings and three have modified tetrazole rings. The two CC analogs with unmodified tetrazole rings exhibited antibacterial activity against MRSA-MW2 with an MIC of 8 mg/L, the same as CC, (Supplementary Figure 7) and interestingly were also active against the CC-resistant mutant MRSA-MW2-S50 generated by serial passage (Supplementary Figure 7A). These two active compounds readily penetrated a simulated S. aureus membrane (Supplementary Figure 7B), permeabilized MRSA-MW2 to Sytox Green (Supplementary Figure 7C),a and exhibited toxicity profiles against mammalian cells similar to CC (Supplementary Figure 7D, E). In contrast, the three CC analogs with modified tetrazole rings (Supplementary Figure 8) lost their antibacterial activity (Supplementary Figure 8A), failed to penetrate the simulated bilayer (Supplementary Figure 8B), did not permeabilize MRSA-MW2 (Supplementary Figure 8C), and had no effect on mammalian cell survival (Supplementary Figure 8D, E). However, an intact tetrazole ring alone is insufficient for antimicrobial activity since candesartan has an unmodified tetrazole but does not block cell growth or permeabilize MRSA-MW2 (Supplementary Figure 8A). Consistent with these findings, MD analysis showed that the unmodified tetrazole containing CC analogs permeated a simulated S. aureus membrane with a deep free energy minimum of 26-20 Å from the bilayer center (Supplementary Figure 9A). In contrast, the analogs with modified tetrazoles do not interact with the membrane in an energetically favorable manner and do not permeate the membrane (Supplementary Figure 9A). Additional MD data shown in Supplementary Figure 9 and discussed in the Supplementary Figure 9 legend are also consistent with the biochemical and physiological data described in this paragraph.

Third, we synthesized six CC analogs with an intact tetrazole ring but modified cilexetil moieties to better understand the structural features of the cilexetil moiety that confer antibacterial activity (Fig. 5). In compound-1, replacing the cyclohexyl ring with isopropyl (Fig. 5A) (Supplementary Information 1) significantly increased the anti-MRSA-MW2 MIC from 8 to 64 mg/L (Fig. 5A). MD simulations showed compound 1 did not permeabilize the membrane (Supplementary movie S8) and that compound 4 has poor permeability (Supplementary movie S10). However, compounds 5, 6, 7 permeated the membrane (Fig. 5B; (Supplementary movies S11-13). The deviations in average bilayer penetration distances Δd of the analogs’ hydrophilic tetrazole pen groups and hydrophobic isopropyl/cyclohexyl/oxanyl groups (Supplementary Figure 10A), and penetration distance of analogs and mean distances from the bilayer center are shown in Supplementary Figure 10B and C. Compound 1 also did not cause MRSA-MW2 membrane permeability (Fig. 5D) and exhibited less mammalian cell toxicity (up to 64 mg/L) than the other compounds that were tested (Fig. 5E). On the other hand, compound-1 still exhibited significant bactericidal activity at 4x the MIC, which is likely due to non-specific toxicity given the high concentration (Fig. 5C). In compound 2, replacing the cyclohexyl ring with a pyran ring but maintaining the same linker length (Supplementary Information 2) caused a complete loss of anti-MRSA activity (MIC >128 mg/L), demonstrating that the cyclohexyl ring is also critical for antibacterial activity). In compounds-4, 5, 6 and 7, retaining the cyclohexyl ring (Fig. 5A) with different length linkages (Supplementary Information 3-5) yielded compounds with the same to moderately reduced antibacterial activity as CC (MIC of 8-32 mg/L (Fig. 5A) and bactericidal activity (Fig. 5C), which largely correlated with their ability to permeabilize MRSA-MW2 membranes (Fig. 5D) and their toxicity to HepG2 cells (Fig. 5E). Interestingly, compounds 5 and 6 were moderately active against the MRSA-MW2 CC-resistant MW2-S50 mutant (Fig. 5A). In compound-7, removing two ester groups in the cilexetil linker (Supplementary Information 6) did not affect antibacterial activity, but compound-7 was not active against CC-resistant MRSA-MW2. Importantly, we evaluated the potential mammalian toxicity of all six analogs using human RBCs. The IC50 values were as follows: CC = 16 mg/L, CC4 = 50 mg/L, CC5 = 25 mg/L, CC6 = 20 mg/L, and CC7 = 16 mg/L, indicating that most analogs retain comparable or reduced toxicity relative to CC.

In vivo efficacy of CC against MRSA-MW2
Finally, we tested the in vivo efficacy of CC in invertebrate and vertebrate models. CC exerted dose-dependent antibacterial activity in a C. elegans – MRSA-MW2 liquid-killing assay, reaching 100% worm-survival at 6.25 mg/L 23, 47 (Supplementary Figure 11A). CC at 50 mg/kg also enhanced the survival of MW2-infected Galleria mellonella (p<0.001) compared to untreated controls (Supplementary Figure 11B).

Pharmacokinetic studies in mice showed a maximum bioavailability of 8 mM CC in serum 12 hours after administration (Fig. 6A). Notably, 50 mg/kg CC did not exhibit gross toxicity based on liver kidney profiling (Fig. 6B-C) and H&E staining of liver, heart, and kidney tissue sections (Fig. 6E).

Injection of 106 S. aureus USA300 cells expressing luciferase into the thighs of Swiss-Webster mice resulted in an active infection site after 24 hours. Compared to untreated controls, 50 mg/kg CC (intraperitoneal) and 30 mg/kg gentamicin (subcutaneous) alone decreased S. aureus abundance per gram of tissue by 1-log -CFU and 2-log -CFU, respectively. Their combination 10 10 decreased bacterial burden by 2.5-3.0-log -CFU (Fig. 6D). Qualitative visual inspection of the 10 images in Fig. 6F suggested that luminescence was reduced at the infection site after CC+gentamicin treatment, whereas in untreated controls, the bacterial luminescence remained essentially unchanged during the same period.

Methods
Data reporting
Methods of this research comply with all relevant ethical regulations approved by The Institutional Biosafety Committee approved the animal protocol (Rhode Island Hospital Institutional Animal Care and Use Committee (RIH IACUC) and Houston Methodist Research Institute) and the experiments were conducted according to the Animal Welfare Act and National Institutes of Health guidelines for animal care and use.

Bacterial strains. The S. aureus methicillin-resistant strain MW2 BAA-1707 (ATCC, Manassas, VA, USA) was used throughout this study. It is a community-acquired, SCCmec Type IV, Panton-Valentine Leucocidin (PVL)-positive strain, which was isolated in 1998 from a female patient in North Dakota, USA 60. The S. aureus strain VRS1 carries a plasmid encoding the vanA gene, which confers resistance to vancomycin 61. The cell wall-defective strain MW2∆tarO expresses an inactive, truncated variant of tarO containing only 80 amino acids 62 and was grown at 37°C in tryptic soy broth (TSB) (Becton, Dickinson (BD ) and Company, Franklin Lakes, NJ, USA).

Compounds. The MicroSource-1 US Drug Collection (http://www.msdiscovery.com/usdrugs.html) is a collection of 1040 compounds that have reached clinical trial stages in the USA. The library was obtained from The Institute of Chemistry and Cell Biology at Harvard Medical School (Boston, MA, USA) in 384-well plates (rows 3 to 22). For high-throughput screening, 0.1 μL of 2 mg/mL compound stock solutions in DMSO were pin-transferred to each well such that the library was screened at a final compound concentration of 2.86 μg/mL. CC was purchased from Cayman Chemical (Ann Arbor, MI, USA), and candesartan was purchased from Tocris (Minneapolis, MN, USA). Tetrazole modified and unmodified CC analogs were purchased from Molport (Toronto Research Chemicals, Canada) and Cayman Chemical and Combiblocks (San Diego, CA, USA). The CC analogs listed in Figure 5 were synthesized at Sai Lifesciences (Watertown, MA, USA).

Nematode strains. The C. elegans glp-4(bn2); sek-1(km4) double mutant strain was used throughout this study. Nematodes were maintained at 15°C on a lawn of E. coli strain HB101 on 10-cm plates 63. The glp-4(bn2) mutation renders the strain incapable of producing progeny at 25°C 64, and the sek-1(km4) mutation enhances sensitivity to various pathogens 65, reducing assay time.

Infection assay for the compound screen. The C. elegans-MRSA screening assay was described earlier23. Briefly, 4,500 glp-4(bn2);sek-1(km4) L1 hatchlings were grown on SK-HB101 agar plates for 52 h at the restrictive temperature of 25°C until animals became sterile young adults and were harvested with M9 buffer. S. aureus MW2 was grown overnight at 37°C in TSB, first under aerobic conditions and later shifted to anaerobic growth conditions at 37°C. The high-throughput screening assay was performed using 384-well plates (Corning no. 3712, Corning, NY, USA), and the total volume in each well was 70 μL, with the final composition being 70% M9 buffer, 19% Sheath solution (Union Biometrica Part no. 300-5101-000, Holliston, MA, USA), 10% TSB, and 1% DMSO or compounds dissolved in DMSO. The bacterial concentration was adjusted to a final optical density (OD) of 0.04. A Union Biometrica Complex Object 600 Parametric Analyzer and Sorter (COPASBioSort) was used to transfer 15 adult worms to each well of the assay plate. After five days of incubation in a humidified chamber at 25°C, the bacteria and other debris were washed from the wells with a microplate washer, leaving 10 μL of assay volume with worms following the final aspiration step. Finally, 60 μL of 0.9 μM Sytox Orange in M9 was dispensed into each well for a final Sytox concentration of 0.7 μM. The plates were incubated overnight at 25°C in a humidified chamber. The plates were imaged the next day using an Image Xpress Micro automated microscope (Molecular Devices, San Jose, CA, USA), capturing both transmitted light and TRITC (535 nm excitation, 610 nm emission) fluorescent images with a 2X objective. The entire assay was done in duplicate 23.

Worm survival quantification using CellProfiler and hit identification. The images were processed using the open-source image analysis software CellProfiler (http://www.cellprofiler.org/) and a pipeline of image processing and analysis modules as described previously 66, 67. The software calculates the ratio of the Sytox worm area to the bright field worm area and the resultant percentage survival data for each well of the assay plates. Z-scores were calculated from the ratio data, and samples with Z >2σ in duplicate assay plates were considered hits.

Antimicrobial susceptibility testing. Compounds (10 mg/mL stock solution in DMSO) were tested by broth microdilution and disc diffusion assays adapted from established protocols 68. Broth microdilution was done in triplicate in 96-well plates. The total volume in each well was 100 μL. Two-fold serial dilutions were carried out to get compounds in the 0.125-64 µg/mL concentration range. The bacterial concentration was adjusted to an initial OD 600 of 0.03. After overnight incubation at 37°C, the absorbance was measured to determine antimicrobial activity 13. The disc diffusion test was done on Mueller Hinton agar (MHA) (BD), and the assay was repeated three times. Empty and sterilized discs (BD) were soaked in either DMSO or 10 mg/mL stock solution and air-dried. Three hundred microliters of an overnight culture of MW2 were spread on tryptic soy agar (TSA). After completely drying the agar plate in a laminar flow hood, DMSO or compound-impregnated discs were overlaid on the plate and incubated at 37°C for 20 h. Antimicrobial susceptibility was determined by measuring the diameter of the zone of inhibition 13.

Killing kinetics assays. These assays were carried out as described previously 14. An inoculum of ~10⁷ CFU/ml (logarithmic phase) was used for time-kill assays (Fig. 1B) to assess CC activity against actively dividing cells, whereas a higher inoculum of ~10⁸ CFU/ml (stationary phase) was used for growth inhibition assays (Fig. 1C and 3B) to evaluate activity against metabolically less active populations. Test compounds were then added at 8x, 4x, 2x, 1x, and 0.5x MIC and incubated with agitation at 37°C. Aliquots were periodically drawn, serially diluted, and plated onto tryptic soy agar (TSA; BD Biosciences). Colony-forming units (CFUs) were counted after overnight incubation, and assays were carried out in duplicate 12.

Membrane permeability. Sytox Green (Life Technologies, Carlsbad, CA, USA) was used to probe the effects of CC on MRSA-MW2 membrane permeabilization as previously described 50. Assays were carried out in duplicate in 96 wells of black flat transparent bottom plates (Corning). Bacterial cells were harvested from logarithmically growing cultures by centrifugation at 3724 g for 5 minutes, washed twice with phosphate-buffered saline (PBS, pH 7.4), and resuspended in PBS to OD nm = 0.2. Sytox Green was added at a final concentration of 5 M, and cells were incubated in the dark for 30 minutes. Cell suspensions (50 L) were added to 50 L of compound (64 g/mL in PBS), and the fluorescence intensity was measured (excitation 485 nm, emission 530 nm) periodically over 60 minutes. DMSO was included as the vehicle control. The membrane-targeting nature of CC was indicated by an increase in cellular fluorescence caused by physical injury of the bacterial membrane, resulting in DNA staining by Sytox Green.

Membrane potential. Membrane potential was measured with a DiSC (5) fluorescence probe 3 (Molecular Probes) using the protocol described earlier 69. MRSA-MW2 cells were added with DiSC (5) and incubated in dark for 30 mins. After incubation, cells were added to a serially diluted compounds and the cellular fluorescence was measured at 620/660 nm.

Membrane fluidity. MRSA-MW2 overnight cultures were inoculated into 25 mL of TSB and allowed to grow to OD nm ~1.5 at 37°C with shaking at 200 rpm. After incubation, the cells were harvested by centrifugation and resuspended in PBS followed by 10-minute incubation with 10 µM Laurdan (Molecular Probes) at room temperature in the dark. The stained cells were washed four times with PBS. Compounds at various concentrations in 100 μL of PBS in 96-well black transparent flat-bottomed plates (Corning) were mixed with Laurdan-treated bacterial cells. After 1-h incubation at room temperature, the Laurdan fluorescence intensities were measured in the dark at 35°C using a spectrophotometer (SpectraMax M2) with emission wavelengths of 440 nm and 490 nm on excitation of 350 nm. The Laurdan GP was calculated using the formula GP = (I – I )/(I + I ). All experiments were conducted in biological triplicate9.

Biofilm studies. The biofilm studies were carried out as described 48, 70 with slight modifications. The biofilm inhibition studies were carried out using the cells grown under glucose supplementation 70. The cells were then added to M HB with serially diluted CC and incubated for 24h. After incubation, planktonic cells were removed, and the plates were washed with sterile PBS (3X) and MHB with Alamar Blue HS was added to the wells to monitor the metabolically alive cells. A mature biofilm eradication assay was carried out using the glucose supplemented TSB grown cells to form a matured biofilm in the 96 well plates. After 24h, planktonic cells were removed and MHB with serially diluted CC was added to the wells. After 24h, the plated were washed with sterile PBS (3X) and MHB with Alamar Blue HS were added to the wells to monitor metabolically alive cells.

Scanning electron microscopy. MRSA-MW2 bacteria were suspended in MHB in the presence or absence of CC (1x, 2x, and 4x MIC) for 90 minutes with agitation. After incubation, the bacteria were harvested by centrifugation at 4000 rpm for 5 minutes, and harvested cells were adhered to coverslips using 1% poly-L-lysine (Sigma-Aldrich, St. Louis, MO, USA). Cells were fixed in a 5% glutaraldehyde (Sigma-Aldrich), 4% paraformaldehyde (Sigma-Aldrich), and 0.1 M sodium cacodylate (Sigma-Aldrich) solution. After fixation, the cells were washed in 0.1M sodium cacodylate buffer and dehydrated by immersion in increasing ethanol concentration (from 30 to 100%) 71. The critical point drying method was applied to dehydrate the samples, and the coverslips were mounted on aluminum stubs and then sputter-coated with gold (Emitech K550, Ashford, Kent, UK). Images were taken on a Hitachi 2700 scanning electron microscope 69 72.

Transmission electron microscopy. CC treated bacterial cells were fixed in 2.5% glutaldehyde in 0.1 M Na Cacodylate and 2% paraformaldehyde with 2mM calcium chloride for 2-3 hours at 4°C and washed with cold 0.15 M Sodium Cacodylate buffer with 2 mM CaCl. Cells were treated with TCH for 20 min followed by rinsing 5 times at 3 min intervals using ddH2O at room temperature, then treated with 2% OsO4 in ddH2O for 30 min at room temperature followed by an overnight treatment with 1% uranyl acetate overnight at 4°C. The cells were washed 5 times at 3 min interval using ddH2O at room temperature and then washed with a Pb aspartate solution in a 60° oven for 30 min, and then washed with 5 times at 3 min interval using ddH2O at room temperature. Cells were dehydrated in graded ethanol in 70%, 90%, 95%, 20 minutes each. Finally, cells were embedded in the resin followed by microscopy.

Atomic Force Microscopy.
Sample Fixation: The samples were fixed in a solution containing 2.5% glutaraldehyde in PBS for 15 minutes at room temperature. After fixation, bacterial cell samples were carefully applied to a pre-APTES-treated* silicon surface and incubated for 20 minutes. For cells grown in 60 mm dishes, fixation and dehydration were performed directly in the dish. Following this step, the silicon-coated surface or the cell-coated dish was rinsed thoroughly with DI water to remove any PBS residues.

Dehydration Procedure: The samples were sequentially immersed in ethanol solutions of increasing concentrations (20%, 40%, 60%, 90%, and 100% in water), spending 15 minutes in each solution. After each step, the samples were spun down to ensure complete processing. Further dehydration was achieved by immersing the samples in tert-butanol for 15 minutes. All chemicals and buffers were sourced from Sigma Aldrich. After dehydration, the tert-butanol solution was removed, and the samples were left to air dry.

AFM Imaging Setup: For AFM imaging, the silicon wafer was mounted onto an AFM ferrometal disc and positioned on the AFM scanner. Alternatively, 60 mm dishes were secured directly to the AFM scanner using a Teflon holder. The imaging was conducted using two Bruker Nano systems: the Multimode-9 AFM system for bacterial cells on silicon wafers and the Biocatalyst AFM for regular cells on dishes. Imaging was performed under ambient conditions in contact mode. The AFM cantilever had a spring constant of 0.4 N/m, a scanning frequency of 1 Hz, and a probe-sample interaction force maintained at 0.1 nN. APTES

APTES Treatment Protocol: APTES, or (3-Aminopropyl)triethoxysilane, was used to prepare the silicon surface. A 0.01 v/v solution of APTES in DI water was applied to the silicon (100) surface and incubated for 2 hours. After incubation, the silicon surface was rinsed three times with DI water to remove any residual APTES and then dried using a stream of dry nitrogen.

Cryo-ET of MRSA cells. MRSA-MW2 cells were treated with CC (10× MIC; 80 mg/L) and handed over to the CryoEM Core Team for CryoET preparation. Approximately 3.5 μL of the sample was applied to a Quantifoil 3.5/1 200Cu grid for vitrification. The sample was blotted using calcium-free blotting paper in a Vitrobot (Mark IV, FEI Corp) with a blot time of 2.5 seconds (1 blot) and immediately plunged into liquid ethane at liquid nitrogen temperature. The grids were then stored in liquid nitrogen until the imaging date. The frozen grids with cells were transferred to either a JEM 3200FSC microscope (JEOL) equipped with a K2 Summit camera and post-column energy filter or a Thermo Scientific Glacios microscope equipped with a Falcon 4 camera. Data from the 3200FSC microscope were collected using SerialEM. Images were recorded at 300 keV with a 40 eV energy filter width on a Gatan 4096 × 4096 pixel K2 Summit direct electron camera in counting mode, with an exposure time of 0.2 seconds per frame and a total exposure of 3 seconds. Single-tilt image series (±45°, 2° increments) were collected at a defocus range of 6–10 μm and a magnification of 10,000×, yielding a pixel size of 3.616 Å/pixel. The total electron dose per tomogram was approximately 60 electrons/Å², consistent with standard cellular cryo-ET protocols.

Tomogram Processing. Image stacks were automatically aligned and reconstructed using e2tomogram in EMAN2, following initial quality assessment of the raw data to ensure proper tilt-series alignment. To improve contrast and reduce noise, the tomograms were averaged by a factor of four, resulting in 3D reconstructed volumes with dimensions of 1K × 1K × 1K and a resolution of 16 Å/pixel. The vitrified cells on the cryo grids were carefully screened prior to imaging to identify regions with optimal preservation and uniform distribution. The reconstructed tomograms were visualized using EMAN2, allowing for a detailed examination of structural features, including mesosome deformities and the integrity of the bacterial cell wall. Observations focused on identifying disruptions in cellular architecture, particularly in regions of potential compound-induced damage. Specific regions of interest, such as the cell wall and intracellular structures, were analyzed in greater detail. To highlight and quantify the clearest features of these findings, further segmentation and rendering were performed using UCSF Chimera, as illustrated in Figure 1L. This analysis allowed for the precise visualization of structural abnormalities and comparative assessments between experimental groups73.

Computational Methodology: Molecular dynamics (MD) simulations. All-atom MD simulations were performed using the AMBER22/AmberTools2374, 75 package to investigate the interactions between CC and polymyxin B in a simulated plasma membrane of S. aureus. The second generation Generalized Amber Force Field (GAFF2)76 and AmberTools antechamber package77 was utilized to create the non-bonded and bonded parameters for CC and polymyxin B. Partial atomic charges were computed using restrained electrostatic potentials (RESP) 78. The structure of CC was built using GaussView, 79 and the geometry was optimized using Gaussian16 80 with the M06-2X/6-31+G(d) method 81, 82 in conjunction with the Polarizable Continuum Model (PCM) 83 to simulate an aqueous environment. The structure of polymyxin B was adapted from a complex of LSD1-CoREST1 in complex with polymyxin B (PDB entry 5L3F) 84, 85 and geometry optimized in Gaussian16 using the B3LYP/6-31+G(d) method 82, 86, 87, 88, 89 and PCM. The Lipid 21 90 and ff14SB 91 forcefields were used to parameterize the DOPC and DOPG (1,2-dioleoyl-sn-glycero-3-phosphocholine/glycerol) lipid bilayer. Construction of the membranes in the xy plane was performed using the PACKMOL-Memgen software 92 (included in AmberTools23), using 200 neutral-charged DOPC and 86 negatively-charged DOPG lipids representing a DOPC:DOPG ratio of 7:3 with dimensions of 100 Å x 100 Å. All systems were charge-neutralized using sodium ions and were solvated explicitly using the TIP3P water model 93. The simulation box had an initial height of 102 Å, which, post-equilibration, stabilized to 73 Å, sufficient to prevent the membrane or solutes from interacting with their periodic images. Long-range electrostatic interactions were treated using the Particle-Mesh-Ewald (PME) method 94, utilizing a default cutoff range of 10 Å. The SHAKE 95 algorithm was used to constrain bonds involving hydrogen atoms, and the time-step for all simulations was set to 2 fs. All simulations were run using the Langevin thermostat 96 (set to 310 K and a collision frequency of 1 ps-1) and Berendsen barostat 97 (set to 1 atm, with semiisotropic pressure scaling).

A solvated DOPC-DOPG membrane bilayer was initially constructed without the drugs present. All water molecules and ions were initially minimized while the lipid bilayer atoms were harmonically restrained using the steepest descent (SD) algorithm for 5,000 steps, followed by 5,000 steps of conjugate gradient (CG) minimization using mixed CPU and GPU-enabled AMBER22 pmemd engines 98, 99. Subsequently, all constraints on the solvent molecules and the lipid bilayer released and the system was minimized using the SD algorithm for 5,000 steps, followed by 5,000 steps of CG minimization. This was followed by a two-step thermalization, where the solvated DOPC-DOPG membrane was constrained and the system was gradually heated from 0 to 100 K using a constant NVT ensemble over 5 ps, followed by an additional heating step from 100 K to 310 K using NVT over 100 ps. To ensure correct equilibration of the membrane, a subsequent 100 ps unconstrained NVT simulation was then performed, and a density-correction step involving NPT equilibration ensured the membrane’s density was stable – a process which took up to 2.5 ns. After a 500 ns initial equilibration of the solvated lipid systems, the drugs (CC, Polymyxin B, or CC + Polymyxin B) were introduced approximately 5-6 Å above the membrane surface into the aqueous region and any overlapping water molecules were removed. A constraint of 2.5 kcal mol-1 Å-2 was placed on the drugs and the newly complexed system was re-equilibrated using the same procedure as the original DOPC-DOPG membrane, with the final step consisting of 100 ns in the NPT ensemble. After this re-equilibration was complete, the constraints were released on the drugs and their interactions with the membrane, including penetration, were further simulated for 500 ns.

Free energy profiles were computed for the translocation of the drug molecules from aqueous environment into the DOPC-DOPG membrane, which is meant to estimate the energetic pathway that is composed of a transfer energy and membrane penetration energy barrier. Umbrella sampling simulations were set up using AMBER’s tleap ‘build’ module, placing the CC, polymyxin B, or a pre-equilibrated CC+Poly complex at the center of the equilibrated membrane. A 50 ns steered MD in the z direction (with the vector defined by the drug and the DOPC-DOPG xy centers-of-mass) from 0 to 36 Å was performed using a harmonic bias with a force constant of 1.1 kcal mol-1 Å-2. Umbrella windows were sampled at distances every 2 Å, restrained using a harmonic bias of 2.5 kcal mol-1 Å-2, and were run for a 50 ns production trajectory. Finally, the weighted histogram analysis (WHAM) method 100, 101 was used to produce PMF profiles to elucidate the free energy change as a function of z displacement.

Freely-solvated systems of the drugs polymyxin B and CC were also constructed using USCF Chimera 102 and simulated in boxes of 5271, 6281 , or 6765 TIP3P water molecules for 1:1, 1:2 and 2:1 ratios of CC:Polymyxin respectively. These followed the same energy minimization and equilibration procedure as the DOPC-DOPG membrane, with three deviations: energy minimization was only performed once using the GPU-based AMBER22 pmemd engine, and NVT equilibrations were run without restraints and using isotropic pressure scaling. Following this, 1500 ns NPT production simulations were performed, and frames with inter-drug distances of less than 8 Å were selected for clustering. To evaluate the dominant clusters of drug aggregates, the K-means algorithm was utilized to generate 10 clusters using RMSD of all drug atoms as the clustering metric. Free energy profiles were computed for the binding energy of these clusters using umbrella sampling, following the same protocol as the umbrella sampling of drug-and-membrane simulations described above. In this case, the last frame of NPT production was chosen at the starting point, and the sampling coordinate was a scalar from the drugs’ contact distance to a COM distance of 20 Å.

Metabolomics and Lipidomics Analysis Sample Preparation: MRSA-MW2 cells were treated with CC or control (DMSO). Six biological samples were processed: control (NC-1, NC-2, NC-3) and CC-treated (CC-1, CC-2, CC-3) and submitted to the MSF Core- UTMB. Cells were extracted using a liquid-liquid extraction with MTBE/methanol/water (10:3:2.5, v/v/v). Isotopically labeled internal standards were added prior to extraction, including a commercial lipid mix (UltimateSPLASH™ ONE) for lipidomics. The aqueous phase was used for metabolomics, and the organic phase for lipidomics. The remaining protein pellet was used to quantify total protein content using a BCA assay.

LC-MS/MS Analysis: Samples were analyzed using an Acquity Premier UHPLC system (Waters) coupled to a QTRAP 6500 mass spectrometer (SCIEX). Quality control (QC) measures included pooled QC (mixture of all samples), reference standard QC (122 metabolites), and internal standard QC. Pooled QCs were injected at the beginning, middle, and end of the batch to monitor signal stability and retention time drift.

Targeted Metabolomics: Dried aqueous phases were resuspended in 80% acetonitrile and separated on a BEH Z-HILIC column (Waters). Mobile phases were: A) 15 mM ammonium bicarbonate + 2.5 µM Agilent InfinityLab deactivator additive, pH 9; B) acetonitrile/15 mM ammonium bicarbonate (90:10, v/v) + 2.5 µM additive. A total of 207 metabolites were monitored, and 121 were detected.

Targeted Lipidomics: Dried organic phases were resuspended in dichloromethane/methanol (1:1, v/v) and separated by HILIC chromatography on a Luna NH2 column (Phenomenex). Mobile phases were: A) acetonitrile/water/hexane (92:6:2, v/v/v) + 10 mM ammonium acetate, pH 9.3; B) acetonitrile/water (1:1, v/v) + 10 mM ammonium acetate, pH 9.3. Lipids were monitored using the Lipidyzer 2.0 targeted panel 103.

Data Analysis:
RNA-seq preprocessing, Differential Expression, and Pathway Analysis:
Raw RNA-sequencing reads (FASTQ files) were processed using Trim Galore to remove adapter sequences and low-quality bases. High-quality reads were aligned to the Staphylococcus aureus strain MW2 reference genome using the STAR aligner (https://doi.org/10.1093/bioinformatics/bts635) with default parameters. Gene-level read counts were generated from the aligned BAM files using feature Counts. Differential gene expression analysis was performed using the DESeq2 package104 in R. Genes with a P value < 0.05 were considered significantly differentially expressed between CC and DMSO samples. Expression patterns of differentially expressed genes were visualized using the pheatmap and EnhancedVolcano R packages. Pathway enrichment analysis of the differentially regulated genes was conducted using the clusterProfiler package (105).

Metabolomics and lipidomics. Peak areas were integrated using MultiQuant v3.0.3 (SCIEX) and normalized to appropriate internal standards using a custom R script (v4.3.1). Statistical analyses were performed in MetaboAnalyst v6.0. Data were log10-transformed and autoscaled. Partial least squares-discriminant analysis (PLS-DA) was performed to compare groups. Volcano plots used thresholds of fold change >2 and p < 0.05. Heatmaps were generated using Euclidean distance and Ward clustering.

Checkerboard assays. Antibacterial synergy was tested using checkerboard assays 28. CC was tested with different combinations of clinical antibiotics. MRSA-MW2 cells were adjusted to OD = 0.06 and added to compound pairs that had been serially diluted in the same 96-well plates, vertically for one compound and horizontally for the other. Assays were carried out in triplicate as described in the antibacterial susceptibility assay sub-section. The fractional inhibitory concentration index (FICI) was calculated as follows: FICI = (MIC combination) / (MIC alone) + (MIC combination) / (MIC alone) FICI ≤ 0.5 indicates synergy, 0.5 < FICI < 4 suggests non-interaction, and FICI ≥ 4 indicates antagonism 106.

Fluorescence microscopy. Fluorescence Microscopy was carried out as described 49. Briefly, the cells were strained with fluorescence probes in agar medium entrapped in the microscopic slide. The slides were then examined under 40x in Nikon Florescence Microscope with red and green filters. For Figures 4 and 6, the microscopes used had a software glitch in scale bars during image acquisition. As a result, the original image data do not contain embedded scale information. In these cases, we report the imaging magnification and acquisition parameters in the corresponding figure legends to provide spatial context.

Bacterial membrane lipid preparation for GC-MS analysis. The membrane lipid profile was analyzed using the MIDI protocol. Bacterial cells were grown overnight in TSB and were used to prepare the log-phase cells with or without CC. After four hours of agitation, cells were harvested by centrifugation followed by saponification. The cells were treated with 1 ml of Reagent 1 (45 g sodium hydroxide, 150 ml methanol, and 150 ml of distilled water), which was added to each tube containing cells, vortexed briefly, and heated in a boiling water bath for five minutes with a vigorous vortex in-between the heat treatment for 5-10 seconds and returned to the water bath to complete the 30-minute heating. After saponification, methylation was carried out by adding 2ml of Reagent 2 (325ml certified 6.0N hydrochloric acid and 275ml methyl alcohol) to cooled tubes. The tubes were capped and briefly vortexed, and the tubes were heated for ten minutes at 80°C. After heat incubation, the fatty acid methyl esters were extracted by adding 1.25 ml of Reagent 3 (200 ml hexane and 200 ml methyl tert-butyl ether) to the cooling tubes and tubes were placed in a rotator for 10 minutes. After incubation, the aqueous (lower) phase was pipetted out and discarded. About 3 ml of Reagent 4 (10.8g sodium hydroxide dissolved in 900 ml distilled water) was added to the organic phase remaining in the tubes and placed in a rotator for 5 minutes. After incubation, about 2/3 of the organic phase was pipetted into a GC vial used for further analysis. GC-MS analyses of the prepared fatty acid methyl esters were performed on an Agilent 7890B instrument coupled to an Agilent 5977A MSD (Agilent Technologies, Palo Alto, CA, USA). An Agilent HP-5MS (30m × 0.25 mm ID, 0.25 m film thickness) capillary column was used for separation, using helium as carrier gas. The instrument method was optimized using a 37-component FAME standard mix (CRM47885, Sigma-Aldrich). The temperature program was as follows: the initial temperature 50°C was held for 1 min, then programmed to increase at 30°C/min to 150°C, hold for 1 min, then increase at 6°C/min to 300° C and hold for 5 min. Characterization and identification of fatty acids were performed by comparing the mass spectra with the NIST reference mass spectra library (NIST, Mass Spectral Database 08, National Institute of Standards and Technology, Gaithersburg, MD, USA). Data acquisition and processing were performed using Agilent MassHunter software for GC/MSD. Results were expressed as relative percentages of each fatty acid.

Bodipy TR cadeverine displacement assay. The affinity of CC for various lipid molecules was determined using the BODIPY TR cadaverine (Molecular Probes) displacement assay. Bacterial membrane constituents (lipopolysaccharide (LPS) or Lipoteichoic acid (LTA) or Lipid A (Sigma-Aldrich))/ fatty acids (strearic acid or oleic acid or arachidic acid (Sigma-Aldrich) at 5 mg/mL and BODIPY TR cadaverine (500 μM) stock solutions were prepared. The mixtures were prepared using the membrane constituents diluted in Tris buffer to yield final concentrations of lipopolysaccharide (LPS) or Lipoteichoic acid or arachidic acid or stearic acid or oleic acid or Lipid A (Sigma-Aldrich) at 50 μg/mL and 5 μM BODIPY TR cadaverine. 50 μl of the mixture (bacterial membrane constituents or fatty acids mixtures+ BODIPY TR cadaverine) was added to 50 μl of CC in serial dilutions in black-bottomed 96-well plates. Fluorescence was measured at 580/620 nm of excitation/emission. Polymyxin B, known to bind and neutralize LPS, was used as the positive control 28.

Cytotoxicity assays. The assays were performed as previously described 107, 108, 109, 110. Briefly, mammalian cell lines were maintained in a conical cell culture flask and 25000 cells/well in 96 well plates were dispensed 24h prior to the experiment. Compounds were serially diluted in serum and antibiotic free medium and added to the monolayer and incubated for 24h. At the end of 20h of incubation, the WST-1 reagent was added and optical density was measured at 420 nm.

Serial passage experiments. Serial passage experiments were performed using the broth microdilution method in 96-well plates. C C was serially diluted in MHB and added to MRSA-MW2 cells, which were then incubated at 35°C for 24 hours. After incubation, optical density measurements were taken, and cells from the well with a sub-MIC concentration of the compound were harvested. The harvested cells were suspended in fresh MHB at a 1:1000 dilution, and this suspension was then added to a new set of wells containing the same sub-MIC concentration of CC, as described previously 11 This process was repeated every 24 hours for a total of 50 days to track changes in bacterial resistance over time.

Genomic DNA isolation, whole-genome sequencing, and assembly. Clones from the serial passaging experiment in sub-lethal concentrations of CC were cultured in Mannitol Salt Agar and isolated single colonies were cultured in TSB for genomic DNA isolation. The genomes of selected CC variants of MRSA- MW2 were sequenced. Total genomic DNA was extracted from cultures of each strain using the DNeasy Blood & Tissue kit (Qiagen, Valencia, CA, USA) and quantified by Qubit dsDNA High-Sensitivity Assay (Invitrogen, Carlsbad, CA, USA). DNA libraries were prepared from 50 ng of DNA per sample using the Nextera XT DNA Sample Preparation Kit (Illumina, San Diego, CA, USA) with recommended modifications for 2 x 250 bp paired-end sequencing. Samples were multiplexed and sequenced on the MiSeq platform at the Massachusetts Eye and Ear Infirmary Ocular Genomics Institute (Boston, MA, USA). Sequence reads were assembled de novo using the CLC Genomics Workbench software (Cambridge, MA, USA). For evolved strains, variants were identified by mapping sequencing reads for each isolate to the annotated reference genome, which was the starting culture of the serial passaging protocol (MRSA-MW2-S1). For confirmation of variant detection, single nucleotide polymorphisms (SNPs) and insertion/deletion (indel) variants were identified using CLC Genomics Workbench software (Cambridge, MA, USA) and confirmed by Pilon (https://doi.org/10.1371/journal.pone.0112963), using the MRSA-MW2 genome as a reference. Later, contigs were annotated through the NCBI Prokaryotic Genome Annotation Pipeline, and the genomes were deposited in the DDBJ/EMBL/GenBank. Pharmacokinetic mouse model. CFW mice (n = 4) were obtained from Charles River Laboratories (Wilmington, MA, USA) and acclimated for 72 h. CC was dissolved in a 1:1 solution of Kolliphor EL (Sigma-Aldrich, St Louis, MO, USA) and ethanol USP (RIH, RI, USA) and then diluted 1:10 in saline USP to a final concentration of 50 mg/kg. 50 mg/kg of CC was administered via IP, and blood samples were collected using the tail vein prick method. About 50 µl of blood samples were collected from each mouse at different time points (0, 1, 2, 4, 8, 14, and 24h) and stored in -80 until use. Pooled blood (100 µL) samples in centrifuge tubes were prepared for targeted analysis of CC by addition of 200 µL acetonitrile, vortex mixing for 30 seconds, and incubation at 4oC for 12 hours 111, 112. The samples were centrifuged at 100 x g for 10 minutes, and the supernatant was transferred to an analysis vial. The sample extracts were analyzed using liquid chromatography-high resolution mass spectrometry (LC-HRMS) using a Thermo QExactive Orbitrap HFX instrument equipped with a Vanquish ultra-high-performance LC (LC-Orbitrap). Extracts were injected into the LC-Orbitrap in 20 µL volumes in triplicate. Reverse-phase LC was used to separate sample components and was performed with a C18 column (Thermo Hypersil Gold Vanquish, 50 mm X 2.1 mm x 1.9 µm) at a constant temperature of 60oC. Mobile phase A contained 2 mM aqueous ammonium acetate, and mobile phase B contained 2 mM ammonium acetate in acetonitrile. CC was eluted from the column at a constant flow rate of 0.5 mL/min using a mobile phase gradient as follows: equilibration with 2.5% B for 1 minute, increase to 100% B over 11 minutes, and held for 2 minutes, and back to 2.5% B over 1 minute and held for 1.5 minutes (total run time 16.5 minutes, data were collected from 0.05 to 12.5 minutes). The MS was operated in full scan dd-MS2 mode (40 NCE) with an inclusion list for CC (m/z 609.2462). Ionization was performed in negative mode with an ionization window of 4.0 m/z. Ions included in the inclusion list were further fragmented in the Higher-energy C-trap dissociation (HCD) collision cell filled with N 2 (produced by a Peak Scientific Nitrogen Generator, Genius NM32LA). For the full scan, the Orbitrap was operated with a resolution of 120,000. For dd-MS2, the Orbitrap was operated with a resolution of 15,000, AGC of 2x105, and a maximum dwell time of 400ms. Quantification was performed in TraceFinder 5.0 General with an external seven-point calibration curve prepared by serial dilutions of a calibration standard. The calibration standard was prepared by dissolving 19.5 mg CC in 3.45 mL dimethyl sulfoxide (9.3 mM). The ion used for quantitation was m/z 609.2462, and the two ions used for confirmation were m/z 309.1031 and m/z 99.0813. The limit of detection (LOD) was determined from seven injections of a calibration standard and calculated as: LOD = [s * t (df, 1 – α = 0.99)] / m; where s is the standard deviation, t is the student's t-value, df is the degrees freedom, α is the significance level (n=7, α =0.01, t=3.14), and m is the slope of the calibration curve 113. The LOD was 0.315 µM in 100 µL blood.

Deep-seated thigh infection mouse model and Live imaging. Six-week-old female CFW mice (n=5/group) were obtained from Charles River Laboratories (Wilmington, MA, USA) and allowed 72 h for acclamation. Animals were maintained in a specific pathogen–free facility with a 12 h light/12 h dark cycle, controlled ambient temperature (20–24 °C), and relative humidity (40–60%), with ad libitum access to food and water. 150 mg/kg of cyclophosphamide (RIH pharmacy, RI, USA) was injected via IP four days before infection, and 100 mg/kg of the same drug was administered on the day before infection to make the mice neutropenic. On the day of infection, 106 cells (approx) of log-phase S. aureus (USA 300 Luciferase- a kind gift from the Hamblin lab) suspended in 50 µl of saline were injected into both sides of the thigh (n=2/mice) of each mouse. After 24h, the mice were anesthetized using Isoflurane USP and imaged under IVIS to confirm the illumination of bacterial load at the infection site. CC was dissolved in a 1:1 solution of Kolliphor EL (Sigma-Aldrich, St Louis, MO, USA) and ethanol USP (RIH, RI, USA) and then diluted 1:10 in saline USP to a final concentration of 50 mg/kg. 50 mg/kg of CC via IP, 25 mg/kg of vancomycin via IP, 30 mg/kg of gentamicin via SC, or CC (50 mg/kG; IP) + gentamicin (30 mg/kg; SC) were administered to the relevant groups of mice (n=5; using both legs of each mouse gave a sample number 10) every 24 hours for 2 days. Control mice were injected with 100 µl of 10% Kolliphor EL/ethanol in saline via IP. The bacterial load was monitored using the IVIS every 24 h once and before euthanization. After euthanizing mice at 48h after treatment, the infected thighs were aseptically excised. The homogenized tissue sample in saline were diluted serially and spot plated on TSA plates. The bacterial burden was recorded as CFU/g tissue. The Institutional Biosafety Committee approved the animal protocol (Rhode Island Hospital Institutional Animal Care and Use Committee (RIH IACUC) and the experiments were conducted according to the Animal Welfare Act and National Institutes of Health guidelines for animal care and use. One-way ANOVA analyzed statistical significance among each group with posthoc Tukey test using GraphPad Prism 7 (GraphPad Software, La Jolla, CA, USA).

In vivo imaging: The S. aureus bacterial strain used in the in vivo imaging experiments was USA300: LUC. Briefly, the cultured strain was injected into each thigh of the mouse. After 24 h, the mouse was anesthetized in a chamber that had a continuous supply of isofluorane (1.75 LPM). After two minutes, the mouse was placed in the IVIS imaging platform and maintained under oxygen (2.5 LPM) and isofluorane anesthesia was supplied (1.75 LPM) through a nose cone. The imaging chamber was closed, and imaging was initiated to see the bioluminescence of bacterial presence in the thigh 114, 115.

Statistical analysis and reproducibility. All statistical analysis was carried out using GraphPad Prism version 10.02 (GraphPad Software, La Jolla CA, USA) and p values of < 0.05 were considered significant. All experiments were repeated a minimum of three times with technical replicate. The representative of experimental data and micrographs values are used to plot or show the experimental data.

Data availability statement:
The whole genome sequencing data generated in this study have been deposited in the NCBI database under accession code, https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA659249. The RNA seq, metabolomics, lipidomics data are available under 10.5281/zenodo.17610396. The processed RNA data code is available at https://doi.org/10.5281/zenodo.17610396. The RNA seq, metabolomics, lipidomics data generated in this study are provided in the Supplementary Information/Source Data file. The raw data that are used to generate graphs in this work are available in the Source Data file. Source data are provided with this manuscript.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study comprehensively reveals the anti-MRSA mechanisms of Candesartan Cilexetil from multiple dimensions. The writing style is professional and standardized, adhering to the requirements of academic papers. The figures are clear and intuitive, with precise English labeling of specialized terms. The references are comprehensive and up-to-date, reflecting the latest research progress in this field. The work exhibits somewhat time-sensitive, and its technical route is complete with prominent innovation. However, the depth of mechanism analysis (e.g., the association between dynamic membrane behavior and metabolic regulation) requires further enhancement, and the rigor of experimental design needs improvement. With revisions, this work is expected to become a landmark study in the field of membrane-targeted antibiotic research. The following suggestions and comments are put forward:\n1. Clearly distinguish between \"main findings\" and \"mechanism interpretation.\" For example, explicitly state \"CC reduces C20:0 fatty acid levels to impair membrane fluidity\" as a standalone mechanism rather than implying it within result descriptions.\n2. Supplement the direct correlation between the membrane structure of MRSA and its drug resistance. In particular, the impact of membrane lipid composition (such as phosphatidylglycerol and cardiolipin) on drug penetration should be emphasized to strengthen the logicality of the research background.\n3. Elaborate on the contrast between \"CC-induced membrane rigidity\" and \"traditional membrane-active agents increasing fluidity\" , and put forward a reasonable explanation based on the membrane lipid phase transition theory.\n4. Discuss potential compensatory mechanisms for the absence of C20:0 synthesis-related genes in resistant mutants.For example, the substitution of other fatty acids or adaptive changes in membrane proteins.\n5. Emphasize integrated analysis of multi-omics data. For example, instead of merely listing data,explore the correlation between the down-regulatation of cell wall synthesis genes in the transcriptome and the reduction of phospholipids in the lipidome.\n6. Validation of membrane disruption mechanisms:\na.Molecular Dynamics Simulation: It is recommended to add simulations with different membrane lipid ratios (such as DOPC/DOPG/CL mixed membranes) to better approximate the real MRSA membrane environment.\nb. Metabolomics and Lipidomics Analysis: It is necessary to verify whether the down-regulation of key metabolites, such as UDP-N- acetylglucosamine, directly leads to cell wall synthesis defects. It is also advisable to analyze the changes in membrane lipid asymmetry after CC treatment to establish a connection with the increase in membrane permeability.\nc. Functional Verification of Mutant Strains: Gene knockout or complementation experiments are required to confirm the contribution of mutant genes (e.g., tarS) to CC resistance, excluding the interference of other SNPs.\n7.Structure-Activity Relationship Research: The scope of toxicity testing on mammalian cell lines needs to be expanded to evaluate the potential mammalian toxicity of CC.\n8. Optimization of In Vivo Models: It is necessary to supplement the monitoring of the local concentration of CC at the infection site, improve its pharmacokinetic data, and clarify its tissue distribution characteristics.", "reviewer_2": "There is a need to develop new therapeutic approaches to combat drug-resistant pathogens such as MRSA. Given the technical and economic challenges associated with antibiotic development, there is justifiable interest in re-purposing FDA-approved drugs as antibacterials. As such, there is a sound rationale for the work described in the manuscript.\nThe authors have clearly done a lot of work, but unfortunately the very large volume of data/number of panels in graphs makes the manuscript hard to follow, particularly since there simply isn’t enough explanation of why certain conditions were chosen, how one experiment led to the next and what the advances are. For example, synergy was reported for gentamicin and polymyxin B, which aren’t the first antibiotics one thinks of for treating MRSA – were others tried? Are these the only ones that showed synergy and are the authors proposing to use polymyxin B to treat MRSA infections?\nI also have concerns about the novelty of the work since reference 21 reported that CC has anti-staphylococcal bactericidal activity, anti-biofilm activity, is antibacterial in vivo, measured host cell cytotoxicity, synergized with gentamicin and caused membrane damage, it’s not clear how much of this work is novel. The authors acknowledge this previous work and provide some additional mechanism, but I’m not convinced that it’s a significant advance.\nAdditional major points\n1. The results are difficult to interpret as there is a lack of consistency in concentrations used throughout the different assays and in some cases, inoculum sizes.\n2. There is a lack of suitable statistics and descriptions of statistics used throughout the paper. The authors also make comparisons between panels, e.g., Line 218: ‘Comparison of Fig. 4A with Fig. 4B’, without performing statistics between these comparisons. I would suggest, when doing this, to show fewer concentrations and combine the panels into one panel. Two-way ANOVA should be performed on time-kill assays.\n3. Fluorescence-based assays, e.g., Sytox and DiSC3(5), should be OD600nm corrected to account for any changes to the bacterial inoculum density.\n4. I am not sure that the DiSC3(5) assay has been performed correctly. When the membrane potential is intact, this voltage-sensitive dye is self-quenched, as the authors show. Compounds capable of depolarizing the membrane cause the dye to leak out, causing an increase in fluorescence. CCCP is also incompatible with this assay due to its interference with the fluorescence of DiSC3(5). Valinomycin or gramicidin would be a more suitable control, see 10.3389/fcell.2016.00029\n5. I would suggest to the authors to reduce some of the modelling and SAR to improve the clarity of the message. This would make for a nice follow up paper.\n6. It’s not clear why Polymyxin was assayed here, since it’s not used to treat infections caused by S. aureus. Some rationale for this should be provided. Daptomycin would be a much more appropriate choice.\n7. Since CC is only active against S. aureus in the pro-drug form, which is converted in gastric epithelial cells, how viable is this as a therapeutic? Are the MIC concentrations reported here achievable in vivo?\nMinor points\nLine 38: I don’t understand why ‘The severity of S. aureus infections is reflected in the wide variety of S. aureus virulence factors’. Some pathogens cause disease via a single toxin. I would understand if range of virulence factors was linked to range of different types of disease but not severity.\nLine39: persistence and tolerance don’t enable resistance. The former confer survival, and the latter the ability to grow, in the presence of antibiotics.\nLine 52: Omit ‘lipopolysaccharides’, as S. aureus does not have LPS and LPS is not a negatively charged phospholipid.\nLine 75: Authors describe a ‘6-log10-CFU/ml’ reduction to the limit of detection. Figure 1B shows a decrease in viability from 107 – 102 CFU/ml, giving a 5-log reduction.\nLine 79: A ~30% reduction in biofilm production, I do not believe, constitutes inhibition of biofilm production. Similarly, I do not think you can categorise CC as having biofilm eradication activity. I would recommend using tetracycline or chloramphenicol as a control for biofilm inhibition\nLine 102: Can the authors comment on why the all-atom MD simulations performed at Vanderbilt are not in agreement with the prior simulations in reference to membrane perturbation?\nLine 93-102: Is CC able to interact with positively charged alterations (lysyl-PG) to the membrane?\nFigure 1: Different starting inocula are used between 1B and 1C. 1I and 1J are lacking labels, scale bars, and quantification. 1K and 1L are lacking scale bars and quantification. Lack of stats for 1B, 1C, 1D, 1E, 1F, 1G. ‘Persisters’ should be labeled as ‘stationary phase’.\nLine 105: A concentration of 10x MIC is used for AFM studies. Can the authors provide a justification for such a high concentration? Is this concentration clinically achievable?\nLine 120: A sub-lethal concentration of CC is used. The authors note in line 116 that they aimed to identify the MOA by which CC kills and identify potential targets, therefore this should be repeated with a lethal concentration of CC.\nLine 126: how do the authors explain down-regulation of genes associated with membrane stress during exposure to CC, which damages the membrane?\nFigure 2. The colour scheme in 2A is very difficult for the reader to follow.\nLine 139-141: Names of metabolites do not need to be capitalised.\nLine 147-149: Based on the author's findings that CC causes a decrease in the abundance of key membrane lipids and its synergy with polymyxin B, CC may also synergise with daptomycin.\nLine 168-179: Transcriptomic analysis indicates a disruption in peptidoglycan synthesis and production of β-lactam resistance determinants, yet synergy with cell-wall acting antibiotics was not investigated.\nLine 170: Please include clarification on how many times the checkerboard analysis was repeated. Is the FICI described the mean/median of multiple replicates?\nLine 173: Please see the above comment on high concentrations and persisters.\nLine 181-184: Is ATP synthesis affected by CC+gentamicin combination?\nFigure 3. A different starting inoculum is used for 3B exponential cells and persisters. Please perform two-way ANOVA analysis on 3B. The labels are cut off on 3D and 3F. Could the authors provide a quantitative measurement of surface disruption/damage in 3E. Concentrations of CC, gentamicin and polymyxin B are not described in the figure legend. How many antibiotics were examined for synergy? Presumably many were tested and only those that showed synergy are shown? 3 of 4 are aminoglycosides, so perhaps not surprising they have similar effects?\nLine 218-219: The Authors make a direct comparison between panels without having performed statistics. Please see comment above on comparisons between graphs.\nLine 223-225: Please see the above comment on comparisons.\nLine 225: Fig. 4N and 4O are referenced before 4F, 4G, 4I, 4J, DK, 4L. Could the authors please re-order Fig. 4 to reflect the text?\nLine 230-239: What is the purpose of Fig. 4M? It is not referenced in the text.\nLine 240: Could the relative abundance of C20:0 be calculated?\nFigure 4. Description of statistics for 4A, 4B, 4C. Stats need to be performed for 4D, 4E. Fluorescence microscopy in 4F-J is too small. Combining panels 4K-L , 4M-O, 4Q-S, 4T-V, 4W-X would give more space for larger microscopy images.\nLine 253: A p-value is provided, but there is no description of what statistics were performed.\nLine 258: A p-value is provided, but there is no description of what statistics were performed.\nLine 267: Needs reference for how pH impacts antibiotic activity.\nLine 271: Could the authors comment on the improved potency at low pH despite not having membrane permeabilisation activity.\nLine 281: A graph should be included of the serial passaging experiment.\nLine 293- Line 295: The authors should investigate the individual involvement of these genes in CC resistance using mutants.\nLine 295- Line 300: A see-saw effect is also seen between daptomycin and 13-lactams, whereby DAPR is associated with increased susceptibility to 13-lactams. Synergy between CC and daptomycin or 13-lactams should be investigated.\nLine 305-306: Drug names do not need to be capitalised.\nLine 359: Does the removal of the ester linker in compound 7 make it less susceptible to esterase activity in the host?\nFigure 5. Please see above comments on stats and combining panels where comparisons are made.\nFigure 6. Please see above comments on stats\nLine 384: What is the difference between membrane perturbation and disruption?\nLine 388-389: These abbreviations have already been introduced to the reader.\nLine 417: If the ester linker is essential for antibacterial activity, then the authors should consider providing information on the compound's stability in host contexts.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "This study found the effectiveness of candesartan cilexetil as an antibiotic against MRSA. In particular, the authors propose that the change in C20:0 fatty acids is associated with the susceptibility of bacteria to CC. While the manuscript presents intriguing data on the potential application of CC as an antibiotic using a variety of experimental approaches, it appears to lack sufficient elucidation of the underlying mechanism and its practical applicability. Moreover, the absence of detailed descriptions for the multi-omics methodology, the low quality of several figures (e.g., small text, missing scale bars), and inaccurate figure legends collectively reduce the overall clarity and credibility of the manuscript.\nMajor concerns\n- The authors should conduct more reliable experiments to identify the potential specific lipid target of CC. The authors should determine the mechanism by which CC treatment reduces the production of C20:0 fatty acids. Additionally, they should analyze whether the susceptibility of MRSA to CC changes when genes involved in the relevant biosynthetic pathway are knocked out or overexpressed.\n- The precise target lipid associated with the antibacterial activity of CC should be identified (e.g., C20:0-containing phosphatidylcholine).\n- While previous studies have correlated increased membrane fluidity with enhanced antibiotic effects as discussed, this study suggests that increased membrane rigidity induced by C20:0 treatment is associated with the antibacterial effect of CC. Further mechanistic studies are needed to clarify this discrepancy.\n- The interpretation of the results regarding C20:0-lipid in this manuscript is difficult for readers to follow. For example, Figures 4Q–Y suggest that the treatment of C20:0 fatty acids enhance MRSA susceptibility to CC. However, Fig 4P shows that treatment with CC, which has antibacterial activity, actually decreases C20:0 lipid levels. This apparent contradiction raises questions about whether C20:0 lipids are indeed the potential target of CC. The authors need to provide a clearer explanation of these findings in the manuscript.\n- CC is known to have side effects such as low blood pressure, elevated potassium levels, and kidney damage. Using the effective dosage shown in Figure 6, these parameters should be measured. In the case of Fig 6B, the lack of statistical significance may be due to the small sample size (low n), yet it shows a trend of increase potentially induced by CC. These measurements should be analyzed with a higher n and not only at the terminal time point, but also across multiple time points.\n- The methods section does not provide details on how the metabolomics and lipidomics analyses shown in Figure 2 were performed.\nMinor concerns\n- In Fig 1L, clearly label which treatment group each image corresponds to.\n- It would be preferable to include scale bars in all microscopy images. (e.g. Fig 1K)\n- In Figure 2, downregulation and upregulation are indicated in red and blue in the pathway graph (Fig. 2A), whereas in the heatmaps (Fig. 2B–D), the colors are reversed, downregulation is shown in blue and upregulation in red. This inconsistency within a single figure may cause confusion in data interpretation, so it would be preferable to standardize the color scheme.\n- In line 132, Extended Data Fig 2E-right shows individual metabolite levels rather than enriched pathways. Additionally, Extended Data Fig 2E-left does not appear to be a volcano plot. There are also other instances in Extended Figure 2 where the figure panels are incorrectly referenced in the main text. Please review and correct the panel labels accordingly.\n- The legend for Figure 4 is incorrectly labeled as Figure 5.\n- The description of the panel in Figure 4P should be clarified. The figure legend states that vancomycin was used as a control, but it is not indicated in the panel itself, which leads to confusion in interpretation.\n- In line 254, please verify whether the panel labeled as Fig. 4G is correctly referenced.\n- In Fig 6D, perform a one-way ANOVA to determine whether the difference between the Genta and CC+Genta groups is statistically significant.\n- The scale bar in Fig 6F is barely visible. Please enlarge it for clarity.\n- In line 411, correct the typo “8:0 mg/L.”"}, "author_response": "1. Clearly distinguish between \"main findings\" and \"mechanism interpretation.\" For example, explicitly state \"CC reduces C20:0 fatty acid levels to impair membrane fluidity\" as a standalone mechanism rather than implying it within result descriptions.\nResponse: We agree with the reviewer’s suggestion. In addition, journal’s 150 words policy, we restrucuted the abstract in the revised manuscript, and we have made a concerted effort to clearly separate the main findings from the mechanistic interpretation. For example, in the Abstract, which was re-arranged to conform with the referee’s concern, we now state that, “Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of bacterial infections, but treatment options are limited due to MRSA multi- drug resistance. The anti-hypertensive drug Candesartan cilexetil (CC) exhibits potent anti-MRSA activity. It permeabilizes the membrane of MRSA cells and potentiates the activity of aminoglycoside antibiotics. Here we used a variety of methods to elucidate the mechanism by which CC disrupts membrane homeostasis. We show that CC binds to bilayer lipid molecules, decreases membrane fluidity, down- regulates cell membrane and cell wall related genes and related metabolites, and decreases C20 fatty acids (C20:0). Decreasing C20:0 fatty acids confers CC- resistance, which can be reversed by C20:0 supplementation. Structural activity relationship analysis shows that the tetrazole ring and ester carbonic acid of CC are critical for antibacterial activity. Finally, CC reduces MRSA-MW2 replication in a murine MRSA abscess model, supporting a potential role of CC as a lead antimicrobial compound/potentiator against MRSA.”. Page 2, Lines 22-33. We believe this revision improves clarity and directly addresses the reviewer’s concern.\n2. Supplement the direct correlation between the membrane structure of MRSA and its drug resistance. In particular, the impact of membrane lipid composition (such as phosphatidylglycerol and cardiolipin) on drug penetration should be emphasized to strengthen the logicality of the research background.\nResponse: As recommended, we have added to the Introduction to more explicitly discuss what is known about the correlation between MRSA membrane structure and drug resistance. Specifically, we added sentences emphasizing how alterations in membrane lipid composition, including phosphatidylglycerol and cardiolipin, impact drug penetration and contribute to resistance. This addition strengthens the rationale for investigating the membrane-targeting activity of CC. The revised Introduction now reads, “In MRSA, alterations in membrane lipid composition, particularly reduced phosphatidylglycerol or altered cardiolipin distribution, have been associated with decreased drug penetration and increased tolerance to membrane- active antibiotics. These adaptive changes stabilize the bilayer and create a barrier to antibiotic entry, thereby contributing directly to drug resistance1-3. Thus, targeting lipid composition and fluidity may represent an effective strategy to overcome intrinsic and acquired resistance mechanisms in MRSA. Pages 3-4, Lines 53-69.\n3. Elaborate on the contrast between \"CC-induced membrane rigidity\" and \"traditional membrane-active agents increasing fluidity\", and put forward a reasonable explanation based on the membrane lipid phase transition theory.\nResponse: We thank the reviewer for this comment. We now added text clarifying the contrast between CC and other membrane-active agents in the context of lipid phase behavior. The revised phrase reads, “This apparent contradiction can be explained by membrane lipid phase transition theory[54]: CC selectively interacts with long-chain saturated fatty acids (C20:0) and other lipids, promoting tighter packing of lipid tails and stabilizing the gel-like phase. This rigidity compromises membrane integrity, dissipates the proton motive force, and increases permeability, resulting in bacterial killing . Page 23, Lines 498-502.\n4, 7, 8\n4. Discuss potential compensatory mechanisms for the absence of C20:0 synthesis-related genes in resistant mutants. For example, the substitution of other fatty acids or adaptive changes in membrane proteins.\nResponse: We acknowledge that none of the CC-resistant MRSA-MW2 mutants that we studied carried mutations in C20:0 biosynthesis genes, despite the importance of these lipids for CC susceptibility. A potential compensatory mechanism may involve the substitution of C20:0 fatty acids with other long-chain saturated or unsaturated fatty acids, which could maintain overall membrane integrity while reducing CC binding. Additionally, adaptive remodeling of membrane-associated proteins, such as transporters, efflux pumps, or enzymes involved in lipid modification, may further stabilize the membrane or modulate drug permeability. Such lipid and protein adaptations could collectively allow MRSA to tolerate CC without direct disruption of C20:0 biosynthesis pathways.\nThe revised text reads, “Although CC-resistant MRSA-MW2 mutants lacked mutations in C20:0 biosynthesis genes, resistance may arise through compensatory mechanisms, such as substituting other long-chain fatty acids for C20:0 or remodeling membrane-associated proteins, maintaining membrane integrity while reducing CC susceptibility9. Future lipidomic and proteomic studies are needed to confirm these conjectures. Discussion, Page- 24, Lines 534-537.\n5. Emphasize integrated analysis of multi-omics data. For example, instead of merely listing data, explore the correlation between the down-regulation of cell wall synthesis genes in the transcriptome and the reduction of phospholipids in the lipidome.\nResponse: We thank the reviewer for the suggestion. We have now added integrated the multi-omics analysis to highlight correlations between transcriptomic and lipidomic changes. Specifically, downregulation of cell wall synthesis genes in the transcriptome correlates with decreased levels of key phospholipids (e.g., phosphatidylglycerol and phosphatidylethanolamine) in the lipidome, suggesting that CC-mediated perturbation of lipid homeostasis is linked to transcriptional regulation of membrane biosynthesis pathways. This integrated view underscores how CC disrupts both membrane composition and biosynthetic processes, contributing to its bactericidal activity.\nThe revised text now reads, “Integrated analysis of multi-omics data revealed that downregulation of cell wall synthesis genes in the transcriptome correlates with decreased levels of key phospholipids in the lipidome, indicating that CC disrupts membrane composition while simultaneously affecting biosynthetic pathways”. Page 21, Lines 450-453. And, “Consistent with the multi-omics findings, the coordinated downregulation of cell wall synthesis genes and the reduction in membrane phospholipids suggest that CC perturbs both structural and biosynthetic components of the S. aureus membrane, highlighting a synergistic disruption of membrane integrity”. Page 21, Lines 458-461.\n6. Validation of membrane disruption mechanisms:\na. Molecular Dynamics Simulation: It is recommended to add simulations with different membrane lipid ratios (such as DOPC/DOPG/CL mixed membranes) to better approximate the real MRSA membrane environment.\nThe 70:30 DOPC/DOPG membrane model employed in the original submitted manuscript is a well-regarded model that has been successfully utilized in multiple high-profile computational and experimental studies.1,2 Nevertheless, the advanced reviewer is correct that validation against additional advanced Gram-positive membrane models would be beneficial. To that end, we performed new simulations exploring the interaction between CC and a “more advanced membrane model” that was previously used to study the antibacterial action of lugdunin in a 2024 Nature Communications article by Ruppelt et al.3,4 To our knowledge, this latter model of a Gram-Positive lipid bilayer is the highest-fidelity available for simulations and contains the 9 most common lipids present in the Gram-Positive model organism Bacillus subtilis, with ratios determined by GC-MS, LC/LC-MS and MALDI-TOF findings. Key structural differences between the Ruppelt et al model and the 70:30 DOPC/DOPG include iso- and anteiso-branched lipid tails and cardiolipin (CL) molecules. In summary, new triplicate MD runs between this advanced membrane model and CC revealed no significant differences in penetration behavior or thermodynamics compared to our original 70:30 model. Unsteered MD simulations confirmed that CC readily penetrates the bilayer membrane (Figure S1while steered MD and PMF analyses (Figure S2) confirmed that this penetration is thermodynamically favored, with a ΔG of around -10 kcal mol-1(compared to -8 kcal mol-1 for the 70:30 DOPC/DOPG simulations). The penetration of CC within this new membrane was slightly shallower ~17-22 Å from the bilayer center (compared to the original penetration depth of ~12-19 Å) but this is thought to be due to an increased variance in simulation box sizes with the Ruppelt et al. membrane model. Due to the parallelized nature of umbrella sampling, this variance cannot be normalized. Our simulations did note the new model yielded some buckling of the membrane plane arising from barostat limitations in the MD software AMBER24. This is likely why we have not found any literature examples of CL-containing membranes simulated using AMBER24, but instead utilizing 70:30 DOPC/DOPG. Overall, the 70:30 DOPC/DOPG model yielded comparable trends to the more advanced model and, more importantly, both models provided support for the proposed penetration of CC reported in the experimental data sections of this manuscript.\nWe decided to NOT include the data with the Ruppelt et al. CL-containing membranes because the paper is already very long and complicated, but these data could be included if necessary.\nReferences:\n1: Kim, W.; Zhu, W.; Hendricks, G. L.; Van Tyne, D.; Steele, A. D.; Keohane, C. E.; Fricke, N.; Conery, A. L.; Shen, S.; Pan, W.; et al. A new class of synthetic retinoid antibiotics effective against bacterial persisters. Nature 2018, 556, 103-107. DOT: 10.1038/nature26157.\n2: Kim, W.; Zou, G.; Hari, T. P. A.; Wilt, T. K.; Zhu, W.; Galle, N.; Faizi, H. A.; Hendricks, G. L.; Tori, K.; Pan, W.; et al. A selective membrane-targeting repurposed antibiotic with activity against persistent methicillin-resistant Staphylococcus aureus. Proc Natl Acad Sci U S A 2019, 116, 16529-16534. DOT: 10.1073/pnas.1904700116.\n3: Ruppelt, D.; Trollmann, M. F. W.; Dema, T.; Wirtz, S. N.; Flegel, H.; Monnikes, S.; Grond, S.; Bockmann, R. A.; Steinem, C. The antimicrobial fibupeptide lugdunin forms water-filled channel structures in lipid membranes. Nat Commun 2024, 15, 3521. DOT: 10.1038/s41467-024-47803-6.\n4: Pogozheva, T. D.; Armstrong, G. A.; Kong, L.; Hartnagel, T. J.; Carpino, C. A.; Gee, S. E.; Picarello, D. M.; Rubin, A. S.; Lee, J.; Park, S.; et al. Comparative Molecular Dynamics Simulation Studies of Realistic Eukaryotic, Prokaryotic, and Archaeal Membranes. J Chem Tnf Model 2022, 62, 1036-1051. DOT: 10.1021/acs.jcim.1c01514.\nb. Metabolomics and Lipidomics Analysis: Tt is necessary to verify whether the down-regulation of key metabolites, such as UDP-N- acetylglucosamine, directly leads to cell wall synthesis defects. Tt is also advisable to analyze the changes in membrane lipid asymmetry after CC treatment to establish a connection with the increase in membrane permeability.\nResponse: To test whether depletion of cell-wall precursors contributes to membrane defects, we supplemented CC- treated MRSA cultures with UDP-N-acetylglucosamine (UDP-NAG) or N-acetylglucosamine (NAG). Supplementation partially restored growth inhibited by CC (growth curves) and reduced CC-induced Sytox Green uptake, indicating decreased membrane permeability. These findings support a mechanistic link between CC-induced depletion of UDP-NAG (observed in our metabolomics) and cell-wall/ membrane defects. We have added these data to the Results (Fig. 2, panels E-H) and summarized them in the Discussion.\nThe revised text in Results now reads: “To test whether depletion of cell-wall precursors contributes to CC- elicited membrane defects, we supplemented cultures with UDP-N-acetylglucosamine (UDP-NAG) or N- acetylglucosamine (NAG). CC-treated MRSA-MW2 show impaired growth and membrane permeability to CC. However, both supplements partially rescued CC- inhibited growth (Fig. 2E; p<0.0001) and reduced Sytox Green permeability (Fig. 2F–G; p<0.0001), supporting a functional link between CC-mediated depletion of UDP-NAG and loss of cell-wall/membrane integrity (Fig. 2H). (Fig. 2H), Page 9, Lines 189- 195\nThe revised figure legend now reads, “E–H. Metabolite supplementation assay. E. To test whether depletion of cell-wall precursors contributes to CC-elicited membrane defects, MRSA-MW2 cultures were supplemented with UDP-N-acetylglucosamine (UDP-NAG) or N-acetylglucosamine (NAG) in the presence of sub-MIC concentrations of CC and incubated with agitation for 4 h. Growth was measured spectrophotometrically and expressed as optical density at 600 nm (OD600). F. MRSA-MW2 cells were treated with CC in the presence of UDP-NAG or NAG, adjusted to an OD600-equivalent cell density, and assessed for membrane permeability using the Sytox Green assay. H. TEM analysis of MRSA-MW2 cells treated under the same conditions to visualize cell-wall and membrane integrity” Figure File- Page 6, Lines 190-199.\nThe revised text in the Discussion now reads, “In addition, rescue experiments using cell-wall constituents (UDP-NAG, NAG) strengthen the connection between transcriptomic/metabolomic down-regulation of cell-wall biosynthesis and the observed lipid/lipidome changes: reduced UDP-NAG compromises peptidoglycan precursor pools, which likely contributes to membrane destabilization and increased permeability” , Page 21- Lines 453-457.\nc. Functional Verification of Mutant Strains: Gene knockout or complementation experiments are required to confirm the contribution of mutant genes (e.g., tarS) to CC resistance, excluding the interference of other SNPs.\nResponse: We agree with the reviewer’s comment regarding the need to functionally verify the role of cell wall/membrane–associated genes in mediating CC susceptibility. However, we did not attempt to directly complement the tarS mutant derived from the passaging experiment because comprehensive genetic analysis is not the focus of this manuscript. Nevertheless, to partially address the referee’s concerns, we performed MIC determinations using wild-type, knockout, and complemented tarO strains (a gift from Dr. Suzzane Walker lab). tarO, like tarS, is involved in the biosynthesis of cell wall teichoic acid. Unexpectedly, however, deletion of tarO resulted in a 2-fold reduction in CC MIC (from 8 mg/L in RN4220 to 4 mg/L in ΔtarO). Restoration of tarO expression from a plasmid complemented strain reverted the MIC back to 8 mg/L, validating the specificity of the phenotype. These results definitively link cell wall architecture to CC activity and validate the functional importance of these genes in modulating susceptibility.\nBased on the observation that deletion of tarO increased rather than decreased susceptibility to CC, we now added the following text in the Results section: “One particular CC-resistant mutant clone carrying mutations in purR, folC, farR, and tarS (strain MRSA-MW2-S50 from day 50 of serial passage (Supplementary Tables 6)) was chosen for further analysis (as described in the section below on structure-activity relationship studies). Interestingly, however, MRSA-MW2-S50 did not exhibit decreased levels of C20:0 lipids (Extended Data 4G), suggesting that a variety of mechanisms could lead to CC-resistance. In this regard, the observation that the CC-resistant strain MRSA-MW2-S50 contained a mutation in the tarS gene, which is required for cell- wall teichoic acid biosynthesis, prompted us to test whether a well-studied S. arueus tarO mutant exhibits CC resistance. Specifically, we tested a previously constructed deletion of the tarO gene, an isogenic derivative of the tarO mutant carrying wild-type tarO expressed on plasmid pTP63, and S. aureus strain Newman, the parent of the tarO mutants. As shown in Extended data 4H, the MICs of the Newman strain, the tarO mutant, and the complemented mutants were unexpectedly 8 mg/L, 4 mg/L and 8 mg/L, respectively. That is, the tarO mutant was more susceptible to CC than the parent strain, in contrast to our expectation. Since the ΔtarO mutant was more sensitive to CC, it is unlikely that TarO or TarS are direct molecular targets of the compound. Instead, these results indicate that TarO–TarS– dependent pathways preserve cell wall architecture and mitigate CC-induced stress. Mutations in tarS may alter WTA composition or surface charge, potentially reducing CC binding or penetration. Collectively, these findings suggest that CC activity is modulated by the integrity and glycosylation status of wall teichoic acids.” Page 15-16, Lines 316-344.\nThe data is now included in Extended data-4H panel.\nPlease refer to the response for Reviewer #2, minor comment #36.\n7. Structure-Activity Relationship Research: The scope of toxicity testing on mammalian cell lines needs to be expanded to evaluate the potential mammalian toxicity of CC.\nResponse: We now report data for mammalian hepatic cells in Fig 6E. We also performed additional mammalian toxicity testing using human RBCs with the CC analogs. The IC50 values for CC and its analogs were as follows: CC = 16 mg/L, CC4 = 50 mg/L, CC5 = 25 mg/L, CC6 = 20 mg/L, and CC7 = 16 mg/L. These results indicate that most analogs exhibit comparable or lower toxicity than CC, supporting their potential safety as antibacterial candidates. We have added these data to the SAR section in the Results (see Fig. 5E and corresponding text).”\nThe revised text reads, “Importantly, we evaluated the potential mammalian toxicity of all six analogs using human RBCs. The IC50 values were as follows: CC = 16 mg/L, CC4 = 50 mg/L, CC5 = 25 mg/L, CC6 = 20 mg/L, and CC7 = 16 mg/L, indicating that most analogs retain comparable or reduced toxicity relative to CC”. Page- 19, Line 408-411.\nThe revised figure legend now reads, “F. Mammalian cell hemolysis assay. Serially diluted test compounds were added to human RBCs and after incubation for an hour, the supernatant was examined by spectroscopy at 540 nm”. Figure file, Page-11, Lines 176-178.\n8. Optimization of In Vivo Models: It is necessary to supplement the monitoring of the local concentration of CC at the infection site, improve its pharmacokinetic data, and clarify its tissue distribution characteristics.\nResponse: To begin addressing the concern of CC stability and local drug concentration in infected tissue, we performed a stability assay of candesartan cilexetil (CC) in mouse thigh muscle homogenate. CC was found to be stable for at least 6 h under the assay conditions, indicating that it does not undergo rapid degradation in muscle tissue.\nResults: The area ratios (analyte/internal standard) demonstrated >95% recovery across all tested time points (0, 5, 15, 30, 60 min), with calculated stability ranging between 97–110% of baseline values. These results confirm that CC maintains chemical stability in the thigh tissue during the relevant timeframe of our in vivo efficacy experiments. While these stability data provide confidence that CC remains intact at the site of infection, we acknowledge the reviewer’s point that full pharmacokinetic (PK) and tissue distribution studies are necessary to complement these findings. Such work is ongoing in our laboratory and will include LC– MS/MS quantification of CC and its active metabolite candesartan in plasma and infected tissues (thigh muscle, liver, kidney) at multiple time points. We plan to include these data in a follow-up study focused on CC analog optimization."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have fully addressed the points raised.", "reviewer_4": "I think the authors responded reasonably to the reviewer’s comments. However, it would be good to carefully check the methodology section and revise it if necessary.\n- In the rebuttal, the authors state that they used HILIC chromatography on a Luna NH2 column for lipidomics, which appears to differ from more conventional lipidomics methods (e.g., using a C18 column). I would recommend that they double-check the methodology."}, "author_response": "We thank the reviewer for constructive feedback throughout the review process.\nWe double-checked that the Luna NH2 was used for lipidomics and cited with reference (PMC9682148) has been now added in the methodology (Page 34, Line 807)."}]

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