A testis-specific E3 ubiquitin ligase complex governs spermiogenesis and male fertility

Domain: chemistry
Published: 2026-02-24
Editorial outcome: major_revision

Abstract
The ubiquitin-proteasome system (UPS) represents an evolutionarily conserved machinery governing proteostasis through spatiotemporal regulation of protein degradation. While spermatogenesis involves multilayered regulatory mechanisms spanning translation to dynamic post-translational modifications (PTMs), the identity of UPS-associated E3 ligases orchestrating germ cell-specific protein turnover remains elusive. Here, we identify a testis-specific E3 ubiquitin ligase complex comprising elongin B/C, Cullin-2 (CUL2), RING-box protein-1 (RBX1), and SOCS box protein ASB9, designated ECSASB9. Genetic ablation of ECSASB9 in mice via ubiquitous Asb9 knockout (KO) or spermatid-specific elongin B/C conditional KO disrupts spermiogenesis and compromises fertility. Mechanistic studies reveal that ECSASB9 engages tubulin beta 4A (TUBB4A) through substrate recognition, catalyzing K48-linked polyubiquitination at lysine 379 (K379) to promote proteasomal degradation. Notably, Tubb4a K379R knock-in (KI) mice phenocopy the spermiogenesis defects observed upon ECSASB9 deficiency. Clinically, we identify three hemizygous missense variants in X-linked ASB9 among Chinese males with idiopathic infertility. Male mice bearing orthologous ASB9 variant exhibit oligoasthenoteratozoospermia (OAT) and subfertility, mirroring human phenotypes. Taken together, our findings establish ECSASB9 as an important regulator of spermatogenic proteostasis and provide mechanistic insights into UPS-mediated tissue-specific degradation, while implicating ASB9 variants in male infertility pathogenesis.

Introduction
Cellular proteostasis relies on dynamic regulation of protein synthesis and degradation, governed by a conserved enzymatic cascade involving E1 ubiquitin-activating enzymes, E2 ubiquitin-conjugating enzymes, and E3 ubiquitin ligases. This cascade initiates with ATP-dependent activation of ubiquitin (Ub) by E1, forming a thioester bond between Ub’s C-terminus and the E1 catalytic cysteine. Ub is subsequently transferred to an E2 enzyme, generating a reactive E2~Ub intermediate. E3 ligases then orchestrate substrate specificity by recruiting both the E2~Ub complex and target proteins, catalyzing Ub transfer to substrate lysine residues. K48-linked polyubiquitination marks substrates for recognition and proteolysis by the 26S proteasome.

The remarkable diversity of E3 ligases (>600 in humans) reflects their evolutionary adaptation to target a broad array of substrates. E3s are classified into three structural families: RING (really interesting new gene), HECT (homologous to E6AP carboxyl terminus), and RBR (RING-between-RING). RING-type E3s, the largest class, utilize zinc-coordinating RING or U-box domains to bind E2~Ub and facilitate Ub transfer. Among these, cullin-RING ligases (CRLs) represent modular multi-subunit complexes assembled on cullin (CUL) scaffolds, RING-box proteins, adaptors, and substrate receptors. A prototypical CRL subclass is the ECS complex (Elongin B/C–CUL–SOCS-box protein), where SOCS-box proteins serve as substrate receptors by interacting with the Elongin B/C (ELOB/C) adaptor dimer. This adaptor recruits either a CUL2-RBX1 or a CUL5-RBX2 catalytic core, completing assembly of a functional E3 ligase that directs substrate polyubiquitination and proteasomal degradation.

Notably, >40 SOCS-box proteins have been identified in mammals, conferring tissue-specific functionality to ECS complexes. For instance, SOCS2 modulates growth hormone signaling through dual regulatory roles, while ASB3 suppresses antiviral immunity by targeting mitochondrial antiviral signaling protein (MAVS) for polyubiquitination and degradation. Tissue-restricted ECS variants include ASB15 in skeletal muscle differentiation, and ASB6 in adipocyte insulin signaling via SH2B2 (SH2B adapter protein 2) degradation. These studies collectively demonstrate that ECS complex composition dictates substrate selectivity and functional specialization across tissues. However, the mechanisms governing ECS assembly heterogeneity and its tissue-specific regulatory logic remain unresolved, necessitating systematic exploration of ECS plasticity in vivo.

In somatic cells, ASB9 is predominantly known to assemble into an ECS complex with CUL5, where it targets mitochondrial proteins such as ubiquitous mitochondrial creatine kinase (uMtCK) for degradation, thereby regulating mitochondrial structure and bioenergetics. This establishes a paradigm where ASB9–CUL5 functions in metabolic regulation. However, whether ASB9 can form alternative, tissue-specific complexes with distinct catalytic cores and functions remains an open question. To address this gap and to systematically explore the heterogeneity of ECS complexes in vivo, we performed tissue-specific proteomic profiling of endogenous ECS complexes in mice, which led to the identification of a testis-specific ECS variant, termed ECSASB9, that is important for spermiogenesis and fertility in mice and humans.

Results
IP-coupled proteomic profiling identifies ECSASB9
To systematically assess the heterogeneity of the endogenous ECS complex across mammalian tissues, we conducted comprehensive interactome mapping in 11 adult mouse tissues using ELOB as bait. ELOB, together with ELOC, forms a stable adaptor dimer that is central to the assembly of ECS-type E3 ligases. This ELOB/C dimer serves a dual function: it specifically recruits SOCS-box-containing substrate receptors while simultaneously binding to the cullin (CUL2 or CUL5) scaffold, thereby bridging the substrate-recognition module to the catalytic core of the ligase.

Profiling revealed tissue-specific variability in ECS complex composition. ELOB/C and CUL2 demonstrated constitutive presence across all examined tissues. In contrast, SOCS-box proteins displayed stringent spatial restriction, with NEURL2 and ASB10 exclusively detected in heart, while VHL showed intestinal specificity. Of note, testicular heterogeneity was observed, characterized by the co-enrichment of four SOCS-box proteins: ASB1, ASB3, ASB9, and TCEB3. Among these, TCEB3 exhibited broad distribution (detected in 5/11 tissues), whereas ASB1/3/9 showed strictly testis-specific enrichment. Notably, uterus uniquely harbored both CRL2- and CRL5-type ECS complexes, with CUL5 showing uterus-specific enrichment. Collectively, our multi-tissue interactome reveals considerable combinatorial diversity in the ECS complex.

In our previous studies, we found that Asb1 and Asb3 showed abundant expression during the postmeiotic spermatid elongation phase in mice. Notably, whereas Asb3-deficient mice maintained normal fertility and spermatogenesis, ASB1 formed a functional E3 ligase complex with ELOB to orchestrate the ubiquitination of sulfide-quinone oxidoreductase (SQOR), thereby modulating hydrogen sulfide (H2S) metabolic homeostasis during spermiogenesis. This mechanistic insight was corroborated by the phenotype of Asb1-KO which exhibited compromised male fertility characterized by sperm defects and significant testicular H2S depletion. However, little is known about the distribution and function of ASB9 during spermatogenesis. To this end, we first performed a cross-species conservation analysis of ASB9, which showed that it is highly conserved across diverse taxa. Notably, the structural integrity of both Ankyrin (ANK) and SOCS domains was maintained throughout evolution, strongly suggesting an evolutionarily conserved functional role for this protein. RT‒PCR identified Asb9 as testis-enriched in both mice and humans. In addition, immunostaining uncovered strict spatiotemporal distribution of ASB9 during spermatogenesis. While core ECS components ELOB/C, CUL2, and RBX1 exhibited constitutive expression across all spermatogenic steps, ASB9 first appeared at step 8 spermatids. Its signal intensified through steps 9-14, then sharply declined by step 15. Intriguingly, TCEB3, a ubiquitously identified ECS substrate receptor, displayed complementary dynamics: present from pachytene spermatocytes to step 8 spermatids but undetectable in later steps. This reciprocally phased expression pattern suggests a model where timed replacement of TCEB3 by ASB9 reprograms ECS complex substrate specificity during late spermiogenesis.

Immunofluorescence imaging of testicular single-cell suspensions revealed a skirt-like structure of ASB9 signals surrounding the elongating spermatid head, a topography reminiscent of the transient microtubule-based manchette that is crucial for sperm head shaping. Co-immunostaining with the manchette marker α-tubulin showed precise colocalization in elongating spermatids, confirming ASB9’s location in manchette. Subsequently, we performed Co-IP assays using testicular lysates to validate the IP-LC-MS/MS results. Anti-ELOB antibody precipitated the immunoprecipitates comprising ELOB/C, CUL2, RBX1, and ASB9, demonstrating constitutive assembly of an ECS complex we term ECSASB9. Together, these data suggest testis-specific ECSASB9 functions as a manchette-resident ubiquitination machinery which may be required for sperm head morphogenesis.

Impaired spermiogenesis and fertility in Asb9-KO mice
To investigate the functional significance of ECSASB9 in male fertility, we established an X-linked ubiquitous Asb9 knockout (Asb9-KO) mouse model via CRISPR/Cas9-mediated genome editing. Sanger sequencing verified a 13-bp deletion causing frameshift mutation in hemizygous Asb9-KO males, with subsequent Western blot and immunostaining confirming complete ASB9 protein ablation in Asb9-KO testes. Asb9-KO males exhibited marked subfertility accompanied by smaller testis sizes and lower testis weights compared to wild-type (WT) mice. Epididymal sperm analysis revealed that the sperm count, motility and progressive motility were significantly reduced in Asb9-KO mice relative to WT mice. Notably, head morphology defects, including amorphous, microcephalic, or tapered configurations, as well as acrosomal abnormalities, such as irregular shaping, fragmentation or near-complete absence, were increased in Asb9-KO mice, while tail abnormalities remained statistically comparable between the two groups. Ultrastructurally, WT sperm displayed intact acrosomal caps overlying densely condensed nuclei, whereas Asb9-KO sperm exhibited malformed heads with acrosomal defects ranging from irregular and fragmented structures to near-complete absence. In contrast, the flagellar ultrastructure of Asb9-KO sperm appeared normal, exhibiting the typical “9+2” microtubule doublet arrangement characteristic of functional sperm tails. To investigate the basis of the motility defect despite normal flagellar structure, we measured intracellular ATP levels. Sperm from Asb9-KO mice exhibited a significant reduction in ATP content compared to WT controls, indicating a state of bioenergetic deficit. These collective anomalies define a classical oligoasthenoteratozoospermia (OAT) phenotype, explaining the observed subfertility in Asb9-KO males.

Histomorphometric assessment of Asb9-KO and WT testes revealed comparable populations of spermatogonia, spermatocytes, Sertoli cells, and Leydig cells between the two groups. However, morphologically compromised spermatids were evident in Asb9-KO tubules. The increased apoptotic signals in the cauda epididymis of Asb9-KO mice suggest that these deformed spermatids may undergo apoptosis. This interpretation is supported by step-resolved morphometric analysis of Periodic acid-Schiff (PAS)- and peanut agglutinin (PNA)-stained spermatids, which revealed normal spermatid numbers and head shaping through step 9 in Asb9-KO mice, but a significant reduction in spermatid number and an increase in head and acrosome defects at steps 10–16 of spermiogenesis.

The manchette, a transient microtubular scaffold composed of α/β-tubulin heterodimers, assembles transiently during mouse spermiogenesis (steps 8–14), serving as the principal architect of postmeiotic nuclear shaping. Given ASB9’s manchette-specific localization, we explored whether the deficiency of ASB9 affects manchette structure across spermiogenesis. Immunofluorescence staining and transmission electron microscopy (TEM) revealed intact manchette assembly in Asb9-KO spermatids at steps 8–9, with normally condensing nuclei and properly overlying acrosomes. However, at step 10, the microtubule bundles in Asb9-KO spermatids were thinner than in WT spermatids, which coincided with the onset of nuclear shaping defects and aberrant acrosomal development. In later steps, aberrant elongation and disorganization of the manchette in Asb9-KO spermatids were accompanied by severe malformation of the sperm head and irregular acrosomal structure. Consequently, by late spermiogenesis when the manchette disassembles, Asb9-KO spermatids failed to complete remodeling, resulting in the production of spermatozoa with misshapen heads and malformed acrosomes. All these results indicate ASB9 as an important modulator of manchette dynamics during murine spermiogenesis, with its deficiency causing OAT through microtubule-dependent nuclear shaping defects.

Given that Asb9 mRNA is detectable in several tissues, with notable expression in the kidney, we assessed whether ASB9 deficiency led to broader pathological consequences. Histopathological examination of key organs, including the brain and lung (tissues rich in motile cilia) and the kidney, revealed no overt morphological abnormalities in Asb9-KO adults compared to WT littermates.

ASB9 facilitates the K48-linked ubiquitination of TUBB4A on K379
To identify substrates of the ECSASB9 complex critical for manchette dynamics, we performed IP-LC-MS/MS on endogenous ASB9 complexes isolated from mouse testes. Comparative analysis of two biological replicates identified 97 overlapping proteins, including ASB9, ELOB/C, and tubulin beta 4A (TUBB4A)—a β-tubulin family member. Reciprocal co-IP validated endogenous interactions between ASB9 and both ELOB/C and TUBB4A in testicular lysates. Immunofluorescence confirmed colocalization of ASB9 and TUBB4A in elongating spermatids. Notably, deficiency of ASB9 increased TUBB4A protein abundance without altering its mRNA levels, implicating post-translational regulation. Consistently, TUBB4A immunoprecipitated from Asb9-KO testes exhibited reduced poly-pan and poly-K48 ubiquitination compared to WT testes, as assessed by pan-ubiquitin and K48-specific antibodies. Given the canonical role of K48 chains in proteasomal degradation, these findings suggest TUBB4A as an ECSASB9 substrate undergoing ubiquitin-dependent degradation.

To mechanistically investigate this regulation, we co-expressed Flag-ASB9 and GFP-TUBB4A in HEK-293T cells. The reciprocal interaction was confirmed by co-IP, while ASB9 overexpression dose-dependently reduced endogenous TUBB4A levels. Cycloheximide (CHX) chase assays revealed decreased TUBB4A stability upon ASB9 overexpression. Moreover, we found that MG132, a proteasome inhibitor, could efficiently restore TUBB4A protein levels even under ASB9 overexpression. In vitro ubiquitination assays revealed that testis-derived ASB9 complexes catalyzed TUBB4A polyubiquitination, a process that was also observed in HEK-293T cells. Ubiquitin mutagenesis narrowed chain specificity to K48, as neither K6/K11/K27/K29/K33/K63 variants nor K48R (Lys48 substituted with arginine) supported conjugation.

Mapping ubiquitination sites using our published ubiquitome data of mouse testis, we identified five candidate lysines (K122, K216, K252, K297, K379) on TUBB4A. Site-directed mutagenesis revealed that only K379 mutation abolished ASB9-mediated polyubiquitination. Correspondingly, TUBB4A-K379R exhibited resistance to ASB9-driven destabilization in CHX assays, pinpointing K379 as the key residue for ECSASB9-dependent proteasomal targeting.

TUBB4A mutation phenocopies the spermiogenesis defects of ASB9 deficiency
To investigate the in vivo role of TUBB4A K379 in manchette integrity, we generated Tubb4a K379R knock-in (KI) mice via CRISPR-Cas9-mediated genome editing. Genotypic validation by Polymerase Chain Reaction (PCR) sequencing confirmed successful introduction of the K379R mutation. As anticipated, Tubb4a-KI mice displayed reduced K48-linked and pan-polyubiquitination of TUBB4A, concomitant with increased TUBB4A protein abundance in testes, while mRNA levels remained unaltered. Although Tubb4a-KI mice developed normally, they exhibited subfertility phenotypes. Testicular appearance and weight showed no significant differences between Tubb4a-KI and WT littermates. However, epididymal sperm analyzes revealed decreased sperm count, sperm motility, and progressive motility in Tubb4a-KI mice compared to WT mice. Furthermore, sperm head and acrosome defects were markedly increased in Tubb4a-KI mice, while tail abnormalities remained comparable to those in WT controls.

Histopathological examination revealed shared spermiogenesis defects between Asb9-KO and Tubb4a-KI mice. As shown in Fig. 4l–o, the elongating spermatids in Tubb4a-KI mice exhibited apparent nuclear and acrosomal deformation, coinciding with aberrant elongation of the manchette cytoskeleton. These structural anomalies correlated temporally with disrupted manchette-mediated nuclear shaping, phenocopying the defects observed in Asb9-KO spermatids. Collectively, these findings uncover TUBB4A K379 as a critical residue governing ubiquitination-dependent regulation of microtubule dynamics during murine spermiogenesis.

ELOB/C maintain ECSASB9 complex integrity to ensure spermiogenesis
While ELOB/C are established binding partners of ASB-family proteins, their functional necessity in ECSASB9-mediated substrate recognition remained undefined. To specifically dissect the role of the late-acting ECSASB9 complex without perturbing earlier ECS functions, we designed a spermatid-targeted knockout strategy. Although ELOB/C are expressed in multiple germ cell types, another ECS substrate receptor, TCEB3, operates from pachytene spermatocytes through step 8 spermatids. Ubiquitous or earlier knockout of Elob/c would disrupt the ECS TCEB3 complex and confound interpretation of phenotypes. To isolate the function of ECSASB9 during late spermiogenesis, we generated Tnp2-Cre transgenic mice and Elob/c-floxed mice. The Tnp2-Cre driver restricts Cre recombinase activity to spermatids beginning at step 8, thereby preserving ECS TCEB3 function while enabling selective ablation of ELOB/C in post-step 8 spermatids. Crossing these strains produced spermatid-specific Elob/c conditional knockout mice (Elob/c-cKO). Immunofluorescence staining confirmed efficient depletion of ELOB and ELOC specifically in elongating spermatids of Elob-cKO and Eloc-cKO testes, respectively.

Breeding assays revealed that Elob/c-cKO males were subfertility despite normal mating behaviors. Testis size and weight remained comparable to WT littermates. However, histopathological analysis of Elob/c-cKO epididymal sperm and testes uncovered an OAT phenotype accompanied by sperm head shaping defects and manchette disorganization, phenocopying Asb9-KO and Tubb4a-KI models.

To dissect the molecular basis of these defects, we interrogated ECSASB9 complex integrity in Elob/c-cKO testes. Immunoblot analyzes revealed coordinated reductions in core components: ELOB/C, ASB9, and RBX1. Additionally, TUBB4A protein levels in Elob/c-cKO testes were significantly increased, consistent with loss of ECSASB9-mediated ubiquitination.

Together, these data reveal ELOB/C as key adaptor for ECSASB9 complex stability, with their ablation triggering aberrant TUBB4A accumulation and consequent failure of manchette-mediated nuclear remodeling during spermiogenesis.

Identification of hemizygous ASB9 variants in four infertile men
To explore a possible role for ASB9 in human male fertility, we screened whole-exome sequencing data from a cohort of 1483 individuals with oligoasthenozoospermia. We identified four infertile men with oligoasthenozoospermia harbored hemizygous X-linked ASB9 missense variants. These included c.542C>T (p.A181V) in families F1 and F3, c.275G>A (p.G92E) in F2, and c.479T>C (p.I160T) in F4. All variants were absent or exceedingly rare in public population databases (gnomAD, 1000 Genomes Project). Furthermore, we specifically examined the data from the large-scale studies involving 2875 infertile men with primary spermatogenic failure and found that none of our reported ASB9 variants were present in that cohort. These ASB9 variants were classified as deleterious by computational predictors (SIFT, PolyPhen-2, CADD). Subsequent Sanger sequencing validated these variants in probands. Notably, all mutated residues reside within the evolutionarily conserved ANK domain of ASB9, as demonstrated by cross-species alignment.

Semen analysis of affected individuals showed severe reductions in sperm count, total motility, and progressive motility. Morphological abnormalities were observed via hematoxylin and eosin (H&E) staining and TEM, including amorphous/tapered/small heads and acrosomal defects. These results suggest that ASB9 also plays an important role in sperm head shaping in humans, and that ASB9 variants are associated with human OAT.

Intracytoplasmic sperm injection (ICSI) using sperm from variant carriers yielded fertilized embryos and blastocysts in all cases. Three of four couples achieved clinical pregnancies, supporting ICSI as an efficient reproductive strategy for men harboring pathogenic ASB9 variants.

To explore the effect of these missense mutations on ASB9 expression, HEK-293T cells were transfected with Flag-tagged ASB9-WT or mutant constructs (p.A181V, p.G92E, and p.I160T). Western blotting revealed markedly reduced ASB9-G92E protein levels compared to WT, concomitant with TUBB4A accumulation. CHX chase assays revealed rapid degradation of ASB9-G92E, which was rescued by MG132-mediated proteasome inhibition. Ubiquitination assays confirmed enhanced polyubiquitination of ASB9-G92E, indicating that p.G92E destabilizes ASB9 via UPS. Notably, despite ASB9-I160T and ASB9-A181V protein levels being comparable to WT, co-IP assays revealed a marked reduction in TUBB4A binding efficiency. This diminished interaction correlated with reduced TUBB4A polyubiquitination and consequent protein accumulation, identifying I160 and A181 as key residues for ASB9-TUBB4A interactions.

Mice bearing orthologous ASB9 variant mirror human phenotypes
Given the important role of Asb9 in murine spermatogenesis was unveiled by KO studies, we further generated CRISPR-Cas9-mediated Asb9G88E (Asb9-KI) mice (orthologous to human ASB9G92E) to validate its functional significance. PCR sequencing confirmed precise targeting of the variant. Similar to our in vitro findings, Asb9-KI testes exhibited increased K48-linked and pan-polyubiquitination of ASB9 alongside markedly reduced ASB9 protein levels, despite unchanged mRNA abundance. Conversely, TUBB4A ubiquitination was diminished in Asb9-KI mice, resulting in elevated TUBB4A protein accumulation without transcriptional alteration.

Notably, while Asb9-KI males exhibited normal mating behavior and testis size, they displayed significant subfertility. Histopathological analysis of Asb9-KI mice revealed OAT phenotypes, characterized by nuclear shaping defects and disorganized manchette structures. Additionally, ICSI using sperm from Asb9-KI males resulted in normal fertilization and development to the 2-cell stage at a rate comparable to the WT control, recapitulating the successful fertilization outcome observed in human carriers. These data conclusively establish the pathogenic cascade initiated by the ASB9G92E variant across species.

Methods
Study participants
In this study, a cohort of 1483 men with idiopathic infertility and oligoasthenozoospermia was recruited from the Reproductive Genetic Hospital of CITIC-Xiangya and the First Affiliated Hospital of Anhui Medical University in China. The recruitment of participants was conducted in accordance with the guidelines of the World Health Organization (WHO, 2010). A clinical investigation revealed that all affected individuals exhibited normal development of male external genitalia. The study also excluded other potential causes of infertility, including reproductive malformations, exposure to drugs, and gonadotoxic factors. Meanwhile, all individuals had a normal 46,XY karyotype and no Y-chromosome AZF microdeletion. This study was conducted in strict accordance with the Declaration of Helsinki and approved by the Ethical Committee of Reproductive Genetic Hospital of CITIC-Xiangya (Approval: LL-CC-2019-034) and the First Affiliated Hospital of Anhui Medical University (Approval: PJ2020-13-10). Written informed consent was obtained from all participants.

Whole exome sequencing (WES), bioinformatic analysis, and Sanger sequencing
Blood samples were collected from participating individuals, and genomic DNA was extracted using a DNA extraction kit (Qiagen, Hilden, Germany). The exome capture was performed using the Agilent SureSelect Human All Exon V6 Kit (Agilent Technologies, Santa Clara, CA, USA), and the sequencing was carried out on the Illumina HiSeq 2000 platform (Illumina, San Diego, CA, USA). Then, the raw sequencing data were aligned to the human genome reference assembly (GRCh37/hg19) using Burrows–Wheeler Aligner software. PCR duplicates were removed, and the quality of variants was evaluated using the Picard software. Functional variants, including single-nucleotide variants and small insertions or deletions, were annotated using ANNOVAR software.

Candidate pathogenic variants were screened using the following criteria: bi-allelic or hemizygous variants, variants with a minor allele frequency below 5% in the public databases, such as the gnomAD database and the 1000 Genomes Project; variants predicted to be deleterious using multiple tools, including Polyphen-2 (genetics.bwh.harvard.edu/pph2), Mutation Taster (www.mutationtaster.org/), SIFT (sift.jcvi.org/) and Combined Annotation Dependent Depletion (cadd.gs.washington.edu/). Subsequently, the candidate variants were further validated through Sanger sequencing in the affected individuals, with the primers listed in Table S4.

Animal models
Asb9, Tubb4a, and Elob/c mutant lines were generated via pronuclear co-injection of Cas9 mRNA and target-specific sgRNAs (Table S5) into C57BL/6J zygotes, followed by oviduct transfer to pseudopregnant females. Founders were backcrossed for five generations with WT C57BL/6J mice to minimize off-target effects.

The Tnp2-Cre line was established using the PiggyBac transposon system. A donor plasmid containing the Tnp2 promoter (−549/−48 bp relative to TSS), Cre recombinase cDNA, and polyA signal was co-injected with transposase mRNA into zygotes prior to embryo transfer. Tnp2-Cre mice were crossed with ROSA26-mT/mG dual-fluorescent reporter mice (Jackson Laboratory, stock no. 007676). Cre-mediated recombination triggered membrane-localized GFP (mG) expression, enabling spatial tracking of recombinase activity.

All genotypes were validated by PCR (primers in Table S4). For fertility assessment, adult males (8 weeks) were individually co-housed with two WT females under standard conditions for 2 months; litter sizes were recorded for statistical analysis.

All male C57BL/6J mice used in this study were 8–12 weeks old. The exact number of animals per experimental group is clearly indicated in the corresponding figure legends. Mice were housed under specific pathogen-free conditions in individually ventilated cages (maximally 5 per cage) with a 12-h light/dark cycle, ambient temperature of 22–26°C, and relative humidity of 50–70%. Sterile food and Mice water were provided ad libitum. Animal health and welfare were monitored daily by trained animal facility staff and the research team; no unexpected mortality or morbidity occurred during the study. At experimental endpoints, mice were euthanized by carbon dioxide inhalation followed by cervical dislocation, and all efforts were made to minimize suffering. All experimental procedures were approved by the Animal Ethical and Welfare Committee of Nanjing Medical University (IACUC licenses: 2004020 and 2402015) and performed in strict accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

Cell culture and transfection
HEK-293T cells (American Type Culture Collection; ATCC, Manassas, VA, USA) were maintained in Dulbecco's Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS; Gibco, Grand Island, NY, USA) and 1% penicillin/streptomycin (Gibco) at 37°C/5% CO2. Expression constructs were generated by cloning the following cDNAs into specified vectors: Flag-tagged ASB9 (WT/mutant) into the pcDNA3.1 vector, HA-tagged Ub (WT/mutant) into the pRK5 vector, and GFP-tagged TUBB4A (WT/mutant) into the pEGFP-N1 vector. Transfections were performed using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) per manufacturer’s protocol. Cells were harvested 48 h post transfection for analysis. Plasmid concentrations were standardized at 0.5 μg/mL unless otherwise indicated in figures. All expression constructs used in this study are detailed in Table S6.

RNA extraction, RT‒PCR, and real-time q‒PCR
Total RNA was isolated from tissues using TRIzol reagent (Invitrogen), with subsequent cDNA synthesis performed via reverse transcription using HiScript III RT SuperMix (Vazyme, Nanjing, China). Commercial human multi-tissue cDNA was purchased from TaKaRa (Shiga, Japan). Gene-specific primers (Table S4) were employed for both RT-PCR (amplification products resolved on 1.5% ethidium bromide-stained agarose gels to confirm amplicon size) and quantitative real-time PCR using SYBR Green chemistry (AceQ MasterMix, Vazyme) on a StepOne System (Applied Biosystems, Waltham, MA, USA). Gene expression levels were calculated by the 2^(−ΔΔCt) method, normalized to 18S rRNA as the internal control.

Western blotting
Protein lysates were prepared in RIPA buffer (Beyotime, Shanghai, China) containing 1% protease inhibitor cocktail (Selleck, Houston, TX, USA), incubated 30 min on ice, and centrifuged (12,000 × g, 50 min, 4°C). Protein concentration was determined by BCA assay (Beyotime). Samples were separated by Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) and transferred to PVDF membranes (0.45 μm, Millipore, Burlington, MA, USA). Membranes were blocked in 5% non-fat milk/tris-buffered saline Tween-20 (TBST) for 2 h followed by primary antibody incubation (overnight, 4°C; antibodies in Table S7). After TBST washes, HRP-conjugated secondary antibodies were applied (2 h; room temperature, RT). Protein signals were detected using SuperSignal West Femto Substrate (Thermo Fisher Scientific, Waltham, MA, USA) on a Tanon 5200 imaging system.

Sperm analysis
Spermatozoa were collected from adult mouse cauda epididymides by mincing in 200 μL modified human tubal fluid (HTF) medium (FUJIFILM Irvine Scientific, Santa Ana, CA, USA) supplemented with 10% FBS (37°C, 10 min). Concentration was quantified via hemocytometer, while motility parameters (total and progressive) were assessed using a Ceros II analyzer (Hamilton Thorne, Beverly, MA, USA), assessing >200 sperm per sample. For morphology analysis, sperm smears were prepared and fixed with 4% paraformaldehyde (PFA) for 15 min. After PBS washes, the smears were stained with Hematoxylin (Solarbio, Wuhan, China) for 8 min, followed by bluing in running water and counterstaining of the cytoplasm with Eosin Y (Solarbio) for 2 min. Finally, the slides were dehydrated through a graded ethanol series, cleared in xylene, and mounted with neutral balsam for morphological examination under a microscope (Axioskop 2 Plus, Zeiss, Oberkochen, Germany).

ATP measurement
Caudal epididymal sperm were lysed using the ATP Assay Kit (Beyotime) according to the manufacturer’s instructions. Following a 15-min centrifugation at 12,000 × g and 4°C, the supernatant was collected for analysis. A standard curve was generated using serial dilutions of ATP stock solution in the provided lysis buffer, yielding final concentrations of 0.01, 0.03, 0.1, 0.3, 1, 3, and 10 μM. The working reagent was equilibrated at room temperature for 3 min prior to use. Supernatants were then mixed with the working reagent, and chemiluminescence was measured using a modular multimode microplate reader (BioTek, Winooski, VT, USA). Total protein concentration in each sample was determined using a BCA Protein Assay Kit (Beyotime). ATP levels were normalized to protein content and expressed as nmol/mg protein.

Histological analysis
Testes, brains, lungs, and kidneys were dissected and immediately fixed in modified Davidson’s fluid for a minimum of 48 h at room temperature. The fixed tissues were then processed through a graded ethanol series (70%, 80%, 95%, and 100%) for dehydration, cleared in xylene, and infiltrated with and embedded in paraffin. For histological evaluation, sections of all tissues (5 μm) were deparaffinized in xylene and rehydrated through a graded ethanol series to distilled water. Testicular sections were subjected to PAS staining, while sections of brain, lung, and kidney were stained with Hematoxylin and Eosin. After dehydration through graded ethanols, clearing in xylene, and mounting with neutral balsam (Solarbio), testicular sections were imaged using a bright-field microscope (Axioskop 2 Plus, Zeiss), while brain, lung, and kidney sections were examined using a DS-Ri2 microscope (Nikon, Tokyo, Japan).

For TEM, tissues and sperm were fixed in 2% glutaraldehyde overnight at 4°C. The samples were then post-fixed with 1% osmium tetroxide (OsO4) for 2 h at room temperature. Following fixation, the samples were dehydrated through a graded ethanol series and infiltrated with a mixture of propylene oxide and Araldite 502 epoxy resin before being embedded in pure resin. Ultrathin sections (80 nm) were cut using an ultramicrotome (UC7, Leica Microsystems, Wetzlar, Germany), collected on copper grids, and counterstained with uranyl acetate and lead citrate. The grids were examined using a JEM-1410 transmission electron microscope (JEOL, Tokyo, Japan) operated at 80 kV.

For SEM, the sperm samples were processed following the similar fixation and dehydration procedures as described for TEM. The dehydrated samples were then dried using a Critical Point Dryer (K850, Quorum, Laughton, UK), sputter-coated with gold, and observed under a scanning electron microscope (JEOL JSM-7900F).

Immunofluorescence analysis
For tissue sections, paraffin-embedded testicular and lung sections (5 μm) were deparaffinized in xylene and rehydrated through a graded ethanol series to distilled water. Antigen retrieval was performed by incubating the sections in pre-heated 10 mM sodium citrate buffer (pH 6.0) and heating in a microwave oven for 15 min. After washing three times with PBS (pH 7.4), sections were blocked with 1% bovine serum albumin (BSA; Solarbio) for 2 h at room temperature. Subsequently, sections were incubated overnight at 4°C with primary antibodies (Table S7). After PBS washes, Alexa Fluor-conjugated secondaries were applied for 2 h at room temperature in the dark. After three additional PBS washes, nuclei were counterstained with DAPI (5 μg/mL; Beyotime). Finally, the slides were washed with PBS and mounted with anti-fade fluorescent mounting medium (Beyotime). Images were acquired using an LSM800 confocal microscope (Zeiss). For single-cell suspensions, testes were enzymatically digested with collagenase IV (1 mg/mL, Invitrogen) for 15 min at 37°C and trypsin (0.25%, Gibco) for 10 min at 37°C, respectively. Reactions were quenched with DMEM/10% FBS. The cell suspension was passed through a 40-μm cell strainer to remove aggregates, and the cells were pelleted by centrifugation at 800 × g for 5 min. Pelleted cells were resuspended, fixed on slides using 4% PFA, and washed three times with PBS. Subsequently, samples were blocked with 1% BSA (Solarbio) for 2 h at room temperature. Immunostaining was then performed following the similar procedure as described for tissue sections.

For sperm, sperm smears were prepared and fixed with 4% PFA for 15 min at room temperature. After PBS washes, samples were blocked with 1% BSA (Solarbio) for 2 h at room temperature. Subsequent immunofluorescence staining steps were performed identically to those described for tissue sections.

Terminal deoxynucleotidyl transferase-dUTP nick-end labeling (TUNEL)
Apoptosis was detected using a commercial kit (Vazyme) according to the manufacturer’s instructions. Briefly, deparaffinized cauda epididymis sections were rehydrated, permeabilized with proteinase K (10 μg/mL) for 10 min at room temperature. After washing three times with PBS, the sections were incubated with Equilibration Buffer for 30 min at room temperature and incubated with Bright Red reagent at 37°C for 60 min. After PBS washes, nuclei were counterstained with DAPI prior to confocal imaging (LSM800, Zeiss).

IP-LC‒MS/MS analysis
Tissues from 8-week-old mice were homogenized on ice in RIPA lysis buffer (Beyotime) containing 1% protease inhibitor cocktail (Selleck). The homogenates were incubated on ice for 30 min and then centrifuged at 16,000 × g for 30 min at 4°C to remove insoluble debris. Lysates were pre-cleared with 20 μL of Dynabeads™ Protein A (Invitrogen) for 3 h at 4°C. The pre-cleared lysates were then incubated overnight at 4°C with gentle rotation with the specified antibody (Table S7). The following day, 40 μL of Dynabeads™ Protein A was added to the lysate-antibody mixture and incubated for an additional 3 h at 4°C with rotation. Precipitates were washed (3 × RIPA buffer), boiled in 2 × SDS buffer (5 min), and resolved by SDS-PAGE. Gel bands were excised (≈1 mm³), destained (H2O→50% ACN→100% ACN), reduced (56°C, 1 h), alkylated (45 min, RT, dark), and digested with trypsin (37°C, 12 h). Peptides were acidified (0.1% TFA), extracted, SpeedVac-dried, desalted via StageTip, and analyzed by LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific).

Raw files were processed in MaxQuant (v1.3.0.5) against the UniProt mouse database with tryptic specificity (≤2 missed cleavages), 1% FDR thresholds, fixed carbamidomethylation (+57.0215 Da), and variable modifications (methionine oxidation, N-terminal acetylation). Label-free quantification ratios were computed, with >10-fold changes defining candidate interactors.

Co-IP assay
For exogenous co-IP, transfected HEK-293T cells were lysed on ice for 30 min in RIPA lysis buffer supplemented with 1% protease inhibitor cocktail. The lysates were centrifuged at 16,000 × g for 15 min at 4°C, and the supernatant was collected. A small aliquot (10%) of the supernatant was saved as the “Input” control. The remaining lysates were incubated overnight at 4°C with anti-Flag magnetic beads (Sigma, St. Louis, MO, USA) or anti-GFP magnetic beads (Proteintech, Rosemont, IL, USA). For endogenous co-IP, testicular lysates underwent primary antibody incubation (overnight, 4°C) followed by 3-h capture with Dynabeads™ Protein A (Invitrogen) at 4°C. In both methodologies, bead-bound complexes were collected using a magnetic rack and subsequently washed with lysis buffer (Beyotime). After the final wash, the bound proteins were eluted by boiling the beads in 2 × SDS buffer at 95°C for 10 min. Both the Input controls and the Co-IP eluates were then subjected to immunoblotting with target-specific antibodies (Table S7).

Cycloheximide chase analysis
Protein stability was assessed using a cycloheximide (CHX) chase assay. In short, HEK-293T cells in 6-well plates were transfected for 48 h. To inhibit new protein synthesis, the cell culture medium was replaced with fresh medium containing 100 μg/mL CHX (Sigma). Cells were then harvested at specific time points post-CHX treatment. Protein degradation kinetics were assessed by immunoblotting.

In vitro ubiquitination assay
ASB9 immunoprecipitates were isolated from mouse testicular lysates via immunoprecipitation and then incubated in reaction buffer (20 mM HEPES pH 7.4, 5 mM MgCl2, 1 mM DTT, 2 mM ATP) containing 100 nM E1, 2 μM UbcH5a (E2), 2.5 μM Ub, and 10 μg His-TUBB4A (Sangon, Shanghai, China). The reaction was incubated at 37°C for 2 h with gentle shaking. Reactions were terminated with SDS buffer (95°C, 10 min) and immunoblotted.

In vivo ubiquitination assay
Tissues or transfected HEK-293T cells were lysed in SDS-denaturing buffer (1% SDS, 50 mM Tris-HCl pH 7.5, 150 mM NaCl). The samples were immediately boiled for 10 min to denature proteins and inactivate cellular enzymes. The lysates were then cooled on ice and diluted tenfold with NP-40 lysis buffer. The diluted lysates were centrifuged at 16,000 × g for 15 min at 4°C, and the supernatants were subjected to immunoprecipitation overnight at 4°C with the appropriate antibody (Table S7). Complexes were resolved by SDS-PAGE and immunoblotted.

ICSI for mice
The sperm from the cauda epididymides of adult WT and Asb9-KO mice was retrieved and added into the HTF medium. For ICSI, cumulus intact oocytes were collected from the oviduct of superovulated 4-week-old C57BL/6J female mice. Subsequently, hyaluronidase from bovine testes (Sigma, H4272) was used to remove the cumulus cells. The sperm head was injected into an oocyte. Then, the injected oocytes were cultured in KSOM medium (Millipore) in 6% CO2 at 37°C. The 2-cell embryo was counted 20 h later.

ICSI for ASB9 mutation-associated infertile men
Four females, whose partner harboring hemizygous ASB9 missense mutations, consented to ICSI treatment. They underwent controlled ovarian hyperstimulation, oocyte retrieval, ICSI, and had pregnancy confirmed. Pregnancy confirmation was achieved through two hCG tests conducted 14 days after embryo transfer. A clinical pregnancy was defined as the visualization of at least a gestational sac with a fetal heartbeat via ultrasound screening 28 days post embryo transfer.

Statistics and reproducibility
Data represent mean ± SD. All experiments were performed at least three independent replicates. Analyzes used GraphPad Prism 8.0: two-tailed unpaired t-tests (two groups) or one-way Analysis of Variance (ANOVA) with Dunnett’s test (multiple groups). Significance was defined as p < 0.05. Biological replicates are specified in figure legends. All schematic diagrams and cartoons presented in this study were originally created by our team using Adobe Illustrator (version 2021).

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.\nThe 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 β-lactams, whereby DAPR is associated with increased susceptibility to β-lactams. Synergy between CC and daptomycin or β-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": "Response: 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 4, 7, 8. Page 23, Lines 498-502.\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 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 pen (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. DOI: 10.1038/nature26157.\n2: Kim, W.; Zou, G.; Hari, T. P. A.; Wilt, I. 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. DOI: 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. DOI: 10.1038/s41467-024-47803-6.\n4: Pogozheva, I. 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 Inf Model 2022, 62, 1036-1051. DOI: 10.1021/acs.jcim.1c01514.\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.\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. The 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.\n\nReviewer #2 (Remarks to the Author):\n1. 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.\nWe thank the reviewer for recognizing the significance and rationale of our study. We agree that repurposing FDA-approved drugs such as CC represents a promising and pragmatic strategy to accelerate the development of new therapeutics against multidrug-resistant pathogens like MRSA. Our findings provide a mechanistic basis supporting this approach by demonstrating that CC exhibits potent antibacterial and synergistic activity through membrane-targeting mechanisms.\n2. The 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.\nResponse: We agree with the reviewer that the paper is very data rich and complicated. We have revised the manuscript to clearly outline the rationale, experimental design, and the scientific advances for each subsection. Below, we summarize these points. Also see below our response to concerns about the novelty of the paper.\n1. CC inhibits the growth of MRSA: CC was identified from a chemical library screen using a C. elegans–MRSA infection model. MICs were determined against MRSA-MW2, clinical strains, and vancomycin-intermediate isolates to establish potency. Killing kinetics showed bactericidal activity against both exponential and stationary-phase cells, including persisters, and demonstrated biofilm inhibition and eradication.\n2. CC disrupts the S. aureus membranes. Rapid permeabilization and membrane depolarization were observed using Sytox Green and DiSC3(5). Fluorescent pH probe studies revealed dose-dependent intracellular alkalization. SEM, AFM, TEM, and cryo-CT confirmed structural and topographical membrane perturbations. Advance: CC kills MRSA through membrane disruption combined with intracellular alkalization, a mechanism distinct from traditional antibiotics.\n3. Comprehensive molecular multi-OMICS analysis: Transcriptomics, metabolomics, and lipidomics identified key pathways and components affected by CC, including fatty acid synthesis, cell wall precursors, purine metabolism, and energy pathways. Lipidomic profiling revealed significant reductions in phosphatidylglycerol and phosphatidylethanolamine, confirming effects on membrane integrity. Advance: Multi-OMICS analyses elucidated molecular targets and pathways underlying CC’s antibacterial mechanism.\n4. CC synergizes with gentamicin, kanamycin, streptomycin, and polymyxin B: Checkerboard and time-kill assays demonstrated strong synergy with membrane-targeting antibiotics, enhancing eradication of both logarithmic and persister cells. Molecular dynamics simulations revealed the formation of CC–antibiotic complexes that promote membrane deformation. Advance: CC effectively potentiates membrane-active antibiotics, providing a mechanistic proof-of-concept for combinatorial therapies. We primarily report molecular-level evidence of direct CC–antibiotic complex formation, as well as its effect on membrane deformation, thereby providing a mechanistic basis for the observed synergy with a structural and biophysical mechanism by which CC–antibiotic complex formation amplifies membrane perturbation and bactericidal efficacy.\n5. Mechanism by which CC disrupts the S. aureus membrane: Laurdan dye studies demonstrated CC increases membrane rigidity. Cerulenin experiments and C20:0 lipid supplementation identified lipid components critical for CC susceptibility. BODIPY TR cadaverine assays confirmed direct interactions between CC and membrane lipids. Advance: CC’s bactericidal activity is mediated through selective targeting of membrane lipids, particularly C20:0 fatty acids. Importantly, we found that CC reduces membrane C20:0 fatty acid levels, which leads to impaired membrane fluidity.\n6. Isolation of CC-resistant mutants: Serial passage generated MRSA-MW2-S50, a partially CC-resistant derivative of MRSA MW2. The mutant retained susceptibility to certain CC analogs, revealing potential resistance pathways and targets. Advance: Identifying resistance mechanisms provides insights into the design of future drugs based on the structure of CC.\n7. Structure-activity relationship (SAR) studies: Commercial and custom CC analogs revealed the tetrazole and cyclohexyl cilexetil moieties as critical for activity and membrane penetration. Analog testing against resistant mutants confirmed that structural modifications can retain or reduce activity. Advance: As described above for the isolation of CC-resistant mutants, SAR studies define structural determinants of antibacterial potency and selectivity, guiding future analog design.\n8. In vivo efficacy of CC against MRSA-MW2: CC demonstrated dose-dependent protection in C. elegans and Galleria mellonella, as well as reduced bacterial burden in a mouse thigh infection model. Pharmacokinetic studies confirmed serum concentrations exceeding MIC with minimal toxicity. Combination with gentamicin enhanced bacterial clearance. Advance: CC exhibits in vivo efficacy against MRSA, with translational potential for membrane-targeted combinatorial strategies.\n3. 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?\nResponse: We tested CC in combination with a range of antibiotics, including β-lactams (oxacillin), vancomycin, daptomycin, and aminoglycosides. CC synergizes with aminoglycosides (gentamicin, kanamycin, streptomycin) and polymyxin. Other conventional anti-MRSA antibiotics such as vancomycin and oxacillin show moderate potentiaton by CC.\nThe choice of gentamicin and polymyxin B for detailed analysis reflects their well-characterized mechanisms of membrane disruption, which complements CC’s membrane-targeting activity, providing a mechanistic proof-of-concept for combinatorial therapy. The rationale for the focus on gentamicin and polymixin has been clarified in the revised manuscript.\nWe are not proposing polymyxin B as a clinical therapy for MRSA infections, but rather using it experimentally to demonstrate that CC can potentiate other membrane-active agents. This approach helps uncover CC’s mechanism of action and informs rational design of future therapeutic combinations with clinically relevant antibiotics. We have clarified this rationale in the revised manuscript.\nWe now added the following text in the discussion, “Gentamicin and polymyxin B were chosen for detailed analysis because their mechanisms complement CC’s membrane-disrupting activity, producing robust and mechanistically interpretable synergy. DiSC3(5) experiments showed that CC combined with aminoglycosides, depolarizing the MRSA membrane more rapidly than CC alone, directly linking the synergy to enhanced membrane disruption. Molecular dynamics simulations further revealed that CC and polymyxin B spontaneously form stable bi- and tri-molecular complexes that penetrate and deform the S. aureus membrane, amplifying the antimicrobial effect beyond what either molecule achieves alone. In contrast, conventional MRSA antibiotics such as vancomycin or oxacillin act primarily on cell wall synthesis and displayed only partial synergy, making them less informative for mechanistic studies”. Page 22, Line 470- 477\n4. I 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.\nOverview: We appreciate the reviewer’s concern regarding the novelty of our study compared to reference 21 (Xu et al 2021). Xu et al. reported antibacterial activity of CC, our work defines its molecular and physiological mechanism, linking C20:0 lipid remodeling, membrane rigidification, and wall teichoic acid homeostasis.\nMembrane Targeting and Lipid Remodeling: In our study, we go well beyond general membrane disruption as a mechanism of action by identifying specific alterations in bacterial membrane lipids, particularly C20:0 fatty acids, that are linked to CC’s antibacterial activity. Importantly, we found that CC reduces membrane C20:0 fatty acid levels, which leads to impaired membrane fluidity. This molecular-level insight was not explored by Xu et al and provides a direct link between membrane composition and drug efficacy.\nStructural-Activity Relationship (SAR) Analysis: Unlike Xu et al, which tested only the parent compound, we evaluated multiple CC analogs, correlating chemical modifications with antibacterial and antipersister activity. Using chemical synthesis, structural analysis, and activity assays, this approach provides a rational framework for optimizing CC derivatives for future therapeutic development.\nExpanded Synergy Studies: We extended synergy testing beyond aminoglycosides to include polymyxins, and we applied multidimensional scaling (MDS) to systematically visualize and quantify antibiotic interactions. These analyses not only reveal enhanced antibacterial activity in combination therapy but also provide mechanistic insights into how CC facilitates antibiotic uptake via membrane permeabilization.\nMulti-Omics and Systems-Level Integration: We employed lipidomics, metabolomics, and transcriptomics to reveal the broader molecular pathways affected by CC, including the cell wall constituent in pathways substitution analysis. This integration provides a systems-level understanding of bacterial response to CC, offering insights that cannot be inferred from phenotypic assays alone, which were the main focus of Xu et al.\nExpanded In Vivo Models: Beyond the topical skin abscess model used in ref. 21, we evaluated CC in a mouse thigh infection models demonstrating efficacy against MRSA in a clinically relevant context. Animal infection models, histopathology, and cytokine assays were used to confirm translational relevance, showing CC’s potential against both extracellular and intracellular persistent infections.\nCollectively, these advances provide significant advances compared to the prior work by Xu et al. Our study integrates mechanistic depth, SAR optimization, expanded synergy analyses with MDS, multi-omics systems biology, advanced imaging, and in vivo models to offer a comprehensive understanding of CC’s antibacterial and anti-persister activity. These innovations represent a substantial advance in the field and address the reviewer’s concerns regarding the originality and impact of our work.\nIn summary, while the 2021 study laid the groundwork for CC's antibacterial potential, our continued research since 2018 on this work, provides a highly detailed mechanistic framework and introduces optimized analogs, thereby advancing the field of CC-based antibacterial therapies.\nAdditional major points:\n5. 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.\nResponse: We acknowledge that the CC concentrations and inoculum sizes vary across the different assays. These variations were intentional and tailored to the specific experimental context:\n1. In vitro MIC, killing kinetics, biofilm, and membrane assays were performed using standardized inocula according to CLSI guidelines, with CC concentrations ranging from sub-MIC to multiple times the MIC to capture bacteriostatic and bactericidal effects, as well as biofilm inhibition and eradication.\n2. Mechanistic and multi-omics studies utilized sublethal concentrations (e.g., 0.5xMIC or 4 mg/L) to detect early transcriptional, metabolic, and lipidomic changes without causing rapid cell death, enabling identification of pathways contributing to CC-mediated killing.\n3. Invertebrate and vertebrate in vivo models employed higher doses (e.g., 50 mg/kg in mice) to achieve serum concentrations exceeding in vitro MICs, while ensuring host safety. These doses were informed by pharmacokinetic studies showing maximum serum bioavailability of 8 mM CC at 12 hours and minimal toxicity to liver, kidney, heart, or overall survival.\n6. 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.\nResponse: We worked on the statistics throughout the paper and the statistics are included here.\nFig 1B.\nTukey's multiple Summary Adjusted\ncomparisons test P Value Fig 1D\nDMSO vs. BP drug (8x MIC ) **** <0.0001\nDMSO vs. BP drug (4x MIC) **** <0.0001 multiple comparisons test Summary Adjusted P Value\nDMSO vs. BP drug (2x MIC ) **** <0.0001 8x vs. 2x ** 0.0019\nDMSO vs. BP drug (1X MIC) **** <0.0001\n8x vs. 1x **** <0.0001\nDMSO vs. Gentamicin (1X) **** <0.0001\n8x vs. 0.5x **** <0.0001\nFig 1C.\nTukey's multiple Summary Adjusted 8x vs. 0.25x **** <0.0001\ncomparisons test P Value 8x vs. 0 **** <0.0001\nPBS+DMSO vs. PBS+ CC 8xMIC **** <0.0001\n4x vs. 2x ns 0.3144\nPBS+DMSO vs. PBS+CC 4xMIC **** <0.0001\n4x vs. 1x ** 0.0014\nPBS+DMSO vs. PBS+Genta 5xMIC **** <0.0001\n4x vs. 0.5x **** <0.0001 Fig 1F\nTukey's multiple comparisons test Summary Adjusted P Value\n4x vs. 0.25x **** <0.0001\n0x vs. 2x **** <0.0001\n4x vs. 0 **** <0.0001\n0x vs. 1x **** <0.0001\n2x vs. 1x ns 0.2567\n0x vs. 0.5x **** <0.0001\n2x vs. 0.5x **** <0.0001\n0x vs. Valinomycin **** <0.0001\n2x vs. 0.25x **** <0.0001\n0x vs. Gramidicin **** <0.0001\n2x vs. 0 **** <0.0001\n1x vs. 0.5x **** <0.0001\n1x vs. 0.25x **** <0.0001 Tukey's multiple comparisons test Summary Adjusted P Value\n1x vs. 0 **** <0.0001 DMSO vs. Candesartan cilexetil 1X *** 0.0003\n0.5x vs. 0.25x **** <0.0001\nDMSO vs. Gentamicin (1X) *** 0.0003\n0.5x vs. 0 **** <0.0001\nDMSO vs. Combination *** 0.0003\n0.25x vs. 0 **** <0.0001\nFig 1E- Inhibition\nTukey's multiple comparisons test Summary Adjusted P Value Fig 3B- Poly\n8x vs. 4x **** <0.0001 Tukey's multiple comparisons Summary Adjusted P\n8x vs. 2x **** <0.0001 test Value\n8x vs. 1x **** <0.0001 DMSO vs. Candesartan cilexetil 1X *** 0.0003\n8x vs. 0.5x **** <0.0001\nDMSO vs. Polymyxin B 1X MIC *** 0.0003\n8x vs. 0x **** <0.0001\n4x vs. 2x **** <0.0001 DMSO vs. Combination *** 0.0003\n4x vs. 1x **** <0.0001\n4x vs. 0.5x **** <0.0001\nFig 3C- Genta\n4x vs. 0x **** <0.0001\n2x vs. 1x ** 0.0051 Tukey's multiple comparisons test Summary Adjusted P Value\n2x vs. 0.5x **** <0.0001\nDMSO vs. Candesartan cilexetil 1X **** <0.0001\n2x vs. 0x **** <0.0001\n1x vs. 0.5x ns 0.7992 DMSO vs. Gentamicin 1X **** <0.0001\n1x vs. 0x * 0.0112 DMSO vs. Combination **** <0.0001\n0.5x vs. 0x ns 0.2820\nFig 1E- Maturation\nFig 3C- Poly\nŠídák's multiple comparisons test Summary Adjusted P Value\nTukey's multiple comparisons test Summary Adjusted P Value\n8x vs. 4x **** <0.0001\nDMSO vs. Candesartan cilexetil 1X *** 0.0002\n8x vs. 2x **** <0.0001 DMSO vs. Polymyxin B 4X MIC **** <0.0001\n8x vs. 1x **** <0.0001 DMSO vs. Combination **** <0.0001\n8x vs. 0.5x **** <0.0001\nFig 4A.\n8x vs. 0.25x **** <0.0001\nDunnett's multiple comparisons test Summary Adjusted P Value\n8x vs. 0 **** <0.0001 Untreated vs. Benzyl Alcohol ** 0.0090\n4x vs. 2x ns 0.9995 Untreated vs. Candesartan cilexetil 64 ug **** <0.0001\n4x vs. 1x ns 0.5264 Untreated vs. Candesartan cilexetil 32 ug **** <0.0001\nUntreated vs. PolymyxinB 64 ug ns 0.0980\n4x vs. 0.5x ns 0.1839\n4x vs. 0.25x * 0.0113\nFig 4B\n4x vs. 0 **** <0.0001 Dunnett's multiple Summary Adjusted\ncomparisons test P\n2x vs. 1x ns 0.5896 Value\n2x vs. 0.5x * 0.0287 Untreated vs. Benzyl Alcohol * 0.0132\n2x vs. 0.25x ** 0.0074 Untreated vs. CC 64 ug * 0.0245\n2x vs. 0 **** <0.0001\nUntreated vs. CC 32 ug ns 0.8927\n1x vs. 0.5x ns >0.9999\nUntreated vs. PolymyxinB 64 ug ** 0.0022\n1x vs. 0.25x * 0.0481\n1x vs. 0 **** <0.0001\nFig. 4C\n0.5x vs. 0.25x ns 0.087 Dunnett's multiple comparisons test Summar Adjusted P\ny Value\n0.5x vs. 0 **** <0.0001\nUntreated vs. Benzyl Alcohol * 0.0485\n0.25x vs. 0 ** 0.0023\nUntreated vs. Candesartan cilexetil 64 **** <0.0001\nug\nUntreated vs. Candesartan cilexetil 32 **** <0.0001\nug\nUntreated vs. PolymyxinB 64 ug ns 0.9404\nFig 4D.\nTukey's multiple comparisons test Summary Adjusted P Value Fig 4E.\nCC 32 ug vs. CC 16 ug **** <0.0001 CC 32 ug vs. CC 16 ug Yes ** 0.0097\nCC 32 ug vs. CC 8 ug **** <0.0001 CC 32 ug vs. CC 8 ug Yes ** 0.0078\nCC 32 ug vs. CC 4 ug **** <0.0001 CC 32 ug vs. CC 4 ug Yes ** 0.0051\nCC 32 ug vs. CC 2ug **** <0.0001 CC 32 ug vs. CC 2ug Yes ** 0.0031\nCC 32 ug vs. CC 0 ug **** <0.0001 CC 32 ug vs. CC 0 ug Yes ** 0.0072\nCC 16 ug vs. CC 8 ug **** <0.0001 CC 16 ug vs. CC 8 ug Yes ** 0.0061\nCC 16 ug vs. CC 4 ug **** <0.0001 CC 16 ug vs. CC 4 ug Yes ** 0.0019\nCC 16 ug vs. CC 2ug **** <0.0001 CC 16 ug vs. CC 2ug Yes ** 0.0013\nCC 16 ug vs. CC 0 ug **** <0.0001 CC 16 ug vs. CC 0 ug Yes ** 0.0048\nCC 8 ug vs. CC 4 ug *** 0.0002 CC 8 ug vs. CC 4 ug Yes ** 0.0028\nCC 8 ug vs. CC 2ug **** <0.0001 CC 8 ug vs. CC 2ug No ns 0.8934\nCC 8 ug vs. CC 0 ug **** <0.0001 CC 8 ug vs. CC 0 ug No ns 0.0784\nCC 4 ug vs. CC 2ug ns 0.6209 CC 4 ug vs. CC 2ug Yes * 0.0411\nCC 4 ug vs. CC 0 ug ns 0.4294 CC 4 ug vs. CC 0 ug Yes * 0.0117\nCC 2ug vs. CC 0 ug ns 0.9997 CC 2ug vs. CC 0 ug No ns 0.7597\nFig 4F Fig 4Q\nTukey's multiple comparisons test Summar Adjusted P Val Tukey's multiple Summary Adjusted P\ny ue comparisons test Value\nMW2 vs. MW2 CC 4xMIC **** <0.0001 CC-0ug vs. CC-2ug ns >0.9999\nMW2 vs. MW2- CC 2xMIC **** <0.0001 CC-0ug vs. CC-4 ug ns 0.9981\nMW2 CC 4xMIC vs. MW2- CC 2x ns >0.9999 CC-0ug vs. CC-8 ug ** 0.0011\nMIC\nCC-0ug vs. CC-16 ug **** <0.0001\nFig 4G.\nTukey's multiple comparisons test Summary Adjusted P CC-0ug vs. CC-32 ug **** <0.0001\nValue CC-0ug vs. CC-64 ug **** <0.0001\nCer-MW2 vs. Cer-Pretreated **** <0.0001\nFig 4R\nMW2 CC-4xMIC\nCer-MW2 vs. Cer-Pretreated **** <0.0001\nCC-0ug vs. CC-2ug ns 0.8764\nMW2 CC-2xMIC\nCer-Pretreated MW2 CC- ns 0.4986 CC-0ug vs. CC-4 ug ns 0.3915\n4xMIC vs.\nCC-0ug vs. CC-8 ug * 0.0205\nCer-Pretreated MW2 CC-2xMIC\nFig 4H. CC-0ug vs. CC-16 ug ** 0.0094\nTukey's multiple comparisons test Summary Adjusted P Value CC-0ug vs. CC-32 ug ** 0.0061\nCC-0ug vs. CC-64 ug ** 0.0035\nControl vs. CC 4x MIC **** <0.0001\nControl vs. Gentamicin 4x MIC **** <0.0001 Fig 4S\nTukey's multiple comparisons test Summary Adjusted P Value\nCC 4x MIC vs. Gentamicin 4x MIC ns >0.9999\nCC-0ug vs. CC-2ug ns >0.9999\nFig 4I\nTukey's multiple comparisons test Summary Adjusted P Value CC-0ug vs. CC-4 ug ns 0.9842\nCC-0ug vs. CC-8 ug ** 0.0082\nControl vs. CC 4x MIC *** 0.0009\nCC-0ug vs. CC-16 ug ** 0.0024\nControl vs. Gentamicin 4x MIC *** 0.0009\nCC-0ug vs. CC-32 ug **** <0.0001\nCC 4x MIC vs. Gentamicin 4x MIC ns >0.9999 CC-0ug vs. CC-64 ug **** <0.0001\nFig 4J\nFig 4T\nTukey's multiple comparisons test Summary Adjusted P Valu\ne Tukey's multiple comparisons test Summary Adjusted P Value\nControl vs. CC 4x MIC **** <0.0001 DMSO vs. CC 4x MIC **** <0.0001\nControl vs. Gentamicin 4x MIC **** <0.0001 DMSO vs. CC 2x MIC **** <0.0001\nCC 4x MIC vs. Gentamicin 4x MI ns >0.9999 DMSO vs. Gentamicin 4x MIC **** <0.0001\nC\nFig 4U\nTukey's multiple compari Below thresh Summ Adjusted P V DMSO vs. CC 4x MIC Yes **** <0.0001\nsons test old? ary alue\nDMSO vs. CC 2x MIC Yes **** <0.0001\nTukey's multiple Summary Adjusted P Value DMSO vs. Gentamicin 4 Yes **** <0.0001\ncomparisons test x MIC\nDMSO vs. CC 4x MIC **** <0.0001\nFig 5C-7\nDMSO vs. CC 2x MIC **** <0.0001 Tukey's multiple Summary Adjusted P\ncomparisons test Value\nDMSO vs. Gentamicin 4x MIC **** <0.0001\nDMSO+MW2 vs. Compound 7 (4x MIC) **** <0.0001\nDMSO+MW2 vs. Compound 7 (1x MIC) **** <0.0001\nFig 4V Compound 7 (4x MIC) vs. ns 0.9998\nTukey's multiple Summary Adjusted P Compound 7 (1x MIC)\ncomparisons test Value\nDMSO vs. CC 4x MIC **** <0.0001 Fig 5D-1\nDMSO vs. CC 2x MIC **** <0.0001 Tukey's multiple Summary Adjusted P Value\ncomparisons test\nDMSO vs. Gentamicin 4x MIC **** <0.0001\n64 mg/L vs. 32 mg/L ns 0.0798\n64 mg/L vs. 16 mg/L * 0.0211\nFig 4W 64 mg/L vs. 8 mg/L ns 0.0716\nTukey's multiple comparisons test Summary Adjusted P Value 64 mg/L vs. 0 mg/L ns 0.1873\nDMSO vs. CC 4x MIC * 0.0399 32 mg/L vs. 16 mg/L ns 0.6267\nDMSO vs. CC 2x MIC ns 0.2534\n32 mg/L vs. 8 mg/L ns 0.4978\nDMSO vs. Gentamicin 4x MIC * 0.0150\n32 mg/L vs. 0 mg/L ns 0.6803\n16 mg/L vs. 8 mg/L ns 0.3887\nFig 4X\nTukey's multiple comparisons test Summary Adjusted P Valu 16 mg/L vs. 0 mg/L ns 0.9988\ne 8 mg/L vs. 0 mg/L ns 0.9548\nDMSO vs. CC 4x MIC **** <0.0001 Fig 5D-4\nDMSO vs. CC 2x MIC **** <0.0001 Tukey's multiple Summary Adjusted P Value\ncomparisons test\nDMSO vs. Gentamicin 4x MIC **** <0.0001 64 mg/L vs. 32 mg/L * 0.0211\n64 mg/L vs. 16 mg/L * 0.0211\nFig 5C-1\n64 mg/L vs. 8 mg/L * 0.0211\nTukey's multiple comparisons test Summary Adjusted P Value\n64 mg/L vs. 0 mg/L * 0.0457\nDMSO+MW2 vs. Compound 1 (4x MIC) **** <0.0001\nDMSO+MW2 vs. Compound 1 (1x MIC) **** <0.0001 32 mg/L vs. 16 mg/L * 0.0215\nCompound 1 (4x MIC) vs. ** 0.0016 32 mg/L vs. 8 mg/L * 0.0211\nCompound 1 (1x MIC) 32 mg/L vs. 0 mg/L ns 0.1475\n16 mg/L vs. 8 mg/L ns 0.1258\nFig 5C-4\nTukey's multiple comparisons test Summary Adjusted P 16 mg/L vs. 0 mg/L ns 0.4297\nValue 8 mg/L vs. 0 mg/L ns 0.9722\nDMSO+MW2 vs. Compound 4 (4x MIC) **** <0.0001\nDMSO+MW2 vs. Compound 4 (1x MIC) **** <0.0001\nFig 5D-5\nCompound 4 (4x MIC) vs. ns 0.9989 Tukey's multiple Summary Adjusted P\nCompound 4 (1x MIC) comparisons test Value\n64 mg/L vs. 16 mg/L **** <0.0001\nFig 5C-5 64 mg/L vs. 8 mg/L * 0.0469\nTukey's multiple comparisons test Summar Adjusted P Valu\n64 mg/L vs. 0 mg/L **** <0.0001\ny e\nDMSO+MW2 vs. Compound 5(4x **** <0.0001 32 mg/L vs. 16 mg/L **** <0.0001\nMIC)\nDMSO+MW2 vs. Compound 5 (1x **** <0.0001 32 mg/L vs. 8 mg/L **** <0.0001\nMIC)\nCompound 5(4x MIC) vs. ns 0.9983 32 mg/L vs. 0 mg/L **** <0.0001\nCompound 5 (1x MIC)\n16 mg/L vs. 8 mg/L **** <0.0001\nFig 5C-6 16 mg/L vs. 0 mg/L **** <0.0001\nTukey's multiple comparisons test Summary Adjusted P Value 8 mg/L vs. 0 mg/L * 0.0411\nDMSO+MW2 vs. Compound 6 (4x MIC) **** <0.0001\nDMSO+MW2 vs. Compound 6 (1x MIC) **** <0.0001\nFig 5D-6\nCompound 6 (4x MIC) vs. ns 0.9909\nCompound 6 (1x MIC) Tukey's multiple comparisons test Summary Adjusted P Value\n64 mg/L vs. 32 mg/L ns 0.9999 CC5 - Vancomycin\n64 mg/L vs. 16 mg/L **** <0.0001 0 ns >0.9999\n64 mg/L vs. 8 mg/L **** <0.0001 2 ns >0.9999\n64 mg/L vs. 0 mg/L **** <0.0001 4 ns >0.9999\n32 mg/L vs. 16 mg/L **** <0.0001 8 ns >0.9999\n32 mg/L vs. 8 mg/L **** <0.0001 16 ns >0.9999\n32 mg/L vs. 0 mg/L **** <0.0001 32 **** <0.0001\n16 mg/L vs. 8 mg/L **** <0.0001 64 **** <0.0001\n16 mg/L vs. 0 mg/L **** <0.0001 128 **** <0.0001\n8 mg/L vs. 0 mg/L ns 0.6354\nFig 5E-6\nFig 5D-7\nŠídák's multiple Summary Adjusted P\nTukey's multiple comparisons test Summary Adjusted P Value comparisons test Value\n64 mg/L vs. 32 mg/L **** <0.0001 CC6 - Vancomycin\n64 mg/L vs. 16 mg/L **** <0.0001 0 ns >0.9999\n64 mg/L vs. 8 mg/L **** <0.0001 2 ns >0.9999\n64 mg/L vs. 0 mg/L **** <0.0001 4 ns 0.9309\n32 mg/L vs. 16 mg/L ns 0.2620 8 ns 0.3273\n32 mg/L vs. 8 mg/L **** <0.0001 16 **** <0.0001\n32 mg/L vs. 0 mg/L **** <0.0001 32 **** <0.0001\n16 mg/L vs. 8 mg/L **** <0.0001 64 **** <0.0001\n16 mg/L vs. 0 mg/L **** <0.0001 128 **** <0.0001\n8 mg/L vs. 0 mg/L ns 0.2258\nFig 5E-6\nFig 5E-1\nŠídák's multiple comparisons test Summary Adjusted P\nŠídák's multiple comparisons test Summary Adjusted P Value\nValue\nCC7 - Vancomycin\nCC1 - Vancomycin\n0 ns >0.9999\n0 ns >0.9999\n2 ns >0.9999\n2 ns 0.9999\n4 ns 0.5892\n4 ns 0.9681\n8 ns 0.9749\n8 ns >0.9999\n16 **** <0.0001\n16 ns >0.9999\n32 **** <0.0001\n32 ns >0.9999\n64 **** <0.0001\n64 ns 0.6603 128 **** <0.0001\n128 **** <0.0001\nFig 5F-1\nFig 5E-4\nTukey's multiple comparisons test Summary Adjusted P Value\nŠídák's multiple Summary Adjusted P Value\ncomparisons test CC-1 vs. Triton-X- % lysis **** <0.0001\nCC4 - Vancomycin\nCC-1 vs. CC **** <0.0001\n0 ns >0.9999\nTriton-X- % lysis vs. CC **** <0.0001\n2 ns 0.9997\n4 ns 0.9925\nFig 5F-4\n8 ns >0.9999\nTukey's multiple comparisons test Summary Adjusted P Value\n16 ns >0.9999\nCC-4 vs. Triton-X- % lysis **** <0.0001\n32 ns 0.3312\nCC-4 vs. CC **** <0.0001\n64 **** <0.0001\nTriton-X- % lysis vs. CC **** <0.0001\n128 **** <0.0001\nFig 5F-5\nFig 5E-5\nTukey's multiple Summary Adjusted P\nŠídák's multiple comparisons test Summary Adjusted P Value comparisons test Value\nCC-5 vs. Triton-X- % lysis **** <0.0001\nFig 6B\nCC-5 vs. CC ** 0.0031\nUnpaired t test\nTriton-X- % lysis vs. CC **** <0.0001\nP value 0.0318\nP value summary *\nFig 5F-6\nTukey's multiple comparisons test Summary Adjusted P\nValue\nCC-6 vs. Triton-X- % lysis **** <0.0001\nCC-6 vs. CC * 0.0221\nTriton-X- % lysis vs. CC **** <0.0001\nFig 6C\nUnpaired t test\nP value 0.5305\nP value summary ns\nFig 5F-7\nTukey's multiple Summary Adjusted P\nSignificantly different (P < 0.05)? No\ncomparisons test Value\nCC-7 vs. Triton-X- % lysis **** <0.0001\nOne- or two-tailed P value? Two-tailed\nCC-7 vs. CC ns 0.2168\nt, df t=0.6654, df=6\nTriton-X- % lysis vs. CC **** <0.0001\nFig 6D\nFig 6A\nDunnett's multiple Summary Adjusted P\nLog-rank (Mantel-Cox) test (conservative)\ncomparisons test Value\nChi square 87.22\nUntreated vs. Vanco **** <0.0001\ndf 6 Untreated vs. Genta **** <0.0001\nP value <0.0001\nUntreated vs. CC *** 0.0005\nP value summary ****\nAre the survival curves sig different? Yes Untreated vs. CC + Genta **** <0.0001\nWe would like to clarify that Figures 4A, 4B, and 4C represent biologically distinct experimental conditions.\nFigure 4A shows MRSA-MW2 cells with standard growth conditions treated with CC.\nFigure 4B, N shows MRSA-MW2 cells grown in the presence of stearic acid and treated with CC.\nFigure 4C, O shows MRSA-MW2 cells grown in the presence of oleic acid and treated with CC.\nBecause the cells in Figures 4B and 4C have altered membrane lipid compositions compared with standard MRSA-MW2, direct statistical comparisons between Figure 4A vs. 4B or 4A vs. 4C are not appropriate, as these represent fundamentally different biological populations. Accordingly, statistical analyses were performed within each growth condition (untreated vs. CC-treated at 4xMIC or 8xMIC), using appropriate tests, as indicated in the figure legend. Differences between fatty acid conditions are described qualitatively, reflecting the trends in membrane rigidity and susceptibility to CC.\nFor clarification, we removed the word “comparison” between groups and the revised text now reads, “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 G) behaves like wild type cells (Fig. 4F), but 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). Page-12, Lines 250-268.\n7. Fluorescence-based assays, e.g., Sytox and DiSC3(5), should be OD600nm corrected to account for any changes to the bacterial inoculum density.\nResponse: We thank the reviewer for this important suggestion. We re-did the original Sytox Green and DiSC3(5) time-course assays normalizing the data to optical densities (OD 600). CC causes rapid membrane permeabilization (Sytox Green Figure 1E and 1F uptake) and rapid dissipation of membrane potential (loss of DiSC3(5) signal) at the concentrations and time points reported. We have replaced the original panels with OD corrected data (revised Fig. 1E and 1F).\n600\n8. 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\nResponse: We thank the reviewer for this important suggestion.\nFollowing the recommendation, we repeated the DiSC₃(5) membrane potential assay with little modifications according to the published study (PMC4829611) suggested by the reviewe#2 and another published study in Nature Communications (PMC11364768). Under optimized conditions using HEPES buffer supplemented with KCl and glucose, and including valinomycin and gramicidin as positive controls as shown in the revised Figure (Fig. 1F), valinomycin and gramicidin induced the expected increase in fluorescence, consistent with membrane depolarization. In contrast, treatment with CC resulted in a decrease in fluorescence in a dose-dependent manner, indicating a distinct effect on membrane potential compared to canonical depolarizing agents. These results suggest that CC does not act as a simple depolarizer but rather alters membrane dynamics in a manner that reduces DiSC₃(5) fluorescence, which is consistent with our lipidomic and fatty acid analyses indicating altered membrane homeostasis. Importantly, these data further support the conclusion that CC perturbs the bacterial membrane, albeit through a mechanism distinct from pore-forming agents. We have replaced the original panel with the new data, which is also OD normalized 600 (revised 1F).\nPMC4829611: Te Winkel, J. D., Gray, D. A., Seistrup, K. H., Hamoen, L. W., & Strahl, H. (2016). Analysis of antimicrobial-triggered membrane depolarization using voltage sensitive dyes. Frontiers in cell and developmental biology, 4, 29.\nPMC11364768: Li, T., Ren, X., Luo, X., Wang, Z., Li, Z., Luo, X., & Cheng, F. (2024). A foundation model identifies broad-spectrum antimicrobial peptides against drug-resistant bacterial infection. Nature Communications, 15(1), 7538.\n9. 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.\nResponse: We sincerely appreciate the reviewer’s suggestion regarding the modeling and SAR sections. While we understand that reducing these sections could streamline the manuscript, we would like to retain them because the SAR studies in particular provide unique mechanistic insights that distinguish our work. As you (Reviewer 2) noted above, you have concerns about the novelty of our study in light of prior reports on CC. We believe that the inclusion of the SAR analyses, combined with the multi-omics and detailed membrane-targeting mechanistic studies, adds substantial new information that goes beyond previous reports. Specifically, the SAR studies reveal structural determinants of CC’s antibacterial activity, demonstrate its interaction with membrane lipids, and identify analogs that retain or lose activity, findings that are not reported elsewhere. We therefore feel that these sections are essential to convey the novelty and significance of our work, while also providing a strong foundation for future follow-up studies.\n10. 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.\nResponse: (also refer to our response to your (referee 2) concern number 3 above). We thank the reviewer for raising this point. We agree that polymyxins are not clinically used against S. aureus. However, we included polymyxin B in our assays for two reasons: (i) polymyxin is a well-characterized cationic, membrane-active antibiotic frequently employed as a model compound to probe bacterial membrane perturbations, and thus served as a useful comparative control to validate CC’s membrane-targeting effects; and (ii) our broader research program is focused on drug repurposing strategies against Gram-negative pathogens, where polymyxins are clinically relevant. For these reasons, we assessed the interaction between CC and polymyxin B to establish potential cross-pathogen therapeutic implications. We agree with the reviewer that daptomycin is the clinically relevant lipopeptide for S. aureus, and in line with the suggestion, we have now performed combination assays of CC with daptomycin. The results show that CC is not a significant potentiator for the activity of daptomycin.\n11. 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?\nResponse: We thank the reviewer for this important question regarding the translational potential of CC. While it is correct that CC is a prodrug, our in vivo studies demonstrate that therapeutically relevant concentrations can be achieved and are effective against MRSA. Specifically, CC exhibited dose-dependent antibacterial activity in vertebrate models. Pharmacokinetic analysis in mice showed a maximum serum concentration of 8 mM CC at 12 hours post-administration, which is well above the MIC values observed in vitro. Importantly, no gross toxicity was observed based on liver and kidney profiling and histological examination. In a murine thigh infection model, 50 mg/kg CC alone reduced bacterial burden by 1-log10-CFU, gentamicin (30 mg/kg) by 2-log10-CFU, and their combination by 2.5–3.0-log10-CFU. Luminescence imaging corroborated bacterial reduction at the infection site. These results indicate that the MIC concentrations reported in vitro are achievable in vivo and that CC, alone or in combination with conventional antibiotics, has potential as a therapeutic agent. This point is now explicitly stated in the Discussion of the revised manuscript. The revised text is now added; “CC is a prodrug and our in vivo data demonstrate that therapeutically relevant concentrations are achievable and effective against MRSA-MW2. Pharmacokinetic profiling in mice showed serum levels relevant to the in vitro combinatorial MIC with clinical agents, and CC treatment significantly reduced bacterial burden in a mouse thigh infection model. These findings support the translational potential of CC as a viable antibacterial or antibiotic-potentiating agent despite its prodrug nature”. Page -24, Lines 529-537.\nMinor points\n1. Line 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.\nResponse: We thank the reviewer for this observation. We agree that severity may not be directly proportional to the number of virulence factors. We have revised the sentence for clarity to emphasize that the diverse range of S. aureus virulence factors contributes to the spectrum of clinical manifestations and infection types, which in combination with persistence and tolerance mechanisms, complicates treatment. This revision clarifies the intended meaning and avoids implying a direct correlation between virulence factor number and severity.\nThe revised sentence now reads: \"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.\" Page 3, Lines 36-39.\n2. Line39: persistence and tolerance don’t enable resistance. The former confer survival, and the latter the ability to grow, in the presence of antibiotics.\nResponse: We thank the reviewer for this important clarification. We agree that persistence and tolerance are distinct from resistance: persistence allows a subpopulation of bacteria to survive antibiotic exposure without growing, whereas tolerance allows growth at reduced rates in the presence of antibiotics. To reflect this distinction, we have revised the relevant text in the introduction. The revised sentence now reads, “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”. Page 3, Lines 36-39.\n3. Line 52: Omit ‘lipopolysaccharides’, as S. aureus does not have LPS and LPS is not a negatively charged phospholipid.\nResponse: We thank the reviewer for pointing this out and we now removed “lipopolysaccharides” from the text and revised the sentence to refer only to relevant negatively charged membrane components, such as phosphatidylglycerol and cardiolipin\n4. Line 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.\nResponse: We thank the reviewer for noting this discrepancy. The text has been corrected to accurately reflect the data shown in Figure 1B.\n5. Line 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\nResponse: We thank the reviewer for this suggestion, and we re-did the assay using Alamar Blue fluorescence assay to monitor the metabolically active cells and revised the text to more accurately reflect the observed effects of CC on MRSA biofilms. CC caused a modest reduction in biofilm eradication, and we agree that the term “inhibition” may overstate the effect at these concentrations. To address the reviewer’s recommendation, tetracycline was included as a control in both biofilm formation and eradication assays, demonstrating that CC’s effects are comparable or slightly less pronounced than those of a standard biofilm-active antibiotic (Fig. 1D).\nWe have therefore revised the text to state:\" Biofilm studies show 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). (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” Page 5-6, Line 94- 100.\nAdditionally, gene expression analyses of key biofilm-associated genes (agrA, sarA, icaA, yycG, fnbA/B) confirmed that CC modulates pathways related to biofilm formation Figure S7A-B. Functional relevance of these effects was further supported by pre-treatment experiments in Galleria mellonella, where CC pre-exposure decreased virulence of MRSA-MW2, resulting in a prolonged survival of CC-pretreated MRSA-MW2 injected larvae relative to CC-untreated controls (Figure S7C).\nTaken together, these results support that CC can impair biofilm formation and reduce biofilm-associated bacterial activity, but we have removed language implying biofilm eradication.\n6. Line 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?\nResponse: The apparent differences likely arise from variations in the simulation design and lipid composition used in the two studies. As a result, the Vanderbilt simulations suggest that CC interacts more dynamically with anionic lipid domains rather than uniformly disrupting the bilayer, which may explain the discrepancy. We also note that simulation timescales and force fields can contribute to different outcomes in terms of membrane perturbation. Taken together, these differences highlight how the specific membrane composition and model parameters can influence the predicted molecular interactions of CC and underscore the importance of integrating both computational and experimental data for mechanistic interpretation.\nReference: Ingólfsson, H. I., et al. (2014). Lipid organization of the plasma membrane. Journal of the American Chemical Society, 136(41), 14554–14559.\n7. Line 93-102: Is CC able to interact with positively charged alterations (lysyl-PG) to the membrane?\nCC, like other negatively-charged synergistic molecules (ethylenediaminetetraacetic acid (Hale., et al) and simeprevir (Wu et al) would show a higher affinity for positively charged alterations to the membrane. Its tetrazole group, which is deprotonated at physiological pH, would allow for even stronger interactions with lysyl headgroups and enhanced amphiphilicity, favoring membrane embedding and disruption. We thank the reviewer for bringing to light this point, as this enhanced affinity is also hypothesized to be the case for synergy with polymyxin B and ethylenediaminetetraacetic acid (Hale., et al) and simeprevir, (Wu., et al) and a similar synergistic mechanism (CC initially disrupting the membrane upon penetration of the complex) may explain enhanced disruption for lysyl-rich membranes.\nReference:\nHale, S. J. M.; Cameron, A. J.; Lux, C. A.; Biswas, K.; Kim, R.; O’Carroll, M.; Harris, P. W. R.; Douglas, R. G.; Wagner Mackenzie, B. Polymyxin B and ethylenediaminetetraacetic acid act synergistically against Pseudomonas aeruginosa and Staphylococcus aureus. Microbiol. Spectr. 2024, 12 (2), e01709-23. DOI: 10.1128/spectrum.01709-23.\nWu, Y.; et al. Simeprevir restores the anti-Staphylococcus activity of polymyxins. AMB Express 2023, 13, 122. DOI: 10.1186/s13568-023-01634-8.\n8. Figure 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’.\nResponse: We thank the reviewer for these observations. The different starting inocula reflect the growth phase of the bacteria and the specific aims of each experiment. In Figure 1B, an inoculum of ~10⁷ CFU/ml (logarithmic phase) was used to evaluate the bactericidal activity of CC under actively dividing conditions. In Figure 1C, a higher inoculum of ~10⁸ CFU/ml (stationary phase) was employed to assess the activity of CC against metabolically less active populations, which are more tolerant to antibiotics. This distinction was intentional to capture the effect of CC across different bacterial physiological states. We have revised the figure legends and Methods section to clarify this rationale. The revised text in Results now reads, “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); Page- 5, Lines 90-92. The revised text in Methods now reads “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.” Page 30, Lins 642- 645.\nPersisters in Figure 1C, is now changed into stationary phase.\nStats were included and please see the response- Reviewer#2, other major point- 6\nWe have now modified Figure 1I, to appear with scale bar and labels. We also included the raw data to show the reviewer how the software includes the scale bar and how we moved to appear.\nReview only file.\nReview only file.\nThe AFM images in Figure 1J were cropped to highlight specific cellular features, which unfortunately made including a scale bar directly in the panel is challenging. However, we now included the raw file here as a review only file\nReview only file.\nTEM: The TEM images in Figure 1K were cropped to highlight specific cellular features, which unfortunately made including a scale bar directly in the panel is challenging. However, we now included the raw file here as a review only file.\n9. Line 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?\nResponse: We used 10×MIC for AFM because high local drug exposure was required to produce rapid, reproducible membrane topography changes that are detectable by AFM within the practical time window of the experiment. AFM in this study was a mechanistic, imaging-oriented assay rather than a direct test of clinical dosing. AFM detects nanoscale topographical and mechanical changes on single cells. To visualize rapid membrane deformation, blebbing, and leakage within the timescale of a single experiment (minutes–hours) we used a supratherapeutic concentration (10×MIC). Lower concentrations produce more subtle, slower changes that are often below AFM signal-to-noise or require extended imaging periods that can introduce artefacts (tip drift, cell drying, biological heterogeneity).\n10. Line 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.\nResponse: We intentionally used a sub-lethal concentration (4 mg/L, 0.5×MIC depending on assay) for the comprehensive transcriptomic, metabolomic, and lipidomic analyses in order to capture primary cellular responses and pathway perturbations that precede irreversible cell death. Lethal concentrations induce rapid loss of viability and widespread secondary degradation, which can obscure upstream events and make it difficult to distinguish primary targets from downstream collapse. For this reason, sub-lethal dosing is a standard strategy when the objective is to identify early mechanistic changes rather than the end-stage consequences of cell lysis. We also emphasize that we have directly characterized CC’s lethal effects using complementary assays performed at bactericidal concentrations (≥2×MIC–10×MIC), including time-kill kinetics (Fig. 1B–C), membrane permeability and depolarization assays (Sytox Green, DiSC3(5), Fig. 1E–G), SEM/AFM/TEM/cryo-CT imaging of membrane damage (Fig. 1I–L and 1J AFM at 10×MIC), and functional killing of persisters and biofilms (Fig. 1B–D). These lethal-concentration data provide direct, orthogonal evidence of membrane disruption and rapid bactericidal activity consistent with the pathways highlighted by the sub-lethal multi-omics experiments (e.g., downregulation of fatty acid and cell-wall biosynthesis and depletion of key PG/PE species). In other words, the sub-lethal omics reveal the early pathway perturbations while the lethal-concentration assays confirm the ultimate phenotype (membrane damage and rapid killing).\nReference:\nSubanovic, M., Frawley, D., Tierney, C. et al. Proteomic and metabolomic responses of priority bacterial pathogens to subinhibitory concentration of antibiotics. npj Antimicrob Resist 3, 80 (2025).\nhttps://doi.org/10.1038/s44259-025-00147-7\nFlores-Vargas G, Korber DR and Bergsveinson J (2023) Sub-MIC antibiotics influence the microbiome, resistome and structure of riverine biofilm communities. Front. Microbiol. 14:1194952. doi: 10.3389/fmicb.2023.1194952\n11. Line 126: how do the authors explain down-regulation of genes associated with membrane stress during exposure to CC, which damages the membrane?\nResponse: We thank the reviewer for this important question. The apparent down-regulation of genes associated with membrane stress, despite CC causing clear membrane damage, is most likely a consequence of using a sub-lethal concentration of CC, the rationale for which is explained above in the preceding response. At this level, cells are exposed to stress sufficient to perturb lipid and membrane metabolism, but not enough to induce rapid lysis. Many canonical membrane stress response genes (e.g., two-component systems, chaperones) are typically upregulated only during acute, high-intensity stress or imminent cell death.\nReference: Tiwari S, Jamal SB, Hassan SS, Carvalho PVSD, Almeida S, Barh D, Ghosh P, Silva A, Castro TLP and Azevedo V (2017) Two-Component Signal Transduction Systems of Pathogenic Bacteria As Targets for Antimicrobial Therapy: An Overview. Front. Microbiol. 8:1878. doi: 10.3389/fmicb.2017.01878\n12. Figure 2. The color scheme in 2A is very difficult for the reader to follow.\nResponse: We reworked on the figure and the revised figure was substituted for the original.\n13. Line 139-141: Names of metabolites do not need to be capitalised.\nResponse: Thank you for pointing out. We modified the text accordingly and the revised phrase now reads, “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”. Page 8, Lines 155-160.\n14. Line 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.\nResponse: To explore whether CC also synergizes with daptomycin, we performed checkerboard assays. The results show that CC is not a significant potentiator for the activity of daptomycin Figure S8. Also see response number 10 above.\n15. Line 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.\nResponse: To explore whether CC also synergizes with b-lactam antibiotics (ampicillin, oxacillin, pencillin G, aztreonam), we performed checkerboard assays. The results showed that CC is not a potent potentiator for the activity of beta-lactam antibiotics (Figure S9).\n16. Line 170: Please include clarification on how many times the checkerboard analysis was repeated.\nResponse: The FICI is the mean of three replicates. We repeated the experiment a minimum of three times with aminoglycosides and polymyxin B as indicated in Figure 3.\n17. Line 173: Please see the above comment on high concentrations and persisters.\nResponse: Please see the response on Reviewer #2, Minor point 8\nThe revised texts in Methods now reads “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.” Page 30, Line no 641- 645\n18. Line 181-184: Is ATP synthesis affected by CC+gentamicin combination?\nResponse: We thank the reviewer for this important question. We measured intracellular ATP (endpoint BacTiter-Glo assay) across CC × gentamicin checkerboards and normalized ATP to viable counts from matched wells. The combination (CC + gentamicin) produces a significantly greater decline in intracellular ATP than either agent alone across multiple concentration pairs (representative data shown in the adjacent figure). These ATP decreases correlate with reduced Alamar Blue signal and with membrane depolarization/permeabilization metrics (DiSC3(5), Sytox Green), supporting a model in which CC perturbs membrane bioenergetics and gentamicin potentiates that energetic collapse, contributing to the rapid bactericidal synergy observed (Figure S11).\n19. Figure 3. A different starting inoculum is used for 3B exponential cells and persisters. Response: Please refer reviewer #2, minor comment 8.\n20. Please perform two-way ANOVA analysis on 3B.\nLog- Genta\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below threshold? Summary Adjusted P Value\nDMSO vs. Candesartan cilexetil 1X 864500000 355291994 to 1373708006 Yes *** 0.0003\nDMSO vs. Gentamicin (1X) 864531833 355323828 to 1373739839 Yes *** 0.0003\nDMSO vs. Combination 864654607 355446601 to 1373862612 Yes *** 0.0003\nCandesartan cilexetil 1X vs. Gentamicin (1X) 31833 -509176172 to 509239839 No ns >0.9999\nCandesartan cilexetil 1X vs. Combination 154607 -509053399 to 509362612 No ns >0.9999\nGentamicin (1X) vs. Combination 122773 -509085232 to 509330779 No ns >0.9999\nLog-Poly\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below threshold? Summary Adjusted P Value\nDMSO vs. Candesartan cilexetil 1X 8.65E+08 355318661 to 1373734672 Yes *** 0.0003\nDMSO vs. Polymyxin B 1X MIC 8.64E+08 355117328 to 1373533339 Yes *** 0.0003\nDMSO vs. Combination 8.65E+08 355458581 to 1373874592 Yes *** 0.0003\nCandesartan cilexetil 1X vs. Polymyxin B 1X MIC -201333 -509409339 to 509006672 No ns >0.9999\nCandesartan cilexetil 1X vs. Combination 139920 -509068086 to 509347926 No ns >0.9999\nPolymyxin B 1X MIC vs. Combination 341253 -508866752 to 509549259 No ns >0.9999\nPersister- genta\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below threshold? Summary Adjusted P Value\nDMSO vs. Candesartan cilexetil 1X 4.11E+08 202910053 to 618156613 Yes **** <0.0001\nDMSO vs. Gentamicin 1X 4.83E+08 275394040 to 690640600 Yes **** <0.0001\nDMSO vs. Combination 5.25E+08 317643520 to 732890080 Yes **** <0.0001\nCandesartan cilexetil 1X vs. Gentamicin 1X 72483987 -135139293 to 280107267 No ns 0.7859\nCandesartan cilexetil 1X vs. Combination 1.15E+08 -92889813 to 322356747 No ns 0.458\nGentamicin 1X vs. Combination 42249480 -165373800 to 249872760 No ns 0.9472\nPersister- poly\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below threshold? Summary Adjusted\nP Value\nDMSO vs. Candesartan cilexetil 1X 3.64E+08 156798552 to 572001448 Yes *** 0.0002\nDMSO vs. Polymyxin B 4X MIC 5.11E+08 303835885 to 719038781 Yes **** <0.0001\nDMSO vs. Combination 5.21E+08 312899059 to 728101955 Yes **** <0.0001\nCandesartan cilexetil 1X vs. Polymyxin B 4X MIC 1.47E+08 -60564115 to 354638781 No ns 0.245\nCandesartan cilexetil 1X vs. Combination 1.56E+08 -51500941 to 363701955 No ns 0.1994\nPolymyxin B 4X MIC vs. Combination 9063173 - No ns 0.9994\n198538275 to 216664621\n21. The labels are cut off on 3D and 3F. Concentrations of CC, gentamicin and polymyxin B are not described in the figure legend.\nResponse: Thank you for noticing the error. We fixed it.\n22. Could the authors provide a quantitative measurement of surface disruption/damage in 3E.\nResponse: Figure 3E represents atomic force microscopy (AFM) images, which we employed primarily to provide qualitative evidence of CC-induced alterations of S. aureus cell surface morphology. While AFM offers nanoscale topographical visualization, it is technically challenging to derive reliable quantitative measurements of surface disruption or damage from these images due to inherent variability in bacterial shape, orientation, and imaging artifacts. In addition, parameters such as roughness or height profiles can be influenced by sample preparation and scanning conditions, making direct quantification less reproducible across biological replicates.\n23. 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?\nResponse: In total, we examined the interaction of CC with approximately 20 clinically relevant antibiotics representing different classes. Of these, only a subset demonstrated reproducible synergy, and we focused our reporting on the most consistent and mechanistically relevant findings. While it is correct that three of the four antibiotics presented are aminoglycosides, our testing also included representatives from β-lactams, fluoroquinolones, glycopeptides, macrolides, and others. The FICI of these combinations were lesser than aminoglycosides and polymyxin B. Therefore were not included in the main figures to maintain clarity and focus. The enrichment of aminoglycosides in the reported data is not due to selective reporting but rather reflects a genuine biological phenomenon where CC appears to potentiate aminoglycoside activity through its membrane-disruptive effects. We have now clarified this point in the revised manuscript.\n24. Line 218-219: The Authors make a direct comparison between panels without having performed statistics. Please see comment above on comparisons between graphs.\nResponse: We removed the word “comparison” between groups and the revised text reads, “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 G) behave like wild type cells (Fig. 4F), but 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). Page-12, Line 250-268\nWe would like to clarify that Figures 4A, 4B, and 4C represent biologically distinct experimental conditions.\nSpecifically:\nFigure 4A shows MRSA-MW2 cells with standard growth conditions treated with CC.\nFigure 4B, G shows MRSA-MW2 cells grown in the presence of stearic acid and treated with CC. Figure 4C, H shows MRSA-MW2 cells grown in the presence of oleic acid and treated with CC. Because the cells in Figures 4B and 4C have altered membrane lipid compositions compared with standard MRSA-MW2, direct statistical comparisons between Figure 4A vs. 4B or 4A vs. 4C are not appropriate, as these represent fundamentally different biological populations. Accordingly, statistical analyses were performed within each growth condition (untreated vs. CC-treated at 4xMIC or 8xMIC), using appropriate tests, as indicated in the figure legend. Differences between fatty acid conditions are described qualitatively, reflecting the trends in membrane rigidity and susceptibility to CC.\n25. Line 223-225: Please see the above comment on comparisons.\nResponse: We thank the reviewer for the comment. For the better clarification, we removed the word “comparison” between groups and revised phrase reads, “Consistent with these results, we found that MRSA-MW2 cell growth in oleic acid (Fig. 4D and G) behave like wild type cells (Fig. 4F), but 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). Page- 12, 255-258.\n26. Line 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?\nResponse: Thank you. We re-ordered it.\n27. Line 230-239: What is the purpose of Fig. 4M? It is not referenced in the text. Relative abundance of C20:0\nResponse: Fig. 4M is now 4F. It is now referred to as Fig. 4F in the text.\n28. Line 240: Could the relative abundance of C20:0 be calculated?\nResponse: We thank the reviewer for this important suggestion. Using our GC-MS datasets, we calculated the relative abundance of C20:0 fatty acids by normalizing the integrated peak areas to the total lipid signal in each condition. The relative abundance of C20:0 was calculated from GC-MS integration peaks as the percentage of total lipid area. In untreated MRSA-MW2 cells, C20:0 represented 13.5 ± 5.1% of total lipids. Following CC treatment, this decreased significantly to 2.7 ± 2.5% (triplicate average), indicating a substantial reduction of C20:0.\n29. Figure 4. Description of statistics for 4A, 4B, 4C.\nResponse: We thank the reviewer for pointing this out.\nStats need to be performed for 4D, 4E.\nResponse: We have now included the description of statistics in Figure legend, performed statistical analysis for 4D and 4E and included in the graph as well as figure legend.\n30. Fluorescence microscopy in 4F-J is too small. Combining panels 4trKea-Ltm ,e n4Mt) -O, 4Q-S, 4T-V, 4W-1X.9 7 would give more space for larger microscopy images.\nResponse: We reorganized the figure.\n31. Line 253: A p-value is provided, but there is no description of what statistics were performed.\nResponse: We have now included the description of statistics in main text\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below threshold? Summary Adjusted P Value\nDMSO vs. CC 4x MIC 11753333 6820127 to 16686540 Yes **** <0.0001\n32. Line 258: A p-value is provided, but there is no description of what statistics were performed.\nResponse: We have now included the description of statistics in main text\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below threshold? Summary Adjusted P Value\nDMSO vs. CC 4x MIC 15032000 12679516 to 17384484 Yes **** <0.0001\n33. Line 267: Needs reference for how pH impacts antibiotic activity.\nResponse: We thank the reviewer for this important suggestion. It is well established that environmental pH can alter antibiotic efficacy by changing drug protonation, membrane permeation, and cellular uptake; notably, aminoglycoside uptake and activity are reduced at low pH and enhanced at alkaline pH (Damper & Epstein 1981; Taber et al. 1987; Lebeaux et al. 2014), and dynamic protonation can dramatically affect membrane permeation of ionizable drugs (Yue et al. 2019). We now cited appropriate references for how pH impacts antibiotic activity. Page 14, Line 307.\nDamper, P. D., & Epstein, W. (1981). Role of the membrane potential in bacterial resistance to aminoglycoside antibiotics. Antimicrobial Agents and Chemotherapy, 20(6), 803–808.\nTaber, H. W., Mueller, J. P., Miller, P. F., & Arrow, A. S. (1987). Bacterial uptake of aminoglycoside antibiotics. Microbiological Reviews, 51(4), 439–457. doi:10.1128/MR.51.4.439-457.1987\nLebeaux, D., Chauhan, A., Létoffé, S., Fischer, F., de Reuse, H., Beloin, C., & Ghigo, J.-M. (2014). pH-mediated potentiation of aminoglycosides kills bacterial persisters and eradicates in vivo biofilms. Journal of Infectious Diseases, 210(9), 1357–1366. doi:10.1093/infdis/jiu286\nYue, Z., Li, C., Voth, G. A., & Swanson, J. M. J. (2019). Dynamic protonation dramatically affects the membrane permeability of drug-like molecules. Journal of the American Chemical Society, 141(34), 13421–13433. doi:10.1021/jacs.9b04387.\n34. Line 271: Could the authors comment on the improved potency at low pH despite not having membrane permeabilisation activity.\nResponse: Indeed, our data show that CC exhibits its highest potency at pH 4 (MIC = 2 µg/mL) even though membrane permeabilization was not detected under these conditions. We hypothesize that this improved activity at low pH may be related to changes in CC’s protonation state, which can alter its solubility, charge distribution, and ability to interact with intracellular targets. Previous studies have shown that pH can influence antibiotic activity by modulating drug uptake, ionization, and intracellular distribution (Damper & Epstein, 1981; Taber et al., 1987; Lebeaux et al., 2014; Yue et al., 2019). At pH 4, protonation of CC may enhance its diffusion across the bacterial cell wall or facilitate stronger interactions with intracellular macromolecules, leading to increased antibacterial potency even in the absence of direct membrane permeabilization. To clarify this point, we have now added text in the Results and Discussion highlighting that CC may exert antibacterial effects through both membrane-dependent and membrane-independent mechanisms, with the latter being more dominant at acidic pH. As suggested by the reviewer, we added the following text in the Results, “Interestingly, despite the absence of detectable membrane permeabilization at pH 4, CC displayed its highest potency (MIC = 2 µg/mL) under these acidic conditions. This suggests that CC may exert antibacterial effects through membrane-independent mechanisms at low pH”, Page 15, Lines 310-314.\n35. Line 281: A graph should be included of the serial passaging experiment.\nResponse: The graph is already included in Extended Data 4F.\n36. Line 293- Line 295: The authors should investigate the individual involvement of these genes in CC resistance using mutants.\nResponse: We agree that assessing the contribution of individual genes to CC resistance using targeted mutants would be the most direct approach. Within our current resources, we performed preliminary MIC testing with a purR mutant from the NTML library, which showed increased susceptibility to CC, consistent with a role for purine metabolism in resistance. We also conducted MIC testing of multiple resistant clones obtained from mutation frequency experiments, which demonstrated a stepwise increase in CC MICs over time. Interestingly, as CC resistance increased, we observed a concurrent reduction in oxacillin MIC (from >32 mg/L at Day 1 to 8 mg/L by Day 50), suggesting a potential trade-off between CC resistance and β-lactam resistance. This pattern indicates that the adaptive mechanisms conferring CC resistance may come at the cost of altered cell wall physiology, reducing resistance to β-lactams. Such collateral sensitivity phenomena have been described in antibiotic resistance evolution and warrant deeper genetic and mechanistic investigation.\nNTML Gene MIC (mg/L)\nidentification CC Tunicamycin Bacitracin Vancomycin\nPlate 3 pur-operon 4 32 16 1\nO1 repressor\nUSA Parent strain 8 >128 32 1\nCC Erythromycin Oxacillin Vancomycin Doxycycline Ciprofloxacin\nDay1 8 0.5 >32 1 0.125 0.25\nDay16 16 0.5 >32 1 0.125 0.25\n(P1)\nDay 23 16 0.5 >32 1 0.125 0.25\n(P2)\nDay 25 32 0.5 >32 1 0.125 0.25\n(P1)\nDay 32 32 0.5 >32 2 0.125 0.25\n(P2)\nDay 35 64 0.5 32 2 0.125 0.25\n(P1)\nDay 43 64 0.5 16 1 0.125 0.25\n(P2)\nDay 45 128 0.5 16 2 0.125 0.25\n(P1)\nDay 47 128 0.5 16 1 0.125 0.25\n(P2)\nDay 48 256 0.5 16 2 0.125 0.25\n(P1)\nDay 50 256 0.5 8 2 0.25 0.25\n(P2)\nWhile these preliminary results provide supportive evidence, we think that a comprehensive functional analysis of purR, folC, farR, and tarS is beyond the scope of the current study and will be pursued in future work\n37. Line 295- Line 300: A see-saw effect is also seen between daptomycin and β-lactams, whereby DAPR is associated with increased susceptibility to β-lactams. Synergy between CC and daptomycin or β-lactams should be investigated.\nTo explore whether CC also synergizes with b-lactam antibiotics (ampicillin, oxacillin, pencillin G, aztreonam), and daptomycin we performed checkerboard assays. The results show CC exerted partial synergy with oxacillin and is not a great potentiator for the activity of other b-lactam antibiotics. Please see response of, Reviewer #2, Minor comments #15\n38. Line 305-306: Drug names do not need to be capitalised.\nResponse: We now changed it.\n39. Line 359: Does the removal of the ester linker in compound 7 make it less susceptible to esterase activity in the host?\nResponse: We revisited the concept and we did in vitro esterase stability assays for CC analogs alongside CC as a control using spectrophotometric analysis, with 4-nitrophenol (4-NOP) as a reference substrate. The results showed that compound CC6 exhibited overlapping absorbance spectra (250–300 nm) in the presence and absence of esterase, indicating minimal enzymatic hydrolysis Figure S10. These findings suggest that CC6 is more stable toward esterase-mediated degradation compared to the other analogs, supporting its improved stability under host-like enzymatic conditions.\nData for review only . Figure S10\nConsidering the length of this current draft, we plan to do it in host in future. The targeted stability assays (human plasma and liver S9/microsomes) and comparative PK analyses versus CC and analogs in future work to confirm whether removal of the ester linker improves in vivo stability.\n40. Figure 5. Please see above comments on stats and combining panels where comparisons are made.\nResponse: We would like to clarify that for Figure 5, statistical analyses were performed for each experiment and are now included in the revised figure legends. However, we did not perform cross-panel statistical comparisons, as each panel depicts distinct experimental readouts for different CC analogs (e.g., MIC determination, membrane permeability, bactericidal activity, cytotoxicity), rather than direct comparisons between the same condition across panels. The intent of Figure 5 is to present a comprehensive view of the structure–activity relationships of CC analogs, with each panel illustrating a different aspect of their activity profile. For this reason, combining panels or performing cross-panel statistics would not be appropriate.\nStatistics for Fig 5C\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted P\nthreshold? V alue\nDMSO+MW2 vs. Compound 1 (4x MIC) 443852120 356079496 to 531624744 Yes **** <0.0001\nDMSO+MW2 vs. Compound 1 (1x MIC) 306933333 219160709 to 394705957 Yes **** <0.0001\nCompound 1 (4x MIC) vs. Compound 1 (1x MIC) -136918787 -224691411 to -49146163 Yes ** 0.0016\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted\nthreshold? P Value\nDMSO+MW2 vs. Compound 4 (4x MIC) 444757760 373543171 to 515972349 Yes **** <0.0001\nDMSO+MW2 vs. Compound 4 (1x MIC) 443488000 372273411 to 514702589 Yes **** <0.0001\nCompound 4 (4x MIC) vs. -1269760 -72484349 to 69944829 No ns 0.9989\nCompound 4 (1x MIC)\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted\nthreshold? P Value\nDMSO+MW2 vs. Compound 5(4x MIC) 445077707 373862188 to 516293225 Yes **** <0.0001\nDMSO+MW2 vs. Compound 5 (1x MIC) 443489333 372273815 to 514704852 Yes **** <0.0001\nCompound 5(4x MIC) vs. -1588373 -72803892 to 69627145 No ns 0.9983\nCompound 5 (1x MIC)\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted\nthreshold? P Value\nDMSO+MW2 vs. Compound 6 (4x MIC) 444979127 373763251 to 516195002 Yes **** <0.0001\nDMSO+MW2 vs. Compound 6 (1x MIC) 441253333 370037458 to 512469209 Yes **** <0.0001\nCompound 6 (4x MIC) vs. -3725793 -74941669 to 67490082 No ns 0.9909\nCompound 6 (1x MIC)\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted\nt hreshold? P Value\nDMSO+MW2 vs. Compound 7 (4x MIC) 444977813 373764059 to 516191568 Yes **** <0.0001\nDMSO+MW2 vs. Compound 7 (1x MIC) 444478667 373264912 to 515692421 Yes **** <0.0001\nCompound 7 (4x MIC) vs. -499147 -71712901 to 70714608 No ns 0.9998\nCompound 7 (1x MIC)\nStatistics for Fig 5D\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted P\nV alue\n64 mg/L vs. 32 mg/L 17.17 14.96 to 19.37 Yes **** <0.0001\n64 mg/L vs. 16 mg/L 21.95 19.74 to 24.15 Yes **** <0.0001\n64 mg/L vs. 8 mg/L 24.98 22.78 to 27.19 Yes **** <0.0001\n64 mg/L vs. 0 mg/L 24.40 22.19 to 26.60 Yes **** <0.0001\n32 mg/L vs. 16 mg/L 4.780 2.574 to 6.985 Yes **** <0.0001\n32 mg/L vs. 8 mg/L 7.819 5.614 to 10.02 Yes **** <0.0001\n32 mg/L vs. 0 mg/L 7.230 5.024 to 9.435 Yes **** <0.0001\n16 mg/L vs. 8 mg/L 3.039 0.8339 to 5.245 Yes ** 0.0030\n16 mg/L vs. 0 mg/L 2.450 0.2441 to 4.655 Yes * 0.0232\n8 mg/L vs. 0 mg/L -0.5897 -2.795 to 1.616 No ns 0.9379\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted P Value\nthreshold?\n64 mg/L vs. 32 mg/L 124.9 122.3 to 127.5 Yes **** <0.0001\n64 mg/L vs. 16 mg/L 156.4 153.8 to 159.0 Yes **** <0.0001\n64 mg/L vs. 8 mg/L 166.0 163.4 to 168.6 Yes **** <0.0001\n64 mg/L vs. 0 mg/L 177.3 174.7 to 179.9 Yes **** <0.0001\n32 mg/L vs. 16 mg/L 31.51 28.92 to 34.10 Yes **** <0.0001\n32 mg/L vs. 8 mg/L 41.09 38.50 to 43.68 Yes **** <0.0001\n32 mg/L vs. 0 mg/L 52.44 49.85 to 55.03 Yes **** <0.0001\n16 mg/L vs. 8 mg/L 9.580 6.990 to 12.17 Yes **** <0.0001\n16 mg/L vs. 0 mg/L 20.94 18.35 to 23.53 Yes **** <0.0001\n8 mg/L vs. 0 mg/L 11.35 8.765 to 13.94 Yes **** <0.0001\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted\nP Value\n64 mg/L vs. 32 mg/L -295.9 -320.1 to -271.7 Yes **** <0.0001\n64 mg/L vs. 16 mg/L -151.0 -175.1 to -126.8 Yes **** <0.0001\n64 mg/L vs. 8 mg/L 74.81 50.62 to 99.01 Yes **** <0.0001\n64 mg/L vs. 0 mg/L 125.3 101.1 to 149.5 Yes **** <0.0001\n32 mg/L vs. 16 mg/L 145.0 120.8 to 169.2 Yes **** <0.0001\n32 mg/L vs. 8 mg/L 370.7 346.5 to 394.9 Yes **** <0.0001\n32 mg/L vs. 0 mg/L 421.2 397.0 to 445.4 Yes **** <0.0001\n16 mg/L vs. 8 mg/L 225.8 201.6 to 250.0 Yes **** <0.0001\n16 mg/L vs. 0 mg/L 276.2 252.0 to 300.4 Yes **** <0.0001\n8 mg/L vs. 0 mg/L 50.48 26.29 to 74.67 Yes **** <0.0001\nTukey's multiple comparisons test Mean Diff. 95.00% CI of diff. Below Summary Adjusted\nthreshold? P Value\n64 mg/L vs. 32 mg/L 54.80 26.22 to 83.37 Yes **** <0.0001\n64 mg/L vs. 16 mg/L 369.3 340.7 to 397.8 Yes **** <0.0001\n64 mg/L vs. 8 mg/L 561.6 533.0 to 590.2 Yes **** <0.0001\n64 mg/L vs. 0 mg/L 591.7 563.1 to 620.2 Yes **** <0.0001\n32 mg/L vs. 16 mg/L 314.5 285.9 to 343.0 Yes **** <0.0001\n32 mg/L vs. 8 mg/L 506.8 478.2 to 535.4 Yes **** <0.0001\n32 mg/L vs. 0 mg/L 536.9 508.3 to 565.4 Yes **** <0.0001\n16 mg/L vs. 8 mg/L 192.3 163.7 to 220.9 Yes **** <0.0001\n16 mg/L vs. 0 mg/L 222.4 193.8 to 251.0 Yes **** <0.0001\n8 mg/L vs. 0 mg/L 30.09 1.509 to 58.66 Yes * 0.0349\nM e a n D i f f . 9 5 . 0 0 % C I o f d i f f . B e l o w t h r e s h o l d ? S u m m a r y A d j u s t e d\nT ukey's multiple comparisons test P Value\n64 mg/L vs. 32 mg/L -249.7 -283.6 to -215.9 Yes **** <0.0001\n64 mg/L vs. 16 mg/L -127.0 -160.9 to -93.18 Yes **** <0.0001\n64 mg/L vs. 8 mg/L 204.6 170.8 to 238.5 Yes **** <0.0001\n64 mg/L vs. 0 mg/L 258.5 224.7 to 292.3 Yes **** <0.0001\n32 mg/L vs. 16 mg/L 122.7 88.87 to 156.5 Yes **** <0.0001\n32 mg/L vs. 8 mg/L 454.3 420.5 to 488.2 Yes **** <0.0001\n32 mg/L vs. 0 mg/L 508.2 474.4 to 542.1 Yes **** <0.0001\n16 mg/L vs. 8 mg/L 331.6 297.8 to 365.5 Yes **** <0.0001\n16 mg/L vs. 0 mg/L 385.5 351.7 to 419.4 Yes **** <0.0001\n8 mg/L vs. 0 mg/L 53.90 20.06 to 87.73 Yes *** 0.0005\n41. Figure 6. Please see above comments on stats\nFigure 6B\nBUN\nUnpaired t test\nP value 0.0318\nP value summary *\nALT\nUnpaired t test\nP value 0.5305\nP value summary ns\n42. Line 384: What is the difference between membrane perturbation and disruption?\nResponse: We thank the reviewer for this helpful comment. By “membrane perturbation,” we refer to subtle biophysical changes such as altered membrane fluidity, lipid packing, or local rigidity that do not necessarily lead to gross leakage of cellular contents. In contrast, “membrane disruption” indicates a more severe effect that compromises membrane integrity, leading to increased permeability or lysis.\n43. Line 388-389: These abbreviations have already been introduced to the reader.\nResponse: We thank the reviewer for this helpful comment. We removed them.\n44. Line 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.\nResponse: We thank the reviewer for this valuable suggestion. Please see the response for Reviewer # 2, Minor comment # 39\nReviewer #3 (Remarks to the Author):\nI 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.\nReviewer #4 (Remarks to the Author):\nThis 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\n1. 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.\nResponse: We thank the reviewer for these suggestions. We agree that identifying the precise lipid target of CC and elucidating the genetic mechanism underlying the reduction of C20:0 fatty acids would provide deeper mechanistic insights; however, these analyses are beyond the scope of the current study. Our preliminary serial passage experiments did not yield mutants with altered C20:0 fatty acid profiles as predicted. Instead, we conducted physiological assays that modulated C20:0 levels and then demonstrated that supplementation with exogenous C20:0 fatty acids restored susceptibility to CC. These findings support the functional link between C20:0 fatty acids and CC activity.\nWe plan to pursue detailed lipidomics and genetic studies in future work\nWe now included this revised text in the Results, “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 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 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 325-344.\nWe also utilized a strain from Nebraska Transposon Mutant Library collection and data is included. Please refer to the response for Reviewer #2, minor comment #36.\n2. The precise target lipid associated with the antibacterial activity of CC should be identified (e.g., C20:0-containing phosphatidylcholine).\nResponse: We thank the reviewer for emphasizing the need to identify the precise lipid targets of CC. We agree with the reviewer that identifying the lipid species associated with CC activity is critical. In our study, comprehensive lipidomic profiling revealed broad remodeling of S. aureus membrane lipids, including significant decreases in multiple phospholipids such as phosphatidylglycerol (PG) and phosphatidylethanolamine (PE), indicating general disruption of membrane integrity. In parallel, functional GC–MS analysis specifically demonstrated a marked decrease in C20:0 fatty acids in MRSA-MW2 membranes upon CC treatment and in cerulenin-treated cells, and supplementation with C20:0 lipids partially restored CC susceptibility. These results together indicate that C20:0-containing lipids play a critical role in CC-mediated membrane disruption. While the exact individual phospholipid species (e.g., phosphatidylcholine, PG, cardiolipin containing C20:0) have not yet been assigned, our data strongly support their involvement. Moreover, our, TEM analysis showed that C20:0 supplementation partially restored cell morphology in CC-treated MRSA cells, we included the revised TEM figure in Fig 2H, providing structural evidence that this lipid contributes to membrane integrity and CC-mediated disruption. While our study demonstrates that C20:0-containing membrane components are important for CC susceptibility, further lipidomic analysis coupled with phospholipid-specific reconstitution assays will be required to determine the precise phospholipid class (e.g., C20:0-containing phosphatidylglycerol or cardiolipin) that acts as the direct target.\n3. 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.\nWhile many membrane-active agents are thought to increase fluidity, there are precedents for increased rigidity or reduced membrane dynamics in the presence of permeabilizing antibiotics or antimicrobial peptides. For example, the AMP LL-37 rigidifies the cytoplasm of E. coli (Zhu, Mohapatra & Weisshaar, 2019) ; daptomycin disrupts fluid membrane microdomains and causes rearrangement of lipids, possibly increasing order in certain membrane regions (Muller et al, 2016); AMPs in model membranes can produce structural domain reorganization (edge instabilities, thinning, etc.) that is not consistent with pure fluidization but suggests increased local order or tension in the bilayer (Zorila et al, 2029)\nWe hypothesize that in the case of CC-mediated rigidification, rather than fluidization, CC perturbs the delicate balance of membrane homeostasis in S. aureus, impairing cell division, membrane protein function, and permeability, thereby leading to bacterial killing. Notably, supplementation with C20:0 lipids partially restores susceptibility, supporting a lipid-specific mechanism. We agree that further studies, including real-time biophysical analyses and molecular-level imaging, will be important to fully reconcile this apparent discrepancy and clarify how CC-induced membrane rigidification contributes to antibacterial efficacy\nReference:\nZhu, Y., Mohapatra, S., & Weisshaar, J. C. (2019). Rigidification of the Escherichia coli cytoplasm by the human antimicrobial peptide LL-37 revealed by superresolution fluorescence microscopy. Proceedings of the National Academy of Sciences of the United States of America, 116(3), 1017–1026.\nhttps://doi.org/10.1073/pnas.1814924116\nMüller, A., Wenzel, M., Strahl, H., Grein, F., Saaki, T. N. V., Kohl, B., Siersma, T., Bandow, J. E., Sahl, H. G., Schneider, T., & Hamoen, L. W. (2016). Daptomycin inhibits cell envelope synthesis by interfering with fluid membrane microdomains. Proceedings of the National Academy of Sciences of the United States of America, 113(45), E7077–E7086. https://doi.org/10.1073/pnas.1611173113\nZorilă, B., Ioanid, N., Ion, A., & Dinescu, A. (2020). Melittin interaction with lipid membranes explored by Laurdan fluorescence and molecular dynamics. Scientific Reports, 10(1), 11658.\nhttps://doi.org/10.1038/s41598-020-68642-4\n4.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.\nWe thank the reviewer for highlighting this important point. We agree that the role of C20:0 lipids in CC-mediated antibacterial activity is complex and requires careful interpretation. Our data indicate that sub-MIC of CC treatment decreases the overall cellular levels of C20:0-containing lipids (Fig. 4P), these lipids appear to be critical for CC’s activity, as shown by functional experiments: supplementation of C20:0 to cerulenin-treated or sub-MIC CC-treated cells partially restores susceptibility to CC (Figs. 4S, 4X). This suggests that CC preferentially interacts with C20:0 lipids in the membrane to exert its antibacterial effect, and that the observed decrease in C20:0 levels after CC treatment likely reflects their consumption, remodeling, or displacement during CC-induced membrane disruption. In other words, C20:0 lipids are functionally important for CC activity even though their abundance decreases following treatment. We have revised the manuscript to explicitly clarify this interpretation, emphasizing the distinction between the absolute lipid level and its functional role as a target of CC.\n5. 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.\nResponse: We thank the reviewer for raising this important point regarding potential side effects of CC. We acknowledge that parameters such as blood pressure, serum potassium levels, and kidney function markers were not measured in the current study and agree that these are critical for evaluating CC’s safety profile. The trends observed in Fig. 6B, although not statistically significant due to the limited sample size, suggest potential alterations that merit further investigation. The observed efficacy at doses below the antihypertensive range suggests a potential therapeutic window, supporting the feasibility of analog optimization before full toxicodynamic profiling. Performing these measurements with a larger cohort and multiple time points represents a different and more extensive scope of work. We plan to conduct these assays in future studies alongside CC analogs in a detailed manner. Therefore, we have included this as a limitation of the current study, and future work will provide a more comprehensive assessment of safety at therapeutically relevant doses.\nWe have included the following text in the revised paper,\n“Although our in vivo studies demonstrate the antibacterial efficacy of CC, parameters such as blood pressure, serum potassium, and kidney function were not evaluated. Assessing these with larger cohorts and multiple time points will be addressed in future work, including evaluations of CC analogs. Page- 23, Lines 534-537.\n6. The methods section does not provide details on how the metabolomics and lipidomics analyses shown in Figure 2 were performed.\nResponse: We now included the procedure in the Methods. Page -38-39, Lines 827- 855.\nMetabolomics 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.\nLC-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.\nTargeted 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.\nTargeted 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.\nData Analysis: Peak areas were integrated using MultiQuant v3.0.3 (SCIEX) and normalized to appropriate internal standards using a custom R script (v4.3.1).\nStatistical 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.\nMinor concerns 1. In Fig 1L, clearly label which treatment group each image corresponds to.\nResponse: In the revised version, we have clearly labeled each image in Fig. 1L with the corresponding treatment group (e.g., “DMSO control,” “10× MIC”) both within the figure panels and in the figure legend for clarity.\n2. It would be preferable to include scale bars in all microscopy images. (e.g. Fig 1K)\nThe TEM images in Figure 1K were cropped to highlight specific cellular features, which unfortunately made including a scale bar directly in the panel challenging. However, we now included the raw file here as a review only file.\nReview only raw data file.\n3. 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.\nResponse: Thank you for noticing. We now made the suggested change.\n4. 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.\nResponse: The incorrect Figure captions have been corrected. We made the suggested change.\n5.The legend for Figure 4 is incorrectly labeled as Figure 5.\nResponse: We made the suggested change.\n6.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.\nResponse: We removed the confusing statement.\n7.In line 254, please verify whether the panel labeled as Fig. 4G is correctly referenced.\nResponse: We have referenced the figure in the text now.\n8.In Fig 6D, perform a one-way ANOVA to determine whether the difference between the Genta and CC+s is statistically significant.\nResponse: Yes. It is significant.\nDunnett's T3 multiple comparisons test Mean Diff. 95.00% CI of diff. Below threshold? Summary Adjusted P Value\nUntreated vs. Vanco 9545666667 1419395990 to 17671937343 Yes * 0.0133\nUntreated vs. Genta 10227666667 2164278158 to 18291055175 Yes ** 0.0064\nUntreated vs. CC 6928000000 -1306107862 to 15162107862 No ns 0.1498\nUntreated vs. CC + Genta 10415293333 2352634530 to 18477952136 Yes ** 0.0053\nVanco vs. Genta 682000000 -484180422 to 1848180422 No ns 0.5723\nVanco vs. CC -2617666667 -4809282950 to -426050384 Yes * 0.0102\nVanco vs. CC + Genta 869626667 -294374685 to 2033628018 No ns 0.2612\nGenta vs. CC -3299666667 -5235651504 to -1363681830 Yes *** 0.0002\nGenta vs. CC + Genta 187626667 78212904 to 297040429 Yes *** 0.0002\nCC vs. CC + Genta 3487293333 1554349975 to 5420236691 Yes **** <0.0001\n9.The scale bar in Fig 6F is barely visible. Please enlarge it for clarity.\nResponse: We worked on the scale bars to be visible. The revised figure is now enclosed here.\n10.In line 411, correct the typo “8:0 mg/L.”\nResponse: We made the change.\nReviewer #2 (Remarks to the Author):\nThe authors have fully addressed the points raised.\nResponse: We thank the reviewer for constructive feedback throughout the review process.\nReviewer #4 (Remarks to the Author):\nI 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.\nResponse: 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)."}, {"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": "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)."}]

Decision letter