Staphylococcus aureus fatty acid metabolism governs saeRS-mediated aggregation in joint infections

Domain: chemistry
Published: 2025-12-21
Editorial outcome: accept

Abstract
Staphylococcus aureus can form aggregates within human synovial fluid, contributing to joint infections that are resistant to conventional therapies. However, the regulatory pathways underlying this process remain unclear. Here, we reveal that the S. aureus two-component system saeRS contributes to synovial aggregation by controlling the expression of fibrinectin-binding proteins A and B (FnbA/B), which bind to human fibrinogen and cause aggregation. We further demonstrate that the fatty acid kinase (fak) system is the primary regulator for SaeS activity under host conditions. Mechanistically, fak is epistatic to saeRS by scavenging exogenous fatty acids and altering the localization of SaeS into functional membrane microdomains (FMMs), which are essential for SaeS function. We also identify that undecanoic acid (UDA) is capable of inhibiting the fak pathway, demonstrating its efficacy and potential applicability in treating joint infections. Altogether, these findings highlight the fak-saeRS axis as a potential therapeutic target for joint infections and underscore UDA as a promising treatment for these infections.

Introduction
Joint infections, either native or prosthetic, represent a formidable clinical challenge in musculoskeletal medicine1. Left untreated, joint infections can lead to irreversible joint damage, chronic pain, functional impairment, and even amputation2,3. Therefore, accurate diagnosis and prompt intervention are urgently needed to mitigate the potentially devastating consequences associated with joint destruction and systemic spread of infection. Although substantial research efforts have advanced our understanding of joint infections1,4, persistent challenges in clinical management and evolving microbial resistance patterns underscore the need for continued refinement of therapeutic strategies and molecular-level understanding of host–pathogen interactions. Further research remains crucial to address critical knowledge gaps and translate mechanistic insights into improved patient outcomes.

Staphylococcus aureus is the primary causative pathogen of septic arthritis and periprosthetic joint infections5,6. Notably, S. aureus strongly tends to aggregate within the synovial fluid, which promotes persistent infection and treatment resistance7. Although closely related, synovial aggregation and biofilm formation are distinct phenomena with different phenotypic manifestations. Synovial aggregates form quickly and develop much larger amounts of biomass. The aggregation process is believed to involve a sophisticated interplay between host and bacterial factors. Host molecules such as fibrinogen and fibronectin serve as substrates for bacterial adhesion via microbial surface proteins such as ClfA/B and FnbA/B8. Despite significant research efforts, the key regulator of this aggregation process and its specific impact on infection outcomes remain incompletely understood, highlighting a critical gap in pathogenesis knowledge. Therefore, elucidating the precise mechanism underlying synovial aggregation is essential for developing targeted therapeutic strategies.

S. aureus transcription is tightly controlled by two-component systems (TCSs). In particular, the saeRS TCS regulates the expression of more than 20 surface-associated adhesins and secreted virulence factors, such as hla (ɑ-haemolysin), coa (coagulase), nuc (thermonuclease), and several proteases9,10. The activity of saeRS is modulated by various factors: it is positively activated by fatty acid kinase (fak) and codY, as well as by the presence of human neutrophil peptides, calprotectin, or hydrogen peroxide. In contrast, free fatty acids inhibit saeRS activity11,12. Notably, fak dysfunction significantly downregulates saeRS, underscoring a close functional relationship13,14. fak is a two-component enzyme that utilizes exogenous fatty acids to generate acyl-phosphate for phospholipid synthesis and cell membrane construction15–17. The fak system consists of a kinase domain protein (FakA) that phosphorylates a fatty acid enveloped by a fatty acid binding protein (FakB1/2)18,19. Parsons et al.15. proposed that fak might activate saeRS activity through phosphorylation of the SaeS protein. However, this was not true in a previous study20. Yet the mechanism by which fak affects saeRS activity has not been determined. The relationships among eFA, fak, and sae need to be thoroughly explored.

Here, the critical role of S. aureus saeRS in synovial aggregation, a key pathogenic mechanism in joint infections, is elucidated. We determined that saeRS activity is regulated by the fak system within the host environment. Mechanistically, fak regulates the localization of the SaeS histidine kinase within functional membrane microdomains (FMMs). Furthermore, our findings demonstrate the high adaptability of S. aureus to the synovial fluid environment to facilitate its pathogenesis. Finally, we identified undecanoic acid (UDA), a medium-chain fatty acid (C11:0) capable of inhibiting the Fak system. UDA effectively inhibits S. aureus aggregation and enhances the efficacy of anti-FASII antibiotics, suggesting its potential as a promising adjuvant therapy for joint infections.

Results
S. aureus synovial aggregation contributes to joint infections
Bacterial synovial aggregation is fundamentally distinct from biofilm, as it rapidly surpasses the phagocytosis size (>10µm in diameter21) within one hour and matures within a few hours (Fig. 1A–C). Currently, no evidence directly demonstrates the contribution of synovial aggregation ability to the severity of septic arthritis. To address this gap, we analysed S. aureus strains from patients with musculoskeletal infections (Fig. 1D). The isolates were classified into joint-involved and joint-not-involved groups (n=41 for JI; n=111 for N-JI). By quantifying their aggregation and biofilm formation abilities, we found that compared with the N-JI group, the JI group presented greater aggregation ability, whereas no difference in biofilm formation ability was observed between the JI and N-JI groups (Fig. 1E, Supplementary Data 1 and 2, and Fig. S1A, B). Moreover, the aggregation ability was not correlated with biofilm ability (Fig. 1F), indicating that aggregation was an independent factor strongly associated with joint infections.

To further verify this, we selected the five strains with the greatest aggregation ability (H1–H5) and the five with the lowest aggregation ability (L1–L5) to establish a knee joint infection mouse model. First, the growth of the representative strains did not differ when cultured in 20% synovial fluid (Fig. S1C). Each mouse was subsequently infected with a high-aggregating isolate at the right knee and a low-aggregating isolate at the left knee. The results revealed that mice infected with high-aggregating S. aureus exhibited more severe infections with greater cartilage impairment (Fig. 1G), limited joint movement (Fig. 1H–J), and greater bacterial burden (Fig. 1K). When cocultured with immune cells or treated with antibiotics, high-aggregating isolates showed significant increases in resistance to immune cell killing (~twofold, Fig. 1L, M), phagocytosis (~twofold, Fig. S1D, E), and antibiotic killing compared with their unaggregated state (100–1000-fold, Fig. S1F–H). The phenotypic difference between the aggregated and unaggregated states of the same strain indicates that synovial aggregation behaviour, rather than other genetic factors, helps the bacteria evade both the host immune response and antimicrobial therapies. This provides a strong link between aggregation and joint infections.

Directed evolution revealed that synovial aggregation depends on SaeRS activity
Our analysis revealed no significant correlation between biofilm formation and aggregation in S. aureus, suggesting that distinct mechanisms govern aggregation. This prompted us to investigate the genetic pathways underlying synovial aggregation. We performed directed evolution by selecting planktonic S. aureus populations within SF (synovial fluid) (Fig. 2A). S. aureus strain USA300 was initially exposed to SF for aggregation; unaggregated cells were then isolated from the planktonic fraction and grown overnight in tryptic soy broth (TSB) before they were reintroduced into SF for the next round of aggregation. The evolution was repeated until aggregation was no longer observable (Fig. 2B). As anticipated, the evolved population exhibited increased susceptibility to antibiotics and reduced resistance to immune cell killing (Figs. 2C and S2A, B). Notably, the evolved population also showed a progressive loss of haemolysis rings on sheep blood agar plates (Fig. 2D). We confirmed that the haemolysis-ring-negative colonies were all nonaggregating (Fig. 2D) and that the mutation was irreversible, even when the cells were cultured for several passages. Whole-genome sequencing of the randomly selected nonaggregating isolates revealed mutations in the saePQRS or fakA locus (Figs. 2E and S2C).

The sae locus, encoding the saeRS TCS and auxiliary saePQ factors, is central to the activation of key virulence factors and adhesins, with a link to fak, as shown in previous studies14,15. We confirmed the roles of fakA and saeRS in aggregation by testing their mutants in SF; ΔsaeRS and ΔfakA were nonaggregating but were restored upon complementation (Fig. 2F). Consistent with these findings, both saeRS and fakA mutants exhibited reduced resistance to phagocytosis and heightened sensitivity to antibiotics and immune cell-mediated killing in SF (Figs. 2G–H and S2D–F). We also demonstrated that bacterial growth was not impaired for the mutants when they were grown in 20% SF (Fig. S2G). We next investigated whether fakA influences aggregation via saeRS by complementing ΔfakA with saeRS (ΔfakA::psaeRS) and ΔsaeRS with fakA (ΔsaeRS::pfakA). Notably, aggregation was restored in only the ΔfakA::psaeRS strain but not in the ΔsaeRS::pfakA strain (Fig. 2I). Furthermore, fakA knockout significantly decreased saeRS transcription, suggesting that fakA is epistatic to saeRS (Fig. 2J, K). These results agree with those of previously published studies13–15.

We subsequently repeated the directed evolution experiment with high-aggregating clinical strains (H1–H5). The results revealed an anticipated decrease in aggregation ability (Fig. S2H). Subsequent genome sequencing revealed DEL/SNPs in the fakA, fakB1, and saePQRS loci (Fig. S2I and Supplementary Data 3), which further supports our findings that the fak–sae axis contributes to synovial aggregation. Molecular docking analysis of FakA binding with FakB1/2 revealed that the SNPs led to changes in amino acids that were critical for binding function, possibly explaining the dysfunction of the fak system (FakA[R246, E311, R314, F422, E532, E548], FakB1[202~209]; Fig. S3A–D and Supplementary Data 3).

FnbA/B-fibrinogen interaction is the key step in synovial aggregation
To further explore the mechanism of aggregation, we first confirmed that the aggregates were primarily composed of proteins. This conclusion was based on the observation that treatment with dispersin-B, DNase-I, RNase-H, and lipase did not inhibit aggregation and that even the nucleation step was not affected (Fig. S4A, B), whereas proteinase K significantly inhibited aggregation (Fig. S4A), suggesting that proteins play a critical role in synovial aggregation. SDS–PAGE of the aggregates revealed enrichment of proteins at 60 and 80 kDa (Fig. 3A, B). Proteomic analysis of aggregates revealed these enriched proteins as immunoglobulins, keratins, fibrinogen, complement proteins, lipoproteins, and fibronectin (Fig. 3C and Supplementary Data 4).

To determine which bacterial adhesins interact with these proteins, we first identified saeRS-regulated genes from a large S. aureus transcriptome dataset with a machine learning method (Fig. 3D)22,23. Among the 34 genes predicted to be downstream of saeR, 23 were verified to be downregulated in the ΔsaeRS RNA-seq data, and 8 were adhesins (Figs. 3E and S5B and Supplementary Data 5). Complementing ΔsaeRS with each of the eight adhesins and inducing overexpression with anhydrotetracycline revealed that only fnbA/B restored aggregation (Figs. 3F, G and S5A). Moreover, the fnbA or fnbB mutant did not completely abolish aggregation, whereas the double mutant eliminated aggregation, suggesting that both fnbA and fnbB are essential for aggregation (Fig. 3H, I).

FnbA/B are proteins that bind fibronectin and fibrinogen24. We then explored how fibronectin and fibrinogen contribute to aggregation. The incubation of S. aureus with fibrinogen, but not fibronectin, induced the adhesin overexpression, which was abolished in the ΔfnbAB strain, indicating that fibrinogen mediates aggregation through binding with FnbA/B adhesins (Fig. 3J, K). We also tested the pathogenesis of fnbAB in a mouse model of joint sepsis. Immunofluorescence detection of S. aureus revealed that the bacterial aggregates around the joint were reduced in the ΔfnbAB and ΔsaeRS groups, although the difference in bacterial burden between the ΔfnbAB and WT groups was not as significant as that in the ΔsaeRS group (Fig. S5C, D). These findings suggest that although fnbA/B are essential for synovial aggregation, other saeRS-regulated genes also contribute to pathogenesis.

S. aureus fak activates saeRS and fnbA/B by scavenging exogenous fatty acids
We subsequently tested how saeRS-fnbA/B responds to the human environment. By analysing the meta-transcriptome data of specimens from patients with S. aureus musculoskeletal infections, we observed higher read counts mapping to the fnbA/B locus in the JI group than in the N-JI group (Fig. 4A). Similarly, when S. aureus was cultured in vitro with SF or serum, upregulation of saeRS-regulated adhesins was observed (Fig. 4B–D and Supplementary Data 6). The upregulation of saeRS-regulated adhesins was more significant in SF than in serum, indicating that saeRS was in an active state within the joint environment.

We hypothesized that fak controls the saeRS-fnbA/B axis in SF. First, we detected no change in the transcription of fakA or fakB1/2 when the cells were incubated with SF (Fig. S5A). The transcription level of saeRS-fnbA/B in response to SF was subsequently compared between the WT and ΔfakA strains, and no difference was detected for the response of the ΔfakA strain to SF (Fig. 4E), suggesting that the upregulation of saeRS observed in synovial fluid is abrogated in the fak mutant. To further explore this phenomenon, we used C-13-labelled oleic acid and acetic acid to trace exogenous (fak) and endogenous fatty acids synthesis pathway (FASII) activity among the high- and low-aggregating strains, respectively; higher fak activity was detected among the high-aggregate strains (Fig. 4F–G). This result implies that differences in fak activity might lead to differences in aggregation ability among clinical strains. Upon comparative analysis of FakA/B1/B2 and SaePQRS protein sequences retrieved from 500 S. aureus strains deposited in public databases, we observed a high degree of polymorphism in FakA amino acid sequences, whereas the SaePQRS and FakB1/2 amino acid sequences were relatively conserved (Figs. 4H and S7). Furthermore, sequencing of our high/low-aggregating strains (H1–5 and L1–5) revealed amino acid polymorphisms with increased binding energy between FakA dimerization and FakA–FakB1 binding (Fig. 4I). Although the absolute binding energy provides little biological meaning, the relative values among strains are still helpful in elucidating the discrepancies in aggregation ability and Fak activity among clinical strains.

The biological function of fak is to exploit exogenous fatty acids (eFAs)15. To explore whether fak influences saeRS-fnbA/B through eFA, we treated S. aureus with common human fatty acids and found that eFA (oleic acid, palmitic acid, and myristic acid) inhibited saeRS-fnbAB transcription (Fig. S6B). We propose that fak, as a consumer of eFA, scavenges fatty acids, thereby alleviating their suppression of saeRS-fnbA/B. To further validate this model, we treated ΔfakA with fatty acid-free bovine serum albumin (FA-free-BSA) to scavenge intracellular fatty acids. A significant increase in sae activity was observed upon treatment of ΔfakA with FA-free BSA. (Fig. 4K). However, the rescue of sae transcription by FA-free-BSA was only partial compared with that of the wild type. This implies that other regulatory mechanisms are involved in the control of saeRS-fnbA/B by fak.

fak regulates SaeS activity by maintaining FMMs integrity
The mechanism by which fak regulates saeRS activity remains to be fully determined. Pathogens alter their cell membrane composition through the fak system to adapt to the host environment16,25. As SaeS is a membrane protein, it is likely influenced by changes in the cell membrane. FMMs, recently identified as crucial membrane structures, have been demonstrated to significantly influence the functionality of membrane-associated proteins26. A review of published FMM proteomic data27,28 revealed enrichment of SaeS in FMMs. Subsequent molecular dynamics simulations indicated potential binding between SaeS and FloA (scaffold protein of FMMs) within the cell membrane (Fig. S8A–D). This finding was confirmed in a bacterial two-hybrid (B2H) assay in which proteins were cloned as prey or bait in Escherichia coli (BTH101). The results revealed FloA–SaeS interactions but not with SaeP/Q (Fig. 5A–C). Furthermore, we performed a Co-IP experiment to verify the results in S. aureus. We first coexpressed USA300 with FloA-6×HIS and SaeS-3×FLAG. Then, FloA was precipitated using anti-HIS beads, and the precipitate was detected with an anti-FLAG antibody for SaeS. The results showed that FloA could bind with SaeS in S. aureus (Fig. 5D). To directly image the SaeS–FloA interaction in S. aureus, we split the mCherry protein, and the resulting N- and C-termini were labelled with FloA and SaeS, respectively, without impairing protein function (Fig. S9A, C, and D). Subcellular localization was observed with high-resolution confocal microscopy, and typical foci around the cell membrane were observed (Fig. S9B–C), indicating that SaeS and FloA interact at the FMMs.

Treating S. aureus with FMM inhibitors (zaragozic acid/ZA and simvastatin) resulted in the downregulation of sae activity (Fig. 5E, F). Moreover, the number of SaeS–FloA foci decreased when the cells were treated with FMM inhibitors (Fig. 5G), confirming the necessity of FMMs for saeS activity. We noticed that the FMM inhibitors were less effective in synovial fluid than in TSB (Fig. 5E, F) and that the FMM inhibitors could inhibit synovial aggregation only at high concentrations (Fig. 5H), where simvastatin started to display antibacterial effects (Fig. 5I). We next assessed the impact of fak on FMMs; the fakA mutant exhibited decreased or eliminated FMMs when grown in TSB and SF, respectively (Fig. S10). These results suggest that the ability of fak to modulate saeRS activity occurred through affecting FMMs structure (illustrated in Fig. 5J).

Metabolite screening reveals undecanoic acid as an inhibitor of the Fak system
Because fak activity is closely linked with eFA, it is hypothesized that eFA or its derivatives may affect the activity of the fak pathway. We tested 124 fatty acid metabolites and derivatives for their ability to inhibit aggregation (Supplementary Data 7), aiming to identify specific Fak–SaeRS inhibitors. Six metabolites significantly inhibited synovial aggregation (Fig. 6A–C), including itaconic acid (ITA) and its derivatives. Itaconic acid has been previously reported to alter carbon flow, reduce fatty acids synthesis, and inhibit bacterial growth at high concentrations (>10–30 mM)29,30, potentially accounting for its aggregation-inhibiting mechanism (Fig. S11). Among the other metabolites, linoleic acid (LA) is a known FASII inhibitor (FabI)31, and UDA has been reported to inhibit fatty acid synthesis in fungi32, although the exact mechanism remains unclear. Studies of 3b-hydroxy-5-cholenoic acid (HCA) have not been reported. S. aureus treated with LA and HCA inhibited growth, demonstrating antimicrobial effects (Fig. 6D). All the metabolites except HCA inhibited sae and fnbAB transcription to varying degrees (Fig. 6E).

To investigate the effects of these candidate compounds on S. aureus fatty acid metabolism, we used C-13-labelled oleic acid and acetic acid to trace exogenous and endogenous fatty acid pathways, respectively. Itaconic acid and its analogues inhibited endogenous FA synthesis but slightly increased the activity of the exogenous pathway. LA and UDA inhibited the endogenous and exogenous pathways, respectively. HCA inhibited endogenous fatty acid synthesis only at high concentrations (Fig. 6F, G).

UDA was the only identified metabolite that inhibited eFA incorporation in this study and exhibited a favourable IC50 (IC50 = 19.5 µM; Fig. 6H). Given the structural similarity of UDA to fatty acids, which are natural substrates for FakB1 and FakB2, we hypothesized that these proteins are the binding targets of UDA. Computational docking of FakB1/2 with UDA or fatty acids revealed favourable binding energy between FakB1/B2 and UDA; MD simulation revealed that UDA forms a stable binding complex with FakB1. Additionally, the RMSF results revealed low values for key residues (FakB1: ILE94, LEU160, and PHE263; FakB2: ILE118, THR259, ILE263, and GLY273), which were also predicted by molecular docking (Fig. S12B–D). Moreover, we adopted surface plasmon resonance (SPR) to investigate the direct binding of UDA with FakB1/2 (Fig. 6I). UDA bound FakB1/2 with an affinity similar to those of its natural ligands (myristic acid-FakB1, oleic acid-FakB2).

To evaluate its efficacy against clinical isolates, we assessed the effects of UDA on fak activity, sae and fnbAB transcription, and synovial aggregation among high-aggregating clinical strains. The results revealed significant inhibition across all the strains tested, even after continuous treatment for 14 days (Fig. S13A–C), underscoring its broad applicability to clinical strains.

UDA coadministration prevents anti-FASII antibiotic resistance and alleviates septic arthritis in mice
Because UDA does not exhibit antibacterial properties, we explored its potential as an antibiotic adjuvant for infection treatment. We tested combinations of UDA with gentamicin, vancomycin, and AFN1252 in culture media supplemented with SF or eFA. The results revealed that UDA enhanced the performance of AFN1252 under both eFA and SF conditions, whereas the efficiency of vancomycin and gentamycin was enhanced only in SF (Fig. 7A–C). AFN1252 inhibits FASII activity, making it an attractive target for antibiotic development33. The fak system is reported to be the cause of anti-FASII antibiotic treatment failure34. We explored whether UDA could hinder the occurrence of anti-FASII resistance. The results revealed that resistance emerged at a frequency of approximately 10−6 in the presence of synovial fluid or exogenous fatty acids, similar to the findings of a previous study35. However, when UDA was introduced, the frequency decreased by 4.68–32.5-fold (Fig. 7D–F). Continuous exposure to low-dose UDA and anti-FASII increased anti-FASII resistance, whereas UDA effectiveness was not impaired (Fig. 7G), suggesting that resistance to UDA is not readily developed.

We further investigated the efficacy of UDA for in vivo application. The initial assessment of biocompatibility revealed no significant impairment in macrophage or fibroblast viability (Fig. S14A, B). Intra-articular injection in a murine model did not result in liver or kidney damage, as confirmed by both histological and biochemical analyses (Fig. S14C–F). In a murine model of septic arthritis (Fig. 7H), the bacterial load was lower in the vancomycin group than in the AFN1252 group at 2 weeks post-treatment. When UDA was coadministered with AFN1252, the treatment efficiency was enhanced, and the treatment efficiency was comparable to that of vancomycin. Accordingly, the combination of UDA with AFN1252 resulted in favourable bacterial clearance, alleviation of local inflammation, removal of bacterial clumps, and recovery of joint function (Figs. 7I, J, L and S15A–C). We also tested the efficacy of UDA in treating the high-aggregating clinical isolates; the results revealed a significant reduction in bacterial load (Fig. 7K). Taken together, these data demonstrated that UDA could enhance the efficacy of AFN1252 in treating septic arthritis.

Methods
Study approval
All experiments with C57BL/6 mice were performed according to protocols approved by the Animal Care and Experiment Committee of Shanghai Sixth People’s Hospital and following standard protocols (approval no: DWLL2025-0007). The collection of human specimens/information in this study was approved by the Ethics Committee of Shanghai Sixth People’s Hospital (approval no. 2024-KY-256K). Written informed consent was obtained from all patients. The clinical data for this study were systematically gathered through a previously described patient cohort, including patients with musculoskeletal infection, from January 2015 to January 202045. The database contained records of 235 patients who were diagnosed with musculoskeletal infections from whom S. aureus was isolated. We excluded repeated isolates from the same patient and isolates from patients with culture-positive polymicrobial infections, focusing on 152 unique patient cases with complete clinical profiles and follow-up data. The study population was stratified into those with joint involvement (n=41) and those without (n=111).

Bacterial strains, plasmids, and growth conditions
The USA300-ΔfnbAB, ΔfnbA, ΔfnbB were graciously provided by Professor Binh An Diep from the University of California, San Francisco, USA. The USA300-ΔsaeRS and USA300-ΔfakA strains were constructed in this study. Additional strains utilized were either maintained in our laboratory or obtained from clinical sources.

The pRMC2 plasmid, a generous gift from Tim Foster (Addgene #68940), was employed as the backbone for gene overexpression in S. aureus. The genes were regulated by a tetracycline-inducible promoter. Sequences of the target genes for overexpression were chemically synthesized and inserted downstream of the tetracycline-inducible promoter using the seamless cloning method. Vectors for the B2H assay were acquired from Euromedex (Cat. EUK001), genes of interest were chemically synthesized and inserted into the MCS site using the seamless cloning method. The pRN11 plasmid (Addgene #84455) was employed as a backbone for the construction of the Split mCherry reporter system. The pKOR1 plasmid (Addgene #133446) was utilized for the construction of gene in-frame knockouts. The pCM29 plasmid46 was employed for green fluorescent protein (GFP) expression in S. aureus. Strains harboring the respective plasmids were maintained in TSB/Tryptic Soy Broth Agar (TSA) supplemented with 10 µg/mL of chloramphenicol. Before each experiment, strains were cultured on TSA plates overnight. Subsequently, a single colony was inoculated into TSB and incubated at 37°C. Detailed information for plasmids and bacterial strains involved in this study is provided in Supplementary Data 8.

Construction of fakA/saeRS mutants
In-frame deletions of the fakA/saeRS genes in the USA300 strain of S. aureus were achieved through allelic replacement using the plasmid pKOR1. The 1000 bp upstream and downstream sequences flanking fakA or saeRS were subsequently cloned and inserted into pKOR1 using the Gibson assembly method, resulting in the generation of recombinant plasmids (pKOR1-ΔfakA and pKOR1-ΔsaeRS), which were subsequently transformed into USA300 via electroporation. Transformants were selected and maintained on medium supplemented with chloramphenicol (10 µg/mL). Mutants were constructed using temperature-sensitive allele replacement45. The recombinant plasmids were transformed into S. aureus USA300 using bacteriophage 11. S. aureus USA300 transformants were then subjected to in-frame allele replacement by growing in temperature shifting between 30°C and 42°C. The candidate mutant strains were selected using the counterselection method (sensitive to chloramphenicol) and then confirmed by Sanger sequencing to validate the desired in-frame deletions.

Biofilm formation and quantification
A suspension of S. aureus (approximately 106 CFU/mL) was inoculated into a 96-well plate containing biofilm medium and incubated at 37°C for 24 h. For proteinaceous biofilms, bacteria were grown in TSB supplemented with 1% glucose (w/v, TSBG); for polysaccharide biofilms, bacteria were grown in TSB supplemented with 4% NaCl (w/v, TSBN). The plate was gently washed with PBS to remove nonadherent bacteria, after which the biofilms were left behind for subsequent analysis. Biofilm formation was quantified using crystal violet staining. First, the wells were washed three times with 200 µL of PBS. Then, the plate was fixed with methanol, air-dried, and stained with 200 µL of 1.5% crystal violet. The crystal violet bound to the wells was dissolved in 200 µL of 33% acetic acid, and 100 µL aliquots from each well were transferred to a new flat-bottom 96-well plate. The optical absorbance at 590 nm was measured using a microplate reader (BioTek Instruments, Inc., United States) to determine the biofilm biomass.

Aggregation assay
All of the human synovial fluid was derived from patients with osteoarthritis knees who received lubrication injections or prosthesis replacement surgery in our hospital. Patients with gout arthritis, joint infections, rheumatoid arthritis, and Charcot’s neuroarthropathy were excluded because the overrepresentation of immunoglobulin and uric acid crystals might affect aggregation and measurement. The samples were pooled together, filtered through a 0.45 µm filter sterilizer before the aggregation assay.

S. aureus was grown in TSB at 37°C, 150 rpm for 6 h to reach the exponential stage, then centrifuged at 3500 × g for 5 min and washed with an equal volume of PBS. Bacteria were stained with 1 µM SYTOX Green (Invitrogen, United States) for 10 min and washed again with PBS. A 200-µL bacterial suspension was mixed with 1.8 mL of 20% synovial fluid in a 12-well plate and incubated at 37°C, 150 rpm for 8 h or the indicated time. Aggregation ability was quantified using a modified method47: 100 µL of planktonic cells were sampled to quantify the fluorescence, aggregates were gently resuspended, and total fluorescence was measured. Relative aggregation was calculated as total fluorescence divided by planktonic fluorescence (As illustrated in Fig. S15).

Directed evolution for nonaggregation mutants
To develop an aggregation-deficient mutant strain of S. aureus, overnight cultures of bacterial suspension were washed and resuspended in an equal volume of sterile PBS. A 200-µL aliquot of this bacterial suspension was mixed with 1.8 mL of 20% human synovial fluid in a 12-well plate and incubated for 8 h at 37°C and 150 rpm. Following incubation, the unaggregated planktonic cells were carefully collected and transferred to fresh TSB (5 mL) overnight growth. The expanded culture was then reintroduced into 20% human synovial fluid to initiate another cycle of aggregation selection. This process was repeated until the evolved bacterial population no longer formed visible aggregates. The evolved population was spread onto sheep blood agar to isolate single colonies, which were cryopreserved and subjected to whole-genome sequencing to characterize the genetic changes associated with the evolved phenotype. Concurrently, a portion of the aggregated cells from each evolution passage was also expanded and cryopreserved to serve as a control group for comparative analysis.

Genomic DNA extraction, whole-genome sequencing, and comparative genomic analysis
Bacteria were collected, and the cell walls were physically disrupted with 0.2 mm silicon beads using a tissue lyser. S. aureus genomic DNA was extracted using a Mag-MK Bacterial Genomic DNA Extraction Kit (Sangon Biotech, Shanghai, China) according to the manufacturer’s instructions. DNA integrity was assessed by 1% agarose gel electrophoresis at 200 V for 30 min, and the DNA concentration was measured using a NanoDrop 2000.

The draft genome of the S. aureus strain was assembled using the SPAdes assembler (v3.15.5)48 with default settings. Genome annotation was performed using Prokka (v1.14.5)49, a prokaryotic annotation program. Comparative genomic analysis was conducted with breseq (v0.38.0)50, employing default settings to identify variations and compare the evolved strain with relevant reference genomes. SNPs and large fragment deletions unique to the nonaggregation groups were considered mutations responsible for the loss of aggregation.

Immune cell killing susceptibility assays
Isolation of human PMN cells. Peripheral blood was collected from healthy donors with informed consent at Shanghai Sixth People’s Hospital, affiliated with Shanghai Jiao Tong University School of Medicine. Fresh peripheral blood from healthy donors was collected using EDTA as an anticoagulant. A 5-mL blood sample was carefully layered over 5 mL of Polymorphprep (Serumwerk Bernburg, Germany) in 15-mL tubes and centrifuged at 550 × g for 30 min to obtain a polymorphonuclear leukocyte (PMN)-rich cell band. The PMNs were then transferred into RPMI 1640 medium supplemented with 10% fetal bovine serum. The remaining red blood cells were lysed using a cell lysis buffer (Sigma-Aldrich), followed by centrifugation at 500 × g for 10 min. The purified PMNs were resuspended in serum-free RPMI 1640 and incubated on a flat tissue cultured dish.

Cell culture. RAW264.7 cells (Catalog: SCSP-5036) were obtained from the Cell Bank of Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences. RAW264.7 cells were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM, Gibco), and human PNM were maintained in Roswell Park Memorial Institute Medium 1640 (RPMI1640, Gibco). Both media were free of fetal bovine serum to avoid bacterial aggregation, and the cells were incubated at 37°C in an atmosphere with 5% CO2.

Bacteria-immune cell co-culture. RAW264.7 cells and PMN cells were seeded in plates and cultured overnight before being co-cultured with different groups of S. aureus for 2 h (Multiplicity of Infection [MOI] = 10 for PMN and 100 for RAW264.7). Subsequently, cells were permeabilized using 0.1% Triton X-100 to release intracellular bacteria. Finally, the total number of viable bacteria was determined using the traditional CFU counting method.

Antibiotic susceptibility test
Each strain was routinely assessed for its minimum inhibitory concentration towards vancomycin and gentamycin to ensure inherent susceptibility to these antibiotics. Following the aggregation procedure described above, S. aureus was treated with antibiotics at a concentration of 5 µg/mL. After incubation with antibiotics for 4 h, the bacteria were resuspended to disrupt the aggregates and were subsequently washed twice with PBS to eliminate antibiotics. The number of surviving bacteria was determined using the CFU counting method.

Anti-FASII antibiotic-resistant generation
To prepare TSB-eFA and TSB-SF. TSB with 5% fatty acid-free bovine serum albumin (FA-free-BSA) was supplemented with 500 µM eFA (167 µM for each component: oleic acid, palmitic acid, myristic acid) or 20% (w/w) synovial fluids. The corresponding TSA plates were prepared by supplementing 1.5% agar. For developing resistance in broth, USA300 wild type strains were grown overnight at 37°C in TSB, TSB-eFA, and TSB-SF for adaptation. Then diluted with the same volume of fresh medium. Triclosan (0.5 µg/mL) or AFN-1252 (5 ng/mL) was added with/without a combination of UDA (100 µM). Incubating at 37°C/150 rpm for 6 h. Sample 100 µL of the bacteria suspension and calculate the whole bacteria count with the serial dilution method. Then, sample another 100 µL bacteria suspension, spread on plates (TSA, TSA-eFA, TSA-SF) containing the corresponding concentration of Triclosan (0.5 µg/mL), or AFN-1252 (5 ng/mL). Plates were incubated at 37°C, and colonies were counted after 48 h. The resistance frequencies were calculated by dividing the resistant colonies number to the total bacterial counts.

For developing resistance on the plate, USA300 wild type strains were grown overnight in TSB, TSB-eFA, and TSB-SF. A 100-µL part of bacterial suspension was plated on corresponding plates (TSA, TSA-eFA, TSA-SF) to obtain a lawn. 1-cm diameter diffusion disc with Triclosan (10 µg/5 µL), AFN-1252 (1 µg/5 µL) were placed on center of the plates. Plates were incubated at 37°C and photographed after 48 h.

Macrophage phagocytosis assay
The macrophage line RAW264.7 was inoculated into a 6-well plate (approximately 1×106 cells/well) and allowed to grow overnight to achieve confluence. S. aureus labelled with GFP or stained with SYTOX green (1 µM, 10 min), either in an aggregated or planktonic state, was subsequently added to the wells and cocultured with RAW264.7 cells for 2 h for bacterial phagocytosis. The MOI was controlled at 100:1. Following the incubation period, the cells were fixed using a 4% paraformaldehyde solution for 1 h. Each well was then stained with PE-Phalloidin for 30 min to visualize the cytoskeleton and with DAPI for 10 min to stain cellular nuclei. After staining, the samples were washed with fresh PBS to remove any unbound stain. Finally, the cells were observed under a fluorescence microscope (Leica, DMi8) to assess the extent of bacterial uptake by the macrophages. The rate of phagocytosis was quantified with ImageJ for each representative microscope field.

Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and Coomassie blue staining for aggregates and synovial fluids
First, the aggregates were collected by removing the supernatant and gently washing with sterile PBS to remove the synovial fluid components attached to their surface. Then, the aggregates were frozen with liquid nitrogen, and the resulting frozen powder was transferred to a 1.5 mL centrifuge tube.

The aggregate powder or synovial fluid was dissolved in PBS. Protein concentrations were determined using the BCA assay. Samples were prepared by diluting them to a final concentration of 5 µg/10 µL. Loading buffer was added to the protein samples, which were then heated at 95°C for 10 min and placed on ice. For electrophoretic separation, equal amounts of protein (5 µg per lane) were loaded onto a sodium dodecyl sulfate–polyacrylamide gel for electrophoresis (SDS–PAGE) and run at a constant voltage to separate proteins based on their molecular weight. After electrophoresis, the gel was stained with Coomassie blue using a staining kit (Sangon, Shanghai, China) at room temperature for 0.5–1 h, followed by decolorization according to the manufacturer’s instructions for 4 h.

LC-MS/MS and proteomics for aggregate protein
Proteins were extracted as described above. A total of 4 biological replicate samples for synovial aggregates were analyzed. For digestion, an equal amount of protein from each sample was reduced with 5 mM dithiothreitol at 55°C for 30 min and alkylated with 10 mM iodoacetamide at room temperature for 15 min. Proteins were then precipitated using 6× volumes of acetone at −20°C for 4 h. After centrifugation at 8000 × g for 10 min, the pellet was air-dried and reconstituted in 50 mM NH₄HCO₃. Digestion was carried out overnight at 37°C with 20 µg/mL trypsin (TPCK-treated). The reaction was quenched by acidification. The resulting peptides were desalted and purified using a SOLA™ SPE 96-well plate (Thermo Fisher Scientific).

Prior to LC-MS/MS injection, an iRT kit standard (Biognosys) was spiked into each peptide sample at a 1:20 (v/v) ratio to serve as an internal reference. Chromatographic separation was performed on a reversed-phase C18 analytical column (15 cm × 75 µm ID, 1.6 µm particles, Ion Opticks) at a constant flow rate of 400 nL/min. The mobile phases consisted of 0.1% (v/v) formic acid in water (buffer A) and 0.1% (v/v) formic acid in acetonitrile (buffer B). The peptide separation was achieved using the following linear gradient: 5% to 22% B over 20 min, 22% to 37% B over 4 min, followed by a rapid increase to 80% B in 3 min and holding at 80% B for 3 min to wash the column. Finally, the column was re-equilibrated to 2% B for 5 min. The total run time was 35 min. The eluted peptides were analyzed using a timsTOF Pro 2 mass spectrometer (Bruker Daltonics) operating in DIA mode with parallel accumulation-serial fragmentation. The source parameters were set as follows: capillary voltage, 1.4 kV; dry gas flow, 3.0 L/min; and dry temperature, 180°C. MS1 surveys were performed over a mass range of 100–1700 m/z. For DIA-PASEF acquisition, ion mobility (1/K0) windows were set from 0.7 to 1.3 Vscm⁻², with collision energies ramping from 20 to 59 eV across the mobility range.

The acquired DIA raw data were processed and analyzed using Spectronaut Pulsar version 18.4 (Biognosys). A direct DIA library-free search strategy was employed against the uniprot-Homo sapiens-9606-2024.2.1.fasta file. The key search parameters were configured as follows: trypsin/P was specified as the enzyme with up to two missed cleavages allowed. Carbamidomethylation of cysteine was set as a fixed modification, while oxidation of methionine and N-terminal acetylation were set as variable modifications. For protein identification and quantification, the false discovery rate was controlled at both the precursor and protein levels using a Q-value cutoff of 0.01. Protein quantification was derived from the MS2-level signal intensities of the fragment ions.

RNA extraction for qPCR and RNA-seq
S. aureus RNA extraction. S. aureus cells at different treatments were pelleted by centrifugation at 3500 × g for 10 min. The bacterial cell walls were then mechanically disrupted using a tissue lyser and 0.2 mm silicon beads (Matrix B, MP biomedicals). Total RNA was extracted according to the manufacturer’s protocol for the EZ-press RNA Purification Kit (EZ Bioscience, China). The integrity and quality of the RNA were assessed using an Agilent 2100 Bioanalyzer (Agilent, USA).

Quantitative PCR (qPCR). Following the elimination of genomic DNA (gDNA) by the addition of DNase, 1 µg of purified RNA was reverse-transcribed into cDNA utilizing an RT-PCR kit (EZ Bioscience, China). The cDNA was then diluted with nuclease-free water (ddH2O) at a ratio of 1:5 and employed as the template for quantitative polymerase chain reaction (qPCR), which was carried out using a 2× SYBR Green qPCR Master Mix (EZ Bioscience, China). The thermal cycling for DNA amplification comprised an initial denaturation step at 95°C for 5 min, followed by 40 cycles of denaturation at 95°C for 10 s and annealing/extension at 60°C for 30 s, all conducted on a Roche LightCycler 480 system (Roche, Switzerland). Detailed primer sequences in this study are delineated in Supplementary Table S1. The relative quantification of gene expression levels was accomplished using the 2−ΔΔCT method, with the expression of gyrB serving as an endogenous control for normalization.

RNA-seq and bioinformatic analysis for bacterial samples grown in vitro. For each in vitro cultured bacteria sample, after the removal of ribosomal RNA, 10 micrograms of RNA were segmented into 200 to 250-base pair fragments and reverse transcribed into complementary DNA (cDNA) with the application of random hexamers. The resulting cDNA libraries underwent sequencing on the HiSeq 2000 platform (Illumina, USA). Raw sequencing data were aligned to a reference genome using the HISAT2 aligner, and the read counts for each gene were determined with the FeatureCounts tool. The gene expression matrix generated was then analyzed and visualized in R, utilizing ggplot2 and DESeq2 for graphical representation and statistical analysis. The raw sequenced data were deposited in the NCIB-SRA database under Bioproject ID PRJNA1209395 and PRJNA1209687.

Bacterial two-hybrid (B2H) assay
A B2H assay was used to assess the interactions among the proteins FloA, SaeP, SaeQ, and SaeS. The coding sequences of floA, saeP, saeQ, and saeS were subsequently cloned and inserted into the B2H expression vectors pKT25 and pUT18C at the site between KpnI and EcoRI, yielding N-terminal fusions with the catalytic domains T25 and T18 of the Bordetella pertussis adenylate cyclase. These constructs enabled the generation of pairwise plasmid combinations, which were subsequently cotransformed into the E. coli BTH101 strain. This strain harbours a lacZ gene under the control of a cAMP-inducible promoter, facilitating the detection of protein–protein interactions via promoter activation. Interacting proteins bring the T25 and T18 catalytic domains into proximity, thereby reconstituting an active adenylate cyclase enzyme. This results in elevated cAMP levels, which in turn induce the expression of the lacZ reporter gene. The transformed E. coli cells were incubated under conditions conducive to interaction (48 h at 30°C). pKT25-zip and pUT18C-zip were cotransferred into BTH101 as positive controls.

To assess β-galactosidase activity quantitatively, the transformants were cultured in LB medium supplemented with ampicillin (100 µg/ml), kanamycin (50 µg/ml), and 0.5 mM IPTG at 30°C for 48 h. The optical density at 600 nm was measured prior to cell permeabilization. Then, o-nitrophenyl-β-d-galactopyranoside (ONPG, 4 mg/ml) was added, and the mixture was incubated for 10 min at 30°C before termination with 1 M Na2CO3. The absorbance readings were recorded at 420 nm and 550 nm. The results are expressed in Miller units using the formula 1000×((OD420−1.75×OD550)/(t×v×OD600)), where t=reaction time and v=reaction volume42.

Coimmunoprecipitation (Co-IP)
The bacteria were physically lysed with silica beads and resuspended in IP buffer containing protease inhibitor cocktails, and the total protein concentration was determined with a BCA assay kit. The lysate supernatant was incubated with anti-His magnetic beads (MCE, HY-K0209) overnight at 4°C, after which the beads were magnetically separated. Then, the immunocomplexes were washed 3 times with IP lysis buffer, and the samples were boiled in SDS–PAGE loading buffer and subjected to subsequent immunoblotting analysis. For the immunoblotting procedure, 20 µg of total protein was separated by electrophoresis on a 10% SDS-polyacrylamide gel (80 V, 1 h). Proteins were transferred from the gel to a PVDF membrane with a pore size of 0.45 µm (400 mA for 30 min). The PVDF membrane was then blocked with skim milk for 1 h and incubated with anti-FLAG (1:10000, cat: 66008-4-Ig, Proteintech, China) or anti-HIS (1:5000, cat: 10001-0-AP, Proteintech, China) antibodies to detect the presence of SaeS-3×FLAG and FloA-6×HIS. After the membrane was incubated with the anti-mouse IgG-HRP secondary antibody (1:5000, cat: SA00001-1, Proteintech, China), proteins were detected using a chemiluminescent substrate kit (Epizyme, China). Images were recorded with an illumination system 5200 (Tanon, China). The full scan images are available in the Source Data file.

Construction of plasmids for gene expression and split mCherry reporter
Gene fragments were chemically synthesized and inserted into the pRMC2 vector at the EcoRI site. The recombinant plasmids were verified by restriction enzyme digestion and Sanger sequencing of the cloning site. Detailed information concerning the overexpression plasmids is provided in Supplementary Data 8. The sequences were deposited in the Mendeley database with the accession numbers listed in Supplementary Data 8.

For the construction of split mCherry, a gram-positive, codon-modified sequence was acquired from pRN11 plasmids. The sequences were first designed in silico and chemically synthesized. For the in silico design of sequences, mCherry was split between 159D and 160G. The N-terminus was ligated with FloA, and mCherry was ligated with SaeS at its C- or N-terminus as a negative control. The generated sequences were inserted into pRN11 to replace the original mCherry sequence. Complete plasmid sequences were deposited in the Mendeley database with the accession numbers provided in Supplementary Data 8.

High-resolution CLSM and quantification
S. aureus strains carrying the pSplitmCherry plasmid were cultured overnight in TSB at 37°C. These strains were then diluted 1:1000 into either TSB or 20% synovial fluid and incubated at 37°C and 150 rpm for 4–6 h. After incubation, the bacterial cultures were washed twice with PBS to remove any residual medium. Then, the cells were stained with SYTOX green for visualization of the bacterial cytosol. Subsequently, 5 µL of the washed bacterial suspension was applied to a glass slide for imaging. Fluorescence microscopy images were captured using a Leica STELLARIS-5 confocal microscope. The fluorescence intensity was quantified with ImageJ 7.0.

Tracing fatty acid metabolism with isotope labeling methods
S. aureus in the exponential growth phase was diluted 1:1 with fresh TSB. C13-labeled oleic acid or acetate was added to the medium to achieve a final concentration of 25 µg/mL for 13C oleic acid and 250 µg/mL for 13C acetate. After a 2-h incubation along with specified treatments, S. aureus was centrifuged and washed three times with deionized water to remove C13-labeled compounds. The supernatant was discarded by centrifugation, sonicated in an ice bath for 30 min. A 2 mL aliquot of chilled extraction solution (1:2 chloroform/methanol, v/v) was added and vortexed for 5 min. An additional 0.5 mL of water and chloroform were added to induce phase separation, followed by vortexing for 1 min. The mixture was centrifuged at 2000 rpm for 10 min, after which the lower layer containing the lipids was collected for subsequent C13 quantification using Liquid Chromatography-Isotope Ratio Mass Spectrometry (LC-IRMS, MAT-253, Thermo). Chromatographic separation was achieved using a reversed-phase C18 column. The mobile phase was comprised of two carbon-free eluents: (A) ultrapure water and (B) HPLC-grade methanol. A linear gradient was applied at a flow rate of 0.8 mL/min as follows: 0–5 min, 80% B; 5–30 min, 80% to 100% B; 30–35 min, hold at 100% B; 35–36 min, 100% to 80% B; followed by a 14-min re-equilibration at 80% B. The effluent was directed into the LC Isolink Interface for detecting the C13/C12 ratio (Rsq). The δ13C value is calculated to express the relative difference in the isotope ratio 13C/12C between the sample and an international reference standard (Vienna Pee Dee Belemnite, VPDB) using the formula δ(‰) = (Rsq/RVPDB - 1) × 1000.

Septic arthritis mouse model
All animal experimental procedures were approved by the Animal Welfare Ethics Committee of Shanghai Jiao Tong University School of Medicine Affiliated Sixth People’s Hospital (approval no: DWLL2025-0007). The replicates for each experiment group are detailed in the corresponding figure legends. Mice were housed at a density of five per cage under standard lighting conditions with a 12-h light/dark cycle and maintained at an appropriate temperature. This study does not involve the effect of sex hormones on S. aureus infection, and animal gender does not affect the experimental results. Male mice were specifically chosen for this study based on their characteristically higher levels of spontaneous activity and locomotor activity, which helps assessment of joint function during gait analysis. Adult male C57BL/6 mice, aged 6–8 weeks, were injected intra-articularly in both knees with the corresponding type of S. aureus suspended in sterile saline at a concentration of approximately 106 CFU/10 µL. Before inoculum, S. aureus single colonies were inoculated into TSB and grown overnight with shaking. Cultures were then sub-cultured (1:10) into fresh TSB and grown for 6 h with shaking (150 rpm) to mid-log phase. These homogeneous cultures were serially diluted and plated for precise CFU enumeration. Aliquots based on these counts were prepared and frozen. On the day of infection, frozen aliquots were thawed and diluted in PBS to achieve the desired, normalized inoculum concentration for all groups, ensuring that any animal received an identical and accurately quantified number of bacteria.

For the models assessing the effects of UDA, AFN1252, and vancomycin treatments, AFN1252 (5 mg/kg) and vancomycin (100 mg/kg) were administered intraperitoneally on days 1, 3, and 10 after the appearance of infection signs (typically 2 days post-infection). UDA (200 µM) was administered with local injection at a concentration of 500 µM, 20 µL, with saline as the control. On Day 14, all mice were anesthetized and euthanized by cervical dislocation. The total knee joints were harvested and homogenized in 5 mL of sterile saline for subsequent bacterial burden quantification. The homogenized suspension was serially diluted by tenfold. A total of 100 µL of the diluted suspension was spread onto sheep blood agar plates. After incubation at 37°C overnight, CFU counts were performed according to the Chinese National Standard GB/T 4789.2 protocol. Bacterial loads were normalized to tissue weight and presented on a log10 scale.

Gait analysis experiment
A gait analysis experiment was conducted at 14 days post-treatment. The mice were placed on the runway of the gait analysis device (VisuGait-XR-FP101, Shanghai, China) and allowed to run from start to end. The average pressure on the runway and foot contact with the ground from every footprint were quantified, and the mean values were calculated for each knee using VisuGait software.

Histopathology and tissue immunofluorescence
Mice were euthanized at specified intervals post-infection, and their lower limbs, including the knee joints, were collected. The specimens were fixed in a 10% formalin solution for 24 h prior to decalcification. Subsequently, the samples were embedded in paraffin wax. The paraffin-embedded tissues were sectioned and prepared for histological examination using Masson staining.

Immunofluorescence (IF) staining was conducted to assess S. aureus aggregation in vivo. Tissue sections underwent deparaffinization and then rehydration through a series of ethanol washes (95%, 80%, 70%), and sections were subjected to antigen retrieval in sodium citrate buffer using microwave treatment and endogenous peroxidase blocking with 0.3% hydrogen peroxide. Nonspecific binding was blocked with 5% goat serum at 20°C for 10 min. Sections were then incubated with the anti-S. aureus primary antibody (ab20920, Abcam, USA) overnight at 4°C, followed by incubation with a horseradish peroxidase-labeled secondary antibody, with fluorescent dye conjugation according to the manufacturer’s instructions (abs50012, Absin, Shanghai, China). Once all procedures were completed, slides were washed in PBST, counterstained with DAPI, and mounted with anti-fluorescence quenching sealing medium. The stained tissue sections were examined under a Leica DMI8 microscope (Leica, Germany). All sections were imaged using either an optical microscope or a DMI6 microscope.

Multiple sequence alignment and phylogenetic analysis
FakA/B1/B2 and SaeP/Q/R/S protein sequences from 500 S. aureus genomes were obtained from the PanX database51. The R packages ggmsa and ggtree52 were used to visualize the results of multisequence alignment and phylogenetic tree construction.

Computational modulation of protein–protein interactions
AlphaFold3 was used to construct protein–protein interaction models53. The model with the highest ranking score was selected as the best binding model. The HawkDOCK SERVER54 was utilized to evaluate the binding free energy of the best binding model and to rank the free binding energies of amino acid residues ites to understand their contribution to the binding interaction. Finally, PyMOL 2.4 software was used to visualize the sites with the greatest contributions to the binding of the two proteins.

Computational modulation of protein–ligand docking
We utilized AutoDock Vina 1.1.2 software to perform molecular docking of myristic acid (PubChem CID: 11005), oleic acid (PubChem CID: 445639), palmitic acid (PubChem CID: 985), and UA (PubChem CID: 8180) with the proteins FakB1 (PDB ID: 6ALW) and FakB2 (PDB ID: 4X9X)55. For FakA docking with ADP, the structure of FakA was predicted with AlphaFold3, and AutoDock Vina 1.1.2 software was subsequently used to perform molecular docking. Protein preprocessing, including the removal of water molecules and extraneous ligands and the addition of hydrogen atoms, was accomplished using PyMOL 2.4. ChemDraw 20.0 was employed for the energy minimization of the compounds. AutoDock Tools 1.5.6 was used to generate PDBQT files for the docking simulations. The docking box was set to encompass the entire protein structure, with all other parameters retaining their default values. The docking results were configured to output nine binding poses. The pose with the lowest binding energy and the highest clustering frequency was considered the most likely binding mode between the ligand and the protein. Finally, the docking outcomes were visualized using PyMOL 2.4 and Discovery Studio 2019 software.

Molecular dynamic modulation for protein interaction
Proteins and DPPG lipid molecules were coarse-grained, and their force fields were constructed using the CHARMM-GUI. The coarse-graining of STX molecules was performed using the parameters available at (https://github.com/cgkmw-durham/cg_param_m3), with the full-atom to coarse-grained correspondence depicted in Supplementary Fig. S7. Molecular dynamics simulations were conducted using GROMACS 2021, employing the MARTINI 3.0 force field. The system was built using PACKMOL, where DPPG and STX were incorporated in an 8:2 ratio to form the membrane, followed by the placement of four FloA proteins and four SaeS proteins within the membrane as required. Water was added, and the system was neutralized by adding Na and Cl ions to balance the overall charge. The simulations were carried out at a temperature of 298.15 K and a pressure of 1 bar. Prior to the run, energy minimization, NVT equilibration, and NPT equilibration were performed to relax the system, followed by a 1000 ns simulation. The production simulation employed the leap-frog algorithm for integrating Newton’s equation of motion with a 20-fs timestep. The V-rescale method was used for temperature coupling, and the Parrinello-Rahman method for pressure coupling. The Verlet method was used for neighbor searching, with a 1.1 nm cutoff for Coulomb and van der Waals interactions, and long-range electrostatics were treated using the reaction-field method. Long-range dispersion corrections were applied to the energy and pressure calculations.

Molecular Dynamics modulation for UDA binding with FakB1/2
For molecular dynamic simulations of UDA binding with FakB1/2, the pose with the highest docking score for each ligand–protein complex was selected from the docking results, and the ligand–bound protein systems were constructed in a 150 mM NaCl aqueous solution. To investigate the stability of the docked ligand–protein poses, 100 ns simulations were performed. All simulations used the program GROMACS 2021. The amber99sb force field was used for the protein, the GAFF2 force field was used for the ligand, and the tip3p model was used for water. The simulation trajectories were analysed for structural fluctuations using root-mean-square deviation and root-mean-square fluctuation calculations.

Surface plasmon resonance (SPR)
The SPR technique was used to identify the interaction affinities between the recombinant FakB1/B2 protein and fatty acid compounds (UDA and myristic acid/oleic acid). The recombinant FakB1/B2 was directly coupled to the CM5 sensor chip (BR-1005-30, Cytiva) for determination. Following incubation, a gradient of fatty acids was introduced into the FakB1 (or FakB2) protein–CM5 system. Binding interactions were analysed using BIAcore T200 evaluation software (v.2.0; GE Healthcare, USA).

Dissecting SaeR-regulated genes from transcriptome datasets with a machine learning method
Genes regulated by SaeR were dissected according to the workflow developed in our previously published research23. Briefly, a total of 506 quality-controlled S. aureus RNA-seq datasets were obtained from publicly available databases. The gene expression data were comprehensively analysed using the independent component analysis algorithm; therefore, genes were divided into several independently regulated gene sets according to their expression mode. Within each independently regulated gene set, gene members were assigned a weight value indicating their association with the gene set. Genes with a weight value above the threshold were determined to have a strong relationship with the gene set and were therefore regarded as being regulated by the regulator of that gene set.

Statistical analyses
Statistical analysis was performed with R (v.4.3.3). Specific details of the individual tests and replicate values are provided in the figure legends. Continuous data were tested for normality using the Shapiro–Wilk test and for homogeneity of variance using Levene’s test. For two-group comparisons, normally distributed data were analysed with an unpaired t test, and non normally distributed data were analysed with the Mann–Whitney U test. For multigroup comparisons, normally distributed data were analysed by one-way ANOVA followed by Tukey’s post hoc test for pairwise comparisons and with Dunnett’s post hoc test versus control values. A significance threshold of P=<0.05 was applied in all the cases. Detailed statistical report for each graph was provided in the Source Data file.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I have carefully reviewed the manuscript by Yu et al., which investigates the link between biofilm formation and the ability to establish synovial infections through a directed evolution approach. This study builds on previous findings and I find little additional insight beyond what has already been published in the field. Furthermore, the manuscript presents technical and conceptual flaws, as well as writting issues, which hinder its clarity and overall impact.\n\nThe authors identify mutations in saeS and fakA that inhibit bacterial aggregation. However, the authors conceptually distinguishes bacterial aggregation from biofilm formation, despite these are closely related processes in Staphylococcus aureus. Unlike in other bacterial species, biofilm formation in S. aureus is primarily driven by adhesin production and cellular aggregation rather than extracellular matrix synthesis. This fundamental misconception affects the interpretation of the experimental results. Additionally, the selection conditions to identify non-aggregating mutants introduce a significant limitation: while these mutants fail to establish infections, the study does not establish a direct causal link between biofilm-defective evolved strains and infection-defective strains. This means, any mutation affecting growth, metabolism, cell wall organization, adhesin production, or membrane integrity would likely impair bacterial aggregation and infection, making it difficult to isolate specific effects. The authors need to demonstrate this causal effect to move forward.\n\nThe methodological approaches present critical shortcomings. For instance, the two-hybrid system lacks essential controls, including plasmid-free conditions, quantitative Miller unit measurements, and both positive and negative controls. The molecular dynamics experiments are limited in temporal scale and lack proper theoretical validation. Similarly, the fluorescence microscopy experiments do not include necessary controls to confirm that the observed signals are not artifacts caused by protein aggregation. Moreover, the manuscript does not address whether the fluorescent protein fusions retain functionality. These examples illustrate broader experimental issues, highlighting fundamental methodological flaws throughout the study.\n\nThere are also recurring issues with nomenclature throughout the text. In molecular biology, the symbol Δ denotes a gene knockout, not a protein deletion, which is an impossible task to make (e.g., ΔsaeS correctly refers to a strain lacking the saeS gene). Gene names should be written in lowercase italics, with an uppercase final letter when denoting a specific locus, whereas protein names should begin with an uppercase letter and not be italicized (e.g., the saeS gene encodes the SaeS protein). The manuscript inconsistently applies these conventions, often misusing Δ to refer to proteins, which is incorrect. It is incorrect because proteins cannot be knocked out, just genes can be knocked out. Additionally, bacterial species names should always be italicized (e.g. line 330).\n\nRegarding the biological findings, many of the conclusions presented in this study have already been extensively documented by other research groups. The role of the SaeSR system in adhesin production and biofilm formation, independent of ica, has been well established (for a review in the field, see PMID: 28581360). Similarly, the influence of fatty acids and Fak on SaeSR activity has been explored in detail by Brinsmade’s laboratory (e.g., PMID: 36135382, 36778227, 38366323, 39382300). The connection between membrane microdomains (FMMs) and SaeS activity has also been previously reported (PMID: 39382300, 34370508).\n\nGiven these considerations, I do not believe the manuscript offers sufficient novelty or experimental rigor to support publication in its current form. I appreciate the authors’ efforts but, regrettably, cannot recommend this work for publication.", "reviewer_2": "In the manuscript “Elucidating the Role of Staphylococcus aureus Fatty Acid Metabolism in SaeRS-Mediated Aggregation and Pathogenesis of Joint Infections,” Yu et al report a role for the SaeRS two-component system in aggregation of staphylococci within synovial fluid. The authors first demonstrate that highly aggregating clinical strains from MSKI patients lead to greater disease pathogenesis in a murine model of septic arthritis (higher CFUs, more gait disturbance, etc). The highly aggregating strains also were less likely to be phagocytosed and more likely to resist antimicrobial therapy in vitro. Mechanistically, the authors discover a role for SaeRS through direct in vitro evolution towards a non-aggregating phenotype. They also find that the fatty acid kinase (Fak) system is involved in activation of SaeS through localization within functional membrane microdomains. Consistent with this mechanism, mutants in Fak or SaeRS had less aggregation, heightened sensitivity to antibiotics, and decreased resistance to immune cell killing. The authors also discover that the medium chain fatty acid undecanoic acid (UDA) inhibits Fak without direct effects on bacterial viability, reduces aggregation in synovial fluid, and improves antibiotic efficacy against the bacteria in vivo.\n\nIn general, the rigor of this study is high. For example: 1) two separate directed evolution experiments with multiple strains led to identification of Fak and Sae mutants with similar phenotypes; 2) multiple clinical strains are used in many experiments; and 3) complementation constructs are also deployed to confirm genotype-phenotype correlation. The authors use a compelling combination of unbiased (e.g. RNAseq, meta-transcriptomics, proteomics, screening of eFA’s) and directed studies (e.g. complementation with 8 different adhesins; bacterial 2-hybrid) to identify the genes regulated by SaeRS that are responsible for the aggregation phenotype (fnbA/B). This knowledge then fueled additional in vivo studies that confirmed a role for both SaeRS and FnbA/B in septic arthritis pathogenesis.\n\nAlthough this manuscript is focused on a very specific clinical problem (septic arthritis), I believe that the research findings will be of broad interest to investigators studying staphylococcal pathogenesis and host-microbe interactions. The data are vast and highly supportive of the newly discovered mechanism, which will undoubtedly fuel exciting additional studies. Most of my critiques of the manuscript are minor, but there are a few areas that need more attention to further improve the rigor of the study.\n\nMajor critiques:\n1. There are a few points within the manuscript where quantification of bacterial growth (as colony forming units, not via optical density) would help to address whether the phenotypes are due to changes in aggregation or inhibition of bacterial growth. This should be tested for the FakA and SaeRS mutants in synovial fluid, representative high- vs. low-aggregating clinical isolates in synovial fluid, and WT S. aureus treated with simvastatin and zaragozic acid as in figure 5.\n2. One of the few pieces of data that is not supportive of the overarching model is the failure of UDA to impact CFUs in the joints of mice infected with USA300. It is also unclear why the authors did not test UDA + vancomycin therapy given that aggregation clearly made bacteria more tolerant to this drug, and UDA disrupts aggregation. The authors should comment on these experimental decisions and consider testing a high-aggregating clinical isolate with UDA therapy in vivo in the septic arthritis model.\n3. Line 591-593: More information is needed regarding the source of synovial fluid. Were these patients enrolled in a study? Were there any underlying diseases that could affect the composition of synovial fluid (e.g. rheumatoid arthritis, infection).\n\nMinor comments:\n1. Lines 46-48: I would remove the comparison of osteomyelitis to septic arthritis, as it is not necessary to convey the severity of septic arthritis and minimizes the high clinical impact that bone infections can have.\n2. Lines 52-53: “Despite its profound clinical implications, joint infection remains relatively understudied compared to other infectious diseases.” This is overstated based on thousands of papers studying the pathogenesis of joint infections such as PJI and septic arthritis. There are also manuscripts over the past 2-3 decades that have specifically investigated the pathogenesis of S. aureus septic arthritis. Please reword.\n3. Figure Panel 1F appears to be lacking labels to describe the graphed data. The legend states “Histograms and correlation analysis of proteinaceous biofilm, polysaccharide biofilm formation abilities, and aggregation ability for 152 clinical strains,” but these features are not denoted on the graph.\n4. Lines 130-133 in Figure 1L-M legend: Please more clearly delineate which shading (grey versus no shading) represents the presence of synovial fluid.\n5. Supplemental Figure 1C: Phagocytosis is misspelled.\n6. Figure 2C: Gentamicin is misspelled.\n7. Line 251: Suggest modifying this heading to “S. aureus Fak Activates SaeRS and FnbA/B by exploiting host fatty acids”\n8. Figure 5E: Y-axis is missing the label.\n9. Line 411: Italicize S. aureus.\n10. Line 441: change “folds” to “fold”\n11. Figure 7ABC: Legend is missing to delineate which bars represent UDA-treated cells\n12. Line 505: Italicize clfA/B, rsbU\n13. Line 515-517: “In our previous study, we demonstrated S. aureus adhesins (MSCRAMMs: FnbA/B, ClfA/B, SdrCDE) could evolve in vivo and contribute to persistent infection. This finding suggests that adhesins are not plausible treatment targets.” In my opinion this is overly concluded. I would remove the sentence regarding adhesins not being plausible treatment targets.\n14. Line 605: Italicize S. aureus\n15. Line 681: Change “resistant” to “resistance”\n16. Line 701: Please rephrase this sentence as it is written as an active protocol statement and not in past tense.\n17. Line 820: here it is stated that UDA was administered as a local injection, but earlier in the text it was characterized as topical. Was this an intra-articular injection? Please provide more detail in methods and be consistent throughout the text.\n18. Line 905-906: Please provide a reference for this data.\n19. Line 936: “40,0000.” Please confirm what number of reads was used as the cutoff, 40,000 or 400,000.\n20. Line 941: Please provide a reference for this patient cohort.\n21. Supplementary Figure 16: “S. aureus” and “FA incorporation” are misspelled.", "reviewer_3": "This study has an overarching theme of the interplay between eFAS, Sae, and synovial infections. There is a lot of really interesting data. The paper is actually very hard to read and understand. The figure legends are not very helpful, and a ton of the needed information is buried in hard-to-find text and supplementary information. Many of the studies lack proper controls that lead to over interpretation of the data. At times, it dismisses already published studies that support the conclusions, but they are not referenced or mentioned in context.\n\nThere is a lot of problems with the statistics. Some panels have no statistics applied, while others have the wrong method applied. There are Mann-Whitney tests, one-way ANOVAs and two-way ANOVAs with no rational explanation as to why a particular method was used. A One-way ANOVA is not appropriate for comparing 2 treatments.\n\nThe introduction is devoid of relevant information on Staphylococcus aureus, Sae, and Fak.\n\nFigure 1C does not indicate sample sizes and does not have error bars. Also, what is the dotted line?\n\nTable S2 is supposed to contain Biofilm data. There are no definitions in the table to explain any of it. I assume B_ indicates biofilm but it is not explained. Figure 1F is equally difficult to understand.\n\nOne of the mutations shown for FakA in Figure S2C is labeled T171K, but the mutation shown does not match that.\n\nMultiple papers connecting fatty acid accumulation, Sae, and FakA from different labs are not cited.\n\nMutations are not labeled conventionally; genetic mutants should be lowercase italics.\n\nThe FakA/Sae relationship studies in figure 2 agree with other published studies and this is not mentioned. It is known that FakA is upstream of SaeS.\n\nIt is really hard to believe that the only SNPs generated by evolution experiment would be in Sae or Fak. What other mutations were there? This information is severely lacking but could be highly relevant.\n\nThe complementation data is not clear. What promoters are used? If fnbA is regulated by SaeRS, then how could adding it back alone complement? Once you dig, you can find it. But this makes little sense when first reading. These details are too buried.\n\nLines 262-264. This does not make sense. If SaeRS is activated by FakA and the SaeP promoter is SaeR dependent, then why is it not lower in a fakA mutant? The same is true for fnb which is also activated by SaeRS. Shouldn’t all these transcripts be down in a fakA mutant? Also, the lines say that there is no difference in a fakA mutant with SF, but then goes on to say that Sae upregulation is FakA dependent. Also, the +SF levels are so low, is it even real expression?\n\nLines 268-272. This is counter to other published studies. Also, it says that 500 mM fatty acid was used. This is higher than the solubility of the fatty acid and seems impossible. Also, that level of 18:1 completely kills S. aureus.\n\nFigure 4F. Do these concentrations of FasII inhibitors impact growth. This was not tested and decreased expression could just be the result growth inhibition and not Sae activity. Growth curves with these inhibitors should be added.\n\nFigure 4H. How is it certain that this is lipid incorporation and not free fatty acid association? A fakA mutant should be used as a control in this experiment. Likewise, 4G should be done in the presence of a FasII inhibitor to show that it is specific to that pathway since acetate can enter metabolism through several metabolic pathways.\n\nThe split mCherry results in Fig S8 are not convincing. In some cases, there appears to be foci, but in additional cells, it looks like it is over most of the membrane surface.\n\nFigure 6 needs to have colors matched for D-G for readability.\n\nThe data with UDA cannot be said to inhibit Fak. That data does not exist in its current. eFA incorporation requires more than just FakA/B. It also involves Pls proteins that may be the target. It is safe to say that it inhibits eFA incorporation but cannot be said that is it is a Fak inhibitor. That is an overstatement. In addition, some evidence exists that eFAs can enter FASII. Is it possible that this is part of what is happening? The data in Fig 7 does partially counter this last point. However, a fakA mutant would have strengthened these studies.\n\nHeat maps lack actual data to validate them.", "reviewer_4": "In this work, Yu et al investigated the mechanism of bacterial infection-induced aggregation in the synovial fluid. Specifically, the authors used an array of experimental and computational tools to first establish the role of S. aureus SaeRS in aggregation and its regulation by Fak. This was done through directed evolution by several rounds of selection of S. aureus variants that were incapable of forming aggregates. The whole genome sequencing then allowed the authors to identify the mutations in two genes, SaeRS and Fak. Then, the extended study attempted to establish Fak as a therapeutic target and identified undecanoic acid (UDA) as an inhibitor for Fak system. This study is important given the lack of detailed understanding of the mechanisms of synovial joints lubrication and the infections targeting the synovial membrane/fluid and the tissues near the joints. The work seems to be comprehensive, and the findings are significant. A few points need to be addressed and clarified.\n\n1) Composition of aggregates: It’s nice to see that the authors identified the primary proteins involved in forming the aggregates. Why is the 40kDa band not visible in the gel, but is prominent in the quantification? The Dispersin-B and DNase-I were used to rule out sugars/glycans and DNA. What about the role of (even in trace amounts) exRNA and lipids, especially during nucleation? Given the variety of proteins and other components involved, Fig. 3K will likely be more complicated. Perhaps include immunoglobin and adhesion proteins?\n\n2) Fig. 4K lists the binding free energies and the caption says the calculation was done using AlphaFold3. While AF3 was used to model the complexes, the free energy calculation was done with the hawkDock, so this needs to be corrected. The MM/GBSA calculations are not very accurate but the relative values are still helpful, so this needs to be discussed/acknowledged.\n\n3) The assays showing direct interactions between FloA-SaeSR are novel. However, the simulations do not seem to provide much insight. A few microseconds are typical for more accurate all-atom simulations and ns-timescales are inadequate for CG simulations to observe any meaningful scaffolding or interactions. Given that tens or hundreds of microseconds can easily be done, it’s unclear why the authors chose CG and not take advantage of the access to longer timescales. Still, free diffusion of the proteins in such simulations is prohibitively slow and unlikely to yield meaningful complexes. A better strategy would be to also include pre-formed complexes (in addition to free proteins) obtained with AF3 or docking and set up the simulations.\n\n4) What about other proteins associated with FMMs (e.g. IsdF, PBP2a)? Do they have any role, or form co-complex with SaeSR/FloA?\n\n5) Given the importance of UDA as potential therapeutic target of FakB1, an all-atom MD would help more accurately position the substrate and identify the interactions. This is because docking is unlikely to produce an accurate pose especially when docked to a modeled or substrate-free apo structure.\n\n6) Fix typos e.g. (“By review published FMMs …”   “Reviewing the published FMMs…”)\n\n7) The simulations are generally expected to be deposited to data sharing sites such as Zenodo."}, "author_response": "We thank the reviewers for their constructive comments. Below are our point-by-point responses to each comment. All corresponding revisions in the manuscript have been highlighted in yellow for the reviewers' convenience.\n\nReviewer1\nComments from Reviewer 1: I have carefully reviewed the manuscript by Yu et al., which investigates the link between biofilm formation and the ability to establish synovial infections through a directed evolution approach. This study builds on previous findings and I find little additional insight beyond what has already been published in the field. Furthermore, the manuscript presents technical and conceptual flaws, as well as writting issues, which hinder its clarity and overall impact.\n\nAuthor response: Thank you very much for your detailed review and for the time and effort you have invested in evaluating our manuscript. We appreciate your constructive critiques, which have enabled us to correct several experimental and technical flaws and to perform additional experiments that strengthen our work.\nWe respectfully disagree with the assessment that our study lacks new insight. Our study builds upon previous literature but goes beyond mere replication of prior research. We believe this impression may stem from a conceptual misunderstanding regarding the distinction between synovial aggregation and biofilms. Aggregates typically arise in highly dynamic fluids—e.g., synovial fluid and blood flow, which makes it critical in the pathogenesis of septic arthritis. Biofilms, in contrast, form on relatively static, abiotic surfaces such as medical implants. The detailed distinct behaviors were provided in the point-by-point response below.\nRegarding the methodological concerns you raised, we have conducted additional experiments to enhance rigor and provide robust support for our conclusions. We have also revised the manuscript to correct the nomenclature issues you identified. Below, we address each of your specific comments point by point.\n\nComments from Reviewer 1: The authors identify mutations in saeS and fakA that inhibit bacterial aggregation. However, the authors conceptually distinguishes bacterial aggregation from biofilm formation, despite these are closely related processes in Staphylococcus aureus. Unlike in other bacterial species, biofilm formation in S. aureus is primarily driven by adhesin production and cellular aggregation rather than extracellular matrix synthesis. This fundamental misconception affects the interpretation of the experimental results.\n\nAuthor response: We thank the reviewer for the comments. We consider synovial aggregation and biofilm formation to be closely related but distinct bacterial behaviors with different phenotypic manifestations. Also, we disagree that synovial aggregation was part of the biofilm formation. First, biofilm is a surface-attached bacterial community, while aggregates are the non-attached floating bacterial population. This means biofilm was always seen in medical implant surfaces, while the aggregates are always present in flowing body fluids, especially in joint spaces and bloodstream. Secondly, the size of a mature aggregate (1-10 mm in diameter, Revised Figure 1A-B) is obviously tens to hundreds larger than the biofilm size (~100 μm thick). Thirdly, synovial aggregation happened rapidly. It forms small aggregates within 30 minutes and matures within 4-6 hours, depending on the concentration of synovial fluid (Revised Figure 1C). In contrast, a typical biofilm requires overnight culture for maturation (Moormeier et al., 2014). Lastly, as demonstrated by a previous study (Perez & Patel, 2015), S. aureus with weak biofilm-forming capacity can still exhibit robust aggregation ability, indicating that the underlying formation mechanisms are different, at least not entirely identical. In our study, we tested the correlation between biofilm-forming capacity and aggregation propensity across 152 clinical isolates from orthopedic infections and found no significant correlation between the two phenotypes (Revised Figure 1F). This again confirms that these two phenotypes are independent of each other. Over the past decade, numerous groups have investigated aggregates as a separate phenotype, and the distinction is now widely accepted by experts in the field (Bidossi et al., 2020; Dastgheyb et al., 2015; Knott et al., 2021; Staats et al., 2022). These studies lead us to believe that past conclusions based on biofilms cannot be directly applied to synovial aggregation. We hope the provided evidence from the published literature and our experimental data could change the impression of synovial aggregation. We have also added this information to the introduction part to provide relevant background (Line 58-61).\n\nRevised Figure 1(A-C, F). (A) Representative fluorescence images of GFP-S. aureus forming aggregates from 0-3 hours since inoculated into 20% human synovial fluid. (B) Representative images of S. aureus aggregation in 20% synovial fluid after 0, 4, 5, and 6 hours of incubation. (C) S. aureus aggregates diameter growth curve measured over a continuous 12-hour observation period (n = 5/time point). The dashed horizontal line at y = 10 µm indicates the size threshold beyond which phagocytes lose the ability to internalize particles. (F) Histograms and correlation analysis of proteinaceous biofilm, polysaccharide biofilm formation abilities, and aggregation ability for 152 clinical strains. Correlation was analyzed with spearman method. i,iii,vi: histogram distributions for polysaccharide biofilm(i), proteinaceous biofilm(iii), and aggregation ability(vi). ii, iv, v: correlation plot between polysaccharide and proteinaceous biofilm(ii), polysaccharide biofilm and aggregation ability(iv), proteinaceous biofilm and aggregation ability(v).\n\nComments from Reviewer 1: Additionally, the selection conditions to identify non-aggregating mutants introduce a significant limitation: while these non-aggregating mutants fail to establish infections, the study does not establish a direct causal link between biofilm-defective evolved strains and infection-defective strains. This means, any mutation affecting growth, metabolism, cell wall organization, adhesin production, or membrane integrity would likely impair bacterial aggregation and infection, making it difficult to isolate specific effects. The authors need to demonstrate this causal effect to move forward.\n\nWe sincerely appreciate the reviewer's insightful critique regarding the potential pleiotropic effects of sae/fak mutations and the need to establish a direct causal link between aggregation defects and the observed infection deficiencies. The reviewer highlights the inherent challenge in definitively isolating the specific contribution of aggregation when sae/fak regulates a broad regulon encompassing virulence factors, metabolism, and cell wall organization. To address the specific contribution of the aggregation phenotype to infection success, we presented multiple lines of evidence throughout the original manuscript to prove this causal link.\n1. Clinical Correlation: We observed a significant enhancement in aggregation capacity among S. aureus clinical isolates specifically recovered from joint infections compared to isolates from non-joint sources. This association suggests a potential role for robust aggregation in the context of joint infection pathogenesis (Figure 1E).\nFigure 1E. Violin plot illustrating the difference in aggregation ability between strains of joint infection (n = 41) and non-joint infection group (n = 111).\n2. Direct Phenotypic Impact in vitro: Critically, we directly compared antibiotic tolerance and immune evasion capabilities between aggregated and non-aggregated states of the same bacterial strains. Aggregated bacteria exhibited substantially higher resistance to clinically relevant antibiotics compared to their planktonic counterparts. Moreover, bacterial aggregates were significantly more resistant to phagocytosis and killing by immune cells. (Figure 1LM, Figure S1F-H)\nRevised Figure 1L-M. CFU count results of human PMN/ RAW264.7 co-cultured with S. aureus of varying aggregation abilities in culture medium with/without 20% synovial fluid (indicated with gray shadow) (n=5/group). Statistical analysis is by one-way ANOVA with Tukey’s post hoc test for pairwise comparison. *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\nRevised Figure S1 (F-H). Quantification for survived clinical isolates of varying aggregation abilities incubated with/without 20% synovial fluids containing gentamicin (5 μg/mL) or vancomycin (5 μg/mL). n = 5/group. Statistical analysis by one-way ANOVA followed with Tukey’s post-hoc test. *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\nThese controlled experiments demonstrated that the aggregated state itself, irrespective of the other effects controlled by the sae regulon, conferred significant advantages against major host defenses encountered during infection. We have explicitly stated it in the revised manuscript (line 121-125)\n3. Exploring the specific effect of fnbAB in vivo: To specifically isolate the causal effect of aggregation on infection. We demonstrated that fbnA/B were critical for synovial aggregation (Revised Figure 3G-K). Then, the fnbAB knock-out strain was used to establish a septic arthritis mouse model. Result showed that fnbAB significantly reduced the infection burden though not to the ΔsaeRS levels in early stage (indicating that sae regulates additional factors contributing to virulence, Figure S5C-D). This partial but significant infection defect, specifically by restoring the aggregation machinery, provides compelling evidence that the loss of aggregation contributes substantially to the infection defect observed in the ΔsaeRS mutant.\nFigure 3G-K. (G). Adhesins were controlled by a tetracycline-inducible promoter. S. aureus was stimulated with/without anhydrotetracycline (1 µg/mL) to induce the adhesin overexpression. n = 5/group. (H-I) Aggregation assay for USA300 of different genotypes in 20% synovial fluid (H), and the quantitative results (I). n = 6/group. (J) Aggregation assay for USA300 of different genotypes in the indicated medium. (K) Illustration for the mechanism of S. aureus adhesins and host extracellular proteins contributing to synovial aggregation. Statistical analysis is by one-way ANOVA with Dunnett’s post-tests versus values in controls. Error bars show the mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\nSupplementary Figure S5C-D. (C)Bacterial burden for mice knee infected with WT, △fnbAB, △saeRS. Seven mice(14 knees) per group. (D)Representative immunofluorescence images for knee joint tissue sections stained with polyclonal antibodies against S. aureus (Green). Statistical analysis is by one-way ANOVA Dunnett’s post-tests versus values in controls. *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\nWhile we fully acknowledge that saeRS regulates multiple pathways beyond aggregation that likely contribute to joint infection success, the convergent evidence from clinical isolates, controlled in vitro functional assays on the aggregation phenotype itself, and the partial in vivo infection defect by fnbAB mutation strongly supports our conclusion that enhanced aggregation is a key mechanism by which sae promotes S. aureus joint infection, primarily by conferring resistance to antibiotics and immune clearance.\nOur study focused specifically on dissecting the contribution of the aggregation phenotype, and the data presented collectively support its significant role. Exploring the relative contributions of other saeRS-regulated pathways is indeed an important avenue for future research beyond the scope of this current work. We hope this clarification supports the causal link between aggregation and infection.\n\nComments from Reviewer 1: The methodological approaches present critical shortcomings. For instance, the two-hybrid system lacks essential controls, including plasmid-free conditions, quantitative Miller unit measurements, and both positive and negative controls. The molecular dynamics experiments are limited in temporal scale and lack proper theoretical validation. Similarly, the fluorescence microscopy experiments do not include necessary controls to confirm that the observed signals are not artifacts caused by protein aggregation. Moreover, the manuscript does not address whether the fluorescent protein fusions retain functionality. These examples illustrate broader experimental issues, highlighting fundamental methodological flaws throughout the study.\n\nAuthor response: We thank the reviewer for constructive suggestions.\n1. For the bacterial two-hybrid (B2H) experiments, the reviewer noted the lack of corresponding positive and negative controls as well as plasmid-free conditions. Since the FloA-FloA interaction has been previously confirmed (García-Fernández et al., 2017; Ukleja et al., 2024), we used it and the ZIP interaction as the positive control. Additionally, we included a plasmid-free condition as a blank control (Revised Figure 5A-B). A vector control, either in combination with plasmids or alone, was included. As per your suggestion, we also incorporated quantitative measurements to quantify the strength of protein interaction (Revised Figure 5C). The results demonstrated a strong interaction between FloA and SaeS. We have improved the presentation of the experimental results by labeling the corresponding control groups and providing detailed annotations in the figure legend for clarity.\nRevised figure 5(A-C) (A) Bacterial two-hybrid (BTH) assay results. Membrane proteins (SaeS, P, Q, and FloA) were expressed as prey (Vector: T25) and bait (Vector: T18C) proteins in E. coli (BTH101). Protein interaction activates lacZ expression, leading to the degradation of X-Gal (blue stain). (B) E. coli (BTH101) transfected with a single plasmid was grown on LB agar to serve as a control. (C) Quantification of interaction efficiency in a β-galactosidase assay. Red box: the ZIP interaction was set as a positive control. Green box: BTH101 carrying no plasmid was set as the blank control. Blue box: BTH101 carrying plasmid vectors was set as the negative control. Z(ZIP), A(FloA), P(SaeP), Q(SaeQ), S(SaeS),V(Vector). (n = 3/group)\n2. To address the concern about simulation timescales, we have extended our CG simulations from the original 1000 ns to 5000 ns (5 µs). Extended simulations confirm the stability of the protein complex (SaeS RMSD reached a plateau at ~5.0 nm after 1 μs; FloA RMSD reached a plateau at 8nm after 4 μs (Revised Supplementary Figure S8B-D). Furthermore, we fully acknowledge that computational predictions necessitate validation through biological experiments. To this end, we have incorporated new co-immunoprecipitation (co-IP) data demonstrating a direct physical interaction between FloA-3xFLAG and SaeS-6xHIS (Revised Figure 5D). Specifically, we co-expressed SaeS-3xFLAG and FloA-6xHIS in USA300 through a plasmid. Anti-HIS magnetic beads were used to immunoprecipitate FloA-6xHIS, and the resulting pull-down product was immunoblotted with an anti-FLAG antibody to detect associated SaeS-3xFLAG. Our results clearly show that FloA binds to SaeS, providing robust biochemical validation that supports the interaction observed in our extensive coarse-grained (CG) molecular dynamics simulations. This was included in the revised manuscript (Line 376-380)\nRevised Supplementary Figure S8 B-D (B) Computational molecular dynamic modulation of FloA and SaeS interaction on the cell membrane (blue: FloA, purple: SaeS). (C) The curves of the total solvent-accessible surface area (SASA) of FLOA and SAES proteins over time. The vertical axis represents the solvent-accessible surface area at the corresponding moment. (D) The RMSD (Root Mean Square Deviation) curve of the SaeS/FloA protein over time.\nRevised Figure 5D: Co-IP and immunoblot analysis of USA300 overexpressing FloA-6xHIS and SaeS-3xFLAG. SDS-PAGE result shows that the protein contents were comparable among the three groups.\n3. Regarding the specificity of our fluorescence microscopy data. We fully agree that rigorous controls are vital to distinguish true biological signals from potential artifacts like non-specific signal or protein aggregation. To directly test whether the observed split-mCherry fluorescence signals could arise from non-specific protein aggregation rather than true protein-protein interaction within FMMs, we employed a functional disruption control using a well-characterized FMM inhibitor (Zaragozic acid)(García-Fernández et al., 2017). We treated bacterial cells expressing the split-mCherry fusion proteins with zaragozic acid. Upon inhibitor treatment, we observed a specific dissolution of the fluorescent foci (Revised Supplementary Figure S9B). This result is consistent with the established mechanism of this inhibitor and demonstrates that the split-mCherry fluorescence signal is dependent on intact, functional FMMs. If the signals were merely due to non-specific protein aggregation (unrelated to FMM localization), they would be expected to persist despite FMM disruption. The inhibitor-induced loss of signal, therefore, provides strong evidence that the observed fluorescence represents specific protein interaction/localization within FMMs, not aggregation artifacts.\nRevised Supplementary Figure S9B. High resolution confocal images for USA300 expression NtmCherry-FloA and SaeS-CtmCherry treated with/without zaragozic acid. NtmCherry-FloA: N-terminal split mCherry was fused to the N-terminal of the FloA protein. SaeS-CtmCherry: C-terminal split mCherry was fused to the C-terminal of SaeS proteins. FloA-NtmCherry: N-terminal split mCherry was fused to the C-terminal of the FloA protein, as a negative control. Negative control: USA300 expression of FloA-NtmCherry and SaeS-CtmCherry.\n4. Regarding the functional validation of our fluorescent protein fusions (FloA and SaeS). We agree that confirming the functionality of tagged proteins is essential, and we have now performed additional experiments to address this concern comprehensively. Our FloA-splitmCherry fusion construct (tagged at the N-terminus) was designed based on established literature demonstrating that FloA retains its ability to localize to functional membrane microdomains (FMMs) regardless of tag placement (N- or C-terminus) (Koch et al., 2017). Also, the full-length GFP tagged to FloA was proven to retain its function according to the data from their published literature (García-Fernández et al., 2017). To rigorously confirm that FloA-splitmCherry retains its function, we constructed a FloA-FloA splitmCherry reporter plasmid. Then treat the S. aureus expressing FloA-FloA splitmCherry reporter plasmid with zaragozic acid (FMM inhibitor). Consistent with previous reports on native FloA function (García-Fernández et al., 2017), we observed a complete disappearance of the fluorescent foci upon zaragozic acid treatment (Revised Supplementary Figure S9C). This result demonstrates that FloA-splitmCherry retains its essential scaffolding function and responds appropriately to FMM perturbation.\nRevised Supplementary Figure S9C. High resolution confocal images for USA300 expression NtmCherry-FloA and FloA-CtmCherry treated with/without zaragozic acid. NtmCherry-FloA: split mCherry was fused to the N-terminal of the FloA protein. FloA-CtmCherry: C-terminal split mCherry was fused to the C-terminal of FloA proteins.\nSaeS is a histidine kinase central to the SaeRS two-component system, regulating the expression of key virulence factors (e.g., hemolysins). A typical phenotype of the SaeRS mutant was the absence of hemolysis ring grown on sheep blood agar. To test if SaeS-splitmCherry retains its signaling function, we complemented the △saeRS strain with a plasmid expressing SaeS-splitRFP and wild-type SaeR. The result showed that SaeRS::pSaeR/SaeS-splitRFP restored the hemolysis ring. Indicating that the splitRFP tag did not impair the protein function (Revised Supplementary Figure S9D).\nRevised Supplementary Figure S9D. Representative images for USA300-ΔsaeRS expressing saeR, and saeS-splitmCherry (or saeS) grown on blood agar plates. Hemolysis rings were absent in ΔsaeRS and ΔsaeRS transfected with only saeR.\n\nComments from Reviewer 1: There are also recurring issues with nomenclature throughout the text. In molecular biology, the symbol Δ denotes a gene knockout, not a protein deletion, which is an impossible task to make (e.g., ΔsaeS correctly refers to a strain lacking the saeS gene). Gene names should be written in lowercase italics, with an uppercase final letter when denoting a specific locus, whereas protein names should begin with an uppercase letter and not be italicized (e.g., the saeS gene encodes the SaeS protein). The manuscript inconsistently applies these conventions, often misusing Δ to refer to proteins, which is incorrect. It is incorrect because proteins cannot be knocked out; just genes can be knocked out. Additionally, bacterial species names should always be italicized (e.g., line 330).\n\nAuthor response: We sincerely appreciate your meticulous attention to nomenclature standards and apologize for our oversight in adhering to these critical formatting conventions. In response to your suggestion, we have gone through the text carefully and fixed all those issues:\n1.Δ symbol is now strictly used before gene names to indicate gene knockouts.\n2. Genes are presented in lowercase italics, and proteins in plain text begin with an uppercase letter.\n3. Species names are now properly italicized everywhere.\n\nComments from Reviewer 1: Regarding the biological findings, many of the conclusions presented in this study have already been extensively documented by other research groups. The role of the SaeSR system in adhesin production and biofilm formation, independent of ica, has been well established (for a review in the field, see PMID: 28581360). Similarly, the influence of fatty acids and Fak on SaeSR activity has been explored in detail by Brinsmade’s laboratory (e.g., PMID: 36135382, 36778227, 38366323, 39382300). The connection between membrane microdomains (FMMs) and SaeS activity has also been previously reported (PMID: 39382300, 34370508).\n\nAuthor response: Thank you for reviewing our manuscript. While it is true that the regulation of sae by fak and fatty acids has been previously reported (as cited in our paper), our study expanded previous findings in a disease-specific environment since all prior studies were conducted in vitro. Also, the connections between SaeS and FMMs were not experimentally verified yet in the previous report mentioned by the reviewer. Below, we would like to highlight the key innovations of our study in detail:\n1. Dissecting genetic regulators for synovial aggregation using a non-biased method.\nInstead of a hypothesis-driven experiment design, we adopted a directed evolution method to dissect regulators of synovial aggregation in S. aureus. This method allows S. aureus to evolve into a non-aggregating mutant, enabling the identification of causative genetic mutations through genome sequencing. This method applies to other pathogens known to aggregate in synovial fluid (e.g., S. epidermidis).\nAs for the biological findings of our study, we disagree that conclusions from biofilm can be directly applied to synovial aggregates, for they exhibit different bacterial behavior as discussed thoroughly above. Moreover, the literature provided by the reviewer just reflects the biological findings of our work. As noted in the review (PMID: 28581360), a complex of adhesins (MSCRAMMs, FnbA/B, SasG et.al. ) and extracellular proteins(PSM, α-hemolysin) contributed to the formation of ica-independent biofilm. These adhesins and proteins are precisely regulated by a complex network of regulators (agrAC, saeRS, sigB, mgrA, rot protein, et.al. ). In this work, we demonstrated that saeRS plays the dominant regulatory role in synovial fluid, emphasizing its unique importance in this specific clinical context—a finding never reported before. Besides, the saeRS mutation totally “abolished” rather than “reduced” aggregation. That means saeRS (or its epistatic regulator) as a promising therapy target for septic arthritis—another unreported insight.\n2. Mechanistic elucidation of Fak/fatty acid regulation of SaeRS via FMMs remodeling\nAlthough the connection between fak and sae activity has been established in previous studies, the precise mechanism by which fak/fatty acids regulate sae remains unclear. In this study, we hypothesize that fak/fatty acids modulate sae activity through FMMs (functional membrane microdomain) remodeling.\nThe reviewer noted that the relationship between FMMs and SaeS has been previously suggested (Alqahtani et al., 2024; Sanson et al., 2021). However, upon careful reading of the references, we found that these prior studies only proposed speculative hypotheses based on previously published FMMs proteomics datasets (García-Fernández et al., 2017), with no subsequent mechanistic investigations and experimental verification. Here, we experimentally demonstrated that fak regulates sae by altering its FMMs architecture. Since the referenced studies merely presented conjectures without experimental evidence for sae localization in FMMs or fak-mediated sae regulation via raft structural changes, our work significantly advances prior theoretical hypotheses by providing robust experimental validation.\n3. Identifying undecanoic acid (UDA) as a Fak inhibitor and promising adjuvant for anti-FASII antibiotics.\nOur study identified UDA as an inhibitor of Fak system. Fak is a promising therapeutic target because it is also responsible for resistance to anti-FASII antibiotics(Balemans et al., 2010). Our data demonstrate that UDA can prevent the development of anti-FASII antibiotic resistance and enhance the in vivo efficacy of these drugs. Furthermore, UDA is pharmacologically inert and serves as a side chain modification for FDA FDA-approved drug (testosterone undecanoate), which underscores its biosafety and biocompatibility.\n\nComments from Reviewer 1: Given these considerations, I do not believe the manuscript offers sufficient novelty or experimental rigor to support publication in its current form. I appreciate the authors’ efforts but, regrettably, cannot recommend this work for publication.\n\nAuthor response: We sincerely appreciate your time and valuable feedback on our manuscript. We fully acknowledge your concerns regarding experimental rigor and nomenclature issues, and have made substantial revisions to address these points. As for the novelty of our work, we have further clarified and emphasized the innovative aspects of our study above. We hope these revisions and the response letter demonstrate the academic merit of our work. Should any further clarifications be needed, we would be glad to provide them.\n\nReviewer #2 (Remarks to the Author):\nIn the manuscript “Elucidating the Role of Staphylococcus aureus Fatty Acid Metabolism in SaeRS-Mediated Aggregation and Pathogenesis of Joint Infections,” Yu et al report a role for the SaeRS two-component system in aggregation of staphylococci within synovial fluid. The authors first demonstrate that highly aggregating clinical strains from MSKI patients lead to greater disease pathogenesis in a murine model of septic arthritis (higher CFUs, more gait disturbance, etc). The highly aggregating strains also were less likely to be phagocytosed and more likely to resist antimicrobial therapy in vitro. Mechanistically, the authors discover a role for SaeRS through direct in vitro evolution towards a non-aggregating phenotype. They also find that the fatty acid kinase (Fak) system is involved in activation of SaeS through localization within functional membrane microdomains. Consistent with this mechanism, mutants in Fak or SaeRS had less aggregation, heightened sensitivity to antibiotics, and decreased resistance to immune cell killing. The authors also discover that the medium chain fatty acid undecanoic acid (UDA) inhibits Fak without direct effects on bacterial viability, reduces aggregation in synovial fluid, and improves antibiotic efficacy against the bacteria in vivo.\n\nIn general, the rigor of this study is high. For example: 1) two separate directed evolution experiments with multiple strains led to identification of Fak and Sae mutants with similar phenotypes; 2) multiple clinical strains are used in many experiments; and 3) complementation constructs are also deployed to confirm genotype-phenotype correlation. The authors use a compelling combination of unbiased (e.g. RNAseq, meta-transcriptomics, proteomics, screening of eFA’s) and directed studies (e.g. complementation with 8 different adhesins; bacterial 2-hybrid) to identify the genes regulated by SaeRS that are responsible for the aggregation phenotype (fnbA/B). This knowledge then fueled additional in vivo studies that confirmed a role for both SaeRS and FnbA/B in septic arthritis pathogenesis.\n\nAlthough this manuscript is focused on a very specific clinical problem (septic arthritis), I believe that the research findings will be of broad interest to investigators studying staphylococcal pathogenesis and host-microbe interactions. The data are vast and highly supportive of the newly discovered mechanism, which will undoubtedly fuel exciting additional studies. Most of my critiques of the manuscript are minor, but there are a few areas that need more attention to further improve the rigor of the study.\n\nAuthor response: We sincerely thank your thoughtful evaluation of our work and for recognizing the broader implications of our findings. Below, we provide point-by-point responses to each comment and have incorporated substantial revisions to address the highlighted areas for improvement. These modifications have strengthened the methodological rigor of the study. We appreciate your constructive feedback, which has undoubtedly enhanced the quality of this manuscript.\n\nMajor critiques:\n1. There are a few points within the manuscript where quantification of bacterial growth (as colony forming units, not via optical density) would help to address whether the phenotypes are due to changes in aggregation or inhibition of bacterial growth. This should be tested for the FakA and SaeRS mutants in synovial fluid, representative high- vs. low-aggregating clinical isolates in synovial fluid, and WT S. aureus treated with simvastatin and zaragozic acid as in figure 5.\n\nAuthor response: Thanks for your suggestions. We agree with your concerns. In the revised manuscript, we have included the bacterial numeration result for the fakA and saeRS mutants, representative high and low-aggregating clinical isolates in the aggregation medium (20% synovial fluid) (Revised Supplementary Figure 1C, Revised Supplementary Figure S2G). The result showed that bacterial growth was not impaired in the fakA and saeRS mutants at the tested time point (6 hours). Also, the representative low-aggregating clinical isolates displayed no impaired growth compared with the high-aggregating counterparts. This was discussed in the revised manuscript (line 112–113 and line 183-185)\nRevised Supplementary Figure S1C. (C) Quantification of the representative high/low aggregation clinical isolates growth within 20% synovial fluid for 6 hours. High aggregating: H1-H5, Low aggregating: L1-5. Three replicates for each strain. unpaired student t test.\nRevised Supplementary Figure S2G. Bacterial growth quantification for USA300 with different genotypes incubating in 20% synovial fluid for 6 hours. Statistical analysis is by one-way ANOVA with Dunnett’s post-tests versus values in controls.\nWe then tested the WT S. aureus treated with simvastatin, zaragozic acid at the indicated 6 hours, the result showed that simvastatin inhibited bacterial growth at high concentration (500μM, Revised Figure 5I), indicating that the inhibition of aggregation might be caused by bacterial growth defect. However, zaragozic acid does not affect bacterial growth. This was discussed in line 388-391 to avoid overinterpretation of the result.\nRevised Figure 5I. Bacterial numeration before and after growing in 20%SF containing simvastatin/zaragozic acid. Statistical analysis is by one-way ANOVA with Dunnett’s post-tests versus values in controls. Error bars show the mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\n\n2. One of the few pieces of data that is not supportive of the overarching model is the failure of UDA to impact CFUs in the joints of mice infected with USA300. It is also unclear why the authors did not test UDA + vancomycin therapy given that aggregation clearly made bacteria more tolerant to this drug, and UDA disrupts aggregation. The authors should comment on these experimental decisions and consider testing a high-aggregating clinical isolate with UDA therapy in vivo in the septic arthritis model.\n\nAuthor response: We sincerely thank the reviewer for raising this important point regarding the efficacy of UDA in reducing CFUs within the joints of mice infected with USA300.\nWhile the reduction in joint CFUs observed with UDA treatment did not reach statistical significance (mean ± SD for no treatment group = 1838 ± 1103 CFU/joint; UDA-treated = 755.0 ± 827.4 CFU/joint), we did note a trend towards lower bacterial burdens. This suggests a potential biological effect that may require larger group sizes or alternative models to fully elucidate.\nAlso, as suggested by reviewer 3, we modified our statistical method. In the original version, data was tested using one-way ANOVA with turkey’s post hoc for pairwise comparison. In the revised version, data was analyzed using one-way ANOVA with dunnet’s post-test for comparison with the untreated group and the raw data was not changed. The new analysis result showed that CFU in the UDA group is significantly lower than in the untreated group (Revised Figure 7J). We have now included the revised version in the manuscript.\nAlso, we agree with the reviewer that testing UDA therapy against a high-aggregating clinical isolate within the septic arthritis model. Therefore, we made an additional experiment in the revised manuscript to further evaluate the therapeutic potential of UDA against clinically relevant strains exhibiting strong aggregation phenotypes (Strain H1). Results showed that UDA treatment could lower the bacterial burden in the septic arthritis MICE model. We have supplied this additional data in the revised manuscript (Revised Figure 7K)\nRevised Figure 7J-K. (J) The bacterial burden of mice infected with USA300 received different treatment strategies. n = 14/group. (K) The bacterial burden of mice infected with the high-aggregating clinical isolate (H1) received different treatment strategies. n = 20/group. Data were analyzed by t-test (K) and one-way ANOVA with Dunnett’s post-tests versus values in the untreated group(J). *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\nRegarding the experimental decision for testing the combination of UDA with AFN1252 instead of with vancomycin. We are pleased to clarify our rationale for testing the combination of UDA and AFN1252. Our in vitro experiments demonstrated that UDA-mediated dispersal of bacterial aggregates enhances susceptibility to multiple antibiotics, including vancomycin and gentamicin. We attribute this effect to improved antibiotic penetration and bioavailability due to reduced aggregation. However, based on prior literature and our preliminary data, we hypothesize that UDA’s ability to potentiate AFN1252 extends beyond aggregate inhibition. Specifically, studies have shown that AFN’s efficacy in vivo is often compromised when bacteria utilize exogenous fatty acids to bypass FASII inhibition, a key mechanism of AFN. We selected the UDA+AFN1252 combination for two key reasons:\n1. Mechanistic validation: Since AFN treatment failure in vivo is linked to bacterial reliance on the Fak exogenous fatty acid system (bypassing FASII), testing UDA+AFN could indirectly confirm whether UDA disrupts exogenous fatty acid uptake. This would strengthen the credibility of our earlier findings.\n2. Clinical relevance: While UDA+vancomycin addresses limitations in treating joint infections, UDA+AFN1252 has broader implications. By countering fatty acid-mediated resistance, this combination could expand AFN1252’s utility across diverse infections, offering greater translational impact.\nWe hope this clarifies the rationale behind our experimental design. Thank you for the opportunity to elaborate. This has been incorporated into the discussion part (Line 619-624).\n\n3. Line 591-593: More information is needed regarding the source of synovial fluid. Were these patients enrolled in a study? Were there any underlying diseases that could affect the composition of synovial fluid (e.g. rheumatoid arthritis, infection).\n\nAuthor response: Thanks for your suggestions. We would like to declare that all of the human synovial fluid was derived from patients with osteoarthritis knees who received lubrication injections or prosthesis replacement surgery in our hospital. Patients with gout arthritis, joint infection, rheumatoid arthritis, and Charcot’s neuroarthropathy were excluded because the overrepresentation of immunoglobulin and uric acid crystals might affect aggregation and measurement, according to our preliminary experiment. The patients with knee osteoarthritis were enrolled for this study to collect their synovial fluid with permission from the ethics board of our institution and written informed consent. The detailed information of the sample source was supplied in the revised manuscript in the method section under the sub-title and highlighted in yellow. (Line 670-674)\n\nMinor comments:\n1. Lines 46-48: I would remove the comparison of osteomyelitis to septic arthritis, as it is not necessary to convey the severity of septic arthritis and minimizes the high clinical impact that bone infections can have.\n\nAuthor response: Thanks for your suggestions. We have removed the comparison of osteomyelitis to septic arthritis.\n\n2. Lines 52-53: “Despite its profound clinical implications, joint infection remains relatively understudied compared to other infectious diseases.” This is overstated based on thousands of papers studying the pathogenesis of joint infections such as PJI and septic arthritis. There are also manuscripts over the past 2-3 decades that have specifically investigated the pathogenesis of S. aureus septic arthritis. Please reword.\n\nAuthor response: We sincerely appreciate your suggestion. In response to this comment, we have revised the statement to more accurately reflect the existing body of literature. The original sentence has been modified to:\n\"While substantial research efforts have advanced our understanding of joint infections(Abram et al., 2020; Chisari et al., 2022), persistent challenges in clinical management and evolving microbial resistance patterns underscore the need for continued refinement of therapeutic strategies and molecular-level understanding of host-pathogen interactions. Further research remains crucial to address critical knowledge gaps and translate mechanistic insights into improved clinical outcomes for these complex infections.\" We thank the reviewer for prompting this important clarification.\n\n3. Figure Panel 1F appears to be lacking labels to describe the graphed data. The legend states “Histograms and correlation analysis of proteinaceous biofilm, polysaccharide biofilm formation abilities, and aggregation ability for 152 clinical strains,” but these features are not denoted on the graph.\n\nAuthor response: Thanks for your suggestions. We have revised Figure 1F to add the corresponding labels. Moreover, the figure legend was revised to improve readability.\nRevised Figure 1F.Histograms and correlation analysis of proteinaceous biofilm, polysaccharide biofilm formation abilities, and aggregation ability for 152 clinical strains. Correlation was analyzed with the Spearman method. i,iii,vi: histogram distributions for polysaccharide biofilm(i), proteinaceous biofilm(iii), and aggregation ability(vi). ii, iv, v: correlation plot between polysaccharide and proteinaceous biofilm(ii), polysaccharide biofilm and aggregation ability(iv), proteinaceous biofilm and aggregation ability(v).\n\n4. Lines 130-133 in Figure 1L-M legend: Please more clearly delineate which shading (grey versus no shading) represents the presence of synovial fluid.\n\nAuthor response: Thanks for your suggestions. Grey shading represents the presence of synovial. It has been labeled in the revised figure and corresponding legend.\nFigure 1L-M. (L-M) CFU count results of human PMN/ RAW264.7 co-cultured with S. aureus of varying aggregation abilities in culture medium with/without 20% synovial fluid (gray shadow represents the presence of synovial fluid) (n=5/group).\n\n5. Supplemental Figure 1C: Phagocytosis is misspelled.\n\nAuthor response: We have corrected it. Thank you for identifying this error.\n\n6. Figure 2C: Gentamicin is misspelled.\n\nAuthor response: We have corrected the misspelling of \"gentamicin\" throughout the manuscript. Thank you for identifying this error..\n\n7. Line 251: Suggest modifying this heading to “S. aureus Fak Activates SaeRS and FnbA/B by exploiting host fatty acids”\n\nAuthor response: We have modified the heading as suggested.\n\n8. Figure 5E: Y-axis is missing the label.\n\nAuthor response: The Y-axis has been added in the revised figure.\nFigure 5E. The relative transcription levels of SaeP in SF/TSB treated with the indicated concentrations of simvastatin. Statistical analysis is by one-way ANOVA with Dunnett’s post-tests versus values in controls. Error bars show the mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\n\n9. Line 411: Italicize S. aureus.\n\nAuthor response: We have italicized the species name as indicated. Species names are now properly italicized everywhere in the manuscript.\n\n10. Line 441: change “folds” to “fold”\n\nAuthor response: We have revised the current version of the manuscript.\n\n11. Figure 7ABC: Legend is missing to delineate which bars represent UDA-treated cells\n\nAuthor response: We have added the legend to Figure 7ABC.\nRevised Figure 7(A-C) Antibiotic susceptibility of S. aureus pretreated with/without UDA in TSB (A) or supplemented with eFA(B), synovial fluids (C). n = 5/group.\n\n12. Line 505: Italicize clfA/B, rsbU\n\nAuthor response: We have italicized clfA/B and rsbU in Line 505 as suggested, and we have thoroughly checked and corrected gene name italicization throughout the manuscript.\n\n13. Line 515-517: “In our previous study, we demonstrated S. aureus adhesins (MSCRAMMs: FnbA/B, ClfA/B, SdrCDE) could evolve in vivo and contribute to persistent infection. This finding suggests that adhesins are not plausible treatment targets.” In my opinion this is overly concluded. I would remove the sentence regarding adhesins not being plausible treatment targets.\n\nAuthor response: We have removed the sentence as suggested.\n\n14. Line 605: Italicize S. aureus\n\nAuthor response: Thanks for your careful review. We have italicized the species name as indicated. Species names are now properly italicized everywhere in the manuscript.\n\n15. Line 681: Change “resistant” to “resistance”\n\nAuthor response: Thanks for pointing this out. We have revised as indicated.\n\n16. Line 701: Please rephrase this sentence as it is written as an active protocol statement and not in past tense.\n\nAuthor response: We have revised the sentence in the passive and past tense. The original sentence has been modified to:\n“The aggregates were collected by removing the supernatant and gently washed with sterile PBS to remove synovial fluid components attached to their surface. Then aggregates were frozen with liquid nitrogen, and the resulting frozen powder was transferred to a 1.5 mL centrifuge tube.”\n\n17. Line 820: here it is stated that UDA was administered as a local injection, but earlier in the text it was characterized as topical. Was this an intra-articular injection? Please provide more detail in methods and be consistent throughout the text.\n\nAuthor response: Thanks for your suggestion. We are sorry for the inconsistency throughout the manuscript, which made it confusing. The UDA was administered via intra-articular injection. The detailed information has been provided in the method sections. Other text (legend for Figure 7H) describing the UDA administration has been revised for consistency throughout the text.\n\n18. Line 905-906: Please provide a reference for this data.\n\nAuthor response: Thanks for your suggestions. We have provided the reference at the end of the sentence.\n[Reference]: Yu, Jinlong, Mingzhang Li, Jin Wang, Musha Hamushan, Feng Jiang, Boyong Wang, Yujie Hu, Pei Han, Jin Tang, Geyong Guo, and Hao Shen. 2023. ‘Identification of Staphylococcus Aureus Virulence-Modulating RNA from Transcriptomics Data with Machine Learning’. Virulence 14(1):2228657. doi:10.1080/21505594.2023.2228657.\n\n19. Line 936: “40,0000.” Please confirm what number of reads was used as the cutoff, 40,000 or 400,000.\n\nAuthor response: We sincerely appreciate your suggestions and apologize for the mistake in labeling the numbers. The cutoff number of reads was 400,000 to ensure > 90% coverage of the bacterial genome. The number has been properly expressed as 400,000 to avoid confusion.\n\n20. Line 941: Please provide a reference for this patient cohort.\n\nAuthor response: Thanks for your suggestions. The corresponding references were cited at the end of the sentence.\n[Reference]: Hamushan, Musha, Jinlong Yu, Feng Jiang, Boyong Wang, Mingzhang Li, Yujie Hu, Jianqiang Wang, Qiong Wu, Jin Tang, Pei Han, and Hao Shen. 2023. ‘Adaptive Evolution of the Clf-Sdr Subfamily Contributes to Staphylococcus Aureus Musculoskeletal Infection: Evidence from Comparative Genomics. Microbiological Research 278:127502. doi:10.1016/j.micres.2023.127502.\n\n21. Supplementary Figure 16: “S. aureus” and “FA incorporation” are misspelled.\n\nAuthor response: We have corrected the misspellings of “S. aureus” and “FA incorporation” in Supplementary Figure 16 (now referred as Revised Supplementary figure S17). Thank you for pointing this out.\n\nReviewer #3\nThis study has an overarching theme of the interplay between eFAS, Sae, and synovial infections. There is a lot of really interesting data. The paper is actually very hard to read and understand. The figure legends are not very helpful, and a ton of the needed information is buried in hard-to-find text and supplementary information. Many of the studies lack proper controls that lead to over interpretation of the data. At times, it dismisses already published studies that support the conclusions, but they are not referenced or mentioned in context.\n\nAuthor response: Thanks for your effort in evaluating our work and your constructive feedback, which really improved the rigor and readability of the manuscript. First, the figure legend and notes for the tables have been revised to improve their readability. We acknowledge that some data was over-interpreted; additional proper controls were added to improve the rigors of the conclusion. Moreover, the published studies that support our conclusion were systematically searched, mentioned, and referenced at their proper place.\n\nThere is a lot of problems with the statistics. Some panels have no statistics applied, while others have the wrong method applied. There are Mann-Whitney tests, one-way ANOVAs and two-way ANOVAs with no rational explanation as to why a particular method was used. A One-way ANOVA is not appropriate for comparing 2 treatments.\n\nAuthor response: Thank you for your valuable feedback regarding the statistical analyses in our manuscript. We sincerely appreciate your rigorous review and have thoroughly revised our statistical methods.\nWe have carefully corrected all statistical inaccuracies:\n For two-group comparisons: the Unpaired two-tailed Student’s t-test (with Welch’s correction if variances are unequal) was adopted for normally distributed data. The Mann-Whitney U test was used for non-normally distributed data.\n For multi-group comparisons (≥3 groups): One-way ANOVA (with Tukey’s post hoc test for pairwise comparisons, with Dunnett’s post-tests versus values in controls) was used for normal distribution. Kruskal-Wallis test (with Dunn’s post hoc test) was adopted for non-normal distribution. All statistical tests are now explicitly stated in the figure legends.\n In addition, we added the following to the Statistical Analysis subsection:\n“Continuous data were tested for normality using the Shapiro-Wilk test and for homogeneity of variance using Levene’s test. For two-group comparisons: Normally distributed data were analyzed using an unpaired t-test; non-normal data were analyzed with the Mann-Whitney U test. For multi-group comparisons: normally distributed data were analyzed by one-way ANOVA followed by Tukey’s post hoc test for pairwise comparisons and with Dunnett’s post-tests versus values in controls; non-normal data were analyzed by the Kruskal-Wallis test with Dunn’s post hoc correction.”\nWe are grateful for your insightful comments, which have significantly strengthened our manuscript. Should further clarifications be needed, we are happy to address them.\n\nThe introduction is devoid of relevant information on Staphylococcus aureus, Sae, and Fak.\n\nAuthor response: Thank you for your valuable suggestion. We have added an additional paragraph (Line 70-82) under the introduction section to include detailed information on Staphylococcus aureus, sae, and fak, supported by relevant references. These additions provide a clearer context for our study.\n\nTable S2 is supposed to contain Biofilm data. There are no definitions in the table to explain any of it. I assume B_ indicates biofilm, but it is not explained. Figure 1F is equally difficult to understand.\n\nAuthor response: We sincerely appreciate your careful review. We have made the following revisions to address your concerns:\n1) For Tables:\nWe have now added clear definitions in the table legend, and abbreviations have been explicitly stated or explained in the table legend. Detailed descriptions of each parameter have been included to improve clarity. This issue has been checked for all of the supplemental tables.\n2) For Figure 1F:\nThe subpanels in Revised Figure 1F were labeled with annotations and have been added to the figure legend to enhance. We hope these modifications improve the clarity of our results. We sincerely appreciate your valuable feedback.\nRevised Figure 1F.Histograms and correlation analysis of proteinaceous biofilm, polysaccharide biofilm formation abilities, and aggregation ability for 152 clinical strains. Correlation was analyzed with the Spearman method. i,iii,vi: histogram distributions for polysaccharide biofilm(i), proteinaceous biofilm(iii), and aggregation ability(vi). ii, iv, v: correlation plot between polysaccharide and proteinaceous biofilm(ii), polysaccharide biofilm and aggregation ability(iv), proteinaceous biofilm and aggregation ability(v).\n\nOne of the mutations shown for FakA in Figure S2C is labeled T171K, but the mutation shown does not match that.\n\nAuthor response: We appreciate your careful review. The FakA mutation label in Figure S2C has been corrected to match the actual sequence change.\nRevised Supplementary Figure S2C\n\nMultiple papers connecting fatty acid accumulation, Sae, and FakA from different labs are not cited.\n\nAuthor response: We sincerely appreciate this insightful suggestion. As already noted above, we included related information about sae and fak in the revised introduction part, and the corresponding references were also cited at the proper lines. Also, our experimental findings that are consistent with previously published literature have been both declared, and the related references were appropriately cited. These additions significantly strengthen the background and context of our study. Thank you for helping us improve the manuscript's completeness\n\nFigure 1C does not indicate sample sizes and does not have error bars. Also, what is the dotted line?\n\nAuthor response: Thank you for your insightful comments. We have updated the figure legend of Figure 1C to include the sample size (n = 5/replicate). Additionally, due to the log scale on the y-axis, the error bars were obscured by the data points, so we have adjusted the presentation to make them visible. The dotted line in the figure represents the 10 µm size threshold, which is critical as particles larger than the cell diameter (>10 µm) cannot be phagocytosed (Schinwald & Donaldson, 2012). This was now explicitly stated in the result part (Line 98-99) and the legend for Figure 1C.\n\nMutations are not labeled conventionally; genetic mutants should be lowercase italics.\n\nAuthor response: Thank you for pointing this out. We have corrected all genetic mutant notations to follow standard conventions (lowercase italics) throughout the manuscript and figures. We appreciate your attention to this important detail.\n\nThe FakA/Sae relationship studies in figure 2 agree with other published studies and this is not mentioned. It is known that FakA is upstream of SaeS.\n\nAuthor response: We thank the reviewer for this important observation. We have now added a discussion in the Results section to explicitly mention that our findings in Figure 2 regarding the FakA/Sae relationship align with published studies, supported by appropriate references (Ericson et al., 2017; Krute et al., 2017; Parsons, Broussard, et al., 2014).\n\nIt is really hard to believe that the only SNPs generated by evolution experiment would be in Sae or Fak. What other mutations were there? This information is severely lacking but could be highly relevant.\n\nAuthor response: Thanks for your comment. During the evolution experiment, many mutations and SNPs were generated both in the control group and the evolution group. We only display the fak and sae SNP/mutations because mutations in the evolution group have a clear location bias toward the fak and sae locus (Figure S2I). That means, fak and sae locus in the evolution group displayed with high frequency of mutation rate compared to the background mutation rate.\nFigure S2I: Summary of the resultant mutations/SNPs identified among 33 randomly selected colonies compared with the corresponding positive control.\nOver the 4-day evolution assays, each strain obtained 1~3 mutations in its genome. Most of the mutations were localized in the intergenic area with undefined function. Other genetic mutations were related to sugar transportation or metabolism pathways, possibly contributing to the adaptation to survive in the synovial fluid environment. We are aware that these SNPs/INDELs could also be worth exploring by some reviewers with their special interest. As this information is important, we upload the summarized mutation table as the Supplementary Table S3 for reference.\nAlso, we added sentences regarding the possibility that other mutations might contribute to bacterial adaptation to the synovial fluid environment in the discussion part (Line 571-573).\nIn addition, a similar comparative analysis method has been previously done in several studies(Nakamura et al., 2020; Noto Guillen et al., 2024). These studies fully demonstrated that the breseq software could efficiently identify genes that related to a specific phenotype.\n\nThe complementation data is not clear. What promoters are used? If fnbA is regulated by SaeRS, then how could adding it back alone complement? Once you dig, you can find it. But this makes little sense when first reading. These details are too buried.\n\nAuthor response: We sincerely appreciate your valuable feedback regarding the clarity of our complementation data. We acknowledge that the experimental details were not sufficiently elucidated, which may have caused confusion for readers. To address your specific questions, we made the following revisions and provided additional experiment results:\n1. Promoter Used in Complementation:\nThe vector utilized for the complementation experiment is pRMC2(Corrigan & Foster, 2009) (as already mentioned in the method section). It is a tetracycline-inducible vector commonly employed for gene overexpression in S. aureus (Addgene plasmid number: #68940). The target genes(e.g., fnbA/B) were inserted into the MCS (multiple cloning site) with seamless cloning methods. The promoter sequences upstream of the MCS site were a tetracycline-inducible promoter. Due to its leaky expression characteristics, transcriptional activity of the target gene occurs even in the absence of anhydrotetracycline induction. The vector sequences were deposited in the Mendeley database, and the corresponding deposit accession numbers were listed in Supplementary Table S8.\n2. Validation of Complementation:\nTo further confirm the complementation, we performed additional aggregation experiments under tetracycline-induced and uninduced conditions (Revised Figure 3F-G). In addition, we also conducted qPCR analyses to demonstrate the high expression levels of the complemented gene under induced conditions. These results are now included in the revised manuscript. (Revised Supplementary Figure S5A )\n3. Improved Clarity in the Manuscript:\nWe fully agree that the initial description was ambiguous and could lead to difficulties in interpreting the experiments. We have added detailed explanations in both the figure legend and the Methods section to ensure transparency and reproducibility. Thank you once again for your insightful comments, which have significantly improved the clarity and rigor of our work.\nRevised Figure 3F-G. Representative images of aggregation assay for USA300-△SaeRS complemented with candidate adhesins (F) and the corresponding quantitative results (G). Adhesins were controlled by a tetracycline-inducible promoter. S. aureus was stimulated with/without anhydrotetracycline (aTc, 1μg/mL) to control the adhesin overexpression. n = 5/group.\nRevised Supplementary Figure S5A. Transcriptional changes for the complemented adhesins under induced/uninduced conditions.\n\nLines 262-264. This does not make sense. If SaeRS is activated by FakA and the SaeP promoter is SaeR dependent, then why is it not lower in a fakA mutant? The same is true for fnb which is also activated by SaeRS. Shouldn’t all these transcripts be down in a fakA mutant? Also, the lines say that there is no difference in a fakA mutant with SF, but then goes on to say that Sae upregulation is FakA dependent. Also, the +SF levels are so low, is it even real expression?\n\nAuthor response: Thank you for raising this point regarding Figure 4E. In our presented data, the saeRS levels and fnbA/B in the △fakA are significantly lower. We are sorry that the original color scheme—specifically, the similar pink and purple colors used for \"△fakA-untreated\" and \"WT-20% SF\" groups could lead to misinterpretation. We also recognize that the accompanying description of results may have contributed to potential confusion. To address this, we have revised the figure with a more distinct color palette that differentiates between experimental groups (Revised Figure 4E). We also revised the description for the result as “Then, the transcription level of saeRS-fnbA/B in response to SF was compared between WT and ΔfakA strains, and no difference was detected for the ΔfakA strain in its response to SF.” The updated version now ensures improved clarity and accuracy in data representation (Line 298-300).\nWe appreciate your careful review, which has significantly enhanced the rigor of our work.\nRevised Figure 4E. The relative transcription level of saeP, fnbA/B after exposure to 20% synovial fluid.\n\nLines 268-272. This is counter to other published studies. Also, it says that 500 mM fatty acid was used. This is higher than the solubility of the fatty acid and seems impossible. Also, that level of 18:1 completely kills S. aureus.\n\nAuthor response: We sincerely apologize for the critical typo in our manuscript. The concentration used was 500 μM, not 500 mM. These concentrations were confirmed to be sublethal in our preliminary trials and others. Additionally, while published literature generally reports that fatty acids inhibit the Sae system in S. aureus, our original data suggest that fatty acids may activate it. We thoroughly searched the literature to understand the reason for these conflicting results and noted that the discrepancy may arise from the addition of high concentrations of fatty acid-free BSA (bovine serum albumin, 10%) in the fatty acid stock solutions in our study, whereas our control group did not contain BSA. BSA was added to promote the binding of fatty acids. We conducted an additional experiment to set the proper control group (10% BSA without eFA). The data confirmed the inhibitory role of eFA on SaeRS, which was consistent with previously published studies (Revised Supplementary Figure S6C). Therefore, we removed the data that contradicts published literature.\nIn the revised version of our results, we acknowledged the inhibitory effect of fatty acids on the Sae system as reported in the literature and have cited the relevant supporting studies. Based on these findings, it is reasonable to propose that FakA, as a fatty acid-binding protein, scavenges fatty acids, thereby alleviating their suppression of the Sae system and leading to its activation. To further prove this model, we treated ΔfakA with eFA-free bovine serum albumin (FA-free-BSA) to scavenge the intracellular fatty acids. A significant increase in sae activity was observed upon treating ΔfakA with FA-free-BSA. (Figure 4K). However, the rescue of sae transcription by FA-free-BSA is only partial compared to the wild type. This implies that other regulatory mechanisms are involved in fak controlling the saeRS-fnbAB.\nIn accordance with the revised additional experimental results, we have changed the section title from “exploiting” to “scavenging”: “S. aureus Fak activates saeRS and fnbA/B by scavenging free fatty acids.” We have also made corresponding modifications to the wording throughout the manuscript.\nWe thank the reviewer for this constructive feedback, which has helped us improve the manuscript's rigor and make the proposed model more compelling.\nRevised Supplementary Figure S6C. The transcriptional changes for USA300 treated with oleic acid(OA, 500μM), palmitic acid (PA, 500 μM), myristic acid (MA, 500 μM), eFA (mixture of OA, PA, MA in a 1:1:1 ratio at a final concentration in TSB of 170 μM each). Fatty acid was dissolved in TSB supplied with 5% fatty acid-free bovine serum albumin (FA-free-BSA). Control group was set as S. aureus grown in TSB with 5% FA-free-BSA. Statistical analysis is by one-way ANOVA Dunnett’s post-tests versus values in controls. *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\nRevised Figure 4K. Transcription level of saeP, fnbA/B for S. aureus treated with eFA-free-BSA(5%) and eFA (500 μM, consisting 1:1:1 ratio of OA, MA and PA)\n\nFigure 4F. Do these concentrations of FasII inhibitors impact growth. This was not tested and decreased expression could just be the result growth inhibition and not Sae activity. Growth curves with these inhibitors should be added.\n\nAuthor response: We thank the reviewer for this important comment. This is indeed a challenging issue. According to literature, the MIC is <0.12 μg/mL for AFN1252 and 0.025~1 mg/L for Triclosan depending on the strains tested (Karlowsky et al., 2009; Suller & Russell, 2000). In our study, AFN1252 and triclosan were regarded as FASII inhibitors based on evidence from the literature. Since the FASII pathway is essential for bacterial growth, its inhibition is inevitably accompanied by growth defects. It is methodologically difficult to assess the specific effect of FASII inhibition on sae activity at concentrations below the MIC, as any observed effects may not be solely attributable to FASII inhibition. Therefore, it remains experimentally challenging to completely separate the impact of FASII inhibitors on Sae-related phenotypes from their general effects on growth. In response to the reviewer’s concern, we have now included representative growth curves of bacteria treated with these inhibitors. Additionally, we have acknowledged this limitation in the corresponding line to avoid overinterpretation (Line 305).\nRevised Supplementary Figure S6B. Growth curve of S. aureus USA300 in TSB containing AFN1252 (5ng/mL) or Triclosan (500ng/mL).\n\nFigure 4H. How is it certain that this is lipid incorporation and not free fatty acid association? A fakA mutant should be used as a control in this experiment. Likewise, 4G should be done in the presence of a FasII inhibitor to show that it is specific to that pathway since acetate can enter metabolism through several metabolic pathways.\n\nAuthor response: We sincerely appreciate the insightful suggestions. We fully acknowledge the concerns regarding Figures 4H and 4G. We recognize that the original experimental results were insufficient to support the conclusions drawn, leading to potential overinterpretation. To address these issues, we have expanded the experimental design as suggested by the reviewer:\n1. For Figure 4H, we added a ΔfakA experimental group. The results show that residual C13 signal in the ΔfakA group was significantly reduced. We defined the difference in values between ΔfakA and the High/Low group as the net lipid incorporation. After correction, the data demonstrate that clinical high-aggregation strains exhibit higher lipid incorporation rates than low-aggregation strains.\n2. For Figure 4G, regarding the acetate metabolic pathway, we agree that acetate indeed enters cells through routes beyond FASII. To specifically analyze FASII-mediated acetate metabolism, we followed the reviewer’s suggestion and introduced an FASII inhibitor group. The results revealed no statistically significant difference in C13 signal ratios between groups with or without the inhibitor, indicating comparable FASII metabolic activity across those two groups.\nWe are grateful for the reviewer’s expertise, which has greatly enhanced the rigor of our conclusions. The revised Figure 4G-H is provided below.\nRevised Figure 4G-H. (G) Endogenous fatty acid synthesis level (exploitation of C13-Acetate) among high-aggregating and low-aggregating S. aureus strains with/without AFN1252 treatment (n = 5/group). (H) Exogenous fatty acid (C13-Oleic acid) incorporation level among high-aggregating and low-aggregating clinically isolated strains (n = 5/group). We defined the difference in values between ΔfakA and the High/Low group as the net lipid incorporation. Statistical analysis is by one-way ANOVA with Tukey’s post hoc test. Error bars show the mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\n\nThe split mCherry results in Fig S8 are not convincing. In some cases, there appears to be foci, but in additional cells, it looks like it is over most of the membrane surface.\n\nAuthor response: We sincerely thank the reviewer for raising this valid concern regarding the apparent inconsistency in the localization patterns observed in the original Figure S8.\nThe variation in mCherry signal distribution—ranging from distinct foci to a more diffuse membrane pattern—was primarily due to limitations in our initial imaging conditions. Our high-resolution confocal microscope uses an exceptionally thin optical section, making the focal plane extremely sensitive. In the original images, high bacterial density resulted in many cells being out of the focal plane; some were dead or lysed, leading to blurred fluorescence and inaccurate localization that manifested as a diffuse membrane signal.\nTo address this, we have repeated the imaging under optimized conditions: we used mid-log phase cultures, significantly reduced the bacterial density during slide preparation, and increased the signal threshold to minimize background noise. These adjustments allow for clearer visualization of individual bacteria in the focal plane, as shown in the Revised Figure S9 provided below.\nFurthermore, to confirm that the observed foci represent functional FMM structures, we included an additional control experiment using a specific FMM inhibitor. Treatment with this inhibitor resulted in the complete disappearance of the fluorescent foci, confirming that the foci signal indeed corresponds to FMM.\nAs a positive control, we constructed the FloA-FloA splitmCherry reporter plasmid. Then treating S. aureus expressing FloA-FloA splitmCherry reporter plasmid with zaragozic acid (FMM inhibitor). Consistent with previous reports on native FloA function (García-Fernández et al., 2017), we observed a complete disappearance of the fluorescent foci upon zaragozic acid treatment (Revised Supplementary Figure S9C).\nWe believe these corrections and additional validation significantly strengthen the reliability of the results. Thank you again for the critical comment, which has helped us improve the quality of our data.\nRevised Supplementary Figure S9B-C. (B)High resolution confocal images for USA300 expression NtmCherry-FloA and SaeS-CtmCherry treated with/without zaragozic acid. NtmCherry-FloA: N-terminal split mCherry was fused to the N-terminal of the FloA protein. Negative control: USA300 expression of FloA-NtmCherry and SaeS-CtmCherry. (C). USA300 expression NtmCherry-FloA and FloA-CtmCherry treated with/without zaragozic acid. NtmCherry-FloA: N-terminal split mCherry fused to the N-terminal of the FloA protein. FloA-CtmCherry: C-terminal split mCherry fused to the C-terminal of FloA proteins. SaeS-CtmCherry: C-terminal splitmCherry was fused to the C-terminal of SaeS proteins. FloA-NtmCherry: N-terminal split mCherry was fused to the C-terminal of the FloA protein, as a negative control.\n\nFigure 6 needs to have colors matched for D-G for readability.\n\nAuthor response: Thank you for this helpful suggestion. We have now standardized the color scheme across panels D-G in Figure 6 to improve clarity and readability.\nRevised Figure 6D-E. (D) Growth curve of S. aureus (USA300) in TSB medium with indicated treatment (100 mM for each compound). (E) Relative transcription level of saeP, fnbA/B in USA300-WT with indicated treatment (100 mM for each compound). n = 3/group. Statistical analysis is by one-way ANOVA with Dunnett’s post-tests versus values in controls. Error bars show the mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.\n\nThe data with UDA cannot be said to inhibit Fak. That data does not exist in its current. eFA incorporation requires more than just FakA/B. It also involves Pls proteins that may be the target. It is safe to say that it inhibits eFA incorporation but cannot be said that is it is a Fak inhibitor. That is an overstatement.\n\nAuthor response: We sincerely appreciate the reviewer’s insightful critique regarding the interpretation of UDA’s inhibitory effects. We fully agree that our initial manuscript overextended the conclusion that UDA directly targets the Fak system based solely on docking data with Fak binding proteins. As rightly noted, eFA incorporation involves additional components (e.g., Pls proteins), and our original data did not substantiate the mechanistic inhibition of Fak. Therefore, the interpretation of our results has been revised as suggested by the reviewer.\nMoreover, to further explore the binding target of UDA and address this gap, we conducted further investigations to test our hypothesis that UDA may interact with FakB1:\n We conducted molecular dynamics (MD) simulations (100 ns) for UDA binding with FakB1/B2. The MD result showed that UDA forms a stable binding complex with FakB1 and FakB2, RMSF result showed a low value for key residues, which were also predicted by molecular docking (Revised Figure S12A-D).\n We deployed surface plasmon resonance (SPR) to investigate the direct binding of UDA with FakB1. After purifying recombinant FakB1 protein and quantifying UDA binding via fluorescence displacement assays, using myristic acid (MA)/oleic acid(OA) as a positive control (Parsons, Broussard, et al., 2014). UDA bound FakB1 with an affinity (K = 4.49 μM) similar to MA (K = 6.93 μM), and its affinity with FakB2 was much lower than OA (2.27 μM for UDA-FakB2, 7.63 μM for OA-FakB2, Revised Figure 6I).\nThese new results demonstrate that UDA directly interacts with FakB1 and FakB2, aligning with our structural prediction. We have revised our conclusion to state that \"UDA inhibits eFA incorporation by targeting FakB1/2\".\nRevised Supplementary Figure S12. (A)Docking model UDA into the structure of the FakB1 and FakB2 structures. Left: fatty acids docking into FakB1/2; Middle: Enlarged view of FakB1/2 bonding with fatty acids; Right: The physicochemical bond between FakB1/2 and fatty acids. (B). Structural stability of UDA binding with FakB1/2 conformations was measured as the RMSD (unit: nm) over a 100-ns time course. (C-D).RMSF (unit: nm) of FakB1(C) or FakB2(D) in complex with UDA throughout the whole protein residues. A vertical line indicates residues predicted to bond with the UDA ligand according to the docking result.\nRevised Figure 6I. The binding between FakB1(or FakB2) and varying concentrations of fatty acid was assessed by SPR.\n\nIn addition, some evidence exists that eFAs can enter FASII. Is it possible that this is part of what is happening? The data in Fig 7 does partially counter this last point. However, a fakA mutant would have strengthened these studies.\n\nAuthor response: We are delighted that our data have provided further insights and inspired additional discussion. Our data in Figure 7 suggest that eFA enables S. aureus to be resistant to the anti-FASII antibiotics. This result agrees with previous literature that anti-FASII were less effective within the host environment because pathogens could bypass the FASII by incorporating environmental eFA(Morvan et al., 2016). To our knowledge, fak was the only pathway that S. aureus used to incorporate eFA. However, the reviewer noted that fatty acids were proven to enter the FASII system and undergo elongation (Parsons, Frank, et al., 2014). Therefore, as suggested by the reviewer, we tested the fakA mutant strain its resistance to anti-FASII antibiotics in the presence of eFA/synovial fluid (Revised Figure 7D-E). The results showed that the fakA mutant did not show an increase in resistance to anti-FASII antibiotics in the presence of eFA/synovial fluid (Revised Figure 7D-E). These data further strengthened previous studies that eFAs can bypass FASII through the fak system. However, this does not mean that eFAs cannot enter FASII. Because, as an inhibitor for FASII, AFN1252 and Triclosan block the elongation process if eFA enters the FASII. Therefore, based on these literatures and additional experimental data, we carefully concluded that eFAs can bypass FASII through the fak system, but cannot exclude the possibility that eFA can enter FASII.\nRevised Figure 7D-E. Number of S. aureus resistance towards Triclosan(D) or AFN1252(E) (tested in TSB either supplied with exogenous fatty acids or synovial fluids, n = 3/group). WT: wild-type USA300; WT+UDA: wild-type USA300 treated with 50μM UDA; △FakA: FakA mutant strain. Data were analyzed by one-way ANOVA with Dunnett’s post-tests versus values in controls.\n\nHeat maps lack actual data to validate them.\n\nAuthor response: Thanks for your comment. All of the actual data is now provided in the source data file (Excel) and will be freely downloaded from the publisher’s website once it is online published. Heatmap produced from RNA-seq data can be reproduced using the raw sequencing data, as we have deposited our rawdata in the GEO database.\n\nReviewer4\nIn this work, Yu et al investigated the mechanism of bacterial infection-induced aggregation in the synovial fluid. Specifically, the authors used an array of experimental and computational tools to first establish the role of S. aureus SaeRS in aggregation and its regulation by Fak. This was done through directed evolution by several rounds of selection of S. aureus variants that were incapable of forming aggregates. The whole genome sequencing then allowed the authors to identify the mutations in two genes, SaeRS and Fak. Then, the extended study attempted to establish Fak as a therapeutic target and identified undecanoic acid (UDA) as an inhibitor for Fak system. This study is important given the lack of detailed understanding of the mechanisms of synovial joints lubrication and the infections targeting the synovial membrane/fluid and the tissues near the joints. The work seems to be comprehensive, and the findings are significant. A few points need to be addressed and clarified.\n\n1) Composition of aggregates: It’s nice to see that the authors identified the primary proteins involved in forming the aggregates. Why is the 40kDa band not visible in the gel, but is prominent in the quantification?\n\nAuthor response: Thanks very much for your careful review. We checked our original data and found that the 40kDa band in the quantification result was unintendedly caused by a contaminated spot near the 40kDa at the middle of the lane (marked with a red arrow in the figures below Figure R1). This spot was not excluded during the quantification for gel data using ImageJ. The software was so sensitive that any unintended noise could lead to a false positive signal.\nTo deal with this, we have repeated the SDS-PAGE in three independent replicates and confirmed that the 40kDa was not enriched in the aggregates (Revised Figure 4A-B). Moreover, the quantification data for each of the three replicates were presented in Revised Figure 4Bi, ii, iii(Figure R2).\nFigure R1. The original SDS-PAGE gel photo was accidentally contaminated by a spot (as indicated by the red arrow). The spot was not excluded from the region of interest during quantification using ImageJ.\nFigure R2 Additional SDS-PAGE experiments were carried and the quantification steps were illustrated.\nRevised Figure 4A-B. (A). Coomassie blue-stained SDS-PAGE of proteins from synovial fluid and synovial aggregates. M: marker; A1-3: synovial aggretates; SF1-3: synovial fluid (B). The corresponding semi-quantitative results (Bi, Bii, Biii for each of the replicates).\n\nThe Dispersin-B and DNase-I were used to rule out sugars/glycans and DNA. What about the role of (even in trace amounts) exRNA and lipids, especially during nucleation?\n\nAuthor response: Thanks for your constructive comment. To address the potential roles of exRNA and lipids, we performed additional experiments using Lipase and RNase A to degrade the corresponding components. Our results show that neither Lipase, RNase A, DNase I, nor Dispersin B had a significant impact on the formation or nucleation of bacterial aggregates. This suggests that these biomolecules are not critically involved in the initial nucleation process under our experimental conditions.\nRevised Supplementary Figure S4A-B. Representative fluorescence micrographs of GFP-labeled S. aureus aggregates following treatment with various enzymes. Images were captured at premature stage (T = 5-15 minutes), nucleus formation (T = ~30 minutes), nucleus aggregate (T = ~1.5 hours), clumping (~2 hours), and maturation (>2-3 hours) time points during aggregate development. (DispersinB: 500 g/mL; DNaseI: 200 g/mL; Proteinase K: 100 g/mL; RNase H: 200 g/mL; Lipase: 2000 U/mL)\n\nGiven the variety of proteins and other components involved, Fig. 3K will likely be more complicated. Perhaps include immunoglobin and adhesion proteins?\n\nAuthor response: As for Figure 3K. The original illustration did not add other protein components in the picture because the corresponding interacting ligands and the mode of linking bacteria are not clearly verified in our study. However, since the immunoglobulin and other proteins were highly enriched according to the proteomic data, we decided to add this information and label them with a question mark. This was also detailed in the figure legend to improve readability.\nRevised Figure 3K. Illustration for the mechanism of S. aureus FnbA/B and host fibrinogen contributing to synovial aggregation. Other host proteins enriched in the synovial aggregates, with no further verification, were labeled with light grey (IgG, Complement, Keratin).\n\nReviewer #4 2) Fig. 4K lists the binding free energies, and the caption says the calculation was done using AlphaFold3. While AF3 was used to model the complexes, the free energy calculation was done with the hawkDock, so this needs to be corrected. The MM/GBSA calculations are not very accurate but the relative values are still helpful, so this needs to be discussed/acknowledged.\n\nAuthor response: We sincerely appreciate your careful reading of the figure legend. We apologize for the incorrect description in the figure caption for Figure. 4K. The calculation was done as described in the method section (Line 970-976): “AlphaFold3 was employed to establish protein-protein interaction models. The model with the highest ranking score was selected as the best binding model. The HawkDOCK SERVER was utilized to evaluate the binding free energy of the best binding model and to rank the free binding energies of amino acid residue sites to understand their contribution to the binding interaction.” We have now corrected this in the revised caption to accurately reflect the methodology.\nMoreover, we fully agree with your observation about the limitations of free energy calculations. In the revised manuscript, we have added an acknowledgment in the results section(Line 316-318): “Although the absolute binding energy value provides little biological meaning, the relative values among strains are still helpful in elucidating the discrepancies in aggregation ability and Fak activity among clinical strains.”\nThank you for bringing these important points to our attention, which have helped improve the precision and transparency of our methodology reporting.\n\n3) The assays showing direct interactions between FloA-SaeSR are novel. However, the simulations do not seem to provide much insight. A few microseconds are typical for more accurate all-atom simulations and ns-timescales are inadequate for CG simulations to observe any meaningful scaffolding or interactions. Given that tens or hundreds of microseconds can easily be done, it’s unclear why the authors chose CG and not take advantage of the access to longer timescales. Still, free diffusion of the proteins in such simulations is prohibitively slow and unlikely to yield meaningful complexes. A better strategy would be to also include pre-formed complexes (in addition to free proteins) obtained with AF3 or docking and set up the simulations.\n\nAuthor response: We appreciate the reviewer's insightful comments regarding the simulations. It is true that all-atom simulation and longer simulation times provide more convincing evidence. All-atom simulation is typically adopted for small molecular interactions with proteins and usually. However, the simulation in our project involves protein-protein interaction, and the simulation was constructed within the bacterial membrane, a non-typical simulation environment. The all-atom simulation for such a large and atypical system requires large computing resources, which is unreachable for our group.\nTo address the concern about simulation timescales, we have extended our CG simulations from the original 1000 ns to 5000 ns (5 µs). Extended simulations confirm the stability of the protein complex (SaeS RMSD reached a plateau at ~5.0 nm after 1 μs; FloA RMSD reached a plateau at 8nm after 4 μs (Revised Figure S8B-D). Furthermore, we fully acknowledge that computational predictions necessitate validation through biological experiments. To this end, we have incorporated new co-immunoprecipitation (co-IP) data demonstrating a direct physical interaction between FloA-3xFLAG and SaeS-6xHIS (Revised Figure 5D). Specifically, we co-expressed SaeS-3xFLAG and FloA-6xHIS in USA300 using a recombinant plasmid. Anti-HIS magnetic beads were used to immunoprecipitate FloA-6xHIS, and the resulting pull-down product was immunoblotted with an anti-FLAG antibody to detect associated SaeS-3xFLAG. Our results clearly show that FloA binds to SaeS, providing robust biochemical validation that supports the interaction observed in our extensive coarse-grained (CG) molecular dynamics simulations. Since the coarse-grained simulation for FloA-SaeS has a timescale limitation, we now moved this part into supplementary data (Revised Figure S8B-D).\nRevised Figure S8 B-D (B) Computational molecular dynamic modulation of FloA and SaeS interaction on the cell membrane (blue: FloA, purple: SaeS). (C) The curves of the total solvent-accessible surface area (SASA) of FloA and SaeS proteins over time. The vertical axis represents the solvent-accessible surface area at the corresponding moment. (D) The RMSD (Root Mean Square Deviation) curve of the SaeS/FloA protein over time.\nRevised Figure 5D: Co-IP and immunoblot analysis of USA300 overexpressing FloA-6xHIS and SaeS-3xFLAG. SDS-PAGE result shows that the protein contents were comparable among the three groups.\n\nReviewer #4: 4) What about other proteins associated with FMMs (e.g. IsdF, PBP2a)? Do they have any role, or form co-complex with SaeSR/FloA?\n\nAuthor response: We appreciate your valuable suggestion. FMMs were reported to be a microdomain containing various functional membrane proteins. IsdF and PBP2a have been demonstrated in a previous study to interact with floA and localized within FMMs structures for their proper function (Adolf et al., 2023; García-Fernández et al., 2017). Impairment of PBP2a could enhance the sensitivity to oxacillin, and dysfunction of isdF was reported to limit its growth in iron-limited conditions. However, in the saeRS mutant strains, we did not observe the increased sensitivity to oxacillin or impaired growth within iron-limited medium (hemoglobin supplied as the sole iron source, Figure R3). These data indicate that IsdF and PBP2a were independently complex with FloA within the FMMs. This was discussed in the revised manuscript, and the references were cited at the proper sites (Line 591-593). As the PbP2a and IsdF were not related to our study topic, we would like to only present the data in this response letter, not included in the manuscript.\nFigure R3.(A). Bacterial enumeration result for S. aureus USA300 (WT or saeRS mutant) treated with oxacillin for 4 hours. (B). Growth of S. aureus USA300 WT, ΔsaeRS in the presence of 1 μg/ml human hemoglobin within iron-limited medium (RPMI+1% casamino acids+10 μM EDDHA)\n\nReviewer #4: 5) Given the importance of UDA as potential therapeutic target of FakB1, an all-atom MD would help more accurately position the substrate and identify the interactions. This is because docking is unlikely to produce an accurate pose especially when docked to a modeled or substrate-free apo structure.\n\nAuthor response: We appreciate this insightful suggestion. While our current docking studies provided initial interaction insights, we agree that all-atom MD simulations would better characterize UDA positioning within the FakB1/2 binding pocket. We have now performed 100 ns MD simulations. The MD result showed that UDA forms a stable binding complex with FakB1/2 (Revised Supplementary Figure 12A-B). Also, the RMSF result showed a low value at key residues (FakB1: ILE94, LEU160, PHE263, FakB2: ILE118, THR259, ILE263, GLY273), which were also predicted by molecular docking. The MD result further confirmed our molecular docking result. The simulation file has been deposited in Mendeley (DOI: 10.17632/wf65dwbkkn.1).\nMoreover, we deployed surface plasmon resonance (SPR) to investigate the direct binding of UDA with FakB1/2 (Revised Figure 6I). UDA bound FakB1/2 with an affinity (K = 4.49 μM for FakB1, K = 2.27 μM for FakB2) similar to its natural ligands (KD = 6.93 μM for myristic acid-FakB1, K = 7.63 μM for oleic acid-FakB2).\nThese new results demonstrate that UDA directly interacts with FakB1, aligning with our structural prediction.\nRevised Supplementary Figure S12. (A)Docking model of UDA into the structure of the FakB1 and FakB2 structures. Left: fatty acids docking into FakB1/2; Middle: Enlarged view of FakB1/2 bonding with fatty acids; Right: The physicochemical bond between FakB1/2 and fatty acids. (B). Structural stability of UDA binding with FakB1/2 conformations was measured as the RMSD (unit: nm) over a 100-ns time course. (C-D).RMSF (unit: nm) of FakB1(C) or FakB2(D) in complex with UDA throughout the whole protein residues. A vertical line indicates residues predicted to bond with the UDA ligand according to the docking result.\nRevised Figure 6I. The binding between FakB1(or FakB2) and varying concentrations of fatty acid was assessed by SPR.\n\n6) Fix typos e.g. (“By review published FMMs …”  “Reviewing the published FMMs…”)\n\nAuthor response: We sincerely appreciate your careful reading of our manuscript. We have now thoroughly reviewed the text and corrected all typographical and grammatical errors, including the example you noted (\"By review published FMMs\" → \"Reviewing the published FMMs\"). Thank you for bringing this to our attention—we apologize for any confusion caused by these oversights.\n\n7) The simulations are generally expected to be deposited to data sharing sites such as Zenodo.\n\nAuthor response: We agree with the importance of data accessibility. As we have deposited some of our data in the Mendeley data center, we chose to deposit the molecular dynamic data in Mendeley. The repository link has been added to the data availability section.\nYu, Jinlong (2025), “UDA-MD data”, Mendeley Data, V1, doi: 10.17632/wf65dwbkkn.1\nYu, Jinlong (2025), “FloA-SaeS simulation”, Mendeley Data, V1, doi: 10.17632/6nx3spyhz8.1\n\nReference:\nAbram, S. G. F., Alvand, A., Judge, A., Beard, D. J., & Price, A. J. (2020). Mortality and adverse joint outcomes following septic arthritis of the native knee: A longitudinal cohort study of patients receiving arthroscopic washout. The Lancet Infectious Diseases, 20(3), 341–349. https://doi.org/10.1016/S1473-3099(19)30419-0\nAdolf, L. 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(2015). Biofilm-like aggregation of Staphylococcus epidermidis in synovial fluid. The Journal of Infectious Diseases, 212(2), 335–336. https://doi.org/10.1093/infdis/jiv096\nSanson, M. A., Vega, L. A., Shah, B., Regmi, S., Cubria, M. B., Horstmann, N., Shelburne, S. A., & Flores, A. R. (2021). The LiaFSR Transcriptome Reveals an Interconnected Regulatory Network in Group A Streptococcus. Infection and Immunity, 89(11), e0021521. https://doi.org/10.1128/IAI.00215-21\nSchinwald, A., & Donaldson, K. (2012). Use of back-scatter electron signals to visualise cell/nanowires interactions in vitro and in vivo; frustrated phagocytosis of long fibres in macrophages and compartmentalisation in mesothelial cells in vivo. Particle and Fibre Toxicology, 9, 34. https://doi.org/10.1186/1743-8977-9-34\nStaats, A., Burback, P. W., Schwieters, A., Li, D., Sullivan, A., Horswill, A. R., & Stoodley, P. (2022). Rapid Aggregation of Staphylococcus aureus in Synovial Fluid Is Influenced by Synovial Fluid Concentration, Viscosity, and Fluid Dynamics, with Evidence of Polymer Bridging. mBio, 13(2), e0023622. https://doi.org/10.1128/mbio.00236-22\nSuller, M. T. E., & Russell, A. D. (2000). Triclosan and antibiotic resistance in Staphylococcus aureus. Journal of Antimicrobial Chemotherapy, 46(1), 11–18. https://doi.org/10.1093/jac/46.1.11\nUkleja, M., Kricks, L., Torrens, G., Peschiera, I., Rodrigues-Lopes, I., Krupka, M., García-Fernández, J., Melero, R., del Campo, R., Eulalio, A., Mateus, A., López-Bravo, M., Rico, A. I., Cava, F., & Lopez, D. (2024). Flotillin-mediated stabilization of unfolded proteins in bacterial membrane microdomains. Nature Communications, 15(1), 5583. https://doi.org/10.1038/s41467-024-49951-1\n\nRESPONSE TO REVIEWERS' COMMENTS\nReviewer #1\nConceptual misunderstanding of biofilms vs. aggregates: The authors persist in a conceptual error regarding the distinction between biofilms and bacterial aggregates. What they refer to as “biofilms” or “aggregates” are essentially the same microbiological phenomenon: a bacterial community embedded in a self-produced extracellular matrix composed of proteins and polysaccharides. Whether this community is attached to a surface or floating is circumstantial and largely influenced by environmental conditions (e.g., agitation, surface properties).\nAuthor response: We thank the reviewer for reiterating this point. We respectfully disagree based on our direct experimental evidence. The core of our argument lies in the lack of a significant correlation between robust biofilm formation on abiotic surfaces and the formation of synovial fluid aggregates in our clinical isolates, as quantitatively demonstrated in Figure 1F. This finding is aligned with prior work from Prof. Robin Patel's laboratory [1]. In their study, the S. epidermidis that form poor biofilms in tryptic soy broth could formed macroscopic clumps in synovial fluid. Indicating that synovial fluid-induced aggregation is a distinct phenotype from canonical biofilm formation.\nReference: [1]. Perez, K. & Patel, R. Biofilm-like aggregation of Staphylococcus epidermidis in synovial fluid. J Infect Dis 212, 335–336 (2015).\nThe morphological and physiological characteristics of a bacterial community embedded in its matrix do not fundamentally change based on surface adhesion. This is well illustrated by decades of work on species such as Bacillus subtilis, where biofilms are routinely observed as floating particles in liquid media (see doi:10.1038/nrmicro2960). Numerous bacterial species form biofilms exclusively as floating aggregates, and the presence or absence of surface adhesion is not a defining biological criterion. The authors themselves identify signaling cascades common to both forms, further supporting this point.\nAuthor response: We thank the reviewer for this comment. The model from Bacillus subtilis is indeed instructive, but its principles cannot be universally generalized to all species. The pathogenic strategies of S. aureus, particularly in clinical niches like synovial fluid may have distinct pathways. Critically, the extensive microscopic and phenotypic evidence we provide in our manuscript (e.g., Figure 1A-B, Figure S4A-B ) reveals fundamental structural differences between synovial fluid aggregates and classic S. aureus biofilms.\nWe systematically profiled the major matrix constituents—including polysaccharides, eDNA, proteins, RNA, and lipids—without a priori assumption of their relative importance to address any potential bias. Our functional assays conclusively demonstrated that proteins were the primary functional component responsible for aggregation integrity.\nAlthough the identified fnbA/B adhesins were also contributors for biofilm, this does not indicate the two forms were the same. Our proteomic analysis(Figure 3C) revealed the enrichment of specific host-derived proteins, such as IgG, within the aggregate matrix. The incorporation of such host factors is a notable distinction from typical S. aureus biofilms and underscores the unique nature of the synovial fluid aggregate microenvironment. This finding not only validates the objectivity of our investigative process but also provides a novel clue suggesting divergent matrix assembly mechanisms between aggregates and biofilms.\nMischaracterization of the extracellular matrix in S. aureus: The authors fail to acknowledge that the extracellular matrix in S. aureus is primarily composed of adhesive proteins (adhesins), with extracellular polysaccharide playing a secondary role. Only a limited number of strains produce sufficient polysaccharide to contribute meaningfully to matrix cohesion. Therefore, the aggregation of S. aureus communities is not predominantly driven by polysaccharide production, contrary to the claims made in the manuscript.\nAuthor response: We thank the reviewer for this comment. We agree entirely that the extracellular matrix in S. aureus synovial aggregates is predominantly protein-based. As shown in Figure 3 (SDS-PAGE and protein mass spectrometry analysis), we specifically identified a rich protein composition as the major component of the matrix in our study system. The subsequent functional experiments were built upon this foundational finding. While we did systematically explore the potential contributions of other matrix components (polysaccharides, eDNA, RNA, and lipids) at the request of another reviewer, our results consistently confirmed that proteins play the primary role in aggregation(as illustrated in Figure 3K).\nUnsubstantiated distinction between aggregates and biofilms: The manuscript suggests that the distinction between aggregates and biofilms is well-established in the field. This is not accurate. In my experience, and in the broader literature, this distinction is not considered biologically meaningful outside of specific clinical contexts—such as differentiating localized infections from those with potential for systemic dissemination (e.g., sepsis).\nAuthor response: We thank the reviewer for raising this important point regarding the distinction between aggregates and biofilms. We respectfully clarify that our insistence on this distinction is precisely grounded in the specific clinical context of our study (joint infection), which aligns with the reviewer's own caveat.\nOur work focuses exclusively on bacterial communities formed in human synovial fluid, a unique environment where the formation of non-attached, matrix-encased aggregates is a well-documented and distinct phenomenon [1-8]. As supported by a body of literature from independent groups, these synovial fluid-induced aggregates exhibit fundamental differences in composition, structure, and regulatory mechanisms compared to canonical surface-attached biofilms. This has led the field to specifically investigate \"synovial fluid-induced aggregation\" and even to the development of artificial synovial fluid models for its study [2].\nTherefore, within the narrow but critical context of joint infection, we believe the distinction is not only biologically meaningful but also essential for accurately modeling the in vivo pathophysiology. Our manuscript does not claim this distinction is universal across all microbiological contexts, but rather that it is a defining feature of the clinical environment we are modeling.\nReference:\n1. Gupta, T. T., Gupta, N. K., Burback, P. & Stoodley, P. Free-Floating Aggregate and Single-Cell-Initiated Biofilms of Staphylococcus aureus. Antibiotics (Basel) 10, 889 (2021).\n2. Stamm, J. et al. Development of an artificial synovial fluid useful for studying Staphylococcus epidermidis joint infections. Front Cell Infect Microbiol 12, 948151 (2022).\n3. Bidossi, A., Bottagisio, M., Savadori, P. & De Vecchi, E. Identification and Characterization of Planktonic Biofilm-Like Aggregates in Infected Synovial Fluids From Joint Infections. Front Microbiol 11, 1368 (2020).\n4. Knott, S. et al. Staphylococcus aureus Floating Biofilm Formation and Phenotype in Synovial Fluid Depends on Albumin, Fibrinogen, and Hyaluronic Acid. Front. Microbiol. 12, 655873 (2021).\n5. Dastgheyb, S. S. et al. Role of Phenol-Soluble Modulins in Formation of Staphylococcus aureus Biofilms in Synovial Fluid. Infect Immun 83, 2966–2975 (2015).\n6. Staats, A. et al. Rapid Aggregation of Staphylococcus aureus in Synovial Fluid Is Influenced by Synovial Fluid Concentration, Viscosity, and Fluid Dynamics, with Evidence of Polymer Bridging. mBio 13, e0023622 (2022).\n7. Pestrak, M. J. et al. Investigation of synovial fluid induced Staphylococcus aureus aggregate development and its impact on surface attachment and biofilm formation. PLOS ONE 15, e0231791 (2020).\n8. Perez, K. & Patel, R. Biofilm-like aggregation of Staphylococcus epidermidis in synovial fluid. J Infect Dis 212, 335–336 (2015).\nLack of demonstrated causality between mutations and biofilm formation: The authors attempt to link specific mutations to biofilm formation based on three arguments: (1) clinical correlation, (2) phenotypic impact in vitro, and (3) in vivo effects of fnbAB. While these observations suggest correlation, they do not establish causality. To illustrate: if an animal’s legs are tied thus it fails to respond to a call, one cannot conclude that the animal has gone deaf. Correlation does not imply causation.\nBiofilm formation is highly dependent on extracellular matrix production and this can be inherently affected by any alteration in growth, metabolism, cell wall organization, adhesin production, or membrane integrity. All this may impair aggregation and infection, making it difficult to isolate specific genetic effects. The authors must demonstrate a direct causal link to support their conclusions.\nAuthor response: We thank the reviewer for this insightful comment and the vivid analogy. We agree that establishing unequivocal causality for specific mutations in clinical isolates is challenging, as any genetic change can have pleiotropic effects. However, within the context of our study, we have built a multi-layered line of evidence. the convergence of our clinical, in vitro, and in vivo data strongly argues against the observed effects being merely indirect or coincidental. We have revised the discussion to more cautiously frame our conclusions\nInaccuracies in Molecular Biology Nomenclature: The comments I previously made regarding molecular biology terminology are not minor or meticulous. They reflect basic principles that suggest the authors lack expertise in molecular biology and are handling key concepts without sufficient rigor.\nAuthor response: In response to this comment, we have conducted a thorough, line-by-line review of the entire manuscript to identify and correct all instances of imprecise molecular biology terminology. We are deeply grateful to Reviewer 1 for holding our work to a high standard. The critique, while firm, has been invaluable in helping us improve the quality and accuracy of our presentation.\nLack of novelty and redundancy with existing literature: As I stated in my previous interaction, many of the conclusions presented in this study have already been extensively documented by other research groups. For example: The role of the SaeSR system in adhesin production and biofilm formation, independent of ica, is well established (see PMID: 28581360). The influence of fatty acids and Fak on SaeSR activity has been explored in detail by Brinsmade’s laboratory (PMID: 36135382, 36778227, 38366323, 39382300). The connection between membrane microdomains (FMMs) and SaeS activity has also been previously reported (PMID: 39382300, 34370508). In light of the conceptual inaccuracies, lack of demonstrated causality, and limited novelty, I do not believe this manuscript meets the standards required for publication in Nature Communications, but this is not for me to say. My role is to highlight the reasons why I believe the work lacks the necessary rigor and originality for this journal.\nAuthor response: We acknowledge the important foundational work conducted in the fields of the saeSR system, fatty acid modulation, and membrane microdomains, including the key studies cited by the reviewer. Our work, however, provides a novel and integrative perspective by demonstrating how these previously reported pathways converge and interact within a unified model in the clinically relevant setting of septic arthritis. To our knowledge, this is the first report to define this cross-regulatory network in a unified model, offering previously unexplored insights into the pathophysiology of this infection.\nReviewer #2\nAll prior critiques have been successfully addressed. However, typos remain:\nLine 303 – Ticlosan – should this be Triclosan?\nLine358: Consisting of\nLine 571 - Occurred is misspelled\nLine 923 – remove “sides”\nFigure 7L label: Mgnificence: Should this be magnification?\nAdditionally, In revised supplementary figure S9B, the description of the negative control is confusing:\nfused to the C-terminal of the FloA protein, as a negative control. Negative control: \"FloA-NtmCherry: N-terminal split mCherry wasfused to the C-terminal of the FloA protein, as a negative control. Negative control: USA300 expression of FloA-NtmCherry and SaeS-CtmCherry\"\nAuthor response: We thank the reviewer for the valuable feedback. All noted typos have been corrected.\nIn response to the comment on legend for Supplementary Figure S9B, we have clarified the figure legend to: \"Negative control: USA300 expression of FloA-NtmCherry (N-terminal split mCherry fused to the C-terminal of FloA) and SaeS-CtmCherry.\"\nReviewer #3\nThis is a much improved manuscript compared to the previous version with considerable consideration of the previous 4 reviews. The research is now of higher impact and better crafted. This includes new original data as well as rewrites that all improve the manuscript. A couple of issues are noted below.\nThe manuscript is full of typos and miss use of appropriate nomenclature. For example, there is consistent incorrect use of lowercase italics when mentioning proteins. The opposite is also true at times. This was noted in the response to reviews but still holds true.\nAuthor response: In response to this comment, we have undertaken a systematic, word-by-word review of the entire manuscript to correct all typographical errors and to ensure strict adherence to standard scientific nomenclature.\nLine 257 should be fnb not snb\nAuthor response: We have corrected the misspelling.\nLine 259 is Figure S5\nAuthor response: We have corrected it. Thank you for identifying this error.\nLine 315 should be figure 4J\nAuthor response: We have corrected it. Thank you for identifying this error.\nLine 300-301. It should not be stated that saeRS upregulation in SF is dependent on Fak since there is no expression in untreated also in the absence of Fak. This should be reworded.\nAuthor response: We have reworded the statement to accurately reflect the data. The text now states that the upregulation of saeRS observed in synovial fluid is abrogated in the fak mutant.\nFigure 4F could simply be because of growth inhibition, as noted. This drastically impacts the interpretation of this data. Since RNA levels were measured, one would assume a loss of growth would have been checked. If Fig S6 is the concentrations used, then Fig 4F should be removed as it misrepresents what it going on.\nAuthor response: We thank the reviewer for this critical insight. While a connection between FASII and the Sae system has been suggested in prior literature [1], we acknowledge that our experimental setup does not allow us to definitively dissociate specific regulatory effects from general growth inhibition. Upon careful reconsideration, given that this dataset is not central to the main conclusions of our study and in order to maintaining the highest level of interpretive rigor, we have removed Figure 4F and the corresponding data in Figure S6B from the manuscript as suggested. The relevant descriptions in the Results section have been revised accordingly.\nReference: 1. Parsons, J. B. et al. Perturbation of Staphylococcus aureus gene expression by the enoyl-acyl carrier protein reductase inhibitor AFN-1252. Antimicrob Agents Chemother 57, 2182–2190 (2013).\nMuch like 4F, what is the impact of these molecules on growth/killing in Figure 5E and F? It is noted that G&H might have antibacterial activity.\nAuthor response: We thank the reviewer for raising this important point. The impact of these molecules on bacterial growth was presented in presented in Figure 5I in the original manuscript. The data show that a significant antibacterial effect for simvastatin was only observed at a much higher concentration (500 µM). We did not observed growth inhibition at low concentration(100 µM) for simvastatin. This was also supported by published study exploring the antimicrobial effect of statins [1]. We have ensured that the revised text in the Results section explicitly references Figure 5I when discussing the data from panels E and F, to make this critical point unequivocal to the reader.\nReference: [1]. Graziano, T. S. et al. Statins and Antimicrobial Effects: Simvastatin as a Potential Drug against Staphylococcus aureus Biofilm. PLOS ONE 10, e0128098 (2015).\nIf they are high aggregating, then how were the CFUs normalized for the animal experiments. Details are lacking on how the inoculums were grown. If they aggregate, then the CFU would be artificially low and more bacteria were infected. As a whole, in the manuscript, this concern is not mentioned or considered.\nAuthor response: We would like to clarify that the \"high-aggregating\" and \"low-aggregating\" phenotypes are specifically and exclusively observed in the context of synovial fluid. Crucially, when the bacterial inoculum was prepared for the animal experiments, they were grown in standard TSB broth under agitation (150 rpm), conditions under which no aggregation occurs for either phenotype. Therefore, the optical density measurements and subsequent CFU plating for normalization were performed on homogeneous, non-aggregated bacterial suspensions.\nThe detailed normalization protocol is as follows:\nSingle colonies were inoculated into TSB and grown overnight with shaking. Cultures were then sub-cultured (1:100) into fresh TSB and grown for 6 hours with shaking (150 rpm) to mid-log phase. These homogeneous cultures were serially diluted and plated for precise CFU enumeration. Aliquots based on these counts were prepared and frozen. On the day of infection, frozen aliquots were thawed and diluted in PBS to achieve the desired, normalized inoculum concentration for all groups, ensuring that any animal received an identical and accurately quantified number of bacteria. We have now comprehensively detailed this process in the Methods section of the revised manuscript to prevent any potential misunderstanding. We thank the reviewer again for prompting us to improve the clarity of this essential experimental detail.\nPlease provide the individual observed mutations in Table S3 as this would be important for the field and, depending on the SNP, may explain changes in Sae activity. This may be particularly relevant since many are in SaeP/Q which are not part of the canonical system.\nAuthor response: We have now expanded Table S3 into a more comprehensive supplementary table that details the observed mutations.\nThe data in Figure 6i is very interesting, but I would still be cautious of saying in the Abstract that UDA if a Fak inhibitor. The data in 6i suggests that to be the case, but the blocking of incorporation in 6F could also still suggest Pls is the target. Alternatively, preventing uptake of oleic acid by UDA could also indicate that it is a competing with oleic acid and is a substrate of FakB and not an inhibitor. Maybe it is preferred at the concentration used and therefore decreases oleic acid utilization. While the authors may be correct, the data could be interpreted a couple of different ways. It should be pointed out that they did a good job addressing this initial concern, but I would soften this statement.\nAuthor response: We thank the reviewer for this thoughtful comment and for acknowledging the interest of our data. In accordance with this feedback, we have softened the statement in the Abstract and throughout the manuscript. Specifically, we have rephrased our conclusions to indicate that UDA \"inhibits the Fak pathway,\" rather than definitively stating it is a direct Fak inhibitor.\nWe believe these changes have strengthened the manuscript by presenting a more nuanced and scientifically precise interpretation of our data, while still highlighting the significant functional role of UDA in disrupting this key metabolic pathway.\nReviewer #4\nThe authors adequately addressed my concerns and revised the manuscript. Specifically, 1) they repeated the SDS-PAGE to verify the quantification, 2) extended the CG simulation that allowed them to achieve stability, 3) performed an All-atom simulation to optimize the binding pose, 4) performed experiments with RNAse H and Lipase.\nAuthor response: We are grateful for the valuable insights and constructive suggestions you have provided, which have been instrumental in greatly improving our manuscript"}]

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