Mapping of Quorum Sensing Landscape of Commensal and Pathogenic Staphylococci Reveals a Largely Inhibitory Interaction Network

Mapping of Quorum Sensing Landscape of Commensal and Pathogenic Staphylococci Reveals a Largely Inhibitory Interaction Network | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Mapping of Quorum Sensing Landscape of Commensal and Pathogenic Staphylococci Reveals a Largely Inhibitory Interaction Network Bengt H. Gless , Benjamin S. Sereika-Bejder , Iben Jensen , Martin S. Bojer , Katerina Tsiko , Sabrina H. Schmied , Ludovica Vitolo , Bruno Toledo-Silva , Sarne De Vliegher , Hanne Ingmer , View ORCID Profile Christian A. Olsen doi: https://doi.org/10.1101/2025.01.30.635662 Bengt H. Gless † Center for Biopharmaceuticals and Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Jagtvej 160, DK-2100, Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: bengt.gless{at}gmail.com cao{at}sund.ku.dk Benjamin S. Sereika-Bejder † Center for Biopharmaceuticals and Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Jagtvej 160, DK-2100, Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Iben Jensen † Center for Biopharmaceuticals and Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Jagtvej 160, DK-2100, Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin S. Bojer ‡ Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Stigbøjlen 4, DK-1870 Frederiksberg C, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katerina Tsiko † Center for Biopharmaceuticals and Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Jagtvej 160, DK-2100, Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sabrina H. Schmied † Center for Biopharmaceuticals and Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Jagtvej 160, DK-2100, Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ludovica Vitolo † Center for Biopharmaceuticals and Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Jagtvej 160, DK-2100, Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bruno Toledo-Silva § M-team & Mastitis and Milk Quality Research Unit, Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University , Salisburylaan 133, B-9820 Merelbeke, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarne De Vliegher § M-team & Mastitis and Milk Quality Research Unit, Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University , Salisburylaan 133, B-9820 Merelbeke, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hanne Ingmer ‡ Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Stigbøjlen 4, DK-1870 Frederiksberg C, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian A. Olsen † Center for Biopharmaceuticals and Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Jagtvej 160, DK-2100, Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian A. Olsen For correspondence: bengt.gless{at}gmail.com cao{at}sund.ku.dk Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Staphylococci utilize secreted autoinducing peptides (AIPs) to regulate group behaviour through a process called quorum sensing (QS). For staphylococcal pathogens such as S. aureus , QS regulates expression of major virulence factors and QS inhibition has been proposed as an alternative to antibiotics for treatment of infections with methicillin resistant S. aureus (MRSA). Here, we surveyed the interaction map between QS systems of the pathogens Staphylococcus aureus, Staphylococcus epidermidis , and Staphylococcus lugdunensis and the 36 currently known AIPs from 22 staphylococcal species. We identified seven of these ribosomally synthesized and post-translationally modified peptides (RiPPs) in this study and all synthetic peptides were assessed for their ability to modulate QS. The mapped interactions of >280 native QS pairings were divided into human- and animal-associated staphylococci showing substantial differences in inhibitory potencies between the groups. In particular, AIPs of the bovine-associated species S. simulans displayed potential as QS inhibitors in the strains investigated in this study and were therefore chosen as starting point for a structure–activity relationship study. This study provides insights into the requirements for QS interference, yielding the most potent inhibitors reported to date for S. epidermidis and S. lugdunensis . Further, we tested an S. simulans AIP as anti-virulence agent in an assay to assess risk of acquired suppression of the inhibitory effect, and we established an assay set-up to successfully monitor agr deactivation of virulent MRSA by the QS inhibitor. Finally, a peptide was shown to attenuate skin infection caused by MRSA in a mouse model. Our results reveal a complex network of staphylococcal interactions and provide further impetus for the development of therapeutic strategies, based on QS modulation to target antibiotic-resistant pathogens. Download figure Open in new tab INTRODUCTION Staphylococci are Gram-positive bacteria that frequently colonize humans and animals representing some of the most abundant microbes found in the human microbiota. 1 Among the numerous staphylococcal species, there are harmless commensal species while others, especially S. aureus , are pathogenic. All staphylococci have genes encoding a quorum sensing (QS) system that enables changes in group behavior and gene expression in response to cell density. 2 , 3 This cell-to-cell communication plays an important role in the transition from harmless skin colonizer to invasive pathogen and is regulated through the secretion and detection of autoinducing peptides (AIPs), which are 7–12 residue peptides, containing a characteristic thiolactone peptide cycle at the C-terminus (lactone for S. intermedius group). 4 – 6 The AIP-mediated QS machinery is encoded by a chromosomal locus termed accessory gene regulator ( agr ), which controls the expression of genes involved in biofilm formation, surface adhesion and toxin production as well as the Agr proteins involved in the QS process (Supplementary Figure S1). 5 , 6 The agr system has been studied in detail for S. aureus but agr loci are found in all staphylococci, suggesting that each species utilizes a unique AIP as QS signaling molecule. Another key feature of AIP secretion is QS interference with agr systems of other staphylococcal species and agr specificity groups within the same species. 2 , 7 This phenomenon has been thoroughly studied for S. aureus and many non-cognate AIPs act as potent inhibitors to its QS system. 8 – 15 QS interference has been less studied in other staphylococci; however, it might be a common occurrence in shared habitats of staphylococci, resulting in altered gene expression levels of co-inhabiting species susceptible to agr inhibition by non-cognate AIPs ( Figure 1 ). Download figure Open in new tab Figure 1. Quorum sensing interference of co-inhabiting staphylococci. The QS interference within habitats of multiple staphylococcal species is complex as each staphylococcal species and agr specificity group secretes a unique AIP. Co-inhabiting staphylococci are exposed to non-cognate AIPs, which can interfere with their QS systems depending on their inhibitory potency and thereby alter gene expression and change group behavior. Determining the influence of QS interference on bacterial multi-species human and animal microbiotas is difficult due to their complex nature and the multitude of non-QS interactions. 3 Nevertheless, recent advances focused on QS interference with S. aureus by commensal staphylococci in the context of atopic dermatitis and therapy. 12 , 16 , 17 Early-phase clinical studies have shown a beneficial effect from the secreted inhibitory AIPs of S. hominis on the outcome of atopic dermatitis caused by S. aureus as a result of QS inhibition. 18 These are promising results from the perspective of investigating anti-virulence strategies based on QS inhibition as alternatives to traditional treatment with antibiotics or as synergistic options together with antibiotics. 19 , 20 QS inhibitor development had and still has a focus on the AIPs of S. aureus and structure–activity relationship studies thereof, which afforded potent inhibitors of S. aureus itself. 21 – 24 However, recent developments in technologies for the identification of AIP, has led to a significant increase of known non-aureus staphylococcal and mammaliicoccal AIPs. 10 – 15 , 25 Several of these AIPs are potent QS inhibitors of S. aureus displaying effective in vivo attenuation of infections caused by methicillin-resistant strains of S. aureus (MRSA). 10 , 13 – 15 The effects of non-cognate AIPs on agr systems different from S. aureus have been more scarcely investigated, including that of the common skin colonizer S. epidermidis , despite its abundance on the human skin. 26 – 29 The roles of S. epidermidis as symbiont are manifold 30 with recent studies even showing a beneficial role for the host, 31 , 32 while at the same time being able to cause medical device infections. 33 A few studies have investigated modifications to the cognate AIPs, to create activators and inhibitors of its agr system. 28 , 34 , 35 Similarly, the human skin commensal S. lugdunensis has been reported to cause severe endocarditis 36 and QS interference with its agr system has been rarely investigated. 37 Thus, the substantial increase in recently identified AIPs, combined with the lack of exploration of their interactions, encouraged us to systematically map the QS interactions of all known AIPs ( 1 – 36 ) with eight agr reporter strains of three therapeutically relevant species S. aureus, S. epidermidis , and S. lugdunensis . Our motivation was two-fold: first, to create a defining data set of QS interactions as a resource to explore trends between bacterial species in the same habitat; and secondly, to discover potent inhibitory interactions, especially against the less studied agr systems of S. epidermidis and S. lugdunensis . RESULTS Identification of new autoinducing peptides and quorum sensing interaction map We previously developed a native chemical ligation-based trapping method for the rapid identification of AIPs from bacterial supernatants, by exploiting the chemo-selective reaction between thioesters and N-terminal cysteine residues, 38 which led to a substantial increase in the number of known AIPs (Supplementary Figure S2). 11 Here, we report the additional identification of seven previously unidentified AIPs from a collection of human and animal isolates, namely: S. capitis AIP-I ( 15 ), S. cohnii AIP-I ( 19 ), S. pasteuri AIP-I ( 22 ), S. devriesei AIP-I ( 23 ), S. succinus AIP-I ( 24 ), as well as S. equorum AIPs I ( 25 ) and II ( 26 ) (Supplementary Figure S2–9). This elevates the number of currently known staphylococcal AIPs to 38 [36 unique structures ( 1 – 36 )], originating from 22 staphylococcal species from across 5 of the 6 phylogenetic species groups as classified through multi-locus sequence typing (Supplementary Table S1). 39 In order to create a comparative and reproducible data set of QS interactions of all known AIPs against S. aureus, S. epidermidis , and S. lugdunensis , we utilized widely-used fluorescent reporter strains, which have a naturally functioning agr system that produces GFP/YFP once the agr dependent promotor P3 is activated. 40 As a first step, we compiled our library of 36 unique AIPs by chemical synthesis (Supplementary Schemes S1–3) and established an assay setup in which the peptides were initially screened at 1 μM and at 50 nM concentration. In cases where we observed >95% inhibition at 50 nM AIP concentration, lower concentrations of 2.5 nM and 0.125 nM were tested ( Figure 2 and Supplementary Figures S10–17). Download figure Open in new tab Figure 2. Quorum sensing (QS) interaction map. a ) Map of human-associated staphylococci. b ) Map of animal-associated staphylococci. c ) Summary of QS interactions with human and animal hosts. Synthetic AIPs were tested at several concentrations (1000 nM, 50 nM, 2.5 nM) against fluorescent reporter strains of S. aureus (SA) agr -I–IV, S. epidermidis (SE) agr -I–III, and S. lugdunensis (SL) agr -I to assess their QS modulation abilities. Blue shading of boxes represents different potencies of QS inhibition (>95% at 2.5 nM, 50 nM and 1000 nM or 5–80% at 1000 nM), white boxes represent no interaction at 1000 nM, green boxes represent QS activation and yellow boxes represent “mixed interference” (activation at 1000 nM and inhibition at 50 nM or partial inhibition over a range of concentrations). To make sure that our systematic survey, using fluorescent reporter strains, correlated with previously reported QS inhibition values, we determined half maximal inhibitory concentration (IC 50 ) values for selected AIPs in both fluorescent reporter strain assays (Supplementary Figures S18–20) and β -lactamase reporter strain assays for QS inhibition of S. aureus agr -I–IV (Supplementary Figures S21– 24), 11 which showed excellent correlation between single concentration data points and full dose– response experiments as well as with previously reported QS inhibition values collected from the literature (Supplementary Tables S2–S3). All staphylococcal species were divided into human- and animal-associated species based on their most commonly reported hosts, 1 , 41 – 44 where S. aureus , a known colonizer of both humans and animals, was included in the human group. The resulting maps contain >280 QS interactions of native AIPs, representing the largest resource of its kind ( Figure 2 ). The likelihood of interactions between certain human and animal-associated species to occur might be low; however, these interactions still represent a promising source for the discovery of potent QS inhibitors. Most measured QS interactions were inhibitory (227 of 288, 79%), where a clear difference between AIPs from human- and animal-associated species could be observed ( Figure 2 ). The QS interactions of human-associated AIPs with S. aureus, S. epidermidis , and S. lugdunensis only exhibited >95% inhibition at 2.5 nM AIP concentration for intra-species interferences between S. aureus specificity groups. Further, most of the combinations that produced no effect at AIP concentration of 1 μM were from the human-associated group of AIPs (37 of 45, 82%) ( Figure 2a and 2c ). In contrast, most QS interactions of animal-associated AIPs (74 of 128, 58%) reached >95% inhibition at 2.5 nM or 50 nM AIP concentration ( Figure 2b and 2c ). The seven AIPs from species primarily associated with bovine colonization ( S. hyicus, S. chromogenes , and S. simulans ), displayed potent interactions, responsible for more than half of the examples of AIPs exhibiting >95% inhibition at 2.5 nM concentration ( Figure 2c ). All AIPs that increased the fluorescence readout compared to the control wells were monitored continuously overnight for growth and fluorescence together with all previously known activators ( Figure 2a and 2b , shown in green). Often an early increase in fluorescence output accompanied by a delay in growth was observed (Supplementary Figures S25,26). Interestingly, we found S. epidermidis AIP-III ( 7 ) to be an activator of S. epidermidis agr -II, in contrast to previous experiments with bacterial supernatant where the AIP had no reported effect. 45 Further, several cross-species activators were discovered: S. hominis AIP-I ( 10 ) activated S. epidermidis agr -I, S. hominis AIP-III ( 12 ) activated S. aureus agr -IV, and S. simulans AIP-I ( 33 ) and AIP-III ( 35 ) activated S. epidermidis agr -III. We observed inconsistent inhibition behavior of several AIPs ( 21 – 23, 25 and 31 ) against some reporter strains ( Figure 2b , shown in yellow). This behavior manifested itself either by causing activation at 1 μM and inhibition at lower concentrations or by showing 70% inhibition at multiple concentrations from 2.5–1000 nM, which was also recently observed for analogs of S. epidermidis AIPs. 35 The QS interaction map identified peptides that were inhibitory across all staphylococcal reporter strains, namely S. hyicus AIP-I ( 27 ) and S. chromogenes AIP-I ( 28 ), which potently inhibited all S. aureus and S. epidermidis agr variants but only weakly inhibited S. lugdunensis agr -I as well as S. simulans AIP-II ( 34 ), which inhibited all eight agr systems ( Figure 2b ). Interestingly, all AIPs that acted as strong inhibitors of S. aureus, S. epidermidis , and S. lugdunensis ( 27, 28, 30 – 35 ) were 9-mer peptides, differing in length from the cognate AIPs of the reporter species (7, 8, and 12 residues), and they were the only AIPs with positively charged residues at the N-terminus (Supplementary Table S1). We found the AIPs of S. simulans interesting, because they displayed strong QS interaction profiles, including the most potent inhibition of S. simulans and the activation of S. epidermidis agr -III by S. simulans AIPs I ( 33 ) and III ( 35 ) but not by S. simulans AIP-II ( 34 ). We therefore peformed a structure–activity relationship study to glean further insights about the function of these molecules. Structure–activity relationship study of autoinducing peptides of S. simulans S. simulans is primarily an animal-associated staphylococcal species, commonly found in bovine livestock, 46 although human infections have also been documented, particularly involving agr -I type strains. 13 The species has three confirmed AIPs ( 33 – 35 ), which share structural features. 11 , 13 S. simulans AIPs I ( 33 ) and III ( 35 ) share an identical exo-tail sequence, KYNP, which is also part of the exo-tail of S. epidermidis AIP-III ( 7 ) and could therefore explain the activating properties towards S. epidermidis agr -III ( Figure 3a ). Further, S. simulans AIPs II ( 34 ) and III ( 35 ) share an identical macrocycle and are both highly potent inhibitors of S. lugdunensis QS, compared to the S. simulans AIP-I ( 33 ) ( Figure 3a ). As starting point for our structure–activity relationship study, we determined the IC 50 values of S. simulans AIP-I–III ( 33 – 35 ) giving sub- or low nanomolar potencies against S. aureus agr -I–III groups as well as S. epidermidis agr -I–II (Supplementary Figures S27–29). For S. aureus agr -IV, the IC 50 value was slightly lower for 33 (10 nM) compared to 34 (32 nM) and 35 (34 nM) and against S. lugdunensis agr -I, 34 and 35 displayed sub- or low nanomolar potencies with the IC 50 value of 35 (0.48 nM) being 400-fold lower compared to the most potent, previously reported inhibitors of S. lugdunensis . 37 S. simulans AIP-II ( 34 ) displayed sub-nanomolar inhibition against S. epidermidis agr -III, while 33 and 35 acted as activators. Download figure Open in new tab Figure 3. Structure–activity relationship study of S. simulans AIP-II (34) and AIP-III (35). a ) Structures of S. simulans AIP-I–III ( 33–35 ) compared to SE-AIP-III ( 7 ). b ) Alanine scan of S. simulans AIP-II ( 34 ). c ) Alanine and truncation scan of S. simulans AIP-III ( 35 ). Synthetic peptides were tested at several concentration (1000 nM, 50 nM, 2.5 nM, 0.125 nM) against fluorescent reporter strains of S. aureus (SA) agr -I–IV, S. epidermidis (SE) agr -I–III, and S. lugdunensis (SL) agr -I. Shading of boxes represents different potencies of QS inhibition (>95% at 0.125 nM, 2.5 nM, 50 nM, and 1000 nM or 5–80% at 1000 nM), white boxes represent no interaction at 1000 nM, green boxes represent QS activation, and yellow boxes represent “mixed interference” (activation at 1000 nM and inhibition at 50 nM or partial inhibition over a range of concentrations). Based on these results, we conducted alanine scans on both S. simulans AIPs II ( 34 ) and III ( 35 ), and tested the peptides to identify important residues for the activation of S. epidermidis agr -III and the inhibition of S. lugdunensis ( Figure 3b and 3c , Supplementary Figures S30–34). The alanine (Ala) mutation of S. simulans AIP-II ( 34 ) starting from the N-terminus, K1A-II ( 37 ) and Y2A-II ( 38 ), showed minor effects to the QS interaction profile ( Figure 3b ). The Y3A-II mutant ( 39 ) also represents the N3A-III mutation of S. simulans AIP-III ( 35 ) as these peptides differ only in this position, and this common mutant acted as an inhibitor of S. epidermidis agr -III. Interestingly, the mutant P4A-II ( 40 ) became an activator of S. aureus agr -III and agr -IV, which we confirmed in a continuous assay (Supplementary Figure S25). Changes to the macrocycle in W6A-II ( 41 ) resulted in decreased inhibitory potency, except against S. aureus agr -III, while reducing structural flexibility by substiting glycine in G7A-II ( 42 ) furnished a decrease against all S. aureus strains. In agreement with the previous consensus, 47 mutations to the C-terminal hydrophobic residues F8A-II ( 43 ) and L9A-II ( 44 ) led to significant loss in potency against all reporter strains. A thioester-to-amide analogue of 34 ( 45 ) (Supplementary Scheme S4), resulted in a decrease in potency against all species apart from S. aureus agr -I ( Figure 3b ). For the Ala-scan of S. simulans AIP-III ( 35 ), the two N-terminal mutants K1A-III ( 46 ) and Y2A-III ( 47 ) both lost the ability to activate S. epidermidis agr -III and showed reduced overall inhibitory potencies ( Figure 3c ). The proline mutation P4A-III ( 48 ) led to an increase in inhibition of S. epidermidis and S. lugdunensis and was the only tested peptide resulting in >95% inhibition at 0.125 nM AIP concentration. Interestingly, the mutation W6A-III ( 49 ) in the macrocycle had no effect on S. epidermidis agr -III activation, while otherwise leading to a weaker inhibition profile. Like G7A-II ( 42 ) also G7A-III ( 50 ) had less effect on inhibition but led to loss of activation of S. epidermidis agr -III. Finally, substitution of the two hydrophobic C-terminal residues in F8A-III ( 51 ) and L9A-III ( 52 ) led to diminished potency of the peptides as also observed for S. simulans AIP-II above. Next, we performed a truncation scan of S. simulans AIP-III ( 35 ), revealing that the inhibition of S. aureus was generally reduced by each truncation going from octamer to pentamer length, with the N-terminus of the pentamer being either acetylated or bis-N-methylated, 48 to circumvent spontaneous rearrangement to the corresponding homodetic pentamer 49 ( 53 – 57 ) ( Figure 3c ). The only exception was an increased inhibition of S. aureus agr -IV by 53 and the truncations had minor effects on S. epidermidis until the 6-mer ( 55 ), except for the loss of S. epidermidis agr -III activation. As anticipated, the macrocycle represented the key feature for potent inhibition of S. lugdunensis agr -I as all truncations including a 5-mer with di-methylated N-terminus ( 57 ) remained highly potent. Finally, we included a known inhibitor of all S. aureus agr groups, S. aureus AIP-III D4A ( 58 ) 22 and observed potent inhibition of S. aureus with the only observed >95% inhibition at 2.5 nM against S. aureus agr -IV but weak interference with S. epidermidis and S. lugdunensis ( Figure 3c ). Having established a foundation to design future QS inhibitors based on S. simulans AIP, we were interested to examine the potential of such compounds as anti-virulence agents. Autoinducing peptide of S. simulans as anti-virulence agent Anti-virulence treatments based on QS inhibition for S. aureus , in particular MRSA, have been postulated since the discovery of agr cross-inhibition and could become an important addition in fighting resistant infections as it may attenuate their severity. 50 However, despite a single recent clinical study with a commensal S. hominis strain, 18 QS-based anti-virulence strategies require further development 51 and certain questions need to be answered: firstly can S. aureus develop resistance towards QS inhibition; and secondly, how effective are QS inhibitors against already virulent bacteria. We attempted to address whether bacteria would develop resistance towards QS inhibitors, by assessing the effects of prolonged treatment of a fluorescent S. aureus agr -I P3-YFP reporter strain with S. simulans AIP-II ( 34 ) ( Figure 4a ). The bacteria were passaged daily for 15 days with and without addition of compound 34 and the agr activity was measured daily by flow cytometry, showing full repression of agr activity at 2 nM dosing of compound 34 over the full period ( Figure 4b ). After the 15 passages, all cultures were passaged once without addition of compound 34 , followed by assessment of the sensitivity of the strains towards inhibition of QS by compound 34 . Thus, all three cultures were exposed to a dilution series of the inhibitory AIP and no change in the potency was observed ( Figure 4c ). Despite the simplicity of this experiment, the results represent a first indicator that repeated treatments with AIP-based anti-virulence agents do not cause rapid resistance development. Download figure Open in new tab Figure 4. QS resistance development and agr deactivation assays. a ) QS resistance development in a S. aureus agr -I P3-YFP reporter strain was examined by passaging cultures daily in the presence of S. simulans AIP-II ( 34 ) for 15 days. On day 16, the cultures were passaged without 34 and dose-response curves against 34 were measured to assess changes in its inhibitory potency. Fluorescence measurements were performed by flow cytometry. b ) Activity of agr over 15 days measured by flow cytometry. The agr activity of untreated cultures decreases during treatment while daily treatments with 34 repressed agr activity at 2 nM and 100 nM. c ) Passaged cultures were treated with 34 (10–0.08 nM) and remained equally susceptible to QS inhibition by 34 as not passaged cultures. d ) The S. aureus agr -I spa -GFP reporter strain (JE2, MRSA) was treated with S. aureus AIP-I ( 1 ) during early growth to activate agr and repress GFP expression. At the start of exponential growth, 34 was added and GFP expression was monitored continuously. Treatment with 34 resulted in immediate increase of fluorescence while induced cultures with 1 remained non-fluorescent. e ) IC 50 values were determined for 34 against S. aureus agr -I spa -GFP induced with different concentrations of 1 . GFP expression was plotted relative to not induced cultures. The majority of QS inhibition data in the literature is performed by treating cultures with inhibitor before the agr system was activated thereby measuring the prevention of agr activation. However, treatments would more often require agr deactivation of virulent bacteria. To better mimic a potential treatment scenario, we therefore transformed JE2, a highly virulent MRSA USA300 isolate of the agr -I type, with a spa -GFP reporter plasmid 52 to create a reporter strain that enables the measurement of agr deactivation. The spa gene is down regulated when agr is activated and will therefore not become fluorescent when the QS system is active ( Figure 4d ). We induced the agr system of reporter cultures with cognate AIP 1 at 100 nM upon inoculation in fresh medium, and once early exponential phase was reached, the inhibitor 34 (40 nM) was added and GFP expression was monitored. Cultures induced with 1 , remained non-fluorescent over the time of the assay, in contrast to cultures treated with 34 , which rapidly started to express GFP and reached fluorescence levels like un-induced cultures because of agr deactivation ( Figure 4d , Supplementary Figure S35). Next, we induced the spa -GFP reporter strain with different concentrations of 1 followed by serial dilutions of 34 , affording IC 50 values for deactivation of agr in the low nanomolar range, which increased when challenged by induction with higher concentrations of 1 ( Figure 4e ). In comparison, the IC 50 value for prevention of agr activation of S. aureus agr -I by compound 34 , measured in the P3-YFP reporter assay (0.45 nM), is 40-fold lower than the highest measured IC 50 value for agr deactivation (18.4 nM at induction with 0.5 μM of 1 ). The combined observations of these two in vitro experiments highlight that QS-based anti-virulence agents are unlikely to induce resistance and can turn off a fully activated agr system in MRSA. Finally, we assessed the potential of S. simulans AIP-II ( 34 ) as an anti-virulence agent in an in vivo MRSA ( agr -I) mouse skin infection model ( Figure 5a–d , Supplementary Table S5). The importance of a functioning agr system for S. aureus during infection to evade the immune response has been established and it was shown that inhibition of agr during early stages of infection can lead to improved disease outcome 48–72 h after its initiation. 50 Thus, S. simulans AIP-II ( 34 ) (100 μM) was added to the MRSA inoculum (10 7 CFU) that was applied to the skin and compared to vehicle and daily treatment with the commercial antibacterial product Fucidin ® (2% fusidic acid). A significant reduction in the skin lesion size was observed after 48 h and 96 h for mice treated with 34 ( P = 0.0174 and P = 0.0249) as well as fusidic acid ( P = 0.0108 and P = 0.0202) compared to the vehicle control ( Figure 5a,c ). Further, a significant decrease in bacterial load (~60-fold, P = 0.009) was observed for mice treated with 34 compared to vehicle control after 4 days, which was comparable to daily treatment with fusidic acid (~38-fold, P = 0.0168) ( Figure 5b ). Download figure Open in new tab Figure 5. S. simulans AIP-II (34) attenuates MRSA infection in a murine skin model. a ) Murine skin infection model performed with vehicle control group [day 1 (n = 16), day 2 (n = 8), day 4 (n = 4)], fusidic acid (daily application of a 38.7 mM ointment (Fucidin) [day 2 (n = 8) and 4 (n = 4)] and 34 (single treatment at day 0 at 100 μM [day 2 (n = 8) and 4 (n = 4)]. MRSA inoculum: 10 7 CFU. Skin lesions measured on day 1, 2, and 4 showing significant reduction in lesion size for mice treated with fusidic acid and 34. b ) CFU count determined after day 1, 2, and 4 showing significant reduction in bacterial load per skin lesion. c ) Representative pictures taken of MRSA lesions at day 4 for untreated and treated mice. d ) Body weight measured at day 1, 2, and 4 showing no statistically significant differences. Data is presented as mean and error bars represent the standard deviation (SD) of the mean. P > 0.05 (ns), P < 0.05 (*), P < 0.01 (**). No statistical difference between treatment or vehicle or time of the infection was found for the body weight, which was expected as no systemic infections were observed ( Figure 5d ). These results are encouraging for the prospects of anti-virulence treatments of staphylococcal infections with non-antibiotic peptides, such as 34 . The high extent of bacterial clearance is most likely a result of a more efficient immune response towards non-virulent MRSA bacteria. DISCUSSION Altering gene expression of co-inhabiting staphylococci through secreted AIPs represents an intriguing ability of the agr system. The agr loci can be found in the genomes of all staphylococcal species and 38 AIPs from 22 species have now been identified from bacterial supernatants, pointing towards a broad utilization of the agr system, including potential QS interference. The colonization of humans and animals by a wide range of staphylococcal species emphasizes that many species share the same environments, providing an arena for biologically relevant inter-staphylococcal interactions, such as QS interference. Here, we report the most comprehensive mapping of QS interference by staphylococcal AIPs performed to date, including seven newly identified AIPs together with all previously reported AIPs. Testing this collection of AIPs against fluorescent reporter strains of S. aureus agr -I–IV, S. epidermidis agr -I–III, and S. lugdunensis agr -I provided a map of >280 QS interactions, which revealed a largely cross-inhibitory network, and lead to the discovery of several potent inhibitors against all the eight tested agr systems as well as previously unknown cross-species activators. To further scrutinize the requirements for the potent inhibition and activation profiles of the S. simulans AIPs, a structure–activity relationship study was performed based on S. simulans AIP-II ( 34 ) and S. simulans AIP-III ( 35 ), including the evaluation of alanine mutants and truncated peptides. The results of this exercise highlighted the importance of different structural features of the peptides. Truncations of the exo-tail affected the ability to inhibit S. aureus the most, while the macrocycle alone was enough to effectively inhibit S. lugdunensis . The S. simulans AIPs represent the first potent inhibitors of QS in S. lugdunensis with a 400-fold increase in potency compared to previously reported inhibitors. 37 Furthermore, S. simulans AIP-II ( 34 ) was investigated for its potential as an anti-virulence agent, by assessing whether MRSA would develop resistance towards the inhibitor upon dosing over two weeks and assessing whether agr could be deactivated in a pathogenic MRSA strain. No detectable resistance development was observed after 15-day long treatment with 34 and we could demonstrate that 34 could shut down the fully active agr system of an MRSA isolate. Finally, a significant effect of this inhibitory AIP on the colonization and pathogenesis of MRSA in viv o was demonstrated in a mouse model, highlighting the power of gene repression through QS inhibition. 10 , 12 , 13 , 18 Our results highlight the potential importance of the agr system and cross-species interference on the colonization of commensal staphylococci and on the pathogenesis of for example S. aureus . Though, the impact of the substantial QS interference among commensal staphylococci on the human microbiota remains to be explored further. It is our hope that the mapping of cross-species QS interactions initiated in the present work will help provide insights into the roles of agr systems in future investigations. Furthermore, our findings highlight the potential utility of natural scaffolds as a promising platform for the development of inhibitors for anti-virulence treatment of Staphylococcus infections. ASSOCIATED CONTENT The Supporting Information contains supplementary figures illustrating AIP trapping experiments, dose–response curves and bar graphs for tested compounds, supplementary schemes showing the compounds syntheses, and supplementary tables containing assay data and library compound sequences. Experimental procedures, materials, methods, and compound characterization data is provided as well as copies of HPLC chromatograms and copies 1 H and 13 C NMR spectra (PDF). AUTHOR INFORMATION Author contributions CRediT: Bengt H. Gless conceptualization, formal analysis, investigation, methodology, data curation, supervision, visualization, writing-original draft, writing-review & editing; Benjamin S. Sereika-Bejder data curation, formal analysis, investigation, methodology, data curation, supervision, visualization, writing-review & editing; Iben Jensen formal analysis, investigation, methodology; Martin S. Bojer investigation, methodology, supervision, writing-review & editing; Katerina Tsiko investigation, methodology; Sabrina H. Schmied investigation; Ludovica Vitolo investigation; Bruno Toledo-Silva investigation; Sarne De Vliegher resources, supervision, writing-review & editing; Hanne Ingmer resources, funding acquisition, supervision, writing-review & editing; Christian A. Olsen conceptualization, resources, funding acquisition, project administration, supervision, visualization, writing-original draft, writing-review & editing. Notes The authors declare no competing interest. ACKNOWLEDGMENTS We thank Prof. Alexander Horswill (Univerity of Colorado) for donation of fluorescent reporter strains. We thank Carina Vingsbo Lundberg and Karen Juhl from Statens Serum Institut (DK) for performing the mouse studies under contract. We thank Peter Damborg (University of Copenhagen) and Paal S. Andersen (Statens Serum Institut (DK)) for contributing bacterial isolates. This work was supported by the Danish Independent Research Council–Natural Sciences (Grant No. 0135-00427B; C.A.O.), the LEO Foundation Open Competition Grant program (LF-OC-19-000039 and LF-OC-21-000901; CAO), and the Novo Nordisk Foundation–Interdisciplinary Synergy Programme (Grant No. 0077593; H.I.). REFERENCES 1. ↵ Otto , M. Staphylococci in the human microbiome: the role of host and interbacterial interactions . Current Opinion in Microbiology 2020 , 53 , 71 – 77 . OpenUrl CrossRef PubMed 2. ↵ Williams , P. ; Hill , P. ; Bonev , B. ; Chan , W. C. Quorum-sensing, intra-and inter-species competition in the staphylococci . Microbiology 2023 , 169 . 3. ↵ Parlet , C. P. ; Brown , M. M. ; Horswill , A. R. Commensal Staphylococci Influence Staphylococcus aureus Skin Colonization and Disease . Trends in Microbiology 2019 , 27 , 497 – 507 . OpenUrl CrossRef PubMed 4. ↵ Ji , G. ; Beavis , R. C. ; Novick , R. P. 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