Identifying a Vaginal Microbiome-Derived Selective Antibiotic Metabolite via Microbiome Pharmacology Analysis

Identifying a Vaginal Microbiome-Derived Selective Antibiotic Metabolite via Microbiome Pharmacology Analysis | 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 Identifying a Vaginal Microbiome-Derived Selective Antibiotic Metabolite via Microbiome Pharmacology Analysis Smrutiti Jena , View ORCID Profile Damilola Lawore , View ORCID Profile Javier Muñoz Briones , Kenzie Birse , Alana Lamont , Romel D. Mackelprang , Laura Noel-Romas , Michelle Perner , Xuanlin Hou , Elizabeth Irungu , Nelly Mugo , Samantha Knodel , View ORCID Profile Stephanie Doran Brubaker , Timothy R. Muwonge , Elly Katabira , Sean M Hughes , Fernanda L. Calienes , View ORCID Profile Raymond Krajci , Rachel Liu , View ORCID Profile Dalí Nemecio , View ORCID Profile Florian Hladik , Jairam Lingappa , View ORCID Profile Adam D Burgener , View ORCID Profile Alicia R Berard , View ORCID Profile Leopold N Green , View ORCID Profile Douglas K. Brubaker doi: https://doi.org/10.1101/2025.08.28.672927 Smrutiti Jena 1 Weldon School of Biomedical Engineering, Purdue University , West Lafayette IN Find this author on Google Scholar Find this author on PubMed Search for this author on this site Damilola Lawore 1 Weldon School of Biomedical Engineering, Purdue University , West Lafayette IN Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Damilola Lawore Javier Muñoz Briones 1 Weldon School of Biomedical Engineering, Purdue University , West Lafayette IN Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Javier Muñoz Briones Kenzie Birse 2 Department of Obstetrics and Gynecology, University of Manitoba , Winnipeg Canada 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alana Lamont 2 Department of Obstetrics and Gynecology, University of Manitoba , Winnipeg Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Romel D. Mackelprang 4 Department of Global Health, University of Washington , Seattle USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Noel-Romas 2 Department of Obstetrics and Gynecology, University of Manitoba , Winnipeg Canada 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michelle Perner 5 Medical Microbiology and Infectious Disease University of Manitoba , Winnipeg Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xuanlin Hou 4 Department of Global Health, University of Washington , Seattle USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth Irungu 6 Partners in Health Research and Development, Kenya Medical Research Institute , Nairobi Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nelly Mugo 4 Department of Global Health, University of Washington , Seattle USA 6 Partners in Health Research and Development, Kenya Medical Research Institute , Nairobi Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samantha Knodel 2 Department of Obstetrics and Gynecology, University of Manitoba , Winnipeg Canada 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephanie Doran Brubaker 7 Graduate School of Public Health, University of Pittsburgh , Pittsburgh USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephanie Doran Brubaker Timothy R. Muwonge 8 Infectious Disease Institute, Makerere University , Makerere, Kampala Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elly Katabira 8 Infectious Disease Institute, Makerere University , Makerere, Kampala Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sean M Hughes 9 Department of Obstetrics and Gynecology, University of Washington , Seattle USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fernanda L. Calienes 10 Fred Hutchinson Cancer Research Center , Seattle USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Raymond Krajci 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raymond Krajci Rachel Liu 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dalí Nemecio 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dalí Nemecio Florian Hladik 9 Department of Obstetrics and Gynecology, University of Washington , Seattle USA 10 Fred Hutchinson Cancer Research Center , Seattle USA 11 Department of Medicine, University of Washington , Seattle USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Florian Hladik Jairam Lingappa 4 Department of Global Health, University of Washington , Seattle USA 11 Department of Medicine, University of Washington , Seattle USA 12 Department of Pediatrics, University of Washington , Seattle USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adam D Burgener 2 Department of Obstetrics and Gynecology, University of Manitoba , Winnipeg Canada 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH 13 Department of Medicine Solna, Karolinska Institutet , Framstegsgatan Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adam D Burgener Alicia R Berard 2 Department of Obstetrics and Gynecology, University of Manitoba , Winnipeg Canada 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alicia R Berard Leopold N Green 1 Weldon School of Biomedical Engineering, Purdue University , West Lafayette IN Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leopold N Green Douglas K. Brubaker 3 Center for Global Health and Disease, Department of Pathology, Case Western Reserve University , Cleveland OH 14 The Blood Heart Lung Immunology Research Center, Case Western Reserve University, University Hospitals of Cleveland, Cleveland , OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Douglas K. Brubaker For correspondence: dkb50{at}case.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT The vaginal microbiome plays a critical role in maintaining immune and epithelial homeostasis in the female reproductive tract. Bacterial Vaginosis (BV) is deleterious to female health, causing the loss of beneficial Lactobacillus species, overgrowth of anaerobic taxa, changes in vaginal pH, breakdown of protective mucins and epithelial barriers, and activation of the immune system. Treatment with gel-based antibiotics (Metronidazole or Clindamycin) resolves BV for 85% of patients, but 50% of those cases recur, indicating a need to identify strategies for overcoming antibiotic resistance and achieving a more durable response. Here, we developed a systems biology approach termed Microbiome Pharmacology Analysis to characterize the antibiotic potential of vaginal microbes, their metabolites and functions, via computational fusion of human cohort multi-omics and post-drug perturbation transcriptomic profiles. We focused on Clindamycin and Metronidazole as candidate drugs and screened 780 vaginal microbiome-drug mimicry candidates to identify candidate taxa and metabolites with antibiotic potential. We demonstrate experimentally that Lactobacillus crispatus -derived Hydroxyisocaproate (HICA) selectively kills Gardnerella vaginalis and that HICA enhances epithelial barrier integrity in a human vagina-on-a-chip system. Our work demonstrates the first use of Pharmacobiome Analysis , for discovering novel, selective antibiotic metabolites for BV with implications for charting the full pharmacologic potential of the vaginal microbiome. INTRODUCTION The female genital tract is a complex microenvironment comprised of an immune-competent epithelial barrier tissue and microbiome that work in concert to maintain mucosal homeostasis and resist invasion by pathogenic organisms 1 . The interactions between epithelial cells in the female reproductive tract (FRT) are governed by intercellular junctional molecules, including tight junctions that regulate paracellular permeability, as well as adherens and desmosomal proteins, which act as a physical defense against the movement of microorganisms across the epithelial barrier 2 . Lactobacilli and their metabolic products may exert direct effects on the mucosa of the FRT, contributing to beneficial outcomes such as improved wound healing, strengthened epithelial barrier integrity 3 , and the maintenance of a non-inflammatory state 4 . The vaginal microbiome is typically comprised of Lactobacillus species, including L. crispatus, L. iners, L jensenii, and L. gasseri , which exhibit many beneficial properties such as the production of lactic acid, maintenance of low vaginal pH 5 , and formation of protective biofilms 6 . Bacterial vaginosis (BV) is a condition of vaginal dysbiosis characterized by loss of Lactobacillus species and overgrowth of facultative anaerobes such as Gardnerella, Mobiluncus, and Prevotella . This change in vaginal microbiome ecology occurs concomitantly with vaginal epithelial disruption, increased production of inflammatory cytokines 7 , and symptoms including odor, discharge, pain during sexual intercourse or urination, and itchiness 8 . Metronidazole and Clindamycin are the clinically recommended antibiotics for treating BV, but recurrent and resistant disease remains a significant challenge 9 . Approximately 50% of patients present with recurrence within one year of treatment 10 , with factors such as past BV infection, sexual behaviors, and ethnicity implicated in recurrence 11 , 12 . Many alternative strategies are being investigated as BV therapeutics 13 , with a strong emphasis on those that target or use the vaginal microbiome directly as a therapeutic agent, including vaginal microbiome transplant 14 , pH modulation 15 – 17 , biofilm disruption 18 , 19 , and probiotics 20 – 22 . The lack of effective therapies for BV is a major challenge for female reproductive health as BV associates with and increases susceptibility to many other conditions such as preterm birth 23 and transmission of sexually transmitted infections 24 . It has also been implicated in pathologies such as infertility 25 , endometriosis 26 and gynecologic cancers 27 , 28 . Since BV impacts as many as 3 million women in United States annually 29 , the discovery of new therapeutic strategies to prevent and treat BV remains a critical need. In this work, we address the need for efficacious alternatives to antibiotics for treating and preventing BV using a systems biology multi-omics modeling approach that fuses microbiome and pharmacology analysis that we term “ Pharmacobiome Analysis” 30 . Pharmacobiome Analysis elucidates novel therapeutic functions of microbiome constituents and byproducts by integrating in vitro drug screening with human in vivo microbiome multi-omics. We compared post-drug treatment transcriptomics data for Clindamycin and Metronidazole from the NIH Common Fund Library of Integrated Network and Cellular Signatures (LINCS) database 31 with vaginal microbiome-host gene expression signatures derived from secondary analysis of vaginal microbiome multi-omics data from the Partners PrEP cohort 32 – 34 . The logic behind this approach is that if a microbiome component (e.g., microbe, metabolite, or function) is associated with a gene expression change in the host that is similar to one caused by one of the drugs, that microbiome factor might itself be a beneficial drug. In total, we screened 780 combinations of vaginal microbiome factors and drug candidates and experimentally tested the most promising candidate identified by Pharmacobiome Analysis using bacterial culture metabolomics, bacterial growth and killing assays, and a vaginal epithelial organ-on-chip system. RESULTS Creation of the vaginal pharmacobiome network resource Pharmacobiome Analysis begins by extracting (1) drug-gene and (2) vaginal microbiome factor-gene signatures for comparison of candidate microbiome factor-drug mimicry relationships ( Figure 1 ). Download figure Open in new tab Figure 1. Overview of Vaginal Pharmacobiome Analysis. (A) Vaginal microbiome multi-omics data is integrated with matched host transcriptomics via Spearman correlation analysis to generate microbe-gene, metabolite-gene, and bacterial functions-gene lists. Human vaginal microbiome-host gene lists are combined with LINCS characteristic direction signatures via Spearman correlation with False Discovery Rate correction to identify vaginal microbiome-drug mimicry associations. To extract the drug-gene signatures, post-drug treatment perturbation gene expression data were obtained from the NIH LINCS database at the level of the Characteristic Direction (CD) score for the “Chemical Perturbations” category for each drug-gene pair for Metronidazole and Clindamycin 31 . CD profiles aggregate the post-treatment gene expression data across all cell lines, drug doses, and time points to obtain a consensus gene signature for the drug. We chose this approach rather than a specific cell line, treatment concentration, or time point because no vaginal cell lines were included in the LINCS database. To extract the vaginal microbiome factor-gene signatures, we obtained vaginal microbiome multi-omics data from a subset of HIV-negative participants enrolled in the Partners PrEP clinical trial 32 , 34 . We analyzed data from 59 participants that included vaginal microbiome composition, vaginal metabolomics, vaginal metaproteomics inferred via mass spectrometry analysis, and vaginal epithelial transcriptomics data obtained from vaginal biopsies. Spearman correlation analysis was used to calculate a per-gene correlation coefficient in the host vaginal transcriptomics data for each microbe, metabolite, and bacterial function variable in the vaginal microbiome composition, metabolomics, or metaproteomics data. The result was a correlation matrix for each microbiome data type quantifying how the abundance of a microbe, metabolite, or bacterial pathway activity is related to the expression of genes in vaginal epithelium, with the coefficients approximating a magnitude and directional change of the gene in response to the microbiome factors. Having extracted the sets of drug-gene relationships and vaginal microbiome factor-gene relationships, we next connected them to infer similarities between the vaginal microbiome factors and drugs. Specifically, we generated a matrix of potential vaginal microbiome factor-drug mimicry inferred associations by calculating a Spearman correlation coefficient for each vector of microbiome factor-gene coefficients and drug-CD gene coefficients from LINCS. At this stage, we applied a Benjamini Hochberg FDR correction to the microbiome-drug correlation coefficient p-values to identify statistically significant linkages for downstream visualization and analysis ( Figure 1 ). We found 100 metabolites, 165 taxa, and 125 bacterial functions potentially associated with host gene signatures that were also affected by the two antibiotics (2 drugs x 390 microbiome factors = 780 candidate mimicry associations). After filtering out missing values and technical dropouts, our total numbers of metabolites, taxa, and bacterial functions associated with host gene signatures impacted by the two antibiotics were 100, 13, and 32, respectively ( Table S1 ). Identifying vaginal bacterial abundance and function relationships to antibiotic drugs Having computed the drug mimicry correlation values for Clindamycin, Metronidazole, and all pairs of vaginal microbes, metabolites, and bacterial pathways, we identified the top candidate microbiome factors likely to mimic the function of these antibiotics ( Figure 2A-C ). We noted that L. iners, L. crispatus, Gardnerella, and Bifidobacterium mapped to the extremes of the distribution. High positive correlation similarity existed between the host gene signature associated with Gardnerella and Bifidobacterium and the cell line gene signatures associated with the antibiotics. In contrast, there was a negative correlation similarity for L. iners and L. crispatus and the antibiotics. Download figure Open in new tab Figure 2. Identification of Vaginal Microbiome Factors with Antibiotic Potential. Shown are correlation similarity scores (Spearman’s rho) for (A) relating the correlation coefficients between vaginal microbes and host gene expression to the CD perturbation values of clindamycin and metronidazole; (B) relating the correlation coefficients between vaginal metabolites and host gene expression to the CD perturbation values for clindamycin and metronidazole; (C) relating the correlation coefficients between vaginal bacterial metabolic functions and host gene expression to the CD perturbation values for clindamycin and metronidazole. See Methods for calculation of correlation similarity scores. We identified several promising drug candidates among the microbiome metabolites based on strong protective gene correlations with both antibiotics. Specifically, we identified Xanthine, Xanthosine, Uracil, Hydroxyisocaproate (HICA), and p-Salicylic acid as potential antibiotics ( Figure 2B ). The anti-correlated metabolites common to both antibiotics (strongly negative correlation similarity scores) included Tryptophan, Ribose 5 Phosphate (R5P), and Imidazole propionic acid. Among these, Imidazole propionic acid was found to be associated with increased inflammation, activation of mTOR signaling, and BV status 35 . Finally, we identified bacterial functions that, when activated, induce similar host gene signatures to Metronidazole and Clindamycin, including the Citrate cycle, Pyrimidine metabolism, and Glycine signaling ( Figure 2C ). Hydroxyisocaproate is a microbiome-derived metabolite selectively antibiotic against BV-associated bacteria In examining our candidate metabolites from Pharmacobiome Analysis, we noted several positive results already known to have antibiotic functions, including p-Salicylic acid and Uracil. Salicylic acid promotes the antimicrobial effect of Carbapenem against P. aeruginosa and has anti-gonococcal activity against azithromycin-resistant N. gonorrhoeae with minimal growth inhibition of commensal vaginal bacteria 36 , 37 . Uracil, while conjugated with Gentamycin or Ciprofloxacin, increases susceptibility of methicillin-resistant Staphylococcus aureus (MRSA) to the antibiotic treatment 38 , 39 . In addition to these metabolites for which antibiotic activities are already known and which can derive from host cell metabolism, we sought to identify novel microbiome-derived molecules that could show antibiotic potential. To identify metabolites of potential vaginal microbiome origin, we performed untargeted metabolomics analysis of conditioned media and matched control media for vaginal strains ( G. vaginalis, L. crispatus, L. iners ) grown in biofilm and suspension conditions ( Figure S1A-B ). Among our top candidate antibiotic metabolites HICA was found to be produced by most vaginal strains, with L. crispatus producing the greatest absolute amount on average (suspension: 7.8, biofilm: 13.0) compared to L. iners (suspension: 3.6) and G. vaginalis (suspension: 5.5, biofilm 1: 6.4, biofilm 2: 0.19) (Figure S1A) . The variation in HICA production when scaled to media ( Figure S1B ) suggests that HICA could be produced by the vaginal microbiome, including G. vaginalis, but depends on specific nutrient availability. Download figure Open in new tab Figure S1. Functional characterization of the antibiotic properties of Hydroxyisocaproate (HICA) (A) HICA production by isolated, cultured type strains of vaginal bacteria in suspension (S) and biofilm (B) conditions. G. vaginalis biofilms were grown in NYCIII (B1) or supplemented BHI (B2) media. (B) HICA production scaled to media (C) G. vaginalis growth inhibition by HICA treatment. (D) L. crispatus growth inhibition by HICA treatment. Next, we performed preliminary experiments to assess the antibiotic potential of HICA by titrating concentrations of 0, 5, 25, and 50 mM cultured with a BV-associated G. vaginalis strain for 24 hours ( Figure S1C ). These experiments indicate that 25mM of HICA is enough to confer 73% growth inhibition of G. vaginalis . As a comparison and control, we characterized the toxicity of 20mM HICA in 24 hour growth with L. crispatus and found only 8% inhibition of growth ( Figure S1D ) . Having identified 20mM HICA as a dose well tolerated by L. crispatus and 25mM as an antibiotic dose for G. vaginalis , we harmonized the doses and protocol and performed time course, dose-response, and pH monitoring experiments of cultured L. crispatus and G. vaginalis to assess the antibiotic potential of HICA at lower doses and earlier time points ( Figure 3 ). We found that for all HICA concentrations (5 mM, 10 mM, 20 mM), L. crispatus continued to grow in biomass over 24 hours and showed no decrease in viability ( Figure 3A ), but a decrease in culture pH potentially due to lactic acid produced by L. crispatus ( Figure 3B ). In contrast to L. crispatus, G. vaginalis showed a decrease in viability at all concentrations of HICA, starting as low as 5mM and achieving significant inhibition with 20mM, that caused near total elimination of G. vaginalis from culture ( Figure 3C ). We measured the pH of the media for G. vaginalis and found no changes in pH during bacterial growth, but that HICA itself reduced the pH of the media in a dose-dependent manner ( Figure 3D ). Therefore, the mechanism by which HICA appears to act as an antibiotic is by modifying environmental pH, a similar manner to which L. crispatus maintains vaginal homeostasis in vivo and a known mechanism by which the vaginal microbiome resists colonization by pathogens and BV-associated bacteria 40 . Download figure Open in new tab Figure 3. (A) L. crispatus growth curve with different concentrations of HICA treatment. (B) Media pH changes at different timepoints of L. crispatus growth with HICA treatments (C) G. vaginalis growth curve with different concentrations of HICA treatment. (D) Media pH changes at different timepoints of G. vaginalis growth with HICA treatments. Hydroxyisocaproate enhances epithelial barrier integrity in a vagina-on-a-chip organoid system Though HICA showed efficacy as a selective vaginal microbiome-derived antibiotic in bacterial culture, the impact of potential therapeutic metabolites on host physiology is equally important to consider for translational viability. To assess the suitability of HICA for translation as a therapeutic for BV in patients from the perspective of impact on vaginal barrier integrity, we implemented the Emulate organ-on-a-chip system to create a vagina-chip (as previously described 41 ). The Emulate microfluidic vagina-chip contains stratified multilayered epithelium and primary human stromal fibroblasts, mimicking the underlying connective tissue of vaginal mucosa. The epithelial layers are connected by tight junctions, adherens junctions and desmosomes, and fibroblasts provide mechanical stability and elasticity to the tissue 1 . This configuration maintains a physiological oxygen gradient besides enabling simulation of physiological parameters including nutrient flow rate and hormone fluctuations 41 . Once the vagina chip system was established, we introduced 20 mM of HICA. Post 48hr incubation, we assessed for the impact of HICA on cellular integrity and barrier permeability ( Figure 4 ). After 48 hours, the HICA-treated system showed increased cellular density and better formation of an epithelial layer compared to the untreated condition, indicating HICA enhanced the quality of the epithelial barrier of the vagina-chip ( Figure 4A ). We also assessed the effect of HICA on inter-epithelial barrier integrity and permeability by Fluorescein isothiocyanate–dextran dye permeability assay. As with the visual inspection via Brightfield microscope showing a more intact epithelial barrier, the permeability experiment quantified an increase in epithelial barrier integrity and reduced permeability ( Figure 4B ). These data together show that the introduction of HICA improved overall vaginal epithelial barrier integrity potentially as the same mechanism as lactic acid by regulating pH 42 , 43 . Together with the bacterial culture experiments, this demonstrates HICA is a potentially translatable selective antibiotic, that inhibits G. vaginalis growth with minimal impact on L. crispatus growth, and eubiotic metabolite for BV treatment. Download figure Open in new tab Figure 4. (A) Brightfield microscope image of vagina-chip system treated with 20mM of HICA versus pre-treatment and media control. ( B ) Apparent permeability of the vagina-chip epithelial layer after 48hours of HICA treatment shows increased barrier integrity (-log10(apparent permeability)). DISCUSSION Here, we leverage a combination of vaginal microbiome multi-omics analysis and drug screening data to create a systems biology platform termed Pharmacobiome Analysis . Using this platform, we identify a candidate BV therapeutic and confirm its activity in the lab. Our novel Pharmacobiome Analysis pinpoints the therapeutic functions of microbial constituents and byproducts. We screened 780 microbiome-drug mimicry candidates to arrive at a ranked list of microbes, metabolites, and bacterial functions with antibiotic potential. Metabolomics analysis of cultured L. iners, L. crispatus, and G. vaginalis allowed us to distinguish between host and microbiome-derived metabolites and prioritize potential therapeutic metabolites by their ability to be produced by the beneficial strain L. crispatus . Finally, screening of HICA, our candidate antimicrobial compound, in bacterial cultures demonstrated its potential as a G. vaginalis -selective antibiotic, and analysis of HICA effects on a vaginal epithelial organ-on-chip showed this metabolite enhances vaginal barrier integrity. Taken together, we present a novel therapeutic strategy with HICA that has the potential to ameliorate multiple hallmarks of BV, including the overgrowth of anaerobic bacteria, support of a Lactobacillus community, and enhancement of vaginal epithelial barrier integrity. HICA is a metabolite in fermented foods, including cheeses, soy sauce, wines, and kimchi 44 . It is derived from the branched-chain amino acid leucine by hydroxyisocaproate dehydrogenases (HicDs) 44 . Evidence suggests that the fermentation process’s specific lactic acid bacterial composition influences HICA production, effectively inhibiting the growth of food spoilage and foodborne pathogens. Its mechanism of action Includes penetrating bacterial cell membranes, leading to depolarization, membrane disruption, and cell death 45 . HICA exhibits broad-spectrum activity against clinical pathogens, including Candida, Aspergillus, Staphylococcus , and Fusobacterium species, and periodontal pathogens such as Porphyromonas gingivalis and Fusobacterium nucleatum 46 . Moreover, HICA has shown efficacy against multi-drug-resistant strains, including Pseudomonas aeruginosa , while being well tolerated in host tissue, even illustrating delayed muscle soreness 47 and repair 48 . To our knowledge, this study represents the first exploration of HICA’s application in the vaginal context, highlighting its potential as a novel antimicrobial agent in this niche. Other potential antibiotic metabolites implicated in our study include Xanthine, Xanthosine, Uracil, and p-Salicylic acid. Our cultured bacterial metabolomics analysis indicated that NAD+, gluconate, glucose-6-phosphate, harmane and dihydroferulic acid can be produced by Lactobacillus strains as well. Of these, gluconate and harmane have the potential to enhance the effect of other antibiotics 49 , 50 with gluconate being able to promote wound healing 51 , further supporting Pharmacobiome Analysis’s ability to identify microbiome-derived metabolites’ with clinically relevant functions. Many of these metabolites have not been tested in the vaginal tract or as therapeutics for BV, making them potential candidates for novel therapeutics along with HICA. Though our overall hypothesis that the magnitude of the similarity score is predictive of drug mimicry seems to hold for metabolites, in the taxa-based Pharmacobiome analysis ( Fig 2A ), we observed that Gardnerella was positively associated with the antibiotics scores and Lactobacilli were negatively associated. This result is opposite of what one might expect given the positive health characteristics associated with a Lactobacillus dominant vaginal community. Our interpretation of this is that since the antibiotics reduce the abundance of Gardnerella the genes positively associated with Gardnerella abundance would decrease and those genes negatively associated would increase. The effect of this would be to invert the expected correlation and produce the apparent, but incorrect inference that higher levels of Gardnerella would induce host gene responses similar to antibiotic treatment. This highlights that accounting for the effect of a drug on the microbiome’s ecology and abundance of taxa is important in taxa-based Pharmacobiome inferences. Our approach takes advantage of Characteristic Direction-averaged gene expression profiles from LINCS to define the vaginal pharmacobiome network, integrating data from various cell lines, time points, and drug concentrations into a single profile. The absence of vaginal cell lines in LINCS is a limitation, since gene expression profiles derived from Clindamycin and Metronidazole-treated vaginal epithelial cells would be more representative of the site where these drugs act. Additionally, optimizing dosing concentrations and time points in drug perturbation transcriptomics experiments could yield higher-fidelity data for vaginal microbiome-based drug discovery. Despite these limitations, the averaged gene expression profiles enabled experimentally testable predictions and the validation of antibiotic-candidate metabolites. The biological context provided by the Partners PrEP multi-omics data likely contributed to overcoming the absence of vaginal cell lines in LINCS. Despite these limitations, we show that the averaged gene profiles of the CD led to experimentally testable predictions and validated antibiotic candidate metabolites. Further refinement of cell type-specific drug perturbation transcriptomics would enhance our analysis and represent a promising future research direction. The logic behind Pharmacobiome Analysis is similar to the Connectivity Map project 31 where perturbation gene expression signatures of drugs were used to identify repurposing candidates. While the Connectivity Map approach has limitations such as a potential high false positive rate 52 , we control for that here through stringent FDR levels, prioritization of microbiome-derived molecules through in vitro metabolomics analysis, and rigorous testing of therapeutic effects experimentally. This study makes a complementary contribution to the body of work harnessing systems biology approaches to understand the vaginal microbiome and its role in BV 34 , 53 , 54 . Indeed, addressing the many critical unmet needs in female reproductive health will require an array of methods tuned to address specific challenges. We envision Pharmacobiome Analysis as a broadly applicable, encompassing framework with the potential to accelerate microbiome-based therapeutic discovery and enable precise characterization of the therapeutic potential of all microbiomes. Further in depth molecular and pathway level studies will facilitate insight into the mechanism of the eubiotic function of the bacteria derived compounds/metabolites advancing towards their therapeutic implementation. METHODS Human Vaginal Microbiome Multi-Omics Data Vaginal microbiome multi-omics data from a subset of 405 HIV-negative women enrolled in the Partners PrEP (Pre-Exposure Prophylaxis) study 32 . The proteomics, metaproteomics, and metabolomics data were previously generated (as described 35 ), and we used data from a subset of 90 participants who were profiled for transcriptomics data from vaginal biopsies as well as the above measures. Our analysis here focused on the subset of matched multi-omic samples from Group 2 participants, with our largest subset of matched data covering 59 patients. Drug Perturbation Transcriptomics Data Post Clindamycin and Metronidazole treatment gene expression profiles were obtained from the Library of Integrated Network and Cellular Signatures (LINCS) Chemical Perturbation SigComm database 31 . Data were obtained at the level of Characteristic Direction (CD) level which summarizes the net perturbation gene expression profile of a drug across multiple cell lines, concentrations, and time points (CD perturbation value). The rationale for using the averaged CD perturbation value rather than cell line-specific responses for the drugs is because there were no vaginal epithelial cell lines in LINCS, thereby making the consensus CD signature the best of the available signature options. Integration of Drug and Microbiome Data Microbiota composition, metaproteomics, and metabolomics data from the Partners PrEP cohort subset were analyzed via Spearman correlation analysis with the vaginal epithelial transcriptomics data to identify a gene signature for each microbe, microbial pathway, and metabolite. The magnitude and direction of the correlation value is treated like a hypothesized regulatory relationship. The vector of gene-based Spearman correlation values for each microbiome factor was then correlated (also Spearman) against the CD perturbation values for Clindamycin and Metronidazole from LINCS to generate a correlation similarity score for each drug-to-microbiome factor pairing. These correlation similarity scores for microbiome factor-drug pairings were subjected to multiple hypothesis correction via Benjamini Hochberg, preserving the multiple comparison correction for the stage of the workflow where the results of the hypothesis test are interpreted. Vaginal Bacterial Metabolomics The cultured bacterial metabolomics data used in this study was performed as described and data are deposited in Metabolomics Workbench: http://dx.doi.org/10.21228/M82M8R . In brief, L. crispatus ATCC 33820, G. vaginalis ATCC 14018, and L. iners ATCC 55195 were cultured for the metabolomics study. For L. crispatus , De Man–Rogosa–Sharpe (MRS) media was used for suspension cultures. NYC III media was used for growing G. vaginalis suspension. L. iners suspension culture was grown in BHI 55 . L. crispatus biofilm was grown in MRS media. G. vaginalis biofilm was grown in NYCIII and Brain Heart Infusion media supplemented with 2% gelatin, 0.5% yeast extract and 0.1% soluble starch (sBHI) separately to attain two types of biofilms based on substrate used. All cultures were incubated at 37 0 C, 5% CO 2 , 24hr shaking for suspension and 48hr static for biofilm. Post incubation, cultures were centrifuged at 13000 x g for 10mins and then sterile filtered using 0.2μm syringe filter. Cell free supernatants were sent in triplicates to Metabolon, NC, USA, for non-targeted metabolomics 34 . Vaginal Bacterial Growth Inhibition Assay Lactobacillus crispatus strain ATCC 33820 and Gardnerella vaginalis strain ATCC 14018 were grown in MRS and TSB+5% sheep blood media respectively. Single colony was picked and inoculated in appropriate broth media and cultured with shaking for 24 hrs. in 5% CO 2 at 37 0 C until 0.4 at OD 570 nm for the MIC experiment of selected metabolites. MIC experiment was performed on 96 well plates (Corning, 3370) with 1:100 culture to media ratio and 5mM, 10mM and 20mM concentrations of L-alpha-Hydroxyisocaproate (Thermo scientific, Cat: 204810050). Temporal growth/inhibition of L. crispatus and G. vaginalis , under the effect of HICA was measured using Biotek synergy H1 microplate reader at OD 570 . The experiment was repeated thrice with triplicates each time. The effect of 20mM, 30mM and 40mM D-Ribose-5-phosphate disodium salt hydrate (Thermo scientific, cat. No. AAJ6681503) was also screened (data not shown). Vagina-on-chip culture Dual channel microfluidic system vagina-on-chip devices used for this study were from Emulate Inc. (Boston, MA). The system was established using a previously published method 41 . Briefly, the apical channel with 1 mm W × 1 mm H was seeded with Primary human vaginal epithelial cells (Lifeline Cell Technology) and the basal channel having 1 mm W × 0.2 mm H seeded with Primary human uterine fibroblasts (ScienCell Research Laboratories, cat. no. 7040). Cocktails of Collagen IV (30 μg/mL) from human placenta (Sigma, cat. no. C7521) with collagen I (200 μg/mL) from rat tail (Corning, cat. no. 354236) and collagen I (200 μg/mL) with poly-L-lysine (15 μg/mL) (ScienCell Research Laboratories, cat. no. 2301) were used for apical and basal chamber ECM coating 2-3 hours prior to introducing cells respectively. Seeding density was 1 × 10 6 cells/mL for fibroblasts and 3 × 10 6 cells/mL for epithelial cells. The chips were incubated at 37°C with 5% CO 2 with continuous media flow. Using Zoe fluidic system (Emulate), the apical chamber was perfused with cervical epithelial cell basal medium (ATCC, cat. No. PCS-480-032) at a flow rate of 20 μL/h and basal chamber using fibroblast growth medium (ScienCell Research Laboratories, cat. no. 70940) at 40 μL/h flow rate. After 4 days, the basal media was replaced with in-house differentiation medium for the basal chamber, composition as described elsewhere 41 . Metabolite treatments The well-established and differentiated vagina-on-chips were treated after 8 days with metabolites for 48 hours at 37 0 C and 5% CO 2 with unchanged flow rates. HICA and D-Ribose-5-phosphate disodium salt hydrate were added to the apical chamber media at concentrations 20mM and 10mM respectively. We used 9 vagina-on-chips (triplicate for each condition including no treatment control) for this experiment. Permeability/barrier integrity assay Fluorescein isothiocyanate–dextran (Sigma, cat. No. FD4-100MG) was used with the apical chamber media at a concentration of 100 μg/mL. After 24 hours, the flow through from the basal and apical chamber outlet was collected and the fluorescence intensity was measured at an excitation wavelength of 485nm and emission at 528nm using plate reader (BioTek, Synergy H1). Apparent permeability (P app ) was calculated using the following equation: where P app is the apparent permeability in units of cm/s, SA is the surface area of the co-culture channel (0.17cm2), Q R & Q D are the fluid flow rates in the receiving and dosing channels, respectively, in units of cm 3 /s, and C R,0 & C D,0 are the recovered concentrations in the receiving and dosing channels, respectively, in any consistent units (Emulate, barrier function analysis protocol). Data and Code Availability The vaginal microbiome multi-omics data used in this study is available in its source publication 34 . The LINC CD signatures can be obtained from the SigComm LINCS database 31 . This paper does not report custom computing code. FUNDING This work was supported by a grant from the Good Ventures Foundation, startup funding from Purdue and Case Western Reserve University, and the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD110367. Generation of the omics data used for this study was supported through funding from R01 AI111738. The Partners PrEP Study was supported through a research grant (ID #47674) from the Bill and Melinda Gates Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AUTHOR CONTRIBUTIONS Conceptualization: SJ & DKB; Methodology, DL, SJ, DKB; Organ-on-chip: SJ, LNG; Visualization: SJ, DKB.; Writing – original draft, SJ & DKB.; Writing – review & editing: DL, JMB, SDB, ARB, KB, AL, RDM, LNR, MP, XH, EI, NM, SK, TRM, EK, SMH, CL, FLC, RK, RL, DN, JMB, CC, FH, JL, LNG, DKB. COMPETING INTERESTS The authors declare no competing interests ACKNOWLEDGEMENTS The authors would like to thank the Partners PrEP-BV Study Team and the participants whose data was used in this study. We are grateful for the opportunity to work with the team to advance other areas of gynecologic health. Funder Information Declared Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health , R01HD110367 National Institutes of Health, https://ror.org/01cwqze88 , AI111738 Bill and Melinda Gates Foundation , 47674 Footnotes Fixed errors in authors and affiliations. REFERENCES 1. ↵ Plesniarski A , Siddik AB , Su RC . The Microbiome as a Key Regulator of Female Genital Tract Barrier Function . Front Cell Infect Microbiol . 2021 ; 11 : 790627 . Epub 20211217. doi: 10.3389/fcimb.2021.790627 . PubMed PMID: 34976864 ; PMCID: PMC8719631 . 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