A systems biology approach to evaluate potential probiotic candidates for women’s vaginal health

A systems biology approach to evaluate potential probiotic candidates for women’s vaginal health | 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 A systems biology approach to evaluate potential probiotic candidates for women’s vaginal health Emma M. Glass , Glynis L. Kolling , Jason A. Papin doi: https://doi.org/10.1101/2025.06.16.659967 Emma M. Glass 1 Department of Biomedical Engineering, University of Virginia , Charlottesville, Virginia, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Glynis L. Kolling 1 Department of Biomedical Engineering, University of Virginia , Charlottesville, Virginia, United States of America 2 Department of Biochemistry & Molecular Genetics, School of Medicine, University of Virginia , Charlottesville, Virginia, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jason A. Papin 1 Department of Biomedical Engineering, University of Virginia , Charlottesville, Virginia, United States of America 2 Department of Biochemistry & Molecular Genetics, School of Medicine, University of Virginia , Charlottesville, Virginia, United States of America 3 Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia , Charlottesville, Virginia United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: papin{at}virginia.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Probiotic supplements are marketed for diverse health benefits, yet species inclusion often lacks functional rationale. We surveyed 352 U.S. probiotic products and found 36 unique microbial species, with most supplements containing only one species and no clear link between species and intended health benefit. To evaluate probiotic function, we developed CoPaPro, a collection of 1,012 genome-scale metabolic models spanning commensal, pathogenic, and probiotic bacteria. Flux balance analysis revealed that current probiotic species fail to capture the metabolic diversity of native commensals. Focusing on vaginal health, we identified commensals with metabolic profiles overlapping Gardnerella vaginalis , a key pathobiont. In vitro spent media assays using 11 vaginal isolates showed variable inhibition of G. vaginalis , primarily driven by D-lactic acid production rather than metabolic similarity. Several non- Lactobacillus species produced inhibitory levels of D-lactate. These findings highlight the need for function-based probiotic design and demonstrate a scalable framework integrating metabolic modeling with experimental validation. Introduction Probiotics are defined as live microorganisms that when administered in adequate amounts confer a health benefit to the host ( 1 ). Many fermented foods containing probiotic lactic acid bacteria ( Lactobacilllus, Bacillus, and Bifidobacterium ) have been consumed for centuries, including kimchi, sauerkraut, and yogurt, among others ( 2 ). Around the year 2000 (S1) research on probiotics and probiotic rich foods began to increase exponentially resulting in a plethora of studies touting the extensive health benefits of probiotics including obesity prevention, improved gut-health, reduced risk of metabolic disorders, modulation of fecal microbiota, improvement of liver metabolism, and reducing cholesterol ( 3 – 6 ). The commercial probiotics market was estimated at $70 billion in 2022 and is expected to hit $374 billion by 2034 ( 7 ). Despite the substantial probiotics market size and rapid growth, there have only been two probiotic products that have been categorized as therapeutic and approved by the FDA in the United States (biologics VOWST and REBYOTA for treating recurrent Clostridioides difficile infection) ( 8 , 9 ). The rest of the probiotic market is occupied by probiotic supplements that are marketed for specific uses such as improving gut health, vaginal health, and even brain health. However, these claims are often not backed by substantial in vitro scientific research, preclinical, or clinical studies, due to the low level of necessary FDA regulation of these products ( 10 , 11 ). This observation highlights the gap in understanding how species included in probiotic supplements can functionally and mechanistically support the targeted use case. It is necessary that we gain a deeper understanding of the range of metabolic functions across the species included in current probiotic supplements to develop more intentional and rationally designed probiotics for specific use cases. One use case that is being increasingly targeted by probiotic companies is women’s health, specifically vaginal microbiome health ( 12 – 15 ). The vaginal microbiome is essential for maintaining vaginal health and protecting against dysbiosis and opportunistic pathogen proliferation ( 16 ). Generally, a vaginal microbiome that is considered a healthy state in reproductive age women is dominated by Lactobacillus , producing lactic acid to maintain an acidic vaginal pH ( 16 ). In a shift to a dysbiotic state, populations of non- Lactobacillus commensal vaginal bacteria take hold, resulting in an increase in species diversity in the microbiome and a higher pH ( 17 ). This shift leaves the vaginal microbiome susceptible to bacterial vaginosis (BV), a dysbiotic vaginal state often characterized by the presence of Gardnerella vaginalis ( 18 , 19 ). However, there are also populations of women with naturally more diverse vaginal microbiomes that do not present with BV-related symptoms. It has been shown that the functional niches of these diverse vaginal microbiomes overlap with both G. vaginalis and Lactobacillus sp. , resulting in prevention of both G. vaginalis proliferation and Lactobacillus dominance ( 20 ). BV is typically treated in the clinic with either oral antibiotics or vaginal antibiotic suppositories; however, there is a high rate of BV recurrence reported in vulnerable populations ( 21 – 23 ). Additionally, the misuse, overuse, and off-target effects of broad-spectrum antibiotics in clinical settings is rapidly leading to the rise of antimicrobial resistant bacteria, resulting in infections that are becoming increasingly difficult to treat ( 24 ). This heterogeneity in vaginal microbiome composition across women of varying demographics and high rates of BV recurrence after antibiotic treatments highlights the need for something other than a one-size-fits all approach to treat BV. One promising avenue to explore is probiotic therapeutics and preventative supplements. Interestingly, there is research showing that oral or vaginal suppositories of Lactobacillus species can restore equilibrium in the vaginal microbiome ( 25 , 26 ). We performed cross-cohort meta-analysis of 32 independent research studies that treated recurrent BV with probiotics, antibiotics, or probiotic/antibiotic combination therapies (S2, S3). Across these 32 studies, we see no statistical difference in clinical outcomes of patients treated with oral antibiotics, intravaginal probiotics, antibiotics, or probiotic/antibiotic combination therapies in terms of BV recurrence. We also observed a large variation in effectiveness at preventing BV recurrence across groups. This underscores the fact that probiotics are a promising avenue for preventing BV recurrence, as they seem to be just as effective as antibiotic therapies. Additionally, because of the large variation in recurrence across treatment modalities, there is an opportunity to mechanistically and rationally design a novel and more effective probiotic supplement to support vaginal microbiome health by preventing BV and Gardnerella vaginalis colonization. In this study, we perform an extensive survey of probiotic supplement products available at the top three pharmacies in the United States to gain a more comprehensive understanding of the probiotic market landscape. Additionally, we use a metabolic network modeling approach to better understand the mechanistic landscape of bacterial species used in probiotic supplements. Genome-scale metabolic network reconstructions (GENREs) are powerful modeling tools that can provide metabolic context to ‘omics data and simulate metabolic phenotypes of bacteria at the strain-level ( 27 ). GENREs enable us to explore the functional similarities and differences between probiotic, commensal, and pathogenic bacteria through species-specific network model generation and constraint-based analysis (COBRA) ( 28 ). Through network model simulation, we identify unique metabolic signatures of probiotic species and determine that there are functional gaps in the landscape of current probiotic species. Secondly, we use a combination of computational and in vitro approaches to identify existing commensal members of the vaginal microbiome that could be candidates for a novel rationally designed probiotic supplement for preventing vaginal dysbiosis. To accomplish this, we leverage GENREs to better understand the similarities and differences in metabolic function of commensal vaginal species. Secondly, we perform in vitro spent media experiments to determine which commensal vaginal bacteria can inhibit G. vaginalis growth. Results Survey of 352 over the counter probiotic supplements To gain a deeper understanding of the landscape of over-the-counter probiotic supplements and the diversity of bacterial species they contain, we performed a survey of all over-the-counter probiotic supplements available at the top three pharmacies in the United States; CVS, Walgreens, and Walmart (9,600, 9,323, and 4,865 store locations respectively) ( 29 ). Across these pharmacies there were 352 unique probiotic supplement products available for purchase. Across these 352 supplements, there were 36 unique probiotic species used (35 bacterial species; 1 fungal species, Saccharomyces boulardii ), and 70 distinct brand names ( Figure 1 A ). Download figure Open in new tab Figure 1. Survey of over the counter probiotic supplements. A) Description of products surveyed across CVS, Walgreens and Walmart. B) Top 10 most prevalent probiotic species across surveyed supplements. C) Histogram of the number of probiotic supplements containing a certain number of probiotic species. D) Box and whisker plot of the number of species contained in a probiotic supplement by brand-name. Only brand-names containing three or more unique products were displayed on this plot. E) PCA plot of the species profiles of each probiotic supplement. Colors of points represent the marketed use for each probiotic supplement. The 10 most prevalent species of bacteria found across the 352 probiotic supplements are detailed in Figure 1 B. The top two species L. rhamnosus and L. acidophilus were present in 46% of all probiotic supplements. Both species are lactic acid-producing bacteria that are also found naturally in fermented dairy products like yogurt and cheese. Additionally, B. coagulans was present in 22% of probiotic supplements, most often appearing in probiotic gummies. B. coagulans spores are added to gummy-based probiotics due to their high survival rates during higher-temperature processing steps required for creating gummies ( 30 ). Other species like Lactobacillus and Bifidobacteria are typically lyophilized and encapsulated ( 31 ). Additionally, we recorded the number of species included in each supplement ( Figure 1 C ) noting over half of the probiotic supplements contain one probiotic species. We observed over half of the probiotic supplements only contained one probiotic species. This result was surprising, because it suggests that the administration of one probiotic species can have a significant impact on health. Studies have shown that multi-strain probiotics are more effective at promoting biological activities and displacing pathogens due to the synergy amongst probiotic strains ( 32 ). This observation suggests there could be a benefit to taking a multi species probiotic, despite most over-the-counter probiotic supplements only containing one species. However, there are several supplements that contain more than five unique species and even several that contain 17 unique species. Because of the variation in number of unique probiotic species included in a supplement, we explored if the number of unique species was linked with brand-name ( Figure 1 D, Brand-names have been de-identified). We observed that name brand #3 probiotics have a median of 12 species in each product. On the other hand, name brand #6 probiotics have a median of 1 species in each product. Additionally, some brand names have a significant spread in the number of probiotic species included across products. For example, name brand #1, name brand #3, and name brand #10 probiotics all range from having one species to 15 species. Because of the apparent inconsistency in the number and type of species used in probiotic supplements, we wanted to determine whether there is a link between the species included in a supplement, and the marketed use for the probiotic. We recorded the targeted use for supplement along with the profile of species either present or absent in each supplement and used this information for dimensionality reduction and visualization with PCA ( Figure 1 E ). We observe no apparent clustering in this PCA plot based on marketed use for the probiotic. This result indicates that across all over-the-counter probiotic supplements, there is no specific combination of species that are consistently used to specifically support gut health, vaginal health, etc., in probiotic supplements. This result highlights the gap in understanding the functional capabilities of individual probiotic species. To elucidate the functional capabilities across probiotic species and how their functions compare to pathogenic and commensal species, we employed computational approach. Ultimately, this work will allow us to gain a deeper understanding of how probiotics can be used to positively impact human health, and is a step toward rational design of probiotic supplements and therapeutics. Metabolic network modeling of probiotic, commensal, and probiotic bacterial species To assess functional capabilities of probiotic, commensal, and pathogenic bacterial species, we generated genome-scale metabolic network reconstructions (GENREs) representing 1,012 bacterial species using the automated GENRE construction tool, Reconstructor ( Figure 2 A, Methods) ( 33 ). We created 779, 198, and 35 GENREs of commensal, pathogen, and probiotic metabolism respectively, which we call the CoPaPro collection. With these GENREs, we explored reaction content across probiotic, commensal, and pathogenic species. Download figure Open in new tab Figure 2. Description of genome-scale metabolic network models of probiotic, pathogen, and commensal metabolism. A) Workflow for generating the CoPaPro GENRE collection. B) Number of unique reactions across the GENREs of each category. C) Number of shared reactions between probiotic, pathogen, and commensal organisms. D-F) Metabolic subsystems of reactions that are unique to commensal (D), pathogen (E), and probiotic (F) GENREs. G) PCA of metabolic phenotypes of probiotic GENREs. Colors represent distinct taxonomic families. Triangles represent cluster centroid, ellipses plotted 2 standard deviations from the centroid. Kruskall-Wallis test for median distances between centroids, p-value = 1.16 e – 112. Cluster standard deviations are as follows: Lactobacillus: 995.19, Streptococcaceae: 533.53, Bacillaceae: 635.82, Bifidobacteriaceae: 609.72, Akkermansiaceae: 248.95, and Enterococcaceae: 488.13. H) PCA of metabolic phenotypes across probiotic, pathogen, and commensal GENREs. Triangles represent cluster centroid, ellipses plotted represent two standard deviations from the centroid. Kruskall-Wallis test for median distances between centroids, p-value = 0. We observed that commensal species have the greatest number of unique reactions across GENREs (4,296 reactions) likely due to the size of the category and diversity of bacterial species it contains ( Figure 2 B ). On the other hand, probiotics have the fewest unique reactions (2,457 reactions), likely due to the small size of the category and the homogeneity of species it contains ( Lactobacillus, Bacillus, Bifidobacteria ). In the upset plot in Figure 2 C, we observe that there is significant overlap in reaction content between the three groups, with pathogens and commensals sharing the greatest number of reactions (3,588). While we observed significant conservation in reaction content across the three groups, there were also reaction sets that were unique to each group. Across these unique reaction sets, there were differences in reaction metabolic subsystems. In commensal bacterial species, most of the unique commensal reactions are a part of the amino acid metabolism ( Figure 2 D ). Amino acid metabolism is an essential process for all organisms, but there have been studies that explicitly suggest that commensal bacteria are essential for the synthesis and absorption of certain amino acids like alanine, glutamate, and tryptophan ( 34 , 35 ). Alternatively, we observed that unique pathogenic reactions were mostly belonging to both the amino acid biosynthesis pathway, and the terpenoid and polyketide biosynthesis pathway ( Figure 2 E ). Many well-known antibiotics such as tetracycline are polyketides, most notably produced by Streptomyces , and some polyketides even act as virulence factors in pathogens ( 36 , 37 ). Finally, we observed that reactions unique to probiotic bacteria most often belonged to carbohydrate metabolism pathways ( Figure 2 F ). This result is consistent with previous studies that show carbohydrate metabolism is one key functions of probiotic bacteria, which aids digestion and promotes short chain fatty acid production, which are essential for fermenting dietary fiber and regulating inflammation ( 38 – 41 ). This presence of unique metabolic reactions and heterogeneity in the metabolic subsystems suggests that bacterial species from commensal, pathogen, and probiotic categories could occupy distinct metabolic niches ( 42 ). Understanding differences in metabolic niche through in silico simulation We used the GENREs of commensal, pathogenic and probiotic metabolism from the CoPaPro collection to probe each species’ genotype-phenotype relationship using constraint-based reconstruction and analysis (COBRA). We generated metabolic phenotypes for each GENRE by generating flux distributions through flux balance analysis. Each generated flux distribution represents a distinct metabolic state of a specific organism. By sampling the flux solution space hundreds of times, we can gain an understanding of the range of all possible metabolic phenotypes for a given organism ( 43 ). We first examined the range of metabolic phenotypes across all probiotic bacteria to gain a better understanding of functional similarities and differences within this category. To visualize flux distributions, we employed principal component analysis ( Figure 2 G ). We observed clustering of metabolic function in probiotics by family (p-value < 0.001). By examining similarities and differences in metabolic function in current probiotic species, we can select probiotic species that capture the widest range of metabolic functions to fill a wide range of metabolic niches. For example, combining species from the Lactobacillacea and Bifidobacteriacea families would cover a wide range of metabolic phenotypes. On the other hand, combining Akkermansiacea and Bifidobacteriacea species together may not have any added benefit than just administering Bifidobacteriacea strains alone. Secondly, we were interested in gaining a deeper understanding of how commensal (blue), pathogenic (yellow), and probiotic (green) metabolic phenotypes are similar and different ( Figure 2 H ). Through this analysis, we observed many pathogenic and commensal metabolic phenotypes not captured by probiotic species. The gap in metabolic overlap we observe across current probiotics suggests that there is a large opportunity to expand species used in probiotic supplements. We will expand upon this analysis and identify ways to fill this metabolic gap in a specific use case; women’s health and the vaginal microbiome. Metabolic function in vaginal probiotic and commensal species Through our survey of commercially available probiotics, we identified 23 supplements that claim vaginal health benefits containing 22 unique probiotic bacterial species with Lactobacillus sp. comprising 15 of the 22 unique species, likely because a Lactobacillus dominant vaginal microbiome is considered healthy ( Figure 3 A ) ( 16 ). However, certain populations do not exhibit Lactobacillus dominance nor symptoms consistent with vaginal dysbiosis ( 20 ) suggesting that non-Lactobacillus commensal members of the vaginal microbiome can be just as important for maintaining homeostasis and could be potential candidates for a novel probiotic consortium to promote vaginal health. To identify vaginal commensal species suitable for this role, we performed a combination of computational and in vitro analyses. Download figure Open in new tab Figure 3. Functional analysis of vaginal commensal and probiotic species. A) Description of probiotic supplements that tout vaginal health benefits. B) PCA plot of metabolic phenotypes of vaginal commensal and probiotic species. Triangles represent cluster centroids, ellipses plotted 2 standard deviations from the centroid Kruskal-wallis test for median distances between centroids, p-value=2.2e-7. Vaginal commensal standard deviation: 897.7. Vaginal probiotic standard deviation: 863.35. C) Hierarchical clustering of metabolic phenotypes across vaginal probiotic species, two G. vaginalis strains, and culturable vaginal commensal species. Metabolic phenotype vectors are calculated as the per-reaction median value across 500 flux samples. We began by identifying vaginal commensal strains and vaginal probiotic species from the larger CoPaPro collection and simulated metabolic phenotypes using flux balance analysis. Through dimensionality reduction and visualization of the metabolic phenotypes, we observe that there are significant differences in metabolism between species used as vaginal probiotics in commercially available supplements, and the native flora of the vaginal microbiome, with the commensal species covering a wider range of metabolic functions (vaginal commensal standard deviation: 897.7; vaginal probiotic standard deviation: 863.35; Kruskal-Wallis test for median distances between centroids: p=2.2e-7) ( Figure 3 B ). Consistent with what we observed in Figure 2 H, there are metabolic functions of vaginal commensals that are not captured with the current suite of vaginal probiotic species. This result presents an opportunity to leverage native commensal members of the vaginal microbiome as possible novel vaginal probiotic strains. Prevention of G. vaginalis growth, frequently associated with BV, is one specific target we could consider when designing next-generation probiotics for the vaginal microbiome ( 44 ) through identifying commensal vaginal species with a metabolic niche most like G. vaginalis , thereby preventing proliferation through resource competition. To identify these species, we began by examining the metabolic profiles of two G. vaginalis strains, commensal vaginal species that can be grown in vitro (necessary for downstream in vitro assays), and vaginal probiotic species, to identify the median metabolic signature of each strain. Then, using hierarchical clustering, we can examine the similarities and differences in metabolic signatures across all strains ( Fig 3 C ). Ultimately, we observe that G. vaginalis strains have very similar functional signatures to Bifidobacterium species that are currently used in vaginal probiotics. Interestingly, the Lactobacillus and Bacillus species used in vaginal probiotics are more functionally like commensal vaginal species than G. vaginalis. This result implies that overlap in metabolic niches may not be the only reason for an effective probiotic. According to these in silico results, Lactobacilli do not have similar metabolic profile to G.vaginalis , but we know that Lactobacilli are essential for maintaining vaginal health through secretion of lactic acid to maintain a low pH environment. This result could suggest that when considering the rational design of probiotics, preventing G. vaginalis colonization may not be a function of only overlapping niche or potential for competition, but may be a more nuanced problem which also considers additional environmental factors. To gain more insight into the balance between resource competition and environment factors driving the prevention of G. vaginalis colonization, we performed an in vitro spent media assay to model competition between G. vaginalis and vaginal commensal species. Spent media assay reveals commensal vaginal species whose spent media inhibits G. vaginalis growth Our computational metabolic phenotype analysis revealed that there are distinct functional groups of vaginal commensal species. We observed that some vaginal commensals have similar metabolic phenotypes to G. vaginalis strains, implying these species could exhibit competitive interactions. Vaginal commensal species that compete with G. vaginalis could be key candidates for a novel probiotic consortium preventing G. vaginalis colonization. To further understand differences in metabolic functionality across vaginal commensals and identify which commensals effectively inhibit G. vaginalis growth, we designed an in vitro spent media assay. This assay is a proxy for assessing competition between vaginal commensals and G. vaginalis ; allowing us to determine which vaginal commensal bacteria produce metabolic byproducts that would inhibit the growth of G. vaginalis. Through this experiment, we can better understand if a stable population of the vaginal commensal could prevent the proliferation of a G. vaginalis population. We began by generating 11 spent media conditions, one for each of the culturable commensal vaginal species shown in Figure 3 C, that were able to be cultured in vitro . Then, an overnight culture of G. vaginalis was inoculated into each species’ spent media and growth was monitored through stationary phase (Methods). We observed some variation in the G. vaginalis growth profiles on the 12 media conditions (PGY (Modified) control, and 11 spent media conditions) ( Figure 4 A ). More specifically, we see three distinct response groups; Non-inhibitory (N), Moderate (M), and Inhibitory (I) (pairwise t-tests of area under the mean growth curve: N vs M: p-value < 0.01, N vs I: p-value < 0.001, and I vs M: p-value < 0.001) ( Figure 4 B ). G. vaginalis grown on non-inhibitory media ( Mobiliuncus curtisii, Veillonellaceae bacterium, and Ezakiella massiliensis spent media) had the same growth dynamics as G. vaginalis grown on the PGY (Modified) media control. In the moderate group, we see a significant change in the area under the growth curve of G. vaginalis grown in Peptoniphilus vaginalis, Anaerococcus marseille, Criibacterium bergeronii, and Aerococcus christensenii spent media compared to the uninhibited group (p-value < 0.01). In the inhibited group, we see a statistically significant difference in the area under the growth curve of Gardnerella vaginalis grown in Fannyhessea vaginae spent media , Lactobacillus jensenii, Anaerococcus lactolyticus, and Anaerococcus tetradius when compared to the uninhibited (p-value < 0.001) and moderate (p-value <0.001) conditions. Download figure Open in new tab Figure 4. In vitro spent media assay and lactic acid quantification. A) Growth curves of G. vaginalis 14018 in 12 media conditions (11 spent media and PGY (Modified) control). Three growth dynamics are specified to the right of the growth curves. N: non-inhibitory, M: moderate, I: inhibitory. B) Quantification of area under the growth curve for each of the three growth dynamic categories. Pairwise t-tests, one tailed: N vs M: p-value = 5.68e-4, n vs I: p-value = 4.19e-7, M vs I: p-value = 6.76e-6. C) pH of the spent media in each growth dynamic category. Pairwise t-tests, one tailed: I vs M: p-value = 6.46e-4, I vs N: p-value = 6.32e-4, M vs N: p-value = 0.4039. D) L-lactic acid concentration in the spent media of each growth dynamic category. Pairwise t-tests, one tailed: I vs M: p-value = 0.133, I vs N: p-value = 0.0532, M vs N: p-value = 0.1605. E) L-lactic acid concentration in the spent media of each growth dynamic category. Pairwise t-tests, one tailed: I vs M: p-value = 0.0292, I vs N: p-value = 0.02418, M vs N: p-value = 0.1632. F) Concentration of L-lactic acid, D-lactic acid, area under the G. vaginalis growth curve, and growth dynamic category of each spent media condition. Asterix (*) indicates that this concentration was beyond the standard curve and was extrapolated. G) Relationship between D-lactic acid and G. vaginalis AUC. R 2 =0.7026, exponential fit equation = y=122.504e -.564x . Bar chart significance key: *** = p < 0.001, ** = p < 0.1, * = p < .1, ns = not significant. The three distinct patterns of G. vaginalis growth dynamics in the 11 spent media conditions suggests that there could be multiple mechanisms of G. vaginalis growth inhibition at play. We next wanted to determine if resource scarcity and competition or environmental factors were driving G. vaginalis growth inhibition in the inhibitory spent media. In vitro assays uncover D-lactic acid as major driver of G. vaginalis growth inhibition As mentioned previously, it is well-known that the Lactobacillus dominance in the vaginal microbiome is responsible for protecting against pathogen invasion by maintaining an acidic environment through lactic acid production ( 16 ). Because of this, we first determined if the differences in growth dynamics of G. vaginalis observed in the different spent media conditions were influenced by pH of the spent media. We observed significant differences in pH across the spent media of the three growth dynamics groups (N, M, and I) ( Figure 4 C ). The spent media that inhibited G. vaginalis growth had a significantly lower pH than the spent media of the moderate and uninhibited groups (I vs N: p-value<0.01, I vs M: p-value 0.8). This result suggests that pH of the spent media plays a major role in inhibiting the growth of G. vaginalis . To determine if the low pH of the inhibitory spent media is due to lactic acid production, we performed both D-lactic acid and L-lactic acid assays (Methods). The mmol/L concentrations of L-lactic acid and D-lactic acid determined by the assays are reported in Fig 4 F. Ultimately, we observed no significant difference in the concentration of L-lactic acid between the inhibitory, non-inhibitory, and moderate spent media ( Figure 4 D ). However, the inhibitory spent media contained more L-lactic acid on average than the moderate and non-inhibitory spent media, and the moderate spent media contained more L-lactic acid on average than the non-inhibitory spent media ( Figure 4 D ). We did see significant differences in D-lactic acid concentration in the inhibitory spent media condition compared to the moderate and non-inhibitory spent media conditions ( Figure 4 E ). Additionally, we observed that there is a relationship between the G. vaginalis area under the curve (AUC) and D-lactic acid concentration of the spent media ( Figure 4 G ). Taken together, these results suggest that D-lactic acid production is a major driver for G. vaginalis growth inhibition in the inhibitory spent media condition. These results are consistent with the general knowledge that Lactobacillus species are typically present in a healthy vagina due to their ability to produce lactic acid and lower vaginal pH. However, we observed significantly high levels of D-lactic acid produced by F. vaginae , A. tetradius , and A. lactolyticus , despite not being Lactobacillus species. These results suggest that any bacteria that produces high levels of D-lactic acid could be just as effective as Lactobacillus at preventing G. vaginalis colonization. While these results help to elucidate that D-lactic acid is important for G. vaginalis growth inhibition, further studies would need to be performed to elucidate the driver of the differences in G. vaginalis growth dynamics observed between the non-inhibitory and moderate spent media groups. Based on preliminary computational analyses presented in S4, there is evidence that metabolic resource competition between commensals and G. vaginalis (resource competition) may be a driver of these observed differences in growth dynamics. In this analysis we observed some clustering of metabolic phenotypes of moderately inhibitory and inhibitory vaginal commensals with G. vaginalis strains suggesting metabolic niche overlap, however this analysis would need to be expanded and refined to better define the potential competitive mechanisms of inhibition. Discussion There has been extensive research on the positive health outcomes linked to probiotic intake, resulting in hundreds of probiotic supplement products being available to consumers over-the-counter ( 3 – 6 ). However, we lack a mechanistic understanding of the bacterial species contained within these supplements and how they can support health of the system of interest. Consequently, we explored the range of metabolic functions across species in over-the-counter probiotic supplements using a metabolic network modeling approach to identify potential opportunities to expand species used in current supplements to capture a broader range of metabolic functions. We observed that the metabolic functions captured across probiotic species used in supplements was only a small subset of the functions captured across all pathogenic and commensal species. Furthermore, we suggest that metabolic network modeling could play a part in rationally designing combinations of probiotic bacteria to add to a supplement to maximize the range of metabolic functions captured. In the future, if sequences of the individual proprietary strains used in probiotic supplements were made available, we could expand this analysis to be strain-specific. Additionally, it would be valuable to understand how species in each supplement function synergistically to determine if combining species could expand metabolic functionality. Furthermore, colonization rates of probiotic species are generally low and quite variable ( 45 ). Consequently, considering competitive and mutualistic interactions between probiotic species and the native microbes should be explored in the future. Using a community modeling approach could help us better identify gaps in probiotic function and how these gaps could be filled with novel probiotic communities rather than individual strains. Additionally, we focused on the vaginal microbiome to identify gaps in probiotics used to support vaginal health. We observed that most vaginal microbiome-related probiotics consist of lactic acid bacteria like Lactobacilli which are known to support vaginal health by maintaining an acidic vaginal pH. In this work we wanted to identify commensal microbes in the native vaginal flora that prevent G. vaginalis growth, which could be candidates for novel vaginal probiotic species. Through spent media experiments to simulate resource competition between G. vaginalis and a commensal vaginal species, we identified four commensal vaginal species ( A. lactolyticus, A. tetradius, L. jensenii, and F. vaginae ) that inhibit G. vaginalis growth through D-lactic acid production. However, D-lactic acid production could depend on the media context; in this study we only used PGY-mod (Methods) to create our spent media conditions. This study could be repeated using spent media generated on media that better mimics the vaginal environment like NYCIII or synthetic vaginal media (SVM). Additionally, we could utilize a co-culture assay to examine competition and growth inhibition in real time, to see how the dynamics of G. vaginalis are altered in the presence of the vaginal commensal. Additionally, there could be an opportunity to explore how G. vaginalis would grow on the spent media of a vaginal commensal community. This would provide an opportunity to see if combinations of the commensal vaginal microbes may be able to more strongly inhibit G. vaginalis growth than individuals. Furthermore, this analysis could be expanded to other microbiomes to identify commensals that serve as competitors for certain infectious pathogens. Overall, we believe this metabolic network modeling approach combined with in vitro experimentation is a promising step toward rationally designed probiotics for targeted uses, specifically for supporting vaginal microbiome health through G. vaginalis growth inhibition. There should not be a one-size fits-all approach to treating bacterial vaginosis; there is a need for patient-specific therapies that can consider variations in vaginal microbiome composition. Specifically, post-menopausal women have been shown to have significantly different vaginal microbiome compositions in both healthy and dysbiotic states due to differences likely due to differences in systemic estrogen ( 46 ). Most BV diagnostics and therapeutics have been designed with only reproductive-age women in mind, which could render these therapeutics less effective for post-menopausal women with BV. The number of post-menopausal women (55+) presenting with vaginitis-associated symptoms in the clinic is not a small portion. According to TriNetX, Of 35,040 reported vaginitis cases in the UVA health system, 7,580 (22%) of these cases were in women > 55 years old (S5, Methods). This emphasizes the need for vaginitis/BV treatment options specifically for post-menopausal women. The systems biology approach presented here could extended to identify possible probiotic candidates to protect against non- Gardnerella dominant BV, which would lend a more patient-specific therapeutic approach. Funding This work was supported by the National Science Foundation (GRFP award number 1842490 to EG) and the National Institutes of Health (1 T 32 GM 145443-1 to EG, R01-AI154242 to JP, R01-AT010253 to JP). Author Contributions Statement E.G. wrote initial manuscript draft E.G. performed computational studies E.G. and G.K. performed experimental analyses J.P. and G.K. supervised the work E.G., G.K., and J.P., edited and approved of the final manuscript Competing Interests Statement Papin has financial stake in Cerillo, the manufacturer of the plate reader used in some experimental analyses. Data and Materials Availability All raw data, scripts, and metabolic network models used in this study are available online: https://github.com/emmamglass/ProbioticsAndWomensHealth List of Supplementary Materials Methods S1-S8 Methods Survey of Commercial Probiotics We performed a survey of probiotics available for purchase on the CVS, Walgreens, and Walmart websites, the top three pharmacies in the USA by number of locations (9,554, 9,398, and 6,860 stores respectively) ( 29 ). In this survey, we collected the name of each probiotic supplement product, specified uses (e.g., gut, vaginal, urinary), and all probiotic strains included in each supplement. Additionally, we determined the total number of strains and the total number of species present in each probiotic supplement. We then created a binary species presence dataframe (row: probiotic supplement product, column: probiotic species, 1: species present, 0: species absent). We then used this dataframe for dimensionality reduction and visualization with PCA (sklearn in Python) to observe clustering patterns ( 47 ). Metabolic Network Reconstruction of Commensal, Pathogenic, and Probiotic Species We generated 1,007 genome scale metabolic network reconstructions (GENREs) of commensal, pathogenic, and probiotic bacterial species. To do this, we began by selecting genome sequences from the BV-BRC ( 48 ). We selected one genome sequence for each of the 35 bacterial probiotic species identified in our survey of commercially available probiotics. The criteria for selecting probiotic genome sequences were as follows: 1) Select the representative or reference sequence of a given species if it exits, 2) If there is no representative or reference sequence, randomly select a sequence that is considered good quality and complete. Of the 35 probiotic sequences selected, 26 were considered reference or representative sequences, and 9 sequences were considered good and complete without the reference or representative designation. We selected pathogen sequences in a similar manner. Using the previously published database of metabolic network models as a guide, we selected all reference and representative sequences of the species included in the PATHGENN database ( 42 ), resulting in 197 pathogenic sequences selected. Finally, we considered commensal bacteria to be any species in the BV-BRC database that was not considered pathogenic or probiotic. We selected all reference and representative commensal sequences that met these criteria, resulting in 775 commensal sequences. After selecting the sequences, we generated an annotated protein sequence through an automated pipeline. We used this annotated protein sequence as an input to Reconstructor, a tool used for automated GENRE creation ( 33 ). We used Reconstructor to generate genome scale metabolic network reconstructions of all 1,007 sequences. All GENREs were created in the context of the same rich media. Reaction and subsystem analysis We identified reactions that were unique to commensal, pathogen, and probiotic bacterial species through model analysis and visualized this data using an upset plot ( Figure 2 B, 2 C). We then took the list of unique metabolic reactions from each group (commensal, pathogenic, probiotic) and identified the metabolic subsystem to which they belong. We did this by querying the KEGG API to identify the general subsystem that each of these unique reactions belongs to ( 49 ). We then displayed the number of unique reactions in each group that were a part of each metabolic subsystem. Flux sampling and metabolic phenotype analysis We used the Gapsplit algorithm for sampling flux distributions ( 50 ). We generated 500 flux distributions for each GENRE to most accurately capture the range of all metabolic functions (metabolic phenotypes). To visualize similarities and differences in flux distributions across commensal, pathogen, and probiotic species, we used principal component analysis (PCA) through the sklearn python package ( 47 ). We reduced each flux distribution to a two-dimensional space (two principal components) to plot. In Figure 2 H, we plotted commensal bacterial species (bottom), then pathogenic (middle), then probiotics (top) to best visualize the metabolic phenotype coverage of group. We used the same method of flux sampling and dimensionality reduction in Figures 2 G and 3 B. Heatmap and hierarchical clustering of metabolic flux through G. vaginalis, vaginal commensal, and vaginal probiotic bacterial species We generated 500 flux distributions per model of interest using Gapsplit ( 50 ). Then, we generated a median flux vector across all 500 samples per each model. We then combined all median flux vectors across models of interest into one data frame, removed low variance reactions (variance threshold of 0.1), and dropped highly correlated reactions (correlation threshold of 0.9). After these pre-processing steps, we generated a cluster map using seaborn ( 51 ), clustering on both rows (species-specific models) and columns (reactions) using the Canberra distance. This method was applied to the heatmaps in Figures 3 C and S4. In vitro spent media assays We obtained 16 vaginal commensal isolates from the DSMZ (strain names in S6). We selected these isolates from the larger collection of identified vaginal commensal organisms for several reasons: 1) the isolates were easily obtainable from one reputable source, 2) we selected strains that were specifically isolated from the human vagina. There were strains of some isolates that were available but were not isolated from the vagina; we did not select these. Ultimately, we were able to successfully grow 11 of these species robustly in anaerobic conditions. After successfully growing these bacterial species in their specified media, we determined a media condition that would successfully grow these 11 species as well as Gardnerella vaginalis. We determined the media that was most successful at robust growth was PGY media (DSMZ) modified with HEPES (6.3 mL) and 10% FBS. We also tried unmodified PGY, NYCIII, and NYCIII media modified with Vitamin K (100uL) and Hemin (5mL) (S7). After determining a universal media condition (PGY modified; PGY-mod that would lead to maximal growth across isolates, we next needed to collect spent media. We inoculated each isolate into 10 mL of PGY-mod. For each isolate, we repeated this 5 times. Each 10mL aliquot of spent media was pooled to generate 50 mL of spent media per isolate. Then, to determine if G. vaginalis would grow on the spent media of each isolate, we performed a spent media assay. This assay involved inoculating G. vaginalis into 5mL of PGY-mod and was allowed to grow for 24 hours. After 24 hours, the culture was spun down at 6500 rpm for 5 minutes, media was aspirated, and culture was resuspended in fresh PGY-mod media. Then, G. vaginalis was inoculated into the spent media in a 12 well plate at an OD of 0.1 and allowed to grow until stationary phase with continuous growth monitoring using a Cerrillo stratus plate reader. We repeated this assay with two different strains of G. vaginalis ; GV14018 (results show in Figure 4 A ) and JCP7672 (S6) to ensure the trends we observed were consistent across strains. We observed inhibition of G. vaginalis JCP767 in A. tetradius, A. lactolyticus, L. jensenii, and F. vaginae spent media as well. We observed growth of JCP7672 in the moderate and Non-inhibitory spent media that were mostly consistent with GV14018, but there was some variation (S8). Area under the growth curve calculations and identifying differences in growth dynamics We calculated area under each growth curve in Figure 4 B using the simps integration method from the scipy package ( 52 ). We used the mean value across replicates for this growth curve calculation. To determine statistical difference between the three observed groups of growth curves, we used a one-way ANOVA test, with subsequent pair-wise t-tests between groups, with a significance value of 0.05. Spent media pH measurements and differences between groups We used a pH meter (Accumet basic AB15 pH meter) to determine the pH of each media condition used in the spent media assay with accuracy to 0.01. Then, to determine statistically significant differences in pH between the three growth dynamics groups we used pairwise Welch’s t-tests, with a significance value of 0.05. L-lactic acid and D-lactic acid assays We quantified concentrations of D-lactic acid and L-lactic acid using the Novus Biologicals D-Lactic Acid/Lactate Assay Kit (Colorimetric) NBP3-25788 and L-lactic Acid Assay Kit (Colorimetric) NBP3-25875 respectively. After completing the specified kit protocol, OD was read at 530nm using a TECAN plate reader. The assay was re-run (standards and samples) at dilution for samples that read above the standard curve range. D-lactic acid and L-lactic acid concentrations were computed according to the standard curve. TriNetX cohot exploration To explore the number of vaginitis cases presented in the UVA health system, we used TriNetX. The data used in this study was collected on June 5, 2025 from the TriNetX University of Virginia Network, which provided access to electronic medical records (diagnoses, procedures, medications, laboratory values, genomic information) from 730,801 patients from health care organization. This retrospective study is exempt from informed consent. The data reviewed is a secondary analysis of existing data, does not involve intervention or interaction with human subjects, and is de-identified per the de-identification standard defined in Section §164.514(a) of the HIPAA Privacy rule. The process by which the data is de-identified is attested to through a formal determination by a qualified expert as defined in section §164.514(b)( 1 ) of the HIPAA Privacy Rule. This formal determination by a qualified expert refreshed on December 2020. Download figure Open in new tab S1. Number of “Probiotics” PubMed articles published each year since before 1950. Download figure Open in new tab S2. Reported BV recurrence rates after probiotic and antibiotic treatments. This data was compiled from 32 independent clinical studies. Oral probiotic: n = 8, Intravaginal probiotic: n = 6, Antibiotic: n = 25, Oral Probiotic + Antibiotic: n = 2, Intravaginal Probiotic + Antibiotic: n = 7. S3 - Bibliography of Studies Used in Meta-analysis Download figure Open in new tab S4. In silico metabolic phenotype comparison between vaginal commensals and G. vaginalis strains. Hierarchical clustering of metabolic phenotypes across two G. vaginalis strains, and culturable vaginal commensal species used in the in vitro spent media experiment, rows are colored by G. vaginalis growth dynamic category. Metabolic phenotype vectors are calculated as the per-reaction median value across 500 flux samples. Download figure Open in new tab S5. Patients presenting with vaginitis associated symptoms in the in the UVA health system. Download figure Open in new tab S6. List of vaginal commensal isolates obtained from the DSMZ. Download figure Open in new tab S7. Comparison of culturable vaginal commensal species growth in four media conditions. Growth over 48 and 72 hour periods in PGY, PGY (Modified), NYCIII, and NYCIII (Modified). Download figure Open in new tab S8. Growth of G. vaginalis JCP7672. This alternative G. vaginalis strain was grown on 11 spent media conditions and PGY (modified) control. Acknowledgements Funder Information Declared National Science Foundation, https://ror.org/021nxhr62 , GRFP award number 1842490 National Institutes of Health, https://ror.org/01cwqze88 , 1 T 32 GM 145443-1 , R01-AI154242 , R01-AT010253 References 1. ↵ C. Hill , F. Guarner , G. Reid , G. R. Gibson , D. J. Merenstein , B. Pot , L. Morelli , R. B. Canani , H. J. Flint , S. Salminen , P. C. Calder , M. E. Sanders , Expert consensus document. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic . Nat Rev Gastroenterol Hepatol 11 , 506 – 514 ( 2014 ). OpenUrl CrossRef PubMed 2. ↵ L. Cuamatzin-García , P. Rodríguez-Rugarcía , E. G. El-Kassis , G. Galicia , M. de L . Meza-Jiménez , Ma . del R. Baños-Lara , D. S. Zaragoza-Maldonado , B. Pérez-Armendáriz , Traditional Fermented Foods and Beverages from around the World and Their Health Benefits . Microorganisms 10 , 1151 ( 2022 ). 3. ↵ E. Song , L. Ang , H. W. Lee , M.-S. Kim , Y. J. Kim , D. Jang , M. S. Lee , Effects of kimchi on human health: a scoping review of randomized controlled trials . Journal of Ethnic Foods 10 , 7 ( 2023 ). 4. S. González , T. Fernández-Navarro , S. Arboleya , C. G. de los Reyes-Gavilán , N. Salazar , M. Gueimonde , Fermented Dairy Foods: Impact on Intestinal Microbiota and Health-Linked Biomarkers . Front. Microbiol . 10 ( 2019 ). 5. P. Batista , M. R. Penas , M. Pintado , P. Oliveira-Silva , Kombucha: Perceptions and Future Prospects . Foods 11 , 1977 ( 2022 ). 6. ↵ C. Raak , T. Ostermann , K. Boehm , F. Molsberger , Regular Consumption of Sauerkraut and Its Effect on Human Health: A Bibliometric Analysis . Glob Adv Health Med 3 , 12 – 18 ( 2014 ). OpenUrl CrossRef PubMed 7. ↵ Probiotics Market Size to Hit USD 374.57 Billion by 2034 . https://www.precedenceresearch.com/probiotics-market . 8. ↵ H. A. Blair , SER-109 (VOWST TM ): A Review in the Prevention of Recurrent Clostridioides difficile Infection . Drugs 84 , 329 – 336 ( 2024 ). OpenUrl CrossRef PubMed 9. ↵ S. Doosetty , C. Umeh , W. Eastwood , I. Samreen , A. Penchala , H. Kaur , C. Chilinga , G. Kaur , T. Mohta , S. Nakka , P. Tangirala , S. Nakka , Efficacy of Fecal Microbiota (REBYOTA) in Recurrent Clostridium difficile Infections: A Systematic Review and Meta-Analysis . Cureus 16 , e58862 ( 2024 ). OpenUrl 10. ↵ D. Kothari , S. Patel , S.-K. Kim , Probiotic supplements might not be universally-effective and safe: A review . Biomedicine & Pharmacotherapy 111 , 537 – 547 ( 2019 ). OpenUrl CrossRef PubMed 11. ↵ V. Venugopalan , K. A. Shriner , A. Wong-Beringer , Regulatory Oversight and Safety of Probiotic Use . Emerg Infect Dis 16 , 1661 – 1665 ( 2010 ). OpenUrl CrossRef PubMed 12. ↵ A. López-Moreno , M. Aguilera , Vaginal Probiotics for Reproductive Health and Related Dysbiosis: Systematic Review and Meta-Analysis . J Clin Med 10 , 1461 ( 2021 ). 13. N. Mashatan , R. Heidari , M. Altafi , A. Amini , M. M. Ommati , M. Hashemzaei , Probiotics in vaginal health . Pathog Dis 81 , ftad012 ( 2023 ). 14. Z. Mei , D. Li , The role of probiotics in vaginal health . Front Cell Infect Microbiol 12 , 963868 ( 2022 ). 15. ↵ J. van de Wijgert , M. C. Verwijs , Lactobacilli-containing vaginal probiotics to cure or prevent bacterial or fungal vaginal dysbiosis: a systematic review and recommendations for future trial designs . BJOG 127 , 287 – 299 ( 2020 ). OpenUrl CrossRef PubMed 16. ↵ M. France , M. Alizadeh , S. Brown , B. Ma , J. Ravel , Towards a deeper understanding of the vaginal microbiota . Nat Microbiol 7 , 367 – 378 ( 2022 ). OpenUrl CrossRef PubMed 17. ↵ V. D. Valeriano , E. Lahtinen , I.-C. Hwang , Y. Zhang , J. Du , I. Schuppe-Koistinen , Vaginal dysbiosis and the potential of vaginal microbiome-directed therapeutics . Front. Microbiomes 3 ( 2024 ). 18. ↵ H. L. Gardner , C. D. Dukes , Haemophilus vaginalis vaginitis: A newly defined specific infection previously classified “nonspecific” vaginitis . American Journal of Obstetrics and Gynecology 69 , 962 – 976 ( 1955 ). OpenUrl CrossRef PubMed Web of Science 19. ↵ A. S. Mondal , R. Sharma , N. Trivedi , Bacterial vaginosis: A state of microbial dysbiosis . Medicine in Microecology 16 , 100082 ( 2023 ). 20. ↵ W. Li , Z. (Sam) Ma , Dominance network analysis of the healthy human vaginal microbiome not dominated by Lactobacillus species . Computational and Structural Biotechnology Journal 18 , 3447 – 3456 ( 2020 ). OpenUrl CrossRef 21. ↵ L. A. Vodstrcil , C. A. Muzny , E. L. Plummer , J. D. Sobel , C. S. Bradshaw , Bacterial vaginosis: drivers of recurrence and challenges and opportunities in partner treatment . BMC Med 19 , 194 ( 2021 ). 22. A. J. Gooding , B. Zhang , F. K. Jahanbani , H. L. Gilmore , J. C. Chang , S. Valadkhan , W. P. Schiemann , The lncRNA BORG Drives Breast Cancer Metastasis and Disease Recurrence . Scientific Reports 7 , 12698 ( 2017 ). 23. ↵ C. S. Bradshaw , A. N. Morton , J. Hocking , S. M. Garland , M. B. Morris , L. M. Moss , L. B. Horvath , I. Kuzevska , C. K. Fairley , High recurrence rates of bacterial vaginosis over the course of 12 months after oral metronidazole therapy and factors associated with recurrence . J Infect Dis 193 , 1478 – 1486 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 24. ↵ C. A. Muzny , J. D. Sobel , The Role of Antimicrobial Resistance in Refractory and Recurrent Bacterial Vaginosis and Current Recommendations for Treatment . Antibiotics (Basel ) 11 , 500 ( 2022 ). 25. ↵ V. Vivekanandan , Z. H. Khan , G. Venugopal , B. Musunuru , P. Mishra , S. Srivastava , B. Ramadass , B. Subhadra , VagiBIOM Lactobacillus suppository improves vaginal health index in perimenopausal women with bacterial vaginosis: a randomized control trial . Sci Rep 14 , 3317 ( 2024 ). 26. ↵ F. Qi , S. Fan , C. Fang , L. Ge , J. Lyu , Z. Huang , S. Zhao , Y. Zou , L. Huang , X. Liu , Y. Liang , Y. Zhang , Y. Zhong , H. Zhang , L. Xiao , X. Zhang , Orally administrated Lactobacillus gasseri TM13 and Lactobacillus crispatus LG55 can restore the vaginal health of patients recovering from bacterial vaginosis . Front Immunol 14 , 1125239 ( 2023 ). 27. ↵ C. Gu , G. B. Kim , W. J. Kim , H. U. Kim , S. Y. Lee , Current status and applications of genome-scale metabolic models . Genome Biology 20 , 121 ( 2019 ). 28. ↵ L. Heirendt , S. Arreckx , T. Pfau , S. N. Mendoza , A. Richelle , A. Heinken , H. S. Haraldsdóttir , J. Wachowiak , S. M. Keating , V. Vlasov , S. Magnusdóttir , C. Y. Ng , G. Preciat , A. Žagare , S. H. J. Chan , M. K. Aurich , C. M. Clancy , J. Modamio , J. T. Sauls , A. Noronha , A. Bordbar , B. Cousins , D. C. El Assal , L. V. Valcarcel , I. Apaolaza , S. Ghaderi , M. Ahookhosh , M. Ben Guebila , A. Kostromins , N. Sompairac , H. M. Le , D. Ma , Y. Sun , L. Wang , J. T. Yurkovich , M. A. P. Oliveira , P. T. Vuong , L. P. El Assal , I. Kuperstein , A. Zinovyev , H. S. Hinton , W. A. Bryant , F. J. Aragón Artacho , F. J. Planes , E. Stalidzans , A. Maass , S. Vempala , M. Hucka , M. A. Saunders , C. D. Maranas , N. E. Lewis , T. Sauter , B. Ø. Palsson , I. Thiele , R. M. T. Fleming , Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0 . Nat Protoc 14 , 639 – 702 ( 2019 ). OpenUrl CrossRef PubMed 29. ↵ Pharmacy Near Me ®: Local Pharmacies & Doctors – Locator & Guide , Pharmacy Near Me ( 2017 ). https://pharmacy-near-me.com/ . 30. ↵ J. Payne , D. Bellmer , R. Jadeja , P. Muriana , The Potential of Bacillus Species as Probiotics in the Food Industry: A Review . Foods 13 , 2444 ( 2024 ). 31. ↵ R. Rajam , P. Subramanian , Encapsulation of probiotics: past, present and future . Beni-Suef Univ J Basic Appl Sci 11 , 46 ( 2022 ). 32. ↵ I. D. Kwoji , O. A. Aiyegoro , M. Okpeku , M. A. Adeleke , Multi-Strain Probiotics: Synergy among Isolates Enhances Biological Activities . Biology (Basel ) 10 , 322 ( 2021 ). 33. ↵ M. L. Jenior , E. M. Glass , J. A. Papin , Reconstructor: a COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling . Bioinformatics 39 , btad367 ( 2023 ). 34. ↵ Y. Chen , J.-Y. Fang , The role of colonic microbiota amino acid metabolism in gut health regulation . Cell Insight 4 , 100227 ( 2025 ). 35. ↵ R. Leitão-Gonçalves , Z. Carvalho-Santos , A. P. Francisco , G. T. Fioreze , M. Anjos , C. Baltazar , A. P. Elias , P. M. Itskov , M. D. W. Piper , C. Ribeiro , Commensal bacteria and essential amino acids control food choice behavior and reproduction . PLOS Biology 15 , e2000862 ( 2017 ). OpenUrl CrossRef PubMed 36. ↵ C. Risdian , T. Mozef , J. Wink , Biosynthesis of Polyketides in Streptomyces . Microorganisms 7 , 124 ( 2019 ). 37. ↵ K. C. Onwueme , C. J. Vos , J. Zurita , J. A. Ferreras , L. E. N. Quadri , The dimycocerosate ester polyketide virulence factors of mycobacteria . Prog Lipid Res 44 , 259 – 302 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 38. ↵ J. N. Pugh , A. J. M. Wagenmakers , D. A. Doran , S. C. Fleming , B. A. Fielding , J. P. Morton , G. L. Close , Probiotic supplementation increases carbohydrate metabolism in trained male cyclists: a randomized, double-blind, placebo-controlled crossover trial . Am J Physiol Endocrinol Metab 318 , E504 – E513 ( 2020 ). OpenUrl CrossRef PubMed 39. Y. Baba , D. Tsuge , R. Aoki , Enhancement of carbohydrate metabolism by probiotic and prebiotic intake promotes short-chain fatty acid production in the gut microbiome: A randomized, double-blind, placebo-controlled crossover trial . Bioscience, Biotechnology, and Biochemistry , zbaf071 ( 2025 ). 40. P. Markowiak-Kopeć , K. Śliżewska , The Effect of Probiotics on the Production of Short-Chain Fatty Acids by Human Intestinal Microbiome . Nutrients 12 , 1107 ( 2020 ). 41. ↵ M. A. R. Vinolo , H. G. Rodrigues , R. T. Nachbar , R. Curi , Regulation of Inflammation by Short Chain Fatty Acids . Nutrients 3 , 858 – 876 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 42. ↵ E. M. Glass , L. R. Dillard , G. L. Kolling , A. S. Warren , J. A. Papin , Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens . PLOS Biology 22 , e3002907 ( 2024 ). OpenUrl CrossRef PubMed 43. ↵ H. A. Herrmann , B. C. Dyson , L. Vass , G. N. Johnson , J.-M. Schwartz , Flux sampling is a powerful tool to study metabolism under changing environmental conditions . npj Syst Biol Appl 5 , 1 – 8 ( 2019 ). OpenUrl PubMed 44. ↵ S. Morrill , N. M. Gilbert , A. L. Lewis , Gardnerella vaginalis as a Cause of Bacterial Vaginosis: Appraisal of the Evidence From in vivo Models . Front Cell Infect Microbiol 10 , 168 ( 2020 ). 45. ↵ J. Walter , M. X. Maldonado-Gómez , I. Martínez , To engraft or not to engraft: An Ecological Framework for Gut Microbiome Modulation with Live Microbes . Curr Opin Biotechnol 49 , 129 – 139 ( 2018 ). OpenUrl CrossRef PubMed 46. ↵ C. M. Mitchell , S. Srinivasan , N. Ma , S. D. Reed , M. C. Wu , N. G. Hoffman , D. J. Valint , S. Proll , T. L. Fiedler , K. J. Agnew , K. A. Guthrie , D. N. Fredricks , Bacterial Communitites Associated With Abnormal Nugent Score in Postmenopausal Versus Premenopasual Women . The Journal of Infectious Diseases 223 , 2048 – 2052 ( 2021 ). OpenUrl CrossRef PubMed 47. ↵ F. Pedregosa , G. Varoquaux , A. Gramfort , V. Michel , B. Thirion , O. Grisel , M. Blondel , P. Prettenhofer , R. Weiss , V. Dubourg , J. Vanderplas , A. Passos , D. Cournapeau , M. Brucher , M. Perrot , É. Duchesnay , Scikit-learn: Machine Learning in Python . Journal of Machine Learning Research 12 , 2825 – 2830 ( 2011 ). OpenUrl 48. ↵ R. D. Olson , R. Assaf , T. Brettin , N. Conrad , C. Cucinell , J. J. Davis , D. M. Dempsey , A. Dickerman , E. M. Dietrich , R. W. Kenyon , M. Kuscuoglu , E. J. Lefkowitz , J. Lu , D. Machi , C. Macken , C. Mao , A. Niewiadomska , M. Nguyen , G. J. Olsen , J. C. Overbeek , B. Parrello , V. Parrello , J. S. Porter , G. D. Pusch , M. Shukla , I. Singh , L. Stewart , G. Tan , C. Thomas , M. VanOeffelen , V. Vonstein , Z. S. Wallace , A. S. Warren , A. R. Wattam , F. Xia , H. Yoo , Y. Zhang , C. M. Zmasek , R. H. Scheuermann , R. L. Stevens , Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR . Nucleic Acids Research 51 , D678 – D689 ( 2023 ). OpenUrl CrossRef PubMed 49. ↵ M. Kanehisa , S. Goto , KEGG: kyoto encyclopedia of genes and genomes . Nucleic Acids Res 28 , 27 – 30 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 50. ↵ T. C. Keaty , P. A. Jensen , GAPSPLIT : efficient random sampling for non-convex constraint-based models . Bioinformatics 36 , 2623 – 2625 ( 2020 ). OpenUrl CrossRef PubMed 51. ↵ M. L. Waskom , seaborn: statistical data visualization . Journal of Open Source Software 6 , 3021 ( 2021 ). 52. ↵ P. Virtanen , R. Gommers , T. E. Oliphant , M. Haberland , T. Reddy , D. Cournapeau , E. Burovski , P. Peterson , W. Weckesser , J. Bright , S. J. van der Walt , M. Brett , J. Wilson , K. J. Millman , N. Mayorov , A. R. J. Nelson , E. Jones , R. Kern , E. Larson , C. J. Carey , İ. Polat , Y. Feng , E. W. Moore , J. VanderPlas , D. Laxalde , J. Perktold , R. Cimrman , I. Henriksen , E. A. Quintero , C. R. Harris , A. M. Archibald , A. H. Ribeiro , F. Pedregosa , P. van Mulbregt , SciPy 1.0: fundamental algorithms for scientific computing in Python . Nat Methods 17 , 261 – 272 ( 2020 ). OpenUrl CrossRef PubMed 1. J. R. Schwebke , R. A. Desmond , A randomized trial of the duration of therapy with metronidazole plus or minus azithromycin for treatment of symptomatic bacterial vaginosis . Clin Infect Dis 44 , 213 – 219 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 2. J. R. Schwebke , R. A. Desmond , Tinidazole vs metronidazole for the treatment of bacterial vaginosis . Am J Obstet Gynecol 204 , 211 . e1 – 6 ( 2011 ). OpenUrl CrossRef PubMed 3. G. Vujic , A. Jajac Knez , V. Despot Stefanovic , V. Kuzmic Vrbanovic , Efficacy of orally applied probiotic capsules for bacterial vaginosis and other vaginal infections: a double-blind, randomized, placebo-controlled study . Eur J Obstet Gynecol Reprod Biol 168 , 75 – 79 ( 2013 ). OpenUrl CrossRef PubMed 4. J. Voorspoels , M. Casteels , J. P. Remon , M. Temmerman , Local treatment of bacterial vaginosis with a bioadhesive metronidazole tablet . Eur J Obstet Gynecol Reprod Biol 105 , 64 – 66 ( 2002 ). OpenUrl CrossRef PubMed 5. M. C. Verwijs , S. K. Agaba , A. C. Darby , J. H. H. M. van de Wijgert , Impact of oral metronidazole treatment on the vaginal microbiota and correlates of treatment failure . American Journal of Obstetrics and Gynecology 222 , 157 . e1 – 157 .e13 ( 2020 ). OpenUrl CrossRef 6. J. Thulkar , A. Kriplani , N. Agarwal , Probiotic and metronidazole treatment for recurrent bacterial vaginosis . Int J Gynaecol Obstet 108 , 251 – 252 ( 2010 ). OpenUrl CrossRef PubMed 7. M. Ratna Sudha , K. A. Yelikar , S. Deshpande , Clinical Study of Bacilluscoagulans Unique IS-2 (ATCC PTA-11748) in the Treatment of Patients with Bacterial Vaginosis . Indian J Microbiol 52 , 396 – 399 ( 2012 ). OpenUrl CrossRef PubMed 8. J. D. Sobel , N. Kaur , N. A. Woznicki , D. Boikov , T. Aguin , G. Gill , R. A. Akins , Prognostic Indicators of Recurrence of Bacterial Vaginosis . J Clin Microbiol 57 , e00227 – 19 ( 2019 ). OpenUrl PubMed 9. E. Shalev , S. Battino , E. Weiner , R. Colodner , Y. Keness , Ingestion of yogurt containing Lactobacillus acidophilus compared with pasteurized yogurt as prophylaxis for recurrent candidal vaginitis and bacterial vaginosis . Arch Fam Med 5 , 593 – 596 ( 1996 ). OpenUrl CrossRef PubMed Web of Science 10. G. Reid , J. Burton , J.-A. Hammond , A. W. Bruce , Nucleic Acid-Based Diagnosis of Bacterial Vaginosis and Improved Management Using Probiotic Lactobacilli . Journal of Medicinal Food 7 , 223 – 228 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 11. I. Raja , A. Basavareddy , D. Mukherjee , B. Meher , Randomized, double-blind, comparative study of oral metronidazole and tinidazole in treatment of bacterial vaginosis . Indian J Pharmacol 48 , 654 ( 2016 ). 12. D. Parent , M. Bossens , D. Bayot , C. Kirkpatrick , F. Graf , F. E. Wilkinson , R. R. Kaiser , Therapy of bacterial vaginosis using exogenously-applied Lactobacilli acidophili and a low dose of estriol: a placebo-controlled multicentric clinical trial . Arzneimittelforschung 46 , 68 – 73 ( 1996 ). OpenUrl PubMed 13. J. Paavonen , C. Mangioni , M. A. Martin , C. P. Wajszczuk , Vaginal clindamycin and oral metronidazole for bacterial vaginosis: a randomized trial . Obstetrics & Gynecology 96 , 256 – 260 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 14. K. Anukam , E. Osazuwa , I. Ahonkhai , M. Ngwu , G. Osemene , A. W. Bruce , G. Reid , Augmentation of antimicrobial metronidazole therapy of bacterial vaginosis with oral probiotic Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri RC-14: randomized, double-blind, placebo controlled trial . Microbes and Infection 8 , 1450 – 1454 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 15. A. Neri , G. Sabah , Z. Samra , Bacterial vaginosis in pregnancy treated with yoghurt . Acta Obstet Gynecol Scand 72 , 17 – 19 ( 1993 ). OpenUrl CrossRef PubMed Web of Science 16. P. Mastromarino , B. Vitali , L. Mosca , Bacterial vaginosis: a review on clinical trials with probiotics . New Microbiol 36 , 229 – 238 ( 2013 ). OpenUrl PubMed 17. R. C. R. Martinez , S. A. Franceschini , M. C. Patta , S. M. Quintana , B. C. Gomes , E. C. P. De Martinis , G. Reid , Improved cure of bacterial vaginosis with single dose of tinidazole (2 g), Lactobacillus rhamnosus GR-1, and Lactobacillus reuteri RC-14: a randomized, double-blind, placebo-controlled trial . Can. J. Microbiol . 55 , 133 – 138 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 18. Z. Ling , X. Liu , W. Chen , Y. Luo , L. Yuan , Y. Xia , K. E. Nelson , S. Huang , S. Zhang , Y. Wang , J. Yuan , L. Li , C. Xiang , The Restoration of the Vaginal Microbiota After Treatment for Bacterial Vaginosis with Metronidazole or Probiotics . Microb Ecol 65 , 773 – 780 ( 2013 ). OpenUrl CrossRef PubMed 19. P.-G. Larsson , E. Brandsborg , U. Forsum , S. Pendharkar , K. K. Andersen , S. Nasic , L. Hammarström , H. Marcotte , Extended antimicrobial treatment of bacterial vaginosis combined with human lactobacilli to find the best treatment and minimize the risk of relapses . BMC Infectious Diseases 11 , 223 ( 2011 ). 20. P.-G. Larsson , B. Stray-Pedersen , K. R. Ryttig , S. Larsen , Human lactobacilli as supplementation of clindamycin to patients with bacterial vaginosis reduce the recurrence rate; a 6-month, double-blind, randomized, placebo-controlled study . BMC Women’s Health 8 , 3 ( 2008 ). 21. M. Kurkinen-Räty , S. Vuopala , M. Koskela , M. Kekki , T. Kurki , J. Paavonen , P. Jouppila , A randomised controlled trial of vaginal clindamycin for early pregnancy bacterial vaginosis . BJOG 107 , 1427 – 1432 ( 2000 ). OpenUrl CrossRef PubMed 22. S. Kovachev , R. Vatcheva-Dobrevski , [Efficacy of combined 5-nitroimidazole and probiotic therapy of bacterial vaginosis: randomized open trial] . Akush Ginekol (Sofiia) 52 , 19 – 26 ( 2013 ). OpenUrl 23. M. Kekki , T. Kurki , J. Pelkonen , M. Kurkinen-Räty , B. Cacciatore , J. Paavonen , Vaginal clindamycin in preventing preterm birth and peripartal infections in asymptomatic women with bacterial vaginosis: a randomized, controlled trial . Obstet Gynecol 97 , 643 – 648 ( 2001 ). OpenUrl CrossRef PubMed Web of Science 24. S. Hantoushzadeh , F. Golshahi , P. Javadian , S. Khazardoost , S. Aram , S. Hashemi , B. Mirarmandehi , S. Borna , Comparative efficacy of probiotic yoghurt and clindamycin in treatment of bacterial vaginosis in pregnant women: A randomized clinical trial . The Journal of Maternal-Fetal & Neonatal Medicine 25 , 1021 – 1024 ( 2012 ). OpenUrl CrossRef 25. A. Hallén , C. Jarstrand , C. Påhlson , Treatment of bacterial vaginosis with lactobacilli . Sex Transm Dis 19 , 146 – 148 ( 1992 ). OpenUrl CrossRef PubMed Web of Science 26. B. Fredricsson , K. Englund , L. Weintraub , A. Olund , C. E. Nord , Bacterial vaginosis is not a simple ecological disorder . Gynecol Obstet Invest 28 , 156 – 160 ( 1989 ). OpenUrl CrossRef PubMed 27. K. Eriksson , B. Carlsson , U. Forsum , P. Larsson , A double-blind treatment study of bacterial vaginosis with normal vaginal lactobacilli after an open treatment with vaginal clindamycin ovules . Acta Dermato-Venereologic a85 , 42 – 46 ( 2005 ). 28. A. Darwish , E. M. Elnshar , S. M. Hamadeh , M. H. Makarem , Treatment options for bacterial vaginosis in patients at high risk of preterm labor and premature rupture of membranes . J Obstet Gynaecol Res 33 , 781 – 787 ( 2007 ). OpenUrl CrossRef PubMed 29. M. Brandt , C. Abels , T. May , K. Lohmann , I. Schmidts-Winkler , U. B. Hoyme , Intravaginally applied metronidazole is as effective as orally applied in the treatment of bacterial vaginosis, but exhibits significantly less side effects . Eur J Obstet Gynecol Reprod Biol 141 , 158 – 162 ( 2008 ). OpenUrl CrossRef PubMed 30. C. S. Bradshaw , M. Pirotta , D. De Guingand , J. S. Hocking , A. N. Morton , S. M. Garland , G. Fehler , A. Morrow , S. Walker , L. A. Vodstrcil , C. K. Fairley , Efficacy of Oral Metronidazole with Vaginal Clindamycin or Vaginal Probiotic for Bacterial Vaginosis: Randomised Placebo-Controlled Double-Blind Trial . PLoS ONE 7 , e34540 ( 2012 ). OpenUrl CrossRef PubMed 31. J. M. Bohbot , E. Daraï , F. Bretelle , G. Brami , C. Daniel , J. M. Cardot , Efficacy and safety of vaginally administered lyophilized Lactobacillus crispatus IP 174178 in the prevention of bacterial vaginosis recurrence . Journal of Gynecology Obstetrics and Human Reproduction 47 , 81 – 86 ( 2018 ). OpenUrl CrossRef 32. K. C. Anukam , E. Osazuwa , G. I. Osemene , F. Ehigiagbe , A. W. Bruce , G. Reid , Clinical study comparing probiotic Lactobacillus GR-1 and RC-14 with metronidazole vaginal gel to treat symptomatic bacterial vaginosis . Microbes and Infection 8 , 2772 – 2776 ( 2006 ). OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted June 21, 2025. 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