Intracellular acidification by bacteria-derived valeric acid is a mechanism of trans-kingdom ecology against Candida parapsilosis colonization

Intracellular acidification by bacteria-derived valeric acid is a mechanism of trans-kingdom ecology against Candida parapsilosis colonization | 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 Intracellular acidification by bacteria-derived valeric acid is a mechanism of trans-kingdom ecology against Candida parapsilosis colonization View ORCID Profile Keiko Yasuma-Mitobe , View ORCID Profile Chen Liao , Tibor Nemeth , View ORCID Profile Kevin Byrne , Audrey Bilipps , Ruben Jose Jesus Faustino Ramos , Cristine Nicole Salinas , Eric Chan , Martina Perissinoto , Ashley M. Sidebottom , George Plitas , Geraldine Butler , Justin R Cross , Eric G Pamer , View ORCID Profile Attila Gacser , View ORCID Profile Joao B Xavier , View ORCID Profile Tobias M. Hohl doi: https://doi.org/10.1101/2025.09.12.675697 Keiko Yasuma-Mitobe 1 Infectious Disease Service, Department of Medicine, Memorial Sloan Kettering Cancer Center , New York, NY, USA 2 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Keiko Yasuma-Mitobe Chen Liao 3 Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chen Liao Tibor Nemeth 4 Department of Biotechnology and Microbiology, Faculty of Science and Informatics, University of Szeged , Szeged, Hungary Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kevin Byrne 5 UCD Conway Institute, University College Dublin , Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kevin Byrne Audrey Bilipps 1 Infectious Disease Service, Department of Medicine, Memorial Sloan Kettering Cancer Center , New York, NY, USA 2 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ruben Jose Jesus Faustino Ramos 6 Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cristine Nicole Salinas 1 Infectious Disease Service, Department of Medicine, Memorial Sloan Kettering Cancer Center , New York, NY, USA 2 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center , New York, NY, USA 7 The Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical Sciences , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric Chan 8 Molecular Cytology Core Facility, Memorial Sloan Kettering Cancer Center , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martina Perissinoto 9 Technical and Quality Department , SILA S. p. A. Advanced Nutrition, Noale, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ashley M. Sidebottom 10 Duchossois Family Institute, University of Chicago , Chicago, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site George Plitas 11 Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site Geraldine Butler 5 UCD Conway Institute, University College Dublin , Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Justin R Cross 6 Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric G Pamer 10 Duchossois Family Institute, University of Chicago , Chicago, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Attila Gacser 4 Department of Biotechnology and Microbiology, Faculty of Science and Informatics, University of Szeged , Szeged, Hungary Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Attila Gacser Joao B Xavier 3 Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joao B Xavier Tobias M. Hohl 1 Infectious Disease Service, Department of Medicine, Memorial Sloan Kettering Cancer Center , New York, NY, USA 2 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tobias M. Hohl For correspondence: hohlt{at}mskcc.org Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract In hematopoietic cell transplant patients, intestinal Candida parapsilosis expansion and translocation cause life-threatening candidemia, yet how commensal intestinal bacteria prevent Candida expansion remains incompletely defined. Here, we trained a machine learning model on supernatant metabolomic profiles of commensal bacteria to identify bacteria-derived inhibitors of fungal growth, with valeric and butyric acid as top hits. Experimental validation confirmed in silico predictions in three systems. First, in patient fecal samples, valeric and butyric acid levels inversely correlated with C. parapsilosis growth. Second, in vitro, valeric acid potently inhibited C. parapsilosis growth by causing intracellular acidification. Third, administration of glycerol valerate, and free or microencapsulated valeric acid blunted C. parapsilosis growth at murine intestinal sites where valeric acid could be detected. Thus, machine learning could identify a mechanistic driver of trans-kingdom ecology limiting C. parapsilosis intestinal expansion and may inform strategies to reduce patient risk of developing candidiasis during high-risk periods. Introduction Candida species are members of the intestinal microbial ecosystem and asymptomatically colonize ∼50-70% of individuals, with expansion checked by bacterial competitors and host immune effectors 1 , 2 . However, Candida species, including C. albicans , C. parapsilosis , C. glabrata , and C. auris, can invade mucosal barriers and cause life-threatening bloodstream infections (BSIs), primarily in critically ill patients, in patients with hematologic malignancies, and in solid organ and hematopoietic cell transplant (HCT) recipient 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . In these high-risk groups, patients often receive antibiotics to prevent or treat bacterial infections, injuring the commensal bacterial ecosystem and facilitating the expansion of Candida species and other pathobionts. For example, longitudinal analysis revealed that ∼20-30% of allogeneic HCT (allo-HCT) patients exhibited Candida intestinal expansion, and that a subset of these patients developed systemic candidiasis despite concurrent receipt of antifungal prophylaxis 14 , 15 . Moreover, C. parapsilosis and C. albicans intestinal expansion have been linked to adverse HCT transplant outcomes, including non-relapse mortality 14 , 16 Analysis of intestinal bacterial populations showed that the loss of Lachnospiraceae 15 and the presence of lactate-producing species, e.g., Lactobacillus , Lactococcus, Klebsiella , and Escherichia, correlated with Candida abundance 17 . These observations and predictive modeling advanced the concept that commensal bacteria engage in trans-kingdom ecology with fungi, namely, through bacteria-derived metabolites that regulate Candida colonization levels. Studies in murine models indicated that commensal bacteria could trigger hypoxia-inducible factor 1α-dependent secretion of cathelidin-related antimicrobial peptide (CRAMP; LL-37 in humans) to limit C. albicans expansion 18 . Host production of C. albicans -specific IgA and antimicrobial peptide YY can preferentially target the C. albicans hyphal morphotype that mediates tissue invasion and virulence 19 , 20 , 21 , 22 , 23 . Moreover, commensal bacteria contribute to hypoxic environments in the intestinal tract, and pharmacologic strategies that promote anaerobiosis limit C. albicans colonization 24 . In vitro studies support the idea that 2-, 3-, and 4-carbon short-chain fatty acids (SCFAs) and bile acids may inhibit Candida growth 20 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , including C. albicans hyphal formation and cell wall remodeling 32 , 33 , 34 . However, it remains unclear whether changes in SCFA or other bacterial metabolites could mediate trans-kingdom ecology in the intestinal tract, and if so, what the underlying mechanisms of Candida growth inhibition are. In this study, we trained a machine learning model using the secreted metabolomes from commensal bacteria as an input to identify bacterial-derived valeric acid as a potent and direct inhibitor of C. parapsilosis growth. This prediction was validated through correlative analysis of valeric acid abundance and C. parapsilosis growth in human fecal specimens, through in vitro studies that revealed valeric acid-mediated loss of C. parapsilosis intracellular pH homeostasis and growth inhibition. Delivery of valeric acid formulations demonstrated C. parapsilosis growth inhibition at gastrointestinal sites where valeric acid could be detected. These results highlight the potential of harnessing machine learning models to identify and characterize bacterial-derived metabolites as a novel strategy to limit Candida colonization and mitigate risk for life-threatening infections. Results Machine learning identifies bacterial-derived valeric acid and other SCFAs as candidate inhibitors of Candida growth To quantify the effects of secreted bacterial metabolites on C. parapsilosis growth, we cultured a clinical C. parapsilosis isolate (strain 1004) in a 1:1 mixture of fresh brain heart infusion (BHI) broth and spent BHI supernatant of 347 human commensal bacteria, primarily Lachnospiraceae 35 , 36 . To account for nutrient depletion, control cultures contained a 1:1 mix of fresh BHI and phosphate-buffered saline (PBS; Figure 1a ). Spent supernatants from Coprococcus comes , Anaerostipes hadrus, and Blautia wexlerae isolates inhibited C. parapsilosis growth, as judged by the area under the curve, compared to control growth conditions ( Figure 1b ). Notably, C. comes and A. hadrus are known SCFA producers 37 , 38 . Download figure Open in new tab Figure 1. Prediction of bacterial metabolites that inhibit C. parapsilosis growth a, Schematic summary of screen, analysis, and mathematical modeling to identify C. parapsilosis growth inhibitory metabolites from spent bacterial supernatants. C. parapsilosis strain 1004 was resuspended in BHI broth and added to an equal volume of spent bacterial supernatant (Sample) or PBS (Control) and growth was measured by OD 600 area under the curve (AUC) analysis at 24 h. Spent bacterial supernatants were analyzed by GC-MS to measure the abundance of 36 targeted metabolites to develop a Random Forest model and predict Candida growth from the metabolomic composition of spent supernatants. SHAP analysis calculated the impact of individual candidate metabolites on C. parapsilosis growth. b , Candida AUC ratio in individual spent bacterial supernatants compared to control samples shown in log 2 scale. Isolates are shown by species, with only species with 5 or more isolates displayed, ordered by median AUC ratio. Unclassified isolates are excluded. c , SHAP analysis of Candida growth AUCs (y axis) in relation to the concentration of butyric, valeric, and 5-aminovaleric acid (x axis). Each dot represents one bacterial isolate. Coprococcus comes , Anaerostipes hadrus , and Blautia wexlerae isolates are highlighted in orange, green, or blue as shown. The means of the absolute SHAP values (μ|SHAP|) are shown. Statistical analysis: b , One sample t-test with Benjamin-Hochberg correction; adjusted P values are shown. To identify candidate inhibitory metabolites, we compared the abundance of 36 target metabolites in 229 spent supernatants with their abundance in fresh BHI broth ( Figure 1a , Extended Figure 1 , Supplementary Table 1). We used these metabolomics data to fit a machine learning model of Candida growth. Then, we used Shapley a dditive exPlanations (SHAP) analysis on a Random Forest model to determine the impact of each bacteria-derived metabolite. This model showed good prediction performance with an R² of 0.75, indicating Candida growth could be explained by the metabolite features ( Extended Figure 2a ). The model identified valeric acid, 5-aminovaleric acid, and butyric acid as inhibitors of Candida growth, as well as other SCFAs and amino acids ( Figure 1c , Extended Figure 2b ). These candidate inhibitors were first investigated in human allo-HCT patient fecal samples. Valeric acid and other SCFA abundances inversely correlate with Candida parapsilosis colonization in hematopoietic cell transplant patient fecal samples To corroborate predicted metabolomic hits with C. parapsilosis intestinal colonization data, we analyzed fecal samples from 22 allo-HCT patients (145 fecal samples, range: 2-17 per patient, Supplementary Table 2 for profiles) that were on micafungin prophylaxis (see Materials and Methods). Each allo-HCT recipient had both fungal culture-positive (n = 64) and fungal culture-negative fecal samples (n = 81). For each sample, we analyzed 43 metabolites by gas chromatography–mass spectrometry (GC-MS), including the abundance of 2- to 5-carbon SCFAs and 5-aminovaleric acid. In addition, the relative abundance of bacterial and fungal taxa was analyzed by 16S and ITS1 rDNA sequencing, respectively, and the C. parapsilosis colony-forming units (CFUs) were enumerated. A patient example illustrates a temporal relationship between fecal SCFA abundances and C. parapsilosis colonization ( Figure 2a ). The patient experienced loss of Lachnospiraceae starting on day −4 of allo-HCT, which preceded a >4 x log 10 reduction in the peak area of valeric acid and other SCFAs on day −1 of allo-HCT. These events occurred prior to C. parapsilosis expansion as judged by the rise in relative C. parapsilosis ITS1 rDNA abundance and the recovery of viable C. parapsilosis CFU on day +1. After the patient experienced engraftment on day +11 and recovery of SCFA fecal abundances on day +16, C. parapsilosis ITS1 expansion declined and the fungus was undetectable by culture. Among all samples tested, fecal valeric acid, 5-amino valeric acid, butyric acid, propionic acid, and acetic acid peak values negatively correlated with fecal fungal CFU ( Figure 2b - 2f ; Extended Figure 3 ). Samples with high peak SCFA abundances were nearly uniformly C. parapsilosis culture-negative, consistent with the idea that valeric acid and other 3-5 carbon SCFAs inhibit C. parapsilosis expansion in the human intestinal tract. Download figure Open in new tab Figure 2. Allo-HCT patient fecal profiles for C. parapsilosis and candidate metabolites a , Longitudinal profile of one allo-HCT patient from day −10 to day +20, with rows indicating antimicrobial medication (top), intestinal fungal colonization by fecal CFU (second), relative abundance of fungal taxa by ITS1 amplicons (third), relative abundance of bacterial taxa by 16S amplicons (fourth), and the abundance of indicated metabolites by GC-MS peak area (bottom). b-f , The plots indicate C. parapsilosis CFU and ( b ) valeric acid, ( c ) 5-aminovaleric acid, ( d ) butyric acid, ( e ) propionic acid and ( f ) acetic acid peak areas in each sample (n = 145 fecal samples from 22 allo-HCT patients). Statistical analysis: b-f, Spearman’s rho and two-tailed P values are shown. Valeric acid inhibits Candida growth in vitro To evaluate in silico predictions, we tested the effect of four SCFAs (i.e., valeric, butyric, propionic, and acetic acid) and 5-aminovaleric acid on the growth of C. parapsilosis and other Candida species. At 6 mM concentration, valeric acid inhibited C. parapsilosis growth in a more potent manner than butyric acid and propionic acid, while acetic acid did not inhibit growth ( Figure 3a - 3b ). Similar results were observed with C. metapsilosis and C. albicans ( Extended Figure 4a-4b ). In contrast, 5-aminovaleric acid did not inhibit C. parapsilosis growth ( Figure 3c ), suggesting that addition of an amino group to valeric acid resulted in loss of antifungal activity. Download figure Open in new tab Figure 3. Effects of short-chain fatty acids on Candida growth (a, c, d) C. parapsilosis 1004 or ( f ) C. auris 1193 growth kinetics by optical density (600 nm) and ( b, e ) C. parapsilosis AUC values at 24 hr after culture in YPD broth with ( a, b ) 0 mM (control, orange symbols) or 6 mM of the indicated SCFAs (AA, acetic acid, light blue symbols; PA, propionic acid, green symbols; BA, butyric acid, red symbols; VA, valeric acid, purple symbols), with ( c ) 0 mM (pH 5.4, orange line or pH 7.0, yellow line; both adjusted with HCl) or 12 mM 5-aminovaleric acid (pink line), with ( d-f ) 0 mM (control, orange symbols), 3 mM (light purple symbols), 6 mM (purple symbols), or 12 mM (dark purple symbols) valeric acid. Data are representative of at least two independent experiments and growth curves were calculated from at least two replicates in an experiment. Statistical analysis: b, e, Adjusted P values versus control, calculated with one-way ANOVA with Dunnett’s multiple comparison test. Download figure Open in new tab Figure 4. Transcriptome analysis of SCFA-treated C. parapsilosis cells Valeric acid-mediated growth inhibition occurred in a dose-dependent manner ( Figure 3d - 3e ) and across all tested C. parapsilosis ( Figure 3e and Extended Figure 4c-4d ), C. orthopsilosis ( Extended Figure 4e-4f ), C. metapsilosis ( Extended Figure 4g-4i ), C. albicans ( Extended Figure 4j-4l ), C. glabrata ( Extended Figure 4m ), C. tropicalis ( Extended Figure 4n ), and C. auris ( Figure 3f ) isolates. Thus, valeric acid exposure inhibits Candida growth across multiple species. Valeric acid exposure alters C. parapsilosis gene expression To understand how bacteria-produced SCFAs may be inhibiting C. parapsilosis growth, we performed transcriptome analysis in the presence of 0 or 25 mM valeric, butyric, propionic, or acetic acid. Principle component analysis (PCA) revealed a minor difference in gene expression between the control and the acetic acid samples, and a large difference between these and propionic, butyric, or valeric acid-treated samples ( Figure 4a ), consistent with the finding that acetic acid did not inhibit Candida growth ( Figure 3a - 3b , Extended Figure 4a-4b ). Overall, 63, 822, 786, and 1,083 genes were downregulated by acetic, propionic, butyric, and valeric acid treatment compared with the control condition, representing a total 1,297 unique genes. Of these, 534 downregulated genes were common to valeric, butyric, and propionic acid exposure ( Figure 4b , Supplementary Table 3). Gene ontology (GO) analysis of these 534 genes revealed that pathways related to ribosomal function and translation were downregulated ( Figure 4d ). Overall, 121, 617, 667, and 674 genes were upregulated by acetic, propionic, butyric, and valeric acid treatment, representing a total of 1,031 unique genes ( Figure 4c ). Of these, 315 upregulated genes were common to valeric, butyric, and propionic acid exposure. GO analysis revealed that pathways related to fatty acid catabolism, fatty acid β-oxidation, and monocarboxylic acid catabolism were upregulated in this subset ( Figure 4e ). Moreover, analysis of the most differentially expressed genes ( 4 log 2 change in expression, adjusted P value <0.001) highlighted an increase in genes implicated in modified β-oxidation, as evidenced by the upregulation of POX1-3 and PXP2 ( Extended Figure 5 ). Since modified β-oxidation has been shown to metabolize toxic intermediates such as propionyl-CoA 39 , the C. parapsilosis transcriptome analysis supports the idea that SCFA exposure activated a detoxification mechanism. a , Principal component analysis of global gene expression by C. parapsilosis cells treated with 0 mM (control, orange dots) or 25 mM acetic (light blue dots), propionic (green dots), butyric (red dots), or valeric acid (purple dots) for 90 min. b-c , Venn diagrams depicting the number of downregulated ( b ) or upregulated genes ( c ) in C. parapsilosis cells treated with valeric acid (VA), butyric acid (BA), propionic acid (PA), or acetic acid (AA), compared to untreated control cells. d, e , Gene Ontology (GO) analysis, based on differential gene expression in C. parapsilosis cells treated with VA, BA, or PA highlighted in ( b, c ), shows ( d ) the 6 most downregulated (black bars, −log 10 (adjusted P value) >40) and ( e ) the 5 most upregulated (white bars, −log 10 (adjusted P value) > 4) biological processes (BP) compared to control cells. Statistical analysis: b, c, Differential gene expression analysis with adjusted P value was performed using DESeq2. Genes with log 2 (fold change) > 1 or < −1 with an adjusted P value < 0.05 were considered significantly differentially expressed. d, e, Gene Ontology analysis was performed using g:Profiler with g:SCS correction for multiple testing. Valeric acid disrupts C. parapsilosis intracellular pH homeostasis To test the idea that valeric acid causes C. parapsilosis growth inhibition in a pH-dependent manner, we neutralized the growth medium supplemented with valeric acid ( pKa = 4.84 at 25 °C) to a pH value of 7.0 with HCl. pH neutralization impaired the Candida growth-inhibitory effect of valeric acid. However, acidification of YPD broth to pH 5.5 (with HCl), the same pH value observed with addition of 6 mM valeric acid, did not inhibit C. parapsilosis or C. metapsilosis growth ( Figure 5a - 5b and Extended Figure 6a ). These results indicate that valeric acid-mediated growth inhibition occurs at an acidic pH which favors the protonated, uncharged species of the monocarboxylic acid. Download figure Open in new tab Figure 5. Valeric acid exposure and loss of C. parapsilosis pH homeostasis a, b, C. parapsilosis 1004 ( a ) growth kinetics by optical density (600 nm) and ( b ) AUC values at 24 hr after culture in YPD broth with 0 mM (pH 5.5, orange symbols or pH 7, yellow symbols) or with 6 mM valeric acid (pH 5.5, purple or neutralized to pH 7, blue symbols). Data are representative of at least two independent experiments and growth curves were calculated from at least two replicates in an experiment. c, pHluorin emission spectrum at pH 5.5 (purple line) and pH 7 (orange line) and experimental scheme with pHluorin-expressing C. parapsilosis cells. d-f , pHluorin-expressing C. parapsilosis 65 was incubated in YPD broth with the indicated concentrations (0-12 mM, 0 mM control, orange) of ( d-f ) valeric acid (purple symbols), ( f ) acetic acid (light blue symbols), ( f ) propionic acid (green symbols), ( f ) butyric acid (red symbols) or ( f ) citric acid (yellow symbols) followed by ( d, e ) fluorescence microscopy emission quantification at 525 nm with 405 and 488 excitation wavelengths or ( f ) fluorescence plater reader emission quantitation at 509 nm with 395 and 475 nm excitation wavelengths. Representative images or data from two independent experiments shown and the intracellular pH is indicated using a standard curve of permeabilized pHluorin-expressing cells by monensin. ( d ), Scale bar, 20 µm. Statistical analysis: b , Adjusted P values versus control, calculated with 2-way ANOVA with Bonferroni’s multiple comparison test. e , Welch’s two-sided test: P < 0.0001 (n = 4). f , Data as mean values are shown with adjusted P values versus pH-adjusted control or indicated pairs ( P value: 12 mM of indicated short-chain fatty acid vs control pH 4, or 6 mM of citric acid vs control pH 4) calculated with 2way ANOVA with Bonferroni’s multiple comparison test. The concept of intracellular acidification as a mechanism of bacterial growth inhibition was discovered in bacterial pathobionts, specifically Salmonella and Enterococcus 40 , 41 . To explore this idea in fungi, we generated C. parapsilosis strains that express a GFP-based ratiometric pH sensor, termed pHluorin ( Figure 5c ) 42 , 43 , 44 , 45 , 46 , 47 , 48 . The pHluorin-expressing C. parapsilosis strains exhibited no growth defect compared to the parental strains and a standard pH reporter curve was generated using permeabilized cells ( Extended Figure 6b-6c ). Download figure Open in new tab Figure 6. Valeric acid-associated inhibition of C. parapsilosis intestinal expansion a, c, e, f, Experimental schemes to test the administration of ( a, c ) glycerol trivalerate (TV, gavage), ( c, e ) valeric acid (VA, c, drinking water ad libitum and e , injection into an ileal loop), and ( f ) microencapsulated valeric acid (ecVA) by addition to DietGel93 feed on C. parapsilosis intestinal colonization. b, d, C. parapsilosis CFU in stomach, small intestine, cecal, or colonic contents, or in the feces of germ-free C57B/6J mice that received 200 µl of 1M TV (purple dots) or glycerol (control, orange dots) and 1 x 10 6 C. parapsilosis by gavage, with mice maintained on 0 mM ( b , both groups; d , control group treated with glycerol) or 150 mM VA in the drinking water ( d , TV group). e, Ileal loop C. parapsilosis CFU 3 hr after injection with 300 μl solution that contained 10 5 C. parapsilosis cells and 0 (control) or 100 mM valeric acid in PBS, adjusted to the same pH. g, C. parapsilosis CFU in stomach, small intestine (SI), cecal, or colonic contents, or in the feces of germ-free, antibiotic-treated C57B/6J mice that received microencapsulated valeric acid or no capsules in feed starting on day −4 to day +7 after gavage with 10 6 C. parapsilosis cells. ( b, d, g ) Results were pooled from two independent experiments or were ( e ) representative of 3 independent experiments, with 7-11 mice per group. Each dots represents one mouse. Statistical analysis: P values were calculated using two-tailed Mann–Whitney U test. Treatment of pHluorin-expressing C. parapsilosis 65 with 12 mM valeric acid caused a decrease in mean intracellular pH from 6.96 to 5.00 ( Figure 5d - 5e ), indicating a loss in intracellular pH homeostasis which is linked to a decrease in translational activity 48 . Using two independent pHluorin-expressing C. parapsilosis strains cultured in 6-12 mM 2-5 carbon chain SCFAs for 1 h, valeric acid caused the largest decrease in intracellular pH, with smaller changes observed with butyric and propionic acid ( Figure 5f , Extended Figure 6d ), and no change observed with acetic acid ( Figure 5f , Extended Figure 6d ). As a control, citric acid did not induce C. parapsilosis intracellular acidification even though it decreased the extracellular pH more than valeric acid and other tested SCFAs ( Figure 5f , Extended Figure 6d ). These results indicate that intracellular C. parapsilosis acidification is specific to certain SCFAs but not a common effect of exposure to other weak organic acids. Valeric acid treatment impacts intestinal Candida growth in mice To test whether valeric acid could directly alter C. parapsilosis colonization in vivo , we infected germ-free C57B/6J mice with 10 6 C. parapsilosis cells followed by treatment with glycerol trivalerate (TV), which consist of a glycerol backbone with three esterified valeric acid chains, and enumeration of CFU in the gastrointestinal tract 4 hr later ( Figure 6a ). Gastric and pancreatic lipases can hydrolyze TV to release free fatty acids 49 , 50 , 51 , 52 , 53 , 54 . TV reduced C. parapsilosis colonization in the stomach and small intestinal contents but did not in the lower intestinal contents or feces, compared to treatment with a glycerol control ( Figure 6b ). To deliver of valeric acid throughout the intestinal tract, we next administered 150 mM valeric acid in the drinking water overnight to germ-free C57BL6/J ( Figure 6c ) or Balb/c mice ( Extended Figure 7a ), followed by gavage of 1×10 6 of C. parapsilosis and TV or control wvehicle with euthanasia 4 h later. Receipt of valeric acid and TV reduced C. parapsilosis colonization in the stomach and small intestines. However, there was no significant difference in C. parapsilosis CFU in lower intestinal segments, as previously observed with TV administration alone ( Figure 6d , Extended Figure 7a-7b ). To determine whether the reduction of C. parapsilosis CFU in the stomach and small intestine was linked to local valeric acid concentrations, we measured valeric acid concentrations in the gastric, small intestinal, cecal, and large intestinal contents. We detected valeric acid in stomach and small intestinal contents but not in cecal and large intestinal contents ( Extended Figure 7c-7d ). These results indicate that valeric acid and TV supplementation increased valeric acid concentrations only in the stomach and small intestine, and that this localized increase correlated with the reduction in C. parapsilosis colonization ( Figure 6c - 6d , Extended Figure 7a-7d ). To test whether valeric acid could directly reduce C. parapsilosis levels in the lower intestinal tract, we developed an externalized ileum loop model in which C. parapsilosis (1 x 10 5 cells) and valeric acid were co-administered. Direct delivery of valeric acid to the ileum inhibited the growth and expansion of C. parapsilosis CFU by ∼2log 10 in the ileal lumen 3 h after murine infection ( Figure 6e ). To release valeric acid along the entire intestinal tract, we encapsulated and coated valeric acid through a spray cooling-based microencapsulation and mixed the resulting capsules into DietGel93M (at a final calculated valeric acid concentration of 88.6 mM) for oral administration ad libitum in germ-free mice (See Materials and Methods) 55 . Germ-free mice were started on vancomycin and ampicillin on day −6 to prevent any bacterial growth. Mice received valeric acid capsules or no capsules on day - 4 and were administered 1 x 10 6 C. parapsilosis cells on day 0 ( Figure 6f ). Receipt of microencapsulated valeric acid in the diet reduced C. parapsilosis CFU in the lower intestinal contents and feces on day 7 ( Figure 6g ). In parallel, we measured valeric acid concentrations in intestinal contents on day +7 and detected valeric acid in the feces of most treated mice, but not in untreated mice ( Extended Figure 6e ). Collectively, these results indicate that valeric acid can inhibit intestinal Candida growth at sites where the compound can be delivered and detected. Discussion In this study, we employed a machine learning approach to identify a bacterial-derived metabolite, valeric acid, that mediates trans-kingdom ecology, inhibiting C. parapsilosis growth. This was validated experimentally through analysis of human allo-HCT fecal samples, in vitro growth assays, and in vivo C. parapsilosis colonization studies in mice. Valeric acid exerted growth-inhibitory effects across a broad range of Candida species, including C. albicans and C. auris , and was more potent in growth inhibition than propionic and butyric acid. Valeric acid resulted in loss of intracellular pH homeostasis in C. parapsilosis and C. albicans (Mishra et al, companion study) and this disruption was linked to a reduction in protein synthetic ability and fungal cell growth. Loss of intracellular pH homeostasis represents can be exploited as an antifungal strategy 48 . For example, antifungal terpenoid phenols cause a burst of Ca 2+ transients, disrupting intracellular pH homeostasis, followed by repression of genes mediating ribosome biogenesis and RNA metabolism 56 . SCFA-mediated loss of intracellular pH homeostasis is conserved across microbial kingdoms and acts on bacterial pathobionts as well, exemplified by studies on Salmonella 57 and Enterobacteriaceae 40 . This work and the companion study by Mishra et al . extends this concept to pathogenic eukaryotes, establishing it as a key mechanism of trans-kingdom ecology of the intestinal microbiota. In a C. albicans study, exposure to 18 mM butyric acid, 52 mM propionic acid, and 75 mM acetic acid led to downregulation of genes involved in RNA synthesis and ribosome biogenesis as well as a reduction in total RNA extracted per unit optical density of C. albicans cultures 30 . In other studies, SCFA exposure impacted C. albicans gene metabolism by regulating histone acylation and chromatin structures, which activate or repress transcription and thus affect cell wall structure and yeast-to-hypha morphogenesis, biofilm formation, and antifungal drug susceptibility 28 , 34 , 58 . The mechanism by which SCFAs enter fungal cells remains poorly understood. Monocarboxylate transporters, such as Jen1, or acetate transporter families, such as Ady2, have been characterized in S. cerevisiae , and orthologs have been identified in Candida species 59 , 60 , 61 . Although no specific transporters have been identified as SCFA transporters in Candida, it is possible that variations in transporter specificity or expression in different Candida species may explain the susceptility of different Candida species or isolates to growth-inhibitory effects of SCFAs. In addition, protonated SCFAs may passively diffuse into fungal cells 62 . The growth-inhibitory effects of SCFAs of varying length may also be related to differences in intracellular metabolism and the potential toxicity of metabolic intermediates.The accumulation of propionyl-CoA, 3-hydroxypropionate, and 3-hydroxypropionyl-CoA exerted toxicity in both host and fungal cells 63 , 64 , 65 . Activation of the modified beta-oxidation pathway may reflect a protective mechanism for detoxifying these intermediates by converting them into acetate and acetyl-CoA. This may partially explain the lack of growth-inhibitory effects of acetic acid at the concentration range tested. Valeric acid may also generate toxic intermediates, including propionyl-CoA, through canonical beta-oxidation 39 , contributing to its inhibitory effect. In contrast, butyric acid undergoes canonical beta-oxidation to form acetyl-CoA, yet still effectively inhibits Candida growth, suggesting that propionyl-CoA accumulation alone may not fully explain SCFA-mediated growth inhibition. Further research is warranted to elucidate the intracellular metabolism and degradation of internalized short chain fatty acids. In allo-HCT patients, intestinal Candida expansion is linked to poor clinical outcomes, in part due to heightened risk of bloodstream translocation 14 , 15 . In allo-HCT patient fecal samples, C. papapsilosis expansion occurred only when SCFA abundances were low. C. parapsilosis expansion did not occur in all samples with low SCFA abundances, due to potential loss of C. parapsilosis from the intestinal microbiota prior to the reduction in SCFA abundance or due to the presence of alternative mechanisms of colonization resistance, e.g., host-derived cathelicidins, hypoxic tissue conditions, the presence of bacterial competitors, or nutrient limitation 18 , 24 , 66 . Receipt of medications that regulate pH along the gastrointestinal tract likely influences the relationship of SCFA abundance and Candida colonization as well 67 . Fecal metabolite analysis in this study was limited in scope and fecal samples with low SCFA concentrations may have contained other compounds that prevented C. parapsilosis growth. For example, we observed that indole abundance inversely correlated with C. parapsilosis growth. Larger untargeted metabolomic studies will be required to identify and test additional host- and bacterial-derived metabolites implicated in Candida growth inhibition. Beyond lowering C. parapsilosis colonization, administration of glycerol trivalerate to mice can inhibit the growth of the bacterial pathogen Clostridiodes difficile 54 . In a study of antibiotic-induced microbiota states that promote C. difficile colonization, administration of cefoperazone reduced intestinal valeric acid levels by 66-fold 68 . Collectively, these studies link changes in valeric acid abundance to C. difficile infectious susceptibility in mice. Intestinal valeric acid abundance has been linked to host immune activation by regulation of mammalian target of rapamycin activity in B cells and CD4 + T cells, with effects on IL-17 and IL-10 release by effector T cell populations 69 , 70 . Butyric acid is a well-known inducer of colonic colonic T regulatory cell differentiation 71 . Thus, it is possible that valeric acid (and other SCFAs) could exert effects on bacterial and Candida intestinal colonization through indirect, immune-mediated mechanisms or through effects on commensal bacteria (Mishra et al., companion study). Some commensal bacterial species, including Megasphaera massiliensis, Anaerostipes hadrus, and Coprococcus comes , produce valeric acid, and its physiological intestinal concentration is typically lower than that of other SCFAs 69 . Since valeric acid is a potent SCFA against Candida species, and its physiological concentration is in the low mM range at baseline, it is possible that strategies to promote a modest increase in valeric acid concentration may be effective in limiting Candida growth. Patients with leukemia and solid organ and bone marrow transplant recipients are highly vulnerable to systemic candidiasis and commonly receive prophylactic antibiotics to prevent life-threatening bacterial infections. Thus, strategies that depend on live commensal bacteria (i.e., fecal microbiota transplantation or delivery of rational bacterial consortia) to reverse microbial dysbiosis are limited by the ongoing need for antibacterial antibiotics during high-risk periods. The strategy of using metabolites to mitigate pathobiont expansion is attractive in these clinical scenarios. A major barrier to clinical success remains the development of strategies to deliver metabolites to intestinal sites of pathobiont expansion. Our findings support the idea that interventions to increase intestinal valeric acid levels may be beneficial for immunocompromised patients with dysbiosis, such as allo-HCT patients, in whom Candida expansion and bloodstream infection originating from intestinal translocation impacts clinical outcomes. Author Contributions The study was conceived by T.M.H. and K.Y-M. In vitro experiments and in vivo experiments were performed by K. Y-M., A. B and C.N.S. C. parapsilosis pHluorin strains were generated by T. N and A.G. Transcriptome analysis was performed by K.Y-M, K.B., and G.B. Imaging analysis was performed by E.C. and K.Y-M. SHAP modeling analysis was performed by C.L. and J.B.X. GC-MS analysis was performed by R.R., J.C., A.S, and E.G.P. Microencapsulated valerate was generated by P.M. Data analysis and visualization were performed by K.Y-M. and C.L. Data interpretation was performed by K.Y-M. and T.M.H. The manuscript was written by K.Y-M. and T.M.H. All authors have read and approved the manuscript. Competing Interests Statement E.G.P. is an inventor on patent application no. WPO2015179437A1, entitled “Methods and compositions for reducing Clostridium difficile infection” and no. WPO2017091753A1, entitled “Methods and compositions for reducing vancomycin resistant Enterococci infection or colonization”; and holds patents that receive royalties from Seres Therapeutics. M. P. is an employee at SILA Advanced Nutrition. The remaining authors declare no competing interests. Resource availability Lead Contact Further information and requests for resources and reagents should be directed to the lead contact, Tobias Hohl ( hohlt{at}mskcc.org ). Materials Availability Requests for materials should be addressed to T.M.H and should be covered by a material transfer agreement. Data and Code Availability The C. parapsilosis RNA-Seq dataset is being deposited in the Sequence Read Archive under BioProject PRJNA1294161 (reviewer link at URL https://dataview.ncbi.nlm.nih.gov/object/PRJNA1294161?reviewer=fiaeishv6qhuim2sk35rrham0t ). Fungal ITS1 and bacterial 16S amplicon sequences previously were deposited under BioProject PRJNA746305 and re-analyzed for the relevant patient fecal samples (see Supplementary Table 4) 14 . All data needed to evaluate the conclusions are present in the paper or in the Supplementary Materials. Any additional information required to re-analyze the data reported in this paper is available from the lead contact upon request. All code for this study is at https://github.com/liaochen1988/Cparaplosis_gut_colonization . Methods Bacteria and Candida strains Commensal bacterial strains isolated from healthy human donor feces were stored in the MSKCC BioBank or the Symbiotic Strain Bank at Duchossois Family Institute at the University of Chicago. C. parapsilosis complex strains are MSK clinical isolates, with C. parapsilosis strains 1004 and 1666 and C. metapsilosis strains 73 and 414 from fecal samples and C. parapsilosis strains 478, 65, and 804, C. orthopsilosis strains 479 and 616, and C. metapsilosis strain 798 from blood culture specimens 72 . C. auris strain 1193 was isolated from human nares. C. albicans strains SC5314 73 , 529L 74 , CHN1 75 , C. glabrata (ATCC 2001), and C. tropicalis (ATCC750) were from the American Tissue Culture Collection (ATCC). All fungal and bacterial strains isolated at MSKCC or at the University of Chicago were classified based on rDNA (16S rDNA for bacteria or ITS1 for fungi) or on whole genome sequencing. In vitro screen of C. parapsilosis growth in bacterial spent supernatants Individual bacterial strains were shaken in 96 well plates at 37°C in brain heart infusion liquid medium (BHI; BD cat no. 237500) supplemented with 0.5 g/L cysteine, 1 mg/L menadione, and 10 mg/L hemin) under anaerobic conditions to yield stationary phase cultures and spent supernatants were collected by centrifugation. An overnight culture of C. parapsilosis strain 1004 was grown in yeast extract peptone dextrose (YPD) broth (BD cat no. 242820) overnight, washed twice in PBS, and diluted to add 400 CFU to 100 μl fresh BHI broth mixed with 100 µl of spent bacterial supernatant or with 100 μl PBS (control) in a 96-well plate. The cultures were incubated in a SpectraMax i3 Microplate Reader (Molecular Devices, CA, USA) at 30°C with shaking under aerobic conditions. Area under the curves (AUC) for 24 h of growth was calculated using Growthcurver in R. To assess the effect of specific metabolites on Candida growth, indicated Candida strains were inoculated into 96-well plates at 400 CFU/well in YPD broth supplemented with the indicated metabolite concentrations, and growth curves were generated in a SPARK microplate reader (Tecan, Switzerland). Fecal sample collection in MSK allo-HCT patients MSK adult allo-HCT patients were invited to participate in an IRB-approved longitudinal collection of fecal samples as described 14 , 15 . Per institutional guidelines, allo-HCT patients received micafungin (100 mg IV every 24 hr) and levofloxacin (500 mg po every 24 hr) for prophylaxis, starting at the beginning of pre-transplant conditioning chemotherapy. For allo-HCT patients who received total body irradiation as part of the conditioning regimen, vancomycin (1 gm IV every 12 hr) was administered from day −2 to day + 7. Micafungin was switched to a mold-active azole drug (i.e, voriconazole or posaconazole) on day +7 for unmodified myeloablative, cord blood, haploidentical, and T cell-depleted allo-HCT. In this study, we included 145 fecal samples from 22 patients with at least one culture-positive C. parapsilosis sample per patient and one culture-negative sample per patient. 56 samples from these 22 patients were excluded from analysis because of (1) concurrent receipt of triazole therapy or (2) sample culture positivity for fungi other than C. parapsilosis complex species. Metabolomic analyses Targeted metabolomics was performed to determine the abundance of 38 metabolites in spent bacterial supernatants at University of Chicago or the abundance of 43 metabolites including short-chain fatty acids in human and mouse fecal samples at MSKCC. Supernatant sample extraction and derivatization Supernatants were extracted and derivatized as described 76 , 77 . Briefly, supernatants were extracted using 4 volumes of 100% methanol spiked with internal standards, centrifuged at 20,000 x g for 20 min at 4°C. Human and mouse sample extraction Human fecal samples and the murine small intestine, cecum, colon, and fecal samples were weighed and transferred into 2 mL microtubes pre-filled with 1.4 mm ceramic beads (Omni International). Human and murine fecal samples were resuspended to a final concentration of 100 mg/mL and 50 mg/mL in 9 volumes of 80:20 methanol:water (Optima LS/MS grade #A456, Fisher Scientific, MA) that contained acetate-d3, propionate-d5, butyrate-d7, and valerate-d9 as internal standards (Cambridge Isotope Laboratories). Homogenization was performed using a Bead Ruptor (Omni International) at a speed of 6 m/s for 3 minutes at 4°C. After homogenization, the samples were centrifuged at 20,000 x g for 20 minutes at 4°C to collect the supernatant. Sample derivatization and quantification by GC-MS Extracted supernatant, human and mouse samples were derivatized for metabolomic analysis similar to methods as described 76 with modifications 77 . Briefly, 1 volume of extract was added to 1 volume of 100 mM borate buffer (pH 10), 4 volumes of 100 mM pentafluorobenzyl bromide (PFBBr, Thermo Scientific) diluted in acetonitrile (Fisher), and 4 volumes of n-hexane or cyclohexane (Acros Organics) and quantified by gas-chromatography-mass spectrometry (Agilent 7890A GC and Agilent 5975C MS detector) operating in negative chemical ionization (nCI) mode, using methane as the reagent gas. For supernatant analysis, semi-quantitative values were calculated from the raw peak area of the compound normalized to the median raw peak area of two baseline media controls. For human and mouse SCFA analysis, the raw peak areas of acetate ( m/z 59) and propionate ( m/z 73) were normalized to acetate-d3 ( m/z 62) and propionate-d5 ( m/z 78), respectively; butyrate and isobutyrate ( m/z 87) were normalized to butyrate-d7 ( m/z 94); 2-methylbutyrate, valerate, and isovalerate ( m/z 101) were normalized to valerate-d9 ( m/z 110). Data was analyzed using the MassHunter Quantitative Analysis software (Version 10.1, Agilent Technologies) and confirmed by comparison to authentic standards. Random Forest model and SHAP analysis A Random Forest (RF) model of 1,000 trees was constructed to predict C. parapsilosis growth based on metabolite concentrations in bacterial supernatants. Two metabolites were excluded from the analysis due to insufficient data points. Metabolite profiling was available for 229 out of 347 isolates used in growth analysis, and these 229 isolates with data on 36 metabolites were used for modeling and SHAP analysis. Both metabolite concentrations and growth curve AUCs were log2-transformed. The RF model hyperparameters were fine-tuned to maximize the R2 score through a grid search in a 5-fold cross-validation framework. These hyperparameters include the number of features considered for the optimal split (max_features = ‘sqrt’, ‘log2’, 0.25, 0.50, 0.75, 1.00), the maximum depth of each tree (max_depth = 2, 4, 8), the minimum number of samples required to split an internal node (min_samples_split = 2, 4, 8), and the minimum number of samples required at a leaf node (min_samples_leaf = 1, 2, 4). The RF model was implemented using the RandomForestRegressor function from Python’s scikit-learn package (version 1.2.2). SHAP (SHapley Additive exPlanations) analysis was performed using the TreeExplainer function from the SHap package (version 0.42.0). pHluorin-expressing Candida parapsilosis strains, pH calculation, and visualization The pHluorin-expressing construct was inserted into the CpNEUT5L locus of C. parapsilosis strains 65 and 804 by CRISPR-Cas9 42 , 44 . The pHluorin ORF was amplified with primers pHluorin_ClaI_F (5’-ttt tat cga tat gtc aaa agg tga aga att att tac tgg-3’) and pHluorin_NheI_R (5’-ttt ttt tgc tag ctt att tat ata att cat cca tac cat gag taa tac c-3’). The amplicon and pNRVL-N5L-CFP were digested with Cla I and Nhe I and ligated to yield pNRVL-N5L-pHluorin (Accession number: PQ261162 ). This plasmid was used as a template to amplify the donor DNA with the primer pair FP_CpN5L_dDNA_F (5’-aac ctc atc tca agg cgc-3’) and FP_CpN5L_dDNA_R (5’-aca caa aaa tac atg att gcg tc-3’). CRISPR/Cas9 engineering and C. parapsilosis transformation of parental strains 65 and 804 was performed as described 47 to generate pHluorin-expressing strains (genotype: cpneut5l::PCaTDH3-pHLuorin-TScURA3/cpneut5l::PCaTDH3-pHLuorin-TScURA3 ). The pHluorin-inserted Cp65 and 804 strains were cultured and inoculated into the media with SCFAs or calibration buffers and incubated for 1 h 43 . Fluorescence intensities were measured using a SPARK microplate reader (Tecan, Switzerland) at 509 nm emission with the excitation at 395 and 475 nm. Imaging was performed on a TCS SP5 confocal microscope (Leica, Germany) with 63x/1.4NA objective lens and fluorescence intensities were measured at 525 nm emission with the excitation at 405 and 488 nm. The image intensity was measured in FIJI (NIH, USA). Intracellular pH was determined based on fluorescence intensity using Graphpad Prism v9.0 and Fiji, based on the standard curve generated by monensin-permeabilizing cells. Candida RNA-seq analysis C. parapsilosis strain 1004 was cultured at 30 °C overnight, diluted 1:100, and grown to an OD 600 of 0.3-0.4 in YPD broth. After a PBS wash, 5 x 10 7 cells were inoculated in 3 ml YPD with 0 or 25 mM SCFA and cultured for 1.5 h at 30°C with shaking, washed in PBS, stored in RNA later (Invitrogen, MA) at −80°C, thawed, and disrupted using RNA pink beads lysis kits (Next Advance, NY). RNA was isolated using TRIzol (Invitrogen, MA), cleaned with RNeasy (Qiagen, Netherland), quantified using RiboGreen (Thermo Scientific, MA), and analyzed for quality on an BioAnalyzer (Agilent, CA). 500 ng of total RNA with RIN values of 8.2-9.5 underwent polyA selection and used for TruSeq library preparation (TruSeq Stranded mRNA LT Kit, RS-122-2102, Illumina), with 8 PCR cycles. Barcoded samples were run on a NovaSeq 6000 in a PE100 run, using the NovaSeq 6000 S4 Reagent Kit (200 Cycles). Each sample generated an average of 52 million paired reads. The raw data were analyzed using the Transcriptome pipeline (Basepair). Only genes with logFC >1 and p-value <0.05 in at least one comparison were included. Annotations for C. albicans and S. cerevisiae orthologs are taken from the Candida Gene Order Browser 78 . Orthologies were assigned on a single gene basis. For large gene families, only one C. parapsilosis gene was assigned as an ortholog. Gene annotation and clustering were performed using g:Profiler. Mice and treatment with valeric acid formulations Specific pathogen-free (SPF) C57BL6/J male and female mice were used in experiments at 8-12 weeks of age. Germ-free C57BL6/J or Balb/c mice were bred and maintained in sterile isolators in the gnotobiotic facility at the Weill Cornell Medicine-MSKCC vivarium. All experiments complied with federal and institutional guidelines and were approved by the MSK Institutional Animal Care and Use Committee (Protocol #13-07-008). Germ-free C57BL6/J or Balb/c mice were administered 150 mM valeric acid or control drinking water, both adjusted to pH 5, starting 18 hr prior to infection with C. parapsilosis strain 478 (inoculum: 10 6 cells) and receipt of 200 μL of 1 M glycerol trivalerate or 1M glycerol in PBS, adjusted to pH 4.5, by oral gavage. Mice were euthanized 4 hr later and intestinal contents were harvested for quantitation of Candida CFU and valeric acid levels. For the ileal loop model, SPF C57B6/J mice (Jackson Laboratories, strain 00664) were treated with ampicillin (1g/L) in the drinking water starting on day −3. On the day of the experiment, mice were anesthetized with ketamine and the ileum was externalized. The ileal loop was injected with 300 μl solution that contained 1×10 5 C. parapsilosis strain 478 cells as well as 0 or 100 mM valeric acid in PBS adjusted to the same pH, mixed immediately prior to the injection. Mice were kept under anesthesia and on a heating pad for 3 hr prior to euthanasia for CFU enumeration from ileal loop contents. Microencapsulated valeric acid was generated by SILA S.p.A. (Noale, Italy), according to the patent EP2352386B1 as follows 55 . Briefly, sodium valerate was generated by mixing valeric acid with an aqueous solution of 50% NaOH until the theoretical equivalence point was reached, ensuring complete conversion to sodium valerate while monitoring pH. The mixture was heated and dried at 120°C overnight, then ground into a fine powder. 41% (w/w) of sodium valerate was slowly added to a melted mixture of vegetable hydrogenated fatty acids and an emulsifier until a homogeneous dispersion was obtained, and the molten mixture was then cooled to room temperature to solidify. The solid product was ground with a low-shear mixer and sieved between 500 and 800 µm. The obtained titer was calculated to be 40.50%. DietGel93M (ClearH2O, USA) was transferred to autoclaved feeders and mixed with or without valerate capsules and fed ad libitum to germ-free mice. To avoid any possible bacterial contamination, germ-free mice were maintained on ampicillin and vancomycin during the experiment and confirmed to be germ-free prior to C. parapsilosis administration. Fecal, intestinal, and ileal loop contents were plated on SAB agar (BD Difco Sabouraud dextrose agar, BD 210930) plates supplemented with or without 10 μg/ml of vancomycin (Hospira, NDC 0409-6510-01) and 100 μg/ml of gentamicin (Gemini, 400108) as previously described 79 . Data analyses and plotting GraphPad Prism (v9 or v10, GraphPad Software Inc.) or R (version 4.1.0, R Development Core Team) were used to generate data plots and perform statistical tests on datasets. Supplementary Information Supplementary Table 1. Bacterial supernatant metabolite profiles (separate file due to size). Abundance of 36 target metabolites in spent supernatants of the 229 commensal bacteria from the library, compared to the BHI growth medium. View this table: View inline View popup Download powerpoint Supplementary Table 2. Clinical profiles of patients who provided fecal samples for analysis Clinical background data of allo-HCT patients with fecal samples that were included in the study (n = 22). Supplementary Table 3. Transcriptome analysis of SCFA-treated C. parapsilosis cells (separate file due to size). List of downregulated or upregulated genes of C.parapsilosis cells treated with acetic, propionic, butyric or valeric acid compared to the untreated cells. Supplementary Table 4. 16S and ITS Sequences relevant for Figure 2a . The data were previously deposited under BioProject PRJNA746305. Extended Data Figures Download figure Open in new tab Extended Figure 1. Heatmap of bacterial supernatant metabolite profiles, related to Figure 1 Concentrations of 36 metabolites in the spent supernatants of Coprococcus comes (orange), Anaerostipes hadrus (green), and Blautia wexlerae (blue) isolates were measured and shown relative to control BHI growth medium. Metabolite production is indicated in green and depletion in purple. Metabolites present in BHI growth medium are highlighted in blue. Download figure Open in new tab Extended Figure 2. Prediction of bacterial metabolites that inhibit C. parapsilosis growth, related to Figure 1 a, Goodness of fit for the Random Forest model in reproducing the observed Candida growth, measured as area under the curve (AUC) relative to the negative control. b, SHAP analysis of Candida growth AUCs (y axis) in relation to metabolite concentrations (x-axis), including acetic acid, 2-methylbutyric acid, tryptophan, phenylacetic acid, alanine, tyramine, and glutamic acid. Each dot represents one bacterial isolate. Coprococcus comes , Anaerostipes hadrus , and Blautia wexlerae isolates are highlighted in orange, green, or blue, respectively. Figure 1c and Extended Figure 2b display the top ten metabolites, ranked by mean absolute SHAP values. SHAP values for all 36 metabolites, including the top ten. Download figure Open in new tab Extended Figure 3. Allo-HCT patient fecal metabolite peak areas and C. parapsilosis growth, related to Figure 2 The plots indicate C. parapsilosis CFU and the GC/MS peak area of indicated metabolites in each sample (n = 145 fecal samples from 22 allo-HCT patients). Statistical analysis: Kendall’s tau and two-tailed P values are shown in blue (negative correlation) or in red (positive correlation). The plots with P <0.05 are shown. Download figure Open in new tab Extended Figure 4. C. parapsilosis and other Candida species growth curves in the presence of valeric acid or other SCFAs, related to Figure 3 a, b, ( a ) Candida metapsilosis 414 and ( b ) C. albicans SC5314 growth by OD 600 measurement in the presence of 0 mM SCFA (control, orange line) or 6 mM acetic acid (blue line), propionic (green line), butyric (red line), or valeric acid (purple line). c-n , Candida growth in 0 mM (control, orange), 3 mM (light purple), 6 mM (purple), or 12 mM (dark blue) valeric acid. ( c, d ) C. parapsilosis 1666 and 478, ( e, f ) C. orthopsilosis 479 and 616, ( g-i ) C. metapsilosis 73, 798, and 414, ( j-l ) C. albicans SC5314, 529L, and CHN1, ( m ) C. glabrata 180, and ( n ) C. tropicalis 179. Growth curves were calculated from at least two replicates per experiment. Download figure Open in new tab Extended Figure 5. Differentially expressed genes in SCFA-treated C. parapsilosis samples, related to Figure 4 The heatmap depicts a subset of highly differentially expressed genes and annotations in propionic acid (PA)-, butyric acid (BA)-, and valeric acid (VA)-treated C. parapsilosis cells compared to untreated control or acetic acid (AA)-treated cells. C. albicans and S. cerevisiae orthologs and known gene functions are indicated in the two right columns. The heatmap includes genes with a log 2 -fold change > 4 or < −4 and an adjusted P value < 0.01 for PA-, BA- and VA-treated cells, and a log 2 -fold change −1 for AA-treated cells. Download figure Open in new tab Extended Figure 6. Characterization of pHluorin-expressing C. parapsilosis strains, related to Figure 5 a, C. metapsilosis strain 414 growth in 0 mM (pH 5.5, orange or pH 7, yellow) or in 6 mM valeric acid (pH 5.5, purple line or pH 7, blue line). b , Growth curves of C. parapsilosis 65 (Cp65), pHluorin-expressing C. parapsilosis 65 (Cp65-pHluorin), C. parapsilosis 804 (Cp804), pHluorin-expressing C. parapsilosis 804 (Cp804-pHluorin) in 0 mM (orange), 3 mM (light purple), 6 mM (purple), and 12 mM valeric acid (dark blue). The lines show the average OD 600 values of duplicate samples. c, Standard curves of monensin-permeabilized Cp65-pHluorin and Cp804-pHluorin cells that indicate the 395nm/475nm emission ratios (y-axis) at the indicated pH (x-axis). d, Intracellular pH of Cp804-pHluorin cells after incubation in YPD broth with the indicated concentrations (0-12 mM, 0 mM control, orange) of acetic acid (light blue), propionic acid (green), butyric acid (red), valeric acid (purple), or citric acid (yellow), as determined by fluorescence emission quantitation at 509 nm with 395 and 475 nm excitation wavelengths using a plate reader. The intracellular pH was calculated using a standard curve of monensin-permeabilized pHluorin-expressing cells as in ( c ). Representative data from two independent experiments shown. Statistical analysis: d , Data as mean values are shown with adjusted P values versus pH-adjusted control or indicated pairs ( P value: 12 mM of indicated short-chain fatty acid vs control pH 4, or 6 mM of citric acid vs control pH 4) calculated with 2way ANOVA with Bonferroni’s multiple comparison test. Download figure Open in new tab Extended Figure 7. Valeric acid-associated inhibition of C. parapsilosis growth and valeric acid abundance in the intestinal tract, related to Figure 6 a , Experimental scheme to test the administration of glycerol trivalerate (TV, gavage), and valeric acid (VA, drinking water ad libitum ) on C. parapsilosis intestinal colonization in germ-free Balb/c mice. b , C. parapsilosis CFU in stomach, small intestine (SI), cecal, or colonic contents, or in the feces of germ-free Balb/c mice that received 200 µl of 1M TV (purple dots) or equivalent glycerol (control, orange dots) and 1 x 10 6 C. parapsilosis by gavage, with mice maintained on 0 mM (control group treated with glycerol) or 150 mM VA in the drinking water (TV group). c-e , Valeric acid levels in indicated intestinal contents from experiments shown in ( c ) Figure 6d , ( d ) Extended Figure 7b , and ( e ) Figure 6g . Results were pooled from two independent with 7-11 mice per group. Each dots represents one mouse. Statistical analysis: b-e , P values were calculated using two-tailed Mann–Whitney U test. Acknowledgments We thank Dr. Julia Köhler (Boston’s Children’s Hospital, Harvard University, USA) for providing the pHluorin construct, the MSK Imaging core facility for microscopy experiments, the Integrated Genomics Operation Core for RNA-seq experiments. Graphical schemes were created with BioRender.com. Portions of this text were edited with the assistance of ChatGPT (OpenAI, 2025). The resulting content was reviewed and further edited by the authors. These studies were supported by a Geoffrey Beene Foundation Award (TMH), a Burroughs Wellcome Fund Investigator in the Pathogenesis of Infectious Diseases Award (TMH) and by NIH grants P01 AI179406 (TMH and JBX), R37 AI093808 (TMH), K99 AI175599 (CL), P30 CA008748 (to MSKCC), an unrestricted award from Tito’s Love, a Takeda Science Foundation Fellowship grant (KYM), and JSPS KAKENHI Grant JP202470035 (KYM). Footnotes https://urldefense.com/v3/__http://www.ncbi.nlm.nih.gov/bioproject/1294161__;!!KVWo1iE!WAwgwWSNmWu4UvFFxEKLUHhacpQzcTRL5j_nNGnI7MId9wxBKSv1orlkrtk9CaRomOcbPkUdeGMmH2nRWOqzfhvbRVnalvFlEV2jduE$ Uncategorized References 1. ↵ Fisher , M.C. & Denning , D.W . The WHO fungal priority pathogens list as a game-changer . Nat Rev Microbiol 21 , 211 – 212 ( 2023 ). OpenUrl CrossRef PubMed 2. ↵ Pappas , P.G. , Lionakis , M.S. , Arendrup , M.C. , Ostrosky-Zeichner , L. & Kullberg , B.J. Invasive candidiasis . Nat Rev Dis Primers 4 , 18026 ( 2018 ). OpenUrl CrossRef PubMed 3. ↵ Wisplinghoff , H. et al. Nosocomial bloodstream infections in US hospitals: Analysis of 24,179 cases from a prospective nationwide surveillance study . Clinical Infectious Diseases 39 , 309 – 317 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 4. ↵ Wisplinghoff , H. et al. Nosocomial bloodstream infections due to Candida spp. in the USA: Species distribution, clinical features and antifungal susceptibilities . International Journal of Antimicrobial Agents 43 , 78 – 81 ( 2014 ). OpenUrl CrossRef PubMed 5. ↵ Magill , S.S. et al. Multistate point-prevalence survey of health care-associated infections . N Engl J Med 370 , 1198 – 1208 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 6. ↵ Kett , D.H. , Azoulay , E. , Echeverria , P.M. & Vincent , J.L . Candida bloodstream infections in intensive care units: analysis of the extended prevalence of infection in intensive care unit study . Crit Care Med 39 , 665 – 670 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 7. ↵ Kullberg , B.J. & Arendrup , M.C . Invasive Candidiasis . N Engl J Med 373 , 1445 – 1456 ( 2015 ). OpenUrl CrossRef PubMed 8. ↵ Wey , S.B. , Mori , M. , Pfaller , M.A. , Woolson , R.F. & Wenzel , R.P. Hospital-acquired candidemia . The attributable mortality and excess length of stay . Arch Intern Med 148 , 2642 – 2645 ( 1988 ). OpenUrl CrossRef PubMed Web of Science 9. ↵ Gudlaugsson , O. et al. Attributable mortality of nosocomial candidemia, revisited . Clin Infect Dis 37 , 1172 – 1177 ( 2003 ). OpenUrl CrossRef PubMed Web of Science 10. ↵ Cornely , F.B. et al. Attributable mortality of candidemia after introduction of echinocandins . Mycoses 63 , 1373 – 1381 ( 2020 ). OpenUrl PubMed 11. ↵ Mazi , P.B. et al. Attributable Mortality of Candida Bloodstream Infections in the Modern Era: A Propensity Score Analysis . Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 75 , 1031 – 1036 ( 2022 ). OpenUrl PubMed 12. ↵ Denning , D.W . Global incidence and mortality of severe fungal disease . Lancet Infect Dis 24 , e428 – e438 ( 2024 ). OpenUrl CrossRef PubMed 13. ↵ Salmanton-Garcia , J. et al. Attributable mortality of candidemia - Results from the ECMM Candida III multinational European Observational Cohort Study . J Infect 89 , 106229 ( 2024 ). 14. ↵ Rolling , T. et al. Haematopoietic cell transplantation outcomes are linked to intestinal mycobiota dynamics and an expansion of Candida parapsilosis complex species . Nat Microbiol 6 , 1505 – 1515 ( 2021 ). OpenUrl PubMed 15. ↵ Zhai , B. et al. High-resolution mycobiota analysis reveals dynamic intestinal translocation preceding invasive candidiasis . Nat Med 26 , 59 – 64 ( 2020 ). OpenUrl CrossRef PubMed 16. ↵ Malard , F. et al. Impact of gut fungal and bacterial communities on the outcome of allogeneic hematopoietic cell transplantation . Mucosal Immunol 14 , 1127 – 1132 ( 2021 ). OpenUrl PubMed 17. ↵ Seelbinder , B. et al. Candida expansion in the gut of lung cancer patients associates with an ecological signature that supports growth under dysbiotic conditions . Nature Communications 14 , 2673 ( 2023 ). OpenUrl PubMed 18. ↵ Fan , D. et al. Activation of HIF-1alpha and LL-37 by commensal bacteria inhibits Candida albicans colonization . Nat Med 21 , 808 – 814 ( 2015 ). OpenUrl PubMed 19. ↵ Pierre , J.F. et al. Peptide YY: A Paneth cell antimicrobial peptide that maintains Candida gut commensalism . Science 381 , 502 – 508 ( 2023 ). OpenUrl CrossRef PubMed 20. ↵ Liang , S.H. et al. The hyphal-specific toxin candidalysin promotes fungal gut commensalism . Nature 627 , 620 – 627 ( 2024 ). OpenUrl CrossRef PubMed 21. ↵ Ost , K.S. et al. Adaptive immunity induces mutualism between commensal eukaryotes . Nature 596 , 114 – 118 ( 2021 ). OpenUrl CrossRef PubMed 22. ↵ Doron , I. et al. Mycobiota-induced IgA antibodies regulate fungal commensalism in the gut and are dysregulated in Crohn’s disease . Nat Microbiol 6 , 1493 – 1504 ( 2021 ). OpenUrl PubMed 23. ↵ Saville , S.P. , Lazzell , A.L. , Monteagudo , C. & Lopez-Ribot , J.L . Engineered control of cell morphology in vivo reveals distinct roles for yeast and filamentous forms of Candida albicans during infection . Eukaryot Cell 2 , 1053 – 1060 ( 2003 ). OpenUrl Abstract / FREE Full Text 24. ↵ Savage , H.P. et al. Epithelial hypoxia maintains colonization resistance against Candida albicans . Cell Host Microbe 32 , 1103 – 1113 e1106 ( 2024 ). OpenUrl CrossRef PubMed 25. ↵ Guinan , J. , Villa , P. & Thangamani , S . Secondary bile acids inhibit Candida albicans growth and morphogenesis . Pathogens and Disease 76 ( 2018 ). 26. ↵ Thangamani , S. et al. Bile Acid Regulates the Colonization and Dissemination of Candida albicans from the Gastrointestinal Tract by Controlling Host Defense System and Microbiota . J Fungi (Basel) 7 ( 2021 ). 27. ↵ Guinan , J. , Wang , S. , Hazbun , T.R. , Yadav , H. & Thangamani , S . Antibiotic-induced decreases in the levels of microbial-derived short-chain fatty acids correlate with increased gastrointestinal colonization of Candida albicans . Sci Rep 9 , 8872 ( 2019 ). OpenUrl PubMed 28. ↵ McCrory , C. , Lenardon , M. & Traven , A . Bacteria-derived short-chain fatty acids as potential regulators of fungal commensalism and pathogenesis . Trends Microbiol ( 2024 ). 29. ↵ Nguyen , L.N. , Lopes , L.C. , Cordero , R.J. & Nosanchuk , J.D . Sodium butyrate inhibits pathogenic yeast growth and enhances the functions of macrophages . J Antimicrob Chemother 66 , 2573 – 2580 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 30. ↵ Cottier , F. et al. The transcriptional stress response of Candida albicans to weak organic acids . G3 (Bethesda) 5 , 497 – 505 ( 2015 ). OpenUrl 31. ↵ Cottier , F. , Tan , A.S. , Xu , X. , Wang , Y. & Pavelka , N . MIG1 Regulates Resistance of Candida albicans against the Fungistatic Effect of Weak Organic Acids . Eukaryot Cell 14 , 1054 – 1061 ( 2015 ). OpenUrl Abstract / FREE Full Text 32. ↵ Hoberg , K.A. , Cihlar , R.L. & Calderone , R.A . Inhibitory effect of cerulenin and sodium butyrate on germination of Candida albicans . Antimicrob Agents Chemother 24 , 401 – 408 ( 1983 ). OpenUrl Abstract / FREE Full Text 33. ↵ Avelar , G.M. et al. Impact of changes at the Candida albicans cell surface upon immunogenicity and colonisation in the gastrointestinal tract . Cell Surf 8 , 100084 ( 2022 ). 34. ↵ McCrory , C. et al. The short-chain fatty acid crotonate reduces invasive growth and immune escape of Candida albicans by regulating hyphal gene expression . mBio 14 , e0260523 ( 2023 ). OpenUrl CrossRef PubMed 35. ↵ Sorbara , M.T. et al. Functional and Genomic Variation between Human-Derived Isolates of Lachnospiraceae Reveals Inter- and Intra-Species Diversity . Cell Host Microbe 28 , 134 – 146 e134 ( 2020 ). OpenUrl CrossRef PubMed 36. ↵ Doran , B.A. et al. An evolution-based framework for describing human gut bacteria . bioRxiv ( 2023 ). 37. ↵ Pan , X. et al. Gut Microbiota, Glucose, Lipid, and Water-Electrolyte Metabolism in Children With Nonalcoholic Fatty Liver Disease . Front Cell Infect Microbiol 11 , 683743 ( 2021 ). 38. ↵ Zeevi , D. et al. Structural variation in the gut microbiome associates with host health . Nature 568 , 43 – 48 ( 2019 ). OpenUrl CrossRef PubMed 39. ↵ Otzen , C. , Bardl , B. , Jacobsen , I.D. , Nett , M. & Brock , M . Candida albicans utilizes a modified beta-oxidation pathway for the degradation of toxic propionyl-CoA . J Biol Chem 289 , 8151 – 8169 ( 2014 ). OpenUrl Abstract / FREE Full Text 40. ↵ Sorbara , M.T. et al. Inhibiting antibiotic-resistant Enterobacteriaceae by microbiota-mediated intracellular acidification . Journal of Experimental Medicine 216 , 84 – 98 ( 2018 ). OpenUrl PubMed 41. ↵ Jacobson , A. et al. A Gut Commensal-Produced Metabolite Mediates Colonization Resistance to Salmonella Infection . Cell Host Microbe 24 , 296 – 307 e297 ( 2018 ). OpenUrl CrossRef PubMed 42. ↵ Liu , N.N. & Kohler , J.R . Antagonism of Fluconazole and a Proton Pump Inhibitor against Candida albicans . Antimicrob Agents Chemother 60 , 1145 – 1147 ( 2016 ). OpenUrl Abstract / FREE Full Text 43. ↵ Diakov , T.T. , Tarsio , M. & Kane , P.M . Measurement of vacuolar and cytosolic pH in vivo in yeast cell suspensions . J Vis Exp ( 2013 ). 44. ↵ Nemeth , T. , Papp , C. , Vagvolgyi , C. , Chakraborty , T. & Gacser , A . Identification and Characterization of a Neutral Locus for Knock-in Purposes in C. parapsilosis . Frontiers in Microbiology 11 ( 2020 ). 45. ↵ Uthayakumar , D. , Sharma , J. , Wensing , L. & Shapiro , R.S . CRISPR-Based Genetic Manipulation of Candida Species: Historical Perspectives and Current Approaches . Front Genome Ed 2 , 606281 ( 2020 ). 46. ↵ Miesenbock , G. , De Angelis , D.A. & Rothman , J.E . Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins . Nature 394 , 192 – 195 ( 1998 ). OpenUrl CrossRef PubMed Web of Science 47. ↵ Nemeth , T. et al. PCR-based CRISPR/Cas9 system for fluorescent tagging: A tool for studying Candida parapsilosis virulence . PLOS ONE 20 , e0312948 ( 2025 ). OpenUrl PubMed 48. ↵ Ramos , J. , Sychrová , H. & Kschischo , M. Kane , P.M. Proton Transport and pH Control in Fungi . In: Ramos , J. , Sychrová , H. & Kschischo , M. (eds). Yeast Membrane Transport . Springer International Publishing : Cham , 2016 , pp 33 – 68 . 49. ↵ Schwartz , B . THE EFFECT OF TEMPERATURE ON THE RATE OF HYDROLYSIS OF TRIGLYCERIDES BY PANCREATIC LIPASE . J Gen Physiol 27 , 113 – 118 ( 1943 ). OpenUrl Abstract / FREE Full Text 50. ↵ Gillis , C.C. et al. Dysbiosis-Associated Change in Host Metabolism Generates Lactate to Support Salmonella Growth . Cell Host & Microbe 23 , 54 – 64 .e56 ( 2018 ). OpenUrl CrossRef PubMed 51. ↵ Vinolo , M.A.R. et al. Suppressive effect of short-chain fatty acids on production of proinflammatory mediators by neutrophils . The Journal of Nutritional Biochemistry 22 , 849 – 855 ( 2011 ). OpenUrl CrossRef PubMed 52. ↵ Glueck , B. , Han , Y. & Cresci , G.A.M . Tributyrin Supplementation Protects Immune Responses and Vasculature and Reduces Oxidative Stress in the Proximal Colon of Mice Exposed to Chronic-Binge Ethanol Feeding . Journal of Immunology Research 2018 , 9671919 ( 2018 ). OpenUrl PubMed 53. ↵ Nguyen , T.D. , Prykhodko , O. , Fåk Hållenius , F. & Nyman , M . Monovalerin and trivalerin increase brain acetic acid, decrease liver succinic acid, and alter gut microbiota in rats fed high-fat diets . European Journal of Nutrition 58 , 1545 – 1560 ( 2019 ). OpenUrl CrossRef PubMed 54. ↵ McDonald , J.A.K. et al. Inhibiting Growth of Clostridioides difficile by Restoring Valerate, Produced by the Intestinal Microbiota . Gastroenterology 155 , 1495 – 1507 .e1415 ( 2018 ). OpenUrl CrossRef PubMed 55. ↵ Lorenzon , M . Process for the production of an n-butyric acid compound in micro encapsulated form, for animal or human consumption . EP2352386B1 : SILA SRL; 2012 . 56. ↵ Rao , A. , Zhang , Y. , Muend , S. & Rao , R . Mechanism of antifungal activity of terpenoid phenols resembles calcium stress and inhibition of the TOR pathway . Antimicrobial Agents and Chemotherapy 54 , 5062 – 5069 ( 2010 ). OpenUrl Abstract / FREE Full Text 57. ↵ Jacobson , A. et al. A Gut Commensal-Produced Metabolite Mediates Colonization Resistance to Salmonella Infection . Cell Host and Microbe 24 , 296 – 307 .e297 ( 2018 ). OpenUrl 58. ↵ Wang , Q. et al. The YEATS Domain Histone Crotonylation Readers Control Virulence-Related Biology of a Major Human Pathogen . Cell Reports 31 ( 2020 ). 59. ↵ He , J. et al. Short-Chain Fatty Acids and Their Association with Signalling Pathways in Inflammation, Glucose and Lipid Metabolism . Int J Mol Sci 21 ( 2020 ). 60. ↵ Alves , R. et al. Carboxylic Acid Transporters in Candida Pathogenesis . mBio 11 ( 2020 ). 61. ↵ Miramón , P. & Lorenz , M.C . A feast for Candida: Metabolic plasticity confers an edge for virulence . PLoS Pathog 13 , e1006144 ( 2017 ). OpenUrl CrossRef PubMed 62. ↵ den Besten , G. et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism . J Lipid Res 54 , 2325 – 2340 ( 2013 ). OpenUrl Abstract / FREE Full Text 63. ↵ Schwab , M.A. et al. Secondary mitochondrial dysfunction in propionic aciduria: A pathogenic role for endogenous mitochondrial toxins . Biochemical Journal 398 , 107 – 112 ( 2006 ). OpenUrl Abstract / FREE Full Text 64. ↵ Brock , M. & Buckel , W . On the mechanism of action of the antifungal agent propionate . European Journal of Biochemistry 271 , 3227 – 3241 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 65. ↵ Brock , M. , Fischer , R. , Linder , D. & Buckel , W . Methylcitrate synthase from Aspergillus nidulans: Implications for propionate as an antifungal agent . Molecular Microbiology 35 , 961 – 973 ( 2000 ). OpenUrl CrossRef PubMed 66. ↵ Rao , C. et al. Multi-kingdom ecological drivers of microbiota assembly in preterm infants . Nature 591 , 633 – 638 ( 2021 ). OpenUrl CrossRef PubMed 67. ↵ Nugent , S.G. , Kumar , D. , Rampton , D.S. & Evans , D.F . Intestinal luminal pH in inflammatory bowel disease: possible determinants and implications for therapy with aminosalicylates and other drugs . Gut 48 , 571 – 577 ( 2001 ). OpenUrl Abstract / FREE Full Text 68. ↵ Theriot , C.M. et al. Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection . Nat Commun 5 , 3114 ( 2014 ). OpenUrl CrossRef PubMed 69. ↵ Luu , M. et al. The short-chain fatty acid pentanoate suppresses autoimmunity by modulating the metabolic-epigenetic crosstalk in lymphocytes . Nature Communications 10 , 760 ( 2019 ). 70. ↵ Luu , M. et al. Microbial short-chain fatty acids modulate CD8+ T cell responses and improve adoptive immunotherapy for cancer . Nature Communications 12 , 4077 ( 2021 ). OpenUrl PubMed 71. ↵ Furusawa , Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells . Nature 504 , 446 – 450 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 72. ↵ Zhai , B. et al. Antifungal heteroresistance causes prophylaxis failure and facilitates breakthrough Candida parapsilosis infections . Nat Med ( 2024 ). 73. ↵ Maestrone , G. & Semar , R . Establishment and treatment of cutaneous Candida albicans infection in the rabbit . Naturwissenschaften 55 , 87 – 88 ( 1968 ). OpenUrl CrossRef PubMed Web of Science 74. ↵ Rahman , D. , Mistry , M. , Thavaraj , S. , Challacombe , S.J. & Naglik , J.R . Murine model of concurrent oral and vaginal Candida albicans colonization to study epithelial host-pathogen interactions . Microbes Infect 9 , 615 – 622 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 75. ↵ Mason , K.L. et al. Interplay between the gastric bacterial microbiota and Candida albicans during postantibiotic recolonization and gastritis . Infect Immun 80 , 150 – 158 ( 2012 ). OpenUrl Abstract / FREE Full Text 76. ↵ Haak , B.W. et al. Impact of gut colonization with butyrate-producing microbiota on respiratory viral infection following allo-HCT . Blood 131 , 2978 – 2986 ( 2018 ). OpenUrl Abstract / FREE Full Text 77. ↵ Zhang , Z.J. et al. Comprehensive analyses of a large human gut Bacteroidales culture collection reveal species- and strain-level diversity and evolution . Cell Host Microbe 32 , 1853 – 1867 e1855 ( 2024 ). OpenUrl CrossRef PubMed 78. ↵ Maguire , S.L. et al. Comparative Genome Analysis and Gene Finding in Candida Species Using CGOB . Molecular Biology and Evolution 30 , 1281 – 1291 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 79. ↵ McDonough , L.D. et al. Candida albicans Isolates 529L and CHN1 Exhibit Stable Colonization of the Murine Gastrointestinal Tract . mBio 12 , e0287821 ( 2021 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted September 12, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Intracellular acidification by bacteria-derived valeric acid is a mechanism of trans-kingdom ecology against Candida parapsilosis colonization Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Intracellular acidification by bacteria-derived valeric acid is a mechanism of trans-kingdom ecology against Candida parapsilosis colonization Keiko Yasuma-Mitobe , Chen Liao , Tibor Nemeth , Kevin Byrne , Audrey Bilipps , Ruben Jose Jesus Faustino Ramos , Cristine Nicole Salinas , Eric Chan , Martina Perissinoto , Ashley M. Sidebottom , George Plitas , Geraldine Butler , Justin R Cross , Eric G Pamer , Attila Gacser , Joao B Xavier , Tobias M. Hohl bioRxiv 2025.09.12.675697; doi: https://doi.org/10.1101/2025.09.12.675697 Share This Article: Copy Citation Tools Intracellular acidification by bacteria-derived valeric acid is a mechanism of trans-kingdom ecology against Candida parapsilosis colonization Keiko Yasuma-Mitobe , Chen Liao , Tibor Nemeth , Kevin Byrne , Audrey Bilipps , Ruben Jose Jesus Faustino Ramos , Cristine Nicole Salinas , Eric Chan , Martina Perissinoto , Ashley M. Sidebottom , George Plitas , Geraldine Butler , Justin R Cross , Eric G Pamer , Attila Gacser , Joao B Xavier , Tobias M. Hohl bioRxiv 2025.09.12.675697; doi: https://doi.org/10.1101/2025.09.12.675697 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17636) Bioengineering (13860) Bioinformatics (41847) Biophysics (21401) Cancer Biology (18536) Cell Biology (25424) Clinical Trials (138) Developmental Biology (13353) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24287) Genetics (15583) Genomics (22463) Immunology (17701) Microbiology (40300) Molecular Biology (17141) Neuroscience (88434) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4813) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9808) Zoology (2268)