Abstract
To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.
Highlights
Melioidosis is a disease caused by the highly pathogenic gram-negative bacterium, Burkholderia pseudomallei (B. pseudomallei)
The diagnostic performances of the identified biomarkers were evaluated using receiver operating characteristic curve (ROC) analysis. In this pilot study, untargeted metabolomics on plasma sample were conducted with the aim to explore potential diagnostic biomarkers and biological pathways involved in host–B. pseudomallei interaction
A total of 22 plasma samples from five patients with newly-diagnosed melioidosis, 24 plasma samples from 24 patients with bacteremia caused by other bacterial species and 30 controls without active infections were included for UHPLC-QTOFMS analysis
Summary
Melioidosis is a disease caused by the highly pathogenic gram-negative bacterium, Burkholderia pseudomallei (B. pseudomallei). Despite being an important pathogen, no studies have reported the use of metabolomics to explore specific biomarkers in plasma of melioidosis patients. To identify potential biomarkers for the non-invasive diagnosis of melioidosis, we applied the metabolomics technology for metabolite profiling of plasma samples from melioidosis patients, using ultra-high-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOFMS). Multi- and univariate statistical analyses of the metabolome data were used to identify specific metabolites that are present in significantly higher levels in plasma of melioidosis patients than in plasma of patients with other bacteremia or controls without infections. The diagnostic performances of the identified biomarkers were evaluated using receiver operating characteristic curve (ROC) analysis In this pilot study, untargeted metabolomics on plasma sample were conducted with the aim to explore potential diagnostic biomarkers and biological pathways involved in host–B. pseudomallei interaction. FFiigguurree 33..MMS/SM/MSSmmasasssspescptercatraandapnrdedpicrteeddicstterductsutrruecstwurieths ewxpitehcteedxpfreacgtemdenfrtaagtimonenptraotfiiolnes opfrothfiele1s2 bofiomthaerk1e2rs biniommealrikoeidrsosiisnpmatieelniotidpolassismap:a(tAie)nLt-opcltaasnmoya:l (cAar)nitLi-noec;ta(Bno) ydleccaarnniotyinlcea; rn(iBti)ned; (eCca)ndooydlceacranniotiynlec;arn(Cit)ined; o(Dde)claynsooyplhcoasrnpihtianteid; yl(eDth)anloylsaomphinoesp(LhyastiodPyEle)(t1h6a:n0o/l0a:m0)i;n(eE) L(LyyssooPPEE(1)(81:60:/00/0:0:0));;(F)(Ep)hoLspyshoaPtiEd(y1l8c:h0/o0l:i0n)e; (PFC) (p1h6o:0sp/h1a6t:0id);yl(cGh)olLinyesoPPCE(1(06::00//1168::00)); ((GH)) LLy-hsoexPaEn(0o:y0l/c1a8r:0n)it(iHne);L(-Ih)esxpahnionyglcoamrnyietilnines; (SIM) s(pdh1i6n:g1o/m16y:0e)l;in(sJ)S2M-d(de1ce6n:1o/1y6lc:0a)r;n(iJt)in2e-d; e(Kce)nSoMylc(dar1n8i:t2i/n1e;6(:0K)); SaMnd(d(1L8):2tr/1an6:s0-)2;adnodde(Lce)ntroaynlsc-a2r-ndiotidneecwenitohylocrawrniitthinoeutwciothmoprawrisitohnotuot ccoommmpaerricsioanllytoavcoamilambleercsitaalnlydaarvdasi.lable standards
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