Salmonella typhi and salmonella paratyphi a elaborate distinct systemic metabolite signatures during enteric fever

1 Salmonella Typhi and Salmonella Paratyphi A elaborate distinct systemic metabolite signatures during enteric fever Elin Näsström 1, Nga Tran Vu Thieu 2, Sabina Dongol 3, Abhilasha Karkey 3, Phat Voong Vinh Tuyen Ha Thanh 2, Anders Johansson 4, Amit Arjyal 2, Guy Thwaites 2,5, Christiane Dolecek 2,5, Buddha Basnyat 3, Stephen Baker 2,5,6†*, Henrik Antti 1* Department of Chemistry, Computational Life Science Cluster, Umeå University, Umeå, Sweden The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University 10 Clinical Research Unit, Ho Chi Minh City, Vietnam 11 Oxford University Clinical Research Unit, Patan Academy of Health Sciences, Kathmandu, Nepal 12 Department of Clinical Microbiology, Umeå University, Umeå, Sweden 13 Centre for Tropical Medicine, Oxford University, Oxford, United Kingdom 14 The London School of Hygiene and Tropical Medicine, London, United Kingdom 15 16 Running head 17 Metabolite profiling of enteric fever 18 Key words 19 Metabolomics, mass spectrometry, two-dimensional gas chromatography, pattern recognition, 20 chemometrics, enteric fever, typhoid, Salmonella Typhi, Salmonella Paratyphi A, diagnostics, 21 biomarkers 22 23 † Corresponding author: Dr Stephen Baker, the Hospital for Tropical Diseases, 764 Vo Van Kiet, Quan 5, Ho 24 Chi Minh City, Vietnam Tel: +84 89241761 Fax: +84 89238904 sbaker@oucru.org 25 * Joint senior authors 26 27 28 29 30 Abstract 31 The host-pathogen interactions induced by Salmonella Typhi and Salmonella Paratyphi A during 32 enteric fever are poorly understood This knowledge gap, and the human restricted nature of these 33 bacteria, limit our understanding of the disease and impede the development of new diagnostic 34 approaches To investigate metabolite signals associated with enteric fever we performed two- 35 dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC/TOFMS) on plasma 36 from patients with S Typhi and S Paratyphi A infections and asymptomatic controls, identifying 695 37 individual metabolite peaks Applying supervised pattern recognition, we found highly significant and 38 reproducible metabolite profiles separating S Typhi cases, S Paratyphi A cases, and controls, 39 calculating that a combination of six metabolites could accurately define the etiological agent For the 40 first time we show that reproducible and serovar specific systemic biomarkers can be detected during 41 enteric fever Our work defines several biologically plausible metabolites that can be used to detect 42 enteric fever, and unlocks the potential of this method in diagnosing other systemic bacterial 43 infections 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 Introduction 59 Enteric fever is a serious bacterial infection caused by Salmonella enterica serovars Typhi (S Typhi) 60 and Paratyphi A (S Paratyphi A) (Parry et al 2002) S Typhi is more prevalent than S Paratyphi A 61 globally, with the best estimates predicting approximately 21 and million new infections with each 62 serovar per year, respectively (Buckle et al 2012; Ochiai et al 2008) Both S Typhi and S Paratyphi 63 A are systemic pathogens that induce clinically indistinguishable syndromes (Maskey et al 2006) 64 However, they exhibit contrary epidemiologies, different geographical distributions, and different 65 propensities to develop resistance to antimicrobials (Karkey et al 2013; Vollaard et al 2004) 66 Additionally, they are genetically and phenotypically distinct, having gone through a lengthy process 67 of convergent evolution to cause an identical disease (Holt et al 2009; Didelot et al 2007) 68 69 The agents of enteric fever induce their effect on the human body by invading the gastrointestinal tract 70 and spreading in the bloodstream (Everest et al 2001) It is this systemic phase of the disease that 71 induces the characteristic symptoms of enteric fever (Glynn et al 1995) However, the host’s reaction 72 to this systemic spread, outside the adaptive immune response, is not well described There is a 73 knowledge gap related to the scope and the nature of the host-pathogen interactions that are induced 74 during enteric fever that limit our understanding of the disease and prevent the development of new 75 diagnostic tests (Baker et al 2010) An accurate diagnosis of enteric fever is important in clinical 76 setting where febrile disease with multiple potential etiologies is common A confirmative diagnostic 77 ensures appropriate antimicrobial therapy to prevents serious complications and death and reduces 78 inappropriate antimicrobial usage (Parry, Vinh, et al 2011; Parry et al 2014) All currently accepted 79 methods for enteric fever diagnosis lack reproducibility and exhibit inacceptable sensitivity and 80 specificity under operational conditions (Moore et al 2014; Parry, Wijedoru, et al 2011) The main 81 roadblock to developing new enteric fever diagnostics is overcoming the lack of reproducible 82 immunological and microbiological signals found in the host during infection 83 84 Metabolomics is a comparatively new in infectious disease research, yet some initial investigations 85 have shown that metabolite signals found in biological samples may have potential as infection 86 “biomarkers” (Lv et al 2011; Langley et al 2013; Antti et al 2013) As S Typhi and S Paratyphi A 87 induce an phenotype via a relatively modest concentration of organisms in the blood (Nga et al 2010; 88 Wain et al 1998), we hypothesized that the host/pathogen interactions during early enteric fever 89 would provide unique metabolite profiles Here we show that enteric fever induces distinct and 90 reproducible serovar specific metabolite profiles in the plasma of enteric fever patients 91 92 Results 93 Plasma metabolites in enteric fever 94 To investigate systemic metabolite profiles associated with enteric fever we selected 75 plasma 95 samples from 50 patients with blood culture confirmed enteric fever (25 with S Typhi and 25 with S 96 Paratyphi A) and 25 age range matched afebrile controls attending the same healthcare facility Mass 97 spectra were generated by an operator that was blinded to the sample group for each of the 75 plasma 98 samples (n=105 including duplicates) in a random order using performed two-dimensional gas 99 chromatography with time-of-flight mass spectrometry (GCxGC/TOFMS) This GCxGC/TOFMS data 100 resulted in a series of 3D landscapes of preliminary metabolites (Figure 1) Following primary data 101 filtering, 988 unique metabolite peaks were retained 102 103 Comparisons to public databases resulted in 178 GCxGC/TOFMS metabolite peaks that could be 104 assigned a structural identity, and a further 62 peaks that could be assigned to a metabolite class We 105 additionally highlighted 10 metabolites, via manual inspection, that were found in less than 50 of the 106 75 samples, which had a diagnostic compatible profile These 10 metabolites were excluded from the 107 initial pattern recognition modeling, but retained for later analysis One of these metabolites was found 108 to be significant and was latterly added to the modeling To further refine the metabolite profiling we 109 aimed to identify profiles that correlated with run order, reducing the risk of instrumental variation 110 into the recognition modeling We identified 279 metabolites that demonstrated a significant 111 correlation with run order (Pearson coefficient > 0.5) These 279 metabolites were excluded from 112 initial pattern recognition modeling but still manually investigated Therefore, 695 unique metabolite 113 peaks (105 samples), were retained for initial pattern recognition modeling 114 115 Principal components analysis (PCA) was used to summarize the systematic variation in the 116 GCxGC/TOFMS data and to generate potential metabolite profiles from the 695 metabolite peaks We 117 first aimed to identify sample outliers that exhibited extreme metabolite profiles as a consequence of 118 analytical error We identified 11/105 samples as analytical outliers using PCA These 11 samples 119 were excluded from further analysis - leaving a total of 94 samples for pattern recognition modeling 120 These remaining samples were comprised of 32 controls (including analytical replicates of seven 121 samples), 29 S Paratyphi A samples (including analytical replicates of four samples), and 33 S Typhi 122 samples (including analytical replicates of eight samples) Calculation of models excluding all 123 analytical replicates was performed to rule out model overestimation due to replicates; no difference in 124 terms of the model significance was observed 125 126 Pattern recognition analysis 127 To investigate the potential of metabolite profiling in enteric fever diagnosis we applied an 128 unsupervised pattern recognition analysis to the filtered metabolite dataset from the cases and controls 129 The resulting PCA score plot is shown in Figure 2a The variation within the unsupervised pattern 130 recognition model outlined obvious differences between the metabolite profiles in the plasma samples 131 from the controls and the enteric fever patients It was evident from these analyses that metabolite 132 profiles in the plasma had a potential diagnostic value for enteric fever However, the samples from 133 patients with S Typhi and S Paratyphi A exhibited substantial overlap, indicating that the metabolite 134 signatures induced by these organisms may be challenging to differentiate 135 136 To obtain a more comprehensive view of the differences between the plasma metabolite profiles 137 between agents of enteric fever we applied a supervised pattern recognition approach We fitted an 138 extension orthogonal partial least squares with discriminant analysis (OPLS-DA) model to 139 differentiate the GCxGC/TOFMS metabolite profiles in relation to the three sample groups (Table 1) 140 The OPLS-DA model generated a Q2 value of 0.45, suggesting reliable differences between the 141 metabolite profiles in relation to the three sample groups Further validation indicated that the OPLS- 142 DA model provided excellent predictive power for distinguishing between the sample groups 143 (p=1.7x10-6; control vs S Typhi vs S Paratyphi A) The OPLS-DA method is interpreted through the 144 scores plot (Figure 2b); the largest between group differences is found along the first component (t[1]) 145 (x-axis) of the model, while less profound differences are found along the second component (t[2]) (y- 146 axis) 147 148 To scrutinize the differences in plasma metabolite profiles between sample groups, new OPLS-DA 149 models were fitted for pairwise comparisons of the sample classes The score plots for these analyses 150 are shown in Figure and the summarized data are shown in Table As predicted, the OPLS-DA 151 models for differentiating plasma metabolite profiles between samples from the afebrile controls and 152 the two agents of enteric fever exhibited robust and significant separation The models between the 153 controls and S Typhi infections and between the controls and S Paratyphi A infections also had high 154 predictive power, generating Q2 values of 0.82 (p=4.1x10-20) and 0.81 (p=4.2x10-18), respectively 155 (Figure 3a/b) The model for differentiating plasma metabolite profiles between the S Typhi infections 156 and the S Paratyphi A infections generated a Q2 value of 0.14 (p=6.7x10-2) (Figure 3c), indicating that 157 the plasma metabolite profiles can also be used to discriminate between the two enteric fever agents 158 159 Using a combination of the OPLS-DA model variable weights (loadings) and univariate p-values we 160 were able to precisely define the number of metabolite peaks separating the sample groups 161 (Supplementary file 1) There were 306, 324, and 58 metabolite peaks separating the controls from the 162 S Typhi infections, the controls from the S Paratyphi A infections, and the S Typhi infections from 163 the S Paratyphi A infections, respectively 164 165 S Typhi and Paratyphi A specific metabolites 166 The presence of 46 metabolites could significantly distinguish between samples from enteric fever 167 cases and control samples, and could also distinguish between samples from S Typhi infected cases 168 and S Paratyphi A infected cases (p≤0.05; two-tailed Student’s t-test) (Table 2) Of these 46 169 informative metabolites, 12 could be annotated Three metabolites that were found to be significant in 170 all three pairwise OPLS-DA models and annotated (phenylalanine, pipecolic acid, and 2-phenyl-2- 171 hydroxybutanoic acid) were selected for confirmation The chromatographic profiles of these peaks 172 were compared using the “raw” GCxGC chromatographic data from one sample in each sample group 173 (Figure 4) Phenylalanine and phenyl-2-hydroxybutanoic acid were confirmed to have the highest 174 concentration in the S Typhi sample and the lowest concentration in control sample, while pipecolic 175 acid had the highest concentration in S Paratyphi A samples and the lowest concentration in control 176 samples (Table 2) In total, seven metabolites (2,4-dihydroxybutanoic acid, 2-phenyl-2- 177 hydroxypropanoic acid, cysteine, gluconic acid, glucose-6-phosphate/mannose-6-phosphate, pentitol- 178 3-desoxy and phenylalanine) exhibited a higher concentration in the plasma from S Typhi infected 179 patients and five (4-methyl-pentanoic acid, ethanolamine, isoleucine, pipecolic acid, and serine) 180 exhibited a higher concentration in the plasma of S Paratyphi A infected patients (Table 2) Of the 34 181 remaining unidentified metabolites, two were classified as saccharides and exhibited a higher 182 concentration in the plasma of S Typhi patients We could not assign a structural identity/class to the 183 remaining 32 metabolites (all metabolites summarized in Supplementary file 1) 184 185 Metabolites with diagnostic potential 186 To investigate the diagnostic potential of the informative metabolites we fitted an OPLS-DA model 187 using the 46 metabolites contributing to the differences between control and infected samples, and 188 between the samples from S Typhi and S Paratyphi A infections (Table 1) The model was highly 189 statistically significant for all pairwise comparisons, (p0.9 for all comparisons) 193 (Figure 5) 194 195 The best identifiable metabolite differentiating S Typhi from S Paratyphi A was 2-phenyl-2- 196 hydroxypropanoic acid, which gave an AUC of 0.693 (Figure 5), and the best unidentified metabolite 197 differentiating S Typhi from S Paratyphi A gave an AUC value of 0.746 The AUC values for the 198 best individual metabolites differentiating controls from S Typhi infections were 0.884 199 (phenylalanine) (Figure 5) and 0.889 (unidentified), and the AUC values for the individual metabolites 200 best differentiating controls from S Paratyphi A infections were 0.925 (phenylalanine) (Figure 5) and 201 0.926 (unidentified) Finally, we investigated the number of metabolites with confirmed identity or 202 metabolite class required to retain diagnostic power We found that a metabolite pattern consisting of 203 six identified/classified metabolites (ethanolamine, gluconic acid, monosaccharide, phenylalanine, 204 pipecolic acid and saccharide) gave ROC values >0.8 for all pairwise comparisons (Figure 6) 205 206 Discussion 207 Our work represents the first application of metabolomics to study enteric fever The potential utility 208 of this method can be observed by the capacity of the metabolite data to successfully identify those 209 with this infection Currently, the ability to accurately diagnose enteric fever is restricted to a positive 210 microbiological culture result or PCR amplification (Nga et al 2010; Parry, Wijedoru, et al 2011) 211 However, blood culture for suspected enteric fever is commonly only positive in up to 50% of cases 212 only, and PCR amplification on blood samples performs less well (Gilman et al 1975) In reality, the 213 fundamental complications of enteric fever diagnostics are the low number of organisms in the blood 214 (Wain et al 1998), and a lack of a generic systemic signal If one combines these limitations with 215 antimicrobial pretreatment and the spectrum of other potential etiological agents circulating in 216 endemic locations, then a substantial technological advance is required to solve the problem of 217 diagnosing enteric fever It is worth stating that this is a problem worth solving, as enteric fever 218 remains rampant in many low to middle-income countries Some may argue that the use of broad- 219 spectrum antimicrobials without diagnosis may be prudent However, this actually compounds the 220 problem, as individuals are often treated with inadequate drugs, inducing treatment failure and 221 facilitating local transmission through fecal shedding (Parry, Vinh, et al 2011) Furthermore, 222 antimicrobial resistance rates are rising in invasive Salmonella, which is associated with treatment 223 failure and complications (Walters et al 2014; Koirala et al 2012) 224 225 We found that 306, 324, and 58 metabolites separated the controls from the S Typhi infections, the 226 controls from the S Paratyphi A infections, and the S Typhi infections from the S Paratyphi A 227 infections, respectively The statistical analyses found that differentiating cases from controls could be 228 performed with considerable power; this was reduced, but still significant, between S Typhi and S 229 Paratyphi A The majority of distinguishing metabolites among the three groups were unknown, 230 however, some were annotated and had a credible explanation For example, elevated metabolites 231 distinguishing cases from controls included, 2,4-dihydroxybutanoic acid, phenylalanine, and pipecolic 232 acid 2,4-dihydroxybutanoic acid is a hydroxyl acid that can be found in low amounts in the blood and 233 urine of healthy individuals, but is also related to hypoxia Many pathogenic bacteria have the ability 234 to induce the activation of hypoxia inducible factor (HIF)-1 and we surmise that invasive Salmonella 235 also play a role in HIF-1 modulation during the inflammatory response induced during early infection 236 (Werth et al 2010) Phenylalanine is an essential amino acid, and higher phenylalanine to tyrosine 237 ratios have been described in the blood of patients with various diseases including sepsis, Hepatitis C 238 (Zoller et al 2012; Herndon et al 1978), and in rats challenged with a number of pathogens 239 (Wannemacher et al 1976) Notably, elevated phenylalanine was also found in during a recent 240 metabolite investigation of primary dengue patients and is intrinsically linked to nitric oxide synthase 241 during infection (Cui et al 2013) Lastly, and most intriguingly, pipecolic acid is a non-protein amino 242 acid and is an essential part of the inducible immunity of plants during challenge from bacterial 243 pathogen and is elevated in the urine of malaria patients (Sengupta et al 2011; Vogel-Adghough et al 244 2013) These metabolites, which were all elevated in the plasma of enteric fever patients, may be 245 generic markers of systemic disease and may prove to be vital in determining other bacterial 246 bloodstream infections 247 248 Our data also allowed us to determine different metabolite profiles between those with enteric fever 249 caused by S Typhi and S Paratyphi A These organisms have a modified physiology in comparison to 250 other Salmonella and enter human tissue with limited intestinal replication and by potentially 251 suppressing gastrointestinal inflammation (Jones & Falkow 1996) Consequently, one of the key 252 features of enteric fever is a lack of gastrointestinal involvement as seen with other, non-invasive, 253 Salmonella serovars The majority of the metabolites distinguishing S Typhi from S Paratyphi A may 254 be explained by these subtle biological differences between these organisms and partly by the presence 255 of the virulence (Vi) capsule on the surface of S Typhi, which is absent from S Paratyphi A Vi is a 256 polysaccharide that has anti-inflammatory properties, limiting complement deposition and restricting 257 immune activation (Jansen et al 2011) The presence and functionality of Vi can be observed in the 258 metabolites differentiating S Typhi from S Paratyphi A as the concentrations of monosaccharide and 259 saccharide were significantly higher in the plasma samples from S Typhi patients than from the S 260 Paratyphi A infections Conversely, ethanolamine was in significantly higher concentrations in the 261 plasma from the S Paratyphi A patients than in S Typhi patients’ plasma Ethanolamine is released by 262 host tissue during inflammation and experimental work in mice has shown that Salmonella S 263 Typhimurium has a growth advantage in an inflamed gut (Thiennimitr et al 2011) Therefore, the 264 differential detection of ethanolamine in plasma samples from enteric fever patients with different 265 infecting serovars, may be explained by Vi negative S Paratyphi A not having the capacity to control 266 gastrointestinal inflammation to the same extent as S Typhi 267 268 The main limitation of our work was that the samples were restricted to one set of enteric fever cases 269 only The reason we restricted analysis to enteric fever, rather than a range of bloodstream infections, 270 we because we felt that this was the most robust test for the methodology Furthermore, as the samples 271 in the study we collected as part of an enteric fever clinical trial we had a range of clinical data and 272 observations on which to link the metabolite profile with We suggest that future studies in this area 273 are designed to address this limitation, both for validation in different enteric fever cohort and for 274 comparison to other bloodstream infections The methodology present here should be applied to future 275 “fever studies” on which there may be a wide array of pathogens The results from this study leads us 276 to hypothesize that this method could be applied to study the differential metabolite signals between 277 enteric fever and multiple invasive infections and could potentially differentiate between an extensive 278 spectrum of causes of systemic disease or both bacterial, viral, and parasitic etiology Our work 279 strongly supports this notion, as the metabolite profiles were able to distinguish between those infected 280 with S Typhi and S Paratyphi A, which until now, with the exception of microbial culture has never 10 502 obtained results The area under the curve (AUC) can be used as an output of the ROC analysis, which 503 can range from 0.5 to 1.0 The higher AUC value a biomarker obtains the higher is the diagnostic 504 potential Here the web-based online tool ROCCET (http://www.roccet.ca/ROCCET/) was used to 505 perform univariate ROC analyses For the individual metabolites the relative concentrations for all 506 samples were used as input, while for the models (metabolite profiles) model scores (t) and cross- 507 validated scores (tcv)(Stone 1974) were used after recalculation by subtracting the lowest score value 508 from all other score values to avoid negative values 509 510 Acknowledgements 511 The authors wish to thank all the unit staff at the Patan Hospital in Kathmandu for assisting in sample, 512 data collection and patient care Peter Haglund and Konstantinos Kouremenos are acknowledged for 513 their valuable input regarding the GCxGC/TOFMS analysis Stephen Baker is a Sir Henry Dale 514 Fellow, jointly funded by the Wellcome Trust and the Royal Society (100087/Z/12/Z) Henrik Antti is 515 funded by the Swedish Research Council (VR-NT 2010-4284) 516 517 Competing Interests 518 The authors state that they have no competing interests 519 520 References 521 522 Antti, H et al., 2013 Metabolic profiling for detection of Staphylococcus aureus infection and antibiotic resistance PloS one, 8(2), 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sample from a 650 patient with enteric fever 651 Image shows a two-dimensional ion chromatogram of unprocessed GCxGC/TOFMS data of a plasma 652 sample from a patient with enteric fever The three-dimensional landscape depicts detected metabolites 653 peaks in the first dimension (seconds – x axis), the second dimension (seconds – y axis), and the 654 concentration intensity of the peak signal (z axis) 655 656 Figure Modeling the variation in the GCxGC/TOFMS data in plasma samples from enteric 657 fever patients and controls 658 a) PCA plot of the first two principal components (t[2] vs t[1]) The PCA plot outlines a separation 659 between the control plasma samples (N=32; including analytical replicates) and the plasma samples 660 from enteric fever cases (S Typhi; N=33 - including analytical replicates, and S Paratyphi A; N=29 661 - including analytical replicates) PCA model incorporates 695 metabolites with eight significant 662 principal components (R2X=0.437, Q2=0.255) b) OPLS-DA scores plot of the two predictive 663 components (tp[2] vs tp[1]; x axis and y axis, respectively) outlining a separation between the control 664 plasma samples (N=32; including analytical replicates) and the plasma samples from enteric fever 665 cases (S Typhi; N=33 - including analytical replicates, and S Paratyphi A; N=29 - including 666 analytical replicates) OPLS-DA model includes 695 metabolites with two predictive and two 667 orthogonal components (R2X=0.269, R2Y=0.837, Q2=0.451, p=1.7x10-6 (CV-ANOVA)) 668 669 Figure Pairwise OPLS-DA models of GCxGC/TOFMS data in plasma samples from controls, 670 S Typhi cases, and S Paratyphi A cases 671 Cross-validated OPLS-DA scores plots of the first predictive component (tcv[1]p) showing the 672 separation between; a) Controls (N=32, including analytical replicates) and S Paratyphi A cases 673 (N=29, including analytical replicates) (p=4.2x10-18) b) Controls and S Typhi cases (N=33, 674 including analytical replicates) (p=4.1x10-20) c) S Typhi cases and S Paratyphi A cases (p=6.7x10- 23 675 676 based on 695 metabolites with one predictive and two orthogonal (a and b), or one predictive and one 677 orthogonal (c) component(s) Additional model information is shown in Table ) Error bars represent mean score values with 95% confidence intervals The OPLS-DA model is 678 679 Figure Verification of metabolite signals in plasma samples from a control and patients with 680 S Typhi and S Paratyphi A infections 681 Three metabolites, in three samples from each sample group that were statistically significant in 682 differentiating between sample classes using pattern recognition modelling, were selected for 683 confirmation using unprocessed chromatographic data a) OPLS-DA scores plot (tp[2] vs tp[1]) 684 highlighting the three selected samples (S Typhi: 45, S Paratyphi A: 19, and control: 60) Panel b-d 685 show one dimensional chromatographic peaks representing each metabolite from the three 686 unprocessed plasma samples (coloured by sample group) Second dimension retention times (s) are 687 shown along the x-axes and the peak intensities along the y-axes b) Phenylalanine (mass: 218, 1st 688 retention time: 1,785 s) c) Pipecolic acid (mass: 156, 1st retention time: 1,130 s) d) 2-phenyl-2- 689 hydroxybutanioc acid (mass: 193, 1st retention time: 1,725 s) Panel e-m show the corresponding two 690 dimensional chromatographic peaks with one peak for each sample and metabolite First and second 691 dimension retention times (s) are shown along the x and y-axes, respectively, and the peak area is 692 shown along the z-axes The peaks are coloured according to area (colour scale is shown to the right) 693 and the top colour for the two lowest peaks for each metabolite is determined according to the colour 694 scale of the highest peak for the same metabolite e, h, k) Phenylalanine for sample 45, 19, and 60, 695 respectively f, i, l) Pipecolic acid for sample 19, 4,5 and 60, respectively g, j, m) 2-phenyl-2- 696 hydroxybutanioc acid for sample 45, 19, and 60, respectively 697 698 Figure The discriminatory power of 46 metabolites to distinguish between plasma samples 699 from controls, S Typhi cases, and S Paratyphi A cases 700 Panels on the left show the ROC-curves based on scores (red lines) and cross-validated scores (black 701 lines) from OPLS-DA models using the 46 most statistically significant (S Typhi against controls 702 and/or S Paratyphi A against controls) metabolites separating enteric fever samples from control 24 703 samples and separating S Typhi samples from S Paratyphi A samples The ROC curve showing the 704 best individual discriminating metabolite is shown by the grey line The scatterplots show pairwise 705 class differences based on scores (t[1]p) (left), cross-validated scores (tcv[1]p) (centre) from OPLS- 706 DA models using the 46 most statistically significant metabolites (as above), and the relative 707 concentration of the best individual discriminating metabolite (right) Data presented for; a) S 708 Paratyphi A vs Controls, (AUC scores: 1.0, AUC CV scores: 0.999, AUC best metabolite: 0.884) b) 709 S Typhi vs Controls (AUC scores: 1.0, AUC CV scores: 0.996, AUC best metabolite: 0.925 c) S 710 Paratyphi A vs S Typhi (AUC scores: 0.951, AUC CV scores: 0.898, AUC best metabolite: 0.693 711 Error bars represent mean score values with 95% confidence intervals 712 713 Figure The discriminatory power of six metabolites to distinguish between plasma samples 714 from controls, S Typhi cases, and S Paratyphi A cases 715 The panels on the left show the ROC-curves based on scores (red lines) and cross-validated scores 716 (black lines) from OPLS-DA models using the six most statistically significant (S Typhi against 717 controls and/or S Paratyphi A against controls) metabolites separating enteric fever samples from 718 control samples and separating S Typhi samples from S Paratyphi A samples The scatterplots show 719 pairwise class differences based on scores (t[1]p) (left), cross-validated scores (tcv[1]p) (right) from 720 OPLS-DA models using the most statistically significant metabolites (as above) Data presented for; 721 a) S Paratyphi A vs Controls, (AUC scores: 0.964, AUC CV scores: 0.948) b) S Typhi vs Controls 722 (AUC scores: 0.934, AUC CV scores: 0.923) and (c) S Paratyphi A vs S Typhi (AUC scores: 0.801, 723 AUC CV scores: 0.796) Error bars represent mean score values with 95% confidence intervals 724 725 726 727 728 729 730 25 731 Tables 732 Table Multivariate modeling of enteric fever metabolites Model a PCA S Paratyphi A, S Typhi, control S Paratyphi A vs control S Typhi vs control S Paratyphi A vs S Typhi S Paratyphi A vs control S Typhi vs control S Paratyphi A vs S Typhi S Paratyphi A vs control S Typhi vs control S Paratyphi A vs S Typhi 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 Number of metabolites included Number of model components b R2X c R2Y c Q2 c CVANOVA d AUC scores e AUC CV scores f 695 695 695 695 695 46 46 46 6 2+2 1+2 1+2 1+1 1+1 1+1 1+1 1+1 1+0 1+0 0.437 0.269 0.261 0.251 0.160 0.416 0.305 0.385 0.543 0.299 0.318 0.837 0.961 0.965 0.714 0.794 0.823 0.565 0.627 0.529 0.300 0.255 0.451 0.815 0.824 0.140 0.718 0.749 0.420 0.567 0.492 0.253 1.7x10-6 4.2x10-18 4.1x10-20 6.7x10-2 8.8x10-15 2.2x10-17 2.3x10-6 1.2x10-9 7.6x10-10 1.8x10-4 1.0 1.0 0.996 1.0 1.0 0.951 0.964 0.934 0.801 0.997 1.0 0.735 0.999 0.996 0.898 0.948 0.923 0.796 a All OPLS-DA models apart from the highlighted PCA The number of predictive components followed by the number of orthogonal model components c R X: The amount of variation in X explained by the model, R2Y: The amount of variation in Y explained by the model, Q2: The amount of variation in Y predicted by the model d p-value based on cross-validated scores showing the degree of significance for the separation e Area under the curve values from receiver operating curves (ROC) calculated from model scores (t) f Area under the curve values from receiver operating curves (ROC) calculated from cross-validated models scores (tcv) b 26 770 771 772 773 774 775 776 777 778 779 780 781 782 783 Table Metabolites with discriminatory power for diagnosing enteric fever Metabolite a RT1 b RT2 b RI1 b p-value P vs C p-value T vs C p-value P vs T Change c P vs C Change c T vs C Change c P vs T 2,4-dihydroxybutanoic acid 1256.4 3.22 1429.6 6.6x10-3 4.9x10-4 4.7x10-2 P T T 2-phenyl-2hydroxypropanoic acid 1724.9 2.61 1692.6 3.7x10-2 1.5x10-4 1.6x10-2 P T T 4-methyl-pentanoic acid 627.6 2.40 1092.8 3.1x10-2 5.9x10-1 1.1x10-2 -2 P - P Cysteine Ethanolamine Gluconic acid 1580.0 880.0 1985.0 2.96 3.88 0.16 1607.6 1233.6 1851.7 1.2x10-3 3.3x10-2 1.7x10 1.4x10-4 3.8x10-2 7.8x10-3 1.2x10-2 P P T T T P T Glucose-6-phosphate /Mannose-6-phosphate 2615.3 3.65 2303.1 6.7x10-4 5.9x10-5 4.1x10-2 P T T -2 -2 Isoleucine Monosaccharide_137 Pentitol-3-desoxy Phenylalanine Pipecolic acid Saccharide_181 Serine Unknown_230 Unknown_231 Unknown_242 Unknown_268 Unknown_270 Unknown_281 Unknown_294 Unknown_303 Unknown_334 Unknown_341 Unknown_364 Unknown_377 Unknown_384 Unknown_397 Unknown_467 Unknown_470 Unknown_490 Unknown_495 Unknown_547 Unknown_604 Unknown_637 Unknown_638 Unknown_676 Unknown_681 Unknown_745 Unknown_798 Unknown_811 Unknown_914 Unknown_949 Unknown_961 Unknown_963 1012.2 1622.5 1490.0 1784.1 1130.0 2529.1 1070.0 549.2 1090.0 1550.0 1895.0 626.4 680.0 725.1 1900.0 2790.0 523.5 775.1 961.1 1010.1 1144.9 1550.4 1570.0 1660.6 1695.0 1995.0 2349.5 2560.7 2561.3 2870.0 2938.1 770.0 855.0 1445.0 3194.9 2661.8 2065.4 1045.1 3.32 4.87 4.22 2.68 3.10 3.99 2.60 2.32 2.42 2.94 3.64 3.90 3.38 2.18 2.57 2.15 2.21 2.25 2.43 2.48 2.75 2.92 4.02 2.27 3.26 2.33 3.27 3.99 2.67 3.28 2.75 3.17 2.36 2.93 2.61 2.07 2.71 2.32 1302.9 1633.8 1557.9 1728.4 1363.1 2237 1332.1 1036.8 1342.3 1590.5 1796.1 1093.1 1124.1 1148.5 1798.5 2443.5 1018.4 1176.3 1275.6 1301.4 1370.6 1590.7 1602.4 1654.6 1675.4 1859.6 2102.0 2261.3 2260.7 2511.6 2570.3 1174.0 1219.7 1532.2 2802.5 2339.1 1905.4 1319.1 1.1x10 6.0x10-3 4.4x10-9 3.0x10-7 2.4x10-5 1.6x10-5 1.7x10-2 1.7x10-3 2.8x10-3 4.0x10-5 1.7x10-2 2.7x10-3 2.1x10-3 9.1x10-3 1.9x10-5 2.5x10-3 6.8x10-3 1.9x10-3 4.9x10-3 2.1x10-2 1.6x10-4 2.3x10-2 2.9x10-5 1.9x10-2 8.9x10-6 1.9x10-7 6.6x10-4 1.6x10-3 4.6x10-3 3.0x10-2 7.1x10-6 2.8x10-2 1.3x10-5 5.5x10-13 -13 -10 1.3x10 2.5x10-3 4.3x10-2 1.9x10-2 1.1x10-2 1.5x10-4 2.8x10-8 2.4x10-2 1.1x10-3 2.3x10-6 1.2x10-2 4.5x10-3 3.2x10-3 2.5x10-3 3.6x10-4 1.1x10-3 1.9x10-9 6.6x10-4 3.6x10-5 4.3x10 6.1x10-3 1.1x10-2 2.4x10-2 3.0x10-2 2.7x10-2 4.8x10-2 9.5x10-3 4.3x10-2 4.4x10-2 2.2x10-2 3.2x10-3 3.1x10-2 1.7x10-2 2.0x10-2 7.8x10-3 2.7x10-2 2.3x10-2 3.1x10-4 2.8x10-2 2.8x10-2 4.7x10-2 1.9x10-2 2.1x10-2 2.0x10-2 3.1x10-2 3.6x10-2 4.0x10-2 7.7x10-3 4.0x10-2 5.3x10-3 3.1x10-2 1.0x10-2 2.5x10-2 3.2x10-2 1.3x10-2 2.2x10-2 4.0x10-2 P C P P P C P P P C P P P P P P P P P P C C P P C C C P P P P P P T T T C C T T T C T T T C T C T C T T T P T T T P T P P P T T P P P T T P P P P P T T T T T P T T T T P P T P T T P Unknown_981 2748.2 2.05 2408.5 1.8x10-4 4.9x10-8 2.1x10-2 P T T a Metabolites with statistically significant differences in two or three pairwise comparisons according to univariate p-values (≤ 0.05) and covariance loadings w* (