Discovery of Infection Associated Metabolic Markers in Human African Trypanosomiasis

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Metabolic Profiling of Central Nervous System Disease in Trypanosoma brucei rhodesiense infection

Acknowledgments. We thank Isabel Garcia-Perez and Maria Lopez-Gonzales, for performing additional mass spectrometry analyses at Imperial College London. Financial support. This work was supported by the Medical Research Council MRC; (to S. D. L.), and Imperial College (MRC doctoral training award G1000390 to S. D. L.), and the Wellcome Trust (grant 082786 to J. M. S. and V. P. A.).Peer reviewedPublisher PD

Metabolic, immune, and gut microbial signals mount a systems response to Leishmania major infection

Parasitic infections such as leishmaniasis induce a cascade of host physiological responses, including metabolic and immunological changes. Infection with Leishmania major parasites causes cutaneous leishmaniasis in humans, a neglected tropical disease that is difficult to manage. To understand the determinants of pathology, we studied L. major infection in two mouse models: the self-healing C57BL/6 strain and the nonhealing BALB/c strain. Metabolic profiling of urine, plasma, and feces via proton NMR spectroscopy was performed to discover parasite-specific imprints on global host metabolism. Plasma cytokine status and fecal microbiome were also characterized as additional metrics of the host response to infection. Results demonstrated differences in glucose and lipid metabolism, distinctive immunological phenotypes, and shifts in microbial composition between the two models. We present a novel approach to integrate such metrics using correlation network analyses, whereby self-healing mice demonstrated an orchestrated interaction between the biological measures shortly after infection. In contrast, the response observed in nonhealing mice was delayed and fragmented. Our study suggests that trans-system communication across host metabolism, the innate immune system, and gut microbiome is key for a successful host response to L. major and provides a new concept, potentially translatable to other diseases

Plasma in HAT patients display different lipid profiles compared with controls.

<p>S-plots of O-PLS-DA model for plasma lipid profiling UPLC-MS features detected in ESI+ mode (A) and ESI- mode (B), whereby each circle represents one feature with a unique combination of m/z and retention time values. Discriminatory features selected surpassed p[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#pntd.0004200.ref001" target="_blank">1</a>] and p(corr)[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#pntd.0004200.ref001" target="_blank">1</a>] threshold criteria, highlighted in red boxes (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#sec002" target="_blank">Methods</a>). See <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#pntd.0004200.s001" target="_blank">S1 Fig</a> for corresponding scores plots. Integrals of features highlighted in the S-plots are shown as bar-chart showing mean averages ± standard error of the mean, for both ESI+ (C) and ESI- (D). Patients levels are shown in red (<i>n</i> = 16) and controls in dark grey (<i>n</i> = 14). Significant differences, as measured via Welch T-test with multiple test correction, are labelled with asterisks, where *** <i>p</i><0.001. Abbreviations: A.U., arbitrary units; LysoPC, lysophosphatidylcholine; PC, phosphatidylcholine.</p

Plasma in HAT patients display different ¹H NMR metabolic profiles compared with controls.

<p>PCA model (A) and O-PLS-DA model (B) score plots of plasma ¹H NMR spectra across HAT patients and controls. Each circle represents a spectra from one sample, whereby patients are presented in red (<i>n</i> = 45) and controls in dark grey (<i>n</i> = 21). Abbreviations: R²X, model fit parameter for variation in spectral data; R²Y, model fit parameter for variation in classifier data (for O-PLS-DA); Q², model predictive parameter for spectral data in PCA (Q²X) and for classifier data in O-PLS-DA (Q²Y). Individual component contribution of R²X are shown on the axes as percentage.</p

Differences in plasma metabolites between HAT patients and controls detected by NMR.

<p>Bar-charts show relative levels of plasma metabolites that were significantly altered between patients (shown in red, <i>n</i> = 45) and controls (shown in grey, <i>n</i> = 21), as measured by ¹H NMR spectroscopy. (A) Metabolites higher in patients than controls. (B). Metabolites lower in patients than controls. Bars represent group mean average with standard error of the mean as error bars. Significance measured via Welch T-test with multiple test correction, shown as asterisks; * <i>p</i><0.05, ** <i>p</i><0.01, *** <i>p</i><0.001. A.U., arbitrary units; NAG, <i>N</i>-acetyl glycoproteins.</p

Nya extra-posten. (N:r 39)

Parasitic infections such as leishmaniasis induce a cascade of host physiological responses, including metabolic and immunological changes. Infection with <i>Leishmania major</i> parasites causes cutaneous leishmaniasis in humans, a neglected tropical disease that is difficult to manage. To understand the determinants of pathology, we studied <i>L. major</i> infection in two mouse models: the self-healing C57BL/6 strain and the nonhealing BALB/c strain. Metabolic profiling of urine, plasma, and feces via proton NMR spectroscopy was performed to discover parasite-specific imprints on global host metabolism. Plasma cytokine status and fecal microbiome were also characterized as additional metrics of the host response to infection. Results demonstrated differences in glucose and lipid metabolism, distinctive immunological phenotypes, and shifts in microbial composition between the two models. We present a novel approach to integrate such metrics using correlation network analyses, whereby self-healing mice demonstrated an orchestrated interaction between the biological measures shortly after infection. In contrast, the response observed in nonhealing mice was delayed and fragmented. Our study suggests that trans-system communication across host metabolism, the innate immune system, and gut microbiome is key for a successful host response to <i>L. major</i> and provides a new concept, potentially translatable to other diseases