Mapping of host-parasite-microbiome interactions reveals metabolic determinants of tropism and tolerance in Chagas disease.

Chagas disease (CD) is a parasitic disease caused by Trypanosoma cruzi protozoa, presenting with cardiomyopathy, megaesophagus, and/or megacolon. To determine the mechanisms of gastrointestinal (GI) CD tissue tropism, we systematically characterized the spatial localization of infection-induced metabolic and microbiome alterations, in a mouse model of CD. Notably, the impact of the transition between acute and persistent infection differed between tissue sites, with sustained large-scale effects of infection in the esophagus and large intestine, providing a potential mechanism for the tropism of CD within the GI tract. Infection affected acylcarnitine metabolism; carnitine supplementation prevented acute-stage CD mortality without affecting parasite burden by mitigating infection-induced metabolic disturbances and reducing cardiac strain. Overall, results identified a previously-unknown mechanism of disease tolerance in CD, with potential for new therapeutic regimen development. More broadly, results highlight the potential of spatially resolved metabolomics to provide insight into disease pathogenesis and infectious disease drug development.

Isotopic correlation for 242Pu composition prediction: Multivariate regresssion approach.

Multivariate regression calibration using multiple linear regression (MLR), principle component regression (PCR) and partial least squares regression (PLSR) algorithm was performed on 238Pu, 239Pu, 240Pu and 241Pu atom% abundances to predict 242Pu isotopic abundance. The MLR algorithm was found to be the best among these three algorithms. The effect of 238Pu composition on the 242Pu abundance prediction was found to be small but significant especially for achieving high accuracy of <0.5%. PCR and PLSR generated nearly identical results and were inferior to the MLR results. A comparison of MLR results with those obtained by employing seven previously reported empirical methods revealed far superior prediction capability of MLR model. Among the seven empirical models, the best prediction capability was found for Bignan correlation containing 238Pu isotopic data. The study clearly demonstrates that the production of 238Pu and 242Pu has some small correlation and the use of 238Pu in isotopic correlation for 242Pu prediction is important to get accurate results.