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Panorganismal metabolic response modeling of an experimental Echinostoma caproni infection in the mouse.

ABSTRACT: Metabolic profiling of host tissues and biofluids during parasitic infections can reveal new biomarker information and aid the elucidation of mechanisms of disease. The multicompartmental metabolic effects of an experimental Echinostoma caproni infection have been characterized in 12 outbred female mice infected orally with 30 E. caproni metacercariae each, using a further 12 uninfected animals as a control group. Mice were killed 36 days postinfection and brain, intestine (colon, ileum, jejeunum), kidney, liver, and spleen were removed. Metabolic profiles of tissue samples were measured using high-resolution magic angle spinning (1)H NMR spectroscopy and biofluids measured by applying conventional (1)H NMR spectroscopy. Spectral data were analyzed via principal component analysis, partial least-squares-derived methods and hierarchical projection analyses. Infection-induced metabolic changes in the tissues were correlated with altered metabolite concentrations in the biofluids (urine, plasma, fecal water) using hierarchical modeling and correlation analyses. Metabolic descriptors of infection were identified in liver, renal cortex, intestinal tissues but not in spleen, brain or renal medulla. The main physiological change observed in the mouse was malabsorption in the small intestine, which was evidenced by decreased levels of various amino acids in the ileum, for example, alanine, taurine, glutamine, and branched chain amino acids. Furthermore, altered gut microbial activity or composition was reflected by increased levels of trimethylamine in the colon. Our modeling approach facilitated in-depth appraisal of the covariation of the metabolic profiles of different biological matrices and found that urine and plasma most closely reflected changes in ileal compartments. In conclusion, an E. caproni infection not only results in direct localized (ileum and jejenum) effects, but also causes remote metabolic changes (colon and several peripheral organs), and therefore describes the panorganismal metabolic response of the infection.

SUBMITTER: Saric J

PROVIDER: S-EPMC2724024 | biostudies-other | 2009 Aug

REPOSITORIES: biostudies-other

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Project description:BackgroundBabesia rossi is a leading cause of morbidity and mortality among the canine population of sub-Saharan Africa, but pathogenesis remains poorly understood. Previous studies of B. rossi infection were derived from clinical cases, in which neither the onset of infection nor the infectious inoculum was known. Here, we performed controlled B. rossi inoculations in canines and evaluated disease progression through clinical tests and whole blood transcriptomic profiling.ResultsTwo subjects were administered a low inoculum (104 parasites) while three received a high (108 parasites). Subjects were monitored for 8 consecutive days; anti-parasite treatment with diminazene aceturate was administered on day 4. Blood was drawn prior to inoculation as well as every experimental day for assessment of clinical parameters and transcriptomic profiles. The model recapitulated natural disease manifestations including anemia, acidosis, inflammation and behavioral changes. Rate of disease onset and clinical severity were proportional to the inoculum. To analyze the temporal dynamics of the transcriptomic host response, we sequenced mRNA extracted from whole blood drawn on days 0, 1, 3, 4, 6, and 8. Differential gene expression, hierarchical clustering, and pathway enrichment analyses identified genes and pathways involved in response to hemolysis, metabolic changes, and several arms of the immune response including innate immunity, adaptive immunity, and response to viral infection.ConclusionsThis work comprehensively characterizes the clinical and transcriptomic progression of B. rossi infection in canines, thus establishing a large mammalian model of severe hemoprotozoal disease to facilitate the study of host-parasite biology and in which to test novel anti-disease therapeutics. The knowledge gained from the study of B. rossi in canines will not only improve our understanding of this emerging infectious disease threat in domestic dogs, but also provide insight into the pathobiology of human diseases caused by Babesia and Plasmodium species.

Project description:Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the "metabolic objective" of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.

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Panorganismal metabolic response modeling of an experimental Echinostoma caproni infection in the mouse.

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