Metabolic modeling helps interpret transcriptomic changes during malaria

Abstract

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Mining the Human Host Metabolome Toward an Improved Understanding of Malaria Transmission

“…Metabolic flux models are particularly helpful for these such joint analyses, as they can combine transcriptional data (which is annotated and thus of known origin) with metabolomics data (from unknown origin). Putting these data together can help to illustrate the predicted activity of various metabolic pathways of either the host or parasite (Chiappino-Pepe et al, 2017;Tang et al, 2018).…”

Systems Biology-Based Investigation of Host–Plasmodium Interactions

“…Malaria systems biology in its most ideal form would include the development of predictive mathematical models that both codify and enhance our understanding of the disease. Beyond the well-trodden field of mathematical modeling of population-scale disease transmission, modeling approaches have also been used to gain greater insights into, for example, the timing of anemia compensation during the course of Plasmodium infection [96], the timing of parasite infection and release before parasitemia can be detected by current methods [5,55], and how models of metabolic pathways may be used to interpret transcriptomic datasets [97]. Efforts like these range from focused models to fit only a few types of physiological measurements to broader pathway-level models, and they have provided noteworthy insight.…”

Perspective: Dimensions of the scientific method

“…Considered by themselves and focusing solely on genes coding for specific enzymes of purine metabolism, the findings showed patterns that did not make sense. However, integrating the changes in a dynamic model revealed that purine metabolism globally shifted, in response to malaria, from guanine compounds to adenine, inosine, and hypoxanthine [51]. (3) Data capturing the dynamics of malaria parasites suggested growth rates that were biologically impossible.…”