Targeting pathogen metabolism without collateral damage to the host

The development of drugs that can inactivate disease-causing cells (e.g. cancer cells or parasites) without causing collateral damage to healthy or to host cells is complicated by the fact that many proteins are very similar between organisms. Nevertheless, due to subtle, quantitative differences between the biochemical reaction networks of target cell and host, a drug can limit the flux of the same essential process in one organism more than in another. We identified precise criteria for this â €network-based' drug selectivity, which can serve as an alternative or additive to structural differences. We combined computational and experimental approaches to compare energy metabolism in the causative agent of sleeping sickness, Trypanosoma brucei, with that of human erythrocytes, and identified glucose transport and glyceraldehyde-3-phosphate dehydrogenase as the most selective antiparasitic targets. Computational predictions were validated experimentally in a novel parasite-erythrocytes co-culture system. Glucose-transport inhibitors killed trypanosomes without killing erythrocytes, neurons or liver cells

LC–MS-based absolute metabolite quantification:Application to metabolic flux measurement in trypanosomes

Human African trypanosomiasis is a neglected tropical disease caused by the protozoan parasite, Trypanosoma brucei. In the mammalian bloodstream, the trypanosome’s metabolism differs significantly from that of its host. For example, the parasite relies exclusively on glycolysis for energy source. Recently, computational and mathematical models of trypanosome metabolism have been generated to assist in understanding the parasite metabolism with the aim of facilitating drug development. Optimisation of these models requires quantitative information, including metabolite concentrations and/or metabolic fluxes that have been hitherto unavailable on a large scale. Here, we have implemented an LC–MS-based method that allows large scale quantification of metabolite levels by using U-13C-labelled E. coli extracts as internal standards. Known amounts of labelled E. coli extract were added into the parasite samples, as well as calibration standards, and used to obtain calibration curves enabling us to convert intensities into concentrations. This method allowed us to reliably quantify the changes of 43 intracellular metabolites and 32 extracellular metabolites in the medium over time. Based on the absolute quantification, we were able to compute consumption and production fluxes. These quantitative data can now be used to optimise computational models of parasite metabolism

Deletion of transketolase triggers a stringent metabolic response in promastigotes and loss of virulence in amastigotes of Leishmania mexicana

Transketolase (TKT) is part of the non-oxidative branch of the pentose phosphate pathway (PPP). Here we describe the impact of removing this enzyme from the pathogenic protozoan Leishmania mexicana. Whereas the deletion had no obvious effect on cultured promastigote forms of the parasite, the Δtkt cells were not infective to mice. Δtkt promastigotes were more susceptible to oxidative stress and various leishmanicidal drugs than wild-type, and metabolomics analysis revealed profound changes to metabolism in these cells. In addition to changes consistent with those directly related to the role of TKT in the PPP, central carbon metabolism was substantially decreased, the cells consumed significantly less glucose, flux through glycolysis diminished, and production of the main end products of metabolism was decreased. Only minor changes in RNA abundance from genes encoding enzymes in central carbon metabolism, however, were detected although fructose-1,6-bisphosphate aldolase activity was decreased two-fold in the knock-out cell line. We also showed that the dual localisation of TKT between cytosol and glycosomes is determined by the C-terminus of the enzyme and by engineering different variants of the enzyme we could alter its sub-cellular localisation. However, no effect on the overall flux of glucose was noted irrespective of whether the enzyme was found uniquely in either compartment, or in both

Regulation of yeast-to-hyphae transition in Yarrowia lipolytica

© 2018 Pomraning et al. The yeast Yarrowia lipolytica undergoes a morphological transition from yeast-to-hyphal growth in response to environmental conditions. A forward genetic screen was used to identify mutants that reliably remain in the yeast phase, which were then assessed by whole-genome sequencing. All the smooth mutants identified, so named because of their colony morphology, exhibit independent loss of DNA at a repetitive locus made up of interspersed ribosomal DNA and short 10- to 40-mer telomere-like repeats. The loss of repetitive DNA is associated with downregulation of genes with stress response elements (5'-CCCCT-3') and upregulation of genes with cell cycle box (5'-ACGCG-3') motifs in their promoter region. The stress response element is bound by the transcription factor Msn2p in Saccharomyces cerevisiae. We confirmed that the Y. lipolytica msn2 (Ylmsn2) ortholog is required for hyphal growth and found that overexpression of Ylmsn2 enables hyphal growth in smooth strains. The cell cycle box is bound by the Mbp1p/Swi6p complex in S. cerevisiae to regulate G1-to-S phase progression. We found that overexpression of either the Ylmbp1 or Ylswi6 homologs decreased hyphal growth and that deletion of either Ylmbp1 or Ylswi6 promotes hyphal growth in smooth strains. A second forward genetic screen for reversion to hyphal growth was performed with the smooth-33 mutant to identify additional genetic factors regulating hyphal growth in Y. lipolytica. Thirteen of the mutants sequenced from this screen had coding mutations in five kinases, including the histidine kinases Ylchk1 and Ylnik1 and kinases of the high-osmolarity glycerol response (HOG) mitogen-activated protein (MAP) kinase cascade Ylssk2, Ylpbs2, and Ylhog1. Together, these results demonstrate that Y. lipolytica transitions to hyphal growth in response to stress through multiple signaling pathways