Dataset Information

Targeted killing of a mammalian cell based upon its specialized metabolic state.

ABSTRACT: Mouse ES cells use a mitochondrial threonine dehydrogenase (TDH) enzyme to catabolize threonine into glycine and acetyl-CoA. Measurements of mRNA abundance have given evidence that ES cells express upwards of 1,000-fold higher levels of TDH mRNA than any of seven other mouse tissues tested. When cell culture medium is deprived of threonine, ES cells rapidly discontinue DNA synthesis, arrest cell division, and eventually die. Such studies led to the conclusion that mouse ES cells exist in a threonine-dependent metabolic state. Proceeding with the assumption that the active TDH enzyme should be essential for the growth and viability of mouse ES cells, we performed a drug screen in search of specific inhibitors of the purified TDH enzyme. Such efforts led to the discovery of a class of quinazolinecarboxamide (Qc) compounds that inhibit the ability of the TDH enzyme to catabolize threonine into glycine and acetyl-CoA. Administration of Qc inhibitors of TDH to mouse ES cells impeded cell growth and resulted in the induction of autophagy. By contrast, the same chemicals failed to affect the growth of HeLa cells at concentrations 300-fold higher than that required to kill mouse ES cells. It was likewise observed that the Qc class of TDH inhibitors failed to affect the growth or viability of ES cell-derived embryoid body cells known to have extinguished TDH expression. These studies demonstrate how it is possible to kill a specific mammalian cell type on the basis of its specialized metabolic state.

SUBMITTER: Alexander PB

PROVIDER: S-EPMC3179072 | biostudies-other | 2011 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

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.

Dataset Information

Targeted killing of a mammalian cell based upon its specialized metabolic state.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets