Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs.

The production of recombinant proteins is frequently enhanced at the levels of transcription, codon usage, protein folding and secretion. Overproduction of heterologous proteins, however, also directly affects the primary metabolism of the producing cells. By incorporation of the production of a heterologous protein into a genome scale metabolic model of the yeast Pichia pastoris, the effects of overproduction were simulated and gene targets for deletion or overexpression for enhanced productivity were predicted. Overexpression targets were localized in the pentose phosphate pathway and the TCA cycle, while knockout targets were found in several branch points of glycolysis. Five out of 9 tested targets led to an enhanced production of cytosolic human superoxide dismutase (hSOD). Expression of bacterial β-glucuronidase could be enhanced as well by most of the same genetic modifications. Beneficial mutations were mainly related to reduction of the NADP/H pool and the deletion of fermentative pathways. Overexpression of the hSOD gene itself had a strong impact on intracellular fluxes, most of which changed in the same direction as predicted by the model. In vivo fluxes changed in the same direction as predicted to improve hSOD production. Genome scale metabolic modeling is shown to predict overexpression and deletion mutants which enhance recombinant protein production with high accuracy.

The sulfur metabolic pathway is involved in basic modes of cellular metabolism, including methylation, cell division, respiratory oscillations and stress responses. Hence, the implicated high reactivity of the sulfur pathway intermediates entails challenges for their quantitative analysis. In particular the unwanted oxidation of the thiol group-containing metabolites glutathione, cysteine, homocysteine, γ-glutamyl cysteine and cysteinyl glycine must be prevented in order to obtain accurate snapshots of this important part of cellular metabolism. Suitable analytical methodologies are therefore needed to support studies of drug metabolism and metabolic engineering. In this work, a novel sample preparation strategy targeting thiolic metabolites was established by implementing thiol group protection with N-ethyl maleimide using a cold methanol metabolite extraction procedure. It was shown that N-ethyl maleimide derivatization is compatible with typical metabolite extraction procedures and also allowed for the stabilization of the instable thiolic metabolites in a fully ¹³C-labeled yeast cell extract. The stable isotope labeled metabolite analogs could be used for internal standardization to achieve metabolite quantification with high precision. Furthermore, a dedicated hydrophilic interaction liquid chromatography tandem mass spectrometry method for the separation of sulfur metabolic pathway intermediates using a sub-2 μm particle size stationary phase was developed. Coupled with tandem mass spectrometry, the presented methodology proved to be robust, and sensitive (absolute detection limits in the low femtomole range), and allowed for the quantification of cysteine, cysteinyl glycine, cystathionine, cystine, glutamic acid, glutamyl cysteine, reduced glutathione, glutathione disulfide, homocysteine, methionine, S-adenosyl homocysteine and serine in a human ovarian carcinoma cell model.