Sorting-free metabolic profiling uncovers the vulnerability of fatty acid ß-oxidation in in vitro quiescence models.

Quiescent cancer cells are rare nondiving cells with the unique ability to evade chemotherapies and resume cell division after treatment. Despite the associated risk of cancer recurrence, how cells can reversibly switch between rapid proliferation and quiescence remains a long-standing open question. By developing a unique methodology for the cell sorting-free separation of metabolic profiles in cell subpopulations in vitro, we unraveled metabolic characteristics of quiescent cells that are largely invariant to basal differences in cell types and quiescence-inducing stimuli. Consistent with our metabolome-based analysis, we show that impairing mitochondrial fatty acid ß-oxidation (FAO) can induce apoptosis in quiescence-induced cells and hamper their return to proliferation. Our findings suggest that in addition to mediating energy and redox balance, FAO can play a role in preventing the buildup of toxic intermediates during transitioning to quiescence. Uncovering metabolic strategies to enter, maintain, and exit quiescence can reveal fundamental principles in cell plasticity and new potential therapeutic targets beyond cancer.

Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism.

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.

A framework for large-scale metabolome drug profiling links coenzyme A metabolism to the toxicity of anti-cancer drug dichloroacetate.

Metabolic profiling of cell line collections has become an invaluable tool to study disease etiology, drug modes of action and to select personalized treatments. However, large-scale in vitro dynamic metabolic profiling is limited by time-consuming sampling and complex measurement procedures. By adapting a mass spectrometry-based metabolomics workflow for high-throughput profiling of diverse adherent mammalian cells, we establish a framework for the rapid measurement and analysis of drug-induced dynamic changes in intracellular metabolites. This methodology is scalable to large compound libraries and is here applied to study the mechanism underlying the toxic effect of dichloroacetate in ovarian cancer cell lines. System-level analysis of the metabolic responses revealed a key and unexpected role of CoA biosynthesis in dichloroacetate toxicity and the more general importance of CoA homeostasis across diverse human cell lines. The herein-proposed strategy for high-content drug metabolic profiling is complementary to other molecular profiling techniques, opening new scientific and drug-discovery opportunities.

Uncertainty budgeting in fold change determination and implications for non-targeted metabolomics studies in model systems.

The p-value is the most prominent established metric for statistical significance in non-targeted metabolomics. However, its adequacy has repeatedly been the subject of discussion criticizing its uncertainty and its dependence on sample size and statistical power. These issues compromise non-targeted metabolomics in model systems, where studies typically investigate 5-10 samples per group. In this paper we propose a different approach for assessing the relevance of fold change (FC) data, where the FC is treated as a quantitative value and is validated by uncertainty budgeting. For the purpose of large-scale application in non-targeted metabolomics, we present a simplified approach for uncertainty propagation using experimental standard deviations of metabolite intensities as type A-summarized standard uncertainties. The resulting expanded FC uncertainty can be used to derive a minimum relevant FC as a complementary criterion in metabolomics data evaluation. This concept overcomes the need for a uniform p-value cut-off for all metabolites by considering the experimental uncertainty for each metabolite individually. The proposed procedure is part of analytical method validation, however the concept has not previously been applied to non-targeted metabolomics. A case study on mesenchymal stem cells cultured in normoxia and hypoxia demonstrates the practical value of this approach, in particular for studies with a small sample size. An online two-dimensional LC method coupled to mass spectrometry was crucial in providing both broad metabolome coverage and excellent experimental precision (<8% CV for peak areas, on average 0.5% CV for retention times) that was required for sensitive differential analysis as low as FC 1.1.

An integrated metabolomics workflow for the quantification of sulfur pathway intermediates employing thiol protection with N-ethyl maleimide and hydrophilic interaction liquid chromatography tandem mass spectrometry.

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 (13)C-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.

Isotopologue analysis of sugar phosphates in yeast cell extracts by gas chromatography chemical ionization time-of-flight mass spectrometry.

Metabolic flux analysis is based on the measurement of isotopologue ratios. In this work, a new GC-MS-based method was introduced enabling accurate determination of isotopologue distributions of sugar phosphates in cell extracts. A GC-TOFMS procedure was developed involving a two-step online derivatization (ethoximation followed by trimethylsilylation) offering high mass resolution, high mass accuracy and the potential of retrospective data analysis typical for TOFMS. The information loss due to fragmentation intrinsic for isotopologue analysis by electron ionization could be overcome by chemical ionization with methane. A thorough optimization regarding pressure of the reaction gas, emission current, electron energy and temperature of the ion source was carried out. For a substantial panel of sugar phosphates both of the glycolysis and the pentose phosphate pathway, sensitive determination of the protonated intact molecular ions together with low abundance fragment ions was successfully achieved. The developed method was evaluated for analysis of Pichia pastoris cell extracts. The measured isotopologue ratios were in the range of 55:1-2:1. The comparison of the experimental isotopologue fractions with the theoretical fractions was excellent, revealing a maximum bias of 4.6% and an average bias of 1.4%.