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.

Expanding the search for small-molecule antibacterials by multidimensional profiling.

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Combining CRISPRi and metabolomics for functional annotation of compound libraries.

Molecular profiling of small molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small-molecule direct targets and secondary effects. However, current profiling methods are limited in either the number of measurable parameters or throughput. Here we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from CRISPR interference (CRISPRi) with 352 genes in all major essential biological processes. Next, on the basis of the comparison of genetic changes with 1,342 drug-induced metabolic changes, we made de novo predictions of compound functionality and revealed antibacterials with unconventional modes of action (MoAs). We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality from bacteria to human cell lines.

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.

Inositol-phosphodihydroceramides in the periodontal pathogen Tannerella forsythia: Structural analysis and incorporation of exogenous myo-inositol.

BACKGROUND: Unique phosphodihydroceramides containing phosphoethanolamine and glycerol have been previously described in Porphyromonas gingivalis. Importantly, they were shown to possess pro-inflammatory properties. Other common human bacteria were screened for the presence of these lipids, and they were found, amongst others, in the oral pathogen Tannerella forsythia. To date, no detailed study into the lipids of this organism has been performed. METHODS: Lipids were extracted, separated and purified by HPTLC, and analyzed using GC-MS, ESI-MS and NMR. Of special interest was how T. forsythia acquires the metabolic precursors for the lipids studied here. This was assayed by radioactive and stable isotope incorporation using carbon-14 and deuterium labeled myo-inositol, added to the growth medium. RESULTS: T. forsythia synthesizes two phosphodihydroceramides (Tf GL1, Tf GL2) which are constituted by phospho-myo-inositol linked to either a 17-, 18-, or 19-carbon sphinganine, N-linked to either a branched 17:0(3-OH) or a linear 16:0(3-OH) fatty acid which, in Tf GL2, is, in turn, ester-substituted with a branched 15:0 fatty acid. T. forsythia lacks the enzymatic machinery required for myo-inositol synthesis but was found to internalize inositol from the medium for the synthesis of both Tf GL1 and Tf GL2. CONCLUSION: The study describes two novel glycolipids in T. forsythia which could be essential in this organism. Their synthesis could be reliant on an external source of myo-inositol. GENERAL SIGNIFICANCE: The effects of these unique lipids on the immune system and their role in bacterial virulence could be relevant in the search for new drug targets.

Complementing reversed-phase selectivity with porous graphitized carbon to increase the metabolome coverage in an on-line two-dimensional LC-MS setup for metabolomics.

Efficient and robust separation methods are indispensable in modern LC-MS based metabolomics, where high-resolution mass spectrometers are challenged by isomeric and isobaric metabolites. The optimization of chromatographic separation hence remains an invaluable tool in the comprehensive analysis of the chemically diverse intracellular metabolome. While it is widely accepted that a single method with comprehensive metabolome coverage does not exist, the potential of combining different chromatographic selectivities in two-dimensional liquid chromatography is underestimated in the field. Here, we introduce a novel separation system combining reversed-phase and porous graphitized carbon liquid chromatography in a heart-cut on-line two-dimensional setup for mass spectrometry. The proposed experimental setup can be readily implemented using standard HPLC equipment with only one additional HPLC pump and a two-position six-port valve. The method proved to be robust with excellent retention time stability (average 0.4%) even in the presence of biological matrix. Testing the presented approach on a test mixture of 82 relevant intracellular metabolites, the number of metabolites that are retained could be doubled as compared to reversed-phase liquid chromatography alone. The presented work further demonstrates how the distinct selectivity of porous graphitized carbon complements reversed-phase liquid chromatography and extends the metabolome coverage of conventional LC-MS based methods in metabolomics to biologically important, but analytically challenging compound groups such as sugar phosphates. Both metabolic profiling and metabolic fingerprinting benefit from this method's increased separation capabilities that enhance sample throughput and the biological information content of LC-MS data. An inter-platform comparison with GC- and LC-tandem MS analyses confirmed the validity of the presented two-dimensional approach in the analysis of yeast cell extracts from P. pastoris.

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%.

Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production.

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 ß-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.

Sample preparation workflow for the liquid chromatography tandem mass spectrometry based analysis of nicotinamide adenine dinucleotide phosphate cofactors in yeast.

The accurate quantification of the highly unstable intracellular cofactor nicotinamide adenine dinucleotide phosphate in its oxidized and reduced forms demands a thorough evaluation of the analytical workflow and dedicated methods reflecting their solution chemistry as well as the biological importance of their ratio. In this work, we present a workflow for the analysis of intracellular levels of oxidized and reduced nicotinamide adenine dinucleotide phosphate in the yeast Pichia pastoris, including hot aqueous extraction, chromatographic separation in reversed-phase conditions employing a 100% wettable stationary phase, and subsequent tandem mass spectrometric analysis. A thorough evaluation and optimization of the sample preparation procedure resulted in excellent biological repeatabilities (on average <10%, N = 3) without employing an internal standardization approach. As a consequence, the methodology proved to be appropriate for the relative assessment of intracellular levels of oxidized and reduced nicotinamide adenine dinucleotide phosphate in different P. pastoris strains. The ratio of reduced versus oxidized nicotinamide adenine dinucleotide phosphate was significantly higher in an engineered strain overexpressing glucose-6-phosphate dehydrogenase than in the corresponding wildtype strain. Interestingly, a difference was also observed in the nicotinamide adenine dinucleotide phosphate pool size, which was significantly higher in the wildtype than in the modified strain.