c-MAF coordinates enterocyte zonation and nutrient uptake transcriptional programs.

Small intestinal villi are structural and functional units present in higher vertebrates and uniquely adapted to nutrient absorption. Villus enterocytes are organized in transcriptional "zones" dedicated to specialized tasks such as absorption of specific nutrients. We report that the transcription factor c-MAF is expressed in differentiated lower and mid-villus enterocytes and is a target of BMP signaling. Maf inactivation perturbed the villus zonation program by increasing carbohydrate-related transcripts while suppressing transcripts linked to amino-acid and lipid absorption. The formation of cytoplasmic lipid droplets, shuttling dietary fat to chylomicrons, was impaired upon Maf loss indicating its role in dietary lipid handling. Maf inactivation under homeostatic conditions expanded tuft cells and led to compensatory gut lengthening, preventing weight loss. However, delayed Maf-/- enterocyte maturation impaired weight recovery after acute intestinal injury, resulting in reduced survival. Our results identify c-MAF as a regulator of the intestinal villus zonation program, while highlighting the importance of coordination between stem/progenitor and differentiation programs for intestinal regeneration.

Mapping choline metabolites in normal and transformed cells.

INTRODUCTION: Choline is an essential human nutrient that is particular important for proliferating cells, and altered choline metabolism has been associated with cancer transformation. Yet, the various metabolic fates of choline in proliferating cells have not been investigated systematically. OBJECTIVES: This study aims to map the metabolic products of choline in normal and cancerous proliferating cells. METHODS: We performed 13C-choline tracing followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis of metabolic products in normal and in vitro-transformed (tumor-forming) epithelial cells, and also in tumor-derived cancer cell lines. Selected metabolites were quantified by internal standards. RESULTS: Untargeted analysis revealed 121 LCMS peaks that were 13C-labeled from choline, including various phospholipid species, but also previously unknown products such as monomethyl- and dimethyl-ethanolamines. Interestingly, we observed formation of betaine from choline specifically in tumor-derived cells. Expression of choline dehydrogenase (CHDH), which catalyzes the first step of betaine synthesis, correlated with betaine synthesis across the cell lines studied. RNAi silencing of CHDH did not affect cell proliferation, although we observed an increased fraction of G2M phase cells with some RNAi sequences, suggesting that CHDH and its product betaine may play a role in cell cycle progression. Betaine cell concentration was around 10 µM, arguing against an osmotic function, and was not used as a methyl donor. The function of betaine in these tumor-derived cells is presently unknown. CONCLUSION: This study identifies novel metabolites of choline in cancer and normal cell lines, and reveals altered choline metabolism in cancer cells.

Mapping metabolic oscillations during cell cycle progression.

Proliferating cells must synthesize a wide variety of macromolecules while progressing through the cell cycle, but the coordination between cell cycle progression and cellular metabolism is still poorly understood. To identify metabolic processes that oscillate over the cell cycle, we performed comprehensive, non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics of HeLa cells isolated in the G1 and SG2M cell cycle phases, capturing thousands of diverse metabolite ions. When accounting for increased total metabolite abundance due to cell growth throughout the cell cycle, 18% of the observed LC-HRMS peaks were at least twofold different between the stages, consistent with broad metabolic remodeling throughout the cell cycle. While most amino acids, phospholipids, and total ribonucleotides were constant across cell cycle phases, consistent with the view that total macromolecule synthesis does not vary across the cell cycle, certain metabolites were oscillating. For example, ribonucleotides were highly phosphorylated in SG2M, indicating an increase in energy charge, and several phosphatidylinositols were more abundant in G1, possibly indicating altered membrane lipid signaling. Within carbohydrate metabolism, pentose phosphates and methylglyoxal metabolites were associated with the cycle. Interestingly, hundreds of yet uncharacterized metabolites similarly oscillated between cell cycle phases, suggesting previously unknown metabolic activities that may be synchronized with cell cycle progression, providing an important resource for future studies.

Mapping Metabolic Events in the Cancer Cell Cycle Reveals Arginine Catabolism in the Committed SG2M Phase.

Alterations in cell-cycle regulation and cellular metabolism are associated with cancer transformation, and enzymes active in the committed cell-cycle phase may represent vulnerabilities of cancer cells. Here, we map metabolic events in the G1 and SG2M phases by combining cell sorting with mass spectrometry-based isotope tracing, revealing hundreds of cell-cycle-associated metabolites. In particular, arginine uptake and ornithine synthesis are active during SG2M in transformed but not in normal cells, with the mitochondrial arginase 2 (ARG2) enzyme as a potential mechanism. While cancer cells exclusively use ARG2, normal epithelial cells synthesize ornithine via ornithine aminotransferase (OAT). Knockdown of ARG2 markedly reduces cancer cell growth and causes G2M arrest, while not inducing compensation via OAT. In human tumors, ARG2 is highly expressed in specific tumor types, including basal-like breast tumors. This study sheds light on the interplay between metabolism and cell cycle and identifies ARG2 as a potential metabolic target.

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing.

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. Moreover, even cultures of a single cell type may contain cells in distinct metabolic states, such as resting or cycling cells. Methods for measuring metabolic activities of such subpopulations are valuable tools for understanding cellular metabolism. Complex cell populations are most commonly separated using a cell sorter, and subpopulations isolated by this method can be analyzed by metabolomics methods. However, a problem with this approach is that the cell sorting procedure subjects cells to stresses that may distort their metabolism. To overcome these issues, we reasoned that the mass isotopomer distributions (MIDs) of metabolites from cells cultured with stable isotope-labeled nutrients are likely to be more stable than absolute metabolite concentrations, because MIDs are formed over longer time scales and should be less affected by short-term exposure to cell sorting conditions. Here, we describe a method based on this principle, combining cell sorting with liquid chromatography-high resolution mass spectrometry (LC-HRMS). The procedure involves analyzing three types of samples: (1) metabolite extracts obtained directly from the complex population; (2) extracts of "mock sorted" cells passed through the cell sorter instrument without gating any specific population; and (3) extracts of the actual sorted populations. The mock sorted cells are compared against direct extraction to verify that MIDs are indeed not altered by the cell sorting procedure itself, prior to analyzing the actual sorted populations. We show example results from HeLa cells sorted according to cell cycle phase, revealing changes in nucleotide metabolism.

Estimation of flux ratios without uptake or release data: Application to serine and methionine metabolism.

Model-based metabolic flux analysis (MFA) using isotope-labeled substrates has provided great insight into intracellular metabolic activities across a host of organisms. One challenge with applying MFA in mammalian systems, however, is the need for absolute quantification of nutrient uptake, biomass composition, and byproduct release fluxes. Such measurements are often not feasible in complex culture systems or in vivo. One way to address this issue is to estimate flux ratios, the fractional contribution of a flux to a metabolite pool, which are independent of absolute measurements and yet informative for cellular metabolism. Prior work has focused on "local" estimation of a handful of flux ratios for specific metabolites and reactions. Here, we perform systematic, model-based estimation of all flux ratios in a metabolic network using isotope labeling data, in the absence of uptake/release data. In a series of examples, we investigate what flux ratios can be well estimated with reasonably tight confidence intervals, and contrast this with confidence intervals on normalized fluxes. We find that flux ratios can provide useful information on the metabolic state, and is complementary to normalized fluxes: for certain metabolic reactions, only flux ratios can be well estimated, while for others normalized fluxes can be obtained. Simulation studies of a large human metabolic network model suggest that estimation of flux ratios is technically feasible for complex networks, but additional studies on data from actual isotopomer labeling experiments are needed to validate these results. Finally, we experimentally study serine and methionine metabolism in cancer cells using flux ratios. We find that, in these cells, the methionine cycle is truncated with little remethylation from homocysteine, and polyamine synthesis in the absence of methionine salvage leads to loss of 5-methylthioadenosine, suggesting a new mode of overflow metabolism in cancer cells. This work highlights the potential for flux ratio analysis in the absence of absolute quantification, which we anticipate will be important for both in vitro and in vivo studies of cancer metabolism.

Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian Cells.

Biological samples such as tissues, blood, or tumors are often complex and harbor heterogeneous populations of cells. Separating out specific cell types or subpopulations from such complex mixtures to study their metabolic phenotypes is challenging because experimental procedures for separation may disturb the metabolic state of cells. To address this issue, we developed a method for analysis of cell subpopulations using stable isotope tracing and fluorescence-activated cell sorting followed by liquid chromatography-high-resolution mass spectrometry. To ensure a faithful representation of cellular metabolism after cell sorting, we benchmarked sorted extraction against direct extraction. While peak areas differed markedly with lower signal for amino acids but higher signal for nucleotides, mass isotopomer distributions from sorted cells were generally in good agreement with those obtained from direct extractions, indicating that they reflect the true metabolic state of cells prior to sorting. In proof-of-principle studies, our method revealed metabolic phenotypes specific to T cell subtypes, and also metabolic features of cells in the committed phase of the cell division cycle. Our approach enables studies of a wide range of adherent and suspension cell subpopulations, which we anticipate will be of broad importance in cell biology and biomedicine.

An aptamer based competition assay for protein detection using CNT activated gold-interdigitated capacitor arrays.

An aptamer can specifically bind to its target molecule, or hybridize with its complementary strand. A target bound aptamer complex has difficulty to hybridize with its complementary strand. It is possible to determine the concentration of target based on affinity separation system for the protein detection. Here, we exploited this property using C-reactive protein (CRP) specific RNA aptamers as probes that were immobilized by physical adsorption on carbon nanotubes (CNTs) activated gold interdigitated electrodes of capacitors. The selective binding ability of RNA aptamer with its target molecule was determined by change in capacitance after allowing competitive binding with CRP and complementary RNA (cRNA) strands in pure form and co-mixtures (CRP:cRNA=0:1, 1:0, 1:1, 1:2 and 2:1). The sensor showed significant capacitance change with pure forms of CRP/cRNA while responses reduced considerably in presence of CRP:cRNA in co-mixtures (1:1 and 1:2) because of the binding competition. At a critical CRP:cRNA ratio of 2:1, the capacitance response was dramatically lost because of the dissociation of adsorbed aptamers from the sensor surface to bind when excess CRP. Binding assays showed that the immobilized aptamers had strong affinity for cRNA (K(d)=1.98 µM) and CRP molecules (K(d)=2.4 µM) in pure forms, but low affinity for CRP:cRNA ratio of 2:1 (K(d)=8.58 µM). The dynamic detection range for CRP was determined to be 1-8 µM (0.58-4.6 µg/capacitor). The approach described in this study is a sensitive label-free method to detect proteins based on affinity separation of target molecules that can potentially be used for probing molecular interactions.