Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution.

The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.

Glycine decarboxylase maintains mitochondrial protein lipoylation to support tumor growth.

The folic acid cycle mediates the transfer of one-carbon (1C) units to support nucleotide biosynthesis. While the importance of serine as a mitochondrial and cytosolic donor of folate-mediated 1C units in cancer cells has been thoroughly investigated, a potential role of glycine oxidation remains unclear. We developed an approach for quantifying mitochondrial glycine cleavage system (GCS) flux by combining stable and radioactive isotope tracing with computational flux decomposition. We find high GCS flux in hepatocellular carcinoma (HCC), supporting nucleotide biosynthesis. Surprisingly, other than supplying 1C units, we found that GCS is important for maintaining protein lipoylation and mitochondrial activity. Genetic silencing of glycine decarboxylase inhibits the lipoylation and activity of pyruvate dehydrogenase and impairs tumor growth, suggesting a novel drug target for HCC. Considering the physiological role of liver glycine cleavage, our results support the notion that tissue of origin plays an important role in tumor-specific metabolic rewiring.

Silica Immobilised Chloro- and Amido-Derivatives of Eremomycine as Chiral Stationary Phases for the Enantioseparation of Amino Acids by Reversed-Phase Liquid Chromatography.

Macrocyclic glycopeptide antibiotics immobilized on silica are one of the effective classes of stationary phases for chiral recognition and HPLC separation of a wide range of optically active compounds. Enantioselectivity primarily depends on the chemical structure of the chiral ligand, immobilization chemistry, and separation conditions. In the present work, three new chiral stationary phases (CSPs) based on macrocyclic antibiotic eremomycin were prepared and investigated for enantioseparation of amino acids. Two eremomycin derivatives, including simple non-substituted amide and bulky adamantyl amide, provided important information on the role of the carboxylic group in the eremomycin structure in the chiral recognition mechanism concerning amino acid optical isomers. One more CSP having a chlorine atom in the same position elucidates the role of the first aromatic ring in the eremomycin structure as a crucial point for chiral recognition. CSP with immobilized chloreremomycin was the most successful among the phases prepared in this work. It was additionally investigated under various separation conditions, including the type and content of the organic solvent in the eluent, the effects of different additives, and the concentration and pH of the buffer. Importantly, an efficient enantioselective separation of amino acids was achieved with pure water as the eluent.

Tumor Reliance on Cytosolic versus Mitochondrial One-Carbon Flux Depends on Folate Availability.

Folate metabolism supplies one-carbon (1C) units for biosynthesis and methylation and has long been a target for cancer chemotherapy. Mitochondrial serine catabolism is considered the sole contributor of folate-mediated 1C units in proliferating cancer cells. Here, we show that under physiological folate levels in the cell environment, cytosolic serine-hydroxymethyltransferase (SHMT1) is the predominant source of 1C units in a variety of cancers, while mitochondrial 1C flux is overly repressed. Tumor-specific reliance on cytosolic 1C flux is associated with poor capacity to retain intracellular folates, which is determined by the expression of SLC19A1, which encodes the reduced folate carrier (RFC). We show that silencing SHMT1 in cells with low RFC expression impairs pyrimidine biosynthesis and tumor growth in vivo. Overall, our findings reveal major diversity in cancer cell utilization of the cytosolic versus mitochondrial folate cycle across tumors and SLC19A1 expression as a marker for increased reliance on SHMT1.

Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions.

Mass spectrometry based metabolomics is a widely used approach in biomedical research. However, current methods coupling mass spectrometry with chromatography are time-consuming and not suitable for high-throughput analysis of thousands of samples. An alternative approach is flow-injection mass spectrometry (FI-MS) in which samples are directly injected to the ionization source. Here, we show that the sensitivity of Orbitrap FI-MS metabolomics methods is limited by ion competition effect. We describe an approach for overcoming this effect by analyzing the distribution of ion m/z values and computationally determining a series of optimal scan ranges. This enables reproducible detection of ~9,000 and ~10,000 m/z features in metabolomics and lipidomics analysis of serum samples, respectively, with a sample scan time of ~15 s and duty time of ~30 s; a ~50% increase versus current spectral-stitching FI-MS. This approach facilitates high-throughput metabolomics for a variety of applications, including biomarker discovery and functional genomics screens.