Glycolysis and fatty acid synthesis are highly active in cancer cells through cytosolic citrate metabolism, with intracellular citrate primarily derived from either glucose or glutamine via the tricarboxylic acid cycle. We show here that extracellular citrate is supplied to cancer cells through a plasma membrane-specific variant of the mitochondrial citrate transporter (pmCiC). Metabolomic analysis revealed that citrate uptake broadly affected cancer cell metabolism through citrate-dependent metabolic pathways. Treatment with gluconate specifically blocked pmCiC and decreased tumor growth in murine xenografts of human pancreatic cancer. This treatment altered metabolism within tumors, including fatty acid metabolism. High expression of pmCiC was associated with invasion and advanced tumor stage across many human cancers. These findings support the exploration of extracellular citrate transport as a novel potential target for cancer therapy. Uptake of extracellular citrate through pmCiC can be blocked with gluconate to reduce tumor growth and to alter metabolic characteristics of tumor tissue. .

Elite endurance athletes are characterized by markedly increased hemoglobin mass (Hbmass). It has been hypothesized that this adaptation may occur as a response to training at a very young age. Therefore, the aim of this study was to monitor changes in Hbmass in children aged 8–14 years following systematic endurance training. In the first study, Hbmass, VO2max, and lean body mass (LBM) were measured in 17 endurance-trained children (13 boys and 4 girls; aged 9.7 ± 1.3 years; training history 1.5±1.8 years; training volume 3.5 ± 1.6 h) twice a year for up to 3.5 years. The same parameters were measured once in a control group of 18 age-matched untrained children. Hbmass and blood volume (BV) were measured using the optimized CO-rebreathing technique, VO2max by an incremental test on a treadmill, and LBM by skin-fold measurements. In the second pilot study, the same parameters were measured in 9 young soccer athletes (aged 7.8 ± 0.2 years), and results were assessed in relation to soccer performance 2.5 years later. The increase in mean Hbmass during the period of study was 50% which was closely related to changes in LBM (r = 0.959). A significant impact of endurance training on Hbmass was observed in athletes exercising more than 4 h/week [+25.4 g compared to the group with low training volume (<2 h/week)]. The greatest effects were related to LBM (11.4 g·kg−1 LBM) and overlapped with the effects of age. A strong relationship was present between absolute Hbmass and VO2max (r = 0.939), showing that an increase of 1 g hemoglobin increases VO2max by 3.6 ml·min−1. Study 2 showed a positive correlation between Hbmass and soccer performance 2.5 years later at age 10.3 ± 0.3 years (r = 0.627, p = 0.035). In conclusion, children with a weekly training volume of more than 4 h show a 7% higher Hbmass than untrained children. Although this training effect is significant and independent of changes in LBM, the major factor driving the increase in Hbmass is still LBM.

Gas chromatography-atmospheric-pressure chemical ionization-time-of-flight mass spectrometry (GC-APCI-TOFMS) was compared to GC × GC-electron ionization (EI)-TOFMS, GC-EI-TOFMS, GC-chemical ionization (CI)-quadrupole mass spectrometry (qMS), and GC-EI-qMS in terms of reproducibility, dynamic range, limit of detection, and quantification using a mix of 43 metabolites and 12 stable isotope-labeled standards. Lower limits of quantification for GC-APCI-TOFMS ranged between 0.06 and 7.81 μM, and relative standard deviations for calibration replicates were between 0.4% and 8.7%. For all compounds and techniques, except in four cases, R(2) values were above 0.99. Regarding limits of quantification, GC-APCI-TOFMS was inferior to only GC × GC-EI-TOFMS, but outperformed all other techniques tested. GC-APCI-TOFMS was further applied to the metabolic fingerprinting of two Escherichia coli strains. Of 45 features that differed significantly (false discovery rate < 0.05) between the strains, 25 metabolites were identified through highly accurate and reproducible (Δm ± SD below 5 mDa over m/z 190-722) mass measurements. Starting from the quasimolecular ion, six additional metabolites were identified that had not been found in a previous study using GC × GC-EI-TOFMS and an EI mass spectral library for identification purposes. Silylation adducts formed in the APCI source assisted the identification of unknown compounds, as their formation is structure-dependent and is not observed for compounds lacking a carboxylic group.

RationaleFor the characterization of the chemical composition of complex matrices such as tobacco smoke, containing more than 6000 constituents, several analytical approaches have to be combined to increase compound coverage across the chemical space. Furthermore, the identification of unknown molecules requiring the implementation of additional confirmatory tools in the absence of reference standards, such as tandem mass spectrometry spectra comparisons and in silico prediction of mass spectra, is a major bottleneck.MethodsWe applied a combination of four chromatographic/ionization techniques (reversed‐phase (RP) – heated electrospray ionization (HESI) in both positive (+) and negative (−) modes, RP – atmospheric pressure chemical ionization (APCI) in positive mode, and hydrophilic interaction liquid chromatography (HILIC) – HESI positive) using a Thermo Q Exactive™ liquid chromatography/high‐resolution accurate mass spectrometry (LC/HRAM‐MS) platform for the analysis of 3R4F‐derived smoke. Compound identification was performed by using mass spectral libraries and in silico predicted fragments from multiple integrated databases.ResultsA total of 331 compounds with semi‐quantitative estimates ≥100 ng per cigarette were identified, which were distributed within the known chemical space of tobacco smoke. The integration of multiple LC/HRAM‐MS‐based chromatographic/ionization approaches combined with complementary compound identification strategies was key for maximizing the number of amenable compounds and for strengthening the level of identification confidence. A total of 50 novel compounds were identified as being present in tobacco smoke. In the absence of reference MS2 spectra, in silico MS2 spectra prediction gave a good indication for compound class and was used as an additional confirmatory tool for our integrated non‐targeted screening (NTS) approach.ConclusionsThis study presents a powerful chemical characterization approach that has been successfully applied for the identification of novel compounds in cigarette smoke. We believe that this innovative approach has general applicability and a huge potential benefit for the analysis of any complex matrices.

The effects of continuous water infusion on efficiency and repeatability of atmospheric pressure chemical ionization of both methyl chloroformate (MCF) and methoxime-trimethylsilyl (MO-TMS) derivatives of metabolites were evaluated using gas chromatography-time-of-flight mass spectrometry. Water infusion at a flow-rate of 0.4 mL/h yielded not only an average 16.6-fold increase in intensity of the quasimolecular ion for 20 MCF-derivatized metabolite standards through suppression of in-source fragmentation but also the most repeatable peak area integrals. The impact of water infusion was the greatest for dicarboxylic acids and the least for (hetero-) aromatic compounds. Water infusion also improved the ability to detect reliably fold changes as small as 1.33-fold for the same 20 MCF-derivatized metabolite standards spiked into a human serum extract. On the other hand, MO-TMS derivatives were not significantly affected by water infusion, neither in their fragmentation patterns nor with regard to the detection of differentially regulated compounds. As a proof of principle, we applied MCF derivatization and GC-APCI-TOFMS to the detection of changes in abundance of metabolites in pancreatic cancer cells upon treatment with 17-DMAG. Water infusion increased not only the number of metabolites identified via their quasimolecular ion but also the reproducibility of peak areas, thereby almost doubling the number of significantly regulated metabolites (false discovery rate < 0.05) to a total of 23.