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Background: Chemotherapy (CT) is central to the treatment of triple negative breast cancer (TNBC), but drug toxicity and resistance place strong restrictions on treatment regimes. Fasting sensitizes cancer cells to a range of chemotherapeutic agents and also ameliorates CT-associated adverse effects. However, the molecular mechanism(s) by which fasting, or short-term starvation (STS), improves the efficacy of CT is poorly characterized.

Methods: The differential responses of breast cancer or near normal cell lines to combined STS and CT were assessed by cellular viability and integrity assays (Hoechst and PI staining, MTT or H2DCFDA staining, immunofluorescence), metabolic profiling (Seahorse analysis, metabolomics), gene expression (quantitative real-time PCR) and iRNA-mediated silencing. The clinical significance of the in vitro data was evaluated by bioinformatical integration of transcriptomic data from patient data bases: The Cancer Genome Atlas (TCGA), European Genome-phenome Archive (EGA), Gene Expression Omnibus (GEO) and a TNBC cohort. We further examined the translatability of our findings in vivo by establishing a murine syngeneic orthotopic mammary tumor-bearing model.

Results: We provide mechanistic insights into how preconditioning with STS enhances the susceptibility of breast cancer cells to CT. We showed that combined STS and CT enhanced cell death and increased reactive oxygen species (ROS) levels, in association with higher levels of DNA damage and decreased mRNA levels for the NRF2 targets genes NQO1 and TXNRD1 in TNBC cells compared to near normal cells. ROS enhancement was associated with compromised mitochondrial respiration and changes in the metabolic profile, which have a significant clinical prognostic and predictive value. Furthermore, we validate the safety and efficacy of combined periodic hypocaloric diet and CT in a TNBC mouse model.

Conclusions: Our in vitro, in vivo and clinical findings provide a robust rationale for clinical trials on the therapeutic benefit of short-term caloric restriction as an adjuvant to CT in triple breast cancer treatment.

R2-like ligand-binding oxidase (R2lox) is a ferritin-like protein that harbours a heterodinuclear manganese–iron active site. Although R2lox function is yet to be established, the enzyme binds a fatty acid ligand coordinating the metal centre and catalyses the formation of a tyrosine–valine ether cross-link in the protein scaffold upon O2 activation. Here, we characterized the ligands copurified with R2lox by mass spectrometry-based metabolomics. Moreover, we present the crystal structures of two new homologs of R2lox, from Saccharopolyspora erythraea and Sulfolobus acidocaldarius, at 1.38 Å and 2.26 Å resolution, respectively, providing the highest resolution structure for R2lox, as well as new insights into putative mechanisms regulating the function of the enzyme.

Medical abzymology has made a great contribution to the development of general autoimmunity theory: it has put the autoantibodies (Ab) as the key brick of the theory to the level of physiological functionality by providing such Ab with the ability to catalyze and mediate direct and independent cytotoxic effect on cellular and molecular targets. Natural catalytic autoantibodies (abzymes) while being a pool of canonical Abs and possessing catalytic activity belong to the new group of physiologically active substances whose features and properties are evolutionary consolidated in one functionally active biomolecule. Therefore, further studies on Ab-mediated autoAg degradation and other targeted Ab-mediated proteolysis may provide biomarkers of newer generations and thus a supplementary tool for assessing the disease progression and predicting disability of the patients and persons at risks. This chapter is a summary of current knowledge and prognostic perspectives toward catalytic Abs in autoimmunity and thus some autoimmune clinical cases, their role in pathogenesis, and the exploitation of both whole molecules and their constituent parts in developing highly effective targeted drugs of the future to come, and thus the therapeutic protocols being individualized.

Tumor cells exhibit a specialized metabolism supporting their superior ability for rapid proliferation, migration, and apoptotic evasion. It is reasonable to assume that the specific metabolic needs of the tumor cells can offer an array of therapeutic windows as pharmacological disturbance may derail the biochemical mechanisms necessary for maintaining the tumor characteristics, while being less important for normally proliferating cells. In addition, the specialized metabolism may leave a unique metabolic signature which could be used clinically for diagnostic or prognostic purposes. Quantitative global metabolic profiling (metabolomics) has evolved over the last two decades. However, despite the technology's present ability to measure 1000s of endogenous metabolites in various clinical or biological specimens, there are essentially no examples of metabolomics investigations being translated into actual utility in the cancer clinic. This review investigates the current efforts of using metabolomics as a tool for translation of tumor metabolism into the clinic and further seeks to outline paths for increasing the momentum of using tumor metabolism as a biomarker and drug target opportunity.

Glutamate-ammonia ligase (GLUL) is important for acid-base homeostasis, ammonia detoxification, cell signaling, and proliferation. Here, we reported that GLUL ablation conferred resistance to several anticancer drugs in specific cancer cell lines while leaving other cell lines non-resistant to the same drugs. To understand the biochemical mechanics supporting this drug resistance, we compared drug-resistant GLUL knockout (KO) A549 non-small-cell lung carcinoma (NSCLC) cells with non-resistant GLUL KO H1299 NSCLC cells and found that the resistant A549 cells, to a larger extent, depended on exogenous glucose for proliferation. As GLUL activity is linked to the tricarboxylic acid (TCA) cycle via reversed glutaminolysis, we probed carbon flux through both glycolysis and TCA pathways by means of ¹³C5 glutamine, ¹³C5 glutamate, and ¹³C6 glucose tracing. We observed increased labeling of malate and aspartate in A549 GLUL KO cells, whereas the non-resistant GLUL KO H1299 cells displayed decreased ¹³C-labeling. The malate and aspartate shuttle supported cellular NADH production and was associated with cellular metabolic fitness. Inhibition of the malate-aspartate shuttle with aminooxyacetic acid significantly impacted upon cell viability with an IC₅₀ of 11.5 μM in resistant GLUL KO A549 cells compared to 28 μM in control A549 cells, linking resistance to the malate-aspartate shuttle. Additionally, rescuing GLUL expression in A549 KO cells increased drug sensitivity. We proposed a novel metabolic mechanism in cancer drug resistance where the increased capacity of the malate-aspartate shuttle increased metabolic fitness, thereby facilitating cancer cells to escape drug pressure.

We report XCMS-MRM and METLIN-MRM (http://xcmsonline-mrm.scripps.edu/ and http://metlin.scripps.edu/), a cloud-based data-analysis platform and a public multiple-reaction monitoring (MRM) transition repository for small-molecule quantitative tandem mass spectrometry. This platform provides MRM transitions for more than 15,500 molecules and facilitates data sharing across different instruments and laboratories.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.

Objectives: The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).

Methods: A discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student’s t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.

Results: Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.

Conclusion: Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.

Considering the physicochemical diversity of the metabolome, untargeted metabolomics will inevitably discriminate against certain compound classes. Efforts are nevertheless made to maximize the metabolome coverage. Contrary to the main steps of a typical liquid chromatography-mass spectrometry (LC-MS) metabolomics workflow, such as metabolite extraction, the sample reconstitution step has not been optimized for maximal metabolome coverage. This sample concentration step typically occurs after metabolite extraction, when dried samples are reconstituted in a solvent for injection on column. The aim of this study was to evaluate the impact of the sample reconstitution solvent composition on metabolome coverage in untargeted LCMS metabolomics. Lysogeny Broth medium samples reconstituted in MeOH/H2O ratios ranging from 0 to 100% MeOH and analyzed with untargeted reversed phase LC-MS showed that the highest number of metabolite features (n = 1500) was detected in samples reconstituted in 100% H2O. As compared to a commonly used reconstitution solvent mixture of 50/50 MeOH/H2O, our results indicate that the small fraction of compounds increasing in peak area response by the addition of MeOH to H2O, 5%, is outweighed by the fraction of compounds with decreased response, 57%. We evaluated our results on human serum samples from lymphoma patients and healthy control subjects. Reconstitution in 100% H2O resulted in a higher number of significant metabolites discriminating between these two groups than both 50% and 100% MeOH. These findings show that the sample reconstitution step has a clear impact on the metabolome coverage of MeOH extracted biological samples, highlighting the importance of the reconstitution solvent composition for untargeted discovery metabolomics.

The cholesterol metabolism is essential for cancer cell proliferation. We found the expression of genes involved in the cholesterol biosynthesis pathway up-regulated in the daunorubicin-resistant leukemia cell line CEM/R2, which is a daughter cell line to the leukemia cell line CCRF-CEM (CEM). Cellular (H2O)-H-2 labelling, mass spectrometry, and isotopomer analysis revealed an increase in lanosterol synthesis which was not accompanied by an increase in cholesterol flux or pool size in CEM/R2 cells. Exogenous addition of lanosterol had a negative effect on CEM/R2 and a positive effect on sensitive CEM cell viability. Treatment of CEM and CEM/R2 cells with cholesterol biosynthesis inhibitors acting on the enzymes squalene epoxidase and lanosterol synthase, both also involved in the 24,25-epoxycholesterol shunt pathway, revealed a connection of this pathway to lanosterol turnover. Our data highlight that an increased lanosterol flux poses a metabolic weakness of resistant cells that potentially could be therapeutically exploited.

Untargeted metabolic profiling has generated large activity in the field of clinical biomarker discovery. Yet, no clinically approved metabolite biomarkers have emerged with failure in validation phases often being a reason. To investigate why, we have applied untargeted metabolic profiling in a retrospective cohort of serum samples representing non-related diseases. Age and gender matched samples from patients diagnosed with pneumonia, congestive heart failure, lymphoma and healthy controls were subject to comprehensive metabolic profiling using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). The metabolic profile of each diagnosis was compared to the healthy control group and significant metabolites were filtered out using t-test with FDR correction. Metabolites found to be significant between each disease and healthy controls were compared and analyzed for overlap. Results show that despite differences in etiology and clinical disease presentation, the fraction of metabolites with an overlap between two or more diseases was 61%. A majority of these metabolites can be associated with immune responses thus representing non-disease specific events. We show that metabolic serum profiles from patients representing non-related diseases display very similar metabolic differences when compared to healthy controls. Many of the metabolites discovered as disease specific in this study have further been associated with other diseases in the literature. Based on our findings we suggest non-related disease controls in metabolomics biomarker discovery studies to increase the chances of a successful validation and future clinical applications.