Yong Jin An

Real-Time Monitoring of Cancer Cell Metabolism and Effects of an Anticancer Agent using 2D In-Cell NMR Spectroscopy†

Altered metabolism is a critical part of cancer cell properties, but real-time monitoring of metabolomic profiles has been hampered by the lack of a facile method. Here, we propose real-time metabolomic monitoring of live cancer cells using (13) C6 -glucose and heteronuclear two-dimensional (2D) NMR. The method allowed for metabolomic differentiation between cancer and normal cells on the basis of time-dependent changes in metabolite concentrations. Cancer cells were found to have large in- and out-flux of pyruvate as well as increased net production of alanine and acetate. The method also enabled evaluation of the metabolic effects of galloflavin whose anticancer effects have been attributed to its specific inhibition of lactate dehydrogenase. Our approach revealed previously unknown functional targets of galloflavin, which were further confirmed at the protein levels. Our method is readily applicable to the study of metabolic alterations in other cellular disease model systems.

An NMR metabolomics approach for the diagnosis of leptomeningeal carcinomatosis in lung adenocarcinoma cancer patients

Leptomeningeal carcinomatosis (LC) is a metastatic cancer invading the central nervous system (CNS). We previously reported a metabolomic diagnostic approach as tested on an animal model and compared with current modalities. Here, we provide a proof of concept by applying it to human LC originating from lung cancer, the most common cause of CNS metastasis. Cerebrospinal fluid from LC (n = 26) and normal groups (n = 41) were obtained, and the diagnosis was established with clinical signs, cytology, MRI and biochemical tests. The cytology on the CSF, the current gold standard, exhibited 69% sensitivity (∼100% specificity) from the first round of CSF tapping. In comparison, the nuclear magnetic resonance spectra on the CSF showed a clear difference in the metabolic profile between the LC and normal groups. Multivariate analysis and cross‐validation yielded the diagnostic sensitivity of 92%, the specificity of 96% and the area under the curve (AUC) of 0.991. Further spectral and statistical analysis identified myo‐inositol (p < 5 × 10−14), creatine (p < 7 × 10−8), lactate (p < 9 × 10−4), alanine (p < 7.9 × 10−3) and citrate (p < 3 × 10−4) as the most contributory metabolites, whose combination exhibited an receiver–operating characteristic diagnostic AUC of 0.996. In addition, the metabolic profile could be correlated with the grading of radiological leptomeningeal enhancement (R2 = 0.3881 and p = 6.66 × 10−4), suggesting its potential utility in grading LC. Overall, we propose that the metabolomic approach might augment current diagnostic modalities for LC, the accurate diagnosis of which remains a challenge.

Metabotyping of the C. elegans sir-2.1 Mutant Using in Vivo Labeling and 13C-Heteronuclear Multidimensional NMR Metabolomics

The roles of sir-2.1 in C. elegans lifespan extension have been subjects of recent public and academic debates. We applied an efficient workflow for in vivo(13)C-labeling of C. elegans and (13)C-heteronuclear NMR metabolomics to characterizing the metabolic phenotypes of the sir-2.1 mutant. Our method delivered sensitivity 2 orders of magnitude higher than that of the unlabeled approach, enabling 2D and 3D NMR experiments. Multivariate analysis of the NMR data showed distinct metabolic profiles of the mutant, represented by increases in glycolysis, nitrogen catabolism, and initial lipolysis. The metabolomic analysis defined the sir-2.1 mutant metabotype as the decoupling between enhanced catabolic pathways and ATP generation. We also suggest the relationship between the metabotypes, especially the branched chain amino acids, and the roles of sir-2.1 in the worm lifespan. Our results should contribute to solidifying the roles of sir-2.1, and the described workflow can be applied to studying many other proteins in metabolic perspectives.

Enhanced Phase II Detoxification Contributes to Beneficial Effects of Dietary Restriction as Revealed by Multi-platform Metabolomics Studies

Dietary restriction (DR) has many beneficial effects, but the detailed metabolic mechanism remains largely unresolved. As diet is essentially related to metabolism, we investigated the metabolite profiles of urines from control and DR animals using NMR and LC/MS metabolomic approaches. Multivariate analysis presented distinctive metabolic profiles and marker signals from glucuronide and glycine conjugation pathways in the DR group. Broad profiling of the urine phase II metabolites with neutral loss scanning showed that levels of glucuronide and glycine conjugation metabolites were generally higher in the DR group. The up-regulation of phase II detoxification in the DR group was confirmed by mRNA and protein expression levels of uridinediphospho-glucuronosyltransferase and glycine-N-acyltransferase in actual liver tissues. Histopathology and serum biochemistry showed that DR was correlated with the beneficial effects of low levels of serum alanine transaminase and glycogen granules in liver. In addition, the Nuclear factor (erythroid-derived 2)-like 2 signaling pathway was shown to be up-regulated, providing a mechanistic clue regarding the enhanced phase II detoxification in liver tissue. Taken together, our metabolomic and biochemical studies provide a possible metabolic perspective for understanding the complex mechanism underlying the beneficial effects of DR.

An NMR metabolomics approach for the diagnosis of leptomeningeal carcinomatosis

Leptomeningeal carcinomatosis (LC) is the third most common metastatic complication of the central nervous system. However, the current modalities to reliably diagnose this condition are not satisfactory. Here, we report a preclinical proof of concept for a metabolomics-based diagnostic strategy using a rat LC model incorporating glioma cells that stably express green fluorescent protein. Cytologic diagnoses gave 66.7% sensitivity for the 7-day LC group and 0% for the 3-day LC group. MR imaging could not diagnose LC at these stages. In contrast, nuclear magnetic resonance-based metabolomics on cerebrospinal fluid detected marked differences between the normal and LC groups. Predictions based on the multivariate model provided sensitivity, specificity, and overall accuracy of 88% to 89% in both groups for LC diagnosis. Further statistical analyses identified lactate, acetate, and creatine as specific for the 7-day LC group, with glucose a specific marker of the normal group. Overall, we showed that the metabolomics approach provided both earlier and more accurate diagnostic results than cytology and MR imaging in current use.

Cancer metabolomics in basic science perspective

As metabolomics investigates metabolic pathways with the focus on metabolites, it is a suitable approach to address the complex metabolic alteration in cancer. In addition, metabolic profiles are affected by environmental and post-natal changes, and therefore, directly measuring many metabolites may provide epigenetically relevant information in cancer. Despite much development in our understanding of cancer metabolism, focus is often directed to signaling or metabolic proteins that modulate the metabolite levels. In this review, we discuss the “metabolite-oriented view” on cancer metabolism. We cover how metabolomics research contributed to our current insights into the basic mechanism of metabolic alterations leading to cancer. Then, we discuss specific metabolites and related enzymatic pathways directly related with tumorigenesis. We particularly pay attention to how metabolites regulate signaling proteins and metabolic enzymes ultimately leading to cancer phenotypes. Finally, we address future prospects and challenges of metabolomics in cancer research.

Alanine-metabolizing enzyme Alt1 is critical in determining yeast life span, as revealed by combined metabolomic and genetic studies.

Alterations in metabolic pathways are gaining attention as important environmental factors affecting life span, but the determination of specific metabolic pathways and enzymes involved in life span remains largely unexplored. By applying an NMR-based metabolomics approach to a calorie-restricted yeast (Saccharomyces cerevisiae) model, we found that alanine level is inversely correlated with yeast chronological life span. The involvement of the alanine-metabolizing pathway in the life span was tested using a deletion mutant of ALT1, the gene for a key alanine-metabolizing enzyme. The mutant exhibited increased endogenous alanine level and much shorter life span, demonstrating the importance of ALT1 and alanine metabolic pathways in the life span. ALT1s effect on life span was independent of the TOR pathway, as revealed by a tor1 deletion mutant. Further mechanistic studies showed that alt1 deletion suppresses cytochrome c oxidase subunit 2 expression, ultimately generating reactive oxygen species. Overall, ALT1 seems critical in determining yeast life span, and our approach should be useful for the mechanistic studies of life span determinations.

Carbon Isotopomer Analysis with Non-Unifom Sampling HSQC NMR for Cell Extract and Live Cell Metabolomics Studies

Isotopomer analysis using either 13C NMR or LC/GC-MS has been an invaluable tool for studying metabolic activities in a variety of systems. Traditional challenges are, however, that 13C-detected NMR is insensitive despite its high resolution, and that MS-based techniques cannot easily differentiate positional isotopomers. In addition, current 13C NMR or LC/GC-MS has limitations in detecting metabolites in living cells. Here, we describe a non-uniform sampling-based 2D heteronuclear single quantum coherence (NUS HSQC) approach to measure metabolic isotopomers in both cell lysates and living cells. The method provides a high resolution that can resolve multiplet structures in the 13C dimension while retaining the sensitivity of the 1H-indirect detection. The approach was tested in L1210 mouse leukemia cells labeled with 13C acetate by measuring NUS HSQC with 25% sampling density. The results gave a variety of metabolic information such as (1) higher usage of acetate in acetylation pathway than aspartate synthesis, (2) TCA cycle efficiency changes upon the inhibition of mitochondrial oxidative phosphorylation by pharmacological agents, and (3) position-dependent isotopomer patterns in fatty acids in living cells. In addition, we were able to detect fatty acids along with other hydrophilic molecules in one sample of live cells without extraction. Overall, the high sensitivity and resolution along with the application to live cells should make the NUS HSQC approach attractive in studying carbon flux information in metabolic studies.

Prediction of glycated hemoglobin levels at 3 months after metabolic surgery based on the 7-day plasma metabolic profile.

Metabolic surgery has been shown to provide better glycemic control for type 2 diabetes than conventional therapies. Still, the outcomes of the surgery are variable, and prognostic markers reflecting the metabolic changes by the surgery are yet to be established. NMR-based plasma metabolomics followed by multivariate regression was used to test the correlation between the metabolomic profile at 7-days after surgery and glycated hemoglobin (HbA1c) levels at 3-months (and up to 12 months with less patients), and to identify the relevant markers. Metabolomic profiles at 7-days could differentiate the patients according to the HbA1c improvement status at 3-months. The HbA1c values were predicted based on the metabolomics profile with partial least square regression, and found to be correlated with the observed values. Metabolite analysis suggested that 3-Hydroxybutyrate (3-HB) and glucose contributes to this prediction, and the [3-HB]/[glucose] exhibited a modest to good correlation with the HbA1c level at 3-months. The prediction of 3-month HbA1c using 7-day metabolomic profile and the suggested new criterion [3-HB]/[glucose] could augment current prognostic modalities and help clinicians decide if drug therapy is necessary.

BMP4 Upregulation Is Associated with Acquired Drug Resistance and Fatty Acid Metabolism in EGFR-Mutant Non-Small-Cell Lung Cancer Cells

Lung cancer is the leading cause of cancer-associated deaths worldwide. In particular, non-small-cell lung cancer (NSCLC) cells harboring epidermal growth factor receptor (EGFR) mutations are associated with resistance development of EGFR tyrosine kinase inhibitor (EGFR-TKI) treatment. Recent findings suggest that bone morphogenetic proteins (BMPs) and microRNAs (miRNAs) might act as oncogenes or tumor suppressors in the tumor microenvironment. In this study, for the first time, we identified the potential roles of BMPs and miRNAs involved in EGFR-TKI resistance by analyzing datasets from a pair of parental cells and NSCLC cells with acquired EGFR-TKI resistance. BMP4 was observed to be significantly overexpressed in the EGFR-TKI-resistant cells, and its mechanism of action was strongly associated with the induction of cancer cell energy metabolism through the modulation of Acyl-CoA synthetase long-chain family member 4. In addition, miR-139-5p was observed to be significantly downregulated in the resistant NSCLC cells. The combination of miR-139-5p and yuanhuadine, a naturally derived antitumor agent, synergistically suppressed BMP4 expression in the resistant cells. We further confirmed that LDN-193189, a small molecule BMP receptor 1 inhibitor, effectively inhibited tumor growth in a xenograft nude mouse model implanted with the EFGR-TKI-resistant cells. These findings suggest a novel role of BMP4-mediated tumorigenesis in the progression of acquired drug resistance in EGFR-mutant NSCLC cells.