Metabolic signatures differentiate ovarian from colon cancer cell lines

A Multi-Omics Approach Reveals Enrichment in Metabolites Involved in the Regulation of the Glutathione Pathway in LIN28B-Dependent Cancer Cells

The RNA-binding protein LIN28B, identified as an independent risk factor in high-risk neuroblastoma patients, is implicated in adverse treatment outcomes linked to metastasis and chemoresistance. Despite its clinical significance, the impact of LIN28B on neuroblastoma cell metabolism remains unexplored. This study employs a multi-omics approach, integrating transcriptome and metabolome data, to elucidate the global metabolic program associated with varying LIN28B expression levels over time. Our findings reveal that escalating LIN28B expression induces a significant metabolic rewiring in neuroblastoma cells. Specifically, LIN28B prompts a time-dependent increase in the release rate of metabolites related to the glutathione and aminoacyl-tRNA biosynthetic pathways, concomitant with a reduction in glucose uptake. These results underscore the pivotal role of LIN28B in governing neuroblastoma cell metabolism and suggest a potential disruption in the redox balance of LIN28B-bearing cells. This study offers valuable insights into the molecular mechanisms underlying LIN28B-associated adverse outcomes in neuroblastoma, paving the way for targeted therapeutic interventions.

Mass spectrometry-based metabolomics approach and in vitro assays revealed promising role of 2,3-dihydroquinazolin-4(1H)-one derivatives against colorectal cancer cell lines

Colorectal cancer (CRC) is the most frequent form of gastrointestinal cancer and one of the major causes of human mortality worldwide. Many of the current CRC therapies have limitations due to multidrug resistance and/or severe side effects. Quinazoline derivatives are promising lead compounds with a wide range of pharmacological actions. In this study, the effect of seven synthesized 2,3-dihydroquinazolin-4(1H)-one analogues as potential anticancer agents against two CRC cell lines (HCT116 and SW480) was investigated using cell viability proliferation, migration, adhesion and invasion assays. A liquid chromatography-mass spectrometry (LC-MS/MS) metabolomics approach was used to identify the underlying biochemical pathways disturbed in treated-HCT116 cells. Cell viability proliferation assay revealed that four compounds (C2, C3, C5, and C7) had IC₅₀ < 10 µM with C5 displaying the most potent cytotoxic effect (IC₅₀ 1.4 and 0.3 µM against HCT116 and SW480, respectively). Additionally, the compounds showed suppression of wound closure after 72 h, and both C2 and C5 significantly decreased the number of adherent cells and suppressed HCT116 cells invasion. Metabolomics study revealed that C5 induced significant perturbations in the level of several metabolites including spermine, polyamines, glutamine, creatine and carnitine, and altered biochemical processes essential for cell proliferation and progression such as amino acids biosynthesis and metabolism, redox homeostasis, energy related processes (e.g., fatty acid oxidation, second Warburg like effect) and one-carbon metabolism. Our findings indicate that 2,3-dihydroquinazolin-4(1H)-one analogues, particularly C5, have promising anticancer properties, and shed light on the role of metabolomics in identifying new therapeutic targets and providing better understanding of the pathways altered in treated cancer cells.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Models for measuring metabolic chemical changes in the metastasis of high grade serous ovarian cancer: fallopian tube, ovary, and omentum

Ovarian cancer (OC) is the most lethal gynecologic malignancy and high grade serous ovarian cancer (HGSOC) is the most common and deadly subtype, accounting for 70-80% of OC deaths. HGSOC has a distinct pattern of metastasis as many believe it originates in the fallopian tube and then it metastasizes first to the ovary, and later to the adipose-rich omentum. Metabolomics has been heavily utilized to investigate metabolite changes in HGSOC tumors and metastasis. Generally, metabolomics studies have traditionally been applied to biospecimens from patients or animal models; a number of recent studies have combined metabolomics with innovative cell-culture techniques to model the HGSOC metastatic microenvironment for the investigation of cell-to-cell communication. The purpose of this review is to serve as a tool for researchers aiming to model the metastasis of HGSOC for metabolomics analyses. It will provide a comprehensive overview of current knowledge on the origin and pattern of metastasis of HGSOC and discuss the advantages and limitations of different model systems to help investigators choose the best model for their research goals, with a special emphasis on compatibility with different metabolomics modalities. It will also examine what is presently known about the role of small molecules in the origin and metastasis of HGSOC.

The accumulation of plasma acylcarnitines are associated with poor immune recovery in HIV-infected individuals

BACKGROUND

Antiretroviral therapy (ART) can reduce opportunistic infections and mortality rates among individuals infected with human immunodeficiency virus (HIV); however, some HIV-infected individuals exhibit poor immune recovery after ART. Hence, we explored the association between metabolome profiles and immune recovery in HIV-infected individuals following ART.

METHODS

An untargeted metabolomics approach was used to analyze plasma samples from 18 HIV-negative individuals and 20 HIV-infected individuals, including 10 immunological non-responders (INR, CD4⁺ T cell rise < 100 cells/μl) and 10 immunological responders (IR, CD4⁺ T cell rise > 300 cells/μl) after 2 years of ART. These individuals were followed for the next 6 years and viral loads and CD4⁺ T cell count were measured regularly. Orthogonal projection on latent structures discriminant analysis (OPLS-DA), ANOVA, correlation, receiver operating characteristic (ROC), and survival analyses were used for selection of discriminant metabolites.

RESULTS

Eighteen lipid metabolites were identified which could distinguish among control, INR, and IR groups. Among them, myristoylcarnitine (MC), palmitoylcarnitine (PC), stearoylcarnitine (SC), and oleoylcarnitine (OC) were significantly elevated in INR plasma samples compared with those from the IR and control groups and were negatively associated with CD4⁺ T cell count. Additionally, ROC analysis using a combination of MC, PC, SC, and OC had high sensitivity and specificity for differentiating INR from IR (AUC = 0.94). Finally, survival analysis for the combination of MC, PC, SC, and OC demonstrated that it could predict CD4⁺ T cell count in patients undergoing long-term ART.

CONCLUSIONS

High levels of lipid metabolites, MC, PC, SC, and OC are associated with poor immune recovery in patients receiving ART and these data provide potential new insights into immune recovery mechanisms.

Targeting the Metabolic Adaptation of Metastatic Cancer

Simple Summary

The search for new therapeutic opportunities to target cancer metastasis is crucial for the improvement of cancer treatment. One of the characteristics of tumoral and metastatic cells is the capacity to reorganize their metabolism, together with the ability to grow faster, migrate and form new tumours in distant sites. Therefore, the pharmaceutical inhibition of metabolic pathways represents a promising strategy to specifically target metastatic cells, especially in colorectal cancer metastasis.

Abstract

Metabolic adaptation is emerging as an important hallmark of cancer and metastasis. In the last decade, increasing evidence has shown the importance of metabolic alterations underlying the metastatic process, especially in breast cancer metastasis but also in colorectal cancer metastasis. Being the main cause of cancer-related deaths, it is of great importance to developing new therapeutic strategies that specifically target metastatic cells. In this regard, targeting metabolic pathways of metastatic cells is one of the more promising windows for new therapies of metastatic colorectal cancer, where still there are no approved inhibitors against metabolic targets. In this study, we review the recent advances in the field of metabolic adaptation of cancer metastasis, focusing our attention on colorectal cancer. In addition, we also review the current status of metabolic inhibitors for cancer treatment.

Inhibitory Effects of Peroxidase from Foxtail Millet Bran on Colitis-Associated Colorectal Carcinogenesis by the Blockage of Glycerophospholipid Metabolism

Abnormal glycerophospholipid (GPL) metabolism represented by phosphatidylcholine (PC) and phosphatidylethanolamine (PE) has been as a universal metabolic hallmark of cancer, which is involved in tumor progression. Our previous finding showed that peroxidase from foxtail millet bran (FMBP) exhibited significant anticolorectal cancer (CRC) activity in vitro and in nude mice. Presently, the potential of FMBP in clinical application was further evaluated by an azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced colitis-associated carcinogenesis (CAC) mice model, revealed the pivotal role of GPL metabolism in anti-CRC effects of FMBP. Excitedly, FMBP significantly reduced the number and volume of CAC polyps of mice and effectively improved physiological indexes of CAC mice. Meanwhile, the elevated expressions of CRC early markers (cyclooxygenase 2, tumor-proliferating nuclear antigen Ki-67, and EGF module-containing mucin-like receptor 1) in CAC mice were efficiently prevented by FMBP treatment. Metabolomics analysis showed that the elevated abundances of PC and PE involved in GPL metabolism in CAC mice were markedly decreased in FMBP-treated groups, which was also verified in human CRC cells. Further, FMBP reduced the expression levels of PE and PC key metabolic enzymes, resulting in the blockage of GPL metabolism and insufficient adenosine triphosphate to maintain CRC growth. Collectively, FMBP has the potential as a preventive and therapeutic candidate for CRC through the blockage of GPL metabolism.

Analysis of the serum peptidome associated with Toxoplasma gondii infection

Toxoplasma gondii is an obligate intracellular parasite that can cause severe disease in immunocompromised individuals and congenitally infected neonates. In order to determine whether serum peptide profile could reveal disease markers or allow determination of toxoplasmosis aggressiveness, mouse sera were collected from acutely infected, chronically infected and control subjects, and analyzed by a quantitative label-free pepdomics approach (LC-MS/MS). Six hundred and seven endogenous peptides were identified among all samples, with peptide profiling of difference that readily distinguished between acutely infected samples and other samples. Among these peptides detected in this study, 81 and 68 differentially expressed peptides (DEPs) were found in the acute and chronic infection stages, respectively. Through Gene Ontology analysis, most of the precursor proteins of these DEPs were associated with biological regulation and binding activity. These findings in this study will help in the search of peptide targets with a key role in disease diagnosis and create new opportunities for the development of better means for the prevention and control of toxoplasmosis. SIGNIFICANCE: Toxoplasma gondii is an unicellular parasite which infects humans and a wide range of warm-blooded animals. The serum peptidome contains a large set of low molecular weight endogenous peptides derived from secretion, protease activity and PTMs. In the present study we quantified the effects of T. gondii infection on the serum peptidome to identify novel disease regulated secretory factors. We developed an optimized label-free LC-MS/MS method to analyze endogenous peptides during toxoplasmosis progression. This resulted in quantification of 607 unique peptides at both acute and chronic infection stages. Collectively, our deep peptidomic analysis of serum revealed that peptide variations were affected by disease development, and peptidomics is an ideal method for quantifying changes in circulating factors on a global scale in response to pathophysiological perturbations such as T. gondii infection.

Decreased Triacylglycerol Content and Elevated Contents of Cell Membrane Lipids in Colorectal Cancer Tissue: A Lipidomic Study

Recent evidence suggests that lipid composition in cancer tissues may undergo multiple alterations. However, no comprehensive analysis of various lipid groups in colorectal cancer (CRC) tissue has been conducted thus far. To address the problem in question, we determined the contents of triacylglycerols (TG), an energetic substrate, various lipids necessary for cell membrane formation, among them phospholipids (phosphatidylcholine, phosphatidylethanolamine), sphingolipids (sphingomyelin) and cholesterol (free, esterified and total), and fatty acids included in complex lipids. ¹H-nuclear magnetic resonance (¹H-NMR) and gas chromatography-mass spectrometry (GC-MS) were used to analyze the lipid composition of colon cancer tissue and normal large intestinal mucosa from 25 patients. Compared with normal tissue, cancer tissues had significantly lower TG content, along with elevated levels of phospholipids, sphingomyelin, and cholesterol. Moreover, the content of oleic acid, the main component of TG, was decreased in cancer tissues, whereas the levels of saturated fatty acids and polyunsaturated fatty acids (PUFAs), which are principal components of polar lipids, were elevated. These lipidome rearrangements were associated with the overexpression of genes associated with fatty acid oxidation, and the synthesis of phospholipids and cholesterol. These findings suggest that reprogramming of lipid metabolism might occur in CRC tissue, with a shift towards increased utilization of TG for energy production and enhanced synthesis of membrane lipids, necessary for the rapid proliferation of cancer cells.

Label-free visualization and characterization of extracellular vesicles in breast cancer

Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.

Characterisation of the Urinary Metabolic Profile of Liver Fluke-Associated Cholangiocarcinoma

BACKGROUND

Human infection with Opisthorchis viverrini, a carcinogenic liver fluke inhabiting the biliary tree, is endemic in Southeast Asia. Chronic infection is associated with a fatal complication, cholangiocarcinoma (CCA), a late-presenting and aggressive malignancy. Currently, annual mortality rates from CCA mirror trends in incidence, due in part to limited availability of efficient prognostic and early diagnostic biomarkers. With ability to detect thousands of urinary metabolites using metabonomics, the urine metabolome holds great potential in providing an insight into system-level alterations in carcinogenesis and in identifying metabolic markers altered in response to disturbed homoeostasis.

METHODS

Global molecular profiling using reversed-phase ultraperformance liquid chromatography mass spectrometry was utilised to acquire the urinary spectral profile of 137 Thai subjects (48 at high risk of infection, 41 with O. viverrini infection, 34 periportal fibrosis and 14 CCA) from Khon Kaen, Thailand.

RESULTS

Multivariate statistical analysis identified perturbation in several molecular classes related to purine metabolism and lipid metabolism in the CCA urine metabolome. These markers mainly reflect changes in energy metabolism to support proliferation (increased fatty acid oxidation and purine recycling), DNA methylation and hepatic injury.

CONCLUSIONS

Several metabolites of biological interest were discovered from this proof-of-principle dataset. Augmenting these findings is essential to accelerate the development of urinary metabolic markers in CCA.

Fanconi Anemia complementation group C protein in metabolic disorders

Given importance of 22-Fanconi Anemia (FA) proteins together to act in a signaling pathway in preventing deleterious clinical symptoms, e.g. severe bone marrow failure, congenital defects, an early onset of aging and cancer, studies on each FA protein become increasingly attractive. However, an unbiased and systematic investigation of cellular effects resulting from each FA protein is missing. Here, we report roles of FA complementation C group protein (FANCC) in the protection from metabolic disorders. This study was prompted by the diabetes-prone feature displayed in FANCC knockout mice, which is not typically shown in patients with FA. We found that in cells expressing FANCC at different levels, there are representative alterations in metabolites associated with aging (glycine, citrulline, ornithine, L-asparagine, L-tyrosine, L-arginine, L-glutamine, L-leucine, L-isoleucine, L-valine, L-proline and L-alanine), Diabetes Mellitus (DM) (carbon monoxide, collagens, fatty acids, D-glucose, fumaric acid, 2-oxoglutaric acid, C3), inflammation (inosine, L-arginine, L-isoleucine, L-leucine, L-lysine, L-phenylalanine, hypoxanthine, L-methionine), and cancer ( L-methionine, sphingomyelin, acetyl-L-carnitine, L-aspartic acid, L-glutamic acid, niacinamide, phospho-rylethanolamine). We also found that FANCC can act in an FA-pathway-independent manner in tumor suppression. Collectively, featured-metabolic alterations are readouts of functional mechanisms underlying reduced tumorigenicity driven by FANCC, demonstrating close links among cancer, aging, inflammation and DM.

Cheminformatics approach to exploring and modeling trait-associated metabolite profiles

Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers.

Live single cell mass spectrometry reveals cancer-specific metabolic profiles of circulating tumor cells

Recently, there has been increased attention on the analysis of circulating tumor cells (CTCs), also known as liquid biopsy, owing to its potential benefits in cancer diagnosis and treatment. Circulating tumor cells are released from primary tumor lesions into the blood stream and eventually metastasize to distant body organs. However, a major hurdle with CTC analysis is their natural scarcity. Existing methods lack sensitivity, specificity, or reproducibility required in CTC characterization and detection. Here, we report untargeted molecular profiling of single CTCs obtained from gastric cancer and colorectal cancer patients, using live single cell mass spectrometry integrated with microfluidics-based cell enrichment techniques. Using this approach, we showed the difference in the metabolomic profile between CTCs originating from different cancer groups. Moreover, potential biomarkers were putatively annotated to be specific to each cancer type.

Peiminine inhibits colorectal cancer cell proliferation by inducing apoptosis and autophagy and modulating key metabolic pathways

Peiminine, a compound extracted from the bulbs of Fritillaria thunbergii and traditionally used as a medication in China and other Asian countries, was reported to inhibit colorectal cancer cell proliferation and tumor growth by inducing autophagic cell death. However, its mechanism of anticancer action is not well understood, especially at the metabolic level, which was thought to primarily account for peiminine's efficacy against cancer. Using an established metabolomic profiling platform combining ultra-performance liquid chromatography/tandem mass spectrometry with gas chromatography/mass spectrometry, we identified metabolic alterations in colorectal cancer cell line HCT-116 after peiminine treatment. Among the identified 236 metabolites, the levels of 57 of them were significantly (p < 0.05) different between peiminine-treated and -untreated cells in which 45 metabolites were increased and the other 12 metabolites were decreased. Several of the affected metabolites, including glucose, glutamine, oleate (18:1n9), and lignocerate (24:0), may be involved in regulation of the phosphoinositide 3-kinase/Akt/mammalian target of rapamycin (mTOR) pathway and in the oxidative stress response upon peiminine exposure. Peiminine predominantly modulated the pathways responsible for metabolism of amino acids, carbohydrates, and lipids. Collectively, these results provide new insights into the mechanisms by which peiminine modulates metabolic pathways to inhibit colorectal cancer cell growth, supporting further exploration of peiminine as a potential new strategy for treating colorectal cancer.

Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

The discovery of biomarkers able to detect cancer at an early stage, to evaluate its aggressiveness, and to predict the response to therapy remains a major challenge in clinical oncology and precision medicine. In this review, we summarize recent achievements in the discovery and development of cancer biomarkers. We also highlight emerging innovative methods in biomarker discovery and provide insights into the challenges faced in their evaluation and validation.

A Metabolomics Pilot Study on Desmoid Tumors and Novel Drug Candidates

Desmoid tumors (aggressive fibromatosis) are locally invasive soft tissue tumors that lack the ability to metastasize. There are no directed therapies or standard treatment plan, and chemotherapeutics, radiation, and surgery often have temporary effects. The majority of desmoid tumors are related to T41A and S45F mutations of the beta-catenin encoding gene (CTNNB1). Using broad spectrum metabolomics, differences were investigated between paired normal fibroblast and desmoid tumor cells from affected patients. There were differences identified, also, in the metabolomics profiles associated with the two beta-catenin mutations, T41A and S45F. Ongoing drug screening has identified currently available compounds which inhibited desmoid tumor cellular growth by more than 50% but did not affect normal fibroblast proliferation. Two drugs were investigated in this study, and Dasatinib and FAK Inhibitor 14 treatments resulted in unique metabolomics profiles for the normal fibroblast and desmoid tumor cells, in addition to the T41A and S45F. The biochemical pathways that differentiated the cell lines were aminoacyl-tRNA biosynthesis in mitochondria and cytoplasm and signal transduction amino acid-dependent mTORC1 activation. This study provides preliminary understanding of the metabolic differences of paired normal and desmoid tumors cells, their response to desmoid tumor therapeutics, and new pathways to target for therapy.

Serum Metabolic Profiling of Oocyst-Induced Toxoplasma gondii Acute and Chronic Infections in Mice Using Mass-Spectrometry

Toxoplasma gondii is an obligate intracellular parasite causing severe diseases in immunocompromised individuals and congenitally infected neonates, such as encephalitis and chorioretinitis. This study aimed to determine whether serum metabolic profiling can (i) identify metabolites associated with oocyst-induced T. gondii infection and (ii) detect systemic metabolic differences between T. gondii-infected mice and controls. We performed the first global metabolomics analysis of mice serum challenged with 100 sporulated T. gondii Pru oocysts (Genotype II). Sera from acutely infected mice (11 days post-infection, dpi), chronically infected mice (33 dpi) and control mice were collected and analyzed using LC-MS/MS platform. Following False Discovery Rate filtering, we identified 3871 and 2825 ions in ESI+ or ESI- mode, respectively. Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) identified metabolomic profiles that clearly differentiated T. gondii-infected and -uninfected serum samples. Acute infection significantly influenced the serum metabolome. Our results identified common and uniquely perturbed metabolites and pathways. Acutely infected mice showed perturbations in metabolites associated with glycerophospholipid metabolism, biosynthesis of amino acid, and tyrosine metabolism. These findings demonstrated that acute T. gondii infection induces a global perturbation of mice serum metabolome, providing new insights into the mechanisms underlying systemic metabolic changes during early stage of T. gondii infection.

Germline whole exome sequencing and large-scale replication identifies FANCM as a likely high grade serous ovarian cancer susceptibility gene

We analyzed whole exome sequencing data in germline DNA from 412 high grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas Project and identified 5,517 genes harboring a predicted deleterious germline coding mutation in at least one HGSOC case. Gene-set enrichment analysis showed enrichment for genes involved in DNA repair (p = 1.8×10-3). Twelve DNA repair genes - APEX1, APLF, ATX, EME1, FANCL, FANCM, MAD2L2, PARP2, PARP3, POLN, RAD54L and SMUG1 - were prioritized for targeted sequencing in up to 3,107 HGSOC cases, 1,491 cases of other epithelial ovarian cancer (EOC) subtypes and 3,368 unaffected controls of European origin. We estimated mutation prevalence for each gene and tested for associations with disease risk. Mutations were identified in both cases and controls in all genes except MAD2L2, where we found no evidence of mutations in controls. In FANCM we observed a higher mutation frequency in HGSOC cases compared to controls (29/3,107 cases, 0.96 percent; 13/3,368 controls, 0.38 percent; P=0.008) with little evidence for association with other subtypes (6/1,491, 0.40 percent; P=0.82). The relative risk of HGSOC associated with deleterious FANCM mutations was estimated to be 2.5 (95% CI 1.3 - 5.0; P=0.006). In summary, whole exome sequencing of EOC cases with large-scale replication in case-control studies has identified FANCM as a likely novel susceptibility gene for HGSOC, with mutations associated with a moderate increase in risk. These data may have clinical implications for risk prediction and prevention approaches for high-grade serous ovarian cancer in the future and a significant impact on reducing disease mortality.

Identification of differential metabolic characteristics between tumor and normal tissue from colorectal cancer patients by gas chromatography-mass spectrometry

Colorectal cancer (CRC) is one of the most common human malignancies and encompasses cancers of the colon and rectum. Although the gold-standard colonoscopy screening method is effective in detecting CRC, this method is invasive and can result in severe complications for patients. The purpose of this study was to determine differences in metabolites between CRC and matched adjacent nontumor tissues from CRC patients, to identify potential biomarkers that may be informative and developed screening methods. Metabolomic analysis was performed on clinically localized CRC tissue and matched adjacent nontumor tissue from 20 CRC patients. Unsupervised analysis, supervised analysis, univariate analysis and pathway analysis were used to identify potential metabolic biomarkers of CRC. The levels of 25 metabolites in CRC tissues were significantly altered compared with the matched adjacent nontumor tissues. Four metabolites (lactic acid, alanine, phosphate and aspartic acid) demonstrated good area under the curve of receiver-operator characteristic with acceptable sensitivities and specificities, indicating their potential as important biomarkers for CRC. Alterations of amino acid metabolism and enhanced glycolysis may be major factors in the development and progression of CRC. Lactic acid, alanine, phosphate, and aspartic acid could be effective diagnostic indicators for CRC.

Metabolic Perturbation and Potential Markers in Patients with Esophageal Cancer

Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.

Brain metabolomic profiling of eastern honey bee (Apis cerana) infested with the mite Varroa destructor

The mite Varroa destructor is currently the greatest threat to apiculture as it is causing a global decrease in honey bee colonies. However, it rarely causes serious damage to its native hosts, the eastern honey bees Apis cerana. To better understand the mechanism of resistance of A. cerana against the V. destructor mite, we profiled the metabolic changes that occur in the honey bee brain during V. destructor infestation. Brain samples were collected from infested and control honey bees and then measured using an untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based global metabolomics method, in which 7918 and 7462 ions in ESI+ and ESI- mode, respectively, were successfully identified. Multivariate statistical analyses were applied, and 64 dysregulated metabolites, including fatty acids, amino acids, carboxylic acid, and phospholipids, amongst others, were identified. Pathway analysis further revealed that linoleic acid metabolism; propanoate metabolism; and glycine, serine, and threonine metabolism were acutely perturbed. The data obtained in this study offer insight into the defense mechanisms of A. cerana against V. destructor mites and provide a better method for understanding the synergistic effects of parasitism on honey bee colonies.

Nesting of colon and ovarian cancer cells in the endothelial niche is associated with alterations in glycan and lipid metabolism

The metabolic phenotype of a cancer cell is determined by its genetic makeup and microenvironment, which dynamically modulates the tumor landscape. The endothelial cells provide both a promoting and protective microenvironment – a niche for cancer cells. Although metabolic alterations associated with cancer and its progression have been fairly defined, there is a significant gap in our understanding of cancer metabolism in context of its microenvironment. We deployed an in vitro co-culture system based on direct contact of cancer cells with endothelial cells (E4⁺EC), mimicking the tumor microenvironment. Metabolism of colon (HTC15 and HTC116) and ovarian (OVCAR3 and SKOV3) cancer cell lines was profiled with non-targeted metabolic approaches at different time points in the first 48 hours after co-culture was established. We found significant, coherent and non-cell line specific changes in fatty acids, glycerophospholipids and carbohydrates over time, induced by endothelial cell contact. The metabolic patterns pinpoint alterations in hexosamine biosynthetic pathway, glycosylation and lipid metabolism as crucial for cancer – endothelial cells interaction. We demonstrated that “Warburg effect” is not modulated in the initial stage of nesting of cancer cell in the endothelial niche. Our study provides novel insight into cancer cell metabolism in the context of the endothelial microenvironment.

Bioluminescent Assays for Glucose and Glutamine Metabolism: High-Throughput Screening for Changes in Extracellular and Intracellular Metabolites

Cancer cell metabolism is a complex, dynamic network of regulated pathways. Interrogation of this network would benefit from rapid, sensitive techniques that are adaptable to high-throughput formats, facilitating novel compound screening. This requires assays that have minimal sample preparation and are adaptable to lower-volume 384-well formats and automation. Here we describe bioluminescent glucose, lactate, glutamine, and glutamate detection assays that are well suited for high-throughput analysis of two major metabolic pathways in cancer cells: glycolysis and glutaminolysis. The sensitivity (1–5 pmol/sample), broad linear range (0.1–100 µM), and wide dynamic range (>100-fold) are advantageous for measuring both extracellular and intracellular metabolites. Importantly, the assays incorporate rapid inactivation of endogenous enzymes, eliminating deproteinization steps required by other methods. Using ovarian cancer cell lines as a model system, the assays were used to monitor changes in glucose and glutamine consumption and lactate and glutamate secretion over time. Homogeneous formats of the lactate and glutamate assays were robust (Z′ = 0.6–0.9) and could be multiplexed with a real-time viability assay to generate internally controlled data. Screening a small-compound library with these assays resulted in the identification of both inhibitors and activators of lactate and glutamate production.

Metabolomic Profiling of the Effects of Melittin on Cisplatin Resistant and Cisplatin Sensitive Ovarian Cancer Cells Using Mass Spectrometry and Biolog Microarray Technology

In the present study, liquid chromatography-mass spectrometry (LC-MS) was employed to characterise the metabolic profiles of two human ovarian cancer cell lines A2780 (cisplatin-sensitive) and A2780CR (cisplatin-resistant) in response to their exposure to melittin, a cytotoxic peptide from bee venom. In addition, the metabolomics data were supported by application of Biolog microarray technology to examine the utilisation of carbon sources by the two cell lines. Data extraction with MZmine 2.14 and database searching were applied to provide metabolite lists. Principal component analysis (PCA) gave clear separation between the cisplatin-sensitive and resistant strains and their respective controls. The cisplatin-resistant cells were slightly more sensitive to melittin than the sensitive cells with IC₅₀ values of 4.5 and 6.8 μg/mL respectively, although the latter cell line exhibited the greatest metabolic perturbation upon treatment. The changes induced by melittin in the cisplatin-sensitive cells led mostly to reduced levels of amino acids in the proline/glutamine/arginine pathway, as well as to decreased levels of carnitines, polyamines, adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide (NAD+). The effects on energy metabolism were supported by the data from the Biolog assays. The lipid compositions of the two cell lines were quite different with the A2780 cells having higher levels of several ether lipids than the A2780CR cells. Melittin also had some effect on the lipid composition of the cells. Overall, this study suggests that melittin might have some potential as an adjuvant therapy in cancer treatment.

Distinct Metabolic Signature of Human Bladder Cancer Cells Carrying an Impaired Fanconi Anemia Tumor-Suppressor Signaling Pathway

Metabolic profiling has great potential to help the diagnosis and prognosis of cancer patients. Fanconi Anemia (FA) tumor-suppressor signaling has been instrumental in understanding human tumorigenesis. However, this instrumental understanding has never been demonstrated at the metabolic level. Here, we show that impaired FA signaling can lead cells to exhibit metabolic signatures of tumorigenesis. This is consistent with our original studies of the roles of FA signaling in suppressing non-FA tumorigenesis at functional and genetic levels. Using ultraperformance liquid chromatography-mass spectroscopy and gas chromatography-mass spectrometry, we characterized metabolic alterations in bladder cancer cells carrying an intact or impaired FA pathway. The latter was obtained by ectopically expressing FAVL (FAVL-high), which we previously found to be capable of inactivating FA signaling. A total of 18 metabolites, end products of cell proliferation or apoptosis, were significantly different between FAVL-high and -low cells. Methionine, phenylalanine, and threonine, resulting from a tumorigenic process, were substantially increased in FAVL-high cells. With this study, we achieved genomic, functional, and metabolomic characterization of the roles of FA signaling in the development of human cancer. Furthermore, this study provides novel insights into how to translate FA basic research into strategies for producing effective biomarkers in human cancer diagnosis and prognosis.