Global metabolomic profiling of trastuzumab resistant gastric cancer cells reveals major metabolic pathways and metabolic signatures based on UHPLC-Q exactive-MS/MS

LC-HRMS-based global metabolomics profiling unravels the distinct metabolic signature of lapatinib-resistant and trastuzumab-resistant HER2+ breast cancer cells

The effectiveness of lapatinib (LAP) and trastuzumab (TRZ), the first-line therapies for HER2+ breast cancer, has been limited owing to the development of acquired resistance in patients with HER2+. This study aimed to investigate the alterations in metabolic signatures in LAP-resistant HCC1954 and TRZ-resistant HCC1954 and pathways in human HER2+ breast cancer cells using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and enrichment analysis. The HCC1954 parental cells were sequentially treated 13 rounds with LAP or TRZ to develop resistant cells and then tested for their cytotoxicity using the MTT assay. Metabolites were prepared from HCC1954 parental (MBXWT), HCC1954-LAP (MBXLAP), and HCC1954-TRZ (MBXTRZ) cells prior to LC-HRMS, chemometric, enrichment, and joint pathway analyses. LAP- and TRZ-resistant cells were successfully developed from HCC1954, and 29 and 17 differentially expressed metabolites (DEMs) were identified between MBXWT-MBXLAP and MBXWT-MBXTRZ, respectively. The analysis of DEMs between MBXWT and MBXLAP revealed significant enrichment in D-amino acid metabolism, while MBXWT and MBXTRZ identified valine, leucine, isoleucine biosynthesis, ascorbate, and aldarate metabolism. Joint pathway enrichment analysis of LAP-resistant DEMs and differentially expressed genes (DEGs) showed enrichment in glutathione metabolism, while that of TRZ-resistance and DEGs showed enrichment in carbohydrate metabolism, namely pentose and glucuronate interconversions, starch and sucrose metabolism, and galactose metabolism. The findings from this study indicate considerable metabolic changes in LAP- and TRZ-resistant HCC1954 cells, which are crucial for understanding the resistance mechanisms and developing strategies to overcome these problems.

Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

BACKGROUND

Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.

RESULTS

Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed.

CONCLUSIONS

Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Rewiring of mitochondrial metabolism in therapy-resistant cancers: permanent and plastic adaptations

Deregulation of tumor cell metabolism is widely recognized as a "hallmark of cancer." Many of the selective pressures encountered by tumor cells, such as exposure to anticancer therapies, navigation of the metastatic cascade, and communication with the tumor microenvironment, can elicit further rewiring of tumor cell metabolism. Furthermore, phenotypic plasticity has been recently appreciated as an emerging "hallmark of cancer." Mitochondria are dynamic organelles and central hubs of metabolism whose roles in cancers have been a major focus of numerous studies. Importantly, therapeutic approaches targeting mitochondria are being developed. Interestingly, both plastic (i.e., reversible) and permanent (i.e., stable) metabolic adaptations have been observed following exposure to anticancer therapeutics. Understanding the plastic or permanent nature of these mechanisms is of crucial importance for devising the initiation, duration, and sequential nature of metabolism-targeting therapies. In this review, we compare permanent and plastic mitochondrial mechanisms driving therapy resistance. We also discuss experimental models of therapy-induced metabolic adaptation, therapeutic implications for targeting permanent and plastic metabolic states, and clinical implications of metabolic adaptations. While the plasticity of metabolic adaptations can make effective therapeutic treatment challenging, understanding the mechanisms behind these plastic phenotypes may lead to promising clinical interventions that will ultimately lead to better overall care for cancer patients.

Pyrroline-5-Carboxylate Reductase-2 Promotes Colorectal Carcinogenesis by Modulating Microtubule-Associated Serine/Threonine Kinase-like/Wnt/β-Catenin Signaling

BACKGROUND

Despite significant progress in clinical management, colorectal cancer (CRC) remains the third most common cause of cancer-related deaths. A positive association between PYCR2 (pyrroline-5-carboxylate reductase-2), a terminal enzyme of proline metabolism, and CRC aggressiveness was recently reported. However, how PYCR2 promotes colon carcinogenesis remains ill understood.

METHODS

A comprehensive analysis was performed using publicly available cancer databases and CRC patient cohorts. Proteomics and biochemical evaluations were performed along with genetic manipulations and in vivo tumor growth assays to gain a mechanistic understanding.

RESULTS

PYCR2 expression was significantly upregulated in CRC and associated with poor patient survival, specifically among PYCR isoforms (PYCR1, 2, and 3). The genetic inhibition of PYCR2 inhibited the tumorigenic abilities of CRC cells and in vivo tumor growth. Coinciding with these observations was a significant decrease in cellular proline content. PYCR2 overexpression promoted the tumorigenic abilities of CRC cells. Proteomics (LC-MS/MS) analysis further demonstrated that PYCR2 loss of expression in CRC cells inhibits survival and cell cycle pathways. A subsequent biochemical analysis supported the causal role of PYCR2 in regulating CRC cell survival and the cell cycle, potentially by regulating the expression of MASTL, a cell-cycle-regulating protein upregulated in CRC. Further studies revealed that PYCR2 regulates Wnt/β-catenin-signaling in manners dependent on the expression of MASTL and the cancer stem cell niche.

CONCLUSIONS

PYCR2 promotes MASTL/Wnt/β-catenin signaling that, in turn, promotes cancer stem cell populations and, thus, colon carcinogenesis. Taken together, our data highlight the significance of PYCR2 as a novel therapeutic target for effectively treating aggressive colon cancer.

Nicolaioidesin C: An Antiausterity Agent Shows Promising Antitumor Activity in a Pancreatic Cancer Xenograft Mouse Model

Human pancreatic tumors are hypovascular in nature, and their tumor microenvironment is often characterized by hypoxia and severe nutrient deprivation due to uncontrolled heterogeneous growth, a phenomenon known as "austerity". However, pancreatic tumor cells have the inherent ability to adapt and thrive even in such low nutrient and hypoxic microenvironments. Anticancer drugs such as gemcitabine and paclitaxel, which target rapidly proliferating cells, are often ineffective against nutrient-deprived pancreatic cancer cells. In order to overcome this limitation, the search for novel agents that can eliminate cancer cells' adaptations to nutrition starvation, also known as "antiausterity" agents, represents a promising strategy to make the cancer cells susceptible to treatment. The natural product (+)-nicolaioidesin C (Nic-C) was found to have potent antiausterity activity against the PANC-1 human pancreatic cancer cell line in a nutrient-deprived condition. However, its efficacy in vivo remained untested. To address this, we synthesized Nic-C in its racemic form and evaluated its antitumor potential in a human pancreatic cancer xenograft model. Nic-C inhibited pancreatic cancer cell migration and colony formation and significantly inhibited tumor growth in MIA PaCa-2 xenografts in a dose-dependent manner. Furthermore, Nic-C inhibited the Akt/mTOR and autophagy signaling pathways in both in vitro and in vivo studies. Metabolomic profiling of in vivo tumor samples suggests that Nic-C downregulates amino acid metabolism while upregulating sphingolipid metabolism.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

[RUNX3 regulates trastuzumab resistance of gastric cancer cells: a metabolomic analysis based on UPLC-Q Exactive Focus Orbitrap mass spectrometry]

OBJECTIVE

To explore the role of Runt-related transcription factor 3 (RUNX3) in metabolic regulation of trastuzumab-resistant gastric cancer cells and investigate the mechanism of RUNX3 knockdown-mediated reversal of trastuzumab resistance.

METHODS

We performed a metabolomic analysis of trastuzumab-resistant gastric cancer cells (NCI N87R) and RUNX3 knockdown cells (NCI N87R/RUNX3) using ultra performance liquid chromatography (UPLC) coupled with Q Exactive Focus Orbitrap mass spectrometry (MS). Multivariate combined with univariate analyses and MS/MS ion spectrums were used to screen the differential variables. MetaboAnalyst 5.0 database was employed for pathway enrichment analysis. Differential metabolites-genes regulatory relationships were constructed based on OmicsNet database. The changes in GSH/GSSG and NADPH/NADP ratios in NCI N87R/RUNX3 cells were measured using detection kits.

RESULTS

The metabolic profile of NCI N87R cells was significantly altered after RUNX3 knockdown, with 81 differential metabolites identified to contribute significantly to the classification, among which 43 metabolites were increased and 38 were decreased (P < 0.01). In NCI N87R cells, RUNX3 knockdown resulted in noticeable alterations in 8 pathways involving glutamine metabolism, glycolysis, glycerophospholipid, nicotinate-nicotinamide and glutathione metabolism, causing also significant reduction of intracellular GSH/GSSG and NADPH/NADP ratios (P < 0.01). The differential metabolites-genes network revealed a regulatory relationship between the metabolic molecules and genes.

CONCLUSION

RUNX3 reverses trastuzumab resistance in gastric cancer cells by regulating energy metabolism and oxidation-reduction homeostasis and may serve as a potential therapeutic target for trastuzumab-resistant gastric cancer.

Lipidomic Analyses Uncover Apoptotic and Inhibitory Effects of Pyrvinium Pamoate on Cholangiocarcinoma Cells via Mitochondrial Membrane Potential Dysfunction

Pyrvinium pamoate (PP), an FDA-approved anthelmintic drug, has been validated as a highly potent anti-cancer agent and patented recently as a potential chemotherapeutic drug for various cancers. The aims of this study were, therefore, to investigate the ability of PP in anti-proliferative activity and focused on the lipid profiles revealing the alteration of specific lipid species in the liver fluke Opisthorchis viverrini (Ov)-associated cholangiocarcinoma (CCA) cells. PP inhibited CCA cell viability through suppressing mitochondrial membrane potential (MMP) and ATP productions, leading to apoptotic cell death. Liquid chromatography-mass spectrometry combined with chemometrics was performed to investigate lipid alteration during PP-induced apoptosis. The lipidomic analyses showed the altered lipid signatures of CCA cell types including S-acetyldihydrolipoamide, methylselenopyruvate, and triglycerides that were increased in PP-treated CCA cells. In contrast, the levels of sphinganine and phosphatidylinositol were lower in the PP-treated group compared with its counterpart. The orthogonal partial-least squares regression analysis revealed that PP-induced MMP dysfunction, leading to remarkably reduced ATP level, was significantly associated with triglyceride (TG) accumulation observed in PP-treated CCA cells. Our findings indicate that PP could suppress the MMP function, which causes inhibition of CCA cell viability through lipid production, resulting in apoptotic induction in CCA cells. These findings provide an anti-cancer mechanism of PP under apoptotic induction ability that may serve as the alternative approach for CCA treatment.

Gut Microbiota Composition and Fecal Metabolic Profiling in Patients With Diabetic Retinopathy

Recent evidence suggests there is a link between metabolic diseases and gut microbiota. To investigate the gut microbiota composition and fecal metabolic phenotype in diabetic retinopathy (DR) patients. DNA was extracted from 50 fecal samples (21 individuals with type 2 diabetes mellitus-associated retinopathy (DR), 14 with type 2 diabetes mellitus but without retinopathy (DM) and 15 sex- and age-matched healthy controls) and then sequenced by high-throughput 16S rDNA analysis. Liquid chromatography mass spectrometry (LC-MS)-based metabolomics was simultaneously performed on the samples. A significant difference in the gut microbiota composition was observed between the DR and healthy groups and between the DR and DM groups. At the genus level, Faecalibacterium, Roseburia, Lachnospira and Romboutsia were enriched in DR patients compared to healthy individuals, while Akkermansia was depleted. Compared to those in the DM patient group, five genera, including Prevotella, were enriched, and Bacillus, Veillonella, and Pantoea were depleted in DR patients. Fecal metabolites in DR patients significantly differed from those in the healthy population and DM patients. The levels of carnosine, succinate, nicotinic acid and niacinamide were significantly lower in DR patients than in healthy controls. Compared to those in DM patients, nine metabolites were enriched, and six were depleted in DR patients. KEGG annotation revealed 17 pathways with differentially abundant metabolites between DR patients and healthy controls, and only two pathways with differentially abundant metabolites were identified between DR and DM patients, namely, the arginine-proline and α-linolenic acid metabolic pathways. In a correlation analysis, armillaramide was found to be negatively associated with Prevotella and Subdoligranulum and positively associated with Bacillus. Traumatic acid was negatively correlated with Bacillus. Our study identified differential gut microbiota compositions and characteristic fecal metabolic phenotypes in DR patients compared with those in the healthy population and DM patients. Additionally, the gut microbiota composition and fecal metabolic phenotype were relevant. We speculated that the gut microbiota in DR patients may cause alterations in fecal metabolites, which may contribute to disease progression, providing a new direction for understanding DR.

Serum Metabolic Profiling of Late-Pregnant Women With Antenatal Depressive Symptoms

Background: Antenatal depression (AD) is a major public health issue worldwide and lacks objective laboratory-based tests to support its diagnosis. Recently, small metabolic molecules have been found to play a vital role in interpreting the pathogenesis of AD. Thus, non-target metabolomics was conducted in serum. Methods: Liquid chromatography-tandem mass spectrometry-based metabolomics platforms were used to conduct serum metabolic profiling of AD and non-antenatal depression (NAD). Orthogonal partial least squares discriminant analysis, the non-parametric Mann-Whitney U test, and Benjamini-Hochberg correction were used to identify the differential metabolites between AD and NAD groups; Spearman's correlation between the key differential metabolites and Edinburgh Postnatal Depression Scale (EPDS) and the stepwise logistic regression analysis was used to identify potential biomarkers. Results: In total, 79 significant differential metabolites between AD and NAD were identified. These metabolites mainly influence amino acid metabolism and glycerophospholipid metabolism. Then, PC (16:0/16:0) and betaine were significantly positively correlated with EPDS. The simplified biomarker panel consisting of these three metabolites [betaine, PC (16:0/16:0) and succinic acid] has excellent diagnostic performance (95% confidence interval = 0.911-1.000, specificity = 95%, sensitivity = 85%) in discriminating AD and NAD. Conclusion: The results suggested that betaine, PC (16:0/16:0), and succinic acid were potential biomarker panels, which significantly correlated with depression; and it could make for developing an objective method in future to diagnose AD.

Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling

Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.

Comprehensive metabolism study of swertiamarin in rats using ultra high-performance liquid chromatography coupled with Quadrupole-Exactive Orbitrap mass spectrometry

Swertiamarin, a natural ingredient with potent pharmacological activities in the iridoid glycoside family, had been reported to have significant therapeutic effects on a variety of human diseases.In this study, a systematic and efficient strategy based on UHPLC-Q-Exactive Orbitrap mass spectrometry was established to reveal the metabolic profile of swertiamarin in rat urine, plasma, and faeces.First of all, post-acquisition data-mining methods, including multiple mass defect filters (MMDFs) and high-resolution extracted ion chromatograms (HREICs), were developed to screen the metabolite candidates of swertiamarin from the complete mass scan data sets.Second, according to the diagnostic product ions (DPIs), neutral loss fragments (NLFs), chromatographic retention time, accurate mass measurement and calculated Clog P values, all metabolite candidates were rapidly identified.As a consequence, 49 metabolites altogether, including archetype compound, were preliminarily characterised. The corresponding in vivo biotransformation processes, such as dehydration, dehydrogenation, hydroxylation, hydrogenation, methylation, sulphonation, N-acetylcysteine (NAC) formation, N-heterocyclisation and their composite reactions, were all discovered in the study.In conclusion, our results not only detailedly elucidated many new metabolites and metabolic pathways of swertiamarin, but also provided a reference for further study of its pharmacological mechanism and evaluation of its safety.

Sex-dependent changes in the skin mucus metabolome of discus fish (Symphysodon haraldi) during biparental care

Discus fish Symphysodon spp. employs an unusual parental care where fry feed on parental skin mucus after hatching. Here, we investigated the mucus metabolites of parental and non-parental discus by using non-targeted metabolomics. Statistical analysis of the skin mucus metabolome revealed sex-dependent changes of mucus between parental and non-parental discus, as well as sex-specific differences between parental fish. Differential metabolites reflected that mucus of both parents was rich in prostaglandin A1, but only male contained more oligosaccharides (gentiobiose and D-melezitose) and nucleotides (guanine and cytosine), and only female detected more thymine. Moreover, differential metabolites revealed the metabolic status of parental discus, including the inhibition of biosynthesis of amino acids, e.g., L-phenylalanine (parents), L-aspartic acid (female) and taurine (male) and the activation of metabolism of these amino acids; the increase of metabolism of fatty acids such as α-Linolenic acid (female), arachidonic acid (female) and linoleic acid (male); the perturbation of metabolism of carbohydrate and energy including starch and sucrose metabolism (parents), ascorbate and aldarate metabolism (parents), amino sugar and nucleotide sugar metabolism (female), pentose and glucuronate interconversions (male) and glyoxylate and dicarboxylate metabolism (male). These results might suggest sex-specific metabolic changes in the skin mucus of discus fish during parental care. SIGNIFICANCE: We detected the low-molecular-weight compounds present in the parental mucus of discus fish evolving for offspring and revealed the possible metabolic changes associated with parental care. These results are helpful to gain further insights on the functional and regulatory aspects of skin mucus of discus during parental care.