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Triple negative breast cancer (TNBC) is a subtype of breast cancer with typically poorer outcomes due to its aggressive clinical behavior and lack of targeted treatment options. Currently, treatment is limited to the administration of high-dose chemotherapeutics, which results in significant toxicities and drug resistance. As such, there is a need to de-escalate chemotherapeutic doses in TNBC while also retaining/improving treatment efficacy. Dietary polyphenols and omega-3 polyunsaturated fatty acids (PUFAs) have been demonstrated to have unique properties in experimental models of TNBC, improving the efficacy of doxorubicin and reversing multi-drug resistance. However, the pleiotropic nature of these compounds has caused their mechanisms to remain elusive, preventing the development of more potent mimetics to take advantage of their properties. Using untargeted metabolomics, we identify a diverse set of metabolites/metabolic pathways that are targeted by these compounds following treatment in MDA-MB-231 cells. Furthermore, we demonstrate that these chemosensitizers do not all target the same metabolic processes, but rather organize into distinct clusters based on similarities among metabolic targets. Common themes in metabolic targets included amino acid metabolism (particularly one-carbon and glutamine metabolism) and alterations in fatty acid oxidation. Moreover, doxorubicin treatment alone generally targeted different metabolites/pathways than chemosensitizers. This information provides novel insights into chemosensitization mechanisms in TNBC.

Aflatoxin B1 (AFB1) is a class 1 carcinogen and mycotoxin known to contribute to the development of hepatocellular carcinoma (HCC), growth impairment, altered immune system modulation, and malnutrition. AFB1 is synthesized by Aspergillus flavus and is known to widely contaminate foodstuffs, particularly maize, wheat, and groundnuts. The mechanism in which AFB1 causes genetic mutations has been well studied, however its metabolomic effects remained largely unknown. A better understanding of how AFB1 disrupts metabolism would provide insight into how this mycotoxin leads to carcinogenesis, growth impairment, and/or immunomodulation, and may reveal potential targets for pharmacological or nutritional interventions to protect against these effects. The current study evaluated the metabolomic effects of various doses (2.5 μM, 5 μM, 10uM) of AFB1 treatment to HepG2 (liver), MDA-MB-231 (breast), and A549 (lung) cells. Treated and control cells’ metabolomic profiles were evaluated via ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Univariate and multivariate analyses revealed significant alterations in metabolite concentrations from each dose of AFB1 treatment in each cell type. Pathway analysis was then used to understand broader biochemical functions affected by AFB1 treatment in each cell type. HepG2 cell pathway analyses revealed significant pathway perturbations in lipid metabolism, carnitine synthesis, catecholamine biosynthesis, purine metabolism, and spermidine and spermine biosynthesis. Analysis of A549 cells found a greater emphasis of perturbations on various amino acids along with lipid synthesis-related pathways, and catecholamine biosynthesis. Finally, analysis of treated MDA-MB-231 cells found spermidine and spermine biosynthesis, carnitine synthesis, plasma membrane-related pathways (phosphatidylcholine synthesis and alpha linolenic acid and linoleic acid metabolism), and various amino acid metabolism pathways to be most affected. These highlighted pathways should be targeted in future investigations to evaluate their potential in mitigating or preventing the development of negative health effects associated with AFB1 exposure.

This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.

Drug resistance continues to be a significant problem in cancer therapy, leading to relapse and associated mortality. Although substantial progress has been made in understanding drug resistance, significant knowledge gaps remain concerning the molecular underpinnings that drive drug resistance and which processes are unique to certain drug classes. The NCI-60 cell line panel program has evaluated the activity of numerous anticancer agents against many common cancer cell line models and represents a highly valuable resource to study intrinsic drug resistance. Furthermore, great efforts have been undertaken to collect high-quality omics datasets to characterize these cell lines. The current study takes these two sources of data—drug response and omics profiles—and uses a multi-omics investigation to uncover molecular networks that differentiate cancer cells that are sensitive or resistant to antifolates, which is a commonly used class of anticancer drugs. Results from a combination of univariate and multivariate analyses showed numerous metabolic processes that differentiate sensitive and resistant cells, including differences in glycolysis and gluconeogenesis, arginine and proline metabolism, beta-alanine metabolism, purine metabolism, and pyrimidine metabolism. Further analysis using multivariate and integrated pathway analysis indicated purine metabolism as the major metabolic process separating cancer cells sensitive or resistant to antifolates. Additional pathways differentiating sensitive and resistant cells included autophagy-related processes (e.g., phagosome, lysosome, autophagy, mitophagy) and adhesion/cytoskeleton-related pathways (e.g., focal adhesion, regulation of actin cytoskeleton, tight junction). Volcano plot analysis and the receiver operating characteristic (ROC) curves of top selected variables differentiating Q1 and Q4 revealed the importance of genes involved in the regulation of the cytoskeleton and extracellular matrix (ECM). These results provide novel insights toward mechanisms of intrinsic antifolate resistance as it relates to interactions between nucleotide metabolism, autophagy, and the cytoskeleton. These processes should be evaluated in future studies to potentially derive novel therapeutic strategies and personalized treatment approaches to improve antifolate response.

This study aimed to investigate metabolic changes following the acquisition of resistance to doxorubicin in the triple-negative breast cancer (TNBC) cell line MDA-MB-231. Two drug-resistant cell lines, DOX-RES-50 and DOX-RES-100, were generated by treating MDA-MB-231 cells with doxorubicin for 24 h and allowing them to recover for six weeks. Both drug-resistant cell lines demonstrated an increase in doxorubicin IC50 values, indicating acquired drug resistance. Metabolomics analysis showed clear separation between the parental MDA-MB-231 cell line and the drug-resistant cell lines. Pathway analysis revealed that arginine and proline metabolism, glutathione metabolism, and beta-alanine metabolism were significantly perturbed in the drug-resistant cell lines compared to the parental cell line. After matching signals to an in-house library of reference standards, significant decreases in short- and medium-chain acylcarnitines and significant increases in long-chain acylcarnitines, 5-oxoproline, and 7-ketodeoxycholic acid were observed in the resistant cell lines as compared to the parental MDA-MB-231 cell line. In addition to baseline metabolic differences, we also investigated differences in metabolic responses in resistant cell lines upon a second exposure at multiple concentrations. Results indicate that whereas the parental MDA-MB-231 cell line had many metabolites that responded to doxorubicin in a dose-dependent manner, the two resistant cell lines lost a dose-dependent response for the majority of these metabolites. The study’s findings provide insight into how metabolism is altered during the acquisition of resistance in TNBC cells and how the metabolic response to doxorubicin changes upon repeated treatment. This information can potentially identify novel targets to prevent or reverse multi-drug resistance in TNBC, and also demonstrate the usefulness of metabolomics technology in identifying new mechanisms of drug resistance in cancer and potential drug targets.

Folate (vitamin B9) is involved in one-carbon transfer reactions and plays a significant role in nucleic acid synthesis and control of cellular proliferation, among other key cellular processes. It is now recognized that the role of folates in different stages of carcinogenesis is complex, and more research is needed to understand how folate reactions become dysregulated in cancers and the metabolic consequences that occur as a result. ALDH1L1 (cytosolic 10-formyltetrahydrofolate dehydrogenase), an enzyme of folate metabolism expressed in many tissues, is ubiquitously downregulated in cancers and is not expressed in cancer cell lines. The RT4 cell line (derived from papillary bladder cancer) which expresses high levels of ALDH1L1 represents an exception, providing an opportunity to explore the metabolic consequences of the loss of this enzyme. We have downregulated this protein in RT4 cells (shRNA driven knockdown or CRISPR driven knockout) and compared metabolomes of ALDH1L1-expressing and -deficient cells to determine if metabolic changes linked to the loss of this enzyme might provide proliferative and/or survival advantages for cancer cells. In this study, cell extracts were analyzed using Ultra High Performance Liquid Chromatography High Resolution Mass Spectrometry (UHPLC-HR-MS). A total of 13,339 signals were identified or annotated using an in-house library and public databases. Supervised and unsupervised multivariate analysis revealed metabolic differences between RT4 cells and ALDH1L1-deficient clones. Glycine (8-fold decrease) and metabolites derived from S-adenosylmethionine utilizing pathways were significantly decreased in the ALDH1L1-deficient clones, compared with RT4 cells. Other changes linked to ALDH1L1 downregulation include decreased levels of amino acids, Krebs cycle intermediates, and ribose-5-phosphate, and increased nicotinic acid. While the ALDH1L1-catalyzed reaction is directly linked to glycine biosynthesis and methyl group flux, its overall effect on cellular metabolism extends beyond immediate metabolic pathways controlled by this enzyme.

Heterotrimeric G proteins were originally discovered through efforts to understand the effects of hormones, such as glucagon and epinephrine, on glucose metabolism. On the other hand, many cellular metabolites, including glucose, serve as ligands for G protein-coupled receptors. Here we investigate the consequences of glucose-mediated receptor signaling, and in particular the role of a Gα subunit Gpa2 and a non-canonical Gβ subunit, known as Asc1 in yeast and RACK1 in animals. Asc1/RACK1 is of particular interest because it has multiple, seemingly unrelated, functions in the cell. The existence of such “moonlighting” operations has complicated the determination of phenotype from genotype. Through a comparative analysis of individual gene deletion mutants, and by integrating transcriptomics and metabolomics measurements, we have determined the relative contributions of the Gα and Gβ protein subunits to glucose-initiated processes in yeast. We determined that Gpa2 is primarily involved in regulating carbohydrate metabolism while Asc1 is primarily involved in amino acid metabolism. Both proteins are involved in regulating purine metabolism. Of the two subunits, Gpa2 regulates a greater number of gene transcripts and was particularly important in determining the amplitude of response to glucose addition. We conclude that the two G protein subunits regulate distinct but complementary processes downstream of the glucose-sensing receptor, as well as processes that lead ultimately to changes in cell growth and metabolism.

This study aimed to elucidate the molecular determinants influencing the response of cancer cells to alkylating agents, a major class of chemotherapeutic drugs used in cancer treatment. The study utilized data from the National Cancer Institute (NCI)-60 cell line screening program and employed a comprehensive multi-omics approach integrating transcriptomic, proteomic, metabolomic, and SNP data. Through integrated pathway analysis, the study identified key metabolic pathways, such as cysteine and methionine metabolism, starch and sucrose metabolism, pyrimidine metabolism, and purine metabolism, that differentiate drug-sensitive and drug-resistant cancer cells. The analysis also revealed potential druggable targets within these pathways. Furthermore, copy number variant (CNV) analysis, derived from SNP data, between sensitive and resistant cells identified notable differences in genes associated with metabolic changes (WWOX, CNTN5, DDAH1, PGR), protein trafficking (ARL17B, VAT1L), and miRNAs (MIR1302-2, MIR3163, MIR1244-3, MIR1302-9). The findings of this study provide a holistic view of the molecular landscape and dysregulated pathways underlying the response of cancer cells to alkylating agents. The insights gained from this research can contribute to the development of more effective therapeutic strategies and personalized treatment approaches, ultimately improving patient outcomes in cancer treatment.

Dysregulation of cellular metabolism is now a well-recognized hallmark of cancer. Studies investigating the metabolic features of cancer cells have shed new light onto processes in cancer cell biology and have identified many potential novel treatment options. The advancement of mass spectrometry-based metabolomics has improved the ability to monitor multiple metabolic pathways simultaneously in various experimental settings. However, questions still remain as to how certain steps in the metabolite extraction process affect the metabolic profiles of cancer cells. Here, we use ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC–HRMS) untargeted metabolomics to investigate the effects of different detachment and lysis methods on the types and abundances of metabolites extracted from MDA-MB-231 cells through the use of in-house standards libraries and pathway analysis software. Results indicate that detachment methods (trypsinization vs. scraping) had the greatest effect on metabolic profiles whereas lysis methods (homogenizer beads vs. freeze–thaw cycling) had a lesser, though still significant, effect. No singular method was clearly superior over others, with certain metabolite classes giving higher abundances or lower variation for each detachment–lysis combination. These results indicate the importance of carefully selecting sample preparation methods for cell-based metabolomics to optimize the extraction performance for certain compound classes.

This multi-omics cross-sectional study investigated differences in metabolomics, proteomics, and epigenomics profiles between two groups of adults matched for age but differing in lifestyle factors such as body composition, diet, and physical activity patterns. Data from prior studies were utilized for a comprehensive integrative analysis. The study included 52 participants in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). Using multi-omics integration software (OmicsNet and Pathview), 96 significantly ( p  < 0.05) enriched pathways were identified that differentiated the LIFE and CON groups. Top pathways significantly ( p  < 2.63 × 10 −5 ) influenced by group status included fatty acid degradation, fatty acid elongation, glutathione metabolism, Parkinson disease, and central carbon metabolism in cancer. This study identified a distinct metabolic signature comprised of metabolites, proteins, and gene methylation sites associated with a healthy lifestyle. These findings provide unique, but complementary, results to previous single-omics analyses using metabolomics and proteomics procedures which showed that the LIFE group exhibited lower plasma bile acid levels, higher levels of beneficial fatty acids, reduced innate immune activation, enhanced lipoprotein metabolism, and increased HDL remodeling. The current multi-omics analysis builds on these previous results by providing a more holistic view of how metabolites, proteins, and methylation sites associated with a healthy lifestyle, providing a larger, more comprehensive list of altered pathways. Additionally, the integrated analysis revealed connections between lifestyle factors and conditions such as cancer and insulin resistance beyond what identified in the single-omics approaches, highlighting the broader metabolic impact of lifestyle on health. Overall, the signatures identified by this multi-omics approach provide a basis for developing more translational biomarkers, such as those that defined the cancer and insulin resistance pathways that can be used to assess one’s state of health and provide guidance on behavior modifications that should be taken to lower disease risk.