scholarly journals Computational metabolomics hints at the relevance of glutamine metabolism in breast cancer

2018 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Elena López-Camacho ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Graphical Models ◽  
A Priori ◽  
Glutamine Metabolism ◽  
Metabolomics Data ◽  
Flux Balance ◽  
Cancer Tumor ◽  
Balance Analysis ◽  
The One ◽  
New Biomarkers

AbstractMetabolomics has a great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without othera prioriinformation; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient’s clinical outcome.

scholarly journals Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Elena López-Camacho ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computational Models ◽  
Glutamine Metabolism ◽  
Metabolomics Data

scholarly journals Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 156 ◽  
Author(s):  
Ellen Kuang ◽  
Matthew Marney ◽  
Daniel Cuevas ◽  
Robert A. Edwards ◽  
Erica M. Forsberg
Keyword(s):  
Mass Spectrometry ◽  
High Resolution Mass Spectrometry ◽  
Metabolic Model ◽  
Mass Spectrometry Data ◽  
Untargeted Metabolomics ◽  
Specific Gene ◽  
Exact Mass ◽  
Metabolomics Data ◽  
Flux Balance ◽  
Balance Analysis

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.

scholarly journals Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

2019 ◽  
Vol 15 (30) ◽  
pp. 3483-3490 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Mariana Díaz-Almirón ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Flux Balance Analysis ◽  
Risk Groups ◽  
Prognostic Models ◽  
Breast Cancer Patients ◽  
Proteomics Data ◽  
Cancer Subtypes ◽  
Flux Balance ◽  
Balance Analysis

Aim: Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux balance analysis is used to explore these differences as well as drug response. Materials & methods: Proteomics data from breast tumors were obtained by mass-spectrometry. Flux balance analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models. Results: Flux activities of vitamin A, tetrahydrobiopterin and β-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups. Conclusion: Flux activities summarize flux balance analysis data and can be associated with prognosis in cancer.

scholarly journals Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

2018 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Mariana Díaz-Almirón ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Flux Balance Analysis ◽  
Risk Groups ◽  
Prognostic Models ◽  
Breast Cancer Patients ◽  
Proteomics Data ◽  
Cancer Subtypes ◽  
Flux Balance ◽  
Balance Analysis

AbstractAims:Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux Balance Analysis is used to explore these differences as well as drug response.Materials & Methods:Proteomics data from breast tumors were obtained by mass-spectrometry. Flux Balance Analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models.Results:Flux activities of vitamin A, tetrahydrobiopterin and beta-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups.Conclusions:Flux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.

scholarly journals Metabolic Footprints and Molecular Subtypes in Breast Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Vera Cappelletti ◽  
Egidio Iorio ◽  
Patrizia Miodini ◽  
Marco Silvestri ◽  
Matteo Dugo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Subtypes ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Glutamine Metabolism ◽  
Enzymatic Reactions ◽  
Tumor Type ◽  
Breast Cancer Patients ◽  
Metabolomics Data ◽  
Luminal B

Cancer treatment options are increasing. However, even among the same tumor histotype, interpatient tumor heterogeneity should be considered for best therapeutic result. Metabolomics represents the last addition to promising “omic” sciences such as genomics, transcriptomics, and proteomics. Biochemical transformation processes underlying energy production and biosynthetic processes have been recognized as a hallmark of the cancer cell and hold a promise to build a bridge between genotype and phenotype. Since breast tumors represent a collection of different diseases, understanding metabolic differences between molecular subtypes offers a way to identify new subtype-specific treatment strategies, especially if metabolite changes are evaluated in the broader context of the network of enzymatic reactions and pathways. Here, after a brief overview of the literature, original metabolomics data in a series of 92 primary breast cancer patients undergoing surgery at the Istituto Nazionale dei Tumori of Milano are reported highlighting a series of metabolic differences across various molecular subtypes. In particular, the difficult-to-treat luminal B subgroup represents a tumor type which preferentially relies on fatty acids for energy, whereas HER2 and basal-like ones show prevalently alterations in glucose/glutamine metabolism.

scholarly journals Melanoma proteomics unravels major differences related to mutational status

2017 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
Mariana Díaz-Almirón ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Tumor Growth Rate ◽  
Biological Processes ◽  
Melanoma Tumor ◽  
Tumor Subtypes ◽  
Flux Balance ◽  
Mutational Status ◽  
Balance Analysis

AbstractThe aim of the study was to explore the molecular differences between melanoma tumor subtypes, based on BRAF and NRAS mutational status. Fourteen formalin-fixed, paraffin- embedded melanoma samples were analyzed using a high-throughput proteomics approach, coupled with probabilistic graphical models and Flux Balance Analysis, to characterize these differences. Proteomics analyses showed differences in expression of proteins related with fatty acid metabolism, melanogenesis and extracellular space between BRAF mutated and BRAF non-mutated melanoma tumors. Additionally, probabilistic graphical models showed differences between melanoma subgroups at biological processes such as melanogenesis or metabolism. On the other hand, Flux Balance Analysis predicts a higher tumor growth rate in BRAF mutated melanoma samples. In conclusion, differential biological processes between melanomas showing a specific mutational status can be detected using combined proteomics and computational approaches.

THE LEVEL OF CYTOKINES IN THE SALIVA OF PATIENTS WITH BREAST CANCER

2019 ◽  
Vol 65 (6) ◽  
pp. 825-831
Author(s):  
Lyudmila Belskaya ◽  
Viktor Kosenok
Keyword(s):  
Breast Cancer ◽  
Control Group ◽  
Early Stages ◽  
Promising Tool ◽  
Urgent Task ◽  
Advanced Stages ◽  
New Biomarkers ◽  
Histological Verification ◽  
Control Study ◽  
Biochemical Examination

Currently, the urgent task is to search for new biomarkers as a promising tool for early detection and monitoring of breast cancer. The aim of the study was to study the level of cytokines in the saliva of patients with breast cancer. In the case-control study volunteers participated, which were divided into 3 groups: the main (breast cancer, n = 43), the comparison group (fibroadenoma, n = 32) and the control group (conditionally healthy, n = 39). All participants were questioned; biochemical examination of saliva, histological verification of the diagnosis was carried out. Intergroup differences are estimated by a nonparametric criterion. It is shown that in the context of breast cancer, the level of cytokines (IL-2, IL-4, IL-6, IL-10 and IL-18) is increasing, except for IL-8, the content of which decreases compared to the control group. When the disease progresses by the nature of the dynamics, the parameters are divided into two groups: IL-2, IL-4, IL-18 and IL-6, IL-8, IL-10. For the first group of cytokines, there was a decrease in content during the transition from the early stages to the more common ones. For the second group, when passing from stages T1-2N0M0 to T1-2NjM0, the level of cytokines remains practically constant. In the future, the level of cytokines is observed for stage T3_4N0_2M0, and for IL-2, IL-4 and IL-10, the level of cytokines reaches values corresponding to early stages, whereas for IL-6, IL-8 and IL-18 in the same direction, a significant increase in indicators was noted. Additionally, the IL-6/IL-8 ratio was calculated depending on the tumor size, as well as the presence / absence of metastasis. It is shown that this ratio is statistically significantly increased in the advanced stages of the disease. Particularly interesting is the increase in this ratio in saliva at the initial stages of the disease.

Dynamic flux balance analysis of biomass and lipid production by Antarctic thraustochytrid Oblongichytrium sp. RT2316‐13

2020 ◽  
Vol 117 (10) ◽  
pp. 3006-3017 ◽  
Author(s):  
Carolina Shene ◽  
Paris Paredes ◽  
Liset Flores ◽  
Allison Leyton ◽  
Juan A. Asenjo ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Lipid Production ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Balance Analysis

scholarly journals A New Paradigm in the Relationship between Melatonin and Breast Cancer: Gut Microbiota Identified as a Potential Regulatory Agent

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3141
Author(s):  
Aurora Laborda-Illanes ◽  
Lidia Sánchez-Alcoholado ◽  
Soukaina Boutriq ◽  
Isaac Plaza-Andrades ◽  
Jesús Peralta-Linero ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gut Microbiota ◽  
Bacterial Population ◽  
Kynurenine Pathway ◽  
New Paradigm ◽  
Bacterial Composition ◽  
Glucuronidase Activity ◽  
The One ◽  
The Relationship ◽  
Melatonin Production

In this review we summarize a possible connection between gut microbiota, melatonin production, and breast cancer. An imbalance in gut bacterial population composition (dysbiosis), or changes in the production of melatonin (circadian disruption) alters estrogen levels. On the one hand, this may be due to the bacterial composition of estrobolome, since bacteria with β-glucuronidase activity favour estrogens in a deconjugated state, which may ultimately lead to pathologies, including breast cancer. On the other hand, it has been shown that these changes in intestinal microbiota stimulate the kynurenine pathway, moving tryptophan away from the melatonergic pathway, thereby reducing circulating melatonin levels. Due to the fact that melatonin has antiestrogenic properties, it affects active and inactive estrogen levels. These changes increase the risk of developing breast cancer. Additionally, melatonin stimulates the differentiation of preadipocytes into adipocytes, which have low estrogen levels due to the fact that adipocytes do not express aromatase. Consequently, melatonin also reduces the risk of breast cancer. However, more studies are needed to determine the relationship between microbiota, melatonin, and breast cancer, in addition to clinical trials to confirm the sensitizing effects of melatonin to chemotherapy and radiotherapy, and its ability to ameliorate or prevent the side effects of these therapies.

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