Systematic investigation of metabolic reprogramming in different cancers based on tissue-specific metabolic models

Cancer cells have different metabolism in contrast to normal cells. The advancement in omics measurement technology enables the genome-wide characterization of altered cellular processes in cancers, but the metabolic flux landscape of cancer is still far from understood. In this study, we compared the well-reconstructed tissue-specific models of five cancers, including breast, liver, lung, renal, and urothelial cancer, and their corresponding normal cells. There are similar patterns in majority of significantly regulated pathways and enriched pathways in correlated reaction sets. But the differences among cancers are also explicit. The renal cancer demonstrates more dramatic difference with other cancer models, including the smallest number of reactions, flux distribution patterns, and specifically correlated pathways. We also validated the predicted essential genes and revealed the Warburg effect by in silico simulation in renal cancer, which are consistent with the measurements for renal cancer. In conclusion, the tissue-specific metabolic model is more suitable to investigate the cancer metabolism. The similarity and heterogenicity of metabolic reprogramming in different cancers are crucial for understanding the aberrant mechanisms of cancer proliferation, which is fundamental for identifying drug targets and biomarkers.

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The Role of PKM2 in Metabolic Reprogramming: Insights into the Regulatory Roles of Non-Coding RNAs

International Journal of Molecular Sciences ◽

10.3390/ijms22031171 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1171

Author(s):

Dexter L. Puckett ◽

Mohammed Alquraishi ◽

Winyoo Chowanadisai ◽

Ahmed Bettaieb

Keyword(s):

Cancer Cells ◽

Pyruvate Kinase ◽

Cancer Metabolism ◽

Cell Metabolism ◽

Metabolic Reprogramming ◽

Circular Rnas ◽

Tissue Specific ◽

Cell Progression ◽

Non Coding Rnas ◽

Regulatory Effects

Pyruvate kinase is a key regulator in glycolysis through the conversion of phosphoenolpyruvate (PEP) into pyruvate. Pyruvate kinase exists in various isoforms that can exhibit diverse biological functions and outcomes. The pyruvate kinase isoenzyme type M2 (PKM2) controls cell progression and survival through the regulation of key signaling pathways. In cancer cells, the dimer form of PKM2 predominates and plays an integral role in cancer metabolism. This predominance of the inactive dimeric form promotes the accumulation of phosphometabolites, allowing cancer cells to engage in high levels of synthetic processing to enhance their proliferative capacity. PKM2 has been recognized for its role in regulating gene expression and transcription factors critical for health and disease. This role enables PKM2 to exert profound regulatory effects that promote cancer cell metabolism, proliferation, and migration. In addition to its role in cancer, PKM2 regulates aspects essential to cellular homeostasis in non-cancer tissues and, in some cases, promotes tissue-specific pathways in health and diseases. In pursuit of understanding the diverse tissue-specific roles of PKM2, investigations targeting tissues such as the kidney, liver, adipose, and pancreas have been conducted. Findings from these studies enhance our understanding of PKM2 functions in various diseases beyond cancer. Therefore, there is substantial interest in PKM2 modulation as a potential therapeutic target for the treatment of multiple conditions. Indeed, a vast plethora of research has focused on identifying therapeutic strategies for targeting PKM2. Recently, targeting PKM2 through its regulatory microRNAs, long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) has gathered increasing interest. Thus, the goal of this review is to highlight recent advancements in PKM2 research, with a focus on PKM2 regulatory microRNAs and lncRNAs and their subsequent physiological significance.

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Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Metabolites ◽

10.3390/metabo10090368 ◽

2020 ◽

Vol 10 (9) ◽

pp. 368

Author(s):

Huan Jin ◽

Joshua M. Mitchell ◽

Hunter N. B. Moseley

Keyword(s):

Metabolic Network ◽

Graph Coloring ◽

Metabolic Networks ◽

Metabolic Flux ◽

Enzyme Commission ◽

Metabolic Model ◽

Metabolic Reprogramming ◽

Automatic Identification ◽

Flux Analysis ◽

Database Curation

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

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Targeting Cellular Metabolism Modulates Head and Neck Oncogenesis

International Journal of Molecular Sciences ◽

10.3390/ijms20163960 ◽

2019 ◽

Vol 20 (16) ◽

pp. 3960 ◽

Cited By ~ 1

Author(s):

Yi-Ta Hsieh ◽

Yi-Fen Chen ◽

Shu-Chun Lin ◽

Kuo-Wei Chang ◽

Wan-Chun Li

Keyword(s):

Head And Neck ◽

Metabolic Pathways ◽

Cancer Metabolism ◽

Molecular Level ◽

Review Article ◽

Metabolic Reprogramming ◽

Prognostic Indicators ◽

Normal Cells ◽

Anti Cancer ◽

Great Energy

Considering the great energy and biomass demand for cell survival, cancer cells exhibit unique metabolic signatures compared to normal cells. Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent neoplasms worldwide. Recent findings have shown that environmental challenges, as well as intrinsic metabolic manipulations, could modulate HNSCC experimentally and serve as clinic prognostic indicators, suggesting that a better understanding of dynamic metabolic changes during HNSCC development could be of great benefit for developing adjuvant anti-cancer schemes other than conventional therapies. However, the following questions are still poorly understood: (i) how does metabolic reprogramming occur during HNSCC development? (ii) how does the tumorous milieu contribute to HNSCC tumourigenesis? and (iii) at the molecular level, how do various metabolic cues interact with each other to control the oncogenicity and therapeutic sensitivity of HNSCC? In this review article, the regulatory roles of different metabolic pathways in HNSCC and its microenvironment in controlling the malignancy are therefore discussed in the hope of providing a systemic overview regarding what we knew and how cancer metabolism could be translated for the development of anti-cancer therapeutic reagents.

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Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

Cancers ◽

10.3390/cancers13164130 ◽

2021 ◽

Vol 13 (16) ◽

pp. 4130

Author(s):

Helena A. Herrmann ◽

Mate Rusz ◽

Dina Baier ◽

Michael A. Jakupec ◽

Bernhard K. Keppler ◽

...

Keyword(s):

Metabolic Flux ◽

Carcinoma Cell ◽

Metabolic Model ◽

Cellular Reprogramming ◽

Metabolic Reprogramming ◽

Metabolomics Data ◽

Extracellular Metabolite ◽

Metabolite Concentrations ◽

Genome Scale ◽

Resistant Cells

Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

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MicroRNA-Mediated Metabolic Reprograming in Renal Cancer

Cancers ◽

10.3390/cancers11121825 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1825 ◽

Cited By ~ 4

Author(s):

Bogusławska ◽

Popławski ◽

Alseekh ◽

Koblowska ◽

Iwanicka-Nowicka ◽

...

Keyword(s):

Renal Cancer ◽

Pentose Phosphate Pathway ◽

Cancer Metabolism ◽

Target Genes ◽

Tricarboxylic Acid Cycle ◽

Cell Cancer ◽

Pentose Phosphate ◽

Metabolic Reprogramming ◽

Gas Chromatography Mass Spectrometry ◽

Arginine Metabolism

Metabolic reprogramming is one of the hallmarks of renal cell cancer (RCC). We hypothesized that altered metabolism of RCC cells results from dysregulation of microRNAs targeting metabolically relevant genes. Combined large-scale transcriptomic and metabolic analysis of RCC patients tissue samples revealed a group of microRNAs that contribute to metabolic reprogramming in RCC. miRNAs expressions correlated with their predicted target genes and with gas chromatography-mass spectrometry (GC-MS) metabolome profiles of RCC tumors. Assays performed in RCC-derived cell lines showed that miR-146a-5p and miR-155-5p targeted genes of PPP (the pentose phosphate pathway) (G6PD and TKT), the TCA (tricarboxylic acid cycle) cycle (SUCLG2), and arginine metabolism (GATM), respectively. miR-106b-5p and miR-122-5p regulated the NFAT5 osmoregulatory transcription factor. Altered expressions of G6PD, TKT, SUCLG2, GATM, miR-106b-5p, miR-155-5p, and miR-342-3p correlated with poor survival of RCC patients. miR-106b-5p, miR-146a-5p, and miR-342-3p stimulated proliferation of RCC cells. The analysis involving >6000 patients revealed that miR-34a-5p, miR-106b-5p, miR-146a-5p, and miR-155-5p are PanCancer metabomiRs possibly involved in global regulation of cancer metabolism. In conclusion, we found that microRNAs upregulated in renal cancer contribute to disturbed expression of key genes involved in the regulation of RCC metabolome. miR-146a-5p and miR-155-5p emerge as a key “metabomiRs” that target genes of crucial metabolic pathways (PPP (the pentose phosphate pathway), TCA cycle, and arginine metabolism).

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Thermodynamic genome-scale metabolic modeling of metallodrug resistance in colorectal cancer

10.1101/2021.06.09.447534 ◽

2021 ◽

Author(s):

Helena A Herrmann ◽

Mate Rusz ◽

Dina Baier ◽

Michael A. Jakupec ◽

Bernhard K. Keppler ◽

...

Keyword(s):

Metabolic Flux ◽

Carcinoma Cell ◽

Metabolic Model ◽

Cellular Reprogramming ◽

Metabolic Reprogramming ◽

Metabolomics Data ◽

Extracellular Metabolite ◽

Metabolite Concentrations ◽

Genome Scale ◽

Resistant Cells

Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

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Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism

Royal Society Open Science ◽

10.1098/rsos.170360 ◽

2017 ◽

Vol 4 (10) ◽

pp. 170360 ◽

Cited By ~ 6

Author(s):

Filmon Eyassu ◽

Claudio Angione

Keyword(s):

Pyruvate Dehydrogenase ◽

Molecular Mechanisms ◽

Cancer Metabolism ◽

Tricarboxylic Acid Cycle ◽

Metabolic Model ◽

Metabolic Reprogramming ◽

Machine Learning Techniques ◽

Low Oxygen ◽

Key Enzyme ◽

Genome Scale

Metabolism is the only biological system that can be fully modelled at genome scale. As a result, metabolic models have been increasingly used to study the molecular mechanisms of various diseases. Hypoxia, a low-oxygen tension, is a well-known characteristic of many cancer cells. Pyruvate dehydrogenase (PDH) controls the flux of metabolites between glycolysis and the tricarboxylic acid cycle and is a key enzyme in metabolic reprogramming in cancer metabolism. Here, we develop and manually curate a constraint-based metabolic model to investigate the mechanism of pyruvate dehydrogenase under hypoxia. Our results characterize the activity of pyruvate dehydrogenase and its decline during hypoxia. This results in lactate accumulation, consistent with recent hypoxia studies and a well-known feature in cancer metabolism. We apply machine-learning techniques on the flux datasets to identify reactions that drive these variations. We also identify distinct features on the structure of the variables and individual metabolic components in the switch from normoxia to hypoxia. Our results provide a framework for future studies by integrating multi-omics data to predict condition-specific metabolic phenotypes under hypoxia.

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Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases

10.1101/2020.06.19.161877 ◽

2020 ◽

Author(s):

Huan Jin ◽

Joshua M. Mitchell ◽

Hunter N.B. Moseley

Keyword(s):

Metabolic Network ◽

Graph Coloring ◽

Metabolic Networks ◽

Metabolic Flux ◽

Enzyme Commission ◽

Metabolic Model ◽

Metabolic Reprogramming ◽

Automatic Identification ◽

Flux Analysis ◽

Database Curation

AbstractMetabolic flux analysis requires both a reliable metabolic model and metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

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Phytochemicals targeting metabolic reprogramming in cancer: an assessment of role, mechanisms, pathways and therapeutic relevance

10.22541/au.160684227.70170021/v1 ◽

2020 ◽

Author(s):

Asifa Khan ◽

Shumaila Siddiqui ◽

Syed Husain ◽

Sybille Mazurek ◽

Mohammad Askandar Iqbal

Keyword(s):

Metabolic Pathways ◽

Cancer Metabolism ◽

Therapeutic Benefit ◽

Metabolic Reprogramming ◽

Normal Cells ◽

Cancer Hallmarks ◽

Metabolic Transformation ◽

Regulation Of Growth ◽

Diverse Plant

The metabolism of cancer is remarkably different from that of normal cells and confers variety of benefits including the promotion of other cancer hallmarks. As the rewired metabolism is a near-universal property of cancer cells, efforts are underway to exploit metabolic vulnerabilities for therapeutic benefit. In the continued search for a safer and effective ways of cancer treatment, structurally diverse plant-based compounds have gained substantial attention. Here, we present an extensive assessment of the role of phytocompounds in modulating cancer metabolism and make a case for the use of plant-based compounds in targeting metabolic vulnerabilities of cancer. We discuss the interactions of phytocompounds with major metabolic pathways and evaluate the role of phytochemicals in the regulation of growth signaling and transcriptional programs involved in metabolic transformation of cancer. Lastly, we examine the potential of these compounds in clinical management of cancer along with limitations and challenges

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The Engaged Role of Tumor Microenvironment in Cancer Metabolism: Focusing on Cancer-Associated Fibroblast and Exosome Mediators

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520620666200910123428 ◽

2020 ◽

Vol 21 (2) ◽

pp. 254-266 ◽

Cited By ~ 1

Author(s):

Khandan Ilkhani ◽

Milad Bastami ◽

Soheila Delgir ◽

Asma Safi ◽

Shahrzad Talebian ◽

...

Keyword(s):

Tumor Microenvironment ◽

Cancer Cells ◽

Cancer Metabolism ◽

Krebs Cycle ◽

Metabolic Reprogramming ◽

Cancer Management ◽

Cancer Associated Fibroblast ◽

Potential Biomarker ◽

Close Relationship ◽

Microenvironmental Factors

: Metabolic reprogramming is a significant property of various cancer cells, which most commonly arises from the Tumor Microenvironment (TME). The events of metabolic pathways include the Warburg effect, shifting in Krebs cycle metabolites, and the rate of oxidative phosphorylation, potentially providing energy and structural requirements for the development and invasiveness of cancer cells. TME and tumor metabolism shifting have a close relationship through bidirectional signaling pathways between stromal and tumor cells. Cancer- Associated Fibroblasts (CAFs), as the most dominant cells of TME, play a crucial role in the aberrant metabolism of cancer. Furthermore, the stated relationship can affect survival, progression, and metastasis in cancer development. Recently, exosomes are considered one of the most prominent factors in cellular communications considering effective content and bidirectional mediatory effect between tumor and stromal cells. In this regard, CAF-Derived Exosomes (CDE) exhibit an efficient obligation to induce metabolic reprogramming for promoting growth and metastasis of cancer cells. The understanding of cancer metabolism, including factors related to TME, could lead to the discovery of a potential biomarker for diagnostic and therapeutic approaches in cancer management. This review focuses on the association between metabolic reprogramming and engaged microenvironmental, factors such as CAFs, and the associated derived exosomes.

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