Metabolic Cancer Biology: Structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment

doi:10.1016/j.semcancer.2014.01.007

Seminars in Cancer Biology

Volume 30, February 2015, Pages 21-29

https://doi.org/10.1016/j.semcancer.2014.01.007 Get rights and content

Abstract

The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies.

Introduction

Cancer is a complex disease which contains multiple types of biological interactions through various physical, sequential, and biological scales. This complexity generates considerable challenges for the description of cancer biology, and inspires the study of cancer in the context of molecular, cellular, and physiological systems. A significant factor contributing to this new synthesis is the observation that several signaling pathways changed in cancer are key regulators of the human metabolic network. This specifies a rational interplay between genetic and metabolic alterations during tumorigenesis without a permanent cause–effect relationship [1]. Otto Warburg first suggested this metabolic modification based on his observations in leukemic cells that altered metabolism of glucose may lead to cancer. This effect is now referred as the “Warburg effect”. Since then, different hypotheses (Fig. 1) have been proposed to find the mechanisms responsible for the Warburg effect [2]. However, the metabolic landscape of cancer is still far from understood, and in particular its regulation. Recently, there has been a resurgence of interest in cancer metabolism [1], [3], [4]. In the last decade, there is a paradigm shift from studying individual enzymes to newer approaches that aims to comprehend altered tumor metabolism as a whole. These new efforts flourish due to increasing availability of high-throughput data from various tumor studies elucidating metabolic concentrations, fluxes and abundance and regulation of the key enzymes. The data can now be analyzed integratively using statistical models to describe cancer metabolism. Beside experimental work, a metabolic network reconstruction is a manually curated, computational framework that empowers the description of gene–protein-reaction relationships [5]. For understanding the metabolic fluxes of a cancer cell, mechanistic genome scale models of cancer metabolism are needed and first attempts are very promising. Mechanistic methods are becoming increasingly feasible not only because of more sophisticated approaches and better data, but also due to hardware improvements enabling to simulate these models on clusters with a couple of hundreds of cores. Several studies have established how such reconstructions of metabolism could guide the development of biological theories and discoveries [6], [7], [8].

In this article we have described recent advances in network-based analysis of cancer as a metabolic disease. In the first section, the topological approach has been explained. In the next section, the constraint-based method (as another network-based approach) has been considered.

Section snippets

Genome-scale metabolic model of human cancer

With the advent of genome-scale metabolic models (GEMs) of various cell types and diseases, a valuable tool to study genetic, epigenetic and metabolic events in combination, has emerged [9]. The convergence of these developments enables the researchers to predict physiological functions and the relevant growth rate of particular human cell types, tissue-specificity and cancer [10], [11], [12]. There are four generic reconstructed genome-scale human metabolic networks: Recon1 [13], Recon2 [14],

Topological approach

The topological analysis of biological networks is an important method in systems biology which allows the investigation of large scale networks such as GEMs [48]. Graph theory is the most useful framework for representation of GEMs. A mathematical representation of a network is a graph G(V,E). Its vertex set (V) consists of all nodes. Two nodes are adjacent if an edge exist between which connects them.

There are many parameters which could be calculated by graph theory method such as clustering

Flux balance analysis

The potential of mathematical computational modeling tools in description, exploration, and prediction of metabolic networks has been realized in recent years [73]. One of the most widely used network analysis approaches is Flux Balance Analysis (FBA), which is based on derivation of the steady-state metabolic capabilities of a system with appropriate constraints and without the need for accurate kinetic data [74]. The main principle of the FBA is that the system will reach a steady-state under

Drug-target prediction and structural analysis

GEMs provide a useful tool for the study of diseases and the development of drugs. Several simulations and modeling methods have been developed to address the issues of drug-target prediction [92], [93], [94], [95], [96]. The structural features of metabolic networks contribute to the robustness and flexibility of the complex biosystems and may explain, in general, the fact that many drug candidates are ineffective (the drug effect is compensated by other pathways in the network) or show

Outline

Aggressive cancers established evidence of a metabolic shift, including upregulation of the pentose phosphate pathway (PPP) and the glutamine transporter genes, downregulation of genes contributed in the TCA cycle, increased acetyl-CoA carboxylase protein, decreased AMPK and PTEN protein levels, and altered promoter methylation of miR-21 and GRB10 [106]. As far as we know, different strategies are available to target metabolic enzymes for cancer therapy including nucleic acid synthesis, amino

Conflict of interest

No conflict of interest exists.

Funding source

No funding.

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ReviewMetabolic Cancer Biology: Structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment

Abstract

Introduction

Section snippets

Genome-scale metabolic model of human cancer

Topological approach

Flux balance analysis

Drug-target prediction and structural analysis

Outline

Conflict of interest

Funding source

Curr Opin Genet Dev

Pharmacol Ther

Cell

Trends Microbiol

FEBS Lett

Biotechnol Adv

BMC Syst Biol

Methods Enzymol

J Biol Chem

Metab Eng

Mol Cell Proteomics

Curr Opin Biotechnol

Trends Biotechnol

Drug Discov Today

Understanding the Warburg effect: the metabolic requirements of cell proliferation

Science

Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection

J Bacteriol

A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1

BMC Syst Biol

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

Mol Syst Biol

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

Nat Biotechnol

A protocol for generating a high-quality genome-scale metabolic reconstruction

Nat Protoc

Global reconstruction of the human metabolic network based on genomic and bibliomic data

Proc Natl Acad Sci U S A

A community-driven global reconstruction of human metabolism

Nat Biotechnol

Compartmentalization of the Edinburgh Human Metabolic Network

BMC Bioinform

Computational prediction of human metabolic pathways from the complete human genome

Genome Biol

HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology

Mol Syst Biol

Drug off-target effects predicted using structural analysis in the context of a metabolic network model

PLoS Comput Biol

Large-scale in silico modeling of metabolic interactions between cell types in the human brain

Nat Biotechnol

iAB-RBC-283: a proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states

BMC Syst Biol

Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions

Mol Syst Biol

Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect

PLoS ONE

Network-based prediction of human tissue-specific metabolism

Nat Biotechnol

Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism

Mol Syst Biol

Catabolic efficiency of aerobic glycolysis: the Warburg effect revisited

BMC Syst Biol

Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect

PLoS Comput Biol

Predicting selective drug targets in cancer through metabolic networks

Mol Syst Biol

Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT

PLoS Comput Biol

A gene atlas of the mouse and human protein-encoding transcriptomes

Proc Natl Acad Sci U S A

HMDB: the human metabolome database

Nucl Acids Res

ChIP-seq: advantages and challenges of a maturing technology

Nat Rev Genet

RNA-Seq: a revolutionary tool for transcriptomics

Nat Rev Genet

Shrinking the metabolic solution space using experimental datasets

PLoS Comput Biol

Context-specific metabolic networks are consistent with experiments

Review
Metabolic Cancer Biology: Structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment