# Presentation: 255784

## Glucose Metabolic Phenotypes in Cancer

**W. Paul Lee, MD (10/2005)**
**David Geffen School of Medicine at UCLA - Los Angeles Biomedical Research Institute at Harbor-UCLA.**

## Introduction

**“****The problems faced by pre- and post-genomic genetics are ... much the same – they all involve bridging the chasm between genotype and phenotype.”  **
** ****- Sydney Brenner, Science 287: 2173-74 (2000)**

## Objectives

**Definition of phenotype – metabolomic  approaches**
**Constraint-based modeling**
**Tracer-based metabolomics**
**Glucose metabolic phenotypes in cancer**

## Definition of Phenotype

**A metabolic phenotype can either be described :**
**By physical composition (metabolite profiling, or metabonomics)**
**By functional properties (tracer-based metabolomics/constraint based models)**

## Defining Phenotype by Composition: Metabolite Profiling

**Substrate or metabolite profiling (metabolomics) generates a data set of steady state concentrations of metabolites in plasma (or other bodily fluids) or in cell extracts.**

## Relation between phenotype (Ai) and Metabolite Profiles (Xij)

**Relation between phenotype (A****i****) and Metabolite Profiles (X****ij****)**
**Distribution of Phenotypic expressions (A****i****)**

**Distribution of each measurement of x**

****** ********1**

****** ********2**

**Distribution of Phenotypic expressions (Ai)**

## Principal Component Analysis of Unrelated Data

**Principal Component Analysis of Unrelated Data**
**(The distribution of A is described by m components (x****im****) instead of k components x****ik****, with m<<k)**

**k=3 m~2**

**k=2 m~1**

- 12  3

## Objective of Metabolite Profiling is to distinguish phenotype A from B

**A**

**B**

**Objective of Metabolite Profiling is to distinguish phenotype A from B**

## N,N

**N,N**

**H,H**

**L,H**

**H,L**

**L,L**

**Objective is to discriminate clustering of populations**
**Functional relationship not required**

## Defining Phenotype by Fluxes: Metabolic profiling

**Metabolic profiling assesses steady state substrate flux of pathways of a metabolic network. **
**It provides a set of dynamic parameters representing the metabolic phenotype. **
**However, such measurements together with metabolites concentration still are inadequate to provide a functional description of the phenotype.**

## A system of 4 variables and eight parameters

_**A system of 4 variables and eight parameters**_
**Solving the system requires: four metabolite measurements, and eight flux measurements, some of which cannot be determined.**
**From a network point of view, metabolite or metabolic profiling alone provides insufficient data for precise characterization of phenotype.  **

## The Uncertainty Principle in Biology

**The addition of metabolic intermediate (metabolic), a change in the kinetics or a branch in the pathway (genomic) will increase the uncertainty of the knowledge of the behavior of the system.**
**The more metabolites that we can measure, the less certain are we of the functions of the system.**
**This Uncertainty Principle is the basis for biological variability in phenotype.**

_**The Uncertainty Principle in Biology**_

## Prof. George Dantzig (1914-2005) Created Linear Programming

**Prof. George Dantzig (1914-2005) Created Linear Programming**

**For m ******** n (underdetermined set), it is possible to obtain a solution space.**

## Constraint-based Modeling

**Constraint-based Modeling**
**Prof. Bernhard O. Palsson, UCSD**

**Incorporating literature or measured kinetic values into a set of balance of flux equations**

**The objective function is to maximize growth.  The solution space of the network is a convex cone bound by extreme pathways.**

## Experimental Approach to Constraint-based Modeling

**Experimental Approach to Constraint-based Modeling**

## Tracer-based Metabolomics

**When a ****13****C labeled substrate is introduced into a biological system, ****13****C is incorporated into a wide range of metabolites collectively known as the metabolome either through exchange or by direct synthesis.  **
**The incorporation of a labeled carbon molecule into a metabolic product generates a “mass” signature (a difference in molecular weight from the naturally existing compound) which permits detection by mass spectrometry or by NMR. **

## Tracer-based metabolomics

**The distribution and destinations of a labeled carbon are determined by the intracellular metabolic pathways that it traverses. **
**The metabolic phenotype determines tracer distribution within individual compounds and distribution among compounds.**
**Such a distribution represents the metabolic functions of the cell and defines its metabolic phenotype.  **

## An Example of Tracer-based Metabolomics

**An Example of Tracer-based Metabolomics**
**(mitochondrial subsystem)**

**Pyruvate carboxylase**

**Pyruvate dehydrogenase**

## Abundance

**145**

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**0**

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**198**

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**Abundance**

**M/Z**

**Ion Fragments of Trifluoroacetamide Butyl Ester of Glutamate from Medium of Cells Incubated with [1, 2 ****13****C****2****]-glucose**

**C1**

**C2**

**C3**

**C4**

**C5**

**C1**

**C2**

**C3**

**C4**

**C5**

**M+1**

**M+2**

**M+2**

**P2**

**P2 or P1**

**P3 + P1**

**M+1**

**P3**

## Isotopomer Distribution Analysis

**Isotopomer Distribution Analysis**

## Constraint-based modeling and Tracer-based Metabolomics 

***Constraint-based modeling***** and *****Tracer-based Metabolomics***** provide a **_**network - system biology -  context**_** to metabolite and flux measurements. **

## Phenotypic Phase Plane Analysis

**I**

**II**

**III**

**IV**

**Line of optimality**

**II :Substrate B  excess**

**III: Substrate A excess**

**IV: Substrate B deficient**

**I : Substrate A deficient**

## Glucose/Butyrate Utilization Phenotypic Phase Plane

_**Glucose/Butyrate Utilization Phenotypic Phase Plane**_

## Glucose/Butyrate Utilization Phenotypic Phase Plane

**0.1 mM butyrate**

**5.0 mM butyrate**

**MIA**

**HT29**

**Line of Differentiation**

**Line of optimality**

## Ribose synthesis/tranketolase Phenotypic Phase Plane

**Oxidative pentose cycle**

**Non-oxidative pentose cycle**

## Ribose synthesis/tranketolase Phenotypic Phase Plane

**Normal**

**TRM**

**Thiamine depleted**

**Thiamine supplement**

**Line of optimality**

**Line of cell cycle arrest**

## Nutrient Environment Gene Interaction on Phenotype (1)

**Genetic factors confer to cells a specific set of metabolic functions which corresponds to a metabolic phenotype under a given nutrient environment.**

**Notes:**
How can the information from tracer-based metabolomics be used to understand gene nutrient interaction in the determination of phenotype?  Genetic factors confer a broad phenotypic space. For a given nutrient environment, the space is limited to the expressed phenotype .

## Phenotypes According to Fluxes

**Phenotypes According to Fluxes**
**       ****(Genetic constraints)**

**Pentose Synthesis**

**Transketolase Flux**

**MIA**

**Normal Fibroblast**

**TRMA Fibroblast**

**Notes:**
Here we have three cell lines carrying different genomic endowment. The phenotypes of these cells in the same culture medium are characterized by the pentose synthesis and transketolase activity.  Mia cell is a cancer cell, normal fibroblast and fibroblast from patient with high affinity thiamine transport defect.  These cell have different rate of proliferation under same culture medium condition and have different rate of pentose synthesis and transketolase activities.

## Nutrient Environment Gene Interaction on Phenotype (2)

**Changes in nutrient environment select cells with the actual observed metabolic phenotypes, which function best under such environments. **

**Notes:**
Since the genotype confers a wide range of possible phenotypes, changes in nutrient environment selects the phenotype that functions best under the altered environment.

## Phenotypes According to Fluxes

**Phenotypes According to Fluxes**
**       ****(Metabolic constraints)**

**Pentose Synthesis**

**Transketolase Flux**

**MIA**

**TRMA Fibroblast**

**Oxythiamine Rx**

**Thiamine Rx**

**Notes:**
This slide shows that the poorly proliferative phenotype of TRMA cells being changes to a more proliferative phenotype when large amount of thiamine is supplemented in the medium.
The converse example is shown by MIA cells under the treatment with oxythiaimine, a transketolase inhibitor.
These cells have the same genotypes but different phenotypes under different nutrient environment.

## Nutrient-Gene Interaction in Cancer Promotion

**The evolution of cancer cells can be understood in terms of metabolic selection.  Genetic mechanism is the source of phenotypic variation, while the final observed species is the result of metabolic selection.**

**Notes:**
How can we extend this idea of phenotype selection to understand the effect of nutrient on cancer promotion. The evolution of cancer cells can be understood in terms of metabolic selection.  Genetic mechanism is the source of phenotypic variation, while the final observed species is the result of metabolic selection.

## Phenotypes Changes in HT29 Cells

**Phenotypes Changes in HT29 Cells**
**       ****(Response to Butyrate)**

**butyrate Utilization**

**glucose Utilization**

**MIA Cells**

**HT29* Cells**

**Butyrate treatment**

**HT29 Cells**

******Butyrate**

**Notes:**
We choose here an example from our work with colon cancer cells HT29.  One can imagine the precursor of HT29 cells as one utilizing butyrate.  When butyrate is limited because of reduced intake of vegetable, the grow of the precursor is limited. Over many cell division, the phenotype that utilizes glucose is selected and compete effectively with neighboring cells.  When butyrate is supplied in the medium, the phenotype of these cells reverted to the less proliferative form because of preexisting metabolic network favoring butyrate utilization.
Such changes were not observed for MIA cells.

## CONCLUSION

## Conclusion (1)

**Genetic inheritance  (genomic constraints) confers a set of possible phenotypes.**
**Selection by metabolic (structural and pathway relationship) and environmental (physical environment and substrate availability) constraints determines the final observed phenotype. **

## Conclusion (2)

**The metabolic phenotype of a cell can be characterized by a set of flux solution space bounded by genomic/ proteomic/metabolomic constraints.**
**Tracer-based metabolomics is a powerful tool for the characterization of metabolic phenotype.**

## Acknowledgement

**Acknowledgement**

**Colleagues/Collaborators:**
**Bill Go, M.D.              Laszlo G Boros, M.D.     Shu Lim, M.S.               Sara Bassilian, M.S.   **
**Marta Cascante, Ph.D.  (University of Barcelona)     Irwin Kurland, M.D., Ph.D. (SUNY Stony Brooks)**
**Supported by:                       NCI  PO1 CA42710-12           NCRR ****MO1 RROO425-31****     NIDDK RO1 DK56090**

## Slide 37

## Conclusion (3)

**It is clear from the metabolic selection hypothesis, nutrient changes are important environmental factors in determining cancer risks. **
**Thus, cancer prevention strategies can be formulated based on our understanding of nutrient gene interaction using constraint-based modeling and metabolic network flux analysis. **

## Other Applications of Tracer-based Metabolomics

**Evaluation of cancer chemotherapies**
**Regulation of hepatic glucose production**
**Insulin sensitivity/resistance**
**Integrated physiology of PPAR******** KO**
**Fatty acid oxidation defects**
**Inborn errors of metabolism**

**Notes:**
In the past ten years, we have worked on a number of projects using tracer-based metabolomics.

## Metabolite Profiling in the Context of A Metabolic Network

**Metabolite Profiling in the Context of A Metabolic Network**