(5bw) Integrative Flux Analysis and Systems Biology in the Investigation of Plant Metabolism and Human Genetic Disorders

In this poster I will present my
graduate and postdoctoral research on flux quantification in metabolic pathways
by isotopomer/bondomer balancing, and the application of flux and systems
approaches to investigate plant metabolism and human genetic disorders.

GRADUATE RESEARCH

In my graduate research in
Jacqueline Shanks' lab at Iowa State University, I developed a comprehensive
flux analysis tool for plant metabolism and applied it to two plant systems:
soybean (Glycine max) embryos and Catharanthus roseus hairy
roots. Although the systemwide measurement of fluxes can serve as a powerful
investigative tool in systems biology, it had received very little attention
compared to other 'omic profiling technologies [1, 2]. This is principally
because of the complexity and subcellular compartmentation inherent in plant
biochemistry, which renders the mathematical models relating labeling data to fluxes highly nonlinear and
nontrivial to solve.

Flux
quantification in plant systems. Metabolic fluxes were evaluated in soybean
embryos and C. roseus hairy roots by using 2-dimensional [¹³C,
¹H] nuclear magnetic resonance (NMR), isotopomer/bondomer balancing,
and global χ² minimization. Comprehensive
isotopomer/bondomer balance models were developed to convert the labeling data
to fluxes. These were solved by employing recent computational developments in
flux analysis and implemented through a computer program, NMR2Flux, developed
in this work. Fluxes of parallel pathways in the cytosolic and plastidic
compartments were identified in the soybean embryos, in addition to
bidirectional fluxes of several pathways in central carbon metabolism. To the
extent of our knowledge, this work [3] is the most comprehensive flux analysis
of a plant system thus far.

Bondomer
and Boolean function mapping. My research introduced a concept called bondomer
and a computational technique called Boolean function mapping to
simplify flux evaluation from labeling experiments that employ uniformly ¹³C-labeled
(U-¹³C) carbon sources [4]. These were shown to result in more
efficient processing of labeling data, and their efficiency is expected to be
valuable while analyzing complex metabolic networks such as those in plant
metabolism.

Flux identifiability and
optimal experiment design. I also investigated flux identifiability
and designed optimal labeling experiments that utilize judicious combinations
of labeled varieties of two carbon sources (sucrose and glutamine), to maximize
the statistical quality of the evaluated fluxes.

POSTDOC RESEARCH

In my postdoctoral research in
Katrina Dipple and James Liao's labs at UCLA, I am applying skills developed in
my Ph.D. toward elucidating the role of flux and systems dynamics in glycerol
kinase deficiency (GKD), a complex, X-linked, single-gene inborn error of metabolism.

Glycerol kinase (GK) is an
important lipogenic enzyme in mammalian liver, adipose tissue, and other
organs. It also performs several 'moonlighting' activities unrelated to its
biochemical function of phosphorylating glycerol [5]. The inherited disorder
GKD exhibits complexities that are not trivially explained by lack of the
biochemical activity of GK. Thus far, there has been no correlation between
genotype and phenotype in patients this disorder. It has been previously
hypothesized that systems dynamics, including flux through metabolic pathways,
can play a significant role in imparting a phenotype that is not easily deduced
from the genotype [6].

Role of metabolic flux and
systems dynamics in GKD. I performed flux analysis of wild type and GK-overexpressing
H4IIE rat hepatoma cells by using ¹³C labeling, gas
chromatography-mass spectrometry and isotopomer balancing; which revealed that
the GK-overexpressing cell lines displayed a substantially higher flux through
the pentose phosphate pathway (PPP). This strengthens our hypothesis of the
involvement of flux in the complexity of GKD. The higher PPP flux is likely due
to increased NADPH requirement in the cytosol, and may be mediated by glycerol
kinase or its network partners. I am currently working on microarray analysis
of the wild type and GK-overexpressing cell lines, which will be followed by a
network component analysis (NCA) of the microarray data. This is expected to
reveal the transcription factor activities altered due to GK overexpression,
and together with the flux results, will shed light on network interactions
involving GK and the role of systems dynamics in GKD.

Mathematical
model of insulin signaling pathway. To elucidate a reported link [7,
8] between GKD and insulin resistance, I developed an extended mathematical
model of the insulin signal transduction pathway and am employing it to predict
insulin sensitivities and phenotypes from experimental gene expression data
including microarray analysis of glycerol kinase-knockout (GK k/o) mice.
Analysis using the model predicted that certain genes with altered expression
in the GK k/o mice confer decreased insulin sensitivity. Ongoing experimental
studies based on the model simulations will shed light on why patients with GKD
develop insulin resistance.

Acknowledgments

Katrina M. Dipple, James C. Liao, UCLA
(postdoc advisors), and Jacqueline V. Shanks, Iowa State University (Ph. D. advisor).

References

1.        
Kruger, N.J. and A. von Schaewen, Curr Opin Plant Biol, 2003. 6:
236-46.

2.        
Sweetlove, L.J., R.L. Last, and A.D. Fernie, Plant Physiol, 2003. 132:
420-425.

3.        
Sriram, G., et al., Plant Physiol, 2004. 136: 3043-3057.

4.        
Sriram, G. and J.V. Shanks, Metab Eng, 2004. 6: 116-132.

5.        
Sriram, G., et al., Am J Hum Genet, 2005. 76: 911-924.

6.        
Dipple, K.M., J.K. Phelan, and E.R. McCabe, Mol Genet Metab, 2001. 74:
45-50.

7.        
Guan, H.P., et al., Nat Med, 2002. 8: 1122-8.

8.        
Gaudet, D., et al., Am J Hum Genet, 2000. 66: 1558-68.