(601e) Computation-Driven Mechanistic Understanding of the Cellular Cost and Regulation of Melanin Production

Computation-Driven Mechanistic
Understanding of the Cellular Cost and Regulation of Melanin Production

Wheaton Schroeder, Jyothi Kumar, Rajib Saha and Steve
Harris

The University of Nebraska – Lincoln, Lincoln, NE

Exophiala
dermatitidis,
a melanized, extremophilic black yeast, is of interest primarily for its ability
to synthesize melanin as a stress protectant. In addition, as an emerging fungal
pathogen, melanin deposition in the cell wall hides this organism from the host’s
immune system, often leading to high mortality rates. Without melanin deposited
in its cell walls, E. dermatitidis’ pathogenic efficacy is significantly
decreased. E. dermatitidis’ genome is small, supporting the organism’s
dual purpose use: first, as a model system for melanocytes to explore
treatments for melanin-related human skin disorders such as albinism, and
second, as a model pathogenic organism to study how infections can be
combated by disrupting melanin production, not only for E. dermatitidis,
but also for other melanized fungal pathogens such as Cryptococcus
neoformans. At present, relatively little is known about the regulation and
kinetics of melanin production in E. dermatitidis, and in particular,
whether synthesis of melanin imposes a severe metabolic cost on the fungus. Computational
modeling, a promising approach that has not yet been systematically applied to
extremophilic fungi or to human fungal pathogens, could fill this knowledge gap.

Computational
modeling of metabolism is now an indispensable tool to drive the processes of
understanding and redesigning of biological systems. Although Flux Balance
Analysis (FBA) is the primary tool used for this purpose, it has significant
limitations due to the lack of reaction kinetics, chemical species
concentration, and metabolic regulation. In contrast, kinetic
models of metabolism (kMMs) provide not only a more accurate method for
designing novel biological systems but also for the characterization of reaction
kinetics, metabolite concentration, and metabolic regulation in these systems. Hence, the development
of a kinetic model framework will create a greater understanding of E.
dermatitidis as a pathogen and as a melanin-producing model organism. However,
the multi-omics data required for kinetic model construction is prohibitive to
their development and widespread use. This is particularly true for understudied
organisms, such as E. dermatitidis whose genome is less than 50%
annotated and for which no current metabolic network model exists. To begin to address
these issues, a
metabolic network model was created for this organism, using its annotated
genome and the metabolic network models from species in the related genus Aspergillus.
The metabolic gaps in this model were then filled using reactions from Saccharomyces
cerevisiae and from the KEGG database, using a novel optimization-based gap
filling tool named OptFill. The completed metabolic network model includes approximately
2000 reactions and 2400 metabolites. FBA was then performed using 36 different
nutrition scenarios that vary the carbon source and limiting micronutrient.

The kMM of E. dermatitidis was developed by applying an
optimization-based method for hypothesizing kinetic mechanisms and regulation pioneered
by this laboratory, named Kinetic OPTimization
using Integer Conditions (KOPTIC). This method circumvents
the omics data requirement and semi-automates kMM construction using reaction
rates and concentration data derived from the metabolic network model to return
plausible kinetic mechanisms. Using the FBA data, KOPTIC predicted the kinetic
mechanisms and metabolic regulation for most reactions. These predictions form
the kMM for E. dermatitidis, which can now be used to explore the important
questions about the biosynthesis, regulation, and metabolic cost of melanin
production. Furthermore, understanding melanin regulation and kinetics can help
establish treatment options for melanin-related human skin disorders such as
albinism, as well as for infections caused by E. dermatitidis and other
melanized pathogens.