(362e) 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 immune system, often
leading to high mortality rates, and without which its pathogenic efficacy is significantly decreased. Thus, with a
small genome E. dermatitidis
may serve for dual purposes: 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 to combat infections by disrupting melanin production,
not only from E. dermatitidis,
but also for other melaninized 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. Our chosen tool for filling this knowledge gap is
computational modeling, which is a promising approach that has
not yet been systematically applied to extremophilic
fungi or to human fungal pathogens.

Computational modeling of
metabolism is now an indispensable tool to drive the processes of
understanding, discovering, 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. Hence, the development a kinetic model
framework will allow for a greater understanding of E. dermatitidis as a pathogen and as a
model organism.  At present, one of the
greatest challenges to this approach is that this organism’s metabolism is not
well studied. Specific challenges to overcome include that the genome is less
than 50% annotated, there is no metabolic model available in either literature
or in public databases such as ModelSeed, and there
are no genes or genome entries for this organism in the Kyoto Encyclopedia of
Genes and Genomes (KEGG). Hence, the necessary first step in computational
modeling is to build a genome-scale model by using the available annotations of
E. dermatitidis,
published models, and homology information of related species (such as Aspergillus-genus).

Once the genome-scale model is
constructed, a novel approach to building a kinetic model framework using FBA,
transcriptomic, metabolomics, experimental, and genomic data is
used. The completed kinetic model framework currently has about 1700
reactions and 2100 metabolites. This modeling framework will
next be used for exploring important questions of biosynthesis,
regulation, and metabolic cost related to melanin production, as a freestanding
organism, as a pathogenic organism, and as a model system for human melanocytes.
In addition, the computational model will be useful for in silico discovery and exploration of possible treatment
options for infections cause by E. dermatitidis and related species by inhibiting or
downregulating melanin synthesis, presenting significant cost savings in
developing viable treatment options. Further, this computational model can be
used as an in silico
model of human melanocytes, to study and hopefully suggest strategies to cure
melanin-related skin disorders such as albinism.