Genome-Scale Metabolic Modeling Allows for Greater Understanding of Gene Function in Cryptosporidium Parvum

Advances in technology have allowed for an increase in our genomic knowledge, stored in a wide range of databases. However, many of these sequenced genes are annotated incorrectly or not at all, so we remain uncertain of their exact function in various organisms. To solve this problem, we use genome-scale metabolic models to identify essential enzymes and metabolites, find their functional homologs in model organisms like E. coli, and engineer growth conditions for the model organisms that allow us to find the genes responsible for the enzymes. The genomic library of the organism of interest is transformed into E. coli recipient strains, and the bacteria that survive in the growth conditions are known to contain the genes of interest. These genes are then sequenced and added to the model.

We are applying this novel approach to Cryptosporidium parvum, a parasite that causes cryptosporidiosis by infects the epithelial cells of mammalian intestinal tracts. This infection causes diarrhea in humans which can result in death in children who are infected. Currently there is one treatment available for infected adults and none available for children. Parasites like Cryptosporidium parvum have had their genomes sequenced, but much of the genome is unannotated and, thus, there are many gaps in our knowledge of their metabolism that we can fill using this metabolic modeling system. The information collected from these models can be used to create drugs and treatments that target these metabolic pathways.