Modeling and High-throughput Experiments for Direct Identification of Gene Functions

Graduate #27
Discipline: Biological Sciences
Subcategory: Microbiology/Immunology/Virology

Shu Pan - University of Wisconsin-Madison
Co-Author(s): Kiel Nikolakakis and Jennifer Reed, University of Wisconsin-Madison, Madison, WI Edward G. Ruby, University of Hawaii, Manoa, HI

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While genomes can be rapidly sequenced, many genes are incompletely and/or erroneously assigned to functions due to a lack of experimental evidence or prior knowledge in sequence databases. We developed an integrated computational modeling and high-throughput experimental approach, model enabled gene search (MEGS), to directly identify gene functions. MEGS takes advantage of genome-scale modeling to identify which metabolic reactions are missing from a network and to design selection experiments so that growth by a host strain requires these missing reactions. MEGS then uses experiments to select genes from a genome that can catalyze these reactions since addition of the genes rescues growth of the host strain. As MEGS uses selection experiments based on gene functions to find genes instead of sequence homology, it can discover genes with unique sequences and/or that have not been studied in the laboratory. To date, we have successfully discovered functions of 11 uncharacterized and/or unique enzymatic and transport genes in 3 microorganisms using our MEGS protocol. This demonstrates MEGS as being a rapid and efficient approach for finding metabolic genes and their functions, including those that are previously uncharacterized or misannotated.

Funder Acknowledgement(s): Gordon and Betty Moore Foundation [grant number 3396].

Faculty Advisor: Jennifer Reed, jennifer.reed@wisc.edu

Role: I conceived and designed the experiments, performed the experiments, performed statistical analysis, and analyzed the data.