Format

Send to

Choose Destination
J Biol Chem. 2017 Jun 16;292(24):10250-10261. doi: 10.1074/jbc.M116.763193. Epub 2017 Apr 26.

Model-enabled gene search (MEGS) allows fast and direct discovery of enzymatic and transport gene functions in the marine bacterium Vibrio fischeri.

Author information

1
From the Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706.
2
the School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China, and.
3
the Pacific Biosciences Research Center, University of Hawaii, Manoa, Hawaii 96813.
4
From the Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, jennifer.reed@wisc.edu.

Abstract

Whereas genomes can be rapidly sequenced, the functions of many genes are incompletely or erroneously annotated because of a lack of experimental evidence or prior functional knowledge in sequence databases. To address this weakness, we describe here a model-enabled gene search (MEGS) approach that (i) identifies metabolic functions either missing from an organism's genome annotation or incorrectly assigned to an ORF by using discrepancies between metabolic model predictions and experimental culturing data; (ii) designs functional selection experiments for these specific metabolic functions; and (iii) selects a candidate gene(s) responsible for these functions from a genomic library and directly interrogates this gene's function experimentally. To discover gene functions, MEGS uses genomic functional selections instead of relying on correlations across large experimental datasets or sequence similarity as do other approaches. When applied to the bioluminescent marine bacterium Vibrio fischeri, MEGS successfully identified five genes that are responsible for four metabolic and transport reactions whose absence from a draft metabolic model of V. fischeri caused inaccurate modeling of high-throughput experimental data. This work demonstrates that MEGS provides a rapid and efficient integrated computational and experimental approach for annotating metabolic genes, including those that have previously been uncharacterized or misannotated.

KEYWORDS:

bacterial metabolism; computational biology; constraint-based model; enzyme selection; functional genomics; genome annotation; genome-scale modeling; metabolism; systems biology

PMID:
28446608
PMCID:
PMC5473228
DOI:
10.1074/jbc.M116.763193
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center