imprint:
Springer Science and Business Media LLC, 2010
Published in:BMC Systems Biology
Language:
English
DOI:
10.1186/1752-0509-4-56
ISSN:
1752-0509
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Background</jats:title>
<jats:p>Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for <jats:italic>Salmonella typhimurium</jats:italic>. We compared our predictions to the viability of experimental knock-outs of <jats:italic>S. typhimurium</jats:italic> and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.</jats:p>
</jats:sec>