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Comput Biol Chem. 2014 Jun;50:29-40. doi: 10.1016/j.compbiolchem.2014.01.011. Epub 2014 Jan 23.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

Author information

1
Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, 24/1400 Beijing (W) Road, Shanghai 200040, PR China.
2
Division of Biomedical Informatics, Cincinnati Children's Hospital Research Foundation, 3333 Burnet Avenue, MLC7024, Cincinnati, OH 45229, USA.
3
Department of Pathology and Laboratory Medicine, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45221, USA.
4
Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, 24/1400 Beijing (W) Road, Shanghai 200040, PR China; Department of Bioengineering (MC 063), University of Illinois at Chicago, 851 S Morgan St, 218 SEO, Chicago, IL 60607, USA. Electronic address: huilu.bioinfo@gmail.com.
5
Division of Biomedical Informatics, Cincinnati Children's Hospital Research Foundation, 3333 Burnet Avenue, MLC7024, Cincinnati, OH 45229, USA; Division of Epidemiology and Biostatistics, Cincinnati Children's Hospital Research Foundation, 3333 Burnet Avenue, MLC7024, Cincinnati, OH 45229, USA; Department of Computer Science, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45221, USA; Department of Environmental Health, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45221, USA; Department of Biomedical Engineering, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45221, USA. Electronic address: long.lu@cchmc.org.

Abstract

BACKGROUND:

Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation".

RESULTS:

In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach.

CONCLUSIONS:

The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to predict an inventory of essential genes in Eukaryotic fungal species, amongst which a preferred subset of suitable drug targets may be selected. By selecting the best new targets, we believe that resultant drugs would exhibit an unparalleled clinical impact against a naive pathogen population. Additional benefits that a compendium of essential genes can provide are important information on cell function and evolutionary biology. Furthermore, mapping essential genes to pathways may also reveal critical check points in the pathogen's metabolism. Finally, this approach is highly reproducible and portable, and can be easily applied to predict essential genes in many more pathogenic microbes, especially those unculturable.

KEYWORDS:

Aspergillus fumigatus; Drug targets; Essential genes; Fungi; Integrative; Machine learning

[Indexed for MEDLINE]

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