Essential genes on metabolic maps

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Within the past five years genome-scale gene essentiality data sets have been published for ten diverse bacterial species. These data are a rich source of information about cellular networks that we are only beginning to explore. The analysis of these data, very heterogeneous in nature, is a challenging task. Even the definition of ‘essential genes’ in various genome-scale studies varies from genes ‘absolutely required for survival’ to those ‘strongly contributing to fitness’ and robust competitive growth. A comparative analysis of gene essentiality across multiple organisms based on projection of experimentally observed essential genes to functional roles in a collection of metabolic pathways and subsystems is emerging as a powerful tool of systems biology.

Introduction

Genome-scale gene essentiality studies are coming of age as datasets are becoming publicly available for a growing number of bacterial species [1, 2, 3, 4, 5, 6•, 7••, 8, 9, 10, 11, 12, 13, 14•, 15••, 16•] (Table 1). As with other genomics techniques, attention is now shifting from generating the data to their meaningful analysis and interpretation. Several key questions need to be addressed. How can gene essentiality be reliably inferred from the raw experimental data? How can observations (essential genes) be translated to conclusions (essential functional roles)? How can gene (function) essentiality be projected between different experimental conditions? How can gene (function) essentiality be projected from model organisms to others? How can the obtained information be used to improve our understanding of cellular networks? And, how can this understanding be applied for biotechnological or therapeutic tasks, such as the identification of potential drug targets?

The emerging approach might be called comparative gene essentiality analysis via projection over functional modules (cellular pathways, subsystems and networks) and is starting to address many of these questions. We illustrate this approach for a subset of genes associated with the metabolic network in several model bacteria. To emphasize the comparative aspect, we have limited the scope of this review by gene essentiality studies in bacteria. Similar studies of Saccharomyces cerevisiae are now in a much more advanced stage, and we refer the reader to several excellent publications and reviews on this subject [17, 18, 19, 20].

Section snippets

Essential genes: concepts and misconceptions

The term ‘essential gene’ perceived as ‘absolutely required for cell viability under any conditions’ can, strictly speaking, be applied to a rather small fraction of genes, encoding largely the information storage and processing functions. For the vast majority of metabolic genes, however, the notion of ‘essentiality’ is meaningful only in the context of specific conditions [8, 21, 22, 23, 24••]. Therefore, the analysis of any genome-wide set of essential genes (experimentally as well as

Comparative analysis and interpretation of essential genes in metabolic pathways

Although many published (and, even more so, unpublished) genome-scale gene essentiality screens were largely motivated by the quest for drug targets (note the abundance of pathogens in Table 1), the analysis and interpretation of these data directly impact several other fundamental research topics. Among these are efforts to deduce an abstraction of the so-called ‘minimal genome’ [23, 24••, 32•] and the related concept of the minimized artificial organism (Hutchison CA, 13 Annual Conference on

Conclusions

Despite some limitations, genome-scale essentiality screens are uniquely valuable for systems biology. They interrogate cellular networks at the functional level, conveying biological meaning beyond many conventional functional genomics techniques. We believe that comparative analysis of the rapidly growing body of genome-scale gene essentiality data will contribute strongly to our understanding of cellular pathways and networks, yielding numerous insights in fundamental and applied areas of

References and recommended reading

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We are grateful to all SEED annotators for building a collection of subsystems that allowed us to perform this analysis and to all members of the SEED/NMPDR development team. We thank Matt DeJongh and Aaron Best for their help with the projection of published metabolic reconstructions over the collection of SEED subsystems. The gene essentiality analysis described in this review was partially supported by the NIAID grants HHSN266200400042C to RS and Ross Overbeek and 1-R01-AI059146-01A2 to AO.

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