Essential genes on metabolic maps
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Although many published (and, even more so, unpublished) genome-scale gene essentiality screens were largely motivated by the quest for drug targets (note the abundance of pathogens in Table 1), the analysis and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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Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes – Biotechnological implications
2022, Biotechnology AdvancesCitation Excerpt :Another hypothesis concerning the role and characteristics of essential genes in interactomes is the so-called “centrality-lethality rule” (Jeong et al., 2001). This thesis proposes that essential genes are interaction hubs and play a central role in inter-relationships such as protein-protein interactions (PPI), metabolic networks (MN) (Gerdes et al., 2006; Basler, 2013; Gatto et al., 2015) and GI (Davierwala et al., 2005). Thus, if essential genes are deactivated, they would have a greater probability of disrupting the network and, as a consequence, cause lethality.
The uridylyltransferase GlnD and tRNA modification GTPase MnmE allosterically control Escherichia coli folylpoly-γ- glutamate synthase FolC
2018, Journal of Biological ChemistrySubtractive proteomics revealed plausible drug candidates in the proteome of multi-drug resistant Corynebacterium diphtheriae
2018, Meta GeneCitation Excerpt :Screening the pathogen specific proteins was crucial in order to avoid cross-reactivity with the host proteins and prevent auto-immune responses (Azam and Shamim, 2014). The cellular procedures required for the organism survival can be obtained by identifying essential genetic elements of an organism (Gerdes et al., 2006). Essential proteins are indispensable for the survival of microorganism within the host thus targeting such proteins can inhibit bacterial growth and survival (Naz et al., 2015).
Gene Essentiality Is a Quantitative Property Linked to Cellular Evolvability
2015, CellCitation Excerpt :Determining whether a gene is essential for cell viability is less straightforward than one might predict. Genes can be essential in one species but not another (Ryan et al., 2013; Sharma et al., 2014) or in one growth condition but not in others (Baba et al., 2006; Gerdes et al., 2006), suggesting that essentiality is not an intrinsic property of a gene but is influenced by genetic and environmental factors. Moreover, some non-essential bacterial genes share numerous features with their essential counterparts (Fang et al., 2005), challenging a clear dichotomy between these two gene subsets.