Variability of Bacterial Essential Genes Among Closely Related Bacteria: The Case of Escherichia coli

Martínez-Carranza, Enrique; Barajas, Hugo R.; Alcaraz, Luis David; Servı́n‐González, Luis; Ponce-Soto, Gabriel Yaxal; Soberón‐Chávez, Gloria

doi:10.3389/fmicb.2018.01059

Cited by 33 publications

(23 citation statements)

References 64 publications

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“…Finally, we find that there are ∼15–30 unique, conditionally essential genes for each of the infection-relevant media examined, with their corresponding biological pathways important for survival only within a particular host tissue environment; these genes may represent a unique set of targets for infection type-specific therapeutics, with the obvious caveat that their essentiality cannot be dependent on microenvironmental factors that vary widely from patient to patient. Interestingly, our study provides insight into differences in gene essentiality between P. aeruginosa and other characterized bacterial species such as E. coli, which differ, for example, in genes required for ATP or fatty acid biosynthesis (45, 46).…”

Section: Discussionmentioning

confidence: 95%

Defining the core essential genome of Pseudomonas aeruginosa

Poulsen

Yang

Clatworthy

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

158

175

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Genomics offered the promise of transforming antibiotic discovery by revealing many new essential genes as good targets, but the results fell short of the promise. While numerous factors contributed to the disappointing yield, one factor was that essential genes for a bacterial species were often defined based on a single or limited number of strains grown under a single or limited number of in vitro laboratory conditions. In fact, the essentiality of a gene can depend on both the genetic background and growth condition. We thus developed a strategy for more rigorously defining the core essential genome of a bacterial species by studying many pathogen strains and growth conditions. We assessed how many strains must be examined to converge on a set of core essential genes for a species. We used transposon insertion sequencing (Tn-Seq) to define essential genes in nine strains of Pseudomonas aeruginosa on five different media and developed a statistical model, FiTnEss, to classify genes as essential versus nonessential across all strain–medium combinations. We defined a set of 321 core essential genes, representing 6.6% of the genome. We determined that analysis of four strains was typically sufficient in P. aeruginosa to converge on a set of core essential genes likely to be essential across the species across a wide range of conditions relevant to in vivo infection, and thus to represent attractive targets for novel drug discovery.

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Section: Discussionmentioning

confidence: 95%

Defining the core essential genome of Pseudomonas aeruginosa

Poulsen

Yang

Clatworthy

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

158

175

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“…Surprisingly, the number of these ancestral genes, belonging to minimal genome [19] was very low ( n = 51), among all E. coli genomes. A similar approach, looking for genes of minimal genome in 63 full E. coli genomes, revealed that as many as 11 genes allocated as minimal genome were absent in some of these genomes [33]. That the presence of essential genes is highly variable is probably due to the phenomenon known as “non-orthologous gene displacement” which refers to the coding for an essential function by genes with no sequence homology [34].…”

Section: Discussionmentioning

confidence: 99%

Stratified reconstruction of ancestral Escherichia coli diversification

2019

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BackgroundPhylogenetic analyses of the bacterial genomes based on the simple classification in core- genes and accessory genes pools could offer an incomplete view of the evolutionary processes, of which some are still unresolved. A combined strategy based on stratified phylogeny and ancient molecular polymorphisms is proposed to infer detailed evolutionary reconstructions by using a large number of whole genomes. This strategy, based on the highest number of genomes available in public databases, was evaluated for improving knowledge of the ancient diversification of E. coli. This staggered evolutionary scenario was also used to investigate whether the diversification of the ancient E. coli lineages could be associated with particular lifestyles and adaptive strategies.ResultsPhylogenetic reconstructions, exploiting 6220 available genomes in Genbank, established the E. coli core genome in 1023 genes, representing about 20% of the complete genome. The combined strategy using stratified phylogeny plus molecular polymorphisms inferred three ancient lineages (D, EB1A and FGB2). Lineage D was the closest to E. coli root. A staggered diversification could also be proposed in EB1A and FGB2 lineages and the phylogroups into these lineages. Several molecular markers suggest that each lineage had different adaptive trajectories. The analysis of gained and lost genes in the main lineages showed that functions of carbohydrates utilization (uptake of and metabolism) were gained principally in EB1A lineage, whereas loss of environmental-adaptive functions in FGB2 lineage were observed, but this lineage showed higher accumulated mutations and ancient recombination events. The population structure of E. coli was re-evaluated including up to 7561 new sequenced genomes, showing a more complex population structure of E. coli, as a new phylogroup, phylogroup I, was proposed.ConclusionsA staggered reconstruction of E. coli phylogeny is proposed, indicating evolution from three ancestral lineages to reach all main known phylogroups. New phylogroups were confirmed, suggesting an increasingly complex population structure of E. coli. However these new phylogroups represent < 1% of the global E. coli population. A few key evolutionary forces have driven the diversification of the two main E. coli lineages, metabolic flexibility in one of them and colonization-virulence in the other.

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“…In any case, the existence of these ‘gene by environment’ effects could account for the presence of accessory genes that may be highly advantageous under some, but not all, conditions. In fact, even gene essentiality can be environment-dependent 44 .…”

Section: Discussionmentioning

confidence: 99%

Selection-based model of prokaryote pangenomes

Domingo-Sananes

McInerney

2019

Preprint

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10The genomes of different individuals of the same prokaryote species can vary widely in gene 11 content, displaying different proportions of core genes, which are present in all genomes, and 12 accessory genes, whose presence varies between genomes. Together, these core and 13 accessory genes make up a species' pangenome. The reasons behind this extensive diversity 14 in gene content remain elusive, and there is an ongoing debate about the contribution of 15 accessory genes to fitness, that is, whether their presence is on average advantageous, 16 neutral, or deleterious. In order to explore this issue, we developed a mathematical model to 17 simulate the gene content of prokaryote genomes and pangenomes. Our model focuses on 18 testing how the fitness effects of genes and their rates of gene gain and loss would affect the 19 properties of pangenomes. We first show that pangenomes with large numbers of low-20 frequency genes can arise due to the gain and loss of neutral and nearly neutral genes in a 21 population. However, pangenomes with large numbers of highly beneficial, low-frequency 22 genes can arise as a consequence of genotype-by-environment interactions when multiple 23 niches are available to a species. Finally, pangenomes can arise, irrespective of the fitness 24 effect of the gained and lost genes, as long as gene gain and loss rates are high. We argue 25 that in order to understand the contribution of different mechanisms to pangenome diversity, 26it is crucial to have empirical information on population structure, gene-by-environment 27 interactions, the distributions of fitness effects and rates of gene gain and loss in different 28 prokaryote groups. 30 42 43Pangenomes arise as a consequence of gene acquisition via horizontal gene transfer (HGT), 44 and gene loss 2 . These processes ultimately result in general patterns observed across 45 prokaryotes, which include an increase in the number of known accessory genes as more 46 genomes from the same species are sequenced -along with a slower decrease in the number 47 of core genes-and a U-shaped gene frequency distribution or spectrum 2,10,11 . The U shape 48 2 of this distribution indicates that there is a large proportion of genes present in a single or very 49 few genomes, few genes present at intermediate frequencies, and substantial proportion of 50 core genes. Although across prokaryotic life there is a certain amount of commonality in the 51 shape of the gene content frequency distributions, different prokaryote species manifest 52 significant differences in the proportions of core and accessory genes [12][13][14] . 54Although we know that HGT and gene loss result in pangenomes, what is less clear are the 55 forces that lead to high diversity in gene content and U-shaped gene frequency distributions. 56According to mathematical models, gain and loss of entirely neutral genes along a simulated 57 phylogeny or population can in principle predict the U-shaped gene frequency distributions of 58 pangenomes 11,15 . However, the fit to real data i...

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Variability of Bacterial Essential Genes Among Closely Related Bacteria: The Case of Escherichia coli

Cited by 33 publications

References 64 publications

Defining the core essential genome of Pseudomonas aeruginosa

Defining the core essential genome of Pseudomonas aeruginosa

Stratified reconstruction of ancestral Escherichia coli diversification

Selection-based model of prokaryote pangenomes

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