The evolution of genetic regulatory systems in bacteria

McAdams, Harley H.; Srinivasan, Balaji; Arkin, Adam P.

doi:10.1038/nrg1292

Cited by 139 publications

(112 citation statements)

References 61 publications

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“…For example, the lac operon, which enables the mammalian commensal E. coli to use lactose as the sole carbon source, is absent from the related enteric species Salmonella enterica serovar Typhimurium, which cannot grown on lactose as the sole carbon source (28). However, such ecological properties may result from the distinct regulation of genes that related species have in common (30)(31)(32). For instance, differences in quantitative properties, such as the level and timing of expression of polymyxin B resistance genes, mediate phenotypic differences in resistance to the antibiotic polymyxin B in enteric bacteria (33).…”

Section: Resultsmentioning

confidence: 99%

Species-Specific Dynamic Responses of Gut Bacteria to a Mammalian Glycan

Raghavan¹,

Groisman²

2015

J. Bacteriol.

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The mammalian intestine provides nutrients to hundreds of bacterial species. Closely related species often harbor homologous nutrient utilization genes and cocolonize the gut, raising questions regarding the strategies mediating their stable coexistence. Here we reveal that related Bacteroides species that can utilize the mammalian glycan chondroitin sulfate (CS) have diverged in the manner in which they temporally regulate orthologous CS utilization genes. Whereas certain Bacteroides species display a transient surge in CS utilization transcripts upon exposure to CS, other species exhibit sustained activation of these genes. Remarkably, species-specific expression dynamics are retained even when the key players governing a particular response are replaced by those from a species with a dissimilar response. Bacteroides species exhibiting distinct expression behaviors in the presence of CS can be cocultured on CS. However, they vary in their responses to CS availability and to the composition of the bacterial community when CS is the sole carbon source. Our results indicate that diversity resulting from regulation of polysaccharide utilization genes may enable the coexistence of gut bacterial species using a given nutrient. IMPORTANCEGenes mediating a specific task are typically conserved in related microbes. For instance, gut Bacteroides species harbor orthologous nutrient breakdown genes and may face competition from one another for these nutrients. How, then, does the gut microbial composition maintain such remarkable stability over long durations? We establish that in the case of genes conferring the ability to utilize the nutrient chondroitin sulfate (CS), microbial species vary in how they temporally regulate these genes and exhibit subtle growth differences on the basis of CS availability and community composition. Similarly to how differential regulation of orthologous genes enables related species to access new environments, gut bacteria may regulate the same genes in distinct fashions to reduce the overlap with coexisting species for utilization of available nutrients.T he mammalian intestine is home to hundreds of gut bacterial species that compete for and cooperate for nutrient sources (1). The gut microbial community benefits the host by degrading otherwise indigestible dietary nutrients (2-4). Alterations in the gut microbial flora have been implicated in numerous disease states, including obesity and diabetes (5, 6). A dominant component of the gut microbiota is the phylum Bacteroidetes (7), which is comprised of a number of species that can break down a wide variety of complex polysaccharides (3). Bacteroides species coexist in the mammalian gut at high densities (10 9 to 10 10 CFU per gram of feces) (8, 9) and share available nutrients that range from intestinal glycans to dietary plant polysaccharides (3). The vast representation of related Bacteroides species colonizing the intestine raises the question: what mechanism(s) drives cohabitation of gut microbial species in an environment in...

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Section: Resultsmentioning

confidence: 99%

Species-Specific Dynamic Responses of Gut Bacteria to a Mammalian Glycan

Raghavan¹,

Groisman²

2015

J. Bacteriol.

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“…Because of the dynamic changes in the cross-reactive affinities, it is not possible to cancel the errors out by a basal level of expression. Secondly, lateral gene transfer (14,15) can introduce exogenous regulatory proteins that can interact with the site, especially when the site is free from its cognate interaction partner. A final source of error arises from residual binding of the designated regulator in its inactive form to its own site: in many cases, the affinity of the inactive regulator is only about one to two orders of magnitude lower than its affinity in the active state (1).…”

Section: Resultsmentioning

confidence: 99%

Rules for biological regulation based on error minimization

Shinar

Dekel

Tlusty

et al. 2006

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

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The control of gene expression involves complex mechanisms that show large variation in design. For example, genes can be turned on either by the binding of an activator (positive control) or the unbinding of a repressor (negative control). What determines the choice of mode of control for each gene? This study proposes rules for gene regulation based on the assumption that free regulatory sites are exposed to nonspecific binding errors, whereas sites bound to their cognate regulators are protected from errors. Hence, the selected mechanisms keep the sites bound to their designated regulators for most of the time, thus minimizing fitness-reducing errors. This offers an explanation of the empirically demonstrated Savageau demand rule: Genes that are needed often in the natural environment tend to be regulated by activators, and rarely needed genes tend to be regulated by repressors; in both cases, sites are bound for most of the time, and errors are minimized. The fitness advantage of error minimization appears to be readily selectable. The present approach can also generate rules for multi-regulator systems. The error-minimization framework raises several experimentally testable hypotheses. It may also apply to other biological regulation systems, such as those involving protein-protein interactions.biological physics ͉ complex networks ͉ systems biology ͉ transcriptional regulation B iological regulation systems convert input signals into specified outputs. The same input-output relationship can generally be carried out by several different mechanisms. For example, transcription regulation is carried out by regulatory proteins that bind specific sites in the promoter region of the regulated genes. A gene that is fully expressed only in the presence of a signal ( Fig. 1) can be regulated by two different mechanisms (1). In the first mechanism, called positive control, an activator binds the promoter to turn on expression. In the second mechanism, called negative control, a repressor binds the promoter to turn expression off. These two mechanisms realize the same input-output relationship: Expression is turned on by the binding of an activator in the positive mode of control and by the unbinding of a repressor in the negative mode of control. More generally, a gene controlled by N regulators, each of which can be either an activator or a repressor, has 2 N possible mechanisms for a given input-output mapping.Among these equivalent mechanisms, evolutionary selection chooses one for each system. Are there rules that govern the selection of mechanisms in biological systems? One possibility is that evolution chooses randomly between equivalent designs. Hence, the selected mechanism is determined by historical precedent. Another possibility is that general principles exist that govern the choice of mechanism in each system.The question of rules for gene regulation was raised by M. A. Savageau (2-6) in his pioneering study of transcriptional control. Savageau found that the mode of control is correlated with the demand...

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“…Bacteria have evolved a diverse set of regulatory pathways that govern various adaptive responses to survive under rapidly changing environmental conditions (40), and feedback loops are common elements of cellular regulatory circuits (15). Feedback regulation is critical for virulence in bacterial pathogens (76), and positive feedback is thought to shape response timing to allow the rapid expression of necessary genes when activated by certain stimuli (43,67).…”

Section: Discussionmentioning

confidence: 99%

VirB-Mediated Positive Feedback Control of the Virulence Gene Regulatory Cascade of Shigella flexneri

Kane

Dorman

2012

J Bacteriol

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Shigella flexneri is a facultative intracellular pathogen that relies on a type III secretion system and its associated effector proteins to cause bacillary dysentery in humans. The genes that encode this virulence system are located on a 230-kbp plasmid and are transcribed in response to thermal, osmotic, and pH signals that are characteristic of the human lower gut. The virulence genes are organized within a regulatory cascade, and the nucleoid-associated protein H-NS represses each of the key promoters. Transcription derepression depends first on the VirF AraC-like transcription factor, a protein that antagonizes H-NS-mediated repression at the intermediate regulatory gene virB . The VirB protein in turn remodels the H-NS–DNA nucleoprotein complexes at the promoters of the genes encoding the type III secretion system and effector proteins, causing these genes to become derepressed. In this study, we show that the VirB protein also positively regulates the expression of its own gene ( virB ) via a cis -acting regulatory sequence. In addition, VirB positively regulates the gene coding for the VirF protein. This study reveals two hitherto uncharacterized feedback regulatory loops in the S. flexneri virulence cascade that provide a mechanism for the enhanced expression of the principal virulence regulatory genes.

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The evolution of genetic regulatory systems in bacteria

Cited by 139 publications

References 61 publications

Species-Specific Dynamic Responses of Gut Bacteria to a Mammalian Glycan

Species-Specific Dynamic Responses of Gut Bacteria to a Mammalian Glycan

Rules for biological regulation based on error minimization

VirB-Mediated Positive Feedback Control of the Virulence Gene Regulatory Cascade of Shigella flexneri

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