Search

The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria. IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia col

Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Enzymes constitute the most prevalent functional gene class in microbial genomes and can be described by their specific catalytic functions using the Enzyme Commission (EC) number. Consequently, the ability to predict EC numbers could substantially reduce the number of un-annotated genes. Here we present a deep learning model, DeepECtransformer, which utilizes transformer layers as a neural network architecture to predict EC numbers. Using the extensively studied Escherichia coli K-12 MG1655 genome, DeepECtransformer predicted EC numbers for 464 un-annotated genes. We experimentally validated the enzymatic activities predicted for three proteins (YgfF, YciO, and YjdM). Further examination of the neural networks reasoning process revealed that the trained neural network relies on functional motifs of enzymes to predict EC numbers. Thus, DeepECtransformer is a method that facilitates the functional annotation of uncharacterized genes.

Although Escherichia coli K-12 strains represent perhaps the best known model bacteria, we do not know the identity or functions of all of their transcription factors (TFs). It is now possible to systematically discover the physiological function of TFs in E. coli BW25113 using a set of synergistic methods; including ChIP-exo, growth phenotyping, conserved gene clustering, and transcriptome analysis. Among 47 LysR-type TFs (LTFs) found on the E. coli K-12 genome, many regulate nitrogen source utilization or amino acid metabolism. However, 19 LTFs remain unknown. In this study, we elucidated the regulation of seven of these 19 LTFs: YbdO, YbeF, YcaN, YbhD, YgfI, YiaU, YneJ. We show that: (1) YbdO (tentatively re-named CitR) regulation has an effect on bacterial growth at low pH with citrate supplementation. CitR is a repressor of the ybdNM operon and is implicated in the regulation of citrate lyase genes (citCDEFG); (2) YgfI (tentatively re-named DhfA) activates the dhaKLM operon that encodes the phosphotransferase system, DhfA is involved in formate, glycerol and dihydroxyacetone utilization; (3) YiaU (tentatively re-named LpsR) regulates the yiaT gene encoding an outer membrane protein, and waaPSBOJYZU operon is also important in determining cell density at the stationary phase and resistance to oxacillin microaerobically; (4) YneJ, re-named here as PtrR, directly regulates the expression of the succinate-semialdehyde dehydrogenase, Sad (also known as YneI), and is a predicted regulator of fnrS (a small RNA molecule). PtrR is important for bacterial growth in the presence of L-glutamate and putrescine as nitrogen/energy sources; and (5) YbhD and YcaN regulate adjacent y-genes on the genome. We have thus established the functions for four LTFs and identified the target genes for three LTFs.

Although Escherichia coli K-12 strains represent perhaps the best known model bacteria, we do not know the identity or functions of all of their transcription factors (TFs). It is now possible to systematically discover the physiological function of TFs in E. coli BW25113 using a set of synergistic methods; including ChIP-exo, growth phenotyping, conserved gene clustering, and transcriptome analysis. Among 47 LysR-type TFs (LTFs) found on the E. coli K-12 genome, many regulate nitrogen source utilization or amino acid metabolism. However, 19 LTFs remain unknown. In this study, we elucidated the regulation of seven of these 19 LTFs: YbdO, YbeF, YcaN, YbhD, YgfI, YiaU, YneJ. We show that: (1) YbdO (tentatively re-named CitR) regulation has an effect on bacterial growth at low pH with citrate supplementation. CitR is a repressor of the ybdNM operon and is implicated in the regulation of citrate lyase genes (citCDEFG); (2) YgfI (tentatively re-named DhfA) activates the dhaKLM operon that encodes the phosphotransferase system, DhfA is involved in formate, glycerol and dihydroxyacetone utilization; (3) YiaU (tentatively re-named LpsR) regulates the yiaT gene encoding an outer membrane protein, and waaPSBOJYZU operon is also important in determining cell density at the stationary phase and resistance to oxacillin microaerobically; (4) YneJ, re-named here as PtrR, directly regulates the expression of the succinate-semialdehyde dehydrogenase, Sad (also known as YneI), and is a predicted regulator of fnrS (a small RNA molecule). PtrR is important for bacterial growth in the presence of L-glutamate and putrescine as nitrogen/energy sources; and (5) YbhD and YcaN regulate adjacent y-genes on the genome. We have thus established the functions for four LTFs and identified the target genes for three LTFs.

The increasing frequency of antibiotic resistance poses myriad challenges to modern medicine. Environmental survival of multidrug-resistant bacteria in health care facilities, including hospitals, creates reservoirs for transmission of these difficult to treat pathogens. To prevent bacterial colonization, these facilities deploy an array of infection control measures, including bactericidal metals on surfaces, as well as implanted devices. Although antibiotics are routinely used in these health care environments, it is unknown whether and how antibiotic exposure affects metal resistance. We identified a multidrug-resistant Enterobacter clinical isolate that displayed heteroresistance to the antibiotic colistin, where only a minor fraction of cells within the population resist the drug. When this isolate was grown in the presence of colistin, a 9-kb DNA region was duplicated in the surviving resistant subpopulation, but surprisingly, was not required for colistin heteroresistance. Instead, the amplified region included a three-gene locus (ncrABC) that conferred resistance to the bactericidal metal, nickel. ncrABC expression alone was sufficient to confer nickel resistance to E. coli K-12. Due to its selection for the colistin-resistant subpopulation harboring the duplicated 9-kb region that includes ncrABC, colistin treatment led to enhanced nickel resistance. Taken together, these data suggest that the use of antibiotics may inadvertently promote enhanced resistance to antimicrobial metals, with potentially profound implications for bacterial colonization and transmission in the health care environment.IMPORTANCE To inhibit bacterial transmission and infection, health care facilities use bactericidal metal coatings to prevent colonization of surfaces and implanted devices. In these environments, antibiotics are commonly used, but their effect on metal resistance is unclear. The data described here reveal that exposure of a human isolate of Enterobacter cloacae to a

The increasing frequency of antibiotic resistance poses myriad challenges to modern medicine. Environmental survival of multidrug-resistant bacteria in health care facilities, including hospitals, creates reservoirs for transmission of these difficult to treat pathogens. To prevent bacterial colonization, these facilities deploy an array of infection control measures, including bactericidal metals on surfaces, as well as implanted devices. Although antibiotics are routinely used in these health care environments, it is unknown whether and how antibiotic exposure affects metal resistance. We identified a multidrug-resistant Enterobacter clinical isolate that displayed heteroresistance to the antibiotic colistin, where only a minor fraction of cells within the population resist the drug. When this isolate was grown in the presence of colistin, a 9-kb DNA region was duplicated in the surviving resistant subpopulation, but surprisingly, was not required for colistin heteroresistance. Instead, the amplified region included a three-gene locus (ncrABC) that conferred resistance to the bactericidal metal, nickel. ncrABC expression alone was sufficient to confer nickel resistance to E. coli K-12. Due to its selection for the colistin-resistant subpopulation harboring the duplicated 9-kb region that includes ncrABC, colistin treatment led to enhanced nickel resistance. Taken together, these data suggest that the use of antibiotics may inadvertently promote enhanced resistance to antimicrobial metals, with potentially profound implications for bacterial colonization and transmission in the health care environment.IMPORTANCE To inhibit bacterial transmission and infection, health care facilities use bactericidal metal coatings to prevent colonization of surfaces and implanted devices. In these environments, antibiotics are commonly used, but their effect on metal resistance is unclear. The data described here reveal that exposure of a human isolate of Enterobacter cloacae to a

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

Bacteria regulate gene expression to adapt to changing environments through transcriptional regulatory networks (TRNs). Although extensively studied, no TRN is fully characterized since the identity and activity of all the transcriptional regulators comprising a TRN are not known. Here, we experimentally evaluate 40 uncharacterized proteins in Escherichia coli K-12 MG1655, which were computationally predicted to be transcription factors (TFs). First, we used a multiplexed chromatin immunoprecipitation method combined with lambda exonuclease digestion (multiplexed ChIP-exo) assay to characterize binding sites for these candidate TFs; 34 of them were found to be DNA-binding proteins. We then compared the relative location between binding sites and RNA polymerase (RNAP). We found 48% (283/588) overlap between the TFs and RNAP. Finally, we used these data to infer potential functions for 10 of the 34 TFs with validated DNA binding sites and consensus binding motifs. Taken together, this study: (i) significantly expands the number of confirmed TFs to 276, close to the estimated total of about 280 TFs; (ii) provides putative functions for the newly discovered TFs and (iii) confirms the functions of four representative TFs through mutant phenotypes.

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to a

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demo

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demo

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in Escherichia coli K-12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non-native substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high-efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity.

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the 'y-ome') which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with 'y' is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in Escherichia coli K-12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non-native substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high-efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity.

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the 'y-ome') which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with 'y' is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

Type II single-span membrane proteins, such as CadC or RodZ, lacking a signal sequence and having a far-downstream hydrophobic segment, require the SecA secretion motor for insertion into the inner membrane of Escherichia coli. Using two chimeric single-span proteins containing a designed hydrophobic segment H, we have determined the requirements for SecA-mediated secretion, the molecular distinction between TM domains and signal peptides, and the propensity for hydrophobic H-segments to remain embedded within the bilayer after targeting. By means of engineered H-segments and a strategically placed SPase I cleavage site, we determined how targeting and stability of the chimeric proteins are affected by the length and hydrophobicity of the H-segment. Very hydrophobic segments (e.g., 16 Leu) are stably incorporated into the inner membrane, resulting in a C-terminal anchored membrane protein, while a 24L construct was not targeted to the membrane by SecA and remained in the cytoplasm. However, a construct carrying preMalE at the N-terminus led to SecA targeting to SecYEG via the native signal sequence and stable insertion of the downstream 24L H-segment. We show that the RseP intramembrane protease degrades weakly stable H-segments and is a useful tool for investigating the borderline between stable and unstable TM segments. Using RseP- cells, we find that moderately hydrophobic sequences (e.g., 5Leu + 11Ala) are targeted to SecYEG by SecA and inserted, but subsequently drop out of the membrane into the cytoplasm. Therefore, the free energy of transfer from translocon to bilayer is different from the transfer free energy from membrane to water.

Biological regulatory network architectures are multi-scale in their function and can adaptively acquire new functions. Gene knockout (KO) experiments provide an established experimental approach not just for studying gene function, but also for unraveling regulatory networks in which a gene and its gene product are involved. Here we study the regulatory architecture of Escherichia coli K-12 MG1655 by applying adaptive laboratory evolution (ALE) to metabolic gene KO strains. Multi-omic analysis reveal a common overall schema describing the process of adaptation whereby perturbations in metabolite concentrations lead regulatory networks to produce suboptimal states, whose function is subsequently altered and re-optimized through acquisition of mutations during ALE. These results indicate that metabolite levels, through metabolite-transcription factor interactions, have a dominant role in determining the function of a multi-scale regulatory architecture that has been molded by evolution.

BACKGROUND:Our understanding of the regulation of gene expression has benefited from the availability of high-throughput technologies that interrogate the whole genome for the binding of specific transcription factors and gene expression profiles. In the case of widely used model organisms, such as Escherichia coli K-12, the new knowledge gained from these approaches needs to be integrated with the legacy of accumulated knowledge from genetic and molecular biology experiments conducted in the pre-genomic era in order to attain the deepest level of understanding possible based on the available data. RESULTS:In this paper, we describe an expansion of RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization in E. coli K-12, to include the genome-wide dataset collections from 32 ChIP and 19 gSELEX publications, in addition to around 60 genome-wide expression profiles relevant to the functional significance of these datasets and used in their curation. Three essential features for the integration of this information coming from different methodological approaches are: first, a controlled vocabulary within an ontology for precisely defining growth conditions; second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated transcription factor binding sites without such support; and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration needed to manage and access such wealth of knowledge. CONCLUSIONS:This version 10.0 of RegulonDB is a first step toward what should become the unifying access point for current and future knowledge on gene regulation in E. coli K-12. Furthermore, this model platform and associated methodologies and criteria can be emulated for gathering knowledge on ot

Transcriptional regulation enables cells to respond to environmental changes. Of the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified, but ChIP methods have been used to fully characterize only a few dozen. Identifying these remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to (i) identify 16 candidate TFs from over a hundred uncharacterized genes; (ii) capture a total of 255 DNA binding peaks for ten candidate TFs resulting in six high-confidence binding motifs; (iii) reconstruct the regulons of these ten TFs by determining gene expression changes upon deletion of each TF and (iv) identify the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of l-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron-limited conditions, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. Diminishing returns epistasis prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern kcats and the whole of metabolism.

Transcriptional regulation enables cells to respond to environmental changes. Of the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified, but ChIP methods have been used to fully characterize only a few dozen. Identifying these remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to (i) identify 16 candidate TFs from over a hundred uncharacterized genes; (ii) capture a total of 255 DNA binding peaks for ten candidate TFs resulting in six high-confidence binding motifs; (iii) reconstruct the regulons of these ten TFs by determining gene expression changes upon deletion of each TF and (iv) identify the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of l-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron-limited conditions, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

Biological regulatory network architectures are multi-scale in their function and can adaptively acquire new functions. Gene knockout (KO) experiments provide an established experimental approach not just for studying gene function, but also for unraveling regulatory networks in which a gene and its gene product are involved. Here we study the regulatory architecture of Escherichia coli K-12 MG1655 by applying adaptive laboratory evolution (ALE) to metabolic gene KO strains. Multi-omic analysis reveal a common overall schema describing the process of adaptation whereby perturbations in metabolite concentrations lead regulatory networks to produce suboptimal states, whose function is subsequently altered and re-optimized through acquisition of mutations during ALE. These results indicate that metabolite levels, through metabolite-transcription factor interactions, have a dominant role in determining the function of a multi-scale regulatory architecture that has been molded by evolution.

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. Diminishing returns epistasis prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern kcats and the whole of metabolism.

BACKGROUND:Our understanding of the regulation of gene expression has benefited from the availability of high-throughput technologies that interrogate the whole genome for the binding of specific transcription factors and gene expression profiles. In the case of widely used model organisms, such as Escherichia coli K-12, the new knowledge gained from these approaches needs to be integrated with the legacy of accumulated knowledge from genetic and molecular biology experiments conducted in the pre-genomic era in order to attain the deepest level of understanding possible based on the available data. RESULTS:In this paper, we describe an expansion of RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization in E. coli K-12, to include the genome-wide dataset collections from 32 ChIP and 19 gSELEX publications, in addition to around 60 genome-wide expression profiles relevant to the functional significance of these datasets and used in their curation. Three essential features for the integration of this information coming from different methodological approaches are: first, a controlled vocabulary within an ontology for precisely defining growth conditions; second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated transcription factor binding sites without such support; and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration needed to manage and access such wealth of knowledge. CONCLUSIONS:This version 10.0 of RegulonDB is a first step toward what should become the unifying access point for current and future knowledge on gene regulation in E. coli K-12. Furthermore, this model platform and associated methodologies and criteria can be emulated for gathering knowledge on ot

A mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory functions of the lost gene. The pgi gene, whose product catalyzes the second step in glycolysis, was deleted in a growth-optimized Escherichia coli K-12 MG1655 strain. The initial knockout (KO) strain exhibited an 80% drop in growth rate that was largely recovered in eight replicate, but phenotypically distinct, cultures after undergoing adaptive laboratory evolution (ALE). Multi-omic data sets showed that the loss of pgi substantially shifted pathway usage, leading to a redox and sugar phosphate stress response. These stress responses were overcome by unique combinations of innovative mutations selected for by ALE. Thus, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.IMPORTANCE A mechanistic understanding of how microbes are able to overcome the loss of a gene through regulatory and metabolic changes is not well understood. Eight independent adaptive laboratory evolution (ALE) experiments with pgi knockout strains resulted in eight phenotypically distinct endpoints that were able to overcome the gene loss. Utilizing multi-omics analysis, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.

The flagellar system in Escherichia coli K12 is expressed under the control of the flhDC-encoded master regulator FlhDC. Transposition of insertion sequence (IS) elements to the upstream flhDC promoter region up-regulates transcription of this operon, resulting in a more rapid motility. Wang and Wood (ISME J 2011;5:1517-1525) provided evidence that insertion of IS5 into upstream activating sites occurs at higher rates in semi-solid agar media in which swarming behaviour is allowed as compared with liquid or solid media where swarming cannot occur. We confirm this conclusion and show that three IS elements, IS1, IS3 and IS5, transpose to multiple upstream sites within a 370 bp region of the flhDC operon control region. Hot spots for IS insertion correlate with positions of stress-induced DNA duplex destabilization (SIDD). We show that IS insertion occurs at maximal rates in 0.24 % agar, with rates decreasing dramatically with increasing or decreasing agar concentrations. In mixed cultures, we show that these mutations preferentially arise from the wild-type parent at frequencies of up to 3×10-3 cell-1 day-1 when the inoculated parental and co-existing IS-activated mutant cells are entering the stationary growth phase. We rigorously show that the apparent increased mutation frequencies cannot be accounted for by increased swimming or by increased growth under the selective conditions used. Thus, our data are consistent with the possibility that appropriate environmental conditions, namely those that permit but hinder flagellar rotation, result in the activation of a mutational pathway that involves IS element insertion upstream of the flhDC operon.

A transcription factor (TF), OmpR, plays a critical role in transcriptional regulation of the osmotic stress response in bacteria. Here, we reveal a genome-scale OmpR regulon in Escherichia coli K-12 MG1655. Integrative data analysis reveals that a total of 37 genes in 24 transcription units (TUs) belong to OmpR regulon. Among them, 26 genes show more than two-fold changes in expression level in an OmpR knock-out strain. Specifically, we find that: 1) OmpR regulates mostly membrane-located gene products involved in diverse fundamental biological processes, such as narU (encoding nitrate/nitrite transporter), ompX (encoding outer membrane protein X), and nuoN (encoding NADH:ubiquinone oxidoreductase); 2) by investigating co-regulation of entire sets of genes regulated by other stress-response TFs, stresses are surprisingly independently regulated among each other; and, 3) a detailed investigation of the physiological roles of the newly discovered OmpR regulon genes reveals that activation of narU represents a novel strategy to significantly improve osmotic stress tolerance of E. coli. Thus, the genome-scale approach to elucidating regulons comprehensively identifies regulated genes and leads to fundamental discoveries related to stress responses.

In bacteria various tactic responses are mediated by the same cellular pathway, but sensing of physical stimuli remains poorly understood. Here, we combine an in-vivo analysis of the pathway activity with a microfluidic taxis assay and mathematical modeling to investigate the thermotactic response of Escherichia coli. We show that in the absence of chemical attractants E. coli exhibits a steady thermophilic response, the magnitude of which decreases at higher temperatures. Adaptation of wild-type cells to high levels of chemoattractants sensed by only one of the major chemoreceptors leads to inversion of the thermotactic response at intermediate temperatures and bidirectional cell accumulation in a thermal gradient. A mathematical model can explain this behavior based on the saturation-dependent kinetics of adaptive receptor methylation. Lastly, we find that the preferred accumulation temperature corresponds to optimal growth in the presence of the chemoattractant serine, pointing to a physiological relevance of the observed thermotactic behavior.

The flagellar system in Escherichia coli K12 is expressed under the control of the flhDC-encoded master regulator FlhDC. Transposition of insertion sequence (IS) elements to the upstream flhDC promoter region up-regulates transcription of this operon, resulting in a more rapid motility. Wang and Wood (ISME J 2011;5:1517-1525) provided evidence that insertion of IS5 into upstream activating sites occurs at higher rates in semi-solid agar media in which swarming behaviour is allowed as compared with liquid or solid media where swarming cannot occur. We confirm this conclusion and show that three IS elements, IS1, IS3 and IS5, transpose to multiple upstream sites within a 370 bp region of the flhDC operon control region. Hot spots for IS insertion correlate with positions of stress-induced DNA duplex destabilization (SIDD). We show that IS insertion occurs at maximal rates in 0.24 % agar, with rates decreasing dramatically with increasing or decreasing agar concentrations. In mixed cultures, we show that these mutations preferentially arise from the wild-type parent at frequencies of up to 3×10-3 cell-1 day-1 when the inoculated parental and co-existing IS-activated mutant cells are entering the stationary growth phase. We rigorously show that the apparent increased mutation frequencies cannot be accounted for by increased swimming or by increased growth under the selective conditions used. Thus, our data are consistent with the possibility that appropriate environmental conditions, namely those that permit but hinder flagellar rotation, result in the activation of a mutational pathway that involves IS element insertion upstream of the flhDC operon.

In bacteria various tactic responses are mediated by the same cellular pathway, but sensing of physical stimuli remains poorly understood. Here, we combine an in-vivo analysis of the pathway activity with a microfluidic taxis assay and mathematical modeling to investigate the thermotactic response of Escherichia coli. We show that in the absence of chemical attractants E. coli exhibits a steady thermophilic response, the magnitude of which decreases at higher temperatures. Adaptation of wild-type cells to high levels of chemoattractants sensed by only one of the major chemoreceptors leads to inversion of the thermotactic response at intermediate temperatures and bidirectional cell accumulation in a thermal gradient. A mathematical model can explain this behavior based on the saturation-dependent kinetics of adaptive receptor methylation. Lastly, we find that the preferred accumulation temperature corresponds to optimal growth in the presence of the chemoattractant serine, pointing to a physiological relevance of the observed thermotactic behavior.

A transcription factor (TF), OmpR, plays a critical role in transcriptional regulation of the osmotic stress response in bacteria. Here, we reveal a genome-scale OmpR regulon in Escherichia coli K-12 MG1655. Integrative data analysis reveals that a total of 37 genes in 24 transcription units (TUs) belong to OmpR regulon. Among them, 26 genes show more than two-fold changes in expression level in an OmpR knock-out strain. Specifically, we find that: 1) OmpR regulates mostly membrane-located gene products involved in diverse fundamental biological processes, such as narU (encoding nitrate/nitrite transporter), ompX (encoding outer membrane protein X), and nuoN (encoding NADH:ubiquinone oxidoreductase); 2) by investigating co-regulation of entire sets of genes regulated by other stress-response TFs, stresses are surprisingly independently regulated among each other; and, 3) a detailed investigation of the physiological roles of the newly discovered OmpR regulon genes reveals that activation of narU represents a novel strategy to significantly improve osmotic stress tolerance of E. coli. Thus, the genome-scale approach to elucidating regulons comprehensively identifies regulated genes and leads to fundamental discoveries related to stress responses.

Escherichia coli K-12 W3110 grows in the presence of membrane-permeant organic acids that can depress cytoplasmic pH and accumulate in the cytoplasm. We conducted experimental evolution by daily diluting cultures in increasing concentrations of benzoic acid (up to 20 mM) buffered at external pH 6.5, a pH at which permeant acids concentrate in the cytoplasm. By 2,000 generations, clones isolated from evolving populations showed increasing tolerance to benzoate but were sensitive to chloramphenicol and tetracycline. Sixteen clones grew to stationary phase in 20 mM benzoate, whereas the ancestral strain W3110 peaked and declined. Similar growth occurred in 10 mM salicylate. Benzoate-evolved strains grew like W3110 in the absence of benzoate, in media buffered at pH 4.8, pH 7.0, or pH 9.0, or in 20 mM acetate or sorbate at pH 6.5. Genomes of 16 strains revealed over 100 mutations, including single-nucleotide polymorphisms (SNPs), large deletions, and insertion knockouts. Most strains acquired deletions in the benzoate-induced multiple antibiotic resistance (Mar) regulon or in associated regulators such as rob and cpxA, as well as the multidrug resistance (MDR) efflux pumps emrA, emrY, and mdtA Strains also lost or downregulated the Gad acid fitness regulon. In 5 mM benzoate or in 2 mM salicylate (2-hydroxybenzoate), most strains showed increased sensitivity to the antibiotics chloramphenicol and tetracycline; some strains were more sensitive than a marA knockout strain. Thus, our benzoate-evolved strains may reveal additional unknown drug resistance components. Benzoate or salicylate selection pressure may cause general loss of MDR genes and regulators. Benzoate is a common food preservative, and salicylate is the primary active metabolite of aspirin. In the gut microbiome, genetic adaptation to salicylate may involve loss or downregulation of inducible multidrug resistance systems. This discovery implies that aspirin therapy may modulate the human gut microbiome to fav

Acid-adapted strains of Escherichia coli K-12 W3110 were obtained by serial culture in medium buffered at pH 4.6 (M. M. Harden, A. He, K. Creamer, M. W. Clark, I. Hamdallah, K. A. Martinez, R. L. Kresslein, S. P. Bush, and J. L. Slonczewski, Appl Environ Microbiol 81:1932-1941, 2015, https://doi.org/10.1128/AEM.03494-14). Revised genomic analysis of these strains revealed insertion sequence (IS)-driven insertions and deletions that knocked out regulators CadC (acid induction of lysine decarboxylase), GadX (acid induction of glutamate decarboxylase), and FNR (anaerobic regulator). Each acid-evolved strain showed loss of one or more amino acid decarboxylase systems, which normally help neutralize external acid (pH 5 to 6) and increase survival in extreme acid (pH 2). Strains from populations B11, H9, and F11 had an IS5 insertion or IS-mediated deletion in cadC, while population B11 had a point mutation affecting the arginine activator adiY The cadC and adiY mutants failed to neutralize acid in the presence of exogenous lysine or arginine. In strain B11-1, reversion of an rpoC (RNA polymerase) mutation partly restored arginine-dependent neutralization. All eight strains showed deletion or downregulation of the Gad acid fitness island. Strains with the Gad deletion lost the ability to produce GABA (gamma-aminobutyric acid) and failed to survive extreme acid. Transcriptome sequencing (RNA-seq) of strain B11-1 showed upregulated genes for catabolism of diverse substrates but downregulated acid stress genes (the biofilm regulator ariR, yhiM, and Gad). Other strains showed downregulation of H2 consumption mediated by hydrogenases (hya and hyb) which release acid. Strains F9-2 and F9-3 had a deletion of fnr and showed downregulation of FNR-dependent genes (dmsABC, frdABCD, hybABO, nikABCDE, and nrfAC). Overall, strains that had evolved in buffered acid showed loss or downregulation of systems that neutralize unbuffered acid and showed altered regulation of catabolism.IMPORTA

Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution.

To study how a bacterium allocates its resources, we compared the costs and benefits of most (86%) of the proteins in Escherichia coli K-12 during growth in minimal glucose medium. The cost or investment in each protein was estimated from ribosomal profiling data, and the benefit of each protein was measured by assaying a library of transposon mutants. We found that proteins that are important for fitness are usually highly expressed, and 95% of these proteins are expressed at above 13 parts per million (ppm). Conversely, proteins that do not measurably benefit the host (with a benefit of less than 5% per generation) tend to be weakly expressed, with a median expression of 13 ppm. In aggregate, genes with no detectable benefit account for 31% of protein production, or about 22% if we correct for genetic redundancy. Although some of the apparently unnecessary expression could have subtle benefits in minimal glucose medium, the majority of the burden is due to genes that are important in other conditions. We propose that at least 13% of the cell's protein is "on standby" in case conditions change.

Van Hofwegen et al. demonstrated that Escherichia coli rapidly evolves the ability to use citrate when long selective periods are provided (D. J. Van Hofwegen, C. J. Hovde, and S. A. Minnich, J Bacteriol 198:1022-1034, 2016, http://dx.doi.org/10.1128/JB.00831-15). This contrasts with the extreme delay (15 years of daily transfers) seen in the long-term evolution experiments of Lenski and coworkers. Their idea of "historical contingency" may require reinterpretation. Rapid evolution seems to involve selection for duplications of the whole cit locus that are too unstable to contribute when selection is provided in short pulses.

To study how a bacterium allocates its resources, we compared the costs and benefits of most (86%) of the proteins in Escherichia coli K-12 during growth in minimal glucose medium. The cost or investment in each protein was estimated from ribosomal profiling data, and the benefit of each protein was measured by assaying a library of transposon mutants. We found that proteins that are important for fitness are usually highly expressed, and 95% of these proteins are expressed at above 13 parts per million (ppm). Conversely, proteins that do not measurably benefit the host (with a benefit of less than 5% per generation) tend to be weakly expressed, with a median expression of 13 ppm. In aggregate, genes with no detectable benefit account for 31% of protein production, or about 22% if we correct for genetic redundancy. Although some of the apparently unnecessary expression could have subtle benefits in minimal glucose medium, the majority of the burden is due to genes that are important in other conditions. We propose that at least 13% of the cell's protein is "on standby" in case conditions change.

DNA-binding motifs that are recognized by transcription factors (TFs) have been well studied; however, challenges remain in determining the in vivo architecture of TF-DNA complexes on a genome-scale. Here, we determined the in vivo architecture of Escherichia coli arginine repressor (ArgR)-DNA complexes using high-throughput sequencing of exonuclease-treated chromatin-immunoprecipitated DNA (ChIP-exo). The ChIP-exo has a unique peak-pair pattern indicating 5' and 3' ends of ArgR-binding region. We identified 62 ArgR-binding loci, which were classified into three groups, comprising single, double and triple peak-pairs. Each peak-pair has a unique 93 base pair (bp)-long (±2 bp) ArgR-binding sequence containing two ARG boxes (39 bp) and residual sequences. Moreover, the three ArgR-binding modes defined by the position of the two ARG boxes indicate that DNA bends centered between the pair of ARG boxes facilitate the non-specific contacts between ArgR subunits and the residual sequences. Additionally, our approach may also reveal other fundamental structural features of TF-DNA interactions that have implications for studying genome-scale transcriptional regulatory networks.

Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated using Escherichia coli K-12 MG1655 on glucose minimal media at 37°C. By keeping E. coli in constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription gene rpoB, an 82-bp deletion between the metabolic pyrE gene and rph, and an IS element between the DNA structural gene hns and tdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.

DNA-binding motifs that are recognized by transcription factors (TFs) have been well studied; however, challenges remain in determining the in vivo architecture of TF-DNA complexes on a genome-scale. Here, we determined the in vivo architecture of Escherichia coli arginine repressor (ArgR)-DNA complexes using high-throughput sequencing of exonuclease-treated chromatin-immunoprecipitated DNA (ChIP-exo). The ChIP-exo has a unique peak-pair pattern indicating 5' and 3' ends of ArgR-binding region. We identified 62 ArgR-binding loci, which were classified into three groups, comprising single, double and triple peak-pairs. Each peak-pair has a unique 93 base pair (bp)-long (±2 bp) ArgR-binding sequence containing two ARG boxes (39 bp) and residual sequences. Moreover, the three ArgR-binding modes defined by the position of the two ARG boxes indicate that DNA bends centered between the pair of ARG boxes facilitate the non-specific contacts between ArgR subunits and the residual sequences. Additionally, our approach may also reveal other fundamental structural features of TF-DNA interactions that have implications for studying genome-scale transcriptional regulatory networks.

Adaptive laboratory evolution (ALE) has emerged as a valuable method by which to investigate microbial adaptation to a desired environment. Here, we performed ALE to 42 °C of ten parallel populations of Escherichia coli K-12 MG1655 grown in glucose minimal media. Tightly controlled experimental conditions allowed selection based on exponential-phase growth rate, yielding strains that uniformly converged toward a similar phenotype along distinct genetic paths. Adapted strains possessed as few as 6 and as many as 55 mutations, and of the 144 genes that mutated in total, 14 arose independently across two or more strains. This mutational recurrence pointed to the key genetic targets underlying the evolved fitness increase. Genome engineering was used to introduce the novel ALE-acquired alleles in random combinations into the ancestral strain, and competition between these engineered strains reaffirmed the impact of the key mutations on the growth rate at 42 °C. Interestingly, most of the identified key gene targets differed significantly from those found in similar temperature adaptation studies, highlighting the sensitivity of genetic evolution to experimental conditions and ancestral genotype. Additionally, transcriptomic analysis of the ancestral and evolved strains revealed a general trend for restoration of the global expression state back toward preheat stressed levels. This restorative effect was previously documented following evolution to metabolic perturbations, and thus may represent a general feature of ALE experiments. The widespread evolved expression shifts were enabled by a comparatively scant number of regulatory mutations, providing a net fitness benefit but causing suboptimal expression levels for certain genes, such as those governing flagellar formation, which then became targets for additional ameliorating mutations. Overall, the results of this study provide insight into the adaptation process and yield lessons important for the future implementat

In bacteria, the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) DNA-targeting complex Cascade (CRISPR-associated complex for antiviral defense) uses CRISPR RNA (crRNA) guides to bind complementary DNA targets at sites adjacent to a trinucleotide signature sequence called the protospacer adjacent motif (PAM). The Cascade complex then recruits Cas3, a nuclease-helicase that catalyzes unwinding and cleavage of foreign double-stranded DNA (dsDNA) bearing a sequence matching that of the crRNA. Cascade comprises the CasA-E proteins and one crRNA, forming a structure that binds and unwinds dsDNA to form an R loop in which the target strand of the DNA base pairs with the 32-nt RNA guide sequence. Single-particle electron microscopy reconstructions of dsDNA-bound Cascade with and without Cas3 reveal that Cascade positions the PAM-proximal end of the DNA duplex at the CasA subunit and near the site of Cas3 association. The finding that the DNA target and Cas3 colocalize with CasA implicates this subunit in a key target-validation step during DNA interference. We show biochemically that base pairing of the PAM region is unnecessary for target binding but critical for Cas3-mediated degradation. In addition, the L1 loop of CasA, previously implicated in PAM recognition, is essential for Cas3 activation following target binding by Cascade. Together, these data show that the CasA subunit of Cascade functions as an essential partner of Cas3 by recognizing DNA target sites and positioning Cas3 adjacent to the PAM to ensure cleavage.

The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements. Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability.

Adaptive laboratory evolution (ALE) has emerged as a valuable method by which to investigate microbial adaptation to a desired environment. Here, we performed ALE to 42 °C of ten parallel populations of Escherichia coli K-12 MG1655 grown in glucose minimal media. Tightly controlled experimental conditions allowed selection based on exponential-phase growth rate, yielding strains that uniformly converged toward a similar phenotype along distinct genetic paths. Adapted strains possessed as few as 6 and as many as 55 mutations, and of the 144 genes that mutated in total, 14 arose independently across two or more strains. This mutational recurrence pointed to the key genetic targets underlying the evolved fitness increase. Genome engineering was used to introduce the novel ALE-acquired alleles in random combinations into the ancestral strain, and competition between these engineered strains reaffirmed the impact of the key mutations on the growth rate at 42 °C. Interestingly, most of the identified key gene targets differed significantly from those found in similar temperature adaptation studies, highlighting the sensitivity of genetic evolution to experimental conditions and ancestral genotype. Additionally, transcriptomic analysis of the ancestral and evolved strains revealed a general trend for restoration of the global expression state back toward preheat stressed levels. This restorative effect was previously documented following evolution to metabolic perturbations, and thus may represent a general feature of ALE experiments. The widespread evolved expression shifts were enabled by a comparatively scant number of regulatory mutations, providing a net fitness benefit but causing suboptimal expression levels for certain genes, such as those governing flagellar formation, which then became targets for additional ameliorating mutations. Overall, the results of this study provide insight into the adaptation process and yield lessons important for the future implementat

In bacteria, the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) DNA-targeting complex Cascade (CRISPR-associated complex for antiviral defense) uses CRISPR RNA (crRNA) guides to bind complementary DNA targets at sites adjacent to a trinucleotide signature sequence called the protospacer adjacent motif (PAM). The Cascade complex then recruits Cas3, a nuclease-helicase that catalyzes unwinding and cleavage of foreign double-stranded DNA (dsDNA) bearing a sequence matching that of the crRNA. Cascade comprises the CasA-E proteins and one crRNA, forming a structure that binds and unwinds dsDNA to form an R loop in which the target strand of the DNA base pairs with the 32-nt RNA guide sequence. Single-particle electron microscopy reconstructions of dsDNA-bound Cascade with and without Cas3 reveal that Cascade positions the PAM-proximal end of the DNA duplex at the CasA subunit and near the site of Cas3 association. The finding that the DNA target and Cas3 colocalize with CasA implicates this subunit in a key target-validation step during DNA interference. We show biochemically that base pairing of the PAM region is unnecessary for target binding but critical for Cas3-mediated degradation. In addition, the L1 loop of CasA, previously implicated in PAM recognition, is essential for Cas3 activation following target binding by Cascade. Together, these data show that the CasA subunit of Cascade functions as an essential partner of Cas3 by recognizing DNA target sites and positioning Cas3 adjacent to the PAM to ensure cleavage.

The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements. Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc.

Gene regulation in bacteria is usually described as an adaptive response to an environmental change so that genes are expressed when they are required. We instead propose that most genes are under indirect control: their expression responds to signal(s) that are not directly related to the genes' function. Indirect control should perform poorly in artificial conditions, and we show that gene regulation is often maladaptive in the laboratory. In Shewanella oneidensis MR-1, 24% of genes are detrimental to fitness in some conditions, and detrimental genes tend to be highly expressed instead of being repressed when not needed. In diverse bacteria, there is little correlation between when genes are important for optimal growth or fitness and when those genes are upregulated. Two common types of indirect control are constitutive expression and regulation by growth rate; these occur for genes with diverse functions and often seem to be suboptimal. Because genes that have closely related functions can have dissimilar expression patterns, regulation may be suboptimal in the wild as well as in the laboratory.

Prokaryotes immunize themselves against transmissible genetic elements by the integration (acquisition) in clustered regularly interspaced short palindromic repeats (CRISPR) loci of spacers homologous to invader nucleic acids, defined as protospacers. Following acquisition, mono-spacer CRISPR RNAs (termed crRNAs) guide CRISPR-associated (Cas) proteins to degrade (interference) protospacers flanked by an adjacent motif in extrachomosomal DNA. During acquisition, selection of spacer-precursors adjoining the protospacer motif and proper orientation of the integrated fragment with respect to the leader (sequence leading transcription of the flanking CRISPR array) grant efficient interference by at least some CRISPR-Cas systems. This adaptive stage of the CRISPR action is poorly characterized, mainly due to the lack of appropriate genetic strategies to address its study and, at least in Escherichia coli, the need of Cas overproduction for insertion detection. In this work, we describe the development and application in Escherichia coli strains of an interference-independent assay based on engineered selectable CRISPR-spacer integration reporter plasmids. By using this tool without the constraint of interference or cas overexpression, we confirmed fundamental aspects of this process such as the critical requirement of Cas1 and Cas2 and the identity of the CTT protospacer motif for the E. coli K12 system. In addition, we defined the CWT motif for a non-K12 CRISPR-Cas variant, and obtained data supporting the implication of the leader in spacer orientation, the preferred acquisition from plasmids harboring cas genes and the occurrence of a sequential cleavage at the insertion site by a ruler mechanism.

Gene regulation in bacteria is usually described as an adaptive response to an environmental change so that genes are expressed when they are required. We instead propose that most genes are under indirect control: their expression responds to signal(s) that are not directly related to the genes' function. Indirect control should perform poorly in artificial conditions, and we show that gene regulation is often maladaptive in the laboratory. In Shewanella oneidensis MR-1, 24% of genes are detrimental to fitness in some conditions, and detrimental genes tend to be highly expressed instead of being repressed when not needed. In diverse bacteria, there is little correlation between when genes are important for optimal growth or fitness and when those genes are upregulated. Two common types of indirect control are constitutive expression and regulation by growth rate; these occur for genes with diverse functions and often seem to be suboptimal. Because genes that have closely related functions can have dissimilar expression patterns, regulation may be suboptimal in the wild as well as in the laboratory.